mRNAs are the molecular templates for the synthesis of proteins. In eukaryotic organisms, the primary gene transcripts, pre- mRNAs, are typically not functional for protein synthesis until internal sequences (introns) are removed and the remaining fragments (exons) are spliced together to generate mature mRNAs (Fig. 1). Indeed, pre- mRNA splicing is essential for the expres- sion of >95% of human genes1,2. The process can also be regulated to generate alternatively spliced mRNAs (Fig. 2) that encode distinct protein variants, which is a mecha- nism often used to maintain cellular homeostasis and to regulate cell differentiation and development1,2.
The splicing process and its regulation are highly relevant for understanding every hallmark of cancer (Fig. 2, Supplementary Table 1), to the point that splic- ing alterations constitute another cancer hallmark3–8. For example, analyses of >8,000 tumours across 32 can- cer types revealed thousands of splicing variants not present in non- malignant tissues, which are likely to generate cancer- specific markers and neoantigens9,10 that could potentially be used as mRNA vaccines11. As another example, the generation of splicing var- iants is frequently responsible for the acquisition of resistance to androgen receptor- targeted therapies in prostate cancer and for resistance to vemurafenib in melanoma12–14. In addition, results indicate that cancer cells impose special demands on the splicing machin- ery such that they become particularly vulnerable to splicing perturbations, a feature that is beginning to be
exploited pharmacologically15–18. In this Review, we outline in detail the splicing process and its alterations in cancer before highlighting opportunities for the development of innovative therapeutic approaches in clinical oncology.
The splicing machinery and cancer The removal of introns involves a chemical mechanism by which specific phosphodiester bonds in the polynu- cleotide chains of RNA are excised, and new ones are formed. This process occurs in two consecutive steps and involves the formation of an unusual 2′–5′ phos- phodiester bond between the 5′ nucleotide of the intron and a key internal adenosine residue (the branch site) located 15–30 nucleotides upstream of the 3′ end of the intron (Fig. 1). This chemical mechanism is identical to that of group II self- catalytic RNAs, possibly reveal- ing the ancestral origin of pre- mRNA introns19. Whereas the sequence of the entire group II intron shapes the elaborate 3D structure that enables their excision in the absence of cofactors, introns in pre- mRNAs har- bour only short consensus sequences at the exon–intron boundaries, known as 5′ and 3′ splice sites, and the removal of these introns relies on one of the most sophis- ticated macromolecular complexes of eukaryotic cells: the spliceosome20,21. The splicing process is an essential step in eukaryotic gene expression and, unsurprisingly, hereditary cancer genes are particularly susceptible to inactivating mutations in splice sites22.
Roles and mechanisms of alternative splicing in cancer — implications for care Sophie C. Bonnal 1,2, Irene López- Oreja 1,2,3 and Juan Valcárcel1,2,4 ✉
Abstract | Removal of introns from messenger RNA precursors (pre- mRNA splicing) is an essential step for the expression of most eukaryotic genes. Alternative splicing enables the regulated generation of multiple mRNA and protein products from a single gene. Cancer cells have general as well as cancer type- specific and subtype- specific alterations in the splicing process that can have prognostic value and contribute to every hallmark of cancer progression, including cancer immune responses. These splicing alterations are often linked to the occurrence of cancer driver mutations in genes encoding either core components or regulators of the splicing machinery. Of therapeutic relevance, the transcriptomic landscape of cancer cells makes them particularly vulnerable to pharmacological inhibition of splicing. Small- molecule splicing modulators are currently in clinical trials and, in addition to splice site- switching antisense oligonucleotides, offer the promise of novel and personalized approaches to cancer treatment.
1Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain. 2Universitat Pompeu Fabra, Barcelona, Spain. 3Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain. 4Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.
✉e- mail: [email protected] crg.eu
https://doi.org/10.1038/ s41571-020-0350- x
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The spliceosome. The spliceosome consists of five small nuclear ribonucleoproteins (snRNPs; U1, U2, U4, U5 and U6, which is tightly bound to U4), each of which is composed of one specific small nuclear RNA (snRNA) and a number of associated proteins, as well as >150 additional polypeptides not directly bound to the snRNPs20. The catalytic core of the spliceosome is assembled anew on each intron substrate from separate subcomplexes and is shaped by RNA–RNA interactions between U2 and U6 snRNAs as well as between snRNAs and the intron splice sites20,21 (Fig. 1). Protein components of the spliceosome help to shape the RNA- based active site of this enzymatic complex and are also essential for its function23–28.
Spliceosome assembly. Spliceosome assembly starts with the recognition of the 5′ splice site by U1 snRNP through base pairing interactions involving 6–8 nucleo- tides of U1 snRNA and the 5′ end of the intron (Fig. 1a). Mutations in this region of U1 snRNA induce predict- able changes in 5′ splice site utilization (Table 1), which affect known cancer driver genes across multiple can- cers and confer an adverse prognosis in patients with chronic lymphocytic leukaemia (CLL)29. In Sonic hedge- hog medulloblastomas, these mutations also inactivate tumour suppressors, such as Patched homologue 1, as well as activating proto- oncoproteins, such as GLI2 and cyclin D2 (reF.30).
Initial identification of the 3′ splice site region by the spliceosome involves three separate sequence elements recognized by three interacting proteins, U2AF1, U2AF2 and SF1 (Fig. 1), which are recurrently mutated in can- cer31. U2AF1 binds to the conserved AG dinucleotide at the 3′ end of the intron20 (Fig. 1). U2AF1 is frequently mutated in myeloid malignancies and lung adenocarci- nomas31–33 (Table 1), but how mutation of a functionally conserved factor that recognizes a conserved sequence at the 3′ splice site contributes to cancer progression remains to be fully understood. Mice expressing mutant U2AF1 have altered haematopoiesis, an altered prefer- ence for the nucleotide preceding the 3′ splice site AG motif (Fig. 3a), and splicing changes in haematopoietic
precursor cells in transcripts encoding RNA- processing factors, ribosomal proteins and mRNAs of genes that are mutated in myelodysplastic syndromes (MDS) and acute myeloid leukaemia (AML), such as BCOR or KMT2D34. Such splicing changes might contribute to disease pro- gression; consistent with this concept, mutant U2AF1 leads to abnormal processing of autophagy- related fac- tor 7 (Atg7) pre- mRNA (which, surprisingly, is related to the selection of a distal cleavage and polyadenylation site; Fig. 3a) and to an autophagy defect that favours the transformation of mouse pro- B cells35. However, given the limited conservation of alternative splicing events between humans and mice, the effects of splicing alter- ations observed in mouse models might be different to those in human tumours. In fact, contrary to the expectation for driver mutations, mouse models have shown that splicing factor- mutant cells have a compro- mised competitive repopulation capacity compared with that of splicing factor- wild- type cells15,34,36–41. Finally, a non- canonical role of U2AF1 in translational repres- sion via direct binding near initiation codons is altered in human cell lines harbouring the common U2AF1S34F mutation, leading to increased expression of the secreted chemokine IL-8, which in turn can contribute to inflammation and tumour progression42.
The next step in spliceosome assembly involves ATP- dependent stable binding of the U2 snRNP around the branch site (Fig. 1). As is the case for recognition of the 5′ splice site by U1 snRNA, recognition of the branch site by U2 snRNP involves base pairing interactions, this time between six nucleotides of U2 snRNA and nucleotides flanking the branch- site adenosine in the pre- mRNA intron20. SF3B1 is a key protein component of U2 snRNP that recognizes the branch site and the U2 snRNA–pre- mRNA helix and, through a confor- mational change induced by binding of the pre- mRNA, facilitates the approximation of the branch- site adeno- sine to the 5′ splice site and the first step of the splicing reaction43,44 (as detailed below). Mutations in SF3B1 are among the most frequent in a variety of cancers31, with a prevalence ranging from 5% in breast cancer to 81% in a class of MDS with ring sideroblasts (Table 1). For exam- ple, SF3B1 mutations are detected in ~10% of patients with CLL, with the K700E mutation being among the most frequent single amino acid change observed in any gene in this disease, and are associated with rapid disease progression and unfavourable overall survival45,46.
As discussed above for U2AF1, it is remarkable that mutations in key core components of the splicing appara- tus contribute to cancer progression rather than causing a general defect in intron removal that would probably compromise cell survival. Mutations affecting SF3B1 disrupt the interaction of this protein with the splicing factor SUGP1 (reF.47) and result in the use of cryptic 3′ splice sites typically within a window of 30 nucleotides upstream of the 3′ splice sites used in wild- type cells48–50 (Fig. 3b). These changes probably contribute to cancer progression by affecting the expression or function of specific genes. For example, a variety of SF3B1 muta- tions result in the use of a cryptic 3′ splice site and in inclusion of an associated cryptic exon in transcripts of BRD9, which introduces premature stop codons into the
• Alternative splicing enables the generation of distinct mRNA and protein isoforms from a single gene. splicing is carried out by the spliceosome, one of the most complex molecular machineries of eukaryotic cells.
• splicing perturbations are common in cancer and are associated with mutations in and/or altered expression of the components of the splicing machinery.
• splicing perturbations contribute to every hallmark of cancer and can generate neoantigens relevant to the design of cancer vaccines and other immunotherapies.
• Cancer cells generate advantageous splicing variants, at the cost of reducing the efficiency or fidelity of the splicing process, thus conferring a special susceptibility to splicing inhibitors and providing a therapeutic window for targeting the splicing process.
• small- molecule modulators of the spliceosome have demonstrated antitumour effects and are particularly active against cancer cells harbouring mutations in spliceosomal components.
• Antisense oligonucleotides offer promise to modulate cancer- relevant alternative splicing decisions, with proof of concept for this type of therapy demonstrated by Nusinersen, a first- in- class treatment for patients with spinal muscular atrophy.
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BRD9 open reading frame that lead to mRNA degra- dation by the process of nonsense- mediated decay51. This process, in turn, results in reduced levels of BRD9 protein, a core component of the non- canonical chro- matin remodelling complex BAF and a potent tumour suppressor, thus promoting uveal melanomagenesis52.
Alternative 3′ splice site usage induced by SF3B1 muta- tions could also be a source of neoantigens for per- sonalized vaccine or adoptive T cell- based therapies53. Surprisingly, a low- level but widespread reduction of intron retention isoforms (that is, enhanced splicing of regulated introns) seems to be the most frequent
5′ splice site GURAGU
YNURAY YYY AG
First catalytic step
Second catalytic step
SF1U1 YNURAY YYY AG
GU RA GU
GU RA GU
YNURAY YYY AG
GU RA GU
YNURAY YYY AG
3′ splice site
U2 U5 U6C, C*
Key RNA–RNA interactions occurring at the different stages of spliceosome assemblysnRNP/protein composition
U1 snRNA GU RA GU
GU RA GU A G
Fig. 1 | Pre-mRNA splicing and the spliceosome assembly pathway. a | The chemical process of intron removal. Two successive transesterifica- tion reactions, involving the breakage and formation of phosphodiester bonds, result in the removal of introns and in the splicing together of exons. The first step generates two reaction intermediates: the 5′ exon and a lariat- shaped structure harbouring an unusual 2′–5′ phosphodiester bond involving the 5′ nucleotide of the intron and an internal adenosine residue (the branch point, indicated in red) located 15–30 nucleotides upstream of the 3′ end of the intron. In the second step, the two exons are spliced together and the intron is released as a lariat product. b | Spliceosome assembly. The spliceosome, composed of five small nuclear ribonucleo- proteins (snRNPs) and numerous additional proteins, assembles anew on each pre- mRNA molecule through sequential steps involving the dynamic remodelling of its composition and conformation mediated by changes in protein–protein, protein–RNA and RNA–RNA interactions (the latter summarized in the right- hand panels). U1 snRNP is recruited through base
pairing between the 5′ splice site and U1 small nuclear RNA (snRNA), whereas SF1, U2AF2 and U2AF1 recognize the branch- site sequence, the polypyrimidine tract and the AG dinucleotide of the 3′ splice site, respectively , leading to the formation of complex E. U2 snRNP is then recruited to the branch site through base pairing interactions with U2 snRNA , leading to complex A , in which the U2 snRNP component SF3B1 facilitates the recognition of the intron branch- site by U2 snRNA. Other protein components of complex A , such as RBM10, RBM39 and RBM15, modulate 3′ splice site recognition and RBM15 is itself regulated by PRMT1. The subsequent recruitment of the U4/U6–U5 tri- snRNP complex to form complex B and the remodelling of numerous interactions within complex B lead to the formation of the catalytically active conformations of the spliceosome (referred to as Bact, B*, C and C* complexes). ClinicalTrials.gov identifiers (NCTs) for trials of inhibitors of PRMT1 and SF3B1 (GSK3368715 and H3B-8800, respectively) are indicated. R , purine; Y, pyrimidine; N, any nucleotide.
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GURAGU YNURAY YYY AG
SR, hnRNP, RBM proteins
Sm site of U1 snRNA
ESS ESE ESSESEISS
• NCT03573310 • NCT02783300 • NCT03614728 • NCT03854227
Intron retention/detained intron (when the intron-containing pre-mRNA remains in the nucleus)
HIF3α4: tumour suppressor
Mutually exclusive exons
Alternative 3′ splice site
Alternative 5′ splice site
HIF3α: regulator of hypoxia- inducible gene expression
PKM1: adult isoform
PKM2: embryonic and tumoural isoform
AR-V7: constitutively active; resistance to enzalutamide
AR: sensitivity to enzalutamide
1 2 3 1 2 3
8 9 10 11 8 9 11
8 10 11
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splicing alteration detected in bone marrow samples of SF3B1- mutated MDS54. Remarkably, SF3B1 is the target of several families of natural and synthetic compounds with antitumour activity in animal models (see below).
Recognition of splice sites by U1 and U2 snRNPs is assisted and modulated by a number of other factors, some of which have become paradigmatic examples of splicing regulators, including members of various families of RNA- binding proteins, such as arginine– serine- rich (SR), heterogeneous nuclear ribonucleo- protein (hnRNP) and RNA- binding motif (RBM) proteins55. These factors recognize specific regulatory sequences located in introns or exons and facilitate or inhibit, in a position- dependent manner, the recogni- tion of neighbouring splice sites by the core splicing machinery55,56 (Fig. 2a). Synonymous mutations that alter splicing regulatory sequences frequently act as driver mutations in cancer by inducing changes in alternative splicing of pre- mRNAs encoded by proto- oncogenes or tumour suppressor genes, leading either to func- tionally distinct protein isoforms or to frameshifts and nonsense- mediated decay51,57–60. A classic example of such regulation is provided by SRSF2, an SR protein that binds to specific exonic splicing enhancers and stimulates recognition of the flanking splice site by U1 or U2 snRNPs (Fig. 2a). SRSF2 is frequently mutated in a variety of myeloid neoplasms (Table 2), including chronic myelomonocytic leukaemia (CMML); at least one of these mutations modifies the relative affinity of SRSF2 for different binding sites in pre- mRNAs, thus altering the consensus of exonic splicing enhancers that are activated, in turn leading to misregulation of exon inclusion (Fig. 3c) and ultimately to anaemia, leukopenia and morphological dysplasia40,61. Mutations in either SF3B1 or SRSF2 have also been shown to gen- erate isoforms, for example, of MAPKKK7 or caspase 8, that activate nuclear factor- κB signalling18. Another example is presented by RBM10, which promotes skip- ping of exon 9 in pre- mRNA encoding the Notch reg- ulator NUMB, leading to increased expression of the anti- proliferative isoform62 (Fig. 4). The gene encoding RBM10 is frequently mutated in a number of solid tumours, including lung adenocarcinomas, and at least one of the mutant forms fails to regulate the splicing of NUMB, probably by affecting its interactions with other
spliceosomal components (this mutant does not have altered RNA- binding properties) and thereby increasing the expression of the pro- proliferative isoform62–64.
In addition to the major spliceosome pathway, a subset of introns harbouring a distinct configuration of splice- site sequences are excised by the minor spliceo- some. This machinery includes U11 and U12 snRNPs, which have roles equivalent to those of U1 and U2 snRNPs in the recognition of the 5′ and 3′ splice sites, respectively65. ZRSR2 is a component of the U11–U12 di- snRNP complex and has a domain organization sim- ilar to that of U2AF1 and, like U2AF1 itself, is involved in 3′ splice site recognition66. Mutations in ZRSR2 are frequently found in MDS31–33. The mutations are dis- tributed throughout the gene, suggesting that loss of ZRSR2 function contributes to MDS, in contrast to the frequent clustering of mutations in U2AF1, SF3B1 or SRSF2 within specific domains or even residues of these proteins31–33. The presence of ZRSR2 mutations is asso- ciated with a global increase in the retention of U12- type introns67 (Fig. 3d), some of which reside within genes with important functions in cell- cycle control68.
Of relevance, U2AF1, SF3B1 and ZRSR2 have direct roles in 3′ splice site recognition, and SRSF2 can also promote this recognition through its association with exonic enhancers20,21. Whereas all of these proteins are functionally linked to U2 snRNP (or, in the case of ZRSR2, its equivalent in the minor spliceosome), mutations in the genes encoding these factors occur in a mutually exclusive manner (with the exception of SF3B1 and SRSF2 mutations in a minority of patients with MDS)18,31,33. This finding suggests that the muta- tions act on a common pathway and, therefore, once the pathway becomes altered by one mutation, other mutations become redundant and are not positively selected within the tumour. Another prominent feature is that the mutations are heterozygous18, suggesting that at least one functional allele is required for cancer cell survival. Collectively, these observations indicate that a delicate balance is established in cancer cells between the generation of splicing variants favourable for tumour growth and preserving a basic gene- expression process generally required for cellular function. As elaborated on in a later section of this Review, these considera- tions might have important implications for the design of splicing- based cancer therapies. Nevertheless, we emphasize that the mechanisms behind the possible carcinogenic effects of splicing- factor mutations remain to be rigorously proven. One outstanding question is whether these effects rely on alterations in the splic- ing of one, or a few, key target genes or whether they reflect the collective effect of a relatively large number of alterations in splicing, or even whether other mech- anistic explanations exist that are not directly related to splicing. For example, augmented formation of R- loops (RNA–DNA hybrids generated during transcription) has been proposed as a unifying mechanism for the oncogenic role of splicing- factor mutations in MDS69. This phenomenon can lead to replication stress and acti- vation of the ATR pathway, suggesting that ATR inhib- itors can provide therapeutic opportunities for splicing factor- mutated MDS70.
Fig. 2 | Alternative splicing. a | Classical mechanisms of alternative splicing regulation. RNA- binding motif (RBM) proteins, arginine–serine- rich (SR) proteins (including SRSF2) and heterogeneous nuclear ribonucleoproteins (hnRNPs) bound to exonic or intronic regulatory elements can promote or prevent the recognition of the 5′ splice site by the U1 small nuclear ribonucleoprotein (snRNP) or of the 3′ splice site by SF1, U2AF2, U2AF1 or U2 snRNP, thus affecting splice site choices and therefore alternative splicing decisions. b | The maturation of snRNPs includes the assembly of the Sm proteins B, D1, D2, D3, E, F and G on specific sequences (Sm sites) of small nuclear RNAs (snRNAs), for example, U1 snRNA; methylation of Sm B, D1 and D3 by PRMT5 facilitates this process, which influences the levels of snRNPs and, consequently , alternative splicing decisions. ClinicalTrials.gov identifiers (NCTs) for trials of inhibitors of PRMT5 are indicated. c | Alternative splicing and effects on cancer. Differential selection of intronic and exonic sequences as well as differential use of alternative promoters and 3′- end formation sites result is the generation of alternative mRNA isoforms. The figure displays different classes of alternative mRNA- processing events and examples of alternatively spliced products with distinct functions in cancer progression. ESE, exonic splicing enhancer; ESS, exonic splicing silencer; ISE, intronic splicing enhancer; ISS, intronic splicing silencer.
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After initial recognition of the splice sites by U1 and U2 snRNPs, the tri- snRNP U4–U6–U5 joins the com- plex, triggering remarkable conformational changes and protein exchanges that enable the formation of the cata- lytic core of the spliceosome and thus completion of the splicing reaction71,72. Mutations affecting factors involved in late stages of spliceosome formation and catalytic acti- vation have also been associated with cancer, including PRPF8, the most evolutionarily conserved spliceosomal component and involved in chaperoning the RNA- based catalytic core28. PRPF8 mutations detected in myeloid neoplasms are associated with defects resulting in mis- splicing, and equivalent mutations in yeast cause defects in the proofreading of splicing at the second
catalytic step73. These observations are consistent with results from various studies indicating that the sophis- ticated conformational dynamics of the catalytic core, occurring at late steps of spliceosome assembly and catalysis, can also be the target of splicing regulation21, with consequences for oncogenic transformation.
Altered expression of splicing factors in cancer. Analysis of The Cancer Genome Atlas data across 33 cancer types indicates that putative cancer driver mutations occur in 119 genes encoding core splicing factors and regulators (about 60% of the components of this machinery)33. In addition to mutations, changes in the levels of expres- sion of splicing factors — often associated with genomic
Table 1 | Recurrent splicing- factor mutations in cancer and associated prognosis
Cancer type Prevalence (%)
Effect on prognosisa
SF3B1 CLL 5–31 (reFs186,187)
Shorter OS45,186, PFS186 and TTT45,188,189 (when clonal: variant allele frequency >12%)190; or no effect on OS190, PFS or ORR187
MDS 7–81 Lower cumulative incidence of disease progression191, longer LFS192,193 (u)194, EFS195 and OS191,193,196–200 (u)194; no effect on OS201
MDS without RS 7 No effect on PFS, AML transformation or OS202
MDS with RS 16–77 Longer LFS203 and OS203,204
Primary myelofibrosis 6.5 No effect on OS205
De novo AML 2.4 Shorter OS and DFS, and lower complete remission rates206
Primary orbital melanoma 36 Tendency for longer OS (in a small cohort of patients)207
Mucosal melanoma 22 Shorter PFS and OS208
Uveal melanoma 15–22 (reFs33,209–211)
Longer EFS and cancer- specific survival210; late onset metastases, intermediate risk of metastases, but worse DFS in disomy 3 group212; tendency for longer OS (in a small cohort of patients)209
Breast cancer 5–10 Shorter OS (luminal B and progesterone receptor- negative disease)213
SRSF2 MDS 4–18 Shorter OS214–216; higher risk of AML transformation215
MDS without RS 10 Shorter PFS202
CMML 25–47 No effect on OS214,217
Primary myelofibrosis 8.5–17 Shorter LFS218,219 and OS219–221
Secondary myelofibrosis 1.0–4.2 Shorter OS (u)222
Secondary (myeloproliferative neoplasm) AML
16–18 Shorter OS223,224
De novo AML 5 Shorter OS and lower complete remission rates206
U1 Sonic hedgehog medulloblastoma
8.8 Increased risk of relapse but no effect on OS30
Hepatocellular carcinoma 5.8 No effect on OS29
CLL 3.8 Shorter TTT but no effect on OS29
U2AF1 MDS 7–17 Increased risk of secondary AML225,226 (including in young, low- risk patients227) and shorter OS226–228 (u)216,229; no effect on OS230
MDS without RS 7 Shorter PFS202
Primary myelofibrosis 16 (65% Q157)
Shorter OS (Q157 mutation)231 (u)232; no effect on LFS231
De novo AML 3 Lower complete remission rates, shorter DFS and OS206
Lung adenocarcinoma 3 Shorter PFS63
AML , acute myeloid leukaemia; CLL , chronic lymphocytic leukaemia; CMML , chronic myelomonocytic leukaemia; DFS, disease- free survival; EFS, event- free survival; LFS, leukaemia- free survival; MDS, myelodysplastic syndrome; OS, overall survival; ORR , objective response rate; PFS, progression- free survival; RS, ring sideroblasts; TTT, time to first treatment; (u), studies demonstrating noted effect on univariate analysis only. aIn comparison with cancers without splicing factor mutations.
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rearrangements — have been associated with oncogene- sis or loss of tumour suppression: 84% of RNA- binding proteins and >70% of splicing factors have been found to be dysregulated at the level of mRNA expression in cancers74–76. Proteomic analyses also indicate a subtype- independent signature of spliceosome dysregulation in CLL77. Furthermore, expression of the SR protein SRSF1 is frequently upregulated in various solid tumours, including breast and lung cancers; SRSF1 is a direct target of MYC and its elevated expression correlates
with increased tumour grade, decreased survival and resistance to chemotherapy78–81. These features are asso- ciated with alterations of alternative splicing in genes controlling cell growth, apoptosis or motility6. Notably, titration of SRSF1 activity using decoy oligonucleotides containing SRSF1- binding sites results in the inhibi- tion of cancer cell growth and apoptosis in vitro and in mouse xenograft models82.
Chromosomal rearrangements resulting in fusions between transcription factors and the splicing regulatory
GURAGU AG YYYYNUR YA YNUR YA
Alternative 3′ splice site
Alternative 3′ splice site
Canonical junction Alternative junction
U12 type intron
C T/ G A/
Fig. 3 | Effect of cancer-associated mutations in splicing factors on alternative splice site selection. a | U2AF1 mutations are associated with differential inclusion of cassette exons harbouring specific nucleotide sequences at positions − 3 and + 1 at the preceding 3′ splice site. Processing patterns favoured by the mutations are shown by thicker lines. The U2AF1S34F mutation is also associated with alternative 3′ splice site usage and alternative polyadenylation. b | SF3B1 mutations are associated with activation of cryptic 3′ splice sites linked to usage of a different branch point. SF3B1 mutations are also associated with enhanced splicing of regulated introns. c | SRSF2 mutations are associated with differential inclusion of cassette exons via preferential recognition of specific exonic splicing enhancer sequences. Processing patterns favoured by the mutations are shown by thicker lines. d | ZRSR2 mutations (ZRSR2 MUT) are associated with retention of U12 introns; these introns are normally removed by the minor spliceosome, of which ZRSR2 is a component. WT, wild type.
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Table 2 | Splicing- based modulation of drug responses
Gene or protein
Isoforms conferring resistance Isoforms conferring sensitivity Tools to revert resistance or promote sensitivity
Mechanism: change in splicing isoform expression
AIMP2 AIMP2- DX2: paclitaxel NA BC- DXI01 to reduce the expression of AIMP2- DX2
AR AR- V7: enzalutamide and abiraterone NA Downregulation of hnRNP A1 by siRNA , quercetin; BRD4 inhibitor to prevent AR- V7–ZFX pathway
Bax2 NA BaxΔ2: chemotherapeutic agents (adriamycin)
BCL2L1 NA Bcl- xS: chemotherapeutic drugs (including cisplatin and 5FdU) and radiation
Antisense to promote Bcl- xS isoform
BCL2L11/BIM Isoform lacking exon 4: TKI (imatinib) NA Antisense to block inclusion of exon 3 and enhance inclusion of exon 4
BCR- ABL BCR- ABL135INS: imatinib NA NA 242
BRAF BRAF3-9: vemurafenib NA SSA and meayamycin B to decrease the expression of BRAF3–9
BRCA1 BRCA1- Δ11q: PARP inhibitor and cisplatin NA Pladienolide B to reduce the level of BRCA1- Δ11q
BRCA2 BRCA2ΔE5 + 7: crosslinking agents (mitomycin C)
NA NA 244
Caspase 2 (CASP2)
CASP-2L- Pro: etoposide, camptothecin and death receptor agonists
NA NA 245
Caspase 3 (CASP3)
Caspase-3s: chemotherapeutic drugs (etoposide and methotrexate)
NA siRNA depletion of caspase-3s 246
CD19 CD19 Δex2: anti- CD19 CAR T cells NA NA 120
Cyclin D1 (CCND1)
Cyclin D1b: oestrogen antagonists NA NA 247
EGFR EGFRvIII: radiation, reversible EGFR inhibitor (gefitinib and erlotinib)
EGFRvIII: irreversible EGFR inhibitor (HKI-272)
ER ERα36: tamoxifen NA NA 250
FPGS Aberrant splice variants: antifolate NA NA 251
HER2 HER2Δ16: trastuzumab NA Quinones to inhibit HER2Δ16 action
HLA- G HLA- G protein isoforms but HLA- G1: NK cell- mediated lysis
NA NA 254
IG20 MADD and DENN- SV: TRAIL NA shRNA depletion of MADD 255,256
IKZF1 IK6: TKI (imatinib and dasatinib) NA NA 257
MCL1 Mcl-1(L): Bcl- x(L) inhibitor ABT-737 NA Meayamycin B to enhance Mcl-1(S) expression
MET NA Exon 14 skipping: MET inhibitors NA 258
MKNK2 MNK2b: gemcitabine MNK2a: chemotherapeutic drugs (doxorubicin, cisplatin and temozolomide)
Antisense to promote MNK2a isoform
PIK3CD PI3KCD- S: PI3Kδ inhibitor (idelalisib) NA NA 260
RON NA RONΔ160: wortmannin NA 261
NA Survivin 2B: antitumour activity in taxane- resistant ovarian cancer
TP53 ∆133p53: 5- fluorouracil NA siRNA depletion of ∆133p53 263
Mechanism: change in splicing factor expression
SRSF1 NA NA siRNA to decrease SRSF1 expression enhances growth inhibition in response to cisplatin and topotecan
5FdU; 5- fluorodeoxyuridine; CAR , chimeric antigen receptor; hnRNP, heterogenous nuclear ribonucleoproteins; NA , not available; NK , natural killer; PARP, poly(ADP- ribose) polymerase; shRNA , short hairpin RNA; siRNA , short interfering RNA; SSA , spliceostatin A; TKI, tyrosine kinase inhibitor; TRAIL , TNF- related apoptosis inducing ligand.
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proteins EWS and RBM15 are characteristic of Ewing sarcomas and paediatric acute megakaryocytic leu- kaemia, respectively83,84. Owing to the RNA- binding function of these proteins, these genetic lesions can also alter alternative splicing of pre- mRNAs of multi- ple cancer- relevant genes, including those encoding the RNA–DNA helicases DHX9 and ARID1A, with oncogenic effects85,86. Finally, substantial differences in the expression of snRNAs across cancer samples have been detected, and genes affected by snRNA levels in a breast cancer cell line were found to be preferentially mis- spliced in a cohort of patients with invasive breast ductal carcinomas87, suggesting that changes in snRNA levels can also contribute to cancer progression.
Splicing programmes in oncogenesis Extensive alternative splicing programmes contribute to the regulation of cell differentiation and organ devel- opment. Classical examples include sex determination in Drosophila and cell pluripotency, epithelial– mesenchymal transition or neuronal synapse formation in vertebrates1,2. Disruption of such programmes in cancer cells can contribute to virtually every aspect of tumour progression3–8,88,89 (Fig. 4, Supplementary Table 1).
For example, retention of a number of so- called detained introns, which leads to accumulation of pre- mRNA in the nucleus until a stimulus triggers splicing and conse- quently rapid expression of gene products, in transcripts associated with proliferation, senescence and apopto- sis contributes to neurogenesis. Programmed intron retention is disrupted in glioblastomas and inhibition of PRMT5, which is an arginine N- methyltransferase important for snRNP biogenesis and detained intron splicing, potently inhibits glioblastoma progression in mouse models, including patient- derived xenograft models16 (Fig. 2b). Interestingly, increased splicing of regulated introns is a major effect of SF3B1 mutations in MDS54, while intron retention is a widespread mecha- nism of tumour- suppressor inactivation by single nucleotide mutations in cancer90.
Reversal of splicing patterns of adult or differenti- ated cells is a common theme in cancer. A paradigmatic example involves the switch from adult to embryonic splicing patterns for a pair of mutually exclusive exons encoded by the pyruvate kinase PKM gene, which con- fers a growth advantage to cancer cells by enabling rapid energy generation through aerobic glycolysis (the Warburg effect) and by shunting glucose towards
NUMB-PRR(L) (↑) <--> NUMB-PRR(S) (↓)
PKM1 (↓) <--> PKM2 (↑)
p120-1A (↑) <--> p120-3A
Bcl-x(S) (↑) <--> Bcl-x(L) (↓)
Genomic instability DNA damage Mutations
RAC1b (↑) <---> RAC1
RONΔ165 (↑) <---> RON
FGFR2 IIIc (↑) <---> FGFR2 IIIb (↓)
HIF1αL <---> HIF1αS (↑)
VEGF165b (↓) <---> VEGF165 (↑)
KLF6-SV1 (↑) <---> KLF6-FL
MAP2K4 <---> MAP2K4Δ (↑)
CEACAM1(L) <---> CEACAM1(S) (↑)
Fig. 4 | Effect of alternative splicing dysregulation on cancer progression. The diagram illustrates different hallmarks of cancer265 along with examples of alternative splicing events that contribute to the regulation of the different processes of tumorigenesis. Arrows up and down indicate the isoforms contributing the most and the least, respectively , to each process. A non- exhaustive list of additional examples is provided in Supplementary Table 1. EMT, epithelial–mesenchymal transition.
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other biosynthesis processes91 (Figs 2c,4, Supplementary Table 1). In another example of developmental repro- gramming during oncogenesis, repression of an alter- natively spliced exon that is enriched in neurons results in inactivation of the tumour suppressor annexin A7 in glioblastoma precursor cells, enabling lineage- specific activation of EGFR signalling92 (Supplementary Table 1).
A number of apoptosis- regulatory genes gener- ate alternatively spliced protein isoforms with oppo- site activities, which is a physiological programme often subverted in tumours to enable cancer cells to escape from intrinsic programmed cell death as well as radiotherapy- induced or chemotherapy- induced cyto- toxicity93. For example, the use of alternative 5′ splice sites in the Bcl- x pre- mRNA generates the anti- apoptotic Bcl- x(L) and pro- apoptotic Bcl- x(S) protein isoforms94 (Figs 2c,4, Supplementary Table 1). Bcl- x(L) is tran- scriptionally upregulated in many cancers and is asso- ciated with chemoresistance and with RAS- induced expression of stemness regulators and maintenance of a cancer- initiating cell phenotype94,95. In addition, inclusion or skipping of the single internal exon respec- tively generate the anti- apoptotic and pro- apoptotic isoforms of MCL1 (reF.96) (Fig. 2c). The switch towards the pro- apoptotic, exon- skipping isoform of MCL1 mediates the cytotoxic effects of splicing- inhibitory anti- tumour drugs (such as gossypol or obatoclax) as well as (re)sensitizing cancer cells to Bcl- x(L) inhibitors97,98.
Numerous examples exist of the effect that alter- ations in the relative expression of particular mRNA isoforms have on almost every hallmark of tumour progression, including cell invasion, angiogenesis, cell metabolism3–6,99 (Fig. 4, Supplementary Table 1) and, of particular relevance for clinical oncology, responses to anticancer therapy and the development of drug resist- ance (reviewed elsewhere7,100) (Table 2). For example, on the one hand, widespread disruption of splicing- factor expression and alternative splicing has been observed in therapy- resistant secondary AML stem cells and MDS progenitor cells101, which, on the other hand, makes them particularly sensitive to splicing- inhibitory drugs (see below) (Fig. 5a).
Further general perturbations of splicing in cancer, revealing thousands of cancer- specific variants, have been profusely reported. These studies reveal patterns of altered splicing both across cancers (affecting cell cycle, cell adhesion and migration, and the insulin sig- nalling pathway102) as well as in cancer type- specific and subtype- specific profiles (with potential prognos- tic value9,58,103–113) and even evidence of intratumoural splicing heterogeneity114.
The generation of neoantigens through altered splic- ing (including intron retention), intron polyadenylation or the generation of fusion transcripts offers important opportunities for the design of cancer vaccines and for chimeric antigen receptor and T cell receptor- engineered T cell- based adoptive cell therapies9–11,18,58,110,115,116. Transcriptome and proteome analyses of breast and ovarian cancers indicate that splicing- derived neoanti- gens are at least twice as prevalent as those created by single amino acid mutations9,58,116. Profiling of splicing changes in tumours might therefore provide biomarkers
for the use of immune- checkpoint inhibitors, such as anti- PD-1 and/or anti- cytotoxic T lymphocyte protein 4 (CTLA-4) antibodies10, and possibly for the design of personalized vaccines117,118. For example, expression of an alternatively spliced form of CD20 in B cell lympho- mas generates immunogenic epitopes that are presented by both major histocompatibility complexes I and II and can thus be recognized by T cells, resulting in the kill- ing of lymphoma cells119. Conversely, the selection of pre- existing alternatively spliced variants of CD19 on malignant B cells leads to escape from anti- CD19 chi- meric antigen receptor T cell immunotherapy, which occurs in 10–20% of paediatric patients with B cell acute lymphoblastic leukaemia120 (Fig. 5b, Table 2).
Thus, both the dysregulation of developmental splice site switches and the generation of cancer- specific splic- ing isoforms contribute transcriptome changes relevant for tumour biology, and these changes are linked to alterations in the activity of splicing factors and regula- tors. Cancer cells therefore have characteristic splicing landscapes.
Splicing addiction of cancer cells Several lines of evidence are consistent with the concept that cancer cells are particularly vulnerable to pertur- bations in the splicing process. As a consequence, their survival depends on certain requirements of splicing activity121 (Fig. 5, box 1). First, as mentioned earlier, can- cer driver mutations in splicing factors are heterozygous, and thus one wild- type allele is required to support cell growth18. Furthermore, the mutations are mostly mutu- ally exclusive31,33, suggesting that each mutation, while conferring some advantage for tumour progression, also imposes a splicing- related burden that, when combined with the effects of other mutations, results in the syn- thetic lethality of cancer cells. Indeed, the co- expression of mutant forms of SF3B1 and SRSF2 in haematopoietic progenitors has been shown to cause cell death18 (box 1). By contrast, the co- expression of mutants of SRSF2 and the epigenetic modifier IDH2 result in more profound splicing changes than each mutation alone and have coordinated effects on the epigenome and RNA splic- ing that promote leukaemogenesis122. More generally, in genome- wide screens, spliceosome- related genes (including SF3B1) are the most enriched category of genes for which reductions in copy number compromise cancer cell survival123,124, again suggesting that limited levels of splicing factors or splicing activity in cancer cells can result in lethality upon further stress in the splicing machinery.
Second, general splicing inhibitors exert stronger, or even more selective, effects on cancer cells than on non- transformed cells101,125,126. Furthermore, the effects of drugs targeting either SF3B1, arginine methylases (which are important for proper snRNP assembly) or other splicing factors are particularly deleterious in cells and tumours harbouring mutations in spliceoso- mal components15,17,18,127,128. Another, possibly related, observation is that melanoma cells that acquire resist- ance to vemurafenib through the generation of splicing variants in BRAF, the target of this drug, revert to the normal pattern of splicing upon treatment with splicing
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inhibitors129 (Fig. 5c, Table 2), perhaps owing to the nega- tive selection of cells generating splicing variants because of their particular sensitivity to these drugs. These find- ings might be explained in terms similar to the synthetic lethal interactions discussed above.
Third, MYC oncogene- activated tumour cells are particularly vulnerable to the depletion or mutation of splicing factors or to treatment with splicing inhib- itors130–132. These observations have been interpreted as a consequence of the high demands imposed on the splicing machinery by the widespread activation of gene expression induced by MYC. This situation might be akin to the global modulation of splicing efficiency in yeast depending upon the expression status of the numerous and abundantly expressed ribosomal protein genes133. These observations again underline the con- cept of the lethal accumulation of splicing stresses in cancer cells. Notably, MYC also drives the expression of a number of splicing regulators, including hnRNP
proteins such as PTB3,131, which have been shown to modulate the alternative splicing of transcripts encoding proteins important for tumour progression, including PKM (explaining the Warburg effect) or the tumour suppressor annexin A7 (explaining lineage- specific enhancement of EGFR signalling)3,92. A number of other networks weaving circuits of transcriptional and post- transcriptional regulation relevant in cancer have been reported75,134,135.
A pervading concept is that alterations in the splicing machinery confer advantages to tumour cells (for exam- ple, through the production of abnormal protein iso- forms or changes in normal cellular isoform ratios) at the cost of reducing the efficiency or fidelity of the splicing process. This precarious equilibrium can be broken by further perturbations of splicing activity (for example, by mutations, inhibitors or excess demand), leading to cytotoxicity and thus revealing splicing as a potential Achilles’ heel of cancer cells (Fig. 5, box 1).
SF3B1 mutations a
BRAF3–9 (resistant to vemurafenib)
AR-V7 (resistant to enzalutamide and abiraterone)
• CD19 • HER2
• AR • ER
eti c l
• CD19 Δex2 splice variant (resistant to anti-CD19 CAR T cells)
• HER2Δ16 (resistant to trastuzumab)
Alternatively spliced receptor
Altered SF expression
• Anti-CD19 CAR T cells
• Enzalutamide or abiraterone
• AR-V7 (resistant to enzalutamide and abiraterone)
• ERα36 (resistant to tamoxifen)
Fig. 5 | Influence of alternative splicing on cancer drug vulnerability and resistance. Mutations or changes in expression of splicing factors can make cancer cells particularly vulnerable to antitumour drugs (synthetic lethality) (part a). Changes in the profile of alternatively spliced isoforms can also generate (part b) or revert resistance to chemotherapy or immunotherapies (part c). The figure provides examples of these three conditions and an extended list is provided in Table 2. AR , androgen receptor; CAR , chimeric antigen receptor; ER , oestrogen receptor; HER , human epidermal growth factor receptor; SF, splicing factor.
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Splicing- based therapeutics in oncology Substantial preclinical work has identified a variety of small- molecule compounds as well as genetic and other approaches to target the spliceosome or its products with potential therapeutic effects (Fig. 6a, Supplementary Table 2). Herein, we focus on two novel approaches with emerging clinical applicability: small- molecule splicing modulators, several of which are currently being tested in clinical trials, and splicing- modifying antisense oligonucleotides.
Small- molecule splicing modulators. Three chemically distinct families of bacterial fermentation pro ducts and synthetic derivatives, FR901464 (including spliceo- statin A, meayamycin and thailanstatins), pladieno- lide B (including E7107, H3B-8800 and FD-895) and GEX1 (including herboxidiene), each sharing a com- mon pharmacophore, have splicing modulatory and anti proliferative or pro- apoptotic activities in vitro and antitumour effects in various mouse models of cancer (reviewed elsewhere7,136–138) (Fig. 6). These com- pounds bind to the HEAT repeats domain of SF3B1 and lock the protein in an open conformation, preventing the transition to a closed conformation that recog- nizes the branch site and flanking pre- mRNA–snRNA helix139,140 (Fig. 6). The establishment of this closed conformation is essential for the first step of catalysis because it helps to bring together the branch- site adeno- sine and the 5′ splice site. How inhibitors of such a fun- damental step can lead to antitumour effects instead of causing general splicing inhibition and cellular toxicity remains an open question. Available evidence indicates that, at concentrations at which the drugs exert anti- tumour effects, these compounds do not induce wide- spread and massive inhibition of the splicing process, but rather retention of particular introns and changes in
alternative splicing in a number of transcripts of genes with functions connected with the control of cell- cycle progression and apoptosis141–144. Understanding the molecular basis for these selective effects could help in the design of antitumour drugs with high potency and specificity towards key target transcripts, thereby limiting potential adverse effects.
E7107, an SF3B1 inhibitor derived from pladieno- lide B, entered clinical testing in 2007 as a first- in- class molecule in open label, phase I dose- escalation studies involving patients with advanced- stage solid tumours not responding to approved therapies (NCT00499499 and NCT00459823). The compound had previously shown promising effects in reducing the growth of human xenograft tumours in mice, without apparent toxicity145. In one clinical study, 8 of 26 patients (31%) had disease stabilization; however, two patients experi- enced optical neuritis and loss of vision (irreversible in one patient) at the maximum tolerated dose (4.3 mg/m2) or at a sub- maximum dose (3.2 mg/m2), leading to dis- continuation of the study146. Another study enrolled 40 patients and, although disease control for 3 months was observed in eight patients (20%), one patient devel- oped bilateral optical neuritis at a dose of 4.0 mg/m2 (reF.147). Why should a general inhibitor of splicing cause specifically ocular adverse effects? One plausible expla- nation is that eye tissues might be particularly sensi- tive to a reduction in splicing activity, perhaps owing to the high proliferative requirements of retinal tissue and/or alterations in the patterns of splicing of genes essential for retinal cell differentiation. Conceivably, these adverse effects could be mechanistically related to the consequences of mutations in splicing factors or in retina- specific alternative exons that have been associated with ocular pathologies, including retinitis pigmentosa148. These mutations affect genes encoding factors such as PRP8, PRP31 or PRP3, which have been implicated in fundamental functions required for the activation of splicing catalysis149. Contrary to expecta- tion, the mutations do not compromise overall organism viability but rather cause ocular pathologies, once again highlighting the functional plasticity of the core splicing machinery150.
H3B-8800 is another pladienolide B- derived SF3B1 inhibitor that has been developed with the specific aim of targeting cancer cells harbouring splicing- factor mutations151. H3B-8800 potently inhibits splicing but preferentially kills epithelial and haematological cancer cells harbouring spliceosomal mutations, possibly by inducing the retention of GC- rich introns in pre- mRNAs encoding other splicing factors151; thus, in this context, H3B-8800 might have synergistic effects on splicing activity that compromise cancer cell viability according to the synthetic lethality paradigm. H3B-8800 entered phase I clinical trials in 2016, with a focus on patients with MDS, AML and CMML (NCT02841540). Initial results revealed dose- dependent target engagement, a predictable pharmacokinetics profile and a favourable safety profile, even with prolonged dosing. Although objective therapeutic responses have not been achieved to date, 14% of patients had reduced requirements for red blood cell or platelet transfusions152.
Box 1 | Splicing-factor mutations and the activity of splicing- based drugs
Sensitization • sF3B1 K700e mutation causes sensitivity to e7107 (reF.15)
• sRsF2 P95H mutation causes sensitivity to e7107 (reF.266)
• u2AF1 s34F mutation causes sensitivity to sudemycins and e7107 (reF.17)
• sF3B1 K700e and sFsR2 P95H mutations cause sensitivity to H3B-8800 (reF.151)
• sF3B1 K700e, sF3B1 K666N, sF3B1 H662Q, sRsF2 P95H and u2AF1 s34F mutations cause sensitivity to e7820 (reF.127)
• sF3B1 Y765C, sF3B1 K700e, u2AF1 s34F, u2AF1 Q157P, sRsF2 P95H and sRsF2 P95l mutations cause sensitivity to inhibition of PRmts128
• several sF3B1 mutations cause sensitivity to sudemycin and ibrutinib, and various mutations in components for the RNA- processing machinery cause sensitivity to sudemycins267
Synthetic lethality • sF3B1 K700e and sRsF2 P95H mutations are synthetically lethal18
• mYC activation is synthetically lethal with inhibition of core components of the spliceosome (by depletion of BuD31, sF3B1, u2AF1 and eFtDu2) and with treatment with sudemycin D6 (reFs130,268)
Resistance • sF3B1 R1074H, sF3B1 v1078A, sF3B1 v1078I and PHF5A Y36C mutations confer
different levels of resistance to H3B-8800, herboxidiene and pladienolide143
• sF3B1 R1074H mutation confers resistance to pladienolide and e7107 (reF.269)
• sF3B1 R1074H and PHF5A Y36C mutations confer resistance to H3B-8800 (reF.151)
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An entirely different mechanism operates in the case of the arylsulfonamides E7820, indisulam, tasi- sulam and chloroquinoxaline, which are experimental anticancer drugs that promote recruitment of the splic- ing factor RBM39 to the E3 ligase substrate receptor
DCAF15, leading to RBM39 ubiquitylation and degra- dation153,154 (Supplementary Table 2). RBM39 is related to the 3′ splice site- recognizing protein U2AF2 and was originally described as a cofactor of steroid- mediated transcriptional and post- transcriptional responses155.
, WNT, JNK
Inhibitors of epigenetic regulators SF/RBM
Core spliceosome inhibitors
mRNA isoform- specific targeting
Inhibition of protein isoforms
ISE ESEISS ESS
Splicing enhancer Steps targeted
Degraded splicing factor
SR, hnRNP or RBM proteins
SF/RBM activity, cellular localization, degradation and expression
Target Inhibitor Clinical trial NCT03901469 NCT02705469
NCT02711956 MK-8628 NCT02259114
Target Inhibitor Clinical trial
SF3B1 H3B-8800 NCT02841540 PRMT1 GSK3368715 NCT03666988
JNJ-6461978 NCT03573310 NCT03614728 NCT02783300
e13 e14a e14b
• Resensitization to chemotherapy
• Inhibition of glioblastoma development
In the presence of drug
In the absence of drug
YYY AGYNUR Y A
Fig. 6 | Approaches to modulate cancer-relevant splicing events. Tools have been implemented to induce splicing changes at various levels, including modulators of signalling pathways regulating RNA- binding motif (RBM) proteins and splicing factors (SFs) involved in splicing; compounds directly targeting spliceosomal components, including antitumour drugs that bind in the interface between SF3B1 and PHF5A components of the U2 small nuclear ribonucleoprotein (snRNP) (the top right inset illustrates how U2 snRNP can be redirected to a decoy , unproductive branch site in the presence of the drug); drugs that target protein–protein or protein–RNA
interactions that affect splice- site accessibility; antisense oligonucleotides that induce switches in splice- site utilization (illustrated in the bottom right inset) or that target specific mRNA isoforms; and isoform- specific antibodies that inhibit protein function. An extended list of examples is provided in Supplementary Table 2. ClinicalTrials.gov identifiers (NCTs) for trials of small- molecule modulators of these steps are indicated as well as their targets. ESE, exonic splicing enhancer; ESS, exonic splicing silencer; hnRNP, heterogeneous nuclear ribonucleoprotein; ISE, intronic splicing enhancer; ISS, intronic splicing silencer; SR , arginine–serine- rich.
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Degradation of RBM39 results in altered cassette exon inclusion and/or skipping and thus in cytotoxicity in a number of cancer cell lines153. Interestingly, haemato- poietic or lymphoid malignancies harbouring splicing factor mutations are particularly sensitive to indisulam cytotoxity127, again supporting the idea of synthetic lethality between different splicing deficiencies. RBM39 regulates a splicing programme that is crucial for the survival of AML cells, owing to the involvement of key leukaemogenesis genes, including HOXA9, BMI1 and GATA2 (reF.127). A phase II study in patients with relapsed and/or refractory AML or high- risk MDS revealed the beneficial effects of indisulam adminis- tration along with chemotherapy in patients stratified according to DCAF15 expression, with an estimated 1- year overall survival of 51% in responders com- pared with 8% in non- responders156. The more general implication of these observations is that, on the basis of structural insights into the RBM39 RNA recognition motif domain and DCAF15 complexes in the presence of existing arylsulfonamide drugs157–160, other arylsulfo- namide derivatives might be generated to specifically target other RNA recognition motif domain- containing RNA- binding proteins.
As alluded to earlier, EPZ015666 (also known as GSK3235025), an inhibitor of the arginine N- methyltransferase PRMT5, prevents splicing of cer- tain introns and reduces the growth of patient- derived glioblastoma xenografts16, as well as mantle- cell lym- phoma xenografts161. Other inhibitors of PRMT5 and of the related enzyme PRMT1 are in phase I clinical trials involving patients with a variety of advanced- stage solid tumours and haematological malignan- cies (NCT03573310, NCT02783300, NCT03614728, NCT03666988 and NCT03854227) (Fig. 6). PRMT5 catalyses symmetric dimethylation of the terminal amino group of the side chain of arginine, whereas PRMT1 catalyses asymmetric dimethylation162. Inhibitors of these enzymes display synergistic effects163, consistent with each having distinct sub strates within the splice- osome. Indeed, PRMT5 modifies snRNP- associated Sm proteins, and PRMT1 modifies the splicing regula- tor RBM15 (reFs164,165). Whether the antitumour effects of these inhibitors occur through these targets, other targets in the spliceosome or proteins of other cellular machiner- ies (such as those involved in transcription or chromatin remodelling) remains to be formally proven.
A number of other drugs targeting splicing factors have shown encouraging preclinical effects in mouse models of cancer. These drugs include inhibitors of SRPK and CLK protein kinases that phosphorylate SR proteins and thereby inhibit angiogenesis by inducing changes in the alternative splicing of VEGF166,167 (Fig. 6). Other splicing inhibitors targeting a variety of spliceo- somal components also reduce cancer cell proliferation in vitro168–171, but their effects in animal models of cancer are not yet known.
Antisense oligonucleotides. Antisense oligonucleotides provide an entirely different therapeutic approach to splicing modulation (Fig. 6, Supplementary Table 2). These molecules bind to pre- mRNA sequences (splice
site or splicing regulatory motifs), thereby preventing their recognition by spliceosomal or regulatory factors and causing splice site switching172. The main advantage of this approach is the selectivity provided by the recog- nition of specific target sequences; the main difficulty is delivering oligonucleotides to particular tissues, as they often accumulate in the liver and kidney172. The use of nusinersen in the treatment of patients with spinal muscular atrophy is paradigmatic of this therapeutic approach. Nusinersen is an antisense oligo nucleotide directed against an intronic silencer in SMN2, which, by enhancing the inclusion of exon 7 in SMN2 mRNAs, restores the levels of functional SMN proteins in patients with spinal muscular atrophy who have inactivating mutations in SMN1 (reFs173,174). Maintenance treatments (consisting of quarterly intrathecal lumbar injections of 12 mg nusinersen) have remarkable therapeutic effects that have improved the quality of life and life expectancy in these patients175. Interestingly, orally bioavailable small- molecule modulators that enhance the recogni- tion of the 5′ splice site of SMN2 exon 7 with considera- ble specificity are under development176; branaplam and risdiplam are currently in clinical trials (NCT02268552, NCT02908685, NCT03779334, NCT03988907, NCT03920865, NCT02913482 and NCT03032172), although — as was the case for E7107 — eye toxicity has been reported in one of these trials.
Preclinical work suggests that antisense oligonucleo- tides might also have therapeutic value in oncology (reviewed elsewhere7) (Supplementary Table 2). For example, antisense oligonucleotide- mediated redirection of Bcl- x pre- mRNA splicing in favour of the Bcl- x(S) isoform induces apoptosis in breast or prostate cancer cells177,178. In mouse models, the administration of such oligonucleotides using lipid nanoparticles resulted in the modification of Bcl- x pre- mRNA splicing in lung metastases of melanoma xenografts and reduced the tumour burden179. Another example is provided by the antisense oligonucleotide- mediated switch between alternatively spliced isoforms of the kinase MKNK2 with antagonistic oncogenic or tumour- suppressor proper- ties (Fig. 6); use of this therapeutic approach activates the p38 MAPK- signalling pathway and inhibits the onco- genicity of glioblastoma cells, re- sensitizing these cells to chemotherapy, and inhibits the growth of a glioblastoma cell line in a mouse xenograft model180. As mentioned earlier, antisense oligonucleotides can also be used as decoys to titrate away tumour- promoting RNA- binding factors as illustrated by the induction of apoptosis and inhibition of cancer growth with antisense oligonucle- otides containing binding sites for transcripts of the SRSF1 oncogene82.
Conclusions In this Review, we have highlighted the functional effect of perturbations in the splicing process and of mutations or changes in the activity of splicing factors in cancer. In addition to changes in other steps of RNA processing, including 3′ end formation, RNA editing and RNA mod- ifications that also contribute to cancer biology (reviewed elsewhere7,181,182), the monitoring of splicing alterations can provide abundant and effective biomarkers for use in
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the diagnosis, prognostication, therapy and monitoring of patients with cancer. For this promise to be realized, an urgent need exists for the development of highly sen- sitive, specific and cost- effective assays for the detection of alternatively spliced isoforms, including solutions to the challenges of detecting transcript variants in single cells and of implementing cost- effective next- generation sequencing methods in the clinic183. Systems biology approaches are also needed to provide an integrated view of the gene- expression landscape of cancers and identify key regulatory networks and hubs75,134,184 that might present particularly important therapeutic vul- nerabilities. However, crucial to our understanding is whether reversal of a pathogenic state can be reached through targeting a small number of key transcriptome changes or whether therapies will need to revert wider regulatory programmes. Related to this concept is the open question of whether cancer- relevant mutations in splicing factors act through unifying mechanisms and common targets69, which could help to identify general therapeutic approaches.
Given the relatively limited conservation of mecha- nisms regulating alternative splicing between mouse and humans, the development of humanized animal models
as well as human organoids will be essential to model cancer initiation and progression as well as to test novel, splicing- based therapeutic approaches. Developing more effective therapeutic agents, including small molecules that target alternative splicing events controlling cell pro- liferation, apoptosis or other key hallmarks with high specificity, is another key challenge. The combination of structural and transcriptomic approaches should help to reveal the molecular basis of the activities of these mol- ecules on spliceosome function and to rationalize their design. The identification of small- molecule modulators of RNA structure able to induce splice site- selection switches is another priority area for future studies185. Further research into chemical modifications that improve the stability, specificity and delivery of antisense oligonucleotides as well as a better understanding of the mechanisms of their cellular uptake will help advance these agents to clinical stages of testing. Conceivably, future personalized therapies will exploit the specificity and versatility of such agents through the use of ‘cock- tails’ of antisense oligonucleotides targeting the specific profile of splicing alterations of each tumour or patient.
Published online 17 April 2020
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Acknowledgements The authors thank Dolors Colomer, Armando López- Guillermo and members of their laboratory for critical reading of the manuscript, and Adrian Krainer, Omar Abdel- Wahab and Rotem Karni for their constructive critique during the review process. I.L.- O. is a recipient of a Severo Ochoa PhD4MD Program Fellowship. The work of the authors is supported by the European Research Council, Worldwide Cancer Research, the Spanish Ministry of Economy and Competitiveness, the Agència de Gestió d’Ajuts Universitaris i de Recerca, and the Centre of Excellence Severo Ochoa Award (to the Centre for Genomic Regulation of the Barcelona Institute of Science and Technology).
Author contributions All authors made a substantial contribution to all aspects of the manuscript.
Competing interests J.V. is a member of the Scientific Advisory Boards of Remix Therapeutics and Stoke Therapeutics. The other authors declare no competing interests.
Reviewer information Nature Reviews Clinical Oncology thanks Seishi Ogawa, Rotem Karni and another, anonymous, reviewer for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information Supplementary information is available for this paper at https://doi.org/10.1038/s41571-020-0350- x.
RelaTed linkS ClinicalTrials.gov: https://www.clinicaltrials.gov
© Springer Nature Limited 2020
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- Roles and mechanisms of alternative splicing in cancer — implications for care
- The splicing machinery and cancer
- The spliceosome.
- Spliceosome assembly.
- Altered expression of splicing factors in cancer.
- Splicing programmes in oncogenesis
- Splicing addiction of cancer cells
- Splicing-factor mutations and the activity of splicing-based drugs
- Splicing-based therapeutics in oncology
- Small-molecule splicing modulators.
- Antisense oligonucleotides.
- Fig. 1 Pre-mRNA splicing and the spliceosome assembly pathway.
- Fig. 2 Alternative splicing.
- Fig. 3 Effect of cancer-associated mutations in splicing factors on alternative splice site selection.
- Fig. 4 Effect of alternative splicing dysregulation on cancer progression.
- Fig. 5 Influence of alternative splicing on cancer drug vulnerability and resistance.
- Fig. 6 Approaches to modulate cancer-relevant splicing events.
- Table 1 Recurrent splicing-factor mutations in cancer and associated prognosis.
- Table 2 Splicing-based modulation of drug responses.
The advent of immunotherapy has revolutionized the treatment of many forms of cancer. It is now well estab- lished that T cells have the ability to reject tumours upon binding to antigenic peptides, derived from endogenous cellular proteins or exogenous viral proteins, presented by the major histocompatibility complex (MHC) on the surface of tumour cells. Several promising immuno- therapeutic anticancer approaches, such as therapeutic vaccines and T cell receptor engineered T cells (TCR- T cells) for adoptive cell therapy, rely on the identification of suitable target antigens1. Historically, the focus has been on three classes of tumour antigens: tumour- specific somatic non- synonymous mutation- derived neoantigens; cancer germline antigens; and antigens derived from viral genes that are expressed by virally infected tumour cells (for example, E6/E7 from human papilloma virus)2. Clinical studies have revealed remarkable outcomes both for TCR- T cell therapy targeting cancer germline anti- gens and for neoantigen- based vaccines1. For instance, TCR- T cells targeting NY- ESO-1, a tumour- specific shared germline antigen, have been shown to mediate sustained antigen- specific antitumour effects in patients with multiple myeloma, as well as several other can- cer types3–5. Further, personalized vaccines targeting mutation- derived neoantigens have been shown to elicit strong neoepitope- specific T cell responses in patients with melanoma (an immunologically ‘hot’ tumour with a high tumour mutational burden (TMB)) and glioblas- toma (an immunologically ‘cold’ tumour with a relatively low TMB)6–10.
Despite the unprecedented durable response rates observed with cancer immunotherapies in some patients, one of the major obstacles for the broader appli- cability of such therapies is the lack of currently known targetable tumour- specific antigens (TSAs) for many cancer types1. The selection of appropriate antigens is
critical to ensure the safety and efficacy of immuno- therapy. Melanoma- associated antigen 3 (MAGE- A3) and melanoma antigen recognized by T cells 1 (MART-1) have been two leading target antigens for TCR- T cell- based cancer therapies due to their frequent expression in several tumour types and their restricted/low expres- sion in normal tissues. However, several clinical cases with unexpected severe off- target toxicities have been reported11–13. For example, in patients with melanoma, immunotherapy with T cells engineered with a high- affinity T cell receptor (F5-TCR) targeting MART-1 showed higher clinical efficacy compared with treatment with T cells engineered with a relatively low- affinity TCR (F4-TCR) but also caused uveitis, vitiligo and hearing loss due to MART-1 expression on melanocytes in the eye, skin and middle ear14. TCR- T cells and vaccines that target neoantigens may enable safer and more durable antitumour effects15, although mutational loads vary widely across different tumour types and identifying suitable targets remains a problem16.
In focusing on somatic mutation- derived neoanti- gens in tumour cells, possible neoepitopes derived from mRNA processing events are often overlooked. With respect to cancer, the most well- studied mRNA process- ing event, and the focus of this review, is mRNA splicing. Nonetheless, processing events such as mRNA poly- adenylation and mRNA editing have also been shown to play a role in tumour development and can result in an increased immunotherapy target space (Box 1). The advent of next- generation sequencing technologies has allowed for a wealth of transcriptomic data to be generated. Such data have helped to illuminate the widespread nature of alternative processing in cancer17 and have the potential to be used to identify neoepitopes derived from tumour- specific mRNA processing events, thereby expanding the repertoire of suitable targets for
Major histocompatibility complex (MHC). A set of genes that code for cell surface proteins (most notably the MHC class I and class II glycoproteins) that are responsible for presenting antigens to lymphocytes.
Adoptive cell therapy A type of immunotherapy approach that uses antigen- specific T cells to treat patients with chronic viral infections or various malignancies.
Non- synonymous mutation A nucleotide mutation that changes the amino acid sequence of a protein.
Alternative mRNA splicing in cancer immunotherapy Luke Frankiw1, David Baltimore 1* and Guideng Li 2,3*
Abstract | Immunotherapies are yielding effective treatments for several previously untreatable cancers. Still, the identification of suitable antigens specific to the tumour that can be targets for cancer vaccines and T cell therapies is a challenge. Alternative processing of mRNA, a phenomenon that has been shown to alter the proteomic diversity of many cancers, may offer the potential of a broadened target space. Here, we discuss the promise of analysing mRNA processing events in cancer cells, with an emphasis on mRNA splicing, for the identification of potential new targets for cancer immunotherapy. Further, we highlight the challenges that must be overcome for this new avenue to have clinical applicability.
1Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA. 2Center of Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China. 3Suzhou Institute of Systems Medicine, Suzhou, China.
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Box 1 | Beyond RNA splicing: non- canonical neoepitopes
RNA splicing is just one of the processing steps that occurs in a pre- messenger RNA transcript (pre- mRNA) before the formation of a mature transcript. Although splicing is the best- studied process with respect to cancer, dysregulation of other processing steps is known to occur. In particular, polyadenylation (pA) and RNA editing have the potential to alter the proteome of a cancer cell and, thus, like RNA splicing, the identification of such events might broaden the immunogenic target space.
Polyadenylation involves the cleavage and addition of a stretch of adenosines, termed the poly(A) tail, to the 3ʹ end of the vast majority of eukaryotic mRNAs. Polyadenylation is complicated by the fact that the majority of human genes contain more than one pA site and that mRNA transcripts are frequently alternatively polyadenylated156. The majority of alternative polyadenylation (APA) sites are in the 3ʹ untranslated region (uTR) and can alter the stability, localization and translation of a given transcript157. However, there are many APA events that are located in intronic regions upstream of the last exon which act to generate either non- coding transcripts or transcripts with truncated coding regions158. The classic intronic polyadenylation (IPA) event occurs in the Igm heavy chain mRNA wherein, upon activation, a proximal IPA site is used, resulting in a shift to the secreted form of the antibody from the membrane- bound form159 (see the figure, part a).
The increased transcriptome complexity created through APA carries with it the risk of gene dysregulation, and it is perhaps no surprise that APA has been associated with tumorigenesis160–163. Recent work has focused on IPA, which has been shown to be a common mechanism of tumour- suppressor inactivation in chronic lymphocytic leukaemia162. Further, it was shown that the kinase CDK12, which is a key regulator of transcription elongation, also has a role in regulating genes involved in DNA repair by suppressing IPA163. In CDK12 mutant tumours, loss of suppression of IPA leads to impaired production of full- length (Fl) gene products for several genes involved in DNA repair. With respect to immunotherapy, the identification of IPA events is exciting due to the potential discovery of tumour- specific peptides. When cancer- specific IPA events occur in the coding region, sequences downstream of the nearest 5ʹ splice site (SS) and upstream of the new polyadenylation site will be translated, creating peptides that might be presented on major histocompatibility complex (mHC) molecules and recognized by the immune system. These IPA events commonly occur in genes that are important for disease progression162,163, which makes such peptides excellent immunotherapy targets. However, peptides derived from tumour- specific IPA events that bind to mHC molecules have yet to be identified and it is uncertain how immunogenic such peptides would be. As more data from methods such as 3ʹ seq164, which is used to identify and quantify polyadenylation site usage, become available, we will better understand the extent to which IPA events can alter the immunotherapeutic target space.
Another step in processing of pre- mRNA is RNA editing (see the figure, part b). The most common form of RNA editing involves the conversion of adenosine to inosine (A- to-I), a process catalysed by the adenosine deaminases acting on RNA (ADARs)165,166. Because most cellular machinery interprets inosine as guanine167, A- to-I editing can alter the amino acid sequence coded by a given transcript. like splicing and polyadenylation, RNA editing has been shown to be dysregulated in many types of cancer168–171, and it was recently reported that peptides derived from over- edited transcripts are presented by mHC molecules in a subset of tumour samples172. most prevalent in ovarian cancer, breast cancer and melanoma, it was further shown that effector CD8+ T cells specific for such peptides were present in the respective tumours, indicating that the peptides are indeed immunogenic. It is important to note that these peptides cannot be considered tumour specific. As editing still occurs in healthy tissue, and peptides derived from editing events are present on mHC molecules of healthy tissues, these over- edited peptides can be classified as tumour- associated shared self- antigens. As such, the therapeutic window that offers efficacy with limited toxic effects would first need to be defined for potential therapies targeted at such peptides.
extending the focus beyond RNA processing, recent work has uncovered several other non- canonical neoantigens that promise to greatly expand the immunotherapy target space. For example, a complete response to anti- programmed cell death 1 (PD-1) therapy was reported to have been mediated by an immune response targeted at an immunogenic peptide derived from a gene fusion event173. The authors suggest that the immunodominant epitope underlying regression of the tumour is probably derived from a DEK–AFF2 fusion expressed in tumour cells. expanding the analysis to 30 different cancer types revealed that 24% of cancers that expressed fusion proteins had a fusion- derived neoepitope predicted to bind to patient- specific mHCs173. Finally, in a cohort of patients with melanoma who responded to anti- PD-1 therapy, it was shown that predicted fusion neoantigens were eliminated, likely due to immune evasion173. This implicates gene fusions as a source of immunogenic neoantigens that could serve as a predictive biomarker for checkpoint inhibitor response.
Downstream pA siteIPA site
IPA isoform FL isoform AAAAAA
Potential IPA-derived neoepitopes
5′ SS 3′ SS 5′ SS 3′ SS
Antigen processing and presentation
Neoantigens Newly formed antigens that have not been previously recognized by the immune system.
Cancer germline antigens Antigens that are normally exclusively expressed in germline cells but have aberrant expression in tumours, such as NY- ESo-1.
Tumour mutational burden (TMB). Also referred to as the tumour mutational load, this is a measurement of mutations carried by tumour tissue taken from a patient.
Tumour- specific antigens (TSAs). Antigens that are exclusively presented by tumour cells but not by any other cells.
RNA editing A molecular process resulting in alteration of the RNA sequence before translating to protein.
Alternative polyadenylation (APA). An RNA- processing event that generates distinct
3ʹ termini on mRNAs and other RNA polymerase II transcripts.
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immunotherapy. Particularly for cancers such as B cell acute lymphoblastic leukaemia, which has a low preva- lence of somatic mutations and copy number variations but displays widespread mRNA splicing aberrations, the expanded target space could lead to the develop- ment of efficient immunotherapies18. Further, because the somatic mutation- derived neoepitope load has been shown to positively correlate with response to immune checkpoint blockade therapy in many cancer types19–24, uncovering processing- derived neoepitopes might offer clinical utility as a predictive biomarker. In this Review, we explore emerging evidence suggesting that mRNA processing- derived neoantigens can be suitable TSAs for cancer immunotherapy and discuss the major challenges that lie ahead.
Alternative mRNA splicing in cancer The processing of pre- mRNA transcripts (pre- mRNAs) represents an essential step in the ultimate function- ality of a gene product. The vast majority of human genes contain multiple exons, with adjoining intronic sequences that need to be spliced from a transcribed pre- mRNA to form the mature mRNA. Alternative splicing, a process by which a single pre- mRNA can be variably spliced into unique mature transcripts, can contribute to transcriptomic and proteomic diversity25–27 (FIg. 1a). This process is tightly regulated in different tissues, cell types and differentiation stages28–33. Of note, one specific
alternative splicing event, intron retention, can be derived from a regulated process affecting select junctions, or from a lack of processing throughout an entire gene30,32. Although the association between dysregulated splicing events with specific cancers has been known for many years34, the recent transcriptomic characterization of cancers has led to the finding that such events are much more frequent than previously predicted18,35–39.
Although our understanding of the extent to which specific alternative splicing events drive tumorigenesis is still evolving, there are several factors that help to explain the widespread dysregulation of splicing in cancer. First and foremost, a surprising finding from the genomic characterization of different cancers was the recur- rent somatic mutations found in genes encoding core spliceosome components as well as in trans- acting splic- ing factors that are essential to the regulation of alter- native splicing40,41. Frequent mutations in components of the spliceosome were initially detected in patients with myelodysplastic syndrome42–44 and chronic lymphocytic leukaemia45,46 but later also found in a wide variety of solid tumours such as breast cancers47–49, pancreatic ductal adenocarcinoma50, uveal melanoma51,52 and lung adenocarcinoma53. Mutations that affect spliceosomal components can alter splicing efficiency and splice- site selection. For example, a recent transcriptomic analysis of chronic lymphocytic leukaemia cells revealed that mutations in the small ribonuclear protein (RNP) U2
Alternative splicing A regulated process during gene expression that results in a single gene coding for multiple proteins.
Intron retention A form of alternative splicing that results in inclusion of introns in the final protein product.
Spliceosome The multi- megadalton ribonucleoprotein complex responsible for removing introns from pre- mRNA sequences.
Alternative 5′ splice sites
Alternative 3′ splice sites
Mutually exclusive exons
a b Mature mRNA
Alternatively spliced protein
Fig. 1 | Alternative splicing and immunotherapy. a | Schematic depicting constitutive splicing, as well as the five common modes of alternative splicing: exon skipping/inclusion, alternative 5′ splice- site selection, alternative 3ʹ splice- site selection, intron retention and mutually exclusive exons. Shown on the right are the mature mRNA transcripts derived from each event. b | Alternative splicing- derived proteins can be processed into 8–11 residue peptides by the proteasome. They are then shuttled into the endoplasmic reticulum (ER) via the transporter associated with antigen processing (TAP), after which they can be loaded onto major histocompatibility complex (MHC) class I. Peptide–MHC complexes can then be recognized by T cells.
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component SF3B1, which is involved in 3ʹ splice- site recognition, resulted in increased levels of alternative 3ʹ splice- site events54. A recent re- analysis of data from The Cancer genome Atlas (TCGA) showed that 119 genes that encode core spliceosome and splicing factors carry putative driver mutations across 33 different tumour types, highlighting the extent to which such mutations affect cancer development55. Beyond the somatic muta- tions found in these splicing- related genes, there is a large body of work implicating the altered expression of genes encoding splicing factors in the widespread dysregulation of alternative splicing in cancer40,56,57. The regulation of alternative splicing is frequently carried out by trans- acting splicing factors, which bind to specific sequence motifs and promote or repress a given splicing event58. Several studies have shown that altered expres- sion of such factors occurs in numerous different cancers and can be linked to malignant transformation59–68. For example, MYC has been shown to upregulate hetero- geneous nuclear RNP (hnRNP) A1, hnRNP A2 and polypyrimidine tract binding protein B (PTB) in glio- mas, promoting the expression of the pyruvate kinase M2 (PKM2) isoform over the PKM1 isoform61,62. PKM2 activity can be regulated through the binding of various allosteric ligands, in turn allowing a cell to shunt glucose carbons towards other biosynthetic processes, which provides a selective advantage for cancer cells.
In addition to the alterations of spliceosome com- ponents and trans- acting splicing factors, mutations found in cis- regulatory elements have also been shown to alter alternative splicing and thereby affect tumori- genesis37,69–72. The conserved cis- regulatory elements of an intron include the 5ʹ and 3ʹ splice sites located at intron– exon junctions, the branch point sequence located near the 3ʹ end of an intron and the polypyrimidine tract located downstream of the branch point73. The dinucleo- tides GT and AG define the 5ʹ and 3ʹ splice sites for ~99% of annotated introns74. The branch point is 15–50 nucleotides upstream of the 3ʹ end of the intron and con- tains an adenine nucleotide that is important for the first transesterification reaction involved in splicing75. Apart from these core elements, numerous motifs exist in both exonic and intronic sequences that act to recruit RNA- binding proteins, which in turn activate or repress splic- ing at neighbouring junctions76. Mutations that create or destroy these cis- regulatory elements change splice- site selection and thus result in multiple RNA isoforms.
One mechanism by which such a mutation can lead to tumour development involves the inactivation of tumour suppressor genes. It has recently been shown that cis mutations leading to unproductive splicing events can act as a mechanism of tumour suppressor inactivation in a range of different cancers37,71. This occurs when an alternative splicing event introduces either a frameshift, which alters the amino acid sequence of a functional protein domain in the gene, or a premature termination codon, which subjects the transcript to degradation via the nonsense- mediated decay machinery77,78. Although splice sites are the most well- studied cis- regulatory ele- ment with respect to cancer- related mutations, intronic and exonic motifs within the pre- mRNA that are bound by RNA- binding proteins have an important role in
splicing regulation and, when mutated in cancer, have been shown to alter splicing decisions72,79,80. It is worth noting that although it may be a good starting point to predict splicing impact based on how a given mutation alters a splicing cis- regulatory element, these elements are not the only factors that constitute the ‘splicing code’. In fact, focusing only on these elements may dramati- cally underestimate how many somatic mutations affect splicing. A recent study analysed 8,656 paired DNA sequencing and RNA sequencing (RNA- seq) samples from the TCGA and found 1,964 somatic mutations that impact splice- site usage37. Such mutations were called splice- site- creating mutations (SCMs). Of the identi fied SCMs, 26% had been previously mis- annotated as mis- sense mutations and 11% had been mis- annotated as silent mutations. An increased wealth of genomic and transcriptomic data, as well as new tools such as the recently published SpliceAI81, allow deeper insights into the “splicing code” and have the potential to dramatically impact our understanding of the relationship between splicing and disease development.
The impact of alternative mRNA splicing on the target space for cancer immunotherapy. Several recent stud- ies have shown that peptides derived from tumour- specific mRNA splicing events have the potential to bind to MHC class I (MHC I) molecules where they serve as neoepitopes37,38,82 (FIg. 1b). One study com- prehensively analysed TCGA data to investigate alter- native splicing across 8,705 patients and found that tumours consistently bear more alternative splicing events compared with healthy tissue38. Restricting the downstream analysis to 63 breast and ovarian cancers that had corresponding mass spectrometry (MS) data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), it was found that 68% of the tumours con- tained one or more alternative splicing- derived neo- epitopes. In contrast, only 30% of the tumours contained a neoepitope derived from a somatic single- nucleotide variant event. Such findings highlight the vast increase in target space that can be gained by analysing splicing- derived neoepitopes in addition to single- nucleotide variant- derived neoepitopes.
Although this work showed the potential broadened target space associated with the inclusion of splicing- derived events, it may in fact still underestimate the true number of splicing- derived neoepitopes. This is because the study only considered neoepitopes with MS evidence from the CPTAC38. However, trypsin is used to generate peptides from full- length proteins for MS, and it has been shown that there is a cleavage bias towards exon–exon junctions83. Trypsin cleaves lysine and argi- nine, which are encoded by conserved nucleotides at exon–exon boundaries83. This, in turn, can obscure the detection of peptides derived from neojunctions. Further, the analysis focused solely on peptides derived from tumour- specific splice junctions. Thus, peptides that either reside completely within a skipped exon, or within a retained intron, were missed. With respect to the latter, a second study looked specifically at intron retention events and concluded that the inclusion of retained intron- derived neoepitopes roughly doubled
The Cancer Genome Atlas (TCgA). The world’s largest and richest collection of genomic data.
Nonsense- mediated decay A translation- coupled mechanism that degrades mRNAs harbouring premature translation- termination codons.
Splice- site-creating mutations (SCMs). genomic mutations that induce splice- site creation. often mis- annotated as missense and silent mutations.
Clinical Proteomic Tumor Analysis Consortium (CPTAC). The first large- scale project that produced proteomics data sets from the mass spectrometric interrogation of tumour samples previously studied by The Cancer genome Atlas programme.
Neojunctions Novel exon–exon junctions found in tumour samples that are not typically found in healthy tissue.
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the estimate of the total neoepitope load82. This find- ing may be surprising considering the fact that many transcripts that retain an intron incorporate a premature termi nation codon and are considered non- functional due to rapid degradation via the nonsense- mediated decay pathway84. However, degradation occurs following the pioneer round of translation and it has been shown that peptides produced during the pioneer round can bind to MHC I molecules85.
The potential for splicing- derived neoepitopes to expand the immunotherapy target space is further sup- ported by the study of paired DNA and RNA samples from the TCGA as described above37. It was found that, on average, an SCM generated more than two times as many neoepitopes per mutation as compared with an average non- synonymous mutation. Furthermore, a substantial number of these neoantigen events were recurrent (present in three or more samples), including events in important cancer- related genes such as GATA3, TP53 and PTEN. Such events have the potential to make excellent immunotherapy targets, although the extent to which they are immunogenic remains unknown.
Although the aforementioned work directly impli- cates splicing in the generation of tumour- specific neoepitopes37,38,82, another notable study published this year indirectly implicates RNA splicing86. Here, the authors developed a proteogenomic strategy involving MS that was capable of identifying aberrantly expressed TSAs, which are cancer- restricted non- mutated epitopes, in a high- throughput manner. They found that a signi- ficant majority of neoepitopes from two murine can- cer cell lines and seven human primary tumours were derived from the translation of out- of-frame coding exons or non- coding regions, many of which proved to be immunogenic. With respect to the former, it can be speculated that such frameshifts could be derived from dysregulated alternative splicing events. However, there are other potential mechanisms, including indels, which can also generate highly immunogenic tumour neoantigens87. Further, several peptides were derived from intronic sequences, again implicating dysregu- lated splicing86. Other non- coding regions that pro- duced aberrantly expressed TSAs include intergenic sequences, non- coding and untranslated regions, and even endogenous retroelements86.
Finally, splicing- derived neoantigen discovery might also be useful as a predictive biomarker for response to immune checkpoint blockade therapy. Immune checkpoint cascades, such as those controlled by pro- grammed cell death 1 (PD-1)88–90 or cytotoxic T lympho- cyte antigen 4 (CTLA-4)91–93, act as negative regulators of immune activation and blocking these with mono- clonal antibodies revolutionized the treatment of many cancers, resulting in unprecedented rates of long- lasting tumour responses94. As might be expected, it was found that the TMB correlates with an increase in neoanti- gens displayed by MHC I and II molecules95,96. Several studies have shown a correlation between the response to checkpoint inhibitors (CPIs) and the TMB in different tumour types19–24, such as melanoma, urothelial carci- noma, head and neck cancer and non- small-cell lung cancer. However, the data clearly show that the TMB is not
the only factor that dictates the response to CPIs. Some patients with high TMB respond poorly to CPIs, whereas some patients with low TMB respond well19–23. Given the number of potential neoepitopes derived from mRNA splicing events, it is reasonable to hypothesize that the splicing- derived neoepitope load might be of use as a clinical biomarker for response to CPIs. In fact, the study of SCMs described above found a higher expres- sion of the programmed cell death 1 ligand 1 (PD- L1) in tumours with an SCM as compared with tumours with- out an SCM, suggesting PD- L1 immunotherapy might be more effective in tumours with SCMs37. However, no association was found between the neoepitope load derived from intron retention events and the clinical benefit from CPIs using data from two cohorts of mela- noma patients treated with these drugs19,82,97. A poten- tial explanation for this result stems from the fact that only intron retention events were analysed and the data only involved patients with melanoma, a cancer with an extremely high TMB16. It is possible that a correlation of the splicing- derived neoepitope load with clinical effi- cacy is more pronounced in tumours with a low TMB. As more clinical data from CPI- treated patients become available, it will be interesting to determine the extent to which the mRNA splicing- derived neoepitope burden can serve as a predictive biomarker for CPI response.
Technological and biological challenges Recent technological innovations have made it possi- ble to identify tumour- specific mRNA splicing- derived neoantigens (FIg. 2). Such neoantigens can present a new class of cancer immunotherapy targets. However, numerous challenges remain for the development and application of immunotherapies targeting these mRNA splicing- derived neoantigens. These challenges include the identification of tumour- specific mRNA splicing events, the validation of peptide presentation, specificity and crossreactivity, immunogenicity and the prevention of tumour escape arising from tumour heterogeneity and evolution.
Identification of tumour- specific splicing events. The accurate identification of tumour- specific mRNA splic- ing events will be central to the ultimate efficacy of immunotherapies targeting neoantigens. With respect to the analysis of RNA- seq data, there are numerous commonly used computational tools that allow the quantification of alternative splicing (Box 2). Still, given that potential off- target effects of cell- based immuno- therapies have drastic consequences12,13, there are several questions regarding the classification of splicing events as tumour specific. A central question concerns the controls that are needed in the mRNA splicing- derived neoepitope identification process to ensure the safety of a potential therapy directed against it. A comparison with matched adjacent healthy tissue is the obvious first step; however, given the fact that splicing is uniquely regulated in different tissues33, is the comparison with matched adjacent healthy tissue sufficient or would dif- ferent tissue types need to be surveyed? As it is not fea- sible to acquire tissues from multiple vital organs from the same patient, a comprehensive comparison with a
Indels Insertion or deletion of nucleotides into genomic DNA, less than 1 kb in length.
Checkpoint inhibitors (CPIs). Types of drug that block the inhibitory checkpoint molecules.
Crossreactivity The recognition of two or more peptide–major histocompatibility complex complexes by a T cell receptor.
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database of human healthy tissues is likely needed to confidently select a given target. Along the same lines, what level of signal in healthy tissue, if any, would be considered acceptable? This is best highlighted in the case of intron retention, where, despite their transient nature, signals from introns can still be captured in actively transcribed pre- mRNAs that do not contain an intron retention event due to the time it takes for splic- ing to occur. Finally, it may also need to be determined whether a splice variant is expressed at other develop- mental stages, as it would be expected that neoepitopes derived from such events will likely suffer from a lack of immunogenicity.
A technical challenge is presented by the fact that mRNA splicing- derived neoepitope analysis has so far relied on bulk RNA- seq37,38,82. Although bulk RNA- seq has provided important insights, the technique lacks the ability to detect splicing effects at the subclonal level. As described in detail below, it has been shown that individual tumours have intratumoural heterogeneity with respect to individual splicing events98. Therapies that target a specific event present in only a fraction of the tumour will therefore lack efficacy. The development
of single- cell RNA- seq (scRNA- seq) offers the potential to identify splicing events that are present in all cells of a tumour. However, the combination of alternative splicing analysis with scRNA- seq is still technically dif- ficult (reviewed elsewhere99) as scRNA- seq relies on a low amount of starting material, which can restrict the analysis to highly abundant transcripts100. These analyses become even more problematic when studying isoform abundance in non- mutually-exclusive cases as non- dominant isoforms tend to be expressed at low levels and, thus, are susceptible to ‘drop- out’99. Further, the low sequence coverage common to scRNA- seq data makes it difficult to accurately characterize splicing variations in low- abundance transcripts. This problem might be alleviated through the development of machine learn- ing algorithms such as the recently published DARTS101, which offers the ability to better characterize splic- ing variations in transcripts with minimal coverage. In conclusion, although it is currently difficult to accu- rately quantify splicing with scRNA- seq, technological advances in both library preparation and sequencing methods, as well as new computational strategies that are tailored to the challenges of scRNA- seq data (namely
AGTCAGTumour tissue specimens
Normal tissue specimens
Identify antigen- specific TCRs
Express TCR in donor T cells
Adoptive transfer of TCR-T cells
Pools of synthetic peptides
Prediction and verification • MHC binding prediction • Mass spectometry
confirmation • T cell assay verification
Alternative splicing analysis Normal vs tumour tissues
Fig. 2 | Schematic illustration of the development of potential immunotherapies targeting mRNA processing- derived neoantigens. Tumour tissue and adjacent normal tissue specimens are obtained from a cancer patient and then subjected to RNA sequencing to identify tumour- specific alternative RNA processing events. This is then followed by a comprehensive comparison with a database of human healthy tissues to avoid selection of targets that might be presented in other healthy tissues. Computational tools are then used to predict the potential target neoepitopes derived from alternative RNA processing events, which are likely to be presented by either major histocompatibility complex (MHC) class I or II. Mass spectrometry of eluted peptides from MHCs and functional T cell activation/cytotoxic assays are performed to narrow down the validated immunogenic neoepitopes. These neoepitopes can hypothetically be used for therapeutic vaccination (for example, peptide, DNA and RNA) to elicit potent antitumour T cell responses in the patients. They can also be used to identify antigen- specific T cell receptors (TCRs) by MHC multimer- based screens or functional T cell expansion assays. The resulting TCRs can be used to engineer T cells to treat cancer patients. TCR- T cell, T cell receptor engineered T cell.
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high technical noise, high processing requirements and misquantification of poorly expressed isoforms due to lack of coverage), offer a great deal of promise102–104.
Prediction and validation of peptide presentation. Although there is experimental evidence for tumour- specific splicing events, the extent to which they contri- bute to proteomic diversity remains a topic of debate. It has been suggested that a large percentage of alternatively
spliced transcripts are not translated into proteins105. Moreover, the detection of proteins derived from alter- native splicing events can be challenging as a large number of alternative splicing events are found in low- abundance transcripts106 and the sensitivity of MS is often not high enough to detect peptides derived from such low- abundance transcripts. Additionally, as dis- cussed above, peptides that span splice junctions are under- represented in MS- based proteomics data sets. Finally, MS requires substrate from large numbers of cells107. In general, the usage of 1 g of primary tissue is the minimum requirement for the detection of a few thousand MHC I or II binding peptides. If such an amount is not available, this may further preclude the detection of low- abundance peptides.
Even in cases where there is clear evidence of trans- lation of a particular mRNA, there is no guarantee that peptides derived from the full- length protein will be pre- sented by surface MHC molecules for immune recog- nition. Our understanding of which peptides will be processed and presented on surface MHC molecules is far from complete. Studies have looked at this question in the context of mRNA splicing- derived neoepitopes using MS and machine learning algorithms37,38,82. MS measurements of peptides eluted from MHC mol- ecules allow the direct identification of peptides bound to MHC molecules. Although such experimental evi- dence is the preferred method to confirm presentation, the sensitivity of this approach may not be sufficient. In the last decade, there has been an emergence of computational programs that utilize machine learn- ing frameworks to predict MHC I ligands108 (TABlE 1). However, the reliability of such in silico analyses has been called into question109 because the quality of their predictions relies on the quality of the training set used for machine learning. Classically, this involved in vitro data from the Immune Epitope Database (IEDB)110. More recently, MS- based immunopeptidome data have been integrated into the training sets, with the hope that such data may better reflect endogenous antigen pro- cessing, and such integration has indeed been shown to improve prediction accuracy111–114. For example, a recently developed neural network trained on MS data generated from cell lines expressing a single HLA allele outperformed previous algorithms trained on data from in vitro measured affinities when compared using two external MS- binding data sets111. Another issue with these in silico tools is the inability to accurately predict the binding of peptides to MHC II molecules, which means an entire arm of cellular immunity is over- looked. The binding of peptides to MHC II molecules is extremely promiscuous and there are limited data to train machine learning algorithms to predict MHC II- binding peptides115. Tools that predict the binding affin- ity for peptide–MHC II complexes are very inaccurate and, thus, lack utility116. In conclusion, current predic- tion tools trained on in vitro measured affinities and MS data offer the ability to narrow down the number of candidate neoepitopes in highly mutated tumours or tumours with a large number of tumour- specific splic- ing events, but further improvements are necessary to increase prediction accuracy.
Box 2 | Computational analysis of RNA splicing
There have been significant developments in the computational tools used to analyse and quantify differential splicing using RNA sequencing (RNA- seq) data. Broadly, such tools fall into two main categories: those that analyse full- length transcripts and those that analyse splicing events. For the analysis of transcripts, the transcriptome is computationally reconstructed and the abundance/relative proportion of full- length mRNA isoforms can be estimated by the RNA- seq reads that have been aligned to a given reference genome. more recently, pseudoalignment tools such as kallisto174 have been developed to perform alignment- independent isoform quantification with extraordinary computational efficiency. However, pseudoalignment tools quantify transcript abundance by directly comparing raw sequencing reads with transcript sequences and determining which transcripts a sequencing read is compatible with. Inherently, this relies on the selection of transcript annotations, which is an important consideration with respect to the discovery of cancer- specific splicing events and is discussed in more detail below. Then, tools such as sleuth can be used in conjunction with the data on transcript abundance that have been quantified with kallisto to determine differential transcript expression175. With respect to the analysis of splicing, differential transcript expression between samples, for example tumour and healthy tissues, is of limited utility as a transcript might have altered expression due to splicing changes, transcriptional changes or both. As such, there are also tools that compute the differential transcript usage (DTu), which quantifies the ratio of expression of a given transcript relative to all other transcripts for a given gene. Notable tools that can calculate DTu include RATs176, SuPPA2 (REF.177) and DRImSeq178.
Still, the identification and quantification of full- length transcripts from short reads is not trivial. The second category of computational tools involves those that analyse splicing events using an event- based approach. These tools detect alternative splicing events by comparing reads at a given junction between multiple samples. In general, the readout is the metric- like percent spliced in (Ψ), which represents the percentage of a given gene’s mRNA transcripts that include a specific splicing event. There are various commonly used tools in this category; for example, mISo179, rmATS180, mAJIQ181, leafCutter182, SplAdder183, Jum184 and Whippet185. of note, the aforementioned tool SuPPA2 can employ a hybrid approach, leveraging transcript quantification to produce both the isoform- centric DTu information and event- centric Ψ information. each tool and approach has its own advantages and various factors dictate which tool works best for a given experiment. Further, it has been proposed that multiple tools should be used to identify all possible significant splicing variants177.
With respect to the discussion of cancer- specific splicing events, an important consideration when comparing tools relates to their reliance on predefined transcript annotations because this may mask disease- specific splicing events. With respect to the transcript- level approach, pseudoalignment tools have made isoform quantification extraordinarily fast and thus scalable to large data sets. However, as previously mentioned, there is an inherent reliance on the choice of transcript annotations, and downstream splicing analyses are thus unable to discover or quantify novel alternative splicing events. It is possible to discover novel transcripts with older transcript- level approaches, for example with Cufflinks186, which can perform a reference- based de novo transcriptome assembly. This allows alternative splicing changes to be quantified based on the annotation of the assembled transcriptome. However, such an approach is both computationally difficult and expensive. With respect to the programs that analyse splicing events, these vary in their reliance on a reference annotation. Broadly speaking, mISo, SuPPA and Whippet rely on an annotation; rmATs, mAJIQ and SplAdder use a transcriptome annotation to guide alternative splicing analysis but can extend the analysis to detect novel splicing events; and leafCutter and Jum are annotation free. The importance of novel junction discovery, as compared with analysis of known annotations, plays a role in the selection of the analysis tool.
Immune Epitope Database (IEDB). A database containing detailed information for more than 100,000 unique immune epitopes related to infectious and immune- mediated diseases.
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Specificity and crossreactivity. Antigen specificity and crossreactivity are two major concerns for immuno- therapy. Regarding the issue of specificity, on- target off- tumour toxicities caused by the expression of target antigen on normal tissues have been reported in several clinical trials of TCR- T cell- based immunotherapies targeting MAGE- A3, MART-1, carcinoembryonic antigen (CEA) and glycoprotein 100 (gp100)11–14. Due to the difficulty of identifying tumour- specific public antigens, person- alized immunotherapies targeting neoantigens arising from mRNA splicing events have the potential to be safer and more effective15. However, as previously mentioned, mRNA splicing can be a noisy process as the dynamic nature of the spliceosome can be a source of stochastic fluctuation117. This fluctuation may prove detri mental if it leads to low levels of expression of the splicing isoform in healthy tissue. Thus, adequate controls are necessary during the splicing- derived tumour- specific peptide identification process to ensure specificity, or, at the very least, a significant enrichment of the targeted splicing- derived peptides in a given tumour. Related to specificity is the problem of crossreactivity of TCRs, which has been found for TCRs targeting both public antigens and neoantigens. A high- affinity TCR targeting an epitope (EVDPIGHLY) derived from MAGE- A3 was found to recognize a peptide derived from the muscle protein titin that has a similar sequence (ESDPIVAQY)13. Further, a recent study found that a neoantigen- specific TCR identified from a patient with ovarian cancer showed crossreactivity against the corresponding wild- type peptide118. These findings highlight the need for careful evaluation of epitope similarity between targeted candidate neoepitopes and similar epitopes known to be presented on healthy tissue. If the peptides derived from the processing event are very similar to the peptides generated from unrelated proteins in normal tissues, TCR crossreactivity will be an issue. Several recent TCR- ligand screening technologies based on trogocytosis, signalling and antigen- presenting bifunctional recep- tors, yeast surface display, DNA barcoded multimers,
TetTCR- seq or organoid co- culture methods have been developed and allow for a better understanding of the problem of crossreactivity119–124.
Immunogenicity. Although a subset of expressed tran- scripts is translated, processed and presented on sur- face MHC molecules, only a fraction of this subset will elicit an immunogenic response125. For example, in four high- profile vaccination trials for melanoma and glio- blastoma, only a portion of somatic mutation- derived candidate neoepitopes (51.7–66% of MHC II- restricted epitopes and 16–43% of MHC I- restricted epitopes) elic- ited CD4+ or CD8+ T cell responses in patients6–9. The immunogenicity of an epitope can be impacted by several factors, including antigen abundance, antigen processing efficiency, peptide binding affinity to MHC molecules, peptide–MHC complex stability and central tolerance due to the expression of other peptides with a similar amino acid composition15,126. As such, experimental vali- dation of immunogenicity is crucial to the development of personalized immunotherapies. Over the past 20 years, there have been numerous examples of mRNA splicing- derived neoepitopes with evidence of immunogenicity (see TABlE 2 for experimentally vali dated splicing- derived peptides that are recognized by T cells)127–136. The alter- native splicing of CD20 in B cell lymphomas offers an excellent example133. The B cell lineage marker CD20 is subjected to an alternative splicing event whereby a 168-nucleotide region within exons 3–7 is spliced out. Although absent from normal resting B cells, this alterna- tive splice variant was present in several patient- derived B cell lines. Importantly, the alternatively spliced variant can give rise to HLA- DR1 binding epitopes and vaccina- tion with a CD20-derived peptide (D393-CD2028–47) was able to elicit epitope- specific CD4+ and CD8+ responses in HLA- A2/HLA- DR1 transgenic mice133. Another recent study showed that peptides derived from alter- natively spliced out- of-frame BCR/ABL transcripts are able to elicit a peptide- specific cytotoxic T lymphocyte response, as suggested by the detection of out- of-frame
Table 1 | Commonly used major histocompatibility complex class I binding prediction tools
Name Training data Allele coverage
MixMHCpred Mass spectrometry Allele specific https://github.com/GfellerLab/MixMHCpred 112,114
NetMHCpan4.0 Binding affinity + mass spectrometry
Pan- class I http://www.cbs.dtu.dk/services/NetMHCpan-4.0/ 113
MHCflurry Binding affinity Allele specific https://github.com/openvax/mhcflurry 187
NetMHC4.0 Binding affinity Allele specific http://www.cbs.dtu.dk/services/NetMHC/ 188
PickPocket Binding affinity Pan- class I http://www.cbs.dtu.dk/services/PickPocket/ 189
NetMHCcons Binding affinity Allele specific http://www.cbs.dtu.dk/services/NetMHCcons/ 190
MHCnuggets Binding affinity Allele specific https://github.com/KarchinLab/mhcnuggets-2.0 191
ConvMHC Binding affinity Pan- class I http://jumong.kaist.ac.kr:8080/convmhc 192
HL A- CNN Binding affinity Allele specific https://github.com/uci- cbcl/HL A- bind 193
NetMHCstabpan Binding stability Pan- class I http://www.cbs.dtu.dk/services/NetMHCstabpan/ 194
NetMHCstab Binding stability Allele specific http://www.cbs.dtu.dk/services/NetMHCstab/ 195
SYFPEITHI Binding affinity + mass spectrometry
Allele specific http://syfpeithi.de/0-Home.htm 196
Trogocytosis A biological process where interacting cells share membrane and membrane- associated proteins.
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peptide- specific IFNγ+CD8+ T cells in patients with chronic myeloid leukaemia and the specific recognition and killing of out- of-frame peptide- pulsed target cells in vitro by these cytotoxic T lymphocytes134.
Although there has been a vast increase in the iden- tification of splicing- derived neoepitopes in the recent past, the percentage of these neoepitopes that are immuno- genic remains unknown. As previously discussed, the process of splicing has some intrinsic noise. This can negatively impact immunogenicity, as low levels of expression of a splicing- derived neoepitope might be expressed in the thymus, inducing central tolerance. Further, a large number of alternative splicing events are found in low- abundance isoforms of proteins106, which may preclude efficient targeting. With respect to immunogenicity, it is important to note that our under- standing of antigen processing and presentation is not complete and, thus, it is difficult to accurately predict which peptides will be immunogenic and which will not. This is especially apparent for class II MHC peptides15,115. Additionally, the measurement of peptide–MHC stabil- ity is the primary experimental method to predict neo- antigen immunogenicity137, but this is not particularly accurate or efficient. It would be more useful to assess antigen immunogenicity using an approach that involves both measuring the T cell response in an in vitro cell co- culture setting and immunizing transgenic mice that express human HLA class I and/or II125,138 molecules.
Tumour heterogeneity and evolution. Immunotherapy can induce long- lasting responses in cancer patients; however, tumour escape mechanisms can significantly impair clinical outcomes. One of the root causes of
resistance is intratumoural heterogeneity139, as shown for chimeric antigen receptor T (CAR T) and TCR- T cell- based adoptive cell therapy140. mRNA splicing- derived neoepitopes are not immune to this obstacle. As pre- viously stated, the identification of such neoepitopes has relied on bulk RNA- seq and, thus, we lack a proper understanding of splicing at the subclonal level37,38,82. This raises the question of what percentage of tumour cells in a given tumour harbour a given splicing- derived neoepitope, and scRNA- seq has shown that there can be significant heterogeneity at the single- cell level98,102,141–143. One of the seminal scRNA- seq studies performed on glioblastomas found considerable cell- to-cell variabil- ity in splicing patterns98. For example, three different variants of the EGFR gene were found to be mutually exclusively expressed across individual cells from the same tumour.
In addition to heterogeneity, tumour evolution poses another challenge to the efficacy of immuno- therapy. Acquired resistance to immunotherapy has been observed in both model systems and clinical tri- als. Several CAR T cell clinical trials suggested that antigen- negative escape is one of the most common mechanisms of acquired resistance to immunotherapy treatment144–146. For example, epitope loss was observed in B cell acute lymphoblastic leukaemia and diffuse large B cell lymphoma patients receiving anti- CD19 and/or anti- CD22 CAR T cell therapy144,146. Further investi- gation of the anti- CD19 CAR T cell trial revealed that, in some instances, epitope loss involved the selection of clones that splice out exon 2 of CD19, which contains the epitope recognized by the antigen- binding moiety of the anti- CD19 CAR T cell. This alternative splicing
Chimeric antigen receptor (CAR). Recombinant receptor protein that has been engineered to direct T cells to target a specific protein on malignant cells.
Table 2 | Experimentally validated mRNA splicing- derived peptides that are recognized by T cells
Tumour type Antigen Peptide sequence HLA type Ref.
Melanoma AIM2 RSDSGQQARY HL A- A1 127
Melanoma NA17-A • VLPDVFIRC • VLPDVFIRCV
HL A- A1 127
Melanoma GP100 VYFFLPDHL HL A- A24 128
Melanoma TRP-2 EVISCKLIKR HL A- A*68011 and HL A- A*3301 129
Melanoma CAMEL MLMAQEAL AFL HL A- A*0201 130
Melanoma CAMEL RTAACFSCTSRCLSRRPWKRSWS Unknown 131
Melanoma CAMEL CLSRRPWKRSWSAGSCPGMPHL HL A- DR7/HL A- DR11/HL A- DR12 131
Melanoma CAMEL MLMAQEAL AFLMAQGAML AA HL A- DR 132
Melanoma CAMEL QGAML AAQERRVPRAAEVPG HL A- DR3 132
Melanoma CAMEL APRGVRMAVPLLRRMEGAPA HL A- DR 132
Lymphomas CD20 KPLFRRMSSLELVIAGIVEN HL A- DRB1 133
Lymphomas CD20 RMSSLELVI HL A- A2 133
Leukaemia BCR/ABL • QQAHCLWCV • GVRGRVEEI • LLREPLQHP • CLWCVPQLR • RLLREPLQH • RVLERSCSH
HL A- A2 and/or HL A- A3 134
Oral cancer Survivin-2B AYACNTSTL HL A- A24 135
Breast cancer melanoma
NY- ESO-ORF2 L AAQERRVPR HL A- A31 136
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event is mediated by SRSF3, a trans- acting splicing factor that acts to promote exon 2 inclusion, and levels of SRSF3 were lower in splicing- mediated relapsed samples146.
It is likely that mRNA splicing- derived events that are targets for cell- based immunotherapies will be vulnerable to similar splicing- mediated mechanisms of immune escape. For instance, when targeting an intron retention event, potential pre- existing clones that efficiently splice the intron would be able to escape immunotherapy (FIg. 3a). In addition to the selection of pre- existing resistant clones, individual tumour cells that are sensitive to immunotherapy might also develop resistance upon treatment. As an example, in response to TCR- T cell therapy, splicing might be altered in tumour cells such that a given neoepitope is no longer translated, resulting in immune escape of tumour cells lacking this epitope (FIg. 3b). Resistance may be less of an issue if an immunotherapy target is derived from an mRNA splicing event that acts as a driver of tumori- genesis, because altering the splicing event will have a negative impact on cancer cell survival. Yet, still, it is unknown how many of these single splicing- mediated driver events exist. It may be possible to overcome the outgrowth of pre- existing antigen- negative clones or emerging antigen- negative clones by targeting multi- ple tumour- specific events, which has been shown in both in vitro and preclinical studies to offset antigen escape and result in increased antitumour activity147,148. However, beyond splicing- mediated mechanisms of
antigen escape, there are many other mechanisms by which antigen escape can occur. An example includes the loss of MHC I expression (HLA- A, HLA- B, HLA- C and β2M) due to simultaneous molecular defects in both copies of the gene or by loss of heterozygosity in one copy of one chromosome and a mutation/deletion in the other homologous gene8,149–155 (FIg. 3c,d). The development of new technologies that either restore antigen presenta- tion or prevent antigen escape will be important for the continued improvement of durable response rates in patients treated with immunotherapy.
Conclusion Mounting evidence indicates that alternative mRNA splicing- derived neoepitopes can be promising immu- notherapy targets. Most of the work has focused on mRNA splicing- derived events, although other forms of mRNA processing have been shown to be dysregulated in cancer and offer promise with regard to immuno- therapy target expansion. Although progress made to expand the immunotherapy target space using tumour- specific mRNA processing events has been significant, a great deal of work is needed. The first critical hurdle involves identifying the most immunogenic epitopes from the numerous candidates derived from mRNA processing events. Current antigen- presentation predic- tion algorithms need to be optimized for the accurate identification of immunogenic neoepitopes. In the last few years, a significant amount of high- quality MHC
T cella b
Pre-existing resistant cell
Alternative spliced isoform
Pre-existing resistant cell
Pre-existing resistance Acquired resistance
Selection of alternative splicing-derived neoantigen clone Loss of alternative splicing-derived neoantigen expression
Selection of HLA-negative clones Loss of MHC expression
Fig. 3 | Potential mechanism of tumour escape from immunotherapy. a | A tumour might consist of a heterogeneous cell population expressing either constitutive splicing or alternative spliced gene products (shown is an intron retention event). The cells in which the immunogenic epitope is constitutively spliced out will not be recognized by T cell receptor engineered T cells (TCR- T cells) that target the alternative splicing- derived epitope and will be selected for in response to TCR- T cell therapy. b | Splicing- mediated mechanisms of antigen- negative escape. When targeting an epitope derived from an intron retention event, acquired resistance to TCR- T cell therapy might occur through epitope loss that is mediated by splicing out the intron. c | Low expression of HL A molecules (for example, class I major histocompatibility complex (MHC) and β2-microglobulin) or alterations in genes encoding components of the antigen- processing machinery and/or HL A molecules can impair antigen presentation to TCR- T cells and result in relapse of antigen- negative tumours following TCR- T cell therapy. d | Loss of MHC expression following TCR- T cell therapy allows antigen- negative tumour cells to escape from T cell attack, rendering these cells resistant to TCR- T cell- directed therapy151–155.
Loss of heterozygosity A common somatic genome event that results in loss of the entire gene and the surrounding chromosomal region.
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immunopeptidome data has been generated. Sharing the published or even unpublished data, together with genomic information, would be valuable for further improvement in the accuracy of computational predic- tions. Furthermore, current experimental validation methods for immunogenicity and crossreactivity are still laborious, non- robust and of low throughput. Thus, there is a need for new technologies that allow for more rapid, robust and precise identification of tumour- specific immunogenic epitopes so that we can accu- rately assess their therapeutic potential. Finally, with the
expanding clinical data on CPIs, more research needs to be carried out to investigate whether splicing- derived neoantigens could aid the prediction of CPI response. The last decade has witnessed great advances in the field of cancer immunotherapy, and incorporating mRNA splicing- derived neoepitopes as potential targets for cell- based and/or vaccination- based immuno therapeutic anticancer approaches may allow more patients to benefit from such treatments.
Published online 30 July 2019
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Acknowledgements Preparation of this review was supported by an endowment provided by the Raymond and Beverly Sackler Foundation, the Parker Institute for Cancer Immunotherapy and the National Cancer Institute (grant 1U54 CA199090-01).
Author contributions The authors contributed equally to all aspects of the article.
Competing interests The authors declare no competing interests.
Peer review information Nature Reviews Immunology thanks Zlatko Trajanoski and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
NATuRe RevIeWS | ImmuNoLogy
R e v i e w s
volume 19 | NovemBeR 2019 | 687
- Alternative mRNA splicing in cancer immunotherapy
- Box 1 | Beyond RNA splicing: non-canonical neoepitopes
- Alternative mRNA splicing in cancer
- The impact of alternative mRNA splicing on the target space for cancer immunotherapy.
- Technological and biological challenges
- Identification of tumour-specific splicing events.
- Computational analysis of RNA splicing
- Prediction and validation of peptide presentation.
- Specificity and crossreactivity.
- Tumour heterogeneity and evolution.
- Fig. 1 Alternative splicing and immunotherapy.
- Fig. 2 Schematic illustration of the development of potential immunotherapies targeting mRNA processing-derived neoantigens.
- Fig. 3 Potential mechanism of tumour escape from immunotherapy.
- Table 1 Commonly used major histocompatibility complex class I binding prediction tools.
- Table 2 Experimentally validated mRNA splicing-derived peptides that are recognized by T cells.