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Microsatellite instability at U2AF-binding polypyrimidic tract sites perturbs alternative splicing during colorectal cancer initiation

Genome Biology volume 25, Article number: 210 (2024) Cite this article

Abstract

Background

Microsatellite instability (MSI) due to mismatch repair deficiency (dMMR) is common in colorectal cancer (CRC). These cancers are associated with somatic coding events, but the noncoding pathophysiological impact of this genomic instability is yet poorly understood. Here, we perform an analysis of coding and noncoding MSI events at the different steps of colorectal tumorigenesis using whole exome sequencing and search for associated splicing events via RNA sequencing at the bulk-tumor and single-cell levels.

Results

Our results demonstrate that MSI leads to hundreds of noncoding DNA mutations, notably at polypyrimidine U2AF RNA-binding sites which are endowed with cis-activity in splicing, while higher frequency of exon skipping events are observed in the mRNAs of MSI compared to non-MSI CRC. At the DNA level, these noncoding MSI mutations occur very early prior to cell transformation in the dMMR colonic crypt, accounting for only a fraction of the exon skipping in MSI CRC. At the RNA level, the aberrant exon skipping signature is likely to impair colonic cell differentiation in MSI CRC affecting the expression of alternative exons encoding protein isoforms governing cell fate, while also targeting constitutive exons, making dMMR cells immunogenic in early stage before the onset of coding mutations. This signature is characterized by its similarity to the oncogenic U2AF1-S34F splicing mutation observed in several other non-MSI cancer.

Conclusions

Overall, these findings provide evidence that a very early RNA splicing signature partly driven by MSI impairs cell differentiation and promotes MSI CRC initiation, far before coding mutations which accumulate later during MSI tumorigenesis.

Background

Genomic instability induces diversity that facilitates the development of the hallmark properties of tumor cells [1]. Following impairment of the DNA mismatch repair (MMR) system, high levels of genomic instability are observed in cancers with the mutator phenotype. This results in the accumulation of numerous mutations, particularly in repetitive DNA sequences such as microsatellites (MS) [2, 3]. This type of genomic instability is characteristic of the microsatellite instability (MSI) phenotype in some tumors [4,5,6]. Tumors presenting MSI can arise in patients with a hereditary cancer syndrome known as Lynch syndrome (LS) or can occur sporadically, as in approximately 15–20% of all colorectal, gastric, and endometrial cancers [7]. In mismatch repair-deficient (dMMR) tumor cells, it has been clearly demonstrated that the level of instability of a MS is positively and strongly correlated with its size [8, 9]. To date, somatic MS alterations leading to changes in gene homeostasis have been primarily observed in small coding MS-containing genes with low instability and in large microsatellites (i.e., > 10 bp) that are not present in the coding portion of the human genome due to the counterselection of such highly unstable sequences during the evolution of species (for review see [10]). Although some of these coding MS mutations have been proposed to play a role in tumorigenesis, there is no real functional evidence behind the claim in many cases; their occurrence has been mostly related to the synthesis of numerous aberrant immunogenic neoantigens [11] and the infiltration of MSI tumors with activated cytotoxic T cell lymphocytes (CTLs) and Th1 cells, thus creating a hostile, antitumor microenvironment. Consequently, MSI colorectal cancer (CRC) has a more favorable prognosis than microsatellite-stable (MSS) CRC, at least for localized tumors [12], and can be treated effectively with immune checkpoint inhibitors [13], although resistance to these new drugs can still develop [14,15,16]. In addition, DNA alterations due to MSI in cancer are much more common in the noncoding region of the genome [8, 9]. However, the functional consequences of MSI noncoding mutations remain largely unknown.

Human genes are generally composed of multiple (coding) exons interspersed with (noncoding) introns that undergo splicing to generate mature messenger RNAs (mRNAs) and subsequently generate proteins (reviewed in [17]). Pre-mRNA splicing is a complex process that involves constitutive intron removal and ligation of most exons in the gene as well as the inclusion or exclusion of certain exons from the final mRNA [17]. Alternative RNA splicing is observed at physiologically regulated exons in more than 95% of the 19,804 transcribed human coding genes [17]. This process enables the production of thousands of alternative mRNAs encoding numerous self-protein isoforms, i.e., proteins endogenously produced by DNA transcription and translation, generated specifically within a tissue during cell differentiation (as opposed to non-self-proteins that are not created within the body of the organism of interest, and subsequently can be targeted and attacked by the immune system) [17, 18]. Alternative mRNA splicing is the main contributor of changes in the transcriptome as stem cells differentiate into tissue progenitor cells, notably in the colonic crypt [19]. Accumulating evidence indicates that both alternative and constitutive exons are aberrantly spliced during cancer development, in part due to somatic mutations resulting from genome instability [20]. Excessive alternative splicing in MSI CRCs compared to MSS CRCs has recently been observed using long-read nanopore sequencing, although the authors did not assess the reason for this observation or the pathophysiological roles that these alternative transcripts may play in these tumors [21]. Interestingly, numerous MS that can be highly unstable in the genome of MSI cancers due to their large size are located near the polypyrimidine tract (PY) of the intron/exon boundary [22, 23]. These noncoding MS are important cis-acting sequence elements for the binding of trans-acting core spliceosomal factors such as U2AF2 upstream of the intronic AG splice acceptor site which binds U2AF1 to guide intron removal during pre-mRNA splicing [24].

The MS distribution within human genes is not homogenous, with intronic regions displaying more frequent and larger MS than coding exons (see Fig. 1A left panel). A highly MS-enriched intronic region is often observed in the PY (Fig. 1A, right panel), and the PY is a major cis-acting determinant of pre-mRNA splicing [25] (Fig. 1B). In the present work, we hypothesized (WH1) that RNA changes specific to MSI CRCs occur because of the high mutational burden of these tumors, and these mutations could destabilize the physiological splicing process, which is dependent on the binding of trans-factor complexes from the spliceosome to the PY cis-sequence sites of pre-mRNAs (Fig. 1B and 1C). We also hypothesized (WH2) that MSI-related mutations disrupt splicing early during tumor evolution, as these changes affect to large microsatellites, which have a very high mutation rate in dMMR cells, and that these alterations thus facilitate MSI tumor initiation (Fig. 1B and 1C). Finally, we assumed (WH3) that the splicing changes due to MSI should mimic those observed in other tumors with disruption of U2AFs complexes, notably composed of U2AF1 and U2AF2, since these spliceosomal factors normally bind RNA at the PY for correct splicing (Fig. 1B and 1C). To assess these hypotheses, whole exome sequencing was performed to study MSI at all the human intron/exon boundaries and up to 50 bp into the intronic sequence. We analyzed a series of dMMR precancerous lesions, i.e., a pool of 10 different dMMR crypt foci from 8 MSI CRC patient and 14 dMMR adenoma patients, with the aim of examining the role of MS in splicing in the very early steps of the MSI-driven tumorigenic process (Fig. 1D). In addition, 46 MSI CRC samples with matched normal mucosa samples were also analyzed to investigate the mutational burden at the same noncoding MS in established colorectal tumors. RNA sequencing was performed on 101 established MSI CRC specimens including 46 MSI CRC specimens with whole-exome sequencing data, 32 MSS CRC specimens, and matched normal mucosa samples (n = 133). Due to the lack of sufficient material in terms of quantity and quality, we were unable to sequence the RNA from the microdissected dMMR crypts and adenoma. Finally, we examined alternative splicing in CRC in relation to the MSI or MSS phenotype at the single-cell (SC) level using a public dataset from patients with MSI (34 patients—33,333 cells) or MSS (28 patients—33,334 cells) CRC [26] (Fig. 1D). Overall, our results provide evidence that a new, very early RNA splicing pathway acts as a mimic of the mutation in trans of U2AF1 in several other cancers. They also establish how this pathway, which is partly driven by MSI at the PY, is likely to promote dMMR cell transformation and MSI CRC initiation by impairing colonic cell differentiation, far before coding MSI mutations which accumulate later in established MSI CRC.

Fig. 1
figure 1

Frequently mutated intronic microsatellites may cause aberrant exon skipping in microsatellite instability (MSI) colorectal cancer (CRC). A Left panel, genomic distribution of microsatellite (MS) mutations (log10 scale) in two gene regions (exonic and intronic) according to repeat length. Right panel, distribution of all flanking MS (3′ splice acceptor site, 3′ FMS) according to their distance from the intron–exon boundary. The distribution of these MS signals is indicated according to their nucleotide composition (violet: thymine, pink: cytosine, green: adenosine, brown: guanine). The background corresponds to the distribution of all MS across the human genome. B Upper panel, schematic representation of physiological splicing. PY, polypyrimidine tract; AG, acceptor site. Lower panel, schematic representation of MSI cis-perturbations of splicing. C Working hypothesis (WH). D Experimental design to investigate splicing cis-perturbations in MSI CRC specimens

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Results

Cis-splicing intronic polypyrimidine tract regions are enriched in DNA microsatellites frequently mutated very early in dMMR colonic crypts and colonic dysplasia prior to their transformation into MSI CRCs

In Fig. 2A, we outlined our approach for longitudinal analysis of MSI at crucial steps during colorectal tumorigenesis, i.e., from MMR crypt to adenoma to established adenocarcinoma. Through quantitative analysis of MSI in the genome using MSICare, MSI was already observed in dMMR crypts, albeit at moderate levels, and rapidly reached a plateau early in adenoma that was sustained adenocarcinoma (Fig. 2B and also Additional file 1: Fig. S1A). Due to differences in microsatellite size, we observed a much greater number of unstable noncoding MS in the vicinity of the PY than unstable coding MS (Fig. 2C right panel). In a dynamic way, our data establish that the level of MSI at noncoding MS whose sizes are frequently greater than 10 nucleotides, reaching 15–20 base pairs or more, is already very high in the dMMR crypts, whereas the instability of coding MS whose lengths exceptionally exceed 10 nucleotides in the human genome is quantitatively much lower (Fig. 2C left panel and Additional file 1: Fig. S1B). Among the dMMR cryptic lesions we were able to examine, the level of MSI gradually increased from monocryptic dMMR foci to slightly more advanced oligo/polycryptic dMMR foci (Fig. 2D). It is clear that in the dMMR crypts, it is the instability of MS very close to the AG site that already prevails within the 3′ portion of PY known to play a key role in splicing, with a further increase in the number of such MSI events during the progression towards cancer (Fig. 2E).

Fig. 2
figure 2

MSI-driven mutational events in non-coding regions lead mRNA changes which appear in the early stages of MSI tumorigenesis. A Histological description of MSI CRC tumorigenesis, from preneoplastic lesions (dMMR crypts) to neoplasms of different severity (adenoma and adenocarcinoma). Immunohistochemical staining of the MSH2 protein was performed. B The MSI status for each sample was defined by MSICare [27]. C Left panel, number of unstable MS by sample according to their length and considering their genomic position (coding or non-coding). C Right panel, boxplot representing the total number of unstable MS according to their region and condition. Statistics: Student’s t-test, *P-value < 0.05, **P-value < 1.10−3, ***P-value < 1.10−4, ****P-value < 1.10.−5. D Loss of MMR in the colonic crypt was categorized into three groups, depending on the number of dMMR crypts in the foci: monocryptic (1), oligocryptic (2–4), and polycryptic (> 4). In all the groups, crypts are identical and morphologically healthy, *P-value < 0.05. E Distribution of 3′ FMS according to the distance of the 3′ splice site (AG); the genomic background distribution of all flanking MS (n = 335,564) is indicated (phantom white bars)

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Massive perturbation of splicing In MSI CRC

We then investigated pre-mRNA splicing and its disruption in MSI CRC by performing RNA sequencing of the bulk tumor. We deliberately limited our analyses to exon insertion/exclusion within mRNAs as exon insertion/exclusion represents the type of RNA alteration that is most likely to be directly influenced by cis-acting elements related to MSI at intron boundaries, notably at the PY. The inclusion or exclusion of all human 3′ exon-flanking a microsatellite (3′ Exon-FMS) was measured by the percent spliced-in index (PSI) as previously described [28]. This is a standard method that represents the ratio between reads that include or exclude specific exons (Additional file 1: Fig. S2). Overall, we identified 985 exons that exhibited significantly altered expression in MSI CRC vs MSS CRC and normal colon tissues (Fig. 3A). These included 839 deregulated skipping events at the 3′ Exon-FMS in MSI CRC tissue compared to normal colonic tissue, and 276 additional events in MSI CRC tissue compared to MSS CRC tissue, of which 146 were new exons to be incorporated into the signature. A greater frequency of deregulated exon splicing events was observed in MSI CRC compared to MSS CRC (two-sided p-value < 2 × 10−20) (Additional file 1: Fig. S3 and Additional file 2: Table S1). Most splicing changes involved exon exclusion (see also the data on aberrant exon inclusion in MSI CRC in Additional file 1: Fig. S3). These changes were detected at high levels in all MSI CRC samples and were a distinctive feature of the transcriptome profile of both hereditary (Lynch syndrome-related) and sporadic MSI CRC, independent of TNM stage (stage 2 or 3) or the presence of BRAF/KRAS driver mutations. When we explored possible association of each exon skipping event from this splicing signature with clinical (i.e., TNM staging, sex, Lynch vs. sporadic inferred status, location of the tumor in the colon) or molecular variables (i.e., KRAS or BRAF mutations, type of MMR defects, MLH1 promoter methylation status), none of the tested associations was significant after adjusting p-values (data not shown). The average number of skipping events per tumor was similar to the number of coding MS mutations in this MSI CRC cohort (Fig. 3B) [8].

Fig. 3
figure 3

MSI-driven mutational events in non-coding regions lead to the skipping of mainly alternatively regulated exons in MSI CRC. A Left panel, Venn diagram showing the overlap between exon skipping events in MSI CRC tissue compared to normal colonic tissue and to MSS CRC tissue. B Bar plot displaying the number of mutations in coding MS (expected frameshift mutations) and the number of skipped exons across the whole exome for 46 non-metastatic MSI tumors (TNM stage 2 or 3). C Left panel, pie chart representing the portion of alternative and constitutive exons in normal colorectal tissue (n = 57,030 exons, n = 133 normal tissue samples). Right panel, pie chart presenting the portion of the same alternative and constitutive exons whose expression was deregulated in MSI tumors. The results of the chi-square test between the two pie charts are indicated. D Distribution of exon skipping events according to their frequency in MSI tumors. Left and right panels, exons classified as constitutive (n = 293) or alternative (n = 692), respectively, in normal colonic tissue. Bottom panel, example of IGV-Sashimi plots showing the read coverage for 2 examples of significantly deregulated exons between tumors and normal tissue (right, constitutive and left, alternative). E Percentage overlap of MSI exon skipping between our study cohort and three independent TCGA cohorts. The number of overlapping events and total number of events analyzed are indicated in brackets. The P-values of the enrichments are indicated. F Cell distribution in MSS (Top panel) or in MSI (Bottom panel) according to sample status (tumor or normal tissue) (Left) and cell subset (Right). The total number of cells used was the same that in the Pelka et al. [26] study (n = 371,223) (more details in Fig. S5, also showing the distribution of the different cell types according to the MSI/MSS status of tumors). However, when we performed the splicing analysis, the total number of cells available was reduced to n = 128,370, but with the same distribution of cell types, i.e., no statistically significant difference was observed in the distribution of cell types in MSI/MSS tumor samples in the population and in the dataset with PSI data. The different number of cells were subsampled, and we performed a bootstrapping analysis (10,000 permutations), considering the same distribution of PSI values according to TA or colonocytes in MSS/MSI. In both situations, more than 100 cells in each condition, allowed to identify statistically significant differences. G Boxplot of quantitative index of alternative splicing events in transit amplifying cells (TA) according to MSI tumor status

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The 985 deregulated exons were then characterized as products of alternative or constitutive splicing. The expression patterns of the exons were first analyzed in 133 normal colorectal mucosa samples (57,030 analyzed exons). As expected, the majority of these exons (88.6%) were constitutively expressed. The remaining exons (11.4%) were alternatively expressed [17, 18]) (Fig. 3C left panel). In MSI CRC samples, a minority of the deregulated exons were constitutive (n = 293/985, 29.7%), whereas the majority (n = 692/985, 70.3%) belonged to the 11.4% of exons that displayed an alternative expression pattern in normal colonic mucosa (significant enrichment by 6.2-fold; two-sided p-value < 2 × 10−16) (Fig. 3C right panel, Additional file 1: Fig. S4). These alternative exons were deregulated at low, moderate, or high frequencies in MSI CRC (Fig. 3D left panel), as opposed to the constitutive exons, which were mostly skipped at lower frequencies (Fig. 3D right panel) (two-sided p-value < 2 × 10−16). Finally, we confirmed that the frequency of exon skipping was also high among the above 839 candidate exons in independent cohorts of patients with MSI tumors from the TCGA. These results were obtained considering not only MSI CRC but also MSI gastric and endometrial cancer, highlighting that the splicing signature is a shared pathophysiological feature of these frequent MSI tumors derived from 3 distinct primary sites and is most likely a common feature across MSI tumors (Fig. 3E).

Confirmation of the increased prevalence of alternative splicing events in MSI versus MSS CRC at the single-cell level

In line with the above results, we wanted to further investigate alternative splicing in CRC in relation to the MSI or MSS phenotype at the single-cell (SC) level. Independent public scRNA sequencing datasets from both MSI and MSS CRC were obtained from the Pelka et al. study [26]. In both types of CRC, i.e., MSI and MSS tumors, the predominance of tumor clones with the same TA (transit amplifying cells) state was observed using Census (https://doi.org/https://doi.org/10.1101/2022.10.19.512926) [29] and CellTypist [30] (Fig. 3F), and the scRNA sequencing analyses therefore focused on these predominant clones within the bulk tumor. With the MARVEL package [31] which enables systematic and integrated splicing and gene expression analysis of single cells to characterize the splicing landscape and reveal biological insights, we were able to measure the degree of alternative exon inclusion from scRNA sequencing data in both MSI and MSS contexts. The overall number of alternative splicing events between transit -amplifying (TA) MSS and TA MSI cell types is shown in Fig. 3G. The results clearly show an overall highly significant increase in the number of such events in TA MSI clones compared to TA MSS malignant clones (p-value < 2 × 10−74). Notably, these analyses cumulatively detected all types of alternative splicing events and not only exon skipping. These findings support a strong link between MSI and alternative splicing during transcription in CRC at the single-cell level (see also Additional file 1: Fig. S5 for further details on the annotation used and the relationship of our annotation with the one used by Pelka et al. [26]).

Eon skipping events in MSI CRCs are partly due to MSI-related somatic deletions in intronic MS at the PY

We then sought to address whether a substantial proportion of splicing events in MSI CRCs are directly induced by MSI through a cis-acting mechanism at the PY intronic site. This was done by assessing whether somatic deletions in noncoding 3′ exon-flanking MS would promote exon skipping in MSI tumors. We examined the status of DNA MS via whole-exome sequencing (up to 50 bp into the intronic sequence) in the initial MSI CRC cohort (n = 46) and compared this to paired normal mucosa (n = 46). Of the 985 deregulated exons, 277 MS/exon pairs were selected for this analysis because they showed sufficiently high coverage and at least one mutation at 3′ noncoding MS in at least one MSI CRC sample to allow association studies (Additional file 3: Table S2). For the remaining 708 MS/Exon couples, a correlation could not be calculated because of the absence of somatic mutations affecting MS at the PY in MSI CRCs, and we assumed that the expression of these exons was not due to a direct mechanism in Cis at the PY.

In Fig. 4A, we show examples of genes in which the expression level of a single exon is closely related to the status of the DNA microsatellite in the PY. Overall, a significant correlation between somatic deletions at flanking 3′ noncoding MS and exon skipping (PSI Index) was observed for 96 of these 277 candidates (34.7%) (Fig. 4B, left panel). As the size of somatic deletions in the intronic MS of MSI tumors became larger, the exclusion of these 96 exons in tumor RNAs increased. Alternative exons were more readily skipped by such cumulative somatic deletions due to MSI compared to constitutive exons in MMR-deficient tumors (Fig. 4B left panel, Additional file 1: Fig. S6, S7 and Additional file 4: Table S3). We further examined the impact of each of these 96 MSI-driven exon skipping on the overall expression of corresponding mRNAs or proteins in MSI CRC using an independent public CPTAC dataset (https://pdc.cancer.gov/pdc/browse/filters/pdc_study_id:PDC000109%7CPDC000116%7CPDC000117) that also included protein-level data. These data are shown for illustrative purposes only, being limited to the analysis of a much smaller number of patients with MSI CRC, without reaching significance in a number of cases, expectedly (Additional file 1: Fig. S8A). Moreover, the majority of exons deregulated in MSI CRC are alternative exons, so that it is not expected that their skipping would have an impact on the overall expression of corresponding mRNAs, these events being more likely to affect the fine tuning of specific alternatively spliced mRNAs and corresponding protein isoforms in MSI tumor cells. By contrast, it also appears that certain microsatellites are unlikely to trigger exon skipping, even when they are frequently unstable in MSI CRC (Additional file 1: Fig. S8B and Additional file 3: Table S2). Compared to others, non-coding MS (n = 96) that were shown to directly influence RNA splicing in MSI CRC were primarily located very close to the intron/exon junction, within the functional polypyrimidine tract, just upstream of the U2AF1 (AG) splice acceptor 3′ site [32] (Fig. 4B, right panel).

Fig. 4
figure 4

Splicing anomalies found in MSI CRCs are frequently caused by MSI-related mutations that occur very early in cancer development, as early as the untransformed dMMR crypt state. A Four examples of genes in which exon skipping is closely related to the status of the DNA MS in the PY at the intronic boundary. B Left panel, boxplots representing the percent spliced included (PSI) values according to deletion size for the 96 MS (ES96). The results of two-sided ANOVA between exons classified as alternative and those classified as constitutive were as follows: ***P-value < .001. In the background, each MS displaying a significant correlation is indicated by a line and dots. Right panel, distribution of 96 MS (ES96) at the intronic boundary (50 to 0 of intron/exon junction), showing an enriched localization very close to the U2AF1/2 binding region in the PY (AG site). C In vitro analysis of HSP110 expression. Upper panel, the RT-PCR products of HSP110 show cDNA fragments with a completed or partial skipped exon (HSP110DE9), in mutated MSI cell lines with large deletion (CO115) and small deletion (HCT116) respectively. Bottom panel: western blotting analysis of HSP110wt and HSP110DE9 mutant proteins in the 3 cell lines (CO115, HCT116 and HCT8). NSB*, non-specific band; MW, molecular weight. Uncropped images are available in additional file 3. D In vitro analysis of TRAF3PIP1 gene. Upper panel, RT-PCR products of TRAF3IP1 showing cDNA fragments with a skipped exon (TRAF3IP1 DE6), in MSI cell lines (CO115 and HCT116). Middle panel, aberrant splicing event is also demonstrated by RT-PCR in endogenous and mutant (intronic MS mutation in the PY) TRAF3IP1 transcripts in both MSI and MSS transfected cells with minigene construction. EV, empty vector; WT, wild type; Splice Ratio = ratio of the intensity of the exon-lacking cDNA fragment to the intensity of the sum of exon-containing + exon lacking cDNA fragments. Lower panel, gel mobility shift assay. Nuclear protein extracts from MSS (left panel) and/or MSS (right panel) cells were incubated with TRAF3IP1 wild type or mutant labeled RNA probe. Shortened RNA probes do no longer allow the formation of large RNA/protein complexes. Uncropped images are available in Additional file 3. E Left panel, bar plots of microsatellites in the ES96 and C129 sub-signatures (consisting of coding MS covered in at least 50% of the samples and mutated in 30% of all lesions, n = 129) according to their mutational frequency in the early stage, i.e., dMMR crypts. The top 25% most frequently mutated MS (ES96/Coding) corresponded to MS in the first quartile when they were ranked by mutational frequency. Coding MS known to be highly mutated in MSI colorectal cancer are write in black. ES96 signature is significantly enriched in the top genes; *P-value < 1.10−2. Right panel, non-exhaustive list of the most frequently mutated MS (ES96/Coding) in the early stage of MSI CRC tumorigenesis (more details in Additional file 7: Table S6). MS are order by mutational frequency in dMMR crypts

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Functional analysis of the impact of MSI on gene splicing in a selected number of MS at the PY using gastrointestinal cancer cell lines

We next examined the impact of MSI on gene splicing by RTPCR in 10 gastrointestinal cancer cell lines, i.e., 5 MSI (SNU-1, Co115, HCT116, HCT8, LoVo) compared to 5 MSS cellular models used as controls (AGS, HGT1, HT29, N87, SW480). This was done by analyzing the effect of endogenous deletions at the RNA level in a small subset of noncoding MS from the ES96 list (Additional file 1: Fig. S9A), i.e., in MRE11, HSP110 (also called HSPH1), KDM6A, and TRAF3IP1 genes. Besides, we also investigated noncoding MS in ATM, DNAJC18, and STAD-US outside the list of mis-spliced genes provided in this study, because previous data from the literature (ATM) or online (DNAJC18, STAD-US) suggested a process of exon instability due to MSI at the PY in non-colorectal MSI tumors. At the protein level, as an example of a gene from the ES96 listing whose MSI-driven exon skipping leads to protein expression changes, we have further analyzed the case of the previously reported HSP110DE9 splicing mutation [22]. In brief, for this candidate gene previously shown to be sensitive to NMD in MSI CRC [33], we show that large deletions of the intronic MS at the PY lead to exon 9 skipping in the RNA and extinction of wild-type HSP110 protein expression. The HSP110DE9 mutant protein is not detected due to degradation of the corresponding mRNA during translation (Fig. 4C). We next selected PY nucleotide MS deletions from single introns of the DNAJC18, PTP4A2, and TRAF3IP1 genes (with 8, 14, and 14 consecutive T, respectively) that are frequently mutated in digestive cancers. These introns, harboring 1 and/or 3 T-deletions in the PY, showed increased level of exclusion of the downstream exon in mRNA of mutated MSI cancer cells (Co115 and HCT116 lines) but not in that of MSS AGS or HGT-1 cancer cells (Fig. 4D and Additional file 1: Fig. S9B). Importantly, these exon skipping events were replicated in minigene transfection experiments [34] regardless of the MSI or MSS status of the recipient cells, suggesting that such exon exclusions were essentially governed in these models by cis-microsatellite elements but not by trans-acting factors (Fig. 4D and Additional file 1: Fig. S9C).

Furthermore, to determine if RNA probes/protein interactions could be evidenced in vitro and modified due to MSI at the PY, we performed gel retardation assays with nuclear protein extracts from both MSI and MSS cells, using various PY as baits. This showed the occurrence of several complexes, in both MSI and MSS tumor cells, with similar migration patterns. Strikingly, the larger complexes disappeared when using probes deleted of 1 (DNAJC18) or 3 U (PTP4A2 and TRAF3IP1), suggesting that spliceosomal subunits could no longer form and/or assemble properly at the intron–exon junction when the PY is mutated (Fig. 4D and Additional file 1: Fig. S9D). In addition, in minigene transfection experiments, silencing U2AF1 gene showed increased skipping of the tested exons (increased Splice Ratios), indicating that U2AF1 is decisive in the control of spacing activity for these 3 minigene substrates (Additional file 1: Fig. S10A). Finally, to determine if U2AF2, the dimerization partner of UAF1 binding RNA at the PY, was able to interact in vivo within the complexes from wild-type or mutant PY, we performed RNA immunoprecipitation experiments. Interaction of U2AF2 with nuclear protein(s) was clearly observed in both MSS and MSI tumor cells in vivo, but we saw no difference between them, showing that some additional determining factors (proteins and/or RNAs) could be essential for splice site recognition in live cells (Additional file 1: Fig. S10B).

Some MSI-driven splicing mutations found in MSI CRCs sometimes occur very early during cancer development

Next, we sought to clarify the kinetics of occurrence of the above MSI-driven splicing mutations during the multistep process that facilitates cancer development in the colorectal mucosa. Among the 96 mutations associated with exon skipping in tumors, a majority, i.e., 61/96 (64.9%), were already present in dMMR colonic crypts, and a number of them were present in high frequencies (e.g., in ORC2L (88%), PDS5A and NUP153 (75%), and RAB3GAP2 (71%)) (Fig. 4E, Additional file 1: Fig. S11 and Additional file 5: Table S4).

Taken together, these results illustrate the earliness of many aberrant MSI-driven splicing MSI mutations in the dMMR crypt, prior to its transformation, in a much more prevalent way than what can be observed at the level of coding microsatellites at this pre-tumoral stage.

The MSI splicing signature primarily deregulates alternative mRNA isoforms that normally drive cell differentiation in the colonic crypt

We next sought to assess the functional relevance of the splicing signature identified here in CRC (n = 985 exons). When we compared our MSI splicing signature (n = 985 exons) to that of Habowski et al., who reported alternatively spliced mRNAs that drive cell differentiation in the normal murine colonic crypt [19], an enrichment in genes whose alternative splicing is modified as stem cells differentiate first into progenitors and then into mature cell types in the normal colonic crypt was observed [19] (Fig. 5A and Additional file 6: Table S5). This enrichment analysis revealed a total of 134 unique genes (DD134 gene signature). Compared to coding MS mutations in CRC, MSI-driven exon skipping events mainly deregulate the fine tuning of self-protein isoforms that are normally tightly regulated in the normal colonic mucosa under physiological conditions (Fig. 5B). Deregulation of these self-protein isoforms, while being expected to be well tolerated from an immune point of view, is likely to impair the differentiation of MMR-deficient cancer cells, in line with the poorly differentiated histological pattern that is a characteristic of MSI CRC as compared to MSS CRC, which is observed in the great majority of MSI tumors [7, 35]. In line with this, the expression of alternative exons in MSI CRC was significantly associated with several cancer-related pathways (Fig. 5C, left panel). Among them, some pathways are known to play an important role in tissue differentiation in the colon along the colonic crypt and also during colorectal cancer initiation, e.g., BMP/TGFBeta, Wnt/Wingless, and Hippo signaling pathways (two-sided p-value < 0.01) [36] (Fig. 5C, right panel). When investigating more particularly the pathways associated with the reduced DD134 exon signature, the association of this reduced signature with TGFBeta signaling remained significant (Fig. 5B, C and Additional file 1: Fig. S12).

Fig. 5
figure 5

Changes in mRNA impair cell differentiation and mimic oncogenic U2AF1 inactivation in MSI CRC. A Schematic representation of the normal colonic crypt. B A total of 134 genes that overlapped between our MSI splicing signature and that of Habowski et al. that regroups alternative mRNA changes that drive cell differentiation in the normal colonic crypt [19]. SC, stem cells; Abs., absorptive; Sec., secretory/deep crypt secretory cells/goblet; EEC, enteroendocrine; Ent, enterocytes, TuftC, tuft cells. The pathway analysis of the 134 genes shows a significative enrichment in TGF-beta signaling pathway (Padj = 0.032), BioPlanet 2019. C Left panel, heatmap displaying pathway analysis of coding mutations, exon skipping signature (All, Alternative, Constitutive, DD134, ES96), and of the exon skipping + coding mutations; colors represent enrich pathway Padj, -log10. Enrichment was realized with EnrichR BioPlanet 2019. Terms were sorted by Padj (< 0.05) and regroup first in pathways (rows, right) then in groups of pathways (rows, left). Columns were sorted by enriched condition parameters. A more detail heatmap showing the different terms is available in Fig. S12. Right panel, interactions between genes related to coding repeat mutations and/or genes with alterations in the splicing of genes involved in Hippo signaling pathways which play a role in CRC and in cell differentiation in the colonic crypt (adapted from KEGG: hsa04390). D Left panel, number of neoepitopes potentially represented by HLA-MHC class I or II for each patient (n = 101 CRC MSI). Middle panel, boxplot displaying the number of unique neoepitopes in average per transcript and per patient. Right panel, boxplot displaying the number of neoepitopes showing high affinity for HLA-MHC class I or II for each patient. E Constitutive exon skipping with a frameshift (ES96) exposing a neoantigen tail; genes were ordered by mutational frequency in the early stage. F Overlapping signature of 125 exons (120 genes) containing common exons between the U2AF-S34F signature and the MSI splicing signature

Full size image

Finally, to further illustrate that some noncoding MSI splicing mutations could affect the function of alternative protein isoforms, we carried out an in silico analysis of 5 of the 11 genes shared by the ES96 and DD134 signatures whose exon skipping due to MSI is an alternative exon. These genes are AMBRA, DHX30, GOLGA4, NUP54 and PAPOLA. For 3 out of these 5 genes, it is highly probable that the loss of the alternative exon following MSI induces a functional change in the protein (i.e., in DHX30, NUP54, and PAPOLA), or even a loss of function due to potential exclusion of key functional domains (see further details in Additional file 1: Fig. S13).

The MSI splicing signature also generates early immunogenic aberrant frameshifts at constitutive exons

In addition, we investigated whether splicing defects affecting constitutive exons in mRNAs might have an early immunogenic impact since this kind of exon skipping is not physiologically observed in normal colonic tissue. Considering the HLA-MHC status of each patient (CRC only) and the presentation of aberrant peptides generated in this setting, many immunogenic neoepitopes were predicted to be presented by the patient’s tumor cells (Fig. 5D). These data strongly suggest an immunogenic nature of the MSI splicing signature, and we further examined its effect on the early stages of MSI CRC tumorigenesis. A significant number of these constitutive immunogenic exons were present in dMMR crypts (20/27, 74.1%), and 10 of them had a high mutational frequency (MF ≥ 50%) (Fig. 5E). These results suggest that immunogenicity in the early stage can stimulate immune infiltration in the normal colonic mucosa, as recently reported [37].

The aberrant splicing events observed in MMR-deficient CRC are similar to those induced by oncogenic dysfunction of the spliceosomal factor U2AF1, which promotes blockade of cell differentiation in several human cancer types

We next investigated whether exon skipping events in MSI CRC could be shared with oncogenic signatures observed following mutations of core spliceosomal proteins or associated trans-acting RNA splicing factors in other cancer types (Fig. 5F left panel). The signature (n = 985 exons) was compared to results from 20 publications that described the exon signature of 9 RNA splicing factors (ESRP1, ESRP2, FUBP1, RBM10, RBM4, SF3B1, SRRM4, SRSF2, U2AF1) in different cancer types. Strikingly, the MSI splicing signature was significantly enriched in exons that were skipped following U2AF1 dysfunction in several cancer types [38,39,40] (Fig. 5F right panel, Additional file 1: Fig. S14 and Additional file 7: Table S6). This enrichment corresponded to a total of 125 unique exons and the results concur with the known role of mutations in U2AF1 in cancer, particularly U2AF1 S34F in lung carcinoma, uterus cervix carcinoma, and acute myeloid leukemia [41, 42]. The present results suggest that, mechanistically, the cis-acting, MSI-driven splicing pathway promotes tumor development by mimicking the oncogenic impact of U2AF1 dysfunction on several gene targets (Fig. 5F left panel). This underlines further that aberrant splicing due to MSI can promote tumor development through cis-somatic MS deletions that may mimic U2AF1 perturbation of function and result in skipping of numerous exons, thereby defining a U2AF1-like MSI-driven oncogenic signature (see also Additional file 1: Fig. S15 for the influence on exon skipping of length and deletion size of these intronic MS). In contrast, the impact of additional mutations that occurred outside MS at the donor (GT) or acceptor (AG) sites on exon exclusion levels was shown to be very low (Additional file 1: Fig. S16).

Discussion

MSI is a common type of genetic instability in human solid tumors, and approximately 700,000 new cases of MSI cancers are diagnosed worldwide every year. To date, this mode of instability and its pathophysiological consequences regarding tumorigenesis have been mostly reported at the DNA coding level [3, 10, 16, 43,44,45,46]. Here, we report for the first time the early impact of MSI on gene splicing in both the untransformed and transformed dMMR colonic mucosa through a newly identified MSI-driven noncoding genomic pathway. In brief, coding MS mutations are frameshift and are usually observed as 1 or 2 bp deletions in short DNA repeats and at low/moderate frequencies [8]. These mutations invariably lead to truncation of the corresponding protein in tumor cells, resulting in aberrant proteins which may have lost their function. Although a role in tumorigenesis has been established for several coding MS mutations, they mostly result in heavy tumor infiltration with cytotoxic T cells and hence an improved prognosis due to their neo-antigenic properties [11, 47]. Our data suggest that these coding MSI variants occur late in tumor development and are thus rarely observed during the early steps of MSI tumorigenesis. In contrast, the MS often found in intronic sequences around the PY are shown here to undergo frequent deletions of one or several bp in MSI cancer cells but also in yet untransformed dMMR colonic crypts, particularly the long MS contained in the cis-regulatory intronic polypyrimidine tract. Strikingly, these MSI-driven noncoding changes early deregulates at high frequencies the expression of dozens of exons which combine with many other exon skipping events unlikely to be directly linked to MSI at the PY site to give rise to a broad splicing signature characteristic of MSI CRC, involving hundreds of exons in all. Other MSI-driven cis-scenarios investigating mutations of microsatellites endowed with functional activity in splicing outside the PY, like in exonic (ESE, ESI) and intronic (SSE, SSI) splicing enhancers and silencers, will have to be developed in future studies to illustrate why MSI-driven PY mutations explain only about 10% of the exon skipping events observed in these cancers [48].

Overall, our data highlight that the number of exon skipping events in established MSI colon tumor, which are in part MSI-driven at the PY, is approximatively similar to the number of somatic mutations in coding MS. Importantly, alternative exons are shown here to constitute the main early target of this process in MSI CRC, probably due to their greater susceptibility to undergo skipping at the intron/exon junction. Though the nature of “ sequencing used in our single-cell RNA study limits our capacity to specifically classify alternative splicing (AS) events, such as exon skipping, we confirm this close link between the MSI phenotype of tumor clones and incrementation of splicing events in an independent, single-cell level dataset. Thus, the splicing pathway we highlight in this study mainly deregulates the fine tuning of many self-protein isoforms that are normally tightly regulated under physiological conditions and govern the process of cell fate determination in the normal colonic mucosa [17,18,19]. Therefore, though our data only report indirect evidence for such a scenario, they highly suggest that this would drastically impair the differentiation of dMMR colonic cells by notably targeting TGFBeta signaling. Besides, a number of constitutive exons are however also targeted by this MSI pathway in MSI cancer cells which may also initiate immunization mechanisms prior to cell transformation. These data are especially meaningful in the context of recent studies where elevated mucosal T cell infiltration was demonstrated in Lynch patients even in the absence of cancer [37].

Of pathophysiological interest also is that our aberrant exon skipping signature is best characterized by its similarity to that of the oncogenic U2AF1S34F mutation in several other non-MSI cancer types [38,39,40]. Without resolving this issue that will have to be the subject of future in-depth, more specialized studies, we bring here functional data which examine the molecular basis of a subset of exon skipping belonging to this signature, i.e., those that are MSI-driven and associated with PY mutations in MSI colorectal cancer cells. Regardless of these mechanistic aspects, the similarity of our RNA splicing signature with the one associated with U2AF1S34F mutation constitutes an important argument for the oncogenic and transforming role of exon skipping events in the dMMR colonic crypt, well before the burst of coding mutations that arises much later in transformed MSI cancer cells. The S34F mutation in U2AF1 is commonly found in myelodysplastic syndromes and secondary acute myeloid leukemia [49]. In line with our findings, it is responsible for blocking erythroid differentiation and causing aberrant alternative splicing in hematopoietic progenitors associated with different disease phenotypes [49, 50]. A number of targets common to both U2AF1 and MSI splicing signatures (e.g., ADAM10 [51], CENP-E [52]) may underpin this functional impact. Strikingly, MSI CRC are frequently characterized by dedifferentiated histopathology [53]. From all our results, we speculate that the RNA splicing defects we report in this work induce early dedifferentiation of multiple dMMR clones in the crypt and then the transformation of some of them at the apex of cancer initiation. It thus appears, that pro-differentiation or splice-correcting agents might be considered as drugs of choice for the early treatment of MSI lesions and more generally for MMR-deficient cancers. These drugs could be proposed to improve the personalized therapy of MSI cancers, at a time when precision medicine for MSI cancers using immune checkpoint inhibitors is growing fast, with the need however to find new targets in combination to overcome resistance to these new drugs, as recently illustrated by our team and others [16].

There are some limitations to this study that have been already highlighted. Mechanistically, though a number of somatic mutations at the PY are likely to participate to the initiation of colon cancer, we have not precisely unraveled in this work their exact consequences on the binding of spliceosome factors to RNA. Besides, alternative scenarios have to be examined in future studies since MSI at the PY only explain a minority of the exon skipping we report here in MSI CRC, with yet a poor understanding of the link between these splicing anomalies and those due to the U2AF1 mutation. From a pathophysiological point of view, some might object that the number of splicing anomalies we report here in MSI CRC is not exceptionally high. This is partly due to the fact that we have willingly chosen to detect only the events with a frequency exceeding 5% in the tumor bulk. Note worthily, this process is here validated in several tumor series including tumor samples from colorectal and other epithelial tissue locations, suggesting that it probably plays a universal major role in MSI tumorigenesis. The next step will be to more accurately describe these splicing anomalies in pan-cancer, using in particular long-read sequencing technology to establish their combinations precisely in tumors, and earlier as we initiated here in pre-tumor lesions.

Conclusions

In this work, we show that alternative splicing is altered partly due to MSI in the intestinal crypt, and that this process is crucial for cell dedifferentiation and initiation of MSI tumorigenesis far before the MSI-driven coding pathway.

Methods

Samples and patients

This study included an experimental cohort composed of 101 MSI patients and 32 MSS CRC patients for whom both tumor tissue and matched colonic mucosa were available. The data were obtained from a retrospectively enrolled cohort of patients who underwent surgical resection of mainly stage II–III CRC from 2004 to 2015 at Saint Antoine Hospital, Paris, France (Cohort Microsplicother, NI14027HLJ). Additionally, 22 precancerous tissues—adenoma (n = 14 samples) and dMMR crypts (n = 8 samples, a pool of 10 crypts foci for each patient)—associated with their normal mucosa (n = 22), i.e., MMR-proficient crypts foci. The data were obtained from a retrospectively cohort of patients who underwent surgical resection of mainly stage II–III CRC from 2013 to 2020 at Lille University Hospital, France. We included patients who presented with a tumor showing loss of MLH1 or MSH2 expression at pathological examination. MSI/dMMR status was identified prospectively at diagnosis using the pentaplex PCR method and immunohistochemistry as previously described [7, 35]. Patients who received preoperative chemotherapy and/or radiation therapy were excluded. Extensive clinical data were available for all MSI CRC patients. This study was approved by our institutional review board/ethics committee (Sorbonne University), and informed consent was obtained. This research was performed in accordance with the Declaration of Helsinki.

RNA sequencing

Before RNA extraction, frozen tissue sections were lysed in QIAzol Lysis Reagent using a Tissue Lyzer (Qiagen). After chloroform separation, RNA extraction was performed using the miRNeasy Mini Kit (Qiagen) with a QIACube instrument (Qiagen). The manufacturer’s instructions were followed, and DNAse treatment was included in the protocol. RNA concentrations were evaluated with a Nanodrop 2000C (Thermo Fischer Scientific) spectrophotometer. RNA integrity was assessed on a Bioanalyzer 2100 (Agilent) using the RNA 6000 Nano Kit. The average RINs (RNA Integrity Number) calculated for tumor and normal adjacent tissues were equal 8.1 and 7.2, respectively. Downstream RNA sequencing experiments were performed on selected pairs of samples, each with RIN ≥ 7 and a DNA yield ≥ 2.5 μg.

Stranded mRNA sequencing was performed by the “Centre National de Recherche en Génomique Humaine, Institut de Biologie François Jacob, CEA.” After complete RNA quality control of each sample (quantification in duplicate and RNA6000 Nano LabChip analysis on Bioanalyzer from Agilent), libraries were prepared using the “TruSeq Stranded mRNA Library Prep Ki” from Illumina according to the manufacturer’s instructions and with an input of 1 μg (selection of poly(A) RNAs). Library quality was checked by Bioanalyzer analysis, and sample libraries were then pooled before sequencing to reach the expected sequencing depth. Sequencing was performed on an Illumina HiSeq 2000 sequencer as paired-end 100 bp reads using Illumina sequencing reagents, 50 M reads. Libraries were pooled with 4 samples per lane. FASTQC v.0.11.7 was used for sequencing quality control, and the reads were aligned against the reference genome hg37 (GRCh37) using TopHat2.

Identification of exon skipping events

MSIsensor [54] (scan method) 0.5 was used to identify genomic MS and annotation was performed by ANNOVAR [55]. We identified all exons flanked by an MS near an intron/exon junction (up to 50 bp into the intronic sequence) and designed a custom junction FASTA reference (50 bases on each side of the junction). The identification of reads spanning an exon-exon junction was performed by BLASTN alignment against our custom reference for each 3′-exon flanked by an MS (3′ Exon-FMS). A spanning read was considered if its sequence matched at least 8 bases on one side of the junction and the remainder of the sequence strictly matched the other side of the junction. Percent spliced in (PSI) [28] scores ranging from zero to one were calculated by + (inclusion reads)/(inclusion + exclusion reads) and considered when the event was covered by at least 5 reads (inclusion or exclusion reads).

For each 3′ Exon-FMS, the PSI of MSI or MSS tumors was compared to that of normal tissues. Adjusted P-values were obtained using Benjamini–Hochberg correction of two-sided t-test (p-adjust < 0.05). In parallel, we also compared the PSI of MSI tumors to the PSI of MSS tumors (p-adjust < 0.05). For 3′ Exon-FMS identified by the two-sided t-test adjusted as skipped, we compared the PSI of each tumor or PSI of each normal matched tissue. The exons considered as skipped had a PSI fold change greater than 1.5 between tumor and normal. Finally, only exons with a mutation frequency > 5% in this cohort were considered. Integrative Genomics Viewer (IGV) was used to generate Sashimi plot. From the 133 available healthy tissues, we considered an exon as an alternative exon if at least 5% of the tissues presented 3 or more reads of exon exclusion. We did not replicate the same analysis on publicly available cohorts because we recently provided clear evidence that the performance of MSISensor for the detection of MSI vs. MSS CRC has to be improved. This was shown in large cohorts of mCRC and nmCRC samples that were previously confirmed as MSI/dMMR or MSS/pMMR by IHC and MSI-PCR methods performed in large, specialized test centers [27].

Tissue recovery and DNA/RNA extraction

Frozen tissues (adenocarcinoma) and formalin-fixed and paraffin-embedded tissues (adenomas and crypts) were recovery by punch technics (adenocarcinomas and adenomas) or by an innovative method developed by our team and based on laser microdissection (LMD7000 Laser, Leica) assisted by IHC (hMSH2 1/125 FE11 clone, Calbochiem) (MMR-deficient and proficient crypts). DNA was purified with the AllPrep DNA/RNA/miRNA Universal Kit (Qiagen) for frozen tissue and AllPrep DNA/RNA FFPE Kit (Ozyme) for FFPE cores, according to the manufacturer’s instructions. Their concentrations were determined by a NanoDrop ND-1000 spectrophotometer at 260/280 nm (NanoDrop Technologies, Inc.) and by the Qubit Fluorometer (Qubit® dsDNA HS Assay Kit). The quality and integrity of the nucleic acids were analyzed with a TapeStation 2200 system (Agilent).

Exome data analyses

For adenocarcinomas, the whole exome sequencing procedure was performed following the manufacturer’s recommendations (SureSelect Human Exon Kit v5, 75 MB; Agilent). The generated reads were mapped to reference genome hg19 (GRCh37).

For adenomas and crypts, the whole exome sequencing procedure was performed following the manufacturer’s recommendations (KAPA HyperExome 45 Mb, Roche, Illumina NovaSeq 6000 S1 Reagent Kit – 2 × 100 cycles), and we used the Unique Molecular Identifier (UMI), following the manufacturer’s recommendations. The exome data analysis was first performed using fastp with a minimum read length of 15 and mapped to the reference genome hg19 (GRCh37) using bwa v0.7.17. Overall, variant calling (single-nucleotide variants and insertions/deletions) was performed using MuTect-2 tool from GATK v4.1.6 [56]. The somatic mutations were filtered as follows: total reads > 10, somatic score > 3, mutated allele frequency in tumor tissue ≥ 5%, mutated allele count in tumor tissue ≥ 3, mutated allele frequency in normal tissue < 4%. A somatic mutation observed at the donor (GU) or acceptor (AG) site is considered to affect splicing if it induces a delta of PSI > 0.1. To analyze mutations extensively at microsatellite sequence sites, mononucleotide MS with lengths greater than or equal to 5 bp were considered if they were covered with at least 10 mapping reads in both normal and tumor paired samples. MSIsensor 0.5 was initially used to generate the read count distribution for each MS. The total number of reads covering each MS was normalized (arbitrary value of 100) in tumor and healthy matched tissue. For each MS, the normalized value of the reads in healthy tissue was subtracted from that in the tumor tissue. If the ratio difference between normal and tumor tissues was > 10% for deletion the MS was identified as mutated. The deletion size considered for each MS corresponds to the largest deletion value with a ratio of difference between normal and tumor > 10%. The branch sites were predicted using SVM-BPfinder with the default parameters [57].

Intronic microsatellite mutations analyses were allowed (from WES) because of the sufficiently high sequencing depth and good median coverage of the flanking sequences of the exons (Additional file 1: Fig. S17 and Additional file 8: Table S7).

DNA preparation and mutation analyses (functional)

Genomic DNA was extracted using a standard phenol–chloroform procedure and was assessed for integrity and quantity with a Bioanalyzer (Agilent, Les Ulis, France). Primers specific for each locus were used to amplify the repeat and short flanking sequences from template DNA using PCR (cycling conditions, parameters and primers sequences available on request). Amplified PCR products were run on an Applied Biosystems PRISM 3100 Genetic Analyzer automated capillary electrophoresis DNA sequencer. Allelic sizes were estimated using gene mapper software (Applied Biosystems).

Cell culture and transfection

We have deliberately used MSI and MSS gastric cancer cellular models to better illustrate the broad scope of our results, which have the potential to apply to other primary MSI cancers than CRC (see also Fig. 3E our results in primary MSI GC and EC). These cellular models have been selected without preconceived ideas, and we have not excluded any others. This series corresponds to cellular models commonly investigated in the literature, which we have historically already used extensively in our laboratory. Cell lines were grown at 37 °C under a humidified atmosphere of 5% CO2 in DMEM (Dulbecco’s modified Eagle’s medium) (Lonza, Saint Beauzire, France), containing 4.5 g L−1 glucose and supplemented with 5% fetal bovine serum without antibiotics (Gibco-Invitrogen, Cergy-Pontoise, France). Cells were plated at a density of 75.104 cells per well in 6-well plates 24 h before transfection. Minigenes (0.75 μg per well) were transfected with Lipofectamine 2000 or 3000 reagent according to the manufacturer’s recommendation (Invitrogen). After 24 h, the cells were harvested, washed in PBS, and used to prepare total RNA. Cell lines were free of mycoplasma contamination.

Minigene constructions

PMSG-1-PolR2G hybrid minigenes have been described previously [34]. The genomic segment encompassing the sequences of interest, containing roughly 150 bp of the upstream intron, the next exon, and 150 bp of the downstream intron, was amplified by PCR from tumor or non-tumor genomic DNA. The resulting plasmids harboring wild type or mutant sequences were verified by sequencing. Plasmid batches for transfection experiments were prepared according to the manufacturer’s procedure (Qiagen).

RNA isolation and RT-PCR analyses

Total RNA was extracted with Tri-Reagent (Ambion, Les Ulis, France) or NucleoSpin RNA kit (Macherey–Nagel), according to the manufacturer’s instructions. RNA was quantified by spectrophotometry, and the integrity was analyzed by gel electrophoresis. One microgram of RNA was reverse-transcribed with M-MLV reverse transcriptase (Biolabs) and random primers or the High-Capacity cDNA Archive Kit (Applied Biosystems). The resulting cDNA was amplified.

RNA immunoprecipitation (RIP)

RIP assays were performed according to the manufacturer’s instructions (Millipore, EZ-Nuclear RIP Kit (Catalog No. 17–10,521)). Briefly, cells were grown in 150-mm Petri dishes. Magnetic beads and anti-U2AF2 or IgG control antibodies were added to the nuclear protein extracts and put on a rotating wheel at 4 °C overnight to immune-precipitate protein-RNA complexes. Beads were washed several times and eluted protein-RNA complexes were dissociated (with 1% SDS and proteinase K) at 60 °C for 30 min with gentle shaking. RNA was then purified using TRIzol, precipitated, resuspended in water, and then treated with DNAse I. The DNAse was inactivated, and the RNA extracted and used for RT-PCR experiments (High-Capacity cDNA Archive Kit and PCR-amplified with Go Taq polymerase (Promega)).

Gel shift assay analyses

Two gel retardation assays were performed using 5′ end-labeled RNA wild type and mutant probes. Samples (4 μg) of nuclear protein extracts were incubated with 2 nmoles of the labeled probe at room temperature for 25 min and run for 1 h in non-denaturing 1.5% agarose gels. The gels were analyzed by fluorescence illumination on an Odyssey™ scanner device (LI-COR). The Cy5.5 signal appeared in red, while the HF750 signal appeared in green.

Immunoblot analyses

Cells were washed in PBS and lysed on ice in cold lysis buffer (RIPA buffer, Thermo Scientific, France) containing protease and phosphatase inhibitors (Halt Protease and Phosphatase Inhibitor Cocktail, Pierce). Proteins were separated by PAGE and transferred following standard protocols before analysis with an Odyssey Infrared Imaging System. Primary antibodies against the following molecules were used: Hsp110 (ab24503 dilution 1:100; Abcam, Cambridge, UK) and actin (926–42,210 dilution 1:5000; Licor, The Netherlands). IRDye680 and IRDye800 secondary antibodies were used (926–68,021 and 926–32,211, respectively, dilution 1:15,000, Licor).

Functional analyses

Genes were analyzed using Enrichr to determine the signaling pathways (BioPlanet 2019) that were enriched in the mutated genes [58].

MHC class I and class II neoepitope prediction

Neoepitopes were predicted through the Ideation@SiRIC pipeline, which integrates various software packages. First, seq2HLA was employed to determine MHC class I and class II types [59], using default parameters for 101 normal-WES preprocessed fastq files via fastp. Next, the alternative transcripts were processed with the pVACbind function from the pVACTools toolkit for neoantigen prediction [60]. In each pVACbind run, NetMHC [61], NetMHCpan, and NetMHCIIpan [62] algorithms, encapsulated in the pVACtools, were employed for epitope prediction. The epitope length was set to 8–10 amino acids for class I and 15 for class II presentation, with default parameters for all other settings. Predicted neoepitopes were filtered based on median affinity binding ≤ 500 nM, representing strong binders. In instances where multiple strong binders were predicted for the same alternative transcript, a unique neoepitope per transcript was defined as the transcript with the lowest binding affinity score. The count of unique neoepitopes per transcript was determined as the overall number of unique filtered transcript peptide sequences per patient.

Single cell analysis

Single cell RNA-seq (scRNA-seq) data were obtained from the Pelka et al. study [26] via dbGaP (phs002407.v1.p1). Raw FASTQ files were aligned to the GRCh38 reference genome using Cell Ranger v2.1.1, to generate BAM files. These BAM files were used as input for STARsolo (bioRxiv, 2021.2005.2005.442755), component of STAR v2.7.8a, to generate gene expression count matrices. The SingCellaR package [63] was then used to filter for high quality cells based on UMI counts and the number of detected genes. The preprocessed data were further analyzed using Scanpy [64] for dimensionality reduction with uniform manifold approximation and projection (UMAP) [65] and plotting.

Cell types were identified using Census (https://doi.org/10.1101/2022.10.19.512926) [29], which implements a collection of hierarchically organized gradient-boosted decision tree models for cell type classification. Specifically, we used Census to identify cancer cells. Additionally, we used CellTypist [30] and its pretrained “Cells_Intestinal_Tract.pkl” model for automated cell type prediction based on gene expression profiles.

Splice junction counts were generated from the FASTQ files using STARsolo. The junction counts and reference GTF files were input into the MARVEL package [31] to create an R object for downstream analyses. MARVEL was used to annotate junctions, filter unannotated and multimapped junctions, and estimate percent spliced-in (PSI) values directly from junction reads. Finally, we performed splicing analysis to compare PSI values between pseudobulk samples using the permutation-based approach of MARVEL [66, 67] to compare transit amplifying (TA) cells based on the MSI/MSS phenotype.

Availability of data and materials

Exome sequencing and RNA sequencing data have been deposited in the European genome–phenome archive. The data deposit is available on EGA with the accession number EGAS00001004863 [68]. All the scripts for analyzing and pre-processing raw data sequences are available under GPL-3.0 license at GitHub: https://github.com/CRSA-MSI/pipelines_and_scripts_Jonchre_Montemont_et_al_2024 [69] and in Zenodo: https://doi.org/10.5281/zenodo.12706170 [70].

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Acknowledgements

We thank the CEPH-Biobank and CNRGH teams for their technical expertise. We thank the Tumorothèque CRB Cancer, Hôpital Tenon, APHP.sorbonne université, Paris, France. We would like to thank Christophe Rachez for his technical help. We thank Ms Prigent who is the technical referent of Histomics (ICM, Sorbonne University).

Peer review information

Ulf Schmitz and Wenjing She were the primary editors of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Review history

The review history is available as Additional file 12.

Funding

This work was supported by grants from SIRIC CURAMUS and the “Ligue Nationale Contre le Cancer.” A. Duval’s team is labeled by the French National League against Cancer. The sponsor was Assistance Publique – Hôpitaux de Paris (Département de la Recherche Clinique et du Développement).” The study was funded by a grant from Programme Hospitalier de Recherche Translationnelle en Cancérologie – PHRT-K 14056 (Ministère de la Santé).” This work was also supported by the France Génomique National infrastructure, funded as part of the “Investissements d’Avenir” program managed by the Agence Nationale pour la Recherche (contract ANR-10-INBS-09).” All contributors from Brest U1078 INSERM unit were supported by grants from the Ligue contre le cancer (departments 29 and 44) and by grants from the GRAMMY EraPerMed European research program.

Author information

Author notes

  1. Vincent Jonchère, Hugo Montémont, and Enora Le Scanf are co-first authors.

  2. Laurent Corcos, Jean-François Deleuze, Ada Collura, and Alex Duval are co-senior authors.

Authors and Affiliations

  1. Sorbonne Université, INSERM, Unité Mixte de Recherche Scientifique 938 and SIRIC CURAMUS, Centre de Recherche Saint-Antoine, Equipe Instabilité Des Microsatellites Et Cancer, Equipe Labellisée Par La Ligue Nationale Contre Le Cancer, 75012, Paris, France

    Vincent Jonchère, Hugo Montémont, Aurélie Siret, Quentin Letourneur, Assane Fall, Karim Labreche, Toky Ratovomanana, Olivier Buhard, Philippe Le Rouzic, Cody Feys, Emmanuelle Despras, Pascale Cervera, Magali Svrcek, Pierre Bourgoin, Jérémie Lefèvre, Yann Parc, Mehdi Touat, Romain Cohen, Thierry André, Florence Coulet, Jean-François Fléjou, Florence Renaud, Agusti Alentorn, Ada Collura & Alex Duval

  2. INSERM, UMR 1078, Université de Brest, Génétique Génomique Fonctionnelle Et Biotechnologies, Etablissement Français du Sang, F-29200, Brest, France

    Enora Le Scanf, Danielle Arzur, Gwennina Cueff, Catherine Le Jossic-Corcos, Gaël Quéré, Gwendal Dujardin, Marc Blondel, Cédric Le Maréchal & Laurent Corcos

  3. CHU de Brest, Inserm, Univ Brest, EFS, UMR 1078, GGB, Brest, F-29200, France

    Enora Le Scanf, Danielle Arzur, Gwennina Cueff, Catherine Le Jossic-Corcos, Gaël Quéré, Gwendal Dujardin, Marc Blondel, Cédric Le Maréchal & Laurent Corcos

  4. Laboratory for Genomics, Foundation Jean Dausset–CEPH (Centre d’Etude du Polymorphisme Humain), Paris, France

    Emmanuel Tubacher, Victor Renault, Habib Zouali, Hélène Blanché & Jean-François Deleuze

  5. Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine (CNRGH), 91057, Evry, France

    Christophe Battail, Anne Boland & Jean-François Deleuze

  6. Genosplice, Paris, France

    Ariane Jolly & Pierre de la Grange

  7. Programme “Cartes d’Identité Des Tumeurs, Ligue Nationale Contre Le Cancer, Paris, France

    Rémy Nicolle & Aurélien de Reyniès

  8. Department of Pathology, Sorbonne Université, AP-HP.Sorbonne UniversitéHôpital Saint-Antoine, 47-83 Boulevard de L’hôpital, 75012, Paris, France

    Pascale Cervera, Magali Svrcek, Pierre Bourgoin & Jean-François Fléjou

  9. Department of Digestive Surgery, Sorbonne Université, AP-HP, Hôpital Saint-Antoine, Paris, France

    Jérémie Lefèvre & Yann Parc

  10. Sorbonne Université, Inserm, CNRS, UMR S 1127 and SIRIC CURAMUS, Institut du Cerveau Et de La Moelle Épinière, ICM, AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Service de Neurologie 2 Mazarin, Paris, France

    Mehdi Touat

  11. Sorbonne Université, Inserm, CNRS, UMR S 1127, Institut du Cerveau Et de La Moelle Épinière, ICM, AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Service de Neuropathologie Laboratoire Escourolle, Paris, France

    Franck Bielle

  12. Department of Medical Oncology, Sorbonne Université, AP-HP, Hôpital Saint-Antoine, Paris, France

    Romain Cohen & Thierry André

  13. Genetics Department, AP-HP.Sorbonne Université, Paris, France

    Florence Coulet & Alex Duval

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  1. Vincent Jonchère
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Contributions

All the authors have made substantial contributions (i) to the conception or design of the work; (ii) the acquisition, analysis, or interpretation of the data; and (iii) the drafting or substantial revision of the work. All authors have approved the submitted version. They have agreed both to be personally accountable for the author’s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even those in which the author was not personally involved, are appropriately investigated and resolved and that the resolution is documented in the literature.

Corresponding author

Correspondence to Alex Duval.

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Ethics approval and consent to participate

This study was approved by our institutional review board/ethic committee (Sorbonne University), and informed consent was recorded. Research has been performed in accordance with the Declaration of Helsinki.

Competing interests

PDLG and AJ (authors) declare a conflict of interest with the Genosplice company.

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Jonchère, V., Montémont, H., Le Scanf, E. et al. Microsatellite instability at U2AF-binding polypyrimidic tract sites perturbs alternative splicing during colorectal cancer initiation. Genome Biol 25, 210 (2024). https://doi.org/10.1186/s13059-024-03340-5

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Genome Biology

ISSN: 1474-760X

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