2. INTRODUCTION ▪ MPM(malignant pleural mesothelioma ) is an aggressive cancer arising from the mesothelial cells of the pleura. About 80% of mesothelioma cases are linked to asbestos exposure; the remainder may be related to prior chest radiation, genetic predisposition or spontaneous occurrence. In the United States, ~3,200 new MPM cases and ~3,000 deaths due to MPM occur annually. ▪ MPM tumors are broadly divided into three histological subtypes: epithelioid, biphasic (or mixed) and sarcomatoid. The prognosis for patients with MPM is dismal, with a median overall survival of 8–36 months depending on stage, but patients with sarcomatoid MPM have particularly poor outcomes compared to patients with epithelioid histology. Although aggressive surgery is effective in patients with early, limited MPM with epithelioid histology, most patients present at advanced stages, and current drug regimens are ineffective.

3. INTRODUCTION ▪ Understanding the genetic alterations that drive MPM is critical for successful development of diagnostics, prognostics and personalized therapeutic modalities. Because MPM is rare, genomic studies are limited and have typically involved a small number of samples. ▪ Previously, loss-of-function mutations in CDKN2A, NF2 and BAP1 have been reported in MPM. In addition, previous studies have reported copy gains and copy losses involving multiple regions of the genome. Familial studies have identified germline BAP1 mutations that predispose carriers to mesothelioma. However, understanding of the mutational landscape of MPM is not yet sufficient to affect classification or treatment strategies. ▸ Here they sequenced transcriptomes and exomes from 216 MPMs. Subtype# of samplesExome Targeted exome sequencing (SPET) RNA-seqSNP arrayWGS TumorNormalPairedTumorNormalPairedTumor NormalPairedTumorNormalPaired 1 Epithelioid 5421 28 5419 444 2 Sarcomatoid 2918 11 2918 777 3 Biphasic-E 7241 27 7239 777 4 Biphasic-S 5618 33 5618 111 5 Unassigned 61114440111111 Total217*99 103 21195 20 ▸ Sample description

4. 1. RESULTS: Expression analysis identifies distinct MPM subtypes [RNA-seq  EXPRESSION] ▸ Obtained RNA-seq data for 211 MPM samples. ▸ RNA-seq libraries were prepared using TruSeq RNA Sample Preparation kit (Illumina). ▸ paired-end (2 × 75-bp) reads per sample. ▸ All sequencing reads were evaluated for quality using the Bioconductor ShortRead package. ▸ RNA-seq reads were aligned to the human genome version NCBI GRCh37 using GSNAP(Genomic Short-read Nucleotide Alignment Program). ▸ Expression counts per gene were obtained by counting the number of reads aligned concordantly within a pair and uniquely to each gene locus as defined by NCBI and Ensembl gene annotations and RefSeq mRNA sequences. Differential gene expression analysis performed using edgeR, and DESeq2. ▸ Mesotheliomas are histologically classified as epithelioid, biphasic or sarcomatoid and are heterogeneous, with different proportions of epithelioid and sarcomatoid features. Several studies have analyzed gene expression in MPMs with microarrays; however, none have established molecular subtypes routinely applied in patient care. ▸ To define molecular subgroups of MPMs, they performed unsupervised consensus clustering of RNA-seq–derived expression data from 211 of the 216 MPM samples and identified four major clusters: sarcomatoid, epithelioid, biphasic- epithelioid (biphasic-E) and biphasic-sarcomatoid (biphasic-S) (Fig. 1).

5. ▸ The sarcomatoid cluster contained all eight samples histologically classified as sarcomatoid/desmoplastic and 21% (13/62) of histologically biphasic samples. Of the remaining 49 histologically biphasic samples, 21 were classified as biphasic-E, 27 as biphasic-S and 1 as epithelioid. The biphasic samples that clustered with sarcomatoid samples contained a high fraction of sarcomatoid cells (Fig. 1a). ▸ Notably, the remaining 62% (88/141) of histologically epithelioid samples, classified as biphasic-E (n = 51), biphasic-S (n = 29) or sarcomatoid (n = 8), showed lower overall survival (log-rank test P < 0.0001; Fig. 1b) with a hazard ratio of 2.5 (Cox proportional hazard model P < 0.001; 95% confidence interval 1.6–3.8). Further, the epithelioid cluster showed greater overall survival than the other groups (Fig. 1c). ▸ The most significantly upregulated gene in the epithelioid group was CLDN15. CLDN15 is downregulated in cells undergoing epithelial-to-mesenchymal transition (EMT). In contrast, LOXL2, known to contribute to EMT, and VIM, which is upregulated during EMT, were among the most significantly upregulated genes in sarcomatoid samples. They found that the log2 ratio of CLDN15/VIM (C/V) expression was significantly different between subtypes (Fig. 1d). A similar trend for C/V ratio discriminating between subtypes was also observed in a previously published microarray data set(supplementary Fig. 5). ▪ Epithelioid group: up-regulated gene, CLDN15 ▪ Sarcomatoid group: up-regulated genes, LOX2 and VIM 1. RESULTS: Expression analysis identifies distinct MPM subtypes

7. 2. RESULTS: MPM mutational profile [Exome-seq  MUTATION] ▸ Obtained exome-seq data for 99 MPM samples. ▸ Exome capture was performed using the Agilent SureSelect Human All Exome kit. ▸ To generate paired-end (2 × 75-bp) reads per sample. ▸ All sequencing reads were evaluated for quality using the Bioconductor ShortRead package. ▸ Sequencing reads were mapped to UCSC human genome (GRCh37/hg19) using BWA software set to default parameters. ▸ Somatic variant calling on tumor and its matched normal BAM file was performed using Strelka(accurate somatic small-variant calling from sequenced tumor-normal sample pairs). Known germline variants represented in dbSNP (Build 131) or 6,515 previously published normal exomes but not represented in COSMIC (v62) were filtered out for all samples. In addition, germline variants that were present in both the tumor and normal samples were removed. [TCGA mutation data] TCGA mutation data used in Figure 2e were retrieved using the GGDSR R package from cBioPortal. [Mutational signatures] ▸ They analyzed the MPM exome sequence data for the frequency of the possible 96 mutation types. They also included in the analysis TCGA exome data for 2,437 samples from 8 other cancer types as provided by the SomaticCancerAlterations Bioconductor package as well as data from two small-cell lung cancer studies. A set of 6 common signatures was detected across the combined data set using non-negative matrix factorization.

8. 2. RESULTS: MPM mutational profile ▸ They identified a total of 2,529 protein-altering somatic mutations, including 2,069 missense, 190 nonsense, 3 stop-loss, 63 essential splice site and 204 frameshift mutations. A majority of the mutations (85%; 2,144/2,529) were novel, as they were not reported in COSMIC or OncoMD. ▸ They did not observe significant differences in mutation rate between molecular subtypes (P = 0.8; Fig. 2a–d). Notably, with the exception of thyroid carcinoma and acute myeloid leukemia, MPMs showed a low protein-altering mutation rate compared to other cancers (Fig. 2e). LAML, acute myeloid leukemia THCA, papillary thyroid carcinoma MESO, mesothelioma BRCA, breast carcinoma GBM, glioblastoma OV, ovarian carcinoma KIRC, clear cell renal carcinoma UCEC, endometrial carcinoma COADREAD, colon and rectal carcinoma STAD, gastric adenocarcinoma LUAD, lung adenocarcinoma BLCA, urothelial bladder carcinoma SCLC, small-cell lung cancer LUSC, lung squamous-cell carcinoma SKCM, cutaneous melanoma HNSC, head and neck squamous cell carcinoma epithelioidbiphasic-E biphasic-S sarcomatoid

9. 2. RESULTS: MPM mutational profile ▸ They found S1 and S2 to be the predominant mutational signatures in MPMs (Fig. 2g). The S1 signature, with no predominant transition or transversion, is probably indicative of a base-agnostic mutagen such as reactive oxygen species (ROS) and is consistent with asbestos exposure known to induce ROS in MPM. ▸ Signature S2 is characterized by C>T transitions at NpCpG trinucleotides and is indicative of the elevated deamination rate of 5-methylcytosine to thymine in CpG islands. Whereas S4 shows C>T transitions characteristic of repair errors at UV-induced pyrimidine dimer sites observed in melanoma (Fig. 2f), S3 is characteristic of C>A transversions indicative of cigarette smoking. Notably, the S3 signature associated with cigarette smoking, though predominant in lung cancer, was not observed in MPMs. Clustering cancers by mutational signatures showed that the mutational processes in MPMs were closer to those observed in ovarian cancers (Fig. 2h). LAML, acute myeloid leukemia THCA, papillary thyroid carcinoma MESO, mesothelioma BRCA, breast carcinoma GBM, glioblastoma OV, ovarian carcinoma KIRC, clear cell renal carcinoma UCEC, endometrial carcinoma COADREAD, colon and rectal carcinoma STAD, gastric adenocarcinoma LUAD, lung adenocarcinoma BLCA, urothelial bladder carcinoma SCLC, small-cell lung cancer LUSC, lung squamous-cell carcinoma SKCM, cutaneous melanoma HNSC, head and neck squamous cell carcinoma

10. 3. RESULTS: Significantly mutated mesothelioma genes ▸ To further assess the relevance of the mutated genes, they identified ten significantly mutated MPM-associated genes (Fig. 3a) that included BAP1, NF2, TP53, SETD2, DDX3X, ULK2, RYR2, CFAP45, SETDB1 and DDX51 (Fig. 3a–g). Among the significantly mutated genes, only BAP1, NF2 and TP53 have been reported in MPM. Consistent with previous reports, they found tumor suppressors BAP1 and NF2 to be mutated in 23% (46/202) and 19% (38/202) of the samples, respectively. ▸ Tumor suppressor TP53 was mutated in 8% (17/202) of MPMs, a number higher than previously reported (3/53)12 but lower than in studies involving a smaller sample size (4/20) or (2/22). Notably, TP53 mutations were absent from the epithelioid subtype. Further, patients with TP53 mutations showed lower overall survival than those with wild-type TP53 in this cohort, indicative of the aggressive nature of TP53-mutant MPMs (Fig. 3h).

11. 4. RESULTS: multiple molecular mechanisms lead to activation and inactivation of genes ▸ In addition to mutations and expression changes, they assessed 95 MPMs for copy number alterations using 2.5M Illumina SNP array and/or whole-genome data. They found regions of recurrent copy loss that include genes such as BAP1, NF2, CDKN2B, LATS2, LATS1 and TP53 (Fig. 4a), consistent with previous reports. Copy number loss correlated with loss of expression in these genes (Supplementary Fig. 12a). Supplementary Fig. 12a

12. 4. RESULTS: multiple molecular mechanisms lead to activation and inactivation of genes ▸ Driver gene fusions have been reported in multiple cancers. They analyzed MPM RNA-seq data for presence of gene fusions. Overall, They identified 43 gene fusions in 22 samples (supplementary Table 10). Although recurrent gene fusions are usually associated with oncogenic activation, they identified many recurrent fusions involving tumor suppressor genes. They found 13 fusions in NF2, 7 in BAP1, 8 in SETD2, 7 in PBRM1, 2 in PTEN and 6 in other genes (Fig. 4). RNA Tumor IDGene 5'Gene 3'Read EvidenceOrthogonal ValidationChromosome # 5'Position 5'Chromosome # 3'Position 3'Fusion Protein Length NF2 predicted fusions 17865241NF2THRB3No2230035201324270492461 17865267NF2IFT1403No2230000101161561151106 9259677NF2CABP75Validated by sanger22300382742230123651207 9259684NF2PIEZO213Validated by sanger2230000101181080727295 9259687NF2OSBP211No223006105322310901951041 9259690NF2PI4KA17Validated by sanger223000010122211930192088 9259709NF2RHOT136Validated by sanger22300382741730530909396 9259750NF2NFATC19No22300001011877170403117 17865239EWSR1NF214Validated by sanger2229664338223003274013 17865285D2HGDHNF23No22426954292230077428459 9259677CABP7NF29Validated by sanger22301237942230067815100 9259683GSTT1NF225Validated by sanger2224384120223003274046 9259709RHOT1NF213Validated by sanger17305299192230050646890 supplementary Table 10

13. ▸ To identify structurally altered transcripts in MPM, they performed de novo prediction of splice variants from RNA-seq data. They identified 26 candidate cancer-specific splice alterations in 14 genes frequently mutated in MPM or other cancers (Supplementary Table 11). 4. RESULTS: multiple molecular mechanisms lead to activation and inactivation of genes Tumor IDSymbolConsequence M680PTABL15' truncation (novel exon) M53PTABL15' truncation (novel exon) M691PTBAP1in-frame deletion M35PTBAP1frame-shift insertion M57PTBAP1in-frame deletion M60PTBAP1frame-shift deletion M632PTBAP1in-frame deletion;frame-shift deletion M683PTBAP1frame-shift deletion 606PTCDKN2A3' truncation 661PTDDX3Xframe-shift deletion M91PTDDX3Xin-frame deletion M632PTFBXW7in-frame deletion M82PTLATS2frame-shift deletion M680PTLATS2frame-shift deletion M59PTLATS2in-frame deletion 651PTLATS2in-frame deletion;frame-shift deletion M635PTMSH2in-frame deletion M4PTNCOR15' truncation M46PTNF2in-frame deletion M79PTNF2in-frame deletion M676PTPTPN145' truncation M3PTRB1in-frame deletion M669PTSETD2in-frame deletion M61PTSETD2frame-shift deletion 667PTSETDB1frame-shift deletion M635PTSTAG2frame-shift deletion Supplementary Table 11 Abstract SGSeq provides a framework for analyzing annotated and previously uncharacterized splice events from RNA-seq data. Input data must be provided as BAM files containing RNA-seq reads aligned against a reference genome. Exons and splice junctions are predicted from aligned reads and are assembled into a genome-wide splice graph. Splice events are identified from the graph and quantified using reads spanning event boundaries. This vignette provides an introduction to SGSeq, including splice event prediction, quantification, annotation and visualization.

14. Package ‘SGSeq’ They use the UCSC knownGene table as reference annotation, which is available as a Bioconductor annotation package TxDb.Hsapiens.UCSC.hg19.knownGene. TxDb.Hsapiens.UCSC.hg19.knownGene ▸ Analysis based on annotated transcripts ▸ Analysis based on de novo prediction Instead of relying on existing annotation, SGSeq can predict features from BAM files directly.

15. 4. RESULTS: multiple molecular mechanisms lead to activation and inactivation of genes Figure 4. (b,c) Mutations, splicing variants and gene fusions that alter NF2 (b) and SETD2 (c) in MPMs, mapped to domain architecture. RNA-seq coverage and junction reads supporting splice alterations in NF2 and SETD2 are shown for patients M79PT and M669PT, respectively. FERM_N, N-terminal ubiquitin-like structural domain of the FERM domain; FERM_central, FERM central domain; FERM_PH, C-terminal PH-like domain; ERM_C, Ezrin/radixin/moesin, C-terminal. (d) Recurrent splicing changes in ABL1 observed in patients M680PT, M53PT. The exon-intron structure of ABL1 and start and stop (*) codons are shown. Top schematic, ABL1 architecture resulting from the normal isoform; bottom schematic, alternate isoform encoding the kinase domain alone. SH2, Src homology 2; SH3, Src homology 3; TyrKc, tyrosine- protein kinase, catalytic domain; FABD, F-actin binding. (e) Quilt plot of alterations in indicated genes. Each column represents a sample.

16. 5. RESULTS: Mutations in the splicing factor SF3B1 are associated with specific alterations in mRNA splicing ▸ Alterations in spliceosomal components that affect splicing have been reported in multiple cancers. Using targeted sequencing, they identified three tumors with missense mutations in SF3B1, which encodes a splice factor with a role in branch-point recognition and U2- snRNP assembly (Fig. 5a). Differential splicing analysis showed that these tumors have distinct splicing patterns (Fig. 5b). RNA-seq analysis in MPMs identified 177 splice events that differ in tumors with SF3B1 mutation, compared to tumors with wild-type SF3B1 (Fig. 5b). ▸ A previous RNA-seq study of cancer cell lines identified an SF3B1 hotspot mutation encoding p.Lys700Glu in NCI-H2595, a mesothelioma cell line. They assessed splice variant usage in the RNA-seq data and found that NCI-H2595 showed splice alterations similar to those observed in SF3B1-mutant tumors (Fig. 5b). ▸ The most common type of splice alteration observed in SF3B1- mutant tumors was a change in the 3′ splice site to an upstream (n = 82, 46%) or downstream 3′ splice site (n = 26, 15%; Fig. 5c). Consistent with previous studies, most 3′ splice site alterations involved increased usage of an alternative 3′ splice site located 10–30 bp upstream (Fig. 5d). ▸ (Fig. 5e,f) Top, SF3B1-associated splice variants in MAP3K7 (e) or SMURF2 (f) in transcript isoforms detected in NCI-H2595. Middle, average per-base read coverage and junction counts after normalizing for read count; splice variants with increased usage in SF3B1 mutants are highlighted in red. Bottom, schematic of effect of cryptic splice site usage on protein architecture. STK, serine/threonine/dual-specificity protein kinase, catalytic domain; HECT, domain homologous to E6-AP C terminus; C2, protein kinase C conserved region 2.

17. 5. RESULTS: Mutations in the splicing factor SF3B1 are associated with specific alterations in mRNA splicing

18. 6. RESULTS: Integrated analysis of pathway alteration observed in MPM ▸ MuSiC pathway analysis identified several significantly altered pathways. Integrated analysis of the genomics data identified Hippo, mTOR, histone methylation, RNA helicases and p53 signaling pathways (Fig. 6) to be altered in MPMs.

19. DISCUSSION ▸ In this study, they performed a comprehensive genomic analysis of transcriptomes and exomes from 216 MPMs. Using RNA-seq, we identified four distinct molecular MPM subtypes. They found differences in mutational rates and signatures between MPMs and other cancers. These differences, along with mutations, expression profiles and gene fusions, have the potential to improve MPM diagnosis, which currently relies on immunohistochemistry and is clinically challenging. ▸ These results substantially expand on previous genomic studies and provide a comprehensive genomics profile of mesothelioma. Incorporating genomic analysis for the detection of actionable alterations as part of MPM patient care will help in developing rational individualized therapy.