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. Author manuscript; available in PMC: 2021 Apr 15.
Published in final edited form as: Clin Cancer Res. 2020 Aug 14;26(20):5477–5486. doi: 10.1158/1078-0432.CCR-20-1767

Evolution of genomic and T cell repertoire heterogeneity of malignant pleural mesothelioma under dasatinib treatment

Runzhe Chen 1,2,*, Won-Chul Lee 2,*, Junya Fujimoto 3, Jun Li 2, Xin Hu 2, Reza Mehran 4, David Rice 4, Stephen Swisher 4, Boris Sepesi 4, Hai Tran 1, Chi-Wan Chow 3, Latasha Little 2, Curtis Gumbs 2, Cara Haymaker 3, John Heymach 1, Ignacio Wistuba 3, J Jack Lee 5, P Andrew Futreal 2, Jianhua Zhang 2, Alexandre Reuben 1, Anne Tsao 1,#, Jianjun Zhang 1,2,#
PMCID: PMC7709879  NIHMSID: NIHMS1619970  PMID: 32816946

Abstract

Purpose:

Malignant pleural mesothelioma (MPM) is considered an orphan disease with few treatment options. Despite multimodality therapy, the majority of MPM recur and eventually become refractory to any systemic treatment. One potential mechanism underlying therapeutic resistance may be intratumor heterogeneity (ITH), making MPM challenging to eradicate. However, the ITH architecture of MPM and its clinical impact have not been well studied.

Experimental design:

We delineated the immunogenomic ITH by multi-region whole exome sequencing (WES) and T cell receptor (TCR) sequencing of 69 longitudinal MPM specimens from 9 patients with resectable MPM, who were treated with dasatinib.

Results:

The median total mutation burden (TMB) before dasatinib treatment was 0.65/Mb, similar with that of post-dasatinib treatment (0.62/Mb). The median proportion of mutations shared by any given pair of two tumor regions within the same tumors was 80% prior to and 83% post-dasatinib treatment indicating a relatively homogenous genomic landscape. T cell clonality, a parameter indicating T cell expansion and reactivity was significantly increased in tumors after dasatinib treatment. Furthermore, on average, 82% of T cell clones were restricted to individual tumor regions, with merely 6% of T cell clones shared by all regions from the same tumors indicating profound TCR heterogeneity. Interestingly, patients with higher T cell clonality and higher portion of T cells present across all tumor regions in post-dasatinib treated tumors had significantly longer survival.

Conclusions:

Despite the homogeneous genomic landscape, the TCR repertoire is extremely heterogeneous in MPM. Dasatinib may potentially induce T cell response leading to improved survival.

Introduction

Malignant pleural mesothelioma (MPM) is a rare and highly aggressive malignancy characterized by unique morphology that commonly grows as an irregular pleural rind within the affected hemithorax (1, 2). MPM is often refractory to aggressive multimodality therapy, combining surgery with chemo- and/or radiotherapy. Recent studies from MPM patients carrying germline mutations in tumor suppressor genes such as BAP1 or DNA repair genes have shown improved survival results (3, 4). However, despite significant efforts to develop novel therapeutics, the median survival of patients with MPM still remains between 12 and 18 months with a 5-year overall survival (OS) rate less than 5%, regardless of stage (2, 5–7). Understanding the mechanisms underlying therapeutic resistance of MPM remains a critical and largely unmet need.

One potential mechanism underlying the aggressiveness and therapeutic resistance in malignancy is intratumor heterogeneity (ITH), wherein different cancer cell clones with distinct molecular and phenotypic features are present within the same tumors, leading to differential responses to treatment (8–12). ITH has been found to associate with therapeutic resistance and survival of patients with different cancer types (8–12). Using a multi-region sequencing approach, our group and others have previously delineated the genomic, epigenetic and transcriptomic ITH architecture of non-small cell lung cancers (NSCLCs) and demonstrated that complex molecular ITH was associated with inferior clinical outcomes (13–19). Given the unique growth pattern of MPM (wide spreading irregular pleural rind across pleura) compared to most other solid tumors (growing as a solid mass), MPM may have profound ITH, which makes it challenging to eradicate by currently available therapeutic modalities.

In addition to ITH of cancer cells, ITH can also be present in the tumor microenvironment, particularly cancer immune contexture, which may have significant impact on cancer biology and clinical outcome. Our recent work has revealed substantial T cell receptor (TCR) repertoire heterogeneity in localized NSCLC, which was associated with inferior survival (15). Investigating the molecular and immune ITH architecture of MPM and its evolution under therapy may provide novel insight into the mechanisms underlying therapeutic resistance and disease progression. In the current study, we performed multi-region whole exome sequencing (WES) and TCR sequencing of 69 MPM specimens from 9 patients with resectable MPM, who were treated with the Src kinase inhibitor dasatinib for 4 weeks prior to surgical resection in a neoadjuvant clinical trial Protocol 2006–0935 (NCT00652574) (6, 20). These longitudinal specimens included 24 baseline specimens before dasatinib treatment and 45 post-dasatinib treatment.

Methods

Patients and sample collection

69 tumor regions consisting of 24 pre-treatment samples (2 to 4 regions per tumor) and 45 post-dasatinib samples (3 to 6 regions per tumor) and their matched peripheral blood samples were collected from 9 patients with MPM. All patients were treated at the University of Texas MD Anderson Cancer Center from 2008 to 2012 on Protocol 2006–0935 (NCT00652574) (Supplementary Table 1). Prior to dastanib treatment, MPM patients underwent extended surgical staging (ESS) with multiple biopsies of the primary pleural tumor to account for tumor heterogeneity. Each tumor biopsy removed from the patient was cut in half with one part flash frozen and the second part formalin fixed for paraffin (6). Surgical specimens were snap frozen in liquid nitrogen immediately after surgical resection and stored at −80°C. All surgical specimens were identified and collected by surgeons per protocol 2006–0935 and submitted to pathologists for multiregional sampling. All the selected samples for this current analysis were subjected to pathological examination to confirm the diagnosis and ensure the sample quality before DNA extraction. Peripheral blood mononuclear cells (PBMC) were immediately isolated from 10 ml whole blood and stored at −80°C. Written informed consent was obtained from all patients included. The study was approved by the Institutional Review Boards (IRB) at University of Texas MD Anderson Cancer Center. The study was conducted in accordance with U.S. Common Rule.

Whole exome sequencing

Genomic DNA was extracted and subjected to library preparation for sequencing with Agilent SureSelect Human All Exon V4 kit according to the manufacturer’s instructions. The 76bp paired-end WES was performed on Illumina HiSeq 2000 platform with mean target coverages of 200x and 100x for tumor and normal samples respectively as previously described (14).

Somatic mutation calling

Somatic single nucleotide variants (SNVs) and somatic small insertions and deletions (INDELs) were called using MuTect (21) and Pindel (22) respectively. Mutations previously reported in public database (dbSNP138, 1000Genomes, ESP6500 and EXAC) with > 1% allele frequency were removed. Next, we applied following mutation filtering criteria: (i) sequencing depth ≥ 50 for tumor and ≥ 30 for normal, (ii) tumor allele frequency ≥ 5% for single nucleotide variants and ≥ 10% for INDELs, and (iii) normal allele frequency < 1%.

Mutational signature analysis

Mutation signatures were determined by deconstructSigs (23) with 30 COSMIC signatures provided by the package.

Somatic copy number aberration (SCNA) analysis

SCNA analysis was done using our in-house SCNA caller, “exomecn” as previously described (14). The “exomecn” is a modified version of HMMcopy (24). Briefly, it calculates read counts of each exon and then calculates log2ratios between tumor and matched normal reference samples by considering the total number of reads as a normalization factor. The resulting normalized log2ratios were segmented using circular binary segmentation (CBS) algorithm implemented in the DNAcopy package of Bioconductor. The copy ratios of segments were then assigned to the overlapping genes by CNTools (25). We defined copy number gains and losses in all tumor samples using +log21.5 for gain and -log21.5 for loss respectively. Since the signal to noise ratio of SCNA could be reduced in the samples with lower tumor purity, we obtained purity-adjusted log2 ratios by log2 ((original copy ratio-1)/purity+1) (26) if any of the paired samples from the same patients passed the original log2 thresholds of +log21.5 and -log21.5. Tumor purity was estimated by Sequenza (27). Copy number gain and loss burden were defined as the number of copy number gains and losses in a given sample, and the total copy number burden is a sum of gains and losses.

Phylogeny inference

To infer phylogenetic trees, mutation data was converted to the binary data with mutations being 1 and wild-type being 0 and fed into Phangorn R package. Tree topologies were estimated by pratchet and branch lengths were inferred by acctran.

Neoantigen prediction

Neoantigens were predicted by NeoPredPipe (28) that uses ANNOVAR and netMHCpan. The SNVs and INDELs were fed into the program with patient-specific HLA types genotyped by HLA-VBSeq (29). Both strong and weak binders were considered predicted neoantigen peptides. The SNVs or INDELs that generated multiple neoantigen peptides with different k-mer settings were only counted once. Trunk neoantigens were defined as predicted neoantigens shared by all the regions per tumor.

TCRβ sequencing and comparison parameters

Sequencing of the CDR3 regions of human TCRβ chains was performed using the protocol of ImmunoSeq (Adaptive Biotechnologies, hsTCRβ Kit) as previously described (15, 30). T cell density was calculated by normalizing TCR-β template counts to the total amount of DNA usable for TCR sequencing, where the amount of usable DNA was determined by PCR-amplification and sequencing of housekeeping genes expected to be present in all nucleated cells. T cell richness is a metric of T cell diversity, and it is calculated by on the T cell unique rearrangements. T cell clonality is a metric of T cell proliferation and reactivity, and it is defined as 1-Peilou’s evenness and is calculated on productive rearrangements by:

1+∑iNpilog2(pi)log2(N)

where pi is the proportional abundance of rearrangement i, and N is the total number of rearrangements. Clonality ranges from 0 to 1: values approaching 0 indicate a very even distribution of frequency of different clones (polyclonal), whereas values approaching 1 indicate a distinct asymmetric distribution in which a few activated clones are present at high frequencies (monoclonal). Statistical analysis was performed in R version 3.2. Morisita index (MOI) is a measure of the similarity in the T cell repertoire between samples ranging from 0 to 1, taking into account the specific rearrangements and their respective frequencies, with an MOI of 1 being an identical T cell repertoire.

Statistical Analysis

Graphs were generated with GraphPad Prism 8.0 (La Jolla, CA). Spearman’s rank correlations were calculated to assess association between 2 continuous variables. Wilcoxon signed-rank test was applied to test the mutational burden, mutation concordance, SCNA burden, SCNA concordance, predicted neoantigens, neoantigen concordance and TCR metrics over time, respectively. Mann-Whitney test was used to compare TCR metrics of MPM and NSCLC. Linear regression was used to model the relationship between TCR metrics with survival. Two-sided p values less than 0.05 were considered to be statistically significant.

Results

Homogenous mutational profiles between different tumor regions from the same MPM

A total of 5,021 non-synonymous mutations (Supplementary Data) were detected in 69 tumor regions with a median total mutational burden (TMB) of 0.65/Mb, consistent with that from TCGA MPM cohort (31) (0.65/Mb, p=0.35). The average TMB in tumors before dasatinib treatment was 0.65/Mb, similar with that of post-dasatinib treatment tumors (0.62/Mb, p=0.5, Supplementary Fig. S1A). The median proportion of shared mutations between any pair of tumor regions was 80% (43% to 90%) prior to and 83% (71%−88%) post-dasatinib treatment (Fig. 1). The average pairwise mutational concordance between any two regions of the same tumors in these 9 MPM patients was no different pre- and post-dasatinib treatment (p=0.3) (Supplementary Fig. S1B) suggesting that dasatinib treatment did not significantly change the mutational ITH complexity. We further predicted neoantigens from these somatic mutations, but did not observe significant changes in total predicted neoantigen burden or proportion of neoantigens shared by different regions within the same tumors before and after dasatinib treatment (Supplementary Fig. S2A–B, Supplementary Fig. S3).

Figure 1. Genomic ITH of 9 MPM tumors before and after dasatinib treatment.

Figure 1.

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Phylogenetic trees were generated from all mutations by Wagner parsimony method. The length of trunk (blue), branch (red) and private branches (green) is proportional to the number of mutations identified in all regions within the same tumor, some but not all regions and only one single tumor region respectively. Pre: prior to dasatinib treatment; Post: post-dasatinib treatment.

We next looked into a set of significantly mutated genes (SMGs) identified from two large mesothelioma cohorts (31, 32) and found 8 mutations in 5 SMGs (BAP1, NF2, TP53, DDX3X and RYR2). All 8 mutations were detected in both pre-treatment and post-treatment tumors and 6 of the 8 mutations were present in all regions within the same tumors (Fig. 1) suggesting these mutations may have been early genomic events during clonal evolution of these MPM tumors. However, two NF2 mutations were heterogeneous mutations. A NF2 stop-gain mutation was detected in both pre-treatment specimens but was missing in one of the post-treatment tumor specimens from patient M4 and a NF2 non-synonymous mutation (p.G123X) was identified in 1 of the 3 pre-treatment tumor specimens and 1 of 6 post-treatment specimens from patient M11 suggesting these two NF2 mutations may be later subclonal mutations in patients M4 and M11.

Mutations are predominantly driven by deficient DNA repair pathways

Understanding how mutational processes shape MPM evolution may inform mechanisms underlying tumor adaptation. We next calculated the contribution of different mutational signatures to investigate the mutational processes operative in this cohort of MPM. In 58/69 (84%) tumor specimens, COSMIC Signature 3 (associated with failure of DNA double-strand break-repair by homologous recombination) or Signature 15 (associated with defective DNA mismatch repair) was most predominant (Fig. 2), suggesting that DNA repair deficiencies played a major role in mutagenesis in this cohort of MPM. The only exception was the tumor from Patient M14, in which COSMIC Signature 4 (associated with cigarette smoking) and Signature 24 (with known exposures to aflatoxin) were the predominant signatures accounting for a median of 35% (2%−44%) and 25% (14%−33%) respectively.

Figure 2. The top 5 mutational signatures in MPM tumors.

Figure 2.

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Pre: prior to dasatinib treatment; Post: post-dasatinib treatment.

Homogeneous somatic copy number aberration (SCNA) profiles in MPM

Somatic copy number aberration (SCNA) is another key feature of human malignancies that could potentially impact expression of large groups of genes and SCNA ITH may have a profound impact on cancer biology and clinical outcome (16). Therefore, we next delineated the SCNA profiles and SCNA ITH architecture of this cohort of MPM. First, we calculated SCNA burden defined as the average number of genes with SCNA for each MPM specimen. As shown in Supplementary Fig. S4, although the SCNA burden varied substantially between different patients, it was very similar between different regions within the same tumors suggesting substantial inter-patient heterogeneity but limited intra-tumor heterogeneity. Furthermore, no significant difference was observed in SCNA burden between tumors prior to versus post-dasatinib treatment (Supplementary Fig. S5A). We next measured concordance for SCNAs across multiple regions from the same tumors (either prior to or post-dasatinib treatment) as the surrogate for SCNA ITH. The average pairwise SCNA concordance between different regions of the same tumors was 0.83 (0.52 to 0.99) indicating homogenous SCNA ITH architecture in this cohort of MPM overall. In addition, dasatinib did not significantly change the SCNA concordance (Supplementary Fig. S5B). Furthermore, we investigated a list of cancer genes reported to be altered by SCNA in two large mesothelioma cohorts (31, 32). As shown in Supplementary Fig. S6, different regions within the same tumors showed a high level of homogeneity of SCNAs in cancer genes including deletions of BAP1, CDKN2A and an amplification of NTRK3.

Substantial T cell repertoire heterogeneity in MPM

We previously demonstrated that a heterogeneous T cell repertoire is associated with inferior clinical outcome in localized NSCLC (15). In this study, we performed multi-region TCR sequencing of 63 tumor regions (2 to 6 regions per tumor) from these 9 MPM patients with available DNA to depict the TCR repertoire and TCR ITH of this cohort of MPM including 19 specimens from 7 patients prior to dasatinib treatment and 44 post-treatment from 9 patients. In pre-treatment tumors, T cell density, an estimate of the proportion of T cells in a sample, ranged from 0.07 to 0.43 (average=0.22) and richness, a measure of T cell diversity, ranged from 2,189 to 11,568 (average=5,946 unique rearrangements), were comparable to localized NSCLC (30) (Supplementary Fig. S7A–B). On the other hand, T cell clonality, a parameter indicating T cell expansion and reactivity, ranged from 0.04 to 0.14 (average=0.08), was significantly lower than in localized NSCLC (Supplementary Fig. S7C, p=0.0005) (30). Interestingly, compared to pre-treatment tumors, post-treatment MPM tumors exhibited similar T cell density (average 0.22 vs 0.22, p>0.99) and richness (average 5,946 vs 6,576, p=0.84) but significantly increased T cell clonality (average 0.08 vs 0.13, p=0.047) (Supplementary Fig. S8A–C) suggesting expansion and activation of T cells post-dasatinib treatment.

To gain insights into spatial heterogeneity of T cell response in MPM, we next investigated the overlap in T cell clones across different regions from the same tumors. As shown in Fig. 3A, the vast majority (average 82%, from 73% to 95%) of T cell clones were restricted to individual tumor regions while only an average of 6% (0.6% to 19%) of T cell clones were trunk TCR detectable across all tumor regions from the same tumors suggesting profound heterogeneity in T cell response in this cohort of MPM. To comprehensively quantify the TCR ITH, we then utilized Morisita index (MOI), a metric taking into consideration the not only the composition of T cell clones but also the abundance of individual T cell clones. MOI ranges from 0 to 1, with 1 indicating identical TCR repertoires and 0 indicating completely distinct TCR repertoires. The average MOI was 0.63 (ranging from 0.40 to 0.93) for this cohort of MPM (Fig. 3B), significantly lower than 0.82 (ranging from 0.61 to 0.93) in NSCLC (Supplementary Fig. S9, p=0.0097) (15).

Figure 3. T cell repertoire ITH in MPM tumors.

Figure 3.

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(A) The proportions of T cell clonotypes detected in all regions of tumors (trunk, blue), shared by at least two regions from the same tumors (branch, red) and restricted to a single region within the tumor (private, green). (B) Quantification of TCR ITH by Morisita index (MOI), a metric taking into consideration the composition of T cell clones and the abundance of individual T cell clones between 2 samples. MOI ranges from 0 to 1, with 1 indicating identical TCR repertoires and 0 indicating completely distinct TCR repertoires. The color scales indicate the MOI between any two tumor regions. Pre: prior to dasatinib treatment. Post: post-dasatinib treatment.

Evolution of TCR repertoire after dasatinib treatment was associated with improved prognosis

Next, we attempted to assess whether the TCR ITH would impact clinical outcomes of these MPM patients although the sample size was small. With a median of 23.1 months follow-up after surgical resection, all 9 patients recurred and expired. Importantly, patients with higher T cell clonality in post-dasatinib treated MPM tumors, had significantly longer overall survival (OS) (Fig. 4A) and a trend of longer progression-free survival (PFS) (Supplementary Fig. S10A). Additionally, the change of clonality after dasatinib treatment (post-treatment clonality – pre-treatment clonality) was also associated with longer OS (Fig. 4B) and a trend of longer PFS (Supplementary Fig. S10B). Furthermore, patients with more homogenous TCR repertoire indicated by higher proportion of trunk TCR detected in all tumor regions within the same tumors or higher MOI, in post-dasatinib treated tumors demonstrated a trend of longer OS (Fig. 4C–D) and PFS (Supplementary Fig. S10C–D). Of note, neither PFS nor OS was associated with TCR parameters in pre-dasatinib treatment tumors. Taken together, these findings suggest that TCR expansion and activation as well as homogeneous T cell response after dasatinib treatment may impact patient outcome.

Figure 4. TCR repertoire and TCR ITH in post-dasatinib treated tumors were associated with overall survival (OS).

Figure 4.

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Correlation between OS and (A) T cell clonality, (B) change of clonality post-dasatinib treatment, (C) proportion of trunk TCR clonotypes detected in all tumor regions with the same tumors and (D) and TCR MOI.

Discussion

ITH is increasingly recognized as a critical component of cancer biology that may have profound impact on outcome of cancer patients (8, 13, 33). ITH could provide diverse genetic elements to foster tumor evolution along with tumor progression and/or during treatment leading to selection of therapeutic resistant cancer cell clones (34). By multi-region WES, we revealed relatively homogenous genomic ITH pattern in this cohort of MPM with the majority of mutations and SCNAs including canonical cancer genes alterations present across all the regions of the same MPM tumors. These findings were surprising for the following reasons. First, MPM grows in a unique pattern -- wide spreading as an irregular pleural rind, which provides adequate space for different cancer clones to evolve in parallel, particularly without effective immune surveillance applying selection pressure, leading to more heterogeneous cancer cell populations. Second, the relatively low response rate to chemotherapy and high incidence of recurrence of MPM are attributed in part to a very high degree of molecular diversity within the tumor (35). One plausible explanation for the homogenous genomic landscape in MPM is that the majority of these mutations are very early molecular events during MPM evolution that have had occurred before these tumors have spread locally. Our recent data in NSCLC have shown that nearly 70% mutations were shared even between primary tumors and distant metastases that developed several years later (36) suggesting that the majority of mutations have occurred prior to distant metastases. Nevertheless, these data indicate that single biopsy analysis might be sufficient to identify the majority of known cancer gene mutations in MPM.

The tumor immune microenvironment, particularly, T cell repertoire plays critical roles in determining cancer biology and clinical behaviors. Our study revealed for the first time the TCR repertoire features of MPM. Of particular interest, when compared to NSCLC, MPM has similar T cell density and richness, but a significantly lower T cell clonality, implying less T cell expansion and activation in MPM. Furthermore, distinct T cell repertoire in different tumor regions could also hamper effective anti-tumor immune response (15). Our multi-region TCR sequencing data has provided a unique opportunity to investigate T cell repertoire ITH architecture within this cohort of MPM. The results have revealed profound TCR ITH with 73% to 95% of all T cell clones restricted to individual tumor regions. Importantly, the average MOI, a surrogate for comprehensive quantification of TCR ITH, was only 0.63, even significantly lower than NSCLC indicating a higher degree of TCR ITH in MPM.

The molecular mechanisms underlying the high TCR ITH in the background of homogeneous genomic landscape in MPM are beyond the scope of the current study. There are several plausible reasons. For example, chromothripsis, a mutational process generating aberrant complex chromosomal rearrangements, is a critical mechanism underlying the evolution of malignant cell clones (37, 38). A recent study by Mansfield et al (39) demonstrated that inter- or intra-chromosomal rearrangements in a pattern of chromothripsis generated the junctions of genes and noncoding DNA with neoantigenic potential in MPM. Chromothripsis-like genome alterations could be heterogeneous and lead to vastly heterogenous neoantigen profiles in different regions of MPM and subsequent heterogeneous T cell response. However, reliable detection of chromothripsis-like genome alterations requires whole-genome level data, while the limited exome sequencing data in our current study was not sufficient for this analysis. Moreover, other “heterogeneous” molecular changes (e.g. DNA methylation (18), acetylation, gene expression (17), post-translation modification) may exist contributing to the “heterogeneous” immune response. Furthermore, in addition to the tumors’ intrinsic characteristics, immune landscape can also be altered by diverse extrinsic factors such as “bystander” T cells within tumors associated with local inflammation and viral infection (30). Future multi-omics studies incorporating comprehensive tumor features as well as relevant extrinsic factors are needed to better understand the molecular features underlying the extremely high TCR heterogeneity in MPM.

Nevertheless, the impaired T cell expansion (low clonality) and profound TCR ITH (low MOI) may lead to an ineffective anti-tumor T cell response, which could be one potential mechanism underlying the frequent recurrence of MPM. Moreover, although immune checkpoint blockade (ICB) targeting T cells have revolutionized the therapeutic landscape across many different cancer types (40–46), the response rates to single agent ICB treatment was only 9%−30% in MPM patients (47–51). The suppressed and heterogeneous T cell response in MPM may be one attributing factor for such suboptimal responses. As such, novel therapeutic strategies with or without ICB are warranted to improve the clinical outcome of MPM patients.

We have previously conducted a neoadjuvant clinical trial using dasatinib in patients with resectable MPM (6, 20). Unfortunately, the clinical trial did not meet the primary endpoint (6). However, patients who with decreased p-SrcTyr419 post-dasatinib treatment had improved PFS suggesting dasatinib may benefit some MPM patients. As a broad-spectrum tyrosine kinase inhibitor, dasatinib has been shown to modulate T cell repertoire by reducing regulatory T populations while enhancing CD8+ anti-tumor T response (52, 53). In the current study, we observed a significant increase in T cell clonality post-dasatinib treatment suggesting dasatinib may have induced T cell expansion and activation. More importantly, regardless of small sample size, higher T cell clonality in the post-treatment MPM specimens (not pre-treatment specimens) and higher level of T cell clonality increase after dasatinib treatment were associated with superior survival. Similarly, higher proportion of trunk TCR in post-dasatinib treated specimens, (but not pre-treatment specimens), indicating more homogenous T cell distribution after dasatinib treatment was associated with superior survival. These results are in line with previous findings that those molecular alterations from on-treatment biopsies were superior than pretreatment biopsies regarding the association with benefit from receiving ICB treatment in patients with melanoma (54). Due to the complexity of cancer biology and substantial inter-patient heterogeneity, it is challenging to identify molecular features associated with therapeutic benefits in the pre-treatment biopsies. However, molecular changes reflecting the actual biological response to therapies from on-treatment biopsies may be a better predictor for clinical response. Although these on-treatment biopsy-based molecular changes are not desirable compared to pretreatment biopsy-based molecular features as potential biomarkers, they can be of value to discontinue ineffective treatment early during the disease course, particularly if these features are predictive of long-term benefit.

Our study has several important limitations. First of all, the sample size was small, which precluded us to make robust conclusions. Second, we did not have transcriptomic data to further dissect the ITH architecture for example distinct molecular subtypes (sarcomatoid, epithelioid, biphasic-epithelioid and biphasic-sarcomatoid components) (12, 32). Third, we did not have enough materials to depict the detailed immunological features of these tumor infiltrating T lymphocytes. However, our previous study on NSCLC has demonstrated that T cell clonality was mainly driven by cytotoxic T lymphocytes and negatively regulated be T regulatory cells (Treg) (30), while MPM is known to enrich for immunosuppressive and anergic immune cells, such as Treg, monocytic myeloid-derived suppressor cells (Gr-MDSC/Mo-MDSC) and M2-polarized tumor associated macrophages (TAMs) (55–59) in line with suppressed T cell repertoire observed in the current study.

With all above limitations fully acknowledged, the multiregional, paired longitudinal specimens before and post treatment from a rare and aggressive malignancy made the data invaluable. In summary, we demonstrated that despite the homogeneous genomic landscape, MPM has suppressed and extremely heterogeneous TCR repertoire. This may led to ineffective host anti-tumor immune surveillance, which could be one potential molecular mechanism underlying high recurrence rate and suboptimal response to immunotherapy in MPM. Future studies are warranted to combine ICB with novel agents that have the potential to induce T cell activation, such as dasatinib, to improve the clinical outcome of patients with MPM.

Supplementary Material

1
2

Translational relevance.

Malignant pleural mesothelioma (MPM) is a rare and highly aggressive malignancy. MPM was believed to have profound intra-tumor heterogeneity (ITH), which makes it challenging to eradicate. In this study, we delineated the genomic and T cell repertoire ITH landscape by multi-region whole exome sequencing (WES) and T cell receptor (TCR) sequencing of 69 MPM specimens from 9 patients with resectable MPM, who were treated with preoperative dasatinib on a neoadjuvant trial. Our results demonstrated a relatively homogenous genomic landscape and extremely heterogeneous TCR repertoire of MPM tumors. T cell clonality significantly increased after treatment with dasatinib, and patients with higher T cell clonality and more homogeneous T cell repertoire in post-dasatinib treated MPM tumors had significantly longer survival. These findings suggest that dasatinib may induce expansion and reactivation of T cells, therefore could potentially serve as an immunomodulator to enhance the efficacy of immunotherapy in MPM patients.

Acknowledgments:

Conquer Cancer Foundation ASCO Young Investigator Award, MD Anderson Physician Scientist Award, University Cancer Foundation Sister Institution Network Fund, Cancer Prevention & Research Institute of Texas (CPRIT) Multiple Investigator Award, TJ Martell Foundation, Aileen M. Dillon and Lee M. Bourg Mesothelioma Fund and Ronald E. and Reba M. Kennedy Endowment for Lung Cancer Research.

Conflicts of interest: A.T. reports other from Genentech, during the conduct of the study; personal fees and other from BMS, personal fees and other from Eli Lilly, personal fees and other from Roche, personal fees and other from Novartis, personal fees and other from Ariad, other from EMD Serono, other from Merck, personal fees and other from Seattle Genetics, personal fees and other from Astra-Zeneca, personal fees and other from Boehringer- Ingelheim, personal fees and other from Sellas Life Science, personal fees and other from Takeda, grants and other from Millennuim, grants and other from Polaris, grants and other from Epizyme, grants and other from EMD Serono, grants and other from Seattle Genetics, outside the submitted work. I.W. reports grants and personal fees from Genentech/Roche, grants and personal fees from Bayer, grants and personal fees from Bristol-Myers Squibb, grants and personal fees from AstraZeneca/Medimmune, grants and personal fees from Pfizer, grants and personal fees from HTG Molecular, grants and personal fees from Merck, personal fees from GlaxoSmithKline, grants and personal fees from Guardant Health, personal fees from MSD, grants from Oncoplex, grants from DepArray, grants from Adaptive, grants from Adaptimmune, grants from EMD Serono, grants from Takeda, grants from Amgen, grants from Karus, grants from Johnson & Johnson, grants from Iovance, grants from 4D, grants from Novartis, grants from Oncocyte, grants from Akoya. J.Z. reports research funding and personal fees from BMS, Johnson and Johnson, AstraZeneca, Geneplus, OrigMed, Innovent and Merck, outside the submitted work.

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NIHMS1619970-supplement-1.xlsx (508KB, xlsx)
2
NIHMS1619970-supplement-2.docx (691.2KB, docx)

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