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Effects on cultured (human and mouse) immune cells

Gene expression signatures used in Figure 1

Table S3 describes multivariate Cox regression analysis of variables in Van Allen's and Snyder's patients.

Syngeneic mouse tumor efficacy, STAT3, and gene expression changes

Patient STAT reductions and reproducibility of replicates

The supplementary figures illustrate Multifactorial deep learning model structures, Correlations of three types of genomic alterations, Immune gene panel profile difference between GCs, Mismatch repair gene mutation frequency of each GC, Gene Set Enrichment Analysis (GSEA) enriched pathways analysis for the stratification, GSEA analysis results of Van Allen's and TCGA melanoma cohorts, Immunotherapy response and prediction survivals of Van Allen's cohort, The mTMB, MSI burden, SCNA burden of Van Allen's cohort in each GC, Deep learning model analysis for combined Van Allen's and Snyder's cohorts, Simple multivariable analysis for combined Van Allen's and Snyder's cohorts, Clinical response analyzed based on gene biomarkers in Van Allen's cohort, Snyder's cohort and combined cohort, Overall survival analyzed based on gene biomarkers in Van Allen's cohort, Snyder's cohort and combined cohort while figures are labeled with S1-12, respectively.

Patient sample details

Patient tumor gene expression data

Purpose:Danvatirsen is a therapeutic antisense oligonucleotide (ASO) that selectively targets STAT3 and has shown clinical activity in two phase I clinical studies. We interrogated the clinical mechanism of action using danvatirsen-treated patient samples and conducted back-translational studies to further elucidate its immunomodulatory mechanism of action.Experimental Design:Paired biopsies and blood samples from danvatirsen-treated patients were evaluated using immunohistochemistry and gene-expression analysis. To gain mechanistic insight, we used mass cytometry, flow cytometry, and immunofluorescence analysis of CT26 tumors treated with a mouse surrogate STAT3 ASO, and human immune cells were treated in vitro with danvatirsen.Results:Within the tumors of treated patients, danvatirsen uptake was observed mainly in cells of the tumor microenvironment (TME). Gene expression analysis comparing baseline and on-treatment tumor samples showed increased expression of proinflammatory genes. In mouse models, STAT3 ASO demonstrated partial tumor growth inhibition and enhanced the antitumor activity when combined with anti–PD-L1. Immune profiling revealed reduced STAT3 protein in immune and stromal cells, and decreased suppressive cytokines correlating with increased proinflammatory macrophages and cytokine production. These changes led to enhanced T-cell abundance and function in combination with anti–PD-L1.Conclusions:STAT3 ASO treatment reverses a suppressive TME and promotes proinflammatory gene expression changes in patients' tumors and mouse models. Preclinical data provide evidence that ASO-mediated inhibition of STAT3 in the immune compartment is sufficient to remodel the TME and enhance the activity of checkpoint blockade without direct STAT3 inhibition in tumor cells. Collectively, these data provide a rationale for testing this combination in the clinic.

Cytokine, T, and NK cell changes in CT26 tumors

Gating and antibodies for flow cytometry and CyTOF

Purpose:Tumor genomic features have been of particular interest because of their potential impact on the tumor immune microenvironment and response to immunotherapy. Due to the substantial heterogeneity, an integrative approach incorporating diverse molecular features is needed to characterize immunologic features underlying primary resistance to immunotherapy and for the establishment of novel predictive biomarkers.Experimental Design:We developed a pan-cancer deep machine learning model integrating tumor mutation burden, microsatellite instability, and somatic copy-number alterations to classify tumors of different types into different genomic clusters, and assessed the immune microenvironment in each genomic cluster and the association of each genomic cluster with response to immunotherapy.Results:Our model grouped 8,646 tumors of 29 cancer types from The Cancer Genome Atlas into four genomic clusters. Analysis of RNA-sequencing data revealed distinct immune microenvironment in tumors of each genomic class. Furthermore, applying this model to tumors from two melanoma immunotherapy clinical cohorts demonstrated that patients with melanoma of different genomic classes achieved different benefit from immunotherapy. Interestingly, tumors in cluster 4 demonstrated a cold immune microenvironment and lack of benefit from immunotherapy despite high microsatellite instability burden.Conclusions:Our study provides a proof for principle that deep learning modeling may have the potential to discover intrinsic statistical cross-modality correlations of multifactorial input data to dissect the molecular mechanisms underlying primary resistance to immunotherapy, which likely involves multiple factors from both the tumor and host at different molecular levels.

Background Tumor mutational burden (TMB) measurements aid in identifying patients who are likely to benefit from immunotherapy; however, there is empirical variability across panel assays and factors contributing to this variability have not been comprehensively investigated. Identifying sources of variability can help facilitate comparability across different panel assays, which may aid in broader adoption of panel assays and development of clinical applications. Materials and methods Twenty-nine tumor samples and 10 human-derived cell lines were processed and distributed to 16 laboratories; each used their own bioinformatics pipelines to calculate TMB and compare to whole exome results. Additionally, theoretical positive percent agreement (PPA) and negative percent agreement (NPA) of TMB were estimated. The impact of filtering pathogenic and germline variants on TMB estimates was assessed. Calibration curves specific to each panel assay were developed to facilitate translation of panel TMB values to whole exome sequencing (WES) TMB values. Results Panel sizes >667 Kb are necessary to maintain adequate PPA and NPA for calling TMB high versus TMB low across the range of cut-offs used in practice. Failure to filter out pathogenic variants when estimating panel TMB resulted in overestimating TMB relative to WES for all assays. Filtering out potential germline variants at >0% population minor allele frequency resulted in the strongest correlation to WES TMB. Application of a calibration approach derived from The Cancer Genome Atlas data, tailored to each panel assay, reduced the spread of panel TMB values around the WES TMB as reflected in lower root mean squared error (RMSE) for 26/29 (90%) of the clinical samples. Conclusions Estimation of TMB varies across different panels, with panel size, gene content, and bioinformatics pipelines contributing to empirical variability. Statistical calibration can achieve more consistent results across panels and allows for comparison of TMB values across various panel assays. To promote reproducibility and comparability across assays, a software tool was developed and made publicly available.

The TonB system of Escherichia coli uses the cytoplasmic membrane protonmotive force (PMF) to energize active transport of nutrients across the otherwise unenergized outer membrane. Because it overcomes limitations for nutrient diffusion through outer membrane size-limiting porins, it provides a growth advantage and is widespread among Gram-negative bacteria. It consists of three known cytoplasmic membrane proteins, TonB, ExbB and ExbD that energize a variety of customized TonB-dependent transporters in the outer membrane. The sole ExbD transmembrane domain is proposed to consist of residues 23-43 (Kampfenkel and Braun, 1992, J. Bacteriol. 174:5485-7). Here we showed that the charge and location of residue Asp25 were essential for activity of the TonB system, thus identifying it as the only PMF-responsive element in the TonB system. The proposed boundaries of the transmembrane domain α-helix were revised to consist of residues 23-39, with residues 40-43 initiating the subsequent disordered region required for signal transduction (Kopp and Postle, 2020, J. Bacteriol. 202, e00687-19). Trapping of disulfide-linked ExbD homodimers through T42C or V43C prevented TonB system activity that was restored by addition of the reducing agent dithiothreitol, indicating a requirement for motion. In vivo photo-cross-linking experiments suggested that motion was rotation of ExbD transmembrane domains. Inactivity of ExbD L132Q, the first ExbD mutant identified, was likely due to steric hindrance. A conserved and defined site of in vivo ExbD interaction with TonB was identified. Exogenous addition of a cyclic peptide based on that site inhibited ExbD-TonB interaction while concomitantly decreasing iron transport efficiency. This suggested that a novel antimicrobial strategy against ESKAPE and other Gram-negative pathogens could be developed by targeting ExbD protein-protein interactions.

Purpose: Danvatirsen is a therapeutic antisense oligonucleotide (ASO) that selectively targets STAT3 and has shown clinical activity in two phase I clinical studies. We interrogated the clinical mechanism of action using danvatirsen-treated patient samples and conducted back-translational studies to further elucidate its immunomodulatory mechanism of action. Experimental Design: Paired biopsies and blood samples from danvatirsen-treated patients were evaluated using immunohistochemistry and gene-expression analysis. To gain mechanistic insight, we used mass cytometry, flow cytometry, and immunofluorescence analysis of CT26 tumors treated with a mouse surrogate STAT3 ASO, and human immune cells were treated in vitro with danvatirsen. Results: Within the tumors of treated patients, danvatirsen uptake was observed mainly in cells of the tumor microenvironment (TME). Gene expression analysis comparing baseline and on-treatment tumor samples showed increased expression of proinflammatory genes. In mouse models, STAT3 ASO demonstrated partial tumor growth inhibition and enhanced the antitumor activity when combined with anti–PD-L1. Immune profiling revealed reduced STAT3 protein in immune and stromal cells, and decreased suppressive cytokines correlating with increased proinflammatory macrophages and cytokine production. These changes led to enhanced T-cell abundance and function in combination with anti–PD-L1. Conclusions: STAT3 ASO treatment reverses a suppressive TME and promotes proinflammatory gene expression changes in patients' tumors and mouse models. Preclinical data provide evidence that ASO-mediated inhibition of STAT3 in the immune compartment is sufficient to remodel the TME and enhance the activity of checkpoint blockade without direct STAT3 inhibition in tumor cells. Collectively, these data provide a rationale for testing this combination in the clinic.

Background: In the Phase 3 CASPIAN study, 1L D+EP significantly improved OS vs EP in pts with ES-SCLC with benefit sustained after >3 years of median follow-up (HR 0.71; 95% CI 0.60-0.86; nominal p=0.0003; median OS [mOS] 12.9 mos vs 10.5 mos). 4 SCLC subtypes with distinct therapeutic vulnerabilities were recently identified using 2 different methods (Rudin et al, 2019; Gay et al, 2021) based on differential gene expression. In this exploratory analysis using RNA sequencing (RNAseq) data from CASPIAN, we explored the concordance between the 2 methods and the association of subtypes with OS. Methods: Pts with ES-SCLC received 4 cycles of D+EP followed by maintenance D; or up to 6 cycles of EP. RNAseq data were generated from FFPE tumor samples collected at screening. Data cutoff: Mar 22, 2021. Results: 57/268 (21.3%) pts in the D+EP arm and 47/269 (17.5%) pts in the EP arm had RNAseq data (biomarker-evaluable population; BEP). In the BEP, the % of pts with WHO PS 1 was slightly higher and the % with brain metastases slightly lower at baseline vs the ITT population. In the BEP, mOS was 11.8 mos in the D+EP arm vs 9.1 mos in the EP arm (HR 0.61; 95% CI 0.40-0.92). Prevalence of neuroendocrine (ASCL1 and NEUROD1) and non-neuroendocrine subtypes (POU2F3 and YAP1 [Rudin] or Inflamed [Gay]) was similar using both methods (Table). However, ASCL1 was more prevalent and NEUROD1 less prevalent with the Rudin method. Inflamed and YAP1 subtypes (11% and 8% prevalence) showed high concordance between methods. Using either method, the mOS in the D+EP arm was higher in the Inflamed or YAP1 subtype vs the other 3 subtypes (Table). Analysis of OS by gene signature will be presented. Conclusions: Among the 4 subtypes, the Inflamed or YAP1 subtype showed the longest OS in the D+EP arm, suggesting this may represent a subgroup primed to respond to immunotherapy. Despite the limited sample size, this finding is consistent with other studies. mOS (95% CI) Rudin et al, 2019 ASCL1 NEUROD1 POU2F3 YAP1* D+EP (n=50) (n=1) (n=2) (n=4) 11.5 (8.4-14.9) 9.5 (NE-NE) 4.8 (2.9-NE) 17.3 (12.8-NE) EP (n=38) (n=2) (n=3) (n=4) 10.7 (8.1-12.4) 7.1 (4.8-NE) 6.1 (1.3-NE) 6.9 (4.5-NE) Gay et al, 2021 ASCL1 NEUROD1 POU2F3 Inflamed D+EP (n=21) (n=25) (n=5) (n=6) 9.5 (6.1-14.9) 14.6 (8.6-16.6) 6.8 (2.9-NE) 17.6 (11.4-NE) EP (n=16) (n=19) (n=7) (n=5) 8.3 (3.0-15.1) 10.5 (7.9-13.6) 7.5 (1.3-10.2) 11.3 (6.3-NE) *A sample is assigned as YAP1 subtype per Rudin et al, 2019, when expression of YAP1 is higher than that of ASCL1, NEUROD1 and POU2F3; NE, not estimable Citation Format: Mingchao Xie, Priti Chugh, Helen Broadhurst, Zhongwu Lai, David Whitston, Luis Paz-Ares, Carl Gay, Lauren Byers, Charles M. Rudin, Ross Stewart, J. Carl Barrett, Yashaswi Shrestha. Durvalumab (D) + platinum-etoposide (EP) in 1L extensive-stage small-cell lung cancer (ES-SCLC): Exploratory analysis of SCLC molecular subtypes in CASPIAN [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr CT024.

Background: Molecular response based on ctDNA dynamics may be predictive of benefit from immunotherapy in NSCLC, complementing radiologic disease assessment and potentially enabling early clinical decision-making, but data in SCLC are lacking. The phase 2, multi-arm, signal-searching BALTIC study (NCT02937818) assessed novel treatment combinations in platinum-refractory/resistant ES-SCLC; we report efficacy, safety, and exploratory ctDNA and PD-L1 analyses from pts who received D + T in Arm A. Methods: Eligible pts had ES-SCLC, progressive disease (PD) during or within 90 days of completing 1L platinum-based CT, WHO PS 0-1, and life expectancy ≥8 weeks. In Arm A, pts received D 1500 mg + T 75 mg q4w for 4 cycles, followed by maintenance D 1500 mg q4w until PD. The primary endpoint was ORR (investigator assessment, RECIST v1.1). Secondary endpoints included DCR, PFS, OS, and safety and tolerability. Prespecified exploratory analyses included assessment of baseline/on-treatment ctDNA levels measured by maximum variant allele frequency (MaxVAF), and PD-L1 expression on tumor cells (TC) and immune cells (IC) (VENTANA PD-L1 [SP263] Assay). Results: 41 pts received treatment with D + T (median age 63 years; 76% male; 76% PS 1). At the data cut-off (DCO; Jun 22, 2020), median treatment duration was 12 weeks, with 3 pts ongoing D treatment (these pts had received ≥38, ≥21, and ≥19 doses of D, respectively, at DCO). Confirmed ORR was 7.3% (95% CI 1.54‒19.92; 3 partial responses [PR]). DCR at 12 weeks was 27%. 21 pts (51%) were PD-L1 evaluable. Of 12 pts with PD-L1 expression (TC or IC) ≥1%, 3 (25%) had a best response of PR or stable disease, compared with 1 (11%) of 9 pts with PD-L1 (TC or IC) Conclusions: Although limited antitumor activity was observed in refractory/resistant ES-SCLC in BALTIC, association of lower baseline ctDNA levels (MaxVAF) with longer OS may reflect the prognostic effect of disease burden. Association of on-treatment reduction in ctDNA level with longer OS suggests potential use of ctDNA as a surrogate of treatment response in ES-SCLC. Further analyses in larger datasets and in a randomized setting are warranted. Citation Format: Niels Reinmuth, Oscar Juan-Vidal, Zsolt Horváth, Dariusz Kowalski, Anna Kryzhanivska, Eszter Csánky, Gabriella Gálffy, Maciej Bryl, David Vicente, Ihor Vynnychenko, Zsolt Pápai-Székely, Jon Armstrong, Tapashi Dalvi, Yashaswi Shrestha, Mingchao Xie, Haiyi Jiang, Igor Bondarenko. Durvalumab (D) plus tremelimumab (T) in platinum-refractory/resistant extensive-stage small cell lung cancer (ES-SCLC): Efficacy, safety and ctDNA dynamics from Arm A of the phase 2 BALTIC study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr CT533.

Clear cell sarcoma (CCS) is a deadly malignancy affecting adolescents and young adults. It is characterized by reciprocal translocations resulting in expression of the chimeric EWSR1-ATF1 or EWSR1-CREB1 fusion proteins, driving sarcomagenesis. Besides these characteristics, CCS has remained genomically uncharacterized. Copy number analysis of human CCSs showed frequent amplifications of the MITF locus and chromosomes 7 and 8. Few alterations were shared with Ewing sarcoma or desmoplastic, small round cell tumors, which are other EWSR1-rearranged tumors. Exome sequencing in mouse tumors generated by expression of EWSR1-ATF1 from the Rosa26 locus demonstrated no other repeated pathogenic variants. Additionally, we generated a new CCS mouse by Cre-loxP-induced chromosomal translocation between Ewsr1 and Atf1, resulting in copy number loss of chromosome 6 and chromosome 15 instability, including amplification of a portion syntenic to human chromosome 8, surrounding Myc. Additional experiments in the Rosa26 conditional model demonstrated that Mitf or Myc can contribute to sarcomagenesis. Copy number observations in human tumors and genetic experiments in mice rendered, for the first time to our knowledge, a functional landscape of the CCS genome. These data advance efforts to understand the biology of CCS using innovative models that will eventually allow us to validate preclinical therapies necessary to achieve longer and better survival for young patients with this disease.

Purpose: Tumor genomic features have been of particular interest because of their potential impact on the tumor immune microenvironment and response to immunotherapy. Due to the substantial heterogeneity, an integrative approach incorporating diverse molecular features is needed to characterize immunologic features underlying primary resistance to immunotherapy and for the establishment of novel predictive biomarkers. Experimental Design: We developed a pan-cancer deep machine learning model integrating tumor mutation burden, microsatellite instability, and somatic copy-number alterations to classify tumors of different types into different genomic clusters, and assessed the immune microenvironment in each genomic cluster and the association of each genomic cluster with response to immunotherapy. Results: Our model grouped 8,646 tumors of 29 cancer types from The Cancer Genome Atlas into four genomic clusters. Analysis of RNA-sequencing data revealed distinct immune microenvironment in tumors of each genomic class. Furthermore, applying this model to tumors from two melanoma immunotherapy clinical cohorts demonstrated that patients with melanoma of different genomic classes achieved different benefit from immunotherapy. Interestingly, tumors in cluster 4 demonstrated a cold immune microenvironment and lack of benefit from immunotherapy despite high microsatellite instability burden. Conclusions: Our study provides a proof for principle that deep learning modeling may have the potential to discover intrinsic statistical cross-modality correlations of multifactorial input data to dissect the molecular mechanisms underlying primary resistance to immunotherapy, which likely involves multiple factors from both the tumor and host at different molecular levels.

BackgroundTumor mutational burden (TMB), defined as the number of somatic mutations per megabase of interrogated genomic sequence, demonstrates predictive biomarker potential for the identification of patients with cancer most likely to respond to immune checkpoint inhibitors. TMB is optimally calculated by whole exome sequencing (WES), but next-generation sequencing targeted panels provide TMB estimates in a time-effective and cost-effective manner. However, differences in panel size and gene coverage, in addition to the underlying bioinformatics pipelines, are known drivers of variability in TMB estimates across laboratories. By directly comparing panel-based TMB estimates from participating laboratories, this study aims to characterize the theoretical variability of panel-based TMB estimates, and provides guidelines on TMB reporting, analytic validation requirements and reference standard alignment in order to maintain consistency of TMB estimation across platforms.MethodsEleven laboratories used WES data from The Cancer Genome Atlas Multi-Center Mutation calling in Multiple Cancers (MC3) samples and calculated TMB from the subset of the exome restricted to the genes covered by their targeted panel using their own bioinformatics pipeline (panel TMB). A reference TMB value was calculated from the entire exome using a uniform bioinformatics pipeline all members agreed on (WES TMB). Linear regression analyses were performed to investigate the relationship between WES and panel TMB for all 32 cancer types combined and separately. Variability in panel TMB values at various WES TMB values was also quantified using 95% prediction limits.ResultsStudy results demonstrated that variability within and between panel TMB values increases as the WES TMB values increase. For each panel, prediction limits based on linear regression analyses that modeled panel TMB as a function of WES TMB were calculated and found to approximately capture the intended 95% of observed panel TMB values. Certain cancer types, such as uterine, bladder and colon cancers exhibited greater variability in panel TMB values, compared with lung and head and neck cancers.ConclusionsIncreasing uptake of TMB as a predictive biomarker in the clinic creates an urgent need to bring stakeholders together to agree on the harmonization of key aspects of panel-based TMB estimation, such as the standardization of TMB reporting, standardization of analytical validation studies and the alignment of panel-based TMB values with a reference standard. These harmonization efforts should improve consistency and reliability of panel TMB estimates and aid in clinical decision-making.

Brain tumor segmentation plays an important role in assisting diagnosis, planning treatment and surgical navigation. In this paper, we propose a convolutional neural network-based learning transfer to support vector machine classifier method for brain tumor segmentation. Our algorithm is composed of two cascaded stages. In the first stage, we trained CNN to learn the mapping from the image space to the tumor label space. During the testing phase, we used the predicted label output from CNN and sent it along with the testing image to an SVM classifier for accurate segmentation. Then we iterate our deep CNN-SVM classifier. Experiments and comparisons demonstrate that the proposed framework is better than the separate SVM-based segmentation or CNN-based segmentation.

Synovial sarcomas are aggressive soft-tissue malignancies that express chromosomal translocation-generated fusion genes, SS18-SSX1 or SS18-SSX2 in most cases. Here, we report a mouse sarcoma model expressing SS18-SSX1, complementing our prior model expressing SS18-SSX2. Exome sequencing identified no recurrent secondary mutations in tumors of either genotype. Most of the few mutations identified in single tumors were present in genes that were minimally or not expressed in any of the tumors. Chromosome 6, either entirely or around the fusion gene expression locus, demonstrated a copy number gain in a majority of tumors of both genotypes. Thus, by fusion oncogene coding sequence alone, SS18-SSX1 and SS18-SSX2 can each drive comparable synovial sarcomagenesis, independent from other genetic drivers. SS18-SSX1 and SS18-SSX2 tumor transcriptomes demonstrated very few consistent differences overall. In direct tumorigenesis comparisons, SS18-SSX2 was slightly more sarcomagenic than SS18-SSX1, but equivalent in its generation of biphasic histologic features. Meta-analysis of human synovial sarcoma patient series identified two tumor-gentoype-phenotype correlations that were not modeled by the mice, namely a scarcity of male hosts and biphasic histologic features among SS18-SSX2 tumors. Re-analysis of human SS18-SSX1 and SS18-SSX2 tumor transcriptomes demonstrated very few consistent differences, but highlighted increased native SSX2 expression in SS18-SSX1 tumors. This suggests that the translocated locus may drive genotype-phenotype differences more than the coding sequence of the fusion gene created. Two possible roles for native SSX2 in synovial sarcomagenesis are explored. Thus, even specific partial failures of mouse genetic modeling can be instructive to human tumor biology.

Introduction: Tumor mutational burden (TMB) is the number of somatic mutations per megabase in a tumor's genome and has shown promise as a predictive biomarker of response to immune checkpoint inhibitors across several cancers. TMB is typically measured by whole exome sequencing (WES TMB) or by targeted next-generation sequencing gene panels (panel TMB). As more assays are developed to estimate TMB, harmonization is emerging as an unmet need and is a key goal of the Friends of Cancer Research (Friends) TMB Harmonization Project. Phase I of the Harmonization Project demonstrated correlation between panel TMB and WES TMB using TCGA data and defined theoretical sources of variability across panels. In phase IIA, sustainable TMB reference standard materials generated from human derived cell lines were used to characterize variability in TMB measurements across panels and assessed for utility in TMB alignment. Phase IIB aims to characterize variability in TMB measurements in clinical samples and to establish best practices for estimating and aligning TMB in order to improve consistency across panels. Methods: Fifteen laboratories (16 targeted gene panels) at different stages of development participated in phase IIB. Thirty formalin-fixed paraffin-embedded (FFPE) samples with >30% tumor content were acquired; tumor DNA was isolated by a single reference lab. TMB values were calculated for DNA extracted from lung (N=10), bladder (N=10), and gastric tumors (N=10) using WES and a uniform bioinformatics pipeline agreed upon by all Consortium members. DNA samples were also sent to all laboratories, and each used their own sequencing and bioinformatics pipelines to estimate TMB from the genes represented in their respective panels. For each tumor sample, a median across panel TMB estimates was calculated; individual panel TMB estimates were translated to fold-changes relative to the sample median to quantify variability. Association between WES TMB (reference) and panel TMB will be assessed by regression analysis; dependence of association on cancer type was investigated. Results: A subset of tumor samples (9 bladder, 7 lung, and 5 gastric) was analyzed using 11 panels at the time of abstract submission. Median panel TMB values ranged 0.60 - 40.26 across samples, with median of median values of 5.35. Fold-change from sample-level medians ranged 0x - 6.67x. Assessment of these clinical samples by WES and all 16 gene panels, as well as regression analysis results, are forthcoming. Conclusions: The Friends TMB Harmonization Project has made substantial progress in characterization of TMB measurement variability and association between WES TMB and panel TMB. These are important steps toward alignment of TMB estimates generated by different gene panels which may improve the interpretation of findings within clinical development programs and ultimately enhance the usefulness of this predictive biomarker in clinical decision making. Citation Format: Diana M. MERINO, Laura M. Yee, Lisa M. McShane, P. Mickey Williams, Tomas Vilimas, Rajesh Patidar, J. Carl Barrett, Shu-Jen Chen, Jen-Hao Cheng, Jeffrey M. Conroy, Dinesh Cyanam, Kenneth R. Eyring, David A. Fabrizio, Vincent Funari, Elizabeth P. Garcia, Sean T. Glenn, Christopher D. Gocke, Vikas Gupta, Lisa M. Haley, Matthew D. Hellmann, Laurel Keefer, Lauryn R. Keeler, Brett Kennedy, Alexander J. Lazar, Laura E. MacConaill, Kristen L. Meier, Arnaud Papin, Naiyer A. Rizvi, Ethan Sokol, Phillip Stafford, John F. Thompson, Warren Tom, Victor J. Weigman, Mingchao Xie, Chen Zhao, Mark D. Stewart, Jeff Allen. Alignment of TMB measured on clinical samples: Phase IIB of the Friends of Cancer Research TMB Harmonization Project [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5671.

Spermatogenesis associated 4 (SPATA4) is a testis-specific gene first cloned by our laboratory, and plays an important role in maintaining the physiological function of germ cells. Accumulated evidence suggests that SPATA4 might be associated with apoptosis. Here we established HeLa cells that stably expressed SPATA4 to investigate the function of SPATA4 in apoptosis. SPATA4 protected HeLa cells from etoposide-induced apoptosis through the mitochondrial apoptotic pathway, in the way that SPATA4 suppressed decrease of the mitochondrial membrane potential, the release of cytochrome c, and subsequent activation of caspase-9 and -3. We further demonstrated that SPATA4 upregulated anti-apoptotic members of Bcl-2 family proteins, Bcl-2, and downregulated the pro-apoptotic member of Bcl-2 family proteins, Bax. Knockdown of SPATA4 in HeLa/SPATA4 cells could partially rescue expression levels of bcl-2 and bax. In conclusion, SPATA4 protects HeLa cells against etoposide-induced apoptosis through the mitochondrial apoptotic pathway. Our findings provide further evidence that SPATA4 plays a role in regulating apoptosis.

Summary MIRMMR predicts microsatellite instability status in cancer samples using methylation and mutation information, in contrast to existing methods that rely on observed microsatellites. Additionally, MIRMMR highlights those genetic alterations contributing to microsatellite instability. Availability and implementation Source code is freely available at https://github.com/ding-lab/MIRMMR under the MIT license, implemented in R and supported on Unix/OS X operating systems. Supplementary information Supplementary data are available at Bioinformatics online.

Several genetic alterations characteristic of leukemia and lymphoma have been detected in the blood of individuals without apparent hematological malignancies. The Cancer Genome Atlas (TCGA) provides a unique resource for comprehensive discovery of mutations and genes in blood that may contribute to the clonal expansion of hematopoietic stem/progenitor cells. Here, we analyzed blood-derived sequence data from 2,728 individuals from TCGA and discovered 77 blood-specific mutations in cancer-associated genes, the majority being associated with advanced age. Remarkably, 83% of these mutations were from 19 leukemia and/or lymphoma-associated genes, and nine were recurrently mutated (DNMT3A, TET2, JAK2, ASXL1, TP53, GNAS, PPM1D, BCORL1 and SF3B1). We identified 14 additional mutations in a very small fraction of blood cells, possibly representing the earliest stages of clonal expansion in hematopoietic stem cells. Comparison of these findings to mutations in hematological malignancies identified several recurrently mutated genes that may be disease initiators. Our analyses show that the blood cells of more than 2% of individuals (5-6% of people older than 70 years) contain mutations that may represent premalignant events that cause clonal hematopoietic expansion.

2624 Background: Tumor mutational burden (TMB) is a predictive biomarker of response to immune checkpoint inhibitors across multiple cancers. In Phase 1 of the Friends of Cancer Research Harmonization Project, we demonstrated a robust correlation between TMB estimated using targeted next-generation sequencing (NGS) gene panels and whole exome sequencing (WES) applied to MC3-TCGA data. These findings demonstrated variability in TMB estimates across different panels. Phase 2 evaluates sustainable TMB reference standard materials for TMB alignment to assess this variability. The goal of this effort is to establish best practices for estimating TMB in order to improve consistency across panels, for the sake of optimizing clinical application and facilitating integration of datasets generated from multiple assays. Methods: Fifteen laboratories with targeted panels at different stages of development participated. We identified a set of reference standards consisting of 10 well-characterized human-derived lung and breast tumor-normal matched cell lines. WES was performed using a uniform bioinformatics pipeline agreed upon by all team members (WES-TMB). Each laboratory used their own sequencing and bioinformatics pipelines (tumor-only and tumor-normal) to estimate TMB according to genes represented in their respective panels (panel-TMB). The association between WES-TMB and each panel-TMB was investigated using regression analyses. Bias (relative to WES-TMB) and variability in TMB estimates across panels were rigorously assessed. All analyses were blinded. Results: The set of reference standards spanned a clinically meaningful TMB range (4.3 to 31.4 mut/Mb). Preliminary data from 12 laboratories shows a good correlation between panel-TMB and WES-TMB in this empirical analysis. Across panels, regression R2 values range 0.77-0.96 with slopes ranging 0.60-1.26. Calibration analyses that seek to minimize variability of TMB estimates across panels using the established set of reference standards are ongoing, as well as investigating cancer type dependence on the relationship between panel-TMB vs. WES-TMB, which will be available at the time of presentation. Conclusions: Preliminary findings demonstrate feasibility of using sustainable reference control cell lines to standardize and align estimation of TMB across different targeted NGS assays. Future studies aim to validate reference standard material as a reliable alignment tool by using formalin-fixed paraffin-embedded human tumor samples.

Transposable element (TE)-derived sequences comprise half of the human genome and DNA methylome and are presumed to be densely methylated and inactive. Examination of genome-wide DNA methylation status within 928 TE subfamilies in human embryonic and adult tissues identified unexpected tissue-specific and subfamily-specific hypomethylation signatures. Genes proximal to tissue-specific hypomethylated TE sequences were enriched for functions important for the relevant tissue type, and their expression correlated strongly with hypomethylation within the TEs. When hypomethylated, these TE sequences gained tissue-specific enhancer marks, including monomethylation of histone H3 at lysine 4 (H3K4me1) and occupancy by p300, and a majority exhibited enhancer activity in reporter gene assays. Many such TEs also harbored binding sites for transcription factors that are important for tissue-specific functions and showed evidence of evolutionary selection. These data suggest that sequences derived from TEs may be responsible for wiring tissue type-specific regulatory networks and may have acquired tissue-specific epigenetic regulation.

The Cancer Genome Atlas (TCGA) has used the latest sequencing and analysis methods to identify somatic variants across thousands of tumours. Here we present data and analytical results for point mutations and small insertions/deletions from 3,281 tumours across 12 tumour types as part of the TCGA Pan-Cancer effort. We illustrate the distributions of mutation frequencies, types and contexts across tumour types, and establish their links to tissues of origin, environmental/carcinogen influences, and DNA repair defects. Using the integrated data sets, we identified 127 significantly mutated genes from well-known (for example, mitogen-activated protein kinase, phosphatidylinositol-3-OH kinase, Wnt/β-catenin and receptor tyrosine kinase signalling pathways, and cell cycle control) and emerging (for example, histone, histone modification, splicing, metabolism and proteolysis) cellular processes in cancer. The average number of mutations in these significantly mutated genes varies across tumour types; most tumours have two to six, indicating that the number of driver mutations required during oncogenesis is relatively small. Mutations in transcriptional factors/regulators show tissue specificity, whereas histone modifiers are often mutated across several cancer types. Clinical association analysis identifies genes having a significant effect on survival, and investigations of mutations with respect to clonal/subclonal architecture delineate their temporal orders during tumorigenesis. Taken together, these results lay the groundwork for developing new diagnostics and individualizing cancer treatment.

We applied a newly developed bioinformatics system called VirusScan to investigate the viral basis of 6,813 human tumors and 559 adjacent normal samples across 23 cancer types and identified 505 virus positive samples with distinctive, organ system- and cancer type-specific distributions. We found that herpes viruses (e.g., subtypes HHV4, HHV5 and HHV6) that are highly prevalent across cancers of the digestive tract showed significantly higher abundances in tumor versus adjacent normal samples, supporting their association with these cancers. We also found three HPV16-positive samples in brain lower grade glioma (LGG). Further, recurrent HBV integration at the KMT2B locus is present in three liver tumors, but absent in their matched adjacent normal samples, indicating that viral integration induced host driver genetic alterations are required on top of viral oncogene expression for initiation and progression of liver hepatocellular carcinoma. Notably, viral integrations were found in many genes, including novel recurrent HPV integrations at PTPN13 in cervical cancer. Finally, we observed a set of HHV4 and HBV variants strongly associated with ethnic groups, likely due to viral sequence evolution under environmental influences. These findings provide important new insights into viral roles of tumor initiation and progression and potential new therapeutic targets.

SummaryDespite the explosive growth of genomic data, functional annotation of regulatory sequences remains difficult. Here, we introduce “comparative epigenomics”—interspecies comparison of DNA and histone modifications—as an approach for annotation of the regulatory genome. We measured in human, mouse, and pig pluripotent stem cells the genomic distributions of cytosine methylation, H2A.Z, H3K4me1/2/3, H3K9me3, H3K27me3, H3K27ac, H3K36me3, transcribed RNAs, and P300, TAF1, OCT4, and NANOG binding. We observed that epigenomic conservation was strong in both rapidly evolving and slowly evolving DNA sequences, but not in neutrally evolving sequences. In contrast, evolutionary changes of the epigenome and the transcriptome exhibited a linear correlation. We suggest that the conserved colocalization of different epigenomic marks can be used to discover regulatory sequences. Indeed, seven pairs of epigenomic marks identified exhibited regulatory functions during differentiation of embryonic stem cells into mesendoderm cells. Thus, comparative epigenomics reveals regulatory features of the genome that cannot be discerned from sequence comparisons alone.

Cancer development is an incremental process. Mutations that initiate clonal hematopoiesis often arise in individuals many years before disease symptoms are apparent. We discovered blood somatic mutations in 8,612 germline blood samples in The Cancer Genome Atlas, drawing fundamental distinctions between these events and germline variants. In a preliminary scan of 5,949 samples, 13,345 blood-specific mutations were identified, preferentially distributed across 26 genes, including ones frequently mutated in hematologic malignancies (e.g., DNMT3A, ASXL1, TET2, JAK2, IDH2, SF3B1, MLL3, and KDM6A) and genes rarely mutated in primary hematologic tumors, including EGFR, PIK3CA, NOTCH2, and PPM1D. Further, we utilized both exome sequencing and SNP array data to conduct the first large-scale survey of blood somatic copy number variation, discovering potentially oncogenic events such as JAK2 amplification. Finally, elderly individuals and smokers have increased likelihoods of undergoing clonal expansion. Our analysis of genomic alterations reveals the origins of hematologic malignancies and can facilitate advancement in early detection and prevention. Citation Format: Kuan-Lin Huang, Mingchao Xie, Yige Wu, Reyka Jayasinghe, Rajees Varghese, R. Jay Mashl, Song Cao, Matthew Wyczalkowski, Wen-wei Liang, Michael C. Wendel, Ryan Fields, Michael D. McLellan, Daniel C. Link, Feng Chen, Li Ding. Genomic alterations in clonal hematopoiesis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3424.

Complex insertions and deletions (indels) are formed by simultaneously deleting and inserting DNA fragments of different sizes at a common genomic location. Here we present a systematic analysis of somatic complex indels in the coding sequences of samples from over 8,000 cancer cases using Pindel-C. We discovered 285 complex indels in cancer-associated genes (such as PIK3R1, TP53, ARID1A, GATA3 and KMT2D) in approximately 3.5% of cases analyzed; nearly all instances of complex indels were overlooked (81.1%) or misannotated (17.6%) in previous reports of 2,199 samples. In-frame complex indels are enriched in PIK3R1 and EGFR, whereas frameshifts are prevalent in VHL, GATA3, TP53, ARID1A, PTEN and ATRX. Furthermore, complex indels display strong tissue specificity (such as VHL in kidney cancer samples and GATA3 in breast cancer samples). Finally, structural analyses support findings of previously missed, but potentially druggable, mutations in the EGFR, MET and KIT oncogenes. This study indicates the critical importance of improving complex indel discovery and interpretation in medical research.

The spermatogenesis associated 4 gene (SPATA4, previously named TSARG2) was first cloned from a mouse testis cDNA library and was reported to be a candidate apoptosis-related gene in male germ cells. In this study, we cloned and characterized the SPATA4 gene from chicken (Gallus gallus). Bioinformatics analysis shows that the chicken SPATA4 gene is located on chromosome 4, is made up of six exons, and contains an 860 bp open reading frame encoding a putative protein of 250 amino acids. Further analysis of the SPATA4 gene sequence indicates that it is highly conserved between avian and mammalian species. Multi-tissue RT-PCR results indicate that the chicken SPATA4 gene is specifically expressed in the testis. Moreover, according to multi-time RT-PCR results, the expression of chicken SPATA4 occurs in a development stage-dependent pattern, and is gradually upregulated during the developmental process in chicken testis. All of these results suggest that SPATA4 may play an important role in the chicken spermatogenesis process.

Large-scale cancer sequencing data enable discovery of rare germline cancer susceptibility variants. Here we systematically analyse 4,034 cases from The Cancer Genome Atlas cancer cases representing 12 cancer types. We find that the frequency of rare germline truncations in 114 cancer-susceptibility-associated genes varies widely, from 4% (acute myeloid leukaemia (AML)) to 19% (ovarian cancer), with a notably high frequency of 11% in stomach cancer. Burden testing identifies 13 cancer genes with significant enrichment of rare truncations, some associated with specific cancers (for example, RAD51C, PALB2 and MSH6 in AML, stomach and endometrial cancers, respectively). Significant, tumour-specific loss of heterozygosity occurs in nine genes (ATM, BAP1, BRCA1/2, BRIP1, FANCM, PALB2 and RAD51C/D). Moreover, our homology-directed repair assay of 68 BRCA1 rare missense variants supports the utility of allelic enrichment analysis for characterizing variants of unknown significance. The scale of this analysis and the somatic-germline integration enable the detection of rare variants that may affect individual susceptibility to tumour development, a critical step toward precision medicine., Published sequencing data sets of cancer samples could be used to identify genetic variants associated with the risk of developing cancer. Here, Lu et al. analyse over 4,000 tumour-normal pairs to reveal variable frequencies of inherited susceptibilities across 12 cancer types and find enrichment of functionally validated missense variants of unknown significance.

Spermatogenesis associated 4 (SPATA4) is a testis-specific gene first cloned by our laboratory, and plays an important role in maintaining the physiological function of germ cells. Accumulated evidence suggests that SPATA4 might be associated with apoptosis. Here we established HeLa cells that stably expressed SPATA4 to investigate the function of SPATA4 in apoptosis. SPATA4 protected HeLa cells from etoposide-induced apoptosis through the mitochondrial apoptotic pathway, in the way that SPATA4 suppressed decrease of the mitochondrial membrane potential, the release of cytochrome c, and subsequent activation of caspase-9 and -3. We further demonstrated that SPATA4 upregulated anti-apoptotic members of Bcl-2 family proteins, Bcl-2, and downregulated the pro-apoptotic member of Bcl-2 family proteins, Bax. Knockdown of SPATA4 in HeLa/SPATA4 cells could partially rescue expression levels of bcl-2 and bax. In conclusion, SPATA4 protects HeLa cells against etoposide-induced apoptosis through the mitochondrial apoptotic pathway. Our findings provide further evidence that SPATA4 plays a role in regulating apoptosis.

Apoptosis and necrosis are distinguished by modality primarily. Here we show an apoptosis occurred instantly, induced by 300 muM W-7 ((N-(6-aminohexyl)-5-chloro-1-naphthalenesulfonamide hydrochloride), inhibitor of calmodulin), which demonstrated necrotic modality. As early as 30 min after W-7 addition, apoptotic (sub-diploid) peak could be detected by fluorescence-activated cell sorter (FACS), "DNA ladders" began to emerge also at this time point, activity of caspase-3 elevated obviously within this period. Absence of mitochondrial membrane potential (MMP) reduction and cytochrome c, AIF (apoptosis inducing factor) release, verified that this rapid apoptosis did not proceed through mitochondria pathway. Activation of caspase-12 and changes of other endoplasmic reticulum (ER) located proteins ascertained that ER pathway mediated this necrosis-like apoptosis. Our findings suggest that it is not credible to judge apoptosis by modality. Elucidation of ER pathway is helpful to comprehend the pathology of diseases associated with ER stress, and may offer a new approach to the therapy of cancer and neurodegenerative diseases.