Identifying somatic fingerprints of cancers defined by germline and environmental risk factors

Numerous studies over the past generation have identified germline variants that increase specific cancer risks. Simultaneously, a revolution in sequencing technology has permitted high-throughput annotations of somatic genomes characterizing individual tumors. However, examining the relationship between germline variants and somatic alteration patterns is hugely challenged by the large numbers of variants in a typical tumor, the rarity of most individual variants, and the heterogeneity of tumor somatic fingerprints. In this article, we propose statistical methodology that frames the investigation of germline-somatic relationships in an interpretable manner. The method uses meta-features embodying biological contexts of individual somatic alterations to implicitly group rare mutations. Our team has used this technique previously through a multilevel regression model to diagnose with high accuracy tumor site of origin. Herein, we further leverage topic models from computational linguistics to achieve interpretable lower-dimensional embeddings of the meta-features. We demonstrate how the method can identify distinctive somatic profiles linked to specific germline variants or environmental risk factors. We illustrate the method using The Cancer Genome Atlas whole-exome sequencing data to characterize somatic tumor fingerprints in breast cancer patients with germline BRCA1/2 mutations and in head and neck cancer patients exposed to human papillomavirus.

Topical hidden genome: discovering latent cancer mutational topics using a Bayesian multilevel context-learning approach

Inferring the cancer-type specificities of ultra-rare, genome-wide somatic mutations is an open problem. Traditional statistical methods cannot handle such data due to their ultra-high dimensionality and extreme data sparsity. To harness information in rare mutations, we have recently proposed a formal multilevel multilogistic "hidden genome" model. Through its hierarchical layers, the model condenses information in ultra-rare mutations through meta-features embodying mutation contexts to characterize cancer types. Consistent, scalable point estimation of the model can incorporate 10s of millions of variants across thousands of tumors and permit impressive prediction and attribution. However, principled statistical inference is infeasible due to the volume, correlation, and noninterpretability of mutation contexts. In this paper, we propose a novel framework that leverages topic models from computational linguistics to effectuate dimension reduction of mutation contexts producing interpretable, decorrelated meta-feature topics. We propose an efficient MCMC algorithm for implementation that permits rigorous full Bayesian inference at a scale that is orders of magnitude beyond the capability of existing out-of-the-box inferential high-dimensional multi-class regression methods and software. Applying our model to the Pan Cancer Analysis of Whole Genomes dataset reveals interesting biological insights including somatic mutational topics associated with UV exposure in skin cancer, aging in colorectal cancer, and strong influence of epigenome organization in liver cancer. Under cross-validation, our model demonstrates highly competitive predictive performance against blackbox methods of random forest and deep learning.

Adaptation of a mutual exclusivity framework to identify driver mutations within oncogenic pathways

Distinguishing genomic alterations in cancer-associated genes that have functional impact on tumor growth and disease progression from the ones that are passengers and confer no fitness advantage have important clinical implications. Evidence-based methods for nominating drivers are limited by existing knowledge on the oncogenic effects and therapeutic benefits of specific variants from clinical trials or experimental settings. As clinical sequencing becomes a mainstay of patient care, applying computational methods to mine the rapidly growing clinical genomic data holds promise in uncovering functional candidates beyond the existing knowledge base and expanding the patient population that could potentially benefit from genetically targeted therapies. We propose a statistical and computational method (MAGPIE) that builds on a likelihood approach leveraging the mutual exclusivity pattern within an oncogenic pathway for identifying probabilistically both the specific genes within a pathway and the individual mutations within such genes that are truly the drivers. Alterations in a cancer-associated gene are assumed to be a mixture of driver and passenger mutations with the passenger rates modeled in relationship to tumor mutational burden. We use simulations to study the operating characteristics of the method and assess false-positive and false-negative rates in driver nomination. When applied to a large study of primary melanomas, the method accurately identifies the known driver genes within the RTK-RAS pathway and nominates several rare variants as prime candidates for functional validation. A comprehensive evaluation of MAGPIE against existing tools has also been conducted leveraging the Cancer Genome Atlas data.

Integration of peripheral blood- and tissue-based biomarkers of response to immune checkpoint blockade in urothelial carcinoma

As predictive biomarkers of response to immune checkpoint inhibitors (ICIs) remain a major unmet clinical need in patients with urothelial carcinoma (UC), we sought to identify tissue-based immune biomarkers of clinical benefit to ICIs using multiplex immunofluorescence and to integrate these findings with previously identified peripheral blood biomarkers of response. Fifty-five pretreatment and 12 paired on-treatment UC specimens were identified from patients treated with nivolumab with or without ipilimumab. Whole tissue sections were stained with a 12-plex mIF panel, including CD8, PD-1/CD279, PD-L1/CD274, CD68, CD3, CD4, FoxP3, TCF1/7, Ki67, LAG-3, MHC-II/HLA-DR, and pancytokeratin+SOX10 to identify over three million cells. Immune tissue densities were compared to progression-free survival (PFS) and best overall response (BOR) by RECIST version 1.1. Correlation coefficients were calculated between tissue-based and circulating immune populations. The frequency of intratumoral CD3+ LAG-3+ cells was higher in responders compared to nonresponders (p = 0.0001). LAG-3+ cellular aggregates were associated with response, including CD3+ LAG-3+ in proximity to CD3+ (p = 0.01). Exploratory multivariate modeling showed an association between intratumoral CD3+ LAG-3+ cells and improved PFS independent of prognostic clinical factors (log HR -7.0; 95% confidence interval [CI] -12.7 to -1.4), as well as established biomarkers predictive of ICI response (log HR -5.0; 95% CI -9.8 to -0.2). Intratumoral LAG-3+ immune cell populations warrant further study as a predictive biomarker of clinical benefit to ICIs. Differences in LAG-3+ lymphocyte populations across the intratumoral and peripheral compartments may provide complementary information that could inform the future development of multimodal composite biomarkers of ICI response. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Adaptation of a Mutual Exclusivity Framework to Identify Driver Mutations within Biological Pathways

Distinguishing genomic alterations in cancer genes that have functional impact on tumor growth and disease progression from the ones that are passengers and confer no fitness advantage has important clinical implications. Evidence-based methods for nominating drivers are limited by existing knowledge on the oncogenic effects and therapeutic benefits of specific variants from clinical trials or experimental settings. As clinical sequencing becomes a mainstay of patient care, applying computational methods to mine the rapidly growing clinical genomic data holds promise in uncovering novel functional candidates beyond the existing knowledge-base and expanding the patient population that could potentially benefit from genetically targeted therapies. We propose a statistical and computational method (MAGPIE) that builds on a likelihood approach leveraging the mutual exclusivity pattern within an oncogenic pathway for identifying probabilistically both the specific genes within a pathway and the individual mutations within such genes that are truly the drivers. Alterations in a cancer gene are assumed to be a mixture of driver and passenger mutations with the passenger rates modeled in relationship to tumor mutational burden. A limited memory BFGS algorithm is used to facilitate large scale optimization. We use simulations to study the operating characteristics of the method and assess false positive and false negative rates in driver nomination. When applied to a large study of primary melanomas the method accurately identified the known driver genes within the RTK-RAS pathway and nominated a number of rare variants with previously unknown biological and clinical relevance as prime candidates for functional validation.

The GENIE BPC NSCLC Cohort: A Real-World Repository Integrating Standardized Clinical and Genomic Data for 1,846 Patients with Non-Small Cell Lung Cancer

PURPOSE

We describe the clinical and genomic landscape of the non-small cell lung cancer (NSCLC) cohort of the American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE) Biopharma Collaborative (BPC).

EXPERIMENTAL DESIGN

A total of 1,846 patients with NSCLC whose tumors were sequenced from 2014 to 2018 at four institutions participating in AACR GENIE were randomly chosen for curation using the PRISSMM data model. Progression-free survival (PFS) and overall survival (OS) were estimated for patients treated with standard therapies.

RESULTS

In this cohort, 44% of tumors harbored a targetable oncogenic alteration, with EGFR (20%), KRAS G12C (13%), and oncogenic fusions (ALK, RET, and ROS1; 5%) as the most frequent. Median OS (mOS) on first-line platinum-based therapy without immunotherapy was 17.4 months [95% confidence interval (CI), 14.9-19.5 months]. For second-line therapies, mOS was 9.2 months (95% CI, 7.5-11.3 months) for immune checkpoint inhibitors (ICI) and 6.4 months (95% CI, 5.1-8.1 months) for docetaxel ± ramucirumab. In a subset of patients treated with ICI in the second-line or later setting, median RECIST PFS (2.5 months; 95% CI, 2.2-2.8) and median real-world PFS based on imaging reports (2.2 months; 95% CI, 1.7-2.6) were similar. In exploratory analysis of the impact of tumor mutational burden (TMB) on survival on ICI treatment in the second-line or higher setting, TMB z-score harmonized across gene panels was associated with improved OS (univariable HR, 0.85; P = 0.03; n = 247 patients).

CONCLUSIONS

The GENIE BPC cohort provides comprehensive clinicogenomic data for patients with NSCLC, which can improve understanding of real-world patient outcomes.

A topic modeling approach reveals the dynamic T cell composition of peripheral blood during cancer immunotherapy

We present TopicFlow, a computational framework for flow cytometry data analysis of patient blood samples for the identification of functional and dynamic topics in circulating T cell population. This framework applies a Latent Dirichlet Allocation (LDA) model, adapting the concept of topic modeling in text mining to flow cytometry. To demonstrate the utility of our method, we conducted an analysis of ∼17 million T cells collected from 138 peripheral blood samples in 51 patients with melanoma undergoing treatment with immune checkpoint inhibitors (ICIs). Our study highlights three latent dynamic topics identified by LDA: a T cell exhaustion topic that independently recapitulates the previously identified LAG-3+ immunotype associated with ICI resistance, a naive topic and its association with immune-related toxicity, and a T cell activation topic that emerges upon ICI treatment. Our approach can be broadly applied to mine high-parameter flow cytometry data for insights into mechanisms of treatment response and toxicity.

Efficacy of PD-(L)1 blockade monotherapy compared with PD-(L)1 blockade plus chemotherapy in first-line PD-L1-positive advanced lung adenocarcinomas: a cohort study

BACKGROUND

Single-agent PD-(L)1 blockade (IO) alone or in combination with chemotherapy (Chemotherapy-IO) is approved first-line therapies in patients with advanced lung adenocarcinomas (LUADs) with PD-L1 expression ≥1%. These regimens have not been compared prospectively. The primary objective was to compare first-line efficacies of single-agent IO to Chemotherapy-IO in patients with advanced LUADs. Secondary objectives were to explore if clinical, pathological, and genomic features were associated with differential response to Chemotherapy-IO versus IO.

METHODS

This was a multicenter retrospective cohort study. Inclusion criteria were patients with advanced LUADs with tumor PD-L1 ≥1% treated with first-line Chemotherapy-IO or IO. To compare the first-line efficacies of single-agent IO to Chemotherapy-IO, we conducted inverse probability weighted Cox proportional hazards models using estimated propensity scores.

RESULTS

The cohort analyzed included 866 patients. Relative to IO, Chemotherapy-IO was associated with improved objective response rate (ORR) (44% vs 35%, p=0.007) and progression-free survival (PFS) in patients with tumor PD-L1≥1% (HR 0.84, 95% CI 0.72 to 0.97, p=0.021) or PD-L1≥50% (ORR 55% vs 38%, p<0.001; PFS HR 0.68, 95% CI 0.53 to 0.87, p=0.002). Using propensity-adjusted analyses, only never-smokers in the PD-L1≥50% subgroup derived a differential survival benefit from Chemotherapy-IO vs IO (p=0.013). Among patients with very high tumor PD-L1 expression (≥90%), there were no differences in outcome between treatment groups. No genomic factors conferred differential survival benefit to Chemotherapy-IO versus IO.

CONCLUSIONS

While the addition of chemotherapy to PD-(L)1 blockade increases the probability of initial response, never-smokers with tumor PD-L1≥50% comprise the only population identified that derived an apparent survival benefit with treatment intensification.

Clinical and Genomic Features of Response and Toxicity to Sotorasib in a Real-World Cohort of Patients With Advanced KRAS G12C-Mutant Non-Small Cell Lung Cancer

PURPOSE

With the recent approval of the KRAS G12C inhibitor sotorasib for patients with advanced KRAS G12C-mutant non-small cell lung cancer (NSCLC), there is a new need to identify factors associated with activity and toxicity among patients treated in routine practice.

MATERIALS AND METHODS

We conducted a multicenter retrospective study of patients treated with sotorasib outside of clinical trials to identify factors associated with real-world progression free survival (rwPFS), overall survival (OS), and toxicity.

RESULTS

Among 105 patients with advanced KRAS G12C-mutant NSCLC treated with sotorasib, treatment led to a 5.3-month median rwPFS, 12.6-month median OS, and 28% real-world response rate. KEAP1 comutations were associated with shorter rwPFS and OS (rwPFS hazard ratio [HR], 3.19; P = .004; OS HR, 4.10; P = .003); no significant differences in rwPFS or OS were observed across TP53 (rwPFS HR, 1.10; P = .731; OS HR, 1.19; P = .631) or STK11 (rwPFS HR, 1.66; P = .098; OS HR, 1.73; P = .168) comutation status. Notably, almost all patients who developed grade 3 or higher treatment-related adverse events (G3+ TRAEs) had previously been treated with anti-PD-(L)1 therapy. Among these patients, anti-PD-(L)1 therapy exposure within 12 weeks of sotorasib was strongly associated with G3+ TRAEs (P < .001) and TRAE-related sotorasib discontinuation (P = .014). Twenty-eight percent of patients with recent anti-PD-(L)1 therapy exposure experienced G3+ TRAEs, most commonly hepatotoxicity.

CONCLUSION

Among patients treated with sotorasib in routine practice, KEAP1 comutations were associated with resistance and recent anti-PD-(L)1 therapy exposure was associated with toxicity. These observations may help guide use of sotorasib in the clinic and may help inform the next generation of KRAS G12C-targeted clinical trials.

Uncovering the hidden structure of dynamic T cell composition in peripheral blood during cancer immunotherapy: a topic modeling approach

Immune checkpoint inhibitors (ICIs), now mainstays in the treatment of cancer treatment, show great potential but only benefit a subset of patients. A more complete understanding of the immunological mechanisms and pharmacodynamics of ICI in cancer patients will help identify the patients most likely to benefit and will generate knowledge for the development of next-generation ICI regimens. We set out to interrogate the early temporal evolution of T cell populations from longitudinal single-cell flow cytometry data. We developed an innovative statistical and computational approach using a Latent Dirichlet Allocation (LDA) model that extends the concept of topic modeling used in text mining. This powerful unsupervised learning tool allows us to discover compositional topics within immune cell populations that have distinct functional and differentiation states and are biologically and clinically relevant. To illustrate the model's utility, we analyzed ∼17 million T cells obtained from 138 pre- and on-treatment peripheral blood samples from a cohort of melanoma patients treated with ICIs. We identified three latent dynamic topics: a T-cell exhaustion topic that recapitulates a LAG3+ predominant patient subgroup with poor clinical outcome; a naive topic that shows association with immune-related toxicity; and an immune activation topic that emerges upon ICI treatment. We identified that a patient subgroup with a high baseline of the naïve topic has a higher toxicity grade. While the current application is demonstrated using flow cytometry data, our approach has broader utility and creates a new direction for translating single-cell data into biological and clinical insights.

[Clinicopathologic characteristics and prognostic analysis of testicular diffuse large B-cell lymphoma]

Objective: To analyze the clinicopathologic characteristics and prognosis of testicular diffuse large B-cell lymphoma (DLBCL) . Methods: A retrospective analysis was performed on 68 patients with testicular DLBCL admitted to Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine from October 2001 to April 2020. The gene mutation profile was evaluated by targeted sequencing (55 lymphoma-related genes) , and prognostic factors were analyzed. Results: A total of 68 patients were included, of whom 45 (66.2% ) had primary testicular DLBCL and 23 (33.8% ) had secondary testicular DLBCL. The proportion of secondary testicular DLBCL patients with Ann Arbor stage Ⅲ-Ⅳ (P<0.001) , elevated LDH (P<0.001) , ECOG score ≥ 2 points (P=0.005) , and IPI score 3-5 points (P<0.001) is higher than that of primary testicular DLBCL patients. Sixty-two (91% ) patients received rituximab in combination with cyclophosphamide, adriamycin, vincristine, and prednisone (R-CHOP) -based first-line regimen, whereas 54 cases (79% ) underwent orchiectomy prior to chemotherapy. Patients with secondary testicular DLBCL had a lower estimated 5-year progression-free survival (PFS) rate (16.5% vs 68.1% , P<0.001) and 5-year overall survival (OS) rate (63.4% vs 74.9% , P=0.008) than those with primary testicular DLBCL, and their complete remission rate (57% vs 91% , P=0.003) was also lower than that of primary testicular DLBCL. The ECOG scores of ≥2 (PFS: P=0.018; OS: P<0.001) , Ann Arbor stages Ⅲ-Ⅳ (PFS: P<0.001; OS: P=0.018) , increased LDH levels (PFS: P=0.015; OS: P=0.006) , and multiple extra-nodal involvements (PFS: P<0.001; OS: P=0.013) were poor prognostic factors in testicular DLBCL. Targeted sequencing data in 20 patients with testicular DLBCL showed that the mutation frequencies of ≥20% were PIM1 (12 cases, 60% ) , MYD88 (11 cases, 55% ) , CD79B (9 cases, 45% ) , CREBBP (5 cases, 25% ) , KMT2D (5 cases, 25% ) , ATM (4 cases, 20% ) , and BTG2 (4 cases, 20% ) . The frequency of mutations in KMT2D in patients with secondary testicular DLBCL was higher than that in patients with primary testicular DLBCL (66.7% vs 7.1% , P=0.014) and was associated with a lower 5-year PFS rate in patients with testicular DLBCL (P=0.019) . Conclusion: Patients with secondary testicular DLBCL had worse PFS and OS than those with primary testicular DLBCL. The ECOG scores of ≥2, Ann Arbor stages Ⅲ-Ⅳ, increased LDH levels, and multiple extra-nodal involvements were poor prognostic factors in testicular DLBCL. PIM1, MYD88, CD79B, CREBBP, KMT2D, ATM, and BTG2 were commonly mutated genes in testicular DLBCL, and the prognosis of patients with KMT2D mutations was poor.

Abstract 4284: Enhancing subclonal reconstruction algorithm for resolving complex tumor phylogenies from multi-sample tumor DNA sequencing

Cancer is characterized by the ongoing accumulation of somatic mutations, providing selective advantages that may lead to dysregulated cellular proliferation. While the cancer genome at diagnosis has been extensively studied, many cancer types still lack strong prognostic biomarkers. The continuous acquisition and selection for driver mutations in a population of cancer cells acts as a Darwinian process, resulting in clonal expansions of progressively more aberrant and fit phenotypes. Reconstructing tumor evolution allows us to understand key events that drive cancer progression and patterns of mutation co-occurrence within clones. These evolutionary features guide our understanding of fundamental mechanisms that lead to disease lethality. Inferring tumor evolution from DNA sequencing data is becoming part of routine analysis in cancer research. As sequencing costs drop, sequencing multiple tumor samples from a patient becomes routine. These multiple samples can represent different spatial regions of a tumor, longitudinal samples from a single region or a combination of both. This provides an opportunity to study tumor evolution in much greater detail and accuracy than was previously feasible through single-sample datasets. The most widely used methods to reconstruct the subclonal evolution of a tumor utilize stochastic-search algorithms. These approaches iterate through a parameter space to select phylogenetic solutions that maximize the likelihood of observed sequencing data. They are optimized for low complexity cases, where the size and number or subclones are relatively limited. As tumor subclonal structure increases in complexity, the parameter space grows exponentially, and stochastic-search algorithms become computationally intractable. For instance, recent benchmarking studies have revealed that many methods fail to reconstruct clone trees for data with as few as ten subclones. To circumvent current computational limitations, we developed a deterministic algorithm for subclonal reconstruction that leverages fundamental principles of cancer biology to encode heuristics that reduce the solution space to biologically plausible phylogenies. When applied to samples (4-36 tumors; median 16) from 12 patients with metastatic breast cancer, our method reduced the average runtime ten-fold. We were able to delineate the evolutionary history of up to 57 distinct subclones per patient, which is infeasible with most current methods. Benchmarking using methods developed for the SMCHet DREAM challenge on real and simulated datasets further quantifies the accuracy, resolution, and scalability. We have thus presented a novel method for rapid and optimized reconstruction of tumor evolutionary histories.

Citation Format: Helena Winata, Daniel Knight, Juber A. Patel, Nicholas K. Wang, Pier Selenica, Stefan E. Eng, Caroline Kostrzewa, Jaron Arbet, Yingjie Zhu, Ronglai Shen, Jorge Reis-Filho, Pedram Razafi, Paul C. Boutros. Enhancing subclonal reconstruction algorithm for resolving complex tumor phylogenies from multi-sample tumor DNA sequencing. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4284.

Abstract 5721: Automated annotation for large-scale clinicogenomic models of lung cancer treatment response and overall survival

The digitization of health records and prompt availability of tumor DNA sequencing results offer a chance to study the determinants of cancer outcomes with unprecedented richness; however, abstraction of key attributes from free text presents a major limitation to large-scale analyses. Using natural language processing (NLP), we derived sites of metastasis, prior treatment at outside institutions, programmed death ligand 1 (PD-L1) levels, and smoking status from records of patients with tumor sequencing to create a richly annotated clinicogenomic cohort. We sought to define whether combining features would improve models of overall survival (OS) and treatment response as validated in a multi-institution, manually curated cohort. We leveraged the manually curated AACR GENIE Biopharma Collaborative (BPC) dataset to train NLP algorithms to abstract the aforementioned features from overlapping records available at Memorial Sloan Kettering (MSK). All models achieved precision and recall &gt; 0.85. We deployed these algorithms to records of all MSK patients with non-small cell lung cancer (NSCLC) and tumor profiling with our FDA-authorized institutional targeted sequencing platform (N=7,015). These labels were combined with genomic, demographic, histopathologic, internal treatment and staging data to train random survival forests (RSF) to predict OS and time-to-next-treatment (TTNT) for molecularly targeted and immunotherapies. RSFs trained on the MSK NSCLC cohort were validated with the curated, non-MSK BPC NSCLC cohort (N=977). The addition of NLP-derived variables to genomic features enhanced RSF predictive power for OS (c-index, 10x bootstrap 95%CI: 0.58, 0.57-0.59 vs 0.75, 0.74-0.76 combined) and targeted and immunotherapy TTNT. The size of the MSK NSCLC cohort enabled discovery of associations between metastatic sites, PD-L1 status, genomics, and TTNTs not apparent in the smaller BPC cohort. We measured the added predictive value of variables not available in BPC with MSK-only cross-validation analyses. White blood cell differential counts and additional tissue genomic features including tumor mutational burden and fraction genome altered added minimally, while circulating tumor DNA sequencing added prognostic power for OS over other factors including disease burden

Using NLP we present a large NSCLC cohort with rich clinicoradiographic annotation, leading to superior models of patient outcomes. Our data uncovers associations not observed in smaller, manually curated cohorts and provides a foundation for further research in therapy choice and prognostication.

Citation Format: Justin Jee, Chris Fong, Karl Pichotta, Thinh Tran, Anisha Luthra, Mirella Altoe, Steven Maron, Ronglai Shen, Si-Yang Liu, Michele Waters, Joseph Kholodenko, Brooke Mastrogiacomo, Susie Kim, A Rose Brannon, Michael F. Berger, Axel Martin, Jason Chang, Anton Safonov, Jorge S. Reis-Filho, Deborah Schrag, Sohrab P. Shah, Pedram Razavi, Bob T. Li, Gregory J. Riely, Nikolaus Schultz. Automated annotation for large-scale clinicogenomic models of lung cancer treatment response and overall survival. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5721.

Predicting Cancer Risk from Germline Whole-exome Sequencing Data Using a Novel Context-based Variant Aggregation Approach

Many studies have shown that the distributions of the genomic, nucleotide, and epigenetic contexts of somatic variants in tumors are informative of cancer etiology. Recently, a new direction of research has focused on extracting signals from the contexts of germline variants and evidence has emerged that patterns defined by these factors are associated with oncogenic pathways, histologic subtypes, and prognosis. It remains an open question whether aggregating germline variants using meta-features capturing their genomic, nucleotide, and epigenetic contexts can improve cancer risk prediction. This aggregation approach can potentially increase statistical power for detecting signals from rare variants, which have been hypothesized to be a major source of the missing heritability of cancer. Using germline whole-exome sequencing data from the UK Biobank, we developed risk models for 10 cancer types using known risk variants (cancer-associated SNPs and pathogenic variants in known cancer predisposition genes) as well as models that additionally include the meta-features. The meta-features did not improve the prediction accuracy of models based on known risk variants. It is possible that expanding the approach to whole-genome sequencing can lead to gains in prediction accuracy.

Significance

There is evidence that cancer is partly caused by rare genetic variants that have not yet been identified. We investigate this issue using novel statistical methods and data from the UK Biobank.

Pathway Alterations in Stage II/III Primary Melanoma

PURPOSE

Genomic classification of melanoma has thus far focused on the mutational status of BRAF, NRAS, and NF1. The clinical utility of this classification remains limited, and the landscape of alterations in other oncogenic signaling pathways is underexplored.

METHODS

Using primary samples from the InterMEL study, a retrospective cohort of cases with specimens collected from an international consortium with participating institutions throughout the United States and Australia, with oversampling of cases who ultimately died of melanoma, we examined mutual exclusivity and co-occurrence of genomic alterations in 495 stage II/III primary melanomas across 11 cancer pathways. Somatic mutation and copy number alterations were analyzed from next-generation sequencing using a clinical sequencing panel.

RESULTS

Mutations in the RTK-RAS pathway were observed in 81% of cases. Other frequently occurring pathways were TP53 (31%), Cell Cycle (30%), and PI3K (18%). These frequencies are generally lower than was observed in The Cancer Genome Atlas, where the specimens analyzed were predominantly obtained from metastases. Overall, 81% of the cases had at least one targetable mutation. The RTK-RAS pathway was the only pathway that demonstrated strong and statistically significant mutual exclusivity. However, this strong mutual exclusivity signal was evident only for the three common genes in the pathway (BRAF, NRAS, and NF1). Analysis of co-occurrence of different pathways exhibited no positive significant trends. However, interestingly, a high frequency of cases with none of these pathways represented was observed, 8.4% of cases versus 4.0% expected (P < .001). A higher frequency of RTK-RAS singletons (with no other pathway alteration) was observed compared with The Cancer Genome Atlas. Clonality analyses suggest strongly that both the cell cycle and RTK-RAS pathways represent early events in melanogenesis.

CONCLUSION

Our results confirm the dominance of mutations in the RTK-RAS pathway. The presence of many mutations in several well-known, actionable pathways suggests potential avenues for targeted therapy in these early-stage cases.

Methylation of nonessential genes in cutaneous melanoma - Rule Out hypothesis

Differential methylation plays an important role in melanoma development and is associated with survival, progression and response to treatment. However, the mechanisms by which methylation promotes melanoma development are poorly understood. The traditional explanation of selective advantage provided by differential methylation postulates that hypermethylation of regulatory 5'-cytosine-phosphate-guanine-3' dinucleotides (CpGs) downregulates the expression of tumor suppressor genes and therefore promotes tumorigenesis. We believe that other (not necessarily alternative) explanations of the selective advantages of methylation are also possible. Here, we hypothesize that melanoma cells use methylation to shut down transcription of nonessential genes - those not required for cell survival and proliferation. Suppression of nonessential genes allows tumor cells to be more efficient in terms of energy and resource usage, providing them with a selective advantage over the tumor cells that transcribe and subsequently translate genes they do not need. We named the hypothesis the Rule Out (RO) hypothesis. The RO hypothesis predicts higher methylation of CpGs located in regulatory regions (CpG islands) of nonessential genes. It also predicts the higher methylation of regulatory CpGs linked to nonessential genes in melanomas compared to nevi and lower expression of nonessential genes in malignant (derived from melanoma) versus normal (derived from nonaffected skin) melanocytes. The analyses conducted using in-house and publicly available data found that all predictions derived from the RO hypothesis hold, providing observational support for the hypothesis.

Nonfood Probiotic, Prebiotic, and Synbiotic Use Reduces All-Cause and Cardiovascular Mortality Risk in Older Adults: A Population-Based Cohort Study

OBJECTIVES

Pro-, pre-, and synbiotic supplements improve cardiovascular risk factors. However, the association between nonfood pro-, pre-, and synbiotics (NPPS) and long-term all-cause and cardiovascular mortality has not been studied. Thus, our objective was to determine the impact of nonfood pro-, pre-, and synbiotics on all-cause and cardiovascular mortality.

DESIGN, SETTING, AND PARTICIPANTS

This was a retrospective, cohort study of 4837 nationally representative American participants aged 65 years or older with a median follow-up duration of 77 months.

MEASUREMENTS

All-cause and cardiovascular mortality were measured.

RESULTS

A total of 1556 participants died during the median 77-month follow-up, and 517 died from cardiovascular disease. Compared with participants without NPPS use, participants who used NPPS experienced a reduced risk of all-cause mortality by nearly 41% (hazard ratio 0.59, 95% CI 0.43 to 0.79) and cardiovascular mortality by 52% (HR 0.48, 95% CI 0.30 to 0.76). Such an effect persisted in most subgroup analyses and complete-case analyses.

CONCLUSION AND RELEVANCE

In this study, we found a protective effect of NPPS against all-cause and cardiovascular mortality in Americans aged 65 years or older. Nonfood pro-, pre-, and synbiotics can be a novel, inexpensive, low-risk treatment addition for all-cause and cardiovascular mortality for older individuals.

InterMEL: An international biorepository and clinical database to uncover predictors of survival in early-stage melanoma

INTRODUCTION

We are conducting a multicenter study to identify classifiers predictive of disease-specific survival in patients with primary melanomas. Here we delineate the unique aspects, challenges, and best practices for optimizing a study of generally small-sized pigmented tumor samples including primary melanomas of at least 1.05mm from AJTCC TNM stage IIA-IIID patients. We also evaluated tissue-derived predictors of extracted nucleic acids' quality and success in downstream testing. This ongoing study will target 1,000 melanomas within the international InterMEL consortium.

METHODS

Following a pre-established protocol, participating centers ship formalin-fixed paraffin embedded (FFPE) tissue sections to Memorial Sloan Kettering Cancer Center for the centralized handling, dermatopathology review and histology-guided coextraction of RNA and DNA. Samples are distributed for evaluation of somatic mutations using next gen sequencing (NGS) with the MSK-IMPACTTM assay, methylation-profiling (Infinium MethylationEPIC arrays), and miRNA expression (Nanostring nCounter Human v3 miRNA Expression Assay).

RESULTS

Sufficient material was obtained for screening of miRNA expression in 683/685 (99%) eligible melanomas, methylation in 467 (68%), and somatic mutations in 560 (82%). In 446/685 (65%) cases, aliquots of RNA/DNA were sufficient for testing with all three platforms. Among samples evaluated by the time of this analysis, the mean NGS coverage was 249x, 59 (18.6%) samples had coverage below 100x, and 41/414 (10%) failed methylation QC due to low intensity probes or insufficient Meta-Mixed Interquartile (BMIQ)- and single sample (ss)- Noob normalizations. Six of 683 RNAs (1%) failed Nanostring QC due to the low proportion of probes above the minimum threshold. Age of the FFPE tissue blocks (p<0.001) and time elapsed from sectioning to co-extraction (p = 0.002) were associated with methylation screening failures. Melanin reduced the ability to amplify fragments of 200bp or greater (absent/lightly pigmented vs heavily pigmented, p<0.003). Conversely, heavily pigmented tumors rendered greater amounts of RNA (p<0.001), and of RNA above 200 nucleotides (p<0.001).

CONCLUSION

Our experience with many archival tissues demonstrates that with careful management of tissue processing and quality control it is possible to conduct multi-omic studies in a complex multi-institutional setting for investigations involving minute quantities of FFPE tumors, as in studies of early-stage melanoma. The study describes, for the first time, the optimal strategy for obtaining archival and limited tumor tissue, the characteristics of the nucleic acids co-extracted from a unique cell lysate, and success rate in downstream applications. In addition, our findings provide an estimate of the anticipated attrition that will guide other large multicenter research and consortia.

Increased tumor glycolysis is associated with decreased immune infiltration across human solid tumors

Response to immunotherapy across multiple cancer types is approximately 25%, with some tumor types showing increased response rates compared to others (i.e. response rates in melanoma and non-small cell lung cancer (NSCLC) are typically 30-60%). Patients whose tumors are resistant to immunotherapy often lack high levels of pre-existing inflammation in the tumor microenvironment. Increased tumor glycolysis, acting through glucose deprivation and lactic acid accumulation, has been shown to have pleiotropic immune suppressive effects using in-vitro and in-vivo models of disease. To determine whether the immune suppressive effect of tumor glycolysis is observed across human solid tumors, we analyzed glycolytic and immune gene expression patterns in multiple solid malignancies. We found that increased expression of a glycolytic signature was associated with decreased immune infiltration and a more aggressive disease across multiple tumor types. Radiologic and pathologic analysis of untreated estrogen receptor (ER)-negative breast cancers corroborated these observations, and demonstrated that protein expression of glycolytic enzymes correlates positively with glucose uptake and negatively with infiltration of CD3⁺ and CD8⁺ lymphocytes. This study reveals an inverse relationship between tumor glycolysis and immune infiltration in a large cohort of multiple solid tumor types.

Landscape of mutations in early stage primary cutaneous melanoma: An InterMEL study

It is unclear why some melanomas aggressively metastasize while others remain indolent. Available studies employing multi-omic profiling of melanomas are based on large primary or metastatic tumors. We examine the genomic landscape of early-stage melanomas diagnosed prior to the modern era of immunological treatments. Untreated cases with Stage II/III cutaneous melanoma were identified from institutions throughout the United States, Australia and Spain. FFPE tumor sections were profiled for mutation, methylation and microRNAs. Preliminary results from mutation profiling and clinical pathologic correlates show the distribution of four driver mutation sub-types: 31% BRAF; 18% NRAS; 21% NF1; 26% Triple Wild Type. BRAF mutant tumors had younger age at diagnosis, more associated nevi, more tumor infiltrating lymphocytes, and fewer thick tumors although at generally more advanced stage. NF1 mutant tumors were frequent on the head/neck in older patients with severe solar elastosis, thicker tumors but in earlier stages. Triple Wild Type tumors were predominantly male, frequently on the leg, with more perineural invasion. Mutations in TERT, TP53, CDKN2A and ARID2 were observed often, with TP53 mutations occurring particularly frequently in the NF1 sub-type. The InterMEL study will provide the most extensive multi-omic profiling of early-stage melanoma to date. Initial results demonstrate a nuanced understanding of the mutational and clinicopathological landscape of these early-stage tumors.

Overall survival with circulating tumor DNA-guided therapy in advanced non-small-cell lung cancer

Circulating tumor DNA (ctDNA) sequencing guides therapy decisions but has been studied mostly in small cohorts without sufficient follow-up to determine its influence on overall survival. We prospectively followed an international cohort of 1,127 patients with non-small-cell lung cancer and ctDNA-guided therapy. ctDNA detection was associated with shorter survival (hazard ratio (HR), 2.05; 95% confidence interval (CI), 1.74-2.42; P < 0.001) independently of clinicopathologic features and metabolic tumor volume. Among the 722 (64%) patients with detectable ctDNA, 255 (23%) matched to targeted therapy by ctDNA sequencing had longer survival than those not treated with targeted therapy (HR, 0.63; 95% CI, 0.52-0.76; P < 0.001). Genomic alterations in ctDNA not detected by time-matched tissue sequencing were found in 25% of the patients. These ctDNA-only alterations disproportionately featured subclonal drivers of resistance, including RICTOR and PIK3CA alterations, and were associated with short survival. Minimally invasive ctDNA profiling can identify heterogeneous drivers not captured in tissue sequencing and expand community access to life-prolonging therapy.

Bioinformatically Expanded Next-Generation Sequencing Analysis Optimizes Identification of Therapeutically Relevant MET Copy Number Alterations in >50,000 Tumors

PURPOSE

Clinical relevance thresholds and laboratory methods are poorly defined for MET amplification, a targetable biomarker across malignancies.

EXPERIMENTAL DESIGN

The utility of next-generation sequencing (NGS) in assessing MET copy number alterations was determined in >50,000 solid tumors. Using fluorescence in situ hybridization as reference, we validated and optimized NGS analysis.

RESULTS

Incorporating read-depth and focality analyses achieved 91% concordance, 97% sensitivity, and 89% specificity. Tumor heterogeneity, neoplastic cell proportions, and genomic focality affected MET amplification assessment. NGS methodology showed superiority in capturing overall amplification status in heterogeneous tumors and defining amplification focality among other genomic alterations. MET copy gains and amplifications were found in 408 samples across 23 malignancies. Total MET copy number inversely correlated with amplified segment size. High-level/focal amplification was enriched in certain genomic subgroups and associated with targeted therapy response.

CONCLUSIONS

Leveraging our integrated bioinformatic approach, targeted therapy benefit was observed across diverse MET amplification contexts.

Genomic Determinants of Early Recurrences in Low-Stage, Low-Grade Endometrioid Endometrial Carcinoma

Low-stage, low-grade endometrioid endometrial carcinoma (EEC), the most common histologic type of endometrial cancer, typically has a favorable prognosis. A subset of these cancers, however, displays an aggressive clinical course with early recurrences, including distant relapses. All statistical tests were 2-sided. Using a combination of whole-exome and targeted capture sequencing of 65 FIGO stage IA and IB grade 1 EECs treated with surgery alone, we demonstrate that chromosome 1q gain (odds ratio [OR] = 8.09, 95% confidence interval [CI] = 1.59 to 54.6; P = .02), PIK3CA mutation (OR = 9.16, 95% CI = 1.95 to 61.8; P = .01), and DNA mismatch repair-deficient molecular subtype (OR = 7.92, 95% CI = 1.44 to 87.6; P = .02) are independent predictors of early recurrences within 3 years in this patient population. Chromosome 1q gain was validated in an independent dataset of stage I grade 1 EECs subjected to whole-exome sequencing. Our findings expand on the repertoire of genomic parameters that should be considered in the evaluation of patients with low-stage, low-grade EEC.

Allelic dosage of RB1 drives CDK4/6 inhibitor treatment resistance in metastatic breast cancer

1010

Background: We recently reported inferior outcomes to CDK4/6 inhibitors and endocrine therapy (CDK4/6i-ET) associated with germline BRCA2 (g BRCA2) in a cohort of estrogen receptor (ER) positive breast cancers. Co-occurrence of gBRCA2 with loss of heterozygosity (LOH) of neighboring RB1 was found to portend particularly poor outcomes. Here, we sought to define the effects of pre-treatment RB1 allelic copy number status on outcomes of CDK4/6i-ET and the likelihood of developing RB1 loss-of-function (LOF) mutations on CDK4/6i through the analysis of an expanded cohort of metastatic ER+ breast cancer patients. Methods: Patients who underwent sequencing on MSK-IMPACT from April 2014 to May 2021 were included. For every sample preceding CDK4/6i-ET, we performed FACETS to infer RB1 allele specific copy number, ploidy, tumor purity and fraction genome altered (FGA). Patients were categorized based on RB1 allelic status: HetLoss (total of one allelic copy), copy neutral LOH (CNLOH), other allelic imbalance including all other aneuploidy states, and diploid. Progression free survival (PFS) was assessed using univariate and multivariate Cox proportional hazard models adjusted for ET partner and FGA. Firth penalized logistic regression was used to study association of pre-treatment RB1 status with acquired RB1 LOF variants in paired post-CDK4/6i samples. Results: Of 2,630 potentially eligible patients, 279 patients had genomic sequencing performed prior to 1st line CDK4/6i-ET. Of these, 75 (26.8%) exhibited RB1 HetLoss, 39 (14.0%) had CNLOH of RB1, 111 (39.7%) exhibited diploid RB1 state, while 54 (19.4%) had other patterns of RB1 allelic imbalance. All non-diploid RB1 states were associated with significantly shortened PFS relative to diploid (univariate HetLoss HR: 2.05, 95% CI: 1.42, 2.97; CNLOH HR: 2.08, 95% CI: 1.32, 3.25; other imbalance HR: 1.70, 95% CI: 1.11, 2.58). Only HetLoss remained significant when adjusted for FGA (HR 1.61, 95% CI: 1.09, 2.38, p = 0.017). RB1 LOF was rare in pre-CDK4/6i tumors (< 1%); excluding these cases did not change our results. Of the 176 patients with paired pre- and post-CDK4/6i samples, only RB1 HetLoss in pre-CDK4/6i sample was significantly associated with development of RB1 LOF mutations in post-CDK4/6i sample (18.4%) as compared to diploid (4.2%, OR 4.25, 95% CI 1.02, 17.7, p = 0.047). These results indicate that tumors with one functional copy of RB1 are more likely to acquire RB1 LOF on CDK4/6i to achieve biallelic RB1 loss as a mechanism of CDK4/6i resistance. Conclusions: We demonstrate that LOH and allelic imbalance of RB1 are associated with shorter PFS on CDK4/6-ET. We postulate this may occur partly as a result of more frequent acquired RB1 LOF mutations under selective pressure of CDK4/6i. These data supports the implementation of more refined allele-specific copy number methods and identifies a high-risk population for escalated monitoring and treatment approaches.

Assessing effectiveness of first-line carboplatin, pemetrexed, and pembrolizumab in patients with recurrent/metastatic lung adenocarcinoma

e21045

Background: Randomized clinical trial data support the addition of immune checkpoint inhibitors to standard first-line platinum-doublet in patients with recurrent/metastatic non-small cell lung cancer. In the landmark KN189 Trial in which patients received carboplatin or cisplatin in combination with pemetrexed and pembrolizumab, the updated analysis (Gadgeel et al, JCO 2020) reported a median overall survival of 22 months. To explore the translation of this clinical trial-proven efficacy to clinical effectiveness in routine practice, we sought to explore patient outcomes with the combination of pemetrexed, carboplatin, and pembrolizumab as initial therapy for patients with recurrent or metastatic non-squamous non-small cell lung cancer. Methods: At two large, urban, academic medical centers (Hackensack University Medical Center and Memorial Sloan Kettering Cancer Center), we reviewed patient treatment data to identify all patient all patients with recurrent/metastatic non-squamous non-small cell lung cancer who received carboplatin, pemetrexed, and pembrolizumab as initial therapy for recurrent/metastatic NSCLC from June 2017 to December 2020. Patients with EGFR mutations or ALK gene fusions were excluded. From the medical record, we obtained baseline clinical characteristics, patient treatments and duration, as well as overall survival. Results: We identified 523 patients treated with carboplatin/pemetrexed/pembrolizumab, with 399 events occurring during the observation period. Baseline characteristics: 47% women and median age 66 (Interquartile range 59-72). The median overall survival from start of therapy was 11 months (95% confidence interval 9-12 months). Conclusions: In routine clinical practice, initiation of chemotherapy + immune checkpoint inhibitor as first-line therapy for patients with recurrent/metastatic NSCLC led to shorter median overall survival than reported in clinical trials. To further evaluate this finding, we will explore patient baseline characteristics including performance status, comorbidities, organ function as well as explore outcomes of a historical cohort of patients administered carboplatin and pemetrexed.

Accounting for Delayed Entry in Analyses of Overall Survival in Clinico-Genomic Databases

BACKGROUND

Clinico-genomic databases favor inclusion of long-term survivors, leading to potentially biased overall survival (OS) analyses. Risk set adjustments relying on the independent delayed entry assumption may mitigate this bias. We aimed to determine whether this assumption is satisfied in a dataset of patients with advanced non-small cell lung cancer (aNSCLC), and to give guidance for clinico-genomic OS analyses when the assumption is not satisfied.

METHODS

We analyzed the association of timing of next-generation sequencing (NGS) testing with real-world OS (rwOS) in patient data from a United States-based nationwide longitudinal deidentified electronic health records-derived database. Estimates of rwOS using risk set adjustment were compared with estimates computed with respect to all patients, regardless of NGS testing.

RESULTS

The independent delayed entry assumption was not satisfied in this database, and later sequencing had a negative association with the hazard of death after sequencing. In a model adjusted for relevant characteristics, each month delay in sequencing was associated with a 2% increase in the hazard of death. However, until the median survival time, estimates of OS using risk set adjustment are similar to estimates computed for all patients, regardless of NGS testing.

CONCLUSIONS

rwOS analyses in clinico-genomic databases should assess the independent delayed entry assumption. Comparisons versus broader population may be useful to evaluate the rwOS differences between calculations using risk set adjustment and patient cohorts where the bias relates to overrepresentation of long survivors.

IMPACT

This study illustrates practices that can increase the interpretability of findings from OS analyses in clinico-genomic databases.

Effects of dezocine combined with dexmedetomidine on adverse reactions and inflammatory factors in patients undergoing HIPEC after intestinal surgery and its protective effect on the heart in the perioperative period

OBJECTIVE

The aim of this study was to explore the effects of dezocine combined with dexmedetomidine on adverse reactions and inflammatory factors in patients undergoing hyperthermic intraperitoneal chemotherapy (HIPEC) after intestinal surgery and its protective effect on the heart in the perioperative period.

PATIENTS AND METHODS

A total of 80 patients treated with HIPEC after intestinal surgery in our hospital from September 2018 to December 2019 were enrolled as research subjects. All patients were evenly divided into two groups using a random number table. As to analgesia and sedation during treatment, dezocine was injected intramuscularly at 30 min before treatment in the control group. Meanwhile, dezocine combined with dexmedetomidine was given in the same way in the observation group. Adverse reactions and changes in numeric rating scale (NRS) pain score during intervention were compared between the two groups. The changes in the levels of inflammatory and myocardial injury-related factors, and vascular endothelial function and regeneration ability among cardiovascular indicators at 12 h after intervention were compared as well. Additionally, the correlations of left ventricular mass index (LVMI) with the changes in the levels of inflammatory factor high-sensitivity C-reactive protein (hs-CRP), myocardial injury-related factor lactic dehydrogenase (LDH), vascular endothelial function indicator endothelin-1 (ET-1) and cardiovascular regeneration ability index vascular endothelial growth factor (VEGF) were analyzed.

RESULTS

Compared with control group, the total prevalence rate of severe pain, respiratory depression, nausea and vomiting, diarrhea, and muscle rigidity during intervention was significantly reduced in the observation group (p<0.05). NRS pain score at 1, 4, 8 and 12 h after intervention decreased remarkably in the observation group compared with the control group (p<0.05). Meanwhile, the levels of inflammatory factors tumor necrosis factor-α (TNF-α) and hs-CRP, and myocardial injury-related factors LDH and creatine kinase MB (CKMB) as well as ET-1 at 12 h after intervention declined remarkably in observation group compared with control group (p<0.05). However, the levels of nitric oxide (NO), VEGF and basic fibroblast growth factor (bFGF) rose significantly in the observation group (p<0.05). Besides, LVMI was positively correlated with hs-CRP and LDH, whereas was negatively associated with ET-1 and VEGF (p<0.05).

CONCLUSIONS

In HIPEC, dezocine combined with dexmedetomidine used for sedation and analgesia is able to effectively reduce adverse reactions and relieve inflammatory responses in vivo, exerting a cardio-protective effect.

Same-Cell Co-Occurrence of RAS Hotspot and BRAF V600E Mutations in Treatment-Naive Colorectal Cancer

PURPOSE

Mitogen-activated protein kinase pathway-activating mutations occur in the majority of colorectal cancer (CRC) cases and show mutual exclusivity. We identified 47 epidermal growth factor receptor/BRAF inhibitor-naive CRC patients with dual RAS hotspot/BRAF V600E mutations (CRC-DD) from a cohort of 4,561 CRC patients with clinical next-generation sequencing results. We aimed to define the molecular phenotypes of the CRC-DD and to test if the dual RAS hotspot/BRAF V600E mutations coexist within the same cell.

MATERIALS AND METHODS

We developed a single-cell genotyping method with a mutation detection rate of 96.3% and a genotype prediction accuracy of 92.1%. Mutations in the CRC-DD cohort were analyzed for clonality, allelic imbalance, copy number, and overall survival.

RESULTS

Application of single-cell genotyping to four CRC-DD revealed the co-occurrence of both mutations in the following percentages of cells per case: NRAS G13D/KRAS G12C, 95%; KRAS G12D/NRAS G12V, 48%; BRAF V600E/KRAS G12D, 44%; and KRAS G12D/NRAS G13V, 14%, respectively. Allelic imbalance favoring the oncogenic allele was less frequent in CRC-DD (24 of 76, 31.5%, somatic mutations) compared with a curated cohort of CRC with a single-driver mutation (CRC-SD; 119 of 232 mutations, 51.3%; P = .013). Microsatellite instability-high status was enriched in CRC-DD compared with CRC-SD (23% v 11.4%, P = .028). Of the seven CRC-DD cases with multiregional sequencing, five retained both driver mutations throughout all sequenced tumor sites. Both CRC-DD cases with discordant multiregional sequencing were microsatellite instability-high.

CONCLUSION

Our findings indicate that dual-driver mutations occur in a rare subset of CRC, often within the same tumor cells and across multiple tumor sites. Their presence and a lower rate of allelic imbalance may be related to dose-dependent signaling within the mitogen-activated protein kinase pathway.

Comparative genomics of primary prostate cancer and paired metastases: insights from 12 molecular case studies

Primary prostate cancer (PCa) can show marked molecular heterogeneity. However, systematic analyses comparing primary PCa and matched metastases in individual patients are lacking. We aimed to address the molecular aspects of metastatic progression while accounting for the heterogeneity of primary PCa. In this pilot study, we collected 12 radical prostatectomy (RP) specimens from men who subsequently developed metastatic castration‐resistant prostate cancer (mCRPC). We used histomorphology (Gleason grade, focus size, stage) and immunohistochemistry (IHC) (ERG and p53) to identify independent tumors and/or distinct subclones of primary PCa. We then compared molecular profiles of these primary PCa areas to matched metastatic samples using whole‐exome sequencing (WES) and amplicon‐based DNA and RNA sequencing. Based on combined pathology and molecular analysis, seven (58%) RP specimens harbored monoclonal and topographically continuous disease, albeit with some degree of intratumor heterogeneity; four (33%) specimens showed true multifocal disease; and one displayed monoclonal disease with discontinuous topography. Early (truncal) events in primary PCa included SPOP p.F133V (one patient), BRAF p.K601E (one patient), and TMPRSS2:ETS rearrangements (eight patients). Activating AR alterations were seen in nine (75%) mCRPC patients, but not in matched primary PCa. Hotspot TP53 mutations, found in metastases from three patients, were readily present in matched primary disease. Alterations in genes encoding epigenetic modifiers were observed in several patients (either shared between primary foci and metastases or in metastatic samples only). WES‐based phylogenetic reconstruction and/or clonality scores were consistent with the index focus designated by pathology review in six out of nine (67%) cases. The three instances of discordance pertained to monoclonal, topographically continuous tumors, which would have been considered as unique disease in routine practice. Overall, our results emphasize pathologic and molecular heterogeneity of primary PCa, and suggest that comprehensive IHC‐assisted pathology review and genomic analysis are highly concordant in nominating the ‘index’ primary PCa area. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Abstract GS4-08: Comprehensive genomic profiling of patients with breast cancer identifies germline-somatic interactions mediating therapy resistance

Background: Germline genetic alterations are established mediators of breast carcinogenesis, often giving rise to specific forms of genomic instability. BRCA1/2 pathogenic variants (PVs) are emblematic of this phenomenon through their induction of homologous recombination deficiency. While specific patterns of genomic instability may sensitize cancers to therapies such as PARP inhibitors (PARPi) or platinum chemotherapy, their implications for lineage-directed therapies such as endocrine therapy (ET) or CDK4/6 inhibitors (CDK4/6i) are unknown. Herein, we systematically investigated the patterns of association of germline alterations with specific somatic alterations and explored the resulting effect on clinical outcomes. Methods: Patients who underwent germline and matched tumor tissue sequencing utilizing MSK-IMPACT from April 2014 to May 2021 and had available germline analysis results were included. The final analysis presented at SABCS will include 6000 tumors from 5,150 patients, anonymized according to established institutional IRB guidelines to allow for germline analysis on the full cohort. We analyzed genomic data to inform the full spectrum of somatic and germline mutations, ploidy, and allele-specific copy number to determine loss of heterozygosity (LOH). We performed gene- and pathway-level enrichment analyses between somatic variants and germline PVs. Univariable and multivariable Cox proportional hazards models were constructed to assess the association of therapy-specific progression-free survival (PFS) with select germline PVs and germline-somatic interactions. Results: The preliminary analysis includes 2,798 tumors from 2,242 patients with germline and somatic sequencing results. The most frequent germline PVs were: BRCA2 (n = 81), BRCA1 (n = 67), CHEK2 (n = 57), ATM (n = 32), PALB2 (n = 19). The cohort robustly confirmed previously established relationships such as mutual exclusivity of gATM and TP53 variants (OR 0.10, 95% CI 0.032 - 0.33, q = 0.005). Alterations of TP53 were seen in 83% (56/67) of gBRCA1 patients; however, this did not achieve significance when adjusted for receptor subtype (OR 3.90, 95% CI 1.34-11.38, q = 0.15). The size of the cohort allowed discovery of several novel relationships. For instance, gBRCA2 loss was associated with alterations in TGF-B pathway components (OR 3.58, 95% CI 1.70 - 7.56, q = 0.002), potentially relevant to metastatic disease progression. PIK3CA mutations were significantly less prevalent in both gBRCA2 (OR 0.52, 95% CI 0.31 - 0.87, q = 0.063) and gBRCA1 PVs (OR 0.21, 95% CI 0.085 - 0.51, q = 0.014). Our analysis uncovered a strong association between gBRCA2 and somatic RB1 pathogenic alterations (OR 3.58, 95% CI 1.70 - 7.56, q = 0.011), with most variants (80%) encountered in metastatic gBRCA2 tumors. Given the essential role of RB1 in CDK4/6i response, we investigated the effect of BRCA2 status on clinical efficacy of CDK4/6i-ET. Strikingly, gBRCA2 PVs were significantly associated with inferior PFS (HR 2.17, 95% CI 1.46-3.22, p &lt; 0.001) on first line treatment with CDK4/6i-ET. We posited the enrichment of somatic RB1 loss as a potential mechanism of resistance to CDK4/6i. Given the proximity of RB1 to BRCA2 on chromosome 13, we hypothesized that co-LOH of BRCA2 and RB1 predisposes the cancer cells to bi-allelic loss under therapeutic pressure of CDK4/6i. Indeed, 18/26 gBRCA2 (69.2%) tumors evaluable for allele-specific copy number had evidence of RB1 LOH. Discussion: Analysis of germline-somatic interactions yielded novel associations relevant to breast cancer progression and treatment resistance. Among these, we demonstrated BRCA2 carriers to have inferior outcomes to first line CDK4/6i-ET with potential implications for optimal first line therapy and sequencing of CDK4/6i vs PARPi in this patient population.

Citation Format: Anton Safonov, Chai Bandlamudi, Paulino Tallón de Lara, Emanuela Ferraro, Fatemeh Derakhshan, Marie Will, Mark Donoghue, Pier Selenica, Joshua Drago, Ezra Rosen, Carlos dos Anjos, Elaine Walsh, Elizabeth A Comen, Mehnaj Ahmed, Barbara Acevedo, Ahmet Zehir, Michael F Berger, David Solit, Larry Norton, Ronglai Shen, Zsofia Stadler, Simon Powell, Jorge S Reis-Filho, Sarat Chandarlapaty, Mark Robson, Pedram Razavi. Comprehensive genomic profiling of patients with breast cancer identifies germline-somatic interactions mediating therapy resistance [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr GS4-08.

FACETS: Fraction and Allele-Specific Copy Number Estimates from Tumor Sequencing

Clinical sequencing studies routinely involve molecular profiling of patients for mutations and copy number alterations. However, detection of "actionable" aberrations to guide treatment decisions require accurate, tumor purity-, ploidy-, and clonal heterogeneity-adjusted integer copy number calls. In this chapter, we describe the FACETS algorithm, an Allele-Specific Copy Number (ASCN) analysis tool with a broad application to whole-genome, whole-exome, as well as targeted panel sequencing platforms to annotate the genome for the detection of copy number alterations including homozygous/heterozygous deletions, copy-neutral loss-of-heterozygosity (LOH) events, allele-specific gains/amplifications, and cellular fraction profiles.We will describe some methodological details on joint segmentation of total and allele-specific copy number, on the estimation of integer copy number calls adjusting for tumor purity, ploidy, and intratumor heterogeneity, along with comprehensive output and integrated visualization. We also provide a tutorial on the installation, application, and tips to run and interpret FACETS.

Implications of Selection Bias Due to Delayed Study Entry in Clinical Genomic Studies

Importance

Real-world data sets that combine clinical and genomic data may be subject to left truncation (when potential study participants are not included because they have already passed the milestone of interest at the time of study recruitment). The lapse between diagnosis and molecular testing can present analytic challenges and threaten the validity and interpretation of survival analyses.

Observations

Effects of ignoring left truncation when estimating overall survival are illustrated using data from the American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange Biopharma Collaborative (GENIE BPC), and a straightforward risk-set adjustment approach is described. Ignoring left truncation results in overestimation of overall survival: unadjusted median survival estimates from diagnosis among patients with stage IV non-small cell lung cancer or stage IV colorectal cancer were overestimated by more than 1 year.

Conclusions and Relevance

Clinicogenomic data are a valuable resource for evaluation of real-world cancer outcomes and should be analyzed using appropriate methods to maximize their potential. Analysts must become adept at application of appropriate statistical methods to ensure valid, meaningful, and generalizable research findings.

Genome-Derived Classification Signature for Ampullary Adenocarcinoma to Improve Clinical Cancer Care

PURPOSE

The clinical behavior of ampullary adenocarcinoma varies widely. Targeted tumor sequencing may better define biologically distinct subtypes to improve diagnosis and management.

EXPERIMENTAL DESIGN

The hidden-genome algorithm, a multilevel meta-feature regression model, was trained on a prospectively sequenced cohort of 3,411 patients (1,001 pancreatic adenocarcinoma, 165 distal bile-duct adenocarcinoma, 2,245 colorectal adenocarcinoma) and subsequently applied to targeted panel DNA-sequencing data from ampullary adenocarcinomas. Genomic classification (i.e., colorectal vs. pancreatic) was correlated with standard histologic classification [i.e., intestinal (INT) vs. pancreatobiliary (PB)] and clinical outcome.

RESULTS

Colorectal genomic subtype prediction was primarily influenced by mutations in APC and PIK3CA, tumor mutational burden, and DNA mismatch repair (MMR)-deficiency signature. Pancreatic genomic-subtype prediction was dictated by KRAS gene alterations, particularly KRAS G12D, KRAS G12R, and KRAS G12V. Distal bile-duct adenocarcinoma genomic subtype was most influenced by copy-number gains in the MDM2 gene. Despite high (73%) concordance between immunomorphologic subtype and genomic category, there was significant genomic heterogeneity within both histologic subtypes. Genomic scores with higher colorectal probability were associated with greater survival compared with those with a higher pancreatic probability.

CONCLUSIONS

The genomic classifier provides insight into the heterogeneity of ampullary adenocarcinoma and improves stratification, which is dictated by the proportion of colorectal and pancreatic genomic alterations. This approach is reproducible with available molecular testing and obviates subjective histologic interpretation.

Exome-Wide Pan-Cancer Analysis of Germline Variants in 8,719 Individuals Finds Little Evidence of Rare Variant Associations

BACKGROUND

Many cancer types show considerable heritability, and extensive research has been done to identify germline susceptibility variants. Linkage studies have discovered many rare high-risk variants, and genome-wide association studies (GWAS) have discovered many common low-risk variants. However, it is believed that a considerable proportion of the heritability of cancer remains unexplained by known susceptibility variants. The "rare variant hypothesis" proposes that much of the missing heritability lies in rare variants that cannot reliably be detected by linkage analysis or GWAS. Until recently, high sequencing costs have precluded extensive surveys of rare variants, but technological advances have now made it possible to analyze rare variants on a much greater scale.

OBJECTIVES

In this study, we investigated associations between rare variants and 14 cancer types.

METHODS

We ran association tests using whole-exome sequencing data from The Cancer Genome Atlas (TCGA) and validated the findings using data from the Pan-Cancer Analysis of Whole Genomes Consortium (PCAWG).

RESULTS

We identified four significant associations in TCGA, only one of which was replicated in PCAWG (BRCA1 and ovarian cancer).

CONCLUSIONS

Our results provide little evidence in favor of the rare variant hypothesis. Much larger sample sizes may be needed to detect undiscovered rare cancer variants.

The use of a next-generation sequencing-derived machine-learning risk-prediction model (OncoCast-MPM) for malignant pleural mesothelioma: a retrospective study

BACKGROUND

Current risk stratification for patients with malignant pleural mesothelioma based on disease stage and histology is inadequate. For some individuals with early-stage epithelioid tumours, a good prognosis by current guidelines can progress rapidly; for others with advanced sarcomatoid cancers, a poor prognosis can progress slowly. Therefore, we aimed to develop and validate a machine-learning tool-known as OncoCast-MPM-that could create a model for patient prognosis.

METHODS

We did a retrospective study looking at malignant pleural mesothelioma tumours using next-generation sequencing from the Memorial Sloan Kettering Cancer Center-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT). We collected clinical, pathological, and routine next-generation sequencing data from consecutive patients with malignant pleural mesothelioma treated at the Memorial Sloan Kettering Cancer Center (New York, NY, USA), as well as the MSK-IMPACT data. Together, these data comprised the MSK-IMPACT cohort. Using OncoCast-MPM, an open-source, web-accessible, machine-learning risk-prediction model, we integrated available data to create risk scores that stratified patients into low-risk and high-risk groups. Risk stratification of the MSK-IMPACT cohort was then validated using publicly available malignant pleural mesothelioma data from The Cancer Genome Atlas (ie, the TCGA cohort).

FINDINGS

Between Feb 15, 2014, and Jan 28, 2019, we collected MSK-IMPACT data from the tumour tissue of 194 patients in the MSK-IMPACT cohort. The median overall survival was higher in the low-risk group than in the high-risk group as determined by OncoCast-MPM (30·8 months [95% CI 22·7-36·2] vs 13·9 months [10·7-18·0]; hazard ratio [HR] 3·0 [95% CI 2·0-4·5]; p<0·0001). No single factor or gene alteration drove risk differentiation. OncoCast-MPM was validated against the TCGA cohort, which consisted of 74 patients. The median overall survival was higher in the low-risk group than in the high-risk group (23·6 months [95% CI 15·1-28·4] vs 13·6 months [9·8-17·9]; HR 2·3 [95% CI 1·3-3·8]; p=0·0019). Although stage-based risk stratification was unable to differentiate survival among risk groups at 3 years in the MSK-IMPACT cohort (31% for early-stage disease vs 30% for advanced-stage disease; p=0·90), the OncoCast-MPM-derived 3-year survival was significantly higher in the low-risk group than in the high-risk group (40% vs 7%; p=0·0052).

INTERPRETATION

OncoCast-MPM generated accurate, individual patient-level risk assessment scores. After prospective validation with the TCGA cohort, OncoCast-MPM might offer new opportunities for enhanced risk stratification of patients with malignant pleural mesothelioma in clinical trials and drug development.

FUNDING

US National Institutes of Health/National Cancer Institute.

Potential biomarkers and lncRNA-mRNA regulatory networks in invasive growth hormone-secreting pituitary adenomas

PURPOSE

Growth hormone-secreting pituitary adenomas (GH-PAs) are common subtypes of functional PAs. Invasive GH-PAs play a key role in restricting poor outcomes. The transcriptional changes in GH-PAs were evaluated.

METHODS

In this study, the transcriptome analysis of six different GH-PA samples was performed. The functional roles, co-regulatory network, and chromosome location of differentially expressed (DE) genes in invasive GH-PAs were explored.

RESULTS

Bioinformatic analysis revealed 101 DE mRNAs and 70 DE long non-coding RNAs (lncRNAs) between invasive and non-invasive GH-PAs. Functional enrichment analysis showed that epithelial cell differentiation and development pathways were suppressed in invasive GH-PAs, whereas the pathways of olfactory transduction, retinol metabolism, drug metabolism-cytochrome P450, and metabolism of xenobiotics by cytochrome P450 had an active trend. In the protein-protein interaction network, 11 main communities were characterized by cell- adhesion, -motility, and -cycle; transport process; phosphorus and hormone metabolic processes. The SGK1 gene was suggested to play a role in the invasiveness of GH-PAs. Furthermore, the up-regulated genes OR51B6, OR52E4, OR52E8, OR52E6, OR52N2, MAGEA6, MAGEC1, ST8SIA6-AS1, and the down-regulated genes GAD1-AS1 and SPINT1-AS1 were identified in the competing endogenous RNA network. The RT-qPCR results further supported the aberrant expression of those genes. Finally, the enrichment of DE genes in chromosome 11p15 and 12p13 regions were detected.

CONCLUSION

Our findings provide a new perspective for studies evaluating the underlying mechanism of invasive GH-PAs.

LAG-3 expression on peripheral blood cells identifies patients with poorer outcomes after immune checkpoint blockade

Immune checkpoint blocking antibodies are a cornerstone in cancer treatment; however, they benefit only a subset of patients and biomarkers to guide immune checkpoint blockade (ICB) treatment choices are lacking. We designed this study to identify blood-based correlates of clinical outcome in ICB-treated patients. We performed immune profiling of 188 ICB-treated patients with melanoma using multiparametric flow cytometry to characterize immune cells in pretreatment peripheral blood. A supervised statistical learning approach was applied to a discovery cohort to classify phenotypes and determine their association with survival and treatment response. We identified three distinct immune phenotypes (immunotypes), defined in part by the presence of a LAG-3⁺CD8⁺ T cell population. Patients with melanoma with a LAG⁺ immunotype had poorer outcomes after ICB with a median survival of 22.2 months compared to 75.8 months for those with the LAG^- immunotype (P = 0.031). An independent cohort of 94 ICB-treated patients with urothelial carcinoma was used for validation where LAG⁺ immunotype was significantly associated with response (P = 0.007), survival (P < 0.001), and progression-free survival (P = 0.004). Multivariate Cox regression and stratified analyses further showed that the LAG⁺ immunotype was an independent marker of outcome when compared to known clinical prognostic markers and previously described markers for the clinical activity of ICB, PD-L1, and tumor mutation burden. The pretreatment peripheral blood LAG⁺ immunotype detects patients who are less likely to benefit from ICB and suggests a strategy for identifying actionable immune targets for further investigation.

Pan-cancer analysis of pathway-based gene expression pattern at the individual level reveals biomarkers of clinical prognosis

Identifying biomarkers to predict the clinical outcomes of individual patients is a fundamental problem in clinical oncology. Multiple single-gene biomarkers have already been identified and used in clinics. However, multiple oncogenes or tumor-suppressor genes are involved during the process of tumorigenesis. Additionally, the efficacy of single-gene biomarkers is limited by the extensively variable expression levels measured by high-throughput assays. In this study, we hypothesize that in individual tumor samples, the disruption of transcription homeostasis in key pathways or gene sets plays an important role in tumorigenesis and has profound implications for the patient's clinical outcome. We devised a computational method named iPath to identify, at the individual-sample level, which pathways or gene sets significantly deviate from their norms. We conducted a pan-cancer analysis and demonstrated that iPath is capable of identifying highly predictive biomarkers for clinical outcomes, including overall survival, tumor subtypes, and tumor-stage classifications.

Clinical utility of next-generation sequencing-based ctDNA testing for common and novel ALK fusions

OBJECTIVES

Liquid biopsy for plasma circulating tumor DNA (ctDNA) next-generation sequencing (NGS) can detect ALK fusions, though data on clinical utility of this technology in the real world is limited.

MATERIALS AND METHODS

Patients with lung cancer without known oncogenic drivers or who had acquired resistance to therapy (n = 736) underwent prospective plasma ctDNA NGS. A subset of this cohort (n = 497) also had tissue NGS. We evaluated ALK fusion detection, turnaround time (TAT), plasma and tissue concordance, matching to therapy, and treatment response.

RESULTS

ctDNA identified an ALK fusion in 21 patients (3%) with a variety of breakpoints and fusion partners, including EML4, CLTC, and PON1, a novel ALK fusion partner. TAT for ctDNA NGS was shorter than tissue NGS (10 vs. 20 days; p < 0.001). Among ALK fusions identified by ctDNA, 93% (13/14, 95% CI 66%-99%) were concordant with tissue evaluation. Among ALK fusions detected by tissue NGS, 54% (13/24, 95% CI 33%-74%) were concordant with plasma ctDNA. ctDNA matched patients to ALK-directed therapy with subsequent clinical response, including four patients matched on the basis of ctDNA results alone due to inadequate or delayed tissue testing. Serial ctDNA analysis detected MET amplification (n = 2) and ALK G1202R mutation (n = 2) as mechanisms of acquired resistance to ALK-directed therapy.

CONCLUSION

Our findings support a complementary role for ctDNA in detection of ALK fusions and other alterations at diagnosis and therapeutic resistance settings.

Overall survival with circulating tumor DNA-guided therapy in advanced non-small cell lung cancer

9009

Background: The effectiveness of circulating tumor DNA (ctDNA) at matching patients to life prolonging therapy has been studied mostly in small cohorts with limited follow up. The prognostic value of ctDNA alterations, particularly those absent on tissue, is also unclear. To address these questions, we studied survival outcomes in a prospective cohort of patients (N = 1002) with non-small cell lung cancer (NSCLC). Methods: Adults with metastatic or recurrent NSCLC were eligible if they had no known driver mutation or a known driver with progression following targeted therapy. Patients were enrolled at Memorial Sloan Kettering Cancer Center (New York, NY) starting October 21, 2016; analysis here is from a snapshot November 1, 2020. All patients had ctDNA sequenced via the Resolution ctDx Lung platform. To reduce inclusion of incidental germline mutations, we excluded non-functionally significant mutations with an allele frequency 35-65% that were present in gnomAD. Patients could also receive, at their provider’s discretion, tissue sequencing with MSK-IMPACT, which filters germline and clonal hematopoietic (CH) mutations with matched white blood cell sequencing. We performed survival analyses using Cox proportional hazards models from time of diagnosis of advanced disease to death, left truncating at time of study entry. Results: Of 1002 patients, 348 (35%) were treated with targeted therapy; in 181 of these (52%) the targetable alteration was detected in ctDNA. Patients treated with targeted therapy had prolonged survival whether matched by tissue-based methods (HR 0.39, 95%CI 0.30-0.51) or ctDNA (HR 0.47, 95%CI 0.37-0.61). These benefits persisted across multiple subgroups. ctDNA alterations themselves were associated with worse survival (HR 2.2, 95%CI 1.8-2.8), in a manner that scaled with allele fraction and burden. Of 401 patients with time-matched tissue sampling, 62 (15%) had ctDNA alterations that were absent on IMPACT (“unique” ctDNA alterations). Three such patients had unique ctDNA EGFR T790M mutations leading to changes in therapy. However, unique ctDNA alterations were generally associated with worse survival than no ctDNA alterations (HR 2.5, 95%CI 1.7-3.7) and even tissue-matched ctDNA alterations (HR 1.7, 95%CI 1.1-2.4). Of 98 unique ctDNA mutations, 48 (49%) were detectable in tissue at subthreshold levels, 12 (12%) were filtered by IMPACT as CH or germline, and 38 mutations (39%) were absent even at subthreshold levels. ctDNA alteration burden correlated with radiographic disease extent. In multivariate models with radiographic disease extent and other clinical variables, ctDNA alterations were the strongest independent predictor of worse survival. Conclusions: Our results show that ctDNA may match patients to life-prolonging targeted therapy and have prognostic importance. ctDNA may provide data about a patient’s cancer missed by spatially restricted tissue sequencing. Clinical trial information: NCT01775072.

Radiation Treatment, ATM, BRCA1/2, and CHEK2*1100delC Pathogenic Variants and Risk of Contralateral Breast Cancer

Whether radiation therapy (RT) affects contralateral breast cancer (CBC) risk in women with pathogenic germline variants in moderate- to high-penetrance breast cancer-associated genes is unknown. In a population-based case-control study, we examined the association between RT; variants in ATM, BRCA1/2, or CHEK2*1100delC; and CBC risk. We analyzed 708 cases of women with CBC and 1399 controls with unilateral breast cancer, all diagnosed with first invasive breast cancer between 1985 and 2000 and aged younger than 55 years at diagnosis and screened for variants in breast cancer-associated genes. Rate ratios (RR) and 95% confidence intervals (CIs) were estimated using multivariable conditional logistic regression. RT did not modify the association between known pathogenic variants and CBC risk (eg, BRCA1/2 pathogenic variant carriers without RT: RR = 3.52, 95% CI = 1.76 to 7.01; BRCA1/2 pathogenic variant carriers with RT: RR = 4.46, 95% CI = 2.96 to 6.71), suggesting that modifying RT plans for young women with breast cancer is unwarranted. Rare ATM missense variants, not currently identified as pathogenic, were associated with increased risk of RT-associated CBC (carriers of ATM rare missense variants of uncertain significance without RT: RR = 0.38, 95% CI = 0.09 to 1.55; carriers of ATM rare missense variants of uncertain significance with RT: RR = 2.98, 95% CI = 1.31 to 6.80). Further mechanistic studies will aid clinical decision-making related to RT.

Bayesian network-driven clustering analysis with feature selection for high-dimensional multi-modal molecular data

Multi-modal molecular profiling data in bulk tumors or single cells are accumulating at a fast pace. There is a great need for developing statistical and computational methods to reveal molecular structures in complex data types toward biological discoveries. Here, we introduce Nebula, a novel Bayesian integrative clustering analysis for high dimensional multi-modal molecular data to identify directly interpretable clusters and associated biomarkers in a unified and biologically plausible framework. To facilitate computational efficiency, a variational Bayes approach is developed to approximate the joint posterior distribution to achieve model inference in high-dimensional settings. We describe a pan-cancer data analysis of genomic, epigenomic, and transcriptomic alterations in close to 9000 tumor samples across canonical oncogenic signaling pathways, immune and stemness phenotype, with comparisons to state-of-the-art clustering methods. We demonstrate that Nebula has the unique advantage of revealing patterns on the basis of shared pathway alterations, offering biological and clinical insights beyond tumor type and histology in the pan-cancer analysis setting. We also illustrate the utility of Nebula in single cell data for immune cell decomposition in peripheral blood samples.

A Genomic-Pathologic Annotated Risk Model to Predict Recurrence in Early-Stage Lung Adenocarcinoma

Importance

Recommendations for adjuvant therapy after surgical resection of lung adenocarcinoma (LUAD) are based solely on TNM classification but are agnostic to genomic and high-risk clinicopathologic factors. Creation of a prediction model that integrates tumor genomic and clinicopathologic factors may better identify patients at risk for recurrence.

Objective

To identify tumor genomic factors independently associated with recurrence, even in the presence of aggressive, high-risk clinicopathologic variables, in patients with completely resected stages I to III LUAD, and to develop a computational machine-learning prediction model (PRecur) to determine whether the integration of genomic and clinicopathologic features could better predict risk of recurrence, compared with the TNM system.

Design, Setting, and Participants

This prospective cohort study included 426 patients treated from January 1, 2008, to December 31, 2017, at a single large cancer center and selected in consecutive samples. Eligibility criteria included complete surgical resection of stages I to III LUAD, broad-panel next-generation sequencing data with matched clinicopathologic data, and no neoadjuvant therapy. External validation of the PRecur prediction model was performed using The Cancer Genome Atlas (TCGA). Data were analyzed from 2014 to 2018.

Main Outcomes and Measures

The study end point consisted of relapse-free survival (RFS), estimated using the Kaplan-Meier approach. Associations among clinicopathologic factors, genomic alterations, and RFS were established using Cox proportional hazards regression. The PRecur prediction model integrated genomic and clinicopathologic factors using gradient-boosting survival regression for risk group generation and prediction of RFS. A concordance probability estimate (CPE) was used to assess the predictive ability of the PRecur model.

Results

Of the 426 patients included in the analysis (286 women [67%]; median age at surgery, 69 [interquartile range, 62-75] years), 318 (75%) had stage I cancer. Association analysis showed that alterations in SMARCA4 (clinicopathologic-adjusted hazard ratio [HR], 2.44; 95% CI, 1.03-5.77; P = .042) and TP53 (clinicopathologic-adjusted HR, 1.73; 95% CI, 1.09-2.73; P = .02) and the fraction of genome altered (clinicopathologic-adjusted HR, 1.03; 95% CI, 1.10-1.04; P = .005) were independently associated with RFS. The PRecur prediction model outperformed the TNM-based model (CPE, 0.73 vs 0.61; difference, 0.12 [95% CI, 0.05-0.19]; P < .001) for prediction of RFS. To validate the prediction model, PRecur was applied to the TCGA LUAD data set (n = 360), and a clear separation of risk groups was noted (log-rank statistic, 7.5; P = .02), confirming external validation.

Conclusions and Relevance

The findings suggest that integration of tumor genomics and clinicopathologic features improves risk stratification and prediction of recurrence after surgical resection of early-stage LUAD. Improved identification of patients at risk for recurrence could enrich and enhance accrual to adjuvant therapy clinical trials.

Pan-cancer identification of clinically relevant genomic subtypes using outcome-weighted integrative clustering

BACKGROUND

Comprehensive molecular profiling has revealed somatic variations in cancer at genomic, epigenomic, transcriptomic, and proteomic levels. The accumulating data has shown clearly that molecular phenotypes of cancer are complex and influenced by a multitude of factors. Conventional unsupervised clustering applied to a large patient population is inevitably driven by the dominant variation from major factors such as cell-of-origin or histology. Translation of these data into clinical relevance requires more effective extraction of information directly associated with patient outcome.

METHODS

Drawing from ideas in supervised text classification, we developed survClust, an outcome-weighted clustering algorithm for integrative molecular stratification focusing on patient survival. survClust was performed on 18 cancer types across multiple data modalities including somatic mutation, DNA copy number, DNA methylation, and mRNA, miRNA, and protein expression from the Cancer Genome Atlas study to identify novel prognostic subtypes.

RESULTS

Our analysis identified the prognostic role of high tumor mutation burden with concurrently high CD8 T cell immune marker expression and the aggressive clinical behavior associated with CDKN2A deletion across cancer types. Visualization of somatic alterations, at a genome-wide scale (total mutation burden, mutational signature, fraction genome altered) and at the individual gene level, using circomap further revealed indolent versus aggressive subgroups in a pan-cancer setting.

CONCLUSIONS

Our analysis has revealed prognostic molecular subtypes not previously identified by unsupervised clustering. The algorithm and tools we developed have direct utility toward patient stratification based on tumor genomics to inform clinical decision-making. The survClust software tool is available at https://github.com/arorarshi/survClust .

Concurrent Mutations in STK11 and KEAP1 Promote Ferroptosis Protection and SCD1 Dependence in Lung Cancer

Concurrent loss-of-function mutations in STK11 and KEAP1 in lung adenocarcinoma (LUAD) are associated with aggressive tumor growth, resistance to available therapies, and early death. We investigated the effects of coordinate STK11 and KEAP1 loss by comparing co-mutant with single mutant and wild-type isogenic counterparts in multiple LUAD models. STK11/KEAP1 co-mutation results in significantly elevated expression of ferroptosis-protective genes, including SCD and AKR1C1/2/3, and resistance to pharmacologically induced ferroptosis. CRISPR screening further nominates SCD (SCD1) as selectively essential in STK11/KEAP1 co-mutant LUAD. Genetic and pharmacological inhibition of SCD1 confirms the essentiality of this gene and augments the effects of ferroptosis induction by erastin and RSL3. Together these data identify SCD1 as a selective vulnerability and a promising candidate for targeted drug development in STK11/KEAP1 co-mutant LUAD.

MET Exon 14-altered Lung Cancers and MET Inhibitor Resistance

PURPOSE

MET tyrosine kinase inhibitors (TKIs) can achieve modest clinical outcomes in MET exon 14-altered lung cancers, likely secondary to primary resistance. Mechanisms of primary resistance remain poorly characterized and comprehensive proteomic analyses have not previously been performed.

EXPERIMENTAL DESIGN

We performed hybrid capture-based DNA sequencing, targeted RNA sequencing, cell-free DNA sequencing, selected reaction monitoring mass spectrometry (SRM-MS), and immunohistochemistry on patient samples of MET exon 14-altered lung cancers treated with a MET TKI. Associations between overall response rate (ORR), progression-free survival (PFS), and putative genomic alterations and MET protein expression were evaluated.

RESULTS

Seventy-five of 168 MET exon 14-altered lung cancers received a MET TKI. Previously undescribed (zygosity, clonality, whole-genome duplication) and known (copy-number focality, tumor mutational burden, mutation region/type) genomic factors were not associated with ORR/PFS (P > 0.05). In contrast, MET expression was associated with MET TKI benefit. Only cases with detectable MET expression by SRM-MS (N = 15) or immunochemistry (N = 22) responded to MET TKI therapy, and cancers with H-score ≥ 200 had a higher PFS than cancers below this cutoff (10.4 vs. 5.5 months, respectively; HR, 3.87; P = 0.02).

CONCLUSIONS

In MET exon 14-altered cancers treated with a MET TKI, a comprehensive analysis of previously unknown and known genomic factors did not identify a genomic mechanism of primary resistance. Instead, MET expression correlated with benefit, suggesting the potential role of interrogating the proteome in addition to the genome in confirmatory prospective trials.