Pan-cancer analysis of biallelic inactivation in tumor suppressor genes identifies KEAP1 zygosity as a predictive biomarker in lung cancer.

The canonical model of tumor suppressor gene (TSG)-mediated oncogenesis posits that loss of both alleles is necessary for inactivation. Here, through allele-specific analysis of sequencing data from 48,179 cancer patients, we define the prevalence, selective pressure for, and functional consequences of biallelic inactivation across TSGs. TSGs largely assort into distinct classes associated with either pan-cancer (Class 1) or lineage-specific (Class 2) patterns of selection for biallelic loss, although some TSGs are predominantly monoallelically inactivated (Class 3/4).

Unlocking the power of multi-institutional data: Integrating and harmonizing genomic data across institutions.

Cancer is a complex disease driven by genomic alterations, and tumor sequencing is becoming a mainstay of clinical care for cancer patients. The emergence of multi-institution sequencing data presents a powerful resource for learning real-world evidence to enhance precision oncology. GENIE BPC, led by American Association for Cancer Research, establishes a unique database linking genomic data with clinical information for patients treated at multiple cancer centers.

Improving Accuracy of Somatic Mutation Profiling in Large Epidemiologic Studies: Addressing Cases without Matched Normal Samples.

Ideally, detection of somatic mutations in a tumor is accomplished using a patient-matched sample of normal cells as the benchmark. In this way somatic mutations can be distinguished from rare germline mutations. In large retrospective studies, archival tissue collection can pose challenges in obtaining samples of normal DNA.

DNA Methylation Classes of Stage II and III Primary Melanomas and Their Clinical and Prognostic Significance.

Purpose: Patients with stage II and III cutaneous primary melanoma vary considerably in their risk of melanoma-related death. We explore the ability of methylation profiling to distinguish primary melanoma methylation classes and their associations with clinicopathologic characteristics and survival.

Materials And Methods: InterMEL is a retrospective case-control study that assembled primary cutaneous melanomas from American Joint Committee on Cancer (AJCC) 8th edition stage II and III patients diagnosed between 1998 and 2015 in the United States and Australia.

Tumor suppressor heterozygosity and homologous recombination deficiency mediate resistance to front-line therapy in breast cancer.

The co-occurrence of germline and somatic oncogenic alterations is frequently observed in breast cancer, but their combined biologic and clinical significance has not been evaluated. To assess the role of germline-somatic interactions on outcomes in routine practice, we developed an integrated clinicogenomic pipeline to analyze the genomes of over 4,500 patients with breast cancer. We find that germline (g) -associated tumors are enriched for loss-of-function mutations and manifest poor outcomes on standard-of-care, front-line CDK4/6 inhibitor (CDK4/6i) combinations.

Spatial Immunophenotyping from Whole-Slide Multiplexed Tissue Imaging Using Convolutional Neural Networks.

The multiplexed immunofluorescence (mIF) platform enables biomarker discovery through the simultaneous detection of multiple markers on a single tissue slide, offering detailed insights into intratumor heterogeneity and the tumor-immune microenvironment at spatially resolved single cell resolution. However, current mIF image analyses are labor-intensive, requiring specialized pathology expertise which limits their scalability and clinical application. To address this challenge, we developed CellGate, a deep-learning (DL) computational pipeline that provides streamlined, end-to-end whole-slide mIF image analysis including nuclei detection, cell segmentation, cell classification, and combined immuno-phenotyping across stacked images.

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.

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.

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.

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.

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).

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.

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

Purpose: With the recent approval of the KRAS G12C inhibitor sotorasib for patients with advanced -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 -mutant NSCLC treated with sotorasib, treatment led to a 5.

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.

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.

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

Unlabelled: 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.

Pathway Alterations in Stage II/III Primary Melanoma.

Purpose: Genomic classification of melanoma has thus far focused on the mutational status of , , and . 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.

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.

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%).