Somatic structural variants drive distinct modes of oncogenesis in melanoma.

The diversity of structural variants (SVs) in melanoma and how they impact oncogenesis are incompletely known. We performed harmonized analysis of SVs across melanoma histological and genomic subtypes, and we identified distinct global properties between subtypes. These included the frequency and size of SVs and SV classes, their relation to chromothripsis events, and the role of topologically associated domain (TAD) boundary altering SVs on cancer-related genes. Following our prior identification of double-stranded break repair deficiency in a subset of triple wild-type cutaneous melanoma, we identified MRE11 and NBN loss-of-function SVs in melanomas with this mutational signature. Experimental knockouts of MRE11 and NBN, followed by olaparib cell viability assays in melanoma cells, indicated that dysregulation of each of these genes may cause sensitivity to PARPi in cutaneous melanomas. Broadly, harmonized analysis of melanoma SVs revealed distinct global genomic properties and molecular drivers, which may have biological and therapeutic impact.

Analysis of evolutionary dynamics and clonal architecture in prostate cancer.

The extent to which clinical and genomic characteristics associate with prostate cancer clonal architecture, tumor evolution, and therapeutic response remains unclear. Here, we reconstructed the clonal architecture and evolutionary trajectories of 845 prostate cancer tumors with harmonized clinical and molecular data. We observed that tumors from patients who self-reported as Black had more linear and monoclonal architectures, despite these men having higher rates of biochemical recurrence. This finding contrasts with prior observations relating polyclonal architecture to adverse clinical outcomes. Additionally, we utilized a novel approach to mutational signature analysis that leverages clonal architecture to uncover additional cases of homologous recombination and mismatch repair deficiency in primary and metastatic tumors and link the origin of mutational signatures to specific subclones. Broadly, prostate cancer clonal architecture analysis reveals novel biological insights that may be immediately clinically actionable and provide multiple opportunities for subsequent investigation. Statement of significance: Tumors from patients who self-reported as Black demonstrate linear and monoclonal evolutionary trajectories yet experience higher rates of biochemical recurrence. In addition, analysis of clonal and subclonal mutational signatures identifies additional tumors with potentially actionable alterations such as deficiencies in mismatch repair and homologous recombination.

Genomic Features of Muscle-invasive Bladder Cancer Arising After Prostate Radiotherapy.

BACKGROUND: Muscle-invasive bladder cancer (MIBC) is a rare but serious event following definitive radiation for prostate cancer. Radiation-associated MIBC (RA-MIBC) can be difficult to manage given the challenges of delivering definitive therapy to a previously irradiated pelvis. The genomic landscape of RA-MIBC and whether it is distinct from non-RA-MIBC are unknown. OBJECTIVE: To define mutational features of RA-MIBC and compare the genomic landscape of RA-MIBC with that of non-RA-MIBC. DESIGN, SETTING, AND PARTICIPANTS: We identified patients from our institution who received radiotherapy for prostate cancer and subsequently developed MIBC. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We performed whole exome sequencing of bladder tumors from RA-MIBC patients. Tumor genetic alterations including mutations, copy number alterations, and mutational signatures were identified and were compared with genetic features of non-RA-MIBC. We used the Kaplan-Meier method to estimate recurrence-free (RFS) and overall (OS) survival. RESULTS AND LIMITATIONS: We identified 19 RA-MIBC patients with available tumor tissue (n = 22 tumors) and clinical data. The median age was 76 yr, and the median time from prostate cancer radiation to RA-MIBC was 12 yr. The median RFS was 14.5 mo and the median OS was 22.0 mo. Compared with a cohort of non-RA-MIBC analyzed in parallel, there was no difference in tumor mutational burden, but RA-MIBCs had a significantly increased number of short insertions and deletions (indels) consistent with previous radiation exposure. We identified mutation signatures characteristic of APOBEC-mediated mutagenesis, aging, and homologous recombination deficiency. The frequency of mutations in many known bladder cancer genes, including TP53, KDM6A, and RB1, as well as copy number alterations such as CDKN2A loss was similar in RA-MIBC and non-RA-MIBC. CONCLUSIONS: We identified unique mutational properties that likely contribute to the distinct biological and clinical features of RA-MIBC. PATIENT SUMMARY: Bladder cancer is a rare but serious diagnosis following radiation for prostate cancer. We characterized genetic features of bladder tumors arising after prostate radiotherapy, and identify similarities with and differences from bladder tumors from patients without previous radiation.

Molecular features of exceptional response to neoadjuvant anti-androgen therapy in high-risk localized prostate cancer.

High-risk localized prostate cancer (HRLPC) is associated with a substantial risk of recurrence and disease mortality. Recent clinical trials have shown that intensifying anti-androgen therapies administered before prostatectomy can induce pathologic complete responses or minimal residual disease, called exceptional response, although the molecular determinants of these clinical outcomes are largely unknown. Here, we perform whole-exome and transcriptome sequencing on pre-treatment multi-regional tumor biopsies from exceptional responders (ERs) and non-responders (NRs, pathologic T3 or lymph node-positive disease) to intensive neoadjuvant anti-androgen therapies. Clonal SPOP mutation and SPOPL copy-number loss are exclusively observed in ERs, while clonal TP53 mutation and PTEN copy-number loss are exclusively observed in NRs. Transcriptional programs involving androgen signaling and TGF-ß signaling are enriched in ERs and NRs, respectively. These findings may guide prospective validation studies of these molecular features in large HRLPC clinical cohorts treated with neoadjuvant anti-androgens to improve patient stratification.

Gene Fusions Create Partner and Collateral Dependencies Essential to Cancer Cell Survival.

Gene fusions frequently result from rearrangements in cancer genomes. In many instances, gene fusions play an important role in oncogenesis; in other instances, they are thought to be passenger events. Although regulatory element rearrangements and copy number alterations resulting from these structural variants are known to lead to transcriptional dysregulation across cancers, the extent to which these events result in functional dependencies with an impact on cancer cell survival is variable. Here we used CRISPR-Cas9 dependency screens to evaluate the fitness impact of 3,277 fusions across 645 cell lines from the Cancer Dependency Map. We found that 35% of cell lines harbored either a fusion partner dependency or a collateral dependency on a gene within the same topologically associating domain as a fusion partner. Fusion-associated dependencies revealed numerous novel oncogenic drivers and clinically translatable alterations. Broadly, fusions can result in partner and collateral dependencies that have biological and clinical relevance across cancer types. SIGNIFICANCE: This study provides insights into how fusions contribute to fitness in different cancer contexts beyond partner-gene activation events, identifying partner and collateral dependencies that may have direct implications for clinical care.

Detection of Pathogenic Variants With Germline Genetic Testing Using Deep Learning vs Standard Methods in Patients With Prostate Cancer and Melanoma.

Importance: Less than 10% of patients with cancer have detectable pathogenic germline alterations, which may be partially due to incomplete pathogenic variant detection. Objective: To evaluate if deep learning approaches identify more germline pathogenic variants in patients with cancer. Design, Setting, and Participants: A cross-sectional study of a standard germline detection method and a deep learning method in 2 convenience cohorts with prostate cancer and melanoma enrolled in the US and Europe between 2010 and 2017. The final date of clinical data collection was December 2017. Exposures: Germline variant detection using standard or deep learning methods. Main Outcomes and Measures: The primary outcomes included pathogenic variant detection performance in 118 cancer-predisposition genes estimated as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The secondary outcomes were pathogenic variant detection performance in 59 genes deemed actionable by the American College of Medical Genetics and Genomics (ACMG) and 5197 clinically relevant mendelian genes. True sensitivity and true specificity could not be calculated due to lack of a criterion reference standard, but were estimated as the proportion of true-positive variants and true-negative variants, respectively, identified by each method in a reference variant set that consisted of all variants judged to be valid from either approach. Results: The prostate cancer cohort included 1072 men (mean [SD] age at diagnosis, 63.7 [7.9] years; 857 [79.9%] with European ancestry) and the melanoma cohort included 1295 patients (mean [SD] age at diagnosis, 59.8 [15.6] years; 488 [37.7%] women; 1060 [81.9%] with European ancestry). The deep learning method identified more patients with pathogenic variants in cancer-predisposition genes than the standard method (prostate cancer: 198 vs 182; melanoma: 93 vs 74); sensitivity (prostate cancer: 94.7% vs 87.1% [difference, 7.6%; 95% CI, 2.2% to 13.1%]; melanoma: 74.4% vs 59.2% [difference, 15.2%; 95% CI, 3.7% to 26.7%]), specificity (prostate cancer: 64.0% vs 36.0% [difference, 28.0%; 95% CI, 1.4% to 54.6%]; melanoma: 63.4% vs 36.6% [difference, 26.8%; 95% CI, 17.6% to 35.9%]), PPV (prostate cancer: 95.7% vs 91.9% [difference, 3.8%; 95% CI, -1.0% to 8.4%]; melanoma: 54.4% vs 35.4% [difference, 19.0%; 95% CI, 9.1% to 28.9%]), and NPV (prostate cancer: 59.3% vs 25.0% [difference, 34.3%; 95% CI, 10.9% to 57.6%]; melanoma: 80.8% vs 60.5% [difference, 20.3%; 95% CI, 10.0% to 30.7%]). For the ACMG genes, the sensitivity of the 2 methods was not significantly different in the prostate cancer cohort (94.9% vs 90.6% [difference, 4.3%; 95% CI, -2.3% to 10.9%]), but the deep learning method had a higher sensitivity in the melanoma cohort (71.6% vs 53.7% [difference, 17.9%; 95% CI, 1.82% to 34.0%]). The deep learning method had higher sensitivity in the mendelian genes (prostate cancer: 99.7% vs 95.1% [difference, 4.6%; 95% CI, 3.0% to 6.3%]; melanoma: 91.7% vs 86.2% [difference, 5.5%; 95% CI, 2.2% to 8.8%]). Conclusions and Relevance: Among a convenience sample of 2 independent cohorts of patients with prostate cancer and melanoma, germline genetic testing using deep learning, compared with the current standard genetic testing method, was associated with higher sensitivity and specificity for detection of pathogenic variants. Further research is needed to understand the relevance of these findings with regard to clinical outcomes.

Integrated molecular drivers coordinate biological and clinical states in melanoma.

We performed harmonized molecular and clinical analysis on 1,048 melanomas and discovered markedly different global genomic properties among subtypes (BRAF, (N)RAS, NF1, triple wild-type (TWT)), subtype-specific preferences for secondary driver genes and active mutational processes previously unreported in melanoma. Secondary driver genes significantly enriched in specific subtypes reflected preferential dysregulation of additional pathways, such as induction of transforming growth factor-ß signaling in BRAF melanomas and inactivation of the SWI/SNF complex in (N)RAS melanomas, and select co-mutation patterns coordinated selective response to immune checkpoint blockade. We also defined the mutational landscape of TWT melanomas and revealed enrichment of DNA-repair-defect signatures in this subtype, which were associated with transcriptional downregulation of key DNA-repair genes, and may revive previously discarded or currently unconsidered therapeutic modalities for genomically stratified melanoma patient subsets. Broadly, harmonized meta-analysis of melanoma whole exomes revealed distinct molecular drivers that may point to multiple opportunities for biological and therapeutic investigation.

Next-Generation Sequencing and the Clinical Oncology Workflow: Data Challenges, Proposed Solutions, and a Call to Action.

PURPOSE: Next-generation sequencing (NGS) of tumor and germline DNA is foundational for precision oncology, with rapidly expanding diagnostic, prognostic, and therapeutic implications. Although few question the importance of NGS in modern oncology care, the process of gathering primary molecular data, integrating it into electronic health records, and optimally using it as part of a clinical workflow remains far from seamless. Numerous challenges persist around data standards and interoperability, and clinicians frequently face difficulties in managing the growing amount of genomic knowledge required to care for patients and keep up to date. METHODS: This review provides a descriptive analysis of genomic data workflows for NGS data in clinical oncology and issues that arise from the inconsistent use of standards for sharing data across systems. Potential solutions are described. RESULTS: NGS technology, especially for somatic genomics, is well established and widely used in routine patient care, quality measurement, and research. Available genomic knowledge bases play an evolving role in patient management but lack harmonization with one another. Questions about their provenance and timeliness of updating remain. Potentially useful standards for sharing genomic data, such as HL7 FHIR and mCODE, remain primarily in the research and/or development stage. Nonetheless, their impact will likely be seen as uptake increases across care settings and laboratories. The specific use case of ASCO CancerLinQ, as a clinicogenomic database, is discussed. CONCLUSION: Because the electronic health records of today seem ill suited for managing genomic data, other solutions are required, including universal data standards and applications that use application programming interfaces, along with a commitment on the part of sequencing laboratories to consistently provide structured genomic data for clinical use.

Genomics of response to immune checkpoint therapies for cancer: implications for precision medicine.

Immune checkpoint blockade (ICB) therapies, which potentiate the body's natural immune response against tumor cells, have shown immense promise in the treatment of various cancers. Currently, tumor mutational burden (TMB) and programmed death ligand 1 (PD-L1) expression are the primary biomarkers evaluated for clinical management of cancer patients across histologies. However, the wide range of responses has demonstrated that the specific molecular and genetic characteristics of each patient's tumor and immune system must be considered to maximize treatment efficacy. Here, we review the various biological pathways and emerging biomarkers implicated in response to PD-(L)1 and cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) therapies, including oncogenic signaling pathways, human leukocyte antigen (HLA) variability, mutation and neoantigen burden, microbiome composition, endogenous retroviruses (ERV), and deficiencies in chromatin remodeling and DNA damage repair (DDR) machinery. We also discuss several mechanisms that have been observed to confer resistance to ICB, such as loss of phosphatase and tensin homolog (PTEN), loss of major histocompatibility complex (MHC) I/II expression, and activation of the indoleamine 2,3-dioxygenase 1 (IDO1) and transforming growth factor beta (TGFß) pathways. Clinical trials testing the combination of PD-(L)1 or CTLA-4 blockade with molecular mediators of these pathways are becoming more common and may hold promise for improving treatment efficacy and response. Ultimately, some of the genes and molecular mechanisms highlighted in this review may serve as novel biological targets or therapeutic vulnerabilities to improve clinical outcomes in patients.

UpSetR: an R package for the visualization of intersecting sets and their properties.

MOTIVATION: Venn and Euler diagrams are a popular yet inadequate solution for quantitative visualization of set intersections. A scalable alternative to Venn and Euler diagrams for visualizing intersecting sets and their properties is needed. RESULTS: We developed UpSetR, an open source R package that employs a scalable matrix-based visualization to show intersections of sets, their size, and other properties. AVAILABILITY AND IMPLEMENTATION: UpSetR is available at https://github.com/hms-dbmi/UpSetR/ and released under the MIT License. A Shiny app is available at https://gehlenborglab.shinyapps.io/upsetr/ . CONTACT: nils@hms.harvard.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.