Novel Genomic Risk Stratification Model for Primary Gastrointestinal Stromal Tumors (GIST) in the Adjuvant Therapy Era.

PURPOSE: Traditional risk stratification schemes in gastrointestinal stromal tumors (GIST) were defined in the pre-imatinib era and rely solely on clinicopathologic metrics. We hypothesize that genomic-based risk stratification is prognostically relevant in the current era of tyrosine kinase inhibitor (TKI) therapeutics. EXPERIMENTAL DESIGN: Comprehensive mutational and copy-number profiling using MSK-IMPACT was performed. We integrated clinicopathologic and genomic parameters and utilized an elastic-net penalized Cox proportional hazards machine learning model for outcome risk stratification. RESULTS: A 3-tier genomic risk stratification model for recurrence-free survival (RFS) in 152 primary localized gastric and 80 small bowel GISTs was proposed. Gastric GISTs were classified as high risk if chr1p deletion or SDHB loss was present, and intermediate risk if chr14q deletion was present or KIT exon 11 mutation was absent. Small bowel GISTs were classified as high risk if MAX/MGA/MYC, CDKN2A, or RB1 alterations were present, and intermediate risk if chr1p deletion or chr5q amplification was present. Compared with conventional risk stratification, genomic risk stratification both upgrades and downgrades, suggesting that conventional risk stratification may underestimate or overtreat some high-risk and low-risk patients, respectively. Longitudinal sequencing detected most KIT-independent genomic alterations at baseline. Subanalysis in 26 SDH-deficient GISTs revealed that presence of TP53 mutations or chr1q amplifications portends worse RFS and disease-free survival. CONCLUSIONS: We developed a novel, next-generation genomic risk stratification model for primary gastric and small bowel GISTs, complementing traditional clinicopathologic models. Future independent validation of our model in external cohorts is essential.

Genomic mapping of metastatic organotropism in lung adenocarcinoma.

We analyzed 2,532 lung adenocarcinomas (LUAD) to identify the clinicopathological and genomic features associated with metastasis, metastatic burden, organotropism, and metastasis-free survival. Patients who develop metastasis are younger and male, with primary tumors enriched in micropapillary or solid histological subtypes and with a higher mutational burden, chromosomal instability, and fraction of genome doublings. Inactivation of TP53, SMARCA4, and CDKN2A are correlated with a site-specific shorter time to metastasis. The APOBEC mutational signature is more prevalent among metastases, particularly liver lesions. Analyses of matched specimens show that oncogenic and actionable alterations are frequently shared between primary tumors and metastases, whereas copy number alterations of unknown significance are more often private to metastases. Only 4% of metastases harbor therapeutically actionable alterations undetected in their matched primaries. Key clinicopathological and genomic alterations in our cohort were externally validated. In summary, our analysis highlights the complexity of clinicopathological features and tumor genomics in LUAD organotropism.

Genetic Ancestry Correlates with Somatic Differences in a Real-World Clinical Cancer Sequencing Cohort.

Accurate ancestry inference is critical for identifying genetic contributors of cancer disparities among populations. Although methods to infer genetic ancestry have historically relied upon genome-wide markers, the adaptation to targeted clinical sequencing panels presents an opportunity to incorporate ancestry inference into routine diagnostic workflows. We show that global ancestral contributions and admixture of continental populations can be quantitatively inferred using markers captured by the MSK-IMPACT clinical panel. In a pan-cancer cohort of 45,157 patients, we observed differences by ancestry in the frequency of somatic alterations, recapitulating known associations and revealing novel associations. Despite the comparable overall prevalence of driver alterations by ancestry group, the proportion of patients with clinically actionable alterations was lower for African (30%) compared with European (33%) ancestry. Although this result is largely explained by population-specific cancer subtype differences, it reveals an inequity in the degree to which different populations are served by existing precision oncology interventions. SIGNIFICANCE: We performed a comprehensive analysis of ancestral associations with somatic mutations in a real-world pan-cancer cohort, including >5,000 non-European individuals. Using an FDA-authorized tumor sequencing panel and an FDA-recognized oncology knowledge base, we detected differences in the prevalence of clinically actionable alterations, potentially contributing to health care disparities affecting underrepresented populations. This article is highlighted in the In This Issue feature, p. 2483.

Clinical sequencing of soft tissue and bone sarcomas delineates diverse genomic landscapes and potential therapeutic targets.

The genetic, biologic, and clinical heterogeneity of sarcomas poses a challenge for the identification of therapeutic targets, clinical research, and advancing patient care. Because there are > 100 sarcoma subtypes, in-depth genetic studies have focused on one or a few subtypes. Herein, we report a comparative genetic analysis of 2,138 sarcomas representing 45 pathological entities. This cohort is prospectively analyzed using targeted sequencing to characterize subtype-specific somatic alterations in targetable pathways, rates of whole genome doubling, mutational signatures, and subtype-agnostic genomic clusters. The most common alterations are in cell cycle control and TP53, receptor tyrosine kinases/PI3K/RAS, and epigenetic regulators. Subtype-specific associations include TERT amplification in intimal sarcoma and SWI/SNF alterations in uterine adenosarcoma. Tumor mutational burden, while low compared to other cancers, varies between and within subtypes. This resource will improve sarcoma models, motivate studies of subtype-specific alterations, and inform investigations of genetic factors and their correlations with treatment response.

Clinical, genomic, and transcriptomic correlates of response to immune checkpoint blockade-based therapy in a cohort of patients with angiosarcoma treated at a single center.

BACKGROUND: Angiosarcoma is a histologically and molecularly heterogeneous vascular neoplasm with aggressive clinical behavior. Emerging data suggests that immune checkpoint blockade (ICB) is efficacious against some angiosarcomas, particularly cutaneous angiosarcoma of the head and neck (CHN). METHODS: Patients with histologically confirmed angiosarcoma treated with ICB-based therapy at a comprehensive cancer center were retrospectively identified. Clinical characteristics and the results of targeted exome sequencing, transcriptome sequencing, and immunohistochemistry analyses were examined for correlation with clinical benefit. Durable clinical benefit was defined as a progression-free survival (PFS) of ≥16 weeks. RESULTS: For the 35 patients included in the analyses, median PFS and median overall survival (OS) from the time of first ICB-based treatment were 11.9 (95% CI 7.4 to 31.9) and 42.5 (95% CI 19.6 to 114.2) weeks, respectively. Thirteen patients (37%) had PFS ≥16 weeks. Clinical factors associated with longer PFS and longer OS in multivariate analyses were ICB plus other therapy regimens, CHN disease, and white race. Three of 10 patients with CHN angiosarcoma evaluable for tumor mutational burden (TMB) had a TMB ≥10. Five of six patients with CHN angiosarcoma evaluable for mutational signature analysis had a dominant mutational signature associated with ultraviolet (UV) light. No individual gene or genomic pathway was significantly associated with PFS or OS; neither were TMB or UV signature status. Analyses of whole transcriptomes from nine patient tumor samples found upregulation of angiogenesis, inflammatory response, and KRAS signaling pathways, among others, in patients with PFS ≥16 weeks, as well as higher levels of cytotoxic T cells, dendritic cells, and natural killer cells. Patients with PFS <16 weeks had higher numbers of cancer-associated fibroblasts. Immunohistochemistry findings for 12 patients with baseline samples available suggest that neither PD-L1 expression nor presence of tumor-infiltrating lymphocytes at baseline appears necessary for a response to ICB-based therapy. CONCLUSIONS: ICB-based therapy benefits only a subset of angiosarcoma patients. Patients with CHN angiosarcoma are more likely to have PFS ≥16 weeks, a dominant UV mutational signature, and higher TMB than angiosarcomas arising from other primary sites. However, clinical benefit was seen in other angiosarcomas also and was not restricted to tumors with a high TMB, a dominant UV signature, PD-L1 expression, or presence of tumor infiltrating lymphocytes at baseline.

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.

Genomic characterization of metastatic patterns from prospective clinical sequencing of 25,000 patients.

Metastatic progression is the main cause of death in cancer patients, whereas the underlying genomic mechanisms driving metastasis remain largely unknown. Here, we assembled MSK-MET, a pan-cancer cohort of over 25,000 patients with metastatic diseases. By analyzing genomic and clinical data from this cohort, we identified associations between genomic alterations and patterns of metastatic dissemination across 50 tumor types. We found that chromosomal instability is strongly correlated with metastatic burden in some tumor types, including prostate adenocarcinoma, lung adenocarcinoma, and HR+/HER2+ breast ductal carcinoma, but not in others, including colorectal cancer and high-grade serous ovarian cancer, where copy-number alteration patterns may be established early in tumor development. We also identified somatic alterations associated with metastatic burden and specific target organs. Our data offer a valuable resource for the investigation of the biological basis for metastatic spread and highlight the complex role of chromosomal instability in cancer progression.

Computational methods and translational applications for targeted next-generation sequencing platforms.

During the past decade, next-generation sequencing (NGS) technologies have become widely adopted in cancer research and clinical care. Common applications within the clinical setting include patient stratification into relevant molecular subtypes, identification of biomarkers of response and resistance to targeted and systemic therapies, assessment of heritable cancer risk based on known pathogenic variants, and longitudinal monitoring of treatment response. The need for efficient downstream processing and reliable interpretation of sequencing data has led to the development of novel algorithms and computational pipelines, as well as structured knowledge bases that link genomic alterations to currently available drugs and ongoing clinical trials. Cancer centers around the world use different types of targeted solid-tissue and blood based NGS assays to analyze the genomic and transcriptomic profile of patients as part of their routine clinical care. Recently, cross-institutional collaborations have led to the creation of large pooled datasets that can offer valuable insights into the genomics of rare cancers.

Therapeutic Implications of Germline Testing in Patients With Advanced Cancers.

PURPOSE: Tumor mutational profiling is increasingly performed in patients with advanced cancer. We determined the extent to which germline mutation profiling guides therapy selection in patients with advanced cancer. METHODS: Patients with cancer undergoing tumor genomic profiling were prospectively consented for germline cancer predisposition gene analysis (2015-2019). In patients harboring germline likely pathogenic or pathogenic (LP/P) alterations, therapeutic actionability was classified using a precision oncology knowledge base. Patients with metastatic or recurrent cancer receiving germline genotype-directed therapy were determined. RESULTS: Among 11,947 patients across > 50 malignancies, 17% (n = 2,037) harbored a germline LP/P variant. By oncology knowledge base classification, 9% (n = 1042) had an LP/P variant in a gene with therapeutic implications (4% level 1; 4% level 3B; < 1% level 4). BRCA1/2 variants accounted for 42% of therapeutically actionable findings, followed by CHEK2 (13%), ATM (12%), mismatch repair genes (11%), and PALB2 (5%). When limited to the 9,079 patients with metastatic or recurrent cancer, 8% (n = 710) harbored level 1 or 3B genetic findings and 3.2% (n = 289) received germline genotype-directed therapy. Germline genotype-directed therapy was received by 61% and 18% of metastatic cancer patients with level 1 and level 3B findings, respectively, and by 54% of BRCA1/2, 75% of mismatch repair, 43% of PALB2, 35% of RAD51C/D, 24% of BRIP1, and 19% of ATM carriers. Of BRCA1/2 patients receiving a poly(ADP-ribose) polymerase inhibitor, 45% (84 of 188) had tumors other than breast or ovarian cancer, wherein the drug, at time of delivery, was delivered in an investigational setting. CONCLUSION: In a pan-cancer analysis, 8% of patients with advanced cancer harbored a germline variant with therapeutic actionability with 40% of these patients receiving germline genotype-directed treatment. Germline sequence analysis is additive to tumor sequence analysis for therapy selection and should be considered for all patients with advanced cancer.

BlackSheep: A Bioconductor and Bioconda Package for Differential Extreme Value Analysis.

Unbiased assays such as shotgun proteomics and RNA-seq provide high-resolution molecular characterization of tumors. These assays measure molecules with highly varied distributions, making interpretation and hypothesis testing challenging. Samples with the most extreme measurements for a molecule can reveal the most interesting biological insights yet are often excluded from analysis. Furthermore, rare disease subtypes are, by definition, underrepresented in cancer cohorts. To provide a strategy for identifying molecules aberrantly enriched in small sample cohorts, we present BlackSheep, a package for nonparametric description and differential analysis of genome-wide data, available from Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/blacksheepr.html) and Bioconda (https://bioconda.github.io/recipes/blksheep/README.html). BlackSheep is a complementary tool to other differential expression analysis methods, which is particularly useful when analyzing small subgroups in a larger cohort.