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.

Analysis and Visualization of Longitudinal Genomic and Clinical Data from the AACR Project GENIE Biopharma Collaborative in cBioPortal.

International cancer registries make real-world genomic and clinical data available, but their joint analysis remains a challenge. AACR Project GENIE, an international cancer registry collecting data from 19 cancer centers, makes data from >130,000 patients publicly available through the cBioPortal for Cancer Genomics (https://genie.cbioportal.org). For 25,000 patients, additional real-world longitudinal clinical data, including treatment and outcome data, are being collected by the AACR Project GENIE Biopharma Collaborative using the PRISSMM data curation model. Several thousand of these cases are now also available in cBioPortal. We have significantly enhanced the functionalities of cBioPortal to support the visualization and analysis of this rich clinico-genomic linked dataset, as well as datasets generated by other centers and consortia. Examples of these enhancements include (i) visualization of the longitudinal clinical and genomic data at the patient level, including timelines for diagnoses, treatments, and outcomes; (ii) the ability to select samples based on treatment status, facilitating a comparison of molecular and clinical attributes between samples before and after a specific treatment; and (iii) survival analysis estimates based on individual treatment regimens received. Together, these features provide cBioPortal users with a toolkit to interactively investigate complex clinico-genomic data to generate hypotheses and make discoveries about the impact of specific genomic variants on prognosis and therapeutic sensitivities in cancer. SIGNIFICANCE: Enhanced cBioPortal features allow clinicians and researchers to effectively investigate longitudinal clinico-genomic data from patients with cancer, which will improve exploration of data from the AACR Project GENIE Biopharma Collaborative and similar datasets.

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.

Intrahepatic Cholangiocarcinoma with Lymph Node Metastasis: Treatment-Related Outcomes and the Role of Tumor Genomics in Patient Selection.

PURPOSE: Lymph node metastasis (LNM) drastically reduces survival after resection of intrahepatic cholangiocarcinoma (IHC). Optimal treatment is ill defined, and it is unclear whether tumor mutational profiling can support treatment decisions. EXPERIMENTAL DESIGN: Patients with liver-limited IHC with or without LNM treated with resection (N = 237), hepatic arterial infusion chemotherapy (HAIC; N = 196), or systemic chemotherapy alone (SYS; N = 140) at our institution between 2000 and 2018 were included. Genomic sequencing was analyzed to determine whether genetic alterations could stratify outcomes for patients with LNM. RESULTS: For node-negative patients, resection was associated with the longest median overall survival [OS, 59.9 months; 95% confidence interval (CI), 47.2-74.31], followed by HAIC (24.9 months; 95% CI, 20.3-29.6), and SYS (13.7 months; 95% CI, 8.9-15.9; P < 0.001). There was no difference in survival for node-positive patients treated with resection (median OS, 19.7 months; 95% CI, 12.1-27.2) or HAIC (18.1 months; 95% CI, 14.1-26.6; P = 0.560); however, survival in both groups was greater than SYS (11.2 months; 95% CI, 14.1-26.6; P = 0.024). Node-positive patients with at least one high-risk genetic alteration (TP53 mutation, KRAS mutation, CDKN2A/B deletion) had worse survival compared to wild-type patients (median OS, 12.1 months; 95% CI, 5.7-21.5; P = 0.002), regardless of treatment. Conversely, there was no difference in survival for node-positive patients with IDH1/2 mutations compared to wild-type patients. CONCLUSIONS: There was no difference in OS for patients with node-positive IHC treated by resection versus HAIC, and both treatments had better survival than SYS alone. The presence of high-risk genetic alterations provides valuable prognostic information that may help guide treatment.

OncoTree: A Cancer Classification System for Precision Oncology.

PURPOSE: Cancer classification is foundational for patient care and oncology research. Systems such as International Classification of Diseases for Oncology (ICD-O), Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT), and National Cancer Institute Thesaurus (NCIt) provide large sets of cancer classification terminologies but they lack a dynamic modernized cancer classification platform that addresses the fast-evolving needs in clinical reporting of genomic sequencing results and associated oncology research. METHODS: To meet these needs, we have developed OncoTree, an open-source cancer classification system. It is maintained by a cross-institutional committee of oncologists, pathologists, scientists, and engineers, accessible via an open-source Web user interface and an application programming interface. RESULTS: OncoTree currently includes 868 tumor types across 32 organ sites. OncoTree has been adopted as the tumor classification system for American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE), a large genomic and clinical data-sharing consortium, and for clinical molecular testing efforts at Memorial Sloan Kettering Cancer Center and Dana-Farber Cancer Institute. It is also used by precision oncology tools such as OncoKB and cBioPortal for Cancer Genomics. CONCLUSION: OncoTree is a dynamic and flexible community-driven cancer classification platform encompassing rare and common cancers that provides clinically relevant and appropriately granular cancer classification for clinical decision support systems and oncology research.