Actionability classification of variants of unknown significance correlates with functional effect.

Genomically-informed therapy requires consideration of the functional impact of genomic alterations on protein expression and/or function. However, a substantial number of variants are of unknown significance (VUS). The MD Anderson Precision Oncology Decision Support (PODS) team developed an actionability classification scheme that categorizes VUS as either "Unknown" or "Potentially" actionable based on their location within functional domains and/or proximity to known oncogenic variants. We then compared PODS VUS actionability classification with results from a functional genomics platform consisting of mutant generation and cell viability assays. 106 (24%) of 438 VUS in 20 actionable genes were classified as oncogenic in functional assays. Variants categorized by PODS as Potentially actionable (N = 204) were more likely to be oncogenic than those categorized as Unknown (N = 230) (37% vs 13%, p = 4.08e-09). Our results demonstrate that rule-based actionability classification of VUS can identify patients more likely to have actionable variants for consideration with genomically-matched therapy.

A functional genomic approach to actionable gene fusions for precision oncology.

Fusion genes represent a class of attractive therapeutic targets. Thousands of fusion genes have been identified in patients with cancer, but the functional consequences and therapeutic implications of most of these remain largely unknown. Here, we develop a functional genomic approach that consists of efficient fusion reconstruction and sensitive cell viability and drug response assays. Applying this approach, we characterize ~100 fusion genes detected in patient samples of The Cancer Genome Atlas, revealing a notable fraction of low-frequency fusions with activating effects on tumor growth. Focusing on those in the RTK-RAS pathway, we identify a number of activating fusions that can markedly affect sensitivity to relevant drugs. Last, we propose an integrated, level-of-evidence classification system to prioritize gene fusions systematically. Our study reiterates the urgent clinical need to incorporate similar functional genomic approaches to characterize gene fusions, thereby maximizing the utility of gene fusions for precision oncology.

Molecular Landscape of BRAF-Mutant NSCLC Reveals an Association Between Clonality and Driver Mutations and Identifies Targetable Non-V600 Driver Mutations.

INTRODUCTION: Approximately 4% of NSCLC harbor BRAF mutations, and approximately 50% of these are non-V600 mutations. Treatment of tumors harboring non-V600 mutations is challenging because of functional heterogeneity and lack of knowledge regarding their clinical significance and response to targeted agents. METHODS: We conducted an integrative analysis of BRAF non-V600 mutations using genomic profiles of BRAF-mutant NSCLC from the Guardant360 database. BRAF mutations were categorized by clonality and class (1 and 2: RAS-independent; 3: RAS-dependent). Cell viability assays were performed in Ba/F3 models. Drug screens were performed in NSCLC cell lines. RESULTS: A total of 305 unique BRAF mutations were identified. Missense mutations were most common (276, 90%), and 45% were variants of unknown significance. F468S and N581Y were identified as novel activating mutations. Class 1 to 3 mutations had higher clonality than mutations of unknown class (p < 0.01). Three patients were treated with MEK with or without BRAF inhibitors. Patients harboring G469V and D594G mutations did not respond, whereas a patient with the L597R mutation had a durable response. Trametinib with or without dabrafenib, LXH254, and lifirafenib had more potent inhibition of BRAF non-V600-mutant NSCLC cell lines than other MEK, BRAF, and ERK inhibitors, comparable with the inhibition of BRAF V600E cell line. CONCLUSIONS: In BRAF-mutant NSCLC, clonality is higher in known functional mutations and may allow identification of variants of unknown significance that are more likely to be oncogenic drivers. Our data indicate that certain non-V600 mutations are responsive to MEK and BRAF inhibitors. This integration of genomic profiling and drug sensitivity may guide the treatment for BRAF-mutant NSCLC.