Identifying Cancer Drivers Using DRIVE: A Feature-Based Machine Learning Model for a Pan-Cancer Assessment of Somatic Missense Mutations.

Sporadic cancer develops from the accrual of somatic mutations. Out of all small-scale somatic aberrations in coding regions, 95% are base substitutions, with 90% being missense mutations. While multiple studies focused on the importance of this mutation type, a machine learning method based on the number of protein-protein interactions (PPIs) has not been fully explored. This study aims to develop an improved computational method for driver identification, validation and evaluation (DRIVE), which is compared to other methods for assessing its performance. DRIVE aims at distinguishing between driver and passenger mutations using a feature-based learning approach comprising two levels of biological classification for a pan-cancer assessment of somatic mutations. Gene-level features include the maximum number of protein-protein interactions, the biological process and the type of post-translational modifications (PTMs) while mutation-level features are based on pathogenicity scores. Multiple supervised classification algorithms were trained on Genomics Evidence Neoplasia Information Exchange (GENIE) project data and then tested on an independent dataset from The Cancer Genome Atlas (TCGA) study. Finally, the most powerful classifier using DRIVE was evaluated on a benchmark dataset, which showed a better overall performance compared to other state-of-the-art methodologies, however, considerable care must be taken due to the reduced size of the dataset. DRIVE outlines the outstanding potential that multiple levels of a feature-based learning model will play in the future of oncology-based precision medicine.

Integrated genomics and functional validation identifies malignant cell specific dependencies in triple negative breast cancer.

Triple negative breast cancers (TNBCs) lack recurrent targetable driver mutations but demonstrate frequent copy number aberrations (CNAs). Here, we describe an integrative genomic and RNAi-based approach that identifies and validates gene addictions in TNBCs. CNAs and gene expression alterations are integrated and genes scored for pre-specified target features revealing 130 candidate genes. We test functional dependence on each of these genes using RNAi in breast cancer and non-malignant cells, validating malignant cell selective dependence upon 37 of 130 genes. Further analysis reveals a cluster of 13 TNBC addiction genes frequently co-upregulated that includes genes regulating cell cycle checkpoints, DNA damage response, and malignant cell selective mitotic genes. We validate the mechanism of addiction to a potential drug target: the mitotic kinesin family member C1 (KIFC1/HSET), essential for successful bipolar division of centrosome-amplified malignant cells and develop a potential selection biomarker to identify patients with tumors exhibiting centrosome amplification.

Anti-Folate Receptor Alpha-Directed Antibody Therapies Restrict the Growth of Triple-negative Breast Cancer.

Purpose: Highly aggressive triple-negative breast cancers (TNBCs) lack validated therapeutic targets and have high risk of metastatic disease. Folate receptor alpha (FRα) is a central mediator of cell growth regulation that could serve as an important target for cancer therapy.Experimental Design: We evaluated FRα expression in breast cancers by genomic (n = 3,414) and IHC (n = 323) analyses and its association with clinical parameters and outcomes. We measured the functional contributions of FRα in TNBC biology by RNA interference and the antitumor functions of an antibody recognizing FRα (MOv18-IgG1), in vitro, and in human TNBC xenograft models.Results: FRα is overexpressed in significant proportions of aggressive basal like/TNBC tumors, and in postneoadjuvant chemotherapy-residual disease associated with a high risk of relapse. Expression is associated with worse overall survival. TNBCs show dysregulated expression of thymidylate synthase, folate hydrolase 1, and methylenetetrahydrofolate reductase, involved in folate metabolism. RNA interference to deplete FRα decreased Src and ERK signaling and resulted in reduction of cell growth. An anti-FRα antibody (MOv18-IgG1) conjugated with a Src inhibitor significantly restricted TNBC xenograft growth. Moreover, MOv18-IgG1 triggered immune-dependent cancer cell death in vitro by human volunteer and breast cancer patient immune cells, and significantly restricted orthotopic and patient-derived xenograft growth.Conclusions: FRα is overexpressed in high-grade TNBC and postchemotherapy residual tumors. It participates in cancer cell signaling and presents a promising target for therapeutic strategies such as ADCs, or passive immunotherapy priming Fc-mediated antitumor immune cell responses. Clin Cancer Res; 24(20); 5098-111. ©2018 AACR.

Profiling lung adenocarcinoma by liquid biopsy: can one size fit all?

BACKGROUND: Cancer is first and foremost a disease of the genome. Specific genetic signatures within a tumour are prognostic of disease outcome, reflect subclonal architecture and intratumour heterogeneity, inform treatment choices and predict the emergence of resistance to targeted therapies. Minimally invasive liquid biopsies can give temporal resolution to a tumour's genetic profile and allow the monitoring of treatment response through levels of circulating tumour DNA (ctDNA). However, the detection of ctDNA in repeated liquid biopsies is currently limited by economic and time constraints associated with targeted sequencing. METHODS: Here we bioinformatically profile the mutational and copy number spectrum of The Cancer Genome Network's lung adenocarcinoma dataset to uncover recurrently mutated genomic loci. RESULTS: We build a panel of 400 hotspot mutations and show that the coverage extends to more than 80% of the dataset at a median depth of 8 mutations per patient. Additionally, we uncover several novel single-nucleotide variants present in more than 5% of patients, often in genes not commonly associated with lung adenocarcinoma. CONCLUSION: With further optimisation, this hotspot panel could allow molecular diagnostics laboratories to build curated primer banks for 'off-the-shelf' monitoring of ctDNA by droplet-based digital PCR or similar techniques, in a time- and cost-effective manner.

PIM1 kinase regulates cell death, tumor growth and chemotherapy response in triple-negative breast cancer.

Triple-negative breast cancers (TNBCs) have poor prognosis and lack targeted therapies. Here we identified increased copy number and expression of the PIM1 proto-oncogene in genomic data sets of patients with TNBC. TNBC cells, but not nonmalignant mammary epithelial cells, were dependent on PIM1 for proliferation and protection from apoptosis. PIM1 knockdown reduced expression of the anti-apoptotic factor BCL2, and dynamic BH3 profiling of apoptotic priming revealed that PIM1 prevents mitochondrial-mediated apoptosis in TNBC cell lines. In TNBC tumors and their cellular models, PIM1 expression was associated with several transcriptional signatures involving the transcription factor MYC, and PIM1 depletion in TNBC cell lines decreased, in a MYC-dependent manner, cell population growth and expression of the MYC target gene MCL1. Treatment with the pan-PIM kinase inhibitor AZD1208 impaired the growth of both cell line and patient-derived xenografts and sensitized them to standard-of-care chemotherapy. This work identifies PIM1 as a malignant-cell-selective target in TNBC and the potential use of PIM1 inhibitors for sensitizing TNBC to chemotherapy-induced apoptotic cell death.