Mahmoud Ghandi

ARID1B is a specific vulnerability in ARID1A-mutant cancers

Recent studies have revealed that ARID1A, encoding AT-rich interactive domain 1A (SWI-like), is frequently mutated across a variety of human cancers and also has bona fide tumor suppressor properties. Consequently, identification of vulnerabilities conferred by ARID1A mutation would have major relevance for human cancer. Here, using a broad screening approach, we identify ARID1B, an ARID1A homolog whose gene product is mutually exclusive with ARID1A in SWI/SNF complexes, as the number 1 gene preferentially required for the survival of ARID1A-mutant cancer cell lines. We show that loss of ARID1B in ARID1A-deficient backgrounds destabilizes SWI/SNF and impairs proliferation in both cancer cells and primary cells. We also find that ARID1A and ARID1B are frequently co-mutated in cancer but that ARID1A-deficient cancers retain at least one functional ARID1B allele. These results suggest that loss of ARID1A and ARID1B alleles cooperatively promotes cancer formation but also results in a unique functional dependence. The results further identify ARID1B as a potential therapeutic target for ARID1A-mutant cancers.

Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features

Abstract Oligomers of length k, or k-mers, are convenient and widely used features for modeling the properties and functions of DNA and protein sequences. However, k-mers suffer from the inherent limitation that if the parameter k is increased to resolve longer features, the probability of observing any specific k-mer becomes very small, and k-mer counts approach a binary variable, with most k-mers absent and a few present once. Thus, any statistical learning approach using k-mers as features becomes susceptible to noisy training set k-mer frequencies once k becomes large. To address this problem, we introduce alternative feature sets using gapped k-mers, a new classifier, gkm-SVM, and a general method for robust estimation of k-mer frequencies. To make the method applicable to large-scale genome wide applications, we develop an efficient tree data structure for computing the kernel matrix. We show that compared to our original kmer-SVM and alternative approaches, our gkm-SVM predicts functional genomic regulatory elements and tissue specific enhancers with significantly improved accuracy, increasing the precision by up to a factor of two. We then show that gkm-SVM consistently outperforms kmer-SVM on human ENCODE ChIP-seq datasets, and further demonstrate the general utility of our method using a Naïve-Bayes classifier. Although developed for regulatory sequence analysis, these methods can be applied to any sequence classification problem.

Pharmacogenomic agreement between two cancer cell line data sets

Large cancer cell line collections broadly capture the genomic diversity of human cancers and provide valuable insight into anti-cancer drug response. Here we show substantial agreement and biological consilience between drug sensitivity measurements and their associated genomic predictors from two publicly available large-scale pharmacogenomics resources: The Cancer Cell Line Encyclopedia and the Genomics of Drug Sensitivity in Cancer databases.

ERK Mutations Confer Resistance to Mitogen-Activated Protein Kinase Pathway Inhibitors

The use of targeted therapeutics directed against BRAF(V600)-mutant metastatic melanoma improves progression-free survival in many patients; however, acquired drug resistance remains a major medical challenge. By far, the most common clinical resistance mechanism involves reactivation of the MAPK (RAF/MEK/ERK) pathway by a variety of mechanisms. Thus, targeting ERK itself has emerged as an attractive therapeutic concept, and several ERK inhibitors have entered clinical trials. We sought to preemptively determine mutations in ERK1/2 that confer resistance to either ERK inhibitors or combined RAF/MEK inhibition in BRAF(V600)-mutant melanoma. Using a random mutagenesis screen, we identified multiple point mutations in ERK1 (MAPK3) and ERK2 (MAPK1) that could confer resistance to ERK or RAF/MEK inhibitors. ERK inhibitor-resistant alleles were sensitive to RAF/MEK inhibitors and vice versa, suggesting that the future development of alternating RAF/MEK and ERK inhibitor regimens might help circumvent resistance to these agents.

Characterizing genomic alterations in cancer by complementary functional associations

Systematic efforts to sequence the cancer genome have identified large numbers of mutations and copy number alterations in human cancers. However, elucidating the functional consequences of these variants, and their interactions to drive or maintain oncogenic states, remains a challenge in cancer research. We developed REVEALER, a computational method that identifies combinations of mutually exclusive genomic alterations correlated with functional phenotypes, such as the activation or gene dependency of oncogenic pathways or sensitivity to a drug treatment. We used REVEALER to uncover complementary genomic alterations associated with the transcriptional activation of β-catenin and NRF2, MEK-inhibitor sensitivity, and KRAS dependency. REVEALER successfully identified both known and new associations, demonstrating the power of combining functional profiles with extensive characterization of genomic alterations in cancer genomes.

Systematic functional characterization of resistance to PI3K inhibition in breast cancer

PIK3CA (which encodes the PI3K alpha isoform) is the most frequently mutated oncogene in breast cancer. Small-molecule PI3K inhibitors have shown promise in clinical trials; however, intrinsic and acquired resistance limits their utility. We used a systematic gain-of-function approach to identify genes whose upregulation confers resistance to the PI3K inhibitor BYL719 in breast cancer cells. Among the validated resistance genes, Proviral Insertion site in Murine leukemia virus (PIM) kinases conferred resistance by maintaining downstream PI3K effector activation in an AKT-independent manner. Concurrent pharmacologic inhibition of PIM and PI3K overcame this resistance mechanism. We also observed increased PIM expression and activity in a subset of breast cancer biopsies with clinical resistance to PI3K inhibitors. PIM1 overexpression was mutually exclusive with PIK3CA mutation in treatment-naïve breast cancers, suggesting downstream functional redundancy. Together, these results offer new insights into resistance to PI3K inhibitors and support clinical studies of combined PIM/PI3K inhibition in a subset of PIK3CA-mutant cancers. SIGNIFICANCE PIM kinase overexpression confers resistance to small-molecule PI3K inhibitors. Combined inhibition of PIM and PI3K may therefore be warranted in a subset of breast cancers. Cancer Discov; 6(10); 1134-47. ©2016 AACR.This article is highlighted in the In This Issue feature, p. 1069.

gkmSVM: an R package for gapped-kmer SVM

UNLABELLED We present a new R package for training gapped-kmer SVM classifiers for DNA and protein sequences. We describe an improved algorithm for kernel matrix calculation that speeds run time by about 2 to 5-fold over our original gkmSVM algorithm. This package supports several sequence kernels, including: gkmSVM, kmer-SVM, mismatch kernel and wildcard kernel. AVAILABILITY AND IMPLEMENTATION gkmSVM package is freely available through the Comprehensive R Archive Network (CRAN), for Linux, Mac OS and Windows platforms. The C ++ implementation is available at www.beerlab.org/gkmsvm CONTACT mghandi@gmail.com or mbeer@jhu.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Robust k-mer frequency estimation using gapped k-mers

Oligomers of fixed length,

Suppression of 19S proteasome subunits marks emergence of an altered cell state in diverse cancers

k