Meenakshi Anurag

Crowd Sourcing a New Paradigm for Interactome Driven Drug Target Identification in Mycobacterium tuberculosis

A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative ‘Connect to Decode’ (C2D) to generate the first and largest manually curated interactome of Mtb termed ‘interactome pathway’ (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach.

A web server for predicting inhibitors against bacterial target GlmU protein.

BackgroundThe emergence of drug resistant tuberculosis poses a serious concern globally and researchers are in rigorous search for new drugs to fight against these dreadful bacteria. Recently, the bacterial GlmU protein, involved in peptidoglycan, lipopolysaccharide and techoic acid synthesis, has been identified as an important drug target. A unique C-terminal disordered tail, essential for survival and the absence of gene in host makes GlmU a suitable target for inhibitor design.ResultsThis study describes the models developed for predicting inhibitory activity (IC50) of chemical compounds against GlmU protein using QSAR and docking techniques. These models were trained on 84 diverse compounds (GlmU inhibitors) taken from PubChem BioAssay (AID 1376). These inhibitors were docked in the active site of the C-terminal domain of GlmU protein (2OI6) using the AutoDock. A QSAR model was developed using docking energies as descriptors and achieved maximum correlation of 0.35/0.12 (r/r2) between actual and predicted pIC50. Secondly, QSAR models were developed using molecular descriptors calculated using various software packages and achieved maximum correlation of 0.77/0.60 (r/r2). Finally, hybrid models were developed using various types of descriptors and achieved high correlation of 0.83/0.70 (r/r2) between predicted and actual pIC50. It was observed that some molecular descriptors used in this study had high correlation with pIC50. We screened chemical libraries using models developed in this study and predicted 40 potential GlmU inhibitors. These inhibitors could be used to develop drugs against Mycobacterium tuberculosis.ConclusionThese results demonstrate that docking energies can be used as descriptors for developing QSAR models. The current work suggests that docking energies based descriptors could be used along with commonly used molecular descriptors for predicting inhibitory activity (IC50) of molecules against GlmU. Based on this study an open source platform, http://crdd.osdd.net/raghava/gdoq, has been developed for predicting inhibitors GlmU.

Loss of MutL Disrupts CHK2-Dependent Cell-Cycle Control through CDK4/6 to Promote Intrinsic Endocrine Therapy Resistance in Primary Breast Cancer

Significant endocrine therapy-resistant tumor proliferation is present in ≥20% of estrogen receptor-positive (ER+) primary breast cancers and is associated with disease recurrence and death. Here, we uncover a link between intrinsic endocrine therapy resistance and dysregulation of the MutL mismatch repair (MMR) complex (MLH1/3, PMS1/2), and demonstrate a direct role for MutL complex loss in resistance to all classes of endocrine therapy. We find that MutL deficiency in ER+ breast cancer abrogates CHK2-mediated inhibition of CDK4, a prerequisite for endocrine therapy responsiveness. Consequently, CDK4/6 inhibitors (CDK4/6i) remain effective in MutL-defective ER+ breast cancer cells. These observations are supported by data from a clinical trial where a CDK4/6i was found to strongly inhibit aromatase inhibitor-resistant proliferation of MutL-defective tumors. These data suggest that diagnostic markers of MutL deficiency could be used to direct adjuvant CDK4/6i to a population of patients with breast cancer who exhibit marked resistance to the current standard of care.Significance: MutL deficiency in a subset of ER+ primary tumors explains why CDK4/6 inhibition is effective against some de novo endocrine therapy-resistant tumors. Therefore, markers of MutL dysregulation could guide CDK4/6 inhibitor use in the adjuvant setting, where the risk benefit ratio for untargeted therapeutic intervention is narrow. Cancer Discov; 7(10); 1168-83. ©2017 AACR.This article is highlighted in the In This Issue feature, p. 1047.

Amplification of TLK2 Induces Genomic Instability via Impairing the G2/M Checkpoint.

Managing aggressive breast cancers with enhanced chromosomal instability (CIN) is a significant challenge in clinics. Previously, we described that a cell cycle–associated kinase called Tousled-like kinase 2 (TLK2) is frequently deregulated by genomic amplifications in aggressive estrogen receptor–positive (ER+) breast cancers. In this study, it was discovered that TLK2 amplification and overexpression mechanistically impair Chk1/2-induced DNA damage checkpoint signaling, leading to a G2–M checkpoint defect, delayed DNA repair process, and increased CIN. In addition, TLK2 overexpression modestly sensitizes breast cancer cells to DNA-damaging agents, such as irradiation or doxorubicin. To our knowledge, this is the first report linking TLK2 function to CIN, in contrast to the function of its paralog TLK1 as a guardian of genome stability. This finding yields new insight into the deregulated DNA damage pathway and increased genomic instability in aggressive ER+ breast cancers. Implications: Targeting TLK2 presents an attractive therapeutic strategy for the TLK2-amplified breast cancers that possess enhanced genomic instability and aggressiveness. Mol Cancer Res; 14(10); 920–7. ©2016 AACR.

The prognostic effects of somatic mutations in ER-positive breast cancer

Here we report targeted sequencing of 83 genes using DNA from primary breast cancer samples from 625 postmenopausal (UBC-TAM series) and 328 premenopausal (MA12 trial) hormone receptor-positive (HR+) patients to determine interactions between somatic mutation and prognosis. Independent validation of prognostic interactions was achieved using data from the METABRIC study. Previously established associations between MAP3K1 and PIK3CA mutations with luminal A status/favorable prognosis and TP53 mutations with Luminal B/non-luminal tumors/poor prognosis were observed, validating the methodological approach. In UBC-TAM, NF1 frame-shift nonsense (FS/NS) mutations were also a poor outcome driver that was validated in METABRIC. For MA12, poor outcome associated with PIK3R1 mutation was also reproducible. DDR1 mutations were strongly associated with poor prognosis in UBC-TAM despite stringent false discovery correction (q = 0.0003). In conclusion, uncommon recurrent somatic mutations should be further explored to create a more complete explanation of the highly variable outcomes that typifies ER+ breast cancer.Unravelling the link between somatic mutation and prognosis in estrogen positive (ER+) breast cancer requires the use of long-term follow-up data. Here, combining archival formalin-fixed paraffin embedded tissue and targeted sequencing in three cohorts of ER+ breast cancer, the authors find associations with clinical outcome for NF1 frame-shift nonsense mutations, PIK3R1 mutation, and DDR1 mutations.

Comprehensive Profiling of DNA Repair Defects in Breast Cancer Identifies a Novel Class of Endocrine Therapy Resistance Drivers

Purpose: This study was undertaken to conduct a comprehensive investigation of the role of DNA damage repair (DDR) defects in poor outcome ER+ disease. Experimental Design: Expression and mutational status of DDR genes in ER+ breast tumors were correlated with proliferative response in neoadjuvant aromatase inhibitor therapy trials (discovery dataset), with outcomes in METABRIC, TCGA, and Loi datasets (validation datasets), and in patient-derived xenografts. A causal relationship between candidate DDR genes and endocrine treatment response, and the underlying mechanism, was then tested in ER+ breast cancer cell lines. Results: Correlations between loss of expression of three genes: CETN2 (P < 0.001) and ERCC1 (P = 0.01) from the nucleotide excision repair (NER) and NEIL2 (P = 0.04) from the base excision repair (BER) pathways were associated with endocrine treatment resistance in discovery dataset, and subsequently validated in independent patient cohorts. Complementary mutation analysis supported associations between mutations in NER and BER genes and reduced endocrine treatment response. A causal role for CETN2, NEIL2, and ERCC1 loss in intrinsic endocrine resistance was experimentally validated in ER+ breast cancer cell lines, and in ER+ patient-derived xenograft models. Loss of CETN2, NEIL2, or ERCC1 induced endocrine treatment resistance by dysregulating G1–S transition, and therefore, increased sensitivity to CDK4/6 inhibitors. A combined DDR signature score was developed that predicted poor outcome in multiple patient cohorts. Conclusions: This report identifies DDR defects as a new class of endocrine treatment resistance drivers and indicates new avenues for predicting efficacy of CDK4/6 inhibition in the adjuvant treatment setting. Clin Cancer Res; 24(19); 4887–99. ©2018 AACR.

Functional Annotation of ESR1 Gene Fusions in Estrogen Receptor-Positive Breast Cancer.

SUMMARY RNA sequencing (RNA-seq) detects estrogen receptor alpha gene (ESR1) fusion transcripts in estrogen receptor-positive (ER+) breast cancer, but their role in disease pathogenesis remains unclear. We examined multiple ESR1 fusions and found that two, both identified in advanced endocrine treatment-resistant disease, encoded stable and functional fusion proteins. In both examples, ESR1-e6>YAP1 and ESR1-e6>PCDH11X, ESR1 exons 1–6 were fused in frame to C-terminal sequences from the partner gene. Functional properties include estrogen-independent growth, constitutive expression of ER target genes, and anti-estrogen resistance. Both fusions activate a metastasis-associated transcriptional program, induce cellular motility, and promote the development of lung metastasis. ESR1-e6>YAP1- and ESR1-e6>PCDH11X-induced growth remained sensitive to a CDK4/6 inhibitor, and a patient-derived xenograft (PDX) naturally expressing the ESR1-e6>YAP1 fusion was also responsive. Transcriptionally active ESR1 fusions therefore trigger both endocrine therapy resistance and metastatic progression, explaining the association with fatal disease progression, although CDK4/6 inhibitor treatment is predicted to be effective.

Abstract 1814: NF1 as an estrogen receptor-α co-repressor in breast cancer

NF1 has been best known as a GAP (GTPase Activating Protein) that inactivates Ras. However, we are now finding evidence that it also functions as an ER co-repressor, whose loss leads to endocrine therapy resistance. Sequencing tumor DNA from >600 ER+ breast cancers treated by tamoxifen adjuvant monotherapy, we found that frameshift (FS) and nonsense (NS) NF1 mutations, which can create an NF1-null state, strongly correlate with relapse risk (HR=2.6, submitted). Surprisingly, no recurrent missense NF1 mutations inactivating GAP activity were found in our cohort, and such mutations are rare in primary cancers in general. We thus posulated that complete loss of NF1 protein (e.g., caused by NS/FS mutations), but not GAP inactivation alone, is required to drive endocrine therapy resistance. Here we demonstrate that NF1 loss (by gene silencing) in ER+ breast cancer cells greatly enhances ligand-dependent ER transcriptional activity in vitro and in vivo, causing estradiol (E2) hypersensitivity and tamoxifen agonism. Mechanistically we show that NF1 can bind directly to ER, an interaction enhanced by tamoxifen but not by E2. Binding is mediated by leucine/isoleucine-rich motifs in NF1, analogous to other ER co-repressors. Mutations in these motifs (some of which are targeted by somatic mutation in cancer) inhibit ER binding and transcriptional activity without impacting GAP activity; conversely, inactivating GAP activity does not impact ER binding and repression. To validate NF1 as an ER co-repressor, we examined proteomic data from >100 breast cancer patients in the CPTAC data base and found that proteins whose levels are positively correlated with NF1 are highly enriched with factors known to bind nuclear receptors; by contrast, levels of another GAP, p120, which lacks ER binding sites, are negatively correlated with these molecules. Importantly, preclinical treatment studies indicate that while NF1-deficient ER+ breast cancer should not be treated by tamoxifen or aromatase inhibitors, fulvestrant, which degrades ER, remains effective. However, fulvestrant monotherapy can activate the Ras-MAP pathway, which may promote cell survival and acquired fulvestrant resistance unless combined with dabrafinib and trametinib to inhibit Raf and MEK —a clinical trial for this combination is in development. Our data suggest that NF1 is a dual negative regulator at the intersection of two potent oncogenic signaling pathways, Ras and ER. Combination therapy targeting both the ER and the Ras-Raf pathways should be investigated for NF1-deficient cancers driven by ER. Citation Format: Eric C. Chang, zeyi Zheng, Meenakshi Anurag, Jin Gao, Burcu Cakar, Xinhui Du, Jing Li, Philip Lavere, Jonathan T. Lei, Purba Singh, Sinem Seker, Wei Song, Jianheng Peng, Tiffany Nguyen, Doug Chan, Xi Chen, Kimberly C. Banks, Richarad B. Lanman, Maryam Shafaee, Susan Hilsenbeck, Charles Foulds, Matthew J. Ellis. NF1 as an estrogen receptor-α co-repressor in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1814.

Abstract 4538: UniConSig: A new algorithm for genome wide quantification of gene functions and disease associations

One of the greatest hurdles for cancer biologists is to identify the cancer-causal genes from thousands of candidate genes suggested by large-scale genomics or deep sequencing studies. In our previous study, we discovered that cancer genes possess a complicated yet distinct “gene concept signature.” Concept Signatures include cancer-related signaling pathways, molecular interactions, transcriptional motifs, protein domains, and gene ontologies. We developed a Concept Signature (or ConSig) analysis that prioritizes the biological importance of candidate genes underlying cancer by computing their strength of association with those cancer-related signature concepts. The ConSig analysis has facilitated the discovery of a recurrent ESR1-CCDC170 gene fusion in more aggressive Luminal B breast cancers (Nat. Commun. 2014) as well as TLK2, MAP3K3, and MYST3 amplifications in aggressive luminal breast cancer (Nat. Commun. 2016, J. Pathol. 2014, Oncogene In press). Nevertheless, current candidate gene prioritization methods, including ConSig, are subject to bias from redundancy in the compiled knowledgebase (also known as gene concept database). This leads to variation of the gene ranking and jeopardizes the reliability of the priority methods. In light of these problems, we developed an innovative, universal algorithm called uniConSig. By penalizing overlapping concepts with a stable parameter, “effective concept number”, we reduced the fluctuation in uniConSig scores, and stabilized the ranking of the genes even with the randomly duplicated gene concept databases. We tested the uniConSig algorithm by identifying known cancer genes based on a cancer gene list, and found that the uniConSig algorithm demonstrated significantly enhanced prioritization of known cancer genes compared to the ConSig algorithm, and the results are stable even in the presence of randomly duplicated gene concept databases. In addition, we used calculations based on the dominant/recessive cancer gene lists, and were able to provide a quantitative measure of the potential dominant/recessive functions of human genes underlying cancer. As an example application of this algorithm, we show that the uniConSig scores can directly reveal the primary oncogene targets from genomic amplicons in breast cancer.To our knowledge, UniConSig is the first tool for genome-wide quantification of gene functions and disease associations. UniConSig has broad applications on gene prioritization for genomic-based studies to discover new disease causal genes or new gene functions. Citation Format: Xu Chi, Meenakshi Anurag, Sartor A. Maureen, Xiaosong Wang. UniConSig: A new algorithm for genome wide quantification of gene functions and disease associations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4538. doi:10.1158/1538-7445.AM2017-4538

Abstract 489: Mismatch repair defects and endocrine therapy resistance in estrogen receptor positive breast cancer

Estrogen receptor positive (ER+) breast cancer is treated with endocrine therapy but intrinsic resistance occurs in ~1/3 of patients and acquired resistance in ~1/5 of the remainder. While many resistance mechanisms have been explored, therapeutic strategies to overcome resistance in the clinical setting have seen mixed outcomes, and appear most effective in the acquired resistance setting. Understanding mechanisms of resistance and finding therapeutic strategies to target them, therefore, remain important challenges facing breast cancer researchers. In this study we systematically examine the role of DNA damage repair defects in inducing endocrine therapy resistance, a relatively understudied question of recent interest. We use in silico analysis of clinical datasets, in vitro experiments evaluating endocrine therapy resistance in response to DDR dysregulation in multiple breast cancer celllines, and in vivo validation using cellline xenograft and patient-derived xenograft models. We also use gene expression microarrays and RPPA data from cell lines, patient-derived xenografts and primary ER+ breast tumors to uncover therapeutic options that are validated in vitro and in vivo and corroborated by clinical trial data. The results of this study uncover an intriguing link between mismatch repair (MMR) deficiency, specifically of the MutL complex (MLH1/3, PMS1/2), and poor prognosis in ER+ disease. We find a direct role for MutL loss in endocrine therapy resistance in vitro and in vivo by knocking down multiple MutL genes using CRISPR and stable shRNA approaches validated using standard rescue experiments. We identify the underlying mechanism: MutL deficiency in ER+ breast cancer abrogates Chk2-mediated feedback inhibition of CDK4/6 that appears necessary for endocrine therapy responsiveness. Consequently, pharmacological targeting of CDK4/6 in vitro and in vivo significantly inhibits growth of endocrine therapy resistant MutL-deficient ER+ breast cancer cells. These results are corroborated by data from a neoadjuvant clinical trial demonstrating that cell cycle regulation of MutL-mutant tumors tends to be estrogen-independent but sensitive to CDK4/6 inhibitors. The results of this study provide important biological and clinically relevant insights. 1) MMR deficiency is unexpectedly causal to intrinsic endocrine therapy resistance 2) This causal effect appears to be mediated by abrogation of cell cycle checkpoint activation in response to endocrine therapy 3) MMR deficiency in a subset of ER+ tumors explains why CDK4/6 inhibition is effective against some de novo endocrine therapy resistant tumors. While there are currently no biomarkers to guide the use of CDK4/6 inhibitors for ER+ breast cancer, markers of MMR dysregulation could identify patients in whom CDK4/6 inhibition should be used to prevent disease recurrence. Citation Format: Svasti Haricharan, Jacob Schmelz, Cheryl Schmidt, Purba Singh, Kimberly R. Holloway, Meenakshi Anurag, Shunqiang Li, Shyam M. Kavuri, Shixia Huang, Dean P. Edwards, Vera Suman, Kelly Hunt, John A. Olson, Jeremy Hoog, Cynthia X. Ma, Matthew N. Bainbridge, Matthew J. Ellis. Mismatch repair defects and endocrine therapy resistance in estrogen receptor positive breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 489. doi:10.1158/1538-7445.AM2017-489