Predicting transcription factor activity using prior biological information.

Dysregulation of normal transcription factor activity is a common driver of disease. Therefore, the detection of aberrant transcription factor activity is important to understand disease pathogenesis. We have developed Priori, a method to predict transcription factor activity from RNA sequencing data. Priori has two key advantages over existing methods. First, Priori utilizes literature-supported regulatory information to identify transcription factor-target gene relationships. It then applies linear models to determine the impact of transcription factor regulation on the expression of its target genes. Second, results from a third-party benchmarking pipeline reveals that Priori detects aberrant activity from 124 single-gene perturbation experiments with higher sensitivity and specificity than 11 other methods. We applied Priori and other top-performing methods to predict transcription factor activity from two large primary patient datasets. Our work demonstrates that Priori uniquely discovered significant determinants of survival in breast cancer and identified mediators of drug response in leukemia.

A framework for considering prior information in network-based approaches to omics data analysis.

For decades, molecular biologists have been uncovering the mechanics of biological systems. Efforts to bring their findings together have led to the development of multiple databases and information systems that capture and present pathway information in a computable network format. Concurrently, the advent of modern omics technologies has empowered researchers to systematically profile cellular processes across different modalities. Numerous algorithms, methodologies, and tools have been developed to use prior knowledge networks (PKNs) in the analysis of omics datasets. Interestingly, it has been repeatedly demonstrated that the source of prior knowledge can greatly impact the results of a given analysis. For these methods to be successful it is paramount that their selection of PKNs is amenable to the data type and the computational task they aim to accomplish. Here we present a five-level framework that broadly describes network models in terms of their scope, level of detail, and ability to inform causal predictions. To contextualize this framework, we review a handful of network-based omics analysis methods at each level, while also describing the computational tasks they aim to accomplish.

Pharmacologic Targeting of TFIIH Suppresses KRAS-Mutant Pancreatic Ductal Adenocarcinoma and Synergizes with TRAIL.

Pancreatic ductal adenocarcinoma (PDAC) typically presents as metastatic disease at diagnosis and remains refractory to treatment. Next-generation sequencing efforts have described the genomic landscape, classified molecular subtypes, and confirmed frequent alterations in major driver genes, with coexistent alterations in KRAS and TP53 correlating with the highest metastatic burden and poorest outcomes. However, translating this information to guide therapy remains a challenge. By integrating genomic analysis with an arrayed RNAi druggable genome screen and drug profiling of a KRAS/TP53 mutant PDAC cell line derived from a patient-derived xenograft (PDCL), we identified numerous targetable vulnerabilities that reveal both known and novel functional aspects of pancreatic cancer biology. A dependence on the general transcription and DNA repair factor TFIIH complex, particularly the XPB subunit and the CAK complex (CDK7/CyclinH/MAT1), was identified and further validated utilizing a panel of genomically subtyped KRAS mutant PDCLs. TFIIH function was inhibited with a covalent inhibitor of CDK7/12/13 (THZ1), a CDK7/CDK9 kinase inhibitor (SNS-032), and a covalent inhibitor of XPB (triptolide), which led to disruption of the protein stability of the RNA polymerase II subunit RPB1. Loss of RPB1 following TFIIH inhibition led to downregulation of key transcriptional effectors of KRAS-mutant signaling and negative regulators of apoptosis, including MCL1, XIAP, and CFLAR, initiating caspase-8 dependent apoptosis. All three drugs exhibited synergy in combination with a multivalent TRAIL, effectively reinforcing mitochondrial-mediated apoptosis. These findings present a novel combination therapy, with direct translational implications for current clinical trials on metastatic pancreatic cancer patients. Significance: This study utilizes functional genetic and pharmacological profiling of KRAS-mutant pancreatic adenocarcinoma to identify therapeutic strategies and finds that TFIIH inhibition synergizes with TRAIL to induce apoptosis in KRAS-driven pancreatic cancer.

Synthetic lethal kinases in Ras/p53 mutant squamous cell carcinoma.

The oncogene Ras and the tumor suppressor gene p53 are frequently co-mutated in human cancer and mutations in Ras and p53 can cooperate to generate a more malignant cell state. To discover novel druggable targets for cancers carrying co-mutations in Ras and p53, we performed arrayed, kinome focused siRNA and oncology drug phenotypic screening utilizing a set of syngeneic Ras mutant squamous cell carcinoma (SCC) cell lines that also carried co-mutations in selected p53 pathway genes. These cell lines were derived from SCCs from carcinogen-treated inbred mice which harbored germline deletions or mutations in Trp53, p19Arf, Atm, or Prkdc. Both siRNA and drug phenotypic screening converge to implicate the phosphoinositol kinases, receptor tyrosine kinases, MAP kinases, as well as cell cycle and DNA damage response genes as targetable dependencies in SCC. Differences in functional kinome profiles between Ras mutant cell lines reflect incomplete penetrance of Ras synthetic lethal kinases and indicate that co-mutations cause a rewiring of survival pathways in Ras mutant tumors. This study describes the functional kinomic landscape of Ras/p53 mutant chemically-induced squamous cell carcinoma in both the baseline unperturbed state and following DNA damage and nominates candidate therapeutic targets, including the Nek4 kinase, for further development.

Associating drug sensitivity with differentiation status identifies effective combinations for acute myeloid leukemia.

Using ex vivo drug screening of primary patient specimens, we identified the combination of the p38 MAPK inhibitor doramapimod (DORA) with the BCL2 inhibitor venetoclax (VEN) as demonstrating broad, enhanced efficacy compared with each single agent across 335 acute myeloid leukemia (AML) patient samples while sparing primary stromal cells. Single-agent DORA and VEN sensitivity was associated with distinct, nonoverlapping tumor cell differentiation states. In particular, increased monocytes, M4/M5 French-American-British classification, and CD14+ immunophenotype tracked with sensitivity to DORA and resistance to VEN but were mitigated with the combination. Increased expression of MAPK14 and BCL2, the respective primary targets of DORA and VEN, were observed in monocytic and undifferentiated leukemias, respectively. Enrichment for DORA and VEN sensitivities was observed in AML with monocyte-like and progenitor-like transcriptomic signatures, respectively, and these associations diminished with the combination. The mechanism underlying the combination's enhanced efficacy may result from inhibition of p38 MAPK-mediated phosphorylation of BCL2, which in turn enhances sensitivity to VEN. These findings suggest exploiting complementary drug sensitivity profiles with respect to leukemic differentiation state, such as dual targeting of p38 MAPK and BCL2, offers opportunity for broad, enhanced efficacy across the clinically challenging heterogeneous landscape of AML.

The TP53 Apoptotic Network Is a Primary Mediator of Resistance to BCL2 Inhibition in AML Cells.

To study mechanisms underlying resistance to the BCL2 inhibitor venetoclax in acute myeloid leukemia (AML), we used a genome-wide CRISPR/Cas9 screen to identify gene knockouts resulting in drug resistance. We validated TP53, BAX, and PMAIP1 as genes whose inactivation results in venetoclax resistance in AML cell lines. Resistance to venetoclax resulted from an inability to execute apoptosis driven by BAX loss, decreased expression of BCL2, and/or reliance on alternative BCL2 family members such as BCL2L1. The resistance was accompanied by changes in mitochondrial homeostasis and cellular metabolism. Evaluation of TP53 knockout cells for sensitivities to a panel of small-molecule inhibitors revealed a gain of sensitivity to TRK inhibitors. We relate these observations to patient drug responses and gene expression in the Beat AML dataset. Our results implicate TP53, the apoptotic network, and mitochondrial functionality as drivers of venetoclax response in AML and suggest strategies to overcome resistance. SIGNIFICANCE: AML is challenging to treat due to its heterogeneity, and single-agent therapies have universally failed, prompting a need for innovative drug combinations. We used a genetic approach to identify genes whose inactivation contributes to drug resistance as a means of forming preferred drug combinations to improve AML treatment.See related commentary by Savona and Rathmell, p. 831.This article is highlighted in the In This Issue feature, p. 813.

Functional Precision Medicine Identifies Novel Druggable Targets and Therapeutic Options in Head and Neck Cancer.

Purpose: Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide, with high mortality and a lack of targeted therapies. To identify and prioritize druggable targets, we performed genome analysis together with genome-scale siRNA and oncology drug profiling using low-passage tumor cells derived from a patient with treatment-resistant HPV-negative HNSCC.Experimental Design: A tumor cell culture was established and subjected to whole-exome sequencing, RNA sequencing, comparative genome hybridization, and high-throughput phenotyping with a siRNA library covering the druggable genome and an oncology drug library. Secondary screens of candidate target genes were performed on the primary tumor cells and two nontumorigenic keratinocyte cell cultures for validation and to assess cancer specificity. siRNA screens of the kinome on two isogenic pairs of p53-mutated HNSCC cell lines were used to determine generalizability. Clinical utility was addressed by performing drug screens on two additional HNSCC cell cultures derived from patients enrolled in a clinical trial.Results: Many of the identified copy number aberrations and somatic mutations in the primary tumor were typical of HPV(-) HNSCC, but none pointed to obvious therapeutic choices. In contrast, siRNA profiling identified 391 candidate target genes, 35 of which were preferentially lethal to cancer cells, most of which were not genomically altered. Chemotherapies and targeted agents with strong tumor-specific activities corroborated the siRNA profiling results and included drugs that targeted the mitotic spindle, the proteasome, and G2-M kinases WEE1 and CHK1 We also show the feasibility of ex vivo drug profiling for patients enrolled in a clinical trial.Conclusions: High-throughput phenotyping with siRNA and drug libraries using patient-derived tumor cells prioritizes mutated driver genes and identifies novel drug targets not revealed by genomic profiling. Functional profiling is a promising adjunct to DNA sequencing for precision oncology. Clin Cancer Res; 24(12); 2828-43. ©2018 AACR.

CTD2 Dashboard: a searchable web interface to connect validated results from the Cancer Target Discovery and Development Network.

Database URL: https://ctd2-dashboard.nci.nih.gov/.

Modeling gene-wise dependencies improves the identification of drug response biomarkers in cancer studies.

Motivation: In recent years, vast advances in biomedical technologies and comprehensive sequencing have revealed the genomic landscape of common forms of human cancer in unprecedented detail. The broad heterogeneity of the disease calls for rapid development of personalized therapies. Translating the readily available genomic data into useful knowledge that can be applied in the clinic remains a challenge. Computational methods are needed to aid these efforts by robustly analyzing genome-scale data from distinct experimental platforms for prioritization of targets and treatments. Results: We propose a novel, biologically motivated, Bayesian multitask approach, which explicitly models gene-centric dependencies across multiple and distinct genomic platforms. We introduce a gene-wise prior and present a fully Bayesian formulation of a group factor analysis model. In supervised prediction applications, our multitask approach leverages similarities in response profiles of groups of drugs that are more likely to be related to true biological signal, which leads to more robust performance and improved generalization ability. We evaluate the performance of our method on molecularly characterized collections of cell lines profiled against two compound panels, namely the Cancer Cell Line Encyclopedia and the Cancer Therapeutics Response Portal. We demonstrate that accounting for the gene-centric dependencies enables leveraging information from multi-omic input data and improves prediction and feature selection performance. We further demonstrate the applicability of our method in an unsupervised dimensionality reduction application by inferring genes essential to tumorigenesis in the pancreatic ductal adenocarcinoma and lung adenocarcinoma patient cohorts from The Cancer Genome Atlas. Availability and Implementation: : The code for this work is available at https://github.com/olganikolova/gbgfa. Contact: : nikolova@ohsu.edu or margolin@ohsu.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Involving undergraduates in the annotation and analysis of global gene expression studies: creation of a maize shoot apical meristem expression database.

Through a multi-university and interdisciplinary project we have involved undergraduate biology and computer science research students in the functional annotation of maize genes and the analysis of their microarray expression patterns. We have created a database to house the results of our functional annotation of >4400 genes identified as being differentially regulated in the maize shoot apical meristem (SAM). This database is located at http://sam.truman.edu and is now available for public use. The undergraduate students involved in constructing this unique SAM database received hands-on training in an intellectually challenging environment, which has prepared them for graduate and professional careers in biological sciences. We describe our experiences with this project as a model for effective research-based teaching of undergraduate biology and computer science students, as well as for a rich professional development experience for faculty at predominantly undergraduate institutions.