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.

The characterization of cancer genomes has provided insight into somatically altered genes across tumors, transformed our understanding of cancer biology, and enabled tailoring of therapeutic strategies. However, the function of most cancer alleles remains mysterious, and many cancer features transcend their genomes. Consequently, tumor genomic characterization does not influence therapy for most patients. Approaches to understand the function and circuitry of cancer genes provide complementary approaches to elucidate both oncogene and non-oncogene dependencies. Emerging work indicates that the diversity of therapeutic targets engendered by non-oncogene dependencies is much larger than the list of recurrently mutated genes. Here we describe a framework for this expanded list of cancer targets, providing novel opportunities for clinical translation.

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The Cancer Target Discovery and Development (CTD2) Network aims to use functional genomics to accelerate the translation of high-throughput and high-content genomic and small-molecule data towards use in precision oncology. As part of this goal, and to share its conclusions with the research community, the Network developed the ‘CTD2 Dashboard’ [https://ctd2-dashboard.nci.nih.gov/], which compiles CTD2 Network-generated conclusions, termed ‘observations’, associated with experimental entities, collected by its member groups (‘Centers’). Any researcher interested in learning about a given gene, protein, or compound (a ‘subject’) studied by the Network can come to the CTD2 Dashboard to quickly and easily find, review, and understand Network-generated experimental results. In particular, the Dashboard allows visitors to connect experiments about the same target, biomarker, etc., carried out by multiple Centers in the Network. The Dashboard’s unique knowledge representation allows information to be compiled around a subject, so as to become greater than the sum of the individual contributions. The CTD2 Network has broadly defined levels of validation for evidence (‘Tiers’) pertaining to a particular finding, and the CTD2 Dashboard uses these Tiers to indicate the extent to which results have been validated. Researchers can use the Network’s insights and tools to develop a new hypothesis or confirm existing hypotheses, in turn advancing the findings towards clinical applications. Database URL: https://ctd2-dashboard.nci.nih.gov/

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. O NE essential component of the success of genomics research has been the development of the field of bioinformatics, which can be defined as the use of information technology for the collection, storage, retrieval, and analysis of genomic data. Collaborations of biologists, computer scientists, and statisticians have become more robust in recent years; current graduate students in genetics commonly receive at least some formal training in computational biology. In addition, bioinformatics graduate degrees are now being offered by several institutions (Zatz 2002). However, it remains a challenge to involve undergraduate biology students, particularly freshmen and sophomores, in genomics and bioinformatics research. Moreover, establishing undergraduate genomics research can be particularly difficult at undergraduate institutions where collaborations between biologists and computer scientists have been slower to develop, or where there historically has not been a strong culture of research.Many undergraduate biology programs introduce cell biology and genetics during freshman introductory courses and require additional courses in cell biology and genetics later in the curriculum (Ledbetter and Campbell 2005). Thus, the beginning biology student's view of biology is largely a cellular and molecular genetics one. Too often students are taken to the brink of understanding the networks and circuitry involved in cell function, but are unable to utilize and develop this knowledge in a research environment. Furthermore,