Ninety-seven percent of drug-indication pairs that are tested in clinical trials in oncology never advance to receive U.S. Food and Drug Administration approval. While lack of efficacy and dose-limiting toxicities are the most common causes of trial failure, the reason(s) why so many new drugs encounter these problems is not well understood. Using CRISPR-Cas9 mutagenesis, we investigated a set of cancer drugs and drug targets in various stages of clinical testing. We show that—contrary to previous reports obtained predominantly with RNA interference and small-molecule inhibitors—the proteins ostensibly targeted by these drugs are nonessential for cancer cell proliferation. Moreover, the efficacy of each drug that we tested was unaffected by the loss of its putative target, indicating that these compounds kill cells via off-target effects. By applying a genetic target-deconvolution strategy, we found that the mischaracterized anticancer agent OTS964 is actually a potent inhibitor of the cyclin-dependent kinase CDK11 and that multiple cancer types are addicted to CDK11 expression. We suggest that stringent genetic validation of the mechanism of action of cancer drugs in the preclinical setting may decrease the number of therapies tested in human patients that fail to provide any clinical benefit.

In castration-resistant prostate cancer (CRPC), the loss of androgen receptor (AR) dependence leads to clinically aggressive tumors with few therapeutic options. We used ATAC-seq (assay for transposase-accessible chromatin sequencing), RNA-seq, and DNA sequencing to investigate 22 organoids, six patient-derived xenografts, and 12 cell lines. We identified the well-characterized AR-dependent and neuroendocrine subtypes, as well as two AR-negative/low groups: a Wnt-dependent subtype, and a stem cell–like (SCL) subtype driven by activator protein–1 (AP-1) transcription factors. We used transcriptomic signatures to classify 366 patients, which showed that SCL is the second most common subtype of CRPC after AR-dependent. Our data suggest that AP-1 interacts with the YAP/TAZ and TEAD proteins to maintain subtype-specific chromatin accessibility and transcriptomic landscapes in this group. Together, this molecular classification reveals drug targets and can potentially guide therapeutic decisions.

The Maternal Embryonic Leucine Zipper Kinase (MELK) has been identified as a promising therapeutic target in multiple cancer types. MELK over-expression is associated with aggressive disease, and MELK has been implicated in numerous cancer-related processes, including chemotherapy resistance, stem cell renewal, and tumor growth. Previously, we established that triple-negative breast cancer cell lines harboring CRISPR/Cas9-induced null mutations in MELK proliferate at wild-type levels in vitro (Lin et al., 2017). Here, we generate several additional knockout clones of MELK and demonstrate that across cancer types, cells lacking MELK exhibit wild-type growth in vitro, under environmental stress, in the presence of cytotoxic chemotherapies, and in vivo. By combining our MELK-knockout clones with a recently described, highly specific MELK inhibitor, we further demonstrate that the acute inhibition of MELK results in no specific anti-proliferative phenotype. Analysis of gene expression data from cohorts of cancer patients identifies MELK expression as a correlate of tumor mitotic activity, explaining its association with poor clinical prognosis. In total, our results demonstrate the power of CRISPR/Cas9-based genetic approaches to investigate cancer drug targets, and call into question the rationale for treating patients with anti-MELK monotherapies.

Regulatory networks containing enhancer to gene edges define cellular state and their rewiring is a hallmark of cancer. While efforts, such as ENCODE, have revealed these networks for reference tissues and cell-lines by integrating multi-omics data, the same methods cannot be applied for large patient cohorts due to the constraints on generating ChIP-seq and three-dimensional data from limited material in patient biopsies. We trained a supervised machine learning model using genomic 3D signatures of physical enhancer-gene connections that can predict accurate connections using data from ATAC-seq and RNA-seq assays only, which can be easily generated from patient biopsies. Our method overcomes the major limitations of correlation-based approaches that cannot distinguish between distinct target genes of given enhancers in different samples, which is a hallmark of network rewiring in cancer. Our model achieved an AUROC (area under receiver operating characteristic curve) of 0.91 and, importantly, can distinguish between active regulatory elements with connections to target genes and poised elements with no connections to target genes. Our predicted regulatory elements are validated by multi-omics data, including histone modification marks from ENCODE, with an average specificity of 0.92. Application of our model on chromatin accessibility and transcriptomic data from 400 cancer patients across 22 cancer types revealed novel cancer-type and subtype-specific enhancer-gene connections for known cancer genes. In one example, we identified two enhancers that regulate the expression of ESR1 in only ER+ breast cancer (BRCA) samples but not in ER-samples. These enhancers are predicted to contribute to the high expression of ESR1 in 93% of ER+ BRCA samples. Functional validation using CRISPRi confirms that inhibition of these enhancers decreases the expression of ESR1 in ER+ samples.

In castration-resistant prostate cancer (CRPC), the loss of androgen receptor (AR)-dependence due to lineage plasticity, which has become more prevalent, leads to clinically highly aggressive tumors with few therapeutic options and is mechanistically poorly defined. To identify the master transcription factors (TFs) of CRPC in a subtype-specific manner, we derived and collected 29 metastatic human prostate cancer organoids and cell lines, and generated ATAC-seq, RNA-seq and DNA sequencing data. We identified four subtypes and their master TFs using novel computational algorithms: AR-dependent; Wnt-dependent, driven by TCF; neuroendocrine, driven by ASCL1 and NEUROD1 and stem cell-like (SCL), driven by the AP-1 family. The transcriptomic signatures of these four subtypes enabled the classification of 370 patients. We find that AP-1 co-operates with the inhibitable YAP/TAZ/TEAD pathway in the SCL subtype, the second most common group of CRPC tumors after AR-dependent. Together, this molecular classification reveals new drug targets and can potentially guide therapeutic decisions.