Compared with normal cells, tumor cells have undergone an array of genetic and epigenetic alterations. Often, these changes underlie cancer development, progression, and drug resistance, so the utility of model systems rests on their ability to recapitulate the genomic aberrations observed in primary tumors. Tumor-derived cell lines have long been used to study the underlying biologic processes in cancer, as well as screening platforms for discovering and evaluating the efficacy of anticancer therapeutics. Multiple -omic measurements across more than a thousand cancer cell lines have been produced following advances in high-throughput technologies and multigroup collaborative projects. These data complement the large, international cancer genomic sequencing efforts to characterize patient tumors, such as The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). Given the scope and scale of data that have been generated, researchers are now in a position to evaluate the similarities and differences that exist in genomic features between cell lines and patient samples. As pharmacogenomics models, cell lines offer the advantages of being easily grown, relatively inexpensive, and amenable to high-throughput testing of therapeutic agents. Data generated from cell lines can then be used to link cellular drug response to genomic features, where the ultimate goal is to build predictive signatures of patient outcome. This review highlights the recent work that has compared -omic profiles of cell lines with primary tumors, and discusses the advantages and disadvantages of cancer cell lines as pharmacogenomic models of anticancer therapies.

MotivationGene Set Enrichment Analysis (GSEA) is routinely used to analyze and interpret coordinate pathway-level changes in transcriptomics experiments. For an experiment where less than seven samples per condition are compared, GSEA employs a competitive null hypothesis to test significance. A gene set enrichment score is tested against a null distribution of enrichment scores generated from permuted gene sets, where genes are randomly selected from the input experiment. Looking across a variety of biological conditions, however, genes are not randomly distributed with many showing consistent patterns of up- or down-regulation. As a result, common patterns of positively and negatively enriched gene sets are observed across experiments. Placing a single experiment into the context of a relevant set of background experiments allows us to identify both the common and experiment-specific patterns of gene set enrichment.ResultsWe compiled a compendium of 442 small molecule transcriptomic experiments and used GSEA to characterize common patterns of positively and negatively enriched gene sets. To identify experiment-specific gene set enrichment, we developed the GSEA-InContext method that accounts for gene expression patterns within a background set of experiments to identify statistically significantly enriched gene sets. We evaluated GSEA-InContext on experiments using small molecules with known targets to show that it successfully prioritizes gene sets that are specific to each experiment, thus providing valuable insights that complement standard GSEA analysis.Availability and implementationGSEA-InContext implemented in Python, Supplementary results and the background expression compendium are available at: https://github.com/CostelloLab/GSEA-InContext.

Highlights d An acute CRISPR/Cas9 assay identifies autophagydependent cancer cell lines d Autophagy-dependent cells can undergo selection to circumvent loss of autophagy d Cancer cells acquire dependence on NRF2 signaling to maintain proteostasis d Adaptation to loss of autophagy increases sensitivity to proteasome inhibition

The immature and dysfunctional vascular network within solid tumors poses a substantial obstacle to immunotherapy because it creates a hypoxic tumor microenvironment that actively limits immune cell infiltration. The molecular basis underpinning this vascular dysfunction is not fully understood. Using genome-scale receptor array technology, we showed here that insulin-like growth factor binding protein 7 (IGFBP7) interacts with its receptor CD93, and we subsequently demonstrated that this interaction contributes to abnormal tumor vasculature. Both CD93 and IGFBP7 were up-regulated in tumor-associated endothelial cells. IGFBP7 interacted with CD93 via a domain different from multimerin-2, the known ligand for CD93. In two mouse tumor models, blockade of the CD93/IGFBP7 interaction by monoclonal antibodies promoted vascular maturation to reduce leakage, leading to reduced tumor hypoxia and increased tumor perfusion. CD93 blockade in mice increased drug delivery, resulting in an improved antitumor response to gemcitabine or fluorouracil. Blockade of the CD93 pathway triggered a substantial increase in intratumoral effector T cells, thereby sensitizing mouse tumors to immune checkpoint therapy. Last, analysis of samples from patients with cancer under anti–programmed death 1/programmed death-ligand 1 treatment revealed that overexpression of the IGFBP7/CD93 pathway was associated with poor response to therapy. Thus, our study identified a molecular interaction involved in tumor vascular dysfunction and revealed an approach to promote a favorable tumor microenvironment for therapeutic intervention.