Cell line drug screening datasets can be utilized for a range of different drug discovery applications from drug biomarker discovery to building translational models of drug response. Previously, we described three separate methodologies to (1) correct for general levels of drug sensitivity to enable drug-specific biomarker discovery, (2) predict clinical drug response in patients and (3) associate these predictions with clinical features to perform in vivo drug biomarker discovery. Here, we unite and update these methodologies into one R package (oncoPredict) to facilitate the development and adoption of these tools. This new OncoPredict R package can be applied to various in vitro and in vivo contexts for drug and biomarker discovery.

Obtaining accurate drug response data in large cohorts of cancer patients is very challenging; thus, most cancer pharmacogenomics discovery is conducted in preclinical studies, typically using cell lines and mouse models. However, these platforms suffer from serious limitations, including small sample sizes. Here, we have developed a novel computational method that allows us to impute drug response in very large clinical cancer genomics data sets, such as The Cancer Genome Atlas (TCGA). The approach works by creating statistical models relating gene expression to drug response in large panels of cancer cell lines and applying these models to tumor gene expression data in the clinical data sets (e.g., TCGA). This yields an imputed drug response for every drug in each patient. These imputed drug response data are then associated with somatic genetic variants measured in the clinical cohort, such as copy number changes or mutations in protein coding genes. These analyses recapitulated drug associations for known clinically actionable somatic genetic alterations and identified new predictive biomarkers for existing drugs.

High-throughput screens in cancer cell lines (CCLs) have been used for decades to help researchers identify compounds with the potential to improve the treatment of cancer and, more recently, to identify genomic susceptibilities in cancer via genome-wide shRNA and CRISPR/Cas9 screens. Additionally, rich genomic and transcriptomic data of these CCLs has allowed researchers to pair this screening data with biological features, enabling efforts to identify biomarkers of treatment response and gene dependencies. In this paper, we review the major CCL screening efforts and the large datasets these screens have made available. We also assess the CCL screens collectively and include a resource with harmonized CCL and compound identifiers to facilitate comparisons across screens. The CCLs in these screens were found to represent a wide range of cancer types, with a strong correlation between the representation of a cancer type and its associated mortality. Patient ages and gender distributions of CCLs were generally as expected, with some notable exceptions of female underrepresentation in certain disease types. Also, ethnicity information, while largely incomplete, suggests that African American and Hispanic patients may be severely underrepresented in these screens. Nearly all genes were targeted in the genetic perturbations screens, but the compounds used for the drug screens target less than half of known cancer drivers, likely reflecting known limitations in our drug design capabilities. Finally, we discuss recent developments in the field and the promise they hold for enabling future screens to overcome previous limitations and lead to new breakthroughs in cancer treatment.

(1) Background: Drug imputation methods often aim to translate in vitro drug response to in vivo drug efficacy predictions. While commonly used in retrospective analyses, our aim is to investigate the use of drug prediction methods for the generation of novel drug discovery hypotheses. Triple-negative breast cancer (TNBC) is a severe clinical challenge in need of new therapies. (2) Methods: We used an established machine learning approach to build models of drug response based on cell line transcriptome data, which we then applied to patient tumor data to obtain predicted sensitivity scores for hundreds of drugs in over 1000 breast cancer patients. We then examined the relationships between predicted drug response and patient clinical features. (3) Results: Our analysis recapitulated several suspected vulnerabilities in TNBC and identified a number of compounds-of-interest. AZD-1775, a Wee1 inhibitor, was predicted to have preferential activity in TNBC (p < 2.2 × 10−16) and its efficacy was highly associated with TP53 mutations (p = 1.2 × 10−46). We validated these findings using independent cell line screening data and pathway analysis. Additionally, co-administration of AZD-1775 with standard-of-care paclitaxel was able to inhibit tumor growth (p < 0.05) and increase survival (p < 0.01) in a xenograft mouse model of TNBC. (4) Conclusions: Overall, this study provides a framework to turn any cancer transcriptomic dataset into a dataset for drug discovery. Using this framework, one can quickly generate meaningful drug discovery hypotheses for a cancer population of interest.

Central nervous system tumors account for the most childhood cancer mortality. Immunotherapies have made major contributions to treat adult cancers, but application of immunotherapy for childhood brain tumors has been limited, in part due to the unique CNS microenvironment and mechanisms of immune escape in this context. To investigate the immunologic context, we query the transcriptomic profile of ~700 primary brain tumors released by the Children’s Brain Tumor Network. An immune subtype classification from The Cancer Genome Atlas project reveals that 81% of high-grade tumors across molecular subtypes are characterized by an immunosuppressive phenotype (C4) while an inflammatory phenotype (C3) is more common in low-grade lesions (p&lt;0.001). Adjusting for histologic grade and extent of resection, C4 associates with worse overall (OS) and progression-free survival (PFS) in this cohort (HR 3.1, p=0.008 and 1.6, p=0.03 respectively). Deconvolution of the transcriptome reveals that relative to C3, C4 tumors have decreased T-cell signature, OR 0.4 (0.2–0.8), and increased macrophage and tumor-proliferation signature (OR 2.0, 1.3–3.3, and 3.1, 2.3–4.2, respectively). In contrast to C3 tumors, T-cell signature in C4 tumors adversely impacts survival and correlates with multiple immunosuppressive genes and cytokines. Among them, the immune checkpoint CD276 has the highest associated impact on survival in C4 tumors (HR of log increase is 1.9, p&lt;0.001). Additionally, high-grade lesions have suppressed expression of antigen-presenting genes. EZH2 is implicated in downregulating antigen presentation and is found to be significantly upregulated in all high-grade lesions in this cohort. Treatment with the EZH1/2 inhibitor valemetostat resulted in upregulation of antigen-presenting genes and tissue differentiation pathways across three murine syngeneic models, one modeling diffuse midline glioma and two embryonal models. Future in-vivo studies with genetic and chemical modification of immunomodulatory genes of interest aim to identify immunotherapeutic targets with potential for broad applicability in pediatric neuro-oncology.