A Landscape of Pharmacogenomic Interactions in Cancer.

Systematic studies of cancer genomes have provided unprecedented insights into the molecular nature of cancer. Using this information to guide the development and application of therapies in the clinic is challenging. Here, we report how cancer-driven alterations identified in 11,289 tumors from 29 tissues (integrating somatic mutations, copy number alterations, DNA methylation, and gene expression) can be mapped onto 1,001 molecularly annotated human cancer cell lines and correlated with sensitivity to 265 drugs. We find that cell lines faithfully recapitulate oncogenic alterations identified in tumors, find that many of these associate with drug sensitivity/resistance, and highlight the importance of tissue lineage in mediating drug response. Logic-based modeling uncovers combinations of alterations that sensitize to drugs, while machine learning demonstrates the relative importance of different data types in predicting drug response. Our analysis and datasets are rich resources to link genotypes with cellular phenotypes and to identify therapeutic options for selected cancer sub-populations.

Chemical genetics identifies Rab geranylgeranyl transferase as an apoptotic target of farnesyl transferase inhibitors.

A chemical genetics approach identified a cellular target of several proapoptotic farnesyl transferase inhibitors (FTIs). Treatment with these FTIs caused p53-independent apoptosis in Caenorhabditis elegans, which was mimicked by knockdown of endosomal trafficking proteins, including Rab5, Rab7, the HOPS complex, and notably the enzyme Rab geranylgeranyl transferase (RabGGT). These FTIs were found to inhibit mammalian RabGGT with potencies that correlated with their proapoptotic activity. Knockdown of RabGGT induced apoptosis in mammalian cancer cell lines, and both RabGGT subunits were overexpressed in several tumor tissues. These findings validate RabGGT, and by extension endosomal function, as a therapeutically relevant target for modulation of apoptosis, and enhance our understanding of the mechanism of action of FTIs.

Biochemical and transcriptional profiling to triage additional activities in a series of IGF-1R/IR inhibitors.

Therapeutic development of a targeted agent involves a series of decisions over additional activities that may be ignored, eliminated or pursued. This paper details the concurrent application of two methods that provide a spectrum of information about the biological activity of a compound: biochemical profiling on a large panel of kinase assays and transcriptional profiling of mRNA responses. Our mRNA profiling studies used a full dose range, identifying subsets of transcriptional responses with differing EC(50)s which may reflect distinct targets. Profiling data allowed prioritization for validation in xenograft models, generated testable hypotheses for active compounds, and informed decisions on the general utility of the series.

Deep learning reveals predictive sequence concepts within immune repertoires to immunotherapy.

T cell receptor (TCR) sequencing has been used to characterize the immune response to cancer. However, most analyses have been restricted to quantitative measures such as clonality that do not leverage the complementarity-determining region 3 (CDR3) sequence. We use DeepTCR, a framework of deep learning algorithms, to reveal sequence concepts that are predictive of response to immunotherapy. We demonstrate that DeepTCR can predict response and use the model to infer the antigenic specificities of the predictive signature and their unique dynamics during therapy. The predictive signature of nonresponse is associated with high frequencies of TCRs predicted to recognize tumor-specific antigens, and these tumor-specific TCRs undergo a higher degree of dynamic changes on therapy in nonresponders versus responders. These results are consistent with a biological model where the hallmark of nonresponders is an accumulation of tumor-specific T cells that undergo turnover on therapy, possibly because of the dysfunctional state of these T cells in nonresponders.

Soluble PD-L1 as an early marker of progressive disease on nivolumab.

BACKGROUND: Soluble PD-L1 (sPD-L1) has been associated with worse prognosis in numerous solid tumors. We determined sPD-L1 levels before and during nivolumab treatment in two prospective clinical trials of metastatic clear cell renal cell carcinoma (RCC) and melanoma patients, and investigated its relationship to clinical factors, biomarkers, and outcome. METHODS: Using a new Single Molecule Array assay, serum sPD-L1 level were determined in RCC (CheckMate 009, n=91) and melanoma (CheckMate 038-Part 1, n=78) prior to, and at two time points on treatment. Gene expression data was obtained from biopsies taken prior to, and at day 28 on treatment. Results were integrated with clinical variables, tumor PD-L1 status from immuno-histochemistry, and genomic mutation status. RESULTS: In RCC patients, sPD-L1 levels were higher in patients with progressive disease as their best response. For both RCC and melanoma patients, progressive or stable disease was associated with an increase in sPD-L1 on nivolumab therapy, whereas mean sPD-L1 levels did not change or declined in patients with objective responses. By categorizing RCC patients into transcriptomic molecular subtypes, we identified a subgroup where the associations between sPD-L1 and progressive disease were particularly evident. In baseline biopsies, we identified six biological processes that were associated with sPD-L1 level in both RCC and melanoma: higher sPD-L1 is associated with lower tumor expression of the Hallmark gene sets 'hypoxia', 'fatty acid metabolism', 'glycolysis', 'MTORC1 signaling' and 'androgen response', and with higher expression of 'KRAS signaling_Down'. CONCLUSION: Baseline and on-therapy sPD-L1 levels in RCC have the potential to predict progressive disease on PD-1 inhibitor nivolumab. In a hypothesis-generating analysis of tumor gene expression, high baseline sPD-L1 is associated with a tumor metabolic state reflecting potentially targetable processes in both melanoma and RCC. In both trials, we observed associations between change in sPD-L1 on treatment and outcome metrics. sPD-L1 levels may further refine a nivolumab-refractory subtype of RCC within transcriptionally based subtypes of RCC.

Biomarker analysis from CheckMate 214: nivolumab plus ipilimumab versus sunitinib in renal cell carcinoma.

BACKGROUND: The phase 3 CheckMate 214 trial demonstrated higher response rates and improved overall survival with nivolumab plus ipilimumab versus sunitinib in first-line therapy for advanced clear-cell renal cell carcinoma (RCC). An unmet need exists to identify patients with RCC who are most likely to benefit from treatment with nivolumab plus ipilimumab. METHODS: In exploratory analyses, pretreatment levels of programmed death ligand 1 were assessed by immunohistochemistry. Genomic and transcriptomic biomarkers (including tumor mutational burden and gene expression signatures) were also investigated. RESULTS: Biomarkers previously associated with benefit from immune checkpoint inhibitor-containing regimens in RCC were not predictive for survival in patients with RCC treated with nivolumab plus ipilimumab. Analysis of gene expression identified an association between an inflammatory response and progression-free survival with nivolumab plus ipilimumab. CONCLUSIONS: The exploratory analyses reveal relationships between molecular biomarkers and provide supportive data on how the inflammation status of the tumor microenvironment may be important for identifying predictive biomarkers of response and survival with combination immunotherapy in patients with RCC. Further validation may help to provide biomarker-driven precision treatment for patients with RCC.

Molecular correlates of response to nivolumab at baseline and on treatment in patients with RCC.

BACKGROUND: Nivolumab is an immune checkpoint inhibitor targeting the programmed death-1 receptor that improves survival in a subset of patients with clear cell renal cell carcinoma (ccRCC). In contrast to other tumor types that respond to immunotherapy, factors such as programmed death ligand-1 (PD-L1) status and tumor mutational burden show limited predictive utility in ccRCC. To address this gap, we report here the first molecular characterization of nivolumab response using paired index lesions, before and during treatment of metastatic ccRCC. METHODS: We analyzed gene expression and T-cell receptor (TCR) clonality using lesion-paired biopsies provided in the CheckMate 009 trial and integrated the results with their PD-L1/CD4/CD8 status, genomic mutation status and serum cytokine assays. Statistical tests included linear mixed models, logistic regression models, Fisher's exact test, and Kruskal-Wallis rank-sum test. RESULTS: We identified transcripts related to response, both at baseline and on therapy, including several that are amenable to peripheral bioassays or to therapeutic intervention. At both timepoints, response was positively associated with T-cell infiltration but not associated with TCR clonality, and some non-Responders were highly infiltrated. Lower baseline T-cell infiltration correlated with elevated transcription of Wnt/ß-catenin signaling components and hypoxia-regulated genes, including the Treg chemoattractant CCL28. On treatment, analysis of the non-responding patients whose tumors were highly T-cell infiltrated suggests association of the RIG-I-MDA5 pathway in their nivolumab resistance. We also analyzed our data using previous transcriptional classifications of ccRCC and found they concordantly identified a molecular subtype that has enhanced nivolumab response but is sunitinib-resistant. CONCLUSION: Our study describes molecular characteristics of response and resistance to nivolumab in patients with metastatic ccRCC, potentially impacting patient selection and first-line treatment decisions. TRIAL REGISTRATION NUMBER: NCT01358721.

Predictive Immune-Checkpoint Blockade Classifiers Identify Tumors Responding to Inhibition of PD-1 and/or CTLA-4.

PURPOSE: Combining anti-PD-1 + anti-CTLA-4 immune-checkpoint blockade (ICB) shows improved patient benefit, but it is associated with severe immune-related adverse events and exceedingly high cost. Therefore, there is a dire need to predict which patients respond to monotherapy and which require combination ICB treatment. EXPERIMENTAL DESIGN: In patient-derived melanoma xenografts (PDX), human tumor microenvironment (TME) cells were swiftly replaced by murine cells upon transplantation. Using our XenofilteR deconvolution algorithm we curated human tumor cell RNA reads, which were subsequently subtracted in silico from bulk (tumor cell + TME) patients' melanoma RNA. This produced a purely tumor cell-intrinsic signature ("InTumor") and a signature comprising tumor cell-extrinsic RNA reads ("ExTumor"). RESULTS: We show that whereas the InTumor signature predicts response to anti-PD-1, the ExTumor predicts anti-CTLA-4 benefit. In PDX, InTumorLO, but not InTumorHI, tumors are effectively eliminated by cytotoxic T cells. When used in conjunction, the InTumor and ExTumor signatures identify not only patients who have a substantially higher chance of responding to combination treatment than to either monotherapy, but also those who are likely to benefit little from anti-CTLA-4 on top of anti-PD-1. CONCLUSIONS: These signatures may be exploited to distinguish melanoma patients who need combination ICB blockade from those who likely benefit from either monotherapy.

Integrative molecular characterization of sarcomatoid and rhabdoid renal cell carcinoma.

Sarcomatoid and rhabdoid (S/R) renal cell carcinoma (RCC) are highly aggressive tumors with limited molecular and clinical characterization. Emerging evidence suggests immune checkpoint inhibitors (ICI) are particularly effective for these tumors, although the biological basis for this property is largely unknown. Here, we evaluate multiple clinical trial and real-world cohorts of S/R RCC to characterize their molecular features, clinical outcomes, and immunologic characteristics. We find that S/R RCC tumors harbor distinctive molecular features that may account for their aggressive behavior, including BAP1 mutations, CDKN2A deletions, and increased expression of MYC transcriptional programs. We show that these tumors are highly responsive to ICI and that they exhibit an immune-inflamed phenotype characterized by immune activation, increased cytotoxic immune infiltration, upregulation of antigen presentation machinery genes, and PD-L1 expression. Our findings build on prior work and shed light on the molecular drivers of aggressivity and responsiveness to ICI of S/R RCC.

Transcriptional profiling of the dose response: a more powerful approach for characterizing drug activities.

The dose response curve is the gold standard for measuring the effect of a drug treatment, but is rarely used in genomic scale transcriptional profiling due to perceived obstacles of cost and analysis. One barrier to examining transcriptional dose responses is that existing methods for microarray data analysis can identify patterns, but provide no quantitative pharmacological information. We developed analytical methods that identify transcripts responsive to dose, calculate classical pharmacological parameters such as the EC50, and enable an in-depth analysis of coordinated dose-dependent treatment effects. The approach was applied to a transcriptional profiling study that evaluated four kinase inhibitors (imatinib, nilotinib, dasatinib and PD0325901) across a six-logarithm dose range, using 12 arrays per compound. The transcript responses proved a powerful means to characterize and compare the compounds: the distribution of EC50 values for the transcriptome was linked to specific targets, dose-dependent effects on cellular processes were identified using automated pathway analysis, and a connection was seen between EC50s in standard cellular assays and transcriptional EC50s. Our approach greatly enriches the information that can be obtained from standard transcriptional profiling technology. Moreover, these methods are automated, robust to non-optimized assays, and could be applied to other sources of quantitative data.

Interplay of somatic alterations and immune infiltration modulates response to PD-1 blockade in advanced clear cell renal cell carcinoma.

PD-1 blockade has transformed the management of advanced clear cell renal cell carcinoma (ccRCC), but the drivers and resistors of the PD-1 response remain incompletely elucidated. Here, we analyzed 592 tumors from patients with advanced ccRCC enrolled in prospective clinical trials of treatment with PD-1 blockade by whole-exome and RNA sequencing, integrated with immunofluorescence analysis, to uncover the immunogenomic determinants of the therapeutic response. Although conventional genomic markers (such as tumor mutation burden and neoantigen load) and the degree of CD8+ T cell infiltration were not associated with clinical response, we discovered numerous chromosomal alterations associated with response or resistance to PD-1 blockade. These advanced ccRCC tumors were highly CD8+ T cell infiltrated, with only 27% having a non-infiltrated phenotype. Our analysis revealed that infiltrated tumors are depleted of favorable PBRM1 mutations and enriched for unfavorable chromosomal losses of 9p21.3, as compared with non-infiltrated tumors, demonstrating how the potential interplay of immunophenotypes with somatic alterations impacts therapeutic efficacy.

Integrative Tumor and Immune Cell Multi-omic Analyses Predict Response to Immune Checkpoint Blockade in Melanoma.

In this study, we incorporate analyses of genome-wide sequence and structural alterations with pre- and on-therapy transcriptomic and T cell repertoire features in immunotherapy-naive melanoma patients treated with immune checkpoint blockade. Although tumor mutation burden is associated with improved treatment response, the mutation frequency in expressed genes is superior in predicting outcome. Increased T cell density in baseline tumors and dynamic changes in regression or expansion of the T cell repertoire during therapy distinguish responders from non-responders. Transcriptome analyses reveal an increased abundance of B cell subsets in tumors from responders and patterns of molecular response related to expressed mutation elimination or retention that reflect clinical outcome. High-dimensional genomic, transcriptomic, and immune repertoire data were integrated into a multi-modal predictor of response. These findings identify genomic and transcriptomic characteristics of tumors and immune cells that predict response to immune checkpoint blockade and highlight the importance of pre-existing T and B cell immunity in therapeutic outcomes.

Conserved Interferon-γ Signaling Drives Clinical Response to Immune Checkpoint Blockade Therapy in Melanoma.

We analyze the transcriptome of baseline and on-therapy tumor biopsies from 101 patients with advanced melanoma treated with nivolumab (anti-PD-1) alone or combined with ipilimumab (anti-CTLA-4). We find that T cell infiltration and interferon-γ (IFN-γ) signaling signatures correspond most highly with clinical response to therapy, with a reciprocal decrease in cell-cycle and WNT signaling pathways in responding biopsies. We model the interaction in 58 human cell lines, where IFN-γ in vitro exposure leads to a conserved transcriptome response unless cells have IFN-γ receptor alterations. This conserved IFN-γ transcriptome response in melanoma cells serves to amplify the antitumor immune response. Therefore, the magnitude of the antitumor T cell response and the corresponding downstream IFN-γ signaling are the main drivers of clinical response or resistance to immune checkpoint blockade therapy.

Chemical genetics reveals an RGS/G-protein role in the action of a compound.

We report here on a chemical genetic screen designed to address the mechanism of action of a small molecule. Small molecules that were active in models of urinary incontinence were tested on the nematode Caenorhabditis elegans, and the resulting phenotypes were used as readouts in a genetic screen to identify possible molecular targets. The mutations giving resistance to compound were found to affect members of the RGS protein/G-protein complex. Studies in mammalian systems confirmed that the small molecules inhibit muscarinic G-protein coupled receptor (GPCR) signaling involving G-alphaq (G-protein alpha subunit). Our studies suggest that the small molecules act at the level of the RGS/G-alphaq signaling complex, and define new mutations in both RGS and G-alphaq, including a unique hypo-adapation allele of G-alphaq. These findings suggest that therapeutics targeted to downstream components of GPCR signaling may be effective for treatment of diseases involving inappropriate receptor activation.

Identification of a nonkinase target mediating cytotoxicity of novel kinase inhibitors.

In developing inhibitors of the LIM kinases, the initial lead molecules combined potent target inhibition with potent cytotoxic activity. However, as subsequent compounds were evaluated, the cytotoxic activity separated from inhibition of LIM kinases. A rapid determination of the cytotoxic mechanism and its molecular target was enabled by integrating data from two robust core technologies. High-content assays and gene expression profiling both indicated an effect on microtubule stability. Although the cytotoxic compounds are still kinase inhibitors, and their structures did not predict tubulin as an obvious target, these results provided the impetus to test their effects on microtubule polymerization directly. Unexpectedly, we confirmed tubulin itself as a molecular target of the cytotoxic kinase inhibitor compounds. This general approach to mechanism of action questions could be extended to larger data sets of quantified phenotypic and gene expression data.

Construction of a reference gene association network from multiple profiling data: application to data analysis.

MOTIVATION: Gene expression profiling is an important tool for gaining insight into biology. Novel strategies are required to analyze the growing archives of microarray data and extract useful information from them. One area of interest is in the construction of gene association networks from collections of profiling data. Various approaches have been proposed to construct gene networks using profiling data, and these networks have been used in functional inference as well as in data visualization. Here, we investigated a non-parametric approach to translate profiling data into a gene network. We explored the characteristics and utility of the resulting network and investigated the use of network information in analysis of variance models and hypothesis testing. RESULTS: Our work is composed of two parts: gene network construction and partitioning and hypothesis testing using sub-networks as groups. In the first part, multiple independently collected microarray datasets from the Gene Expression Omnibus data repository were analyzed to identify probe pairs that are positively co-regulated across the samples. A co-expression network was constructed based on a reciprocal ranking criteria and a false discovery rate analysis. We named this network Reference Gene Association (RGA) network. Then, the network was partitioned into densely connected sub-networks of probes using a multilevel graph partitioning algorithm. In the second part, we proposed a new, MANOVA-based approach that can take individual probe expression values as input and perform hypothesis testing at the sub-network level. We applied this MANOVA methodology to two published studies and our analysis indicated that the methodology is both effective and sensitive for identifying transcriptional sub-networks or pathways that are perturbed across treatments.

Genome-wide association analysis identifies genetic correlates of immune infiltrates in solid tumors.

Therapeutic options for the treatment of an increasing variety of cancers have been expanded by the introduction of a new class of drugs, commonly referred to as checkpoint blocking agents, that target the host immune system to positively modulate anti-tumor immune response. Although efficacy of these agents has been linked to a pre-existing level of tumor immune infiltrate, it remains unclear why some patients exhibit deep and durable responses to these agents while others do not benefit. To examine the influence of tumor genetics on tumor immune state, we interrogated the relationship between somatic mutation and copy number alteration with infiltration levels of 7 immune cell types across 40 tumor cohorts in The Cancer Genome Atlas. Levels of cytotoxic T, regulatory T, total T, natural killer, and B cells, as well as monocytes and M2 macrophages, were estimated using a novel set of transcriptional signatures that were designed to resist interference from the cellular heterogeneity of tumors. Tumor mutational load and estimates of tumor purity were included in our association models to adjust for biases in multi-modal genomic data. Copy number alterations, mutations summarized at the gene level, and position-specific mutations were evaluated for association with tumor immune infiltration. We observed a strong relationship between copy number loss of a large region of chromosome 9p and decreased lymphocyte estimates in melanoma, pancreatic, and head/neck cancers. Mutations in the oncogenes PIK3CA, FGFR3, and RAS/RAF family members, as well as the tumor suppressor TP53, were linked to changes in immune infiltration, usually in restricted tumor types. Associations of specific WNT/beta-catenin pathway genetic changes with immune state were limited, but we noted a link between 9p loss and the expression of the WNT receptor FZD3, suggesting that there are interactions between 9p alteration and WNT pathways. Finally, two different cell death regulators, CASP8 and DIDO1, were often mutated in head/neck tumors that had higher lymphocyte infiltrates. In summary, our study supports the relevance of tumor genetics to questions of efficacy and resistance in checkpoint blockade therapies. It also highlights the need to assess genome-wide influences during exploration of any specific tumor pathway hypothesized to be relevant to therapeutic response. Some of the observed genetic links to immune state, like 9p loss, may influence response to cancer immune therapies. Others, like mutations in cell death pathways, may help guide combination therapeutic approaches.

Immunomodulatory Activity of Nivolumab in Metastatic Renal Cell Carcinoma.

PURPOSE: Nivolumab, an anti-PD-1 immune checkpoint inhibitor, improved overall survival versus everolimus in a phase 3 trial of previously treated patients with metastatic renal cell carcinoma (mRCC). We investigated immunomodulatory activity of nivolumab in a hypothesis-generating prospective mRCC trial. EXPERIMENTAL DESIGN: Nivolumab was administered intravenously every 3 weeks at 0.3, 2, or 10 mg/kg to previously treated patients and 10 mg/kg to treatment-naïve patients with mRCC. Baseline and on-treatment biopsies and blood were obtained. Clinical activity, tumor-associated lymphocytes, PD-L1 expression (Dako immunohistochemistry; ≥5% vs. <5% tumor membrane staining), tumor gene expression (Affymetrix U219), serum chemokines, and safety were assessed. RESULTS: In 91 treated patients, median overall survival [95% confidence interval (CI)] was 16.4 months [10.1 to not reached (NR)] for nivolumab 0.3 mg/kg, NR for 2 mg/kg, 25.2 months (12.0 to NR) for 10 mg/kg, and NR for treatment-naïve patients. Median percent change from baseline in tumor-associated lymphocytes was 69% (CD3+), 180% (CD4+), and 117% (CD8+). Of 56 baseline biopsies, 32% had ≥5% PD-L1 expression, and there was no consistent change from baseline to on-treatment biopsies. Transcriptional changes in tumors on treatment included upregulation of IFNγ-stimulated genes (e.g., CXCL9). Median increases in chemokine levels from baseline to C2D8 were 101% (CXCL9) and 37% (CXCL10) in peripheral blood. No new safety signals were identified. CONCLUSIONS: Immunomodulatory effects of PD-1 inhibition were demonstrated through multiple lines of evidence across nivolumab doses. Biomarker changes from baseline reflect nivolumab pharmacodynamics in the tumor microenvironment. These data may inform potential combinations. Clin Cancer Res; 22(22); 5461-71. ©2016 AACR.

Model systems in drug discovery: chemical genetics meets genomics.

Animal model systems are an intricate part of the discovery and development of new medicines. The sequencing of not only the human genome but also those of the various pathogenic bacteria, the nematode Caenorhabditis elegans, the fruitfly Drosophila, and the mouse has enabled the discovery of new drug targets to push forward at an unprecedented pace. The knowledge and tools in these "model" systems are allowing researchers to carry out experiments more efficiently and are uncovering previously hidden biological connections. While the history of bacteria, yeast, and mice in drug discovery are long, their roles are ever evolving. In contrast, the history of Drosophila and C. elegans at pharmaceutical companies is short. We will briefly review the historic role of each model organism in drug discovery and then update the readers as to the abilities and liabilities of each model within the context of drug development.