An integrated single-cell RNA-seq map of human neuroblastoma tumors and preclinical models uncovers divergent mesenchymal-like gene expression programs

Neuroblastoma is a common pediatric cancer, where preclinical studies suggest that a mesenchymal-like gene expression program contributes to chemotherapy resistance. However, clinical outcomes remain poor, implying we need a better understanding of the relationship between patient tumor heterogeneity and preclinical models. Here, we generated single-cell RNA-seq maps of neuroblastoma cell lines, patient-derived xenograft models (PDX), and a genetically engineered mouse model (GEMM). We developed an unsupervised machine learning approach ('automatic consensus nonnegative matrix factorization' (acNMF)) to compare the gene expression programs found in preclinical models to a large cohort of patient tumors. We confirmed a weakly expressed, mesenchymal-like program in otherwise adrenergic cancer cells in some pre-treated high-risk patient tumors, but this appears distinct from the presumptive drug-resistance mesenchymal programs evident in cell lines. Surprisingly however, this weak-mesenchymal-like program was maintained in PDX and could be chemotherapy-induced in our GEMM after only 24 hours, suggesting an uncharacterized therapy-escape mechanism. Collectively, our findings improve the understanding of how neuroblastoma patient tumor heterogeneity is reflected in preclinical models, provides a comprehensive integrated resource, and a generalizable set of computational methodologies for the joint analysis of clinical and pre-clinical single-cell RNA-seq datasets.

A spatial cell atlas of neuroblastoma reveals developmental, epigenetic and spatial axis of tumor heterogeneity

Neuroblastoma is a pediatric cancer arising from the developing sympathoadrenal lineage with complex inter- and intra-tumoral heterogeneity. To chart this complexity, we generated a comprehensive cell atlas of 55 neuroblastoma patient tumors, collected from two pediatric cancer institutions, spanning a range of clinical, genetic, and histologic features. Our atlas combines single-cell/nucleus RNA-seq (sc/scRNA-seq), bulk RNA-seq, whole exome sequencing, DNA methylation profiling, spatial transcriptomics, and two spatial proteomic methods. Sc/snRNA-seq revealed three malignant cell states with features of sympathoadrenal lineage development. All of the neuroblastomas had malignant cells that resembled sympathoblasts and the more differentiated adrenergic cells. A subset of tumors had malignant cells in a mesenchymal cell state with molecular features of Schwann cell precursors. DNA methylation profiles defined four groupings of patients, which differ in the degree of malignant cell heterogeneity and clinical outcomes. Using spatial proteomics, we found that neuroblastomas are spatially compartmentalized, with malignant tumor cells sequestered away from immune cells. Finally, we identify spatially restricted signaling patterns in immune cells from spatial transcriptomics. To facilitate the visualization and analysis of our atlas as a resource for further research in neuroblastoma, single cell, and spatial-omics, all data are shared through the Human Tumor Atlas Network Data Commons at www.humantumoratlas.org.

A CRISPR-drug perturbational map for identifying compounds to combine with commonly used chemotherapeutics

Combination chemotherapy is crucial for successfully treating cancer. However, the enormous number of possible drug combinations means discovering safe and effective combinations remains a significant challenge. To improve this process, we conduct large-scale targeted CRISPR knockout screens in drug-treated cells, creating a genetic map of druggable genes that sensitize cells to commonly used chemotherapeutics. We prioritize neuroblastoma, the most common extracranial pediatric solid tumor, where ~50% of high-risk patients do not survive. Our screen examines all druggable gene knockouts in 18 cell lines (10 neuroblastoma, 8 others) treated with 8 widely used drugs, resulting in 94,320 unique combination-cell line perturbations, which is comparable to the largest existing drug combination screens. Using dense drug-drug rescreening, we find that the top CRISPR-nominated drug combinations are more synergistic than standard-of-care combinations, suggesting existing combinations could be improved. As proof of principle, we discover that inhibition of PRKDC, a component of the non-homologous end-joining pathway, sensitizes high-risk neuroblastoma cells to the standard-of-care drug doxorubicin in vitro and in vivo using patient-derived xenograft (PDX) models. Our findings provide a valuable resource and demonstrate the feasibility of using targeted CRISPR knockout to discover combinations with common chemotherapeutics, a methodology with application across all cancers.

Random-effects meta-analysis of effect sizes as a unified framework for gene set analysis

Gene set analysis (GSA) remains a common step in genome-scale studies because it can reveal insights that are not apparent from results obtained for individual genes. Many different computational tools are applied for GSA, which may be sensitive to different types of signals; however, most methods implicitly test whether there are differences in the distribution of the effect of some experimental condition between genes in gene sets of interest. We have developed a unifying framework for GSA that first fits effect size distributions, and then tests for differences in these distributions between gene sets. These differences can be in the proportions of genes that are perturbed or in the sign or size of the effects. Inspired by statistical meta-analysis, we take into account the uncertainty in effect size estimates by reducing the influence of genes with greater uncertainty on the estimation of distribution parameters. We demonstrate, using simulation and by application to real data, that this approach provides significant gains in performance over existing methods. Furthermore, the statistical tests carried out are defined in terms of effect sizes, rather than the results of prior statistical tests measuring these changes, which leads to improved interpretability and greater robustness to variation in sample sizes.

The role of gene set analysis is to identify groups of genes that are perturbed in a genomics experiment. There are many tools available for this task and they do not all test for the same types of changes. Here we propose a new way to carry out gene set analysis that involves first working out the distribution of the group effect in the gene set and then comparing this distribution to the equivalent distribution in other genes. Tests performed by existing tools for gene set analysis can be related to different comparisons in these distributions of group effects. A unified framework for gene set analysis provides for more explicit null hypotheses against which to test sets of genes for different types of responses to the experimental conditions. These results are more interpretable, because the group effect distributions can be compared visually, providing an indication of how the experimental effect differs between the gene sets.

Abstract 1907: Combocat: A high-throughput framework for drug combination studies

Drug combinations are the basis of treatment for modern diseases but arriving at successful combination therapies is fraught with challenges. Decades ago, the limited number of drugs represented a tractable candidate list from which to design combination experiments. However, the current pool of single-agent drugs to potentially combine is far too large to brute-force screen, and purely computational predictions have performed poorly. Suitable screening methods are needed, but the design of experimental approaches has proven to be highly complex; researchers need to carefully balance many variables such as appropriate drug concentration ranges, number of doses, inclusion of replicates, and throughput. Perhaps the most significant obstacle facing these studies is the approach to data analysis, where conflicting definitions of synergy and unintuitive metrics serve to confuse researchers and render largely uninterpretable results. Collectively, these challenges hamper the progress of drug combination research and ultimately translational impact. To overcome these limitations, we have developed a fully self-contained framework to handle both the experimental design and analysis of drug combination experiments. Our method, called Combocat, provides a straightforward way to test and analyze any number of drug combinations and samples, and is suitable for high-throughput. Combocat provides a high-resolution of concentration combinations compared to most current approaches. This is automated by common instruments and uses scripts included within our protocol. Through careful template design, we were able to include 3 replicates of each 10x10 matrix, single-agent drugs, and controls - all within a single 384-well plate. We found our method to work robustly with varying sample types (Human cancer, bacteria, fungi), and readouts. After data generation, files can simply be dragged into our Combocat analysis tool directly. We provide a free, web-based software suite to fully automate the analysis after data collection. The Combocat web tool is intuitive and facilitates interactive exploration of synergy. It also provides a rich array of information such as dose-response curves, IC50 values, synergy matrices, ranked hit plots, and more. Data normalization, synergy algorithms, scoring functions, and other complex calculations are run swiftly and automatically in the background with no need for user input. Notably, we employ statistical testing by taking advantage of experimental replicates, which is a feature we found lacking in most methods. We use a well-documented synergy metric but also decided to formulate our own Combocat score which considers statistical measurements and assay quality. The Combocat score provides an easy interpretation of results and facilitates quick identification of top hits. Collectively, our platform will be used to enhance and expedite the selection of effective drug combinations.

Citation Format: William C. Wright, Min Pan, Hyeong-Min Lee, Gregory A. Phelps, Jonathan Low, Duane Currier, Richard E. Lee, Taosheng Chen, Paul Geeleher. Combocat: A high-throughput framework for drug combination studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1907.

Abstract 4075: Meta-analysis of neuroblastoma single cell RNA-seq datasets identifies conserved and divergent gene expression programs across human and preclinical models

Neuroblastoma is a highly heterogeneous disease not only in the clinical presentation of individual patients, but also in the cellular composition of any given tumor. Insights into this diversity have only recently been enabled due to advancements in single cell technologies, which have facilitated investigation of this disease at unprecedented resolution and detail. Coinciding with the growing number of scRNA-seq technologies, so too are the number of single cell datasets encompassing neuroblastoma patients across several institutions. However, due to the rarity of the affliction and sample access, the cohort pool in each aforementioned scRNA-seq study is limited to a reduced representation of the spectrum of disease classifications, which limits the ability of any single study to draw conclusions about neuroblastoma as a whole. Moreover, inconsistencies in data acquisition and analytical approaches across these studies have led to diverging interpretations. As such, we decided to amass the entirety of publicly available neuroblastoma scRNA-seq studies, representing a more comprehensive cross-section of patient presentations, towards the goal of conducting an exhaustive meta-analysis of the underlying data. To this end, we have implemented a generalizable non-negative matrix factorization (NMF)-based framework targeted at discovering conserved gene expression programs in malignant neuroblastoma as well as the supporting microenvironment. Using graph-based network analyses for classification of gene expression programs, we have identified conserved signatures of malignant and non-tumor cell types in neuroblastoma. In addition to defining the landscape of expression programs in human neuroblastoma patients, we have also utilized the NMF analysis to assess the alignment of several preclinical models to human signatures. We have identified gene expression programs that align to malignant human expression programs as well as signatures more closely related to non-tumor cell types. These include previously characterized divergent mesenchymal and adrenergic programs, as well as undescribed liver/metabolic, neuronal, and glial signatures. When considering the affinity of neuroblastoma models to malignant human profiles we observed specific agreement between certain preclinical signatures and subtype classifications found in patient samples. Careful consideration of these results will allow researchers to guide preclinical studies by cross-referencing neuroblastoma models of interest with patient profiles. Overall, we characterize the most updated view of the landscape of neuroblastoma by documenting the full repertoire of gene expression programs across patient and preclinical models.

Citation Format: Richard Chapple, Charlie Wright, Min Pan, Paul Geeleher. Meta-analysis of neuroblastoma single cell RNA-seq datasets identifies conserved and divergent gene expression programs across human and preclinical models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4075.

Abstract 456: Jointly leveraging spatial transcriptomics and deep learning models for image annotation achieves better-than-pathologist performance in cell type identification in tumors

For over 100 years, the traditional tools of pathology, such as tissue-marking dyes (e.g. the H&E stain) have been used to study the disorganization and dysfunction of cells within tissues. This has represented a principal diagnostic and prognostic tool in cancer. However, in the last 5 years, new technologies have promised to revolutionize histopathology, with Spatial Transcriptomics technologies allowing us to measure gene expression directly in pathology-stained tissue sections. In parallel with these developments, Artificial Intelligence (AI) applied to histopathology tissue images now approaches pathologist level performance in cell type identification. However, these new technologies still have severe limitations, with Spatial Transcriptomics suffering difficulties distinguishing transcriptionally similar cell types, and AI-based pathology tools often performing poorly on real world out-of-batch test datasets. Thus, century-old techniques still represent standard-of-care in most areas of clinical cancer diagnostics and prognostics. Here, we present a new frontier in digital pathology: describing a conceptually novel computational methodology, based on Bayesian probabilistic modelling, that allows Spatial Transcriptomics data to be leveraged together with the output of deep learning-based AI used to computationally annotate H&E-stained sections of the same tumor. By leveraging cell-type annotations from multiple independent pathologists, we show that this integrated methodology achieves better performance than any given pathologist’s manual tissue annotation in the task of identifying regions of immune cell infiltration in breast cancer, and easily outperforms either technology alone. We also show that on a subset of histopathology slides examined, the methodology can identify regions of clinically relevant immune cell infiltration that were missed entirely by an initial pathologist’s manual annotation. While this use case has clear diagnostic and prognostic value in cancer (e.g. predicting response to immunotherapy), our methodology is generalizable to any type of pathology images and also has broad applications in spatial transcriptomics data analytics, where most applications (such as identifying cell-cell interactions) rely on correct cell type annotations having been established a priori. We anticipate that this work will spur many follow-up studies, including new computational innovations building on the approach. The work sets the stage for better-than-pathologist performance in other cell-type annotation tasks, with relevant applications in diagnostics and prognostics across almost all cancers.

Citation Format: Asif Zubair, Rich Chapple, Sivaraman Natarajan, William C. Wright, Min Pan, Hyeong-Min Lee, Heather Tillman, John Easton, Paul Geeleher. Jointly leveraging spatial transcriptomics and deep learning models for image annotation achieves better-than-pathologist performance in cell type identification in tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 456.

The chemotherapeutic CX-5461 primarily targets TOP2B and exhibits selective activity in high-risk neuroblastoma

Survival in high-risk pediatric neuroblastoma has remained around 50% for the last 20 years, with immunotherapies and targeted therapies having had minimal impact. Here, we identify the small molecule CX-5461 as selectively cytotoxic to high-risk neuroblastoma and synergistic with low picomolar concentrations of topoisomerase I inhibitors in improving survival in vivo in orthotopic patient-derived xenograft neuroblastoma mouse models. CX-5461 recently progressed through phase I clinical trial as a first-in-human inhibitor of RNA-POL I. However, we also use a comprehensive panel of in vitro and in vivo assays to demonstrate that CX-5461 has been mischaracterized and that its primary target at pharmacologically relevant concentrations, is in fact topoisomerase II beta (TOP2B), not RNA-POL I. This is important because existing clinically approved chemotherapeutics have well-documented off-target interactions with TOP2B, which have previously been shown to cause both therapy-induced leukemia and cardiotoxicity-often-fatal adverse events, which can emerge several years after treatment. Thus, while we show that combination therapies involving CX-5461 have promising anti-tumor activity in vivo in neuroblastoma, our identification of TOP2B as the primary target of CX-5461 indicates unexpected safety concerns that should be examined in ongoing phase II clinical trials in adult patients before pursuing clinical studies in children.

Long non-coding RNA transcriptome of uncharacterized samples can be accurately imputed using protein-coding genes

Long non-coding RNAs (lncRNAs) play an important role in gene regulation and are increasingly being recognized as crucial mediators of disease pathogenesis. However, the vast majority of published transcriptome datasets lack high-quality lncRNA profiles compared to protein-coding genes (PCGs). Here we propose a framework to harnesses the correlative expression patterns between lncRNA and PCGs to impute unknown lncRNA profiles. The lncRNA expression imputation (LEXI) framework enables characterization of lncRNA transcriptome of samples lacking any lncRNA data using only their PCG profiles. We compare various machine learning and missing value imputation algorithms to implement LEXI and demonstrate the feasibility of this approach to impute lncRNA transcriptome of normal and cancer tissues. Additionally, we determine the factors that influence imputation accuracy and provide guidelines for implementing this approach.

Facilitating Drug Discovery in Breast Cancer by Virtually Screening Patients Using In Vitro Drug Response Modeling

(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.

Abstract 3578: Comprehensive pharmacogenomic analysis establishes lncRNAs as protein-coding independent biomarker of drug response in human cancers

Somatic alterations in the cancer genome influence a patient’s response to anti-cancer therapeutics. Thus, identifying context-specific alterations associated with differential drug sensitivity can improve patient response prediction and therapeutic selection. The discovery of pervasive dysregulation of long non-coding RNAs (lncRNA) in human cancers and their perceived role in gene regulation has led to the speculation that lncRNAs may also play a role in determining cancer drug response. However, it is unclear whether lncRNAs can augment the existing, predominantly protein coding, repertoire of pharmacogenomic biomarkers of anti-cancer agents.

We performed comprehensive analysis of whole genome and transcriptome data from over 1000 cancer cell lines and drug screening data for over 500 anti-cancer agents to systematically analyze over 5 million lncRNA-drug associations. We developed statistical and machine-learning frameworks to study these associations while controlling for the effects of confounding PCGs. Sparse regression and network-based methods demonstrated the predictive ability and biological relevance of literature-supported and novel lncRNAs biomarkers. Using regression analysis of lncRNA expression controlling for neighboring PCGs against drug response, we established that a large proportion of the lncRNAs candidates are PCG-independent biomarkers and potent predictors of cancer drug response. We further identified response-associated somatic alterations specifically in lncRNA genome that do not overlap PCGs. In addition, we demonstrated that collinear lncRNAs might be biologically relevant determinants of drug response. As an example, we identified EGFR-AS1 and MIR205HG as novel predictors of anti-EGFR therapy, which explain a significantly larger proportion of variability in erlotinib and gefitinib response as compared to EGFR mutations and copy-number alterations in drug screens and in patient data. We validated our findings in 16 non-small cell lung cancer and erlotinib-resistant cell lines. Our knockdown experiments revealed mechanisms of erlotinib sensitivity mediated by the two lncRNAs without influencing EGFR expression.

In conclusion, our comprehensive analysis generated unprecedented insights into the role of lncRNAs in cancer drug response that reaches beyond PCGs. This study will serve as a foundation for future cancer pharmacogenomic studies and will be an invaluable resource for investigators seeking insights into mechanisms of drug response.

Citation Format: Aritro Nath, Eunice Y. Lau, Adam M. Lee, Paul Geeleher, William C. Cho, R. Stephanie Huang. Comprehensive pharmacogenomic analysis establishes lncRNAs as protein-coding independent biomarker of drug response in human cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3578.

Cancer expression quantitative trait loci (eQTLs) can be determined from heterogeneous tumor gene expression data by modeling variation in tumor purity

Expression quantitative trait loci (eQTLs) identified using tumor gene expression data could affect gene expression in cancer cells, tumor-associated normal cells, or both. Here, we have demonstrated a method to identify eQTLs affecting expression in cancer cells by modeling the statistical interaction between genotype and tumor purity. Only one third of breast cancer risk variants, identified as eQTLs from a conventional analysis, could be confidently attributed to cancer cells. The remaining variants could affect cells of the tumor microenvironment, such as immune cells and fibroblasts. Deconvolution of tumor eQTLs will help determine how inherited polymorphisms influence cancer risk, development, and treatment response.

Abstract 4271: Most expression quantitative trait loci discovered in tumors cannot be attributed to cancer cells

Genome-wide association studies (GWAS) have identified hundreds of inherited genetic variants that increase cancer risk. However, most cancer risk variants are in non-coding regions of the genome and modulate risk by affecting gene regulation. Thus, determining how inherited genetic variation affects gene expression in cancer is critically important to understanding disease development. Consequently, by leveraging large genomics datasets like The Cancer Genome Atlas (TCGA), previous studies have examined the effect of inherited genetic variation on gene expression in tumors; an approach known as expression quantitative trait locus (eQTL) mapping. However, tumors are mixtures of both cancer and normal cells, for example, immune cells and stroma. No previous cancer eQTL study has accounted for this mixture of cell types, essentially having treated bulk tumor expression as representative of gene expression in cancer cells.

Building on research in deconvolution of mixtures of cell types, we have developed a new approach that can accurately account for the effect of tumor-infiltrating normal cells on cancer eQTLs. The approach involves first estimating the proportion of tumor-infiltrating normal cells (tumor purity) using a combined estimate from genomics data and H&E staining. Then, we developed a statistical model that can account for the effect of tumor purity on eQTLs by modeling the interaction of the tumor purity estimate and genotype. Intuitively, this works by determining how the magnitude of the association between gene expression and genotype changes as a function of tumor purity, then extrapolating this effect to 100% cancer cells. The model's accuracy was validated using simulated data and on expression profiles from purified cell types.

We used this model to map eQTLs in the TCGA breast cancer cohort. Remarkably, while 57,189 eQTLs were identified in TCGA breast cancer patients using a conventional model, only 8,833 (15.4%) could be attributed to cancer cells when tumor purity was accounted for. Analysis of the Genotype-Tissue Expression (GTEx) data provided evidence that almost 50% of tumor eQTLs inferred using a conventional approach affect gene expression in tumor-infiltrating immune cells and fibroblasts. We also investigated the eQTL profiles of cancer risk variants from a GWAS for breast cancer risk. Strikingly, for 33% of breast cancer risk variants identified as eQTLs using a conventional approach, we found strong evidence of an effect in tumor-infiltrating normal cells, but no evidence of an effect in cancer cells. This suggests cancer risk is mediated by the effect of inherited genetic variation on gene regulation in the cells of the tumor microenvironment, as well as in cancer and pre-cancer cells. Our findings profoundly challenge the current interpretation of inherited genetic regulation in cancer and should be considered in functional validation of results from all cancer risk GWAS.

Citation Format: Paul Geeleher, Fan Wang, Zheny Zhang, Robert L. Grossman, Cathal Seoighe, R. Stephanie Huang. Most expression quantitative trait loci discovered in tumors cannot be attributed to cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4271.

Exploring the Link between the Germline and Somatic Genome in Cancer

<b/> Carter and colleagues propose a systematic analysis of the germline and somatic genome in cancer. They identify interactions that occur between germline and somatic variants. This elucidates the function of the germline genome in the context of cancer risk and development. Cancer Discov; 7(4); 354-5. ©2017 AACRSee related article by Carter et al., p. 410.

Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies

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.

Abstract 3481: Leveraging protein coding gene expression profiles to accurately impute lncRNA transcriptome of cancer cells

Long noncoding RNAs (lncRNAs) represent a large, diverse and tissue-specific class of transcripts that are involved in gene regulation. Recent large-scale cancer sequencing efforts indicate that lncRNAs are an important component of the cancer transcriptome, and may play a critical role in carcinogenesis and drug sensitivity. Accurate profiling of lncRNAs however remains a challenge owing to significantly lower expression levels than mRNA, requiring deep paired-end total RNA sequencing, which can be prohibitively expensive. Additionally, previous generation microarrays that constitute a vast majority of GEO and ArrayExpress datasets do not provide comprehensive lncRNA coverage.

Here we propose a lncRNA expression imputation (LEXI) framework to reconstruct the lncRNA transcriptome of cancer cells using their mRNA expression profiles. Our goal is to provide a tool that enables the harnessing of enormous wealth of publicly available cancer mRNA datasets and discover novel lncRNAs associated with carcinogenesis and drug sensitivity. The LEXI approach is based on learning patterns of mRNA expression associated with each lncRNA across a diverse cohort of cancer cells and then predict lncRNA expression profile of uncharacterized cells.

We developed LEXI by evaluating the performance of various machine-learning algorithms benchmarked in a cross-validation study across a cohort of 675 cancer cell lines and 9755 pan-cancer tissues. We adapted the LEXI framework based on optimal performance and computation time and show that LEXI accurately predicts lncRNA expression profiles in both cell lines and tissues. To demonstrate the utility of LEXI, we reconstruct the lncRNA transcriptome of over 1000 cell lines and 2000 TCGA samples, and compare with RNAseq measured lncRNA levels in corresponding samples. We further show expression levels of MALAT1, HOTAIR, CCAT1 and other established cancer-associated lncRNAs are accurately predicted across cancer types, and can be used to discover novel associations in uncharacterized phenotypes. LEXI will be available as a free resource for researchers to easily obtain lncRNA profiles using their own mRNA data.

Citation Format: Aritro Nath, Paul Geeleher, R. Stephanie Huang. Leveraging protein coding gene expression profiles to accurately impute lncRNA transcriptome of cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3481. doi:10.1158/1538-7445.AM2017-3481

Abstract 5035: IDWAS: Imputing drug response in large cohorts of cancer patients to discover novel predictive biomarkers

There are currently only 10 cancer genes with FDA approved drug treatment options (source: OncoKB). Here, we present a conceptually novel analytical method that can rapidly expand this small list of clinically effective cancer drug treatment biomarkers.

Large sequencing studies, such as The Cancer Genome Atlas (TCGA), have greatly accelerated our understanding of the molecular basis of cancer. However, because of the difficulty in collecting drug response data in large cohorts of cancer patients, these studies have not been effectively used for finding new biomarkers of drug response (i.e. pharmacogenomics discovery). Thus, much cancer pharmacogenomics research is conducted in pre-clinical disease models such as cell lines (e.g. the Genomics of Drug Sensitivity in Cancer (GDSC) project); but these studies are (among other limitations) always restricted by comparatively smaller sample sizes. Here, we present an analytical method that integrates data from large clinical studies (e.g. TCGA) with data from pre-clinical disease models (e.g. GDSC) and overcomes these critical obstacles, allowing studies such as TCGA to now be effectively used for pharmacogenomics discovery. We refer to this approach as an “Imputed drug-wide association study” (IDWAS).

The method works by fitting a statistical model relating gene expression and drug response in pre-clinical data (here we use the GDSC cancer cell lines), then using this model to impute drug response from tumor gene expression data in a clinical cohort (here we use TCGA). Next, we compare these imputed drug response data to measured variants (e.g. somatic mutations, copy number changes) in TCGA, thus finding new biomarkers of drug response. We show that we can recapitulate known clinically effective biomarkers and we have validated new clinically relevant biomarkers, which remarkably could not have been identified using conventional approaches.

Our method will set the stage for many future studies. Crucially, this approach could easily be applied to any of the vast number of clinical cancer sequencing studies now undertaken, meaning that it will be possible to use all of these datasets for pharmacogenomics research. We have included a set of computational tools to allow easy application of our method and replication of our results. Given that this is a conceptually novel methodology, it is also likely that many other studies will attempt to improve upon our proposed implementation.

Furthermore, members of our group are currently leading the development of the new Genomic Data Commons (GDC; https://gdc.cancer.gov/), which is the NCI’s new access portal for TCGA data. We are currently working towards making the imputed drug response data directly accessible on GDC, along with all other TCGA data. This means that our imputed drug response data will be easily accessible to the thousands of researchers already accessing TCGA via the GDC.

Citation Format: Paul Geeleher, Zhenyu Zhang, Fan Wang, Aritro Nath, Steven Bhutra, Robert Grossman, R. Stephanie Huang. IDWAS: Imputing drug response in large cohorts of cancer patients to discover novel predictive biomarkers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5035. doi:10.1158/1538-7445.AM2017-5035

Institutional Profile: Pharmacogenomic research in R Stephanie Huang Laboratory

The Huang Lab was established in 2009 at the University of Chicago and has since been active in conducting pharmacogenomic research. Our laboratory's main research focus is translational pharmacogenomics with a particular interest in the pharmacogenomics of anticancer agents. By systematically evaluating the human genome and its relationships to drug response and toxicity, our goal is to develop clinically useful models that predict risk for adverse drug reactions and nonresponse prior to administration of chemotherapy. Specifically, the theme of our research evolved around the idea of cell-based pharmacogenomics, which utilizes in vitro models for biomarker discovery and prediction-model construction, followed by in vivo validation. We routinely use cell lines (derived from healthy and diseased individuals as well as commercially available cancer cell lines) and clinical samples to discover and functionally characterize genetic variation and gene, miRNA, and long noncoding RNA expression for their roles in drug sensitivity.

Cancer biomarker discovery is improved by accounting for variability in general levels of drug sensitivity in pre-clinical models

We show that variability in general levels of drug sensitivity in pre-clinical cancer models confounds biomarker discovery. However, using a very large panel of cell lines, each treated with many drugs, we could estimate a general level of sensitivity to all drugs in each cell line. By conditioning on this variable, biomarkers were identified that were more likely to be effective in clinical trials than those identified using a conventional uncorrected approach. We find that differences in general levels of drug sensitivity are driven by biologically relevant processes. We developed a gene expression based method that can be used to correct for this confounder in future studies.

Abstract 2039: Exploring the longitudinal transcriptomic landscape of tyrosine kinase inhibitor treatment response in chronic myeloid leukemia patients

The interpretation and implementation of large-scale genetic profiles into clinical practice remains a challenge despite substantial growth in our understanding of genetic contributors to drug response. Most current omic studies focus on identifying genetic features that are distinct between normal and tumor samples, but fail to capture the dynamics of association between omic profiles, treatment response and disease progression over time. The focus of this research is to analyze the longitudinal transcriptomic profile of chronic myeloid leukemia patients (CML) in context of tyrosine kinase inhibitor (TKI) treatment and clinical status. The main objectives were to compare a series of post-TKI treatment transcriptome profiles to their baseline levels, and characterize the impact of TKI treatment and CML disease status on the individual's transcriptome over time. Our ultimate goal is to develop TKI response predictors using the longitudinal expression data collected over the treatment course.

Peripheral blood samples, buccal swabs and detailed clinical data were collected from each study participant (screened for BCR-ABL1 translocation) for a period of 6 months, in addition to pre-therapy baseline. RNA was extracted from granulocytes isolated from peripheral blood samples, and profiled using RNA sequencing. RNAseq profiles over TKI treatment course were compared to baseline, as well as against hematologic response (complete blood count), cytogenetic response (FISH), and clinical disease progression.

We investigated dynamic trends in RNAseq profiles associated TKI response, as well as with the clinical status of the patient over time. We identified genetic features that were either 1) Differentially expressed between baseline and post-TKI time points; 2) Showed non-random spikes in expression levels at specific time points; 3) Associated with hematological and clinical phenotypes, including white blood cell count, percentage granulocytes and percentage cells with BCR-ABL1 translocation; 4) Demonstrated highly correlated patterns of expression over time. Through clustering and enrichment analysis of the selected transcripts, we identified several pathways and molecular features associated with TKI-response, and altered disease state. Of note, we found mTOR signaling, and pro-apoptotic pathways to be significantly altered between baseline and TKI-responding individuals. In addition, we observed significant changes in transcription regulatory network of several transcription factors, notably AP-1, over the treatment time course.

To our knowledge, this is the first study to establish the utility of comprehensive longitudinal multiple transcriptome profile analysis of TKI-response in CML. We believe this study will pave way for future large-scale longitudinal omic profiling of CML and other cancer-types.

Citation Format: Aritro Nath, Fan Wang, Divya Lenkala, Bonnie LaCroix, Nancy Glavin, Kristen Kipping-Johnson, Paul Geeleher, Michael Thirman, Lucy Godley, Gordana Raca, Richard Larson, R. Stephanie Huang. Exploring the longitudinal transcriptomic landscape of tyrosine kinase inhibitor treatment response in chronic myeloid leukemia patients. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2039.

Abstract 2100: Imputation of drug sensitivity levels in The Cancer Genome Atlas using machine learning identifies novel predictors of chemotherapeutic response

The Cancer Genome Atlas (TCGA) represents a landmark undertaking that has collected genomic data from tumors of almost 15,000 cancer patients. Most tumors have been assayed using many high-throughput genomics techniques, such as whole-genome DNA copy number, gene expression, mutation status and DNA methylation. The project has also compiled a large set of matching clinical data; however, the utility of TCGA for pharmacogenomic discovery has been severely limited by the lack of cleanly measured drug response data. The absence of such data owes to the difficulty in obtaining this information from cancer patients, because individuals are typically treated with complex multi-drug regimes, which renders the precise measurement of a drug specific response phenotype intractable. However, precisely measured drug response data can be readily obtained in pre-clinical models, such as cell lines. Thus, in this study, we have implemented a machine learning based approach, where gene expression based predictive models of drug response are constructed on approximately 700 cancer cell lines; these models are then applied to nearly 15,000 TCGA tumor samples, for which gene expression data is also available, yielding a predicted drug sensitivity value in each TCGA sample. We used this approach to impute a drug sensitivity estimate for 138 drugs that were treated against the cell lines.

Our approach allowed us to transform TCGA into a vast pharmacogenomics dataset, on an unprecedented scale (and thus power), which could be mined for novel associations relevant to chemotherapeutic response. As a proof-of-concept, we first investigated whether the existing small number of known clinically relevant associations could be recapitulated in these imputed data. As an example, we identified the predicted difference in sensitivity to Lapatinib, which interrupts ERBB2, as significantly greater in ERBB2 amplified tumors (P = 6 × 10-12), an association that is drug specific. In ERBB2 amplified tumors, large chunks of chromosome 17, containing many genes, are typically amplified; this recurring phenomenon often renders it impossible to identify the causative genes in genome wide copy number data using conventional approaches. Strikingly however, by interrogating the observed effect size of genes in this amplicon, ERBB2 could be identified as the precise causative drug target in our data, demonstrating the impressive increase in power obtained from this approach. Additionally, the second most significant association for Lapatinib is for EGFR, which is the known secondary target of this drug (P = 5 × 10-4).

We also identified many novel associations; e.g. that a copy number amplification in ERLIN2, a gene shown to play a role in stabilizing microtubules, is associated with resistance to a class of chemotherapeutics that interact with the microtubules. This was subsequently validated with follow-up experiments.

Citation Format: Paul Geeleher, R. Stephanie Huang, Fan Wang, Gladys Morrison. Imputation of drug sensitivity levels in The Cancer Genome Atlas using machine learning identifies novel predictors of chemotherapeutic response. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2100.

ID: 26: EXPLORING THE LONGITUDINAL TRANSCRIPTOMIC LANDSCAPE OF TYROSINE KINASE INHIBITOR TREATMENT RESPONSE IN CHRONIC MYELOID LEUKEMIA PATIENTS

The interpretation and implementation of large-scale genetic profiles into clinical practice remains a challenge despite substantial growth in our understanding of genetic contributors to drug response. Most current omic studies focus on identifying genetic features that are distinct between normal and tumor samples, but fail to capture the dynamics of association between omic profiles, treatment response and disease progression over time. The focus of this research is to analyze the longitudinal transcriptomic profile of chronic myeloid leukemia patients (CML) in context of tyrosine kinase inhibitor (TKI) treatment and clinical status. The main objectives were to compare a series of post-TKI treatment transcriptome profiles to their baseline levels, and characterize the impact of TKI treatment and CML disease status on the individual's transcriptome over time. Our ultimate goal is to develop TKI response predictors using the longitudinal expression data collected over the treatment course.

Peripheral blood samples, buccal swabs and detailed clinical data were collected from each study participant (screened for BCR-ABL1 translocation) for a period of 6 months, in addition to pre-therapy baseline. RNA was extracted from granulocytes isolated from peripheral blood samples, and profiled using RNA sequencing. RNAseq profiles over TKI treatment course were compared to baseline, as well as against hematologic response (complete blood count), cytogenetic response (FISH), and clinical disease progression.

We investigated dynamic trends in RNAseq profiles associated TKI response, as well as with the clinical status of the patient over time. We identified genetic features that were either 1) Differentially expressed between baseline and post-TKI time points; 2) Showed non-random spikes in expression levels at specific time points; 3) Associated with hematological and clinical phenotypes, including white blood cell count, percentage granulocytes and percentage cells with BCR-ABL1 translocation; 4) Demonstrated highly correlated patterns of expression over time. Through clustering and enrichment analysis of the selected transcripts, we identified several pathways and molecular features associated with TKI-response, and altered disease state. Of note, we found mTOR signaling, and pro-apoptotic pathways to be significantly altered between baseline and TKI-responding individuals. In addition, we observed significant changes in transcription regulatory network of several transcription factors, notably AP-1, over the treatment time course.

To our knowledge, this is the first study to establish the utility of comprehensive longitudinal multiple transcriptome profile analysis of TKI-response in CML. We believe this study will pave way for future large-scale longitudinal omic profiling of CML and other cancer-types.

Abstract A1-48: Whole genome expression based drug repurposing

Currently, personalizing cancer chemotherapy relies on pathology and more recently molecular biomarker-based approaches. However, as the driving biology is normally not fully understood, the majority of existing biomarkers do not capture a substantial proportion of variability in drug response. This partly explains the commonly observed lack of reproducibility of findings (e.g. from many conventional gene expression signatures) when these markers are applied to new datasets. We developed an approach to predict in vivo drug sensitivity that leverages whole-genome gene expression microarray data and allows the expression of every gene to influence the prediction by a small amount. Our approach builds statistical models from gene expression and drug sensitivity data in a very large panel of cell lines, then applies these models to gene expression data from primary tumor biopsies. In this study, we applied this approach to tumor samples collected in The Cancer Genome Atlas (TCGA). We derived predicted sensitivity for over one hundred drugs in each of the tumor samples. As a proof-of-concept, we demonstrated that a targeting agent (lapatinib) designed against a specific tumor marker (HER2 positive) is indeed predicted to be more sensitive in HER2 positive breast cancers when compared to the other type of cancers. Meanwhile, we identified other agents that exhibit similar or superior sensitivity when compared to commonly prescribed agents in different disease settings. These findings warrant further evaluation of these agents to be repurposed for possible new indications. Interestingly, some of our derived drug sensitivity predictive estimates are correlated with observed survival outcomes in certain cancer patients. When screening all tumor types based on their molecular profiles, we defined several classes of drugs that maybe differentially effective based on tumor molecular profiles. In conclusion, a genome-wide expression drug sensitivity model built in cell lines can be a powerful approach in repurposing drug in cancer treatment.

Citation Format: Paul Geeleher, Steven Bhutra, Jacqueline Wang, R. Stephanie Huang. Whole genome expression based drug repurposing. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-48.

Abstract B1-01: Whole genome expression based drug repurposing

Currently, personalizing cancer chemotherapy relies on pathology and more recently molecular biomarker-based approaches. However, as the driving biology is normally not fully understood, the majority of existing biomarkers do not capture a substantial proportion of variability in drug response. This partly explains the commonly observed lack of reproducibility of findings (e.g. from many conventional gene expression signatures) when these markers are applied to new datasets. We developed an approach to predict in vivo drug sensitivity that leverages whole-genome gene expression microarray data and allows the expression of every gene to influence the prediction by a small amount. Our approach builds statistical models from gene expression and drug sensitivity data in a very large panel of cell lines, then applies these models to gene expression data from primary tumor biopsies. In this study, we applied this approach to tumor samples collected in The Cancer Genome Atlas (TCGA). We derived predicted sensitivity for over one hundred drugs in each of the tumor samples. As a proof-of-concept, we demonstrated that a targeting agent (lapatinib) designed against a specific tumor marker (HER2 positive) is indeed predicted to be more sensitive in HER2 positive breast cancers when compared to the other type of cancers. Meanwhile, we identified other agents that exhibit similar or superior sensitivity when compared to commonly prescribed agents in different disease settings. These findings warrant further evaluation of these agents to be repurposed for possible new indications. Interestingly, some of our derived drug sensitivity predictive estimates are correlated with observed survival outcomes in certain cancer patients. When screening all tumor types based on their molecular profiles, we defined several classes of drugs that maybe differentially effective based on tumor molecular profiles. In conclusion, a genome-wide expression drug sensitivity model built in cell lines can be a powerful approach in repurposing drug in cancer treatment.

Citation Format: Paul Geeleher, Steven Bhutra, Jacqueline Wang, R. Stephanie Huang. Whole genome expression based drug repurposing. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-01.

Seq-ing improved gene expression estimates from microarrays using machine learning

BACKGROUND

Quantifying gene expression by RNA-Seq has several advantages over microarrays, including greater dynamic range and gene expression estimates on an absolute, rather than a relative scale. Nevertheless, microarrays remain in widespread use, demonstrated by the ever-growing numbers of samples deposited in public repositories.

RESULTS

We propose a novel approach to microarray analysis that attains many of the advantages of RNA-Seq. This method, called Machine Learning of Transcript Expression (MaLTE), leverages samples for which both microarray and RNA-Seq data are available, using a Random Forest to learn the relationship between the fluorescence intensity of sets of microarray probes and RNA-Seq transcript expression estimates. We trained MaLTE on data from the Genotype-Tissue Expression (GTEx) project, consisting of Affymetrix gene arrays and RNA-Seq from over 700 samples across a broad range of human tissues.

CONCLUSION

This approach can be used to accurately estimate absolute expression levels from microarray data, at both gene and transcript level, which has not previously been possible. This methodology will facilitate re-analysis of archived microarray data and broaden the utility of the vast quantities of data still being generated.

Predicting Response to Histone Deacetylase Inhibitors Using High-Throughput Genomics

BACKGROUND

Many disparate biomarkers have been proposed as predictors of response to histone deacetylase inhibitors (HDI); however, all have failed when applied clinically. Rather than this being entirely an issue of reproducibility, response to the HDI vorinostat may be determined by the additive effect of multiple molecular factors, many of which have previously been demonstrated.

METHODS

We conducted a large-scale gene expression analysis using the Cancer Genome Project for discovery and generated another large independent cancer cell line dataset across different cancers for validation. We compared different approaches in terms of how accurately vorinostat response can be predicted on an independent out-of-batch set of samples and applied the polygenic marker prediction principles in a clinical trial.

RESULTS

Using machine learning, the small effects that aggregate, resulting in sensitivity or resistance, can be recovered from gene expression data in a large panel of cancer cell lines.This approach can predict vorinostat response accurately, whereas single gene or pathway markers cannot. Our analyses recapitulated and contextualized many previous findings and suggest an important role for processes such as chromatin remodeling, autophagy, and apoptosis. As a proof of concept, we also discovered a novel causative role for CHD4, a helicase involved in the histone deacetylase complex that is associated with poor clinical outcome. As a clinical validation, we demonstrated that a common dose-limiting toxicity of vorinostat, thrombocytopenia, can be predicted (r = 0.55, P = .004) several days before it is detected clinically.

CONCLUSION

Our work suggests a paradigm shift from single-gene/pathway evaluation to simultaneously evaluating multiple independent high-throughput gene expression datasets, which can be easily extended to other investigational compounds where similar issues are hampering clinical adoption.

Genome-wide interrogation of longitudinal FEV1 in children with asthma

RATIONALE

Most genomic studies of lung function have used phenotypic data derived from a single time-point (e.g., presence/absence of disease) without considering the dynamic progression of a chronic disease.

OBJECTIVES

To characterize lung function change over time in subjects with asthma and identify genetic contributors to a longitudinal phenotype.

METHODS

We present a method that models longitudinal FEV1 data, collected from 1,041 children with asthma who participated in the Childhood Asthma Management Program. This longitudinal progression model was built using population-based nonlinear mixed-effects modeling with an exponential structure and the determinants of age and height.

MEASUREMENTS AND MAIN RESULTS

We found ethnicity was a key covariate for FEV1 level. Budesonide-treated children with asthma had a slight but significant effect on FEV1 when compared with those treated with placebo or nedocromil (P < 0.001). A genome-wide association study identified seven single-nucleotide polymorphisms nominally associated with longitudinal lung function phenotypes in 581 white Childhood Asthma Management Program subjects (P < 10(-4) in the placebo ["discovery"] and P < 0.05 in the nedocromil treatment ["replication"] group). Using ChIP-seq and RNA-seq data, we found that some of the associated variants were in strong enhancer regions in human lung fibroblasts and may affect gene expression in human lung tissue. Genetic mapping restricted to genome-wide enhancer single-nucleotide polymorphisms in lung fibroblasts revealed a highly significant variant (rs6763931; P = 4 × 10(-6); false discovery rate < 0.05).

CONCLUSIONS

This study offers a strategy to explore the genetic determinants of longitudinal phenotypes, provide a comprehensive picture of disease pathophysiology, and suggest potential treatment targets.

Abstract 5561: Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines

Robust prediction of in vivo chemotherapeutic response, using baseline gene expression and drug sensitivity data gathered on cancer cell lines, has been a profoundly important, long standing and controversial problem in pharmacogenomics. Here, we present for the first time, a solution to this problem.

Currently, personalizing cancer chemotherapy relies on pathology and more recently molecular biomarker-based approaches (e.g. ERBB2 amplification in breast cancer). However, as the driving biology are normally not fully understood, the majority of existing biomarkers do not capture a substantial proportion of variability in drug response. This partly explains the commonly observed lack of reproducibility of findings (e.g. from many conventional gene expression signatures) when these markers are applied to new datasets.

In this study, we developed an approach to predict in vivo drug sensitivity that leverages whole-genome gene expression microarray data and allows the expression of every gene to influence the prediction by a small amount. The method works by fitting a ridge regression model of baseline genome-wide gene expression levels against in vitro drug sensitivity in a very large panel of approximately 700 cancer cell lines. Then, after a (crucial) data homogenization step, these models are applied to baseline expression levels from primary tumor biopsies. Our method successfully predicted patient response to different chemotherapeutic agents in three (of four total suitable) independent, publicly available clinical trials, each investigating different drugs and different types of cancer. In each of these cases, we predicted drug response at least as accurately as previously published models that had been derived from the clinical data itself. Interestingly, our approach could also predict clinical response in the absence of any known drug sensitivity biomarker. We effectively enriched for drug responders in breast, myeloma and lung cancers, treated with docetaxel, bortezomib and erlotinib respectively, thus identifying responders to both cytotoxic and targeted agents.

Many previous clinical trials and in vitro assays have attempted to discover biomarkers of drug sensitivity, but found that the genes/aberrations which they had identified, performed poorly as predictors, once applied to out-of-batch sets of samples. Our models, on the other hand, are trained on an independent set of cancer cell lines and performed well on three completely separate and independent clinical trial datasets (all assessed using different microarray platforms). These results have far-reaching implications for personalized medicine and drug development (e.g. for the development of companion diagnostics). All datasets and bioinformatics tools to reproduce our results are publicly available.

Citation Format: Paul Geeleher, Nancy Cox, R. Stephanie Huang. Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5561. doi:10.1158/1538-7445.AM2014-5561

Abstract 2746: An evaluation of poly (ADP-ribose) polymerase inhibitor efficacy in head and neck cancer

Background: Synthetic lethality induced by poly (ADP-ribose) polymerase (PARP) inhibitors can occur in tumors without BRCA mutations. This “BRCAness” phenomenon has been observed in ovarian and triple negative breast cancers, but its role in other malignancies is not known. This study aims to evaluate the potency of PARP inhibitors in head and neck cancer, where BRCA mutations are rare.

Methods: First, we compared three PARP inhibitors (veliparib, olaparib and rucaparib). We subsequently established dose response curves for rucaparib for ten head and neck cancer cell lines and compared with two BRCA deficient breast cancer cell lines. Furthermore, we used immunofluorescent staining for γH2AX and RAD51 to study the capability for the DNA repair mechanism homologous recombination.

Results: We identified rucaparib as the most potent of the three PARP inhibitors tested and found a subset of tumors that show high rucaparib sensitivity (IC50 values: 7.0µM, 10.3µM and 11.7µM) comparable to a BRCA deficient breast cancer cell line (IC50 value: 8.9µM). Foci formation of the homologous recombination marker RAD51 did not serve as a reliable post-treatment biomarker.

Conclusion: In conclusion, we demonstrate that PARP inhibitors are effective in a subset of head and neck cancer cell lines, suggesting that these compounds could play a role in the treatment of a subset of head and neck tumors that exhibit the “BRCAness” phenotype. Further studies regarding the underlying mechanism of this phenotype are warranted.

Citation Format: Jana Heitmann, Paul Geeleher, Michaela Keck, Zhixiang Zuo, Arun Khattri, Susanne Tepper, Michael Beckett, Ralph R. Weichselbaum, Sebastian Fetscher, Everett E. Vokes, Tanguy Seiwert. An evaluation of poly (ADP-ribose) polymerase inhibitor efficacy in head and neck cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2746. doi:10.1158/1538-7445.AM2014-2746

Poly (ADP-ribose) polymerase inhibitor efficacy in head and neck cancer

OBJECTIVES

Poly (ADP-ribose) polymerase inhibitors (PARPi) have shown single agent activity against tumors with deficiencies in the DNA repair mechanism homologous recombination including, but not limited to those harboring BRCA mutations. We hypothesized that, in the context of homologous recombination deficiency (HRD), PARPi could have an effect in head and neck cancer (HNC).

MATERIALS AND METHODS

We evaluated TCGA data for evidence of HRD using a copy number data signature established for breast cancer. The comparative potency of three PARPi was evaluated using cell viability assays in a panel of HNC cell lines and response was compared to BRCA-deficient breast cancer cell lines. The change in foci formation of γH2AX and RAD51 was assessed with immunofluorescent staining after exposure to a PARPi. Baseline gene expression was analyzed using microarray data.

RESULTS

We found a subgroup in the TCGA HNC cohort harboring genomic aberrations consistent with HRD in breast cancer. Rucaparib activity was superior to olaparib and veliparib and showed single agent activity in a subset of HNC cell lines that was comparable to BRCA-deficient breast cancer cell lines. Rucaparib-sensitive and rucaparib-resistant groups showed significant differences in γH2AX and RAD51 foci formation after rucaparib exposure. Expression of genes involved in chromosome structure was strongly associated with rucaparib resistance.

CONCLUSION

We demonstrate that PARPi are effective in a subset of HNC cell lines and propose that HRD may be present in HNC in vivo suggesting that these compounds could play a role in the treatment of HNC.

Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines

We demonstrate a method for the prediction of chemotherapeutic response in patients using only before-treatment baseline tumor gene expression data. First, we fitted models for whole-genome gene expression against drug sensitivity in a large panel of cell lines, using a method that allows every gene to influence the prediction. Following data homogenization and filtering, these models were applied to baseline expression levels from primary tumor biopsies, yielding an in vivo drug sensitivity prediction. We validated this approach in three independent clinical trial datasets, and obtained predictions equally good, or better than, gene signatures derived directly from clinical data.

Gene-set analysis is severely biased when applied to genome-wide methylation data

MOTIVATION

DNA methylation is an epigenetic mark that can stably repress gene expression. Because of its biological and clinical significance, several methods have been developed to compare genome-wide patterns of methylation between groups of samples. The application of gene set analysis to identify relevant groups of genes that are enriched for differentially methylated genes is often a major component of the analysis of these data. This can be used, for example, to identify processes or pathways that are perturbed in disease development. We show that gene-set analysis, as it is typically applied to genome-wide methylation assays, is severely biased as a result of differences in the numbers of CpG sites associated with different classes of genes and gene promoters.

RESULTS

We demonstrate this bias using published data from a study of differential CpG island methylation in lung cancer and a dataset we generated to study methylation changes in patients with long-standing ulcerative colitis. We show that several of the gene sets that seem enriched would also be identified with randomized data. We suggest two existing approaches that can be adapted to correct the bias. Accounting for the bias in the lung cancer and ulcerative colitis datasets provides novel biological insights into the role of methylation in cancer development and chronic inflammation, respectively. Our results have significant implications for many previous genome-wide methylation studies that have drawn conclusions on the basis of such strongly biased analysis.

CONTACT

cathal.seoighe@nuigalway.ie

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The regulatory effect of miRNAs is a heritable genetic trait in humans

BACKGROUND

microRNAs (miRNAs) have been shown to regulate the expression of a large number of genes and play key roles in many biological processes. Several previous studies have quantified the inhibitory effect of a miRNA indirectly by considering the expression levels of genes that are predicted to be targeted by the miRNA and this approach has been shown to be robust to the choice of prediction algorithm. Given a gene expression dataset, Cheng et al. defined the regulatory effect score (RE-score) of a miRNA as the difference in the gene expression rank of targets of the miRNA compared to non-targeted genes.

RESULTS

Using microarray data from parent-offspring trios from the International HapMap project, we show that the RE-score of most miRNAs is correlated between parents and offspring and, thus, inter-individual variation in RE-score has a genetic component in humans. Indeed, the mean RE-score across miRNAs is correlated between parents and offspring, suggesting genetic differences in the overall efficiency of the miRNA biogenesis pathway between individuals. To explore the genetics of this quantitative trait further, we carried out a genome-wide association study of the mean RE-score separately in two HapMap populations (CEU and YRI). No genome-wide significant associations were discovered; however, a SNP rs17409624, in an intron of DROSHA, was significantly associated with mean RE-score in the CEU population following permutation-based control for multiple testing based on all SNPs mapped to the canonical miRNA biogenesis pathway; of 244 individual miRNA RE-scores assessed in the CEU, 214 were associated (p < 0.05) with rs17409624. The SNP was also nominally significantly associated (p = 0.04) with mean RE-score in the YRI population. Interestingly, the same SNP was associated with 17 (8.5% of all expressed) miRNA expression levels in the CEU. We also show here that the expression of the targets of most miRNAs is more highly correlated with global changes in miRNA regulatory effect than with the expression of the miRNA itself.

CONCLUSIONS

We present evidence that miRNA regulatory effect is a heritable trait in humans and that a polymorphism of the DROSHA gene contributes to the observed inter-individual differences.