A systematic analysis of genomics-based modeling approaches for prediction of drug response to cytotoxic chemotherapies

MESIA: multi-epigenome sample integration approach for precise peak calling

The assay for transposase-accessible chromatin with sequencing (ATAC-seq) is the most widely used method for measuring chromatin accessibility. Researchers have included multi-sample replication in ATAC-seq experimental designs. In epigenomic analysis, researchers should measure subtle changes in the peak by considering the read depth of individual samples. It is important to determine whether the peaks of each replication have an integrative meaning for the region of interest observed during multi-sample integration. We developed multi-epigenome sample integration approach for precise peak calling (MESIA), which integrates replication with high representativeness and reproducibility in multi-sample replication and determines the optimal peak. After identifying the reproducibility between all replications, our method integrated multiple samples determined as representative replicates. MESIA detected 6.06 times more peaks, and the value of the peaks was 1.32 times higher than the previously used method. MESIA is a shell-script-based open-source code that provides researchers involved in the epigenome with comprehensive insights.

Tissue-specific identification of multi-omics features for pan-cancer drug response prediction

Current statistical models for drug response prediction and biomarker identification fall short in leveraging the shared and unique information from various cancer tissues and multi-omics profiles. We developed mix-lasso model that introduces an additional sample group penalty term to capture tissue-specific effects of features on pan-cancer response prediction. The mix-lasso model takes into account both the similarity between drug responses (i.e., multi-task learning), and the heterogeneity between multi-omics data (multi-modal learning). When applied to large-scale pharmacogenomics dataset from Cancer Therapeutics Response Portal, mix-lasso enabled accurate drug response predictions and identification of tissue-specific predictive features in the presence of various degrees of missing data, drug-drug correlations, and high-dimensional and correlated genomic and molecular features that often hinder the use of statistical approaches in drug response modeling. Compared to tree lasso model, mix-lasso identified a smaller number of tissue-specific features, hence making the model more interpretable and stable for drug discovery applications.

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Pan-cancer cell lines provide a test bench for exploring gene-drug relationships

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Multi-omics data were integrated with pharmacological profiles for joint modeling

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Mix-lasso identifies tissue-specific biomarkers predictive of multi-drug responses

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Mix-lasso provides small number of stable features for drug discovery applications

Canine and murine models of osteosarcoma

Osteosarcoma (OS) is the most common malignant bone tumor in children. Despite efforts to develop and implement new therapies, patient outcomes have not measurably improved since the 1980s. Metastasis continues to be the main source of patient mortality, with 30% of cases developing metastatic disease within 5 years of diagnosis. Research models are critical in the advancement of cancer research and include a variety of species. For example, xenograft and patient-derived xenograft (PDX) mouse models provide opportunities to study human tumor cells in vivo while transgenic models have offered significant insight into the molecular mechanisms underlying OS development. A growing recognition of naturally occurring cancers in companion species has led to new insights into how veterinary patients can contribute to studies of cancer biology and drug development. The study of canine cases, including the use of diagnostic tissue archives and clinical trials, offers a potential mechanism to further canine and human cancer research. Advancement in the field of OS research requires continued development and appropriate use of animal models. In this review, animal models of OS are described with a focus on the mouse and tumor-bearing pet dog as parallel and complementary models of human OS.

Prospective clinical trial testing COXEN-based gene expression models of chemosensitivity in dogs with spontaneous osteosarcoma

BACKGROUND

This study is a prospective clinical trial in dogs with osteosarcoma testing a gene expression model (GEM) predicting the chemosensitivity of tumors to carboplatin (CARBO) or doxorubicin (DOX) developed using the COXEN method.

PATIENTS AND METHODS

Sixty dogs with appendicular osteosarcoma were enrolled in this trial. RNA isolation and gene expression profiling were conducted with 2 biopsies for 54/63 screened tumors, and with a single biopsy for 9 tumors. Resulting gene expression data were used for calculation of a COXEN score for CARBO and DOX based on a previous study showing the significance of this predictor on patient outcome utilizing retrospective data (BMC Bioinformatics 17:93). Dogs were assigned adjuvant CARBO, DOX or the combination based on the results of the COXEN score following surgical removal of the tumor via amputation and were monitored for disease progression by chest radiograph every 2 months.

RESULTS

The COXEN predictor of chemosensitivity to CARBO or DOX was not a significant predictor of progression-free interval or overall survival for the trial participants. The calculation of DOX COXEN score using gene expression data from two independent biopsies of the same tumor were highly correlated (P < 0.0001), whereas the calculated CARBO COXEN score was not (P = 0.3039).

CONCLUSION

The COXEN predictor of chemosensitivity to CARBO or DOX is not a significant predictor of outcome when utilized in this prospective study. This trial represents the first prospective trial of a GEM predictor of chemosensitivity and establishes pet dogs with cancer as viable surrogates for prospective trials of prognostic indicators.

Bipartite graph-based approach for clustering of cell lines by gene expression-drug response associations

MOTIVATION

In pharmacogenomic studies, the biological context of cell lines influences the predictive ability of drug-response models and the discovery of biomarkers. Thus, similar cell lines are often studied together based on prior knowledge of biological annotations. However, this selection approach is not scalable with the number of annotations, and the relationship between gene-drug association patterns and biological context may not be obvious.

RESULTS

We present a procedure to compare cell lines based on their gene-drug association patterns. Starting with a grouping of cell lines from biological annotation, we model gene-drug association patterns for each group as a bipartite graph between genes and drugs. This is accomplished by applying sparse canonical correlation analysis (SCCA) to extract the gene-drug associations, and using the canonical vectors to construct the edge weights. Then, we introduce a nuclear norm-based dissimilarity measure to compare the bipartite graphs. Accompanying our procedure is a permutation test to evaluate the significance of similarity of cell line groups in terms of gene-drug associations. In the pharmacogenomics datasets CTRP2, GDSC2, and CCLE, hierarchical clustering of carcinoma groups based on this dissimilarity measure uniquely reveals clustering patterns driven by carcinoma subtype rather than primary site. Next, we show that the top associated drugs or genes from SCCA can be used to characterize the clustering patterns of haematopoietic and lymphoid malignancies. Finally, we confirm by simulation that when drug responses are linearly-dependent on expression, our approach is the only one that can effectively infer the true hierarchy compared to existing approaches.

AVAILABILITY

Bipartite graph-based hierarchical clustering is implemented in R and can be obtained from CRAN: https://CRAN.R-project.org/package=hierBipartite. The source code is available at https://github.com/CalvinTChi/hierBipartite.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Impact of between-tissue differences on pan-cancer predictions of drug sensitivity

Increased availability of drug response and genomics data for many tumor cell lines has accelerated the development of pan-cancer prediction models of drug response. However, it is unclear how much between-tissue differences in drug response and molecular characteristics may contribute to pan-cancer predictions. Also unknown is whether the performance of pan-cancer models could vary by cancer type. Here, we built a series of pan-cancer models using two datasets containing 346 and 504 cell lines, each with MEK inhibitor (MEKi) response and mRNA expression, point mutation, and copy number variation data, and found that, while the tissue-level drug responses are accurately predicted (between-tissue ρ = 0.88–0.98), only 5 of 10 cancer types showed successful within-tissue prediction performance (within-tissue ρ = 0.11–0.64). Between-tissue differences make substantial contributions to the performance of pan-cancer MEKi response predictions, as exclusion of between-tissue signals leads to a decrease in Spearman’s ρ from a range of 0.43–0.62 to 0.30–0.51. In practice, joint analysis of multiple cancer types usually has a larger sample size, hence greater power, than for one cancer type; and we observe that higher accuracy of pan-cancer prediction of MEKi response is almost entirely due to the sample size advantage. Success of pan-cancer prediction reveals how drug response in different cancers may invoke shared regulatory mechanisms despite tissue-specific routes of oncogenesis, yet predictions in different cancer types require flexible incorporation of between-cancer and within-cancer signals. As most datasets in genome sciences contain multiple levels of heterogeneity, careful parsing of group characteristics and within-group, individual variation is essential when making robust inference.

One of the central goals for precision oncology is to tailor treatment of individual tumors by their molecular characteristics. While drug response predictions have traditionally been sought within each cancer type, it has long been hoped to develop more robust predictions by jointly considering diverse cancer types. While such pan-cancer approaches have improved in recent years, it remains unclear whether between-tissue differences are contributing to the reported pan-cancer prediction performance. This concern stems from the observation that, when cancer types differ in both molecular features and drug response, strong predictive information can come mainly from differences among tissue types. Our study finds that both between- and within-cancer type signals provide substantial contributions to pan-cancer drug response prediction models, and about half of the cancer types examined are poorly predicted despite strong overall performance across all cancer types. We also find that pan-cancer prediction models perform similarly or better than cancer type-specific models, and in many cases the advantage of pan-cancer models is due to the larger number of samples available for pan-cancer analysis. Our results highlight tissue-of-origin as a key consideration for pan-cancer drug response prediction models, and recommend cancer type-specific considerations when translating pan-cancer prediction models for clinical use.

Predicting chemosensitivity using drug perturbed gene dynamics

Background

One of the current directions of precision medicine is the use of computational methods to aid in the diagnosis, prognosis, and treatment of disease based on data driven approaches. For instance, in oncology, there has been a particular focus on development of algorithms and biomarkers that can be used for pre-clinical and clinical applications. In particular large-scale omics-based models to predict drug sensitivity in in vitro cancer cell line panels have been used to explore the utility and aid in the development of these models as clinical tools. Additionally, a number of web-based interfaces have been constructed for researchers to explore the potential of drug perturbed gene expression as biomarkers including the NCI Transcriptional Pharmacodynamic Workbench. In this paper we explore the influence of drug perturbed gene dynamics of the NCI Transcriptional Pharmacodynamics Workbench in computational models to predict in vitro drug sensitivity for 15 drugs on the NCI60 cell line panel.

Results

This work presents three main findings. First, our models show that gene expression profiles that capture changes in gene expression after 24 h of exposure to a high concentration of drug generates the most accurate predictive models compared to the expression profiles under different dosing conditions. Second, signatures of 100 genes are developed for different gene expression profiles; furthermore, when the gene signatures are applied across gene expression profiles model performance is substantially decreased when gene signatures developed using changes in gene expression are applied to non-drugged gene expression. Lastly, we show that the gene interaction networks developed on these signatures show different network topologies and can be used to inform selection of cancer relevant genes.

Conclusion

Our models suggest that perturbed gene signatures are predictive of drug response, but cannot be applied to predict drug response using unperturbed gene expression. Furthermore, additional drug perturbed gene expression measurements in in vitro cell lines could generate more predictive models; but, more importantly be used in conjunction with computational methods to discover important drug disease relationships.