Identification of gene-drug interactions that impact patient survival in TCGA

Human NTHL1 expression and subcellular distribution determines cisplatin sensitivity in human lung epithelial and non-small cell lung cancer cells

Base excision repair is critical for maintaining genomic stability and for preventing malignant transformation. NTHL1 is a bifunctional DNA glycosylase/AP lyase that initiates repair of oxidatively damaged pyrimidines. Our recent work established that transient over-expression of NTHL1 leads to acquisition of several hallmarks of cancer in non-tumorigenic immortalized cells likely through interaction with nucleotide excision repair protein XPG. Here, we investigate how NTHL1 expression levels impact cellular sensitivity to cisplatin in non-tumorigenic immortalized cells and five non-small cell lung carcinomas cell lines. The cell line with lowest expression of NTHL1 (H522) shows the highest resistance to cisplatin indicating that decrease in NTHL1 levels may modulate resistance to crosslinking agents in NSCLC tumors. In a complementation study, overexpression of NTHL1 in H522 cell line sensitized it to cisplatin. Using NTHL1 N-terminal deletion mutants defective in nuclear localization we show that cisplatin treatment can alter NTHL1 subcellular localization possibly leading to altered protein-protein interactions and affecting cisplatin sensitivity. Experiments presented in this study reveal a previously unknown link between NTHL1 expression levels and cisplatin sensitivity of NSCLC tumor cells. These findings provide an opportunity to understand how altered NTHL1 expression levels and subcellular distribution can impact cisplatin sensitivity in NSCLC tumor cells.

Methylation of CpG Sites as Biomarkers Predictive of Drug-Specific Patient Survival in Cancer

Background:

Though the development of targeted cancer drugs continues to accelerate, doctors still lack reliable methods for predicting patient response to standard-of-care therapies for most cancers. DNA methylation has been implicated in tumor drug response and is a promising source of predictive biomarkers of drug efficacy, yet the relationship between drug efficacy and DNA methylation remains largely unexplored.

Method:

In this analysis, we performed log-rank survival analyses on patients grouped by cancer and drug exposure to find CpG sites where binary methylation status is associated with differential survival in patients treated with a specific drug but not in patients with the same cancer who were not exposed to that drug. We also clustered these drug-specific CpG sites based on co-methylation among patients to identify broader methylation patterns that may be related to drug efficacy, which we investigated for transcription factor binding site enrichment using gene set enrichment analysis.

Results:

We identified CpG sites that were drug-specific predictors of survival in 38 cancer-drug patient groups across 15 cancers and 20 drugs. These included 11 CpG sites with similar drug-specific survival effects in multiple cancers. We also identified 76 clusters of CpG sites with stronger associations with patient drug response, many of which contained CpG sites in gene promoters containing transcription factor binding sites.

Conclusion:

These findings are promising biomarkers of drug response for a variety of drugs and contribute to our understanding of drug-methylation interactions in cancer. Investigation and validation of these results could lead to the development of targeted co-therapies aimed at manipulating methylation in order to improve efficacy of commonly used therapies and could improve patient survival and quality of life by furthering the effort toward drug response prediction.

Integrative analysis of TCGA data identifies miRNAs as drug-specific survival biomarkers

Biomarkers predictive of drug-specific outcomes are important tools for personalized medicine. In this study, we present an integrative analysis to identify miRNAs that are predictive of drug-specific survival outcome in cancer. Using the clinical data from TCGA, we defined subsets of cancer patients who suffered from the same cancer and received the same drug treatment, which we call cancer-drug groups. We then used the miRNA expression data in TCGA to evaluate each miRNA’s ability to predict the survival outcome of patients in each cancer-drug group. As a result, the identified miRNAs are predictive of survival outcomes in a cancer-specific and drug-specific manner. Notably, most of the drug-specific miRNA survival markers and their target genes showed consistency in terms of correlations in their expression and their correlations with survival. Some of the identified miRNAs were supported by published literature in contexts of various cancers. We explored several additional breast cancer datasets that provided miRNA expression and survival data, and showed that our drug-specific miRNA survival markers for breast cancer were able to effectively stratify the prognosis of patients in those additional datasets. Together, this analysis revealed drug-specific miRNA markers for cancer survival, which can be promising tools toward personalized medicine.

Identifying gene expression patterns associated with drug-specific survival in cancer patients

The ability to predict the efficacy of cancer treatments is a longstanding goal of precision medicine that requires improved understanding of molecular interactions with drugs and the discovery of biomarkers of drug response. Identifying genes whose expression influences drug sensitivity can help address both of these needs, elucidating the molecular pathways involved in drug efficacy and providing potential ways to predict new patients’ response to available therapies. In this study, we integrated cancer type, drug treatment, and survival data with RNA-seq gene expression data from The Cancer Genome Atlas to identify genes and gene sets whose expression levels in patient tumor biopsies are associated with drug-specific patient survival using a log-rank test comparing survival of patients with low vs. high expression for each gene. This analysis was successful in identifying thousands of such gene–drug relationships across 20 drugs in 14 cancers, several of which have been previously implicated in the respective drug’s efficacy. We then clustered significant genes based on their expression patterns across patients and defined gene sets that are more robust predictors of patient outcome, many of which were significantly enriched for target genes of one or more transcription factors, indicating several upstream regulatory mechanisms that may be involved in drug efficacy. We identified a large number of genes and gene sets that were potentially useful as transcript-level biomarkers for predicting drug-specific patient survival outcome. Our gene sets were robust predictors of drug-specific survival and our results included both novel and previously reported findings, suggesting that the drug-specific survival marker genes reported herein warrant further investigation for insights into drug mechanisms and for validation as biomarkers to aid cancer therapy decisions.

Proteomics Approach of Rapamycin Anti-Tumoral Effect on Primary and Metastatic Canine Mammary Tumor Cells In Vitro

Rapamycin is an antifungal drug with antitumor activity and acts inhibiting the mTOR complex. Due to drug antitumor potential, the aim of this study was to evaluate its effect on a preclinical model of primary mammary gland tumors and their metastases from female dogs. Four cell lines from our cell bank, two from primary canine mammary tumors (UNESP-CM1, UNESP-CM60) and two metastases (UNESP-MM1, and UNESP-MM4) were cultured in vitro and investigated for rapamycin IC₅₀. Then, cell lines were treated with rapamycin IC₅₀ dose and mRNA and protein were extracted in treated and non-treated cells to perform AKT, mTOR, PTEN and 4EBP1 gene expression and global proteomics by mass spectrometry. MTT assay demonstrated rapamycin IC₅₀ dose for all different tumor cells between 2 and 10 μM. RT-qPCR from cultured cells, control versus treated group and primary tumor cells versus metastatic tumor cells, did not shown statistical differences. In proteomics were found 273 proteins in all groups, and after data normalization 49 and 92 proteins were used for statistical analysis for comparisons between control versus rapamycin treatment groups, and metastasis versus primary tumor versus metastasis rapamycin versus primary tumor rapamycin, respectively. Considering the two statistical analysis, four proteins, phosphoglycerate mutase, malate dehydrogenase, l-lactate dehydrogenase and nucleolin were found in decreased abundance in the rapamycin group and they are related with cellular metabolic processes and enhanced tumor malignant behavior. Two proteins, dihydrolipoamide dehydrogenase and superoxide dismutase, also related with metabolic processes, were found in higher abundance in rapamycin group and are associated with apoptosis. The results suggested that rapamycin was able to inhibit cell growth of mammary gland tumor and metastatic tumors cells in vitro, however, concentrations needed to reach the IC₅₀ were higher when compared to other studies.

Personalized Network Modeling of the Pan-Cancer Patient and Cell Line Interactome

PURPOSE

Personalized network inference on diverse clinical and in vitro model systems across cancer types can be used to delineate specific regulatory mechanisms, uncover drug targets and pathways, and develop individualized predictive models in cancer.

METHODS

We developed TransPRECISE (personalized cancer-specific integrated network estimation model), a multiscale Bayesian network modeling framework, to analyze the pan-cancer patient and cell line interactome to identify differential and conserved intrapathway activities, to globally assess cell lines as representative models for patients, and to develop drug sensitivity prediction models. We assessed pan-cancer pathway activities for a large cohort of patient samples (> 7,700) from the Cancer Proteome Atlas across ≥ 30 tumor types, a set of 640 cancer cell lines from the MD Anderson Cell Lines Project spanning 16 lineages, and ≥ 250 cell lines' response to > 400 drugs.

RESULTS

TransPRECISE captured differential and conserved proteomic network topologies and pathway circuitry between multiple patient and cell line lineages: ovarian and kidney cancers shared high levels of connectivity in the hormone receptor and receptor tyrosine kinase pathways, respectively, between the two model systems. Our tumor stratification approach found distinct clinical subtypes of the patients represented by different sets of cell lines: patients with head and neck tumors were classified into two different subtypes that are represented by head and neck and esophagus cell lines and had different prognostic patterns (456 v 654 days of median overall survival; P = .02). High predictive accuracy was observed for drug sensitivities in cell lines across multiple drugs (median area under the receiver operating characteristic curve > 0.8) using Bayesian additive regression tree models with TransPRECISE pathway scores.

CONCLUSION

Our study provides a generalizable analytic framework to assess the translational potential of preclinical model systems and to guide pathway-based personalized medical decision making, integrating genomic and molecular data across model systems.

PANOPLY: Omics-Guided Drug Prioritization Method Tailored to an Individual Patient

PURPOSE

The majority of patients with cancer receive treatments that are minimally informed by omics data. We propose a precision medicine computational framework, PANOPLY (Precision Cancer Genomic Report: Single Sample Inventory), to identify and prioritize drug targets and cancer therapy regimens.

MATERIALS AND METHODS

The PANOPLY approach integrates clinical data with germline and somatic features obtained from multiomics platforms and applies machine learning and network analysis approaches in the context of the individual patient and matched controls. The PANOPLY workflow uses the following four steps: selection of matched controls to the patient of interest; identification of patient-specific genomic events; identification of suitable drugs using the driver-gene network and random forest analyses; and provision of an integrated multiomics case report of the patient with prioritization of anticancer drugs.

RESULTS

The PANOPLY workflow can be executed on a stand-alone virtual machine and is also available for download as an R package. We applied the method to an institutional breast cancer neoadjuvant chemotherapy study that collected clinical and genomic data as well as patient-derived xenografts to investigate the prioritization offered by PANOPLY. In a chemotherapy-resistant patient-derived xenograft model, we found that that the prioritized drug, olaparib, was more effective than placebo in treating the tumor ( P < .05). We also applied PANOPLY to in-house and publicly accessible multiomics tumor data sets with therapeutic response or survival data available.

CONCLUSION

PANOPLY shows promise as a means to prioritize drugs on the basis of clinical and multiomics data for an individual patient with cancer. Additional studies are needed to confirm this approach.

GDISC: a web portal for integrative analysis of gene-drug interaction for survival in cancer

Summary

Survival analysis has been applied to The Cancer Genome Atlas (TCGA) data. Although drug exposure records are available in TCGA, existing survival analyses typically did not consider drug exposure, partly due to naming inconsistencies in the data. We have spent extensive effort to standardize the drug exposure data, which enabled us to perform survival analysis on drug-stratified subpopulations of cancer patients. Using this strategy, we integrated gene copy number data, drug exposure data and patient survival data to infer gene-drug interactions that impact survival. The collection of all analyzed gene-drug interactions in 32 cancer types are organized and presented in a searchable web-portal called gene-drug Interaction for survival in cancer (GDISC). GDISC allows biologists and clinicians to interactively explore the gene-drug interactions identified in the context of TCGA, and discover interactions associated to their favorite cancer, drug and/or gene of interest. In addition, GDISC provides the standardized drug exposure data, which is a valuable resource for developing new methods for drug-specific analysis.

Availability and Implementation

GDISC is available at https://gdisc.bme.gatech.edu/.

Contact

peng.qiu@bme.gatech.edu.