High-Throughput Architecture for Discovering Combination Cancer Therapeutics

Abstract

Purpose: The amount of available next-generation sequencing data of tumors, in combination with relevant molecular and clinical data, has significantly increased in the last decade and transformed translational cancer research. Even with the progress made through data-sharing initiatives, there is a clear unmet need for easily accessible analyses tools. These include capabilities to efficiently process large sequencing database projects to present them in a straightforward and accurate way. Another urgent challenge in cancer research is to identify more effective combination therapies.

Methods: We have created a software architecture that allows the user to integrate and analyze large-scale sequencing, clinical, and other datasets for efficient prediction of potential combination drug targets. This architecture permits predictions for all genes pairs; however, Food and Drug Administration-approved agents are currently lacking for most of the identified gene targets.

Results: By applying this approach, we performed a comprehensive study and analyzed all possible combination partners and identified potentially synergistic target pairs for 38 approved targets currently in clinical use. We further showed which genes could be synergistic prediction markers and potential targets with MAPK/ERK inhibitors for the treatment of melanoma. Moreover, we integrated a graph analytics technique in this architecture to identify pathways that could be targeted synergistically to enhance the efficacy of certain therapeutics in cancer.

Conclusion: The architecture and the results presented provide a foundation for discovering effective combination therapeutics.

Fig 1.

The high-throughput computing architecture. Clinical and gene expression data are distributed and permuted as RDDs and used to calculate the survival statistics (see algorithms 1 and 2 in the text) that make up the primary results. These primary survival results are then filtered and merged, using the CGE, into a knowledge graph built from publicly available databases representing known protein associations. This knowledge graph was used to explore and identify interesting relationships between genes and pathways that showed significant association with survivability. Tableau Desktop and Cytoscape were used to visualize these data and their relationships. CGE, Cray Graph Engine; HDFS, Hadoop Distributed File System; LUAD, lung adenocarcinoma; MESO, mesothelioma; OV, ovarian serous cystadenocarcinoma; RDD, resilient distributed data set; SKCM, skin cutaneous melanoma.

Fig 2.

Combination predictions for US Food and Drug Administration-approved targeted therapeutics. (A) Drug-target gene and survival correlations are denoted by colored rectangles; the color represents the ratio of survival in the groups in which both targets are low (below median) or both are high (above median). The x- and y-axes show the same genes. Black rectangle in the LGG panel highlights the VEGFA–BTK combination, for which (B, C) individual and (D) combined survival analyses are shown. Blue rectangles denote the MAP2K1 (MEK1) and CDK4/6 combinations with Kaplan-Meier curves shown for (E) breast cancer, (F) mesothelioma, and (G) pancreatic cancer. BRCA, breast invasive carcinoma; LAML, acute myeloid leukemia; LGG, brain lower grade glioma; MESO, mesothelioma; PAAD, pancreatic adenocarcinoma.

Fig 3.

Predicted combinations to target the MAPK/ERK pathway in melanoma. Median survival in months is shown for the following four groups of patients with melanoma: expression of both genes are above the median expression levels (high-high group), the expression of one gene is above while the other is below median expression (high-low group), the expression of the first gene is below while the second is above median expression (low-high group), and the expression of both genes are below the median expression levels (low-low group). One of the combination partners is always MAPK1, and the other gene is shown on the x-axis. (A) Color represents gene expression. MAPK1–PKN3 survival analysis is shown as an example. (B) Blue: MAPK1 low, PKN3 low; red: MAPK1 low/PKN3 high; yellow: MAPK1 high/PKN3 low; gray: MAPK1 high/PKN3 high, together with (C) expression analysis of these genes, and (D) a correlation analysis of sorafenib concentration achieving half-maximal response and MAPK1 and PKN3 expression in the Cancer Cell Line Encyclopedia database. BRCA, breast invasive carcinoma; LGG, brain lower grade glioma; MESO, mesothelioma; PAAD, pancreatic adenocarcinoma; SKCM, skin cutaneous melanoma; TPM, transcripts per million.

Fig 4.

Pathway analysis of predicted potential targets to combine with MAPK/ERK inhibition, using graph analytics. Genes predicted to be potential MAPK1 combination partners are shown in circles; the red background color indicates median gene expression. MAPK1 and PKN3 are highlighted with blue. Pathways associated with these genes in the Reactome database are connected to the gene names and shown at (A) the top pathway level and also in the signal transduction pathway group at (B) the second level.