Biomarker-driven drug repurposing on biologically similar cancers with DNA-repair deficiencies

Abstract

Similar molecular and genetic aberrations among diseases can lead to the discovery of jointly important treatment options across biologically similar diseases. Oncologists closely looked at several hormone-dependent cancers and identified remarkable pathological and molecular similarities in their DNA repair pathway abnormalities. Although deficiencies in Homologous Recombination (HR) pathway plays a significant role towards cancer progression, there could be other DNA-repair pathway deficiencies that requires careful investigation. In this paper, through a biomarker-driven drug repurposing model, we identified several potential drug candidates for breast and prostate cancer patients with DNA-repair deficiencies based on common specific biomarkers and irrespective of the organ the tumors originated from. Normalized discounted cumulative gain (NDCG) and sensitivity analysis were used to assess the performance of the drug repurposing model. Our results showed that Mitoxantrone and Genistein were among drugs with high therapeutic effects that significantly reverted the gene expression changes caused by the disease (FDR adjusted p-values for prostate cancer =1.225e-4 and 8.195e-8, respectively) for patients with deficiencies in their homologous recombination (HR) pathways. The proposed multi-cancer treatment framework, suitable for patients whose cancers had common specific biomarkers, has the potential to identify promising drug candidates by enriching the study population through the integration of multiple cancers and targeting patients who respond poorly to organ-specific treatments.

Keywords: DNA repair; drug repurposing; homologous recombination; mitoxantrone; multi cancer treatment; personalized medicine.

FIGURE 1

The Homologous Recombination pathway. The genes are represented in the rectangular boxes, with the shades of blue representing down-regulated genes for prostate cancer patients.

FIGURE 2

Framework proposed for data-driven drug repurposing for biologically similar cancers—(A): Genes within each of the DNA repair pathways, i.e., HR (Homologous Recombination), BER (Base Excision Repair), MMR (Mismatch Repair, NER(Nucleotide Excision Repair) and NHEJ (Non-Homologous End Joining) were identified using KEGG database. Subset of breast and prostate cancer patients with mutations in DNA repair genes were identified and grouped based on DNA repair deficiency. (B): Differentially expressed genes (DEGs) were identified on each cohort separately. (C): Drugs for each cohort were identified using Drug-Disease Similarity. Framework was validated using NDCG (Normalized Discounted Cumulative Gain) and sensitivity scores; and network interaction analysis was used for validating the utility of the drugs.

FIGURE 3

Target genes consistent with the hypothesis considered: In (A), the signs of the DE genes shown in red (+) and blue (−) match the signs of their respective incoming edges, suggesting that the upstream regulator u is activated. In (B), the signs of the DE genes shown in red (+) and blue (−) are opposite to the signs of their edges, suggesting that the upstream regulator u is inhibited.

FIGURE 4

Performance comparison of the drug-disease similarity model on DNA-repair deficient patient subpopulations using NDCG (left) and sensitivity analysis (right). The NDCG/sensitivity values (vertical axes) of all drug–indication associations using different DNA repair deficient subpopulations are shown according to different cutoff values (horizontal axis). The NDCG results clearly demonstrate that the HR-deficient subpopulations result in drugs that are clinically more relevant with more FDA-approved/investigational drugs compared with other DNA-repair pathway deficiencies. The plot has further shown that identifying homogeneous subpopulations through common biomarkers result in better performances when compared to all patients combined. The sensitivity values demonstrate that the list of breast/prostate cancer drugs retrieved for all cutoff levels are clinically relevant and indicates an overall better performance relative to random controls (shown as the black curve).

FIGURE 5

(A) The mechanism through which Mitoxantone act on the genes measured to be DE for prostate cancer. Note that, out of the 19 downstream DE genes that Mitoxantone is interacting with, 15 were consistent with the hypothesis, i.e., the drug was able to revert the expression changes caused by disease 15 out of 19 times. All 15 genes were shown on the figure with three down-regulated genes (blue circles) being activated, and 12 up-regulated genes (red circles) being inhibited with the exposure of the drug. (B) Fold changes reported for cell lines treated with Mitoxantrone. The upregulated genes are highlighted in red, and the downregulated genes are highlighted in blue.

FIGURE 6

Drugs proposed using cross cancer genes. (A) Breast, prostate and ovarian cancer expression data was used to predict the tissue type and the disease type using multi-label classification—auto encoder (MLC-AE). SHAP Explanation model was used to identify the contribution of each gene towards the prediction using SHAP values that rank the genes. (B) Network interaction analysis was used to perform meta analysis and predict novel drugs.

FIGURE 7

(A) Significant genes identified using SHAP based on contribution scores for all three tissues (breast, prostate and ovary). (T) Denotes solid tumor and (N) denotes solid normal tissue. Figure utilized from Zhou et al. (2021) (B) Top eight drugs proposed for repurposing using cross cancer genes.

FIGURE 8

The drug target genes within the HR pathway and their interactions with the proposed drugs. The differential expression analysis here were conducted on patients with breast cancer with HR deficiencies vs. adjacent normal tissue.

FIGURE 9

(A) The DE genes of prostate cancer associated with the cell cycle downstream of Mitoxantrone and (B) The DE genes of prostate cancer associated with the cell cycle downstream of Genistein.