Disparity between Inter-Patient Molecular Heterogeneity and Repertoires of Target Drugs Used for Different Types of Cancer in Clinical Oncology

Abstract

Inter-patient molecular heterogeneity is the major declared driver of an expanding variety of anticancer drugs and personalizing their prescriptions. Here, we compared interpatient molecular heterogeneities of tumors and repertoires of drugs or their molecular targets currently in use in clinical oncology. We estimated molecular heterogeneity using genomic (whole exome sequencing) and transcriptomic (RNA sequencing) data for 4890 tumors taken from The Cancer Genome Atlas database. For thirteen major cancer types, we compared heterogeneities at the levels of mutations and gene expression with the repertoires of targeted therapeutics and their molecular targets accepted by the current guidelines in oncology. Totally, 85 drugs were investigated, collectively covering 82 individual molecular targets. For the first time, we showed that the repertoires of molecular targets of accepted drugs did not correlate with molecular heterogeneities of different cancer types. On the other hand, we found that the clinical recommendations for the available cancer drugs were strongly congruent with the gene expression but not gene mutation patterns. We detected the best match among the drugs usage recommendations and molecular patterns for the kidney, stomach, bladder, ovarian and endometrial cancers. In contrast, brain tumors, prostate and colorectal cancers showed the lowest match. These findings provide a theoretical basis for reconsidering usage of targeted therapeutics and intensifying drug repurposing efforts.

Algorithmically Deduced FREM2 Molecular Pathway Is a Potent Grade and Survival Biomarker of Human Gliomas

“…Also, quality measuring of hierarchical clustering was applied to CGGA datasets for the same goal. This was performed by the Watermelon multisection method [33] that returns WM metric which positively reflects the quality of clustering of samples into pre-defined groups.…”

“…In addition, hierarchical clustering of samples from two batches of the CGGA project (CGGA_325 and CGGA_693) showed that samples were clustering by batch ID rather by glioma type (LGG or GBM). This was also quantitatively measured by the Watermelon multisection method [33] that returns WM metric which positively reflects the quality of clustering of samples into pre-defined groups, with WM metrics 0.839 and 0.159 for batchand glioma type clustering, respectively (data not shown).…”

Better Agreement of Human Transcriptomic and Proteomic Cancer Expression Data at the Molecular Pathway Activation Level

“…We observed that Pearson and Spearman correlations for pathway activation levels were statistically significantly higher compared to the single gene expression levels in the same four out of seven cancer types (Figures 4, 5, 9 and 11). For the remaining three cancer types, i.e., hepatocellular carcinoma, ovarian serous cystadenocarcinoma, and uterine corpus endometrial carcinoma, Pearson and Spearman correlations showed poor statistical significance (Figures 6, 8 Paired gene-to-gene and PAL-to-PAL correlation between RNA and protein expression levels estimated within Lung Adenocarcinoma biosamples using Pearson (left) and Spearman (right) correlation coefficients for (A) the total set of genes and molecular pathways; (B) the set of drug target genes and molecular pathways [17].…”

Recent Trends in Cancer Genomics and Bioinformatics Tools Development

“…Victor Tkachev and colleagues [27] discussed sequencing data processing in global machine learning methods in clinical oncology. Marianna Zolotovskaia et al [28] studied molecular heterogeneity of target drugs in oncology.…”