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. 2020 Feb 26;21(5):1580.
doi: 10.3390/ijms21051580.

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

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Free PMC article

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

Marianna A Zolotovskaia et al. Int J Mol Sci. .
Free PMC article

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.

Keywords: cancer drugs; chemotherapy; clinical oncology; genome; molecular diagnostics; mutations; personalized medicine; targeted therapeutics; transcriptome; tumor heterogeneity.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Correlation between molecular variability and sampling size for cancer groups under investigation. Standard deviation (SD) was used as a measure of variation, R is Pearson correlation coefficient. (a) Correlation of dataset size and SD for gene expression (logarithmic on base 10). Each dot represents one gene, totally 36304 genes investigated for 13 cancer types. (b) Correlation of cancer group size and SD for normalized gene mutation rate. Each dot represents one gene, totally 19618 genes investigated for 13 cancer types. (c) Distribution of correlation coefficients between SD and number of samples in a group for 1000 randomly formed datasets of gene expression data (logarithmic on base 10). (d) Distribution of correlation coefficients between SD and number of samples in a group for 1000 randomly formed datasets of normalized gene mutation rate.
Figure 2
Figure 2
Intragroup tumor heterogeneities of molecular profiles in comparison with number of targets for National Comprehensive Cancer Network (NCCN)-recommended drugs. Expression data means logarithmic Deseq2-normalized expression counts, mutation data means normalized mutation rate. Measures of heterogeneity: (a) pairwise intragroup distances for expression data, (b) pairwise intragroup distances for mutation data, (c) WM area as indicator of clustering quality for the expression (blue bins) and mutation (red bins) data. (d) Correlation of average pairwise distance per group with numbers of molecular targets for the respective NCCN-recommended drugs, for expression and mutation data. (e) Correlation of clustering quality (WM area) with number of molecular targets for the respective NCCN-recommended drugs, for expression and mutation data. Cancer type abbreviations: BLCA -Bladder Urothelial Carcinoma, GBM—Glioblastoma multiforme, LGG—Lower Grade Glioma, BRCA—Breast invasive carcinoma, CESC—Cervical squamous cell carcinoma and endocervical adenocarcinoma, COAD—Colon adenocarcinoma, READ—Rectum adenocarcinoma, KIRC—Kidney renal clear cell carcinoma, KIRP—Kidney renal papillary cell carcinoma, LIHC—Liver Hepatocellular carcinoma, LUAD—Lung adenocarcinoma, LUSC—Lung squamous cell carcinoma, OV—Ovarian serous cystadenocarcinoma, PRAD—Prostate adenocarcinoma, STAD—Stomach adenocarcinoma, THCA—Thyroid carcinoma, UCEC—Uterine Corpus Endometrial Carcinoma.
Figure 3
Figure 3
Intragroup tumor heterogeneities of molecular profiles for drug target genes in comparison with number of targets for NCCN-recommended drugs. Expression data means logarithmic Deseq2-normalized expression counts, mutation data means normalized mutation rate. Measures of heterogeneity: (a) pairwise intragroup distances for expression data, (b) pairwise intragroup distances for mutation data, (c) WM area as indicator of clustering quality for the expression (blue bins) and mutation (red bins) data. (d) Correlation of average pairwise distance per group with numbers of molecular targets for the respective NCCN-recommended drugs, for expression and mutation data. (e) Correlation of clustering quality (WM area) with number of molecular targets for the respective NCCN-recommended drugs, for expression and mutation data. Cancer type abbreviations: BLCA -Bladder Urothelial Carcinoma, GBM—Glioblastoma multiforme, LGG—Lower Grade Glioma, BRCA—Breast invasive carcinoma, CESC—Cervical squamous cell carcinoma and endocervical adenocarcinoma, COAD—Colon adenocarcinoma, READ—Rectum adenocarcinoma, KIRC—Kidney renal clear cell carcinoma, KIRP—Kidney renal papillary cell carcinoma, LIHC—Liver Hepatocellular carcinoma, LUAD—Lung adenocarcinoma, LUSC—Lung squamous cell carcinoma, OV—Ovarian serous cystadenocarcinoma, PRAD—Prostate adenocarcinoma, STAD—Stomach adenocarcinoma, THCA—Thyroid carcinoma, UCEC—Uterine Corpus Endometrial Carcinoma.
Figure 4
Figure 4
Correlation between cancer type-specific average pairwise distances for gene expression and mutation data. Each dot corresponds to one cancer type, R is Pearson correlation coefficient. Correlations are shown for molecular profiles of all genes (a) and for drug target genes (b).
Figure 5
Figure 5
Clustering of thirteen cancer types by molecular profiles and targets of NCCN-recommended drugs. Clustering by (a) averaged expression profiles (logarithm of Deseq2 normalized expression counts) for NCCN-recommended drug target genes, (b) averaged mutation profiles (NMR) for NCCN-recommended drug target genes, (c) molecular targets of NCCN-recommended drugs, (d) expression profiles (logarithm of Deseq2 normalized expression counts) for all genes, (e) mutation profiles (NMR) for all genes. Cancer type abbreviations: BLCA—Bladder Urothelial Carcinoma, GBM—Glioblastoma multiforme, LGG—Lower Grade Glioma, BRCA—Breast invasive carcinoma, CESC—Cervical squamous cell carcinoma and endocervical adenocarcinoma, COAD—Colon adenocarcinoma, READ—Rectum adenocarcinoma, KIRC—Kidney renal clear cell carcinoma, KIRP—Kidney renal papillary cell carcinoma, LIHC—Liver Hepatocellular carcinoma, LUAD—Lung adenocarcinoma, LUSC—Lung squamous cell carcinoma, OV—Ovarian serous cystadenocarcinoma, PRAD—Prostate adenocarcinoma, STAD—Stomach adenocarcinoma, THCA—Thyroid carcinoma, UCEC—Uterine Corpus Endometrial Carcinoma.

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References

    1. Bedard P.L., Hansen A.R., Ratain M.J., Siu L.L. Tumour heterogeneity in the clinic. Nature. 2013;501:355–364. doi: 10.1038/nature12627. - DOI - PMC - PubMed
    1. Jamal-Hanjani M., Quezada S.A., Larkin J., Swanton C. Translational Implications of Tumor Heterogeneity. Clin. Cancer Res. 2015;21:1258. doi: 10.1158/1078-0432.CCR-14-1429. - DOI - PMC - PubMed
    1. Grzywa T.M., Paskal W., Włodarski P.K. Intratumor and Intertumor Heterogeneity in Melanoma. Transl. Oncol. 2017;10:956–975. doi: 10.1016/j.tranon.2017.09.007. - DOI - PMC - PubMed
    1. Miyauchi T., Yaguchi T., Kawakami Y. Inter-patient and Intra-tumor Heterogeneity in the Sensitivity to Tumor-targeted Immunity in Colorectal Cancer. Japanese J. Clin. Immunol. 2017;40:54–59. doi: 10.2177/jsci.40.54. - DOI - PubMed
    1. Cusnir M., Cavalcante L. Inter-tumor heterogeneity. Hum. Vaccin. Immunother. 2012;8:1143–1145. doi: 10.4161/hv.21203. - DOI - PMC - PubMed
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