MOTIVATION: Traditional cancer therapy is focused on eradicating fast proliferating population of tumor cells. However, existing evidences suggest survival of sub-population of cancer cells that can resist chemotherapy by entering a 'persister' state of minimal growth. These cells eventually survive to produce cells resistant to drugs. The identifying of appropriate targets that can eliminate the drug-tolerant 'persisters' remains a challenge. Hence, a deeper understanding of the distinctive genetic signatures that lead to resistance is of utmost importance to design an appropriate therapy. RESULTS: In this study, deep-sequencing of mRNA was performed in osteosarcoma (OS) cells, exposed to the widely used drug, cisplatin which is an integral part of current treatment regime for OS. Transcriptomic analysis was performed in (i) untreated OS; (ii) persister sub-population of cells post-drug shock; (iii) cells which evade growth bottleneck and (iv) drug-resistant cells obtained after several rounds of drug shock and revival. The transcriptomic signatures and pathways regulated in each group were compared; the transcriptomic pipeline to the acquisition of resistance was analyzed and the core network of genes altered during the process was delineated. Additionally, our transcriptomic data were compared with OS patient data obtained from Gene Ontology Omnibus. We observed a sub-set of genes to be commonly expressed in both data sets with a high correlation (0.81) in expression pattern. To the best of our knowledge, this study is uniquely designed to understand the series of genetic changes leading to the emergence of drug-resistant cells, and implications from this study have a potential therapeutic impact. AVAILABILITY AND IMPLEMENTATION: All raw data can be accessed from GEO database (https://www.ncbi.nlm.nih.gov/geo/) under the GEO accession number GSE86053. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Traditional cancer therapy is focused on eradicating fast proliferating population of tumor cells. However, existing evidences suggest survival of sub-population of cancer cells that can resist chemotherapy by entering a 'persister' state of minimal growth. These cells eventually survive to produce cells resistant to drugs. The identifying of appropriate targets that can eliminate the drug-tolerant 'persisters' remains a challenge. Hence, a deeper understanding of the distinctive genetic signatures that lead to resistance is of utmost importance to design an appropriate therapy. RESULTS: In this study, deep-sequencing of mRNA was performed in osteosarcoma (OS) cells, exposed to the widely used drug, cisplatin which is an integral part of current treatment regime for OS. Transcriptomic analysis was performed in (i) untreated OS; (ii) persister sub-population of cells post-drug shock; (iii) cells which evade growth bottleneck and (iv) drug-resistant cells obtained after several rounds of drug shock and revival. The transcriptomic signatures and pathways regulated in each group were compared; the transcriptomic pipeline to the acquisition of resistance was analyzed and the core network of genes altered during the process was delineated. Additionally, our transcriptomic data were compared with OS patient data obtained from Gene Ontology Omnibus. We observed a sub-set of genes to be commonly expressed in both data sets with a high correlation (0.81) in expression pattern. To the best of our knowledge, this study is uniquely designed to understand the series of genetic changes leading to the emergence of drug-resistant cells, and implications from this study have a potential therapeutic impact. AVAILABILITY AND IMPLEMENTATION: All raw data can be accessed from GEO database (https://www.ncbi.nlm.nih.gov/geo/) under the GEO accession number GSE86053. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.