Pathway-centric generalizable computational framework uncovers pathway markers governing chemoresistance across cancers
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Epsi, Nusrat.
Pathway-centric generalizable computational framework uncovers pathway markers governing chemoresistance across cancers. Retrieved from
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TitlePathway-centric generalizable computational framework uncovers pathway markers governing chemoresistance across cancers
Date Created2020
Other Date2020-08 (degree)
Extent1 online resource (ix, 79 pages)
DescriptionBackground: Despite recent advances in discovering a wide array of novel chemotherapy agents, identification of patients with poor and favorable treatment response prior to treatment administration remains a major challenge in clinical oncology and cancer management.
Methods: We have developed a genome-wide systematic computational framework to uncover an interplay between transcriptomic and epigenomic mechanisms that elucidate the complexity of chemotherapy response in cancer patients. Our approach integrates transcriptomic (i.e., mRNA expression) and epigenomic (i.e., DNA methylation) patient profiles to uncover molecular pathways with significant alterations on transcriptomic and epigenomic levels that can distinguish favorable from poor treatment response.
Results: We have tested our approach on patients with lung adenocarcinoma who received a carboplatin and paclitaxel combination chemotherapy (i.e., carboplatin-paclitaxel), a standard-of-care for treating advanced lung cancer. Our integrative approach identified seven molecular pathways with significant alterations on transcriptomic and epigenomic levels that distinguish favorable from poor carboplatin-paclitaxel response, including chemokine receptors bind chemokines, mRNA splicing, G alpha (s) signalling events, immune network for IgA production, etc. We have demonstrated that these pathways can classify patients based on their risk to developing carboplatin- paclitaxel resistance in an independent patient cohort (log-rank p-value = 0.0081) and their predictive ability is independent of and is not affected by (i) signatures of lung cancer aggressiveness, and (ii) commonly utilized covariates, such as age, gender, and disease stage at diagnosis (adjusted hazard ratio = 14.0). To demonstrate generalizability of our approach, we have applied our algorithm across additional chemotherapy regimens (i.e., cisplatin-vinorelbine, oxaliplatin-fluorouracil) and cancer types (i.e., lung squamous cell carcinoma, and colorectal adenocarcinoma); and have demonstrated our method’s ability to accurately predict patients’ treatment response.
Conclusions: We propose that our approach can be utilized to identify transcriptomic and epigenomic altered pathways implicated in primary chemoresponse and effectively classify patients who would benefit from specific chemotherapy regimens or are at risk of resistance, which will significantly improve personalized therapeutic strategies and informed clinical decision making.
NotePh.D.
NoteIncludes bibliographical references
Genretheses, ETD doctoral
LanguageEnglish
CollectionSchool of Health Professions ETD Collection
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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