pathCHEMO, a generalizable computational framework uncovers molecular pathways of chemoresistance in lung adenocarcinoma

被引:8
作者
Epsi, Nusrat J. [1 ]
Panja, Sukanya [1 ]
Pine, Sharon R. [2 ,3 ]
Mitrofanova, Antonina [1 ,4 ]
机构
[1] Rutgers Biomed & Hlth Sci, Rutgers Sch Hlth Profess, Dept Hlth Informat, Newark, NJ 07107 USA
[2] Univ Med & Dent New Jersey, Rutgers Canc Inst New Jersey, Dept Pharmacol, New Brunswick, NJ 08901 USA
[3] Univ Med & Dent New Jersey, Rutgers Canc Inst New Jersey, Dept Med, New Brunswick, NJ 08901 USA
[4] Rutgers State Univ, Rutgers Canc Inst New Jersey, New Brunswick, NJ 08901 USA
关键词
EPITHELIAL-MESENCHYMAL TRANSITION; GENE-EXPRESSION PROFILES; DNA METHYLATION; NONSMALL CELL; ACQUIRED-RESISTANCE; ADJUVANT CHEMOTHERAPY; PROTEIN-KINASE; MOUSE MODEL; CANCER; MUTATION;
D O I
10.1038/s42003-019-0572-6
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Despite recent advances in discovering a wide array of novel chemotherapy agents, identification of patients with poor and favorable chemotherapy response prior to treatment administration remains a major challenge in clinical oncology. To tackle this challenge, we present a generalizable genome-wide computational framework pathCHEMO that uncovers interplay between transcriptomic and epigenomic mechanisms altered in biological pathways that govern chemotherapy response in cancer patients. Our approach is tested on patients with lung adenocarcinoma who received adjuvant standard-of-care doublet chemotherapy (i.e., carboplatin-paclitaxel), identifying seven molecular pathway markers of primary treatment response and demonstrating their ability to predict patients at risk of carboplatinpaclitaxel resistance in an independent patient cohort (log-rank p-value = 0.008, HR = 10). Furthermore, we extend our method to additional chemotherapy-regimens and cancer types to demonstrate its accuracy and generalizability. We propose that our model can be utilized to prioritize patients for specific chemotherapy-regimens as a part of treatment planning.
引用
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页数:13
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