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

Nusrat J. Epsi, Sukanya Panja, Sharon R. Pine, Antonina Mitrofanova

Research output: Contribution to journalArticle

Abstract

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 carboplatin-paclitaxel 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.

Original languageEnglish (US)
Article number334
JournalCommunications Biology
Volume2
Issue number1
DOIs
StatePublished - Dec 1 2019

Fingerprint

Chemotherapy
adenocarcinoma
drug therapy
lungs
Drug Therapy
paclitaxel
Carboplatin
Paclitaxel
neoplasms
Oncology
Medical Oncology
Proxy
Standard of Care
transcriptomics
Epigenomics
epigenetics
adjuvants
Adenocarcinoma of lung
Neoplasms
Therapeutics

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • Medicine (miscellaneous)

Cite this

@article{340cb91c6e4c4946bfca858dff958407,
title = "pathCHEMO, a generalizable computational framework uncovers molecular pathways of chemoresistance in lung adenocarcinoma",
abstract = "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 carboplatin-paclitaxel 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.",
author = "Epsi, {Nusrat J.} and Sukanya Panja and Pine, {Sharon R.} and Antonina Mitrofanova",
year = "2019",
month = "12",
day = "1",
doi = "10.1038/s42003-019-0572-6",
language = "English (US)",
volume = "2",
journal = "Communications Biology",
issn = "2399-3642",
number = "1",

}

pathCHEMO, a generalizable computational framework uncovers molecular pathways of chemoresistance in lung adenocarcinoma. / Epsi, Nusrat J.; Panja, Sukanya; Pine, Sharon R.; Mitrofanova, Antonina.

In: Communications Biology, Vol. 2, No. 1, 334, 01.12.2019.

Research output: Contribution to journalArticle

TY - JOUR

T1 - pathCHEMO, a generalizable computational framework uncovers molecular pathways of chemoresistance in lung adenocarcinoma

AU - Epsi, Nusrat J.

AU - Panja, Sukanya

AU - Pine, Sharon R.

AU - Mitrofanova, Antonina

PY - 2019/12/1

Y1 - 2019/12/1

N2 - 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 carboplatin-paclitaxel 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.

AB - 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 carboplatin-paclitaxel 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.

UR - http://www.scopus.com/inward/record.url?scp=85071980378&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85071980378&partnerID=8YFLogxK

U2 - 10.1038/s42003-019-0572-6

DO - 10.1038/s42003-019-0572-6

M3 - Article

AN - SCOPUS:85071980378

VL - 2

JO - Communications Biology

JF - Communications Biology

SN - 2399-3642

IS - 1

M1 - 334

ER -