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.

Mechanism-Centric Approaches for Biomarker Detection and Precision Therapeutics in Cancer

“…Recently, pathway-based biomarker algorithms, such as pathCHEMO ( Epsi et al, 2019 ) and pathER ( Rahem et al, 2020 ), have demonstrated that discovery approaches that encompass information from biological pathways significantly outperform gene-centric methods which do not take into account pathway membership.…”

“…In the past two decades, the advancement of high-throughput technologies has led to the discovery of genomic, transcriptomic, and epigenomic modalities involved in cancer initiation, progression, and treatment response. Multiple groups have started to effectively utilize molecular data produced by high-throughput oncology experiments to identify biomarkers of progression and therapeutic response in cancer patients ( Sorlie et al, 2001 ; Zhang et al, 2001 ; van’t Veer et al, 2002 ; Zhan et al, 2002 , 2006 ; Sotiriou et al, 2003 ; Ayers et al, 2004 ; Allen et al, 2006 ; Jain et al, 2009 ; Lim et al, 2009 ; Petty et al, 2009 ; Zhao et al, 2009 ; Carro et al, 2010 ; Lefebvre et al, 2010 ; Shaughnessy et al, 2011 ; Bae et al, 2013 ; Aytes et al, 2014 , 2018 ; Mitrofanova et al, 2015 ; Robinson et al, 2015 ; Wang et al, 2016 ; Giulietti et al, 2017 ; Heng et al, 2017 ; Hoadley et al, 2018 ; Abida et al, 2019 ; Epsi et al, 2019 ; Arriaga et al, 2020 ; Panja et al, 2020 ; Rahem et al, 2020 ). Yet, our understanding of the mechanisms involving these modalities, their upstream regulation, and effective therapeutic targeting remains incomplete.…”

“…Several groups have addressed this problem by developing biomarker discovery methods based on mechanism-centric approaches, which are not focused on single genes and take into account complex mechanisms implicated in cancer initiation, progression, and treatment response. In this review, we will discuss the mechanism-centric approaches based on construction and mining of co-expression networks ( Freeman, 1977 ; Zhang and Horvath, 2005 ; Zhang and Huang, 2014 ; Han et al, 2016 ), regulatory networks ( Basso et al, 2005 ; Lefebvre et al, 2010 ; Alvarez et al, 2016 ; Dhingra et al, 2017 ), protein–protein interaction (PPI) networks ( Chuang et al, 2007 ), and molecular pathways ( Epsi et al, 2019 ; Rahem et al, 2020 ; Figure 1 ). Through an in-depth understanding of upstream and downstream molecular mechanisms, such techniques open a door for the discovery of functionally interpretable molecular drivers (rather than passengers) and potential targets for precision therapeutics.…”

Annexin A13 predicts poor prognosis for lung adenocarcinoma patients and accelerates the proliferation and migration of lung adenocarcinoma cells by modulating epithelial–mesenchymal transition

“…Lung adenocarcinoma (LUAD) is the most familiar histological type of lung cancer [3]. Although researchers have studied for decades to fight LUAD, their therapeutic effects are not ideal, with the 5-year survival rate only 17.4% [4,5]. Hence, to elevate the survival, it is momentous to discover novel molecular markers that might be beneficial for LUAD diagnosis and treatment.…”

Dynamical network analysis reveals key microRNAs in progressive stages of lung cancer

“…The mechanisms behind cancer evolution are extremely complex, impeding accurate and reliable prognosis as well as effective treatment [3]. A standard existing approach to monitoring tumor progress and detecting/ascertaining the underlying mechanism is mRNA transcriptomics, which has led to a large number of significant prognostic biomarkers and therapeutic targets [4][5][6]. When combined with other omics information, such as microRNA, methylation and epigenetic data, mRNA transcriptomics has provided valuable insights into the mechanisms of lung cancers [7][8][9][10].…”