Clinical Validation of a Machine-learning–derived Signature Predictive of Outcomes from First-line Oxaliplatin-based Chemotherapy in Advanced Colorectal Cancer

Jim P. Abraham, Daniel Magee, Chiara Cremolini, Carlotta Antoniotti, David D. Halbert, Joanne Xiu, Phillip Stafford, Donald A. Berry, Matthew J. Oberley, Anthony F. Shields, John L. Marshall, Mohamed E. Salem, Alfredo Falcone, Axel Grothey, Michael J. Hall, Alan P. Venook, Heinz Josef Lenz, Anthony Helmstetter, W. Michael Korn, David B. Spetzler

Research output: Contribution to journalArticlepeer-review

35 Scopus citations

Abstract

Purpose: FOLFOX, FOLFIRI, or FOLFOXIRI chemotherapy with bevacizumab is considered standard first-line treatment option for patients with metastatic colorectal cancer (mCRC). We developed and validated a molecular signature predictive of efficacy of oxaliplatin-based chemotherapy combined with bevacizumab in patients with mCRC. Experimental Design: A machine-learning approach was applied and tested on clinical and next-generation sequencing data from a real-world evidence (RWE) dataset and samples from the prospective TRIBE2 study resulting in identification of a molecular signature, FOLFOXai. Algorithm training considered time-to-next treatment (TTNT). Validation studies used TTNT, progression-free survival, and overall survival (OS) as the primary endpoints. Results: A 67-gene signature was cross-validated in a training cohort (N = 105) which demonstrated the ability of FOLFOXai to distinguish FOLFOX-treated patients with mCRC with increased benefit from those with decreased benefit. The signature was predictive of TTNT and OS in an independent RWE dataset of 412 patients who had received FOLFOX/bevacizumab in first line and inversely predictive of survival in RWE data from 55 patients who had received first-line FOLFIRI. Blinded analysis of TRIBE2 samples confirmed that FOLFOXai was predictive of OS in both oxaliplatin-containing arms (FOLFOX HR, 0.629; P = 0.04 and FOLFOXIRI HR, 0.483; P = 0.02). FOLFOXai was also predictive of treatment benefit from oxaliplatin-containing regimens in advanced esophageal/gastro-esophageal junction cancers, as well as pancreatic ductal adenocarcinoma. Conclusions: Application of FOLFOXai could lead to improvements of treatment outcomes for patients with mCRC and other cancers because patients predicted to have less benefit from oxaliplatin-containing regimens might benefit from alternative regimens.

Original languageEnglish
Pages (from-to)1174-1183
Number of pages10
JournalClinical Cancer Research
Volume27
Issue number4
DOIs
StatePublished - Feb 15 2021

Keywords

  • Adult
  • Aged
  • Aged, 80 and over
  • Antineoplastic Combined Chemotherapy Protocols/pharmacology
  • Bevacizumab/pharmacology
  • Biomarkers, Tumor/genetics
  • Clinical Trials as Topic
  • Colorectal Neoplasms/drug therapy
  • Datasets as Topic
  • Drug Resistance, Neoplasm/genetics
  • Female
  • Follow-Up Studies
  • High-Throughput Nucleotide Sequencing
  • Humans
  • Machine Learning
  • Male
  • Middle Aged
  • Models, Genetic
  • Oxaliplatin/pharmacology
  • Prognosis
  • Progression-Free Survival
  • Prospective Studies
  • Risk Assessment/methods
  • Young Adult

Fingerprint

Dive into the research topics of 'Clinical Validation of a Machine-learning–derived Signature Predictive of Outcomes from First-line Oxaliplatin-based Chemotherapy in Advanced Colorectal Cancer'. Together they form a unique fingerprint.

Cite this