Oncotarget

Research Papers:

Early detection of treatment futility in patients with metastatic colorectal cancer

Jodi I. Rattner, Karen A. Kopciuk, Hans J. Vogel, Patricia A. Tang, Jeremy D. Shapiro, Dongsheng Tu, Derek J. Jonker, Lillian L. Siu, Chris J. O’Callaghan and Oliver F. Bathe _

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Abstract

Jodi I. Rattner1, Karen A. Kopciuk2, Hans J. Vogel3, Patricia A. Tang1,4, Jeremy D. Shapiro5, Dongsheng Tu6, Derek J. Jonker7, Lillian L. Siu8, Chris J. O’Callaghan6 and Oliver F. Bathe1,4

1 Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, Canada

2 Department of Mathematics and Statistics, Faculty of Science, University of Calgary, Calgary, Canada

3 Department Biological Sciences, Faculty of Science, University of Calgary, Calgary, Canada

4 Department of Surgery and Oncology, Cumming School of Medicine, University of Calgary, Calgary, Canada

5 Department of Medical Oncology, Monash University, Melbourne, Victoria, Australia

6 Department of Community Health and Epidemiology, Queens University, Kingston, Canada

7 Division of Medical Oncology, Ottawa Hospital Cancer Centre, Ottawa, Canada

8 Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, Canada

Correspondence to:

Oliver F. Bathe, email: bathe@ucalgary.ca

Keywords: colorectal cancer; metabolomics; response biomarkers; radiographic imaging; chemotherapy

Received: August 25, 2021     Accepted: December 10, 2021     Published: January 07, 2022

Copyright: © 2022 Rattner et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

ABSTRACT

Purpose: Chemotherapy options for treating CRC have rapidly expanded in recent years, and few have predictive biomarkers. Oncologists are challenged with evidence-based selection of treatments, and response is evaluated retrospectively based on serial imaging beginning after 2–3 months. As a result, cumulative toxicities may appear in patients who will not benefit. Early recognition of non-benefit would reduce cumulative toxicities. Our objective was to determine treatment-related changes in the circulating metabolome corresponding to treatment futility.

Methods: Metabolomic studies were performed on serial plasma samples from patients with CRC in a randomized controlled trial of cetuximab vs. cetuximab + brivanib (N = 188). GC-MS quantified named 94 metabolites and concentrations were evaluated at baseline, Weeks 1, 4 and 12 after treatment initiation. In a discovery cohort (N = 68), a model distinguishing changes in metabolites associated with radiographic disease progression and response was generated using OPLS-DA. A cohort of 120 patients was used for validation of the model.

Results: By one week after treatment, a stable model of 21 metabolites could distinguish between progression and partial response (R2Y = 0.859; Q2Y = 0.605; P = 5e-4). In the validation cohort, patients with the biomarker had a significantly shorter OS (P < 0.0001). In a separate cohort of patients with HCC on axitinib, appearance of the biomarker also signified a shorter PFS (1.7 months vs. 9.2 months, P = 0.001).

Conclusion: We have identified changes in the metabolome that appear within 1 week of starting treatment associated with treatment futility. The novel approach described is applicable to future efforts in developing a biomarker for early assessment of treatment efficacy.


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