Early detection of treatment futility in patients with metastatic colorectal cancer
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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
|Oliver F. Bathe,||email:||email@example.com|
Keywords: colorectal cancer; metabolomics; response biomarkers; radiographic imaging; chemotherapy
Received: August 25, 2021 Accepted: December 10, 2021 Published: January 07, 2022
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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