Early detection of treatment futility in patients with metastatic colorectal cancer

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.

Clinical and metabolomic characterization of Brivanib‐Induced hypertension in metastatic colorectal cancer

“…Gas chromatography–mass spectrometry (GC–MS) was performed as previously described 12 . Briefly, metabolite extraction was based on the methods of Bligh and Dyer 13 .…”

“…2.3 | Gas chromatography-mass spectrometry Gas chromatography-mass spectrometry (GC-MS) was performed as previously described. 12 Briefly, metabolite extraction was based on the methods of Bligh and Dyer. 13 Derivatization was performed using methoxyaminehydrochloride in pyridine solution, and the silylating agent N-Methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA; Millipore-Sigma).…”

Identifying metabolic features of colorectal cancer liability using Mendelian randomization

“…Although previous observational studies have investigated associations between the circulating metabolome and CRC risk, these studies may have been influenced by confounding bias which should be less relevant to MR analyses. [22][23][24][25][26][27][28][29][30][31] Additionally, these studies focussed on adults, who commonly take medications which may confound metabolite associations, further complicating interpretations.…”

Identifying metabolic features of colorectal cancer liability using Mendelian randomization

“…Given the suspected importance of the circulating metabolome in CRC development, employing both reverse MR and conventional forward MR for metabolites in the same study may be an efficient approach for revealing causal and predictive biomarkers for CRC. Although previous observational studies have investigated associations between the circulating metabolome and CRC risk, these studies may have been influenced by confounding bias, which should be less relevant to MR analyses ( Gao et al, 2023 ; Rattner et al, 2022 ; Leichtle et al, 2012 ; Ritchie et al, 2010 ; Nishiumi et al, 2012 ; Ma et al, 2012 ; Bertini et al, 2012 ; Zhang et al, 2014 ; Farshidfar et al, 2016 ; Farshidfar et al, 2012 ). Additionally, these studies focussed on adults, who commonly take medications which may confound metabolite associations, further complicating interpretations.…”