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Cancer Treatment Response Prediction

Metabolomic screening with fecal samples is advantageous because it is minimally invasive, can be applied on a large scale, and provides significant information about patient stratification.

The Metabolon Global Discovery Panel showed that the metabolomic fingerprint of fecal samples, collected before therapy, acts as a predictive biomarker to treatment response. Prospective identification of patients that will benefit from immune checkpoint inhibitor therapy could improve patient stratification, thus avoiding ineffective therapeutic strategies.

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The Metabolon Discovery: Global Panel showed that the metabolomic fingerprint of fecal samples, collected before therapy, acts as a predictive biomarker to treatment response. Prospective identification of patients that will benefit from immune checkpoint inhibitor therapy could improve patient stratification, thus avoiding ineffective therapeutic strategies.

Talk With An Expert About Your Project

The Challenge: Understanding Immune Checkpoint Inhibitor Therapy Response

The incidence of skin cancers has been increasing over the past decades. Globally, two to three million people suffer from skin cancer each year.1 Melanoma is the most aggressive skin cancer with the highest risk of death. While less common than other skin cancers, melanoma is more dangerous due to its ability to rapidly spread to other organs if left untreated.

Immune checkpoints regulate the immune system by preventing an immune response from attacking healthy cells. However, cancer cells can find ways to activate these checkpoints to avoid being attacked by the immune system. Immune checkpoint inhibitors work by blocking immune checkpoints, allowing the immune system to kill cancer cells. Immune checkpoint inhibitors are approved to treat a variety of cancer types, including melanomas. These inhibitors achieve durable remissions in up to 50% of patients with metastatic melanoma.2 However, those who fail to benefit from immune checkpoint inhibitor therapy have a poor prognosis. In this setting, investigators have sought to identify host or tumor characteristics that impact the outcome of immune checkpoint inhibitors. The discovery of biomarkers that can predict which patients are most likely to respond and benefit from immune checkpoint inhibitor therapy will improve clinical decision-making and treatment efficacy.

Metabolon Insight: Metabolomics Identifies Differential Metabolites in Responders to Immune Checkpoint Inhibitor Therapy

Metabolon helped characterize the effects of the human gut microbiome on immune checkpoint inhibitor therapy response in metastatic melanoma patients.3 To carry out this study, the Metabolon Global Discovery Panel was used to analyze fecal metabolites from metastatic melanoma patients prior to initiating immune checkpoint inhibitors. All samples were collected before the beginning of the treatment with the aim to find predictive metabolites (biomarkers) of the response to immune checkpoint inhibitors.

The Solution: Immune Checkpoint Inhibitor Therapy Responders Have Different Metabolomic Profiles Compared to Non-responders

The study authors conducted a metabolomic study on metastatic melanoma patients initiating immune checkpoint inhibitor therapy. Metastatic melanoma patients (n = 39) provided pretreatment fecal samples and then underwent immune checkpoint inhibitor treatment. Once patients completed the treatment, follow-up exams and scans evaluated whether treatment was effective or not at reducing tumor size. Out of the 39 patients, 24 responded well or remained stable after treatment, while 15 showed cancer progression. Metabolon performed global metabolomic profiling on the fecal samples and detected significant differences in the metabolite composition between responders and those with progressive disease. When comparing the responder to the progressive group, 83 metabolites were significantly different (49 increased, 34 decreased). These 83 metabolites are involved in numerous metabolic pathways.

The study team also analyzed fecal samples from the same patients via metagenomic shotgun sequencing (MSS). MSS allowed them to detect differences in the composition of the gut microbiota of the responder and progressive group. Responder microbiomes were significantly enriched with several bacterial strains, such as Bacteroides caccae, compared to those with cancer progression. MSS also uncovered differences in the microbiome gene content between responder and progressive microbiomes. Among all treatment recipients, responder microbiomes were significantly enriched with bacterial enzymes involved in fatty acid synthesis.

The Outcome: Metabolomics Identifies Predictive Biomarkers to Treatment Response

Using the Metabolon Global Discovery Panel, the researchers were able to identify specific gut metabolites that were associated with response to three different immune checkpoint inhibitor therapies. This study sheds light on the effects of human microbiota on immune checkpoint inhibitor therapy response in metastatic melanoma patients. Future larger clinical studies using metabolomics could reveal that the metabolomic fingerprint of fecal samples, collected before therapy, has the potential to act as a biomarker to predict treatment response. Prospective identification of patients that will benefit from immune checkpoint inhibitor therapy could improve patient stratification, thus avoiding ineffective therapeutic strategies. Identification of gut metabolites can also be used to evaluate clinical response to other cancer therapies. Moreover, the use of fecal samples for screening is advantageous because it is minimally invasive, can be applied on a large scale, and provides significant information about patient stratification.

References

1. WHO. Radiation: Ultraviolet (UV), radiation and skin cancer. 2017: World Health Organization; 2017.

2. Larkin J, Chiarion-Sileni V, Gonzalez R, et al. Combined Nivolumab and Ipilimumab or Monotherapy in Untreated Melanoma [published correction appears in N Engl J Med. 2018 Nov 29;379(22):2185]. N Engl J Med. 2015;373(1):23-34. doi:10.1056/NEJMoa1504030

3. Frankel AE, Coughlin LA, Kim J, et al. Metagenomic Shotgun Sequencing and Unbiased Metabolomic Profiling Identify Specific Human Gut Microbiota and Metabolites Associated with Immune Checkpoint Therapy Efficacy in Melanoma Patients. Neoplasia. 2017;19(10):848-855. doi:10.1016/j.neo.2017.08.004

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References

1. Zgoda-Pols, J.R., et al., Metabolomics analysis reveals elevation of 3-indoxyl sulfate in plasma and brain during chemically-induced acute kidney injury in mice: investigation of nicotinic acid receptor agonists. Toxicol Appl Pharmacol, 2011. 255(1): p. 48-56.

2. Bryant, J.A., et al., The impact of an oral purified microbiome therapeutic on the gastrointestinal microbiome. Nat Med, 2026. 32(1): p. 186-196

3. McGovern, B .H., et al., SER-109, an Investigational Microbiome Drugto Reduce Recurrence After Clostridioides difficile Infection: Lessons Learned From a Phase 2 Trial. Clin Infect Dis, 2021. 72(12): p. 2132-2140.

4. Feuerstadt, P., et al., SER-109, an Oral Microbiome Therapy for Recurrent Clostridioides difficile Infection. N Engl J Med, 2022. 386(3): p. 220-229.

5. Hu, Z., et al., Targeted metabolomics reveals novel diagnostic biomarkers for colorectal cancer. Mol Oncol, 2025. 19(6): p. 1737-1750.

6. Butler, F.M., et al., Vegetarian Dietary Patterns and Diet-Related Metabolites Are Associated With Kidney Function in the Adventist Health Study-2 Cohort. J Ren Nutr, 2025.

7. Stanford, J., et al., Metabolomic Profiling and Diet Quality Scoring in a Randomized Crossover Trial of Healthy and Typical Dietary Patterns. Mol Nutr Food Res, 2025 . 69(23): p. e70271.

8. O’Connor, L.E., et al., Metabolomic Profiling of an Ultraprocessed Dietary Pattern in a Domiciled Randomized Controlled Crossover Feeding Trial. J Nutr, 2023. 153(8): p. 2181-2192.

9. Fritsch, D.A., et al., Microbiome function underpins the efficacy of a fiber-supplemented dietary intervention in dogs with chronic large bowel diarrhea. BMC Vet Res, 2022. 18(1): p. 245.

10. Leal, L.N., et al., Preweaning nutrient supply improves lactation productivity and reduces the risk of culling in Holstein cows. J Dairy Sci, 2025. 108(6): p. 5875-5888.

11. Ahsin, M., et al., Soil and pasture health underlie improved beef nutrient density determined by untargeted metabolomics in Southern US grass finished beef systems. NPJ Sci Food, 2025. 9(1): p. 151.

12. Yin, W., et al., Plasma lipid profiling across species for the identification of optimal animal models of human dyslipidemia. J Lipid Res, 2012. 53(1): p. 51-65.

13. Porter, F .D., et al., Cholesterol oxidation products are sensitive and specific blood-based biomarkers for Niemann-Pick C1 disease. Sci Transl Med, 2010. 2(56): p. 56ra81.

14. Needham, B .D., et al., Plasma and Fecal Metabolite Profiles in Autism Spectrum Disorder. Biol Psychiatry, 2021. 89(5): p. 451-462

15. Li, C., et al., Estradiol and mTORC2 cooperate to enhance prostaglandin biosynthesis and tumorigenesis in TSC2-deficient LAM cells. J Exp Med, 2014. 211(1): p. 15-28.

16. Green, P.G., et al., Metabolic flexibility and reverse remodelling of the failing human heart. Eur Heart J, 2025. 46(25): p. 2422-2433.

17. Maekawa, H., et al., SGLT2 inhibition protects kidney function by SAM-dependent epigenetic repression of inflammatory genes under metabolic stress. J Clin Invest, 2025. 135(19).

18. Wu, D., et al., Integrated screens reveal that guanine nucleotide depletion, which is irreversible via targeting IMPDH2, inhibits pancreatic cancer and potentiates KRAS inhibition. Gut, 2026.

19. Schwerdtfeger, L.A., et al., Gut microbiota and metabolites are linked to disease progression in multiple sclerosis. Cell Rep Med, 2025. 6(4): p. 102055.

20. Wu, H., et al., Microbiome-metabolome dynamics associated with impaired glucose control and responses to lifestyle changes. Nat Med, 2025. 31(7): p. 2222-2231.

21. Jacobs, J.P., et al., Cognitive behavioral therapy for irritable bowel syndrome induces bidirectional alterations in the brain-gut-microbiome axis associated with gastrointestinal symptom improvement. Microbiome, 2021. 9(1): p. 236.

22. Pietzner, M., et al., Plasma metabolites to profile pathways in noncommunicable disease multimorbidity. Nat Med, 2021. 27(3): p. 471-479.

23. Faquih, T.O., et al., Robust Metabolomic Age Prediction Based on a Wide Selection of Metabolites. J Gerontol A Biol Sci Med Sci, 2025. 80(3).

24. Scherer, N., et al., Coupling metabolomics and exome sequencing reveals graded effects of rare damaging heterozygous variants on gene function and human traits. Nat Genet, 2025. 57(1): p. 193-205.

25. Holmes, Z.C., et al., Untargeted metabolomic analysis of human milk from healthy mothers reveals drivers of metabolite variability. Sci Rep, 2024. 14(1): p. 20827.

26. Titz, B., et al., Implications of Ocular Confounding Factors for Aqueous Humor Proteomic and Metabolomic Analyses in Retinal Diseases. Transl Vis Sci Technol, 2024. 13(6): p. 17.

27. Bloom, S.M., et al., Cysteine dependence of Lactobacillus iners is a potential therapeutic target for vaginal microbiota modulation. Nat Microbiol, 2022. 7(3): p. 434-450.

28. Leimer, E.M., et al., Lipid profile of human synovial fluid following intra-articular ankle fracture. J Orthop Res, 2017. 35(3): p. 657-666.