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COMMON PART

Project Number22-75-10029

Project titleGut microbiota and response to immunotherapy for malignant tumors: from reproducible biomarkers to effect modulation

Project LeadOlekhnovich Evgenii

AffiliationLopukhin Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency,

Research area 05 - FUNDAMENTAL RESEARCH IN MEDICINE, 05-403 - Medical microbiology and virology

Keywordsgut microbiota, immunotherapy of malignant tumors, metagenomics, next generation sequencing

PROJECT CONTENT

Annotation
In addition to targeted therapies, tumor immunotherapy has recently entered the practice of oncologists. The essence of immunotherapy, or so-called biological therapy, is to increase the body's natural anti-tumor defense, aimed at the elimination of tumor cells. One of the directions of immunotherapy based on the use of monoclonal antibodies - immune checkpoint inhibitors (ICIs) - has become a significant breakthrough in anti-tumor therapy and significantly improved patient survival [Hoos A. 2016]. Blocking this interaction serves as a way to keep T cells active, allowing a sustained destruction of cancer cells by the immune system. ICIs targeting the programmed cell death 1 protein (PD-1) and programmed death-ligand 1 (PD-L1), alone or in combination with the cytotoxic T lymphocyte–associated antigen 4 (CTLA-4), are now used as first-line therapy for late-stage cancers, including metastatic melanoma (MM), non–small cell lung cancer (NSCLC), and renal cell carcinoma (RCC). In fact, 5-year overall survival rates for patients with MM soared from 16% to 52% after the introduction of an anti–CTLA-4 + anti–PD-1 combination therapy [Larkin J, et al 2019, Pollack LA, et al. 2011]. Nevertheless, half of the patients do not respond to the ICIs treatment, and predicting the responder (R) or nonresponder (NR) phenotype is difficult. In recent years, the efficacy of ICIs therapy has been associated with specific mutations in tumor cells, expression of certain genes/proteins, infiltration by tumor-associated dendritic cells and T-cells, lymphocyte/monocyte/eosinophil levels, and the composition of the gut microbiota. The interest in the microbiota stems from the fact that, unlike most other predictors, the microbiome can be modulated. Preclinical studies on model animals have demonstrated that the composition of the microbiota has a significant impact on the efficacy of ICIs therapy as well as on the development of side effects related to immune activation. Also, it was shown that experimental interventions, such as fecal microbiota transplantation, can restore the response to therapy in animals and reduce side effects, in particular colitis [Vétizou M, et al. 2015; Sivan A, et al. 2015]. In the future, causal relationships between specific taxa and their ability to influence the results of the ICIs therapy have been shown for humans [Matson et al., 2018; Gopalakrishnan et al., 2018; Routy et al., 2018; Frankel et al., 2017]. One of such works was performed by the project applicants [Fedorov et al., 2020]. Together with BIOCAD company within the framework of the agreement of the Ministry of Education and Science of the Russian Federation № 05.604.21.0215 "Development of method for evaluation of metagenomic markers of human gut microbiota associated with the response to cancer immunotherapy" a study was carried out to find differences in the structure of microbiota of melanoma patients with different responses to immunotherapy by prolgolimab. Analysis of the experimental data demonstrated differences in the composition of the microbiota between the experimental groups, once again highlighting the strong correlation between the composition of the gut microbiota and the results of this type of therapy. In turn, the expansion of such studies will be useful for the accumulation of relevant data and may contribute to the development of additional tools to predict the outcome of anticancer immunotherapy, as well as improve its effectiveness. Despite their high scientific significance and relevance, the works presented in the world literature have limitations in the form of local sampling of patients, which can significantly complicate the associative analysis. To improve the effectiveness of immunotherapy for cancer diseases, there is a need to identify the characteristics of the structure of the gut microbiota involved in the regulatory process, as well as a thorough study of the biological phenomena underlying it. In our study, we plan to summarize published metagenomic data from cancer patients who received ICIs. We plan to study at least 500 metagenomic samples from third-party repositories (NCBI, EBI). We plan to study at least 500 metagenomic stool samples of cancer patients with different responses to immunotherapy obtained from third-party repositories (NCBI, EBI). An analysis of the collected dataset will be performed using bioinformatics tools and concepts accepted in the world scientific community. We plan to obtain cross-study reproducible metagenomic biomarkers of the human gut microbiota involved in a positive response to immunotherapy. At the moment there are no cheap methods for predicting the immunotherapy outcome widely introduced into medical practice. The collected metagenomic data set will be used to build a predictive model of the immunotherapy outcome based on machine learning approaches. In addition, experimental validation of the obtained biomarkers using animal models will be carried out. Thus, the hypothesis on the possibility of reducing the human gut microbiome structure to individual components (microorganisms or groups of microorganisms) involved in the process of a positive response to immunotherapy will be tested. There is already research on the use of fecal transplantation using patients with a positive response to therapy as donors to increase the probability of response in patients with a negative outcome [Davar et al., 2021; Baruch et al., 2021]. However, this procedure comes with a high risk for cancer patients. Our strategy may allow us to reduce the risk by excluding concomitant components that are not necessary to achieve the desired outcome. We believe that this study will offer conceptual solutions in improving existing cancer immunotherapy regimens. The results may be important for additional personalization of cancer therapy and, in the long term, for targeted correction of the human microbiota to increase the effectiveness of cancer immunotherapy. Referenses: [1] Hoos A. Development of immuno-oncology drugs—from CTLA4 to PD1 to the next generations // Nature reviews Drug discovery. – 2016. – V. 15. – №. 4. – P. 235-247. [2] Larkin J. et al. Five-year survival with combined nivolumab and ipilimumab in advanced melanoma // New England Journal of Medicine. – 2019. – V. 381. – №. 16. – P. 1535-1546. [3] Pollack L. A. et al. Melanoma survival in the United States, 1992 to 2005 // Journal of the American Academy of Dermatology. – 2011. – V. 65. – №. 5. – P. S78. e1-S78. e10. [4] Vétizou M. et al. Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota // Science. – 2015. – V. 350. – №. 6264. – P. 1079-1084. [5] Matson V. et al. The commensal microbiome is associated with anti–PD-1 efficacy in metastatic melanoma patients // Science. – 2018. – V. 359. – №. 6371. – P. 104-108. [6] Gopalakrishnan V. et al. Gut microbiome modulates response to anti–PD-1 immunotherapy in melanoma patients // Science. – 2018. – V. 359. – №. 6371. – P. 97-103. [7] Routy B. et al. The gut microbiota influences anticancer immunosurveillance and general health // Nature Reviews Clinical Oncology. – 2018. – V. 15. – №. 6. – P. 382-396. [8] Frankel A. E., Frankel E. P. Melanoma metamorphoses: advances in biology and therapy // J Cancer Sci Ther. – 2017. – V. 9. – P. 325-335. [9] Sivan A. et al. Commensal Bifidobacterium promotes antitumor immunity and facilitates anti–PD-L1 efficacy // Science. – 2015. – V. 350. – №. 6264. – P. 1084-1089. [10] Pierrard J., Seront E. Impact of the gut microbiome on immune checkpoint inhibitor efficacy—a systematic review // Current Oncology. – 2019. – V. 26. – №. 6. – P. 395-403. [11] Baruch E. N. et al. Fecal microbiota transplant promotes response in immunotherapy-refractory melanoma patients // Science. – 2021. – V. 371. – №. 6529. – P. 602-609. [12] Davar D. et al. Fecal microbiota transplant overcomes resistance to anti–PD-1 therapy in melanoma patients // Science. – 2021. – V. 371. – №. 6529. – P. 595-602.

Expected results
1. Human gut microbiota biomarkers differentiating cancer patients with different responses to immunotherapy will be identified. This step will be performed using data obtained from open sources (for example, NCBI Sequence Read Archive). We plan to identify reproducible biomarkers (between all studies added to the analysis) that could be useful in predicting response to immunotherapy. In addition, we plan to create a classifier based on machine learning approaches to test the hypothesis about the possible use of the gut microbiota structure to predict the immunotherapy outcome. This work step will be focused on melanoma patients due to the value of accumulated data in comparison with other tumor types. 2. In silico hypothesis testing of the identified biomarkers universality to predict the immunotherapy outcome in other types of malignant tumors will be carried out (for example, in non-small cell lung cancer and cervical cancer). 3. Additionally, gut microbial DNA sequences that significantly distinguish patients groups by immunotherapy outcome will be identified. Obtaining such DNA sequences can be useful for further hypothesis testing about the possibility of developing universal test systems for predicting the cancer immunotherapy outcome for use in clinical practice. At the moment, there are no solutions that could predict the immunotherapy outcome based on the gut microbiota structure. It should be noted that the existing methods of such prediction based on immunohistochemistry have not found wide application in clinical practice due to their labor intensity and high cost. 4. An experimental hypothesis testing about the possibility of reducing the human gut microbiota to individual components involved in the process of a positive immunotherapy outcome using model animals will be performed. Thus, the obtained biomarkers of positive immunotherapy outcome will be tested in conditions close to real translational research. It should be noted that at the moment there is no approved medical scheme for modulating the immunotherapy outcome in cancer patients. Thus, we plan to use modern global concepts, computational and translational medicine methods to predict and modulate the immunotherapy outcome in cancer patients. Thus, we will plan to use modern global concepts such as computational and translational medicine to predict and modulate the immunotherapy outcome in cancer patients.

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