Differences in commensal microbiome influence how we respond to cancer treatment

Author: Lily Thompson


Fig 1A. Comparison of the 62 OTUs with varying abundance in responders (R) and nonresponders (NR). Red corresponds to a higher abundance while blue corresponds to low abundance.

DOI: 10.1126/science.aao3290

Melanoma is the deadliest form of skin cancer; the American Cancer Society estimates that in the United States alone, over 178,000 will be diagnosed with melanoma and close to 10,000 will die in 2018 (1). While melanoma is curable if caught early on, once it metastasizes, patients have a 16% five-year survival rate and treatments are limited (2)(3). Immunotherapy using PD-1 inhibitors, which consists of antibodies against PD-1, is commonly used. PD-1, which stands for programmed cell death 1, is expressed by cancer cells and known to be implicated in their escape of immune destruction (4)(5). Anti-PD-1 immunotherapy is used clinically to treat patients with metastatic melanoma; however, the efficacy of the treatment is limited to a subset of patients who exhibit T cell response in the tumor microenvironment prior to immunotherapy (6). Thus, current research is oriented toward exploring factors involved in T cell infiltration. Interestingly, studies point towards a link between the commensal microbiome and response to immunotherapy, which occurs by modulation of the inflammatory process contributing to cancer and cancer therapy (7). Building off of this, Matson et al from the University of Chicago explored how microbiome composition might impact patient response to anti-PD-1 immunotherapy.

In Biochemistry 441, we covered a paper on the human gut microbiome and the impact of different diets on diversity over subsequent generations. The paper we read studied how the microbiome has changed over the years but didn’t assess how this might affect human health. The Matson et al researchers study how the microbiome impacts health. Additionally, the paper we read in BCM 441 stressed biodiversity, and this paper provides possible evidence towards the importance of having the “right” kind of diversity.

In their paper, Matson et al explored a possible relationship between commensal microbiome composition and response to anti-PD-1-based immunotherapy. They first retrieved stool samples from patients who were responsive (R) and nonresponsive (NR) to anti-PD-1 treatment. A comparison of the microbiome composition revealed 39 operational taxonomic units (OTUs) more abundant in R and 23 more abundant in NR (Fig 1A). Using screening methods, such as BLAST and qPCR, they narrowed these numbers down to 8 and 2 respectively. In order to investigate the possible correlation between the microbiome and response to immunotherapy, they injected fecal material from R and NR cohorts into germ-free mice. They found that, following melanoma implantation, the mice segregated into two phenotypes: a slower growing tumor and faster growing tumor group. To test whether this difference was due to host immunity, the researchers performed an Enzyme-Linked Immunospot (ELISPOT) of ex vivo SIY-stimulated splenocytes. SIY is an epitope found on the surface of the melanoma cell line used in this paper and can be recognized by immune cells (9). ELISPOT assays are similar in principle to ELISAs and are most often used to monitor immune response. By measuring response to SIY via ELISPOT, the authors found that the R mice exhibited a greater number of activated T cells. Additional analysis of the tumor microenvironment revealed they were mostly SIY-specific cytotoxic T cells, which kill cancer cells, not regulatory T cells, which suppress immune response. Finally, qPCR of the fecal DNA from the mice confirmed that they exhibited the same pattern of enrichment as seen in patients. The authors concluded that response to anti-PD-1 treatment was indeed influenced by composition of commensal microbiota.

These results will likely impact how we treat patients, not only with melanomas, but other forms of cancer as well. Immunotherapy relies heavily on a patient’s ability to generate an immune response, and the microbiome composition could be used as a model to predict this response (10). While the authors recognize that additional factors may affect this, their results provide a powerful addition to combination therapy, which in itself is a cornerstone of cancer therapy. In the future, therapies might combine the knowledge that certain bacteria provide higher response to treatment with the treatment itself. This knowledge may also be used to assess a patient’s response to a treatment and therefore impact whether they receive one therapy over another. Similarly, the David Solit lab has research paralleling that of Matson et al. Dr. Solit’s lab performs genomic sequencing on individuals who respond favorably to a cancer treatment. After finding what differs between these individuals and nonresponsive patients, they decide whether one therapy over another may be more effective. Matson et al follow a new direction in cancer research, which involves research into treatments that are tailored to the specific needs of the individual.

Future studies might involve exploring the mechanism(s) behind how certain bacteria confer a better immune response than others. Research into this subject is limited; however, it has been suggested that certain drugs might alter the composition of the microbiota, which then induces movement of certain bacteria species into secondary lymphoid organs where they stimulate the generation of specific types of T cells (8). While intriguing, whether this is the case with anti-PD-1 therapy is unknown yet unlikely; the drug from the study previously mentioned involved an alkylating agent which differs significantly from an antibody. Research into the mechanism behind how antibodies in particular can alter the microbiome remains to be explored.



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