Around one out of six deaths worldwide is related to cancer. The best known treatment methods include chemotherapy and immunotherapy. These therapies work by inhibiting the further division of cancer cells or by supporting the immune system in killing the tumour cells. However, despite today's well-developed anti-cancer therapies, their efficiency is not as high as desired.

We know that the intestinal microbiome plays an important role in therapeutic success. So all the microorganisms living in our gut can somehow influence the outcome of therapeutic measures in cancer treatment. To this end, we analysed metagenomic sequencing data from the stool samples of cancer patients treated with chemotherapy or a combination of chemotherapy and immunotherapy. The experimental group consisted of patients with eight different types of cancer – a novelty since comparable studies have so far concentrated on only one type of cancer.

Fig. 1. Human gut microbiota is associated with patients response to anti-cancer treatment.

Nevertheless, we were able to identify some common features in the stool analysis. The intestinal microbiomes of those cancer patients who responded well to the therapy show a greater microbial diversity. In addition, the bacterial species found in their intestine differ from those of patients who responded less well to therapy. More specifically, the species Bacteroides ovatus and Bacteroides xylanisolvens have a positive effect on the course of therapy, whereas Clostridium symbiosum and Ruminococcus gnavus were found in higher amount in the patients whose anti-cancer therapy was less successful. With the help of these findings, our team has developed a prediction model based on machine learning. This model makes it possible to calculate the probability of successful anti-cancer treatment before therapy begins, regardless of the type of cancer. Moreover, the model proved to be highly accurate in another patient group.

We also tested the effect of these responder bacteria (Bacteroides ovatus and Bacteroides xylanisolvens) in experimental mice with lung cancer induced. Feeding the mice with those bacteria helped increase the efficacy of erlotinib (an anti-cancer drug) and further reduced the tumour volume by 46% compared to the control. Such enhancement of chemotherapy efficacy may be achieved by synergistically upregulating the expression of chemokines, CXCL9 and IFN-γ, involved in the recruitment of immune T cells.

Fig. 2. Increased anti-tumor efficacy of chemotherapy in the presence of B. ovatusand B. xylanisolvens. (A) Experimental design: Male 6-week C57BL6/N mice (n=5-8) were treated with antibiotic cocktail in drinking water for 1 week before bacterial oral gavage. Control PBS, B. ovatus and B. xylanisolvens, and C. symbiosum and R. gnavus were orally gavaged into mice 1 week prior to tumor cell inoculation. A total of 10⁷ Lewis lung cancer cells in 200 µl PBS were subcutaneously injected into the mice to induce tumor formation. Mice were treated with erlotinib(60 mg/kg body weight) once the tumor size reached approximately 250-500 mm³. Time in days is relative to tumor cells injection. (B) Tumor size measurement at day 14. (C) Tumor growth curve after Lewis lung carcinoma cellinoculation. Dark dots indicate the application of erlotinib.

Our study suggests a predictable impact of specific members of the gut microbiota on tumour growth and cancer treatment outcomes with implications for prognosis. For better therapeutic outcomes, the administration of specific probiotic bacteria could be a potential supplemental treatment in combination with anticancer therapies.

For more details please check: https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-020-00811-2

Predictable modulation of cancer treatment outcomes by the gut microbiota. Microbiome 8, 28 (2020)