Background
Despite improvements in breast cancer therapy, many patients do not respond to treatment: the
response rate varies between 31 and 80% (Waks and Winer 2019). The tumor microenvironment
contains a microbiome which is both cancer type-specific and different to surrounding normal tissue
(Nejman et al. 2020). It plays an important role in tumour microenvironment crosstalk with tumor
cells and has been implicated in disease pathogenesis, being recognised in the latest hallmarks of
cancer (Hanahan 2022). It has also successfully predicted treatment response (Chen et al. 2022;
Hermida, Gertz, and Ruppin 2022).
Here, we use conventional microbiome analysis alongside our proprietary mechanistically-powered
technology platform to compare the intratumoral bacteria between responders and non-responders.
We hypothesise that there are significant differences between those groups regarding composition
and mechanistic function of the bacteria.
Methods
The tumor microbiome can be deduced from existing sequencing data of tumor biopsy samples.
Using 34 tumor DNA samples from 5 breast cancer studies, we compared the intratumoral bacterial
profiles of responders to non-responders of various treatments. We performed microbiome analysis
in R, including alpha diversity, compositional abundance profiles, and PERMANOVA (permutational
analysis of variance). Additionally, BioCorteX CarbonMirror™ version dated 2023-02-14 was used to
infer mechanistic links between bacterial species and the up- and downregulation of genes
implicated in the hallmarks of cancer.
Results
The results show significant differences in diversity and compositional profile between responders
and non-responders: Non-responders have significantly higher alpha diversity (p<0.05) (Figure 1) and
a higher proportion of Proteobacteria (Figure 2). PERMANOVA analysis revealed that while
responders are more likely to have commensal bacteria, non-responder tumour microbiomes are
more likely to harbor pathogenic species such as Mycoplasmopsis fermentans (Figure 3).
Mechanistic analysis showed that for all responders the tumor microbiome is consistently promoting
tumor suppressor genes and downregulating proto-oncogenes. In non-responders, however, the
tumor microbiome is upregulating and downregulating both with unclear consistency
Conclusion
Our results show microbiome differences between responders and non-responders which were
mechanistically implicated in tumor pathogenesis. Further analysis divided by the hallmarks of
cancer is required to fully understand the non-responder microbiome links. The reported findings
could make the tumor microbiome a useful biomarker aiding patient stratification for both prognosis
and treatment choice. They also highlight a potentially meaningful mechanistic link between tumour
microenvironment and treatment response that could be leveraged as a novel therapeutic avenue
References
Chen, Yan, Fa-Hong Wu, Peng-Qiang Wu, Hong-Yun Xing, and Tao Ma. 2022. ‘The Role of The Tumor Microbiome in Tumor Development and Its Treatment’. Frontiers in Immunology 13. https://www.frontiersin.org/articles/10.3389/fimmu.2022.935846.
Hanahan, Douglas. 2022. ‘Hallmarks of Cancer: New Dimensions’. Cancer Discovery 12 (1): 31–46. https://doi.org/10.1158/2159-8290.CD-21-1059.
Hermida, Leandro C., E. Michael Gertz, and Eytan Ruppin. 2022. ‘Predicting Cancer Prognosis and Drug Response from the Tumor Microbiome’. Nature Communications 13 (1): 2896. https://doi.org/10.1038/s41467-022-30512-3.
Nejman, Deborah, Ilana Livyatan, Garold Fuks, Nancy Gavert, Yaara Zwang, Leore T Geller, Aviva Rotter-Maskowitz, et al. 2020. ‘The Human Tumor Microbiome Is Composed of Tumor Type-Specific Intracellular Bacteria’. Science (New York, N.Y.) 368 (6494): 973–80. https://doi.org/10.1126/science.aay9189.
Waks, Adrienne G., and Eric P. Winer. 2019. ‘Breast Cancer Treatment: A Review’. JAMA 321 (3): 288. https://doi.org/10.1001/jama.2018.19323.
