High resolution molecular profiling platform to investigate the role of tumour microbiota in anti-tumour immunity

The tumour microenvironment (TME) plays fundamental roles in cancer pathology and response to therapies. The intratumour microbiota has emerged as a non-negligible active component of the TME.

The diversity and spatial distribution of the intratumour microbiota are closely associated with prognosis in different cancer types. Moreover, there is mounting evidence that it regulates cancer pathogenesis and efficacy of anticancer therapies via various mechanisms, including immune regulation and metabolic alterations of the host cells. However, mechanisms by which host-microbiota interactions mediate anti-tumour effects via modulating the immune TME are largely unknown.

In spite of the potential for intratumor microbiota to be a prognostic indicator, and of their interactions with host cells to unravel new strategies for precision medicine in cancer, research in this field is moving forward at a slow pace. The fundamental challenge is the low biomass of the intratumour microbiota and the integration of multi-modal data.

Here we are tackling this challenge by establishing an innovative platform that combines complementary cutting-edge spatial-profiling technologies, Raman spectroscopy and mass spectrometry (imaging and proteomics), with advanced AI-driven image analysis and bioinformatics. Together, these will map the intratumour microbiome, cell type specific metabolic states and the proteome associated with the presence of intratumour microbes in situ in clinical tissue samples.

In parallel, we will also develop novel microfluidic platforms that will uniquely enable the enrichment of the microbiome for in-depth genetic investigations.

When combined, the data from the Raman, metabolic and proteomic imaging and bioinformatics will enable a massively holistic view of the cancer cell's biology with high spatial resolution and detailed molecular information.

As proof of concept, we will apply this platform to tubo-ovarian high grade serous (HGS) tumour tissue samples. HGS ovarian cancer is the most lethal gynaecological malignancy in the developed world with limited therapeutic opportunities for the patients. While immunotherapies have revolutionised anti-cancer treatments due to long-term survival benefits, their effectiveness has been limited in HGS ovarian cancer patients due to the immunosuppressive TME. Finding ways to predict which patients respond to immunotherapies and the development of new treatments to revert immunosuppression could be a game changer for these patients. Of particular interest here is the prospect of investigating mechanisms behind the intratumoral microbiome's close association with the immune TME and prognosis in HGS ovarian cancer.

The data generated with our platforms have the potential to identify microbiome biomarkers of immunosuppression. Moreover, Raman spectroscopy offers unprecedented speed to determine the diversity of the microbiome, hence the potential of the technology developed here for rapid patient stratification for treatment regimens and clinical outcomes. Furthermore, our data on host-microbiota interactions can lead to generating hypotheses on mechanistic functions of the microbiota in regulating tumour immunity, ultimately advancing the development of new therapies to boost tumour immunity and response to immunotherapies.

More broadly, our platform will impact other cancer types and even other fields beyond cancer because of its applicability to any tissue samples.