Computational methods and challenges in analyzing intratumoral microbiome data

Abstract

The human microbiome is intimately related to cancer biology and plays a vital role in the efficacy of cancer treatments, including immunotherapy. Extraordinary evidence has revealed that several microbes influence tumor development through interaction with the host immune system, that is, immuno-oncology-microbiome (IOM). This review focuses on the intratumoral microbiome in IOM and describes the available data and computational methods for discovering biological insights of microbial profiling from host bulk, single-cell, and spatial sequencing data. Critical challenges in data analysis and integration are discussed. Specifically, the microorganisms associated with cancer and cancer treatment in the context of IOM are collected and integrated from the literature. Lastly, we provide our perspectives for future directions in IOM research.

Keywords: cancer treatment and diagnosis; computational methods; immuno-oncology-microbiome; immunotherapy; intratumoral microbiome.

Figure 1.. Key Figure. Schematic overview of computational studies of immuno–oncology-microbiome (IOM).

To analyze the effects of gut microbiota on host tumors, researchers can acquire the gut microbial composition using the 16S ribosomal RNA (rRNA) data, metagenomic data, or metatranscriptomic data from stool samples. Correlation analysis between gut microbial composition and cancer patients undergoing immunotherapy can help researchers understand clinical response heterogeneity. For host tumor tissues, microbial profiling can be derived from existing host sequencing data, including bulk sequencing, single-cell sequencing, and spatial transcriptome data. The computational analyses of these data can enable researchers to obtain the tissue-specific, cell-type-specific, or spatial-specific microbial signatures for further IOM studies and clinical applications.

Figure 2.. Roadmap for computationally studying IOM.

The tasks in IOM studies are to investigate tumor type-specific and clinical response-specific microbial signatures as well as cellular interactions and systematic mechanisms of IOM. By using various sequencing technologies on host fecal and tissue samples, researchers can obtain data interpretation of IOM, including microbial profiling, host-microbe interactions, and host immune profiling. Computational methods, such as statistical tests and deep learning, reveal the associations between microbes and tumor-type/clinical response or host antitumor immune response by using the collected data interpretation. The processes highlighted in red arrows represent the microbial profiling mining of IOM analysis involving intratumoral microbes, which fills in the missing part of current IOM studies (i.e., the effect of the intratumoral microbiota in the TME).

Figure 3.. Clinical application of IOM studies.

Outcomes from computational methods in IOM studies help researchers to understand the IOM and guide more precise cancer treatments. (A) IOM can help researchers understand the mechanisms of tumor development. For example, the dysregulation of local microbiota can promote lung cancer development via γδ T cells [24]. (B) Microbes may contribute to the heterogeneity in the clinical response of cancer patients receiving the same treatments. For example, the differential composition of the commensal microbiome of metastatic melanoma patients may affect the effect of the immunotherapy [12]. (C) IOM can guide cancer treatment. An example is the modulation of pulmonary microbiota by antibiotic treatment, which promotes immunosurveillance against melanoma metastases to the lung [25]. (D) Microbial therapies provide a new opportunity for treating cancers. For example, tumor cell lysis triggered by oncolytic T-VEC releases TDA, GM-CSF, and new viral particles, which can enhance the activation of dendritic cells and initiate a systemic antitumor adaptive immune response in advanced melanoma patients [64]. Areg: amphiregulin; NK cell: natural killer cell; T-VEC: talimogene laherparepvec; TDA: tumor-derived antigens; GM-CSF: granulocyte-macrophage colony-stimulating factor.