Anna Karenina Principle, Immune-Oncology-Microbiome Trio and Cancer Microbiome Therapy.

The immune-oncology-microbiome (IOM) trio highlights the significant role of microbiomes in cancer progression by modulating immune evasion, genomic instability, and inflammation-key hallmarks of cancer. While microbiomes can exert both protective and detrimental effects on tumor development and treatment response, the mechanistic underpinnings-particularly those involving intratumoral microbiomes-remain poorly understood. To elucidate these dynamics, we frame the interplay between cancer, immune cells, and microbiomes through the lens of the Anna Karenina Principle (AKP)-using Leo Tolstoy's aphorism: "All happy families are alike; each unhappy family is unhappy in its own way." In biomedical terms, this translates to: all healthy systems including intratumoral microbiomes are alike, but each dysfunctional system fails in its own way. We hypothesize that either AKP or its inverse (anti-AKP) may govern microbial interactions that influence cancer progression. Analyzing four published cancer tissue microbiome datasets (Nejman 2020, Science), we identified two distinct patterns: AKP-driven increased microbial heterogeneity in lung and ovarian cancers, and anti-AKP-driven decreased heterogeneity in breast and colon cancers. We further propose cancer microbiome therapy (CMT) as an emerging frontier in microbiome-based therapeutics. The CMT may include the following strategies: (i) Restoring a healthy microbiome (including barrier tissue, tumor, blood microbiomes) to enhance immune function through ecosystem engineering; (ii) Developing specific microbial agents (species or their metabolites) to modulate crossroads of cancer immunotherapy; (iii) Engineering microbial agents to suppress cancer-causing microbes (oncomicrobes and complicit) and halt cancer progression; (iv) Reviving historical approaches like Coley's toxin and oncolytic viruses for direct cancer cell targeting. This article is categorized under: Cancer > Genetics/Genomics/Epigenetics Cancer > Computational Models.