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