In a groundbreaking advancement that could revolutionize cancer diagnostics, researchers at the University of Geneva (UNIGE) have unveiled an innovative method to detect colorectal cancer through analysis of the human gut microbiota at an unprecedentedly detailed subspecies level. This pioneering study leverages machine learning algorithms to decipher the complex microbial signatures contained in simple stool samples, offering a promising, non-invasive, and cost-effective alternative to the traditional colonoscopy, potentially transforming early cancer detection globally.
Colorectal cancer remains one of the deadliest malignancies worldwide, ranking as the second leading cause of cancer-related deaths. One of the major hurdles in improving patient outcomes lies in early diagnosis. Colonoscopy, though highly effective, is costly and uncomfortable, deterring many from regular screening and consequently delaying detection until advanced stages when treatment options are limited and prognosis worsens. The UNIGE team’s novel approach directly addresses this challenge by providing a simpler route to identifying cancer presence through the microbiome’s intricate composition.
Central to this breakthrough is the recognition that not all bacteria within the gut microbiota contribute equally to disease development. Previous research established a link between microbial communities and colorectal cancer but treating bacterial species as uniform entities glossed over critical differences. Remarkably, strains within a single species can diverge functionally — some may promote carcinogenesis, while others remain benign. By developing a framework to identify these organisms at the subspecies level, the researchers have pinpointed more relevant microbial actors with finer resolution, bringing clarity to a previously murky biological landscape.
The team, led by Professor Mirko Trajkovski of the Department of Cell Physiology and Metabolism at UNIGE, innovated by moving beyond traditional taxonomic classifications. They explained that subspecies-level analysis strikes a balance: it resolves bacterial groups sufficiently to capture functional diversity but remains sufficiently consistent across individuals and populations to yield meaningful, reproducible insights. This middle ground overcomes the variability that complicates attempts to use strain-level data clinically, which often suffers from extreme heterogeneity.
To achieve this, the researchers amassed and processed vast datasets encompassing the human gut microbiome. Matija Trickovic, the study’s first author and a PhD student under Trajkovski, tackled this bioinformatic challenge by creating the first exhaustive catalogue of human gut microbiota subspecies. Utilizing cutting-edge machine learning techniques, they developed computational methods capable of efficiently parsing extensive microbiome data to identify subspecies signatures correlated with colorectal cancer presence.
Combining this subspecies catalogue with clinical data from patients, the research team trained predictive models capable of diagnosing colorectal cancer solely based on the bacteria found in stool samples. The results were extraordinary: the diagnostic model detected 90% of colorectal cancer cases, approaching the 94% sensitivity conventionally achieved by colonoscopy. Notably, this performance outstripped that of all existing non-invasive detection methods. This validation confirms the power of subspecies microbiota analysis in medical diagnostics.
The implications extend well beyond screening. By integrating additional clinical information, the model’s predictive power is anticipated to improve further, potentially matching or even surpassing colonoscopy accuracy. Such a tool could be deployed in routine screenings worldwide, reserving invasive procedures for patients at high risk. This paradigm shift would not only enhance early cancer detection but also reduce healthcare costs and improve patient compliance due to the non-invasive nature of stool sampling.
Currently, UNIGE is collaborating with Geneva University Hospitals (HUG) to initiate clinical trials aimed at refining the detection capabilities of the model. A key objective is to determine the earliest cancer stages and specific lesions identifiable through microbiome signatures, paving the way for personalized medical interventions. These trials represent a significant step towards clinical translation and widespread use.
Beyond colorectal cancer, this subspecies-based microbiome analysis opens a vast frontier in understanding the gut’s impact on human health. Different subspecies of the same bacterial species can exert opposing physiological roles, influencing disease pathways from metabolic disorders to immune dysfunction. Capturing this nuanced microbial diversity provides a powerful lens through which researchers may unravel complex host-microbiome interactions that underlie numerous diseases.
The technological foundation of this breakthrough lies in the synergy between bioinformatics, machine learning, and microbiology. High-throughput sequencing generates massive data describing microbiome composition, but extracting biologically relevant information demands sophisticated algorithms capable of detecting subtle patterns. The UNIGE team’s approach exemplifies how interdisciplinary innovation can harness big data to address unmet medical needs effectively.
Furthermore, the non-invasive nature of stool sample analysis aligns with patient-centered care principles, potentially increasing participation in cancer screening programs. Regular microbiota profiling could enable longitudinal monitoring of gut health and early disease detection, fundamentally changing preventive medicine. The technique offers scalability and accessibility, especially in resource-limited settings where colonoscopy infrastructure is scarce.
Professor Trajkovski emphasizes that this research ushers in a new era for microbiome studies—not merely cataloging species but deciphering the functional and pathological implications hidden at finer taxonomic levels. This breakthrough exemplifies how sub-microscopic variations in microbial populations shape human health and disease, challenging scientists to rethink current diagnostic and therapeutic strategies.
The success of this study also exemplifies how machine learning can transform biological research. By training algorithms on catalogued microbial data aligned with clinical outcomes, the researchers created predictive tools that learn and improve over time. This dynamic capacity positions microbiome analysis as a cornerstone technology for next-generation diagnostics across a wide spectrum of diseases.
As this subspecies-focused diagnostic technology matures, it holds promise for integration with other omics data—such as metabolomics and genomics—to build multifaceted disease prediction platforms. The potential to detect subtle shifts in microbial communities before clinical symptoms arise could vastly improve early intervention and patient prognosis.
In conclusion, the UNIGE team has redefined the frontiers of cancer diagnostics by unveiling a microbiome-based, machine learning-powered tool for early colorectal cancer detection. This innovative method not only rivals established colonoscopy standards but also heralds a future where non-invasive, microbiota-informed diagnostics augment the fight against cancer and other diseases. As research progresses, it promises to democratize screening, reduce the burden of invasive procedures, and deepen our understanding of the microscopic ecosystems within us that ultimately influence our health.
Subject of Research: People
Article Title: Subspecies of the human gut microbiota carry implicit information for in-depth microbiome research
News Publication Date: 13-Aug-2025
Web References: 10.1016/j.chom.2025.07.015
Keywords: colorectal cancer, gut microbiota, subspecies, machine learning, non-invasive diagnostics, microbiome, early cancer detection, bioinformatics, stool sample screening, personalized medicine, microbiota catalog, cancer biomarkers
