Multiomic Study of MSS CRC Identifies Vulnerabilities Pointing to Therapeutic Possibilities

An international study headed by scientists at RCSI University of Medicine and Health Sciences, and The Institute of Cancer Research, London, has identified potential new ways to treat microsatellite stable (MSS) colorectal cancer (CRC). The team integrated multiomic, single-cell, and clinical data from around 2,000 CRC tumors and developed an AI tool, iPhenMap, to create biological tumor maps.

The study uncovered vulnerabilities in these tumors and identified the drug combinations—including drugs already approved to treat other tumor types—that in vivo experiments in models indicated could be used to target CRC, potentially enabling more effective treatments. The researchers suggest that their results provide a clear roadmap for repurposing existing drugs and for new drug development to create more effective, personalized treatments for patients who currently have limited options.

Research co-lead Professor Annette Byrne, PhD, head of the RCSI Precision Cancer Medicine Group, Department of Physiology and Medical Physics, said “Patients with MSS RAS-mutant metastatic colorectal cancer urgently need better treatment options, particularly when their disease no longer responds to standard therapy. While these findings remain preclinical, they highlight promising therapeutic opportunities and provide a strong foundation for further development. The next step will be to identify the biological features that predict response and determine how these approaches can be advanced safely towards clinical evaluation.”

Professor Byrne is co-senior and co-corresponding author of the team’s published paper in Cancer Cell, titled “Multiomic characterization of microsatellite-stable colorectal cancer reveals distinct biology and targetable vulnerabilities.” In their report the researchers concluded, “Collectively, the results present a rich resource for the CRC research community and provide evidence for combinatorial treatment approaches with translational potential.”

Microsatellite stable colorectal cancer accounts for 80-85% of the 2,500 new bowel cancer cases diagnosed annually in Ireland, but remains challenging to treat, with limited response to many newer therapies. “Between 40% and 50% of these cases harbor mutations in the retrovirus-associated sequences (RAS) gene superfamily, 8–10 with RAS-mutant (RAS-mt) patients consistently displaying inferior outcomes compared with their RAS-wild-type (RAS-wt) counterparts,” the authors wrote.

Trials combining chemotherapy with newer targeted drugs or immunotherapies have delivered only modest improvements over current standard treatments. As the authors further pointed out, “As MSS CRC patient survival in particular has not significantly improved in recent decades, a concerted effort to unravel disease complexity is warranted.” And integrated analyses of multiomics datasets could provide key insights, they suggested.

Using 2,000 patient tumor samples—including more than 300 MSS CRCs that had not previously been profiled—the researchers analyzed genetic, metabolic protein, and clinical data with the help of artificial intelligence (AI) tools, revealing previously unrecognized ways in which the cancer cells divide, grow, adapt and interact with their environment. “To our knowledge, no study to date has presented a global integration of bulk genomics, epigenomics, proteomics, and metabolomics with single-nucleus/single-cell RNA sequencing (sn/scRNA-seq) and clinical parameters (phenome) in the MSS CRC setting,” they stated in their paper.

To do this the team developed a machine-learning AI tool called iPhenMap, which can create a biological map of the tumor. Using this map, they were able to spot connections between the different elements of the tumor—such as the genetic and metabolic features—that have been missed in previous studies that looked at the data in isolation.

Importantly, they found previously unidentified vulnerabilities in the tumors. They then tested these vulnerabilities in laboratory models and showed that certain drug combinations could work well to target them and treat the cancer. For example, the targeted drug trametinib—approved to treat various cancers including melanoma, lung cancer, and thyroid cancer, with a specific mutation—was shown to significantly reduce tumor growth and extend survival in the laboratory models when combined with other drugs.

The researchers hope this work will accelerate the path to clinical testing of new treatment strategies for colorectal cancer using drugs which have already been developed and approved. In addition, the team believe their AI approach could be used to identify more effective drugs for other tumor types.

In their paper the investigators wrote, “In conclusion, the in-depth molecular characterization of MSS CRC has revealed biological insights and promising therapeutic opportunities. By seamlessly integrating genomic, epigenomic, proteomic, metabolomic, and snRNA-seq/scRNA-seq expression analyses, we have substantially advanced understanding of the disease’s complex and heterogeneous biology and in parallel have identified promising new contexts of vulnerabilities with further translational potential.” Given what the team describes as “… the dearth of available effective treatments for MSS CRC patients …” the newly identified vulnerabilities may represent “the foundation for precision oncology treatment frameworks aimed at improving outcomes in this intractable disease setting.”

Research co-lead and co-senior author Professor Anguraj Sadanandam, PhD, group leader, Systems and Precision Cancer Medicine Group, The Institute of Cancer Research, London, said, “A major strength of this research is the scale and diversity of the dataset, which was only possible through collaboration with European partners. By bringing together data from patients across multiple countries and integrating DNA, RNA, epigenetic, protein, metabolic, single-cell and clinical data at this scale, we were able to expose connections between tumor biology and therapeutic dependency that would otherwise be missed. AI and machine learning will increasingly help cancer researchers to bring these complex data together, not just to classify tumors, but to understand how they behave, identify their vulnerabilities and guide the development and repurposing of treatments, so that patients can live longer and healthier lives.”

Rodrigo Dienstmann, PhD, Vall d’Hebron Institute of Oncology, Barcelona and University of Vic–Central University of Catalonia was co-senior author of the study.

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