Multiomic Mapping Finds Drug Targets in Hard-to-Treat Colorectal Cancer

A map of tumor biology points to treatment leads

For microsatellite-stable colorectal cancer, a large share of cases still have few effective treatment options. A new international study offers a detailed view of the disease’s biology and identifies drug combinations that curbed tumor growth in laboratory models. The findings are preclinical, but they could help guide drug repurposing and further treatment development.

Researchers led by RCSI University of Medicine and Health Sciences and The Institute of Cancer Research in London analyzed data from around 2,000 colorectal tumors. The set included more than 300 MSS tumors that had not previously been profiled. By combining genomic, epigenomic, protein, metabolic, single-cell and clinical information, the team examined how tumor cells grow, adapt and interact with their surroundings.

The group built a machine-learning tool called iPhenMap to connect these data in biological maps of tumors. That approach brought relationships into view that can be missed when each data type is studied separately, including links between genetic and metabolic features.

Why MSS disease remains a difficult target

MSS colorectal cancer accounts for 80–85% of the 2,500 new bowel cancer cases diagnosed each year in Ireland, according to the researchers. Between 40% and 50% of these cases carry mutations in the RAS gene superfamily. Patients with RAS-mutant disease have consistently had poorer outcomes than those with RAS-wild-type tumors.

Trials combining chemotherapy with targeted drugs or immunotherapies have brought only modest gains over standard treatment. The researchers point to the lack of significant improvement in MSS patient survival over recent decades as a reason to examine the disease’s complexity through integrated data.

“Patients with MSS RAS-mutant metastatic colorectal cancer urgently need better treatment options, particularly when their disease no longer responds to standard therapy,” said co-lead researcher Annette Byrne, head of the RCSI Precision Cancer Medicine Group. She emphasized that the results remain preclinical. The next steps, she said, are to identify biological features that predict response and determine how the treatment approaches could progress safely toward clinical evaluation.

Drug combinations tested in models

Using the tumor maps, the researchers identified vulnerabilities and tested them in laboratory models. Some combinations showed promise, including regimens built around trametinib, a targeted drug approved for certain cancers, including melanoma, lung and thyroid cancers. In the models, trametinib combined with other drugs significantly reduced tumor growth and extended survival.

Those results do not establish that the combinations will work in patients. They do, however, give researchers a set of testable leads, including drugs already used in other cancer settings. The team hopes that this could shorten the path toward clinical testing of colorectal cancer strategies and inform the development of new treatments.

Byrne is co-senior and co-corresponding author of the study, published in Cancer Cell as “Multiomic characterization of microsatellite-stable colorectal cancer reveals distinct biology and targetable vulnerabilities.” Co-lead and co-senior author Anguraj Sadanandam, group leader of the Systems and Precision Cancer Medicine Group at The Institute of Cancer Research, said the dataset’s scale and diversity depended on collaboration with European partners.

Sadanandam said that combining DNA, RNA, epigenetic, protein, metabolic, single-cell and clinical data helped reveal connections between tumor biology and treatment dependencies. He sees AI and machine learning as tools for understanding how tumors behave and finding vulnerabilities, beyond simply classifying them. Rodrigo Dienstmann of the Vall d’Hebron Institute of Oncology in Barcelona and the University of Vic–Central University of Catalonia was also a co-senior author.

The researchers describe the work as a resource for the colorectal cancer research community and evidence supporting combination treatments with translational potential. Their broader approach may also help investigators study other tumor types. For MSS colorectal cancer, the next test is whether the biological signals identified here can predict which patients might benefit from particular drug combinations.

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