Immunotherapy resistance in solid tumors remains one of oncology’s most significant therapeutic challenges, with multiple cancer types showing limited response to current approaches. Microsatellite stable colorectal cancer (MSS CRC) exemplifies this challenge, with over 90% of MSS CRC cases failing to respond to checkpoint inhibitor monotherapy, while resistance patterns also emerge in many other immunologically ‘cold’ solid tumors.
3T Biosciences aims to address these therapeutic barriers through its 3T-TRACE platform, which combines high-diversity target libraries with active machine learning to identify novel shared T-cell receptor (TCR) targets. Traditional approaches—such as mass spectrometry and homology-based modeling—rely on the known proteome and may overlook cryptic targets.
Beyond the known proteome
In a nutshell
Therapeutic challenge: Current immunotherapies show minimal efficacy against solid tumors, with MSS colorectal cancer showing over 90% resistance to checkpoint inhibitors.
Standard approach limitations: Mass spectrometry and computer modeling only identify known proteins without testing actual biological interactions.
3T’s platform innovation: Yeast-display technology tests billions of protein fragments against tumor-derived immune cells, enabling direct observation of interactions and cross-reactivities.
Technical advantages:
- Direct biological interaction testing
- Novel target identification beyond known proteome
- Early safety risk detection
- Machine learning-enhanced validation
Commercial strategy: Early candidate de-risking reduces late-stage development costs while accelerating clinical-stage pipeline advancement.
“Traditional methods like mass spectrometry or homology-based modeling rely on the known proteome,” said Dr. Stefan Scherer, CEO of 3T Biosciences. These approaches identify protein sequences but cannot directly test their biological interactions and cross-reactivities. “In contrast, our platform is fully functional. We basically display up to a billion peptides per HLA on the surface of yeast. Then we take TCRs from tumor samples and run them over that large, complex library, which can go beyond 100 billion targets when factoring in the Stanford-licensed yeast-display technology.” The approach lets the company not only see TCR binding, but also spot off-target cross-reactivities. “That comprehensive, functional testing is what truly sets us apart and helps us select the safest and most promising candidates,” Scherer said.
Coupling this library of potential targets with machine learning further refines target validation. Collaborations with institutions like Oxford University, VIB, KU Leuven, and UCSF are helping validate this strategy, with KU Leuven specifically focusing on colorectal cancer, including the MSS subtype where traditional immunotherapies have shown limited efficacy.
De-risking through functional validation
“A number of groups are trying to predict TCR–peptide–HLA interactions using structural or in silico methods,” Scherer said. “At this point, the question is whether we can fully eliminate the need for functional testing in the wet lab. There’s promising progress, but we’re still not there yet. It might be possible within the next few years, but right now, we still rely on functional data to de-risk our pipeline. The real value here is in deciding which targets not to take into the clinic, because that’s where most of the cost savings come in.”
Through this process of early de-risking, 3T’s pipeline systematically eliminates a large fraction of nonviable candidates before they reach expensive trial phases. “The cost you save isn’t so much in discovery,” Scherer continued. “Those costs are relatively modest. The real savings come from knowing early which candidates aren’t likely to succeed in the clinic. That way, you avoid sinking millions into manufacturing and clinical development for something that would ultimately fail.”
“We’ve essentially reached the stage of truly transforming into a clinical-stage biopharma,” Scherer said. “From the start, we were a research and platform company, but now we’ve become clinical-stage. That’s a very important transformation of the company and one that fosters investor confidence as we move closer to delivering novel, tumor-specific therapies.”
Filed Under: Oncology
