Scientists Develop AI-Powered Model to Enhance Cancer Immunotherapies
Scientists at Ludwig Cancer Research With the use of AI, have developed a predictive model that can identify the cancer-killing immune cells that have the highest potential to be used in immunotherapies. This tool, TRTpred, is especially described, in detail, in the high-rated journal Nature Biotechnology.
Personalizing cancer treatments
The TRTpred model supported by algorithms can be integrated into individualized cancer treatments and tailored therapies based on the signature of each patient’s precancerous cells. As Mr. Alexander Harari from Ludwig Lausanne, the head researcher of the work, mentioned, such a novel technique may brandish a new set of subsidies to the patients.
The immune cells found in cancer transferred into a patient, known as tumor-infiltrating lymphocytes (TILs), form the basis of cell-based immunotherapy. These TILs then could be optionally modulated to increase their intrinsic synergistic capabilities against cancer and then reintroduced to the body after being scaled up in culture. Nevertheless, not every TIL successfully attends to tumor-suspicious cells, with just a fraction being tumor-reactive.
Harari and his team came up with TRTpred, an AI-driven predictive modeling approach that allows them to rank T cell receptors (TCRs) by their tumor reactivity. As TRTpred identifies genes responsible for tumor secretion, it can create a rule that will be applied to a new population and then accurately predict whether a TCR is a tumor-reactive cell or not.
TRTpred: An AI-driven game-changer
The scientists boosted the quality of the former step with algorithmic filters with a scope to find tumor antigen-bearing T-cells of high avidity, namely those capable of strong antigen binding. Furthermore, a third filter was added with the aim of better differentiation of antigens for tumor cells, which contributes to the targeting of multiple antigens.
The team extracted the TIL’s TCRs, and used MixTRTpred (a combination of the TRTpred technique and filters of the algorithms) to identify the T cells that could be helpful in attacking tumors which are high avidity and specific for multiple tumor antigens. These engineered T cells were successfully introduced into the mice, where the elimination of tumors was evident when the xenograft was completed, thereby providing proof of the method.
George Coukos, the Director of Ludwig Lausanne and co-author of the study, intends to initiate a Phase I clinical trial as soon as possible in humans to test the technology. He stated that he had great hope in the method’s performance as he believed that it would oblige the current shortcomings of TIL-based therapies. Specifically, those patients whose tumors do not react the way they are supposed to today.
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Scientists Develop AI-Powered Model to Enhance Cancer Immunotherapies
Scientists at Ludwig Cancer Research With the use of AI, have developed a predictive model that can identify the cancer-killing immune cells that have the highest potential to be used in immunotherapies. This tool, TRTpred, is especially described, in detail, in the high-rated journal Nature Biotechnology.
Personalizing cancer treatments
The TRTpred model supported by algorithms can be integrated into individualized cancer treatments and tailored therapies based on the signature of each patient’s precancerous cells. As Mr. Alexander Harari from Ludwig Lausanne, the head researcher of the work, mentioned, such a novel technique may brandish a new set of subsidies to the patients.
The immune cells found in cancer transferred into a patient, known as tumor-infiltrating lymphocytes (TILs), form the basis of cell-based immunotherapy. These TILs then could be optionally modulated to increase their intrinsic synergistic capabilities against cancer and then reintroduced to the body after being scaled up in culture. Nevertheless, not every TIL successfully attends to tumor-suspicious cells, with just a fraction being tumor-reactive.
Harari and his team came up with TRTpred, an AI-driven predictive modeling approach that allows them to rank T cell receptors (TCRs) by their tumor reactivity. As TRTpred identifies genes responsible for tumor secretion, it can create a rule that will be applied to a new population and then accurately predict whether a TCR is a tumor-reactive cell or not.
TRTpred: An AI-driven game-changer
The scientists boosted the quality of the former step with algorithmic filters with a scope to find tumor antigen-bearing T-cells of high avidity, namely those capable of strong antigen binding. Furthermore, a third filter was added with the aim of better differentiation of antigens for tumor cells, which contributes to the targeting of multiple antigens.
The team extracted the TIL’s TCRs, and used MixTRTpred (a combination of the TRTpred technique and filters of the algorithms) to identify the T cells that could be helpful in attacking tumors which are high avidity and specific for multiple tumor antigens. These engineered T cells were successfully introduced into the mice, where the elimination of tumors was evident when the xenograft was completed, thereby providing proof of the method.
George Coukos, the Director of Ludwig Lausanne and co-author of the study, intends to initiate a Phase I clinical trial as soon as possible in humans to test the technology. He stated that he had great hope in the method’s performance as he believed that it would oblige the current shortcomings of TIL-based therapies. Specifically, those patients whose tumors do not react the way they are supposed to today.
Read More