Summary
The efficacy of standard cancer therapies varies, and while some patients respond to a particular treatment, other patients do not gain any benefit. In response, an era of individualized cancer treatments is emerging which are based on the identification of biomarkers that characterize the state of a tumor. Many solid tumors (e.g., breast cancers and sarcomas) stiffen as they grow within a normal tissue. Tumor stiffening is a known factor leading to compromised efficacy of therapeutics. Repurposing drugs in order to alleviate tumor stiffness before the initiation of therapy has been tested in preclinical studies and has recently made it to the clinic. Despite recent success of these strategies, optimization of their application is understudied. Here, we aim to harness the power of deep learning (DL) methods in order to construct a robust biomarker based on ultrasound shear wave elastography (SWE). The biomarker will aim to: (i) predict the tumor’s response to treatment with chemo- and immuno-therapy and (ii) monitor treatment outcomes, in the case of strategies that target tumor stiffness. The project will capitalize on the existing strengths of the applicant in medical image processing and DL and the expertise of the host in tumor mechanopathology and in vivo experiments. In past experiments, the host acquired a large number of SWE tumor data, which will enable the development of the DL biomarker. Through the experimental part of the project, additional data on murine cancer models will be acquired enabling the validation of the biomarker. Formulation of the developed DL-derived biomarker in a user-friendly software will allow for potential clinical translation and further exploitation through IPR. The applicant will acquire new knowledge in the fields of cancer therapy, tumor biology and in vivo experimental design. The attained knowledge and skills will be instrumental to the applicant’s ambition to lead the field of artificial intelligence in biomedicine.
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| Web resources: | https://cordis.europa.eu/project/id/101065325 |
| Start date: | 01-09-2023 |
| End date: | 31-08-2025 |
| Total budget - Public funding: | - 148 488,00 Euro |
Cordis data
Original description
The efficacy of standard cancer therapies varies, and while some patients respond to a particular treatment, other patients do not gain any benefit. In response, an era of individualized cancer treatments is emerging which are based on the identification of biomarkers that characterize the state of a tumor. Many solid tumors (e.g., breast cancers and sarcomas) stiffen as they grow within a normal tissue. Tumor stiffening is a known factor leading to compromised efficacy of therapeutics. Repurposing drugs in order to alleviate tumor stiffness before the initiation of therapy has been tested in preclinical studies and has recently made it to the clinic. Despite recent success of these strategies, optimization of their application is understudied. Here, we aim to harness the power of deep learning (DL) methods in order to construct a robust biomarker based on ultrasound shear wave elastography (SWE). The biomarker will aim to: (i) predict the tumor’s response to treatment with chemo- and immuno-therapy and (ii) monitor treatment outcomes, in the case of strategies that target tumor stiffness. The project will capitalize on the existing strengths of the applicant in medical image processing and DL and the expertise of the host in tumor mechanopathology and in vivo experiments. In past experiments, the host acquired a large number of SWE tumor data, which will enable the development of the DL biomarker. Through the experimental part of the project, additional data on murine cancer models will be acquired enabling the validation of the biomarker. Formulation of the developed DL-derived biomarker in a user-friendly software will allow for potential clinical translation and further exploitation through IPR. The applicant will acquire new knowledge in the fields of cancer therapy, tumor biology and in vivo experimental design. The attained knowledge and skills will be instrumental to the applicant’s ambition to lead the field of artificial intelligence in biomedicine.Status
SIGNEDCall topic
HORIZON-MSCA-2021-PF-01-01Update Date
09-02-2023
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