20/06/2024
🔬Biophysical changes are an emerging hallmark of cancers and their sensitivity to therapy, including pharmaceutical and radiation approaches. Tomographic Phase Microscopy (TPM) is a new method able to capture specific 3D biophysical features at the single-cell level without fluorescent labelling thanks to Digital Holography (DH).
🤖By flow cytometry (FC), the TPM can reach an unprecedented statistical power able to answer to the need for large and informative datasets typical of Artificial Intelligence (AI), thus making fast, automatic, and objective the cell phenotyping for multiple endpoints. In the framework of MUR PRIN2017 project (MORFEO-2017N7R2CJ), a completely new TPM setup has been developed in FC mode (TPM-FC), thus proofing for the first time that the high-throughput of stain-free cells can be achieved non-invasively by retrieving 3D morphology of cells flowing in microfluidic channels [1]. In a recent paper [2] has been proved that TPM-FC can furnish stain-free identification/measurements of nuclei by computational analysis of the 3D spatial distribution of the refractive index (RI) of cancer cells. Such results have opened a fully new opportunity of research about the planning and follow-up of tailored therapeutic procedures in cancer therapy.
☢ A notable example is radiotherapy(RT), an essential component of cancer treatment used by over 50% of patients. RT treatment planning is adapted to patient morphology as visualized by CT or MRI images, yet the treatment schedule is not individualized. In fact, general dose and fractionation prescription are used for all patients, irrespective of the characteristics of the individual cancer. RT is therefore still missing a place in personalized medicine [3]. The quest for biomarkers or predictive assays of radiation sensitivity has been as yet generally unsuccessful, even if it is acknowledged as of primary relevance to adapt the protocol to the patient’s characteristics and to decide the type of therapy to be pursued, e.g. if patients should undergo X-ray [4].
📝 Here we propose to investigate TPM-FC as personalized predictive assay in cancer RT. The project will include in-vitro tests on human cancer cell lines having different intrinsic radiosensitivity after being exposed to graded doses of radiation. Then, from TPM-FC data, an AI-driven system will identify relevant biophysical parameters related to radiosensitivity as specific biomarkers. The ultimate-goal is to offer a new AI-driven histology advanced single-cell analysis able not only to characterize the tumor characteristics but also to predict its response to the RT treatment, thus making a remarkable step forward the tailoring of therapeutic strategies.
References:
[1] https://lnkd.in/dNDpMciQ
[2] https://lnkd.in/dCPHQDzv
[3] https://lnkd.in/dRRpzU4N
[4] https://lnkd.in/d2vXbmP8
