Research database

REAP - Revealing drug tolerant persister cells in cancer using contrast enhanced optical coherence and photoacoustic tomography

Duration:
48 months (2024)
Principal investigator(s):
Project type:
UE-funded research - H2020 - Industrial Leadership – LEIT - ICT
Funding body:
COMMISSIONE EUROPEA
Project identification number:
PoliTo role:
Partner

Abstract

The objective of this project is to reveal the drug tolerant persister cells (DTPs) in cancer using contrast enhanced optical coherence and photoacoustic tomography. Although people have gained unprecedented insight into the molecular mechanism of cancer, the drug resistance of cancer is still the Gordian knot for targeted therapy options, especially for cancers in advanced stages. The ringleader for this resistance can be traced to the DTPs, which can survive treatment. Detection of the DTPs, therefore, is of key importance for cancer treatment. However, due to the scarcity of the DTPs, tracking and analyzing them are extremely challenging with commercially available methods. In this proposal, we aim to reveal these DTPs by multimodal optical imaging. Firstly, a triple modal two-photon laser scanning optical coherence photoacoustic microscopy (2PLS-OC-PAM) system will be built for in vitro measurements of cancer organoids. Secondly, a dual modality optical coherence photoacoustic tomography (OC-PAT) system will be implemented to visualize the tumors in vivo in a mouse model. A genetically modified mouse model of triple negative breast cancer will be dedicated in this study. As a contrast enhancement measure, nanoparticles will be designed and biofunctionalized to label the DTPs, enabling greatly increased sensitivity and specificity. To improve the image resolution, novel photoacoustic detectors will be developed based on microring technology. Furthermore, the image acquisition speed is expected to be increased by an order of magnitude by bringing in innovative laser sources to be developed in this proposal. Last but not least, real time data handling will be explored in this project as well as deep learning based automatic analysis algorithms. With the combined expertise in laser sources, detector technology, nanoparticle, and deep learning-based algorithms, this proposal has the potential to create completely new applications in imaging.

Structures

Partners

  • Medical University of Vienna - Coordinator
  • POLITECNICO DI TORINO - AMMINISTRAZIONE CENTRALE
  • AUSTRIAN INSTITUTE OF TECHNOLOGY GMBH
  • UNIVERSIDAD DE SANTIAGO DE COMPOSTELA
  • PICOPHOTONICS Oy
  • TAMPERE UNIVERSITY FOUNDATION SR
  • INNOLAS LASER GMBH
  • LAVISION GMBH
  • LIONIX INTERNATIONAL BV
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Keywords

ERC sectors

LS4_6 - Cancer and its biological basis
LS7_2 - Diagnostic tools (e.g. genetic, imaging)
PE2_9 - Optics, non-linear optics and nano-optics

Sustainable Development Goals

Obiettivo 3. Assicurare la salute e il benessere per tutti e per tutte le età

Budget

Total cost: € 6,185,978.75
Total contribution: € 6,185,978.75
PoliTo total cost: € 480,151.25
PoliTo contribution: € 480,151.25

Communication activities