Project LIDER

 Ovarian cancer stands as one of the most challenging battles in modern medicine, often detected at advanced stages, complicating treatment options and diminishing survival rates. However, amidst these challenges lies a beacon of hope: liquid biopsies. In our research, we delve into the potential of liquid biopsies, specifically focusing on single-cell sequencing of blood cells and RNA sequencing of platelets that reflect host response to disease.  By combining machine learning and the world of big data, we hope to transform the landscape of ovarian cancer diagnosis and treatment in the future. 

 Traditional methods of cancer detection and monitoring are invasive, often carry risks, and in many cases are not feasible due to geographic and infrastructure limitations. Liquid biopsies offer a non-invasive alternative, drawing upon the analysis of bodily fluids such as blood, to provide invaluable insights into the presence and progression of cancer. Our research aims to harness the power of liquid biopsies to revolutionize ovarian cancer care. 

 At the forefront of our research is single-cell sequencing, a cutting-edge technique that allows us to delve into the intricate molecular landscape of ovarian cancer at unprecedented resolution. By analyzing individual cancer cells circulating in the bloodstream, we can uncover crucial RNA signatures, paving the way for earlier detection and more precise treatment strategies. 

 Complementing our approach is platelet RNA sequencing, a novel method that capitalizes on the dynamic interplay between cancer cells and platelets. Platelets, often overlooked in traditional cancer diagnostics, carry a wealth of information encoded in their RNA signatures. By deciphering these molecular messages, we can unravel the dialogue between cancer cells and their microenvironment, offering invaluable insights into disease progression and treatment response. 

 Our ultimate goal extends beyond mere detection. we envision a future where ovarian cancer treatment is truly personalized. By leveraging the wealth of data from liquid biopsies, we aim to tailor treatment regimens to the unique molecular profiles of individual patients. This paradigm shift holds the promise of improved outcomes, minimized side effects, and enhanced quality of life for ovarian cancer patients. 

 Our NCBiR LIDER project is complemented by other initiatives that relate to artificial intelligence in medicine and increasing the entrepreneurial innovation capacity of higher education institutions in data science in healthcare. We organize workshops, podcasts, matchmaking events, debates and other events which shape patients management of the future. 

Platelet RNA samples acquired from : 

66 Ovarian cancer patients

25 Benign controls

7 Healthy donors 

Single-cell sequencing samples acquired from:

13 Ovarian cancer patients

3 Bening control

4 Healthy donors

Analyzed AMNIS images of ovarian cancer samples

more images available at:

 www.ctcatlas.org  

Published manuscripts

Scientific conference posters

Patent applications:

1. Granted patent: P.435990  Classification of biological material in detecting cancer 
2. Patent application: P.447214 (EP24219459.5)  Detection of ovarian cancer using the RT-QPCR probe panel 
3. Patent application: P.450192 Ovarian cancer diagnostics using machine learning