I3LUNG is a European project funded under the framework of the H2020 call “Ensuring access to innovative, sustainable and high-quality health care”. Our consortium gathers 16 partners located worldwide characterized by different expertise, with the common goal of providing better assistance and individualize treatment for patients affected by metastatic lung cancer.

I3LUNG will enroll more than 2000 patients with metastatic non-small cell lung cancer (mNSCLC), the most common subtype of lung cancer, with the aim of investigating their individualized response to immunotherapy. This is a cancer treatment that uses the power of the body’s own immune system to prevent, control, and eliminate cancer. In our specific case, immunotherapy used for the treatment of mNSCLC are molecules called checkpoint inhibitors, that act by boosting immune cells to better recognize cancer cells and consequently eliminate tumor.

Immunotherapy is now the standard of care for this class of mNSCLC patients, administered in combination or not with chemotherapy, and often presenting with strong toxicity, unfortunately not always leading to a sustained clinical efficacy and moreover being very costly for the health system.

Biomarkers are biological molecules found in blood, other body fluids or samples, or tumor tissues which can be used to see how well the body responds to a treatment for a disease or condition. In mNSCLC, programmed Death-Ligand 1 (PD-L1) (a protein expressed in the tumor cell) still remains the only biomarker used to predict patient outcome to immunotherapy, despite its less-than-ideal predictive performance, highlighting how more comprehensive molecular/translational analysis are desperately needed in this setting of patients.

Today, there is not a way to predict response to immunotherapy, outside PD-L1 and biomarkers remain a critical missing link in attempting to identify appropriate candidates for immunotherapy and tailoring most effective treatment regimens.

In recent years, the explosion of Artificial Intelligence (AI) and machine learning (ML) has created the exciting opportunity to use a new set of tools to assess the vast amounts of data generated from clinical trials and research.

Thanks to the collection of biological, molecular, radiological, and clinical data from more than 2000 mNSCLC patients, I3LUNG will integrate all the collected information and thanks to the power of AI, generate a ML algorithm that will be able to predict the individual response to immunotherapy regimens. This tool will help to properly stratify mNSCLC patients and create a tailored treatment for each case, moving lung cancer care away from a “one-size-fits-all” approach to more of a personalized treatment plan.

This individualized patient selection strategy will also help to reduce the European economic burden and improve patient outcomes by better matching available treatments to patients.

Check what Chiara Giangregorio, our partner from Politecnico di Milano, has to say about this tool that holistically evaluates patients quality of life throughout their therapeutic journey.

The I3LUNG project will run for 5 years. Starting on June 1, 2022 and ending in May 2027.

• June 2022: Kick-off I3LUNG Annual Meeting in Milan (Italy) [Event]

 

• November 2022: “Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology” (Nature Communications: link) [Paper]

• February 2023: The EU-funded I3LUNG Project: Integrative Science, Intelligent Data Platform for Individualized LUNG Cancer Care With Immunotherapy (Clinical Lung Cancer: link) [Paper]

 

• April 2023: Lung Cancer (NSCLC) and adherence to best practice guidelines (The Swedish Health Economics Association (SHEA) Conference: link) [Congress]

 

• May 2023:
  • AI for Oncology Congress 2023 in Milan (Italy) [Event]
  • Artificial Intelligence in Clinical Oncology: From Data to Digital Pathology and Treatment (ASCO Educational Book: link) [Paper]

 

• June 2023:
  • Second I3LUNG Annual Meeting in Barcelona (Spain) [Event]
  • I3LUNG: Integrative Science, Intelligent Data Platform for Individualized LUNG Cancer Care with Immunotherapy (Deutsches Zentrum für Lungenforschung Conference (DZL): link) [Congress]

• January 2024: Real World Data and Digital Oncology – Applications and Promises of Artificial Intelligence (AI) in Oncology (ESMO Webinar Series: link) [Congress]

 

• May 2024:
  • AI for Oncology Congress 2024 in Milan (Italy) [Event]
  • The impact of decision tools during oncological consultation with lung cancer patients: a systematic review within the I3LUNG project (Cancer Medicine: link) [Paper]
  • I3LUNG: A machine learning framework to improve treatment selection in lung cancer (National Scientific forum of Romanian young oncologists (FSTMO): link) [Congress]
  • Therapy selection gets AI assistance [Nature: link) [Paper]

 

• June 2024:
  • Third I3LUNG Annual Meeting in Spetses (Greece) [Event]
  • I3LUNG: Integrative Science, Intelligent Data Platform for Individualized LUNG Cancer Care with Immunotherapy – UPDATE: AI Based prediction (Deutsches Zentrum für Lungenforschung Conference (DZL): link) [Congress]