First-line monotherapy with immune checkpoint inhibitors (anti-PD1/PDL1) is recommended for patients with advanced non-small cell lung cancer (NSCLC) who do not have targetable oncogenic mutations (driver mutations) and with PDL1 receptor expression in more than 50% of cells. However, many patients do not respond to the treatment or develop resistance after an initial positive response. Therefore, there is an urgent need to distinguish patients who will benefit from anti-PD1 monotherapy from those who will require more aggressive combination therapies.

The development of three-dimensional (3D) primary dynamic microfluidic cell cultures (microfluidic chip) can allow visualization of the dynamic interaction between tumor cells and immune cells, identify specific biomarkers, and contribute to the selection of appropriate therapy (precision medicine) through reverse translation. Indeed, recent studies have shown that dynamic microfluidic melanoma cultures exposed to anti-PD1 produce levels of chemokines CCL19 and CXCL13 that can positively correlate with patients’ clinical responses to the same drug. The application of similar approaches in lung cancer remains an open field.

The aim of BioOnChip is to investigate the potential of applying the innovative 3D dynamic culture of tumor tissue, known as tumor-on-chip, to predict in real time the response of NSCLC patients to anti-PD1 therapy.

Bronchoscopic tumor biopsies are minimally invasive, obtained from almost all NSCLC patients, and therefore ideal for use in drug selection platforms. The goal of the program is to develop the first-ever bronchoscopic biopsy-on-chip cultures worldwide (BioOnChip).

In this context, a clinical study will be organized to simultaneously examine the clinical response of advanced NSCLC patients to anti-PD1 monotherapy and the reaction of their bronchoBOCs upon exposure to anti-PD1. The responses of bronchoBOCs to anti-PD1 therapy will be characterized using state-of-the-art technologies combining mass spectrometry (LC-MS/MS), flow cytometry (FACS), multiplex protein detection platforms (CBA, cytometric bead arrays), and imaging techniques.

Methods of Artificial Intelligence and Machine Learning will be applied to correlate BioOnChip molecular data with patients’ clinical responses in order to predict biomarkers with higher accuracy, while simple algorithms will be developed that can be applied to large-scale data to predict treatment response with a satisfactory success rate.

The final products of this program will be:

1. a standardized preclinical platform for drug screening,
2. a study of the socioeconomic impact (Health Technology Assessment) of the new technology,
3. a user-friendly clinical prediction algorithm and an online portal,
4. the first Greek spin-out company offering tumor-on-chip platforms for drug screening in the pharmaceutical industry.