The Virtual Conference on

Lung Cancer

Avi Schroeder, PhD

Associate Professor of Chemical Engineering, Technion – Israel Institute of Technology, Haifa 32000, ISRAEL;    avids@technion.ac.il

Medicine is taking its first steps towards patient-specific cancer care. Nanoparticles have many potential benefits for treating cancer, including the ability to transport complex molecular cargoes including siRNA and protein, as well as targeting to specific cell populations.

The talk will discuss ‘barcoded nanoparticles’ that target sites of cancer where they perform a programmed therapeutic task. Specifically, liposomes that diagnose the tumor and metastasis for their sensitivity to different medications, providing patient-specific drug activity information that can be used to improve the medication choice.

The talk will also describe how the liposomal lipid composition can be used as multi-functional systems for degrading the pancreatic stroma to allow subsequent drug penetration into pancreatic adenocarcinoma, and how the nanoparticle configuration can be leveraged to induce an anti-tumor immune response.

The evolution of drug delivery systems into synthetic cells, programmed nanoparticles that have an autonomous capacity to synthesize diagnostic and therapeutic proteins inside the body, and their promise for treating cancer and immunotherapy, will be discussed.

References:

1) Theranostic barcoded nanoparticles for personalized cancer medicine, Yaari et al. Nature Communications, 2016, 7, 13325

2)  Synthetic Cells Synthesize Therapeutic Proteins inside Tumors, Krinsky et al., Advanced Healthcare Materials, 2017

3)  Collagenase nanoparticles enhance the penetration of drugs into pancreatic tumors, Zinger et al., ACS Nano, 2019

Immunotherapy based on immune checkpoint inhibitors (ICIs) has made a paradigm shift in oncology. Despite a remarkable success of prolonged survival, only a small portion of patients benefit from this treatment modality. Major efforts are therefore made to identify strategies to identify the patient population that will benefit from ICI therapy. The host (Patient), in response to anti-cancer therapy, generates pro-tumorigenic biological processes which in turn can induce tumor regrowth and resistance to therapy. Here we present the PROphet^Ò platform for systemic host response proteomic profiling in liquid biopsies for biomarker discovery in ICI therapy.

The host response to immunotherapy treatment may generate processes that support and facilitate tumor growth and spread. We have previously shown that machine learning based, proteomic analysis of the host response may serve as a predictive tool for response to immunotherapy for NSCLC and Melanoma. The PROphet^Ò utilizes a blood-based proteomic profiling and a machine-learning approach to identify host response biomarkers for predicting clinical outcome in ICI-treated non-small cell lung cancer (NSCLC) patients.