Deep learning derived mechanical biomarkers for cancer therapy prediction

Introduction

In the fight against cancer, it is well recognized that tumors are highly heterogeneous and they might differ considerably not only between tumor types but also among tumors of the same type or even for the same tumor during progression. As a result, the efficacy of standard cancer therapies varies, and while some patients respond to a particular treatment, other patients do not gain any benefit. Consequently, crucial in cancer therapy is the prediction of a patient’s response to treatment.

Background

Failure of standard therapies has led to the introduction of a new era of personalized, patient-specific treatments, which are based on the identification of biomarkers that characterize the state of a particular tumor. Many solid tumors (e.g., breast cancers and sarcomas) stiffen as they grow in a host’s normal tissue. Tumor stiffening is a known factor leading to compromised efficacy of therapeutics.

Recent Developments

Recently, it has been demonstrated by our team and co-workers that repurposing of common drugs with anti-fibrotic properties, known as “mechanotherapeutics,” target tumor stiffness and enhance therapy. Here, we aim to harness the power of deep learning (DL) methods in order to develop a robust biomarker based on ultrasound shear wave elastography (SWE).

Objectives

This biomarker will aim to:

1. Predict patient’s response to treatment, separating responders and non-responders.
2. Monitor treatment outcomes, in the case of strategies that target tumor stiffness (i.e., mechanotherapeutics).

Methodology

The DL algorithms will be applied to a large set of existing preclinical data and to additional new data. A proof of concept clinical study on sarcoma patients will accommodate the clinical translation of the biomarker.

Commercialization

Furthermore, we propose to develop a software product to be used as a commercial tool for the measurement of the DL-derived, SWE biomarker. A planned market research will highlight the best options for commercialization.