Pharmaceuticals
Methods of diagnosing cancer and predicting responsiveness to therapy (No. T4-2066)

6018
Overview

Immunotherapy was proven very efficient for some patients revolutionizing cancer treatment; however, many patients fail to respond to the treatment. It is currently unknown which patients will respond to immunotherapy. The current technology enables to diagnose cancer and to pre-determine a patient’s likelihood to respond to immunotherapy based on the tumor microbiome.

Background and Unmet Need

Immunotherapy achieved remarkable clinical responses in some patients. However, large proportions of the patients do not acquire any significant therapeutic response or develop resistance that results in disease progression. For instance, immune checkpoint inhibitors (ICI) have demonstrated significant clinical efficacy in metastatic melanoma; however, 40‑60% of patients fail to respond. Many of the responders experience tumor relapse within two years1. It is currently unknown which of the patients will respond to immunotherapies and which of the available treatments will be the most optimal for any individual.

The Solution

The current technology offers a method for cancer diagnosis and for determining if a patient is likely to respond to immunotherapy based on the tumor microbiome.

Technology Essence

Dr. Ravid Straussman and his team tested seven different cancer types including breast, lung, ovary, pancreas, melanoma, bone, and brain tumors. Overall, 1526 samples from 9 centers were tested, identifying 528 bacterial species in tumors. The team showed that bacterial components are found in solid tumors to different extents, ranging from only 14.3% in melanoma to >60% in breast, pancreatic, and bone tumors. Interestingly, breast cancer has a particularly rich and diverse microbiome. The team further showed that different tumor types have distinct microbial compositions. Most importantly, the team found that each tumor has a unique microbiome signature, which correlates with the tumor potential responsiveness to immunotherapy. Furthermore, the team found that patients with a favorable response-associated tumor-microbiome signature had prolonged progression-free survival compared to those without this signature (Figure 1)2.

Figure 1: (A) Volcano plot demonstrating the bacterial taxa enriched in melanoma patients who responded to immune checkpoint inhibitors (ICI) vs. non-responders. (B) Differentially prevalent bacterial taxa from panel A (n=46) were used to stratify the melanoma patients cohort according to the presence or absence of a favorable bacterial signature. Progression-free survival is demonstrated for both groups of patients.

Applications and Advantages
  • Cancer diagnostics
  • A tool to determine a patient’s likelihood to respond to immunotherapy
Development Status

The team characterized tumor microbiome of seven different cancer types (breast, lung, ovary, pancreas, melanoma, bone, and brain), and identified microbiome signatures of responders vs. non-responders to cancer immunotherapies.

 

 

 

 

 

References:

Imbert C, Montfort A, Fraisse M, et al. Resistance of melanoma to immune checkpoint inhibitors is overcome by targeting the sphingosine kinase-1. Nat Commun. 2020;11(1):437. doi:10.1038/s41467-019-14218-7

Nejman D, Livyatan I, Fuks G, et al. The human tumor microbiome is composed of tumor type–specific intracellular bacteria. Science. 2020;368(6494):973-980. doi:10.1126/science.aay9189

Senior Scientist Ravid Straussman

Ravid Straussman

Faculty of Biology
Molecular Cell Biology
All projects (1)
Contact for more information

Dr. Yael Klionsky

Director of Business Development, Life Science

+972-8-9344293 Linkedin