Functional Genomics Approach To Identify New Combination Therapies For Cancer Treatment
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The harnessing of the immune system to fight cancer is not a new concept. William Coley, a surgeon in the 1890s, observed that cancer patients who got infections following surgery fared considerably better than those who didn’t get an infection. This led to the belief that infection is beneficial in the clearance of tumors since the bacteria stimulates the patient’s immune system, allowing them to mount a better response to fight cancer.
Coley’s mixture of killed bacteria, commonly referred to as Coley’s toxins, was inferred to boost the immune system and have anti-tumor activity through the production of proteins such as tumor necrosis factor and interleukin 12. The idea of boosting the immune system to better attack and kill cancer cells is now commonly referred to as immunotherapy.
Current day immunotherapies are vast, including oncolytic viruses, CAR T cells, and immune checkpoint therapy, such as antibodies to CTLA-4, PD-1, and PD-L1. Over the past decade, the clinical value of PD-1 targeting therapies has been shown across many cancer types, with their utilization growing exponentially. While a subset of patients receiving these therapies experience durable responses, many fail to respond, highlighting the importance of other mechanisms influencing immune responsiveness in these tumors. Combining therapies that enhance anti-tumor immunity has therefore been an area of great interest to the entire cancer community. This is reflected by the number of clinical trials exploring combinations aimed at enhancing response to this relatively new class of anti-cancer drugs, which has soared from a single trial in 2009 to over 1100 in 2017 (Nature 552, S67-S69 (2017)).
In our recent publication in Science Advances, we describe the use of an in vivo-based functional genomics screen to identify genes whose inhibition potentiates a response to anti-PD-1 immunotherapy. Specifically, we define a novel mechanism whereby targeting the collagen receptor, discoidin domain receptor 2 (DDR2), elicits a significantly enhanced response to immune checkpoint blockade with PD-1 inhibitors. Of specific note is the observation this combination is robust across multiple tumor models including melanoma, sarcoma, breast, bladder and colon cancer indicating that DDR2 expression is an important and broadly used mechanism by cancer cells to escape checkpoint blockade therapy.
A customized shRNA was transfected into tumor cells and implanted into syngeneic mice. Tumor outgrowth was allowed, and mice were then treated with anti-PD-1. These tumors were subsequently analyzed to identify shRNA clones which were lost from tumor samples treated with anti-PD-1, compared to the control-treated group. This analysis is based on the premise of synthetic lethality, where tumor cells which lack a critical gene for immune evasion of anti-PD-1 therapy would be killed off, and as a result, be underrepresented in the bulk tumor pool. Analysis of the sequencing data through three different methods, consistently placed DDR2 at the top as a candidate gene. Single gene validation in vivo showed that tumors with a knocked-down expression of DDR2 were much more susceptible to treatment with anti-PD-1 compared to control tumors expressing wild-type levels of DDR2.
To further validate these findings in a clinically-translatable manner, combination therapy was pursued in wildtype tumors with PD-1 and DDR2 inhibition. DDR2 is a target of several FDA-approved drugs, with dasatinib being the most potent of these inhibitors. Established tumors which were treated with the combination of anti-PD-1 and dasatinib showed tumor size regression, and in some cases complete clearance of the tumor. Cytometry by a time of flight (CyTOF) analysis of the tumors revealed a significant increase in tumor-infiltrating CD8+ T cells in the combination-treated tumors. To identify associations between DDR2 expression and human tumors, the CIBERSORT method was performed on bulk tumor RNAseq data from The Cancer Genome Atlas (TCGA) to infer immune infiltration and relative abundance of the different immune cell subsets. Low DDR2 expression is associated with increased CD8+ T cells, and activated dendritic cell infiltration, suggestive of an anti-tumor immune microenvironment, and correlates with better patient outcomes.
Our data supports further exploration of DDR2 and anti-PD-1 combinations and demonstrates a rational approach to discovering novel immunotherapy combinations that have robust efficacy across multiple preclinical models. This study addresses an increasingly significant problem, with the emergence of an unprecedented number of clinical trials, and scientific findings which can be used to rationally guide clinical trial development.
This work was the result of a collaboration with Bristol-Myers Squibb. There is currently an ongoing clinical trial sponsored by Bristol-Myers Squibb to test combination treatments in patients with advanced non-small cell lung cancer, titled FRACTION-Lung (NCT02750514). This study is actively testing the clinical efficacy of combined PD-1 and DDR2 inhibition, with the FDA-approved agents nivolumab and dasatinib, which inhibit these targets, respectively.
These findings are described in the article entitled Targeting DDR2 enhances tumor response to anti–PD-1 immunotherapy, recently published in the journal Science Advances.