Colony stimulating factor-1 (CSF-1) is produced by a variety of cancers and recruits myeloid cells that suppress antitumor immunity, including myeloid-derived suppressor cells (MDSCs.) Here, we show that both CSF-1 and its receptor (CSF-1R) are frequently expressed in tumors from cancer patients, and that this expression correlates with tumor-infiltration of MDSCs. Furthermore, we demonstrate that these tumor-infiltrating MDSCs are highly immunosuppressive but can be reprogrammed toward an antitumor phenotype in vitro upon CSF-1/CSF-1R signaling blockade. Supporting these findings, we show that inhibition of CSF-1/CSF-1R signaling using an anti-CSF-1R antibody can regulate both the number and the function of MDSCs in murine tumors in vivo. We further find that treatment with anti-CSF-1R antibody induces antitumor T-cell responses and tumor regression in multiple tumor models when combined with CTLA-4 blockade therapy. However, this occurs only when administered after or concurrent with CTLA-4 blockade, indicating that timing of each therapeutic intervention is critical for optimal antitumor responses. Importantly, MDSCs present within murine tumors after CTLA-4 blockade showed increased expression of CSF-1R and were capable of suppressing T cell proliferation, and CSF-1/CSF-1R expression in the human tumors was not reduced after treatment with CTLA-4 blockade immunotherapy. Taken together, our findings suggest that CSF-1R-expressing MDSCs can be targeted to modulate the tumor microenvironment and that timing of CSF-1/CSF-1R signaling blockade is critical to improving responses to checkpoint based immunotherapy.Significance: Infiltration by immunosuppressive myeloid cells contributes to tumor immune escape and can render patients resistant or less responsive to therapeutic intervention with checkpoint blocking antibodies. Our data demonstrate that blocking CSF-1/CSF-1R signaling using a monoclonal antibody directed to CSF-1R can regulate both the number and function of tumor-infiltrating immunosuppressive myeloid cells. In addition, our findings suggest that reprogramming myeloid responses may be a key in effectively enhancing cancer immunotherapy, offering several new potential combination therapies for future clinical testing. More importantly for clinical trial design, the timing of these interventions is critical to achieving improved tumor protection.

1539 Background: Clinical trial eligibility increasingly requires information found in NGS tests; lack of structured NGS results hinders the automation of trial matching for this criterion, which may be a deterrent to open biomarker-driven trials in certain sites. We developed a machine learning tool that infers the presence of NGS results in the EHR, facilitating clinical trial matching. Methods: The Flatiron Health EHR-derived database contains patient-level pathology and genetic counseling reports from community oncology practices. An internal team of clinical experts reviewed a random sample of patients across this network to generate labels of whether each patient had been NGS tested. A supervised ML model was trained by scanning documents in the EHR and extracting n-gram features from text snippets surrounding relevant keywords (i.e. 'Lung biomarker', 'Biomarker negative'). Through k-fold cross-validation and l2-regularization, we found that a logistic regression was able to classify patients' NGS testing status. The model's offline performance on a 20% hold-out test set was measured with standard classification metrics: sensitivity, specificity, positive predictive value (PPV) and NPV. In an online setting, we integrated the tool into Flatiron's clinical trial matching software OncoTrials by including in each patient's profile an indicator of "likely NGS tested" or "unlikely NGS tested" based on the classifier's prediction. For patients inferred as tested, the model linked users to a test report view in the EHR. In this online setting, we measured sensitivity and specificity of the model after user review in two community oncology practices. Results: This NGS testing status inference model was characterized using a test sample of 15,175 patients. The model sensitivity and specificity (95%CI) were 91.3% (90.2, 92.3) and 96.2% (95.8, 96.5), respectively; PPV was 84.5% (83.2, 85.8) and NPV was 98.0% (97.7, 98.2). In the validation sample (N = 200 originated from 2 distinct care sites), users identified NGS testing status with a sensitivity of 95.2% (88.3%, 98.7%). Conclusions: This machine learning model facilitates the screening for potential patient enrollment in biomarker-driven trials by automatically surfacing patients with NGS test results at high sensitivity and specificity into a trial matching application to identify candidates. This tool could mitigate a key barrier for participation in biomarker-driven trials for community clinics.