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Abstract Image-guided percutaneous cryoablation is an established minimally invasive oncologic treatment. We hypothesized that cryoablation may modify the immune microenvironment through direct modulation of the tumor, thereby generating an anti-tumor response in tumors refractory to immune checkpoint inhibition (ICI). In this non-randomized phase II single-center study (NCT03290677), subjects with unresectable melanoma progressing on ICI underwent cryoablation of an enlarging metastasis, and ICI was continued for a minimum of two additional cycles. The primary endpoints were safety, feasibility and tumor response in non-ablated lesions. From May 2018 through July 2020, 17 patients were treated on study. The study met its primary endpoints with the combination strategy found to be safe and feasible with an objective response rate of 23.5% and disease control rate of 41% (4 partial response, 3 stable disease). Our data support further study of this synergistic therapeutic approach.

Non-small cell lung cancer (NSCLC) accounts for 85 % of all lung cancers. In NSCLC, 10–20 % of Caucasian patients and 30–50 % of Asian patients have tumors with activating mutations in the Epidermal Growth Factor Receptor (EGFR). A high percentage of these patients exhibit favorable responses to treatment with tyrosine kinase inhibitors (TKI). Unfortunately, a majority of these patients develop therapeutic resistance with progression free survival lasting 9–18 months. The mechanisms that underlie the tumorigenic effects of EGFR and the ability of NSCLC to develop resistance to TKI therapies, however, are poorly understood. Here we demonstrate that CHI3L1 is produced by EGFR activation of normal epithelial cells, transformed epithelial cells with wild type EGFR and cells with cancer-associated, activating EGFR mutations. We also demonstrate that CHI3L1 auto-induces itself and feeds back to stimulate EGFR and its ligands via a STAT3-dependent mechanism(s). Highly specific antibodies against CHI3L1 (anti-CHI3L1/FRG) and TKI, individually and in combination, abrogated the effects of EGFR activation on CHI3L1 and the ability of CHI3L1 to stimulate the EGFR axis. Anti-CHI3L1 also interacted with osimertinib to reverse TKI therapeutic resistance and induce tumor cell death and inhibit pulmonary metastasis while stimulating tumor suppressor genes including KEAP1. CHI3L1 is a downstream target of EGFR that feeds back to stimulate and activate the EGFR axis. Anti-CHI3L1 is an exciting potential therapeutic for EGFR mutant NSCLC, alone and in combination with osimertinib or other TKIs.

Summary: Background: Pre-treatment FDG-PET/CT scans were analyzed with machine learning to predict progression of lung malignancies and overall survival (OS). Methods: A retrospective review across three institutions identified patients with a pre-procedure FDG-PET/CT and an associated malignancy diagnosis. Lesions were manually and automatically segmented, and convolutional neural networks (CNNs) were trained using FDG-PET/CT inputs to predict malignancy progression. Performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Image features were extracted from CNNs and by radiomics feature extraction, and random survival forests (RSF) were constructed to predict OS. Concordance index (C-index) and integrated brier score (IBS) were used to evaluate OS prediction. Findings: 1168 nodules (n=965 patients) were identified. 792 nodules had progression and 376 were progression-free. The most common malignancies were adenocarcinoma (n=740) and squamous cell carcinoma (n=179). For progression risk, the PET+CT ensemble model with manual segmentation (accuracy=0.790, AUC=0.876) performed similarly to the CT only (accuracy=0.723, AUC=0.888) and better compared to the PET only (accuracy=0.664, AUC=0.669) models. For OS prediction with deep learning features, the PET+CT+clinical RSF ensemble model (C-index=0.737) performed similarly to the CT only (C-index=0.730) and better than the PET only (C-index=0.595), and clinical only (C-index=0.595) models. RSF models constructed with radiomics features had comparable performance to those with CNN features. Interpretation: CNNs trained using pre-treatment FDG-PET/CT and extracted performed well in predicting lung malignancy progression and OS. OS prediction performance with CNN features was comparable to a radiomics approach. The prognostic models could inform treatment options and improve patient care. Funding: NIH NHLBI training grant (5T35HL094308-12, John Sollee).

Lung cancer is the second most common cancer in the United States, and small cell lung cancer (SCLC) accounts for about 15% of all lung cancers. In SCLC, more than other malignancies, the standard of care is based on clinical demonstration of efficacy, and less on a mechanistic understanding of why certain treatments work better than others. This is in large part due to the virulence of the disease, and lack of clinically or biologically relevant biomarkers beyond routine histopathology. While first line therapies work in the majority of patients with extensive stage disease, development of resistance is nearly universal. Although neuroendocrine features, Rb and p53 mutations are common, the current lack of actionable biomarkers has made it difficult to develop more effective treatments. Some progress has been made with the application of immune checkpoint inhibitors. There are new agents, such as lurbinectedin, that have completed late-phase clinical testing while other agents are still in the pre-clinical phase. ONC201/TIC10 is an imipridone with strong in vivo and in vitro antitumor properties and activity against neuroendocrine tumors in phase 1 clinical testing. ONC201 activates the cellular integrated stress response and induces the TRAIL pro-apoptotic pathway. Combination treatment of lurbinectedin with ONC201 are currently being investigated in preclinical studies that may facilitate translation into clinical trials for SCLC patients.