Bestvina, Christine M.

The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Small Cell Lung Cancer provide recommendations for diagnostic workup, staging, and treatment. These NCCN Guidelines Insights highlight recent updates, with a particular focus on changes to systemic therapies and radiation treatment in the NCCN Guidelines for Small Cell Lung Cancer.

8634 Background: Patients with unresectable non-small cell lung cancer (NSCLC) with an inadequate response to immune checkpoint inhibitors (ICI) have limited therapeutic options and expected median overall survival (mOS) of 11.6 to 14.5m (PMID: 35658002 ). The immunosuppressive tumor microenvironment and the lack of a specific T cell response underpin inadequate clinical response. CAN-2409 is a replication-defective adenovirus encoding the HSV-tk gene. Transduced cells activate orally delivered valacyclovir, resulting in immunogenic cell death, and in situ immunization against the patient’s tumor antigens, eliciting a specific immune response against the injected tumor and uninjected metastases. Methods: NCT04495153 is an open-label, phase 2 clinical trial of CAN-2409 + valacyclovir in combination with continued ICI in patients with non-resectable, stage III/IV NSCLC, refractory or resistant to anti-PD-(L)1. Patients were enrolled in 2 cohorts (C) depending on disease status at enrolment: C1, stable disease or C2, progressive disease. Two doses of CAN-2409 (5x1011vp) were given 5-7w apart via bronchoscopic or percutaneous injection into lung tumor, disease-positive lymph node, or peripheral metastasis, each followed by oral prodrug. Patients were assessed for safety, immunologic biomarkers, and OS. Results: As of 16Jan2024, 73 patients were treated (safety population: ≥1 dose of CAN-2409) and 44 were evaluable per protocol (received 2 doses of CAN-2409+ valacyclovir and 12-week CT scan). Baseline treated pt median age was 67 [43-88] yrs, 44% female, 68% on aPD(L)1 alone and 32% aPD(L)1 + pemetrexed regimen. We observed no dose-limiting toxicities or ≥grade 4 treatment-related AEs. mOS of the evaluable population was estimated as 22.0m (95% CI: 14.3m, NA; median follow up (mFU) time of 18.2m). In C2 (n = 38) mOS was 20.6 (mFU 19.2.m) and in C1 (n = 6) was N.A. (mFU 15.2m). mOS in the safety population (n = 73) was 14.3.m (mFU16.8m). 64% of evaluable patients had systemic clinical response with shrinkage of both injected and uninjected lesions. We found a significant correlation between increases in T cell populations after the second injection of CAN-2409 (circulating memory and effector memory CD4+ and CD8+T cells, CD4+ Ki67+IFNg+ and CD4+granzymeB+ T cells, CD8+granzymeB+Ki67+ T cells, T regulatory cells) and subsequent OS (R2 between 0.181 and 0.354 and p = 0.043 and 0.004). Conclusions: Experimental treatment with CAN-2409+valacyclovir in NSCLC pts following an inadequate response to ICI is well tolerated and results in the induction of a cytotoxic and memory circulating T cell response associated with shrinkage of injected and uninjected lesions and a raised tail of the survival curve. mOS of the evaluable population was estimated as 22.0m, markedly longer than the expected mOS of 11.6-14.5m. Clinical trial information: NCT04495153 .

TPS9162 Background: Immune checkpoint inhibitors (ICI) are a standard of care (SOC) treatment that have improved outcomes for patients (pts) with non-small cell lung cancer (NSCLC). However, most pts progress, signaling a need for combination approaches to further improve outcomes. CAN-2409 is a replication-defective adenoviral construct encoding the herpes simplex virus thymidine kinase (HSV-tk) gene that is injected intratumorally followed by 2 weeks of prodrug (valacyclovir or acyclovir). HSV-tk converts the prodrug in the tumor microenvironment into a toxic metabolite, resulting in immunogenic cell death, while the adenoviral proteins provide a strong pro-inflammatory signal. Together, this leads to in situ vaccination against the patient’s own tumor. As a result, CD8+ cytotoxic T cells are educated on how to recognize and kill tumor cells in both the injected tumor and the uninjected metastases. A completed neoadjuvant phase I trial in resectable NSCLC demonstrated safety and immune stimulation (Predina et al 2021), warranting further investigation of CAN-2409 + prodrug as a therapy that can lead to improved long-term outcomes for pts with NSCLC who have a suboptimal response to ICI. Methods: LuTK02 (NCT04495153) is an open-label, multi-arm, phase II clinical trial to evaluate CAN-2409 + prodrug added to SOC ICI (anti-PD-(L)1) therapy in pts with stage III/IV NSCLC presenting with inadequate response (as defined below) to ICI. Cohort 1 includes pts with nonresponding but stable disease ≥18 weeks after starting ICI; cohort 2 includes pts with progressive disease ≥18 weeks after starting ICI. Key eligibility criteria include RECIST-evaluable disease; an injectable lesion (via bronchoscopy or US/IR guidance); ability to continue ICI for the treatment period; no change of ICI or prior interruptions of > 4 weeks within 6 months of enrollment; and no focal therapy at > 3 sites of disease within 12 months prior to enrollment. Key exclusion criteria include a history of ICI immune-related adverse events and known alterations in EGFR, ALK, or ROS1. Eligible patients receive 2 injections of CAN-2409 approximately 6 weeks apart followed by prodrug for 14 days. After treatment of ~40 evaluable pts in cohort 2 with two injections of CAN-2409, the protocol has been amended to include a new cohort (n~20) of pts treated with three injections of CAN-2409 approximately 4 weeks apart, each followed by prodrug for 14 days, to further define the optimal therapeutic regimen. Primary endpoints are overall response and/or disease control rate per RECIST 1.1 criteria and safety of CAN-2409 + prodrug. Secondary endpoints include duration of response, PFS, OS, and biomarker studies. Exploratory endpoints include evaluation of effects in non-injected lesions, changes in non-target (per RECIST) lesions, tumor growth trajectory, and viral shedding. Clinical trial information: NCT04495153 .

8549 Background: First-line treatment of early stage non-small cell lung cancer (NSCLC) is surgical resection, but with a 5-year survival of only 54%, rates of future relapse are high. Identifying patients at high risk of relapse can help guide adjuvant treatment decisions. Deep convolutional neural network (DCNN) AI models trained on tumor histology have shown incredible flexibility as potential biomarkers, both in lung cancer and more generally across many malignancies. While DCNN models can obtain extremely accurate results when used for routine purposes such as diagnosis, subtyping, and grading of malignancies, most models trained for prognostication or treatment response prediction do not reach performance sufficient for clinical application. Uncertainty quantification (UQ) – a family of techniques that give DCNN models the ability to report confidence alongside predictions – is an underexplored avenue in cancer AI that may help further improve performance and clinical application of models designed to provide clinicians with estimations of risk. Methods: To explore the potential use of UQ in clinically-oriented DCNN models, we trained models on a single-institution, retrospective digital tumor histology slide cohort from patients with Stage I-III NSCLC who underwent surgical resection to predict risk of future relapse. Estimation of uncertainty was performed using dropout as a Bayesian approximation, and uncertainty thresholds were calculated from training sets using a novel method to identify and remove low-confidence predictions. For comparison, a separate multivariate logistic regression was trained in cross-validation using known clinical risk factors. Results: We trained DCNN models on slides from 198 patients (40 relapsed, 158 without relapse). In this cohort, 130 patients had stage I disease (65.6%), 42 had stage II (21.2%), and 26 had stage III (13.2%). The average age was 69 years, 85% were current or previous smokers, and 38 received guideline-concordant adjuvant chemotherapy. Without UQ, a DCNN model predicted risk of future relapse with an average area under receiver operator curve (AUROC) of 0.74 across three-fold cross-validation. Using UQ estimation, 63% of slides were reported with high confidence. Relapse prediction was significantly improved in the high-confidence cohort, with an average AUROC of 0.83 in cross-validation. With a specificity of 70%, this corresponds to an average sensitivity of 86.8% across the three cross-folds (79.1%, 85.5%, and 95.7%). In comparison, a clinical-only multivariate regression model predicted relapse with a cross-validated AUROC of 0.67. Conclusions: This method of uncertainty quantification appears to be a powerful tool to predict lung cancer recurrence risk from digital histopathology while simultaneously providing clinicians with a measurement of algorithm trustworthiness.