Purpose: PD-1/PD-L1 inhibitors are approved for multiple tumor types. However, resistance poses substantial clinical challenges. Patients and Methods: We conducted a phase I trial of CD40 agonist APX005M (sotigalimab) and CSF1R inhibitor cabiralizumab with or without nivolumab using a 3+3 dose-escalation design (NCT03502330). Patients were enrolled from June 2018 to April 2019. Eligibility included patients with biopsy-proven advanced melanoma, non–small cell lung cancer (NSCLC), or renal cell carcinoma (RCC) who progressed on anti-PD-1/PD-L1. APX005M was dose escalated (0.03, 0.1, or 0.3 mg/kg i.v.) with a fixed dose of cabiralizumab with or without nivolumab every 2 weeks until disease progression or intolerable toxicity. Results: Twenty-six patients (12 melanoma, 1 NSCLC, and 13 RCC) were enrolled in six cohorts, 17 on nivolumab-containing regimens. Median duration of follow-up was 21.3 months. The most common treatment-related adverse events were asymptomatic elevations of lactate dehydrogenase (n = 26), creatine kinase (n = 25), aspartate aminotransferase (n = 25), and alanine aminotransferase (n = 19); periorbital edema (n = 17); and fatigue (n = 13). One dose-limiting toxicity (acute respiratory distress syndrome) occurred in cohort 2. The recommended phase 2 dose was APX005M 0.3 mg/kg, cabiralizumab 4 mg/kg, and nivolumab 240 mg every 2 weeks. Median days on treatment were 66 (range, 23–443). Median cycles were 4.5 (range, 2–21). One patient had unconfirmed partial response (4%), 8 stable disease (31%), 16 disease progression (62%), and 1 unevaluable (4%). Pro-inflammatory cytokines were upregulated 4 hours post-infusion. CD40 and MCSF increased after therapy. Conclusions: This first in-human study of patients with anti-PD-1/PD-L1–resistant tumors treated with dual macrophage-polarizing therapy, with or without nivolumab demonstrated safety and pharmacodynamic activity. Optimization of the dosing frequency and sequence of this combination is warranted.

Accurate risk-stratification can facilitate precision therapy in oropharyngeal squamous cell carcinoma (OPSCC). We explored the potential added value of baseline positron emission tomography (PET)/computed tomography (CT) radiomic features for prognostication and risk stratification of OPSCC beyond the American Joint Committee on Cancer (AJCC) 8th edition staging scheme. Using institutional and publicly available datasets, we included OPSCC patients with known human papillomavirus (HPV) status, without baseline distant metastasis and treated with curative intent. We extracted 1037 PET and 1037 CT radiomic features quantifying lesion shape, imaging intensity, and texture patterns from primary tumors and metastatic cervical lymph nodes. Utilizing random forest algorithms, we devised novel machine-learning models for OPSCC progression-free survival (PFS) and overall survival (OS) using “radiomics” features, “AJCC” variables, and the “combined” set as input. We designed both single- (PET or CT) and combined-modality (PET/CT) models. Harrell’s C-index quantified survival model performance; risk stratification was evaluated in Kaplan–Meier analysis. A total of 311 patients were included. In HPV-associated OPSCC, the best “radiomics” model achieved an average C-index ± standard deviation of 0.62 ± 0.05 (p = 0.02) for PFS prediction, compared to 0.54 ± 0.06 (p = 0.32) utilizing “AJCC” variables. Radiomics-based risk-stratification of HPV-associated OPSCC was significant for PFS and OS. Similar trends were observed in HPV-negative OPSCC. In conclusion, radiomics imaging features extracted from pre-treatment PET/CT may provide complimentary information to the current AJCC staging scheme for survival prognostication and risk-stratification of HPV-associated OPSCC.

Background Little is known about tumor-associated vasogenic edema in brain metastasis, yet it causes significant morbidity and mortality. Our purpose was to characterize edema in patients treated with anti-PD-1 and to study potential causes of vessel leakage in humans and in pre-clinical models. Methods We analyzed tumor and edema volume in 18 non-small cell lung (NSCLC) and 18 melanoma patients with untreated brain metastases treated with pembrolizumab on a phase II clinical trial. Melanoma brain metastases were stained with anti-CD34 to assess vessel density and its association with edema. We employed an in vitro model of the blood-brain barrier using short-term cultures from melanoma brain and extracranial metastases to determine tight junction resistance as a measure of vessel leakiness. Results Edema volumes are similar in NSCLC and melanoma brain metastases. While larger tumors tended to have more edema, the correlation was weak (R² = 0.30). Patients responding to pembrolizumab had concurrent shrinkage of edema volume and vice versa (R² = 0.81). Vessel density was independent of the degree of edema (R² = 0.037). Melanoma brain metastasis cells in culture caused loss of tight junction resistance in an in vitro blood-brain barrier model system in some cases, whereas extracerebral cell cultures did not. Conclusions Edema itself should not preclude using anti-PD-1 with caution, as sensitive tumors have resultant decreases in edema, and anti-PD-1 itself does not exacerbate edema in sensitive tumors. Additional factors aside from tumor mass effect and vessel density cause perilesional edema. Melanoma cells themselves can cause decline in tight junction resistance in a system void of immune cells, suggesting they secrete factors that cause leakiness, which might be harnessed for pharmacologic targeting in patients with significant perilesional edema.

Glioma and brain metastasis can be difficult to distinguish on conventional magnetic resonance imaging (MRI) due to the similarity of imaging features in specific clinical circumstances. Multiple studies have investigated the use of machine learning (ML) models for non-invasive differentiation of glioma from brain metastasis. Many of the studies report promising classification results, however, to date, none have been implemented into clinical practice. After a screening of 12,470 studies, we included 29 eligible studies in our systematic review. From each study, we aggregated data on model design, development, and best classifiers, as well as quality of reporting according to the TRIPOD statement. In a subset of eligible studies, we conducted a meta-analysis of the reported AUC. It was found that data predominantly originated from single-center institutions (n = 25/29) and only two studies performed external validation. The median TRIPOD adherence was 0.48, indicating insufficient quality of reporting among surveyed studies. Our findings illustrate that despite promising classification results, reliable model assessment is limited by poor reporting of study design and lack of algorithm validation and generalizability. Therefore, adherence to quality guidelines and validation on outside datasets is critical for the clinical translation of ML for the differentiation of glioma and brain metastasis.

• Radiomics quantitatively captures visually inappreciable imaging features. • PET/CT radiomics provides wholistic metabolic and structural tumor characterization. • Machine-learning algorithms can generate radiomics-based biomarkers for OPSCC. • PET/CT radiomics can predict post-radiotherapy locoregional progression in HPV-associated OPSCC. • Such biomarkers may improve patient selection for treatment de-intensification trials.