Sarcoidosis-like reaction in metastatic triple negative breast cancer treated by anti-PD-L1.

We report the first case of sarcoidosis-like reaction in a patient treated by anti-PD-L1 for a breast cancer. A 69-year-old woman presented with a histologically confirmed lung metastasis of a triple negative breast cancer. She was treated by nab-paclitaxel plus anti-PD-L1 in first line. After 2 months, a dramatic lung response was noticed but an involvement of mediastinal lymph nodes appeared. Endoscopic ultrasound-guided fine-needle aspiration of these lymph nodes revealed multiple epitheloid granulomas without caseating necrosis in favour of a sarcoidosis-like reaction. The patient remained free of symptom and in complete lung response on anti-PD-L1 treatment as a maintenance therapy.

Ranking the most influential predictors of CT-based radiomics feature values in metastatic lung adenocarcinoma.

PURPOSE: To investigate which acquisition, post-processing, tumor, and patient characteristics contribute the most to the value of radiomics features (RFs) in lung adenocarcinoma in order to better understand and order the potential sources of bias in radiomics studies in a multivariate setting. METHODS: This single-center retrospective study included all consecutive patients with newly-diagnosed lung adenocarcinoma treated between December 2016 and September 2018 who had pre-treatment contrast-enhanced CT-scan showing ≥ 2 target lesions per response evaluation criteria in solid tumors (RECIST) v1.1. All measurable lesions were manually segmented; 49 RFs were extracted using LIFEx v7.0.0. Afterwards, we reverted the usual radiomics approach (i.e., predicting a clinical outcome base on multiple RFs). To do so, for each RF, random forests and linear regression algorithms were trained using cross-validation to predict the RF value depending on the following variables: patient, mutational status, phase of CT-scan acquisition, discretization (binsize), lesion location, lesion volume, and best response obtained during the first line of treatment (partial response per RECIST vs other). The most important contributors to the value of reproducible RFs (intra-class correlation coefficient > 0.80) according to the best random forests model (selected via R-squared) were ranked. RESULTS: 101 patients (median age: 62.3) were included, with a median of 5 target lesions per patient (range: 2-10) providing 466 segmented lesions. Twenty-nine RFs were reproducible. The most important predictors of the reproducible RFs values were, in order: tumor volume, binsize, tumor location, CT-scan phase, KRAS mutation, and treatment response (average importance: 61.7%, 57.4%, 8.1%, 3.3%, 3%, and 2.7%, respectively). The treatment response and KRAS and EGFR/ROS1/ALK mutational status remained independently correlated with the RF value for 64.3%, 32.1%, and 50% reproducible RFs, respectively. CONCLUSION: Tumor volume, location, acquisition and post-processing parameters should systematically be incorporated in radiomics-based modeling; however, most reproducible RFs do have significant relationships with mutational status and treatment response.

Atezolizumab for the treatment of renal cell carcinoma.

INTRODUCTION: The therapeutic landscape of renal cell cancer has evolved rapidly over the past 2 years with nivolumab and ipilimumab for patients with metastatic disease and an intermediate or poor prognosis, in the first line setting. More recently, data from trials combining antiangiogenic agents and immune checkpoint inhibitors demonstrated a major benefit of this treatment approach for all patients. AREAS COVERED: One of three recent trials evaluated the combination of atezolizumab, an anti-programmed death ligand 1 antibody, with bevacizumab, an anti-vascular endothelial growth factor monoclonal antibody. In this manuscript, we summarize the preclinical, clinical, and safety data on atezolizumab for treatment of renal cell carcinoma and describe ongoing trials. EXPERT OPINION: Atezolizumab was evaluated in combination with an antiangiogenic agent. These trials were designed based on the hypothesis that selecting patients according to the expression of programmed death ligand 1 would increase the benefit of the treatment combination. Despite positive effects on the primary endpoints progression-free survival and response rate in this selected population, overall survival in the global population did not meet the criteria for significance at the time of the intermediate analysis. The major information was a proposed tumor gene expression signature. The signature was predictive of the sensitivity to anti-angiogenic and/or immune checkpoint inhibitor therapy.