While immune checkpoint inhibitors are disrupting the management of patients with cancer, anecdotal occurrences of rapid progression (i.e., hyperprogressive disease or HPD) under these agents have been described, suggesting potentially deleterious effects of these drugs. The prevalence, the natural history, and the predictive factors of HPD in patients with cancer treated by anti-PD-1/PD-L1 remain unknown. Medical records from all patients ( = 218) prospectively treated in Gustave Roussy by anti-PD-1/PD-L1 within phase I clinical trials were analyzed. The tumor growth rate (TGR) prior ("REFERENCE"; REF) and upon ("EXPERIMENTAL"; EXP) anti-PD-1/PD-L1 therapy was compared to identify patients with accelerated tumor growth. Associations between TGR, clinicopathologic characteristics, and overall survival (OS) were computed. HPD was defined as a RECIST progression at the first evaluation and as a ≥2-fold increase of the TGR between the REF and the EXP periods. Of 131 evaluable patients, 12 patients (9%) were considered as having HPD. HPD was not associated with higher tumor burden at baseline, nor with any specific tumor type. At progression, patients with HPD had a lower rate of new lesions than patients with disease progression without HPD ( < 0.05). HPD is associated with a higher age ( < 0.05) and a worse outcome (overall survival). Interestingly, REF TGR (before treatment) was inversely correlated with response to anti-PD-1/PD-L1 ( < 0.05) therapy. A novel aggressive pattern of hyperprogression exists in a fraction of patients treated with anti-PD-1/PD-L1. This observation raises some concerns about treating elderly patients (>65 years old) with anti-PD-1/PD-L1 monotherapy and suggests further study of this phenomenon. .

Our study suggests that HPD is more common with PD-1/PD-L1 inhibitors compared with chemotherapy in pretreated patients with NSCLC and is also associated with high metastatic burden and poor prognosis in patients treated with PD-1/PD-L1 inhibitors. Additional studies are needed to determine the molecular mechanisms involved in HPD.

No abstract

High-throughput genomic analyses may improve outcomes in patients with advanced cancers. MOSCATO 01 is a prospective clinical trial evaluating the clinical benefit of this approach. Nucleic acids were extracted from fresh-frozen tumor biopsies and analyzed by array comparative genomic hybridization, next-generation sequencing, and RNA sequencing. The primary objective was to evaluate clinical benefit as measured by the percentage of patients presenting progression-free survival (PFS) on matched therapy (PFS2) 1.3-fold longer than the PFS on prior therapy (PFS1). A total of 1,035 adult patients were included, and a biopsy was performed in 948. An actionable molecular alteration was identified in 411 of 843 patients with a molecular portrait. A total of 199 patients were treated with a targeted therapy matched to a genomic alteration. The PFS2/PFS1 ratio was >1.3 in 33% of the patients (63/193). Objective responses were observed in 22 of 194 patients (11%; 95% CI, 7%-17%), and median overall survival was 11.9 months (95% CI, 9.5-14.3 months). This study suggests that high-throughput genomics could improve outcomes in a subset of patients with hard-to-treat cancers. Although these results are encouraging, only 7% of the successfully screened patients benefited from this approach. Randomized trials are needed to validate this hypothesis and to quantify the magnitude of benefit. Expanding drug access could increase the percentage of patients who benefit. .

Medical image processing and analysis (also known as Radiomics) is a rapidly growing discipline that maps digital medical images into quantitative data, with the end goal of generating imaging biomarkers as decision support tools for clinical practice. The use of imaging data from routine clinical work-up has tremendous potential in improving cancer care by heightening understanding of tumor biology and aiding in the implementation of precision medicine. As a noninvasive method of assessing the tumor and its microenvironment in their entirety, radiomics allows the evaluation and monitoring of tumor characteristics such as temporal and spatial heterogeneity. One can observe a rapid increase in the number of computational medical imaging publications-milestones that have highlighted the utility of imaging biomarkers in oncology. Nevertheless, the use of radiomics as clinical biomarkers still necessitates amelioration and standardization in order to achieve routine clinical adoption. This Review addresses the critical issues to ensure the proper development of radiomics as a biomarker and facilitate its implementation in clinical practice.

No abstract

Purpose RECIST evaluation does not take into account the pre-treatment tumor kinetics and may provide incomplete information regarding experimental drug activity. Tumor Growth Rate (TGR) allows for a dynamic and quantitative assessment of the tumor kinetics. How TGR varies along the introduction of experimental therapeutics and is associated with outcome in phase I patients remains unknown. Experimental designs Medical records from all patients (n=253) prospectively treated in 20 phase I trials were analyzed. TGR was computed during the pre-treatment period (REFERENCE) and the EXPERIMENTAL period. Associations between TGR, standard prognostic scores (RMH score) and outcome (PFS, OS) were computed (multivariate analysis). Results We observed a reduction of TGR between the REFERENCE vs. EXPERIMENTAL periods (38% vs. 4.4%, P<.00001). Although most patients were classified as stable disease (65%) or progressive disease (25%) by RECIST at the first evaluation, 82% and 65% of them exhibited a decrease in TGR, respectively. In a multivariate analyses, only the decrease of TGR was associated with PFS (P=.004), whereas the RMH score was the only variable associated with OS (P=.0008). Only the investigated regimens delivered were associated with a decrease of TGR (P<.00001, multivariate analysis). Computing TGR profiles across different clinical trials reveals specific patterns of antitumor activity. Conclusions Exploring TGR in phase I patients is simple and provides clinically relevant information: (i) an early and subtle assessment of signs of antitumor activity; (ii) indpendent association with PFS; and (iii) It reveals drug-specific profiles; suggesting potential utility for guiding the further development of the investigational drugs.

Standard endpoints often poorly predict overall survival (OS) with immunotherapies. We investigated the predictive performance of model-based tumor growth inhibition (TGI) metrics using data from atezolizumab clinical trials in patients with non-small cell lung cancer. OS benefit with atezolizumab versus docetaxel was observed in both POPLAR (phase II) and OAK (phase III), although progression-free survival was similar between arms. A multivariate model linking baseline patient characteristics and on-treatment tumor growth rate constant (KG), estimated using time profiles of sum of longest diameters (RECIST 1.1) to OS, was developed using POPLAR data. The model was evaluated to predict OAK outcome based on estimated KG at TGI data cutoffs ranging from 10 to 122 weeks. In POPLAR, TGI profiles in both arms crossed at 25 weeks, with more shrinkage with docetaxel and slower KG with atezolizumab. A log-normal OS model, with albumin and number of metastatic sites as independent prognostic factors and estimated KG, predicted OS HR in subpopulations of patients with varying baseline PD-L1 expression in both POPLAR and OAK: model-predicted OAK HR (95% prediction interval), 0.73 (0.63-0.85), versus 0.73 observed. The POPLAR OS model predicted greater than 97% chance of success of OAK (significant OS HR, < 0.05) from the 40-week data cutoff onward with 50% of the total number of tumor assessments when a successful study was predicted from 70 weeks onward based on observed OS. KG has potential as a model-based early endpoint to inform decisions in cancer immunotherapy studies. .