Charles Ferté

Hyperprogressive Disease Is a New Pattern of Progression in Cancer Patients Treated by Anti-PD-1/PD-L1

Purpose: 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. Experimental Design: Medical records from all patients (N = 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. Results: 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 (P < 0.05). HPD is associated with a higher age (P < 0.05) and a worse outcome (overall survival). Interestingly, REF TGR (before treatment) was inversely correlated with response to anti-PD-1/PD-L1 (P < 0.05) therapy. Conclusions: 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. Clin Cancer Res; 23(8); 1920–8. ©2016 AACR. See related commentary by Sharon, p. 1879

Exomics and immunogenics: Bridging mutational load and immune checkpoints efficacy

Anti-PD-1/PD-L1 antibodies are emerging as promising anticancer therapeutics. Interestingly, elevated response rates to these agents are mostly documented among patients with tumors that bear high level of somatic mutations, like melanoma or non-small cell lung carcinoma. We herein formulate the hypothesis that high levels of mutational heterogeneity in the tumor could be the key for the success of immune checkpoint-targeting therapies.

Circulating Cell-Free Tumor DNA Analysis of 50 Genes by Next-Generation Sequencing in the Prospective MOSCATO Trial

Purpose: Liquid biopsies based on circulating cell-free DNA (cfDNA) analysis are described as surrogate samples for molecular analysis. We evaluated the concordance between tumor DNA (tDNA) and cfDNA analysis on a large cohort of patients with advanced or metastatic solid tumor, eligible for phase I trial and with good performance status, enrolled in MOSCATO 01 trial (clinical trial NCT01566019). Experimental Design: Blood samples were collected at inclusion and cfDNA was extracted from plasma for 334 patients. Hotspot mutations were screened using next-generation sequencing for 50 cancer genes. Results: Among the 283 patients with tDNA–cfDNA pairs, 121 had mutation in both, 99 in tumor only, 5 in cfDNA only, and for 58 patients no mutation was detected, leading to a 55.0% estimated sensitivity [95% confidence interval (CI), 48.4%–61.6%] at the patient level. Among the 220 patients with mutations in tDNA, the sensitivity of cfDNA analysis was significantly linked to the number of metastatic sites, albumin level, tumor type, and number of lines of treatment. A sensitivity prediction score could be derived from clinical parameters. Sensitivity is 83% in patients with a high score (≥8). In addition, we analyzed cfDNA for 51 patients without available tissue sample. Mutations were detected for 22 patients, including 19 oncogenic variants and 8 actionable mutations. Conclusions: Detection of somatic mutations in cfDNA is feasible for prescreening phase I candidates with a satisfactory specificity; overall sensitivity can be improved by a sensitivity score allowing to select patients for whom cfDNA constitutes a reliable noninvasive surrogate to screen mutations. Clin Cancer Res; 22(12); 2960–8. ©2016 AACR.

Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology

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.

Impact of centralization on aCGH-based genomic profiles for precision medicine in oncology

BACKGROUNDnComparative genomic hybridization (CGH) arrays are increasingly used in personalized medicine programs to identify gene copy number aberrations (CNAs) that may be used to guide clinical decisions made during molecular tumor boards. However, analytical processes such as the centralization step may profoundly affect CGH array results and therefore may adversely affect outcomes in the precision medicine context.nnnPATIENTS AND METHODSnThe effect of three different centralization methods: median, maximum peak, alternative peak, were evaluated on three datasets: (i) the NCI60 cell lines panel, (ii) the Cancer Cell Line Encyclopedia (CCLE) panel, and (iii) the patients enrolled in prospective molecular screening trials (SAFIR-01 n = 283, MOSCATO-01 n = 309), and compared with karyotyping, drug sensitivity, and patient-drug matching, respectively.nnnRESULTSnUsing the NCI60 cell lines panel, the profiles generated by the alternative peak method were significantly closer to the cell karyotypes than those generated by the other centralization strategies (P < 0.05). Using the CCLE dataset, selected genes (ERBB2, EGFR) were better or equally correlated to the IC50 of their companion drug (lapatinib, erlotinib), when applying the alternative centralization. Finally, focusing on 24 actionable genes, we observed as many as 7.1% (SAFIR-01) and 6.8% (MOSCATO-01) of patients originally not oriented to a specific treatment, but who could have been proposed a treatment based on the alternative peak centralization method.nnnCONCLUSIONnThe centralization method substantially affects the call detection of CGH profiles and may thus impact precision medicine approaches. Among the three methods described, the alternative peak method addresses limitations associated with existing approaches.

Limits of radiomic-based entropy as a surrogate of tumor heterogeneity: ROI-area, acquisition protocol and tissue site exert substantial influence

Entropy is a promising quantitative imaging biomarker for characterizing cancer imaging phenotype. Entropy has been associated with tumor gene expression, tumor metabolism, tumor stage, patient prognosis, and treatment response. Our hypothesis states that tumor-specific biomarkers such as entropy should be correlated between synchronous metastases. Therefore, a significant proportion of the variance of entropy should be attributed to the malignant process. We analyzed 112 patients with matched/paired synchronous metastases (SM#1 and SM#2) prospectively enrolled in the MOSCATO-01 clinical trial. Imaging features were extracted from Regions Of Interest (ROI) delineated on CT-scan using TexRAD software. We showed that synchronous metastasis entropy was correlated across 5 Spatial Scale Filters: Spearman’s Rho ranged between 0.41 and 0.59 (Pu2009=u20090.0001, Bonferroni correction). Multivariate linear analysis revealed that entropy in SM#1 is significantly associated with (i) primary tumor type; (ii) entropy in SM#2 (same malignant process); (iii) ROI area size; (iv) metastasis site; and (v) entropy in the psoas muscle (reference tissue). Entropy was a logarithmic function of ROI area in normal control tissues (aorta, psoas) and in mathematical models (Pu2009<u20090.01). We concluded that entropy is a tumor-specific metric only if confounding factors are corrected.

The MET/AXL/FGFR Inhibitor S49076 Impairs Aurora B Activity and Improves the Antitumor Efficacy of Radiotherapy

Several therapeutic agents targeting HGF/MET signaling are under clinical development as single agents or in combination, notably with anti-EGFR therapies in non–small cell lung cancer (NSCLC). However, despite increasing data supporting a link between MET, irradiation, and cancer progression, no data regarding the combination of MET-targeting agents and radiotherapy are available from the clinic. S49076 is an oral ATP-competitive inhibitor of MET, AXL, and FGFR1-3 receptors that is currently in phase I/II clinical trials in combination with gefitinib in NSCLC patients whose tumors show resistance to EGFR inhibitors. Here, we studied the impact of S49076 on MET signaling, cell proliferation, and clonogenic survival in MET-dependent (GTL16 and U87-MG) and MET-independent (H441, H460, and A549) cells. Our data show that S49076 exerts its cytotoxic activity at low doses on MET-dependent cells through MET inhibition, whereas it inhibits growth of MET-independent cells at higher but clinically relevant doses by targeting Aurora B. Furthermore, we found that S49076 improves the antitumor efficacy of radiotherapy in both MET-dependent and MET-independent cell lines in vitro and in subcutaneous and orthotopic tumor models in vivo. In conclusion, our study demonstrates that S49076 has dual antitumor activity and can be used in combination with radiotherapy for the treatment of both MET-dependent and MET-independent tumors. These results support the evaluation of combined treatment of S49076 with radiation in clinical trials without patient selection based on the tumor MET dependency status. Mol Cancer Ther; 16(10); 2107–19. ©2017 AACR.

TumorTracer: a method to identify the tissue of origin from the somatic mutations of a tumor specimen

BackgroundA substantial proportion of cancer cases present with a metastatic tumor and require further testing to determine the primary site; many of these are never fully diagnosed and remain cancer of unknown primary origin (CUP). It has been previously demonstrated that the somatic point mutations detected in a tumor can be used to identify its site of origin with limited accuracy. We hypothesized that higher accuracy could be achieved by a classification algorithm based on the following feature sets: 1) the number of nonsynonymous point mutations in a set of 232 specific cancer-associated genes, 2) frequencies of the 96 classes of single-nucleotide substitution determined by the flanking bases, and 3) copy number profiles, if available.MethodsWe used publicly available somatic mutation data from the COSMIC database to train random forest classifiers to distinguish among those tissues of origin for which sufficient data was available. We selected feature sets using cross-validation and then derived two final classifiers (with or without copy number profiles) using 80xa0% of the available tumors. We evaluated the accuracy using the remaining 20xa0%. For further validation, we assessed accuracy of the without-copy-number classifier on three independent data sets: 1669 newly available public tumors of various types, a cohort of 91 breast metastases, and a set of 24 specimens from 9 lung cancer patients subjected to multiregion sequencing.ResultsThe cross-validation accuracy was highest when all three types of information were used. On the left-out COSMIC data not used for training, we achieved a classification accuracy of 85xa0% across 6 primary sites (with copy numbers), and 69xa0% across 10 primary sites (without copy numbers). Importantly, a derived confidence score could distinguish tumors that could be identified with 95xa0% accuracy (32xa0%/75xa0% of tumors with/without copy numbers) from those that were less certain. Accuracy in the independent data sets was 46xa0%, 53xa0% and 89xa0% respectively, similar to the accuracy expected from the training data.ConclusionsIdentification of primary site from point mutation and/or copy number data may be accurate enough to aid clinical diagnosis of cancers of unknown primary origin.

rCGH: a comprehensive array-based genomic profile platform for precision medicine

Summary: We present rCGH, a comprehensive array-based comparative genomic hybridization analysis workflow, integrating computational improvements and functionalities specifically designed for precision medicine. rCGH supports the major microarray platforms, ensures a full traceability and facilitates profiles interpretation and decision-making through sharable interactive visualizations. Availability and implementation: The rCGH R package is available on bioconductor (under Artistic-2.0). The aCGH-viewer is available at https://fredcommo.shinyapps.io/aCGH_viewer, and the application implementation is freely available for installation at https://github.com/fredcommo/aCGH_viewer. Contact: frederic.commo@gustaveroussy.fr Supplementary information: Supplementary data are available at Bioinformatics online.

TGR analysis in phase I clinical trials--response.

We thank Dienstmann and Tabernero for their comments on our article ([1][1]). They underscore the high translational potential of using tumor growth rates (TGR) to assess the therapeutic effect independently from the natural course of the disease and ultimately guide the “go - no go decision