Relatlimab and Nivolumab versus Nivolumab in Untreated Advanced Melanoma

2022 ◽  
Vol 386 (1) ◽  
pp. 24-34
Author(s):  
Hussein A. Tawbi ◽  
Dirk Schadendorf ◽  
Evan J. Lipson ◽  
Paolo A. Ascierto ◽  
Luis Matamala ◽  
...  
Keyword(s):  
2008 ◽  
Vol 39 (7) ◽  
pp. 9
Author(s):  
NEIL OSTERWEIL
Keyword(s):  

2020 ◽  
Vol 13 (1) ◽  
pp. 24-31 ◽  
Author(s):  
Angelo Castello ◽  
Egesta Lopci

Background: Immune checkpoint inhibitors (ICI) have achieved astonishing results and improved overall survival (OS) in several types of malignancies, including advanced melanoma. However, due to a peculiar type of anti-cancer activity provided by these drugs, the response patterns during ICI treatment are completely different from that with “old” chemotherapeutic agents. Objective: To provide an overview of the available literature and potentials of 18F-FDG PET/CT in advanced melanoma during the course of therapy with ICI in the context of treatment response evaluation. Methods: Morphologic criteria, expressed by Response Evaluation Criteria in Solid Tumors (RECIST), immune-related response criteria (irRC), irRECIST, and, more recently, immune-RECIST (iRECIST), along with response criteria based on the metabolic parameters with 18F-Fluorodeoxyglucose (18FFDG), have been explored. Results: To overcome the limits of traditional response criteria, new metabolic response criteria have been introduced on time and are being continuously updated, such as the PET/CT Criteria for the early prediction of Response to Immune checkpoint inhibitor Therapy (PECRIT), the PET Response Evaluation Criteria for Immunotherapy (PERCIMT), and “immunotherapy-modified” PET Response Criteria in Solid Tumors (imPERCIST). The introduction of new PET radiotracers, based on monoclonal antibodies combined with radioactive elements (“immune-PET”), are of great interest. Conclusion: Although the role of 18F-FDG PET/CT in malignant melanoma has been widely validated for detecting distant metastases and recurrences, evidences in course of ICI are still scarce and larger multicenter clinical trials are needed.


2021 ◽  
Vol 91 (S2) ◽  
pp. 3-13
Author(s):  
B. Mark Smithers ◽  
Robyn P. M. Saw ◽  
David E. Gyorki ◽  
Richard C. W. Martin ◽  
Victoria Atkinson ◽  
...  

Author(s):  
Nina Zila ◽  
Christoph Hoeller ◽  
Verena Paulitschke

SummaryIn malignant diseases, targeting of immune checkpoints successfully changed the therapeutic landscape and helped to unleash anti-tumor T cell responses, resulting in durable clinical outcomes, but only in up to 50% of patients. The success of these therapies and the need to overcome intrinsic and acquired therapy resistance stimulated research to identify new pathways and targets. Numerous clinical trials are currently evaluating novel checkpoint inhibitors or recently developed strategies like modulating the tumor microenvironment, mostly in combination with approved therapies. This short review briefly discusses promising therapeutic targets, currently still under investigation, with the chance to realize clinical application in the foreseeable future.


2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A711-A711
Author(s):  
Matthew Robinson ◽  
Kevin Vervier ◽  
Simon Harris ◽  
David Adams ◽  
Doreen Milne ◽  
...  

BackgroundThe gut microbiome of cancer patients appears to be associated with response to Immune Checkpoint Inhibitor (ICIs) treatment.1–4 However, the bacteria linked to response differ between published studies.MethodsLongitudinal stool samples were collected from 69 patients with advanced melanoma receiving approved ICIs in the Cambridge (UK) MELRESIST study. Pretreatment samples were analysed by Microbiotica, using shotgun metagenomic sequencing. Microbiotica’s sequencing platform comprises the world’s leading Reference Genome Database and advanced Microbiome Bioinformatics to give the most comprehensive and precise mapping of the gut microbiome. This has enabled us to identify gut bacteria associated with ICI response missed using public reference genomes. Published microbiome studies in advanced melanoma,1–3renal cell carcinoma (RCC) and non-small cell lung cancer (NSCLC)4 were reanalysed with the same platform.ResultsAnalysis of the MELRESIST samples showed an overall change in the microbiome composition between advanced melanoma patients and a panel of healthy donor samples, but not between patients who subsequently responded or did not respond to ICIs. However, we did identify a discrete microbiome signature which correlated with response. This signature predicted response with an accuracy of 93% in the MELRESIST cohort, but was less predictive in the published melanoma cohorts.1–3 Therefore, we developed a bioinformatic analytical model, incorporating an interactive random forest model and the MELRESIST dataset, to identify a microbiome signature which was consistent across all published melanoma studies. This model was validated three times by accurately predicting the outcome of an independent cohort. A final microbiome signature was defined using the validated model on MELRESIST and the three published melanoma cohorts. This was very accurate at predicting response in all four studies combined (91%), or individually (82–100%). This signature was also predictive of response in a NSCLC study and to a lesser extent in RCC. The core of this signature is nine bacteria significantly increased in abundance in responders.ConclusionsAnalysis of the MELRESIST study samples, precision microbiome profiling by the Microbiotica Platform and a validated bioinformatic analysis, have enabled us to identify a unique microbiome signature predictive of response to ICI therapy in four independent melanoma studies. This removes the challenge to the field of different bacteria apparently being associated with response in different studies, and could represent a new microbiome biomarker with clinical application. Nine core bacteria may be driving response and hold potential for co-therapy with ICIs.Ethics ApprovalThe study was approved by Newcastle & North Tyneside 2 Research Ethics Committee, approval number 11/NE/0312.ReferencesMatson V, Fessler J, Bao R, et al. The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science 2018;359(6371):104–108.Gopalakrishnan V, Spencer CN, Nezi L, et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 2018;359(6371):97–103.Frankel AE, Coughlin LA, Kim J, et al. Metagenomic shotgun sequencing and unbiased metabolomic profiling identify specific human gut microbiota and metabolites associated with immune checkpoint therapy efficacy in melanoma patients. Neoplasia 2017;19(10):848–855.Routy B, Le Chatelier E, Derosa L, et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 2018;359(6371):91–97.


2012 ◽  
Vol 13 (5) ◽  
pp. 349-354 ◽  
Author(s):  
Katherine A. Lyseng-Williamson ◽  
Mark Sanford
Keyword(s):  

Sign in / Sign up

Export Citation Format

Share Document