| Literature DB >> 31331337 |
Paolo A Ascierto1, Sanjiv S Agarwala2, Gerardo Botti3, Alfredo Budillon4, Michael A Davies5, Reinhard Dummer6, Marc Ernstoff7, Soldano Ferrone8, Silvia Formenti9, Thomas F Gajewski10, Claus Garbe11, Omid Hamid12, Roger S Lo13, Jason J Luke14, Oliver Michielin15, Giuseppe Palmieri16, Laurence Zitvogel17, Francesco M Marincola18, Giuseppe Masucci19, Corrado Caracò20, Magdalena Thurin21, Igor Puzanov22.
Abstract
Diagnosis of melanocytic lesions, correct prognostication of patients, selection of appropriate adjuvant and systemic therapies, and prediction of response to a given therapy remain very real challenges in melanoma. Recent studies have shown that immune checkpoint blockade that represents a forefront in cancer therapy, provide responses but they are not universal. Improved understanding of the tumor microenvironment, tumor immunity and response to therapy has prompted extensive translational and clinical research in melanoma. Development of novel biomarker platforms may help to improve diagnostics and predictive accuracy for selection of patients for specific treatment. There is a growing evidence that genomic and immune features of pre-treatment tumor biopsies may correlate with response in patients with melanoma and other cancers they have yet to be fully characterized and implemented clinically. For example, advancements in sequencing and the understanding of the tumor microenvironment in melanoma have led to the use of genome sequencing and gene expression for development of multi-marker assays that show association with inflammatory state of the tumor and potential to predict response to immunotherapy. As such, melanoma serves as a model system for understanding cancer immunity and patient response to immunotherapy, either alone or in combination with other treatment modalities. Overall, the aim for the translational and clinical studies is to achieve incremental improvements through the development and identification of optimal treatment regimens, which increasingly involve doublet as well as triplet combinations, as well as through development of biomarkers to improve immune response. These and other topics in the management of melanoma were the focus of discussions at the fourth Melanoma Bridge meeting (November 29th-December 1st, 2018, Naples, Italy), which is summarised in this report.Entities:
Keywords: Adjuvant; Anti-CTLA-4; Anti-PD-1; BRAF inhibitor; Biomarkers; Combination strategies; Immunotherapy; MEK inhibitor; Melanoma; Neoadjuvant; Target therapy
Mesh:
Substances:
Year: 2019 PMID: 31331337 PMCID: PMC6647284 DOI: 10.1186/s12967-019-1979-z
Source DB: PubMed Journal: J Transl Med ISSN: 1479-5876 Impact factor: 5.531
Comparison of anti-PD-1 adjuvant trials
| Data comparison in adjuvant melanoma trials | ||||||
|---|---|---|---|---|---|---|
| Patient population | CM238 [ | COMBI-AD [ | KN054 [ | |||
| Completely resected stage IIIB/C or IV melanoma | Completely resected, | Completely resected stage IIIA/B/C melanoma | ||||
| Treatment | Nivo | Lpi | Dab/Tram | Placebo | Pembro | Placebo |
| N | 453 | 453 | 438 | 432 | 514 | 505 |
| RFS HR | 0.65 (97.56% CI 0.51–0.83), P < 0.0001 | 0.47 (95% CI 0.39–0.58), P < 0.001 | 0.57 (98.4% CI 0.43–0.74), P < 0.001 | |||
| 1-year RFS rate (%) |
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| 18-months RFS rate (%) | 66 | 53 | N/A | N/A | 71 | 53 |
| 3-years OS rate (%) | N/A | N/A | 86 | 77 | N/A | N/A |
| Grade 3–5 TRAEs (%) |
| 46 |
| 5 |
| 3 |
| DC rate due to AEs (%) |
| 43 |
| 3 |
| 2 |
In italic values for 1-y RFS Rate (%), patients treated with nivolumab Grade 3-5 TREs (%) and patients treated with nivolumab DC Rate due to AEs (%)
Comparison of three trials showing a consistent beneficial effect on RFS with adjuvant PD-1 inhibitor therapy, although OS data is largely still awaited
Fig. 1How to decipher the clinical relevance of the compositions of the gut microbiome? Various methods can be utilized to help better understand the clinical relevance of the composition of the gut microbiome
Fig. 2A clinical endpoint of translational research. A clinical goal of translational research is a multi-system predictive model to guide treatment decision-making in metastatic melanoma