Background: Covid-19 pandemic by the new coronavirus SARS-Cov-2 has produced devastating effects on the health care system, affecting also cancer patient care. Data about COVID-19 infection in cancer patients are scarce, and they point out a higher risk of complications due to the viral infection in this population. Moreover, cancer treatments could increase viral complications, specially those treatments based on the use of immunotherapy with checkpoints antibodies. There are no clinical data about the safety of immune check point antibodies in cancer patients when they become infected by SARS-CoV-2. As checkpoint inhibitors, mainly anti PD-1 and anti CTLA-4 antibodies, are an effective treatment for most melanoma patients, avoiding their use during the pandemic could lead to a decrease in the chances of curing melanoma. Methods: In Spain we have started a national registry of melanoma patients infected by SARS-Cov-2 since April 1st, 2020. A retrospective analysis of patients included in the Spanish registery has been performed weekly since the activation of the study. Interim analysis shows unexpected findings about cancer treatment safety in SARS-Cov-2 infected melanoma patients, so a rapid communication to the scientific community is mandatory Results: Fifty patients have been included as of May 17th, 2020. Median age is 69 years (range 6 to 94 years), 27 (54%) patients are males and 36 (70%) patients have stage IV melanoma. Twenty-two (44%) patients were on active anticancer treatment with anti PD-1 antibodies, 16 (32%) patients were on treatment with BRAF plus MEK inhibitors and 12 (24%) patients were not on active cancer treatment. COVID-19 episode has been resolved in 43 cases, including 30 (70%) patients cured, four (9%) patients that have died due to melanoma progression, and nine (21%) patients that have died from COVID-19. Mortality rates from COVID-19 according to melanoma treatment type were 16%, 15% and 36% for patients on immunotherapy, targeted drugs, and for those that were not undergoing active cancer treatment, respectively. Conclusion: These preliminary findings show that the risk of death in those patients undergoing treatment with anti PD-1 antibodies does not exceed the global risk of death in this population. These results could be relevant in order to select melanoma therapy during the COVID-19 pandemic

Immunotherapy improves the survival of patients with advanced melanoma, 40% of whom become long-term responders. However, not all patients respond to immunotherapy. Further knowledge of the processes involved in the response and resistance to immunotherapy is still needed. In this study, clinical paraffin samples from fifty-two advanced melanoma patients treated with anti-PD-1 inhibitors were assessed via high-throughput proteomics and RNA-seq. The obtained proteomics and transcriptomics data were analyzed using multi-omics network analyses based on probabilistic graphical models to identify those biological processes involved in the response to immunotherapy. Additionally, proteins related to overall survival were studied. The activity of the node formed by the proteins involved in protein processing in the endoplasmic reticulum and antigen presentation machinery was higher in responders compared to non-responders; the activity of the immune and inflammatory response node was also higher in those with complete or partial responses. A predictor for overall survival based on two proteins (AMBP and PDSM5) was defined. In summary, the response to anti-PD-1 therapy in advanced melanoma is related to protein processing in the endoplasmic reticulum, and also to genes involved in the immune and inflammatory responses. Finally, a two-protein predictor can define survival in advanced disease. The molecular characterization of the mechanisms involved in the response and resistance to immunotherapy in melanoma leads the way to establishing therapeutic alternatives for patients who will not respond to this treatment.

Cutaneous melanoma is the most aggressive of skin tumors. In order to discover new biomarkers that could help us improve prognostic prediction in melanoma patients, we have searched for germline DNA variants associated with melanoma progression. Thus, after exome sequencing of a set of melanoma patients and healthy control individuals, we identified rs1042602, an SNP within TYR, as a good candidate. After genotyping rs1042602 in 1025 patients and 773 healthy donors, we found that the rs1042602-A allele was significantly associated with susceptibility to melanoma (CATT test: p = 0.0035). Interestingly, we also observed significant differences between patients with good and bad prognosis (5 years of follow-up) (n = 664) (CATT test for all samples p = 0.0384 and for men alone p = 0.0054). Disease-free-survival (DFS) analyses also showed that patients with the A allele had shorter DFS periods. In men, the association remained significant even in a multivariate Cox Proportional-hazards model, which was adjusted for age at diagnosis, Breslow thickness, ulceration and melanoma subtype (HR 0.4; 95% confidence interval (CI) 0.20–0.83; p = 0.0139). Based on our results, we propose that rs1042602-A is a risk allele for melanoma, which also seems to be responsible for a poorer prognosis of the disease, particularly in men.

Immunotherapy based on anti-PD1 antibodies has improved the outcome of advanced melanoma. However, prediction of response to immunotherapy remains an unmet need in the field. Tumor PD-L1 expression, mutational burden, gene profiles and microbiome profiles have been proposed as potential markers but are not used in clinical practice. Probabilistic graphical models and classificatory algorithms were used to classify melanoma tumor samples from a TCGA cohort. A cohort of patients with advanced melanoma treated with PD-1 inhibitors was also analyzed. We established that gene expression data can be grouped in two different layers of information: immune and molecular. In the TCGA, the molecular classification provided information on processes such as epidermis development and keratinization, melanogenesis, and extracellular space and membrane. The immune layer classification was able to distinguish between responders and non-responders to immunotherapy in an independent series of patients with advanced melanoma treated with PD-1 inhibitors. We established that the immune information is independent than molecular features of the tumors in melanoma TCGA cohort, and an immune classification of these tumors was established. This immune classification was capable to determine what patients are going to respond to immunotherapy in a new cohort of patients with advanced melanoma treated with PD-1 inhibitors Therefore, this immune signature could be useful to the clinicians to identify those patients who will respond to immunotherapy.