Добірка наукової літератури з теми "Leeds melanoma Cohort"

Purpose A cohort study was carried out to test the hypothesis that higher vitamin D levels reduce the risk of relapse from melanoma. Methods A pilot retrospective study of 271 patients with melanoma suggested that vitamin D may protect against recurrence of melanoma. We tested these findings in a survival analysis in a cohort of 872 patients recruited to the Leeds Melanoma Cohort (median follow-up, 4.7 years). Results In the retrospective study, self-reports of taking vitamin D supplements were nonsignificantly correlated with a reduced risk of melanoma relapse (odds ratio = 0.6; 95% CI, 0.4 to 1.1; P = .09). Nonrelapsers had higher mean 25-hydroxyvitamin D3 levels than relapsers (49 v 46 nmol/L; P = .3; not statistically significant). In the cohort (prospective) study, higher 25-hydroxyvitamin D3 levels were associated with lower Breslow thickness at diagnosis (P = .002) and were independently protective of relapse and death: the hazard ratio for relapse-free survival (RFS) was 0.79 (95% CI, 0.64 to 0.96; P = .01) for a 20 nmol/L increase in serum level. There was evidence of interaction between the vitamin D receptor (VDR) BsmI genotype and serum 25-hydroxyvitamin D3 levels on RFS. Conclusion Results from the retrospective study were consistent with a role for vitamin D in melanoma outcome. The cohort study tests this hypothesis, providing evidence that higher 25-hydroxyvitamin D3 levels, at diagnosis, are associated with both thinner tumors and better survival from melanoma, independent of Breslow thickness. Patients with melanoma, and those at high risk of melanoma, should seek to ensure vitamin D sufficiency. Additional studies are needed to establish optimal serum levels for patients with melanoma.

9016 Background: Vitamin D has pleiotropic effects, which may moderate the interaction between patients and their tumors. Two studies were carried out to test the hypothesis that higher vitamin D levels reduce the risk of relapse from melanoma. Methods: A pilot retrospective case-control study in 271 melanoma patients suggested that vitamin D may protect against recurrence of melanoma. We then tested these findings in a survival analysis in a cohort of 872 cases recruited to the Leeds Melanoma Cohort (median follow up of 4.7 years). Results: Pilot study Results suggested that taking vitamin D reduced the risk of relapse from melanoma (OR 0.6, 95% CI: 0.4, 1.1). Non-relapsers had higher mean 25-dihydroxyvitamin D3 levels than relapsers (49 nmol/L compared with 46, p=0.3). Cohort study Higher 25-dihydroxyvitamin D3 levels were associated with lower Breslow thickness at diagnosis and were independently protective of relapse and death: hazard ratio (HR) for relapse free survival (RFS) 0.76, 95% CI: (0.64, 0.96), for a 20nmol/L increase in serum level. There was evidence of interaction between the vitamin D receptor (VDR) BsmI genotype and serum 25-dihydroxyvitamin D3 levels on RFS. Conclusions: The pilot study provided preliminary evidence for a role for vitamin D in outcome from melanoma. The cohort study provided further evidence that higher 25-dihydroxyvitamin D3 levels, at diagnosis, were associated both with thinner tumors and better survival, independent of Breslow thickness, from melanoma. Melanoma patients should avoid vitamin D deficiency. Further studies are needed to establish optimal serum levels for melanoma patients. No significant financial relationships to disclose.

Background: Survival from melanoma is strongly related to patient sex, with females having a survival rate almost twice that of males. Many explanations have been proposed but have not withstood critical scrutiny. Prior analysis of different cancers with a sex bias has identified six X-linked genes that escape X chromosome inactivation in females and are, therefore, potentially involved in sex differences in survival. Four of the genes are well-known epigenetic regulators that are known to influence the expression of hundreds of other genes and signaling pathways in cancer. Methods: Survival and interaction analysis were performed on the skin cutaneous melanoma (SKCM) cohort in The Cancer Genome Atlas (TCGA), comparing high vs. low expression of KDM6A, ATRX, KDM5C, and DDX3X. The Leeds melanoma cohort (LMC) on 678 patients with primary melanoma was used as a validation cohort. Results: Analysis of TCGA data revealed that two of these genes—KDM6A and ATRX—were associated with improved survival from melanoma. Tumoral KDM6A was expressed at higher levels in females and was associated with inferred lymphoid infiltration into melanoma. Gene set analysis of high KDM6A showed strong associations with immune responses and downregulation of genes associated with Myc and other oncogenic pathways. The LMC analysis confirmed the prognostic significance of KDM6A and its interaction with EZH2 but also revealed the expression of KDM5C and DDX3X to be prognostically significant. The analysis also confirmed a partial correlation of KDM6A with immune tumor infiltrates. Conclusion: When considered together, the results from these two series are consistent with the involvement of X-linked epigenetic regulators in the improved survival of females from melanoma. The identification of gene signatures associated with their expression presents insights into the development of new treatment initiatives but provides a basis for exploration in future studies.

BackgroundPatients with BRAF-mutant and wild-type melanoma have different response rates to immune checkpoint blockade therapy. However, the reasons for this remain unknown. To address this issue, we investigated the precise immune composition resulting from BRAF mutation in treatment-naive melanoma to determine whether this may be a driver for different response to immunotherapy.MethodsIn this study, we characterized the treatment-naive immune context in patients with BRAF-mutant and BRAF wild-type (BRAF-wt) melanoma using data from single-cell RNA sequencing, bulk RNA sequencing, flow cytometry and immunohistochemistry (IHC).ResultsIn single-cell data, BRAF-mutant melanoma displayed a significantly reduced infiltration of CD8+ T cells and macrophages but also increased B cells, natural killer (NK) cells and NKT cells. We then validated this finding using bulk RNA-seq data from the skin cutaneous melanoma cohort in The Cancer Genome Atlas and deconvoluted the data using seven different algorithms. Interestingly, BRAF-mutant tumors had more CD4+ T cells than BRAF-wt samples in both primary and metastatic cohorts. In the metastatic cohort, BRAF-mutant melanoma demonstrated more B cells but less CD8+ T cell infiltration when compared with BRAF-wt samples. In addition, we further investigated the immune cell infiltrate using flow cytometry and multiplex IHC techniques. We confirmed that BRAF-mutant melanoma metastases were enriched for CD4+ T cells and B cells and had a co-existing decrease in CD8+ T cells. Furthermore, we then identified B cells were associated with a trend for improved survival (p=0.078) in the BRAF-mutant samples and Th2 cells were associated with prolonged survival in the BRAF-wt samples.ConclusionsIn conclusion, treatment-naive BRAF-mutant melanoma has a distinct immune context compared with BRAF-wt melanoma, with significantly decreased CD8+ T cells and increased B cells and CD4+ T cells in the tumor microenvironment. These findings indicate that further mechanistic studies are warranted to reveal how this difference in immune context leads to improved outcome to combination immune checkpoint blockade in BRAF-mutant melanoma.

e14104 Background: Immune checkpoint blockers have dramatically improved the survival of patients (pts) suffering from advanced metastasized melanoma. In pts with melanoma brain metastases (MBM) a combination with radiotherapy (RT) is routinely used. Methods: We prospectively included 106 pts with MBM in a non-randomized observational trial with 7 treatment cohorts. Patients in cohort 1-4 were treated with ipilimumab (+/- nivolumab) and either stereotactic (up to 3 MBM) or whole brain RT (≥ 4 MBM) before or after the start of immunotherapy. Cohort 5 and 6 included pts who received RT with an ipilimumab-free systemic treatment and cohort 7 pts were treated with ipilimumab (+/- nivolumab) and no RT. Primary endpoints were immunological response in the peripheral blood (FACS of T cell subsets, ELISpots against melanoma antigens) and radiological response, secondary endpoints were progression free and overall survival. Results: Included pts were in median 61 years old, 72% were male. At trial inclusion, 31% of pts had an elevated LDH. 39% of ipilimumab treated pts received combination therapy with nivolumab. Clinically, ipilimumab treated pts in the early RT groups had better responses of both intra- and extracranial disease (p = 0.04 for both). Multivariate analyses showed a better PFS for pts with early RT (p = 0.02) and normal LDH (p = 0.049). Type of radiation (p = 0.6) and immune therapy (p = 0.8) had no significant influence in this small cohort of pts. Immune monitoring revealed that ipilimumab leads to an increase in activated CD4+ and CD8+ T cells in the peripheral blood which was maintained in responding pts and higher in pts receiving early RT. Treg were not depleted in general but activated by ipilimumab. However, responders displayed a temporary decrease of Treg and activated Treg under treatment. An increase in the detection of melanoma antigens could be observed after 2 cycles of ipilimumab which was higher in pts with combined radioimmunotherapy compared to ipilimumab only. Conclusions: Preliminary data from this small observational trial might lead to a preference of a treatment sequence with radiotherapy first, followed by checkpoint inhibition in pts with MBM.

e24079 Background: Melanoma accounts for 5.5% of new cancer diagnoses in the United States, and the 5-year overall survival is 93%. Overall, 7% of patients develop a recurrence, and 4-8% develop a second primary melanoma. This study aimed to assess how the standards set by the American College of Surgeons Commission on Cancer (CoC) to provide survivorship care plans (SCP) to patients may improve adherence to surveillance visits. Methods: All patients treated for invasive melanoma at our institution between 8/2018-2/2020 were included. SCP containing stage, treatment summary, and surveillance plan were delivered in-person to patients and sent to primary providers and dermatologists as outlined by CoC Standards for Optimal Care. Psychosocial distress (PSD) screening was performed using the National Comprehensive Cancer Network Distress Thermometer, with scores > 4 requiring further evaluation by oncology social worker. SCP and PSD were provided during the initiation phase of our cancer care program, and half the patients received services. Surveillance adherence was determined from chart review. The two groups were compared by t-test for continuous or chi-square test for categorical variables. Multiple regression analysis with odds ratios were performed. Mann-Whitney analysis was performed to assess the impact of SCP on PSD. Results: Of 146 patients identified for our cohort, 73 received SCP and PSD screening. Stage IA was the most common diagnosis (44%), followed by IB (13%) and IIIC (9%). Ninety-eight patients (67%) were adherent to all surveillance visits, and 55 of these received SCPs. Most patients noted low distress without the need for further support (79%), and 12 (21%) scored ≥4, benefiting from emotional and financial support and appointment and health insurance navigation. High PSD score did not correlate with advanced stage. Reception of SCP (p = 0.036) and close distance to treating facility (p = 0.016) improved adherence to surveillance visits. For patients who did not receive SCP, likelihood to follow up decreased by a factor of 0.469 (95% CI 0.231 - 0.952). Sex, age, PSD score, and stage did not affect surveillance adherence (p = NS). There were 6 recurrences, of which 4 were physician-detected during surveillance, and 8 patients developed second primary melanomas, all physician-detected. Conclusions: Delivery of SCP, a component of which includes counseling regarding signs and symptoms of recurrence or possibility of second primary melanoma, leads to significantly higher rates of surveillance adherence. This was shown for all stages. Melanoma survivors require close clinical follow-up, as demonstrated by our study finding that even with patient education, most recurrences and all new primary melanomas were physician-detected. PSD among melanoma patients is common, and all patients regardless of stage should undergo screening, as even early-stage patients exhibited distress.

Abstract T cell-mediated immunity can produce durable anti-melanoma responses resulting in improved survival of metastatic melanoma patients. Gamma-interferon-inducible lysosomal thiol reductase (GILT) is critical for MHC class II-restricted presentation of multiple melanoma antigens to CD4+ T cells and facilitates cross-presentation on MHC class I for activation of CD8+ T cells. Our prior study found that GILT expression is increased in melanocytes of malignant melanoma specimens compared with benign nevi. To determine whether GILT expression is associated with inflammation, expression in halo nevi specimens was compared to nevi without lymphocytic infiltrate by immunohistochemistry. A halo nevus is a benign nevus with a lymphocytic infiltrate which leads to regression of the nevus. GILT, but not MHC class II, expression was increased in melanocytes of halo nevi compared to nevi without lymphocytic infiltrate. Analysis of a publicly available gene expression profiling cohort of 457 cutaneous melanoma specimens revealed that GILT expression was associated with IFN-γ, TNF-α, and IL-1β expression in human melanoma. In vitro exposure of human melanoma cell lines to IFN-γ or inflammatory cytokines, such as TNF-α, induced GILT expression in melanoma cell lines, which lacked GILT expression at baseline. Vemurafenib, a BRAF inhibitor used in the treatment of metastatic melanoma, enhanced IFN-γ-induced GILT and MHC class II expression in a melanoma cell line. Together these data demonstrate that inflammation, alone or in combination with a current therapeutic agent, induces high levels of GILT expression in human melanoma.

BackgroundTo assess the additive value of dual-energy CT (DECT) over single-energy CT (SECT) to radiomics-based response prediction in patients with metastatic melanoma preceding immunotherapy.Material and methodsA total of 140 consecutive patients with melanoma (58 female, 63±16 years) for whom baseline DECT tumor load assessment revealed stage IV and who were subsequently treated with immunotherapy were included. Best response was determined using the clinical reports (81 responders: 27 complete response, 45 partial response, 9 stable disease). Individual lesion response was classified manually analogous to RECIST 1.1 through 1291 follow-up examinations on a total of 776 lesions (6.7±7.2 per patient). The patients were sorted chronologically into a study and a validation cohort (each n=70). The baseline DECT was examined using specialized tumor segmentation prototype software, and radiomic features were analyzed for response predictors. Significant features were selected using univariate statistics with Bonferroni correction and multiple logistic regression. The area under the receiver operating characteristic curve of the best subset was computed (AUROC). For each combination (SECT/DECT and patient response/lesion response), an individual random forest classifier with 10-fold internal cross-validation was trained on the study cohort and tested on the validation cohort to confirm the predictive performance.ResultsWe performed manual RECIST 1.1 response analysis on a total of 6533 lesions. Multivariate statistics selected significant features for patient response in SECT (min. brightness, R²=0.112, padj. ≤0.001) and DECT (textural coarseness, R²=0.121, padj. ≤0.001), as well as lesion response in SECT (mean absolute voxel intensity deviation, R²=0.115, padj. ≤0.001) and DECT (iodine uptake metrics, R²≥0.12, padj. ≤0.001). Applying the machine learning models to the validation cohort confirmed the additive predictive power of DECT (patient response AUROC SECT=0.5, DECT=0.75; lesion response AUROC SECT=0.61, DECT=0.85; p<0.001).ConclusionThe new method of DECT-specific radiomic analysis provides a significant additive value over SECT radiomics approaches for response prediction in patients with metastatic melanoma preceding immunotherapy, especially on a lesion-based level. As mixed tumor response is not uncommon in metastatic melanoma, this lends a powerful tool for clinical decision-making and may potentially be an essential step toward individualized medicine.

Malignant melanoma, a cancer arising from melanocytes, is reported to have one of the fastest growing incidence rates worldwide, and is considered to be one of the most aggressive human malignancies. According to the World Health Organization (WHO), current statistics indicate that 132,000 cases occur globally each year and is set to rise by 2-3% every year. If detected early, a complete surgical excision of the tumor can be performed. However, in many cases, diagnosis and treatment is delayed, leading to poor prognosis, with a survival expectation of a mere 6-9 months in the case of metastatic melanoma. Due to high incidence rates, difficulty in early diagnosis and rapid progression to metastasis, melanoma is an important malignancy to be studied. Diagnosis and stage classification of many diseases including cancers is still a major challenge. Melanoma is a highly heterogeneous disease, with multiple sources of heterogeneity. It can arise in many regions of the body, with varied incidence patterns. It is also linked to various alterations at the molecular level, with the pattern of alterations varying widely across patients. As a result of this heterogeneity, existing diagnostic and therapeutic methods can lead to poor outcomes in a subset of patients even if it is effective in a different subset. A large amount of genomic and transcriptomic data of tumor samples from patients is now available, which provides new opportunities for understanding disease mechanisms and identifying specific molecular features characteristic of the disease stage and sub-type. In this context, a feature refers to a gene or a pool of genes or even a pathway. Identification of molecular features from such large complex data is still a major challenge in many diseases, and is currently a highly pursued objective. The multi-level complexities involved in the disease and the need to study large patient data to understand the perturbations at a systems level, necessitates the use of large scale computational approaches. While melanoma, like many other diseases, can be associated with variations at multiple levels, the onset of the disease is due to mutations in critical genes. Metabolic alterations are also known to occur in the disease, which cater to the high energy needs of a progressive tumor. Viewing the disease from a different perspective, modulation of the immune processes has also been studied, which has now shown that the tumor evades the immune system and manages to proliferate. In this work, a computational systems biology approach was used to identify the molecular features which addresses all these aspects and are responsible for the progression of melanoma. As a first objective in Chapter 2, a new network-based computational pipeline combined with machine learning method which utilizes publicly available transcriptomic data of melanoma patient samples was developed to identify signature genes which can efficiently classify metastatic melanoma and primary melanoma. These genes can be potential biomarkers for the identification of progression in melanoma patients. To begin with, a condition-specific protein-protein interaction network was constructed for the three conditions, normal skin (NS), primary melanoma (PM) and metastatic melanoma (MM). Further, the active paths in each of the networks were computed based on the shortest-path approach. The paths different in MM compared to NS, PM compared to NS and MM compared to PM were identified using a string similarity metric. These perturbed paths were further pruned based on the influence they wield on the entire system. To do this, network communities were identified and genes in them scored based on the number of communities they spanned. Using this, the most influential, differentially expressed genes in all the three comparisons were identified and were taken as a short-list of markers. The shortlisted genes were further evaluated by a machine learning approach and ranked by their discriminatory capacities. Based on a feature elimination exercise, a minimal gene-set with the maximum efficiency for classification between the pair of conditions, was identified using a Random Forest classifier. From this, a panel of 6 genes, ALDH1A1, HSP90AB1, KIT, KRT16, SPRR3 and TMEM45B whose expression values discriminated metastatic from primary melanoma by 87% classification accuracy was identified. In an independent transcriptomic data set derived from 703 primary melanomas from a collaborator’s laboratory, it was observed that all six genes in the panel were significant in predicting melanoma specific survival (MSS) in a univariate analysis, which was also consistent with AJCC staging. Further, 3 of these genes, HSP90AB1, SPRR3 and KRT16 remained significant predictors of MSS in a joint analysis of the 6 genes (HR=2.3, P=0.03), although HSP90AB1 (HR=1.9, P=2x10-4) alone remained predictive after adjusting for clinical predictors. A panel of 20 genes with high discriminatory capacity to classify PM vs. NS and a panel of 25 genes for MM vs. NS were also identified. Chapter 3 describes the work carried out to identify potential driver mutations in melanoma. Melanoma is a malignancy with a high mutation burden, with the onset of the disease attributed to mutations caused by external stresses. The mutations are observed to modulate multiple pathways, with the landscape of mutations varying across patients. In some cases, different mutations resulting in the same end effect can also be seen. These observations highlight the extent of heterogeneity among melanoma patients. A novel algorithm, DMIN (Driver Mutation identification using Influence Network) was designed to identify patient-specific potential driver mutations using the mutation information and gene expression variation of 362 melanoma patients from the TCGA dataset which was integrated with a comprehensive protein-protein interaction network. The active paths based on the shortest-paths principles were computed from the mutated node as a source, to all other nodes as possible destinations in the 362 patient-specific networks. The paths were further scored and prioritized in each patient to identify the mutations and differentially expressed nodes, referred to as outliers. A tripartite graph was constructed consisting of patient (P), mutations (M) and outlier (O) as three connected node sets. The M nodes were ranked based on the betweenness centrality and based on percentile threshold, 59 potential driver mutations were identified which were found to be statistically significant. The performance of the DMIN method was further validated by comparing with three other existing methods and DMIN method was found to outperform others. Co-occurring mutation combinations were also computed and shortlisted based on their effect on the survival of the patients. In total, 68 combinations ranging from 2-12 genes with a high hazard to survival were identified. Finally, driver mutations were computed in the patients based on their clinicopathological information such as the sample type, mutation subtype, AJCC stage, Breslow thickness, gender and ulceration and pathways enriched in each of these conditions are described. Metabolic rewiring is an important characteristic of the tumor cells. The pathway rewiring accounts for the increased energy requirements and also aid in the proliferation of the cells. In Chapter 4, Flux balance based analysis was carried out using a genome scale metabolic model to identify the variations associated with disease progression of cancer. To begin with, a melanoma metabolic model was constructed using a general human metabolic model and gene expression data of NS, PM and MM samples. The flux level variations were computed between PM-NS, MM-NS and MM-PM conditions and sub-systems that varied were identified. The reactions belonging to ROS detoxification, Warburg effect and tyrosine metabolism were found to be largely varied in the melanoma condition. In addition, Vitamin A and Vitamin C metabolism variation were observed between MM and PM. Gene essentiality analysis on the metabolic model identified 5 important genes needed for the cancer proliferation and can be validated for being important as therapeutic targets. In chapter 5, the molecular features of the immune system involved in progression of melanoma were investigated. FOXP3+ regulatory T cells are the immune cell types involved in maintaining an immune check by suppressing the immune activation function of effector T cells. High Treg leads to a bad prognosis of disease, whereas a high Teff population is linked to good prognosis. High FOXP3 expression levels correlate well with a low survival of melanoma patients. Consistent with this, a low ratio of Treg: Teff cells in the tumor microenvironment is attributed to the success of IL-2 based immunotherapy. A network based analysis was carried out using transcriptome expression values of FOXP3_high and FOXP3_low primary melanoma patients. Active paths in FOXP3_high patients were identified and genes reported. PTEN and FOS were predicted to modulate the expression of FOXP3 leading to an immunosuppressed environment. A simple deterministic model was also constructed to mimic the population interplay between Treg and Teff cells in the tumor microenvironment, which provides a basis to predict the disease outcome and prognosis of survival in a given patient. In summary, this thesis presents an integrated approach for identification of molecular markers, metabolic variations and driver mutations of melanoma. The outcome of the work holds promise in efficient classification of the various stages involved and also aid in predicting prognosis of the melanoma disease. The methods developed for identification of biomarkers and driver mutations are fairly general and can easily be adapted for studying other diseases as well.