Dissection of immune gene networks in primary melanoma tumors critical for antitumor surveillance of patients with stage II-III resectable disease

An update on methods for detection of prognostic and predictive biomarkers in melanoma

The approval of immunotherapy for stage II-IV melanoma has underscored the need for improved immune-based predictive and prognostic biomarkers. For resectable stage II-III patients, adjuvant immunotherapy has proven clinical benefit, yet many patients experience significant adverse events and may not require therapy. In the metastatic setting, single agent immunotherapy cures many patients but, in some cases, more intensive combination therapies against specific molecular targets are required. Therefore, the establishment of additional biomarkers to determine a patient's disease outcome (i.e., prognostic) or response to treatment (i.e., predictive) is of utmost importance. Multiple methods ranging from gene expression profiling of bulk tissue, to spatial transcriptomics of single cells and artificial intelligence-based image analysis have been utilized to better characterize the immune microenvironment in melanoma to provide novel predictive and prognostic biomarkers. In this review, we will highlight the different techniques currently under investigation for the detection of prognostic and predictive immune biomarkers in melanoma.

HEV-associated dendritic cells are observed in metastatic tumor-draining lymph nodes of cutaneous melanoma patients with longer distant metastasis-free survival after adjuvant immunotherapy

Introduction

Tissue biomarkers that aid in identifying cutaneous melanoma (CM) patients who will benefit from adjuvant immunotherapy are of crucial interest. Metastatic tumor-draining lymph nodes (mTDLN) are the first encounter site between the metastatic CM cells and an organized immune structure. Therefore, their study may reveal mechanisms that could influence patients´ outcomes.

Methods

Twenty-nine stage-III CM patients enrolled in clinical trials to study the vaccine VACCIMEL were included in this retrospective study. After radical mTDLN dissection, patients were treated with VACCIMEL (n=22) or IFNα-2b (n=6), unless rapid progression (n=1). Distant Metastasis-Free Survival (DMFS) was selected as an end-point. Two cohorts of patients were selected: one with a good outcome (GO) (n=17; median DMFS 130.0 months), and another with a bad outcome (BO) (n=12; median DMFS 8.5 months). We analyzed by immunohistochemistry and immunofluorescence the expression of relevant biomarkers to tumor-cell biology and immune cells and structures in mTDLN, both in the tumor and peritumoral areas.

Results

In BO patients, highly replicating Ki-67+ tumor cells, low tumor HLA-I expression and abundant FoxP3+ lymphocytes were found (p=0.037; p=0.056 and p=0.021). In GO patients, the most favorable biomarkers for prolonged DMFS were the abundance of peri- and intra-tumoral CD11c+ cells (p=0.0002 and p=0.001), peri-tumoral DC-LAMP+ dendritic cells (DCs) (p=0.001), and PNAd+ High Endothelial Venules (HEVs) (p=0.004). Most strikingly, we describe in GO patients a peculiar, heterogeneous structure that we named FAPS (Favoring Antigen-Presenting Structure), a triad composed of DC, HEV and CD62L+ naïve lymphocytes, whose postulated role would be to favor tumor antigen (Ag) priming of incoming naïve lymphocytes. We also found in GO patients a preferential tumor infiltration of CD8+ and CD20+ lymphocytes (p=0.004 and p=0.027), as well as peritumoral CD20+ aggregates, with no CD21+ follicular dendritic cells detected (p=0.023). Heterogeneous infiltration with CD64+CD68-CD163-, CD64+CD68+CD163- and CD64+CD68+CD163+ macrophages were observed in both cohorts.

Discussion

The analysis of mTDLN in GO and BO patients revealed marked differences. This work highlights the importance of analyzing resected mTDLN from CM patients and suggests a correlation between tumor and immune characteristics that may be associated with a spontaneous or vaccine-induced long DMFS. These results should be confirmed in prospective studies.

A deep learning model based on whole slide images to predict disease-free survival in cutaneous melanoma patients

The application of deep learning on whole-slide histological images (WSIs) can reveal insights for clinical and basic tumor science investigations. Finding quantitative imaging biomarkers from WSIs directly for the prediction of disease-free survival (DFS) in stage I-III melanoma patients is crucial to optimize patient management. In this study, we designed a deep learning-based model with the aim of learning prognostic biomarkers from WSIs to predict 1-year DFS in cutaneous melanoma patients. First, WSIs referred to a cohort of 43 patients (31 DF cases, 12 non-DF cases) from the Clinical Proteomic Tumor Analysis Consortium Cutaneous Melanoma (CPTAC-CM) public database were firstly annotated by our expert pathologists and then automatically split into crops, which were later employed to train and validate the proposed model using a fivefold cross-validation scheme for 5 rounds. Then, the model was further validated on WSIs related to an independent test, i.e. a validation cohort of 11 melanoma patients (8 DF cases, 3 non-DF cases), whose data were collected from Istituto Tumori 'Giovanni Paolo II' in Bari, Italy. The quantitative imaging biomarkers extracted by the proposed model showed prognostic power, achieving a median AUC value of 69.5% and a median accuracy of 72.7% on the public cohort of patients. These results remained comparable on the validation cohort of patients with an AUC value of 66.7% and an accuracy value of 72.7%, respectively. This work is contributing to the recently undertaken investigation on how treat features extracted from raw WSIs to fulfil prognostic tasks involving melanoma patients. The promising results make this study as a valuable basis for future research investigation on wider cohorts of patients referred to our Institute.

Sentinel Lymph Node Gene Expression Signature Predicts Recurrence-Free Survival in Cutaneous Melanoma

We sought to develop a sentinel lymph node gene expression signature score predictive of disease recurrence in patients with cutaneous melanoma. Gene expression profiling was performed on SLN biopsies using U133A 2.0 Affymetrix gene chips. The top 25 genes associated with recurrence-free survival (RFS) were selected and a penalized regression function was used to select 12 genes with a non-zero coefficient. A proportional hazards regression model was used to evaluate the association between clinical covariates, gene signature score, and RFS. Among the 45 patients evaluated, 23 (51%) had a positive SLN. Twenty-one (46.7%) patients developed disease recurrence. For the top 25 differentially expressed genes (DEG), 12 non-zero penalized coefficients were estimated (CLGN, C1QTNF3, ADORA3, ARHGAP8, DCTN1, ASPSCR1, CHRFAM7A, ZNF223, PDE6G, CXCL3, HEXIM1, HLA-DRB). This 12-gene signature score was significantly associated with RFS (p < 0.0001) and produced a bootstrap C index of 0.888. In univariate analysis, Breslow thickness, presence of primary tumor ulceration, SLN positivity were each significantly associated with RFS. After simultaneously adjusting for these prognostic factors in relation to the gene signature, the 12-gene score remained a significant independent predictor for RFS (p < 0.0001). This SLN 12-gene signature risk score is associated with melanoma recurrence regardless of SLN status and may be used as a prognostic factor for RFS.

Prognostic biomarkers of cutaneous melanoma

BACKGROUND/PURPOSE

Melanomas account for only approximately 4% of diagnosed skin cancers in the United States but are responsible for the majority of deaths caused by skin cancer. Both genetic factors and ultraviolet (UV) radiation exposure play a role in the development of melanoma. Although melanomas have a strong propensity to metastasize when diagnosed late, melanomas that are diagnosed and treated early pose a low mortality risk. In particular, the identification of patients with increased metastatic risk, who may benefit from early adjuvant therapies, is crucial, especially given the advent of new melanoma treatments. However, the accuracy of classic clinical and histological variables, including the Breslow thickness, presence of ulceration, and lymph node status, might not be sufficient to identify such individuals. Thus, there is a need for the development of additional prognostic melanoma biomarkers that can improve early attempts to stratify melanoma patients and reliably identify high-risk subgroups with the aim of providing effective personalized therapies.

METHODS

In our current work, we discuss and assess emerging primary melanoma tumor biomarkers and prognostic circulating biomarkers.

RESULTS

Several promising biomarkers show prognostic value (eg, exosomal MIA (ie, melanoma inhibitory activity), serum S100B, AMLo signatures, and mRNA signatures); however, the scarcity of reliable data precludes the use of these biomarkers in current clinical applications.

CONCLUSION

Further research is needed on several promising biomarkers for melanoma. Large-scale studies are warranted to facilitate the clinical translation of prognostic biomarker applications for melanoma in personalized medicine.

Molecular Pathology of Skin Melanoma: Epidemiology, Differential Diagnostics, Prognosis and Therapy Prediction

Similar to other malignancies, TCGA network efforts identified the detailed genomic picture of skin melanoma, laying down the basis of molecular classification. On the other hand, genome-wide association studies discovered the genetic background of the hereditary melanomas and the susceptibility genes. These genetic studies helped to fine-tune the differential diagnostics of malignant melanocytic lesions, using either FISH tests or the myPath gene expression signature. Although the original genomic studies on skin melanoma were mostly based on primary tumors, data started to accumulate on the genetic diversity of the progressing disease. The prognostication of skin melanoma is still based on staging but can be completed with gene expression analysis (DecisionDx). Meanwhile, this genetic knowledge base of skin melanoma did not turn to the expected wide array of target therapies, except the BRAF inhibitors. The major breakthrough of melanoma therapy was the introduction of immune checkpoint inhibitors, which showed outstanding efficacy in skin melanoma, probably due to their high immunogenicity. Unfortunately, beyond BRAF, KIT mutations and tumor mutation burden, no clinically validated predictive markers exist in melanoma, although several promising biomarkers have been described, such as the expression of immune-related genes or mutations in the IFN-signaling pathway. After the initial success of either target or immunotherapies, sooner or later, relapses occur in the majority of patients, due to various induced genetic alterations, the diagnosis of which could be developed to novel predictive genetic markers.

Culture shock: microglial heterogeneity, activation, and disrupted single-cell microglial networks in vitro

BACKGROUND

Microglia, the resident immune cells of the brain, play a critical role in numerous diseases, but are a minority cell type and difficult to genetically manipulate in vivo with viral vectors and other approaches. Primary cultures allow a more controlled setting to investigate these cells, but morphological and transcriptional changes upon removal from their normal brain environment raise many caveats from in vitro studies.

METHODS

To investigate whether cultured microglia recapitulate in vivo microglial signatures, we used single-cell RNA sequencing (scRNAseq) to compare microglia freshly isolated from the brain to primary microglial cultures. We performed cell population discovery, differential expression analysis, and gene co-expression module analysis to compare signatures between in vitro and in vivo microglia. We constructed causal predictive network models of transcriptional regulators from the scRNAseq data and identified a set of potential key drivers of the cultured phenotype. To validate this network analysis, we knocked down two of these key drivers, C1qc and Prdx1, in primary cultured microglia and quantified changes in microglial activation markers.

RESULTS

We found that, although often assumed to be a relatively homogenous population of cells in culture, in vitro microglia are a highly heterogeneous population consisting of distinct subpopulations of cells with transcriptional profiles reminiscent of macrophages and monocytes, and are marked by transcriptional programs active in neurodegeneration and other disease states. We found that microglia in vitro presented transcriptional activation of a set of "culture shock genes" not found in freshly isolated microglia, characterized by strong upregulation of disease-associated genes including Apoe, Lyz2, and Spp1, and downregulation of homeostatic microglial markers, including Cx3cr1, P2ry12, and Tmem119. Finally, we found that cultured microglia prominently alter their transcriptional machinery modulated by key drivers from the homeostatic to activated phenotype. Knockdown of one of these drivers, C1qc, resulted in downregulation of microglial activation genes Lpl, Lyz2, and Ccl4.

CONCLUSIONS

Overall, our data suggest that when removed from their in vivo home environment, microglia suffer a severe case of "culture shock", drastically modulating their transcriptional regulatory network state from homeostatic to activated through upregulation of modules of culture-specific genes. Consequently, cultured microglia behave as a disparate cell type that does not recapitulate the homeostatic signatures of microglia in vivo. Finally, our predictive network model discovered potential key drivers that may convert activated microglia back to their homeostatic state, allowing for more accurate representation of in vivo states in culture. Knockdown of key driver C1qc partially attenuated microglial activation in vitro, despite C1qc being only weakly upregulated in culture. This suggests that even genes that are not strongly differentially expressed across treatments or preparations may drive downstream transcriptional changes in culture.

New Prognostic Biomarkers and Drug Targets for Skin Cutaneous Melanoma via Comprehensive Bioinformatic Analysis and Validation

Skin cutaneous melanoma (SKCM) is the most aggressive and fatal type of skin cancer. Its highly heterogeneous features make personalized treatments difficult, so there is an urgent need to identify markers for early diagnosis and therapy. Detailed profiles are useful for assessing malignancy potential and treatment in various cancers. In this study, we constructed a co-expression module using expression data for cutaneous melanoma. A weighted gene co-expression network analysis was used to discover a co-expression gene module for the pathogenesis of this disease, followed by a comprehensive bioinformatics analysis of selected hub genes. A connectivity map (CMap) was used to predict drugs for the treatment of SKCM based on hub genes, and immunohistochemical (IHC) staining was performed to validate the protein levels. After discovering a co-expression gene module for the pathogenesis of this disease, we combined GWAS validation and DEG analysis to identify 10 hub genes in the most relevant module. Survival curves indicated that eight hub genes were significantly and negatively associated with overall survival. A total of eight hub genes were positively correlated with SKCM tumor purity, and 10 hub genes were negatively correlated with the infiltration level of CD4+ T cells and B cells. Methylation levels of seven hub genes in stage 2 SKCM were significantly lower than those in stage 3. We also analyzed the isomer expression levels of 10 hub genes to explore the therapeutic target value of 10 hub genes in terms of alternative splicing (AS). All 10 hub genes had mutations in skin tissue. Furthermore, CMap analysis identified cefamandole, ursolic acid, podophyllotoxin, and Gly-His-Lys as four targeted therapy drugs that may be effective treatments for SKCM. Finally, IHC staining results showed that all 10 molecules were highly expressed in melanoma specimens compared to normal samples. These findings provide new insights into SKCM pathogenesis based on multi-omics profiles of key prognostic biomarkers and drug targets. GPR143 and SLC45A2 may serve as drug targets for immunotherapy and prognostic biomarkers for SKCM. This study identified four drugs with significant potential in treating SKCM patients.

Transcriptomic analysis identifies differences in gene expression in actinic keratoses after treatment with imiquimod and between responders and non responders

The presence of actinic keratoses (AKs) increases a patient’s risk of developing squamous cell carcinoma by greater than six-fold. We evaluated the effect of topical treatment with imiquimod on the tumor microenvironment by measuring transcriptomic differences in AKs before and after treatment with imiquimod 3.75%. Biopsies were collected prospectively from 21 patients and examined histologically. RNA was extracted and transcriptomic analyses of 788 genes were performed using the nanoString assay. Imiquimod decreased number of AKs by study endpoint at week 14 (p < 0.0001). Post-imiquimod therapy, levels of CDK1, CXCL13, IL1B, GADPH, TTK, ILF3, EWSR1, BIRC5, PLAUR, ISG20, and C1QBP were significantly lower (adjusted p < 0.05). Complete responders (CR) exhibited a distinct pattern of inflammatory gene expression pre-treatment relative to incomplete responders (IR), with alterations in 15 inflammatory pathways (p < 0.05) reflecting differential expression of 103 genes (p < 0.05). Presence of adverse effects was associated with improved treatment response. Differences in gene expression were found between pre-treatment samples in CR versus IR, suggesting that higher levels of inflammation pre-treament may play a part in regression of AKs. Further characterization of the immune micro-environment in AKs may help develop biomarkers predictive of response to topical immune modulators and may guide therapy.

Automated digital TIL analysis (ADTA) adds prognostic value to standard assessment of depth and ulceration in primary melanoma

Accurate prognostic biomarkers in early-stage melanoma are urgently needed to stratify patients for clinical trials of adjuvant therapy. We applied a previously developed open source deep learning algorithm to detect tumor-infiltrating lymphocytes (TILs) in hematoxylin and eosin (H&E) images of early-stage melanomas. We tested whether automated digital (TIL) analysis (ADTA) improved accuracy of prediction of disease specific survival (DSS) based on current pathology standards. ADTA was applied to a training cohort (n = 80) and a cutoff value was defined based on a Receiver Operating Curve. ADTA was then applied to a validation cohort (n = 145) and the previously determined cutoff value was used to stratify high and low risk patients, as demonstrated by Kaplan–Meier analysis (p ≤ 0.001). Multivariable Cox proportional hazards analysis was performed using ADTA, depth, and ulceration as co-variables and showed that ADTA contributed to DSS prediction (HR: 4.18, CI 1.51–11.58, p = 0.006). ADTA provides an effective and attainable assessment of TILs and should be further evaluated in larger studies for inclusion in staging algorithms.

Prognostic significance of an 11-gene RNA assay in archival tissue of cutaneous melanoma stage I-III patients

PURPOSE

The purpose of this study was to validate the results of an 11-gene expression profiling (GEP) assay which aims to improve the precision of individual prognosis beyond conventional American Joint Committee on Cancer staging for patients with cutaneous melanoma.

METHODS

The reverse transcriptase polymerase chain reaction test of 11 prospectively selected genes was performed on 291 formalin-fixed, paraffin-embedded primary tumours of patients with stage I-III cutaneous melanoma. The expression levels of eight prognostic and three reference genes were used in a predefined algorithm to calculate a numerical score (-0.84 to 3.53) and then assign each patient to a preselected risk group (low versus high score) for melanoma-specific survival (MSS).

RESULTS

One hundred twenty-seven patients were allocated to the low-score group, with a corresponding five-year disease-free survival (DFS) and MSS of 95% and 99%, respectively. 164 patients were allocated to the high-score group, with a corresponding five-year DFS and MSS of 78% and 88%. Continuous regression analysis demonstrated decreasing MSS probabilities with increasing scores. In a multivariate cox regression, only the 11-GEP, tumour thickness and age were statistically associated with MSS (p = 0.0068, 0.002 and 0.0159).

CONCLUSIONS

The 11-GEP has been validated as an independent predictor of outcome for melanoma patients. More specifically, using an 11-GEP score cut-off of ≤0, the assay can identify patient cohorts with 10-year survival probabilities well above 90%. This information may be used in the decision-making for a potential adjuvant therapy.

Predictive network modeling in human induced pluripotent stem cells identifies key driver genes for insulin responsiveness

Insulin resistance (IR) precedes the development of type 2 diabetes (T2D) and increases cardiovascular disease risk. Although genome wide association studies (GWAS) have uncovered new loci associated with T2D, their contribution to explain the mechanisms leading to decreased insulin sensitivity has been very limited. Thus, new approaches are necessary to explore the genetic architecture of insulin resistance. To that end, we generated an iPSC library across the spectrum of insulin sensitivity in humans. RNA-seq based analysis of 310 induced pluripotent stem cell (iPSC) clones derived from 100 individuals allowed us to identify differentially expressed genes between insulin resistant and sensitive iPSC lines. Analysis of the co-expression architecture uncovered several insulin sensitivity-relevant gene sub-networks, and predictive network modeling identified a set of key driver genes that regulate these co-expression modules. Functional validation in human adipocytes and skeletal muscle cells (SKMCs) confirmed the relevance of the key driver candidate genes for insulin responsiveness.

Insulin resistance is characterized by a defective response (“resistance”) to normal insulin concentrations to uptake the glucose present in the blood, and is the underlying condition that leads to type 2 diabetes (T2D) and increases the risk of cardiovascular disease. It is estimated that 25–33% of the US population are insulin resistant enough to be at risk of serious clinical consequences. For more than a decade, large population studies have tried to discover the genes that participate in the development of insulin resistance, but without much success. It is now increasingly clear that the complex genetic nature of insulin resistance requires novel approaches centered in patient specific cellular models. To fill this gap, we have generated an induced pluripotent stem cell (iPSC) library from individuals with accurate measurements of insulin sensitivity, and performed gene expression and key driver analyses. Our work demonstrates that iPSCs can be used as a revolutionary technology to model insulin resistance and to discover key genetic drivers. Moreover, they can develop our basic knowledge of the disease, and are ultimately expected to increase the therapeutic targets to treat insulin resistance and type 2 diabetes.

Risk stratification of cutaneous melanoma reveals carcinogen metabolism enrichment and immune inhibition in high-risk patients

Cutaneous melanoma (CM) is the most lethal form of skin cancer. Risk assessment should facilitate stratified surveillance and guide treatment selection. Here, based on the mRNA-seq data from 419 CM patients in the Cancer Genome Atlas (TCGA), we developed a prognostic 21-gene signature to distinguish the outcomes of high- and low-risk patients, which was further validated in two external cohorts. The signature achieved a higher C-index as compared with other known biomarkers and clinical characteristics in both the TCGA and validation cohorts. Notably, in high-risk patients the expression levels of three driver genes, BRAF, NRAS, and NF1 in the MAPK pathway, were lower but exhibited a stronger positive correlation as compared with low-risk patients. Moreover, the genes involved in nicotinamide adenine dinucleotide metabolism were negatively correlated with the expression of BRAF in the high-risk group. Function analysis revealed that the upregulated genes in the high-risk group were enriched in the cytochrome P450-mediated metabolism of chemical carcinogens. Furthermore, the low-risk group had high levels of gamma delta T cells infiltration, while regulatory T cells were accumulated in the high-risk group. The present study offers a promising new prognostic signature for CM, and provides insight into the mechanisms of melanoma progression.

Risk prediction in cutaneous melanoma patients from their clinico-pathological features: superiority of clinical data over gene expression data

Risk assessment in cutaneous melanoma (CM) patients is one of the major challenges in the effective treatment of CM patients. Traditionally, clinico-pathological features such as Breslow thickness, American Joint Committee on Cancer (AJCC) tumor staging, etc. are utilized for this purpose. However, due to advancements in technology, most of the upcoming risk prediction methods are gene-expression profile (GEP) based. In this study, we have tried to develop new GEP and clinico-pathological features-based biomarkers and assessed their prognostic strength in contrast to existing prognostic methods. We developed risk prediction models using the expression of the genes associated with different cancer-related pathways and got a maximum hazard ratio (HR) of 2.52 with p-value ~10-8 for the apoptotic pathway. Another model, based on combination of apoptotic and notch pathway genes boosted the HR to 2.57. Next, we developed models based on individual clinical features and got a maximum HR of 2.45 with p-value ~10-6 for Breslow thickness. We also developed models using the best features of clinical as well as gene-expression data and obtained a maximum HR of 3.19 with p-value ~10-9. Finally, we developed a new ensemble method using clinical variables only and got a maximum HR of 6.40 with p-value ~10-15. Based on this method, a web-based service and an android application named 'CMcrpred' is available at (https://webs.iiitd.edu.in/raghava/cmcrpred/) and Google Play Store respectively to facilitate scientific community. This study reveals that our new ensemble method based on only clinico-pathological features overperforms methods based on GEP based profiles as well as currently used AJCC staging. It also highlights the need to explore the full potential of clinical variables for prognostication of cancer patients.

Linking Transcriptomic and Imaging Data Defines Features of a Favorable Tumor Immune Microenvironment and Identifies a Combination Biomarker for Primary Melanoma

Patients with resected stage II-III melanoma have approximately a 35% chance of death from their disease. A deeper understanding of the tumor immune microenvironment (TIME) is required to stratify patients and identify factors leading to therapy resistance. We previously identified that the melanoma immune profile (MIP), an IFN-based gene signature, and the ratio of CD8+ cytotoxic T lymphocytes (CTL) to CD68+ macrophages both predict disease-specific survival (DSS). Here, we compared primary with metastatic tumors and found that the nuclei of tumor cells were significantly larger in metastases. The CTL/macrophage ratio was significantly different between primary tumors without distant metastatic recurrence (DMR) and metastases. Patients without DMR had higher degrees of clustering between tumor cells and CTLs, and between tumor cells and HLA-DR+ macrophages, but not HLA-DR- macrophages. The HLA-DR- subset coexpressed CD163+CSF1R+ at higher levels than CD68+HLA-DR+ macrophages, consistent with an M2 phenotype. Finally, combined transcriptomic and multiplex data revealed that densities of CD8 and M1 macrophages correlated with their respective cell phenotype signatures. Combination of the MIP signature with the CTL/macrophage ratio stratified patients into three risk groups that were predictive of DSS, highlighting the potential use of combination biomarkers for adjuvant therapy. SIGNIFICANCE: These findings provide a deeper understanding of the tumor immune microenvironment by combining multiple modalities to stratify patients into risk groups, a critical step to improving the management of patients with melanoma.

Adjuvant Treatment of Melanoma: Recent Developments and Future Perspectives

Surgical excision is the treatment of choice for early stage melanoma, and this strategy is initially curative for the vast majority of patients. However, only approximately 40-60% of high-risk patients who undergo surgery alone will be disease-free at 5 years. These patients will ultimately experience loco-regional relapse or relapse at distant sites. The main aim of adjuvant therapies is to reduce the recurrence rate of radically operated patients at high risk and to potentially improve survival. Recent practice changing results with immune checkpoint inhibitors and targeted therapies have been published in stage III/IV melanoma patients, after surgical complete resection, and have dramatically improved the landscape of adjuvant therapy. Interferon-α, ipilimumab, and more recently anti-programmed cell death protein-1 antibodies and BRAF inhibitors plus MEK inhibitors have been approved in the adjuvant setting by the US Food and Drug Administration; similarly, the same drugs are approved by the European Medicines Agency with the exception of ipilimumab. A completely new scenario is emerging in the neoadjuvant setting as well: in locally advanced or metastatic disease, patients may partially respond to neoadjuvant therapy and become virtually resectable with systemic control of disease. This review summarizes the current state of the field and describes new strategies tracing the history of adjuvant therapy in melanoma, with a view on future projects.

A Th1/IFNγ Gene Signature Is Prognostic in the Adjuvant Setting of Resectable High-Risk Melanoma but Not in Non-Small Cell Lung Cancer

PURPOSE

Immune components of the tumor microenvironment (TME) have been associated with disease outcome. We prospectively evaluated the association of an immune-related gene signature (GS) with clinical outcome in melanoma and non-small cell lung cancer (NSCLC) tumor samples from two phase III studies.

EXPERIMENTAL DESIGN

The GS was prospectively validated using an adaptive signature design to optimize it for the sample type and technology used in phase III studies. One-third of the samples were used as "training set"; the remaining two thirds, constituting the "test set," were used for the prospective validation of the GS.

RESULTS

In the melanoma training set, the expression level of eight Th1/IFNγ-related genes in tumor-positive lymph node tissue predicted the duration of disease-free survival (DFS) and overall survival (OS) in the placebo arm. This GS was prospectively and independently validated as prognostic in the test set. Building a multivariate Cox model in the test set placebo patients from clinical covariates and the GS score, an increased number of melanoma-involved lymph nodes and the GS were associated with DFS and OS. This GS was not associated with DFS in NSCLC, although expression of the Th1/IFNγ-related genes was associated with the presence of lymphocytes in tumor samples in both indications.

CONCLUSIONS

These findings provide evidence that expression of Th1/IFNγ genes in the TME, as measured by this GS, is associated with clinical outcome in melanoma. This suggests that, using this GS, patients with stage IIIB/C melanoma can be classified into different risk groups.

Health-related quality of life in patients with fully resected BRAFV600 mutation-positive melanoma receiving adjuvant vemurafenib

AIM OF STUDY

The aim of the study was to assess the impact of treatment with adjuvant vemurafenib monotherapy on health-related quality of life (HRQOL) in patients with resected stage IIC-IIIC melanoma.

METHODS

The phase 3 BRIM8 study (NCT01667419) randomised patients with BRAF^V600 mutation-positive resected stage IIC-IIIC melanoma to 960 mg of vemurafenib twice daily or matching placebo for 52 weeks (13 × 28-day cycles). Patients completed the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30) version 3 at baseline, cycle 1 (days 1, 15 and 22), cycle 2 (days 1 and 15), day 1 of every subsequent 4-week cycle, the end-of-treatment visit and each visit during the follow-up period.

RESULTS

Completion rates for the EORTC QLQ-C30 questionnaire were high (>80%). There was a mean decline in the global health status (GHS)/quality of life (QOL) score of 17.4 (±22.9) and 17.3 (±24.1) points at days 15 and 22 of cycle 1, respectively, among vemurafenib-treated patients who recovered to approximately 10 points below baseline for the remainder of the treatment period. A similar trend was observed in all functional scales except for cognitive function (<10-point change from baseline at all visits) and in the symptom scores for appetite loss, fatigue and pain. As observed for the GHS/QOL score, all scores rapidly returned to baseline after completion of planned vemurafenib treatment or treatment discontinuation.

CONCLUSIONS

The schedule of HRQOL assessments allowed for an accurate and complete evaluation of the impact of acute treatment-related symptoms. Vemurafenib-treated patients experience clinically meaningful moderate worsening in some treatment- or disease-related symptoms and GHS/QOL that resolve over time.

Deep Learning Based on Standard H&E Images of Primary Melanoma Tumors Identifies Patients at Risk for Visceral Recurrence and Death

PURPOSE

Biomarkers for disease-specific survival (DSS) in early-stage melanoma are needed to select patients for adjuvant immunotherapy and accelerate clinical trial design. We present a pathology-based computational method using a deep neural network architecture for DSS prediction.

EXPERIMENTAL DESIGN

The model was trained on 108 patients from four institutions and tested on 104 patients from Yale School of Medicine (YSM, New Haven, CT). A receiver operating characteristic (ROC) curve was generated on the basis of vote aggregation of individual image sequences, an optimized cutoff was selected, and the computational model was tested on a third independent population of 51 patients from Geisinger Health Systems (GHS).

RESULTS

Area under the curve (AUC) in the YSM patients was 0.905 (P < 0.0001). AUC in the GHS patients was 0.880 (P < 0.0001). Using the cutoff selected in the YSM cohort, the computational model predicted DSS in the GHS cohort based on Kaplan-Meier (KM) analysis (P < 0.0001).

CONCLUSIONS

The novel method presented is applicable to digital images, obviating the need for sample shipment and manipulation and representing a practical advance over current genetic and IHC-based methods.

Validation of Melanoma Immune Profile (MIP), a Prognostic Immune Gene Prediction Score for Stage II-III Melanoma

PURPOSE

Biomarkers are needed to stratify patients with stage II-III melanoma for clinical trials of adjuvant therapy because, while immunotherapy is protective, it also confers the risk of severe toxicity. We previously defined and validated a 53-immune gene melanoma immune profile (MIP) predictive both of distant metastatic recurrence and of disease-specific survival (DSS). Here, we test MIP on a third independent population.

EXPERIMENTAL DESIGN

A retrospective cohort of 78 patients with stage II-III primary melanoma was analyzed using the NanoString assay to measure expression of 53 target genes, and MIP score was calculated. Statistical analysis correlating MIP with DSS, overall survival, distant metastatic recurrence, and distant metastasis-free interval was performed using ROC curves, Kaplan-Meier curves, and standard univariable and multivariable Cox proportional hazards models.

RESULTS

MIP significantly distinguished patients with distant metastatic recurrence from those without distant metastatic recurrence using ROC curve analysis (AUC = 0.695; P = 0.008). We defined high- and low-risk groups based on the cutoff defined by this ROC curve and find that MIP correlates with both DSS and overall survival by ROC curve analysis (AUC = 0.719; P = 0.004 and AUC = 0.698; P = 0.004, respectively). Univariable Cox regression reveals that a high-risk MIP score correlates with DSS (P = 0.015; HR = 3.2).

CONCLUSIONS

MIP identifies patients with low risk of death from melanoma and may constitute a clinical tool to stratify patients with stage II-III melanoma for enrollment in clinical trials.

Identification of risk in cutaneous melanoma patients: Prognostic and predictive markers

New therapeutic modalities for melanoma promise benefit in selected individuals. Efficacy appears greater in patients with lower tumor burden, suggesting an important role for risk-stratified surveillance. Robust predictive markers might permit optimization of agent to patient, while low-risk prognostic markers might guide more conservative management. This review evaluates protein, gene, and multiplexed marker panels that may contribute to better risk assessment and improved management of patients with cutaneous melanoma.

Identification of patients at risk of metastasis using a prognostic 31-gene expression profile in subpopulations of melanoma patients with favorable outcomes by standard criteria

BACKGROUND

A substantial number of patients who relapse and die from cutaneous melanoma (CM) are categorized as being at low risk by traditional staging factors. The 31-gene expression profile (31-GEP) test independently stratifies metastatic risk of patients with CM as low (Class 1, with 1A indicating lowest risk) or high (Class 2,with 2B indicating highest risk).

OBJECTIVE

To assess risk prediction by the 31-GEP test within 3 low-risk (according to the American Joint Committee on Cancer) populations of patients with CM: those who are sentinel lymph node (SLN) negative, those with stage I to IIA tumors, and those with thin (≤1 mm [T1]) tumors.

METHODS

A total of 3 previous validation studies provided a nonoverlapping cohort of 690 patients with 31-GEP results, staging information, and survival outcomes. Kaplan-Meier and Cox regression analysis were performed.

RESULTS

The results included the identification of 70% of SLN-negative patients who experienced metastasis as Class 2, the discovery of reduced recurrence-free survival for patients with thin tumors and Class 2B biology compared with that of those with Class 1A biology (P < .0001); and determination of the 31-GEP test as an independent predictor of risk compared with traditional staging factors in patients with stage I to IIA tumors.

LIMITATIONS

Diagnoses spanned multiple versions of pathologic staging criteria.

CONCLUSIONS

The 31-GEP test identifies high-risk patients who are likely to experience recurrence or die of melanoma within low-risk groups of subpopulations of patients with CM who have SLN-negative disease, stage I to IIA tumors, and thin tumors.

IKZF1 Enhances Immune Infiltrate Recruitment in Solid Tumors and Susceptibility to Immunotherapy

Immunotherapies are some of the most promising emergent treatments for several cancers, yet there remains a majority of patients who do not benefit from them due to immune-resistant tumors. One avenue for enhancing treatment for these patients is by converting these tumors to an immunoreactive state, thereby restoring treatment efficacy. By leveraging regulatory networks we previously characterized in autoimmunity, here we show that overexpression of the master regulator IKZF1 leads to enhanced immune infiltrate recruitment and tumor sensitivity to PD1 and CTLA4 inhibitors in several tumors that normally lack IKZF1 expression. This work provides proof of concept that tumors can be rendered susceptible by hijacking immune cell recruitment signals through molecular master regulators. On a broader scale, this work also demonstrates the feasibility of using computational approaches to drive the discovery of novel molecular mechanisms toward treatment.

Pan-cancer adaptive immune resistance as defined by the Tumor Inflammation Signature (TIS): results from The Cancer Genome Atlas (TCGA)

The Tumor Inflammation Signature (TIS) is an investigational use only (IUO) 18-gene signature that measures a pre-existing but suppressed adaptive immune response within tumors. The TIS has been shown to enrich for patients who respond to the anti-PD1 agent pembrolizumab. To explore this immune phenotype within and across tumor types, we applied the TIS algorithm to over 9000 tumor gene expression profiles downloaded from The Cancer Genome Atlas (TCGA). As expected based on prior evidence, tumors with known clinical sensitivity to anti-programmed cell death protein 1 (PD-1) blockade had higher average TIS scores. Furthermore, TIS scores were more variable within than between tumor types, and within each tumor type a subset of patients with elevated scores was identifiable although with different prevalence associated with each tumor type, the latter consistent with the observed clinical responsiveness to anti PD-1 blockade. Notably, TIS scores only minimally correlated with mutation load in most tumors and ranking tumors by median TIS score showed differing association to clinical sensitivity to PD-1/PD-1 ligand 1 (PD-L1) blockade than ranking of the same tumors by mutation load. The expression patterns of the TIS algorithm genes were conserved across tumor types yet appeared to be minimally prognostic in most cancers, consistent with the TIS score serving as a pan-cancer measurement of the inflamed tumor phenotype. Characterization of the prevalence and variability of TIS will lead to increased understanding of the immune status of untreated tumors and may lead to improved indication selection for testing immunotherapy agents.

Altered Immune-Related Gene Expressions Indicate Oral Cancer Nodal Disease

Lymph nodal disease (LN+) is the most significant prognostic factor of oral squamous cell carcinoma (OSCC). Current risk indicator(s) for guiding elective neck dissection (END) is insufficient for clinically node-negative (cN0) patients, resulting in under- or overtreatment. While the role of immunological events in tumorigenesis and metastasis is evident, the prognostic implication in OSCC remains unclear. The study objective was to investigate large-scale immune-related gene expression and determine its prognostic value on node-free survival (NFS). We analyzed patients who received intent-to-cure surgery with at least 3 y of follow-up and known outcome of LN through a pan-Canadian surgical trial. Total RNA was extracted from surgical tissues with >70% tumor content and analyzed on a 730-gene panel (NanoString nCounter® PanCancer Immune Panel). We first profiled gene expression in a fresh-frozen (FF) discovery set to identify differentially expressed (DE) genes, which were then used in unsupervised clustering analysis to identify patient subgroups. The prognostic value of the identified DE genes was then validated on formalin-fixed, paraffin-embedded (FFPE) samples. A total of 177 RNA samples were derived from 89 FF and 88 FFPE surgical tissues, of which 45 (51%) and 40 (45%), respectively, were from patients who developed LN+. We identified 6 DE genes overexpressed in LN+ tumors (false discovery rate <0.001; log2 fold change >1). Clustering analysis separated the patients into 2 subgroups (CM1, CM2), with CM2 exhibiting significantly increased expression and worse 5-y NFS rate (28%; P < 0.001). The prognostic value of these 6 candidate genes was validated on FFPE samples, which were also separated into 2 distinct prognostic groups, confirming the association between increased gene expression and poor 5-y NFS (CM1, 70.3%; CM2, 43.3%; P = 0.01). This is the first study identifying a panel of immune-related genes associated with NFS that can potentially be used clinically stratifying the risk of LN+ at the time of OSCC diagnosis.

Quantitative Analysis of Immune Infiltrates in Primary Melanoma

Novel methods to analyze the tumor microenvironment (TME) are urgently needed to stratify melanoma patients for adjuvant immunotherapy. Tumor-infiltrating lymphocyte (TIL) analysis, by conventional pathologic methods, is predictive but is insufficiently precise for clinical application. Quantitative multiplex immunofluorescence (qmIF) allows for evaluation of the TME using multiparameter phenotyping, tissue segmentation, and quantitative spatial analysis (qSA). Given that CD3⁺CD8⁺ cytotoxic lymphocytes (CTLs) promote antitumor immunity, whereas CD68⁺ macrophages impair immunity, we hypothesized that quantification and spatial analysis of macrophages and CTLs would correlate with clinical outcome. We applied qmIF to 104 primary stage II to III melanoma tumors and found that CTLs were closer in proximity to activated (CD68⁺HLA-DR⁺) macrophages than nonactivated (CD68⁺HLA-DR^-) macrophages (P < 0.0001). CTLs were further in proximity from proliferating SOX10⁺ melanoma cells than nonproliferating ones (P < 0.0001). In 64 patients with known cause of death, we found that high CTL and low macrophage density in the stroma (P = 0.0038 and P = 0.0006, respectively) correlated with disease-specific survival (DSS), but the correlation was less significant for CTL and macrophage density in the tumor (P = 0.0147 and P = 0.0426, respectively). DSS correlation was strongest for stromal HLA-DR⁺ CTLs (P = 0.0005). CTL distance to HLA-DR^- macrophages associated with poor DSS (P = 0.0016), whereas distance to Ki67^- tumor cells associated inversely with DSS (P = 0.0006). A low CTL/macrophage ratio in the stroma conferred a hazard ratio (HR) of 3.719 for death from melanoma and correlated with shortened overall survival (OS) in the complete 104 patient cohort by Cox analysis (P = 0.009) and merits further development as a biomarker for clinical application. Cancer Immunol Res; 6(4); 481-93. ©2018 AACR.

Cell-of-origin of diffuse large B-cell lymphomas determined by the Lymph2Cx assay: better prognostic indicator than Hans algorithm

Diffuse large B-cell lymphomas (DLBCLs) are clinically heterogeneous and need a biomarker that can predict the outcome of treatments accurately. To assess the prognostic significance of the cell-of-origin type for DLBCLs, we applied the Lymph2Cx assay using a NanoString gene expression platform on formalin-fixed paraffin wax-embedded pretreatment tissues obtained from 82 patients with de novo DLBCL, not otherwise specified. All patients were treated with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) as the first line of chemotherapy. Based on the expression levels of Bcl-6, CD10, and MUM-1 measured by immunohistochemistry, cases were subdivided into germinal center B-cell (GCB) and non-GCB types according to the Hans algorithm. NanoString assay was performed on 82 cases. The Lymph2Cx assay successfully classified 82 cases into three categories: activated B-cell (ABC), GCB, and unclassified types. The concordance rate between the Lymph2Cx assay and the Hans algorithm was 73.6%. The Lymph2Cx-defined ABC type had significantly poorer outcomes compared with the GCB type (5-year overall survival, GCB vs. ABC, 96.6% vs. 77.1%, P = 0.020; 5-year disease-free survival, GCB vs. ABC, 96.6% vs. 79.2%, P = 0.018). In contrast, no significant differences were observed in survival between the two patient subgroups with DLBCL types classified by the Hans algorithm. The Lymph2Cx assay is a robust, reliable method for predicting the outcome of patients with DLBCL treated with R-CHOP chemotherapy.

Addressing the challenges of applying precision oncology

Precision oncology is described as the matching of the most accurate and effective treatments with the individual cancer patient. Identification of important gene mutations, such as BRCA1/2 that drive carcinogenesis, helped pave the way for precision diagnosis in cancer. Oncoproteins and their signaling pathways have been extensively studied, leading to the development of target-based precision therapies against several types of cancers. Although many challenges exist that could hinder the success of precision oncology, cutting-edge tools for precision diagnosis and precision therapy will assist in overcoming many of these difficulties. Based on the continued rapid progression of genomic analysis, drug development, and clinical trial design, precision oncology will ultimately become the standard of care in cancer therapeutics.

Immune cell profiling in cancer: molecular approaches to cell-specific identification

The immune system has many important regulatory roles in cancer development and progression. Given the emergence of effective immune therapies against many cancers, reliable predictors of response are needed. One method of determining response is by evaluating immune cell populations from treated and untreated tumor samples. The amount of material obtained from tumor biopsies can be limited; therefore, gene-based or protein-based analyses may be attractive because they require minimal tissue. Cell-specific signatures are being analyzed with use of the latest technologies, including NanoString’s nCounter technology, intracellular staining flow cytometry, cytometry by time-of-flight, RNA-Seq, and barcoding antibody-based protein arrays. These signatures provide information about the contributions of specific types of immune cells to bulk tumor samples. To date, both tumor tissue and immune cells have been analyzed for molecular expression profiles that can assess genes and proteins that are specific to immune cells, yielding results of varying specificity. Here, we discuss the importance of profiling tumor tissue and immune cells to identify immune-cell-associated genes and proteins and specific gene profiles of immune cells. We also discuss the use of these signatures in cancer treatment and the challenges faced in molecular expression profiling of immune cell populations.

Underexpression of Specific Interferon Genes Is Associated with Poor Prognosis of Melanoma

Because the prognosis of melanoma is challenging and inaccurate when using current clinical approaches, clinicians are seeking more accurate molecular markers to improve risk models. Accordingly, we performed a survival analysis on 404 samples from The Cancer Genome Atlas (TCGA) cohort of skin cutaneous melanoma. Using our recently developed gene network model, we identified biological signatures that confidently predict the prognosis of melanoma (p-value < 10-5). Our model predicted 38 cases as low-risk and 54 cases as high-risk. The probability of surviving at least 5 years was 64% for low-risk and 14% for high-risk cases. In particular, we found that the overexpression of specific genes in the mitotic cell cycle pathway and the underexpression of specific genes in the interferon pathway are both associated with poor prognosis. We show that our predictive model assesses the risk more accurately than the traditional Clark staging method. Therefore, our model can help clinicians design treatment strategies more effectively. Furthermore, our findings shed light on the biology of melanoma and its prognosis. This is the first in vivo study that demonstrates the association between the interferon pathway and the prognosis of melanoma.

Inflammatory Marker Testing Identifies CD74 Expression in Melanoma Tumor Cells, and Its Expression Associates with Favorable Survival for Stage III Melanoma

PURPOSE

Inflammatory marker expression in stage III melanoma tumors was evaluated for association with outcome, using two independent cohorts of stage III melanoma patients' tumor tissues.

EXPERIMENTAL DESIGN

Fifteen markers of interest were selected for analysis, and their expression in melanoma tissues was determined by immunohistochemistry. Proteins associating with either overall survival (OS) or recurrence-free survival (RFS) in the retrospective discovery tissue microarray (TMA; n = 158) were subsequently evaluated in an independent validation TMA (n = 114). Cox proportional hazards regression models were used to assess the association between survival parameters and covariates, the Kaplan-Meier method to estimate the distribution of survival, and the log-rank test to compare distributions.

RESULTS

Expression of CD74 on melanoma cells was unique, and in the discovery TMA, it associated with favorable patient outcome (OS: HR, 0.53; P = 0.01 and RFS: HR, 0.56; P = 0.01). The validation data set confirmed the CD74 prognostic significance and revealed that the absence of macrophage migration inhibitory factor (MIF) and inducible nitric oxide synthase (iNOS) was also associated with poor survival parameters. Consistent with the protein observation, tumor CD74 mRNA expression also correlated positively (P = 0.003) with OS in the melanoma TCGA data set.

CONCLUSIONS

Our data validate CD74 as a useful prognostic tumor cell protein marker associated with favorable RFS and OS in stage III melanoma. Low or negative expression of MIF in both TMAs and of iNOS in the validation set also provided useful prognostic data. A disease-specific investigation of CD74's functional significance is warranted, and other markers appear intriguing to pursue. Clin Cancer Res; 22(12); 3016-24. ©2016 AACR.

Prognostic and predictive biomarkers in melanoma: an update

Malignant melanoma is one of the most aggressive cancers. Several new therapeutic strategies that focus on immuno- and/or targeted therapy have been developed, which have entered clinical trials or already been approved. This review provides an update on prognostic and predictive biomarkers in melanoma that may be used to improve the clinical management of patients. Prognostic markers include conventional histopathological characteristics, chromosomal aberrations, gene expression patterns and miRNA profiles. There is a trend towards multi-marker assays and whole-genome molecular screening methods to determine the prognosis of individual patients. Predictive biomarkers, including targeted components of signal transduction, developmental or transcriptional pathways, can be used to determine patient response towards a particular treatment or combination thereof. The rapid evolution of sequencing technologies and multi-marker screening will change the spectrum of patients who become candidates for therapeutic agents, and in addition create new ethical and regulatory challenges.

Revisiting determinants of prognosis in cutaneous melanoma

The American Joint Committee on Cancer staging system for cutaneous melanoma is based on primary tumor thickness and the presence of ulceration, mitoses, lymph node spread, and distant metastases as determinants of prognosis. Although this cutaneous melanoma staging system has evolved over time to more accurately reflect patient prognosis, improvements are still needed, because current understanding of the particular factors (genetic mutation, expression alteration, host response, etc) that are critical for predicting patient outcomes is incomplete. Given the clinical and biologic heterogeneity of primary melanomas, new prognostic tools are needed to more precisely identify patients who are most likely to develop advanced disease. Such tools would affect clinical surveillance strategies and aid in patient selection for adjuvant therapy. The authors reviewed the literature on prognostic molecular and immunologic markers in primary cutaneous melanoma, their associations with clinicopathologic and survival outcomes, and their potential for incorporation into current staging models. Overall, the studies considered in this review did not define prognostic markers that could be readily incorporated into the current staging system. Therefore, efforts should be continued in these and other directions to maximize the likelihood of identifying clinically useful prognostic biomarkers for cutaneous melanoma.

Evaluating Robustness and Sensitivity of the NanoString Technologies nCounter Platform to Enable Multiplexed Gene Expression Analysis of Clinical Samples

Analysis of clinical trial specimens such as formalin-fixed paraffin-embedded (FFPE) tissue for molecular mechanisms of disease progression or drug response is often challenging and limited to a few markers at a time. This has led to the increasing importance of highly multiplexed assays that enable profiling of many biomarkers within a single assay. Methods for gene expression analysis have undergone major advances in biomedical research, but obtaining a robust dataset from low-quality RNA samples, such as those isolated from FFPE tissue, remains a challenge. Here, we provide a detailed evaluation of the NanoString Technologies nCounter platform, which provides a direct digital readout of up to 800 mRNA targets simultaneously. We tested this system by examining a broad set of human clinical tissues for a range of technical variables, including sensitivity and limit of detection to varying RNA quantity and quality, reagent performance over time, variability between instruments, the impact of the number of fields of view sampled, and differences between probe sequence locations and overlapping genes across CodeSets. This study demonstrates that Nanostring offers several key advantages, including sensitivity, reproducibility, technical robustness, and utility for clinical application.

Aloe-emodin exerts a potent anticancer and immunomodulatory activity on BRAF-mutated human melanoma cells

Aim of this study was to extend the knowledge on the antineoplastic effect of aloe-emodin (AE), a natural hydroxyanthraquinone compound, both in metastatic human melanoma cell lines and in primary stem-like cells (melanospheres). Treatment with AE caused reduction of cell proliferation and induction of SK-MEL-28 and A375 cells differentiation, characterized by a marked increase of transamidating activity of transglutaminase whose expression remained unmodified. In vitro antimetastatic property of AE was evaluated by adhesion and Boyden chamber invasion assays. The effect of AE on melanoma cytokines/chemokines production was determined by a multiplex assay: interestingly AE showed an immunomodulatory activity through GM-CSF and IFN-γ production. We report also that AE significantly reduced the proliferation, stemness and invasive potential of melanospheres. Moreover, AE treatment significantly enhanced dabrafenib (a BRAF inhibitor) antiproliferative activity in BRAF mutant cell lines. Our results confirm that AE possesses remarkable antineoplastic properties against melanoma cells, indicating this anthraquinone as a promising agent for differentiation therapy of cancer, or as adjuvant in chemotherapy and targeted therapy. Further, its mechanisms of action support a potential efficacy of AE treatment to counteract resistance of BRAF-mutated melanoma cells to target therapy.

Divergent Phenotypes of Human Regulatory T Cells Expressing the Receptors TIGIT and CD226

Regulatory T cells (Tregs) play a central role in counteracting inflammation and autoimmunity. A more complete understanding of cellular heterogeneity and the potential for lineage plasticity in human Treg subsets may identify markers of disease pathogenesis and facilitate the development of optimized cellular therapeutics. To better elucidate human Treg subsets, we conducted direct transcriptional profiling of CD4(+)FOXP3(+)Helios(+) thymic-derived Tregs and CD4(+)FOXP3(+)Helios(-) T cells, followed by comparison with CD4(+)FOXP3(-)Helios(-) T conventional cells. These analyses revealed that the coinhibitory receptor T cell Ig and ITIM domain (TIGIT) was highly expressed on thymic-derived Tregs. TIGIT and the costimulatory factor CD226 bind the common ligand CD155. Thus, we analyzed the cellular distribution and suppressive activity of isolated subsets of CD4(+)CD25(+)CD127(lo/-) T cells expressing CD226 and/or TIGIT. We observed TIGIT is highly expressed and upregulated on Tregs after activation and in vitro expansion, and is associated with lineage stability and suppressive capacity. Conversely, the CD226(+)TIGIT(-) population was associated with reduced Treg purity and suppressive capacity after expansion, along with a marked increase in IL-10 and effector cytokine production. These studies provide additional markers to delineate functionally distinct Treg subsets that may help direct cellular therapies and provide important phenotypic markers for assessing the role of Tregs in health and disease.

Future perspectives in melanoma research: meeting report from the "Melanoma Bridge", Napoli, December 5th-8th 2013

The fourth "Melanoma Bridge Meeting" took place in Naples, December 5 to 8th, 2013. The four topics discussed at this meeting were: Diagnosis and New Procedures, Molecular Advances and Combination Therapies, News in Immunotherapy, and Tumor Microenvironment and Biomarkers.

Integrated genome-wide association, coexpression network, and expression single nucleotide polymorphism analysis identifies novel pathway in allergic rhinitis

BACKGROUND

Allergic rhinitis is a common disease whose genetic basis is incompletely explained. We report an integrated genomic analysis of allergic rhinitis.

METHODS

We performed genome wide association studies (GWAS) of allergic rhinitis in 5633 ethnically diverse North American subjects. Next, we profiled gene expression in disease-relevant tissue (peripheral blood CD4+ lymphocytes) collected from subjects who had been genotyped. We then integrated the GWAS and gene expression data using expression single nucleotide (eSNP), coexpression network, and pathway approaches to identify the biologic relevance of our GWAS.

RESULTS

GWAS revealed ethnicity-specific findings, with 4 genome-wide significant loci among Latinos and 1 genome-wide significant locus in the GWAS meta-analysis across ethnic groups. To identify biologic context for these results, we constructed a coexpression network to define modules of genes with similar patterns of CD4+ gene expression (coexpression modules) that could serve as constructs of broader gene expression. 6 of the 22 GWAS loci with P-value ≤ 1x10-6 tagged one particular coexpression module (4.0-fold enrichment, P-value 0.0029), and this module also had the greatest enrichment (3.4-fold enrichment, P-value 2.6 × 10-24) for allergic rhinitis-associated eSNPs (genetic variants associated with both gene expression and allergic rhinitis). The integrated GWAS, coexpression network, and eSNP results therefore supported this coexpression module as an allergic rhinitis module. Pathway analysis revealed that the module was enriched for mitochondrial pathways (8.6-fold enrichment, P-value 4.5 × 10-72).

CONCLUSIONS

Our results highlight mitochondrial pathways as a target for further investigation of allergic rhinitis mechanism and treatment. Our integrated approach can be applied to provide biologic context for GWAS of other diseases.

Defining the role of CD2 in disease progression and overall survival among patients with completely resected stage-II to -III cutaneous melanoma

BACKGROUND

Accurate assessment of prognosis remains clinically challenging in stage II to III cutaneous melanoma. Studies have implicated CD2 in immune surveillance, T-cell activation, and antitumor immunity, but its role in melanoma progression warrants further investigation.

OBJECTIVE

We sought to investigate the prognostic role of CD2 in primary cutaneous melanoma.

METHODS

Patients with American Joint Committee on Cancer stage II and III cutaneous melanoma were identified by retrospective review of dermatopathology databases from 2001 to 2010 at Mount Sinai Medical Center and Geisinger Medical Center. Additional patients were provided by New York University Medical Center based on retrospective review and tissue availability. Immunohistochemistry was performed on tumors from 90 patients with known recurrence status and documented follow-up.

RESULTS

Primary tumors from patients who developed recurrent disease had fewer CD2(+) cells (P = .0003). In multivariable analyses including standard clinicopathologic predictors, CD2 was an independent predictor of disease recurrence (P = .008) and overall survival (P = .007). CD2 count correlated with characterization of tumor-infiltrating lymphocytes (P = .0004). Among the intermediate prognosis group of patients with nonbrisk tumor-infiltrating lymphocytes, CD2 count was predictive of disease recurrence (P = .0006) and overall survival (P = .0318).

LIMITATIONS

Our retrospective design may have resulted in incomplete representation of patients lacking documented follow-up.

CONCLUSIONS

CD2 may be an independent predictor of disease recurrence and overall survival among patients with primary cutaneous melanoma.