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

An artificial intelligence-based model exploiting H&E images to predict recurrence in negative sentinel lymph-node melanoma patients

BACKGROUND

Risk stratification and treatment benefit prediction models are urgent to improve negative sentinel lymph node (SLN-) melanoma patient selection, thus avoiding costly and toxic treatments in patients at low risk of recurrence. To this end, the application of artificial intelligence (AI) could help clinicians to better calculate the recurrence risk and choose whether to perform adjuvant therapy.

METHODS

We made use of AI to predict recurrence-free status (RFS) within 2-years from diagnosis in 94 SLN- melanoma patients. In detail, we detected quantitative imaging information from H&E slides of a cohort of 71 SLN- melanoma patients, who registered at Istituto Tumori "Giovanni Paolo II" in Bari, Italy (investigational cohort, IC). For each slide, two expert pathologists firstly annotated two Regions of Interest (ROIs) containing tumor cells alone (TUMOR ROI) or with infiltrating cells (TUMOR + INF ROI). In correspondence of the two kinds of ROIs, two AI-based models were developed to extract information directly from the tiles in which each ROI was automatically divided. This information was then used to predict RFS. Performances of the models were computed according to a 5-fold cross validation scheme. We further validated the prediction power of the two models on an independent external validation cohort of 23 SLN- melanoma patients (validation cohort, VC).

RESULTS

The TUMOR ROIs have revealed more informative than the TUMOR + INF ROIs. An Area Under the Curve (AUC) value of 79.1% and 62.3%, a sensitivity value of 81.2% and 76.9%, a specificity value of 70.0% and 43.3%, an accuracy value of 73.2% and 53.4%, were achieved on the TUMOR and TUMOR + INF ROIs extracted for the IC cohort, respectively. An AUC value of 76.5% and 65.2%, a sensitivity value of 66.7% and 41.6%, a specificity value of 70.0% and 55.9%, an accuracy value of 70.0% and 56.5%, were achieved on the TUMOR and TUMOR + INF ROIs extracted for the VC cohort, respectively.

CONCLUSIONS

Our approach represents a first effort to develop a non-invasive prognostic method to better define the recurrence risk and improve the management of SLN- melanoma patients.

5-Methylcytosine immunohistochemistry for predicting cutaneous melanoma prognosis

There is a correlation between DNA methylation and the diseased stage and poor survival. 5-methylcytosine (5-mC) is one of the epigenetic modifications of bases that researchers focus on. Staining with 5-mC immunohistochemistry was used to examine pathological samples taken from individuals diagnosed with cutaneous melanoma. Between Breslow levels 2 and 4, there was a significant difference in the H-score of 5-mC expression (p = 0.046). A significant reduction in 5-mC expression H-scores was seen in patients who were diagnosed with ulcers (p = 0.039). It was shown that patients with low 5-mC had a significantly worse overall survival rate (p = 0.027).

Correlation Studies between S100 Protein Level and Soluble MIA or Tissue MelanA and gp100 (HMB45) Expression in Cutaneous Melanoma

(1) Background: Cutaneous melanoma (CM) originates from melanocytes and causes 90% of skin cancer deaths; therefore, the comparison of different soluble and tissue markers could be valuable in the detection of melanoma progression and therapy monitoring. The present study is focused on the potential correlations between soluble S100B and MIA protein levels in different melanoma stages or with tissue expression of S100, gp100 (HMB45), and MelanA biomarkers. (2) Methods: Soluble S100B and MIA levels were evaluated by means of immunoassay methods in blood samples from 176 patients with CM, while tissue expressions of S100, MelanA, and gp100 (HMB45) were detected by means of immunohistochemistry in 76 melanomas. (3) Results: Soluble S100B correlated with MIA in stages III (r = 0.677, p < 0.001) and IV (r = 0.662, p < 0.001) but not in stages I and II; however, 22.22% and 31.98% of stage I and II patients, respectively, had high values for at least one of the two soluble markers. S100 tissue expression correlated with both MelanA (r = 0.610, p < 0.001) and HMB45 (r = 0.476, p < 0.01), while HMB45 and MelanA also significantly positively correlated (r = 0.623, p < 0.001). (4) Conclusions: Blood levels of S100B and MIA corroborated with melanoma tissue markers expression could help to improve the stratification process for patients with a high risk of tumor progression.

Blood immune cells as potential biomarkers predicting relapse-free survival of stage III/IV resected melanoma patients treated with peptide-based vaccination and interferon-alpha

Introduction

Despite the recent approval of several therapies in the adjuvant setting of melanoma, tumor relapse still occurs in a significant number of completely resected stage III-IV patients. In this context, the use of cancer vaccines is still relevant and may increase the response to immune checkpoint inhibitors. We previously demonstrated safety, immunogenicity and preliminary evidence of clinical efficacy in stage III/IV resected melanoma patients subjected to a combination therapy based on peptide vaccination together with intermittent low-dose interferon-α2b, with or without dacarbazine preconditioning (https://www.clinicaltrialsregister.eu/ctr-search/search, identifier: 2008-008211-26). In this setting, we then focused on pre-treatment patient immune status to highlight possible factors associated with clinical outcome.

Methods

Multiparametric flow cytometry was used to identify baseline immune profiles in patients' peripheral blood mononuclear cells and correlation with the patient clinical outcome. Receiver operating characteristic curve, Kaplan-Meier survival and principal component analyses were used to evaluate the predictive power of the identified markers.

Results

We identified 12 different circulating T and NK cell subsets with significant (p ≤ 0.05) differential baseline levels in patients who later relapsed with respect to patients who remained free of disease. All 12 parameters showed a good prognostic accuracy (AUC>0.7, p ≤ 0.05) and 11 of them significantly predicted the relapse-free survival. Remarkably, 3 classifiers also predicted the overall survival. Focusing on immune cell subsets that can be analyzed through simple surface staining, three subsets were identified, namely regulatory T cells, CD56^dimCD16^- NK cells and central memory γδ T cells. Each subset showed an AUC>0.8 and principal component analysis significantly grouped relapsing and non-relapsing patients (p=0.034). These three subsets were used to calculate a combination score that was able to perfectly distinguish relapsing and non-relapsing patients (AUC=1; p=0). Noticeably, patients with a combined score ≥2 demonstrated a strong advantage in both relapse-free (p=0.002) and overall (p=0.011) survival as compared to patients with a score <2.

Discussion

Predictive markers may be used to guide patient selection for personalized therapies and/or improve follow-up strategies. This study provides preliminary evidence on the identification of peripheral blood immune biomarkers potentially capable of predicting the clinical response to combined vaccine-based adjuvant therapies in melanoma.

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.

Optimal surveillance strategies for patients with stage 1 cutaneous melanoma post primary tumour excision: three systematic reviews and an economic model

BACKGROUND

Malignant melanoma is the fifth most common cancer in the UK, with rates continuing to rise, resulting in considerable burden to patients and the NHS.

OBJECTIVES

The objectives were to evaluate the effectiveness and cost-effectiveness of current and alternative follow-up strategies for stage IA and IB melanoma.

REVIEW METHODS

Three systematic reviews were conducted. (1) The effectiveness of surveillance strategies. Outcomes were detection of new primaries, recurrences, metastases and survival. Risk of bias was assessed using the Cochrane Collaboration's Risk-of-Bias 2.0 tool. (2) Prediction models to stratify by risk of recurrence, metastases and survival. Model performance was assessed by study-reported measures of discrimination (e.g. D-statistic, Harrel's c-statistic), calibration (e.g. the Hosmer-Lemeshow 'goodness-of-fit' test) or overall performance (e.g. Brier score, R ²). Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). (3) Diagnostic test accuracy of fine-needle biopsy and ultrasonography. Outcomes were detection of new primaries, recurrences, metastases and overall survival. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Review data and data from elsewhere were used to model the cost-effectiveness of alternative surveillance strategies and the value of further research.

RESULTS

(1) The surveillance review included one randomised controlled trial. There was no evidence of a difference in new primary or recurrence detected (risk ratio 0.75, 95% confidence interval 0.43 to 1.31). Risk of bias was considered to be of some concern. Certainty of the evidence was low. (2) Eleven risk prediction models were identified. Discrimination measures were reported for six models, with the area under the operating curve ranging from 0.59 to 0.88. Three models reported calibration measures, with coefficients of ≥ 0.88. Overall performance was reported by two models. In one, the Brier score was slightly better than the American Joint Committee on Cancer scheme score. The other reported an R ² of 0.47 (95% confidence interval 0.45 to 0.49). All studies were judged to have a high risk of bias. (3) The diagnostic test accuracy review identified two studies. One study considered fine-needle biopsy and the other considered ultrasonography. The sensitivity and specificity for fine-needle biopsy were 0.94 (95% confidence interval 0.90 to 0.97) and 0.95 (95% confidence interval 0.90 to 0.97), respectively. For ultrasonography, sensitivity and specificity were 1.00 (95% confidence interval 0.03 to 1.00) and 0.99 (95% confidence interval 0.96 to 0.99), respectively. For the reference standards and flow and timing domains, the risk of bias was rated as being high for both studies. The cost-effectiveness results suggest that, over a lifetime, less intensive surveillance than recommended by the National Institute for Health and Care Excellence might be worthwhile. There was considerable uncertainty. Improving the diagnostic performance of cancer nurse specialists and introducing a risk prediction tool could be promising. Further research on transition probabilities between different stages of melanoma and on improving diagnostic accuracy would be of most value.

LIMITATIONS

Overall, few data of limited quality were available, and these related to earlier versions of the American Joint Committee on Cancer staging. Consequently, there was considerable uncertainty in the economic evaluation.

CONCLUSIONS

Despite adoption of rigorous methods, too few data are available to justify changes to the National Institute for Health and Care Excellence recommendations on surveillance. However, alternative strategies warrant further research, specifically on improving estimates of incidence, progression of recurrent disease; diagnostic accuracy and health-related quality of life; developing and evaluating risk stratification tools; and understanding patient preferences.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42018086784.

FUNDING

This project was funded by the National Institute for Health Research Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol 25, No. 64. See the NIHR Journals Library website for further project information.

Using Immune-Related lncRNA Signature for Prognosis and Response to Immunotherapy in Cutaneous Melanoma

Background

Cutaneous melanoma is a highly malignant skin tumor, and most patients have a poor prognosis. In recent years, immunotherapy has assumed an important role in the treatment of advanced cutaneous melanoma, but only a small percentage of patients benefit from immunotherapy. A growing number of studies have demonstrated that the prognosis of patients with cutaneous melanoma is closely related to long non-coding RNA and the tumor immune microenvironment.

Methods

We downloaded RNA expression data and immune-related gene lists of cutaneous melanoma patients separately from The Cancer Genome Atlas database and ImmPort website and identified immune-related lncRNAs by co-expression analysis. The prognostic model was constructed by applying least absolute shrinkage and selection operator regression, and all patients were classified into high- and low-risk groups according to the risk score of the model. We evaluated the differences between the two groups in terms of survival outcomes, immune infiltration, pathway enrichment, chemotherapeutic drug sensitivity and immune checkpoint gene expression to verify the impact of lncRNA signature on clinical prognosis and immunotherapy efficacy.

Results

By correlation analysis and LASSO regression analysis, we constructed an immune-related lncRNA prognostic model based on five lncRNA: HLA-DQB1-AS1, MIR205HG, RP11-643G5.6, USP30-AS1 and RP11-415F23.4. Based on this model, we plotted Kaplan-Meier survival curves and time-dependent ROC curves and analyzed its ability as an independent prognostic factor for cutaneous melanoma in combination with clinicopathological features. The results showed that these lncRNA signature was an independent prognostic factor of cutaneous melanoma with favorable prognostic ability. Our results also show a higher degree of immune infiltration, higher expression of immune checkpoint-associated genes, and better outcome of immunotherapy in the low-risk group of the lncRNA signature.

Conclusion

The 5 immune-related lncRNA signatures constructed in our study can predict the prognosis of cutaneous melanoma and contribute to the selection of immunotherapy.

Prognostic Value and Immunological Characteristics of a Novel RNA Binding Protein Signature in Cutaneous Melanoma

Background

The existing studies indicate that RNA binding proteins (RBPs) are closely correlated with the genesis and development of cancers. However, the role of RBPs in cutaneous melanoma remains largely unknown. Therefore, the present study aims to establish a reliable prognostic signature based on RBPs to distinguish cutaneous melanoma patients with different prognoses and investigate the immune infiltration of patients.

Methods

After screening RBPs from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, Cox and least absolute shrinkage and selection operator (LASSO) regression analysis were then used to establish a prediction model. The relationship between the signature and the abundance of immune cell types, the tumor microenvironment (TME), immune-related pathways, and immune checkpoints were also analyzed.

Results

In total, 7 RBPs were selected to establish the prognostic signature. Patients categorized as a high-risk group demonstrated worse overall survival (OS) rates compared to those of patients categorized as a low-risk group. The signature was validated in an independent external cohort and indicated a promising prognostic ability. Further analysis indicated that the signature wasan independent prognostic indicator in cutaneous melanoma. A nomogram combining risk score and clinicopathological features was then established to evaluate the 3- and 5-year OS in cutaneous melanoma patients. Analyses of immune infiltrating, the TME, immune checkpoint, and drug susceptibility revealed significant differences between the two groups. GSEA analysis revealed that basal cell carcinoma, notch signaling pathway, melanogenesis pathways were enriched in the high-risk group, resulting in poor OS.

Conclusion

We established and validated a robust 7-RBP signature that could be a potential biomarker to predict the prognosis and immunotherapy response of cutaneous melanoma patients, which provides new insights into cutaneous melanoma immunotherapeutic strategies.

Risk Stratification of Patients with Stage I Cutaneous Melanoma Using 31-Gene Expression Profiling

BACKGROUND

While patients with localized cutaneous melanoma (CM) generally have good five-year melanoma-specific survival rates, identifying patients with localized disease at a high risk of recurrence could allow them access to additional follow-up or surveillance.

OBJECTIVE

We sought to examine the prognostic value of the 31-gene expression profile (31-GEP) test for the risk of recurrence in stage I CM patients according to 31-GEP main class (low risk: Class 1 vs. high-risk: Class 2) and the lowest and highest risk 31-GEP subclasses (Class 1A vs. Class 2B).

METHODS

Data from a previously described meta-analysis detailing the 31-GEP results for patients with stage I CM (N = 623) were re-analyzed to determine 31-GEP accuracy.

RESULTS

Patients with stage I CM and a Class 1 31-GEP result were less likely to have a recurrence (15/556; 2.7% vs. 6/67; 9.0%; p=0.018) than patients with a Class 2 result and had a higher five-year recurrence-free survival (RFS) (96% vs. 85%). Patients with a Class 2 result were 2.8 times as likely to experience a recurrence (positive likelihood ratio: 2.82; 95% confidence interval: 1.38-5.77). In a subset of patients with stage I CM stratified further into 31-GEP subclasses (n = 206), patients with a Class 1A result had a higher five-year RFS than those with a Class 2B result (98% vs. 73%). Patients with a Class 2B result were also 6.5 times as likely to experience a recurrence (positive likelihood ratio: 6.45; 95% confidence interval: 2.44-17.00) than those with a Class 1A result, and the 31-GEP had a negative predictive value of 96.3% (95% confidence interval: 92.3%-98.4%).

CONCLUSION

The 31-GEP test significantly differentiates between low and high recurrence risk in patients with stage I CM.

The human melanoma proteome atlas-Defining the molecular pathology

The MM500 study is an initiative to map the protein levels in malignant melanoma tumor samples, focused on in-depth histopathology coupled to proteome characterization. The protein levels and localization were determined for a broad spectrum of diverse, surgically isolated melanoma tumors originating from multiple body locations. More than 15,500 proteoforms were identified by mass spectrometry, from which chromosomal and subcellular localization was annotated within both primary and metastatic melanoma. The data generated by global proteomic experiments covered 72% of the proteins identified in the recently reported high stringency blueprint of the human proteome. This study contributes to the NIH Cancer Moonshot initiative combining detailed histopathological presentation with the molecular characterization for 505 melanoma tumor samples, localized in 26 organs from 232 patients.

Antibody-Based Targeted Interventions for the Diagnosis and Treatment of Skin Cancers

BACKGROUND

Cutaneous malignancies most commonly arise from skin epidermal cells. These cancers may rapidly progress from benign to a metastatic phase. Surgical resection represents the gold standard therapeutic treatment of non-metastatic skin cancer while chemo- and/or radiotherapy are often used against metastatic tumors. However, these therapeutic treatments are limited by the development of resistance and toxic side effects, resulting from the passive accumulation of cytotoxic drugs within healthy cells.

OBJECTIVE

This review aims to elucidate how the use of monoclonal Antibodies (mAbs) targeting specific Tumor Associated Antigens (TAAs) is paving the way to improved treatment. These mAbs are used as therapeutic or diagnostic carriers that can specifically deliver cytotoxic molecules, fluorophores or radiolabels to cancer cells that overexpress specific target antigens.

RESULTS

mAbs raised against TAAs are widely in use for e.g. differential diagnosis, prognosis and therapy of skin cancers. Antibody-Drug Conjugates (ADCs) particularly show remarkable potential. The safest ADCs reported to date use non-toxic photo-activatable Photosensitizers (PSs), allowing targeted Photodynamic Therapy (PDT) resulting in targeted delivery of PS into cancer cells and selective killing after light activation without harming the normal cell population. The use of near-infrared-emitting PSs enables both diagnostic and therapeutic applications upon light activation at the specific wavelengths.

CONCLUSION

Antibody-based approaches are presenting an array of opportunities to complement and improve current methods employed for skin cancer diagnosis and treatment.

Genetic markers for characterization and prediction of prognosis of melanoma subtypes: a 2021 update

In this article we examined the most important genetic markers involved in melanoma susceptibility, initiation and progression, and their impact on the prognosis of the disease. Current knowledge in melanoma genetics identifies distinct pathways to the development of different melanoma subtypes characterized by specific clinico-pathological features and partially known genetic markers, modulated by high, low or absence of cumulative sun damage. The most prevalent somatic mutations are related to the activation of the MAPK pathway, which are classified into four major subtypes: BRAF mutant, NRAS mutant, NF1 mutant and triple wild type. Moreover, germinal mutations are also involved in the characterization and predictions of prognosis in melanoma. Currently, CDKN2A is seen as the main high-risk gene involved in melanoma susceptibility being mutated in around 20% of melanoma-prone families. Other high-risk susceptibility genes described include CDK4, POT1, BAP1, TERT promoter, ACD, and TERF2IP. Melanoma is one of the most genetically predisposed among all cancers in humans, and ultraviolet light from the sun is the main environmental factor. This genetic predisposition is starting to be understood, impacting not only on the risk of developing melanoma but also on the risk of developing other types of cancer, as well as on the prognosis of the disease.

Expression of SEC62 Oncogene in Benign, Malignant and Borderline Melanocytic Tumors-Unmasking the Wolf in Sheep's Clothing?

Simple Summary

Amplification and overexpression of the SEC62 oncogene was reported in a variety of human cancers and was associated with poor prognosis as well as lymph node and distant metastases. In this study, SEC62 expression was analyzed in benign, borderline, and malignant melanocytic lesions of 209 patients. We found the highest expression in Spitz nevi, followed by melanoma metastases, primary melanoma, congenital nevi, and blue nevi. In melanoma patients, high Sec62 levels correlated with shorter overall and progression-free survival. Significantly higher Sec62 levels were found in melanomas with lymph node and distant metastases compared with those without. Taken together, these data suggest a relevant role of SEC62 as a metastasis-stimulating oncogene in melanoma development, which represents a promising therapeutic target.

Abstract

SEC62 oncogene located at chromosomal region 3q26 encodes for a transmembrane protein of the endoplasmic reticulum (ER) and is expressed at high levels in numerous human malignancies. SEC62 overexpression has been associated with worse prognosis and high risk for lymphatic and distant metastases in head and neck cancer, cervical cancer, hepatocellular cancer, and lung cancer. However, its role in the development and tumor biology of melanocytic lesions has not been investigated so far. An immunohistochemical study including 209 patients with melanocytic lesions (malignant melanoma (MM), n = 93; melanoma metastases (MET), n = 28; Spitz nevi (SN), n = 29; blue nevi (BN), n = 21; congenital nevi (CN), n = 38) was conducted and SEC62 expression was correlated with clinical data including patient survival and histopathological characteristics. SN showed the highest SEC62 expression levels followed by MET, MM, CN, and BN. High SEC62 expression correlated with a shorter overall and progression-free survival in MM patients. Additionally, high Sec62 levels correlated significantly with higher tumor size (T stage), the presence of tumor ulceration, and the presence of lymph node as well as distant metastases. Strikingly, SEC62 expression showed a strong correlation with Clark level. Taken together, these data demonstrate that SEC62 is a promising prognostic marker in MM and has the potential to predict biological behavior and clinical aggressiveness of melanocytic lesions.

BRAF Gene and Melanoma: Back to the Future

As widely acknowledged, 40-50% of all melanoma patients harbour an activating BRAF mutation (mostly BRAF V600E). The identification of the RAS-RAF-MEK-ERK (MAP kinase) signalling pathway and its targeting has represented a valuable milestone for the advanced and, more recently, for the completely resected stage III and IV melanoma therapy management. However, despite progress in BRAF-mutant melanoma treatment, the two different approaches approved so far for metastatic disease, immunotherapy and BRAF+MEK inhibitors, allow a 5-year survival of no more than 60%, and most patients relapse during treatment due to acquired mechanisms of resistance. Deep insight into BRAF gene biology is fundamental to describe the acquired resistance mechanisms (primary and secondary) and to understand the molecular pathways that are now being investigated in preclinical and clinical studies with the aim of improving outcomes in BRAF-mutant patients.

Cost-Benefit Implication of Gene Expression Profiling and Adjuvant Therapy in Stage IIIA Melanoma

BACKGROUND

Indiscriminate use of adjuvant therapy in stage IIIA melanoma is controversial. We sought to model the clinical impact and cost of implementing a gene expression profile (GEP) test to guide adjuvant therapy.

STUDY DESIGN

A Markov decision-analysis model was created to represent resected stage IIIA melanoma with 3 treatment options: observation (OBS), adjuvant pembrolizumab for all patients (ALL), and selective adjuvant therapy (SEL). In the SEL option, only high-risk patients based on GEP stratification were treated with pembrolizumab. Cost of adjuvant therapy was normalized to reflect Medicare reimbursement schedules. The primary outcome was cost per mortality avoided at 10 years.

RESULTS

Model projections for 10-year overall survival were 68% for OBS, 73% for SEL, and 76% for ALL. The estimated incremental cost-per-mortality-avoided (compared to OBS) was $2.1 million for SEL and $2.4 million for ALL. These translate to costs of $583.0K and $697.1K per life-year for the SEL and ALL strategies, respectively.

CONCLUSIONS

Routine adjuvant pembrolizumab for stage IIIA melanoma is costly, and risk-stratification by GEP only marginally improves the value of therapy.

BRAF as a positive predictive biomarker: Focus on lung cancer and melanoma patients

In the era of personalized medicine, BRAF mutational assessment is mandatory in advanced-stage melanoma and non-small cell lung cancer (NSCLC) patients. The identification of actionable mutations is crucial for the adequate management of these patients. To date various drugs have been implemented in clinical practice. Similarly, various methods may be adopted for the identification of BRAF mutations. Here, we briefly review the current literature on BRAF in melanoma and NSCLC, focusing attention in particular on the different methods and drugs adopted in these patients. In addition, an overview of the real-world practice in different Italian laboratories with high expertise in molecular predictive pathology testing is provided.

Reporting regression in primary cutaneous melanoma. Part 2: prognosis, evaluation and management

The effect of histological regression on patient prognosis for primary cutaneous melanoma is controversial. Some authors hypothesize that regression indicates a robust systemic immune response and may decrease risk of metastasis. Others argue that histological regression calls into question a T0 diagnosis because there may have been an invasive component of the melanoma that is no longer visible but is still active. The literature to date does not suggest that histological regression is associated with increased risk of positive sentinel lymph node status, metastasis or increased risk of mortality. Thus, the presence of histological regression should not change patient staging, evaluation or management. The criteria used for reporting regression have varied dramatically across studies, and standardized reporting is needed to foster evidence-based practices in the future.

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.

Identification of CD38 as a potential biomarker in skin cutaneous melanoma using bioinformatics analysis

Skin cutaneous melanoma (SKCM) is the most aggressive type of skin cancer, with a high rate of metastasis and mortality; however, identification of biomarkers for the treatment of SKCM is required. Cluster of differentiation (CD)38 has emerged as an effective target for therapeutic drugs in several types of cancer, such as chronic lymphocytic leukemia and multiple myeloma. In the present study, to determine the contribution of CD38 to the diagnosis of SKCM, Gene Expression Profiling Interactive Analysis 2 and University of Alabama Cancer Database online tools were used to analyze The Cancer Genome Atlas-SKCM dataset. Moreover, Search Tool for the Retrieval of Interacting Genes/Proteins and GeneMANIA databases were used to determine protein-protein interaction networks and potential functions. To the best of our knowledge, the results of the present study indicated for the first time that high expression levels of CD38 were a favorable diagnostic factor for SKCM. Moreover, a correlation between CD38 expression levels and the survival probability of patients with SKCM was identified. Integrative analysis predicted that nine genes were correlated with CD38 in SKCM, and the similarity of these genes in SKCM expression and a survival heatmap was verified. Gene ontology enrichment analysis using the Metascape tool revealed that CD38 and its correlated genes were significantly enriched in lymphocyte activation and T cell differentiation regulation. Collectively, the bioinformatics analysis revealed that CD38 might serve as a potential diagnostic predictor for SKCM.

Factors predicting survival in thick melanoma: Do all thick melanomas have the same prognosis?

BACKGROUND

It is unknown whether all thick melanomas share the same prognostic features. We present a large, multi-institutional study on thick melanoma, evaluating for factors prognostic of survival.

METHODS

We queried the database of the Sentinel Lymph Node Working Group for patients with thick melanoma (>4 mm) who had a sentinel lymph node biopsy from 1993 to 2018. Clinicopathologic characteristics were correlated with overall survival.

RESULTS

There were 1,235 patients with a median follow-up of 28 months. Median thickness was 5.9 mm, with 713, 356, and 166 cases having a thickness of >4 to 6, >6 to 10, and >10 mm, respectively. Ulceration was seen in 51.2% of cases, while sentinel lymph node metastases were seen in 439 of 1,235 (35.5%) cases. For melanomas >4 to 6 mm, age, thickness, ulceration, lymphovascular invasion, and sentinel lymph node metastasis were correlated with overall survival (all P < .05), but for melanomas >6 to 10 mm, only sex and sentinel lymph node metastasis were prognostic of overall survival (both P < .05). For melanomas >10 mm, only sentinel lymph node metastasis predicted overall survival on multivariable analyses (P < .05).

CONCLUSION

Prognostic markers of overall survival for thick melanoma include thickness, ulceration, and sentinel lymph node metastasis, but also include other unique factors such as lymphovascular invasion. Moreover, certain prognostic markers for survival are associated with different subgroups of thick melanoma, which vary based on thickness group.

Combining texture features of whole slide images improves prognostic prediction of recurrence-free survival for cutaneous melanoma patients

BACKGROUND

Accurate prediction of recurrence-free survival (RFS) is important for the prognosis of cutaneous melanoma patients. The image-based pathological examination remains as the gold standard for diagnosis. It is of clinical interest to account for computer-aided processing of pathology image when performing prognostic analysis.

METHODS

We enrolled in this study a total of 152 patients from TCGA-SKCM (The Cancer Genome Atlas Skin Cutaneous Melanoma project) with complete information in recurrence-related survival time, baseline variables (clinicopathologic variables, mutation status of BRAF and NRAS genes), gene expression data, and whole slide image (WSI) features. We preprocessed WSI to segment global or nucleus areas, and extracted 3 types of texture features from each region. We performed cross validation and used multiple evaluation metrics including C-index and time-dependent AUC to determine the best model of predicting recurrence events. We further performed differential gene expression analysis between the higher and lower-risk groups within AJCC pathologic tumor stage III patients to explore the underlying molecular mechanisms driving risk stratification.

RESULTS

The model combining baseline variables and WSI features had the best performance among models with any other types of data integration. The prognostic risk score generated by this model could provide a higher-resolution risk stratification within pathologically defined subgroups. We found the selected image features captured important immune-related variations, such as the aberration of expression in T cell activation and proliferation gene sets, and therefore contributed to the improved prediction.

CONCLUSIONS

Our study provided a prognostic model based on the combination of baseline variables and computer-processed WSI features. This model provided more accurate prediction than models based on other types of data combination in recurrence-free survival analysis.

TRIAL REGISTRATION

This study was based on public open data from TCGA and hence the study objects were retrospectively registered.

A twelve-gene signature for survival prediction in malignant melanoma patients

Background

Melanoma is defined as a highly mutational heterogeneous disease containing numerous alternations of the molecule. However, due to the phenotypically and genetically heterogeneity of malignant melanoma, conventional clinical characteristics remain restricted or limited in the ability to accurately predict individual outcomes and survival. This study aimed to establish an accurate gene expression signature to predict melanoma prognosis.

Methods

In this study, we established an RNA sequencing-based 12-gene signature data of melanoma patients obtained from 2 independent databases: the Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. We evaluated the quality of each gene to predict survival conditions in each database by employing univariate and multivariate regression models. A prognostic risk score based on a prognostic signature was determined. This prognostic gene signature further classified patients into low-risk and high-risk groups.

Results

Based on a prognostic signature, a prognostic risk score was determined. This prognostic gene signature further divided the patients into low-risk and high-risk groups. In the chemotherapy and radiotherapy groups of the TCGA cohort and V-raf murine sarcoma viral oncogene homolog B1 (BRAF) expression group in the GEO cohort, patients in the low-risk group had a longer survival duration compared to patients in the high-risk group. Nevertheless, the immunotherapy group in the TCGA database and neuroblastoma RAS viral oncogene homolog (NRAS) expression group in the GEO database had no significant differences in statistics. Moreover, this gene signature was associated with patient prognosis regardless of whether the Breslow depth was greater than or less than 3.75 mm. Stratified gene set enrichment analysis (GSEA) revealed that certain immune-related pathways, such as the T-cell signaling pathway, chemokine signaling pathway, and primary immunodeficiency, were significantly enriched in the low-risk group of both TCGA and GEO cohorts. This information implied the immune-related properties of the 12-gene signature.

Conclusions

Our study emphasizes the significance of the gene expression signature in that it may be an indispensable prognostic predictor in melanoma and has great potential for application in personalized treatment.

A Machine-Learning Approach to Identify a Prognostic Cytokine Signature That Is Associated With Nivolumab Clearance in Patients With Advanced Melanoma

Lower clearance of immune checkpoint inhibitors is a predictor of improved overall survival (OS) in patients with advanced cancer. We investigated a novel approach using machine learning to identify a baseline composite cytokine signature via clearance, which, in turn, could be associated with OS in advanced melanoma. Peripheral nivolumab clearance and cytokine data from patients treated with nivolumab in two phase III studies (n = 468 (pooled)) and another phase III study (n = 158) were used for machine-learning model development and validation, respectively. Random forest (Boruta) algorithm was used for feature selection and classification of nivolumab clearance. The 16 top-ranking baseline inflammatory cytokines reflecting immune-cell modulation were selected as a composite signature to predict nivolumab clearance (area under the curve (AUC) = 0.75; accuracy = 0.7). Predicted clearance (high vs. low) via the cytokine signature was significantly associated with OS across all three studies (P < 0.01), regardless of treatment (nivolumab vs. chemotherapy).

Differential Diagnosis and Precision Therapy of Two Typical Malignant Cutaneous Tumors Leveraging Their Tumor Microenvironment: A Photomedicine Strategy

Elevated hydrogen peroxide (H2O2) in biological tissues is generally recognized to be relevant to the carcinogenesis process that regulates the proliferative activity of cancer cells and the transformation of malignant features. Inspired by this observation, it can be hypothesized that imaging H2O2 in the tumor microenvironment (TME) could help diagnose tumor types and malignancy, and even guide precise therapy. Thus, in this study, a noninvasive photomedicine strategy is demonstrated that leverages the different levels of H2O2 in the TME, and two representative skin cancers, malignant melanoma (MM, clinically higher incidence of metastasis and recurrence) and cutaneous squamous cell carcinoma (cSCC, relatively less dangerous), are differentially diagnosed. The working probe used here is one we previously developed, namely, intelligent H2O2 responsive ABTS-loaded HRP@Gd nanoprobes (iHRANPs). In this study, iHRANPs have advantages over ratiometric imaging due to their bimodal imaging elements, in which the inherent magnetic resonance imaging (MR) mode can be used as the internal imaging reference and the H2O2 responsive photoacoustic (PA) imaging modality can be used for differential diagnosis. Results showed that after intravenous injection of iHRANPs, the tumor signals on both MM and cSCC are obviously enhanced without significant difference under the MR modality. However, under the PA modality, MM and cSCC can be easily distinguished with obvious variations in signal enhancement. Particularly, guided by PA imaging, photothermal therapy (PTT) can be precisely applied on MM, and a strong antitumor effect was achieved owing to the excessive H2O2 in the TME of MM. Furthermore, exogenous H2O2 was injected into cSCC to remedy H2O2 deficiency in the TME of cSCC, and an evident therapeutic efficacy on cSCC can also be realized. This study demonstrated that MM can be differentially diagnosed from cSCC by noninvasive imaging of H2O2 in the TME with iHRANPs; meanwhile, it further enabled imaging-guided precision PTT ablation, even for those unsatisfactory tumor types (cSCC) through exogenously delivering H2O2.

Prognostic genes of melanoma identified by weighted gene co-expression network analysis and drug repositioning using a network-based method

Melanoma is one of the most malignant types of skin cancer. However, the efficacy and utility of available drug therapies for melanoma are limited. The objective of the present study was to identify potential genes associated with melanoma progression and to explore approved therapeutic drugs that target these genes. Weighted gene co-expression network analysis was used to construct a gene co-expression network, explore the associations between genes and clinical characteristics and identify potential biomarkers. Gene expression profiles of the GSE65904 dataset were obtained from the Gene Expression Omnibus database. RNA-sequencing data and clinical information associated with melanoma obtained from The Cancer Genome Atlas were used for biomarker validation. A total of 15 modules were identified through average linkage hierarchical clustering. In the two significant modules, three network hub genes associated with melanoma prognosis were identified: C-X-C motif chemokine receptor 4 (CXCR4), interleukin 7 receptor (IL7R) and phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit γ (PIK3CG). The receiver operating characteristic curve indicated that the mRNA levels of these genes exhibited excellent prognostic efficiency for primary and metastatic tumor tissues. In addition, the proximity between candidate genes associated with melanoma progression and drug targets obtained from DrugBank was calculated in the protein interaction network, and the top 15 drugs that may be suitable for treating melanoma were identified. In summary, co-expression network analysis led to the selection of CXCR4, IL7R and PIK3CG for further basic and clinical research on melanoma. Utilizing a network-based method, 15 drugs that exhibited potential for the treatment of melanoma were identified.

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.