Development and validation of a nomogram to predict recurrence and melanoma-specific mortality in patients with negative sentinel lymph nodes

Establishment and validation of a nomogram model for predicting the specific mortality risk of melanoma in upper limbs based on the SEER database

For patients with upper limb melanoma, the significance of specific death is more important than that of all-cause death, and traditional survival analysis may overestimate the mortality rate of patients. Therefore, the nomogram model for predicting the specific mortality risk of melanoma in the upper limbs was developed. A population with melanoma in the upper limbs, diagnosed from 2010 to 2015, were selected from the National Cancer Institute database of Surveillance, Epidemiology, and End Results (SEER). The independent predictive factors of specific death were confirmed by the competing risk model of one-factor analysis and multi-factor analysis, and the nomogram was constructed according to the independent predictive factors. 17,200 patients with upper limb melanoma were enrolled in the study (training cohort: n = 12,040; validation cohort: n = 5160). Multi-factor analysis of the competing risk model showed that age, marital status, gender, tumor stage, T stage, M stage, regional lymph node surgery information, radiotherapy, chemotherapy, mitotic cell count, ulcer and whether there were multiple primary cancers, were independent factors affecting the specific death of upper limb melanoma patients (P < 0.05). The nomogram has good predictive ability regarding the specific mortality risk of melanoma in the upper limbs, and could be of great help to formulate prognostic treatment strategies and follow-up strategies that are conducive to survival.

Development and validation of a novel model to predict recurrence-free survival and melanoma-specific survival after sentinel lymph node biopsy in patients with melanoma: an international, retrospective, multicentre analysis

BACKGROUND

The introduction of adjuvant systemic treatment for patients with high-risk melanomas necessitates accurate staging of disease. However, inconsistencies in outcomes exist between disease stages as defined by the American Joint Committee on Cancer (8th edition). We aimed to develop a tool to predict patient-specific outcomes in people with melanoma rather than grouping patients according to disease stage.

METHODS

Patients older than 13 years with confirmed primary melanoma who underwent sentinel lymph node biopsy (SLNB) between Oct 29, 1997, and Nov 11, 2013, at four European melanoma centres (based in Berlin, Germany; Amsterdam and Rotterdam, the Netherlands; and Warsaw, Poland) were included in the development cohort. Potential predictors of recurrence-free and melanoma-specific survival assessed were sex, age, presence of ulceration, primary tumour location, histological subtype, Breslow thickness, sentinel node status, number of sentinel nodes removed, maximum diameter of the largest sentinel node metastasis, and Dewar classification. A prognostic model and nomogram were developed to predict 5-year recurrence-free survival on a continuous scale in patients with stage pT1b or higher melanomas. This model was also calibrated to predict melanoma-specific survival. Model performance was assessed by discrimination (area under the time-dependent receiver operating characteristics curve [AUC]) and calibration. External validation was done in a cohort of patients with primary melanomas who underwent SLNB between Jan 30, 1997, and Dec 12, 2013, at the Melanoma Institute Australia (Sydney, NSW, Australia).

FINDINGS

The development cohort consisted of 4071 patients, of whom 2075 (51%) were female and 1996 (49%) were male. 889 (22%) had sentinel node-positive disease and 3182 (78%) had sentinel node-negative disease. The validation cohort comprised 4822 patients, of whom 1965 (41%) were female and 2857 (59%) were male. 891 (18%) had sentinel node-positive disease and 3931 (82%) had sentinel node-negative disease. Median follow-up was 4·8 years (IQR 2·3-7·8) in the development cohort and 5·0 years (2·2-8·9) in the validation cohort. In the development cohort, 5-year recurrence-free survival was 73·5% (95% CI 72·0-75·1) and 5-year melanoma-specific survival was 86·5% (85·3-87·8). In the validation cohort, the corresponding estimates were 66·1% (64·6-67·7) and 83·3% (82·0-84·6), respectively. The final model contained six prognostic factors: sentinel node status, Breslow thickness, presence of ulceration, age at SLNB, primary tumour location, and maximum diameter of the largest sentinel node metastasis. In the development cohort, for the model's prediction of recurrence-free survival, the AUC was 0·80 (95% CI 0·78-0·81); for prediction of melanoma-specific survival, the AUC was 0·81 (0·79-0·84). External validation showed good calibration for both outcomes, with AUCs of 0·73 (0·71-0·75) and 0·76 (0·74-0·78), respectively.

INTERPRETATION

Our prediction model and nomogram accurately predicted patient-specific risk probabilities for 5-year recurrence-free and melanoma-specific survival. These tools could have important implications for clinical decision making when considering adjuvant treatments in patients with high-risk melanomas.

FUNDING

Erasmus Medical Centre Cancer Institute.

Prognostic nomograms for predicting long-term overall survival in spindle cell melanoma: a population-based study

Background

There are few research findings on the survival prognosis of spindle cell melanoma (SCM), which is an unusual kind of melanoma. The purpose of this study was to develop a thorough nomogram for predicting the overall survival (OS) of patients with SCM and to assess its validity by comparing it with the conventional American Joint Committee on Cancer (AJCC) staging system.

Methods

The Surveillance, Epidemiology, and End Results database was searched, and 2,015 patients with SCM were selected for the analysis. The patients were randomly divided into training (n = 1,410) and validation (n = 605) cohorts by using R software. Multivariate Cox regression was performed to identify predictive factors. A nomogram was established based on these characteristics to predict OS in SCM. The calibration curve, concordance index (C-index), area under the receiver operating characteristic curve, and decision-curve analysis were utilized to assess the accuracy and reliability of the model. The net reclassification improvement and integrated discrimination improvement were also applied in this model to evaluate its differences with the AJCC model.

Results

The developed nomogram suggests that race, AJCC stage, chemotherapy status, regional node examination status, marital status, and sex have the greatest effects on OS in SCM. The nomogram had a higher C-index than the AJCC staging system (0.751 versus 0.633 in the training cohort and 0.747 versus 0.650 in the validation cohort). Calibration plots illustrated that the model was capable of being calibrated. These criteria demonstrated that the nomogram outperforms the AJCC staging system alone.

Conclusion

The nomogram developed in this study is sufficiently reliable for forecasting the risk and prognosis of SCM, which may facilitate personalized treatment recommendations in upcoming clinical trials.

Prognostic and predictive biomarkers in melanoma

Biomarkers help to inform the clinical management of patients with melanoma. For patients with clinically localised primary melanoma, biomarkers can help to predict post-surgical outcome (including via the use of risk prediction tools), better select patients for sentinel lymph node biopsy, and tailor catch-all follow-up protocols to the individual. Systemic drug treatments, including immune checkpoint inhibitor (ICI) therapies and BRAF-targeted therapies, have radically improved the prognosis of metastatic (stage III and IV) cutaneous melanoma patients, and also shown benefit in the earlier setting of stage IIB/C primary melanoma. Unfortunately, a response is far from guaranteed. Here, we review clinically relevant, established, and emerging, prognostic, and predictive pathological biomarkers that refine clinical decision-making in primary and metastatic melanoma patients. Gene expression profile assays and nomograms are emerging tools for prognostication and sentinel lymph node risk prediction in primary melanoma patients. Biomarkers incorporated into clinical practice guidelines include BRAF V600 mutations for the use of targeted therapies in metastatic cutaneous melanoma, and the HLA-A∗02:01 allele for the use of a bispecific fusion protein in metastatic uveal melanoma. Several predictive biomarkers have been proposed for ICI therapies but have not been incorporated into Australian clinical practice guidelines. Further research, validation, and assessment of clinical utility is required before more prognostic and predictive biomarkers are fluidly integrated into routine care.

Predicting Recurrence-Free and Overall Survival for Patients With Stage II Melanoma: The MIA Calculator

PURPOSE

Improvements in recurrence-free survival (RFS) were demonstrated in two recent randomized trials for patients with sentinel node (SN)-negative stage IIB or IIC melanoma receiving adjuvant systemic therapy (pembrolizumab/nivolumab). However, adverse events also occurred. Accurate individualized prognostic estimates of RFS and overall survival (OS) would allow patients to more accurately weigh the risks and benefits of adjuvant therapy. Since the current American Joint Committee on Cancer eighth edition (AJCC-8) melanoma staging system focuses on melanoma-specific survival, we developed a multivariable risk prediction calculator that provides estimates of 5- and 10-year RFS and OS for these patients.

METHODS

Data were extracted from the Melanoma Institute Australia (MIA) database for patients diagnosed with stage II (clinical or pathological) melanoma (n = 3,220). Survival prediction models were developed using multivariable Cox regression analyses (MIA models) and externally validated twice using data sets from the United States and the Netherlands. Each model's performance was assessed using C-statistics and calibration plots and compared with Cox models on the basis of AJCC-8 staging (stage models).

RESULTS

The 5-year and 10-year RFS C-statistics were 0.70 and 0.73 (MIA-model) versus 0.61 and 0.60 (stage-model), respectively. For OS, the 5-year and 10-year C-statistics were 0.71 and 0.75 (MIA-model) compared with 0.62 and 0.61 (stage-model), respectively. The MIA models were well calibrated and externally validated.

CONCLUSION

The MIA models offer accurate and personalized estimates of both RFS and OS in patients with stage II melanoma even in the absence of pathological staging with SN biopsy. These models were robust on external validations and may be used in everyday practice both with (ideally) and without performing SN biopsy to identify high-risk patients for further management strategies. An online tool will be available at the MIA website (Risk Prediction Tools).

Predictive accuracy of risk prediction models for recurrence, metastasis and survival for early-stage cutaneous melanoma: a systematic review

OBJECTIVES

To identify prognostic models for melanoma survival, recurrence and metastasis among American Joint Committee on Cancer stage I and II patients postsurgery; and evaluate model performance, including overall survival (OS) prediction.

DESIGN

Systematic review and narrative synthesis.

DATA SOURCES

Searched MEDLINE, Embase, CINAHL, Cochrane Library, Science Citation Index and grey literature sources including cancer and guideline websites from 2000 to September 2021.

ELIGIBILITY CRITERIA

Included studies on risk prediction models for stage I and II melanoma in adults ≥18 years. Outcomes included OS, recurrence, metastases and model performance. No language or country of publication restrictions were applied.

DATA EXTRACTION AND SYNTHESIS

Two pairs of reviewers independently screened studies, extracted data and assessed the risk of bias using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist and the Prediction study Risk of Bias Assessment Tool. Heterogeneous predictors prevented statistical synthesis.

RESULTS

From 28 967 records, 15 studies reporting 20 models were included; 8 (stage I), 2 (stage II), 7 (stages I-II) and 7 (stages not reported), but were clearly applicable to early stages. Clinicopathological predictors per model ranged from 3-10. The most common were: ulceration, Breslow thickness/depth, sociodemographic status and site. Where reported, discriminatory values were ≥0.7. Calibration measures showed good matches between predicted and observed rates. None of the studies assessed clinical usefulness of the models. Risk of bias was high in eight models, unclear in nine and low in three. Seven models were internally and externally cross-validated, six models were externally validated and eight models were internally validated.

CONCLUSIONS

All models are effective in their predictive performance, however the low quality of the evidence raises concern as to whether current follow-up recommendations following surgical treatment is adequate. Future models should incorporate biomarkers for improved accuracy.

PROSPERO REGISTRATION NUMBER

CRD42018086784.

Establishment and validation of a nomogram for predicting immune-related prognostic features in trunk melanoma-specific death

Background

Trunk melanoma is one of the most common and deadly types of melanomas. Multiple factors are associated with the prognosis of patients with trunk melanoma. Currently, direct, and reliable clinical tools for early assessment of individual specific risk of death are limited, and most of them are prediction models for all-cause death. Their accuracy in predicting competitiveness events, which make up a relatively large portion, may be substantially compromised. Hence, we conducted this study to investigate the risk factors of trunk melanoma-specific death to establish a comprehensive prediction model suitable for clinical application.

Methods

Patients with trunk melanoma analyzed in this study were from the SEER program [2010-2015]. The random sampling method was used to split the included cases into the training and validation cohorts at a ratio of 7:3. Univariate and multivariate competing risk models were used to screen the independent influencing factors of specific death, and then a nomogram covering these independent predictors was constructed. The concordance index (C-index) and a calibration curve were used to evaluate the calibration degree and accuracy of the nomogram.

Results

We identified 21,198 patients with trunk melanoma from the SEER database, and 3,814 of them died (17.99%). Among the death cases, deaths from other causes accounted for 66.50%The prognostic nomogram included 8 variables and 16 independent influencing factors. The overall C-index in the training set was 0.89, and the receiver operating characteristic (ROC) curve for predicting 1-, 3-, and 5-year survival was 0.928 [95% confidence interval (CI): 0.911-0.945], 0.907 (95% CI: 0.895-0.918), and 0.891 (95% CI: 0.879-0.902), respectively. The C-index of the model in the validation set was 0.89, and the area under the ROC curve (AUC) for predicting 1-, 3-, and 5-year cancer-specific death (CSD) was 0.927 (95% CI: 0.899-0.955), 0.916 (95% CI: 0.901-0.930), and 0.905 (95% CI: 0.899-0.921). Both the training set and the validation set showed the ideal calibration degree.

Conclusions

This model can be used as a potential tool for prognostic risk management of trunk melanoma in the presence of many competing events.

Optimizing treatment approaches for patients with cutaneous melanoma by integrating clinical and pathologic features with the 31-gene expression profile test

BACKGROUND

Many patients with low-stage cutaneous melanoma will experience tumor recurrence, metastasis, or death, and many higher staged patients will not.

OBJECTIVE

To develop an algorithm by integrating the 31-gene expression profile test with clinicopathologic data for an optimized, personalized risk of recurrence (integrated 31 risk of recurrence [i31-ROR]) or death and use i31-ROR in conjunction with a previously validated algorithm for precise sentinel lymph node positivity risk estimates (i31-SLNB) for optimized treatment plan decisions.

METHODS

Cox regression models for ROR were developed (n = 1581) and independently validated (n = 523) on a cohort with stage I-III melanoma. Using National Comprehensive Cancer Network cut points, i31-ROR performance was evaluated using the midpoint survival rates between patients with stage IIA and stage IIB disease as a risk threshold.

RESULTS

Patients with a low-risk i31-ROR result had significantly higher 5-year recurrence-free survival (91% vs 45%, P < .001), distant metastasis-free survival (95% vs 53%, P < .001), and melanoma-specific survival (98% vs 73%, P < .001) than patients with a high-risk i31-ROR result. A combined i31-SLNB/ROR analysis identified 44% of patients who could forego sentinel lymph node biopsy while maintaining high survival rates (>98%) or were restratified as being at a higher or lower risk of recurrence or death.

LIMITATIONS

Multicenter, retrospective study.

CONCLUSION

Integrating clinicopathologic features with the 31-GEP optimizes patient risk stratification compared to clinicopathologic features alone.

Clinically Significant Risk Thresholds in the Management of Primary Cutaneous Melanoma: A Survey of Melanoma Experts

BACKGROUND

Risk-based thresholds to guide management are undefined in the treatment of primary cutaneous melanoma but are essential to advance the field from traditional stage-based treatment to more individualized care.

METHODS

To estimate treatment risk thresholds, hypothetical clinical melanoma scenarios were developed and a stratified random sample was distributed to expert melanoma clinicians via an anonymous web-based survey. Scenarios provided a defined 5-year risk of recurrence and asked for recommendations regarding clinical follow-up, imaging, and adjuvant therapy. Marginal probability of response across the spectrum of 5-year recurrence risk was estimated. The risk at which 50% of respondents recommended a treatment was defined as the risk threshold.

RESULTS

The overall response rate was 56% (89/159). Three separate multivariable models were constructed to estimate the recommendations for clinical follow-up more than twice/year, for surveillance cross-sectional imaging at least once/year, and for adjuvant therapy. A 36% 5-year risk of recurrence was identified as the threshold for recommending clinical follow-up more than twice/year. The thresholds for recommending cross-sectional imaging and adjuvant therapy were 30 and 59%, respectively. Thresholds varied with the age of the hypothetical patient: at younger ages they were constant but increased rapidly at ages 60 years and above.

CONCLUSIONS

To our knowledge, these data provide the first estimates of clinically significant treatment thresholds for patients with cutaneous melanoma based on risk of recurrence. Future refinement and adoption of thresholds would permit assessment of the clinical utility of novel prognostic tools and represents an early step toward individualizing treatment recommendations.

Using a Clinicopathologic and Gene Expression (CP-GEP) Model to Identify Stage I-II Melanoma Patients at Risk of Disease Relapse

Background: The current standard of care for patients without sentinel node (SN) metastasis (i.e., stage I−II melanoma) is watchful waiting, while >40% of patients with stage IB−IIC will eventually present with disease recurrence or die as a result of melanoma. With the prospect of adjuvant therapeutic options for patients with a negative SN, we assessed the performance of a clinicopathologic and gene expression (CP-GEP) model, a model originally developed to predict SN metastasis, to identify patients with stage I−II melanoma at risk of disease relapse. Methods: This study included patients with cutaneous melanoma ≥18 years of age with a negative SN between October 2006 and December 2017 at the Sahlgrenska University Hospital (Sweden) and Erasmus MC Cancer Institute (The Netherlands). According to the CP-GEP model, which can be applied to the primary melanoma tissue, the patients were stratified into high or low risk of recurrence. The primary aim was to assess the 5-year recurrence-free survival (RFS) of low- and high-risk CP-GEP. A secondary aim was to compare the CP-GEP model with the EORTC nomogram, a model based on clinicopathological variables only. Results: In total, 535 patients (stage I−II) were included. CP-GEP stratification among these patients resulted in a 5-year RFS of 92.9% (95% confidence interval (CI): 86.4−96.4) in CP-GEP low-risk patients (n = 122) versus 80.7% (95%CI: 76.3−84.3) in CP-GEP high-risk patients (n = 413; hazard ratio 2.93 (95%CI: 1.41−6.09), p < 0.004). According to the EORTC nomogram, 25% of the patients were classified as having a ‘low risk’ of recurrence (96.8% 5-year RFS (95%CI 91.6−98.8), n = 130), 49% as ‘intermediate risk’ (88.4% 5-year RFS (95%CI 83.6−91.8), n = 261), and 26% as ‘high risk’ (61.1% 5-year RFS (95%CI 51.9−69.1), n = 137). Conclusion: In these two independent European cohorts, the CP-GEP model was able to stratify patients with stage I−II melanoma into two groups differentiated by RFS.

Development and validation of a nomogram incorporating gene expression profiling and clinical factors for accurate prediction of metastasis in patients with cutaneous melanoma following Mohs micrographic surgery

BACKGROUND

There is a need to improve prognostic accuracy for patients with cutaneous melanoma. A 31-gene expression profile (31-GEP) test uses the molecular biology of primary tumors to identify individual patient metastatic risk.

OBJECTIVE

Develop a nomogram incorporating 31-GEP with relevant clinical factors to improve prognostic accuracy.

METHODS

In an IRB-approved study, 1124 patients from 9 Mohs micrographic surgery centers were prospectively enrolled, treated with Mohs micrographic surgery, and underwent 31-GEP testing. Data from 684 of those patients with at least 1-year follow-up or a metastatic event were included in nomogram development to predict metastatic risk.

RESULTS

Logistic regression modeling of 31-GEP results and T stage provided the simplest nomogram with the lowest Bayesian information criteria score. Validation in an archival cohort (n = 901) demonstrated a significant linear correlation between observed and nomogram-predicted risk of metastasis. The resulting nomogram more accurately predicts the risk for cutaneous melanoma metastasis than T stage or 31-GEP alone.

LIMITATIONS

The patient population is representative of Mohs micrographic surgery centers. Sentinel lymph node biopsy was not performed for most patients and could not be used in the nomogram.

CONCLUSIONS

Integration of 31-GEP and T stage can gain clinically useful prognostic information from data obtained noninvasively.

Improved cutaneous melanoma survival stratification through integration of 31-gene expression profile testing with the American Joint Committee on Cancer 8th Edition Staging

Cutaneous melanoma (CM) survival is assessed using averaged data from the American Joint Committee on Cancer 8th edition (AJCC8). However, subsets of AJCC8 stages I-III have better or worse survival than the predicted average value. The objective of this study was to determine if the 31-gene expression profile (31-GEP) test for CM can further risk-stratify melanoma-specific mortality within each AJCC8 stage. This retrospective multicenter study of 901 archival CM samples obtained from patients with stages I-III CM assessed 31-GEP test predictions of 5-year melanoma-specific survival (MSS) using Kaplan-Meier and Cox proportional hazards. In stage I-III CM population, patients with a Class 2B result had a lower 5-year MSS (77.8%) than patients with a Class 1A result (98.7%) and log-rank testing demonstrated significant stratification of MSS [χ2 (2df, n = 901) = 99.7, P < 0.001). Within each stage, 31-GEP data provided additional risk stratification, including in stage I [χ2 (2df, n = 415) = 11.3, P = 0.004]. Cox regression multivariable analysis showed that the 31-GEP test was a significant predictor of melanoma-specific mortality (MSM) in patients with stage I-III CM [hazard ratio: 6.44 (95% confidence interval: 2.61-15.85), P < 0.001]. This retrospective study focuses on Class 1A versus Class 2B results. Intermediate results (Class 1B/2A) comprised 21.6% of cases with survival rates between Class 1A and 2B, and similar to 5-year MSS AJCC stage values. Data from the 31-GEP test significantly differentiates MSM into lower (Class 1A) and higher risk (Class 2B) groups within each AJCC8 stage. Incorporating 31-GEP results into AJCC8 survival calculations has the potential to more precisely assess survival and enhance management guidance.

Characteristics, Prognosis, and Competing Risk Nomograms of Cutaneous Malignant Melanoma: Evidence for Pigmentary Disorders

PURPOSE

Cutaneous malignant melanoma (CMM) always presents as a complex disease process with poor prognosis. The objective of the present study was to explore the influence of solitary or multiple cancers on the prognosis of patients with CMM to better understand the landscape of CMM.

METHODS

We reviewed the records of CMM patients between 2004 and 2015 from the Surveillance, Epidemiology, and End Results Program. The cumulative incidence function was used to represent the probabilities of death. A novel causal inference method was leveraged to explore the risk difference to death between different types of CMM, and nomograms were built based on competing risk models.

RESULTS

The analysis cohort contained 165,043 patients with CMM as the first primary malignancy. Patients with recurrent CMM and multiple primary tumors had similar overall survival status (p = 0.064), while their demographics and cause-specific death demonstrated different characteristics than those of patients with solitary CMM (p < 0.001), whose mean survival times are 75.4 and 77.3 months and 66.2 months, respectively. Causal inference was further applied to unveil the risk difference of solitary and multiple tumors in subgroups, which was significantly different from the total population (p < 0.05), and vulnerable groups with high risk of death were identified. The established competing risk nomograms had a concordance index >0.6 on predicting the probabilities of death of CMM or other cancers individually across types of CMM.

CONCLUSION

Patients with different types of CMM had different prognostic characteristics and different risk of cause-specific death. The results of this study are of great significance in identifying the high risk of cause-specific death, enabling targeted intervention in the early period at both the population and individual levels.

A rare case of solitary gallbladder metastasis from an early cutaneous melanoma

Solitary gallbladder metastasis from melanoma is a rare phenomenon, in this case manifesting as biliary symptoms during and following pregnancy. It is important to consider uncommon causes of biliary symptoms to aid in prompt diagnosis and treatment. This patient was successfully treated with laparoscopic cholecystectomy and adjuvant immunotherapy.

Predicting recurrence in patients with sentinel node-negative melanoma: validation of the EORTC nomogram using population-based data

BACKGROUND

Identifying patients with sentinel node (SN)-negative melanoma who are at greatest risk of recurrence is important. The European Organization for Research and Treatment of Cancer (EORTC) Melanoma Group proposed a prognostic model that has not been validated in population-based data. The EORTC nomogram includes Breslow thickness, ulceration status and anatomical location as parameters. The aim of this study was to validate the EORTC model externally using a large national data set.

METHODS

Adults with histologically proven, invasive cutaneous melanoma with a negative SN biopsy in the Netherlands between 2000 and 2014 were identified from the Dutch Pathology Registry, and relevant data were extracted. The EORTC nomogram was used to predict recurrence-free survival. The predictive performance of the nomogram was assessed by discrimination (C-statistic) and calibration.

RESULTS

A total of 8795 patients met the eligibility criteria, of whom 14·7 per cent subsequently developed metastatic disease. Of these recurrences, 20·9 per cent occurred after the first 5 years of follow-up. Validation of the EORTC nomogram showed a C-statistic of 0·70 (95 per cent c.i. 0·68 to 0·71) for recurrence-free survival, with excellent calibration (R2 = 0·99; P = 0·999, Hosmer-Lemeshow test).

CONCLUSION

This population-based validation confirmed the value of the EORTC nomogram in predicting recurrence-free survival in patients with SN-negative melanoma. The EORTC nomogram could be used in clinical practice for personalizing follow-up and selecting high-risk patients for trials of adjuvant systemic therapy.

Novel Prognostic Models Predicting the Cancer-Specific Survival in Patients with Cutaneous Melanoma Based on Metastatic Lymph Node Status

BACKGROUND

In cutaneous melanoma (CM), the present methods of lymph node (LN) staging have not sufficiently utilized the prognostic information of metastatic LNs. In this study, we aimed to construct prognostic nomograms based on the number of positive LNs (PLNs) and other clinicopathologic characteristics of CM patients.

METHODS

Two prognostic models were constructed in the none/single PLN (PLN_none/single) and multiple PLN (PLN_multiple) cohorts, respectively. Independent prognostic predictors associated with cancer-specific survival (CSS) in the above two cohorts were integrated to construct two nomograms for predicting the probability of 2-, 4-, and 6-year CSS in the PLN_none/single and PLN_multiple cohorts. The nomograms were evaluated by the area under the receiver operating characteristic curves (AUC), the calibration plots, and the decision curve analyses (DCAs).

RESULTS

A total of 31,065 CM cases were included in this study. Factors included in the prognostic nomogram for patients in the PLN_none/single cohort were age, sex, race, marital status, insurance, primary tumor site, T stage, and number of PLNs, while factors included in the nomogram for cases in the PLN_multiple cohort included age, sex, marital status, insurance, primary tumor site, T stage, and number of PLNs. The AUC values for 2-, 4-, and 6-year CSS in the validation group of the PLN_none/single cohort were 0.833, 0.811, and 0.818, respectively, while in the validation group of the PLN_multiple cohort, the AUC values for 2-, 4,- and 6-year CSS were 0.720, 0.723, and 0.745, respectively. Compared with the American Joint Committee on Cancer 7th edition staging system, our two nomograms showed better predictive values. Additionally, the calibration plots and DCA curves for 2-, 4-, and 6-year CSS prediction demonstrated good coordination and net benefit in both the PLN_none/single and PLN_multiple cohorts.

CONCLUSION

Our nomograms, based on the number of PLNs and other clinicopathologic characteristics, showed good predictive ability for predicting the survival of CM patients.

A Novel Nomogram and Risk Classification System Predicting the Cancer-Specific Mortality of Patients with Initially Diagnosed Metastatic Cutaneous Melanoma

BACKGROUND

Cutaneous melanoma and distant organ metastasis has varying outcomes. Considering all prognostic indicators in a prediction model might assist in selecting cases who could benefit from a personalized therapy strategy.

OBJECTIVE

This study aimed to develop and validate a prognostic model for patients with metastatic melanoma.

METHODS

A total of 1535 cases diagnosed with metastatic cutaneous melanoma (stage IV) were identified from the Surveillance, Epidemiology, and End Results database. Patients were randomly divided into the training (n = 1023) and validation (n = 512) cohorts. A prognostic nomogram was established based predominantly on results from the competing-risk regression model for predicting cancer-specific death (CSD). The area under the time-dependent receiver operating characteristic curve (AUC), calibration curves, and decision curve analyses (DCAs) were used to evaluate the nomogram.

RESULTS

No significant differences were observed in the clinical characteristics between the training and validation cohorts. In the training cohort, patient-, tumor-, and treatment-related predictors of CSD for metastatic melanoma included age, sex, race, marital status, insurance, American Joint Committee on Cancer T and N stage, number of metastatic organs, surgical treatment, and chemotherapy. All these factors were used for nomogram construction. The time-dependent AUC values of the training and validation cohorts suggested a favorable performance and discrimination of the nomogram. The 6-, 12-, and 18-month AUC values were 0.706, 0.700, and 0.706 in the training cohort, and 0.702, 0.670, and 0.656 in the validation cohort, respectively. The calibration curves for the probability of death at 6, 12, and 18 months showed acceptable agreement between the values predicted by the nomogram and the observed outcomes in both cohorts. DCA curves showed good positive net benefits in the prognostic model among most of the threshold probabilities at different time points (death at 6, 12, and 18 months). Based on the total nomogram scores of each case, all patients were divided into the low-risk (n = 511), intermediate-risk (n = 512), and high-risk (n = 512) groups, and the risk classification could identify cases with a high risk of death in both cohorts.

CONCLUSIONS

A predictive nomogram and a corresponding risk classification system for CSD in patients with metastatic melanoma were developed in this study, which may assist in patient counseling and in guiding clinical decision making for cases with metastatic melanoma.

The PD-1/PD-L1-Checkpoint Restrains T cell Immunity in Tumor-Draining Lymph Nodes

PD-1/PD-L1-checkpoint blockade therapy is generally thought to relieve tumor cell-mediated suppression in the tumor microenvironment but PD-L1 is also expressed on non-tumor macrophages and conventional dendritic cells (cDCs). Here we show in mouse tumor models that tumor-draining lymph nodes (TDLNs) are enriched for tumor-specific PD-1⁺ T cells which closely associate with PD-L1⁺ cDCs. TDLN-targeted PD-L1-blockade induces enhanced anti-tumor T cell immunity by seeding the tumor site with progenitor-exhausted T cells, resulting in improved tumor control. Moreover, we show that abundant PD-1/PD-L1-interactions in TDLNs of nonmetastatic melanoma patients, but not those in corresponding tumors, associate with early distant disease recurrence. These findings point at a critical role for PD-L1 expression in TDLNs in governing systemic anti-tumor immunity, identifying high-risk patient groups amendable to adjuvant PD-1/PD-L1-blockade therapy.

Prognostic Implications of Metabolism Related Gene Signature in Cutaneous Melanoma

Metabolic reprogramming is closely related to melanoma. However, the prognostic role of metabolism-related genes (MRGs) remains to be elucidated. We aimed to establish a nomogram by combining MRGs signature and clinicopathological factors to predict melanoma prognosis. Eighteen prognostic MRGs between melanoma and normal samples were identified using The Cancer Genome Atlas (TCGA) and GSE15605. WARS (HR = 0.881, 95% CI = 0.788–0.984, P = 0.025) and MGST1 (HR = 1.124, 95% CI = 1.007–1.255, P = 0.037) were ultimately identified as independent prognostic MRGs with LASSO regression and multivariate Cox regression. The MRGs signature was established according to these two genes and externally validated in the Gene Expression Omnibus (GEO) dataset. Kaplan-Meier survival analysis indicated that patients in the high-risk group had significantly poorer overall survival (OS) than those in the low-risk group. Furthermore, the MRGs signature was identified as an independent prognostic factor for melanoma survival. An MRGs nomogram based on the MRGs signature and clinicopathological factors was developed in TCGA cohort and validated in the GEO dataset. Calibration plots showed good consistency between the prediction of nomogram and actual observation. The receiver operating characteristic curve and decision curve analysis indicated that MRGs nomogram had better OS prediction and clinical net benefit than the stage system. To our knowledge, we are the first to develop a prognostic nomogram based on MRGs signature with better predictive power than the current staging system, which could assist individualized prognosis prediction and improve treatment.

A novel predictive model incorporating immune-related gene signatures for overall survival in melanoma patients

Melanoma is the most invasive type of skin cancer, in which the immune system plays a vital role. In this study, we aimed to establish a prognostic prediction nomogram for melanoma patients that incorporates immune-related genes (IRGs). Ninety-seven differentially expressed IRGs between melanoma and normal skin were screened using gene expression omnibus database (GEO). Among these IRGs, a two-gene signature consisting of CCL8 and DEFB1 was found to be closely associated with patient prognosis using the cancer genome atlas (TCGA) database. Survival analysis verified that the IRGs score based on the signature gene expressions efficiently distinguished between high- and low-risk patients, and was identified to be an independent prognostic factor. A nomogram integrating the IRGs score, age and TNM stage was established to predict individual prognosis for melanoma. The prognostic performance was validated by the TCGA/GEO-based concordance indices and calibration plots. The area under the curve demonstrated that the nomogram was superior than the conventional staging system, which was confirmed by the decision curve analysis. Overall, we developed and validated a nomogram for prognosis prediction in melanoma based on IRGs signatures and clinical parameters, which could be valuable for decision making in the clinic.

External validation of a prognostic model to predict survival of patients with sentinel node-negative melanoma

Background

Identifying patients with sentinel node‐negative melanoma at high risk of recurrence or death is important. The European Organisation for Research and Treatment of Cancer (EORTC) recently developed a prognostic model including Breslow thickness, ulceration and site of the primary tumour. The aims of the present study were to validate this prognostic model externally and to assess whether it could be improved by adding other prognostic factors.

Methods

Patients with sentinel node‐negative cutaneous melanoma were included in this retrospective single‐institution study. The β values of the EORTC prognostic model were used to predict recurrence‐free survival and melanoma‐specific survival. The predictive performance was assessed by discrimination (c‐index) and calibration. Seeking to improve the performance of the model, additional variables were added to a Cox proportional hazards model.

Results

Some 4235 patients with sentinel node‐negative cutaneous melanoma were included. The median follow‐up time was 50 (i.q.r. 18·5–81·5) months. Recurrences and deaths from melanoma numbered 793 (18·7 per cent) and 456 (10·8 per cent) respectively. Validation of the EORTC model showed good calibration for both outcomes, and a c‐index of 0·69. The c‐index was only marginally improved to 0·71 when other significant prognostic factors (sex, age, tumour type, mitotic rate) were added.

Conclusion

This study validated the EORTC prognostic model for recurrence‐free and melanoma‐specific survival of patients with negative sentinel nodes. The addition of other prognostic factors only improved the model marginally. The validated EORTC model could be used for personalizing follow‐up and selecting high‐risk patients for trials of adjuvant systemic therapy.