Maximizing the clinical usefulness of a nomogram to select patients candidate to sentinel node biopsy for cutaneous melanoma

Molecular-Guided Therapy for Melanoma in Canada: Overview of Current Practices and Recommendations

The emergence of pathologist-driven molecular reflex testing for tumoural biomarkers is a significant advancement in cancer diagnostics, facilitating targeted cancer therapy for our patients. Based on our experience, the Canadian landscape of pathologist-driven reflex biomarker testing for melanoma lacks standardization and is plagued by a lack of awareness by pathologists and clinicians. This paper comprehensively examines the approaches to reflex biomarker testing for melanoma patients across Canada, highlighting the regional variations in the criteria for initiating molecular testing, the biomarkers tested, and the molecular techniques employed. We also discuss the clinical relevance of biomarkers, emphasizing their alignment with the National Comprehensive Cancer Network® (NCCN®) Clinical Practice Guidelines in Oncology (NCCN Guidelines®) as well as ancillary tests such as BRAF VE1 immunohistochemistry to detect BRAF V600E mutation and molecular techniques such as real-time polymerase chain reaction, matrix-assisted laser desorption ionization-time of flight mass spectrometry and next-generation sequencing. Our proposed standardized minimum criteria for reflex testing prioritize melanomas with Breslow thickness >4 mm or disseminated disease, who will most benefit from enhanced delivery of biomarkers and expedited access to targeted therapies while attempting to balance cost-effectiveness and utilization of public healthcare resources with patient outcomes.

Are Nomograms Useful for Predicting Sentinel Lymph Node Status in Melanoma Patients?

BACKGROUND AND OBJECTIVES

Clinical nomograms have been developed to predict sentinel lymph node (SLN) status in early-stage melanoma patients, but the clinical utility of these tools remains debatable. We created and validated a nomogram using data from a randomized clinical trial and assessed its accuracy against the well-validated Melanoma Institute Australia (MIA) nomogram.

METHODS

We developed our model to predict SLN status using logistic regression on clinicopathological patient data from the Multicenter Selective Lymphadenectomy Trial-I. The model was externally validated using the National Cancer Database (NCDB) data set, and its performance was compared to that of the MIA nomogram.

RESULTS

Our model had good discrimination between positive and negative SLNs, with a training set area under the curve (AUC) of 0.706 (0.661-0.751). Our model achieved an AUC of 0.715 (0.706-0.724) compared to 0.723 (0.715-0.731) with the MIA model, using the NCDB set.

CONCLUSION

Our model performed similarly to the MIA model, confirming that despite using different clinical features and data sets, no clinical nomogram is currently accurate enough for clinical use.

Sentinel lymph node positivity in melanoma: Which risk prediction tool is most accurate?

BACKGROUND

Sentinel lymph node biopsy for melanoma determines treatment and prognostic factors and improves disease-specific survival. To risk-stratify patients for sentinel lymph node biopsy consideration, Memorial Sloan Kettering Cancer Center and Melanoma Institute Australia developed nomograms to predict sentinel lymph node positivity. We aimed to compare the accuracy of these 2 nomograms.

METHODS

A multi-institutional study of patients with melanoma receiving sentinel lymph node biopsy between September 2018 and December 2022 was performed. The accuracy of the 2 risk prediction tools in determining a positive sentinel lymph node biopsy was analyzed using receiver operating characteristic curves and area under the curve.

RESULTS

In total, 532 patients underwent sentinel lymph node biopsy for melanoma; 98 (18.4%) had positive sentinel lymph node. Increasing age was inversely related to sentinel lymph node positivity (P < .01); 35.7% of patients ≤30 years had positive sentinel lymph node compared with 9.7% of patients ≥75 years. When we analyzed the entire study population, accuracy of the 2 risk prediction tools was equal (area under the curveMemorial Sloan Kettering Cancer Center: 0.693; area under the curveMIA: 0.699). However, Memorial Sloan Kettering Cancer Center tool was a better predictor in patients aged ≥75 years (area under the curveMemorial Sloan Kettering Cancer Center: 0.801; area under the curveMelanoma Institute Australia: 0.712, P < .01) but Melanoma Institute Australia tool performed better in patients with a higher mitotic index (mitoses/mm2 ≥2; area under the curveMemorial Sloan Kettering Cancer Center: 0.659; area under the curveMelanoma Institute Australia: 0.717, P = .027). Both models were poor predictors of sentinel lymph node positivity in young patients (age ≤30 years; area under the curveMemorial Sloan Kettering Cancer Center: 0.456; area under the curveMelanoma Institute Australia: 0.589, P = .283).

CONCLUSION

The current study suggests that the 2 risk stratification tools differ in their abilities to predict sentinel lymph node positivity in specific populations: Memorial Sloan Kettering Cancer Center tool is a better predictor for older patients, whereas Melanoma Institute Australia tool is more accurate in patients with a higher mitotic index. Both nomograms performed poorly in predicting sentinel lymph node positivity in young patients.

Population-Based Validation of the MIA and MSKCC Tools for Predicting Sentinel Lymph Node Status

Importance

Patients with melanoma are selected for sentinel lymph node biopsy (SLNB) based on their risk of a positive SLN. To improve selection, the Memorial Sloan Kettering Cancer Center (MSKCC) and Melanoma Institute Australia (MIA) developed predictive models, but the utility of these models remains to be tested.

Objective

To determine the clinical utility of the MIA and MSKCC models.

Design, Setting, and Participants

This was a population-based comparative effectiveness research study including 10 089 consecutive patients with cutaneous melanoma undergoing SLNB from the Swedish Melanoma Registry from January 2007 to December 2021. Data were analyzed from May to August 2023.

Main Outcomes and Measures,

The predicted probability of SLN positivity was calculated using the MSKCC model and a limited MIA model (using mitotic rate as absent/present instead of count/mm2 and excluding the optional variable lymphovascular invasion) for each patient. The operating characteristics of the models were assessed and compared. The clinical utility of each model was assessed using decision curve analysis and compared with a strategy of performing SLNB on all patients.

Results

Among 10 089 included patients, the median (IQR) age was 64.0 (52.0-73.0) years, and 5340 (52.9%) were male. The median Breslow thickness was 1.8 mm, and 1802 patients (17.9%) had a positive SLN. Both models were well calibrated across the full range of predicted probabilities and had similar external area under the receiver operating characteristic curves (AUC; MSKCC: 70.8%; 95% CI, 69.5-72.1 and limited MIA: 69.7%; 95% CI, 68.4-71.1). At a risk threshold of 5%, decision curve analysis indicated no added net benefit for either model compared to performing SLNB for all patients. At risk thresholds of 10% or higher, both models added net benefit compared to SLNB for all patients. The greatest benefit was observed in patients with T2 melanomas using a threshold of 10%; in that setting, the use of the nomograms led to a net reduction of 8 avoidable SLNBs per 100 patients for the MSKCC nomogram and 7 per 100 patients for the limited MIA nomogram compared to a strategy of SLNB for all.

Conclusions and Relevance

This study confirmed the statistical performance of both the MSKCC and limited MIA models in a large, nationally representative data set. However, decision curve analysis demonstrated that using the models only improved selection for SLNB compared to biopsy in all patients when a risk threshold of at least 7% was used, with the greatest benefit seen for T2 melanomas at a threshold of 10%. Care should be taken when using these nomograms to guide selection for SLNB at the lowest thresholds.

Clinical Utility of Melanoma Sentinel Lymph Node Biopsy Nomograms

BACKGROUND

For patients with melanoma, the decision to perform sentinel lymph node biopsy (SLNB) is based on the estimated risk of lymph node metastasis. We assessed 3 melanoma SLNB risk-prediction models' statistical performance and their ability to improve clinical decision making (clinical utility) on a cohort of melanoma SLNB cases.

STUDY DESIGN

Melanoma patients undergoing SLNB at a single center from 2003 to 2021 were identified. The predicted probabilities of sentinel lymph node positivity using the Melanoma Institute of Australia, Memorial Sloan Kettering Cancer Center (MSK), and Friedman nomograms were calculated. Receiver operating characteristic and calibration curves were generated. Clinical utility was assessed via decision curve analysis, calculating the net SLNBs that could have been avoided had a given model guided selection at different risk thresholds.

RESULTS

Of 2,464 melanoma cases that underwent SLNB, 567 (23.0%) had a positive sentinel lymph node. The areas under the receiver operating characteristic curves for the Melanoma Institute of Australia, MSK, and Friedman models were 0.726 (95% CI, 0.702 to 0.750), 0.720 (95% CI, 0.697 to 0.744), and 0.721 (95% CI, 0.699 to 0.744), respectively. For all models, calibration was best at predicted positivity rates below 30%. The MSK model underpredicted risk. At a 10% risk threshold, only the Friedman model would correctly avoid a net of 6.2 SLNBs per 100 patients. The other models did not reduce net avoidable SLNBs at risk thresholds of ≤10%.

CONCLUSIONS

The tested nomograms had comparable performance in our cohort. The only model that achieved clinical utility at risk thresholds of ≤10% was the Friedman model.

External validation of the Melanoma Institute Australia Sentinel Node Metastasis Risk Prediction Tool using the National Cancer Database

BACKGROUND

To improve patient selection for sentinel node (SN) biopsy, the Melanoma Institute of Australia (MIA) created a predictive model based on readily available clinicopathologic factors.

OBJECTIVES

Validation of the MIA nomogram using the National Cancer Database (NCDB), a nationwide oncology outcomes database for >1500 Commission-accredited cancer programs in the United States.

METHODS

A total of 60,165 patients were included in the validation. The probability of SN positivity was calculated for each patient. Using calculated probabilities, a receiver operating characteristic curve was generated to assess the model's discrimination ability.

RESULTS

At baseline, the NCDB cohort had different clinicopathologic characteristics compared with the original MIA data set. Despite these differences, the MIA nomogram retained high-predictive accuracy within the NCDB dataset (C-statistic, 0.733 [95% CI, 0.726-0.739]), although calibration weakened for the highest risk decile.

LIMITATIONS

The NCDB collects data from hospital registries accredited by the Commission on Cancer.

CONCLUSIONS

In conclusion, this study validated the use of the MIA nomogram in a nationwide oncology outcomes database collected from >1500 Commission-accredited cancer programs in the United States, demonstrating the potential for this nomogram to predict SN positivity and reduce the number of negative SN biopsies.

A Clinical Decision Tool to Calculate Pretest Probability of Sentinel Lymph Node Metastasis in Primary Cutaneous Melanoma

BACKGROUND

Although sentinel lymph node biopsy (SLNB) status is a strong prognostic indicator for cutaneous melanoma, unnecessary SLNBs have substantial cost and morbidity burden.

OBJECTIVE

This study was designed to develop, validate, and present a personalized, clinical, decision-making tool using nationally representative data with clinically actionable probability thresholds (Expected Lymphatic Metastasis Outcome [ELMO]).

METHODS

Data from the Surveillance, Epidemiology, and End Results (SEER) Registry from 2000 to 2017 and the National Cancer Database (NCDB) from 2004 to 2015 were used to develop and internally validate a logistic ridge regression predictive model for SLNB positivity. External validation was done with 1568 patients at a large tertiary referral center.

RESULTS

The development cohort included 134,809 patients, and the internal validation cohort included 38,518 patients. ELMO (AUC 0.85) resulted in a 29.54% SLNB reduction rate and greater sensitivity in predicting SLNB status for T1b, T2a, and T2b tumors than previous models. In external validation, ELMO had an accuracy of 0.7586 and AUC of 0.7218. Limitations of this study are potential miscoding, unaccounted confounders, and effect modification.

CONCLUSIONS

ELMO ( https://melanoma-sentinel.herokuapp.com/ ) has been developed and validated (internally and externally) by using the largest publicly available dataset of melanoma patients and was found to have high accuracy compared with other published models and gene expression tests. Individualized risk estimates for SLNB positivity are critical in facilitating thorough decision-making for healthcare providers and patients with melanoma.

Are the MIA and MSKCC nomograms useful in selecting patients with melanoma for sentinel lymph node biopsy?

BACKGROUND AND METHODS

The Melanoma Institute of Australia (MIA) and Memorial Sloan Kettering Cancer Center (MSKCC) nomograms were developed to help guide sentinel lymph node biopsy (SLNB) decisions. Although statistically validated, whether these prediction models provide clinical benefit at National Comprehensive Cancer Network guideline-endorsed thresholds is unknown. We conducted a net benefit analysis to quantify the clinical utility of these nomograms at risk thresholds of 5%-10% compared to the alternative strategy of biopsying all patients. External validation data for MIA and MSKCC nomograms were extracted from respective published studies.

RESULTS

The MIA nomogram provided added net benefit at a risk threshold of 9% but net harm at 5%-8% and 10%. The MSKCC nomogram provided added net benefit at risk thresholds of 5% and 9%-10% but net harm at 6%-8%. When present, the magnitude of net benefit was small (1-3 net avoidable biopsies per 100 patients).

CONCLUSION

Neither model consistently provided added net benefit compared to performing SLNB for all patients.

DISCUSSION

Based on published data, use of the MIA or MSKCC nomograms as decision-making tools for SLNB at risk thresholds of 5%-10% does not clearly provide clinical benefit to patients.

MelRisk: Using neutrophil-to-lymphocyte ratio to improve risk prediction models for metastatic cutaneous melanoma in the sentinel lymph node

BACKGROUND

Identifying metastatic melanoma in the sentinel lymph node (SLN) is important because 80% of SLN biopsies are negative and 11% of patients develop complications. The neutrophil-to-lymphocyte ratio (NLR), a biomarker of micrometastatic disease, could improve prediction models for SLN status. We externally validated existing models and developed 'MelRisk' prognostic score to better predict SLN metastasis.

METHODS

The models were externally validated using data from a multicenter cohort study of 1,251 adults. Additionally, we developed and internally validated a new prognostic score `MelRisk', using candidate predictors derived from the extant literature.

RESULTS

The Karakousis model had a C-statistic of 0.58 (95% CI, 0.54-0.62). The Sondak model had a C-statistic of 0.57 (95% CI 0.53-0.61). The MIA model had a C-statistic of 0.60 (95% CI. 0.56-0.64). Our 'MelRisk' model (which used Breslow thickness, ulceration, age, anatomical site, and the NLR) showed an adjusted C-statistic of 0.63 (95% CI, 0.56-0.64).

CONCLUSION

Our prediction tool is freely available in the Google Play Store and Apple App Store, and we invite colleagues to externally validate its performance .

Bioinformatic and Machine Learning Applications in Melanoma Risk Assessment and Prognosis: A Literature Review

Over 100,000 people are diagnosed with cutaneous melanoma each year in the United States. Despite recent advancements in metastatic melanoma treatment, such as immunotherapy, there are still over 7000 melanoma-related deaths each year. Melanoma is a highly heterogenous disease, and many underlying genetic drivers have been identified since the introduction of next-generation sequencing. Despite clinical staging guidelines, the prognosis of metastatic melanoma is variable and difficult to predict. Bioinformatic and machine learning analyses relying on genetic, clinical, and histopathologic inputs have been increasingly used to risk stratify melanoma patients with high accuracy. This literature review summarizes the key genetic drivers of melanoma and recent applications of bioinformatic and machine learning models in the risk stratification of melanoma patients. A robustly validated risk stratification tool can potentially guide the physician management of melanoma patients and ultimately improve patient outcomes.

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.

Predicting sentinel node positivity in patients with melanoma: external validation of a risk-prediction calculator (the Melanoma Institute Australia nomogram) using a large European population-based patient cohort

BACKGROUND

A nomogram to predict sentinel node (SN) positivity [the Melanoma Institute Australia (MIA) nomogram] was recently developed and externally validated using two large single-institution databases. However, there remains a need to further validate the nomogram's performance using population-based data.

OBJECTIVES

To perform further validation of the nomogram using a European national patient cohort.

METHODS

Patients with cutaneous melanoma who underwent SN biopsy in the Netherlands between 2000 and 2014 were included. Their data were obtained from the Dutch Pathology Registry. The predictive performance of the nomogram was assessed by discrimination (C-statistic) and calibration. Negative predictive values (NPVs) were calculated at various predicted probability cutoffs.

RESULTS

Of the 3049 patients who met the eligibility criteria, 23% (691) were SN positive. Validation of the MIA nomogram (including the parameters Breslow thickness, ulceration, age, melanoma subtype and lymphovascular invasion) showed a good C-statistic of 0·69 (95% confidence interval 0·66-0·71) with excellent calibration (R² = 0·985, P = 0·40). The NPV of 90·1%, found at a 10% predicted probability cutoff for having a positive SN biopsy, implied that by using the nomogram, a 16·3% reduction in the rate of performing an SN biopsy could be achieved with an error rate of 1·6%. Validation of the MIA nomogram considering mitotic rate as present or absent showed a C-statistic of 0·70 (95% confidence interval 0·68-0·74).

CONCLUSIONS

This population-based validation study in European patients with melanoma confirmed the value of the MIA nomogram in predicting SN positivity. Its use will spare low-risk patients the inconvenience, cost and potential risks of SN biopsy while ensuring that high-risk patients are still identified.

Development and validation of a Brazilian nomogram to assess sentinel node biopsy positivity in melanoma

BACKGROUND

Although well-established, sentinel node biopsy (SNB) for melanoma is not free from controversies and sometimes it can be questionable if SNB should be considered even for patients who meet the criteria for the procedure. Mathematical tools such as nomograms can be helpful and give more precise answers for both clinicians and patients. We present a nomogram for SNB positivity that has been internally validated.

METHODS

Retrospective analysis of patients who underwent SNB from 2000 to 2015 in a single institution. Single logistic regressions were used to identify variables that were associated to SNB positivity. All variables with a p value < 0.05 were included in the final model. Overall performance, calibration, and discriminatory power of the final multiple logistic regression model were all assessed. Internal validation of the multiple logistic regression model was performed via bootstrap analysis based on 1000 replications.

RESULTS

Site of primary lesion, Breslow thickness, mitotic rate, histologic regression, lymphatic invasion, and Clark level were statistically related to SNB positivity. After internal validation, a good performance was observed as well as an adequate power of discrimination (area under the curve 0.751).

CONCLUSIONS

We have presented a nomogram that can be helpful and easily used in daily practice for assessing SNB positivity.

Improved Risk Prediction Calculator for Sentinel Node Positivity in Patients With Melanoma: The Melanoma Institute Australia Nomogram

PURPOSE

For patients with primary cutaneous melanoma, the risk of sentinel node (SN) metastasis varies according to several clinicopathologic parameters. Patient selection for SN biopsy can be assisted by National Comprehensive Cancer Network (NCCN) and ASCO/Society of Surgical Oncology (SSO) guidelines and the Memorial Sloan Kettering Cancer Center (MSKCC) online nomogram. We sought to develop an improved online risk calculator using alternative clinicopathologic parameters to more accurately predict SN positivity.

PATIENTS AND METHODS

Data from 3,477 patients with melanoma who underwent SN biopsy at Melanoma Institute Australia (MIA) were analyzed. A new nomogram was developed by replacing body site and Clark level from the MSKCC model with mitotic rate, melanoma subtype, and lymphovascular invasion. The predictive performance of the new nomogram was externally validated using data from The University of Texas MD Anderson Cancer Center (n = 3,496).

RESULTS

The MSKCC model receiver operating characteristic curve had a predictive accuracy of 67.7% (95% CI, 65.3% to 70.0%). The MIA model had a predictive accuracy of 73.9% (95% CI, 71.9% to 75.9%), a 9.2% increase in accuracy over the MSKCC model (P < .001). Among the 2,748 SN-negative patients, SN biopsy would not have been offered to 22.1%, 13.4%, and 12.4% based on the MIA model, the MSKCC model, and NCCN or ASCO/SSO criteria, respectively. External validation generated a C-statistic of 75.0% (95% CI, 73.2% to 76.7%).

CONCLUSION

A robust nomogram was developed that more accurately estimates the risk of SN positivity in patients with melanoma than currently available methods. The model only requires the input of 6 widely available clinicopathologic parameters. Importantly, the number of patients undergoing unnecessary SN biopsy would be significantly reduced compared with use of the MSKCC nomogram or the NCCN or ASCO/SSO guidelines, without losing sensitivity. An online calculator is available at www.melanomarisk.org.au.

The neutrophil-lymphocyte ratio and locoregional melanoma: a multicentre cohort study

OBJECTIVES

The neutrophil-lymphocyte ratio (NLR) is an inflammatory biomarker which is useful in cancer prognostication. We aimed to investigate the differences in baseline NLR between patients with localised and metastatic cutaneous melanoma and how this biomarker changed over time with the recurrence of disease.

METHODS

This multicentre cohort study describes patients treated for Stage I-III cutaneous melanoma over 10 years. The baseline NLR was measured immediately prior to surgery and again at the time of discharge or disease recurrence. The odds ratios (OR) for sentinel node involvement are estimated using mixed-effects logistic regression. The risk of recurrence is estimated using multivariable Cox regression.

RESULTS

Overall 1489 individuals were included. The mean baseline NLR was higher in patients with palpable nodal disease compared to those with microscopic nodal or localised disease (2.8 versus 2.4 and 2.3, respectively; p < 0.001). A baseline NLR ≥ 2.3 was associated with 30% higher odds of microscopic metastatic melanoma in the sentinel lymph node [adjusted OR 1.3 (95% CI 1.3, 1.3)]. Following surgery, 253 patients (18.7%) developed recurrent melanoma during surveillance although there was no statistically significant association between the baseline NLR and the risk of recurrence [adjusted HR 0.9 (0.7, 1.1)].

CONCLUSION

The NLR is associated with the volume of melanoma at presentation and may predict occult sentinel lymph metastases. Further prospective work is required to investigate how NLR may be modelled against other clinicopathological variables to predict outcomes and to understand the temporal changes in NLR following surgery for melanoma.

A nomogram to predict node positivity in patients with thin melanomas helps inform shared patient decision making

OBJECTIVE

To develop a nomogram to estimate the probability of positive sentinel lymph node (+SLN) for patients with thin melanoma and to characterize its potential impact on sentinel lymph node biopsy (SLNB) rates.

METHODS

Patients diagnosed with thin (0.5-1.0 mm) melanoma were identified from the National Cancer Database 2012 to 2015. A multivariable logistic regression model was used to examine factors associated with +SLN, and a nomogram to predict +SLN was constructed. Nomogram performance was evaluated and diagnostic test statistics were calculated.

RESULTS

Of the 21 971 patients included 10 108 (46.0%) underwent SLNB, with a 4.0% +SLN rate. On multivariable analysis, age, Breslow thickness, lymphovascular invasion, ulceration, and Clark level were significantly associated with SLN status. The area under the receiver operating curve was 0.67 (95% confidence interval, 0.65-0.70). While 15 249 (69.4%) patients had either T1b tumors or T1a tumors with at least one adverse feature, only 2846 (13.0%) had a nomogram predicted probability of a +SLN ≥5%. Using this cut-off, the indication for a SLNB in these patients would be reduced by 81.3% as compared to the American Joint Committee on Cancer 8th edition staging criteria.

CONCLUSIONS

The risk predictions obtained from the nomogram allow for more accurate selection of patients who could benefit from SLNB.

Clinical protein science in translational medicine targeting malignant melanoma

Melanoma of the skin is the sixth most common type of cancer in Europe and accounts for 3.4% of all diagnosed cancers. More alarming is the degree of recurrence that occurs with approximately 20% of patients lethally relapsing following treatment. Malignant melanoma is a highly aggressive skin cancer and metastases rapidly extend to the regional lymph nodes (stage 3) and to distal organs (stage 4). Targeted oncotherapy is one of the standard treatment for progressive stage 4 melanoma, and BRAF inhibitors (e.g. vemurafenib, dabrafenib) combined with MEK inhibitor (e.g. trametinib) can effectively counter BRAFV600E-mutated melanomas. Compared to conventional chemotherapy, targeted BRAFV600E inhibition achieves a significantly higher response rate. After a period of cancer control, however, most responsive patients develop resistance to the therapy and lethal progression. The many underlying factors potentially causing resistance to BRAF inhibitors have been extensively studied. Nevertheless, the remaining unsolved clinical questions necessitate alternative research approaches to address the molecular mechanisms underlying metastatic and treatment-resistant melanoma. In broader terms, proteomics can address clinical questions far beyond the reach of genomics, by measuring, i.e. the relative abundance of protein products, post-translational modifications (PTMs), protein localisation, turnover, protein interactions and protein function. More specifically, proteomic analysis of body fluids and tissues in a given medical and clinical setting can aid in the identification of cancer biomarkers and novel therapeutic targets. Achieving this goal requires the development of a robust and reproducible clinical proteomic platform that encompasses automated biobanking of patient samples, tissue sectioning and histological examination, efficient protein extraction, enzymatic digestion, mass spectrometry-based quantitative protein analysis by label-free or labelling technologies and/or enrichment of peptides with specific PTMs. By combining data from, e.g. phosphoproteomics and acetylomics, the protein expression profiles of different melanoma stages can provide a solid framework for understanding the biology and progression of the disease. When complemented by proteogenomics, customised protein sequence databases generated from patient-specific genomic and transcriptomic data aid in interpreting clinical proteomic biomarker data to provide a deeper and more comprehensive molecular characterisation of cellular functions underlying disease progression. In parallel to a streamlined, patient-centric, clinical proteomic pipeline, mass spectrometry-based imaging can aid in interrogating the spatial distribution of drugs and drug metabolites within tissues at single-cell resolution. These developments are an important advancement in studying drug action and efficacy in vivo and will aid in the development of more effective and safer strategies for the treatment of melanoma. A collaborative effort of gargantuan proportions between academia and healthcare professionals has led to the initiation, establishment and development of a cutting-edge cancer research centre with a specialisation in melanoma and lung cancer. The primary research focus of the European Cancer Moonshot Lund Center is to understand the impact that drugs have on cancer at an individualised and personalised level. Simultaneously, the centre increases awareness of the relentless battle against cancer and attracts global interest in the exceptional research performed at the centre.

Tumor infiltrating lymphocytes in acral lentiginous melanoma: a study of a large cohort of cases from Latin America

PURPOSE

Acral lentiginous melanoma (ALM) is a poor prognosis subtype and is the most prevalent in non-Caucasian populations. The presence of tumor infiltrating lymphocytes (TILs) has been associated with poor prognosis in melanoma. A large cohort of ALM cases was studied to determine status of TIL and its association with outcome.

METHODS

All patients with cutaneous melanoma presenting from 2005 to 2012 at Instituto Nacional de Enfermedades Neoplasicas in Peru were retrospectively identified. Clinicopathological information was obtained from the medical charts. A prospective evaluation of TIL was performed. Analysis of association between ALM and clinicopathological features including TIL as well as survival analysis compared the outcome of ALM to whole group and extremity NALM was performed.

RESULTS

537 ALM from a total of 824 cutaneous melanoma cases were studied. Older age (p = 0.022), higher Breslow (p = 0.008) and ulceration (p < 0.001) were found to be more frequent in ALM. Acral had worse overall survival (OS) compared with the whole group (p = 0.04). Clinical stage (CS) I-II patients had a median OS of 5.3 (95% CI 4.3-6.2) for ALM and 9.2 (95% CI 5.0-7.0) for extremity NALM (p = 0.016). Grade 0 (absence of TIL), I, II and III were found in 7.5, 34.5, 32.1, and 25.9%, respectively. Lower TIL grade was associated with larger tumor size (p = 0.003), higher Breslow (p = 0.001), higher Clark level (p = 0.007), higher CS (p = 0.002), extremity location (p = 0.048), histological subtype ALM (p = 0.024) and better OS (p = 0.001).

CONCLUSIONS

ALM is highly prevalent in Peru and carries poor outcome. Lower TIL levels were associated with poor outcome and ALM.

Validation of a nomogram predicting sentinel lymph node status in melanoma in an Irish population

BACKGROUND

Sentinel lymph node (SLN) positivity is an important prognostic factor in cutaneous melanoma. A nomogram has been developed at Memorial Sloan-Kettering Cancer Centre (MSKCC) to predict SLN positivity and this may be useful to select patients for SLN biopsy.

AIMS

We aimed to determine whether this nomogram would be of clinical use in an Irish population.

METHODS

Age, Breslow thickness, Clark's level, presence of ulceration and tumour location indices were used to calculate the probability of SLN positivity with the MSKCC nomogram in 124 patients who underwent SLN biopsy in Beaumont Hospital between 2006 and 2012. Discrimination and calibration of the nomogram were evaluated. Negative predictive value (NPV) of the nomogram was calculated, using a cut-off of nomogram predicted probability of <9%.

RESULTS

SLN biopsy was positive in 25 patients (20.16%). Overall predictive accuracy of the nomogram was found to be significant with an area under the curve of 0.805 (95% confidence interval 0.710-0.899). The mean predicted probability correlated well with observed risk (r = 0.887). The NPV was 92.86% with an error rate of 3.23%. This would lead to a reduction in SLN biopsy rate of 45.16%.

CONCLUSIONS

This nomogram is valid and accurate at predicting SLN positivity in an Irish population. This may facilitate the clinical decision to perform a SLN biopsy in malignant melanoma.

Lymphatic biomarkers in primary melanomas as predictors of regional lymph node metastasis and patient outcomes

Recently developed lymphatic-specific immunohistochemical markers can now be utilized to assess intratumoral and/or peritumoral lymphatic vessel density (LVD), to detect lymphatic vessel invasion (LVI) by melanoma cells and to identify lymphatic marker expression in melanoma cells themselves. We systematically reviewed the available evidence for the expression of lymphatic markers as predictors of regional node metastasis and survival in melanoma patients. The currently available evidence suggests that LVD (particularly in a peritumoral location) and LVI are predictors of sentinel node metastasis and poorer survival. Nevertheless, adherence to international guidelines in the conduct and reporting of the studies was generally poor, with wide methodologic variations and heterogeneous findings. Larger, carefully conducted and well-reported studies that confirm these preliminary findings are required before it would be appropriate to recommend the routine application of costly and time-consuming immunohistochemistry for lymphatic markers in the routine clinical assessment of primary cutaneous melanomas.