Faculty Bibliography

Although personal melanoma risk factors are well established, the contribution of socioeconomic factors, including clothing styles, social norms, medical paradigms, perceptions of tanned skin, economic trends, and travel patterns, to melanoma incidence has not been fully explored. We analyzed artwork, advertisements, fashion trends, and data regarding leisure-time activities to estimate historical changes in UV skin exposure. We used data from national cancer registries to compare melanoma incidence rates with estimated skin exposure and found that they rose in parallel. Although firm conclusions about melanoma causation cannot be made in an analysis such as this, we provide a cross-disciplinary, historical framework in which to consider public health and educational measures that may ultimately help reverse melanoma incidence trends. (Am J Public Health. Published online ahead of print September 11, 2014: e1-e8. doi:10.2105/AJPH.2014.302185).

BACKGROUND: Identifying individuals at increased risk for melanoma could potentially improve public health through targeted surveillance and early detection. Studies have separately demonstrated significant associations between melanoma risk, melanocortin receptor (MC1R) polymorphisms, and indoor ultraviolet light (UV) exposure. Existing melanoma risk prediction models do not include these factors; therefore, we investigated their potential to improve the performance of a risk model. METHODS: Using 875 melanoma cases and 765 controls from the population-based Minnesota Skin Health Study we compared the predictive ability of a clinical melanoma risk model (Model A) to an enhanced model (Model F) using receiver operating characteristic (ROC) curves. Model A used self-reported conventional risk factors including mole phenotype categorized as "none", "few", "some" or "many" moles. Model F added MC1R genotype and measures of indoor and outdoor UV exposure to Model A. We also assessed the predictive ability of these models in subgroups stratified by mole phenotype (e.g. nevus-resistant ("none" and "few" moles) and nevus-prone ("some" and "many" moles)). RESULTS: Model A (the reference model) yielded an area under the ROC curve (AUC) of 0.72 (95% CI = 0.69, 0.74). Model F was improved with an AUC = 0.74 (95% CI = 0.71-0.76, p<0.01). We also observed substantial variations in the AUCs of Models A & F when examined in the nevus-prone and nevus-resistant subgroups. CONCLUSIONS: These results demonstrate that adding genotypic information and environmental exposure data can increase the predictive ability of a clinical melanoma risk model, especially among nevus-prone individuals.

BACKGROUND: Steadily high melanoma mortality rates urge for the availability of novel biomarkers with a more personalized ability to predict melanoma clinical outcomes. Germline risk variants are promising candidates for this purpose; however, their prognostic potential in melanoma has never been systematically tested. METHODS: We examined the effect of 108 melanoma susceptibility single nucleotide polymorphisms (SNPs), associated in recent GWAS with melanoma and melanoma-related phenotypes, on recurrence-free survival (RFS) and overall survival (OS), in 891 prospectively accrued melanoma patients. Cox proportional hazards models (Cox PH) were used to test the associations between 108 melanoma risk SNPs and RFS and OS adjusted by age at diagnosis, gender, tumor stage, histological subtype and other primary tumor characteristics. RESULTS: We identified significant associations for rs7538876 (RCC2) with RFS (HR = 1.48, 95% CI = 1.20-1.83, p = 0.0005) and rs9960018 (DLGAP1) with both RFS and OS (HR = 1.43, 95% CI = 1.07-1.91, p = 0.01, HR = 1.52, 95% CI = 1.09-2.12, p = 0.01, respectively) using multivariable Cox PH models. In addition, we developed a logistic regression model that incorporates rs7538876, rs9960018, primary tumor histological type and stage at diagnosis that has an improved discriminatory ability to classify 3-year recurrence (AUC = 82%) compared to histological type and stage alone (AUC = 78%). CONCLUSIONS: We identified associations between melanoma risk variants and melanoma outcomes. The significant associations observed for rs7538876 and rs9960018 suggest a biological implication of these loci in melanoma progression. The observed predictive patterns of associated variants with clinical end-points suggest for the first time the potential for utilization of genetic risk markers in melanoma prognostication.

Objectives: Age is an understudied factor when considering treatment options for melanoma. Here, we examine the impact of age on primary melanoma treatment in a prospective cohort of patients. Methods: We used logistic regression models to examine the associations between age and initial treatment, using recurrence and melanoma-specific survival as endpoints. Results: 444 primary melanoma patients were categorized into three groups by age at diagnosis: 19-45 years (24.3%), 46-70 (50.2%), and 71-95 (25.5%). In multivariate models, older patients experienced a higher risk of recurrence (hazard ratio 3.34, 95% confidence interval, CI, 1.53-7.25; p < 0.01). No significant differences were observed in positive biopsy margin rates or extent of surgical margins across age groups. Patients in the middle age group were more likely to receive adjuvant therapy than those in the older group (odds ratio 2.78, 95% CI 1.19-6.45; p = 0.02) and showed a trend to longer disease-free survival when receiving adjuvant therapy (p = 0.09). Conclusion: Our data support age as an independent negative prognostic factor in melanoma. Our data suggest that age does not affect primary surgical treatment but may affect decisions of whether or not patients receive postoperative treatment(s). Further work is needed to better understand the biological variables affecting treatment decisions and efficacy in older patients.

Background: Although patient age at diagnosis is not currently included in guidelines for treatment of primary melanoma, several lines of evidence suggest that patient age is an important, yet understudied, factor when considering treatment options. Here, we attempt to address the limited knowledge of the impact of age on primary melanoma treatment. Methods: In a prospectively enrolled and followed-up cohort of melanoma patients at NYU, we used logistic regression models to evaluate the association between patient age at diagnosis, tumor baseline characteristics, including BRAF and NRAS mutation status, and likelihood of receiving and responding to adjuvant therapy. We examined adjuvant therapy effectiveness using recurrence and melanoma-specific survival as endpoints. Results: 444 primary melanoma patients were included in the study (median follow-up: 6.3 years; age range: 19-95 years). Age was categorized into three groups spanning the range of age at presentation: younger (19-45 years; 24%), middle (46-70 years; 50%), and older (71-95 years; 26%). Older patients were significantly more likely to have advanced stage, nodular subtype (P < 0.01, both variables), and BRAF wildtype tumors (P = 0.04). Controlling for these factors as well as gender, older patients experienced a higher risk of recurrence (HR older vs. younger 3.34, 95% CI 1.53-7.25; P < 0.01). Of the 128/444 (29%) patients who were eligible for adjuvant treatment (clinical stage IIB), only 67/128 (52%) received treatment. Using a propensity score that accounts for stage at presentation, patients in the middle age group were more likely to receive adjuvant therapy than those in the older group (OR 2.61, 95% CI 1.12-6.08; P = 0.03). In addition, a trend suggesting benefit from adjuvant therapy (defined as longer melanoma-specific survival) was observed only in the middle age group (P = 0.07). Conclusions: Our data suggest that older melanoma patients, despite having a significantly worse prognosis, are less likely to receive and bene!

Background: Patients with metastatic melanoma are eligible for BRAF inhibitor therapy if the BRAF V600E mutation can be identified in their tumor specimen. Patients lacking an available specimen for genotyping are unable to receive inhibitor therapy. We developed two mutation-specific genotyping platforms and tested their ability to detect BRAF and NRAS mutations in archived plasma and tumor samples to determine the potential utility of blood-based tumor genotyping in melanoma. Methods: We analyzed a group of 96 patients with stage III or IV melanoma, prospectively enrolled and followed in the NYU Melanoma Biorepository program. Each patient had a plasma sample and one or more tumor samples available for analysis. We used a combination of allele-specific PCR (Taqman) and SNaPshot assays to identify BRAF V600 and NRAS Q61 mutations in the tumor and plasma samples. Results: Among the 96 patients, 51 had stage III disease at the time of analysis; 45 had stage IV disease. Seventy-two patients had 2 or more tumor samples available for analysis, for a total of 204 tumors analyzed. In total, 52/96 (54%) patients had one or more BRAF or NRAS mutant tumors, including one patient with separate BRAF and NRAS mutant tumors (BRAF, n=35 (36%); NRAS, n=18 (19%)). We successfully amplified plasma DNA from 39/52 (75%) patients with tumor-associated mutations. Among those patients with amplifiable plasma DNA we detected mutations in 7 (18%) patients including 3 BRAF V600E, one V600K, 2 NRAS Q61K and one Q61L. Plasma-based mutations matched tumor-associated mutations in all 7 patients. All 7 patients had active disease at the time of blood draw. There were 32 patients with tumor-associated mutations in which a mutation could not be detected in the plasma. Only 15 of those 32 (47%) had active disease at the time of blood draw. There were no mutations detected in the plasma of the 44 patients whose tumors lacked BRAF or NRAS mutations. Conclusions: These data suggest that plasma-based detection of BRAF and NRAS mut!

BACKGROUND: Studies have shown low satisfaction levels among dermatology residents with respect to dermoscopy training. Many desire additional instruction. OBJECTIVE: We surveyed graduating chief residents to assess current education practices among US dermatology training programs with respect to the role of dermoscopy as an aid in the management of pigmented lesions. METHODS: An online survey was sent to 139 chief residents of US dermatology training programs. RESULTS: A 59% response rate was achieved. Of responding chief residents, 94% use dermoscopy. Although 92% of chief residents received dermoscopy training, only 48% trained with a pigmented lesion specialist. Among those training without a specialist, less than half received classroom or bedside teaching compared with 77% of those who trained with a specialist. Of those who trained with a specialist, 77% were satisfied with their training compared with only 30% who trained without a specialist (P < .0001). Those who trained with a specialist were more likely to agree that dermoscopy can help differentiate melanoma from benign lesions (77% vs 47%; P = .0065). LIMITATIONS: Response bias and limiting the survey to chief residents potentially limits our ability to generalize these results to all US dermatology trainees. CONCLUSION: Although many residents use dermoscopy as a diagnostic tool, the lack of dedicated dermoscopy training remains a potential barrier to increasing residents' diagnostic confidence in the management of pigmented lesions. Increasing the amount of dedicated instruction on this topic is one possible approach to enhance resident satisfaction, potentially increasing their competency in the management of atypical nevi.

The prognostic value of mitotic rate in melanoma is increasingly recognized, particularly in thin melanoma in which the presence or absence of a single mitosis/mm can change staging from T1a to T1b. Still, accurate mitotic rate calculation (mitoses/mm) on hematoxylin and eosin (H&E)-stained sections can be challenging. Antimonoclonal mitotic protein-2 (MPM-2) and antiphosphohistone-H3 (PHH3) are 2 antibodies reported to be more mitosis-specific than other markers of proliferation such as Ki-67. We used light microscopy and computer-assisted image analysis software to quantify MPM-2 and PHH3 staining in melanoma. We then compared mitotic rates by each method with conventional H&E-based mitotic rate for correlation with clinical outcomes. Our study included primary tissues from 190 nonconsecutive cutaneous melanoma patients who were prospectively enrolled at New York University Langone Medical Center with information on age, gender, and primary tumor characteristics. The mitotic rate was quantified manually by light microscopy of corresponding H&E-stained, MPM-2-stained, and PHH3-stained sections. Computer-assisted image analysis was then used to quantify immunolabeled mitoses on the previously examined PHH3 and MPM-2 slides. We then analyzed the association between mitotic rate and both progression-free and melanoma-specific survival. Univariate analysis of PHH3 found significant correlation between increased PHH3 mitotic rate and decreased progression-free survival (P=0.04). Computer-assisted image analysis enhanced the correlation of PHH3 mitotic rate with progression-free survival (P=0.02). Regardless of the detection method, neither MPM-2 nor PHH3 offered significant advantage over conventional H&E determination of mitotic rate.