PD-1, PD-L1, and BIM as Predictors of Sentinel Lymph Node Metastasis in Primary Cutaneous Melanoma.
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ABSTRACT:
SUBMITTER: Kung FF
PROVIDER: S-EPMC7541661 | biostudies-literature | 2020 Nov
REPOSITORIES: biostudies-literature
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PD-1, PD-L1, and BIM as Predictors of Sentinel Lymph Node Metastasis in Primary Cutaneous Melanoma.
Kung Felix F FF Arias-Mejias Suzette M SM Quattrocchi Enrica E Sominidi-Damodaran Sindhuja S Bridges Alina G AG Lehman Julia S JS Weaver Amy L AL Meves Alexander A
The Journal of investigative dermatology 20200408 11
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| S-EPMC6461196 | biostudies-literature
Project description:National guidelines recommend sentinel lymph node biopsy (SLNB) be offered to patients with > 10% likelihood of sentinel lymph node (SLN) positivity. On the other hand, guidelines do not recommend SLNB for patients with T1a tumors without high-risk features who have < 5% likelihood of a positive SLN. However, the decision to perform SLNB is less certain for patients with higher-risk T1 melanomas in which a positive node is expected 5%-10% of the time. We hypothesized that integrating clinicopathologic features with the 31-gene expression profile (31-GEP) score using advanced artificial intelligence techniques would provide more precise SLN risk prediction. An integrated 31-GEP (i31-GEP) neural network algorithm incorporating clinicopathologic features with the continuous 31-GEP score was developed using a previously reported patient cohort (n = 1,398) and validated using an independent cohort (n = 1,674). Compared with other covariates in the i31-GEP, the continuous 31-GEP score had the largest likelihood ratio (G2 = 91.3, P < .001) for predicting SLN positivity. The i31-GEP demonstrated high concordance between predicted and observed SLN positivity rates (linear regression slope = 0.999). The i31-GEP increased the percentage of patients with T1-T4 tumors predicted to have < 5% SLN-positive likelihood from 8.5% to 27.7% with a negative predictive value of 98%. Importantly, for patients with T1 tumors originally classified with a likelihood of SLN positivity of 5%-10%, the i31-GEP reclassified 63% of cases as having < 5% or > 10% likelihood of positive SLN, for a more precise, personalized, and clinically actionable SLN-positive likelihood estimate. These data suggest the i31-GEP could reduce the number of SLNBs performed by identifying patients with likelihood under the 5% threshold for performance of SLNB and improve the yield of positive SLNBs by identifying patients more likely to have a positive SLNB.
| S-EPMC8457832 | biostudies-literature
Project description:BackgroundAlthough sentinel lymph node biopsy (SLNB) status is a strong prognostic indicator for cutaneous melanoma, unnecessary SLNBs have substantial cost and morbidity burden.ObjectiveThis 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]).MethodsData 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.ResultsThe 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.ConclusionsELMO ( 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.
| S-EPMC9961302 | biostudies-literature
Project description:BackgroundThe Clinicopathological and Gene Expression Profile (CP-GEP) model was developed to accurately identify patients with T1-T3 primary cutaneous melanoma at low risk for nodal metastasis.ObjectivesTo validate the CP-GEP model in an independent Dutch cohort of patients with melanoma.MethodsPatients (aged ≥ 18 years) with primary cutaneous melanoma who underwent sentinel lymph node biopsy (SLNB) between 2007 and 2017 at the Erasmus Medical Centre Cancer Institute were eligible. The CP-GEP model combines clinicopathological features (age and Breslow thickness) with the expression of eight target genes involved in melanoma metastasis (ITGB3, PLAT, SERPINE2, GDF15, TGFBR1, LOXL4, CXCL8 and MLANA). Using the pathology result of SLNB as the gold standard, performance measures of the CP-GEP model were calculated, resulting in CP-GEP high risk or low risk for nodal metastasis.ResultsIn total, 210 patients were included in the study. Most patients presented with T2 (n = 94, 45%) or T3 (n = 70, 33%) melanoma. Of all patients, 27% (n = 56) had a positive SLNB, with nodal metastasis in 0%, 30%, 54% and 16% of patients with T1, T2, T3 and T4 melanoma, respectively. Overall, the CP-GEP model had a negative predictive value (NPV) of 90·5% [95% confidence interval (CI) 77·9-96.2], with an NPV of 100% (95% CI 72·2-100) in T1, 89·3% (95% CI 72·8-96·3) in T2 and 75·0% (95% CI 30·1-95·4) in T3 melanomas. The CP-GEP indicated high risk in all T4 melanomas.ConclusionsThe CP-GEP model is a noninvasive and validated tool that accurately identified patients with primary cutaneous melanoma at low risk for nodal metastasis. In this validation cohort, the CP-GEP model has shown the potential to reduce SLNB procedures in patients with melanoma.
| S-EPMC8247350 | biostudies-literature
Project description:The metabolic hormone leptin has been implicated in the pathogenesis of various malignancies and may contribute to the high rate of cancer in obese individuals. We reported that leptin and its receptor are expressed by melanoma tumors and cell lines, and that leptin stimulates proliferation of cultured melanoma cells. Here, we tested the hypothesis that leptin contributes to early melanoma progression by assessing its association with sentinel node positivity in cutaneous melanoma patients. The study enrolled 72 patients who were scheduled to undergo lymphatic mapping and sentinel node biopsy. Fasting blood was obtained before surgery, and serum leptin levels were measured by enzyme-linked immunosorbent assay (ELISA) with a "raw" (assay value) and an "adjusted" value (raw value divided by body mass index). Leptin levels and other clinicopathologic parameters were compared between sentinel node positive and negative groups. Logistic regression models were used to predict sentinel node status using leptin and other relevant clinical parameters. The raw and adjusted leptin levels were significantly higher in the 15 patients with positive sentinel nodes. These findings could not be attributed to differences in body mass indices. Univariate models revealed raw leptin, adjusted leptin, Breslow thickness, and mitotic rate as significant predictors of sentinel node status. Leptin levels and Breslow thickness remained significant in multivariate models. Survival and follow-up analysis revealed more aggressive disease in diabetic patients. Elevated serum leptin levels predict sentinel node metastasis in melanoma. Validation of this finding in larger cohorts should enable better stratification of early stage melanoma patients.
| S-EPMC4839916 | biostudies-literature
Project description:ImportanceA neural network-based model (i31-GEP-SLNB) that uses clinicopathologic factors (thickness, mitoses, ulceration, patient age) plus molecular analysis (31-gene expression profiling) has become commercially available to guide selection for sentinel lymph node (SLN) biopsy in cutaneous melanoma, but its clinical utility is not well characterized.ObjectiveTo determine if use of the i31-GEP-SLNB model is associated with clinical benefit when used to select patients for SLN biopsy.Design, setting, and participantsThis decision-analytic study used data derived from a published external validation study of the i31-GEP-SLNB prediction model. Participants included patients with primary cutaneous melanoma.Main outcomes and measuresThe primary outcome was the net benefit associated with using the i31-GEP-SLNB model for SLN biopsy selection compared with other selection strategies (SLN biopsy for all patients and SLN biopsy for no patients) at a 5% risk threshold. Analyses were stratified by American Joint Committee on Cancer (AJCC) T category. The reduction in the number of avoidable SLN biopsies and relative utility were also calculated.ResultsCompared with other SLN biopsy selection strategies, use of the i31-GEP-SLNB model had greater net benefit for patients with T1b (+0.012), T2a (+0.002), and T2b melanoma (+0.002) but not for those with high-risk T1a (-0.003) disease. The improvement in relative utility was +22% in patients with T1b, +1% in T2a, and +2% in T2b melanoma. Compared with SLN biopsy for all patients, use of the model would equate to a 23% decrease in SLN biopsies among patients with T1b disease without an SLN metastasis with no increase in the number of patients with an SLN metastasis left untreated; among patients with T2a and T2b melanoma, the net decrease in avoidable biopsies compared with SLN biopsy for all was 3% and 4%, respectively.Conclusions and relevanceThe findings of this decision-analytic study suggest that i31-GEP SLNB has significant potential for risk-stratifying patients with T1b melanoma if using a 5% risk threshold; its role among patients with T1a and T2 melanoma or using other risk thresholds requires further study. A prospective validation study confirming the added clinical benefit and cost-effectiveness of i31-GEP-SLNB compared with free clinicopathologic-based prediction models is needed in patients with T1b melanoma.
| S-EPMC9047749 | biostudies-literature
Project description:We sought to develop a sentinel lymph node gene expression signature score predictive of disease recurrence in patients with cutaneous melanoma. Gene expression profiling was performed on SLN biopsies using U133A 2.0 Affymetrix gene chips. The top 25 genes associated with recurrence-free survival (RFS) were selected and a penalized regression function was used to select 12 genes with a non-zero coefficient. A proportional hazards regression model was used to evaluate the association between clinical covariates, gene signature score, and RFS. Among the 45 patients evaluated, 23 (51%) had a positive SLN. Twenty-one (46.7%) patients developed disease recurrence. For the top 25 differentially expressed genes (DEG), 12 non-zero penalized coefficients were estimated (CLGN, C1QTNF3, ADORA3, ARHGAP8, DCTN1, ASPSCR1, CHRFAM7A, ZNF223, PDE6G, CXCL3, HEXIM1, HLA-DRB). This 12-gene signature score was significantly associated with RFS (p < 0.0001) and produced a bootstrap C index of 0.888. In univariate analysis, Breslow thickness, presence of primary tumor ulceration, SLN positivity were each significantly associated with RFS. After simultaneously adjusting for these prognostic factors in relation to the gene signature, the 12-gene score remained a significant independent predictor for RFS (p < 0.0001). This SLN 12-gene signature risk score is associated with melanoma recurrence regardless of SLN status and may be used as a prognostic factor for RFS.
| S-EPMC9599365 | biostudies-literature
Project description:BackgroundAlthough a well-established causative relationship exists between smoking and several epithelial cancers, the association of smoking with metastatic progression in melanoma is not well studied. We hypothesized that smokers would be at increased risk for melanoma metastasis as assessed by sentinel lymph node (SLN) biopsy.MethodsData from the first international Multicenter Selective Lymphadenectomy Trial (MSLT-I) and the screening-phase of the second trial (MSLT-II) were analyzed to determine the association of smoking with clinicopathologic variables and SLN metastasis.ResultsCurrent smoking was strongly associated with SLN metastasis (p = 0.004), even after adjusting for other predictors of metastasis. Among 4231 patients (1025 in MSLT-I and 3206 in MSLT-II), current or former smoking was also independently associated with ulceration (p < 0.001 and p < 0.001, respectively). Compared with current smoking, never smoking was independently associated with decreased Breslow thickness in multivariate analysis (p = 0.002) and with a 0.25 mm predicted decrease in thickness.ConclusionThe direct correlation between current smoking and SLN metastasis of primary cutaneous melanoma was independent of its correlation with tumor thickness and ulceration. Smoking cessation should be strongly encouraged among patients with or at risk for melanoma.
| S-EPMC5553293 | biostudies-literature
Project description:Sentinel lymph node (SLN) biopsy (SLNB) usually need not be simultaneously performed with breast-conserving surgery (BCS) for patients diagnosed with ductal carcinoma in situ (DCIS) by preoperative core needle biopsy (CNB), but must be performed once there is invasive carcinoma (IC) found postoperatively. This study aimed to investigate the factors contributing to SLN metastasis in underestimated IC patients with an initial diagnosis of DCIS by CNB. We retrospectively reviewed 1240 consecutive cases of DCIS by image-guided CNB from January 2010 to December 2017 and identified 316 underestimated IC cases with SLNB. Data on clinical characteristics, radiologic features, and final pathological findings were examined. Twenty-three patients (7.3%) had SLN metastasis. Multivariate analysis indicated that an IC tumor size > 0.5 cm (odds ratio: 3.11, p = 0.033) and the presence of lymphovascular invasion (odds ratio: 32.85, p < 0.0001) were independent risk predictors of SLN metastasis. In the absence of any predictors, the incidence of positive SLNs was very low (2.6%) in the total population and extremely low (1.3%) in the BCS subgroup. Therefore, omitting SLNB may be an acceptable option for patients who initially underwent BCS without risk predictors on final pathological assessment. Further prospective studies are necessary before clinical application.
| S-EPMC8392104 | biostudies-literature
Project description:BackgroundRecent studies evaluating patients with a positive sentinel lymph node biopsy (SLNB+) show no melanoma-specific survival difference between patients undergoing lymph node basin surveillance and completion lymph node dissection (CLND). This has been broadly applied, despite underrepresentation of head and neck (HN) cutaneous melanoma patients. We evaluated whether this was upheld in the HN melanoma cohort.MethodsPatients with HN melanoma with a SLNB+ were selected from the National Cancer Database (NCDB) from 2012 to 2019. Overall survival (OS) of patients who underwent SLNB only versus SLNB + CLND were compared. Subgroup analyses were performed based on pathologic N (pN) and receipt of immunotherapy. Adjusted hazard ratio (aHR) and 95% confidence interval (CI) were calculated.ResultsAnalysis of 634 patients with multivariable Cox regression showed no difference in OS in SLNB only versus SLNB + CLND cohorts (hazard ratio [HR] 1.13; 95% confidence interval [CI] 0.71-1.81; p = 0.610). Charlson-Deyo score (CDS) 1 versus 0 (HR 1.70; 95% CI 1.10-2.63; p = 0.016), pN2+ versus pN1 (HR 1.74; 95% CI 1.23-2.45; p = 0.002), and lymphovascular invasion (LVI) versus no (HR 2.07; 95% CI 1.34-3.19; p = 0.001) were associated with worse prognosis. Subgroup analysis by pN showed no OS benefit for CLND in either pN1 (HR 1.04; 95% CI 0.51-2.10; p = 0.922) or pN2+ (HR 1.31; 95% CI 0.67-2.57; p = 0.427) patients or in patients who received immunotherapy (HR 1.32; 95% CI 0.54-3.22; p = 0.549).ConclusionsThis study of SLNB + HN melanoma patients showed no OS difference in SLNB only versus SLNB + CLND. Further studies need to be performed to better define the role of CLND.
| S-EPMC10883719 | biostudies-literature