Bellomo, Domenico

AbstractBackground and ObjectivesOf clinically node‐negative (cN0) cutaneous melanoma patients with sentinel lymph node (SLN) metastasis, between 10% and 30% harbor additional metastases in non‐sentinel lymph nodes (NSLNs). Approximately 80% of SLN‐positive patients have a single positive SLN.MethodsTo assess whether state‐of‐the‐art clinicopathologic models predicting NSLN metastasis had adequate performance, we studied a single‐institution cohort of 143 patients with cN0 SLN‐positive primary melanoma who underwent subsequent completion lymph node dissection. We used sensitivity (SE) and positive predictive value (PPV) to characterize the ability of the models to identify patients at high risk for NSLN disease.ResultsAcross Stage III patients, all clinicopathologic models tested had comparable performances. The best performing model identified 52% of NSLN‐positive patients (SE = 52%, PPV = 37%). However, for the single SLN‐positive subgroup (78% of cohort), none of the models identified high‐risk patients (SE > 20%, PPV > 20%) irrespective of the chosen probability threshold used to define the binary risk labels. Thus, we designed a new model to identify high‐risk patients with a single positive SLN, which achieved a sensitivity of 49% (PPV = 26%).ConclusionFor the largest SLN‐positive subgroup, those with a single positive SLN, current model performance is inadequate. New approaches are needed to better estimate nodal disease burden of these patients.

e22088 Background: In recent years, adjuvant therapy trials in stage III melanoma have been successful and trials have started with the inclusion of stage IIB/C patients. However, stage IIA melanoma patients are currently not eligible for adjuvant therapy, even though a large part of all melanoma related deaths occur in this patient group. Therefore, a strong clinical need has emerged for diagnostic tools that can identify high-risk patients who currently have no access to adjuvant therapy. Here, we sought to assess the ability of a recently introduced clinicopathologic gene expression model (CP-GEP) (Bellomo et al., JCO Precis Oncol. 2020: in press) to select stage IIA patients at high risk for disease relapse, upon design of a stage-specific operating point. Methods: We assessed the prognostic performance of the CP-GEP model in all 141 stage IIA patients from a Mayo Clinic cohort of 837 consecutive melanoma patients who had a sentinel lymph node biopsy (SLNb) performed within 90 days of their diagnosis. The CP-GEP model combines Breslow thickness and patient age, with the expression of 8 genes in the primary tumor. Moreover, it stratifies patients according to their risk of relapse: CP-GEP High Risk or CP-GEP Low Risk, based on an operating point that was specifically developed for stage IIA. This stage-specific operating point was selected to fulfill the following criteria: hazard ratio RFS > 2 with a p-value < 0.05, and risk groups of similar size. The main clinical endpoint was five-year relapse free survival (RFS). Results: The CP-GEP High Risk group corresponds to 45% (63/141) of all stage IIA patients and captures 62% (18/29) of the total relapses in this substage. Moreover, CP-GEP High Risk patients relapse more frequently than CP-GEP Low Risk patients (RFS of 56% versus 78%; HR, 2.23; P < 0.05). The prognosis for stage IIA CP-GEP High Risk patients in our cohort is worse than for stage IIC/IIIA patients with reported RFS ranging from 63% to 77%. Conclusions: The CP-GEP model can be optimized by designing a stage-specific operating point, to identify a subset of stage IIA patients with an increased risk for disease relapse, not very different from IIC/IIIA patients. Therefore, stage IIA CP-GEP High Risk patients may be considered for inclusion in adjuvant trials. Independent validation studies are ongoing for the newly developed operating point. [Table: see text]

10068 Background: The identification of early stage melanoma patients at high risk for relapse is still difficult. Roughly 50% of melanoma deaths occur in patients who were initially diagnosed with nonmetastatic melanoma. Therefore, a strong clinical need has emerged for diagnostic tools that can identify melanoma patients at high risk for relapse. Here, we assessed the performance of a recently developed model (Bellomo et al., JCO Precis Oncol. 2020: in press), combining clinicopathologic and gene expression variables (CP-GEP), in identifying melanoma patients that have a high risk for disease relapse. Methods: We assessed the prognostic performance of the CP-GEP model in a cohort of 837 consecutive melanoma patients from Mayo Clinic who had a sentinel lymph node biopsy (SLNb) performed within 90 days of their diagnosis. The CP-GEP model combines Breslow thickness and patient age, with the expression of 8 genes in the primary tumor, to stratify patients according to their risk of relapse: CP-GEP High Risk or CP-GEP Low Risk. The main clinical endpoint of this study was five-year relapse free survival (RFS). Results: Patients were stratified based on SLNb status and CP-GEP classification. 76% of the patients were SLNb negative and had an RFS of 79% versus 52% for SLNb positive patients; HR, 3.21; P < 0.0001. 60% of the patients were identified as CP-GEP High Risk and had an RFS of 62% versus 87% for CP-GEP Low Risk patients; HR, 4.12; P < 0.0001. Within the SLNb negative group (637 patients of which 65% stage I), 51% of patients were classified as CP-GEP High Risk. Here, RFS was 70% for CP-GEP High Risk patients versus 89% for CP-GEP Low Risk patients; HR, 3.61; P < 0.0001. The prognosis of these CP-GEP High Risk patients is similar to stage IIC/IIIA patients with reported RFS ranging from 63% to 77%. This confirms the heterogeneity in prognosis among patients with stage I/II melanoma disease. Conclusions: The CP-GEP model can be successfully used to stratify patients based on their risk for relapse. In particular, it can be used to identify SLNb negative patients with a high risk for disease relapse who may benefit from therapeutic interventions. Independent validation studies are ongoing to validate the CP-GEP model in various patient populations. [Table: see text]

PURPOSEMore than 80% of patients who undergo sentinel lymph node (SLN) biopsy have no nodal metastasis. Here, we describe a model that combines clinicopathologic and molecular variables to identify patients with thin- and intermediate-thickness melanomas who may forgo the SLN biopsy procedure because of their low risk of nodal metastasis.PATIENTS AND METHODSGenes with functional roles in melanoma metastasis were discovered by analysis of next-generation sequencing data and case-control studies. We then used polymerase chain reaction to quantify gene expression in diagnostic biopsy tissue across a prospectively designed archival cohort of 754 consecutive thin- and intermediate-thickness primary cutaneous melanomas. Outcome of interest was SLN biopsy metastasis within 90 days of melanoma diagnosis. A penalized maximum likelihood estimation algorithm was used to train logistic regression models in a repeated cross-validation scheme to predict the presence of SLN metastasis from molecular, clinical, and histologic variables.RESULTSExpression of genes with roles in epithelial-to-mesenchymal transition (glia-derived nexin, growth differentiation factor 15, integrin-β3, interleukin 8, lysyl oxidase homolog 4, transforming growth factor-β receptor type 1, and tissue-type plasminogen activator) and melanosome function (melanoma antigen recognized by T cells 1) were associated with SLN metastasis. The predictive ability of a model that only considered clinicopathologic or gene expression variables was outperformed by a model that included molecular variables in combination with the clinicopathologic predictors Breslow thickness and patient age (area under the receiver operating characteristic curve, 0.82; 95% CI, 0.78 to 0.86; SLN biopsy reduction rate, 42%; negative predictive value, 96%).CONCLUSIONA combined model that included clinicopathologic and gene expression variables improved the identification of patients with melanoma who may forgo the SLN biopsy procedure because of their low risk of nodal metastasis.