Identification of stage I/II melanoma patients at high risk for recurrence using a model combining clinicopathologic factors with gene expression profiling (CP-GEP).

PURPOSE: Patients with cutaneous melanoma stage I/IIA disease are currently not eligible for adjuvant therapy, despite their risk for relapses and death. This study validates the ability of a model combining clinicopathologic factors with gene expression profiling (CP-GEP) to identify patients at high risk for disease recurrence in stage I/II and subgroup stage I/IIA. PATIENTS AND METHODS: 543 patients with stage I/II primary cutaneous melanoma from the University of Tuebingen diagnosed between 2000 and 2017 were analysed. All patients received sentinel lymph node biopsy (SLNB). Analysis was conducted for a separate group of 80 patients who did not undergo SLNB. RESULTS: CP-GEP stratified 424 stage I/IIA patients (78% of the cohort) according to their risk for recurrence, with five-year relapse-free survival (RFS) rates of 77.8% and 93% for CP-GEP high risk (195 patients) and low risk (229 patients), respectively, and hazard ratio of 3.53 (p-value <0.001). In patients who did not receive SLNB biopsy, CP-GEP captured 6 out of 7 relapses. CONCLUSION: CP-GEP can be used to identify primary cutaneous melanoma patients with a high risk for disease recurrence - especially for stage I/IIA, who are considered low risk by AJCC 8th. These patients may benefit from adjuvant therapy. Also, in the future, when SLNB may become irrelevant, CP-GEP may serve as a risk stratification tool.

Clinicopathologic models predicting non-sentinel lymph node metastasis in cutaneous melanoma patients: Are they useful for patients with a single positive sentinel node?

BACKGROUND AND OBJECTIVES: Of 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. METHODS: To 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. RESULTS: Across 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%). CONCLUSION: For 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.

Genome-Resolved Metagenomics of a Photosynthetic Bioreactor Performing Biological Nutrient Removal.

Enhanced biological phosphorus removal (EBPR) is an economically and environmentally significant wastewater treatment process for removing excess phosphorus by harnessing the metabolic physiologies of enriched microbial communities. We present a genome-resolved metagenomic data set consisting of 86 metagenome-assembled genome sequences from a photosynthetically operated lab-scale bioreactor simulating EBPR.

Identification of stage I/IIA melanoma patients at high risk for disease relapse using a clinicopathologic and gene expression model.

PURPOSE: Patients with stage I/IIA cutaneous melanoma (CM) are currently not eligible for adjuvant therapies despite uncertainty in relapse risk. Here, we studied the ability of a recently developed model which combines clinicopathologic and gene expression variables (CP-GEP) to identify stage I/IIA melanoma patients who have a high risk for disease relapse. PATIENTS AND METHODS: Archival specimens from a cohort of 837 consecutive primary CMs were used for assessing the prognostic performance of CP-GEP. The CP-GEP model combines Breslow thickness and patient age, with the expression of eight genes in the primary tumour. Our specific patient group, represented by 580 stage I/IIA patients, was stratified based on their risk of relapse: CP-GEP High Risk and CP-GEP Low Risk. The main clinical end-point of this study was five-year relapse-free survival (RFS). RESULTS: Within the stage I/IIA melanoma group, CP-GEP identified a high-risk patient group (47% of total stage I/IIA patients) which had a considerably worse five-year RFS than the low-risk patient group; 74% (95% confidence interval [CI]: 67%-80%) versus 89% (95% CI: 84%-93%); hazard ratio [HR] = 2.98 (95% CI: 1.78-4.98); P < 0.0001. Of patients in the high-risk group, those who relapsed were most likely to do so within the first 3 years. CONCLUSION: The CP-GEP model can be used to identify stage I/IIA patients who have a high risk for disease relapse. These patients may benefit from adjuvant therapy.