PowerPoint Presentation Patients with T1–T2 tumors from previously published multicenter cohort studies who had undergone the SLNB procedure were analyzed by both the i31-GEP and the MSKCC nomogram (n=465).6 Accuracy metrics were compared using <5% predicted risk as a negative result and ≥5% as a positive result. The i31-GEP for sentinel lymph node (SLN) biopsy outperforms the MSKCC nomogram in predicting the risk of having a positive SLN in patients with cutaneous melanoma Background › National Comprehensive Cancer Network (NCCN) guidelines recommend foregoing sentinel lymph node biopsy (SLNB) if the population-based point- estimate risk of positivity is <5% (T1a with no high-risk features), discuss and consider SLNB if the risk is 5-10% (T1a with high-risk feature(s), T1b), and recommend SLNB if the risk is >10% (T2-T4).1 › With the current SLNB positivity rate at approximately 12%,2,3 better tools are needed to refine patient selection for the procedure to avoid potential complications and additional healthcare costs. Such methods that improve patient selection of those who will have a positive SLNB, while identifying the correct patients with low enough risk (<5% positivity risk by current guidelines) that they can safely forego SLNB could reduce the number of unnecessary surgical procedures, lower healthcare costs, and improve patient care. › Two tools, the i31-gene expression profile test for SLNB (i31-GEP for SLNB)4 and the nomogram developed at the Memorial Sloan Kettering Cancer Center (MSKCC)5,6 predict the risk of SLN positivity in patients with cutaneous melanoma (CM) by combining a tumor’s molecular risk profile, clinical and pathological factors (i31-GEP test) clinical and pathological factors only (MSKCC). › We compared the performance of the i31-GEP for SLNB result prediction to that of the MSKCC nomogram. Acknowledgments & Disclosures References › Funding provided by Castle Biosciences. › DZ and NB have no conflicts of interest. SKM is an employee and stock/options holder at Castle Biosciences. Methods Presented at the 2023 Winter Clinical-Miami Dermatology Conference. Danny Zakria, MD1, Sonia K. Morgan-Linnell, PhD2, Nicholas Brownstone, MD3 1Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, NY; 2Castle Biosciences, Inc., Friendswood, TX; 3Department of Dermatology, Temple Health, Philadelphia, PA › The MSKCC nomogram using clinical and pathological factors alone had a 10% miss rate in patients it predicted to have <5% risk of SLN positivity—worse than AJCC staging alone. › The i31-GEP for SLNB missed 2.7%, significantly lower than MSKCC—better than both MSKCC and AJCC staging. › The i31-GEP for SLNB showed an 89% increase in the number of patients who could forego SLNB compared with current guidelines (36:1 vs. 19:1 true to false negative ratio) compared with a 53% decrease if using MSKCC. › The i31-GEP for SLNB has demonstrated clinical utility to guide SLNB decisions in patients with T1-T2 tumors as well as guiding subsequent treatment plans with risk-of-recurrence. Conclusions Scan or click here for more info 1. National Comprehensive Cancer Guidelines, v2, 2022. 2. Joyce KM, et al. Ir J Med Sci 186:847–853, 2017. 3. Chen et al. Oncotarget 7:45671-45677. 4. Whitman ED, et al. JCO Precision Oncology 1466–1479, 2021. 5. Wong SL, et al. Ann Surg Oncol 12:282–288, 2005. 6. Pasquali S, et al. Eur J Surg Oncol 37:675–80, 2011. Figure 1. i31-GEP SLNB guidance results in fewer missed positive SLNs than the MSKCC nomogram or standard staging guidance › The i31-GEP had a significantly lower false negative rate (3/111; 2.7%) than the MSKCC nomogram (11/110; 10.0%) (p=0.026). › The i31-GEP also had a lower false negative rate than using AJCC staging, which does not recommend SLNB if the risk is 5% or less. Table 1: Variables included in i31-GEP test or MSKCC Model Prediction Variables Included in i31-GEP Test Included in MSKCC Model 31-GEP continuous score √ Breslow thickness √ √ Mitotic Rate √ Ulceration √ √ Age √ √ Clark Level √ Tumor location √ Table 3. Reclassification of risk in patients with 5-10% risk (NCCN/AJCC T1b tumors) for whom guidance is not definitive Test Predicted <5% risk Positivity rate in <5% group Predicted >10% risk Positivity rate in >10% group Total reclassified i31-GEP 36.2% (46/127) 2.2% (1/46) 15.7% (20/127) 15% (3/20) 52.0% (66/127) MSKCC 29.9% (38/127) 7.9% (3/38) 2.4% (3/127) 0% (0/3) 32.3% (41/127) All results are % (n/N). i31-GEP MSKCCAJCC Table 2. The i31-GEP for SLNB has higher accuracy than MSKCC for predicting SLN positivity in patients with T1-T2 tumors Test Sensitivity Specificity Negative predictive value Positive predictive value i31-GEP 94.8% 26.5% 97.3% 15.5% MSKCC 81.0% 24.3% 90.0% 13.2% 19:1 9:136:1 Missed positive SLNTrue negative SLN (safely forego SLNB) 31-GEP: Better than standard of care MSKCC: Worse than standard of care 1 missed positive SLN per 37 considered low-risk 1 missed positive SLN per 20 considered low-risk 1 missed positive SLN per 10 considered low-risk › Using only clinical and pathologic features to predict SLN status limits the ability to identify tumors with spread to the SLN (e.g., 12% positivity rate). Integrating clinical and pathological factors with molecular tumor biology assessed by the prospectively validated gene expression risk profile (31-GEP) improves risk stratification to guide personalized patient care. › Beyond the utility for SLNB guidance, the integrated 31-GEP ( i31- GEP) also provides a precise risk for recurrence, metastasis, and disease-specific mortality for an individual patient. Clinical Impact 31-GEP score, Breslow thickness, mitotic rate, age, and Clark level were continuous variables. Ulceration was present or absent, and tumor location was entered as trunk, extremity, or head/neck. Tumor location was evaluated in development of the i31-GEP but was not significant for prediction; Clark level was not evaluated because most providers use Breslow thickness instead. https://castlebiosciences.com/research-development/publications/ Slide 1