Using digital-image analysis of tumor-infiltrating lymphocytes to predict survival outcomes in primary melanoma.

Authors

null

Margaret Chou

The Ronald O. Perelman Department of Dermatology, New York University School of Medicine, New York, NY

Margaret Chou , Irineu Illa-Bochaca , Ben Minxi , Keith M. Giles , Farbod Darvishian , George Jour , Una Moran , Richard L. Shapiro , Russell S. Berman , Iman Osman , Hua Zhong

Organizations

The Ronald O. Perelman Department of Dermatology, New York University School of Medicine, New York, NY, School of Life Sciences, Fudan University, Shanghai, NY, China, Department of Pathology, New York University School of Medicine, New York, NY, NYU Langone Medical Center, New York, NY, Division of Surgical Oncology, Department of Surgery, New York University School of Medicine, New York, NY, Department of Population Health, New York University School of Medicine, New York, NY

Research Funding

Other
P50 CA225450 NYU Melanoma SPORE, Other Foundation, P30 CA016087 Cancer Center Support Grant

Background: Inclusion of tumor-infiltrating lymphocytes (TIL) into AJCC staging criteria has been proposed due to evidence suggesting its prognostic significance. However, subjective inter-observer discordance prevents adoption of semi-quantitative TIL grading (e.g. absent, non-brisk, brisk) into clinical practice. We hypothesize that digital-image analysis (DIA) of TIL can provide a standardized, quantitative scoring system that more accurately predicts survival compared to currently used semi-quantitative grading methods. Methods: Clinical data and tumor specimens were analyzed from prospectively enrolled primary melanoma patients in the New York University Interdisciplinary Melanoma Cooperative Group with median follow-up of 5 years. H&E-stained slides were digitized using an Aperio ScanScope at 20X magnification. QuPath software was used for automated TIL quantification. Cox regression analysis was used to assess the improved prognostic value of TIL on recurrence-free (RFS) and overall survival (OS). Patients were separated into high- and low-TIL groups using a score threshold determined by the Youden Index. Results: 453 patients (18% stage I, 42% stage II, 40% stage III) were scored using automated TIL assessment and scores were significantly correlated with better RFS and OS per 10% increase in TIL (stage adjusted hazard ratio [aHR] = 0.92 [0.84-1.00] for RFS and aHR = 0.90 [0.83-0.99] for OS). A model combining TIL score with stage increased prognostic ability for both RFS (0.68 to 0.70, P = 0.02) and OS (0.62 to 0.64, P = 0.01), as assessed by concordance indices (C-index). Kaplan-Meier curves of high- ( > 16.6%) versus low-TIL (≤16.6%) patients showed clear separation in RFS and OS (median RFS = 155 vs 48 months, P < 0.001; median OS = 155 vs 89 months, P = 0.002). For comparison, a subset of the cohort (n = 250) was semi-quantitatively graded (absent, non-brisk, brisk) by an attending melanoma pathologist; however, this did not significantly differentiate RFS between groups (P > 0.05). Conclusions: A standardized, quantitative TIL scoring system significantly improved prediction of RFS and OS in primary melanoma patients compared with semi-quantitative TIL grading. Incorporation of quantitative TIL scoring into prognostic algorithms, such as AJCC criteria, should be considered.

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Abstract Details

Meeting

2020 ASCO Virtual Scientific Program

Session Type

Poster Session

Session Title

Melanoma/Skin Cancers

Track

Melanoma/Skin Cancers

Sub Track

Local-Regional Disease

Citation

J Clin Oncol 38: 2020 (suppl; abstr 10066)

DOI

10.1200/JCO.2020.38.15_suppl.10066

Abstract #

10066

Poster Bd #

415

Abstract Disclosures