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An open source automated tumor infiltrating lymphocyte algorithm for prognosis in melanoma.

ABSTRACT: Assessment of tumor infiltrating lymphocytes (TILs) as a prognostic variable in melanoma has not seen broad adoption due to lack of standardization. Automation could represent a solution. Here, using open source software, we build an algorithm for image-based automated assessment of TILs on hematoxylin-eosin stained sections in melanoma. Using a retrospective collection of 641 melanoma patients comprising four independent cohorts; one training set (N?=?227) and three validation cohorts (N?=?137, N?=?201, N?=?76) from 2 institutions, we show that the automated TIL scoring algorithm separates patients into favorable and poor prognosis cohorts, where higher TILs scores were associated with favorable prognosis. In multivariable analyses, automated TIL scores show an independent association with disease-specific overall survival. Therefore, the open source, automated TIL scoring is an independent prognostic marker in melanoma. With further study, we believe that this algorithm could be useful to define a subset of patients that could potentially be spared immunotherapy.

SUBMITTER: Acs B

PROVIDER: S-EPMC6884485 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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An open source automated tumor infiltrating lymphocyte algorithm for prognosis in melanoma.

Acs Balazs B Ahmed Fahad Shabbir FS Gupta Swati S Wong Pok Fai PF Gartrell Robyn D RD Sarin Pradhan Jaya J Rizk Emanuelle M EM Gould Rothberg Bonnie B Saenger Yvonne M YM Rimm David L DL

Nature communications 20191129 1

Assessment of tumor infiltrating lymphocytes (TILs) as a prognostic variable in melanoma has not seen broad adoption due to lack of standardization. Automation could represent a solution. Here, using open source software, we build an algorithm for image-based automated assessment of TILs on hematoxylin-eosin stained sections in melanoma. Using a retrospective collection of 641 melanoma patients comprising four independent cohorts; one training set (N = 227) and three validation cohorts (N = 137, ...[more]

PMID: 31784511

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Project description:PurposeEffective treatment options are limited for patients with advanced (metastatic or unresectable) melanoma who progress after immune checkpoint inhibitors and targeted therapies. Adoptive cell therapy using tumor-infiltrating lymphocytes has demonstrated efficacy in advanced melanoma. Lifileucel is an autologous, centrally manufactured tumor-infiltrating lymphocyte product.MethodsWe conducted a phase II open-label, single-arm, multicenter study in patients with advanced melanoma who had been previously treated with checkpoint inhibitor(s) and BRAF ± MEK targeted agents. Lifileucel was produced from harvested tumor specimens in central Good Manufacturing Practice facilities using a streamlined 22-day process. Patients received a nonmyeloablative lymphodepletion regimen, a single infusion of lifileucel, and up to six doses of high-dose interleukin-2. The primary end point was investigator-assessed objective response rate (ORR) per RECIST, version 1.1.ResultsSixty-six patients received a mean of 3.3 prior therapies (anti-programmed death 1 [PD-1] or programmed death ligand 1 [PD-L1]: 100%; anticytotoxic T-lymphocyte-associated protein-4: 80%; BRAF ± MEK inhibitor: 23%). The ORR was 36% (95% CI, 25 to 49), with two complete responses and 22 partial responses. Disease control rate was 80% (95% CI, 69 to 89). Median duration of response was not reached after 18.7-month median study follow-up (range, 0.2-34.1 months). In the primary refractory to anti-PD-1 or PD-L1 therapy subset, the ORR and disease control rate were 41% (95% CI, 26 to 57) and 81% (95% CI, 66 to 91), respectively. Safety profile was consistent with known adverse events associated with nonmyeloablative lymphodepletion and interleukin-2.ConclusionLifileucel demonstrated durable responses and addresses a major unmet need in patients with metastatic melanoma with limited treatment options after approved therapy, including the primary refractory to anti-PD-1 or PD-L1 therapy subset.

Project description:BackgroundArtificial intelligence (AI) technology represented by deep learning has made remarkable achievements in digital pathology, enhancing the accuracy and reliability of diagnosis and prognosis evaluation. The spatial distribution of CD3 + and CD8 + T cells within the tumor microenvironment has been demonstrated to have a significant impact on the prognosis of colorectal cancer (CRC). This study aimed to investigate CD3 CT (CD3 + T cells density in the core of the tumor [CT]) prognostic ability in patients with CRC by using AI technology.MethodsThe study involved the enrollment of 492 patients from two distinct medical centers, with 358 patients assigned to the training cohort and an additional 134 patients allocated to the validation cohort. To facilitate tissue segmentation and T-cells quantification in whole-slide images (WSIs), a fully automated workflow based on deep learning was devised. Upon the completion of tissue segmentation and subsequent cell segmentation, a comprehensive analysis was conducted.ResultsThe evaluation of various positive T cell densities revealed comparable discriminatory ability between CD3 CT and CD3-CD8 (the combination of CD3 + and CD8 + T cells density within the CT and invasive margin) in predicting mortality (C-index in training cohort: 0.65 vs. 0.64; validation cohort: 0.69 vs. 0.69). The CD3 CT was confirmed as an independent prognostic factor, with high CD3 CT density associated with increased overall survival (OS) in the training cohort (hazard ratio [HR] = 0.22, 95% confidence interval [CI]: 0.12-0.38, P <0.001) and validation cohort (HR = 0.21, 95% CI: 0.05-0.92, P = 0.037).ConclusionsWe quantify the spatial distribution of CD3 + and CD8 + T cells within tissue regions in WSIs using AI technology. The CD3 CT confirmed as a stage-independent predictor for OS in CRC patients. Moreover, CD3 CT shows promise in simplifying the CD3-CD8 system and facilitating its practical application in clinical settings.

Dataset Information

An open source automated tumor infiltrating lymphocyte algorithm for prognosis in melanoma.

Publications

An open source automated tumor infiltrating lymphocyte algorithm for prognosis in melanoma.

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