A30 Tumor-Infiltrating Lymphocytes (TILs) Found Elevated in Lung Adenocarcinomas (LUAD) Using Automated Digital Pathology Masks Derived from Deep-Learning Models

AbstractThe nuclear protein Ki-67 and Tumor infiltrating lymphocytes (TILs) have been introduced as prognostic factors in predicting both tumor progression and probable response to chemotherapy. The value of Ki-67 index and TILs in approach to heterogeneous tumors such as Breast cancer (BC) that is the most common cancer in women worldwide, has been highlighted in literature. Considering that estimation of both factors are dependent on professional pathologists’ observation and inter-individual variations may also exist, automated methods using machine learning, specifically approaches based on deep learning, have attracted attention. Yet, deep learning methods need considerable annotated data. In the absence of publicly available benchmarks for BC Ki-67 cell detection and further annotated classification of cells, In this study we propose SHIDC-BC-Ki-67 as a dataset for the aforementioned purpose. We also introduce a novel pipeline and backend, for estimation of Ki-67 expression and simultaneous determination of intratumoral TILs score in breast cancer cells. Further, we show that despite the challenges that our proposed model has encountered, our proposed backend, PathoNet, outperforms the state of the art methods proposed to date with regard to harmonic mean measure acquired. Dataset is publicly available in http://shiraz-hidc.com and all experiment codes are published in https://github.com/SHIDCenter/PathoNet.

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A template to quantify the location and density of CD3 + and CD8 + tumor-infiltrating lymphocytes in colon cancer by digital pathology on whole slides for an objective, standardized immune score assessment

Cancer Immunology Immunotherapy ◽

10.1007/s00262-020-02834-y ◽

2021 ◽

Author(s):

Dordi Lea ◽

Martin Watson ◽

Ivar Skaland ◽

Hanne R. Hagland ◽

Melinda Lillesand ◽

...

Keyword(s):

Colon Cancer ◽

Digital Pathology ◽

Tumor Infiltrating Lymphocytes ◽

Risk Groups ◽

Cd8 Cells ◽

Invasive Margin ◽

Cell Counts ◽

Tumor Center ◽

Infiltrating Lymphocytes ◽

Immune Score

Abstract Background In colon cancer, the location and density of tumor-infiltrating lymphocytes (TILs) can classify patients into low and high-risk groups for prognostication. While a commercially available ‘Immunoscore®’ exists, the incurred expenses and copyrights may prevent universal use. The aim of this study was to develop a robust and objective quantification method of TILs in colon cancer. Methods A consecutive, unselected series of specimens from patients with colon cancer were available for immunohistochemistry and assessment of TILs by automated digital pathology. CD3 + and CD8 + cells at the invasive margin and in tumor center were assessed on consecutive sections using automated digital pathology and image analysis software (Visiopharm®). An algorithm template for whole slide assessment, generated cell counts per square millimeters (cells/mm2), from which the immune score was calculated using distribution volumes. Furthermore, immune score was compared with clinical and histopathological characteristics to confirm its relevance. Results Based on the quantified TILs numbers by digital image analyses, patients were classified into low (n = 83, 69.7%), intermediate (n = 14, 11.8%) and high (n = 22, 18.5%) immune score groups. High immune score was associated with stage I–II tumors (p = 0.017) and a higher prevalence of microsatellite instable (MSI) tumors (p = 0.030). MSI tumors had a significantly higher numbers of CD3 + TILs in the invasive margin and CD8 + TILs in both tumor center and invasive margin, compared to microsatellite stable (MSS) tumors. Conclusion A digital template to quantify an easy-to-use immune score corresponds with clinicopathological features and MSI in colon cancer.

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1124O Prediction of distant relapse in patients with invasive breast cancer from deep learning models applied to digital pathology slides

Annals of Oncology ◽

10.1016/j.annonc.2021.08.766 ◽

2021 ◽

Vol 32 ◽

pp. S921

Author(s):

I.J. Garberis ◽

C. Saillard ◽

D. Drubay ◽

B. Schmauch ◽

V. Aubert ◽

...

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Invasive Breast Cancer ◽

Digital Pathology ◽

Learning Models ◽

Distant Relapse

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Predicting response to pembrolizumab in non-small cell lung cancer, by analyzing the spatial arrangement of tumor infiltrating lymphocytes using deep learning.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.9045 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. 9045-9045

Author(s):

Efrat Ofek ◽

Jair Bar ◽

Alona Zer ◽

Damien Urban ◽

Chen Mayer ◽

...

Keyword(s):

Deep Learning ◽

Clinical Outcome ◽

Single Agent ◽

Tumor Infiltrating Lymphocytes ◽

Spatial Arrangement ◽

Spatial Features ◽

Validation Set ◽

Nsclc Patients ◽

Infiltrating Lymphocytes ◽

Whole Slide Images

9045 Background: Immune checkpoint inhibitors (ICI) have become the standard treatment for metastatic NSCLC, although only a small proportion of patients derive durable benefit. PDL1 expression is the only approved biomarker to select NSCLC patients for treatment with single-agent pembrolizumab, however its predictive value is limited and better predictive biomarkers are needed. The spatial arrangement of immune cells in the tumor microenvironment (TME), namely tumor infiltrating lymphocytes (TILs), emerges as a potential biomarker for ICI efficacy. In this work, we utilized deep-learning (DL) models to extract TME features from digitized H&E slides and evaluated their predictive and prognostic role in patients with mNSCLC treated with Pembrolizumab. Methods: NSCLC patients (n=90) treated with single-agent 1st line pembrolizumab in two centers were identified. 47 patients from one center were used to train the model, and 43 patients from another center were used for validating the model. Pre-treatment H&E whole slide images (WSI) were analyzed using a deep-learning model to identify and classify tumor cells, TILs, tumor and stromal areas, and spatial features were calculated. Spatial features were correlated with clinical outcome data to train a binary classifier that identifies patients with a favorable clinical outcome. The resulting classifier combined three spatial features and three clinical features. The classifier was then applied to the validation set and differences in duration of treatment (DOT), and overall survival (OS) between patients with positive and negative scores were assessed. Results: The classifier identified patients in the validation set to have either positive (n=18) or negative (n=25) scores. Baseline patient characteristics and PDL1 score were similar between the positive and negative groups. In a Kaplan-Meier (KM) analysis, OS was significantly higher in patients with a positive score compared to patients with a negative score (HR=0.35, 95% CI 0.13-0.98; p<0.05). Positive patients had a significantly higher median OS (NR vs.17.8m, p<0.05) and 2-year OS (70.8% vs. 33%, p=0.02) than negative patients. Median DOT was also higher in positive patients compared to negative patients (10.1m vs. 6.5m). Conclusions: Deep-learning models that analyze the TME from H&E whole-slide images can identify NSCLC patients with durable benefit on Pembrolizumab. Identifying NSCLC patients who are exceptionally sensitive to anti-PD-1 therapy as monotherapy may improve clinical decision making and spare patients the unnecessary adverse effects associated with the addition chemotherapy or another IO agent.

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Abstract P5-03-08: Development of a histology-based digital pathology image analysis algorithm for assessment of tumor infiltrating lymphocytes in HER2+ breast cancer

10.1158/1538-7445.sabcs17-p5-03-08 ◽

2018 ◽

Cited By ~ 1

Author(s):

M Barnes ◽

A Sarkar ◽

R Redman ◽

C Bechert ◽

C Srinivas

Keyword(s):

Breast Cancer ◽

Image Analysis ◽

Digital Pathology ◽

Tumor Infiltrating Lymphocytes ◽

Analysis Algorithm ◽

Her2 Breast Cancer ◽

Image Analysis Algorithm ◽

Infiltrating Lymphocytes ◽

Pathology Image

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Unmasking the tissue microecology of ductal carcinoma in situ with deep learning

10.1101/812735 ◽

2019 ◽

Cited By ~ 1

Author(s):

Priya Lakshmi Narayanan ◽

Shan E Ahmed Raza ◽

Allison H. Hall ◽

Jeffrey R. Marks ◽

Lorraine King ◽

...

Keyword(s):

Deep Learning ◽

Ductal Carcinoma ◽

Digital Pathology ◽

Spatial Relationship ◽

Learning Networks ◽

Deep Convolutional Neural Networks ◽

Technological Developments ◽

Local Ecology ◽

Infiltrating Lymphocytes

AbstractDespite increasing evidence supporting the clinical relevance of tumour infiltrating lymphocytes (TILs) in invasive breast cancer, TIL spatial distribution pattern surrounding ductal carcinoma in situ (DCIS) and its association with progression is not well understood.To characterize the tissue microecology of DCIS, we designed and tested a new deep learning pipeline, UNMaSk (UNet-IM-Net-SCCNN), for the automated detection and simultaneous segmentation of DCIS ducts. This new method achieved the highest sensitivity and recall over cutting-edge deep learning networks in three patient cohorts, as well as the highest concordance with DCIS identification based on CK5 staining.Following automated DCIS detection, spatial tessellation centred at each DCIS duct created the boundary in which local ecology can be studied. Single cell identification and classification was performed with an existing deep learning method to map the distribution of TILs. In a dataset comprising grade 2-3 pure DCIS and DCIS adjacent to invasive cancer (adjacent DCIS), we found that pure DCIS cases had more TILs compared to adjacent DCIS. However, TILs co-localise significantly less with DCIS ducts in pure DCIS compared with adjacent DCIS, suggesting a more inflamed tissue ecology local to adjacent DCIS cases.Our experiments demonstrate that technological developments in deep convolutional neural networks and digital pathology can enable us to automate the identification of DCIS as well as to quantify the spatial relationship with TILs, providing a new way to study immune response and identify new markers of progression, thereby improving clinical management.

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Unmasking the immune microecology of ductal carcinoma in situ with deep learning

npj Breast Cancer ◽

10.1038/s41523-020-00205-5 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Priya Lakshmi Narayanan ◽

Shan E. Ahmed Raza ◽

Allison H. Hall ◽

Jeffrey R. Marks ◽

Lorraine King ◽

...

Keyword(s):

Deep Learning ◽

Ductal Carcinoma In Situ ◽

Carcinoma In Situ ◽

Ductal Carcinoma ◽

Digital Pathology ◽

Deep Convolutional Neural Networks ◽

Technological Developments ◽

Infiltrating Lymphocytes ◽

Spatial Architecture

AbstractDespite increasing evidence supporting the clinical relevance of tumour infiltrating lymphocytes (TILs) in invasive breast cancer, TIL spatial variability within ductal carcinoma in situ (DCIS) samples and its association with progression are not well understood. To characterise tissue spatial architecture and the microenvironment of DCIS, we designed and validated a new deep learning pipeline, UNMaSk. Following automated detection of individual DCIS ducts using a new method IM-Net, we applied spatial tessellation to create virtual boundaries for each duct. To study local TIL infiltration for each duct, DRDIN was developed for mapping the distribution of TILs. In a dataset comprising grade 2–3 pure DCIS and DCIS adjacent to invasive cancer (adjacent DCIS), we found that pure DCIS cases had more TILs compared to adjacent DCIS. However, the colocalisation of TILs with DCIS ducts was significantly lower in pure DCIS compared to adjacent DCIS, which may suggest a more inflamed tissue ecology local to DCIS ducts in adjacent DCIS cases. Our study demonstrates that technological developments in deep convolutional neural networks and digital pathology can enable an automated morphological and microenvironmental analysis of DCIS, providing a new way to study differential immune ecology for individual ducts and identify new markers of progression.

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Characterizing Immune Responses in Whole Slide Images of Cancer With Digital Pathology and Pathomics

Current Pathobiology Reports ◽

10.1007/s40139-020-00217-7 ◽

2020 ◽

Vol 8 (4) ◽

pp. 133-148

Author(s):

Rajarsi Gupta ◽

Han Le ◽

John Van Arnam ◽

David Belinsky ◽

Mahmudul Hasan ◽

...

Keyword(s):

Lung Cancer ◽

Digital Pathology ◽

Tumor Infiltrating Lymphocytes ◽

Screening Tests ◽

Cancer Tissue ◽

Tissue Samples ◽

Wide Range ◽

Novel Biomarkers ◽

Infiltrating Lymphocytes ◽

Whole Slide Images

Abstract Purpose of Review Our goal is to show how readily available Pathomics tissue analytics can be used to study tumor immune interactions in cancer. We provide a brief overview of how Pathomics complements traditional histopathologic examination of cancer tissue samples. We highlight a novel Pathomics application, Tumor-TILs, that quantitatively measures and generates maps of tumor infiltrating lymphocytes in breast, pancreatic, and lung cancer by leveraging deep learning computer vision applications to perform automated analyses of whole slide images. Recent Findings Tumor-TIL maps have been generated to analyze WSIs from thousands of cases of breast, pancreatic, and lung cancer. We report the availability of these tools in an effort to promote collaborative research and motivate future development of ensemble Pathomics applications to discover novel biomarkers and perform a wide range of correlative clinicopathologic research in cancer immunopathology and beyond. Summary Tumor immune interactions in cancer are a fascinating aspect of cancer pathobiology with particular significance due to the emergence of immunotherapy. We present simple yet powerful specialized Pathomics methods that serve as powerful clinical research tools and potential standalone clinical screening tests to predict clinical outcomes and treatment responses for precision medicine applications in immunotherapy.

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