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Pages
Posts
Portfolio item number 1
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Portfolio item number 2
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publications
High Expression of FAP in Colorectal Cancer
Published in Frontiers in Oncology, 2020
This study highlights the significance of the tumor stroma in the tumor microenvironment, focusing on FAP protein expression in cancer-associated fibroblasts. We demonstrated its role in tumor progression, immunoregulatory processes, and patient prognosis.
Recommended citation: Coto-Llerena M., Ercan C. et al. "High expression of FAP in colorectal cancer is associated with angiogenesis and immunoregulation processes." Frontiers in oncology 10 (2020): 979.
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Genomic analysis of focal nodular hyperplasia with associated hepatocellular carcinoma
Published in communications medicine , 2022
This project explored tumor heterogeneity in a liver carcinoma adjacent to a benign lesion (focal nodular hyperplasia). Spatial genomic analysis revealed a clonal relationship between the lesions, providing the first genomic evidence of malignant transformation from a lesion previously considered benign.
Recommended citation: Ercan, Caner, et al. "Genomic analysis of focal nodular hyperplasia with associated hepatocellular carcinoma unveils its malignant potential: a case report." Communications medicine 2.1 (2022): 11.
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AI(H): A deep-learning-based model for assessment of autoimmune hepatitis from histology
Published in , 2024
This paper is about development of a computational model for autoimmune hepatitis.
Recommended citation: Ercan, Caner, et al. "A deep-learning-based model for assessment of autoimmune hepatitis from histology: AI (H)." Virchows Archiv (2024): 1-11.
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Deep learning-based whole-slide image analysis for immune ecology and cancer prediction in Barrett’s oesophagus
Published in Medical Imaging with Deep Learning. 2024, 2024
As my main projecy in postdoc, this project focuses on the spatially computational analysis of Barrett’s Esophagus biopsy images to identify cancer progression-related changes. These include abnormalities in DNA content and alterations in the spatial immune microenvironment. This project has financed by (SNF Postdoc.Mobility) [https://data.snf.ch/grants/grant/214162]. The aim of the projct is firstly detecting aneuploidy in biopsies using weakly supervised methods. Afterwards, investigating the microenviromental heterogeniety in biopsies and its relevance with aneuoploidy related regions within biopsies. The aneuploid detection related study was presented in multiple meetings. The immune environment investigation part is on ging and the reliminary results were submitted to AACR 2025 meeting.
Recommended citation: Ercan, Caner, et al. "Predicting DNA Content Abnormalities in Barrett’s Esophagus: A Weakly Supervised Learning Paradigm." Medical Imaging with Deep Learning. 2024.
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Hepatocellular Carcinoma Immune Microenvironment Analysis
Published in Clinical Cancer Research, 2024
As the main project of my PhD, this study investigated the tumor immune microenvironment in hepatocellular carcinoma (HCC) for the first time following the Immuno-Oncology Working Group’s recommendations. The project demonstrated the applicability and significance of microscopic evaluation of immune responses and their relationship with patient prognosis, histology, and tumor heterogeneity. Additionally, we developed a computational pathology tool to analyze slides with accuracy, interpretability, and reproducibility.
Recommended citation: Ercan, Caner, et al. "Hepatocellular carcinoma immune microenvironment analysis: A comprehensive assessment with computational and classical pathology." Clinical Cancer Research 30.22 (2024): 5105-5115.
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research
talks
Expression of FAP-1 in Colorectal Adenocarcinoma Associated Fibroblasts
Published:
Poster on FAP-1 expression in cancer-associated fibroblasts in colorectal adenocarcinoma, presented at ECP 2019.
Tumour Budding in Colorectal Carcinoma: Results of TCGA Dataset
Published:
Poster on tumour budding in colorectal carcinoma using TCGA dataset analysis, presented at ECP 2020.
AI(H): Deep Learning Model for Staging and Grading Autoimmune Hepatitis from Histology
Published:
Poster on AI(H), a deep learning model for staging and grading autoimmune hepatitis from histology, presented at AASLD The Liver Meeting.
QuPath for Digital Pathology
Published:
Short presentation on QuPath for digital pathology analysis, delivered at the Swiss Digital Pathology Consortium (SDiPath) workshop.
Establishing Quantitative Image Analysis Methods for Tumour Microenvironment Evaluation
Published:
Poster on quantitative image analysis methods for tumour microenvironment evaluation in hepatocellular carcinoma, presented at the AACR Annual Meeting.
AI(H): Deep Learning Model for Staging and Grading Autoimmune Hepatitis from Histology
Published:
Poster on AI(H), a deep learning model for staging and grading autoimmune hepatitis from histology. Awarded Second Best Poster of the Conference at ECP 2022.
Establishing Quantitative Image Analysis Methods for Tumor Microenvironment Evaluation
Published:
Oral presentation on quantitative image analysis methods for tumour microenvironment evaluation, presented at the Triennial Congress of the Italian Society of Pathology (SIAPeC-IAP 2022).
Predicting DNA Content Abnormalities in Barrett’s Esophagus: A Weakly Supervised Learning Paradigm
Published:
Flash talk on weakly supervised deep learning for predicting DNA content abnormalities from routine H&E whole-slide images of Barrett’s esophagus biopsies, presented at the New York Academy of Sciences.
Predicting DNA Content Abnormalities in Barrett’s Esophagus: A Weakly Supervised Learning Paradigm
Published:
Poster on weakly supervised deep learning for predicting DNA content abnormalities from routine H&E whole-slide images of Barrett’s esophagus biopsies, presented at MIDL 2024.
A Novel Approach to Barrett’s Esophagus Risk Stratification: Whole-Slide Image Analysis for Aneuploidy Detection
Published:
Poster on whole-slide image analysis for aneuploidy detection in Barrett’s esophagus risk stratification, presented at the AACR Annual Meeting.
Path-omics: Computational Pathology Approach to Predict Barrett’s Esophagus Progression
Published:
Poster presenting Path-omics: a computational pathology approach to predict Barrett’s esophagus progression using aneuploidy and ecological microenvironmental metrics.
Path-omics: Computational Pathology Approach to Predict Barrett’s Esophagus Progression
Published:
Poster presenting Path-omics: a computational pathology approach to predict Barrett’s esophagus progression using aneuploidy and ecological microenvironmental metrics.
Spatial Co-Evolution of Molecular Features and Immune Ecology Predicts Cancer Progression Risk in Barrett’s Esophagus
Published:
Short talk on spatial co-evolution of molecular features and immune ecology in Barrett’s esophagus, presented at the EMBO Workshop on The Many Faces of Cancer Evolution.
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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