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Your search keyword '"Irineu Illa-Bochaca"' showing total 3 results
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3 results on '"Irineu Illa-Bochaca"'

1. Quantitative multiplex immunohistochemistry reveals inter-patient lymphovascular and immune heterogeneity in primary cutaneous melanoma

Author

Julia Femel, Cameron Hill, Irineu Illa Bochaca, Jamie L. Booth, Tina G. Asnaashari, Maria M. Steele, Ata S. Moshiri, Hyungrok Do, Judy Zhong, Iman Osman, Sancy A. Leachman, Takahiro Tsujikawa, Kevin P. White, Young H. Chang, and Amanda W. Lund

Subjects
melanoma, vasculature, inflammation, tertiary lymphoid structures, lymphatic vessels, metastasis, immune microenvironment, Immunologic diseases. Allergy, RC581-607
Abstract

IntroductionQuantitative, multiplexed imaging is revealing complex spatial relationships between phenotypically diverse tumor infiltrating leukocyte populations and their prognostic implications. The underlying mechanisms and tissue structures that determine leukocyte distribution within and around tumor nests, however, remain poorly understood. While presumed players in metastatic dissemination, new preclinical data demonstrates that blood and lymphatic vessels (lymphovasculature) also dictate leukocyte trafficking within tumor microenvironments and thereby impact anti-tumor immunity. Here we interrogate these relationships in primary human cutaneous melanoma. MethodsWe established a quantitative, multiplexed imaging platform to simultaneously detect immune infiltrates and tumor-associated vessels in formalin-fixed paraffin embedded patient samples. We performed a discovery, retrospective analysis of 28 treatment-naïve, primary cutaneous melanomas. ResultsHere we find that the lymphvasculature and immune infiltrate is heterogenous across patients in treatment naïve, primary melanoma. We categorized five lymphovascular subtypes that differ by functionality and morphology and mapped their localization in and around primary tumors. Interestingly, the localization of specific vessel subtypes, but not overall vessel density, significantly associated with the presence of lymphoid aggregates, regional progression, and intratumoral T cell infiltrates. DiscussionWe describe a quantitative platform to enable simultaneous lymphovascular and immune infiltrate analysis and map their spatial relationships in primary melanoma. Our data indicate that tumor-associated vessels exist in different states and that their localization may determine potential for metastasis or immune infiltration. This platform will support future efforts to map tumor-associated lymphovascular evolution across stage, assess its prognostic value, and stratify patients for adjuvant therapy.

Published
2024
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2. Optimization of an automated tumor-infiltrating lymphocyte algorithm for improved prognostication in primary melanoma

Author

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

Subjects
0301 basic medicine, Adult, Male, Skin Neoplasms, Databases, Factual, Biopsy, H&E stain, chemical and pharmacologic phenomena, Risk Assessment, Article, Pathology and Forensic Medicine, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Lymphocytes, Tumor-Infiltrating, Predictive Value of Tests, Risk Factors, Image Interpretation, Computer-Assisted, medicine, Tumor Microenvironment, Humans, Diagnosis, Computer-Assisted, Lymphocyte Count, Melanoma, Aged, Neoplasm Staging, Retrospective Studies, Automation, Laboratory, Microscopy, business.industry, Tumor-infiltrating lymphocytes, Cancer, Reproducibility of Results, hemic and immune systems, Ajcc staging, Middle Aged, medicine.disease, Progression-Free Survival, Clinical Practice, 030104 developmental biology, 030220 oncology & carcinogenesis, Disease Progression, Female, Neoplasm Recurrence, Local, business, Algorithm
Abstract

Tumor-infiltrating lymphocytes (TIL) have potential prognostic value in melanoma and have been considered for inclusion in the American Joint Committee on Cancer (AJCC) staging criteria. However, interobserver discordance continues to prevent the adoption of TIL into clinical practice. Computational image analysis offers a solution to this obstacle, representing a methodological approach for reproducibly counting TIL. We sought to evaluate the ability of a TIL-quantifying machine learning algorithm to predict survival in primary melanoma. Digitized hematoxylin and eosin (HE) slides from prospectively enrolled patients in the NYU melanoma database were scored for % TIL using machine learning and manually graded by pathologists using Clark's model. We evaluated the association of % TIL with recurrence-free survival (RFS) and overall survival (OS) using Cox proportional hazards modeling and concordance indices. Discordance between algorithmic and manual TIL quantification was assessed with McNemar's test and visually by an attending dermatopathologist. In total, 453 primary melanoma patients were scored using machine learning. Automated % TIL scoring significantly differentiated survival using an estimated cutoff of 16.6% TIL (log-rank P 0.001 for RFS; P = 0.002 for OS). % TIL was associated with significantly longer RFS (adjusted HR = 0.92 [0.84-1.00] per 10% increase in % TIL) and OS (adjusted HR = 0.90 [0.83-0.99] per 10% increase in % TIL). In comparison, a subset of the cohort (n = 240) was graded for TIL by melanoma pathologists. However, TIL did not associate with RFS between groups (P 0.05) when categorized as brisk, nonbrisk, or absent. A standardized and automated % TIL scoring algorithm can improve the prognostic impact of TIL. Incorporation of quantitative TIL scoring into the AJCC staging criteria should be considered.

Published
2020

3. Radiation Acts on the Microenvironment to Affect Breast Carcinogenesis by Distinct Mechanisms that Decrease Breast Cancer Latency and Affect Tumor Type

Author

Shraddha A. Ravani, Daniel Medina, Alexander D. Borowsky, Jae Hong Seo, Jiri Zavadil, Jorge S. Reis-Filho, D. Joseph Jerry, David H. Nguyen, Mary Helen Barcellos-Hoff, Hellen A. Oketch-Rabah, Karen A. Dunphy, Irineu Illa-Bochaca, Felipe C. Geyer, Jian-Hua Mao, and Sandra Z. Haslam

Subjects
Cancer Research, Neoplasms, Radiation-Induced, Time Factors, Breast Neoplasms, Biology, medicine.disease_cause, Article, Ionizing radiation, Transforming Growth Factor beta1, Mice, Chimera (genetics), Mammary Glands, Animal, Reaction Time, Tumor Microenvironment, medicine, Animals, Gene Regulatory Networks, Carcinogen, Mice, Knockout, Mice, Inbred BALB C, Molecular pathology, Gene Expression Profiling, Dose-Response Relationship, Radiation, Epithelial Cells, Cell Biology, Epithelium, Tumor Burden, Gene Expression Regulation, Neoplastic, Cell Transformation, Neoplastic, medicine.anatomical_structure, Receptors, Estrogen, Oncology, Radiation Chimera, Immunology, Cancer research, Female, Cancer biomarkers, Tumor Suppressor Protein p53, Stem cell, Carcinogenesis, Whole-Body Irradiation
Abstract

SummaryTissue microenvironment is an important determinant of carcinogenesis. We demonstrate that ionizing radiation, a known carcinogen, affects cancer frequency and characteristics by acting on the microenvironment. Using a mammary chimera model in which an irradiated host is transplanted with oncogenic Trp53 null epithelium, we show accelerated development of aggressive tumors whose molecular signatures were distinct from tumors arising in nonirradiated hosts. Molecular and genetic approaches show that TGFβ mediated tumor acceleration. Tumor molecular signatures implicated TGFβ, and genetically reducing TGFβ abrogated the effect on latency. Surprisingly, tumors from irradiated hosts were predominantly estrogen receptor negative. This effect was TGFβ independent and linked to mammary stem cell activity. Thus, the irradiated microenvironment affects latency and clinically relevant features of cancer through distinct and unexpected mechanisms.

Published
2011

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