Skip to main content
Grant Details

Grant Number: 7U01CA220401-03 Interpret this number
Primary Investigator: Cooper, Lee
Organization: Northwestern University At Chicago
Project Title: Informatics Tools for Quantitative Digital Pathology Profiling and Integrated Prognostic Modeling
Fiscal Year: 2019
Back to top


Abstract

PROJECT SUMMARY Accurate biomarker-driven prognostic stratification, response prediction, and cohort enrichment are critical for realizing precision treatment strategies and population health management approaches that optimize quality of life and survival for cancer patients. Genomics holds promise for improving classification and prognostication of malignancies, yet oncology practice continues to rely heavily on immunohistochemistry (IHC) as a fundamental tool due to its practicality and ability to provide protein-level and subcellular localization information. The goal of this proposal is to create an open-source software resource for the quantitative analysis of IHC stained tissues and effective integration of IHC, genomic, and clinical features for cancer classification and prognostication. This proposal builds on our collective experience in computer-assisted analysis of microscopic images (including IHC images), development of machine-learning methods to address the challenges of classification and prognostication with heterogeneous and high-dimensional data, and leadership in collection and large-scale analysis of cancer outcomes involving collaboration with multiple medical centers. This effort for the first time will create tools to integrate quantitative IHC imaging, clinical, and genomic information that will in turn enable the research community to explore strategies for the classification of malignancies and prediction of outcomes. The proposed tools will be developed and extensively validated in close collaboration with clinical, genomic, and digital pathology data from the NCI-supported Lymphoma Epidemiology of Outcomes (LEO) cohort study. The software tools produced by this proposal will enable the characterization of subcellular protein expression in cell nuclei, membranes and cytoplasmic compartments. Spatial features of protein expression heterogeneity, along with patient-level summaries of protein expression will be used to develop machine-learning classifiers for cancer subtypes, using diffuse large b-cell lymphomas as a driving application. Technology for automatic tuning of machine learning algorithms will enable a broad class of clinically and biologically motivated users to utilize these tools in their investigations. We will also provide an interactive dashboard that enables users to integrate genomic and IHC-based features to explore prognostic models of patient survival. These tools will be released and documented under an open-source model, integrated with HistomicsTK (https://histomicstk.readthedocs.io/en/latest/), and available to the broader cancer research community.

Back to top


Publications

Clustering of cutaneous T-cell lymphoma is associated with increased levels of the environmental toxins benzene and trichloroethylene in the state of Georgia.
Authors: Clough L. , Bayakly A.R. , Ward K.C. , Khan M.K. , Chen S.C. , Lechowicz M.J. , Flowers C.R. , Allen P.B. , Switchenko J.M. .
Source: Cancer, 2020-04-15; 126(8), p. 1700-1707.
EPub date: 2020-01-14.
PMID: 31943154
Related Citations

Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells.
Authors: Chandradevan R. , Aljudi A.A. , Drumheller B.R. , Kunananthaseelan N. , Amgad M. , Gutman D.A. , Cooper L.A.D. , Jaye D.L. .
Source: Laboratory investigation; a journal of technical methods and pathology, 2020 01; 100(1), p. 98-109.
EPub date: 2019-09-30.
PMID: 31570774
Related Citations

Structured crowdsourcing enables convolutional segmentation of histology images.
Authors: Amgad M. , Elfandy H. , Hussein H. , Atteya L.A. , Elsebaie M.A.T. , Abo Elnasr L.S. , Sakr R.A. , Salem H.S.E. , Ismail A.F. , Saad A.M. , et al. .
Source: Bioinformatics (Oxford, England), 2019-09-15; 35(18), p. 3461-3467.
PMID: 30726865
Related Citations

Digital pathology and artificial intelligence.
Authors: Niazi M.K.K. , Parwani A.V. , Gurcan M.N. .
Source: The Lancet. Oncology, 2019 05; 20(5), p. e253-e261.
PMID: 31044723
Related Citations

Informatics Approaches to Address New Challenges in the Classification of Lymphoid Malignancies.
Authors: Jordan J. , Goldstein J.S. , Jaye D.L. , Gurcan M. , Flowers C.R. , Cooper L.A.D. .
Source: JCO clinical cancer informatics, 2018; 2, .
EPub date: 2018-02-09.
PMID: 30637363
Related Citations




Back to Top