Integration of epidemiology, pathology, immunology and outcomes in colorectal cancer

Active Project Grant Awarded Last action

Award ID
R01CA263318
Recipient
Awarding agency
Obligated
$4,799,607

Award facts

Assistance listing
93.394 Cancer Detection and Diagnosis Research
Completion date 82% complete
Place of performance
Irwindale, CA 91010, USA

Award details come from USAspending.gov.

Description

As the agency reported it.

Abstract
Machine learning has the potential to transform pathologic diagnosis and to address very limited accessibility of expert pathology in low-income countries. Routine histology images of solid tumors contain an immense number of visual features that can be extracted and processed by artificial intelligence tools like machine learning, which excels at basic image analysis tasks such as tumor detection. In addition, machine learning can also predict clinically relevant features directly from histology images including microsatellite instability and immune features that independently predict prognosis response to therapy. This large, multicultural, racially and ethnically diverse study uses images of whole slides from routinely collected clinical specimens and applies computational pathology methods and digital spatial expression profiling to quantifiably improve CRC diagnosis, prognosis and predictive models together with clinical, epidemiologic and genetic data. The study goals will be accomplished through three specific aims. In aim 1, we will apply novel machine learning algorithms from whole slide images to reproducibly identify MSI, histopathologic and immune features of colorectal cancer in racially/ethnically diverse populations. We will study h & e slides from 6,751 CRC cases, digitizing existing slides from 5,551 CRC cases and 1,200 new cases of CRC with contemporaneous clinical and epidemiologic data. Then, we will apply deep learning methods to accurately identify histopathologic features and immune characteristics of CRC. We will use a robust training validation, and testing design (70%/15%/15%) to ensure the rigor and reproducibility of our findings. In aim 2, we will test whether machine learning algorithms that predict MSI and immune features related to CRC prognosis improve with the addition of clinical, epidemiologic, and germline genetic data. We will use machine learning statistical methods to test whether algorithms developed in aim 1 improve prediction of overall survival and response to therapy with the addition of supplemental information beyond whole slide digital images. Finally, in aim 3, we will compare the information derived from digital spatial profiling of expressed proteins in colorectal tumors with the information derived from immunoscore quantification of lymphocyte populations at the tumor center (CT) and the invasive margin (IM), and explore whether these measures improve the models developed in aims 1 and 2 in a subset of samples. We will perform geomx digital spatial profiling of 56 proteins expressed in 150 stage i-III TNM colorectal cancers to compare the performance of digital spatial profiling to immunoscore, a scoring system relying exclusively on expression patterns of CD3+ and CD8+ t cells. This study takes advantage of pathologic, epidemiologic, clinical, immunologic and germline genetic data from racially/ethnically diverse CRC patients from california, detroit, new york, florida, puerto rico, israel and spain. Our overarching goal is to improve the efficient diagnosis of colorectal cancer with clinically impactful immune profiles.

Transactions

Action Obligated Date
Amendment $688,362
Amendment $688,965
Amendment $670,826
Amendment $0
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Amendment $650,087
Amendment $650,087
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Amendment $725,640
Original award $725,640
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Subawards

Subrecipient Amount Awarded
Albert Einstein College Of Medicine Dr. Campbell is a Professor in the Dept. of Epidemiology and Population Health at the Albert Einstein (AE) College of Medicine and AE Cancer Center. He has more… $51,928
Subrecipient not on record The major aim of the study is to combine epidemiologic and clinical data of the study participants with the findings in the tumor (as expressed by the pathology… $56,160
Technische Universitaet Dresden Outline and target variables: The aim of this sub-project is to use Deep Learning to infer clinically relevant variables directly from routine histology… $20,038
Fundacio Institut D Investigacio Biomedica De Bellvitge Idibell Prof. V. Moreno, the PI of the Barcelona sub-contract, carried out by IDIBELL, will be responsible for providing data and samples from 800 colorectal cancer… $29,678
Wayne State University Under the supervision of Dr. Kristen Purrington, the following terms of work will be completed for a collaborative research agreement on the planned project… $42,649
Cleveland Clinic Lerner College Of Medicine Of Case Western Reserve University Dr. Schmit is a longstanding collaborator on the Molecular Epidemiology of Colorectal Cancer (MECC) Study. She will guide Dr. Tsai on her team to generate… $69,614

Quick answers

Who received award R01CA263318?

R01CA263318 was awarded to Beckman Research Institute Of The City Of Hope by National Cancer Institute (National Institutes of Health) on September 22, 2022.

How much is award R01CA263318 worth?

$4,799,607 has been obligated on R01CA263318.

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