The Informatics Technology for Cancer Research Education Resource FOA (UE5 Clinical Trial Not Allowed)
Funding Opportunity Announcement (FOA) RFA-CA-19-042
The purpose of these Funding Opportunity Announcements (FOAs) is to encourage applications from currently funded NCI R01, U01 and U24 projects proposing to expand or accelerate progress of the parent study by incorporating informatics methods, tools or resources developed through the ITCR program. Awards are meant to spur novel collaborations and to incentivize the adoption, adaptation, and integration of ITCR technologies.
Application Due Date November 20, 2019
View Funding Opportunities:
The 27thConference on Intelligent Systems for Molecular Biology (ISMB) and the 18thEuropean Conference on Computational Biology (ECCB) Annual Meeting in Basel, Switzerland July 21-25, 2019.
Visit the National Cancer Institute (NCI), Informatics Technology for Cancer Research (ITCR) Program Exhibit Booth #8 to meet ITCR investigators and program staff to learn about the ITCR tools and funding opportunities!
ITCR 2019 Annual Meeting was held in Salt Lake City/Park City, Utah, May 28-31 2019. Presentations are now available on the NCIP Hub https://nciphub.org/groups/itcr/annual_meeting_2019
ITCR funded Cancer Deep Phenotype Extraction (DeepPhe) tool version 0.3.0 is now available! The tool extracts deep phenotypic information from the clinical narrative at the document-, episode-, and patient-level. The final output is FHIR compliant patient-level phenotypic summary which can be consumed by research warehouses or the DeepPhe native visualization tool. ITCR PI Dr. Guergana Savova, Boston Children's Hospital and Harvard Medical School.
New Items:
Pre-Application Webinar for Population-Based Researchers on NCI's Informatics Technologies for Cancer Research (ITCR) Funding Opportunities
Tuesday, May 7, 1:30-3:00 p.m. ET
Juli Klemm, Ph.D.
Acting Branch Chief, Cancer Informatics Branch
Center for Biomedical Informatics and Information Technology (CBIIT), National Cancer institute
Creative thinking can pay dividends for researchers taking on cancer. Anant Madabhushi (Case Western Reserve University) uses computer algorithms to analyze key data from medical images to improve the accuracy of cancer diagnosis and to determine prognosis and optimum treatments for people with lung, head and neck, prostate or breast cancers.
The next application receipt date for ITCR funding opportunities is November 20, 2019. All funding opportunties can be found here.
American Association of Cancer Research (AACR) Annual Meeting, March 29-April 3, 2019
Several ITCR investigators and program staff will be presenting at the American Association of Cancer Research Annual Meeting in Atlanta, GA, USA.
The ITCR program renewal was approved by the NCI Board of Scientific Advisors (BSA) on December 4th, 2018. Dr. Juli Klemm’s presentation to the BSA is available on the NIH Videocast, starting at 4:56:45. Pre-application webinar for population-based researchers is available here.
In addition to continuing the technology development funding opportunities, several revisions were approved for the program:
Work by ITCR investigator Anant Madabhushi (Case Western Reserve University) on developing novel computational pathology predictors for identifying cancer patients who will benefit from chemotherapy was identified as one of the top 10 medical breakthrough technologies of 2018 by Prevent Magazine! See https://www.prevention.com/health/g25423574/top-medical-breakthroughs/
Patient-derived tumor xenograft (PDX) mouse models are a versatile oncology research platform for studying tumor biology and for testing chemotherapeutic approaches tailored to genomic characteristics of individual patients’ tumors. PDX models are generated and distributed by a diverse group of academic labs, multi-institution consortia and contract research organizations.
Save the Date: ITCR 2019 Annual Meeting will be held in Salt Lake City/Park City, Utah, May 28-31 2019.
Several ITCR investigators will be attending the Radiological Society of North America (RSNA) Annual Meeting in Chicago, November 25-30, 2018. Download the schedule for the presentations by ITCR investigators.
RSNA Annual Meeting, November 25-30, 2018
In this ITCR funded study Dr. Min Jin Ha and Dr. Veera Baladandayuthapani from the University of Texas MD Anderson Cancer Center and University of Michigan, assessed pan-cancer pathway activities for >7700 patients across 32 tumor types from The Cancer Proteome Atlas by developing a personalized cancer-specific integrated network estimation (PRECISE) model. The paper can be accessed here https://www.nature.com/articles/s41598-018-32682-x
The Galaxy-P team (University of Minnesota, PI Tim Griffin) and the Cravat team (Johns Hopkins University, PI Rachel Karchin) have developed an automated workflow within the Galaxy for Proteomics (Galaxy-P) platform, which leverages the Cancer-Related Analysis of Variants Toolkit (CRAVAT) and makes it interoperable with proteogenomic results.
The NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) aims to develop a framework for functional mapping the human body with cellular resolution to enhance our understanding of cellular organization-function. HuBMAP will accelerate the development of the next generation of tools and techniques to generate 3D tissue maps using validated high-content, high-throughput imaging and omics assays, and establish an open data platform for integrating, visualizing data to build multi-dimensional maps.
ITCR funded Cancer Deep Phenotype Extraction (DeepPhe) tool new release is now available! The tool extracts deep phenotypic information from the clinical narrative at the document-, episode-, and patient-level. The final output is FHIR compliant patient-level phenotypic summary which can be consumed by research warehouses or the DeepPhe native visualization tool. PI Dr. Guergana Savova, Boston Children's Hospital and Harvard Medical School
The next application receipt date for ITCR funding opportunities is November 20, 2018. All funding opportunties can be found here.
Computational DREAM Challenge to generate computational methods for extracting information from the cancer proteome and for linking those data to genomic and transcriptomic information.