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Developing deep learning models for precision oncology

Award Number: K99LM013089
ORGANIZATION: NATIONAL LIBRARY OF MEDICINE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP
PERIOD OF PERFORMANCE START DATE: 12/01/2019
PERIOD OF PERFORMANCE END DATE: 01/31/2022

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Developing deep learning models for precision oncology - Project Summary/Abstract The goal of this study is to develop machine learning methods, especially deep learning models (DLMs), to learn a better representation of activation states of cellular signaling pathways in an individual tumor and use such information to predict its sensitivity to anti-cancer drugs. Cancer is mainly caused by somatic genome alterations (SGAs) that perturb cellular signaling pathways, and aberrations in pathways eventually lead to cancer development. Precision oncology aims to accurately detect and target tumor-specific aberrations, but challenges remain. Currently, there is no well-established method to detect the activation states of signaling pathways, and the common practice of using mutation status of a targeted gene as the indicator for prescribing a molecularly targeted drug has limitations. To overcome such limitation, we hypothesize that, by closely simulating the hierarchical organization of cellular signaling systems, DLMs can be used to systematically identify major cancer signaling pathways, to detect tumor-specific aberrations in signaling pathways, and to predict cancer cell sensitivity to anti-cancer drugs. We will develop models that more precisely represent the state of signaling systems in cancer cells and use such information to enhance precision oncology. I will design and apply innovative DLMs to cancer big data, including large-scale pharmacogenomic data and cancer omics data to learn unified representation of aberrations in signaling systems caused by driver SGAs in cancer cell, despite of their different growth conditions, such as in cell culture, PDX and real tumor. This will enable us to transfer the models trained using cell lines and PDXs to clinical setting (real tumors) in future. By the nature of drugs that may share common target proteins, we develop model DLM-MLT (the combination of DLM and multi-task learning) to predict the sensitivity of tumor samples to multiple drugs at once. Furthermore, we will develop model BioSI-DLM to use various perturbations (ex. SGA/LINCS perturbation data) as side information to learn better representation that potentially map latent variables in a DLM to biological entities. We hypothesize that the representation learned from our designed models will significantly improve the prediction accuracy compared with the conventional indication for drug treatment (ex. mutation state of the drug targeting protein). In summary, our study uses deep learning based machine learning methods to learn better and concise representation embedded in the cancer omics data to reflect the personalized genomic changes, which could be used to guide the personalized treatment. Our study could significantly contribute to the development of cancer ontology and promote the development of precision medicine.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2022 ( Subtotal = -$17,926 )
2022	2021	UNIVERSITY OF PITTSBURGH - OF THE COMMONWEALTH SYSTEM OF HIGHER EDUCATION	4200 FIFTH AVENUE	PITTSBURGH	PA	15260	ALLEGHENY	USA	Medical Library Assistance	001	2	4/20/2022	NON-COMPETING CONTINUATION	-$17,926
2022	2021	UNIVERSITY OF PITTSBURGH, THE	4200 5TH AVE	PITTSBURGH	PA	15260	ALLEGHENY	USA	Medical Library Assistance	000	2	12/15/2021	NON-COMPETING CONTINUATION	$0
														Subtotal = -$17,926

Issue Date FY: 2021 ( Subtotal = $91,506 )
2021	2021	UNIVERSITY OF PITTSBURGH THE	3520 FIFTH AVE	PITTSBURGH	PA	15213	ALLEGHENY	USA	Medical Library Assistance	000	2	10/29/2020	NON-COMPETING CONTINUATION	$82,354
2021	2021	UNIVERSITY OF PITTSBURGH THE	3520 FIFTH AVE	PITTSBURGH	PA	15213	ALLEGHENY	USA	Medical Library Assistance	001	2	2/19/2021	NON-COMPETING CONTINUATION	$9,152
														Subtotal = $91,506

Issue Date FY: 2020 ( Subtotal = $93,908 )
2020	2020	UNIVERSITY OF PITTSBURGH THE	3520 FIFTH AVE	PITTSBURGH	PA	15213	ALLEGHENY	USA	Medical Library Assistance	000	1	11/21/2019	NEW	$93,908
														Subtotal = $93,908

Grand Total All Awards = $167,488

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Developing deep learning models for precision oncology

Award Number: K99LM013089

ORGANIZATION: NATIONAL LIBRARY OF MEDICINE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP

PERIOD OF PERFORMANCE START DATE: 12/01/2019

PERIOD OF PERFORMANCE END DATE: 01/31/2022

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