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Penalized mixture cure models for identifying genomic features associated with outcome in acute myeloid leukemia

Award Number: R01LM013879
ORGANIZATION: NATIONAL LIBRARY OF MEDICINE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 01/01/2022
PERIOD OF PERFORMANCE END DATE: 12/31/2026

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Penalized mixture cure models for identifying genomic features associated with outcome in acute myeloid leukemia - Molecular features associated with time-to-event outcomes, such as overall or disease-free survival, may be prognostically relevant or potential therapeutic targets. Therefore, analyzing data from high-throughput genomic assays with clinical follow-up data has been of growing interest. The Cancer Genome Atlas (TCGA) Project has collected baseline demographic, clinical characteristics, and follow-up data for 11,125 patients for 32 different cancer types and corresponding tissue samples were processed for examining SNPs, copy number, methylation, miRNA expression, and mRNA expression. Because the number of variables (P ) exceeds the sample size (N), one strategy frequently employed when associating molecular features to survivorship data is to ﬁt univariable Cox proportional hazards (PH) models followed by adjustment for multiple hypothesis tests using a false discovery rate approach. However, most chronic conditions and diseases, including cancer, are likely caused by multiple dysregulated genes or mutations. It is therefore critical to ﬁt multivariable models in the presence of a high- dimensional covariate space. Traditional statistical methods cannot be used when the number of features exceeds the sample size (e.g., P > N), though penalized methods perform automatic variable selection and accommodate the P > N scenario. Penalized approaches including LASSO, smoothly clipped absolute deviation (SCAD), adaptive LASSO, and Bayesian LASSO have all been extended to Cox's PH model for handling high-dimensional covariate spaces. However, when modeling survival or other time-to-event outcomes, the Cox PH model assumes that all subjects will experience the event of interest, which is violated when a subset of subjects are cured. Instead, when a subset of subjects in the data are cured, mixture cure models should be ﬁt. Although mixture cure models have been described for traditional settings where the number of samples exceeds the number of covariates, limited variable selection methods and no methods for high-dimensional model ﬁtting currently exist for mixture cure models. Therefore, this project will overcome a critical barrier to progress in this ﬁeld by developing penalized parametric and semi-parametric mixture cure models applicable for high-dimensional datasets. The speciﬁc aims of this application are to: (1) Develop penalized parametric mixture cure models for high-dimensional datasets; and (2) Develop a penalized semi-parametric proportional hazards mixture cure model for high-dimensional datasets. For both aims we will characterize the performance of the methods using extensive simulation studies, develop software, and distribute R packages to CRAN. In aim (3) we will identify molecular features associated with cure and survival using our large unique AML dataset from the Alliance for Clinical Trials in Oncology and assess robustness of ﬁndings using AML datasets from Gene Expression Omnibus and The Cancer Genome Atlas project. This research will ﬁll a critical gap as there are currently no mixture cure models for high-dimensional data. We anticipate application of our methods to our AML data will enhance existing risk stratiﬁcation systems used in daily clinical practice that determine treatment intensity and modality.


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: 2026 ( Subtotal = $0 )
2026	2025	OHIO STATE UNIVERSITY, THE	1960 KENNY RD	COLUMBUS	OH	43210	FRANKLIN	USA	Medical Library Assistance	000	4	10/16/2025	NON-COMPETING CONTINUATION	$0
														Subtotal = $0

Issue Date FY: 2025 ( Subtotal = $258,203 )
2025	2025	OHIO STATE UNIVERSITY, THE	1960 KENNY RD	COLUMBUS	OH	43210	FRANKLIN	USA	Medical Library Assistance	000	4	12/13/2024	NON-COMPETING CONTINUATION	$232,382
2025	2025	OHIO STATE UNIVERSITY, THE	1960 KENNY RD	COLUMBUS	OH	43210	FRANKLIN	USA	Medical Library Assistance	001	4	6/6/2025	NON-COMPETING CONTINUATION	$25,821
														Subtotal = $258,203

Issue Date FY: 2024 ( Subtotal = $258,743 )
2024	2024	OHIO STATE UNIVERSITY, THE	1960 KENNY RD	COLUMBUS	OH	43210	FRANKLIN	USA	Medical Library Assistance	001	3	4/30/2024	NON-COMPETING CONTINUATION	$25,874
2024	2024	OHIO STATE UNIVERSITY, THE	1960 KENNY RD	COLUMBUS	OH	43210	FRANKLIN	USA	Medical Library Assistance	000	3	12/6/2023	NON-COMPETING CONTINUATION	$232,869
														Subtotal = $258,743

Issue Date FY: 2023 ( Subtotal = $259,253 )
2023	2023	OHIO STATE UNIVERSITY, THE	1960 KENNY RD	COLUMBUS	OH	43210	FRANKLIN	USA	Medical Library Assistance	000	2	12/7/2022	NON-COMPETING CONTINUATION	$259,253
														Subtotal = $259,253

Issue Date FY: 2022 ( Subtotal = $259,734 )
2022	2022	OHIO STATE UNIVERSITY, THE	1960 KENNY RD	COLUMBUS	OH	43210	FRANKLIN	USA	Medical Library Assistance	000	1	12/29/2021	NEW	$259,734
														Subtotal = $259,734

Grand Total All Awards = $1,035,933

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Penalized mixture cure models for identifying genomic features associated with outcome in acute myeloid leukemia

Award Number: R01LM013879

ORGANIZATION: NATIONAL LIBRARY OF MEDICINE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 01/01/2022

PERIOD OF PERFORMANCE END DATE: 12/31/2026

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