Document text
Principal Investigator: Yue Qin
Organization: BROAD INSTITUTE, INC.
Fiscal Year: 2024
Award: $95,948
Funding agency: National Cancer Institute
Project Summary/Abstract
Understanding of genetic interactions can lead to therapeutic design for individual cancer patients by targeting
the specific genetic vulnerability in the cancer genome. For example, by identifying gene pairs that pose severe
fitness defects when knocked out simultaneously (compared to separate knockouts), one can selectively kill
cancer cells that harbor loss-of-function mutation in one protein by inhibiting its synthetic-lethal partner. Despite
generation of large-scale data delineating the tumor transcriptome, proteome, metabolome, imaging, and so on,
little is known regarding how different genes interact with each other and it is unclear how one can design
targeted treatments based on the ‘omics data available. To address these challenges, the proposed research
will develop a “visible” machine learning framework to systematically understand the higher-order genetic
interactions (i.e. di-genic and tri-genic interactions) in cancer and design targeted treatments.
The first step for the proposed framework is to gain a holistic view of cancer pathways through combining
the ‘omics data available. Multiple approaches have been applied to integrate data of similar forms, but there yet
lacks an effective solution for integrating data of vastly different qualities and formats. To address this challenge,
Yue Qin has developed a method to infer a hierarchical cancer cell map capturing cancer pathways at multi-
scale resolution by fusing immunofluorescence (IF) imaging data and affinity purification-mass spectrometry (AP-
MS).
During the F99 phase of the proposed research, by tying the architecture of a deep neural network to the
hierarchical cancer cell map, Yue will develop a “visible” neural network (VNN) that can predict the cancer cell
fitness from genetic perturbation (i.e. knockouts) and genomic backgrounds (i.e. mutations) while providing
mechanistic insights in cancer pathways critical for genotype-phenotype prediction.
During the K00 phase of the award, Yue will develop genetic engineering approaches to experimentally
map higher-order genetic interactions in cancer cells based on the mechanistic insights obtained from VNN
during genotype-phenotype prediction. The data generated experimentally can directly inspire targeted treatment
designs. In addition, the new data can be integrated into the hierarchical cancer cell map to improve accuracy
and resolution of the inferred pathways, thus further improving the “visibility” of VNN in genotype-phenotype
prediction.
The combination of a computational focused training during F99 phase and experimental focused training
during K00 phase will fully prepare Yue leading her own interdisciplinary research in cancer biology. In addition,
the personalized training plan covering aspects including mentoring and teaching, scientific writing, and oral
presentation will ensure Yue acquiring skills necessary for her future establishment as an independent
investigator.
Terms: <Address><Affinity Chromatography><Architecture><Award><Awareness><Binding Proteins><Biological><CRISPR editing screen><CRISPR screen><CRISPR-based screen><CRISPR/Cas9 screen><Cancer Biology><Cancer Patient><Cancer cell line><Cancers><Cell Body><Cell Components><Cell Structure><Cells><Cellular Structures><Complex><Critical Paths><Critical Pathways><Data><Defect><Educational process of instructing><Engineering / Architecture><Ensure><Future><Generations><Genes><Genetic><Genetic Alteration><Genetic Change><Genetic Engineering><Genetic Engineering Biotechnology><Genetic Engineering Molecular Biology><Genetic Predisposition><Genetic Predisposition to Disease><Genetic Susceptibility><Genetic defect><Genetic propensity><Genome engineering><Genomics><Genotype><Image><Immunofluorescence><Immunofluorescence Immunologic><Individual><Inherited Predisposition><Inherited Susceptibility><Interdisciplinary Research><Interdisciplinary Study><Investigation><Investigators><Knock-out><Knockout><Lead><Learning><Ligand Binding Protein><Ligand Binding Protein Gene><Machine Learning><Malignant Cell><Malignant Neoplasms><Malignant Tumor><Maps><Mass Photometry/Spectrum Analysis><Mass Spectrometry><Mass Spectroscopy><Mass Spectrum><Mass Spectrum Analyses><Mass Spectrum Analysis><Mentors><Methods><Multidisciplinary Collaboration><Multidisciplinary Research><Mutation><Nature><Oral><Pathway interactions><Pb element><Phase><Phenotype><Postdoc><Postdoctoral Fellow><Protein Binding><Proteins><Proteome><R-Series Research Projects><R01 Mechanism><R01 Program><Recombinant DNA Technology><Research><Research Associate><Research Grants><Research Personnel><Research Project Grants><Research Projects><Research Resources><Researchers><Resolution><Resources><System><Teaching><Therapeutic><Training><Writing><affinity purification><biologic><bound protein><cancer cell><cancer cell genome><cancer genome><clustered regularly interspaced short palindromic repeats screen><combinatorial><data integration><data modalities><deep learning based neural network><deep learning neural network><deep neural net><deep neural network><design><designing><empowerment><experiment><experimental research><experimental study><experiments><fitness><gene interaction><genetic etiology><genetic mechanism of disease><genetic vulnerability><genetically engineered><genetically predisposed><genome mutation><global gene expression><global transcription profile><heavy metal Pb><heavy metal lead><imaging><improved><insight><intervention design><knockout gene><large scale data><large scale data sets><large scale datasets><loss of function mutation><machine based learning><machine learning based framework><machine learning framework><malignancy><metabolome><metabonome><nanometer><neoplasm/cancer><neural net architecture><neural network><neural network architecture><pathway><post-doc><post-doctoral><post-doctoral trainee><preservation><research associates><resolutions><skill acquisition><skill development><skills><synergism><synthetic lethal interaction><targeted drug therapy><targeted drug treatments><targeted therapeutic><targeted therapeutic agents><targeted therapy><targeted treatment><therapeutically effective><therapy design><transcriptome><treatment design><tumor><tumor genome>