grant

Comprehensive identification of germline-somatic interactions

Organization UNIVERSITY OF CALIFORNIA, SAN DIEGOLocation LA JOLLA, UNITED STATESPosted 1 Jul 2022Deadline 30 Jun 2027
NIHUS FederalResearch GrantFY202521+ years oldAddressAdultAdult HumanAffectAgeAge of OnsetAttentionBioinformaticsCancer BiologyCancer GenesCancer TreatmentCancer-Promoting GeneCancersCell FunctionCell PhysiologyCell ProcessCellular FunctionCellular PhysiologyCellular ProcessChildhood CancersClinicalCommunitiesComputer softwareDNA Damage RepairDNA RepairDNA mutationDataData SetDetectionDevelopmentDiagnosisDiseaseDisorderDrug ScreeningEarly DiagnosisEventEvolutionGene Action RegulationGene Expression RegulationGene RegulationGene Regulation ProcessGene variantGenesGeneticGenetic ChangeGenetic DiversityGenetic VariationGenetic defectGenetic mutationGenetics-MutagenesisGenomeGenomicsGerm LinesGerm-Line MutationGoalsHereditaryHereditary MutationHeterogeneityHumanImmuneImmune SurveillanceImmune mediated therapyImmune systemImmunesImmunityImmunologic SurveillanceImmunologically Directed TherapyImmunosurveillanceImmunotherapyIndividualInheritedInter-tumoral heterogeneityInvestigatorsLiteratureMalignant Childhood NeoplasmMalignant Childhood TumorMalignant Neoplasm TherapyMalignant Neoplasm TreatmentMalignant NeoplasmsMalignant Pediatric NeoplasmMalignant Pediatric TumorMalignant TumorMalignant childhood cancerModern ManMolecularMorbidityMorbidity - disease rateMutagenesisMutagenesis Molecular BiologyMutationMutation AnalysisNGS MethodNGS systemNatureNormal TissueNormal tissue morphologyOn-Line SystemsOncogenesOncogenesisOncogenicOnline SystemsOutcomePathway interactionsPatientsPatternPenetrancePopulationPrevention therapyProcessPrognosisProgression-Free SurvivalsResearchResearch PersonnelResearch ResourcesResearchersResourcesRetinal NeuroblastomaRetinoblastomaRisk-associated variantRoleSample SizeSelection CriteriaShapesSignal PathwaySoftwareSomatic MutationSourceStatistical MethodsSubcellular ProcessSurvival RateTechnologyTestingTransforming GenesTumor BiologyTumor CellTumor Suppressor ProteinsUnscheduled DNA SynthesisValidationVariantVariationadulthoodagesallelic variantanti-cancer researchanti-cancer therapybio-informatics pipelinebio-informatics toolbioinformatics pipelinebioinformatics toolcancer cell genomecancer genomecancer genomicscancer in a childcancer in childrencancer predispositioncancer preventioncancer progressioncancer researchcancer riskcancer therapycancer-directed therapychild with cancerchildhood malignancyclinical phenotypeclinical relevanceclinically relevantcomputational resourcescomputing resourcesdesigndesigningdevelopmentaldisease riskdisorder riskearly detectionentire genomeexomefitnessfull genomegenetic profilinggenetic variantgenome mutationgenomic datagenomic datasetgenomic profilesgenomic variantgerm-line defectgermline variantimmune therapeutic approachimmune therapeutic interventionsimmune therapeutic regimensimmune therapeutic strategyimmune therapyimmune-based therapiesimmune-based treatmentsimmuno therapyinsightinter-individual variabilityinter-individual variationintertumoral heterogeneityloss of functionmalignancymeetingmeetingsmolecular phenotypemortalityneoplasm progressionneoplasm/cancerneoplastic cellneoplastic progressionnext gen sequencingnext generation sequencingnextgen sequencingnoveloncogenomicsonline computerpathwaypediatric cancerpediatric malignancypersonalized health interventionpersonalized interventionprecision interventionsresponseresponse to therapyresponse to treatmentretina neuroblastomarisk allelerisk generisk genotyperisk locirisk locusrisk variantsocial rolesomatic variantstatistic methodssuccesstargeted drug therapytargeted drug treatmentstargeted therapeutictargeted therapeutic agentstargeted therapytargeted treatmenttherapeutic responsetherapy responsetooltreatment responsetreatment responsivenesstumortumor genometumor progressiontumor suppressortumorigenesisvalidationsweb basedwhole genome
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Full Description

Cancer remains a significant source of morbidity and mortality worldwide. Advances in next generation
sequencing technologies have allowed extensive profiling of the genetic variants present in tumors and revealed

daunting levels of inter-tumoral heterogeneity. Tumor genomic datasets have been extensively mined to reveal

germline risk variants and to characterize heterogeneous patterns of somatic alteration that drive tumor

progression. However, little attention has been given to interactions between genetic background and somatic

changes, which could represent a major driver of heterogeneity. New evidence suggests that germline-somatic

interactions are prevalent and our preliminary data support that some such interactions directly influence

individual disease risk and potential to respond to therapies. This proposal will develop computational strategies

to identify germline-somatic interactions and to characterize them in the context of molecular and clinical

phenotypes, enabling new understanding of their role in inter-tumoral heterogeneity.

Germline-somatic interactions have been challenging to study due to the limited amount of available data. To

address this challenge, we have compiled tumor genomic data from public sources to boost our sample size to

almost 45,000 tumor whole-exome and whole-genome sequences. Our analysis will focus on three major forces

that shape the tumor genome: (i) the mutational processes that generate somatic mutations, (ii) the molecular

organization of oncogenic pathways which determines the genes that can effectively drive cancer, and (iii) the

immune system which acts as a selective force throughout tumor development. We will focus hypothesis testing

with strict criteria for selecting germline variants and somatically altered genes likely to interact based on

established tumor biology. To identify and characterize germline-somatic interactions we will:

1) Elucidate germline variants affecting the somatic mutational landscapes of human cancers

2) Reveal germline variants that modify somatic activation of hallmark oncogenic pathways

3) Establish the role of pathway-specific variant burden in cancer predisposition, overall survival, and response

to immunotherapy

Our team of co-investigators includes strong complementary expertise in analysis of mutational processes,

genetic variation effects on molecular pathways and immunity, cancer biology, statistical methods and

bioinformatic software dissemination. Careful attention will be given to statistical considerations including power,

controlling false discovery rates, and validation in independent datasets. This proposal will produce A) novel

bioinformatics tools designed specifically to detect and annotate germline-somatic interactions, B) new

understanding of the contribution of germline variation to tumor progression, and C) a set of validated germline-

somatic interactions affecting cancer risk, tumor evolution and immunotherapy response.

Grant Number: 5R01CA269919-04
NIH Institute/Center: NIH

Principal Investigator: Hannah Carter

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