SBIR/STTR Research Award

Novel Glyco-tests to Predict Chemoresistance

Award identifier c3e9146ef1dd914ff8fc424d63fce2eeb33e9b14dfa86bb235038611682fbd17Awarding agency Department of Health and Human Services · National Institutes of Health

Award amount

$497,265

Program and phase

SBIR · Phase I

Award year

2017

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Award details

Awarding agency
Department of Health and Human Services
Branch
National Institutes of Health
Recipient company
Accudava
Proposal award date
2017-09-20
Contract end date
2019-08-31
UEI
LLG1HDTXS2Q4
Solicitation number
PA18-591
Topic code
102

Research abstract

Novel Glyco Tests to Predict Chemoresistance Project summary Two carbohydrate structures whose expression levels on ovarian tumor cells are directly linked to tumor responses to platinum based chemotherapypt CHEMOsare identifiedWhile not found on healthy ovarian cellsthese glycansglycomarkersandare expressed to varying degrees on cancerous cellsrendering each of these molecules a reliable indicator in predicting responses to pt CHEMOsA highly sensitive lectin histochemistryLHCassay was developed to detect each glycomarker on Formalin fixedParaffin embeddedFFPEtissue sectionsThis LHC performance was initially confirmed for specificityreproducibilityand sensitivityusing various staining systemsIts feasibility for clinical use was consequently confirmed on a training panel composed ofhealthy and cancerous ovarian tissuesIn a retrospective study withovarian cancer specimenscollected before any treatmentLHC correctly predicted the response to firstline chemotherapy inofandoftested for glycomarkersandrespectivelyThe preliminary cut points of the glycomarker expressions were deduced for three clinical groups of refractoryfailing the initial chemotherapyresistantrecurring within six months following chemotherapyand sensitivein remission for at leastmonths following chemotherapyandgtIn Phase I of the proposed studythe LHC validation for chemo reactivity will be ascertained by testing the glycomarker expressions on a large setof un treated ovarian cancer specimens whose response to ptCHEMOs will be available for retrospective correlation analysisTriplxed immunofluorescent with a panel of three markers consisting of CAovarian cancer markerPanCKepithelial markerand a glycomarkerwill quantify the glycomarker expressions on tumor sections prior to treatmentBoth digital cytometry and scoring by two pathologistsin blind fashionwill be used to analyze the LHC assayStatistical analysis with a large data set will address the following study aimsAimto determine the correlation between drug response and glycomarker expression levels andAimto define the cut points for each glycomarker expression on ovarian cancer patients in three clinical classificationsrefractory resistant sensitiveThe key milestone in Phase I will be completing the statistical validationto determine whether the LHC can be offered as a laboratory developed testLDTnot requiring FDA approvalMeanwhilewe will be developing required datain Phase IIto get prepared for FDA submissionThe final commercial product will be clinical kits to provide guidance for personalized chemotherapyOur approach to predicting resistance to pt CHEMOs will also have prospective applications to other cancer types that express these glycomarkers and are also treated with pt CHEMOSOverallthe LHC offers a companion diagnostic test for Pt CHEMOs that identifies chemoresistant patientsthereby giving them a hope at being treated sooner and with more promising second line optionsand consequentlyincreasing their chance of survival and quality of life Novel Glyco tests to Predict Chemoresistance Narrative Today many cancer patients die due to tumors that are non responsive to standard chemotherapy and unfortunately to date there is no way to identify these patientsOur proposed novel glycan based method will reduce mortality by identifying chemo resistant patients prior to the start of therapyand changing the treatment strategyThis new method will potentially create a paradigm shift in the treatment of cancer and will ultimately save many lives

Technology keywords

Not reported

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Agency intelligence

Department of Health and Human Services

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