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 chemotherapy (pt-CHEMOs) are identified. While not found on healthy ovarian cells, these glycans (glycomarkers 1 and 2) are expressed to varying degrees on cancerous cells, rendering each of these molecules a reliable indicator in predicting responses to pt-CHEMOs. A highly sensitive lectin histochemistry (LHC) assay was developed to detect each glycomarker on Formalin-fixed, Paraffin-embedded, (FFPE) tissue sections. This LHC performance was initially confirmed for specificity, reproducibility, and sensitivity, using various staining systems. Its feasibility for clinical use was consequently confirmed on a training panel composed of 64 healthy and cancerous ovarian tissues. In a retrospective study with 27 ovarian cancer specimens, collected before any treatment, LHC correctly predicted the response to first- line chemotherapy in 25 of 27 (92%) and 19 of 27 (70%) tested for glycomarkers 1 and 2, respectively; The preliminary cut-points of the glycomarker expressions were deduced for three clinical groups of refractory (failing the initial chemotherapy) 0-10%; resistant (recurring within six months following chemotherapy)10-30%; and sensitive (in remission for at least 6 months following chemotherapy) >30%. In Phase I of the proposed study, the LHC validation for chemo reactivity will be ascertained by testing the glycomarker expressions on a large set (140) of un-treated ovarian cancer specimens whose response to pt- CHEMOs will be available for retrospective correlation analysis. Triplxed-immunofluorescent with a panel of three markers consisting of CA125 (ovarian cancer marker), PanCK (epithelial marker) and a glycomarker, will quantify the glycomarker expressions on tumor sections prior to treatment. Both digital cytometry and scoring by two pathologists, in blind fashion, will be used to analyze the LHC assay. Statistical analysis with a large data set will address the following study aims;
Aim 1) to determine the correlation between drug response and glycomarker expression levels and;
Aim 2) to define the cut-points for each glycomarker expression on ovarian cancer patients in three clinical classifications (refractory/resistant/sensitive). The key milestone in Phase-I will be completing the statistical validation, to determine whether the LHC can be offered as a laboratory-developed test (LDT), not requiring FDA approval. Meanwhile, we will be developing required data, in Phase II, to get prepared for FDA submission. The final commercial product will be clinical kits to provide guidance for personalized chemotherapy. Our 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-CHEMOS. Overall, the LHC offers a companion diagnostic test for Pt-CHEMOs that identifies chemoresistant patients, thereby giving them a hope at being treated sooner and with more promising second line options, and consequently, increasing their chance of survival and quality of life.

Public Health Relevance

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 patients. Our proposed novel glycan-based method will reduce mortality by identifying chemo-resistant patients prior to the start of therapy, and changing the treatment strategy. This new method will potentially create a paradigm shift in the treatment of cancer and will ultimately save many lives.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Small Business Innovation Research Grants (SBIR) - Phase I (R43)
Project #
3R43CA213766-01A1S1
Application #
9663542
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Evans, Gregory
Project Start
2017-09-20
Project End
2018-08-31
Budget Start
2017-09-20
Budget End
2018-08-31
Support Year
1
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Accudava, Inc.
Department
Type
DUNS #
625144303
City
La Jolla
State
CA
Country
United States
Zip Code
92037