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Updated: Apr 24, 2026
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
A pilot study utilizing multi-omic molecular profiling to find potential targets and select individualized treatments
Gayle S Jameson1, Emanuel F Petricoin, Jasgit Sachdev
1Virginia G Piper Cancer Center - Clinical Trials, Scottsdale Healthcare, Scottsdale, AZ, USA, GJameson@SHC.org.
Abstract:
The primary objective was to determine if multi-omic molecular profiling (MMP) informed selection of approved cancer treatments could change the clinical course of disease for patients with previously treated metastatic breast cancer (MBC) (i.e., produce a growth modulation index (GMI) ≥1.3). GMI was calculated as the ratio of progression free survival on MMP-selected therapy/time to progression on last prior treatment. To meet the primary objective at least 35 % of the subjects should demonstrate a GMI ≥1.3. Secondary endpoints included determining the response rate (according to RECIST 1.1), the percent of patients with non-progression at 4 months, and overall survival in patients whose therapy is selected by molecular profiling and proteomic analysis. Eligible patients had MBC, with ≥3 prior lines of therapy. A multi-omic based approach was performed incorporating multiplexed immunohistochemistry, c-DNA microarray, and phosphoprotein pathway activation mapping by reverse phase protein array. MMP was performed on fresh core biopsies; results were generated and sent to a Treatment Selection Committee (TSC) for review and treatment selection. Three sites enrolled 28 patients, of which 25 were evaluable. The median range of prior treatment was 7 (range 3-12). The MMP analysis and treatment recommendation were delivered within a median of 15.5 days from biopsy (range 12-23). The TSC selected MMP-rationalized treatment in 100 % (25/25) of cases. None of the MMP-based therapies were the same as what the clinician would have selected if the MMP had not been performed. GMI ≥1.3 was reported in 11/25 (44 %) patients. Partial responses were noted in 5/25 (20 %), stable disease in 8/25 (32 %) and 9/25 (36 %) had no progression at 4 months. This pilot study demonstrates the feasibility of finding possible treatments for patients with previously treated MBC using a multiplexed MMP-rationalized treatment recommendation. This MMP approach merits further investigation.
Insights
Multi-omic molecular profiling (MMP) helped select treatments for metastatic breast cancer (MBC) patients, with 44% showing a growth modulation index (GMI) of 1.3 or higher. This approach offers a feasible strategy for personalized cancer therapy.
Area of Science:
- Oncology
- Genomics
- Proteomics
- Translational Medicine
Background:
- Metastatic breast cancer (MBC) poses significant treatment challenges, especially after multiple prior therapies.
- Personalized medicine approaches are crucial for improving outcomes in refractory cancer patients.
Purpose of the Study:
- To evaluate if multi-omic molecular profiling (MMP) guided treatment selection could alter the clinical course of previously treated MBC.
- To assess the feasibility and efficacy of MMP in identifying effective therapies for advanced breast cancer.
Main Methods:
- Conducted a pilot study on 25 evaluable patients with previously treated MBC (≥3 prior lines of therapy).
- Utilized multiplexed immunohistochemistry, c-DNA microarray, and reverse phase protein array for MMP.
- Treatment Selection Committee (TSC) reviewed MMP results to recommend targeted therapies.
Main Results:
- 44% (11/25) of patients achieved a growth modulation index (GMI) ≥1.3, indicating clinical benefit.
- Partial responses were observed in 20% (5/25), stable disease in 32% (8/25), and 36% (9/25) had no progression at 4 months.
- MMP-guided treatment recommendations were unique compared to standard clinical choices.
Conclusions:
- Multi-omic molecular profiling is a feasible approach for guiding treatment selection in patients with previously treated MBC.
- MMP-rationalized treatment recommendations show potential for improving clinical outcomes and warrant further investigation.
