Functional Proteomics for Breast Cancer Prognosis
Author Information
Author(s): Gonzalez-Angulo Ana M, Hennessy Bryan T, Meric-Bernstam Funda, Sahin Aysegul, Liu Wenbin, Ju Zhenlin, Carey Mark S, Myhre Simen, Speers Corey, Deng Lei, Broaddus Russell, Lluch Ana, Aparicio Sam, Brown Powel, Pusztai Lajos, Symmans W Fraser, Alsner Jan, Overgaard Jens, Borresen-Dale Anne-Lise, Hortobagyi Gabriel N, Coombes Kevin R, Mills Gordon B
Primary Institution: The University of Texas MD Anderson Cancer Center
Hypothesis
Functional proteomics can improve breast cancer classification and predict pathological complete response in patients receiving neoadjuvant therapy.
Conclusion
A 10-protein biomarker panel was developed that classifies breast cancer into prognostic groups, potentially aiding in patient management.
Supporting Evidence
- Six breast cancer subgroups were identified by a 10-protein biomarker panel.
- The 10-protein score was associated with recurrence-free survival in both training and test sets.
- There was a significant association between the prognostic score and likelihood of pCR to NST.
Takeaway
Scientists created a special test that looks at proteins in breast cancer to help doctors understand how serious the cancer is and how well treatment might work.
Methodology
Reverse phase protein array (RPPA) was used on tumor samples to identify protein markers associated with breast cancer prognosis.
Potential Biases
Potential biases may arise from the varied treatments affecting prognosis and response predictions.
Limitations
The study's findings may be limited by the diverse types of systemic treatments received by patient cohorts.
Participant Demographics
The training set included 712 patients with breast cancer, with a median age of 62 years.
Statistical Information
P-Value
3.2E-13
Confidence Interval
(0.65, 0.751)
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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