Oncotarget

Priority Research Papers:

Clinical outcomes based on multigene profiling in metastatic breast cancer patients

Reva K. Basho, Debora de Melo Gagliato, Naoto T. Ueno, Chetna Wathoo, Huiqin Chen, Maryam Shariati, Caimiao Wei, Ricardo H. Alvarez, Stacy L. Moulder, Aysegul A. Sahin, Sinchita Roy-Chowdhuri, Mariana Chavez-MacGregor, Jennifer K. Litton, Vincent Valero, Raja Luthra, Jia Zeng, Kenna R. Shaw, John Mendelsohn, Gordon B. Mills, Debu Tripathy and Funda Meric-Bernstam _

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Oncotarget. 2016; 7:76362-76373. https://doi.org/10.18632/oncotarget.12987

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Abstract

Reva K. Basho1,*, Debora de Melo Gagliato2,*, Naoto T. Ueno2, Chetna Wathoo3, Huiqin Chen4, Maryam Shariati5, Caimiao Wei4,6, Ricardo H. Alvarez2,7, Stacy L. Moulder2, Aysegul A. Sahin8, Sinchita Roy-Chowdhuri8, Mariana Chavez-MacGregor2,9, Jennifer K. Litton2, Vincent Valero2, Raja Luthra8, Jia Zeng3, Kenna R. Shaw3, John Mendelsohn3,10, Gordon B. Mills3,11, Debu Tripathy2 and Funda Meric-Bernstam3,5,12

1 Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

2 Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

3 Sheikh Khalifa Bin Zayed Al Nahyan Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

4 Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

5 Investigational Cancer Therapeutics (Phase I Trials Program), The University of Texas MD Anderson Cancer Center, Houston, TX, USA

6 Pfizer, Inc, New York, NY, USA

7 The Cancer Treatment Centers of America, Chicago, IL, USA

8 Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

9 Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

10 Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

11 Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

12 Breast Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

* These authors have contributed equally to this work

Correspondence to:

Funda Meric-Bernstam, email:

Keywords: metastatic breast cancer, genomics, TP53, PIK3CA

Received: April 06, 2016 Accepted: October 13, 2016 Published: October 28, 2016

Abstract

BACKGROUND: Identifying the clinical impact of recurrent mutations can help define their role in cancer. Here, we identify frequent hotspot mutations in metastatic breast cancer (MBC) patients and associate them with clinical outcomes.

PATIENTS AND METHODS: Hotspot mutation testing was conducted in 500 MBC patients using an 11 gene (N = 126) and/or 46 or 50 gene (N = 391) panel. Patients were stratified by hormone receptor (HR) and human epidermal growth factor 2 (HER2) status. Clinical outcomes were retrospectively collected.

RESULTS: Hotspot mutations were most frequently detected in TP53 (30%), PIK3CA (27%) and AKT1 (4%). Triple-negative breast cancer (TNBC) patients had the highest incidence of TP53 (58%) and the lowest incidence of PIK3CA (9%) mutations. TP53 mutation was associated with shorter relapse-free survival (RFS) (median 22 vs 42months; P < 0.001) and overall survival (OS) from diagnosis of distant metastatic disease (median 26 vs 51months; P < 0.001). Conversely, PIK3CA mutation was associated with a trend towards better clinical outcomes including RFS (median 41 vs 30months; P = 0.074) and OS (52 vs 40months; P = 0.066). In HR-positive patients, TP53 mutation was again associated with shorter RFS (median 30 vs 46months; P = 0.017) and OS (median 30 vs 55months; P = 0.001). When multivariable analysis was performed for RFS and OS, TP53 but not PIK3CA mutation remained a significant predictor of outcomes in the overall cohort and in HR-positive patients.

CONCLUSIONS: Clinical hotspot sequencing identifies potentially actionable mutations. In this cohort, TP53 mutation was associated with worse clinical outcomes, while PIK3CA mutation did not remain a significant predictor of outcomes after multivariable analysis.


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