Differential Response to Neoadjuvant Chemotherapy Among 7 Triple-Negative Breast Cancer Molecular Subtypes
The clinical relevancy of the 7-subtype classification of triple-negative breast cancer (TNBC) reported by Lehmann and colleagues is unknown. We investigated the clinical relevancy of TNBC heterogeneity by determining pathologic complete response (pCR) rates after neoadjuvant chemotherapy, based on...
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| Published in | Clinical cancer research Vol. 19; no. 19; pp. 5533 - 5540 |
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| Main Authors | , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Philadelphia, PA
American Association for Cancer Research
01.10.2013
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| Subjects | |
| Online Access | Get full text |
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| Abstract | The clinical relevancy of the 7-subtype classification of triple-negative breast cancer (TNBC) reported by Lehmann and colleagues is unknown. We investigated the clinical relevancy of TNBC heterogeneity by determining pathologic complete response (pCR) rates after neoadjuvant chemotherapy, based on TNBC subtypes.
We revalidated the Lehmann and colleagues experiments using Affymetrix CEL files from public datasets. We applied these methods to 146 patients with TNBC with gene expression microarrays obtained from June 2000 to March 2010 at our institution. Of those, 130 had received standard neoadjuvant chemotherapy and had evaluable pathologic response data. We classified the TNBC samples by subtype and then correlated subtype and pCR status using Fisher exact test and a logistic regression model. We also assessed survival and compared the subtypes with PAM50 intrinsic subtypes and residual cancer burden (RCB) index.
TNBC subtype and pCR status were significantly associated (P = 0.04379). The basal-like 1 (BL1) subtype had the highest pCR rate (52%); basal-like 2 (BL2) and luminal androgen receptor had the lowest (0% and 10%, respectively). TNBC subtype was an independent predictor of pCR status (P = 0.022) by a likelihood ratio test. The subtypes better predicted pCR status than did the PAM50 intrinsic subtypes (basal-like vs. non basal-like).
Classifying TNBC by 7 subtypes predicts high versus low pCR rate. We confirm the clinical relevancy of the 7 subtypes of TNBC. We need to prospectively validate whether the pCR rate differences translate into long-term outcome differences. The 7-subtype classification may spur innovative personalized medicine strategies for patients with TNBC. |
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| AbstractList | Abstract
Purpose: The clinical relevancy of the 7-subtype classification of triple-negative breast cancer (TNBC) reported by Lehmann and colleagues is unknown. We investigated the clinical relevancy of TNBC heterogeneity by determining pathologic complete response (pCR) rates after neoadjuvant chemotherapy, based on TNBC subtypes.
Experimental Design: We revalidated the Lehmann and colleagues experiments using Affymetrix CEL files from public datasets. We applied these methods to 146 patients with TNBC with gene expression microarrays obtained from June 2000 to March 2010 at our institution. Of those, 130 had received standard neoadjuvant chemotherapy and had evaluable pathologic response data. We classified the TNBC samples by subtype and then correlated subtype and pCR status using Fisher exact test and a logistic regression model. We also assessed survival and compared the subtypes with PAM50 intrinsic subtypes and residual cancer burden (RCB) index.
Results: TNBC subtype and pCR status were significantly associated (P = 0.04379). The basal-like 1 (BL1) subtype had the highest pCR rate (52%); basal-like 2 (BL2) and luminal androgen receptor had the lowest (0% and 10%, respectively). TNBC subtype was an independent predictor of pCR status (P = 0.022) by a likelihood ratio test. The subtypes better predicted pCR status than did the PAM50 intrinsic subtypes (basal-like vs. non basal-like).
Conclusions: Classifying TNBC by 7 subtypes predicts high versus low pCR rate. We confirm the clinical relevancy of the 7 subtypes of TNBC. We need to prospectively validate whether the pCR rate differences translate into long-term outcome differences. The 7-subtype classification may spur innovative personalized medicine strategies for patients with TNBC. Clin Cancer Res; 19(19); 5533–40. ©2013 AACR. The clinical relevancy of the 7-subtype classification of triple-negative breast cancer (TNBC) reported by Lehmann and colleagues is unknown. We investigated the clinical relevancy of TNBC heterogeneity by determining pathologic complete response (pCR) rates after neoadjuvant chemotherapy, based on TNBC subtypes. We revalidated the Lehmann and colleagues experiments using Affymetrix CEL files from public datasets. We applied these methods to 146 patients with TNBC with gene expression microarrays obtained from June 2000 to March 2010 at our institution. Of those, 130 had received standard neoadjuvant chemotherapy and had evaluable pathologic response data. We classified the TNBC samples by subtype and then correlated subtype and pCR status using Fisher exact test and a logistic regression model. We also assessed survival and compared the subtypes with PAM50 intrinsic subtypes and residual cancer burden (RCB) index. TNBC subtype and pCR status were significantly associated (P = 0.04379). The basal-like 1 (BL1) subtype had the highest pCR rate (52%); basal-like 2 (BL2) and luminal androgen receptor had the lowest (0% and 10%, respectively). TNBC subtype was an independent predictor of pCR status (P = 0.022) by a likelihood ratio test. The subtypes better predicted pCR status than did the PAM50 intrinsic subtypes (basal-like vs. non basal-like). Classifying TNBC by 7 subtypes predicts high versus low pCR rate. We confirm the clinical relevancy of the 7 subtypes of TNBC. We need to prospectively validate whether the pCR rate differences translate into long-term outcome differences. The 7-subtype classification may spur innovative personalized medicine strategies for patients with TNBC. |
| Author | MASUDA, Hiroko FRASER SYMMANS, W YING WANG MERIC-BERNSTAM, Funda UENO, Naoto T PIETENPOL, Jennifer A LEHMANN, Brian D HORTOBAGYI, Gabriel N GONZALEZ-ANGULO, Ana Maria YA ZHANG BAGGERLY, Keith A VALERO, Vicente |
| AuthorAffiliation | 5 Department of Biochemistry, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 1 Morgan Welch Inflammatory Breast Cancer Research Program and Clinic, The University of Texas MD Anderson Cancer Center, Houston, TX 3 Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 2 Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 4 Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 6 Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX |
| AuthorAffiliation_xml | – name: 1 Morgan Welch Inflammatory Breast Cancer Research Program and Clinic, The University of Texas MD Anderson Cancer Center, Houston, TX – name: 3 Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX – name: 4 Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX – name: 6 Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX – name: 2 Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX – name: 5 Department of Biochemistry, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN |
| Author_xml | – sequence: 1 givenname: Hiroko surname: MASUDA fullname: MASUDA, Hiroko organization: Morgan Welch Inflammatory Breast Cancer Research Program and Clinic, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States – sequence: 2 givenname: Keith A surname: BAGGERLY fullname: BAGGERLY, Keith A organization: Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States – sequence: 3 givenname: W surname: FRASER SYMMANS fullname: FRASER SYMMANS, W organization: Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States – sequence: 4 givenname: Naoto T surname: UENO fullname: UENO, Naoto T organization: Morgan Welch Inflammatory Breast Cancer Research Program and Clinic, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States – sequence: 5 surname: YING WANG fullname: YING WANG organization: Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States – sequence: 6 surname: YA ZHANG fullname: YA ZHANG organization: Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States – sequence: 7 givenname: Ana Maria surname: GONZALEZ-ANGULO fullname: GONZALEZ-ANGULO, Ana Maria organization: Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States – sequence: 8 givenname: Funda surname: MERIC-BERNSTAM fullname: MERIC-BERNSTAM, Funda organization: Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States – sequence: 9 givenname: Vicente surname: VALERO fullname: VALERO, Vicente organization: Morgan Welch Inflammatory Breast Cancer Research Program and Clinic, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States – sequence: 10 givenname: Brian D surname: LEHMANN fullname: LEHMANN, Brian D organization: Department of Biochemistry, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States – sequence: 11 givenname: Jennifer A surname: PIETENPOL fullname: PIETENPOL, Jennifer A organization: Department of Biochemistry, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States – sequence: 12 givenname: Gabriel N surname: HORTOBAGYI fullname: HORTOBAGYI, Gabriel N organization: Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=27788806$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/23948975$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1172/JCI45014 10.1200/JCO.2006.10.3523 10.1093/nar/gng015 10.1200/JCO.2007.14.4147 10.1200/JCO.2008.17.9952 10.1002/cncr.22618 10.1186/1471-2164-7-96 10.1073/pnas.191367098 10.1200/JCO.2011.38.8595 10.1158/1078-0432.CCR-04-2421 10.1200/JCO.2008.18.1370 10.1093/jnci/djr071 10.1001/jama.2011.593 10.1186/gb-2007-8-5-r76 10.1634/theoncologist.2012-0397 10.1200/JCO.2007.10.6823 10.1186/bcr2635 10.1634/theoncologist.11-8-868 10.1073/pnas.0932692100 10.1200/JCO.2010.28.4273 10.4137/CIN.S9983 10.1158/1078-0432.CCR-06-1109 10.1200/JCO.2006.05.6861 |
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| Issue | 19 |
| Keywords | Mammary gland diseases Chemotherapy Breast disease Treatment Malignant tumor Neoadjuvant treatment Triple negative breast cancer Cancer |
| Language | English |
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| Snippet | The clinical relevancy of the 7-subtype classification of triple-negative breast cancer (TNBC) reported by Lehmann and colleagues is unknown. We investigated... Abstract Purpose: The clinical relevancy of the 7-subtype classification of triple-negative breast cancer (TNBC) reported by Lehmann and colleagues is unknown.... |
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| SubjectTerms | Adult Aged Antineoplastic agents Antineoplastic Combined Chemotherapy Protocols - therapeutic use Biological and medical sciences Cluster Analysis Female Gene Expression Profiling Genetic Heterogeneity Gynecology. Andrology. Obstetrics Humans Mammary gland diseases Medical sciences Middle Aged Multiple tumors. Solid tumors. Tumors in childhood (general aspects) Neoplasm Grading Neoplasm Staging Pharmacology. Drug treatments Recurrence Reproducibility of Results Treatment Outcome Triple Negative Breast Neoplasms - drug therapy Triple Negative Breast Neoplasms - genetics Triple Negative Breast Neoplasms - mortality Triple Negative Breast Neoplasms - pathology Tumors |
| Title | Differential Response to Neoadjuvant Chemotherapy Among 7 Triple-Negative Breast Cancer Molecular Subtypes |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/23948975 https://pubmed.ncbi.nlm.nih.gov/PMC3813597 |
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