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3. Data from A Molecular Signature of the Nottingham Prognostic Index in Breast Cancer

Author

Patrick Tan, Chow Yin Wong, Siew Bok Wee, Ga Sze Hong, Puay Hoon Tan, Chee How Lee, and Kun Yu

Abstract

The Nottingham prognostic index (NPI) is a widely used clinicopathological staging system for breast cancer prognostication. Using a step-wise classification approach where breast tumor expression profiles were first divided into general “molecular subtypes” [estrogen receptor (ER)+, ER−, ERBB2+], followed by an independent analysis of each subtype, we identified a 62-gene expression signature (NPI-ES) highly correlated to the NPI in ER+ tumors. The NPI-ES classified the majority of ER+ tumors into two distinct groups with high confidence and was significantly correlated to NPI status in two independent sets of ER+ tumors derived from different centers. The NPI-ES is comparable to the classical NPI in identifying patients likely to exhibit a poor clinical prognosis, as well as to a recently described “prognosis signature” for breast cancer. Our findings demonstrate how expression profiling can complement classical staging systems employing histopathological parameters scored over a continuous range of values.

Published
2023
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4. A molecular signature of the Nottingham prognostic index in breast cancer

Author

Chow Yin Wong, Kun Yu, Ga Sze Hong, Chee How Lee, Siew Bok Wee, Patrick Tan, and Puay Hoon Tan

Subjects
Oncology, Cancer Research, medicine.medical_specialty, Pathology, Receptor, ErbB-2, Mammary gland, Estrogen receptor, Breast Neoplasms, Breast tumor, Breast cancer, Internal medicine, mental disorders, medicine, Cluster Analysis, Humans, Survival rate, Staging system, Neoplasm Staging, business.industry, Gene Expression Profiling, medicine.disease, Prognosis, humanities, Gene expression profiling, Survival Rate, medicine.anatomical_structure, Receptors, Estrogen, Nottingham Prognostic Index, business
Abstract

The Nottingham prognostic index (NPI) is a widely used clinicopathological staging system for breast cancer prognostication. Using a step-wise classification approach where breast tumor expression profiles were first divided into general “molecular subtypes” [estrogen receptor (ER)+, ER−, ERBB2+], followed by an independent analysis of each subtype, we identified a 62-gene expression signature (NPI-ES) highly correlated to the NPI in ER+ tumors. The NPI-ES classified the majority of ER+ tumors into two distinct groups with high confidence and was significantly correlated to NPI status in two independent sets of ER+ tumors derived from different centers. The NPI-ES is comparable to the classical NPI in identifying patients likely to exhibit a poor clinical prognosis, as well as to a recently described “prognosis signature” for breast cancer. Our findings demonstrate how expression profiling can complement classical staging systems employing histopathological parameters scored over a continuous range of values.

Published
2004

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