Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer.

By A. Ivshina, J. George, O. Senko, B. Mow, T. Putti, J. Smeds, T. Lindahl, Y. Pawitan, P. Hall, H. Nordgren and 5 more. Cancer Research, 2006

688 citations recorded by the source corpus.

Histologic grading of breast cancer defines morphologic subtypes informative of metastatic potential, although not without considerable interobserver disagreement and clinical heterogeneity particularly among the moderately differentiated grade 2 (G2) tumors. We posited that a gene expression signature capable of discerning tumors of grade 1 (G1) and grade 3 (G3) histology might provide a more objective measure of grade with prognostic benefit for patients with G2 disease. To this end, we studied the expression profiles of 347 primary invasive breast tumors analyzed on Affymetrix microarrays. Using class prediction algorithms, we identified 264 robust grade-associated markers, six of which could accurately classify G1 and G3 tumors, and separate G2 tumors into two highly discriminant classes (termed G2a and G2b genetic grades) with patient survival outcomes highly similar to those with G1 and G3 histology, respectively. Statistical analysis of conventional clinical variables further distinguished G2a and G2b subtypes from each other, but also from histologic G1 and G3 tumors. In multivariate analyses, genetic grade was consistently found to be an independent prognostic indicator of disease recurrence comparable with that of lymph node status and tumor size. When incorporated into the Nottingham prognostic index, genetic grade enhanced detection of patients with less harmful tumors, likely to benefit little from adjuvant therapy. Our findings show that a genetic grade signature can improve prognosis and therapeutic planning for breast cancer patients, and support the view that low- and high-grade disease, as defined genetically, reflect independent pathobiological entities rather than a continuum of cancer progression.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/036c98e4cea6998174d7bc29ea00c776ce412fdb

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Citations in the corpus

Biological and molecular heterogeneity of breast cancers correlates with their cancer stem cell content. (2010, methodology): “We employed two data sets published by Ivshina et al. (2006) and Pawitan et al. (2005), from which we extracted the information relative to poorly differentiated (G3) and well-differentiated (G1) tumors (Extended Experimental Procedures).”
Predictive, personalized, preventive, participatory (P4) cancer medicine (2011, background): “and stratifying them into distinct subtypes (each with differing combinations of disease-perturbed networks)”
A Mutant-p53/Smad complex opposes p63 to empower TGFbeta-induced metastasis. (2009, neutral): “A point in case are tumors classified as intermediate (grade 2) by the Nottingham scale, that represent the majority of tumors and whose prognosis is uncertain (Ivshina et al., 2006).”
Breast cancer intrinsic subtype classification, clinical use and future trends. (2015, background): “This signature could stratify grade II cancers into grade I-like (with a low frequency of distant relapses) and grade III-like (having a clinical behavior similar to that of grade III) cancers [45, 52].”
SIRT3 is a Mitochondrial Localized Tumor Suppressor Required for Maintenance of Mitochondrial Integrity and Metabolism During Stress (2010, methodology): “, 2006), as a function of (E) Elston (G-1, G-2, or G-3) Grade (Ivshina et al., 2006), or (F) pathological (well, moderately, or poorly differentiated) differentiation (Sotiriou et al.”
Thresholds for therapies: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2009 (2009, background): “Gene expression signatures are likely to indicate a prognostically relevant dichotomy (low grade versus high grade), though the implications of this observation for therapy require further study [87, 88].”
Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures (2008, background): “Confirming previous studies [18,32], the effect of histological grade is much reduced and can be explained by the refinement of intermediate grade into two groups with very different survival rates.”
SurvExpress: An Online Biomarker Validation Tool and Database for Cancer Gene Expression Data Using Survival Analysis (2013, methodology): “To test the biomarker in several conditions, the datasets were chosen to reflect patients suitable for the test (Wang [27] and Ivshina [26]), patients with partial information besides different event (TCGA [25]), and patients without clinical information (Kao [15]).”
Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome (2011, background): “markers such as proliferation [6–9] or grade [10–12].”
An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients (2010, neutral): “2 1 [31] GSE3494 GPL96 213 (85%) 84 (33%) NA NA 67/128/54 62±14 2.”
GOBO: Gene Expression-Based Outcome for Breast Cancer Online (2011, neutral): “GSE4922 40 NA NA NA NA NA NA NA NA 0/40/0 NA NA [36]”