A real-world retrospective study of the use of Ki-67 testing and treatment patterns in patients with HR+, HER2- early breast cancer in the United States.

BACKGROUND: The National Comprehensive Cancer Network recommends that patients with hormone receptor-positive early breast cancer be considered for adjuvant endocrine therapy (ET) after primary treatment like surgical excision. Adjuvant chemotherapy (CT) use primarily depends on risk of recurrence. Biomarkers such as Ki-67 potentially have most value in patients with intermediate risk factors, such as involvement of 1-3 positive nodes. This study evaluated the use of Ki-67 testing and treatment patterns in patients with HR+, human epidermal growth factor receptor 2-negative early breast cancer. METHODS: This was an observational retrospective cohort study of patients with electronic medical records from January 2010 to August 2018 treated for HR+, HER2- early breast cancer at Sarah Cannon sites in the United States (US). Overall, 567 patients were randomly selected after using the eligibility criteria: female or male ≥18 years, without distant metastases, and with available physician and pathology reports. Multivariable logistic regression was used to investigate factors predicting Ki-67 testing and test results. Descriptive analyses were applied to treatment patterns. RESULTS: Multivariable logistic regression analyses found no clinical or pathological factors that predicted whether Ki-67 testing had been ordered by physicians. Of all tested patients (N = 130), having Grade-2 tumors (OR, 7.95 [95% CI: 2.05, 30.9]; p = 0.0027) or Grade-3 tumors (OR, 95.3 [95% CI, 11.9, 760.7]; p < 0.001) at initial diagnosis was a predictor of high Ki-67 expression (≥20%). Ki-67 expression was tested in 23.6% (61/258) of patients with 1-3 positive nodes; 54.1% of them (33/61) had high Ki-67 expression (≥20%). While having a higher grade tumor predicted high Ki-67 (≥20%), 28.6% of patients with Grade-1 tumors also had high Ki-67 expression. Neo-adjuvant therapy was received by 16.0% of patients (91/567), most of whom (66/91; 72.5%) received CT alone. Adjuvant therapy, either endocrine and/or chemotherapy, was received by 92.6% (525/567) of patients and by 67.0% (61/91) of those who received neo-adjuvant therapy. Most (428/525, 81.5%) received ET in the adjuvant treatment setting. CONCLUSIONS: High grade tumors predicted high Ki-67 (≥20%) expression, but Ki-67 testing was not widely used in these US patients. Most HR+, HER2- early breast cancers were treated with adjuvant ET, with or without CT.

GeneSigDB: a manually curated database and resource for analysis of gene expression signatures.

GeneSigDB (http://www.genesigdb.org or http://compbio.dfci.harvard.edu/genesigdb/) is a database of gene signatures that have been extracted and manually curated from the published literature. It provides a standardized resource of published prognostic, diagnostic and other gene signatures of cancer and related disease to the community so they can compare the predictive power of gene signatures or use these in gene set enrichment analysis. Since GeneSigDB release 1.0, we have expanded from 575 to 3515 gene signatures, which were collected and transcribed from 1604 published articles largely focused on gene expression in cancer, stem cells, immune cells, development and lung disease. We have made substantial upgrades to the GeneSigDB website to improve accessibility and usability, including adding a tag cloud browse function, facetted navigation and a 'basket' feature to store genes or gene signatures of interest. Users can analyze GeneSigDB gene signatures, or upload their own gene list, to identify gene signatures with significant gene overlap and results can be viewed on a dynamic editable heatmap that can be downloaded as a publication quality image. All data in GeneSigDB can be downloaded in numerous formats including .gmt file format for gene set enrichment analysis or as a R/Bioconductor data file. GeneSigDB is available from http://www.genesigdb.org.

GeneSigDB--a curated database of gene expression signatures.

The primary objective of most gene expression studies is the identification of one or more gene signatures; lists of genes whose transcriptional levels are uniquely associated with a specific biological phenotype. Whilst thousands of experimentally derived gene signatures are published, their potential value to the community is limited by their computational inaccessibility. Gene signatures are embedded in published article figures, tables or in supplementary materials, and are frequently presented using non-standard gene or probeset nomenclature. We present GeneSigDB (http://compbio.dfci.harvard.edu/genesigdb) a manually curated database of gene expression signatures. GeneSigDB release 1.0 focuses on cancer and stem cells gene signatures and was constructed from more than 850 publications from which we manually transcribed 575 gene signatures. Most gene signatures (n = 560) were successfully mapped to the genome to extract standardized lists of EnsEMBL gene identifiers. GeneSigDB provides the original gene signature, the standardized gene list and a fully traceable gene mapping history for each gene from the original transcribed data table through to the standardized list of genes. The GeneSigDB web portal is easy to search, allows users to compare their own gene list to those in the database, and download gene signatures in most common gene identifier formats.