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Development of a Novel Proteomic Risk-Classifier for Prognostication of Patients With Early-Stage Hormone Receptor-Positive Breast Cancer.

ABSTRACT: Use of proteomic strategies to identify a risk classifier that estimates probability of distant recurrence in early-stage hormone receptor (HR)-positive breast cancer is relevant to physiological cellular function and therefore to intrinsic tumor biology. We used a 298-sample retrospective training set to develop an immunohistochemistry-based novel risk classifier called CanAssist-Breast (CAB) which combines 5 prognostically relevant biomarkers and 3 clinico-pathological parameters to arrive at probability of distant recurrence within 5?years from diagnosis. Five selected biomarkers, namely, CD44, ABCC4, ABCC11, N-cadherin, and pan-cadherin, were chosen based on their role in tumor metastasis. The chosen biomarkers represent the hallmarks of cancer and are distinct from other proliferation and gene expression-based prognostic signatures. The 3 clinico-pathological parameters integrated into the machine learning-based CAB algorithm are tumor size, tumor grade, and node status. These features are used to calculate a "CAB risk score" that classifies patients into low- or high-risk groups and predicts probability of distant recurrence in 5?years. Independent clinical validation of CAB in a retrospective study comprising 196 patients indicated that distant metastasis-free survival (DMFS) was significantly different in the 2 risk groups. The difference in DMFS between the low- and high-risk categories was 19% in the validation cohort (P?=?.0002). In multivariate analysis, CAB risk score was the most significant independent predictor of distant recurrence with a hazard ratio of 4.3 (P?=?.0003). CanAssist-Breast is a precise and unique machine learning-based proteomic risk-classifier that can assist in risk stratification of patients with early-stage HR+ breast cancer.

SUBMITTER: Ramkumar C

PROVIDER: S-EPMC6066801 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Development of a Novel Proteomic Risk-Classifier for Prognostication of Patients With Early-Stage Hormone Receptor-Positive Breast Cancer.

Ramkumar Charusheila C Buturovic Ljubomir L Malpani Sukriti S Kumar Attuluri Arun A Basavaraj Chetana C Prakash Chandra C Madhav Lekshmi L Doval Dinesh Chandra DC Mehta Anurag A Bakre Manjiri M MM

Biomarker insights 20180730

Use of proteomic strategies to identify a risk classifier that estimates probability of distant recurrence in early-stage hormone receptor (HR)-positive breast cancer is relevant to physiological cellular function and therefore to intrinsic tumor biology. We used a 298-sample retrospective training set to develop an immunohistochemistry-based novel risk classifier called CanAssist-Breast (CAB) which combines 5 prognostically relevant biomarkers and 3 clinico-pathological parameters to arrive at ...[more]

PMID: 30083053

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Project description:ImportanceRace disparities persist in breast cancer mortality rates. One factor associated with these disparities may be differences in symptom burden, which may reduce chemotherapy tolerance and increase early treatment discontinuation.ObjectivesTo compare symptom burden by race among women with early-stage breast cancer before starting chemotherapy and quantify symptom differences explained by baseline characteristics.Design, setting, and participantsA cross-sectional analysis of symptom burden differences by race among Black and White women with a diagnosis of stage I to III, hormone receptor-positive breast cancer who had a symptom report collected before chemotherapy initiation in a large cancer center in the southern region of the US from January 1, 2007, through December 31, 2015. Analyses were conducted from November 1, 2019, to March 31, 2021. Blinder-Oaxaca decomposition was used, adjusting for baseline sociodemographic and clinical characteristics.Main outcomes and measuresFour symptom composite scores with a mean (SD) of 50 (10) were reported before starting chemotherapy (baseline) and were derived from symptom items: general physical symptoms (11 items), treatment adverse effects (8 items), acute distress (4 items), and despair (7 items). Patients rated the severity of each symptom they experienced in the past week on a scale of 0 to 10 (where 0 indicates not a problem and 10 indicates as bad as possible).ResultsA total of 1338 women (mean [SD] age, 54.6 [11.6] years; 420 Black women [31.4%] and 918 White women [68.6%]) were included in the study. Before starting chemotherapy, Black women reported a statistically significantly higher (ie, worse) symptom composite score than White women for adverse effects (44.5 vs 43.8) but a lower acute distress score (48.5 vs 51.0). Decomposition analyses showed that Black patients' characteristics were associated with higher symptom burden across all 4 scores. However, these differences were offset by relatively greater, statistically significant, unexplained physical, distress, and despair symptom reporting by White patients.Conclusions and relevanceIn this study, before starting chemotherapy, Black patients with early-stage breast cancer reported significantly higher burden for symptoms that may be exacerbated with chemotherapy and lower distress symptoms compared with White patients. Future studies should explore how symptoms change before and after treatment and differ by racial/ethnic groups and how they are associated with treatment adherence and mortality disparities.

Project description:BACKGROUND:BRCA1-mutated cancers exhibit deficient homologous recombination (HR) DNA repair, resulting in extensive copy number alterations and genome instability. HR deficiency can also arise in tumors without a BRCA1 mutation. Compared with other breast tumors, HR-deficient, BRCA1-like tumors exhibit worse prognosis but selective chemotherapeutic sensitivity. Presently, patients with triple negative breast cancer (TNBC) who do not respond to hormone endocrine-targeting therapy are given cytotoxic chemotherapy. However, more recent evidence showed a similar genomic profile between BRCA1-deficient TNBCs and hormone-receptor-positive tumors. Characterization of the somatic alterations of BRCA1-like hormone-receptor-positive breast tumors as a group, which is currently lacking, can potentially help develop biomarkers for identifying additional patients who might respond to chemotherapy. METHODS:We retrained and validated a copy-number-based support vector machine (SVM) classifier to identify HR-deficient, BRCA1-like breast tumors. We applied this classifier to The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) breast tumors. We assessed mutational profiles and proliferative capacity by covariate-adjusted linear models and identified differentially methylated regions using DMRcate in BRCA1-like hormone-receptor-positive tumors. RESULTS:Of the breast tumors in TCGA and METABRIC, 22% (651/2925) were BRCA1-like. Stratifying on hormone-receptor status, 13% (302/2405) receptor-positive and 69% (288/417) triple-negative tumors were BRCA1-like. Among the hormone-receptor-positive subgroup, BRCA1-like tumors showed significantly increased mutational burden and proliferative capacity (both P < 0.05). Genome-scale DNA methylation analysis of BRCA1-like tumors identified 202 differentially methylated gene regions, including hypermethylated BRCA1. Individually significant CpGs were enriched for enhancer regions (P < 0.05). The hypermethylated gene sets were enriched for DNA and chromatin conformation (all Bonferroni P < 0.05). CONCLUSIONS:To provide insights into alternative classification and potential therapeutic targeting strategies of BRCA1-like hormone-receptor-positive tumors we developed and applied a novel copy number classifier to identify BRCA1-like hormone-receptor-positive tumors and their characteristic somatic alteration profiles.

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Development of a Novel Proteomic Risk-Classifier for Prognostication of Patients With Early-Stage Hormone Receptor-Positive Breast Cancer.

Publications

Development of a Novel Proteomic Risk-Classifier for Prognostication of Patients With Early-Stage Hormone Receptor-Positive Breast Cancer.

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