Genome Instability Profiles Predict Disease Outcome in a Cohort of 4,003 Patients with Breast Cancer

Abstract

◥Purpose: The choice of therapy for patients with breast cancer is often based on clinicopathologic parameters, hormone receptor status, and HER2 amplification. To improve individual prognostication and tailored treatment decisions, we combined clinicopathologic prognostic data with genome instabilty profiles established by quantitative measurements of the DNA content.Experimental Design: We retrospectively assessed clinical data of 4,003 patients with breast cancer with a minimum postoperative follow-up period of 10 years. For the entire cohort, we established genome instability profiles. We applied statistical methods, including correlation matrices, Kaplan-Meier curves, and multivariable Cox proportional hazard models, to ascertain the potential of standard clinicopathologic data and genome instability profiles as independent predictors of disease-specific survival in distinct subgroups, defined clinically or with respect to treatment.Results: In Cox regression analyses, two parameters of the genome instability profiles, the S-phase fraction and the stemline scatter index, emerged as independent predictors in premenopausal women, outperforming all clinicopathologic parameters. In postmenopausal women, age and hormone receptor status were the predominant prognostic factors. However, by including Sphase fraction and 2.5c exceeding rate, we could improve disease outcome prediction in pT1 tumors irrespective of the lymph node status. In pT3-pT4 tumors, a higher S-phase fraction led to poorer prognosis. In patients who received adjuvant endocrine therapy, chemotherapy or radiotherapy, or a combination, the ploidy profiles improved prognostication.Conclusions: Genome instability profiles predict disease outcome in patients with breast cancer independent of clinicopathologic parameters. This applies especially to premenopausal patients. In patients receiving adjuvant therapy, the profiles improve identification of high-risk patients.

High Levels of Chromosomal Copy Number Alterations and TP53 Mutations Correlate with Poor Outcome in Younger Breast Cancer Patients

“…17,52 This observation, however, is not universally applicable, and the complex biology of breast cancer in young women, with its potentially aggressive features, remains poorly understood. 53 This study addressed the variability in disease outcome by selecting younger breast cancer patients with short and long survival (mean DSS, <4 versus >19 years) from over 5000 cases treated in the greater Stockholm area between 1986 and 2001. The potential genetic basis of the disparate outcome was explored by determining the genetic instability profile using miFISH, which allows for the enumeration of copy numbers of cancer genes in individual cells across the tumor cell population and an assessment of the degree of ITH.…”

Unique ER PR expression pattern in breast cancers with CHEK2 mutation: a hormone receptor and HER2 analysis based on germline cancer predisposition genes

“…In recent decades, genomic instability has risen as a predictor of cancer outcomes and offers opportunity for targeted treatment. In a large cohort of breast cancer patients, genome instability profiles were found to predict mortality outcomes independent of clinicopathological parameters [ 1 ]. A number of breast cancer predisposition genes ( ATM, BRCA1, BRCA2, CHEK2, TP53, and PALB2 ) are important gatekeepers to maintaining DNA replication fidelity by regulating DNA damage repair.…”

Identification of Mutator-Derived lncRNA Signatures of Genomic Instability for Promoting the Clinical Outcome in Hepatocellular Carcinoma

“…Genomic instability often relates to tumor-prone phenotypes and required for the acquisition of tumor-initiating mutations [ 5 ]. More importantly, many researchers indicated that genomic instability accumulation refers to patients' tumor progression and prognosis [ 6 , 7 ]. Even though genomic instability mechanisms have not been wholly discovered, abnormal epigenetics has been reported related to genomic instability [ 8 ], indicating molecular signature's capability as a quantitative analysis for genomic instability.…”