PAM50 Provides Prognostic Information When Applied to the Lymph Node Metastases of Advanced Breast Cancer Patients

Abstract

Purpose Transcriptional pathway activity and the molecular subtypes of breast cancer metastases have been shown to significantly influence patient postrelapse survival. Here, we further determine the relevance of clinically employed gene signatures in the advanced breast cancer (ABC) setting. Experimental Design Sufficient RNA for expression profiling was obtained from distant metastatic or inoperable locoregional relapse tissue by fine-needle aspiration from 109 patients of the Swedish TEX clinical trial. Gene signatures (GGI, 70 gene, recurrence score, cell-cycle score, risk of recurrence score, and PAM50) were applied to all metastases, and their relationship to long- (5-year) and short-term (1.5-year) postrelapse survival at all and locoregional lymph nodes (n = 40) versus other metastatic sites (n = 69) combined was assessed using Kaplan-Meier and/or multivariate Cox regression analyses. Results The majority of metastases were classified into intermediate or high-risk groups by all signatures, and a significant association was found between metastatic signature subgroups and primary tumor estrogen receptor status and histologic grade (P < 0.05). When considering all sites of metastasis, only PAM50 was statistically significant in Kaplan–Meier analysis (Log-rank P = 0.008 and 0.008 for long- and short-term postrelapse breast cancer–specific survival, respectively). This significance remained in both uni- and multivariate models when restricting analyses to lymph node metastases only, and a similar trend was observed in other metastatic sites combined, but did not reach formal significance. Conclusions Our findings are the first to demonstrate that the PAM50 signature can provide prognostic information from the lymph node metastases of ABC patients.

A cell-cycle signature classifier for pan-cancer analysis

“…It also allows association studies between the frequency of DNA alterations and activity scores of pathways with fundamental biological role of cancer. The cell-cycle pathways represent a compelling choice for such pathway activity-based analysis [1,2], and the cell-cycle score signature (CCS) applied by Lundberg et al here has been previously validated by the same research group in a number of breast cancer samples [3,4]. In this issue of journal, Lundberg et al hypothesised that a correlation between cell-cycle pathways and genomic features of a tumour can be made for a variety of malignancies, which could significantly expand the clinical application of the methodology in the provision of independent prognostic information.…”

“…The previously invented CCS by the authors had cut-offs specific to the breast cancer, so the authors modified the existing CCS system for the current analysis, by creating new tertiles on the basis of the continuous CCS in the pancancer dataset alone. This novel CCS score is generated by methodology utilising 463 genes that have previously been identified to have implications on general cell-cycle activity based on the amalgamation of three different databases-KEGG, HNGC and Cyclebase 3.0 [3,4]. The CCS score was then used for each individual tumour type in the TCGA pan-cancer dataset to generate low, intermediate and high CCS subgroups.…”

An integrative multi-omics network-based approach identifies key regulators for breast cancer

“…Second, knowledge-driven signatures revealed molecular characteristics of breast cancer from the gene expression level. Intrinsic subtyping, prognostic and predictive signatures of breast cancer based on gene expression profiling have been found for correlation with molecular subtypes, including proliferation, prognosis, and therapeutic response [33] , [34] , [64] , [65] . Third, since biological molecules interact within and across multiple biological levels, we constructed disease networks from genomic, transcriptomic and proteomics scales based on data-driven gene sets and identified the key genes [8] , [11] .…”

Profiling of Invasive Breast Carcinoma Circulating Tumour Cells—Are We Ready for the ‘Liquid’ Revolution?

“…This so-called intrinsic IBC classification has been reproduced by hierarchical cluster analyses of gene expression profiles, favoured by modern approach to IBC subtyping, enabling a more comprehensive evaluation of mRNA levels of genes included in molecular signatures established in microarray experiments [27,30,31,32]. The PAM50 is the most widely used IBC gene signature and is considered a robust counterpart of classification based on immunohistochemical subtyping [30,33,34,35,36,37]. In the last years, many more predictive panels have been constructed and introduced to the management of IBC patients [32,38,39,40,41].…”