Prognostic significance of bcl-2 expression in stage III breast cancer patients who had received doxorubicin and cyclophosphamide followed by paclitaxel as adjuvant chemotherapy

BACKGROUND: Bcl-2 is positively regulated by hormonal receptor pathways in breast cancer. A study was conducted to assess the prognostic significances of clinico-pathologic variables and of ER, PR, p53, c-erbB2, bcl-2, or Ki-67 as markers of relapse in breast cancer patients who had received the identical adjuvant therapy at a single institution. METHODS: A cohort of 151 curatively resected stage III breast cancer patients (M:F = 3:148, median age 46 years) who had 4 or more positive lymph nodes and received doxorubicin and cyclophosphamide followed by paclitaxel (AC/T) as adjuvant chemotherapy was analyzed for clinico-pathologic characteristics including disease-free survival (DFS) and overall survival (OS). Patients with positive ER and/or PR expression received 5 years of tamoxifen following AC/T. The protein expressions of biomarkers were assessed immunohistochemically. RESULTS: The median follow-up duration was 36 months, and 37 patients (24.5%) experienced a recurrence. Univariate analyses indicated that the tumor size (P = 0.038) and the number of involved lymph nodes (P < 0.001) significantly affected the recurrences. However, the type of surgery, the histology, histologic grade, the presence of endolymphatic emboli, and a close resection margin did not. Moreover, ER positivity (P = 0.013), bcl-2 positivity (P = 0.002) and low p53 expression (P = 0.032) were found to be significantly associated with a prolonged DFS. Furthermore, multivariate analysis identified 10 or more involved lymph nodes (HR 7.366; P < 0.001), negative bcl-2 expression (HR 2.895; P = 0.030), and c-erbB2 over-expression (HR 3.535; P = 0.001) as independent indicators of poorer DFS. In addition, bcl-2 expression was found to be significantly correlated with the expressions of ER and PR, and inversely correlated with the expressions of p53, c-erbB2 and Ki-67. Patients with bcl-2 expression had a significantly longer DFS than those without, even in the ER (+) subgroup. Moreover, OS was significantly affected by ER, bcl-2 and c-erbB2. CONCLUSION: Bcl-2 is an independent prognostic factor of DFS in curatively resected stage III breast cancer patients and appears to be a useful prognostic factor in combination with c-erbB2 and the number of involved lymph nodes

Correlation of microarray-based breast cancer molecular subtypes and clinical outcomes: implications for treatment optimization

<p>Abstract</p> <p>Background</p> <p>Optimizing treatment through microarray-based molecular subtyping is a promising method to address the problem of heterogeneity in breast cancer; however, current application is restricted to prediction of distant recurrence risk. This study investigated whether breast cancer molecular subtyping according to its global intrinsic biology could be used for treatment customization.</p> <p>Methods</p> <p>Gene expression profiling was conducted on fresh frozen breast cancer tissue collected from 327 patients in conjunction with thoroughly documented clinical data. A method of molecular subtyping based on 783 probe-sets was established and validated. Statistical analysis was performed to correlate molecular subtypes with survival outcome and adjuvant chemotherapy regimens. Heterogeneity of molecular subtypes within groups sharing the same distant recurrence risk predicted by genes of the Oncotype and MammaPrint predictors was studied.</p> <p>Results</p> <p>We identified six molecular subtypes of breast cancer demonstrating distinctive molecular and clinical characteristics. These six subtypes showed similarities and significant differences from the Perou-Sørlie intrinsic types. Subtype I breast cancer was in concordance with chemosensitive basal-like intrinsic type. Adjuvant chemotherapy of lower intensity with CMF yielded survival outcome similar to those of CAF in this subtype. Subtype IV breast cancer was positive for ER with a full-range expression of HER2, responding poorly to CMF; however, this subtype showed excellent survival when treated with CAF. Reduced expression of a gene associated with methotrexate sensitivity in subtype IV was the likely reason for poor response to methotrexate. All subtype V breast cancer was positive for ER and had excellent long-term survival with hormonal therapy alone following surgery and/or radiation therapy. Adjuvant chemotherapy did not provide any survival benefit in early stages of subtype V patients. Subtype V was consistent with a unique subset of luminal A intrinsic type. When molecular subtypes were correlated with recurrence risk predicted by genes of Oncotype and MammaPrint predictors, a significant degree of heterogeneity within the same risk group was noted. This heterogeneity was distributed over several subtypes, suggesting that patients in the same risk groups require different treatment approaches.</p> <p>Conclusions</p> <p>Our results indicate that the molecular subtypes established in this study can be utilized for customization of breast cancer treatment.</p

Tumour sampling method can significantly influence gene expression profiles derived from neoadjuvant window studies

Patient-matched transcriptomic studies using tumour samples before and after treatment allow inter-patient heterogeneity to be controlled, but tend not to include an untreated comparison. Here, Illumina BeadArray technology was used to measure dynamic changes in gene expression from thirty-seven paired diagnostic core and surgically excised breast cancer biopsies obtained from women receiving no treatment prior to surgery, to determine the impact of sampling method and tumour heterogeneity. Despite a lack of treatment and perhaps surprisingly, consistent changes in gene expression were identified during the diagnosis-surgery interval (48 up, 2 down; Siggenes FDR 0.05) in a manner independent of both subtype and sampling-interval length. Instead, tumour sampling method was seen to directly impact gene expression, with similar effects additionally identified in six published breast cancer datasets. In contrast with previous findings, our data does not support the concept of a significant wounding or immune response following biopsy in the absence of treatment and instead implicates a hypoxic response following the surgical biopsy. Whilst sampling-related gene expression changes are evident in treated samples, they are secondary to those associated with response to treatment. Nonetheless, sampling method remains a potential confounding factor for neoadjuvant study design

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe