Association between a germline OCA2 polymorphism at chromosome 15q13.1 and estrogen receptor-negative breast cancer survival

Genetic ancestry is related to potential sources of breast cancer health disparities among Colombian women

Breast cancer health disparities are linked to clinical-pathological determinants, socioeconomic inequities, and biological factors such as genetic ancestry. These factors collectively interact in complex ways, influencing disease behavior, especially among highly admixed populations like Colombians. In this study, we assessed contributing factors to breast cancer health disparities according to genetic ancestry in Colombian patients from a national cancer reference center. We collected non-tumoral paraffin embedded (FFPE) blocks from 361 women diagnosed with breast cancer at the National Cancer Institute (NCI) to estimate genetic ancestry using a 106-ancestry informative marker (AIM) panel. Differences in European, Indigenous American (IA) and African ancestry fractions were analyzed according to potential sources of breast cancer health disparities, like etiology, tumor-biology, treatment administration, and socioeconomic-related factors using a Kruskal-Wallis test. Our analysis revealed a significantly higher IA ancestry among overweight patients with larger tumors and those covered by a subsidized health insurance. Conversely, we found a significantly higher European ancestry among patients with smaller tumors, residing in middle-income households, and affiliated to the contributory health regime, whereas a higher median of African ancestry was observed among patients with either a clinical, pathological, or stable response to neoadjuvant treatment. Altogether, our results suggest that the genetic legacy among Colombian patients, measured as genetic ancestry fractions, may be reflected in many of the clinical-pathological variables and socioeconomic factors that end up contributing to health disparities for this disease.

Key molecules associated with thyroid carcinoma prognosis: A study based on transcriptome sequencing and GEO datasets

Background

Thyroid carcinoma (THCA) has a low mortality rate, but its incidence has been rising over the years. We need to pay attention to its progression and prognosis. In this study, a transcriptome sequencing analysis and bioinformatics methods were used to screen key genes associated with THCA development and analyse their clinical significance and diagnostic value.

Methods

We collected 10 pairs of THCA tissues and noncancerous tissues, these samples were used for transcriptome sequencing to identify disordered genes. The gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. Comprehensive analysis of thyroid clinicopathological data using The Cancer Genome Atlas (TCGA). R software was used to carry out background correction, normalization and log2 conversion. We used quantitative real-time PCR (qRT–PCR) and Western blot to determine differentially expressed genes (DEGs) expression in samples. We integrated the DEGs expression, clinical features and progression-free interval (PFI). The related functions and immune infiltration degree were established by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and single-sample Gene Set Enrichment Analysis (ssGSEA). The UALCAN database was used to analyse the methylation level.

Results

We evaluated DEGs between normal tissue and cancer. Three genes were identified: regulator of G protein signaling 8 (RGS8), diacylglycerol kinase iota (DGKI) and oculocutaneous albinism II (OCA2). The mRNA and protein expression levels of RGS8, DGKI and OCA2 in normal tissues were higher than those in THCA tissues. Better survival outcomes were associated with higher expression of RGS8 (HR=0.38, P=0.001), DGKI (HR=0.52, P=0.022), and OCA2 (HR=0.41, P=0.003). The GO analysis, KEGG analysis and GSEA proved that the coexpressed genes of RGS8, DGKI and OCA2 were related to thyroid hormone production and peripheral downstream signal transduction effects. The expression levels of RGS8, DGKI and OCA2 were linked to the infiltration of immune cells such as DC cells. The DNA methylation level of OCA2 in cancer tissues was higher than that in the normal samples.

Conclusions

RGS8, DGKI and OCA2 might be promising prognostic molecular markers in patients with THCA and reveal the clinical significance of RGS8, DGKI and OCA2 in THCA.

Role of germline variants in the metastasis of breast carcinomas

Most cancer-related deaths in breast cancer patients are associated with metastasis, a multistep, intricate process that requires the cooperation of tumour cells, tumour microenvironment and metastasis target tissues. It is accepted that metastasis does not depend on the tumour characteristics but the host’s genetic makeup. However, there has been limited success in determining the germline genetic variants that influence metastasis development, mainly because of the limitations of traditional genome-wide association studies to detect the relevant genetic polymorphisms underlying complex phenotypes. In this work, we leveraged the extreme discordant phenotypes approach and the epistasis networks to analyse the genotypes of 97 breast cancer patients. We found that the host’s genetic makeup facilitates metastases by the dysregulation of gene expression that can promote the dispersion of metastatic seeds and help establish the metastatic niche—providing a congenial soil for the metastatic seeds.

A functional variant near XCL1 gene improves breast cancer survival via promoting cancer immunity

Most genome-wide association studies (GWASs) identify genetic variants for breast cancer occurrence. In contrast, few are for recurrence and mortality. We conducted a GWAS on breast cancer survival after diagnosis in estrogen receptor-positive patients, including 953 Taiwanese patients with 159 events. Through Cox proportional hazard models estimation, we identified 24 risk SNPs with p < 1 × 10^-5 . Based on imputation and integrated analysis, one SNP, rs1024176 (located in 1q24.2, p = 2.43 × 10^-5 ) was found to be a functional variant associated with breast cancer survival and XCL1 gene expression. A series of experimental approaches, including cell-based analyses and CRISPR/Cas9 genome-editing system, were then used and identified the transcription factor MYBL2 was able to discriminately bind to the A allele of rs1024176, the protective variant for breast cancer survival, which promoted XCL1 expression, but not to the G allele of rs1024176. The chemokine XCL1 attracts type 1 dendritic cells (DC1s) to the tumor microenvironment. In breast cancer tissues, we applied a two-step Mendelian randomization analysis, using expression quantitative trait loci as instrumental variables, to confirm higher XCL1 expression was correlated with higher DC1 signatures and favorable disease progression, through the causal effect of rs1024176-A allele. Our study supports the genetic effect on preventing breast cancer survival through XCL1-induced DC1 recruitment in tumor microenvironment.

Integrating of genomic and transcriptomic profiles for the prognostic assessment of breast cancer

PURPOSE

To evaluate the prognostic effect of the integration of genomic and transcriptomic profiles in breast cancer.

METHODS

Eight hundred and ten samples from the Cancer Genome Atlas (TCGA) data sets were randomly divided into the training set (540 subjects) and validation set (270 subjects). We first selected single-nucleotide polymorphism (SNPs) and genes associated with breast cancer prognosis in the training set to construct the prognostic prediction model, and then replicated the prediction efficiency in the validation set.

RESULTS

Four SNPs and three genes associated with the prognosis of breast cancer in the training set were included in the prognostic model. Patients were divided into the high-risk group and low-risk group based on the four SNPs and three genes signature-based genetic prognostic index. High-risk patients showed a significant worse overall survival [Hazard Ratio (HR) 9.43, 95% confidence interval (CI) 3.81-23.33, P < 0.001] than the low-risk group. Compared to the model constructed with only gene expression, the C statistics for the signature-based genetic prognostic index [area under curves (AUC) = 0.79, 95% CI 0.72-0.86] showed a significant increase (P < 0.001). Additionally, we further replicated the prognostic prediction model in the validation set as patients in the high-risk group also showed a significantly worse overall survival (HR 4.55, 95% CI 1.50-13.88, P < 0.001), and the C statistics for the signature-based genetic prognostic index was 0.76 (95% CI 0.65-0.86). The following time-dependent ROC revealed that the mean of AUCs were 0.839 and 0.748 in the training set and the validation set, respectively.

CONCLUSIONS

Our findings suggested that integrating genomic and transcriptomic profiles could greatly improve the predictive efficiency of the prognosis of breast cancer patients.

Genome-wide association study of germline variants and breast cancer-specific mortality

BACKGROUND

We examined the associations between germline variants and breast cancer mortality using a large meta-analysis of women of European ancestry.

METHODS

Meta-analyses included summary estimates based on Cox models of twelve datasets using ~10.4 million variants for 96,661 women with breast cancer and 7697 events (breast cancer-specific deaths). Oestrogen receptor (ER)-specific analyses were based on 64,171 ER-positive (4116) and 16,172 ER-negative (2125) patients. We evaluated the probability of a signal to be a true positive using the Bayesian false discovery probability (BFDP).

RESULTS

We did not find any variant associated with breast cancer-specific mortality at P < 5 × 10-8. For ER-positive disease, the most significantly associated variant was chr7:rs4717568 (BFDP = 7%, P = 1.28 × 10-7, hazard ratio [HR] = 0.88, 95% confidence interval [CI] = 0.84-0.92); the closest gene is AUTS2. For ER-negative disease, the most significant variant was chr7:rs67918676 (BFDP = 11%, P = 1.38 × 10-7, HR = 1.27, 95% CI = 1.16-1.39); located within a long intergenic non-coding RNA gene (AC004009.3), close to the HOXA gene cluster.

CONCLUSIONS

We uncovered germline variants on chromosome 7 at BFDP < 15% close to genes for which there is biological evidence related to breast cancer outcome. However, the paucity of variants associated with mortality at genome-wide significance underpins the challenge in providing genetic-based individualised prognostic information for breast cancer patients.

Risk, Prediction and Prevention of Hereditary Breast Cancer - Large-Scale Genomic Studies in Times of Big and Smart Data

Over the last two decades genetic testing for mutations in BRCA1 and BRCA2 has become standard of care for women and men who are at familial risk for breast or ovarian cancer. Currently, genetic testing more often also includes so-called panel genes, which are assumed to be moderate-risk genes for breast cancer. Recently, new large-scale studies provided more information about the risk estimation of those genes. The utilization of information on panel genes with regard to their association with the individual breast cancer risk might become part of future clinical practice. Furthermore, large efforts have been made to understand the influence of common genetic variants with a low impact on breast cancer risk. For this purpose, almost 450 000 individuals have been genotyped for almost 500 000 genetic variants in the OncoArray project. Based on first results it can be assumed that – together with previously identified common variants – more than 170 breast cancer risk single nucleotide polymorphisms can explain up to 18% of familial breast cancer risk. The knowledge about genetic and non-genetic risk factors and its implementation in clinical practice could especially be of use for individualized prevention. This includes an individualized risk prediction as well as the individualized selection of screening methods regarding imaging and possible lifestyle interventions. The aim of this review is to summarize the most recent developments in this area and to provide an overview on breast cancer risk genes, risk prediction models and their utilization for the individual patient.

Smoking and FGFR2 rs2981582 variant independently modulate male breast cancer survival: A population-based study in Tuscany, Italy

AIM

Male breast cancer (MBC) is a rare disease and recommendations for its clinical management are often extrapolated from those for female breast cancer, even if breast cancer (BC) has different characteristics in the two sexes. The purpose of this study was to assess the influence of several individual characteristics including clinico-pathological, lifestyle and genetic factors on overall survival (OS) of a relatively large and well characterized population-based series of 166 MBCs enrolled in Tuscany.

METHODS

We genotyped MBC cases at BRCA1/2 genes and at 9 candidate BC susceptibility SNPs. Kaplan-Meier method and multivariate Cox regression, adjusted for several individual characteristics were used. To reduce a possible selection bias related to the interval between diagnosis and enrolment of MBC cases into the study, we used the date of blood donation as the date of the start of observation for survival analysis.

RESULTS

Only smoking habits had a significant effect on OS at 10 years (for current smokers, HR: 3.34; 95% CI 1.45-7.68; p = 0.004), while lymph node status fell short of reaching statistical significance (for pN positive, HR: 2.07; 95% CI 0.93-4.55; p = 0.07). In the same multivariate analysis we found a significantly higher OS in cases with FGFR2 rs2981582 variant in the dominant transmission model (HR: 0.29; 95% CI: 0.13-0.62; p = 0.028). A sensitivity analysis with left truncation showed similar results.

CONCLUSIONS

Our results may contribute to shed light on factors influencing MBC survival suggesting an important role for cigarette smoking and FGFR2 rs2981582 variant, and provide clues for better patient management.

Genome-wide association study identifies the GLDC/IL33 locus associated with survival of osteosarcoma patients

Survival rates for osteosarcoma, the most common primary bone cancer, have changed little over the past three decades and are particularly low for patients with metastatic disease. We conducted a multi-institutional genome-wide association study (GWAS) to identify germline genetic variants associated with overall survival in 632 patients with osteosarcoma, including 523 patients of European ancestry and 109 from Brazil. We conducted a time-to-event analysis and estimated hazard ratios (HR) and 95% confidence intervals (CI) using Cox proportional hazards models, with and without adjustment for metastatic disease. The results were combined across the European and Brazilian case sets using a random-effects meta-analysis. The strongest association after meta-analysis was for rs3765555 at 9p24.1, which was inversely associated with overall survival (HR = 1.76; 95% CI 1.41-2.18, p = 4.84 × 10-7 ). After imputation across this region, the combined analysis identified two SNPs that reached genome-wide significance. The strongest single association was with rs55933544 (HR = 1.9; 95% CI 1.5-2.4; p = 1.3 × 10-8 ), which localizes to the GLDC gene, adjacent to the IL33 gene and was consistent across both the European and Brazilian case sets. Using publicly available data, the risk allele was associated with lower expression of IL33 and low expression of IL33 was associated with poor survival in an independent set of patients with osteosarcoma. In conclusion, we have identified the GLDC/IL33 locus on chromosome 9p24.1 as associated with overall survival in patients with osteosarcoma. Further studies are needed to confirm this association and shed light on the biological underpinnings of this susceptibility locus.

Germline copy number variations are associated with breast cancer risk and prognosis

Breast cancer is one of the most common cancers among women, and susceptibility is explained by genetic, lifestyle and environmental components. Copy Number Variants (CNVs) are structural DNA variations that contribute to diverse phenotypes via gene-dosage effects or cis-regulation. In this study, we aimed to identify germline CNVs associated with breast cancer susceptibility and their relevance to prognosis. We performed whole genome CNV genotyping in 422 cases and 348 controls using Human Affymetrix SNP 6 array. Principal component analysis for population stratification revealed 84 outliers leaving 366 cases and 320 controls of Caucasian ancestry for association analysis; CNVs with frequency > 10% and overlapping with protein coding genes were considered for breast cancer risk and prognostic relevance. Coding genes within the CNVs identified were interrogated for gene- dosage effects by correlating copy number status with gene expression profiles in breast tumor tissue. We identified 200 CNVs associated with breast cancer (q-value < 0.05). Of these, 21 CNV regions (overlapping with 22 genes) also showed association with prognosis. We validated representative CNVs overlapping with APOBEC3B and GSTM1 genes using the TaqMan assay. Germline CNVs conferred dosage effects on gene expression in breast tissue. The candidate CNVs identified in this study warrant independent replication.

Predicting Triple-Negative Breast Cancer Subtype Using Multiple Single Nucleotide Polymorphisms for Breast Cancer Risk and Several Variable Selection Methods

INTRODUCTION

Studies of triple-negative breast cancer have recently been extending the inclusion criteria and incorporating additional molecular markers into the selection criteria, opening up scope for targeted therapies. The screening phases required for studies of this type are often prolonged, since the process of determining the molecular subtype and carrying out additional biomarker assessment is time-consuming. Parameters such as germline genotypes capable of predicting the molecular subtype before it becomes available from pathology might be helpful for treatment planning and optimizing the timing and cost of screening phases. This appears to be feasible, as rapid and low-cost genotyping methods are becoming increasingly available. The aim of this study was to identify single nucleotide polymorphisms (SNPs) for breast cancer risk capable of predicting triple negativity, in addition to clinical predictors, in breast cancer patients.

METHODS

This cross-sectional observational study included 1271 women with invasive breast cancer who were treated at a university hospital. A total of 76 validated breast cancer risk SNPs were successfully genotyped. Univariate associations between each SNP and triple negativity were explored using logistic regression analyses. Several variable selection and regression techniques were applied to identify a set of SNPs that together improve the prediction of triple negativity in addition to the clinical predictors of age at diagnosis and body mass index (BMI). The most accurate prediction method was determined by cross-validation.

RESULTS

The SNP rs10069690 (TERT, CLPTM1L) was the only significant SNP (corrected p = 0.02) after correction of p values for multiple testing in the univariate analyses. This SNP and three additional SNPs from the genes RAD51B, CCND1, and FGFR2 were selected for prediction of triple negativity. The addition of these SNPs to clinical predictors increased the cross-validated area under the curve (AUC) from 0.618 to 0.625. Age at diagnosis was the strongest predictor, stronger than any genetic characteristics.

CONCLUSION

Prediction of triple-negative breast cancer can be improved if SNPs associated with breast cancer risk are added to a prediction rule based on age at diagnosis and BMI. This finding could be used for prescreening purposes in complex molecular therapy studies for triple-negative breast cancer.

Survival in familial and non-familial breast cancer by age and stage at diagnosis

We aimed to compare the survival in familial and sporadic breast cancer (BC) patients who were diagnosed at an identical age and TNM stage. The Nationwide Swedish Family-Cancer Database including all Swedes born after 1931 and their biological parents, totalling >14.7 million individuals, was used. Hazard ratios (HRs) were calculated for women with BC in a first-degree relative (FDR) versus BC patients without positive family history. There was no difference in survival of familial BC patients who were diagnosed at higher TNM status or older age (>40) compared to sporadic BC cases diagnosed at the same late TNM stage. Young BC patients (age <40) in early stages had the worst survival when their FDR was diagnosed with single (HR: 2.0-3.7) or multiple (HR: 2.4-7.1) BC at any age. We concluded that there is no difference in survival of familial and non-familial BC patients who are diagnosed at higher TNM status or older ages (>40). Young familial BC patients (age <40), diagnosed at early stage, have the poorer survival compared to sporadic cases. Our results urge the need for identifying the underling genetic component for such a difference in survival of familial BC.

Inbreeding and homozygosity in breast cancer survival

Genome-wide association studies (GWASs) help to understand the effects of single nucleotide polymorphisms (SNPs) on breast cancer (BC) progression and survival. We performed multiple analyses on data from a previously conducted GWAS for the influence of individual SNPs, runs of homozygosity (ROHs) and inbreeding on BC survival. (I.) The association of individual SNPs indicated no differences in the proportions of homozygous individuals among short-time survivors (STSs) and long-time survivors (LTSs). (II.) The analysis revealed differences among the populations for the number of ROHs per person and the total and average length of ROHs per person and among LTSs and STSs for the number of ROHs per person. (III.) Common ROHs at particular genomic positions were nominally more frequent among LTSs than in STSs. Common ROHs showed significant evidence for natural selection (iHS, Tajima's D, Fay-Wu's H). Most regions could be linked to genes related to BC progression or treatment. (IV.) Results were supported by a higher level of inbreeding among LTSs. Our results showed that an increased level of homozygosity may result in a preference of individuals during BC treatment. Although common ROHs were short, variants within ROHs might favor survival of BC and may function in a recessive manner.

Polymorphism at 19q13.41 Predicts Breast Cancer Survival Specifically after Endocrine Therapy

PURPOSE

Although most patients with estrogen receptor (ER)-positive breast cancer benefit from endocrine therapies, a significant proportion do not. Our aim was to identify inherited genetic variations that might predict survival among patients receiving adjuvant endocrine therapies.

EXPERIMENTAL DESIGN

We performed a meta-analysis of two genome-wide studies; Helsinki Breast Cancer Study, 805 patients, with 240 receiving endocrine therapy and Prospective study of Outcomes in Sporadic versus Hereditary breast cancer, 536 patients, with 155 endocrine therapy patients, evaluating 486,478 single-nucleotide polymorphisms (SNP). The top four associations from the endocrine treatment subgroup were further investigated in two independent datasets totaling 5,011 patients, with 3,485 receiving endocrine therapy.

RESULTS

A meta-analysis identified a common SNP rs8113308, mapped to 19q13.41, associating with reduced survival among endocrine-treated patients [hazard ratio (HR), 1.69; 95% confidence interval (CI), 1.37-2.07; P = 6.34 × 10(-7)] and improved survival among ER-negative patients, with a similar trend in ER-positive cases not receiving endocrine therapy. In a multivariate analysis adjusted for conventional prognostic factors, we found a significant interaction between the rs8113308 and endocrine treatment, indicating a predictive, treatment-specific effect of the SNP rs8113308 on breast cancer survival, with the per-allele HR for interaction 2.16 (95% CI, 1.30-3.60; Pinteraction = 0.003) and HR = 7.77 (95% CI, 0.93-64.71) for the homozygous genotype carriers. A biologic rationale is suggested by in silico functional analyses.

CONCLUSIONS

Our findings suggest carrying the rs8113308 rare allele may identify patients who will not benefit from adjuvant endocrine treatment.

Association of breast cancer risk loci with breast cancer survival

The survival of breast cancer patients is largely influenced by tumor characteristics, such as TNM stage, tumor grade and hormone receptor status. However, there is growing evidence that inherited genetic variation might affect the disease prognosis and response to treatment. Several lines of evidence suggest that alleles influencing breast cancer risk might also be associated with breast cancer survival. We examined the associations between 35 breast cancer susceptibility loci and the disease over-all survival (OS) in 10,255 breast cancer patients from the National Cancer Institute Breast and Prostate Cancer Cohort Consortium (BPC3) of which 1,379 died, including 754 of breast cancer. We also conducted a meta-analysis of almost 35,000 patients and 5,000 deaths, combining results from BPC3 and the Breast Cancer Association Consortium (BCAC) and performed in silico analyses of SNPs with significant associations. In BPC3, the C allele of LSP1-rs3817198 was significantly associated with improved OS (HRper-allele =0.70; 95% CI: 0.58-0.85; ptrend  = 2.84 × 10(-4) ; HRheterozygotes  = 0.71; 95% CI: 0.55-0.92; HRhomozygotes  = 0.48; 95% CI: 0.31-0.76; p2DF  = 1.45 × 10(-3) ). In silico, the C allele of LSP1-rs3817198 was predicted to increase expression of the tumor suppressor cyclin-dependent kinase inhibitor 1C (CDKN1C). In the meta-analysis, TNRC9-rs3803662 was significantly associated with increased death hazard (HRMETA =1.09; 95% CI: 1.04-1.15; ptrend  = 6.6 × 10(-4) ; HRheterozygotes  = 0.96 95% CI: 0.90-1.03; HRhomozygotes  = 1.21; 95% CI: 1.09-1.35; p2DF =1.25 × 10(-4) ). In conclusion, we show that there is little overlap between the breast cancer risk single nucleotide polymorphisms (SNPs) identified so far and the SNPs associated with breast cancer prognosis, with the possible exceptions of LSP1-rs3817198 and TNRC9-rs3803662.

Common germline polymorphisms associated with breast cancer-specific survival

INTRODUCTION

Previous studies have identified common germline variants nominally associated with breast cancer survival. These associations have not been widely replicated in further studies. The purpose of this study was to evaluate the association of previously reported SNPs with breast cancer-specific survival using data from a pooled analysis of eight breast cancer survival genome-wide association studies (GWAS) from the Breast Cancer Association Consortium.

METHODS

A literature review was conducted of all previously published associations between common germline variants and three survival outcomes: breast cancer-specific survival, overall survival and disease-free survival. All associations that reached the nominal significance level of P value <0.05 were included. Single nucleotide polymorphisms that had been previously reported as nominally associated with at least one survival outcome were evaluated in the pooled analysis of over 37,000 breast cancer cases for association with breast cancer-specific survival. Previous associations were evaluated using a one-sided test based on the reported direction of effect.

RESULTS

Fifty-six variants from 45 previous publications were evaluated in the meta-analysis. Fifty-four of these were evaluated in the full set of 37,954 breast cancer cases with 2,900 events and the two additional variants were evaluated in a reduced sample size of 30,000 samples in order to ensure independence from the previously published studies. Five variants reached nominal significance (P <0.05) in the pooled GWAS data compared to 2.8 expected under the null hypothesis. Seven additional variants were associated (P <0.05) with ER-positive disease.

CONCLUSIONS

Although no variants reached genome-wide significance (P <5 x 10(-8)), these results suggest that there is some evidence of association between candidate common germline variants and breast cancer prognosis. Larger studies from multinational collaborations are necessary to increase the power to detect associations, between common variants and prognosis, at more stringent significance levels.

Prediction of breast cancer survival using clinical and genetic markers by tumor subtypes

PURPOSE

To identify the genetic variants associated with breast cancer survival, a genome-wide association study (GWAS) was conducted of Korean breast cancer patients.

METHODS

From the Seoul Breast Cancer Study (SEBCS), 3,226 patients with breast cancer (1,732 in the discovery and 1,494 in the replication set) were included in a two-stage GWAS on disease-free survival (DFS) by tumor subtypes based on hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2). The associations of the re-classified combined prognostic markers through recursive partitioning analysis (RPA) of DFS for breast cancer were assessed with the Cox proportional hazard model. The prognostic predictive values of the clinical and genetic models were evaluated by Harrell's C.

RESULTS

In the two-stage GWAS stratified by tumor subtypes, rs166870 and rs10825036 were consistently associated with DFS in the HR+ HER2- and HR- HER2- breast cancer subtypes, respectively (Prs166870 = 2.88 × 10(-7) and Prs10825036 = 3.54 × 10(-7) in the combined set). When patients were classified by the RPA in each subtype, genetic factors contributed significantly to differentiating the high risk group associated with DFS inbreast cancer, specifically the HR+ HER2- (P discovery=1.18 × 10(-8) and P replication = 2.08 × 10(-5)) and HR- HRE2- subtypes (P discovery = 2.35 × 10(-4) and P replication = 2.60 × 10(-2)). The inclusion of the SNPs tended to improve the performance of the prognostic models consisting of age, TNM stage and tumor subtypes based on ER, PR, and HER2 status.

CONCLUSION

Combined prognostic markers that include clinical and genetic factors by tumor subtypes could improve the prediction of survival in breast cancer.

Biomarkers in Patients with Metastatic Breast Cancer and the PRAEGNANT Study Network

Progress has been made in the treatment of metastatic breast cancer in recent decades, but very few therapies use patient or tumor-specific characteristics to tailor individualized treatment. More than ten years after the publication of the reference human genome sequence, analysis methods have improved enormously, fostering the hope that biomarkers can be used to individualize therapies and offer precise treatment based on tumor and patient characteristics. Biomarkers at every level of the system (genetics, epigenetics, gene expression, micro-RNA, proteomics and others) can be used for this. This has led to changes in clinical study designs, with drug developments often only focusing on small or very small subgroups of patients and tumors. The screening and registration of patients and their molecular tumor data has therefore become very important for the successful completion of clinical studies. This new form of medicine presents particular challenges for patients and physicians. Even in this new age of genome-wide analysis, the focus should still be on the patients' quality of life. This review summarizes recent developments and describes how the PRAEGNANT study network manages the aforementioned medical challenges and changes to create a professional infrastructure for patients and physicians.

Breast Cancer Update 2014 - Focus on the Patient and the Tumour

The therapy for patients with breast cancer has developed markedly in the past ten years. Our understanding of the molecular biology of tumours and the characteristics of the patients has shaped the recent advances. In this review we present the latest knowledge about the therapy for breast cancer. There are new tests and options not only in the field of anti-HER2 therapy but also in the management of triple negative and hormone receptor-positive patients. Comprehension of prognosis and therapeutic response to chemotherapies is little by little helping to define patient groups who will not respond to chemotherapy or who do not need treatment because their prognosis is extremely good. In the field of anti-HER2 therapy, work is continuing on the development of drugs suitable for and able to overcome trastuzumab resistance. For hormone receptor-positive cancers, we now have a better understanding of which therapy groups will benefit from which anti-endocrine drugs, and which will be able to overcome a possible resistance (treatment of the PI3K pathways, inhibition of the cell cycle). Molecular tests are still being evaluated with regard to the clinical situations in which they may have the greatest relevance for therapeutic decision-making; however, evidence is also increasing as to the fields in which good predictions for the prognosis can be obtained. On the whole, more work is needed to promote our understanding of the new developments in diagnostics and therapy and to involve both physicians and patients equally in the procedures.

A genome wide meta-analysis study for identification of common variation associated with breast cancer prognosis

OBJECTIVE

Genome wide association studies (GWAs) of breast cancer mortality have identified few potential associations. The concordance between these studies is unclear. In this study, we used a meta-analysis of two prognostic GWAs and a replication cohort to identify the strongest associations and to evaluate the loci suggested in previous studies. We attempt to identify those SNPs which could impact overall survival irrespective of the age of onset.

METHODS

To facilitate the meta-analysis and to refine the association signals, SNPs were imputed using data from the 1000 genomes project. Cox-proportional hazard models were used to estimate hazard ratios (HR) in 536 patients from the POSH cohort (Prospective study of Outcomes in Sporadic versus Hereditary breast cancer) and 805 patients from the HEBCS cohort (Helsinki Breast Cancer Study). These hazard ratios were combined using a Mantel-Haenszel fixed effects meta-analysis and a p-value threshold of 5×10(-8) was used to determine significance. Replication was performed in 1523 additional patients from the POSH study.

RESULTS

Although no SNPs achieved genome wide significance, three SNPs have significant association in the replication cohort and combined p-values less than 5.6×10(-6). These SNPs are; rs421379 which is 556 kb upstream of ARRDC3 (HR = 1.49, 95% confidence interval (CI) = 1.27-1.75, P = 1.1×10(-6)), rs12358475 which is between ECHDC3 and PROSER2 (HR = 0.75, CI = 0.67-0.85, P = 1.8×10(-6)), and rs1728400 which is between LINC00917 and FOXF1.

CONCLUSIONS

In a genome wide meta-analysis of two independent cohorts from UK and Finland, we identified potential associations at three distinct loci. Phenotypic heterogeneity and relatively small sample sizes may explain the lack of genome wide significant findings. However, the replication at three SNPs in the validation cohort shows promise for future studies in larger cohorts. We did not find strong evidence for concordance between the few associations highlighted by previous GWAs of breast cancer survival and this study.

Germline DNA variations in breast cancer predisposition and prognosis: a systematic review of the literature

Breast cancer is the most common cancer and the second leading cause of death in women worldwide. The disease is caused by a combination of genetic, environmental, lifestyle, and reproductive risk factors. Linkage and family-based studies have identified many pathological germline mutations, which account for around 20% of the genetic risk of familial breast cancer. In recent years, single nucleotide polymorphism-based genetic association studies, especially genome-wide association studies (GWASs), have been very successful in uncovering low-penetrance common variants associated with breast cancer risk. These common variants alone may explain up to an additional 30% of the familial risk of breast cancer. With the advent of available genetic resources and growing collaborations among researchers across the globe, the much needed large sample size to capture variants with small effect sizes and low population frequencies is being addressed, and hence many more common variants are expected to be discovered in the coming days. Here, major GWASs conducted for breast cancer predisposition and prognosis until 2013 are summarized. Few studies investigating other forms of genetic variations contributing to breast cancer predisposition and disease outcomes are also discussed. Finally, the potential utility of the GWAS-identified variants in disease risk models and some future perspectives are presented.

Systematic pathway enrichment analysis of a genome-wide association study on breast cancer survival reveals an influence of genes involved in cell adhesion and calcium signaling on the patients' clinical outcome

Genome-wide association studies (GWASs) may help to understand the effects of genetic polymorphisms on breast cancer (BC) progression and survival. However, they give only a focused view, which cannot capture the tremendous complexity of this disease. Therefore, we investigated data from a previously conducted GWAS on BC survival for enriched pathways by different enrichment analysis tools using the two main annotation databases Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The goal was to identify the functional categories (GO terms and KEGG pathways) that are consistently overrepresented in a statistically significant way in the list of genes generated from the single nucleotide polymorphism (SNP) data. The SNPs with allelic p-value cut-offs 0.005 and 0.01 were annotated to the genes by excluding or including a 20 kb up-and down-stream sequence of the genes and analyzed by six different tools. We identified eleven consistently enriched categories, the most significant ones relating to cell adhesion and calcium ion binding. Moreover, we investigated the similarity between our GWAS and the enrichment analyses of twelve published gene expression signatures for breast cancer prognosis. Five of them were commonly used and commercially available, five were based on different aspects of metastasis formation and two were developed from meta-analyses of published prognostic signatures. This comparison revealed similarities between our GWAS data and the general and the specific brain metastasis gene signatures as well as the Oncotype DX signature. As metastasis formation is a strong indicator of a patient’s prognosis, this result reflects the survival aspect of the conducted GWAS and supports cell adhesion and calcium signaling as important pathways in cancer progression.

HER2 and TOP2A amplification in a hospital-based cohort of breast cancer patients: associations with patient and tumor characteristics

Gene amplification is an important factor for altered gene expression in breast cancers. TOP2A-amplification often occurs concomitantly with HER2 amplification, and it has been suggested to be predictive for the response to anthracycline chemotherapy. This study assessed the correlation between HER2 status and TOP2A co-amplification, the possible association of TOP2A single-nucleotide polymorphisms with the frequency of this co-amplification as well as confirmation of association with outcome. HER2 and TOP2A amplification were analyzed in a tissue microarray from a clinical cohort study. Additionally, a common genetic variant (rs13695) in the TOP2A gene was genotyped in germline DNA. HER2 gene amplification was compared with HER2-IHC findings assessed during clinical routine work, and the association between all the biomarkers analyzed and the clinical outcome was determined. As an exploratory aim, rs13695 genotypes were compared with TOP2A amplification status. HER2 amplification was seen in 101 of 628 (16.1 %) and TOP2A amplification in 32 (5.1 %) cancers. No TOP2A amplification occurred without HER2 co-amplification. HER2 amplification was found in 8, 13.6, and 55.1 % of patients with HER2-IHC 0/1+, 2+, and 3+ tumors, respectively. HER2-IHC was not associated with an effect on the prognosis, but HER2-FISH was. There was an association between the rs13695 genotype and TOP2A amplification status (P = 0.03). Although there was a significant correlation between HER2 status determined by IHC and HER2 by FISH, only HER2 gene amplification status by FISH was correlated with outcome indicating greater utility for FISH in routine clinical settings.

Genome-wide association study of survival in patients with pancreatic adenocarcinoma

BACKGROUND AND OBJECTIVE

Survival of patients with pancreatic adenocarcinoma is limited and few prognostic factors are known. We conducted a two-stage genome-wide association study (GWAS) to identify germline variants associated with survival in patients with pancreatic adenocarcinoma.

METHODS

We analysed overall survival in relation to single nucleotide polymorphisms (SNPs) among 1005 patients from two large GWAS datasets, PanScan I and ChinaPC. Cox proportional hazards regression was used in an additive genetic model with adjustment for age, sex, clinical stage and the top four principal components of population stratification. The first stage included 642 cases of European ancestry (PanScan), from which the top SNPs (p≤10(-5)) were advanced to a joint analysis with 363 additional patients from China (ChinaPC).

RESULTS

In the first stage of cases of European descent, the top-ranked loci were at chromosomes 11p15.4, 18p11.21 and 1p36.13, tagged by rs12362504 (p=1.63×10(-7)), rs981621 (p=1.65×10(-7)) and rs16861827 (p=3.75×10(-7)), respectively. 131 SNPs with p≤10(-5) were advanced to a joint analysis with cases from the ChinaPC study. In the joint analysis, the top-ranked SNP was rs10500715 (minor allele frequency, 0.37; p=1.72×10(-7)) on chromosome 11p15.4, which is intronic to the SET binding factor 2 (SBF2) gene. The HR (95% CI) for death was 0.74 (0.66 to 0.84) in PanScan I, 0.79 (0.65 to 0.97) in ChinaPC and 0.76 (0.68 to 0.84) in the joint analysis.

CONCLUSIONS

Germline genetic variation in the SBF2 locus was associated with overall survival in patients with pancreatic adenocarcinoma of European and Asian ancestry. This association should be investigated in additional large patient cohorts.

Breast Cancer Risk - From Genetics to Molecular Understanding of Pathogenesis

Several advancements over the last decade have triggered the developments in the field of breast cancer risk research. One of them is the availability of the human genome sequence along with cheap genotyping possibilities. Another is the globalization of research, which has led to the growth of research collaboration into large international consortia that facilitate the pooling of clinical and genotype data of hundreds of thousands of patients and healthy control individuals. This review concerns with the recent developments in breast cancer risk research and focuses on the discovery of new genetic breast cancer risk factors and their meaning in the context of established non-genetic risk factors. Finally the clinical application is highly dependent on the accuracy of breast cancer risk prediction models, not only for all breast cancer patients, but also for molecular subtypes, preferably for those which are associated with an unfavorable prognosis. Recently risk prediction incorporates all possible risk factors, which include epidemiological risk factors, mammographic density and genetic risk factors.

Genetic ancestry and risk of mortality among U.S. Latinas with breast cancer

Multiple studies have reported that Latina women in the United States are diagnosed with breast cancer at more advanced stages and have poorer survival than non-Latina White women. However, Latinas are a heterogeneous group with individuals having different proportions of European, Indigenous American, and African genetic ancestry. In this study, we evaluated the association between genetic ancestry and survival after breast cancer diagnosis among 899 Latina women from the San Francisco Bay area. Genetic ancestry was estimated from single-nucleotide polymorphisms from an Affymetrix 6.0 array and we used Cox proportional hazards models to evaluate the association between genetic ancestry and breast cancer-specific mortality (tests were two-sided). Women were followed for an average of 9 years during which 75 died from breast cancer. Our results showed that Individuals with higher Indigenous American ancestry had increased risk of breast cancer-specific mortality [HR: 1.57 per 25% increase in Indigenous American ancestry; 95% confidence interval (CI): 1.08-2.29]. Adjustment for demographic factors, tumor characteristics, and some treatment information did not explain the observed association (HR: 1.75; 95%CI, 1.12-2.74). In an analysis in which ancestry was dichotomized, the hazard of mortality showed a two-fold increase when comparing women with less than 50% Indigenous American ancestry to women with 50% or more [HR, 1.89, 95%CI, 1.10-3.24]. This was also reflected by Kaplan-Meier survival estimates (P for log-rank test of 0.003). Overall, results suggest that genetic factors and/or unmeasured differences in treatment or access to care should be further explored to understand and reduce ethnic disparities in breast cancer outcomes.

The seed and soil hypothesis revisited: current state of knowledge of inherited genes on prognosis in breast cancer

The crucial event in the course of malignancies such as breast cancer is its metastatic spread from the primary tumor of origin to distant organs. The natural history of a tumor is determined by the expression of its genes, and in this sense, knowledge has advanced dramatically in recent decades. However, much less is known about the role that the patient plays in the behavior of a tumor. In this article, we review the evidence regarding the genetic background of the host in metastatic tumor dissemination, providing information from epidemiological studies as well as from animal models and human studies. Undoubtedly, the elucidation of possible interpersonal variability in susceptibility to developing metastases would significantly contribute to improve management of cancer patients.

Genome-wide association study identifies common variants in SLC39A6 associated with length of survival in esophageal squamous-cell carcinoma

We conducted a genome-wide scan of SNPs to identify variants associated with length of survival in 1,331 individuals with esophageal squamous-cell carcinoma (ESCC), with associations validated in 2 independent sets including 1,962 individuals with this cancer. We identified rs1050631 in SLC39A6 as associated with the survival times of affected individuals, with the hazard ratio for death from ESCC in the combined sample being 1.30 (95% confidence interval (CI) = 1.19-1.43; P = 3.77 × 10(-8)). rs7242481, located in the 5' UTR of SLC39A6, disturbs a transcriptional repressor binding site and results in upregulation of SLC39A6 expression. Immunohistochemical staining of ESCC tissues showed that higher expression of SLC39A6 protein was correlated with shorter length of survival in individuals with advanced ESCC (P = 0.013). Knockdown of SLC39A6 expression suppressed proliferation and invasion in ESCC cells. These results suggest that SLC39A6 has an important role in the prognosis of ESCC and may be a potential therapeutic target.

Multiple independent variants at the TERT locus are associated with telomere length and risks of breast and ovarian cancer

TERT-locus SNPs and leukocyte telomere measures are reportedly associated with risks of multiple cancers. Using the Illumina custom genotyping array iCOGs, we analyzed ∼480 SNPs at the TERT locus in breast (n = 103,991), ovarian (n = 39,774) and BRCA1 mutation carrier (n = 11,705) cancer cases and controls. Leukocyte telomere measurements were also available for 53,724 participants. Most associations cluster into three independent peaks. The minor allele at the peak 1 SNP rs2736108 associates with longer telomeres (P = 5.8 × 10(-7)), lower risks for estrogen receptor (ER)-negative (P = 1.0 × 10(-8)) and BRCA1 mutation carrier (P = 1.1 × 10(-5)) breast cancers and altered promoter assay signal. The minor allele at the peak 2 SNP rs7705526 associates with longer telomeres (P = 2.3 × 10(-14)), higher risk of low-malignant-potential ovarian cancer (P = 1.3 × 10(-15)) and greater promoter activity. The minor alleles at the peak 3 SNPs rs10069690 and rs2242652 increase ER-negative (P = 1.2 × 10(-12)) and BRCA1 mutation carrier (P = 1.6 × 10(-14)) breast and invasive ovarian (P = 1.3 × 10(-11)) cancer risks but not via altered telomere length. The cancer risk alleles of rs2242652 and rs10069690, respectively, increase silencing and generate a truncated TERT splice variant.

Prognosis Research Strategy (PROGRESS) 2: prognostic factor research

Prognostic factor research aims to identify factors associated with subsequent clinical outcome in people with a particular disease or health condition. In this article, the second in the PROGRESS series, the authors discuss the role of prognostic factors in current clinical practice, randomised trials, and developing new interventions, and explain why and how prognostic factor research should be improved.

Percent Mammographic Density and Dense Area as Risk Factors for Breast Cancer

Purpose: Mammographic characteristics are known to be correlated to breast cancer risk. Percent mammographic density (PMD), as assessed by computer-assisted methods, is an established risk factor for breast cancer. Along with this assessment the absolute dense area (DA) of the breast is reported as well. Aim of this study was to assess the predictive value of DA concerning breast cancer risk in addition to other risk factors and in addition to PMD. Methods: We conducted a case control study with hospital-based patients with a diagnosis of invasive breast cancer and healthy women as controls. A total of 561 patients and 376 controls with available mammographic density were included into this study. We describe the differences concerning the common risk factors BMI, parital status, use of hormone replacement therapy (HRT) and menopause between cases and controls and estimate the odds ratios for PMD and DA, adjusted for the mentioned risk factors. Furthermore we compare the prediction models with each other to find out whether the addition of DA improves the model. Results: Mammographic density and DA were highly correlated with each other. Both variables were as well correlated to the commonly known risk factors with an expected direction and strength, however PMD (ρ = -0.56) was stronger correlated to BMI than DA (ρ = -0.11). The group of women within the highest quartil of PMD had an OR of 2.12 (95 % CI: 1.25-3.62). This could not be seen for the fourth quartile concerning DA. However the assessment of breast cancer risk could be improved by including DA in a prediction model in addition to common risk factors and PMD. Conclusions: The inclusion of the parameter DA into a prediction model for breast cancer in addition to established risk factors and PMD could improve the breast cancer risk assessment. As DA is measured together with PMD in the process of computer-assisted assessment of PMD it might be considered to include it as one additional breast cancer risk factor that is obtained from breast imaging.

Common genetic polymorphisms of microRNA biogenesis pathway genes and breast cancer survival

Background

Although the role of microRNA’s (miRNA’s) biogenesis pathway genes in cancer development and progression has been well established, the association between genetic variants of this pathway genes and breast cancer survival is still unknown.

Methods

We used genotype data available from a previously conducted case–control study to investigate association between common genetic variations in miRNA biogenesis pathway genes and breast cancer survival. We investigated the possible associations between 41 germ-line single-nucleotide polymorphisms (SNPs) and both disease free survival (DFS) and overall survival (OS) among 488 breast cancer patients. During the median follow-up of 6.24 years, 90 cases developed disease progression and 48 cases died.

Results

Seven SNPs were significantly associated with breast cancer survival. Two SNPs in AGO2 (rs11786030 and rs2292779) and DICER1 rs1057035 were associated with both DFS and OS. Two SNPs in HIWI (rs4759659 and rs11060845) and DGCR8 rs9606250 were associated with DFS, while DROSHA rs874332 and GEMIN4 rs4968104 were associated with only OS. The most significant association was observed in variant allele of AGO2 rs11786030 with 2.62-fold increased risk of disease progression (95% confidence interval (CI), 1.41-4.88) and in minor allele homozygote of AGO2 rs2292779 with 2.94-fold increased risk of death (95% CI, 1.52-5.69). We also found cumulative effects of SNPs on DFS and OS. Compared to the subjects carrying 0 to 2 high-risk genotypes, those carrying 3 or 4–6 high-risk genotypes had an increased risk of disease progression with a hazard ratio of 2.16 (95% CI, 1.18- 3.93) and 4.47 (95% CI, 2.45- 8.14), respectively (P for trend, 6.11E-07).

Conclusions

Our results suggest that genetic variants in miRNA biogenesis pathway genes may be associated with breast cancer survival. Further studies in larger sample size and functional characterizations are warranted to validate these results.

The role of genetic breast cancer susceptibility variants as prognostic factors

Recent genome-wide association studies identified 11 single nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk. We investigated these and 62 other SNPs for their prognostic relevance. Confirmed BC risk SNPs rs17468277 (CASP8), rs1982073 (TGFB1), rs2981582 (FGFR2), rs13281615 (8q24), rs3817198 (LSP1), rs889312 (MAP3K1), rs3803662 (TOX3), rs13387042 (2q35), rs4973768 (SLC4A7), rs6504950 (COX11) and rs10941679 (5p12) were genotyped for 25 853 BC patients with the available follow-up; 62 other SNPs, which have been suggested as BC risk SNPs by a GWAS or as candidate SNPs from individual studies, were genotyped for replication purposes in subsets of these patients. Cox proportional hazard models were used to test the association of these SNPs with overall survival (OS) and BC-specific survival (BCS). For the confirmed loci, we performed an accessory analysis of publicly available gene expression data and the prognosis in a different patient group. One of the 11 SNPs, rs3803662 (TOX3) and none of the 62 candidate/GWAS SNPs were associated with OS and/or BCS at P<0.01. The genotypic-specific survival for rs3803662 suggested a recessive mode of action [hazard ratio (HR) of rare homozygous carriers=1.21; 95% CI: 1.09-1.35, P=0.0002 and HR=1.29; 95% CI: 1.12-1.47, P=0.0003 for OS and BCS, respectively]. This association was seen similarly in all analyzed tumor subgroups defined by nodal status, tumor size, grade and estrogen receptor. Breast tumor expression of these genes was not associated with prognosis. With the exception of rs3803662 (TOX3), there was no evidence that any of the SNPs associated with BC susceptibility were associated with the BC survival. Survival may be influenced by a distinct set of germline variants from those influencing susceptibility.

Novel genetic markers of breast cancer survival identified by a genome-wide association study

Only two genome-wide association studies (GWAS) have been conducted to date to identify potential markers for total mortality after diagnosis of breast cancer. Here, we report the identification of two single-nucleotide polymorphisms (SNP) associated with total mortality from a two-stage GWAS conducted among 6,110 Shanghai-resident Chinese women with tumor-node-metastasis (TNM) stage I to IV breast cancer. The discovery stage included 1,950 patients and evaluated 613,031 common SNPs. The top 49 associations were evaluated in an independent replication stage of 4,160 Shanghai patients with breast cancer. A consistent and highly significant association with total mortality was documented for SNPs rs3784099 and rs9934948. SNP rs3784099, located in the RAD51L1 gene, was associated with total morality in both the discovery stage (P = 1.44 × 10(-8)) and replication stage (P = 0.06; P-combined = 1.17 × 10(-7)). Adjusted HRs for total mortality were 1.41 [95% confidence interval (CI), 1.18-1.68] for the AG genotype and 2.64 (95% CI, 1.74-4.03) for the AA genotype, when compared with the GG genotype. The variant C allele of rs9934948, located on chromosome 16, was associated with a similarly elevated risk of total mortality (P-combined = 5.75 × 10(-6)). We also observed this association among 1,145 patients with breast cancer of European ancestry from the Nurses' Health Study (NHS; P = 0.006); the association was highly significant in a combined analysis of NHS and Chinese data (P = 1.39 × 10(-7)). Similar associations were observed for these two SNPs with breast cancer-specific mortality. This study provides strong evidence suggesting that the RAD51L1 gene and a chromosome 16 locus influence breast cancer prognosis.

Breast Cancer Risk - Genes, Environment and Clinics

The information available about breast cancer risk factors has increased dramatically during the last 10 years. In particular, studies of low-penetrance genes and mammographic density have improved our understanding of breast cancer risk. In addition, initial steps have been taken in investigating interactions between genes and environmental factors. This review concerns with actual data on this topic. Several genome-wide association studies (GWASs) with a case-control design, as well as large-scale validation studies, have identified and validated more than a dozen single nucleotide polymorphisms (SNPs) associated with breast cancer risk. They are located not only in or close to genes known to be involved in cancer pathogenesis, but also in genes not previously associated with breast cancer pathogenesis, or may even not be related to any genes. SNPs have also been identified that alter the lifetime risk in BRCA mutation carriers. With regard to nongenetic risk factors, studies of postmenopausal hormone replacement therapy (HRT) have revealed important information on how to weigh up the risks and benefits of HRT. Mammographic density (MD) has become an accepted and important breast cancer risk factor. Lifestyle and nutritional considerations have become an integral part of most studies of breast cancer risk, and some improvements have been made in this field as well. More than 10 years after the publication of the first breast cancer prevention studies with tamoxifen, other substances such as raloxifene and aromatase inhibitors have been investigated and have also been shown to have preventive potential. Finally, mammographic screening systems have been implemented in most Western countries during the last decade. These may be developed further by including more individualized methods of predicting the patient's breast cancer risk.

Capturing changes in gene expression dynamics by gene set differential coordination analysis

Analyzing gene expression data at the gene set level greatly improves feature extraction and data interpretation. Currently most efforts in gene set analysis are focused on differential expression analysis--finding gene sets whose genes show first-order relationship with the clinical outcome. However the regulation of the biological system is complex, and much of the change in gene expression dynamics do not manifest in the form of differential expression. At the gene set level, capturing the change in expression dynamics is difficult due to the complexity and heterogeneity of the gene sets. Here we report a systematic approach to detect gene sets that show differential coordination patterns with the rest of the transcriptome, as well as pairs of gene sets that are differentially coordinated with each other. We demonstrate that the method can identify biologically relevant gene sets, many of which do not show first-order relationship with the clinical outcome.

Familial mortality and familial incidence in cancer

PURPOSE

An overwhelming majority of data on familial risk in cancer is based on incident cancer, whereas familiality in cancer mortality is largely unknown. If fatal form of cancer was a highly familial subtype, then familial risk for mortality may exceed that of incidence, which would be particularly relevant for clinical decision making and counseling.

PATIENTS AND METHODS

The individuals in the nationwide Swedish Family-Cancer Database were classified according to family history of fatal and nonfatal cancer. Familial risks of incident and fatal concordant cancer were calculated for offspring based on their parental family history using a Cox model with hazard ratio (HR); offspring without family history were the reference.

RESULTS

Most HRs for offspring incident cancers were somewhat higher for fatal compared with nonfatal parental family history. For breast (HR, 1.87 fatal v 1.66 nonfatal; P < .001) and prostate (HR, 2.30 fatal v 1.84 nonfatal; P < .001) cancers, 51.0% of patients with familial breast cancer and 56.6% of patients with prostate cancer had fatal family history. HRs for death in offspring according to a fatal compared with nonfatal family history were significantly increased for colorectal (HR, 1.76 v 1.47, respectively; P = .02), breast (HR, 1.97 v 1.51, respectively; P = .002), and prostate (HR, 2.03 v 1.59, respectively; P = .002) cancers. TNM classification did not seem to differ between the family histories. We showed also that an overwhelming proportion of offspring were diagnosed after the parental death.

CONCLUSION

Familial breast, prostate, and colorectal cancers might have a yet unidentified genetic component associated with poorer survival. It may be useful to record survival data in family history records.

Evidence for SMAD3 as a modifier of breast cancer risk in BRCA2 mutation carriers

INTRODUCTION

Current attempts to identify genetic modifiers of BRCA1 and BRCA2 associated risk have focused on a candidate gene approach, based on knowledge of gene functions, or the development of large genome-wide association studies. In this study, we evaluated 24 SNPs tagged to 14 candidate genes derived through a novel approach that analysed gene expression differences to prioritise candidate modifier genes for association studies.

METHODS

We successfully genotyped 24 SNPs in a cohort of up to 4,724 BRCA1 and 2,693 BRCA2 female mutation carriers from 15 study groups and assessed whether these variants were associated with risk of breast cancer in BRCA1 and BRCA2 mutation carriers.

RESULTS

SNPs in five of the 14 candidate genes showed evidence of association with breast cancer risk for BRCA1 or BRCA2 carriers (P < 0.05). Notably, the minor alleles of two SNPs (rs7166081 and rs3825977) in high linkage disequilibrium (r² = 0.77), located at the SMAD3 locus (15q22), were each associated with increased breast cancer risk for BRCA2 mutation carriers (relative risk = 1.25, 95% confidence interval = 1.07 to 1.45, P(trend) = 0.004; and relative risk = 1.20, 95% confidence interval = 1.03 to 1.40, P(trend) = 0.018).

CONCLUSIONS

This study provides evidence that the SMAD3 gene, which encodes a key regulatory protein in the transforming growth factor beta signalling pathway and is known to interact directly with BRCA2, may contribute to increased risk of breast cancer in BRCA2 mutation carriers. This finding suggests that genes with expression associated with BRCA1 and BRCA2 mutation status are enriched for the presence of common genetic modifiers of breast cancer risk in these populations.

Germline genetic variation, cancer outcome, and pharmacogenetics

Studies of the role of germline or inherited genetic variation on cancer outcome can fall into three distinct categories. First, the impact of highly penetrant but lowly prevalent mutations of germline DNA on cancer prognosis has been studied extensively for BRCA1 and BRCA2 mutations as well as mutations related to hereditary nonpolyposis colorectal cancer syndrome. These mainly modest-sized analyses have produced conflicting results. Although some associations have been observed, they may not be independent of other known clinical or molecular prognostic factors. Second, the impact of germline polymorphisms on cancer prognosis is a burgeoning field of research. However, a deeper understanding of potentially confounding somatic changes and larger multi-institutional, multistage studies may be needed before consistent results are seen. Third, research examining the impact of germline genetic variation on differential treatment response or toxicity (pharmacogenetics) has produced some proof-of-principle results. Putative germline pharmacogenetic predictors of outcome include DPYD polymorphisms and fluorouracil toxicity, UGT1A1 variation and irinotecan toxicity, and CYP2D6 polymorphisms and tamoxifen efficacy, with emerging data on predictors of molecularly targeted or biologic drugs. Here we review data pertaining to these germline outcome and germline toxicity relationships.