Background

It is unclear whether estrogen receptor (ER)-status of first primary breast cancer is associated with risk of metachronous (non-simultaneous) contralateral breast cancer (CBC), and to what extent endocrine therapy affects this association.

Methods

We studied the effect of ER-status of the first cancer on the risk of CBC overall, and for different ER-subtypes of CBC, using a large, population-based cohort. The cohort consisted of all women diagnosed with breast cancer in the Stockholm region 1976–2005; 25715 patients, of whom 940 suffered CBC. The relative risk was analyzed mainly using standardized incidence ratios (SIR).

Results

Women with breast cancer had a doubled risk of CBC compared to the risk of breast cancer in the general female population (SIR: 2.22 [2.08–2.36]), for women with a previous ER-positive cancer: SIR = 2.30 (95% CI:2.11–2.50) and for women with a previous ER-negative cancer: SIR = 2.17 (95% CI:1.82–2.55). The relative risk of ER-positive and ER-negative CBC was very similar for women with ER-positive first cancer (SIR = 2.02 [95%CI: 1.80–2.27] and SIR = 1.89 [95%CI: 1.46–2.41] respectively) while for patients with ER-negative first cancer the relative risk was significantly different (SIR = 1.27 [95% CI:0.94–1.68] for ER-positive CBC and SIR = 4.96 [95%CI:3.67–6.56] for ER-negative CBC). Patients with ER-positive first cancer who received hormone therapy still had a significantly higher risk of CBC than the risk of breast cancer for the general female population (SIR = 1.74 [95% CI:1.47–2.03]).

Conclusion

The risk of CBC for a breast cancer patient is increased to about two-fold, compared to the risk of breast cancer in the general female population. This excess risk decreases, but does not disappear, with adjuvant endocrine therapy. Patients with ER-positive first cancers have an increased risk for CBC of both ER subtypes, while patients with ER-negative first cancer have a specifically increased risk of ER-negative CBC.

Background

Although screening for maternal red blood cell antibodies during pregnancy is a standard procedure, the prevalence and clinical consequences of non-anti-D immunization are poorly understood. The objective was to create a national database of maternal antibody screening results that can be linked with population health registers to create a research resource for investigating these issues.

Study Design and Methods

Each birth in the Swedish Medical Birth Register was uniquely identified and linked to the text stored in routine maternal antibody screening records in the time window from 9 months prior to 2 weeks after the delivery date. These text records were subjected to a computerized search for specific antibodies using regular expressions. To illustrate the research potential of the resulting database, selected antibody prevalence rates are presented as tables and figures, and the complete data (from more than 60 specific antibodies) presented as online moving graphical displays.

Results

More than one million (1,191,761) births with valid screening information from 1982–2002 constitute the study population. Computerized coverage of screening increased steadily over time and varied by region as electronic records were adopted. To ensure data quality, we restricted analysis to birth records in areas and years with a sustained coverage of at least 80%, representing 920,903 births from 572,626 mothers in 17 of the 24 counties in Sweden. During the study period, non-anti-D and anti-D antibodies occurred in 76.8/10,000 and 14.1/10,000 pregnancies respectively, with marked differences between specific antibodies over time.

Conclusion

This work demonstrates the feasibility of creating a nationally representative research database from the routine maternal antibody screening records from an extended calendar period. By linkage with population registers of maternal and child health, such data are a valuable resource for addressing important clinical questions, such as the etiological significance of non-anti-D antibodies.

Background

The link between squamous cell cervical carcinoma and HPV 16/18 is well-established but the magnitude of the risk association is uncertain and the importance of other high-risk HPV types unclear.

Methods

In two prospective nested case-control series among women participating in cytological screening in Sweden, we collected 2772 cervical smears from 515 women with cancer in situ (CIS), 315 with invasive squamous cell carcinoma (SCC), and individually matched controls. All smears were tested for HPV with PCR assays and the median follow-up until diagnosis was 5-7 years. Conditional logistic regression was used to estimate relative risks (RR) and 95% confidence intervals (CI).

Results

Presence of HPV16/18 in the first smear was associated with 8.5-fold (95% CI 5.3-13.7), and 18.6-fold (95% CI 9.0-38.9) increased risks of CIS and SCC, respectively, compared to women negative for HPV. Infection with other high-risk HPV types in the first smear was also associated with significantly increased risks for both CIS and SCC. Persistence of HPV16 infection conferred a RR of 18.5 (95% CI, 6.5-52.9) for CIS and 19.5 (95% CI 4.7-81.7) for SCC. The HPV16/18 attributable risk proportion was estimated to 30-50% of CIS, and 41-47% of SCC. Other high-risk HPV types also conferred significant proportions.

Conclusions

Our large population-based study provides quantification of risks for different HPV types and prospective evidence that non-16/18 high-risk HPV types increase the risk for future cervical cancer.

Impact

This study gives further insights into cervical cancer risk stratification with implications for HPV-based prevention strategies.

Introduction

To better understand trends in sexually transmitted infection (STI) prevention, specifically low prevalence of condom use with temporary partners, the aim of this study was to examine factors associated with condom use and perceptions of STI risk amongst individuals at risk, with the underlying assumption that STI risk perceptions and STI prevention behaviors are correlated.

Methods

A national population-based survey on human papillomavirus (HPV) and sexual habits of young adults aged 18–30 was conducted in Sweden in 2007, with 1712 men and 8855 women participating. Regression analyses stratified by gender were performed to measure condom use with temporary partners and STI risk perception.

Results

Men's condom use was not associated with STI risk perception while women's was. Awareness of and disease severity perceptions were not associated with either condom use or risk perception though education level correlated with condom use. Women's young age at sexual debut was associated with a higher risk of non-condom use later in life (OR 1.95 95% CI: 1.46–2.60). Women with immigrant mothers were less likely to report seldom/never use of condoms with temporary partners compared to women with Swedish-born mothers (OR 0.53 95% CI: 0.37–0.77). Correlates to STI risk perception differ substantially between sexes. Number of reported temporary partners was the only factor associated for both men and women with condom use and STI risk perception.

Conclusions

Public health interventions advocating condom use with new partners could consider employing tactics besides those which primarily aim to increase knowledge or self-perceived risk if they are to be more effective in STI reduction. Gender-specific prevention strategies could be effective considering the differences found in this study.

Polymorphisms associated with prostate cancer include those in three genes encoding major secretory products of the prostate: KLK2 (encoding kallikrein-related peptidase 2; hK2), KLK3 (encoding prostate-specific antigen; PSA), and MSMB (encoding beta-microseminoprotein). PSA and hK2, members of the kallikrein family, are elevated in serum of men with prostate cancer. In a comprehensive analysis which included sequencing of all coding, flanking, and 2kb of putative promoter regions of all 15 kallikrein (KLK) genes spanning ≈280 Kb on chromosome 19q, we identified novel SNPs and genotyped 104 SNPs in 1419 cancer cases and 736 controls in CAPS1, with independent replication in 1267 cases and 901 controls in CAPS2. This verified prior associations of SNPs in KLK2 and in MSMB (but not in KLK3) with prostate cancer. Twelve SNPs in KLK2 and KLK3 were associated with levels of PSA forms or hK2 in plasma of control subjects. Based on our comprehensive approach, this is likely to represent all common KLK variants associated with these phenotypes. A T allele at rs198977 in KLK2 associated with increased cancer risk and a striking decrease of hK2 levels in blood. We also found a strong interaction between rs198977 genotype and hK2 levels in blood in predicting cancer risk. Based on this strong association, we developed a model for predicting prostate cancer risk from standard biomarkers, rs198977 genotype, and rs198977 x hK2 interaction; this model had greater accuracy than did biomarkers alone (AUC 0.874 vs 0.866), providing proof in principle to clinical application for our findings.

Human papillomaviruses (HPV) are established as a major cause of cervical carcinoma. However, causality inference is dependent on prospective evidence showing that exposure predicts risk for future disease. Such evidence is available for squamous cell carcinoma, but not for cervical adenocarcinoma. We followed a population-based cohort of 994 120 women who participated in cytological screening in Sweden for a median of 6.7 years. Baseline smears from women who developed adenocarcinoma during follow-up (118 women with in situ disease and 164 with invasive disease) and their individually matched controls (1434 smears) were analyzed for HPV using PCR. Conditional logistic regression was used to estimate odds ratios (OR) of future adenocarcinoma with 95% confidence intervals (CI). Being positive for HPV 16 in the first cytologically normal smear was associated with increased risks for both future adenocarcinoma in situ (OR 11.0, 95 % CI 2.6–46.8) and invasive adenocarcinoma (OR 16.0, 95 % CI 3.8–66.7), compared to being negative for HPV 16. Similarly, an HPV 18 positive smear was associated with increased risks for adenocarcinoma in situ (OR 26.0, 95 % CI 3.5–192) and invasive adenocarcinoma (OR 28.0, 95 % CI 3.8–206), compared to an HPV 18 negative smear. Being positive for HPV 16/18 in two subsequent smears was associated with an infinite risk of both in situ and invasive adenocarcinoma. In conclusion, infections with HPV 16 and 18 are detectable up to at least 14 years before diagnosis of cervical adenocarcinoma. Our data provide prospective evidence that the association of HPV16/18 with cervical adenocarcinoma is strong and causal.

Background

Joint analysis of transcriptomic and proteomic data taken from the same samples has the potential to elucidate complex biological mechanisms. Most current methods that integrate these datasets allow for the computation of the correlation between a gene and protein but only after a one-to-one matching of genes and proteins is done. However, genes and proteins are connected via biological pathways and their relationship is not necessarily one-to-one. In this paper, we investigate the use of Correlated Factor Analysis (CFA) for modeling the correlation of genome-scale gene and protein data. Unlike existing approaches, CFA considers all possible gene-protein pairs and utilizes all gene and protein information in its modeling framework. The Generalized Singular Value Decomposition (gSVD) is another method which takes into account all available transcriptomic and proteomic data. Comparison is made between CFA and gSVD.

Results

Our simulation study indicates that the CFA estimates can consistently capture the dominant patterns of correlation between two sets of measurements; in contrast, the gSVD estimates cannot do that. Applied to real cancer data, the list of co-regulated genes and proteins identified by CFA has biologically meaningful interpretation, where both the gene and protein expressions are pointing to the same processes. Among the GO terms for which the genes and proteins are most correlated, we observed blood vessel morphogenesis and development.

Conclusion

We demonstrate that CFA is a useful tool for gene-protein data integration and modeling, where the main question is in finding which patterns of gene expression are most correlated with protein expression.

We present the rationale, the background and the structure for version 2.0 of the GENESTAT information portal (www.genestat.org) for statistical genetics. The fast methodological advances, coupled with a range of standalone software, makes it difficult for expert as well as non-expert users to orientate when designing and analysing their genetic studies. The ultimate ambition of GENESTAT is to guide on statistical methodology related to the broad spectrum of research in genetic epidemiology. GENESTAT 2.0 focuses on genetic association studies. Each entry provides a summary of a topic and gives links to key papers, websites and software. The flexibility of the internet is utilised for cross-referencing and for open editing. This paper gives an overview of GENESTAT and gives short introductions to the current main topics in GENESTAT, with additional entries on the website. Methods and software developers are invited to contribute to the portal, which is powered by a Wikipedia-type engine and allows easy additions and editing.

Background

While the use of complementary and alternative medicine (CAM) among cancer patients is common and widespread, levels of commitment to CAM vary. "Committed" CAM use is important to investigate, as it may be associated with elevated risks and benefits, and may affect use of biomedically-oriented health care (BHC). Multiple methodological approaches were used to explore and map patterns of CAM use among individuals postulated to be committed users, voluntarily reporting exceptional experiences associated with CAM use after cancer diagnosis.

Method

The verbatim transcripts of thirty-eight unstructured interviews were analyzed in two steps. First, manifest content analysis was used to elucidate and map participants' use of CAM, based on the National Center for Complementary Medicine (NCCAM)'s classification system. Second, patterns of CAM use were explored statistically using principal component analysis.

Findings

The 38 participants reported using a total of 274 specific CAM (median = 4) consisting of 148 different therapeutic modalities. Most reported therapies could be categorized using the NCCAM taxonomy (n = 224). However, a significant number of CAM therapies were not consistent with this categorization (n = 50); consequently, we introduced two additional categories: Spiritual/health literature and Treatment centers. The two factors explaining the largest proportion of variation in CAM usage patterns were a) number of CAM modalities used and b) a category preference for Energy therapies over the categories Alternative Medical Systems and Treatment centers or vice versa.

Discussion

We found considerable heterogeneity in patterns of CAM use. By analyzing users' own descriptions of CAM in relation to the most commonly used predefined professional taxonomy, this study highlights discrepancies between user and professional conceptualizations of CAM not previously addressed. Beyond variations in users' reports of CAM, our findings indicate some patterns in CAM usage related to number of therapies used and preference for different CAM categories.

Many recent microarrays hold an enormous number of probe sets, thus raising many practical and theoretical problems in controlling the false discovery rate (FDR). Biologically, it is likely that most probe sets are associated with un-expressed genes, so the measured values are simply noise due to non-specific binding; also many probe sets are associated with non-differentially-expressed (non-DE) genes. In an analysis to find DE genes, these probe sets contribute to the false discoveries, so it is desirable to filter out these probe sets prior to analysis. In the methodology proposed here, we first fit a robust linear model for probe-level Affymetrix data that accounts for probe and array effects. We then develop a novel procedure called FLUSH (Filtering Likely Uninformative Sets of Hybridizations), which excludes probe sets that have statistically small array-effects or large residual variance. This filtering procedure was evaluated on a publicly available data set from a controlled spiked-in experiment, as well as on a real experimental data set of a mouse model for retinal degeneration. In both cases, FLUSH filtering improves the sensitivity in the detection of DE genes compared to analyses using unfiltered, presence-filtered, intensity-filtered and variance-filtered data. A freely-available package called FLUSH implements the procedures and graphical displays described in the article.

Background

Many procedures for finding differentially expressed genes in microarray data are based on classical or modified t-statistics. Due to multiple testing considerations, the false discovery rate (FDR) is the key tool for assessing the significance of these test statistics. Two recent papers have generalized two aspects: Storey et al. (2005) have introduced a likelihood ratio test statistic for two-sample situations that has desirable theoretical properties (optimal discovery procedure, ODP), but uses standard FDR assessment; Ploner et al. (2006) have introduced a multivariate local FDR that allows incorporation of standard error information, but uses the standard t-statistic (fdr2d). The relationship and relative performance of these methods in two-sample comparisons is currently unknown.

Methods

Using simulated and real datasets, we compare the ODP and fdr2d procedures. We also introduce a new procedure called S2d that combines the ODP test statistic with the extended FDR assessment of fdr2d.

Results

For both simulated and real datasets, fdr2d performs better than ODP. As expected, both methods perform better than a standard t-statistic with standard local FDR. The new procedure S2d performs as well as fdr2d on simulated data, but performs better on the real data sets.

Conclusion

The ODP can be improved by including the standard error information as in fdr2d. This means that the optimality enjoyed in theory by ODP does not hold for the estimated version that has to be used in practice. The new procedure S2d has a slight advantage over fdr2d, which has to be balanced against a significantly higher computational effort and a less intuititive test statistic.

Background

Molecular markers and the rich biological information they contain have great potential for cancer diagnosis, prognostication and therapy prediction. So far, however, they have not superseded routine histopathology and staging criteria, partly because the few studies performed on molecular subtyping have had little validation and limited clinical characterization.

Methods

We obtained gene expression and clinical data for 412 breast cancers obtained from population-based cohorts of patients from Stockholm and Uppsala, Sweden. Using the intrinsic set of approximately 500 genes derived in the Norway/Stanford breast cancer data, we validated the existence of five molecular subtypes – basal-like, ERBB2, luminal A/B and normal-like – and characterized these subtypes extensively with the use of conventional clinical variables.

Results

We found an overall 77.5% concordance between the centroid prediction of the Swedish cohort by using the Norway/Stanford signature and the k-means clustering performed internally within the Swedish cohort. The highest rate of discordant assignments occurred between the luminal A and luminal B subtypes and between the luminal B and ERBB2 subtypes. The subtypes varied significantly in terms of grade (p < 0.001), p53 mutation (p < 0.001) and genomic instability (p = 0.01), but surprisingly there was little difference in lymph-node metastasis (p = 0.31). Furthermore, current users of hormone-replacement therapy were strikingly over-represented in the normal-like subgroup (p < 0.001). Separate analyses of the patients who received endocrine therapy and those who did not receive any adjuvant therapy supported the previous hypothesis that the basal-like subtype responded to adjuvant treatment, whereas the ERBB2 and luminal B subtypes were poor responders.

Conclusion

We found that the intrinsic molecular subtypes of breast cancer are broadly present in a diverse collection of patients from a population-based cohort in Sweden. The intrinsic gene set, originally selected to reveal stable tumor characteristics, was shown to have a strong correlation with progression-related properties such as grade, p53 mutation and genomic instability.

Background

Postmenopausal hormone-replacement therapy (HRT) increases breast-cancer risk. The influence of HRT on the biology of the primary tumor, however, is not well understood.

Methods

We obtained breast-cancer gene expression profiles using Affymetrix human genome U133A arrays. We examined the relationship between HRT-regulated gene profiles, tumor characteristics, and recurrence-free survival in 72 postmenopausal women.

Results

HRT use in patients with estrogen receptor (ER) protein positive tumors (n = 72) was associated with an altered regulation of 276 genes. Expression profiles based on these genes clustered ER-positive tumors into two molecular subclasses, one of which was associated with HRT use and had significantly better recurrence free survival despite lower ER levels. A comparison with external data suggested that gene regulation in tumors associated with HRT was negatively correlated with gene regulation induced by short-term estrogen exposure, but positively correlated with the effect of tamoxifen.

Conclusion

Our findings suggest that post-menopausal HRT use is associated with a distinct gene expression profile related to better recurrence-free survival and lower ER protein levels. Tentatively, HRT-associated gene expression in tumors resembles the effect of tamoxifen exposure on MCF-7 cells.

Introduction

Adjuvant breast cancer therapy significantly improves survival, but overtreatment and undertreatment are major problems. Breast cancer expression profiling has so far mainly been used to identify women with a poor prognosis as candidates for adjuvant therapy but without demonstrated value for therapy prediction.

Methods

We obtained the gene expression profiles of 159 population-derived breast cancer patients, and used hierarchical clustering to identify the signature associated with prognosis and impact of adjuvant therapies, defined as distant metastasis or death within 5 years. Independent datasets of 76 treated population-derived Swedish patients, 135 untreated population-derived Swedish patients and 78 Dutch patients were used for validation. The inclusion and exclusion criteria for the studies of population-derived Swedish patients were defined.

Results

Among the 159 patients, a subset of 64 genes was found to give an optimal separation of patients with good and poor outcomes. Hierarchical clustering revealed three subgroups: patients who did well with therapy, patients who did well without therapy, and patients that failed to benefit from given therapy. The expression profile gave significantly better prognostication (odds ratio, 4.19; P = 0.007) (breast cancer end-points odds ratio, 10.64) compared with the Elston–Ellis histological grading (odds ratio of grade 2 vs 1 and grade 3 vs 1, 2.81 and 3.32 respectively; P = 0.24 and 0.16), tumor stage (odds ratio of stage 2 vs 1 and stage 3 vs 1, 1.11 and 1.28; P = 0.83 and 0.68) and age (odds ratio, 0.11; P = 0.55). The risk groups were consistent and validated in the independent Swedish and Dutch data sets used with 211 and 78 patients, respectively.

Conclusion

We have identified discriminatory gene expression signatures working both on untreated and systematically treated primary breast cancer patients with the potential to spare them from adjuvant therapy.

Background

There are currently a number of competing techniques for low-level processing of oligonucleotide array data. The choice of technique has a profound effect on subsequent statistical analyses, but there is no method to assess whether a particular technique is appropriate for a specific data set, without reference to external data.

Results

We analyzed coregulation between genes in order to detect insufficient normalization between arrays, where coregulation is measured in terms of statistical correlation. In a large collection of genes, a random pair of genes should have on average zero correlation, hence allowing a correlation test. For all data sets that we evaluated, and the three most commonly used low-level processing procedures including MAS5, RMA and MBEI, the housekeeping-gene normalization failed the test. For a real clinical data set, RMA and MBEI showed significant correlation for absent genes. We also found that a second round of normalization on the probe set level improved normalization significantly throughout.

Conclusion

Previous evaluation of low-level processing in the literature has been limited to artificial spike-in and mixture data sets. In the absence of a known gold-standard, the correlation criterion allows us to assess the appropriateness of low-level processing of a specific data set and the success of normalization for subsets of genes.