Collateral damage: the impact on outcomes from cancer surgery of the COVID-19 pandemic.

BACKGROUND: Cancer diagnostics and surgery have been disrupted by the response of health care services to the coronavirus disease 2019 (COVID-19) pandemic. Progression of cancers during delay will impact on patients' long-term survival. PATIENTS AND METHODS: We generated per-day hazard ratios of cancer progression from observational studies and applied these to age-specific, stage-specific cancer survival for England 2013-2017. We modelled per-patient delay of 3 and 6 months and periods of disruption of 1 and 2 years. Using health care resource costing, we contextualise attributable lives saved and life-years gained (LYGs) from cancer surgery to equivalent volumes of COVID-19 hospitalisations. RESULTS: Per year, 94 912 resections for major cancers result in 80 406 long-term survivors and 1 717 051 LYGs. Per-patient delay of 3/6 months would cause attributable death of 4755/10 760 of these individuals with loss of 92 214/208 275 life-years, respectively. For cancer surgery, average LYGs per patient are 18.1 under standard conditions and 17.1/15.9 with a delay of 3/6 months (an average loss of 0.97/2.19 LYGs per patient), respectively. Taking into account health care resource units (HCRUs), surgery results on average per patient in 2.25 resource-adjusted life-years gained (RALYGs) under standard conditions and 2.12/1.97 RALYGs following delay of 3/6 months. For 94 912 hospital COVID-19 admissions, there are 482 022 LYGs requiring 1 052 949 HCRUs. Hospitalisation of community-acquired COVID-19 patients yields on average per patient 5.08 LYG and 0.46 RALYGs. CONCLUSIONS: Modest delays in surgery for cancer incur significant impact on survival. Delay of 3/6 months in surgery for incident cancers would mitigate 19%/43% of LYGs, respectively, by hospitalisation of an equivalent volume of admissions for community-acquired COVID-19. This rises to 26%/59%, respectively, when considering RALYGs. To avoid a downstream public health crisis of avoidable cancer deaths, cancer diagnostic and surgical pathways must be maintained at normal throughput, with rapid attention to any backlog already accrued.

Lymphocyte density determined by computational pathology validated as a predictor of response to neoadjuvant chemotherapy in breast cancer: secondary analysis of the ARTemis trial.

BACKGROUND: We have previously shown lymphocyte density, measured using computational pathology, is associated with pathological complete response (pCR) in breast cancer. The clinical validity of this finding in independent studies, among patients receiving different chemotherapy, is unknown. PATIENTS AND METHODS: The ARTemis trial randomly assigned 800 women with early stage breast cancer between May 2009 and January 2013 to three cycles of docetaxel, followed by three cycles of fluorouracil, epirubicin and cyclophosphamide once every 21 days with or without four cycles of bevacizumab. The primary endpoint was pCR (absence of invasive cancer in the breast and lymph nodes). We quantified lymphocyte density within haematoxylin and eosin (H&E) whole slide images using our previously described computational pathology approach: for every detected lymphocyte the average distance to the nearest 50 lymphocytes was calculated and the density derived from this statistic. We analyzed both pre-treatment biopsies and post-treatment surgical samples of the tumour bed. RESULTS: Of the 781 patients originally included in the primary endpoint analysis of the trial, 609 (78%) were included for baseline lymphocyte density analyses and a subset of 383 (49% of 781) for analyses of change in lymphocyte density. The main reason for loss of patients was the availability of digitized whole slide images. Pre-treatment lymphocyte density modelled as a continuous variable was associated with pCR on univariate analysis (odds ratio [OR], 2.92; 95% CI, 1.78-4.85; P < 0.001) and after adjustment for clinical covariates (OR, 2.13; 95% CI, 1.24-3.67; P = 0.006). Increased pre- to post-treatment lymphocyte density showed an independent inverse association with pCR (adjusted OR, 0.1; 95% CI, 0.033-0.31; P < 0.001). CONCLUSIONS: Lymphocyte density in pre-treatment biopsies was validated as an independent predictor of pCR in breast cancer. Computational pathology is emerging as a viable and objective means of identifying predictive biomarkers for cancer patients. CLINICALTRIALS.GOV: NCT01093235.

PD-L1 protein expression in breast cancer is rare, enriched in basal-like tumours and associated with infiltrating lymphocytes.

BACKGROUND: Expression of programmed death ligand 1 (PD-L1) in solid tumours has been shown to predict whether patients are likely to respond to anti-PD-L1 therapies. To estimate the therapeutic potential of PD-L1 inhibition in breast cancer, we evaluated the prevalence and significance of PD-L1 protein expression in a large collection of breast tumours. PATIENTS AND METHODS: Correlations between CD274 (PD-L1) copy number, transcript and protein levels were evaluated in tumours from 418 patients recruited to the METABRIC genomic study. Immunohistochemistry was used to detect PD-L1 protein in breast tumours in tissue microarrays from 5763 patients recruited to the SEARCH population-based study (N = 4079) and the NEAT randomised, controlled trial (N = 1684). RESULTS: PD-L1 protein data was available for 3916 of the possible 5763 tumours from the SEARCH and NEAT studies. PD-L1 expression by immune cells was observed in 6% (235/3916) of tumours and expression by tumour cells was observed in just 1.7% (66/3916). PD-L1 was most frequently expressed in basal-like tumours. This was observed both where tumours were subtyped by combined copy number and expression profiling [39% (17/44) of IntClust 10 i.e. basal-like tumours were PD-L1 immune cell positive; P < 0.001] and where a surrogate IHC-based classifier was used [19% (56/302) of basal-like tumours were PD-L1 immune cell positive; P < 0.001]. Moreover, CD274 (PD-L1) amplification was observed in five tumours of which four were IntClust 10. Expression of PD-L1 by either tumour cells or infiltrating immune cells was positively correlated with infiltration by both cytotoxic and regulatory T cells (P < 0.001). There was a nominally significant association between PD-L1 and improved disease-specific survival (hazard ratio 0.53, 95% confidence interval 0.26-1.07; P = 0.08) in ER-negative disease. CONCLUSIONS: Expression of PD-L1 is rare in breast cancer, markedly enriched in basal-like tumours and is correlated with infiltrating lymphocytes. PD-L1 inhibition may benefit the 19% of patients with basal-like tumours in which the protein is expressed. NEAT CLINICALTRIALSGOV: NCT00003577.

Evidence for a time-dependent association between FOLR1 expression and survival from ovarian carcinoma: implications for clinical testing. An Ovarian Tumour Tissue Analysis consortium study.

BACKGROUND: Folate receptor 1 (FOLR1) is expressed in the majority of ovarian carcinomas (OvCa), making it an attractive target for therapy. However, clinical trials testing anti-FOLR1 therapies in OvCa show mixed results and require better understanding of the prognostic relevance of FOLR1 expression. We conducted a large study evaluating FOLR1 expression with survival in different histological types of OvCa. METHODS: Tissue microarrays composed of tumour samples from 2801 patients in the Ovarian Tumour Tissue Analysis (OTTA) consortium were assessed for FOLR1 expression by centralised immunohistochemistry. We estimated associations for overall (OS) and progression-free (PFS) survival using adjusted Cox regression models. High-grade serous ovarian carcinomas (HGSC) from The Cancer Genome Atlas (TCGA) were evaluated independently for association between FOLR1 mRNA upregulation and survival. RESULTS: FOLR1 expression ranged from 76% in HGSC to 11% in mucinous carcinomas in OTTA. For HGSC, the association between FOLR1 expression and OS changed significantly during the years following diagnosis in OTTA (Pinteraction=0.01, N=1422) and TCGA (Pinteraction=0.01, N=485). In OTTA, particularly for FIGO stage I/II tumours, patients with FOLR1-positive HGSC showed increased OS during the first 2 years only (hazard ratio=0.44, 95% confidence interval=0.20-0.96) and patients with FOLR1-positive clear cell carcinomas (CCC) showed decreased PFS independent of follow-up time (HR=1.89, 95% CI=1.10-3.25, N=259). In TCGA, FOLR1 mRNA upregulation in HGSC was also associated with increased OS during the first 2 years following diagnosis irrespective of tumour stage (HR: 0.48, 95% CI: 0.25-0.94). CONCLUSIONS: FOLR1-positive HGSC tumours were associated with an increased OS in the first 2 years following diagnosis. Patients with FOLR1-negative, poor prognosis HGSC would be unlikely to benefit from anti-FOLR1 therapies. In contrast, a decreased PFS interval was observed for FOLR1-positive CCC. The clinical efficacy of FOLR1-targeted interventions should therefore be evaluated according to histology, stage and time following diagnosis.

Association between CD8+ T-cell infiltration and breast cancer survival in 12,439 patients.

BACKGROUND: T-cell infiltration in estrogen receptor (ER)-negative breast tumours has been associated with longer survival. To investigate this association and the potential of tumour T-cell infiltration as a prognostic and predictive marker, we have conducted the largest study of T cells in breast cancer to date. PATIENTS AND METHODS: Four studies totalling 12 439 patients were used for this work. Cytotoxic (CD8+) and regulatory (forkhead box protein 3, FOXP3+) T cells were quantified using immunohistochemistry (IHC). IHC for CD8 was conducted using available material from all four studies (8978 samples) and for FOXP3 from three studies (5239 samples)-multiple imputation was used to resolve missing data from the remaining patients. Cox regression was used to test for associations with breast cancer-specific survival. RESULTS: In ER-negative tumours [triple-negative breast cancer and human epidermal growth factor receptor 2 (human epidermal growth factor receptor 2 (HER2) positive)], presence of CD8+ T cells within the tumour was associated with a 28% [95% confidence interval (CI) 16% to 38%] reduction in the hazard of breast cancer-specific mortality, and CD8+ T cells within the stroma with a 21% (95% CI 7% to 33%) reduction in hazard. In ER-positive HER2-positive tumours, CD8+ T cells within the tumour were associated with a 27% (95% CI 4% to 44%) reduction in hazard. In ER-negative disease, there was evidence for greater benefit from anthracyclines in the National Epirubicin Adjuvant Trial in patients with CD8+ tumours [hazard ratio (HR) = 0.54; 95% CI 0.37-0.79] versus CD8-negative tumours (HR = 0.87; 95% CI 0.55-1.38). The difference in effect between these subgroups was significant when limited to cases with complete data (P heterogeneity = 0.04) and approached significance in imputed data (P heterogeneity = 0.1). CONCLUSIONS: The presence of CD8+ T cells in breast cancer is associated with a significant reduction in the relative risk of death from disease in both the ER-negative [supplementary Figure S1, available at Annals of Oncology online] and the ER-positive HER2-positive subtypes. Tumour lymphocytic infiltration may improve risk stratification in breast cancer patients classified into these subtypes. NEAT ClinicalTrials.gov: NCT00003577.

Astronomical algorithms for automated analysis of tissue protein expression in breast cancer.

BACKGROUND: High-throughput evaluation of tissue biomarkers in oncology has been greatly accelerated by the widespread use of tissue microarrays (TMAs) and immunohistochemistry. Although TMAs have the potential to facilitate protein expression profiling on a scale to rival experiments of tumour transcriptomes, the bottleneck and imprecision of manually scoring TMAs has impeded progress. METHODS: We report image analysis algorithms adapted from astronomy for the precise automated analysis of IHC in all subcellular compartments. The power of this technique is demonstrated using over 2000 breast tumours and comparing quantitative automated scores against manual assessment by pathologists. RESULTS: All continuous automated scores showed good correlation with their corresponding ordinal manual scores. For oestrogen receptor (ER), the correlation was 0.82, P<0.0001, for BCL2 0.72, P<0.0001 and for HER2 0.62, P<0.0001. Automated scores showed excellent concordance with manual scores for the unsupervised assignment of cases to 'positive' or 'negative' categories with agreement rates of up to 96%. CONCLUSION: The adaptation of astronomical algorithms coupled with their application to large annotated study cohorts, constitutes a powerful tool for the realisation of the enormous potential of digital pathology.

Public health genomics and personalized prevention: lessons from the COGS project.

Using the principles of public health genomics, we examined the opportunities and challenges of implementing personalized prevention programmes for cancer at the population level. Our model-based estimates indicate that polygenic risk stratification can potentially improve the effectiveness and cost-effectiveness of screening programmes. However, compared with 'one-size-fits-all' screening programmes, personalized screening adds further layers of complexity to the organization of screening services and raises ethical, legal and social challenges. Before polygenic inheritance is translated into population screening strategy, evidence from empirical research and engagement with and education of the public and the health professionals are needed.

A common variant in BRCA2 is associated with both breast cancer risk and prenatal viability.

Inherited mutations in the gene BRCA2 predispose carriers to early onset breast cancer, but such mutations account for fewer than 2% of all cases in East Anglia. It is likely that low penetrance alleles explain the greater part of inherited susceptibility to breast cancer; polymorphic variants in strongly predisposing genes, such as BRCA2, are candidates for this role. BRCA2 is thought to be involved in DNA double strand break-repair. Few mice in which Brca2 is truncated survive to birth; of those that do, most are male, smaller than their normal littermates and have high cancer incidence. Here we show that a common human polymorphism (N372H) in exon 10 of BRCA2 confers an increased risk of breast cancer: the HH homozygotes have a 1.31-fold (95% CI, 1.07-1.61) greater risk than the NN group. Moreover, in normal female controls of all ages there is a significant deficiency of homozygotes compared with that expected from Hardy-Weinberg equilibrium, whereas in males there is an excess of homozygotes: the HH group has an estimated fitness of 0.82 in females and 1.38 in males. Therefore, this variant of BRCA2 appears also to affect fetal survival in a sex-dependent manner.

Aurora kinase A outperforms Ki67 as a prognostic marker in ER-positive breast cancer.

BACKGROUND: Proliferation has emerged as a major prognostic factor in luminal breast cancer. The immunohistochemical (IHC) proliferation marker Ki67 has been most extensively investigated but has not gained widespread clinical acceptance. METHODS: We have conducted a head-to-head comparison of a panel of proliferation markers, including Ki67. Our aim was to establish the marker of the greatest prognostic utility. Tumour samples from 3093 women with breast cancer were constructed as tissue microarrays. We used IHC to detect expression of mini-chromosome maintenance protein 2, Ki67, aurora kinase A (AURKA), polo-like kinase 1, geminin and phospho-histone H3. We used a Cox proportional-hazards model to investigate the association with 10-year breast cancer-specific survival (BCSS). Missing values were resolved using multiple imputation. RESULTS: The prognostic significance of proliferation was limited to oestrogen receptor (ER)-positive breast cancer. Aurora kinase A emerged as the marker of the greatest prognostic significance in a multivariate model adjusted for the standard clinical and molecular covariates (hazard ratio 1.3; 95% confidence interval 1.1-1.5; P=0.005), outperforming all other markers including Ki67. CONCLUSION: Aurora kinase A outperforms other proliferation markers as an independent predictor of BCSS in ER-positive breast cancer. It has the potential for use in routine clinical practice.

PREDICT Plus: development and validation of a prognostic model for early breast cancer that includes HER2.

BACKGROUND: Predict (www.predict.nhs.uk) is an online, breast cancer prognostication and treatment benefit tool. The aim of this study was to incorporate the prognostic effect of HER2 status in a new version (Predict+), and to compare its performance with the original Predict and Adjuvant!. METHODS: The prognostic effect of HER2 status was based on an analysis of data from 10 179 breast cancer patients from 14 studies in the Breast Cancer Association Consortium. The hazard ratio estimates were incorporated into Predict. The validation study was based on 1653 patients with early-stage invasive breast cancer identified from the British Columbia Breast Cancer Outcomes Unit. Predicted overall survival (OS) and breast cancer-specific survival (BCSS) for Predict+, Predict and Adjuvant! were compared with observed outcomes. RESULTS: All three models performed well for both OS and BCSS. Both Predict models provided better BCSS estimates than Adjuvant!. In the subset of patients with HER2-positive tumours, Predict+ performed substantially better than the other two models for both OS and BCSS. CONCLUSION: Predict+ is the first clinical breast cancer prognostication tool that includes tumour HER2 status. Use of the model might lead to more accurate absolute treatment benefit predictions for individual patients.

Meta-analysis of the association between emphysematous change on thoracic computerized tomography scan and recurrent pneumothorax.

OBJECTIVES: At least a third of patients go on to suffer a recurrence following a first spontaneous pneumothorax. Surgical intervention reduces the risk of recurrence and has been advocated as a primary treatment for pneumothorax. But surgery exposes patients to the risks of anaesthesia and in some cases can cause chronic pain. Risk stratification of patients to identify those most at risk of recurrence would help direct the most appropriate patients to early intervention. Many studies have addressed the role of thoracic computerized tomography (CT) in identifying those individuals at increased risk of recurrence, but a consensus is lacking. AIM: Our objective was to clarify whether CT provides valuable prognostic information for recurrent pneumothorax. DESIGN: Meta-analysis. METHODS: We conducted an exhaustive search of the literature for thoracic CT imaging and pneumothorax, and then performed a meta-analysis using a random effects model to estimate the common odds ratio and standard error. RESULTS: Here, we show by meta-analysis of data from 2475 individuals that emphysematous change on CT scan is associated with a significant increased odds ratio for recurrent pneumothorax ipsilateral to the radiological abnormality (odds ratio 2.49, 95% confidence interval 1.51-4.13). CONCLUSIONS: The association holds true for primary spontaneous pneumothorax when considering emphysematous changes including blebs and bullae. Features, such as bullae at the azygoesophageal recess or increased Goddard score similarly predicted recurrent secondary pneumothorax, as shown by subgroup analysis. Our meta-analysis suggests that CT scanning has value in risk stratifying patients considering surgery for pneumothorax.

Comparison of methods for handling missing data on immunohistochemical markers in survival analysis of breast cancer.

BACKGROUND: Tissue micro-arrays (TMAs) are increasingly used to generate data of the molecular phenotype of tumours in clinical epidemiology studies, such as studies of disease prognosis. However, TMA data are particularly prone to missingness. A variety of methods to deal with missing data are available. However, the validity of the various approaches is dependent on the structure of the missing data and there are few empirical studies dealing with missing data from molecular pathology. The purpose of this study was to investigate the results of four commonly used approaches to handling missing data from a large, multi-centre study of the molecular pathological determinants of prognosis in breast cancer. PATIENTS AND METHODS: We pooled data from over 11,000 cases of invasive breast cancer from five studies that collected information on seven prognostic indicators together with survival time data. We compared the results of a multi-variate Cox regression using four approaches to handling missing data - complete case analysis (CCA), mean substitution (MS) and multiple imputation without inclusion of the outcome (MI-) and multiple imputation with inclusion of the outcome (MI+). We also performed an analysis in which missing data were simulated under different assumptions and the results of the four methods were compared. RESULTS: Over half the cases had missing data on at least one of the seven variables and 11 percent had missing data on 4 or more. The multi-variate hazard ratio estimates based on multiple imputation models were very similar to those derived after using MS, with similar standard errors. Hazard ratio estimates based on the CCA were only slightly different, but the estimates were less precise as the standard errors were large. However, in data simulated to be missing completely at random (MCAR) or missing at random (MAR), estimates for MI+ were least biased and most accurate, whereas estimates for CCA were most biased and least accurate. CONCLUSION: In this study, empirical results from analyses using CCA, MS, MI- and MI+ were similar, although results from CCA were less precise. The results from simulations suggest that in general MI+ is likely to be the best. Given the ease of implementing MI in standard statistical software, the results of MI+ and CCA should be compared in any multi-variate analysis where missing data are a problem.

Identification of a novel prostate cancer susceptibility variant in the KLK3 gene transcript.

Genome-wide association studies (GWAS) have identified more than 30 prostate cancer (PrCa) susceptibility loci. One of these (rs2735839) is located close to a plausible candidate susceptibility gene, KLK3, which encodes prostate-specific antigen (PSA). PSA is widely used as a biomarker for PrCa detection and disease monitoring. To refine the association between PrCa and variants in this region, we used genotyping data from a two-stage GWAS using samples from the UK and Australia, and the Cancer Genetic Markers of Susceptibility (CGEMS) study. Genotypes were imputed for 197 and 312 single nucleotide polymorphisms (SNPs) from HapMap2 and the 1000 Genome Project, respectively. The most significant association with PrCa was with a previously unidentified SNP, rs17632542 (combined P = 3.9 × 10(-22)). This association was confirmed by direct genotyping in three stages of the UK/Australian GWAS, involving 10,405 cases and 10,681 controls (combined P = 1.9 × 10(-34)). rs17632542 is also shown to be associated with PSA levels and it is a non-synonymous coding SNP (Ile179Thr) in KLK3. Using molecular dynamic simulation, we showed evidence that this variant has the potential to introduce alterations in the protein or affect RNA splicing. We propose that rs17632542 may directly influence PrCa risk.

A large-scale meta-analysis to refine colorectal cancer risk estimates associated with MUTYH variants.

BACKGROUND: defective DNA repair has a causal role in hereditary colorectal cancer (CRC). Defects in the base excision repair gene MUTYH are responsible for MUTYH-associated polyposis and CRC predisposition as an autosomal recessive trait. Numerous reports have suggested MUTYH mono-allelic variants to be low penetrance risk alleles. We report a large collaborative meta-analysis to assess and refine CRC risk estimates associated with bi-allelic and mono-allelic MUTYH variants and investigate age and sex influence on risk. METHODS: MUTYH genotype data were included from 20 565 cases and 15 524 controls. Three logistic regression models were tested: a crude model; adjusted for age and sex; adjusted for age, sex and study. RESULTS: all three models produced very similar results. MUTYH bi-allelic carriers demonstrated a 28-fold increase in risk (95% confidence interval (CI): 6.95-115). Significant bi-allelic effects were also observed for G396D and Y179C/G396D compound heterozygotes and a marginal mono-allelic effect for variant Y179C (odds ratio (OR)=1.34; 95% CI: 1.00-1.80). A pooled meta-analysis of all published and unpublished datasets submitted showed bi-allelic effects for MUTYH, G396D and Y179C (OR=10.8, 95% CI: 5.02-23.2; OR=6.47, 95% CI: 2.33-18.0; OR=3.35, 95% CI: 1.14-9.89) and marginal mono-allelic effect for variants MUTYH (OR=1.16, 95% CI: 1.00-1.34) and Y179C alone (OR=1.34, 95% CI: 1.01-1.77). CONCLUSIONS: overall, this large study refines estimates of disease risk associated with mono-allelic and bi-allelic MUTYH carriers.

COGENT (COlorectal cancer GENeTics): an international consortium to study the role of polymorphic variation on the risk of colorectal cancer.

It is now recognised that a part of the inherited risk of colorectal cancer (CRC) can be explained by the co-inheritance of low-penetrance genetic variants. The accumulated experience to date in identifying these variants has served to highlight difficulties in conducting statistically and methodologically rigorous studies and follow-up analyses. The COGENT (COlorectal cancer GENeTics) consortium includes 20 research groups in Europe, Australia, the Americas, China and Japan. The overarching goal of COGENT is to identify and characterise low-penetrance susceptibility variants for CRC through association-based analyses. In this study, we review the rationale for identifying low-penetrance variants for CRC and our proposed strategy for establishing COGENT.

Variation in genes required for normal mitosis and risk of breast cancer.

The down-regulation of genes involved in normal cell division can cause aberrant mitoses and increased cell death. Surviving cells exhibit aneuploidy and/or polyploidy. Since mitotic disruption has been linked with tumor development and progression, alterations in the expression or activity of these mitotic regulators may contribute to breast tumor formation. We evaluated associations between common inherited variation in these genes and breast cancer risk. Two hundred and five tagging and candidate functional single nucleotide polymorphisms in 30 genes required for normal cell division were genotyped in 798 breast cancer cases and 843 controls from the Mayo Clinic breast cancer study. Two variants in EIF3A (rs10787899 and rs3824830; P < 0.01) and four variants in SART1 (rs660118, rs679581, rs754532, and rs735942; P(trend) < or = 0.02) were significantly associated with an altered risk of breast cancer along with single variants in RRM2, PSCD3, C11orf51, CDC16, SNW1, MFAP1, and CDC2 (P < 0.05). Variation in both SART1 (P = 0.009) and EIF3A (P = 0.02) was also significant at the gene level. Analyses suggested that SART1 SNPs rs660118 and rs679581 accounted for the majority of the association of that gene with breast cancer. The observed associations between breast cancer risk and genetic variation in the SART1 and EIF3A genes that are required for maintenance of normal mitosis suggest a direct role for these genes in the development of breast cancer.

Prevalent cases in observational studies of cancer survival: do they bias hazard ratio estimates?

Observational epidemiological studies often include prevalent cases recruited at various times past diagnosis. This left truncation can be dealt with in non-parametric (Kaplan-Meier) and semi-parametric (Cox) time-to-event analyses, theoretically generating an unbiased hazard ratio (HR) when the proportional hazards (PH) assumption holds. However, concern remains that inclusion of prevalent cases in survival analysis results inevitably in HR bias. We used data on three well-established breast cancer prognosticators - clinical stage, histopathological grade and oestrogen receptor (ER) status - from the SEARCH study, a population-based study including 4470 invasive breast cancer cases (incident and prevalent), to evaluate empirically the effectiveness of allowing for left truncation in limiting HR bias. We found that HRs of prognostic factors changed over time and used extended Cox models incorporating time-dependent covariates. When comparing Cox models restricted to subjects ascertained within six months of diagnosis (incident cases) to models based on the full data set allowing for left truncation, we found no difference in parameter estimates (P=0.90, 0.32 and 0.95, for stage, grade and ER status respectively). Our results show that use of prevalent cases in an observational epidemiological study of breast cancer does not bias the HR in a left truncation Cox survival analysis, provided the PH assumption holds true.

Tagging single-nucleotide polymorphisms in candidate oncogenes and susceptibility to ovarian cancer.

Low-moderate risk alleles that are relatively common in the population may explain a significant proportion of the excess familial risk of ovarian cancer (OC) not attributed to highly penetrant genes. In this study, we evaluated the risks of OC associated with common germline variants in five oncogenes (BRAF, ERBB2, KRAS, NMI and PIK3CA) known to be involved in OC development. Thirty-four tagging SNPs in these genes were genotyped in approximately 1800 invasive OC cases and 3000 controls from population-based studies in Denmark, the United Kingdom and the United States. We found no evidence of disease association for SNPs in BRAF, KRAS, ERBB2 and PIK3CA when OC was considered as a single disease phenotype; but after stratification by histological subtype, we found borderline evidence of association for SNPs in KRAS and BRAF with mucinous OC and in ERBB2 and PIK3CA with endometrioid OC. For NMI, we identified a SNP (rs11683487) that was associated with a decreased risk of OC (unadjusted P(dominant)=0.004). We then genotyped rs11683487 in another 1097 cases and 1792 controls from an additional three case-control studies from the United States. The combined odds ratio was 0.89 (95% confidence interval (CI): 0.80-0.99) and remained statistically significant (P(dominant)=0.032). We also identified two haplotypes in ERBB2 associated with an increased OC risk (P(global)=0.034) and a haplotype in BRAF that had a protective effect (P(global)=0.005). In conclusion, these data provide borderline evidence of association for common allelic variation in the NMI with risk of epithelial OC.

Validating genetic risk associations for ovarian cancer through the international Ovarian Cancer Association Consortium.

The search for genetic variants associated with ovarian cancer risk has focused on pathways including sex steroid hormones, DNA repair, and cell cycle control. The Ovarian Cancer Association Consortium (OCAC) identified 10 single-nucleotide polymorphisms (SNPs) in genes in these pathways, which had been genotyped by Consortium members and a pooled analysis of these data was conducted. Three of the 10 SNPs showed evidence of an association with ovarian cancer at P< or =0.10 in a log-additive model: rs2740574 in CYP3A4 (P=0.011), rs1805386 in LIG4 (P=0.007), and rs3218536 in XRCC2 (P=0.095). Additional genotyping in other OCAC studies was undertaken and only the variant in CYP3A4, rs2740574, continued to show an association in the replication data among homozygous carriers: OR(homozygous(hom))=2.50 (95% CI 0.54-11.57, P=0.24) with 1406 cases and 2827 controls. Overall, in the combined data the odds ratio was 2.81 among carriers of two copies of the minor allele (95% CI 1.20-6.56, P=0.017, p(het) across studies=0.42) with 1969 cases and 3491 controls. There was no association among heterozygous carriers. CYP3A4 encodes a key enzyme in oestrogen metabolism and our finding between rs2740574 and risk of ovarian cancer suggests that this pathway may be involved in ovarian carcinogenesis. Additional follow-up is warranted.