David C Greenberg

PREDICT: a new UK prognostic model that predicts survival following surgery for invasive breast cancer

IntroductionThe aim of this study was to develop and validate a prognostication model to predict overall and breast cancer specific survival for women treated for early breast cancer in the UK.MethodsUsing the Eastern Cancer Registration and Information Centre (ECRIC) dataset, information was collated for 5,694 women who had surgery for invasive breast cancer in East Anglia from 1999 to 2003. Breast cancer mortality models for oestrogen receptor (ER) positive and ER negative tumours were derived from these data using Cox proportional hazards, adjusting for prognostic factors and mode of cancer detection (symptomatic versus screen-detected). An external dataset of 5,468 patients from the West Midlands Cancer Intelligence Unit (WMCIU) was used for validation.ResultsDifferences in overall actual and predicted mortality were <1% at eight years for ECRIC (18.9% vs. 19.0%) and WMCIU (17.5% vs. 18.3%) with area under receiver-operator-characteristic curves (AUC) of 0.81 and 0.79 respectively. Differences in breast cancer specific actual and predicted mortality were <1% at eight years for ECRIC (12.9% vs. 13.5%) and <1.5% at eight years for WMCIU (12.2% vs. 13.6%) with AUC of 0.84 and 0.82 respectively. Model calibration was good for both ER positive and negative models although the ER positive model provided better discrimination (AUC 0.82) than ER negative (AUC 0.75).ConclusionsWe have developed a prognostication model for early breast cancer based on UK cancer registry data that predicts breast cancer survival following surgery for invasive breast cancer and includes mode of detection for the first time. The model is well calibrated, provides a high degree of discrimination and has been validated in a second UK patient cohort.

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.

CYP2D6 gene variants: association with breast cancer specific survival in a cohort of breast cancer patients from the United Kingdom treated with adjuvant tamoxifen

IntroductionTamoxifen is one of the most effective adjuvant breast cancer therapies available. Its metabolism involves the phase I enzyme, cytochrome P4502D6 (CYP2D6), encoded by the highly polymorphic CYP2D6 gene. CYP2D6 variants resulting in poor metabolism of tamoxifen are hypothesised to reduce its efficacy. An FDA-approved pre-treatment CYP2D6 gene testing assay is available. However, evidence from published studies evaluating CYP2D6 variants as predictive factors of tamoxifen efficacy and clinical outcome are conflicting, querying the clinical utility of CYP2D6 testing. We investigated the association of CYP2D6 variants with breast cancer specific survival (BCSS) in breast cancer patients receiving tamoxifen.MethodsThis was a population based case-cohort study. We genotyped known functional variants (n = 7; minor allele frequency (MAF) > 0.01) and single nucleotide polymorphisms (SNPs) (n = 5; MAF > 0.05) tagging all known common variants (tagSNPs), in CYP2D6 in 6640 DNA samples from patients with invasive breast cancer from SEARCH (Studies of Epidemiology and Risk factors in Cancer Heredity); 3155 cases had received tamoxifen therapy. There were 312 deaths from breast cancer, in the tamoxifen treated patients, with over 18000 years of cumulative follow-up. The association between genotype and BCSS was evaluated using Cox proportional hazards regression analysis.ResultsIn tamoxifen treated patients, there was weak evidence that the poor-metaboliser variant, CYP2D6*6 (MAF = 0.01), was associated with decreased BCSS (P = 0.02; HR = 1.95; 95% CI = 1.12-3.40). No other variants, including CYP2D6*4 (MAF = 0.20), previously reported to be associated with poorer clinical outcomes, were associated with differences in BCSS, in either the tamoxifen or non-tamoxifen groups.ConclusionsCYP2D6*6 may affect BCSS in tamoxifen-treated patients. However, the absence of an association with survival in more frequent variants, including CYP2D6*4, questions the validity of the reported association between CYP2D6 genotype and treatment response in breast cancer. Until larger, prospective studies confirming any associations are available, routine CYP2D6 genetic testing should not be used in the clinical setting.

A population-based validation of the prognostic model PREDICT for early breast cancer

INTRODUCTION Predict (www.predict.nhs.uk) is a prognostication and treatment benefit tool developed using UK cancer registry data. The aim of this study was to compare the 10-year survival estimates from Predict with observed 10-year outcome from a British Columbia dataset and to compare the estimates with those generated by Adjuvant! (www.adjuvantonline.com). METHOD The analysis was based on data from 3140 patients with early invasive breast cancer diagnosed in British Columbia, Canada, from 1989-1993. Demographic, pathologic, staging and treatment data were used to predict 10-year overall survival (OS) and breast cancer specific survival (BCSS) using Adjuvant! and Predict models. Predicted outcomes from both models were then compared with observed outcomes. RESULTS Calibration of both models was excellent. The difference in total number of deaths estimated by Predict was 4.1 percent of observed compared to 0.7 percent for Adjuvant!. The total number of breast cancer specific deaths estimated by Predict was 3.4 percent of observed compared to 6.7 percent for Adjuvant! Both models also discriminate well with similar AUC for Predict and Adjuvant! respectively for both OS (0.709 vs 0.712) and BCSS (0.723 vs 0.727). Neither model performed well in women aged 20-35. CONCLUSION In summary Predict provided accurate overall and breast cancer specific survival estimates in the British Columbia dataset that are comparable with outcome estimates from Adjuvant! Both models appear well calibrated with similar model discrimination. This study provides further validation of Predict as an effective predictive tool following surgery for invasive breast cancer.

Population based time trends and socioeconomic variation in use of radiotherapy and radical surgery for prostate cancer in a UK region: continuous survey

Objective To examine variation in the management of prostate cancer in patients with different socioeconomic status. Design Survey using UK regional cancer registry data. Setting Regional population based cancer registry. Participants 35 171 patients aged ≥51 with a diagnosis of prostate cancer, 1995-2006. Main outcome measures Use of radiotherapy and radical surgery. Socioeconomic status according to fifths of small area deprivation index. Results Over the nine years of the study, information on stage at diagnosis was available for 15 916 of 27 970 patients (57%). During the study period, the proportion of patients treated with radiotherapy remained at about 25%, while use of radical surgery increased significantly (from 2.9% (212/7201) during 1995-7 to 8.4% (854/10 211) during 2004-6, P<0.001). Both treatments were more commonly used in least deprived compared with most deprived patients (28.5% v 21.0% for radiotherapy and 8.4% v 4.0% for surgery). In multivariable analysis, increasing deprivation remained strongly associated with lower odds of radiotherapy or surgery (odds ratio 0.92 (95% confidence interval 0.90 to 0.94), P<0.001, and 0.91 (0.87 to 0.94), P<0.001, respectively, per incremental deprivation group). There were consistently concordant findings with multilevel models for clustering of observations by hospital of diagnosis, with restriction of the analysis to patients with information on stage, and with sequential restriction of the analysis to different age, stage, diagnosis period, and morphology groups. Conclusions After a diagnosis of prostate cancer, men from lower socioeconomic groups were substantially less likely to be treated with radical surgery or radiotherapy. The causes and impact on survival of such differences remain uncertain.

Variation in advanced stage at diagnosis of lung and female breast cancer in an English region 2006-2009.

Background:Understanding variation in stage at diagnosis can inform interventions to improve the timeliness of diagnosis for patients with different cancers and characteristics.Methods:We analysed population-based data on 17 836 and 13 286 East of England residents diagnosed with (female) breast and lung cancer during 2006–2009, with stage information on 16 460 (92%) and 10 435 (79%) patients, respectively. Odds ratios (ORs) of advanced stage at diagnosis adjusted for patient and tumour characteristics were derived using logistic regression.Results:We present adjusted ORs of diagnosis in stages III/IV compared with diagnosis in stages I/II. For breast cancer, the frequency of advanced stage at diagnosis increased stepwise among old women (ORs: 1.21, 1.46, 1.68 and 1.78 for women aged 70–74, 75–79, 80–84 and ⩾85, respectively, compared with those aged 65–69 , P<0.001). In contrast, for lung cancer advanced stage at diagnosis was less frequent in old patients (ORs: 0.82, 0.74, 0.73 and 0.66, P<0.001). Advanced stage at diagnosis was more frequent in more deprived women with breast cancer (OR: 1.23 for most compared with least deprived, P=0.002), and in men with lung cancer (OR: 1.14, P=0.011). The observed patterns were robust to sensitivity analyses approaches for handling missing stage data under different assumptions.Conclusion:Interventions to help improve the timeliness of diagnosis of different cancers should be targeted at specific age groups.

Glioblastoma in England: 2007-2011.

AIMS Glioblastoma (GBM) is the most common and aggressive primary malignant brain tumour in adults, with a poor prognosis. Changing treatment paradigms suggest improved outcome, but whole nation data for England is scarce. The aim of this report is to examine the incidence of patients with glioblastoma in England, and to assess the influence of gender, age, geographical region and treatment on outcome. METHODS A search strategy encompassing all patients coded with GBM and treated from January 2007 to December 2011 was obtained from data linkage between the National Cancer Registration Service and Hospital Episode Statistics for England. RESULTS There were 10,743 patients coded with GBM in this 5-year period (6451 male, 4292 female), giving an overall national age standardised incidence of 4.64/100,000/year. Incidence increases with age. Median survival overall was 6.1 months. One, 2 and 5-year survivals, were 28.4%, 11.5% and 3.4% respectively. Age stratified median survivals decreased significantly (p<0.0001) with increasing age from 16.2 months for the 20-44 year age group, to 7.9 months for the 45-69 years, and 3.2 months for 70+years. In the maximal treatment subgroup, patients aged up to 69 years had a median survival of 14.9 months. Patients over 60 years were less likely to receive maximal combination treatment but median survival was better with maximal treatment at all ages. CONCLUSIONS The overall outcome for patients with GBM remains poor. However, aggressive treatment at every age group is associated with extended survival similar to that described in clinical trials.

Changing presentation of prostate cancer in a UK population – 10 year trends in prostate cancer risk profiles in the East of England

Background:Prostate cancer incidence is rising in the United Kingdom but there is little data on whether the disease profile is changing. To address this, we interrogated a regional cancer registry for temporal changes in presenting disease characteristics.Methods:Prostate cancers diagnosed from 2000 to 2010 in the Anglian Cancer Network (n=21 044) were analysed. Risk groups (localised disease) were assigned based on NICE criteria. Age standardised incidence rates (IRs) were compared between 2000–2005 and 2006–2010 and plotted for yearly trends.Results:Over the decade, overall IR increased significantly (P<0.00001), whereas metastasis rates fell (P<0.0007). For localised disease, IR across all risk groups also increased but at different rates (P<0.00001). The most striking change was a three-fold increase in intermediate-risk cancers. Increased IR was evident across all PSA and stage ranges but with no upward PSA or stage shift. In contrast, IR of histological diagnosis of low-grade cancers fell over the decade, whereas intermediate and high-grade diagnosis increased significantly (P<0.00001).Conclusion:This study suggests evidence of a significant upward migration in intermediate and high-grade histological diagnosis over the decade. This is most likely to be due to a change in histological reporting of diagnostic prostate biopsies. On the basis of this data, increasing proportions of newly diagnosed cancers will be considered eligible for radical treatment, which will have an impact on health resource planning and provision.

Mortality Among Men with Advanced Prostate Cancer Excluded from the ProtecT Trial

Background Early detection and treatment of asymptomatic men with advanced and high-risk prostate cancer (PCa) may improve survival rates. Objective To determine outcomes for men diagnosed with advanced PCa following prostate-specific antigen (PSA) testing who were excluded from the ProtecT randomised trial. Design, setting, and participants Mortality was compared for 492 men followed up for a median of 7.4 yr to a contemporaneous cohort of men from the UK Anglia Cancer Network (ACN) and with a matched subset from the ACN. Outcome measurements and statistical analysis PCa-specific and all-cause mortality were compared using Kaplan-Meier analysis and Coxs proportional hazards regression. Results and limitations Of the 492 men excluded from the ProtecT cohort, 37 (8%) had metastases (N1, M0 = 5, M1 = 32) and 305 had locally advanced disease (62%). The median PSA was 17 μg/l. Treatments included radical prostatectomy (RP; n = 54; 11%), radiotherapy (RT; n = 245; 50%), androgen deprivation therapy (ADT; n = 122; 25%), other treatments (n = 11; 2%), and unknown (n = 60; 12%). There were 49 PCa-specific deaths (10%), of whom 14 men had received radical treatment (5%); and 129 all-cause deaths (26%). In matched ProtecT and ACN cohorts, 37 (9%) and 64 (16%), respectively, died of PCa, while 89 (22%) and 103 (26%) died of all causes. ProtecT men had a 45% lower risk of death from PCa compared to matched cases (hazard ratio 0.55, 95% confidence interval 0.38–0.83; p = 0.0037), but mortality was similar in those treated radically. The nonrandomised design is a limitation. Conclusions Men with PSA-detected advanced PCa excluded from ProtecT and treated radically had low rates of PCa death at 7.4-yr follow-up. Among men who underwent nonradical treatment, the ProtecT group had a lower rate of PCa death. Early detection through PSA testing, leadtime bias, and group heterogeneity are possible factors in this finding. Patient summary Prostate cancer that has spread outside the prostate gland without causing symptoms can be detected via prostate-specific antigen testing and treated, leading to low rates of death from this disease.

An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation

BackgroundPREDICT is a breast cancer prognostic and treatment benefit model implemented online. The overall fit of the model has been good in multiple independent case series, but PREDICT has been shown to underestimate breast cancer specific mortality in women diagnosed under the age of 40. Another limitation is the use of discrete categories for tumour size and node status resulting in ‘step’ changes in risk estimates on moving between categories. We have refitted the PREDICT prognostic model using the original cohort of cases from East Anglia with updated survival time in order to take into account age at diagnosis and to smooth out the survival function for tumour size and node status.MethodsMultivariable Cox regression models were used to fit separate models for ER negative and ER positive disease. Continuous variables were fitted using fractional polynomials and a smoothed baseline hazard was obtained by regressing the baseline cumulative hazard for each patients against time using fractional polynomials. The fit of the prognostic models were then tested in three independent data sets that had also been used to validate the original version of PREDICT.ResultsIn the model fitting data, after adjusting for other prognostic variables, there is an increase in risk of breast cancer specific mortality in younger and older patients with ER positive disease, with a substantial increase in risk for women diagnosed before the age of 35. In ER negative disease the risk increases slightly with age. The association between breast cancer specific mortality and both tumour size and number of positive nodes was non-linear with a more marked increase in risk with increasing size and increasing number of nodes in ER positive disease.The overall calibration and discrimination of the new version of PREDICT (v2) was good and comparable to that of the previous version in both model development and validation data sets. However, the calibration of v2 improved over v1 in patients diagnosed under the age of 40.ConclusionsThe PREDICT v2 is an improved prognostication and treatment benefit model compared with v1. The online version should continue to aid clinical decision making in women with early breast cancer.