The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups.

The elucidation of breast cancer subgroups and their molecular drivers requires integrated views of the genome and transcriptome from representative numbers of patients. We present an integrated analysis of copy number and gene expression in a discovery and validation set of 997 and 995 primary breast tumours, respectively, with long-term clinical follow-up. Inherited variants (copy number variants and single nucleotide polymorphisms) and acquired somatic copy number aberrations (CNAs) were associated with expression in ~40% of genes, with the landscape dominated by cis- and trans-acting CNAs. By delineating expression outlier genes driven in cis by CNAs, we identified putative cancer genes, including deletions in PPP2R2A, MTAP and MAP2K4. Unsupervised analysis of paired DNA­RNA profiles revealed novel subgroups with distinct clinical outcomes, which reproduced in the validation cohort. These include a high-risk, oestrogen-receptor-positive 11q13/14 cis-acting subgroup and a favourable prognosis subgroup devoid of CNAs. Trans-acting aberration hotspots were found to modulate subgroup-specific gene networks, including a TCR deletion-mediated adaptive immune response in the 'CNA-devoid' subgroup and a basal-specific chromosome 5 deletion-associated mitotic network. Our results provide a novel molecular stratification of the breast cancer population, derived from the impact of somatic CNAs on the transcriptome.

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

BACKGROUND: PREDICT 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. METHODS: Multivariable 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. RESULTS: In 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. CONCLUSIONS: The 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.

Effects of the addition of gemcitabine, and paclitaxel-first sequencing, in neoadjuvant sequential epirubicin, cyclophosphamide, and paclitaxel for women with high-risk early breast cancer (Neo-tAnGo): an open-label, 2×2 factorial randomised phase 3 trial.

BACKGROUND: Anthracyclines and taxanes have been the standard neoadjuvant chemotherapies for breast cancer in the past decade. We aimed to assess safety and efficacy of the addition of gemcitabine to accelerated paclitaxel with epirubicin and cyclophosphamide, and also the effect of sequencing the blocks of epirubicin and cyclophosphamide and paclitaxel (with or without gemcitabine). METHODS: In our randomised, open-label, 2×2 factorial phase 3 trial (Neo-tAnGo), we enrolled women (aged >18 years) with newly diagnosed breast cancer (tumour size >20 mm) at 57 centres in the UK. Patients were randomly assigned via a central randomisation procedure to epirubicin and cyclophosphamide then paclitaxel (with or without gemcitabine) or paclitaxel (with or without gemcitabine) then epirubicin and cyclophosphamide. Four cycles of each component were given. The primary endpoint was pathological complete response (pCR), defined as absence of invasive cancer in the breast and axillary lymph nodes. This study is registered with EudraCT (2004-002356-34), ISRCTN (78234870), and ClinicalTrials.gov (NCT00070278). FINDINGS: Between Jan 18, 2005, and Sept 28, 2007, we randomly allocated 831 participants; 207 received epirubicin and cyclophosphamide then paclitaxel; 208 were given paclitaxel then epirubicin and cyclophosphamide; 208 had epirubicin and cyclophosphamide followed by paclitaxel and gemcitabine; and 208 received paclitaxel and gemcitabine then epirubicin and cyclophosphamide. 828 patients were eligible for analysis. Median follow-up was 47 months (IQR 37-51). 207 (25%) patients had inflammatory or locally advanced disease, 169 (20%) patients had tumours larger than 50 mm, 413 (50%) patients had clinical involvement of axillary nodes, 276 (33%) patients had oestrogen receptor (ER)-negative disease, and 191 (27%) patients had HER2-positive disease. Addition of gemcitabine did not increase pCR: 70 (17%, 95% CI 14-21) of 404 patients in the epirubicin and cyclophosphamide then paclitaxel group achieved pCR compared with 71 (17%, 14-21) of 408 patients who received additional gemcitabine (p=0·98). Receipt of a taxane before anthracycline was associated with improved pCR: 82 (20%, 95% CI 16-24) of 406 patients who received paclitaxel with or without gemcitabine followed by epirubicin and cyclophosphamide achieved pCR compared with 59 (15%, 11-18) of 406 patients who received epirubicin and cyclophosphamide first (p=0·03). Grade 3 toxicities were reported at expected levels: 173 (21%) of 812 patients who received treatment and had full treatment details had grade 3 neutropenia, 66 (8%) had infection, 41 (5%) had fatigue, 41 (5%) had muscle and joint pains, 37 (5%) had nausea, 36 (4%) had vomiting, 34 (4%) had neuropathy, 23 (3%) had transaminitis, 16 (2%) had acute hypersensitivity, and 20 (2%) had a rash. 86 (11%) patients had grade 4 neutropenia and 3 (<1%) had grade 4 infection. INTERPRETATION: Although addition of gemcitabine to paclitaxel and epirubicin and cyclophosphamide chemotherapy does not improve pCR, sequencing chemotherapy so that taxanes are received before anthracyclines could improve pCR in standard neoadjuvant chemotherapy for breast cancer. FUNDING: Cancer Research UK, Eli Lilly, Bristol-Myers Squibb.

Inclusion of KI67 significantly improves performance of the PREDICT prognostication and prediction model for early breast cancer.

BACKGROUND: PREDICT (http://www.predict.nhs.uk) is a prognostication and treatment benefit tool for early breast cancer (EBC). The aim of this study was to incorporate the prognostic effect of KI67 status in a new version (v3), and compare performance with the Predict model that includes HER2 status (v2). METHODS: The validation study was based on 1,726 patients with EBC treated in Nottingham between 1989 and 1998. KI67 positivity for PREDICT is defined as >10% of tumour cells staining positive. ROC curves were constructed for Predict models with (v3) and without (v2) KI67 input. Comparison was made using the method of DeLong. RESULTS: In 1274 ER+ patients the predicted number of events at 10 years increased from 196 for v2 to 204 for v3 compared to 221 observed. The area under the ROC curve (AUC) improved from 0.7611 to 0.7676 (p=0.005) in ER+ patients and from 0.7546 to 0.7595 (p=0.0008) in all 1726 patients (ER+ and ER-). CONCLUSION: Addition of KI67 to PREDICT has led to a statistically significant improvement in the model performance for ER+ patients and will aid clinical decision making in these patients. Further studies should determine whether other markers including gene expression profiling provide additional prognostic information to that provided by PREDICT.

Predictors of recurrence for ductal carcinoma in situ after breast-conserving surgery.

Ductal carcinoma in situ (DCIS) constitutes a major public health problem, with up to half of screen-detected cancers representing pure forms of DCIS without evidence of invasion. A proportion of cases detected with routine screening would not have progressed to a life-threatening form of breast cancer during the patient's lifetime, and overdiagnosis of breast cancer is a cause for concern. Once DCIS has been detected, treatment is obligatory and present technologies do not allow accurate risk stratification such that intensity of treatment can be tailored to risk of recurrence and progression to invasive disease. Present management strategies are based on prognostic and predictive information derived from conventional histopathological and host factors. With increasing molecular characterisation of these preinvasive lesions, data will be available for how factors such as oestrogen receptor, progesterone receptor, HER2, and indicators of proliferative activity can provide additional information about both prognosis and benefit from adjuvant treatments such as radiotherapy and hormonal therapy. Low-risk patients are especially poorly defined in terms of need for adjuvant therapies, which can be associated with both short-term adverse sequelae and long-term effects (eg, cardiotoxicity) that can affect all-cause mortality. Optimum risk prediction in the future is likely to be achieved by integration of both conventional and molecular factors, which should be incorporated into a validated predictive model to help with clinical decision making.

An updated PREDICT breast cancer prognostic model including the benefits and harms of radiotherapy.

PREDICT Breast ( www.breast .predict.nhs.uk ) is a prognostication tool for early invasive breast cancer. The current version was based on cases diagnosed in 1999-2003 and did not incorporate the benefits of radiotherapy or the harms associated with therapy. Since then, there has been a substantial improvement in the outcomes for breast cancer cases. The aim of this study was to update PREDICT Breast to ensure that the underlying model is appropriate for contemporary patients. Data from the England National Cancer Registration and Advisory Service for invasive breast cancer cases diagnosed 2000-17 were used for model development and validation. Model development was based on 35,474 cases diagnosed and registered by the Eastern Cancer Registry. A Cox model was used to estimate the prognostic effects of the year of diagnosis, age at diagnosis, tumour size, tumour grade and number of positive nodes. Separate models were developed for ER-positive and ER-negative disease. Data on 32,408 cases from the West Midlands Cancer Registry and 100,551 cases from other cancer registries were used for validation. The new model was well-calibrated; predicted breast cancer deaths at 5-, 10- and 15-year were within 10 per cent of the observed validation data. Discrimination was also good: The AUC for 15-year breast cancer survival was 0.809 in the West Midlands data set and 0.846 in the data set for the other registries. The new PREDICT Breast model outperformed the current model and will be implemented in the online tool which should lead to more accurate absolute treatment benefit predictions for individual patients.

Leveraging AI and patient metadata to develop a novel risk score for skin cancer detection.

Melanoma of the skin is the 17th most common cancer worldwide. Early detection of suspicious skin lesions (melanoma) can increase 5-year survival rates by 20%. The 7-point checklist (7PCL) has been extensively used to suggest urgent referrals for patients with a possible melanoma. However, the 7PCL method only considers seven meta-features to calculate a risk score and is only relevant for patients with suspected melanoma. There are limited studies on the extensive use of patient metadata for the detection of all skin cancer subtypes. This study investigates artificial intelligence (AI) models that utilise patient metadata consisting of 23 attributes for suspicious skin lesion detection. We have identified a new set of most important risk factors, namely "C4C risk factors", which is not just for melanoma, but for all types of skin cancer. The performance of the C4C risk factors for suspicious skin lesion detection is compared to that of the 7PCL and the Williams risk factors that predict the lifetime risk of melanoma. Our proposed AI framework ensembles five machine learning models and identifies seven new skin cancer risk factors: lesion pink, lesion size, lesion colour, lesion inflamed, lesion shape, lesion age, and natural hair colour, which achieved a sensitivity of 80.46 ± 2.50 % and a specificity of 62.09 ± 1.90 % in detecting suspicious skin lesions when evaluated using the metadata of 53,601 skin lesions collected from different skin cancer diagnostic clinics across the UK, significantly outperforming the 7PCL-based method (sensitivity 68.09 ± 2.10 % , specificity 61.07 ± 0.90 % ) and the Williams risk factors (sensitivity 66.32 ± 1.90 % , specificity 61.71 ± 0.6 % ). Furthermore, through weighting the seven new risk factors we came up with a new risk score, namely "C4C risk score", which alone achieved a sensitivity of 76.09 ± 1.20 % and a specificity of 61.71 ± 0.50 % , significantly outperforming the 7PCL-based risk score (sensitivity 73.91 ± 1.10 % , specificity 49.49 ± 0.50 % ) and the Williams risk score (sensitivity 60.68 ± 1.30 % , specificity 60.87 ± 0.80 % ). Finally, fusing the C4C risk factors with the 7PCL and Williams risk factors achieved the best performance, with a sensitivity of 85.24 ± 2.20 % and a specificity of 61.12 ± 0.90 % . We believe that fusing these newly found risk factors and new risk score with image data will further boost the AI model performance for suspicious skin lesion detection. Hence, the new set of skin cancer risk factors has the potential to be used to modify current skin cancer referral guidelines for all skin cancer subtypes, including melanoma.

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

INTRODUCTION: The 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. METHODS: Using 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. RESULTS: Differences 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). CONCLUSIONS: We 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.

Incidence of severe capsular contracture following implant-based immediate breast reconstruction with or without postoperative chest wall radiotherapy using 40 Gray in 15 fractions.

PURPOSE: To determine the incidence of capsular contracture (CC) requiring revisional surgery in patients receiving postoperative radiotherapy (RT) or no RT following mastectomy and immediate breast reconstruction. MATERIAL AND METHODS: One hundred and seventy-eight immediate breast reconstructions performed at the Cambridge Breast Unit between 1.1.2001 and 31.12.2005 were identified. RT was delivered using a standard UK scheme of 40 Gray in 15 fractions over 3 weeks. The influence of hormones and chemotherapy as well as postoperative RT on time to development of severe CC after implant-based reconstruction was explored in univariate and multivariate analysis. RESULTS: One hundred and ten patients had implant-based reconstructions with a median follow-up of 51 months. In the RT group (41 patients), there were 8 patients with severe CC requiring revisional surgery, a crude rate of 19.5%, with actuarial rates of 0%, 5%, 5%, 21%, 30% and 30% at 1, 2, 3, 4, 5 and 6 years follow-up. In the unirradiated group, there were no cases of severe CC. This difference is highly significant (p<0.001). Hormones and chemotherapy were not significantly associated with severe CC. CONCLUSIONS: This series showed a significantly higher rate of severe CC with postoperative RT. This finding has important clinical implications, when counselling patients for immediate breast reconstruction.

Metabolic-flow relationships in primary breast cancer: feasibility of combined PET/dynamic contrast-enhanced CT.

PURPOSE: To assess the feasibility and first experience of combined (18)F-FDG-PET)/dynamic contrast-enhanced (DCE) CT in evaluating breast cancer. METHODS: Nine consecutive female patients (mean age 64.2 years, range 52-74 years) with primary breast carcinoma were prospectively recruited for combined (18)F-FDG PET/DCE-CT. Dynamic CT data were used to calculate a range of parameters of tumour vascularity, and tumour (18)F-FDG uptake (standardized uptake value, SUVmax) was used as a metabolic indicator. RESULTS: One tumour did not enhance and was excluded. The mean tumour SUVmax was 7.7 (range 2.4-26.1). The mean values for tumour perfusion, perfusion normalized to cardiac output, standard perfusion value (SPV) and permeability were 41 ml/min per 100 g (19-59 ml/min per 100 g), 0.56%/100 g (0.33-1.09%/100 g), 3.6 (2.5-5.9) and 0.15/min (0.09-0.30/min), respectively. Linear regression analysis showed a positive correlation between tumour SUV and tumour perfusion normalized to cardiac output (r=0.55, p=0.045) and a marginal correlation between tumour SUV and tumour SPV (r=0.19, p=0.065). There were no significant correlations between tumour SUV and tumour perfusion (r=0.29, p=0.401) or permeability (r=0.03, p=0.682). CONCLUSION: The first data from combined (18)F-FDG-PET/DCE-CT in breast cancer are reported. The technique was successful in eight of nine patients. Breast tumour metabolic and vascular parameters were consistent with previous data from (15)O-H(2)O-PET.

Accuracy of the online prognostication tools PREDICT and Adjuvant! for early-stage breast cancer patients younger than 50 years.

IMPORTANCE: Online prognostication tools such as PREDICT and Adjuvant! are increasingly used in clinical practice by oncologists to inform patients and guide treatment decisions about adjuvant systemic therapy. However, their validity for young breast cancer patients is debated. OBJECTIVE: To assess first, the prognostic accuracy of PREDICT's and Adjuvant! 10-year all-cause mortality, and second, its breast cancer-specific mortality estimates, in a large cohort of breast cancer patients diagnosed <50 years. DESIGN: Hospital-based cohort. SETTING: General and cancer hospitals. PARTICIPANTS: A consecutive series of 2710 patients without a prior history of cancer, diagnosed between 1990 and 2000 with unilateral stage I-III breast cancer aged <50 years. MAIN OUTCOME MEASURES: Calibration and discriminatory accuracy, measured with C-statistics, of estimated 10-year all-cause and breast cancer-specific mortality. RESULTS: Overall, PREDICT's calibration for all-cause mortality was good (predicted versus observed) meandifference: -1.1% (95%CI: -3.2%-0.9%; P = 0.28). PREDICT tended to underestimate all-cause mortality in good prognosis subgroups (range meandifference: -2.9% to -4.8%), overestimated all-cause mortality in poor prognosis subgroups (range meandifference: 2.6%-9.4%) and underestimated survival in patients < 35 by -6.6%. Overall, PREDICT overestimated breast cancer-specific mortality by 3.2% (95%CI: 0.8%-5.6%; P = 0.007); and also overestimated it seemingly indiscriminately in numerous subgroups (range meandifference: 3.2%-14.1%). Calibration was poor in the cohort of patients with the lowest and those with the highest mortality probabilities. Discriminatory accuracy was moderate-to-good for all-cause mortality in PREDICT (0.71 [95%CI: 0.68 to 0.73]), and the results were similar for breast cancer-specific mortality. Adjuvant!'s calibration and discriminatory accuracy for both all-cause and breast cancer-specific mortality were in line with PREDICT's findings. CONCLUSIONS: Although imprecise at the extremes, PREDICT's estimates of 10-year all-cause mortality seem reasonably sound for breast cancer patients <50 years; Adjuvant! findings were similar. Prognostication tools should be used with caution due to the intrinsic variability of their estimates, and because the threshold to discuss adjuvant systemic treatment is low. Thus, seemingly insignificant mortality overestimations or underestimations of a few percentages can significantly impact treatment decision-making.

Indocyanine green fluorescence mapping for sentinel lymph node biopsy in early breast cancer.

INTRODUCTION: A recent feasibility study (ICG-10) has confirmed high sensitivity of ICG fluorescence mapping for sentinel SLN detection in early breast cancer with 95% of nodes both blue and fluorescent. This follow-on study has specifically evaluated a combination of ICG and blue dye for SLN localization. METHODS: Fifty consecutive patients (49 female; 1 male) with unilateral clinically node negative invasive (37) and non-invasive (13) breast cancer underwent SLN biopsy with blue dye and ICG. Median patient age was 48 years and median invasive tumour size 19 mm for primary surgical patients. All patients had a normal pre-operative axillary ultrasound. Nodal and procedural detection rates were calculated for ICG alone and in combination with blue dye. RESULTS: A total of 87 nodes were retrieved with an average nodal count of 1.8 per patient (range 1-4). Eighty four nodes were blue and fluorescent and 3 fluorescent only. Nodal detection rates for ICG alone and combined with blue dye were 100% (87/87) and 96% (84/87) respectively. Metastases were present in 18 nodes (all blue and fluorescent) with 10 patients node positive overall (20%). The procedural detection rate for blue dye and ICG was 96% (48/50) and 2 patients had fluorescent only nodes which were deemed sentinel (4%). CONCLUSION: Fluorescent imaging with ICG is a sensitive, valuable and safe method for SLN biopsy. A combination of blue dye and ICG is useful dual approach when radioisotope is unavailable. ICG has the potential to be a sole tracer agent with improved patient convenience and costs.

Severe systemic reaction to (99m)Tc-methylene diphosphonate: a case report.

We report an unusual severe systemic reaction that occurred in a woman after a (99m)Tc-methylene diphosphonate bone scan and for which no alternative explanation could be found. The bone scintigram showed diffusely increased uptake in the liver and kidneys accompanied by reversible dysfunction of these organs and dermatologic manifestations. We speculate that an immune-mediated mechanism may have caused this unusual reaction.

Nurse practitioner training in breast examination.

BACKGROUND: To date, there have been no published guidelines, either locally or nationally, to advise nurse practitioners on training and assessment in breast and axillary examination. This study is a prospective audit of the clinical competence of a nurse practitioner in breast and axillary clinical examination, following an 18-month period of clinical training and supervision by two consultant breast surgeons. CONCLUSION: The results of the audit show that the nurse achieved a high level of concordance with the findings of consultant breast surgeons. This training and audit process could be incorporated into the training and assessment of future nurse practitioners in this specialist area.

Nurse specialists in breast care: a developing role.

This article describes how one regional breast unit has incorporated the traditional role of the breast care nurse and the developing role of the nurse practitioner into valued and complementary members of the multidisciplinary team. It also details how specialist nurse roles have evolved in the unit.

Effect of PREDICT on chemotherapy/trastuzumab recommendations in HER2-positive patients with early-stage breast cancer.

PREDICT is an online prognostication tool for early-stage breast cancer, which incorporates human epidermal growth factor 2 (HER2) status and stratifies absolute treatment benefits for hormone therapy, chemotherapy and trastuzumab. The present study compared historical multidisciplinary team (MDT) decisions regarding adjuvant treatment with PREDICT estimates, to determine whether certain patients are being over- or undertreated, particularly when stratified by age and oestrogen-receptor (ER) status. HER2-positive early-stage breast cancer cases over a five-year period at the Cambridge Breast Unit (Addenbrooke's Hospital, Cambridge, UK) were retrospectively reviewed. Patients receiving neo-adjuvant therapy were excluded. Adjuvant chemotherapy/trastuzumab recommendations based on PREDICT (<3%, no benefit; 3-5%, discuss treatment; and >5%, recommend treatment) were compared with actual MDT decisions. In total, 109 eligible patients were identified. The average age at diagnosis was 59.6 years, with 21 patients older than 70 years (19%). Four patients were predicted to gain an absolute benefit of >5% from chemotherapy/ trastuzumab, but were not offered treatment (all >70 years). Amongst the 19 patients aged >70 years predicted to benefit >3%, six were not offered treatment (32%). In the patients aged <69 years, there was evidence of overtreatment with adjuvant chemotherapy/trastuzumab in 8 out of 12 cases with <3% benefit using PREDICT. For all 20 patients with ER-negative tumours, the MDT and PREDICT decisions correlated, whilst for ER-positive cases, more than half (8 out of 14) were offered treatment despite a <3% predicted benefit. PREDICT can aid decision-making in HER2-positive early-stage breast cancer by identifying older patients at risk of undertreatment with chemotherapy/trastuzumab, and by reducing the overtreatment of patients with little predicted benefit, particularly in ER-positive disease.

The Cambridge post-mastectomy radiotherapy (C-PMRT) index: a practical tool for patient selection.

BACKGROUND AND PURPOSE: Post mastectomy radiotherapy (PMRT) reduces loco-regional recurrence (LRR) and has been associated with survival benefit. It is recommended for patients with T3/T4 tumours and/or ⩾ 4 positive lymph nodes (LN). The role of PMRT in 1-3 positive LN and LN negative patients is contentious. The C-PMRT index has been designed for selecting PMRT patients, using independent prognostic factors for LRR. This study reports a 10 year experience using this index. MATERIALS AND METHODS: The C-PMRT index was constructed using the following prognostic factors (a) number of positive LN/lymphovascular invasion, (b) tumour size (c) margin status and (d) tumour grade. Patients were categorised as high (H) risk, intermediate (I) risk and low (L) risk. PMRT was recommended for H and I risk patients. The LRR, distant metastasis and overall survival (OS) rates were measured from the day of mastectomy. RESULTS: From 1999 to 2009, 898 invasive breast cancers in 883 patients were treated by mastectomy (H: 323, I: 231 and L: 344). At a median follow up of 5.2 years, 4.7% (42/898) developed LRR. The 5-year actuarial LRR rates were 6%, 2% and 2% for the H, I and L risk groups, respectively. 1.6% (14/898) developed isolated LRR (H risk n = 4, I risk group n = 0 and L risk n = 10). The 5-year actuarial overall survival rates were 67%, 77% and 90% for H, I and L risk groups, respectively. CONCLUSION: Based on published literature, one would have expected a higher LRR rate in the I risk group without adjuvant RT. We hypothesise that the I risk group LRR rates have been reduced to that of the L risk group by the addition of RT. Apart from LN status and tumour size, other prognostic factors should also be considered in selecting patients for PMRT. This pragmatic tool requires further validation.