Chemotherapy effectiveness in trial-underrepresented groups with early breast cancer:A retrospective cohort study

BACKGROUND: Adjuvant chemotherapy in early stage breast cancer has been shown to reduce mortality in a large meta-analysis of over 100 randomised trials. However, these trials largely excluded patients aged 70 years and over or with higher levels of comorbidity. There is therefore uncertainty about whether the effectiveness of adjuvant chemotherapy generalises to these groups, hindering patient and clinician decision-making. This study utilises administrative healthcare data-real world data (RWD)-and econometric methods for causal analysis to estimate treatment effectiveness in these trial-underrepresented groups. METHODS AND FINDINGS: Women with early breast cancer aged 70 years and over and those under 70 years with a high level of comorbidity were identified and their records extracted from Scottish Cancer Registry (2001-2015) data linked to other routine health records. A high level of comorbidity was defined as scoring 1 or more on the Charlson comorbidity index, being in the top decile of inpatient stays, and/or having 5 or more visits to specific outpatient clinics, all within the 5 years preceding breast cancer diagnosis. Propensity score matching (PSM) and instrumental variable (IV) analysis, previously identified as feasible and valid in this setting, were used in conjunction with Cox regression to estimate hazard ratios for death from breast cancer and death from all causes. The analysis adjusts for age, clinical prognostic factors, and socioeconomic deprivation; the IV method may also adjust for unmeasured confounding factors. Cohorts of 9,653 and 7,965 were identified for women aged 70 years and over and those with high comorbidity, respectively. In the ≥70/high comorbidity cohorts, median follow-up was 5.17/6.53 years and there were 1,935/740 deaths from breast cancer. For women aged 70 years and over, the PSM-estimated HR was 0.73 (95% CI 0.64-0.95), while for women with high comorbidity it was 0.67 (95% CI 0.51-0.86). This translates to a mean predicted benefit in terms of overall survival at 10 years of approximately3% (percentage points) and 4%, respectively. A limitation of this analysis is that use of observational data means uncertainty remains both from sampling uncertainty and from potential bias from residual confounding. CONCLUSIONS: The results of this study, as RWD, should be interpreted with caution and in the context of existing and emerging randomised data. The relative effectiveness of adjuvant chemotherapy in reducing mortality in patients with early stage breast cancer appears to be generalisable to the selected trial-underrepresented groups.</p

Additional file 2 of PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients

Additional file 2: Table S1. Description of the studies included in the analyses

Additional file 2 of PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients

Additional file 2: Table S1. Description of the studies included in the analyses

Additional file 3 of PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients

Additional file 1: Table S3. Patient and primary breast cancer characteristics per study

Additional file 2 of PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients

Additional file 2: Table S1. Description of the studies included in the analyses

Additional file 3 of PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients

Additional file 1: Table S3. Patient and primary breast cancer characteristics per study

Additional file 1 of Colorectal cancer incidences in Lynch syndrome: a comparison of results from the prospective lynch syndrome database and the international mismatch repair consortium

Additional file 1

Additional file 3 of PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients

Additional file 1: Table S3. Patient and primary breast cancer characteristics per study

Additional file 1 of Colorectal cancer incidences in Lynch syndrome: a comparison of results from the prospective lynch syndrome database and the international mismatch repair consortium

Additional file 1

PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients

Abstract Background Prediction of contralateral breast cancer (CBC) risk is challenging due to moderate performances of the known risk factors. We aimed to improve our previous risk prediction model (PredictCBC) by updated follow-up and including additional risk factors. Methods We included data from 207,510 invasive breast cancer patients participating in 23 studies. In total, 8225 CBC events occurred over a median follow-up of 10.2 years. In addition to the previously included risk factors, PredictCBC-2.0 included CHEK2 c.1100delC, a 313 variant polygenic risk score (PRS-313), body mass index (BMI), and parity. Fine and Gray regression was used to fit the model. Calibration and a time-dependent area under the curve (AUC) at 5 and 10 years were assessed to determine the performance of the models. Decision curve analysis was performed to evaluate the net benefit of PredictCBC-2.0 and previous PredictCBC models. Results The discrimination of PredictCBC-2.0 at 10 years was higher than PredictCBC with an AUC of 0.65 (95% prediction intervals (PI) 0.56–0.74) versus 0.63 (95%PI 0.54–0.71). PredictCBC-2.0 was well calibrated with an observed/expected ratio at 10 years of 0.92 (95%PI 0.34–2.54). Decision curve analysis for contralateral preventive mastectomy (CPM) showed the potential clinical utility of PredictCBC-2.0 between thresholds of 4 and 12% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. Conclusions Additional genetic information beyond BRCA1/2 germline mutations improved CBC risk prediction and might help tailor clinical decision-making toward CPM or alternative preventive strategies. Identifying patients who benefit from CPM, especially in the general breast cancer population, remains challenging