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18th February 2022
Adjuvant pembrolizumab added to chemotherapy post surgery significantly improves event-free survival compared to chemotherapy alone. This was the conclusion of a study by researchers from the Centre of Experimental Cancer Medicine, Barts Cancer Institute, London, UK.
Triple negative breast cancer constitutes 10-15% of female breast cancers and is a more aggressive cancer with more frequent recurrence and worse survival compared with the non-triple negative form. Clinical trial data has revealed improved disease-free survival with postoperative chemotherapy in patients with either triple-negative or HER2-positive breast cancer who had residual disease after neoadjuvant chemotherapy. Whether the addition of pembrolizumab to neoadjuvant chemotherapy would increase the proportion of patients with early triple-negative breast cancer who had a pathological complete response after surgery was unclear until publication in 2020, of the KEYNOTE-522 trial. The trial concluded that the percentage of patients with a pathological complete response was significantly higher among those who received pembrolizumab plus neoadjuvant chemotherapy than among those who received placebo plus neoadjuvant chemotherapy.
For the present study, the authors have reported on an updated analysis of the KEYNOTE-522 trial and in particular, event-free survival as well as additional efficacy and updated safety information. Participants were adults with confirmed triple negative breast cancer or those with newly diagnosed and previously untreated non-metastatic disease with primary tumour and regional lymph node involvement. During the adjuvant phase, patients with previously untreated stage II or III triple negative breast cancer, were randomised 2:1 to receive either pembrolizumab (the pembrolizumab–chemotherapy group) or placebo (the placebo–chemotherapy group), administered once every 3 weeks. After surgery, patients received either pembrolizumab or placebo and chemotherapy every 3 weeks for up to nine cycles. The primary endpoint was pathological complete response and event-free survival.
Adjuvant Pembrolizumab and event-free survival
A total of 1174 patients were randomly assigned to either arm and during follow-up, 123 (15.7%) patients assigned to the pembrolizumab arm had an event or died compared to 93 (23.8%) in the placebo-chemotherapy arm.
The estimated event-free survival after 36 months was 84.5% in the adjuvant pembrolizumab group and 76.8% in the placebo arm, giving a hazard ratio, HR, for an event or death = 0.63, 95% CI 0.48 – 0.82, p < 0.001.
Data on overall survival were described as immature at the time of the analysis and the estimated overall survival at 36 months was 89.7% in the adjuvant pembrolizumab group and 86.9% in the placebo-chemotherapy group. With respect to safety, the authors reported that the reported adverse events were consistent with the established safety profile of both pembrolizumab and chemotherapy.
The authors concluded that among those with early triple negative breast cancer, neoadjuvant pembrolizumab plus chemotherapy and then followed by adjuvant pembrolizumab after surgery, was associated with a significantly longer event-free survival than neoadjuvant chemotherapy alone.
Schmid P et al. Event-free Survival with Pembrolizumab in Early Triple-Negative Breast Cancer N Engl J Med 2022
13th December 2021
An MRI radiomics model based on deep learning has shown good predictive accuracy at identifying whether patients with triple negative breast cancer (TNBC) would have systemic recurrence within three years of neoadjuvant chemotherapy, according to researchers from the Department of Radiology, Peking University, Beijing, China.
A triple negative breast cancer describes an aggressive tumour which lacks expression of oestrogen receptor, progesterone receptor and HER2 and accounts for around 15% of all breast cancers. Moreover, TNBC has been found to have the lowest 4-year survival rate at 77%.
Neoadjuvant chemotherapy is seen as the mainstay of treatment for early stage TNBC followed by definitive surgery although relapse within 3 years has been found to occur in up to 97% of patients. Magnetic resonance imaging (MRI) has been used to enable prediction of disease recurrence although these MRI radiomics models have required a radiologist to manually draw regions of interest (ROIs) as the input which is very time-consuming.
In the present study, the Chinese team used radiomics based on deep learning models and which utilised automated segmented ROIs of breast tumours from MRI images to predict whether patients with TNBC who had received neoadjuvant chemotherapy, would experience disease recurrence within three years of treatment. The team undertook a retrospective analysis of consecutive female patients with unilateral primary TNBC and for whom pre- and post-neoadjuvant MRI imaging was available. The researchers also collected clinico-pathologic factors from the patient medical records, e.g., menopausal status, the histological type of cancer, the clinical T and N stages. They developed a MRI radiomics model based on three separate features; pre-treatment features (model 1), post-treatment features (model 2) and a combination of pre and post-treatment MRI features (model 3). Multivariate analysis was used to assess associations between clinical factors and disease recurrence and the predictive performance of the models was assessed using the receiver operating characteristic curves and the area under the curves (AUC).
A total of 147 women with a median age of 49.5 years were included in the analysis, 104 (22 with disease recurrence) were used for the training cohort and 43 for the testing cohort (9 with disease recurrence) and the median time to disease recurrence was 17 months.
In both the univariate and multivariate analysis the clinical T stage and pathological T stage were significantly associated with systemic disease recurrence within three years of treatment (p < 0.05). Using these two variables in a clinical model yielded AUCs of 0.75 for the training cohort and 0.74 in the testing cohort. In comparison, the AUCs for each of the MRI radiomics model was 0.88 (model 1), 0.91 (model 2) and 0.96 (model 3).
Model 3 had a perfect sensitivity (i.e., 1) and a specificity of 0.85 compared to a sensitivity of 0.67 and specificity of 0.82 for the clinical model and this difference was statistically significant (p < 0.05). However, neither model 1 or 2 were significantly better than the clinical model.
Commenting on their findings, the authors speculated that the predictive ability of the third MRI radiomics model was likely to have a greater prognostic value by inclusion of post-neoadjuvant chemotherapy features that were reflective of treatment-induced changes.
They concluded that the value of their non-invasive model was in being able to more easily identify those at risk of disease recurrence and to therefore strengthen the treatment of these patients after surgery to improve their prognosis.