Refining patient selection for breast cancer immunotherapy: beyond PD-L1

ESMO Open ◽  
2021 ◽  
Vol 6 (5) ◽  
pp. 100257
Author(s):  
M. Kossai ◽  
N. Radosevic-Robin ◽  
F. Penault-Llorca
2021 ◽  
pp. 20210295
Author(s):  
Christina Schröder ◽  
Sebastian Kirschke ◽  
Eyck Blank ◽  
Sophia Rohrberg ◽  
Robert Förster ◽  
...  

Objective: To prospectively analyze the feasibility of an algorithm for patient preparation, treatment planning and selection for deep inspiration breath-hold (DIBH) treatment of left-sided breast cancer. Methods: From 02/2017 to 07/2019, 135 patients with left-sided breast cancer were selected and prepared for radiotherapy in DIBH. 99 received radiotherapy for the breast alone and 36 for the breast including the lymphatic drainage (RNI). Treatment plans DIBH and free breathing (FB) were calculated. Dosimetrical analyses were performed and criteria were defined to assess whether a patient would dosimetrically profit from DIBH. Results: Of the 135 patients, 97 received a DIBH planning CT and 72 were selected for treatment in DIBH according to predefined criteria. When using DIBH there was a mean reduction of the DmeanHeart of 2.8 Gy and DmeanLAD of 4.2 Gy. seven patients did not benefit from DIBH regarding DmeanHeart, 23 regarding DmeanLAD. For the left lung the V20Gy was reduced by 4.9%, the V30Gy by 2.7% with 15 and 29 patients not benefitting from DIBH, respectively. In the 25 patients treated in FB, the benefit of DIBH would have been lower than for patients treated with DIBH (ΔDmeanHeart0.7 Gy vs 3.4 Gy). Conclusion: Dosimetrically, DIBH is no “one fits all” approach. However, there is a statistically significant benefit when looking at a larger patient population. DIBH should be used for treatment of left-sided breast cancer in patients fit for DIBH. Advances in knowledge: This analysis offers a well-designed dosimetrical analysis in patients treated with DIBH radiotherapy in an “every day” cohort.


2019 ◽  
Author(s):  
Andrés López-Cortés ◽  
Alejandro Cabrera-Andrade ◽  
José M. Vázquez-Naya ◽  
Alejandro Pazos ◽  
Humberto Gonzáles-Díaz ◽  
...  

ABSTRACTBackgroundBreast cancer (BC) is a heterogeneous disease characterized by an intricate interplay between different biological aspects such as ethnicity, genomic alterations, gene expression deregulation, hormone disruption, signaling pathway alterations and environmental determinants. Due to the complexity of BC, the prediction of proteins involved in this disease is a trending topic in drug design.MethodsThis work is proposing accurate prediction classifier for BC proteins using six sets of protein sequence descriptors and 13 machine learning methods. After using a univariate feature selection for the mix of five descriptor families, the best classifier was obtained using multilayer perceptron method (artificial neural network) and 300 features.ResultsThe performance of the model is demonstrated by the area under the receiver operating characteristics (AUROC) of 0.980 ± 0.0037 and accuracy of 0.936 ± 0.0056 (3-fold cross-validation). Regarding the prediction of 4504 cancer-associated proteins using this model, the best ranked cancer immunotherapy proteins related to BC were RPS27, SUPT4H1, CLPSL2, POLR2K, RPL38, AKT3, CDK3, RPS20, RASL11A and UBTD1; the best ranked metastasis driver proteins related to BC were S100A9, DDA1, TXN, PRNP, RPS27, S100A14, S100A7, MAPK1, AGR3 and NDUFA13; and the best ranked RNA-binding proteins related to BC were S100A9, TXN, RPS27L, RPS27, RPS27A, RPL38, MRPL54, PPAN, RPS20 and CSRP1.ConclusionsThis powerful model predicts several BC-related proteins which should be deeply studied to find new biomarkers and better therapeutic targets. The script and the results are available as a free repository at https://github.com/muntisa/neural-networks-for-breast-cancer-proteins.


Cancers ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 259 ◽  
Author(s):  
Mikaela Dell’Oro ◽  
Eileen Giles ◽  
Amy Sharkey ◽  
Martin Borg ◽  
Caroline Connell ◽  
...  

Background: Several studies have investigated cardiac dose reduction when utilizing the deep inspiration breath hold (DIBH) technique in patients undergoing radiotherapy for left-sided breast cancer. This paper aims to recommend potential selection criteria based on a retrospective single institute study of free breathing (FB) and DIBH computed tomography (CT) simulation planning scans. Methods: Dosimetric comparisons were performed retrospectively for 20 patients correlating the dose reduction and patient anatomical factors (anatomical variation of chest shape, chest wall separation, total lung volume (TLV) and others). Results: Paired t-tests demonstrated significant cardiac dose reduction for most patients but not all. Minimal cardiac dose reduction was observed for three patients using their DIBH plan, with one patient receiving a higher dose. Linear regression analysis identified a positive correlation between the patient’s TLV (on the FB CT simulation scan) and the magnitude of dosimetric benefit received (0.4045 R2). Conclusion: The TLV measured on a FB plan could potentially be utilised to predict cardiac exposure and assist with patient selection for DIBH. This is important in resource allocation, as DIBH may be unnecessarily recommended for some patients with little dosimetric benefit.


Sign in / Sign up

Export Citation Format

Share Document