scholarly journals Tumour-infiltrating regulatory T cell density before neoadjuvant chemoradiotherapy for rectal cancer does not predict treatment response

Oncotarget ◽  
2017 ◽  
Vol 8 (12) ◽  
pp. 19803-19813 ◽  
Author(s):  
Melanie J. McCoy ◽  
Chris Hemmings ◽  
Chidozie C. Anyaegbu ◽  
Stephanie J. Austin ◽  
Tracey F. Lee-Pullen ◽  
...  

2015 ◽  
Vol 113 (12) ◽  
pp. 1677-1686 ◽  
Author(s):  
M J McCoy ◽  
C Hemmings ◽  
T J Miller ◽  
S J Austin ◽  
M K Bulsara ◽  
...  




Cancers ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 563 ◽  
Author(s):  
Elisabeth S. Gruber ◽  
Georg Oberhuber ◽  
Dietmar Pils ◽  
Theresa Stork ◽  
Katharina Sinn ◽  
...  

Background: T cell density in colorectal cancer (CRC) has proven to be of high prognostic importance. Here, we evaluated the influence of a hyperfractionated preoperative short-term radiation protocol (25 Gy) on immune cell density in tumor samples of rectal cancer (RC) patients and on patient survival. In addition, we assessed spatial tumor heterogeneity by comparison of analogue T cell quantification on full tissue sections with digital T cell quantification on a virtually established tissue microarray (TMA). Methods: A total of 75 RC patients (60 irradiated, 15 treatment-naïve) were defined for retrospective analysis. RC samples were processed for immunohistochemistry (CD3, CD8, PD-1, PD-L1). Analogue (score 0–3) as well as digital quantification (TMA: 2 cores vs. 6 cores, mean T cell count) of marker expression in 2 areas (central tumor, CT; invasive margin, IM) was performed. Survival was estimated on the basis of analogue as well as digital marker densities calculated from 2 cores (Immunoscore: CD3/CD8 ratio) and 6 cores per tumor area. Results: Irradiated RC samples showed a significant decrease in CD3 and CD8 positive T cells, independent of quantification mode. T cell densities of 6 virtual cores approximated to T cell densities of full tissue sections, independent of individual core density or location. Survival analysis based on full tissue section quantification demonstrated that CD3 and CD8 positive T cells as well as PD-1 positive tumor infiltrating leucocytes (TILs) in the CT and the IM had a significant impact on disease-free survival (DFS) as well as overall survival (OS). In addition, CD3 and CD8 positive T cells as well as PD-1 positive TILs in the IM proved as independent prognostic factors for DFS and OS; in the CT, PD-1 positive TILs predicted DFS and CD3 and CD8 positive T cells as well as PD-1 positive TILs predicted OS. Survival analysis based on virtual TMA showed no impact on DFS or OS. Conclusion: Spatial tumor heterogeneity might result in inadequate quantification of immune marker expression; however, if using a TMA, 6 cores per tumor area and patient sample represent comparable amounts of T cell densities to those quantified on full tissue sections. Consistently, the tissue area used for immune marker quantification represents a crucial factor for the evaluation of prognostic and predictive biomarker potential.



Author(s):  
Yusuke Kitagawa ◽  
Takashi Akiyoshi ◽  
Noriko Yamamoto ◽  
Toshiki Mukai ◽  
Yukiharu Hiyoshi ◽  
...  


Author(s):  
Yoshihiro Kurata ◽  
Koichi Hayano ◽  
Gaku Ohira ◽  
Shunsuke Imanishi ◽  
Toru Tochigi ◽  
...  


2004 ◽  
Vol 189 (10) ◽  
pp. 1811-1820 ◽  
Author(s):  
Donna Mildvan ◽  
Ronald J. Bosch ◽  
Ryung S. Kim ◽  
John Spritzler ◽  
David W. Haas ◽  
...  


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhihui Li ◽  
Xiaolu Ma ◽  
Fu Shen ◽  
Haidi Lu ◽  
Yuwei Xia ◽  
...  

Abstract Background To validate and compare various MRI-based radiomics models to evaluate treatment response to neoadjuvant chemoradiotherapy (nCRT) of rectal cancer. Methods A total of 80 patients with locally advanced rectal cancer (LARC) who underwent surgical resection after nCRT were enrolled retrospectively. Rectal MR images were scanned pre- and post-nCRT. The radiomics features were extracted from T2-weighted images, then reduced separately by least absolute shrinkage and selection operator (LASSO) and principal component analysis (PCA). Four classifiers of Logistic Regression, Random Forest (RF), Decision Tree and K-nearest neighbor (KNN) models were constructed to assess the tumor regression grade (TRG) and pathologic complete response (pCR), respectively. The diagnostic performances of models were determined with leave-one-out cross-validation by generating receiver operating characteristic curves and decision curve analysis. Results Three features related to the TRG and 11 features related to the pCR were obtained by LASSO. Top five principal components representing a cumulative contribution of 80% to overall features were selected by PCA. For TRG, the area under the curve (AUC) of RF model was 0.943 for LASSO and 0.930 for PCA, higher than other models (P < 0.05 for both). As for pCR, the AUCs of KNN for LASSO and PCA were 0.945 and 0.712, higher than other models (P < 0.05 for both). The DCA showed that LASSO algorithm was clinically superior to PCA. Conclusion MRI-based radiomics models demonstrated good performance for evaluating the treatment response of LARC after nCRT and LASSO algorithm yielded more clinical benefit.



Sign in / Sign up

Export Citation Format

Share Document