scholarly journals Clinical and prognostic implications of an immune‐related risk model based on TP53 status in lung adenocarcinoma

Author(s):  
Xuming Song ◽  
Qiang Chen ◽  
Jifan Wang ◽  
Qixing Mao ◽  
Wenjie Xia ◽  
...  
2020 ◽  
Author(s):  
Xuming Song ◽  
Qiang Chen ◽  
Jifan Wang ◽  
Qixing Mao ◽  
Wenjie Xia ◽  
...  

Abstract Background: TP53 mutation is the most widespread mutation in lung adenocarcinoma (LUAD), meanwhile p53 (encoded by TP53) has recently been implicated in immune responses. However, it is still unknown whether TP53 mutation may remodel tumor microenvironment to influence tumor progression and prognosis in LUAD.Methods: we developed a six-gene immune-related model (IRM) to predict the survival of patients with LUAD in TCGA cohort based on TP53 status using LASSO Cox analysis, which was also confirmed the predictive ability in two independent cohorts.Results: The mutation of TP53 led to a decrease in the immune response in LUAD. Further analysis revealed that patients in the high-index group had observably lower relative infiltration of memory B cells and regulatory T cells, together with significantly higher relative infiltration proportions neutrophils and resting memory CD4+ T cells. Additionally, the IRM index positively correlated with expression of critical immune checkpoint genes including PDCD1 (encoding PD-1) and CD274 (encoding PD-L1), which was validated in Nanjing cohort. Furthermore, the IRM index as an independent prognostic factor was used to establish a nomogram for clinical application.Conclusion: This immune-related model may serve as a powerful prognostic tool to further optimize immunotherapies for LUAD.


2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Qun-Xian Zhang ◽  
Ye Yang ◽  
Heng Yang ◽  
Qiang Guo ◽  
Jia-Long Guo ◽  
...  

2020 ◽  
Vol 27 (4) ◽  
pp. 525-532
Author(s):  
Hua Geng ◽  
Shixiong Li ◽  
Yixian Guo ◽  
Fang Yan ◽  
Yuebin Han ◽  
...  

2020 ◽  
Vol 26 ◽  
Author(s):  
Fan Zhang ◽  
Suzhen Xie ◽  
Zhenyu Zhang ◽  
Huanhuan Zhao ◽  
Zijun Zhao ◽  
...  

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3308
Author(s):  
Won Sang Shim ◽  
Kwangil Yim ◽  
Tae-Jung Kim ◽  
Yeoun Eun Sung ◽  
Gyeongyun Lee ◽  
...  

The prognosis of patients with lung adenocarcinoma (LUAD), especially early-stage LUAD, is dependent on clinicopathological features. However, its predictive utility is limited. In this study, we developed and trained a DeepRePath model based on a deep convolutional neural network (CNN) using multi-scale pathology images to predict the prognosis of patients with early-stage LUAD. DeepRePath was pre-trained with 1067 hematoxylin and eosin-stained whole-slide images of LUAD from the Cancer Genome Atlas. DeepRePath was further trained and validated using two separate CNNs and multi-scale pathology images of 393 resected lung cancer specimens from patients with stage I and II LUAD. Of the 393 patients, 95 patients developed recurrence after surgical resection. The DeepRePath model showed average area under the curve (AUC) scores of 0.77 and 0.76 in cohort I and cohort II (external validation set), respectively. Owing to low performance, DeepRePath cannot be used as an automated tool in a clinical setting. When gradient-weighted class activation mapping was used, DeepRePath indicated the association between atypical nuclei, discohesive tumor cells, and tumor necrosis in pathology images showing recurrence. Despite the limitations associated with a relatively small number of patients, the DeepRePath model based on CNNs with transfer learning could predict recurrence after the curative resection of early-stage LUAD using multi-scale pathology images.


2016 ◽  
Vol 41 (6) ◽  
pp. E13 ◽  
Author(s):  
Ruth Prieto ◽  
José María Pascual ◽  
Maria Rosdolsky ◽  
Inés Castro-Dufourny ◽  
Rodrigo Carrasco ◽  
...  

OBJECTIVE Craniopharyngioma (CP) adherence strongly influences the potential for achieving a radical and safe surgical treatment. However, this factor remains poorly addressed in the scientific literature. This study provides a rational, comprehensive description of CP adherence that can be used for the prediction of surgical risks associated with the removal of these challenging lesions. METHODS This study retrospectively analyzes the evidence provided in pathological, neuroradiological, and surgical CP reports concerning 3 components of the CP attachment: 1) the intracranial structures attached to the tumor; 2) the morphology of the adhesion; and 3) the adhesion strength. From a total of 1781 CP reports published between 1857 and 2016, a collection of 500 CPs providing the best information about the type of CP attachment were investigated. This cohort includes autopsy studies (n = 254); surgical studies with a detailed description or pictorial evidence of CP adherence (n = 298); and surgical CP videos (n = 61) showing the technical steps for releasing the attachment. A predictive model of CP adherence in hierarchical severity levels correlated with surgical outcomes was generated by multivariate analysis. RESULTS The anatomical location of the CP attachment occurred predominantly at the third ventricle floor (TVF) (54%, n = 268), third ventricle walls (23%, n = 114), and pituitary stalk (19%, n = 94). The optic chiasm was involved in 56% (n = 281). Six morphological patterns of CP attachment were identified: 1) fibrovascular pedicle (5.4%); 2) sessile or patch-like (21%); 3) cap-like (over the CP top, 14%); 4) bowl-like (around the CP bottom, 13.5%); 5) ring-like (encircling central band, 19%); and 6) circumferential (enveloping the entire CP, 27%). Adhesion strength was classified in 4 grades: 1) loose (easily dissectible, 8%); 2) tight (requires sharp dissection, 32%); 3) fusion (no clear cleavage plane, 40%); and 4) replacement (loss of brain tissue integrity, 20%). The types of CP attachment associated with the worst surgical outcomes are the ring-like, bowl-like, and circumferential ones with fusion to the TVF or replacement of this structure (p < 0.001). The CP topography is the variable that best predicts the type of CP attachment (p < 0.001). Ring-like and circumferential attachments were observed for CPs invading the TVF (secondary intraventricular CPs) and CPs developing within the TVF itself (infundibulo-tuberal CPs). Brain invasion and peritumoral gliosis occurred predominantly in the ring-like and circumferential adherence patterns (p < 0.001). A multivariate model including the variables CP topography, tumor consistency, and the presence of hydrocephalus, infundibulo-tuberal syndrome, and/or hypothalamic dysfunction accurately predicts the severity of CP attachment in 87% of cases. CONCLUSIONS A comprehensive descriptive model of CP adherence in 5 hierarchical levels of increased severity—mild, moderate, serious, severe, and critical—was generated. This model, based on the location, morphology, and strength of the attachment can be used to anticipate the surgical risk of hypothalamic injury and to plan the degree of removal accordingly.


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