The tumor organismal environment: Role in tumor development and cancer immunotherapy

Bikfalvi, Andréas

doi:10.1016/j.semcancer.2019.12.021

Cited by 31 publications

(28 citation statements)

References 134 publications

(138 reference statements)

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“…These three radiomic zones had different standardized uptake value (SUV) characteristics based on heterogeneous molecular uptake of the radiolabeled 11 C‐choline tracer. In this study, zone‐1 was the region of metabolic tumor volume, zone‐2 was the proximal peripheral tumor region, and zone‐3 was the extended peripheral tumor region 16,17 …”

Section: Introductionmentioning

confidence: 99%

Utility of radiomic zones for risk classification and clinical outcome predictions using supervised machine learning during simultaneous ¹¹C‐choline PET/MRI acquisition in prostate cancer patients

Tran

Teo

et al. 2021

Medical Physics

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Purpose In most radiomic studies related to cancer research, the traditional tumor‐centric view has predominated. In this retrospective study, we go beyond the single‐tumor region and investigate the utility of proposed radiomic zones for risk classification and clinical outcome predictions using radiomic features extracted from 11C‐choline positron emission tomography (PET) imaging and supervised machine learning in prostate tumors. Materials and Methods Seventy‐seven prostate tumors were selected and delineated. The prostate organ was divided into three radiomic zones, with zone‐1 being the metabolic tumor zone, zone‐2 the proximal peripheral tumor zone, and zone‐3 the extended peripheral tumor zone. LIFEx was used for PET‐radiomic feature extraction. Risk groups were created using Gleason scores (GS), prostate‐specific antigen (PSA) levels, clinical TNM staging, and progression‐free survival (PFS). Random forest (RF) and AdaBoost advanced machine learning algorithms were used for supervised machine learning. Accuracy, positive predictive value, area under the receiver operating characteristic curve (AreaROC), and other metrics were calculated for comparisons of predictive performance between zones. Results For the GS risk classification group, the accuracies of risk classification predictions were 71%, 71%, and 67% using RF and 65%, 64%, and 63% using AdaBoost for zones −1, −2, and −3, respectively. For the PSA group, the accuracies of risk classification predictions were 74%, 65%, and 64% using RF and 76%, 66%, and 67% using AdaBoost for zones −1, −2, and −3, respectively. For the TNM group, the accuracies of risk classification predictions were 68%, 76%, and 78% using RF and 66%, 75%, and 80% using AdaBoost for zones −1, −2, and −3, respectively. For the PFS group, the accuracies of clinical outcome predictions were 77%, 75%, and 83% using RF and 77%, 74%, and 83% using AdaBoost in zones −1, −2, and −3, respectively. Conclusions We proposed three radiomic zones with different standard uptake value characteristics and created four risk groups of prostate cancer patients for testing this idea. We showed that these radiomic zones have different predicting strengths in classifying risk groups and might allow us to identify a radiomic zone with higher accuracy for patient outcome prediction.

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Section: Introductionmentioning

confidence: 99%

Utility of radiomic zones for risk classification and clinical outcome predictions using supervised machine learning during simultaneous ¹¹C‐choline PET/MRI acquisition in prostate cancer patients

Tran

Teo

et al. 2021

Medical Physics

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“…Although the success of immunotherapy is exciting, immunotherapy still faces many challenges 32 . Through in-depth exploration, researchers have realized that the TME is complex and diverse in terms of immune status, and different aspects of the TME have been shown to have important impacts on prognosis and the efficacy of immunotherapy 33 , 34 . In this study, gastric cancer patients were stratified according to characteristics of the TME, and these results will deepen the understanding of the effects of the TME on antitumor and immune responses and will guide more effective immunotherapy strategies.…”

Section: Discussionmentioning

confidence: 99%

Immune cell infiltration signatures identified molecular subtypes and underlying mechanisms in gastric cancer

et al. 2021

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Increasing evidence has clarified that the tumor microenvironment (TME) is closely related to the prognosis and therapeutic efficacy of cancer. However, there is no reliable TME evaluation system used to accurately predict the prognosis of and therapeutic efficacy in gastric cancer. We evaluated the immune microenvironment score (IMS) of 1422 gastric cancer samples based on 51 immune cell signatures. We explored the relationship between the IMS and prognosis, immune cell infiltration, cancer subtype, and potential immune escape mechanisms. The results show that activation of the stroma and decreased levels of immune infiltration were associated with a low IMS. A high IMS was characterized by Epstein–Barr virus infection, increased mutation load, microsatellite instability, and immune cell infiltration. A high IMS was also related to high expression of immune checkpoint molecules (PD-1/PD-L1). Finally, patients with a high IMS had a better response to PD-1/PD-L1 inhibitors and may be more suitable for immune checkpoint inhibitors (area under the curve = 0.81). In addition, a low IMS may be converted into the immune-infiltrating subtype after romidepsin treatment. Stratification based on the IMS may enable gastric cancer patients to benefit more from immunotherapy and help identify new cancer treatment strategies.

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“…With characteristics of insidious onset and early metastasis, ~80% of patients are diagnosed at a late stage of disease ( 32 ). At present, tumor immunotherapy results in marked effects in cancer treatment ( 33 ). The impact of the immune microenvironment on tumor cells has been demonstrated in numerous studies ( 34 , 35 ).…”

Section: Discussionmentioning

confidence: 99%

Comprehensive analysis of prognostic immune‑related genes associated with the tumor microenvironment of pancreatic ductal adenocarcinoma

et al. 2020

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Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with a specific tumor immune microenvironment (TIME). Therefore, investigating prognostic immune-related genes (IRGs) that are closely associated with TIME to predict PDAC clinical outcomes is necessary. In the present study, 459 samples of PDAC from the Genotype-Tissue Expression database, The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) were included and a survival-associated module was identified using weighted gene co-expression network analysis. Based on the Cox regression analysis and least absolute shrinkage and selection operator analysis, four IRGs (2′-5′-oligoadenylate synthetase 1, MET proto-oncogene, receptor tyrosine kinase, interleukin 1 receptor type 2 and interleukin 20 receptor subunit β) were included in the prognostic model to calculate the risk score (RS), and patients with PDAC were divided into high- and low-RS groups. Kaplan-Meier survival and receiver operating characteristic curve analyses demonstrated that the low-RS group had significantly improved survival conditions compared with the high-RS group in TCGA training set. The prognostic function of the model was also validated using ICGC and GEO cohorts. To investigate the mechanism of different overall survival between the high- and low-RS groups, the present study included Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data and Cell Type Identification by Estimating Relative Subset of Known RNA Transcripts algorithms to investigate the state of the tumor microenvironment and immune infiltration inpatients in the cohort from TCGA. In summary, four genes associated with the TIME of PDAC were identified, which may provide a reference for clinical treatment.

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The tumor organismal environment: Role in tumor development and cancer immunotherapy

Cited by 31 publications

References 134 publications

Utility of radiomic zones for risk classification and clinical outcome predictions using supervised machine learning during simultaneous ¹¹C‐choline PET/MRI acquisition in prostate cancer patients

Utility of radiomic zones for risk classification and clinical outcome predictions using supervised machine learning during simultaneous ¹¹C‐choline PET/MRI acquisition in prostate cancer patients

Immune cell infiltration signatures identified molecular subtypes and underlying mechanisms in gastric cancer

Comprehensive analysis of prognostic immune‑related genes associated with the tumor microenvironment of pancreatic ductal adenocarcinoma

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The tumor organismal environment: Role in tumor development and cancer immunotherapy

Cited by 31 publications

References 134 publications

Utility of radiomic zones for risk classification and clinical outcome predictions using supervised machine learning during simultaneous 11C‐choline PET/MRI acquisition in prostate cancer patients

Utility of radiomic zones for risk classification and clinical outcome predictions using supervised machine learning during simultaneous 11C‐choline PET/MRI acquisition in prostate cancer patients

Immune cell infiltration signatures identified molecular subtypes and underlying mechanisms in gastric cancer

Comprehensive analysis of prognostic immune‑related genes associated with the tumor microenvironment of pancreatic ductal adenocarcinoma

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Utility of radiomic zones for risk classification and clinical outcome predictions using supervised machine learning during simultaneous ¹¹C‐choline PET/MRI acquisition in prostate cancer patients

Utility of radiomic zones for risk classification and clinical outcome predictions using supervised machine learning during simultaneous ¹¹C‐choline PET/MRI acquisition in prostate cancer patients