Browse
Submit Data
Databases
API
Help

Dataset Information

12 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Scoring the tumor-stroma ratio in colon cancer: procedure and recommendations.

ABSTRACT: The tumor-stroma ratio (TSR) has been reported as a strong, independent prognostic parameter in colon cancer as well as in other epithelial cancer types, and may be implemented to routine pathology diagnostics. The TSR is an easy technique, based on routine hematoxylin and eosin stained histological sections, estimating the amount of stroma present in the primary tumor. It links tumors with high stromal content to poor prognosis. The analysis time is less than 2 min with a low inter-observer variation. Scoring of the TSR has been validated in a number of independent international studies. In this manuscript, we provide a detailed technical description of estimating the TSR in colon cancer, including examples, pitfalls, and recommendations.

SUBMITTER: van Pelt GW

PROVIDER: S-EPMC6182321 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Scoring the tumor-stroma ratio in colon cancer: procedure and recommendations.

van Pelt G W GW Kjær-Frifeldt S S van Krieken J H J M JHJM Al Dieri R R Morreau H H Tollenaar R A E M RAEM Sørensen F B FB Mesker W E WE

Virchows Archiv : an international journal of pathology 20180720 4

The tumor-stroma ratio (TSR) has been reported as a strong, independent prognostic parameter in colon cancer as well as in other epithelial cancer types, and may be implemented to routine pathology diagnostics. The TSR is an easy technique, based on routine hematoxylin and eosin stained histological sections, estimating the amount of stroma present in the primary tumor. It links tumors with high stromal content to poor prognosis. The analysis time is less than 2 min with a low inter-observer var ...[more]

PMID: 30030621

Similar Datasets

The Stroma Liquid Biopsy Panel Contains a Stromal-Epithelial Gene Signature Ratio That Is Associated with the Histologic Tumor-Stroma Ratio and Predicts Survival in Colon Cancer.

Project description:Liquid biopsy has emerged as a novel approach to tumor characterization, offering advantages in sample accessibility and tissue heterogeneity. However, as mutational analysis predominates, the tumor microenvironment has largely remained unacknowledged in liquid biopsy research. The current work provides an explorative transcriptomic characterization of the Stroma Liquid BiopsyTM (SLB) proteomics panel in colon carcinoma by integrating single-cell and bulk transcriptomics data from publicly available repositories. Expression of SLB genes was significantly enriched in tumors with high histologic stromal content in comparison to tumors with low stromal content (median enrichment score 0.308 vs. 0.222, p = 0.036). In addition, we identified stromal-specific and epithelial-specific expression of the SLB genes, that was subsequently integrated into a gene signature ratio. The stromal-epithelial signature ratio was found to have prognostic significance in a discovery cohort of 359 colon adenocarcinoma patients (OS HR 2.581, 95%CI 1.567-4.251, p < 0.001) and a validation cohort of 229 patients (OS HR 2.590, 95%CI 1.659-4.043, p < 0.001). The framework described here provides transcriptomic evidence for the prognostic significance of the SLB panel constituents in colon carcinoma. Plasma protein levels of the SLB panel may reflect histologic intratumoral stromal content, a poor prognostic tumor characteristic, and hence provide valuable prognostic information in liquid biopsy.

| S-EPMC8750571 | biostudies-literature

e-Learning for Instruction and to Improve Reproducibility of Scoring Tumor-Stroma Ratio in Colon Carcinoma: Performance and Reproducibility Assessment in the UNITED Study.

Project description:BackgroundThe amount of stroma in the primary tumor is an important prognostic parameter. The tumor-stroma ratio (TSR) was previously validated by international research groups as a robust parameter with good interobserver agreement.ObjectiveThe Uniform Noting for International Application of the Tumor-Stroma Ratio as an Easy Diagnostic Tool (UNITED) study was developed to bring the TSR to clinical implementation. As part of the study, an e-Learning module was constructed to confirm the reproducibility of scoring the TSR after proper instruction.MethodsThe e-Learning module consists of an autoinstruction for TSR determination (instruction video or written protocol) and three sets of 40 cases (training, test, and repetition sets). Scoring the TSR is performed on hematoxylin and eosin-stained sections and takes only 1-2 minutes. Cases are considered stroma-low if the amount of stroma is ≤50%, whereas a stroma-high case is defined as >50% stroma. Inter- and intraobserver agreements were determined based on the Cohen κ score after each set to evaluate the reproducibility.ResultsPathologists and pathology residents (N=63) with special interest in colorectal cancer participated in the e-Learning. Forty-nine participants started the e-Learning and 31 (63%) finished the whole cycle (3 sets). A significant improvement was observed from the training set to the test set; the median κ score improved from 0.72 to 0.77 (P=.002).Conclusionse-Learning is an effective method to instruct pathologists and pathology residents for scoring the TSR. The reliability of scoring improved from the training to the test set and did not fall back with the repetition set, confirming the reproducibility of the TSR scoring method.Trial registrationThe Netherlands Trial Registry NTR7270; https://www.trialregister.nl/trial/7072.International registered report identifier (irrid)RR2-10.2196/13464.

| S-EPMC8122297 | biostudies-literature

Low Tumor-to-Stroma Ratio Reflects Protective Role of Stroma against Prostate Cancer Progression.

Project description:Tumor-to-stroma ratio (TSR) is a prognostic factor that expresses the relative amounts of tumor and intratumoral stroma. In this study, its clinical and molecular relevance was evaluated in prostate cancer (PCa). The feasibility of automated quantification was tested in digital scans of tissue microarrays containing 128 primary tumors from 72 PCa patients stained immunohistochemically for epithelial cell adhesion molecule (EpCAM), followed by validation in a cohort of 310 primary tumors from 209 PCa patients. In order to investigate the gene expression differences between tumors with low and high TSR, we applied multigene expression analysis (nCounter® PanCancer Progression Panel, NanoString) of 42 tissue samples. TSR scores were categorized into low (<1 TSR) and high (≥1 TSR). In the pilot cohort, 31 patients (43.1%) were categorized as low and 41 (56.9%) as high TSR score, whereas 48 (23.0%) patients from the validation cohort were classified as low TSR and 161 (77.0%) as high. In both cohorts, high TSR appeared to indicate the shorter time to biochemical recurrence in PCa patients (Log-rank test, p = 0.04 and p = 0.01 for the pilot and validation cohort, respectively). Additionally, in the multivariate analysis of the validation cohort, TSR predicted BR independent of other factors, i.e., pT, pN, and age (p = 0.04, HR 2.75, 95%CI 1.07-7.03). Our data revealed that tumors categorized into low and high TSR score show differential expression of various genes; the genes upregulated in tumors with low TSR score were mostly associated with extracellular matrix and cell adhesion regulation. Taken together, this study shows that high stroma content can play a protective role in PCa. Automatic EpCAM-based quantification of TSR might improve prognostication in personalized medicine for PCa.

| S-EPMC8622253 | biostudies-literature

Combined Assessment of the Tumor-Stroma Ratio and Tumor Immune Cell Infiltrate for Immune Checkpoint Inhibitor Therapy Response Prediction in Colon Cancer.

Project description:The best current biomarker strategies for predicting response to immune checkpoint inhibitor (ICI) therapy fail to account for interpatient variability in response rates. The histologic tumor-stroma ratio (TSR) quantifies intratumoral stromal content and was recently found to be predictive of response to neoadjuvant therapy in multiple cancer types. In the current work, we predicted the likelihood of ICI therapy responsivity of 335 therapy-naive colon adenocarcinoma tumors from The Cancer Genome Atlas, using bioinformatics approaches. The TSR was scored on diagnostic tissue slides, and tumor-infiltrating immune cells (TIICs) were inferred from transcriptomic data. Tumors with high stromal content demonstrated increased T regulatory cell infiltration (p = 0.014) but failed to predict ICI therapy response. Consequently, we devised a hybrid tumor microenvironment classification of four stromal categories, based on histological stromal content and transcriptomic-deconvoluted immune cell infiltration, which was associated with previously established transcriptomic and genomic biomarkers for ICI therapy response. By integrating these biomarkers, stroma-low/immune-high tumors were predicted to be most responsive to ICI therapy. The framework described here provides evidence for expansion of current histological TIIC quantification to include the TSR as a novel, easy-to-use biomarker for the prediction of ICI therapy response.

| S-EPMC8616493 | biostudies-literature

Tumour-stroma ratio outperforms tumour budding as biomarker in colon cancer: a cohort study.

Project description:The tumour-stroma ratio (TSR) and tumour budding (TB) are two high-risk factors with potential to be implemented in the next TNM classification. The aim of the current study was to evaluate the practical application of the two biomarkers based on reproducibility, independency and prognostic value. Patients diagnosed with stage II or III colon cancer who underwent surgery between 2005 and 2016 were included. Both TSR and TB were scored on haematoxylin and eosin-stained tissue sections. The TSR, based on the relative amount of stroma, was scored in increments of 10%. TB was scored following the consensus guidelines; a bud was defined as ≤ 4 tumour cells. For analysis, three categories were used. Cohen's kappa was used for reproducibility. The prognostic value was determined with survival analysis. In total, 246 patients were included. The TSR distribution was N = 137 (56%) stroma-low and N = 109 (44%) stroma-high. The TB distribution was TB-low N = 194 (79%), TB-intermediate N = 35 (14%) and TB-high N = 17 (7%). The reproducibility of the TSR was good (interobserver agreement kappa = 0.83 and intraobserver agreement kappa = 0.82), whereas the inter- and intraobserver agreement for scoring TB was moderate (kappa 0.47 and 0.45, respectively). The survival analysis showed an independent prognostic value for disease-free survival for TSR (HR 1.57; 95% CI 1.01-2.44; p = 0.048) and for TB-high (HR 2.01; 95% CI 1.02-3.96; p = 0.043). Based on current results, we suggest the TSR is a more reliable parameter in daily practice due to better reproducibility and independent prognostic value for disease-free survival.

| S-EPMC8589816 | biostudies-literature

Integrating Tumor Stroma Biomarkers With Clinical Indicators for Colon Cancer Survival Stratification.

Project description:The tumor stroma plays an important role in tumor progression and chemotherapeutic resistance; however, its role in colon cancer (CC) survival prognosis remains to be investigated. Here, we identified tumor stroma biomarkers and evaluated their role in CC prognosis stratification. Four independent datasets containing a total of 1,313 patients were included in this study and were divided into training and testing sets. Stromal scores calculated using the estimation of stromal and immune cells in malignant tumors using expression data (ESTIMATE) algorithm were used to assess the tumor stroma level. Kaplan-Meier curves and the log-rank test were used to identify relationships between stromal score and prognosis. Tumor stroma biomarkers were identified by cross-validation of multiple datasets and bioinformatics methods. Cox proportional hazards regression models were constructed using four prognosis factors (age, tumor stage, the ESTIMATE stromal score, and the biomarker stromal score) in different combinations for prognosis prediction and compared. Patients with high stromal scores had a lower overall survival rate (p = 0.00016), higher risk of recurrence (p < 0.0001), and higher probability of chemotherapeutic resistance (p < 0.0001) than those with low scores. We identified 16 tumor stroma biomarkers and generated a new prognosis indicator termed the biomarker stromal score (ranging from 0 to 16) based on their expression levels. Its addition to an age/tumor stage-based model significantly improved prognosis prediction accuracy. In conclusion, the tumor stromal score is significantly negatively associated with CC survival prognosis, and the new tumor stroma indicator can improve CC prognosis stratification.

| S-EPMC7750539 | biostudies-literature

Does heterogeneity matter in the estimation of tumour budding and tumour stroma ratio in colon cancer?

Project description:Tumour budding (TB) and Tumour Stroma Ratio (TSR) may be rewarding in the treatment stratification of patients with stage II colon cancer. However, lack of standardization may exclude these parameters from being used in a clinical setting. The purpose of this methodologic study was to compare stereology with semi-quantitative estimations of TSR, to investigate the intra-tumoural heterogeneity of TB and TSR, and to assess the intra- and inter-observer agreement.Three paraffin embedded tumour blocks, one of them representing the deepest invasive front, were selected from each of 43 patients treated for stage II colon cancer. TSR was estimated in H&E sections semi-quantitatively using conventional microscopy, and stereologically on scanned slides, using the newCAST stereology platform. TB was scored across 10 high power fields at the invasive front in cytokeratin AE1/AE3 stained sections.Subjective, semi-quantitative estimates of TSR significantly correlated to the stereological estimates, with the best correlation found for sections with the deepest invasive tumour penetration (??=?0.621, p <?0.001). Inter-observer agreement was moderate to substantial for both TB (?appa?=?0.46-0.73) and TSR (?appa?=?0.70-0.75). The Intraclass correlation coefficient (ICC) for TSR varied from 0.322 based on stereological hotspot estimation to 0.648 for the semi-quantitative evaluation. For TB, ICC varied from 0.646 based on continuous data to 0.698 based on categorical data (cut-off: 10 buds). Thus, the intra-tumoural heterogeneity for both TB and the semi-quantitative estimation of TSR was low.We recommend using only one tissue section representing the deepest invasive tumour area for estimation of TSR. For TB we recommend using one tissue section; however due to low representation of high-budding tumours, results must be considered with caution.

| S-EPMC5859415 | biostudies-literature

Impact of Tumor-Stroma Ratio on the Prognosis of Colorectal Cancer: A Systematic Review.

Project description:BackgroundIt is critical to develop a reliable and cost-effective prognostic tool for colorectal cancer (CRC) stratification and treatment optimization. Tumor-stroma ratio (TSR) may be a promising indicator of poor prognosis in CRC patients. As a result, we conducted a systematic review on the predictive value of TSR in CRC.MethodsThis study was carried out according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guideline. An electronic search was completed using commonly used databases PubMed, CENTRAL, Cochrane Central Register of Controlled Trials, and Google scholar till the last search up to May 30, 2021. STATA version 13 was used to analyze the data.ResultsA total of 13 studies [(12 for disease-free survival (DFS) and nine studies for overall survival (OS)] involving 4,857 patients met the inclusion criteria for the systematic review in the present study. In individuals with stage II CRC, stage III CRC, or mixed stage CRC, we observed a significantly higher pooled hazard ratio (HR) in those with a low TSR/greater stromal content (HR, 1.54; 95% CI: 1.20 to 1.88), (HR, 1.90; 95% CI: 1.35 to 2.45), and (HR, 1.70; 95% CI: 1.45 to 1.95), respectively, for predicting DFS. We found that a low TSR ratio had a statistically significant predictive relevance for stage II (HR, 1.43; 95% CI: 1.09 to 1.77) and mixed stages of CRC (HR, 1.65; 95% CI: 1.31 to 2.0) for outcome OS.ConclusionIn patients with CRC, low TSR was found to be a prognostic factor for a worse prognosis (DFS and OS).

| S-EPMC8635241 | biostudies-literature

Tenascin C, Fibronectin, and Tumor-Stroma Ratio in Pancreatic Ductal Adenocarcinoma.

Project description:OBJECTIVES:Pancreatic ductal adenocarcinoma (PDAC) is characterized by abundant stroma with increased expression of tenascin C and fibronectin. Their role and tumor-stroma ratio in PDAC are not well known. The aim of this study was to evaluate tenascin C and fibronectin expression and tumor-stroma ratio and their prognostic relevance in PDAC. METHODS:Ninety-five resected PDACs were immunohistochemically stained for tenascin C and fibronectin, and the expression was separately assessed in tumor bulk and front. Tumor-stroma ratio was determined with sections stained with hematoxylin-eosin. RESULTS:Tenascin C and fibronectin were abundantly expressed in the stroma of PDAC, but absent in adjacent normal pancreatic tissue. Fibronectin expression of the bulk was associated with high T class (P = 0.045). In the main analysis, tenascin C and fibronectin expression and tumor-stroma ratio were not associated with patient survival. In a subgroup analysis of early-stage PDAC (T1-T2 tumors), high tenascin C expression in the tumor bulk was associated with poor prognosis (hazard ratio, 8.23; 95% confidence interval, 2.71-24.96). CONCLUSIONS:Tenascin C and fibronectin are abundantly expressed in PDAC, but they seem to have no major association with patient survival. However, in early-stage PDAC, tenascin C expression of the tumor bulk may have prognostic impact. Tumor-stroma ratio has no prognostic value in PDAC.

| S-EPMC6296849 | biostudies-other

Expression of pim-1 in tumors, tumor stroma and tumor-adjacent mucosa co-determines the prognosis of colon cancer patients.

Project description:Provirus integration site for Moloney murine leukemia virus (pim-1) is a proto-oncogene that is linked to the development and progression of several cancers. In this study, we evaluated pim-1 expression in tumors, tumor stroma and tumor-adjacent mucosa together as an independent prognostic factor for colon cancer patients. The study included 343 colon cancer patients. Immunohistochemical staining was used to detect pim-1. Multivariate cox regression for disease-free survival (DFS) were used to identify independent prognostic factors. Analytic hierarchy process (AHP) was used to calculate the weight of pim-1 in tumors, tumor stroma and tumor-adjacent mucosa in order to obtain a Pim-1 total score (PTS) for recurrence and survival. Kaplan-Meier DFS curves and OS curves for patients with different pim-1 expression levels were compared using the log-rank test. In this study, four independent prognostic factors were identified for colon cancer patients: pim-1 expression in tumors, tumor stroma, tumor-adjacent mucosa, as well as tumor stage. It has been established that clinical stage is an important prognostic factor for colon cancer patients. However, PTS can identify the patients who are likely to recur not only in the whole radical excision group but also within each stage of this group. Based on the results of this study we can conclude that the PTS combined with clinical staging system may be a better predictor of colon cancer patients' prognosis than using the clinical stage system alone. ClinicalTrials.gov Number: ChiCTR-PRCH-12002842.

| S-EPMC3792018 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data