A high tumour-stroma ratio (TSR) in colon tumours and its metastatic lymph nodes predicts poor cancer-free survival and chemo resistance

PURPOSE

Despite known high-risk features, accurate identification of patients at high risk of cancer recurrence in colon cancer remains a challenge. As tumour stroma plays an important role in tumour invasion and metastasis, the easy, low-cost and highly reproducible tumour-stroma ratio (TSR) could be a valuable prognostic marker, which is also believed to predict chemo resistance.

METHODS

Two independent series of patients with colon cancer were selected. TSR was estimated by microscopic analysis of 4 µm haematoxylin and eosin (H&E) stained tissue sections of the primary tumour and the corresponding metastatic lymph nodes. Patients were categorized as TSR-low (≤ 50%) or TSR-high (> 50%). Differences in overall survival and cancer-free survival were analysed by Kaplan-Meier curves and cox-regression analyses. Analyses were conducted for TNM-stage I-II, TNM-stage III and patients with an indication for chemotherapy separately.

RESULTS

We found that high TSR was associated with poor cancer-free survival in TNM-stage I-II colon cancer in two independent series, independent of other known high-risk features. This association was also found in TNM-stage III tumours, with an additional prognostic value of TSR in lymph node metastasis to TSR in the primary tumour alone. In addition, high TSR was found to predict chemo resistance in patients receiving adjuvant chemotherapy after surgical resection of a TNM-stage II-III colon tumour.

CONCLUSION

In colon cancer, the TSR of both primary tumour and lymph node metastasis adds significant prognostic value to current pathologic and clinical features used for the identification of patients at high risk of cancer recurrence, and also predicts chemo resistance.

Standardization of the tumor-stroma ratio scoring method for breast cancer research

Purpose

The tumor-stroma ratio (TSR) has repeatedly proven to be correlated with patient outcomes in breast cancer using large retrospective cohorts. However, studies validating the TSR often show variability in methodology, thereby hampering comparisons and uniform outcomes.

Method

This paper provides a detailed description of a simple and uniform TSR scoring method using Hematoxylin and Eosin (H&E)-stained core biopsies and resection tissue, specifically focused on breast cancer. Possible histological challenges that can be encountered during scoring including suggestions to overcome them are reported. Moreover, the procedure for TSR estimation in lymph nodes, scoring on digital images and the automatic assessment of the TSR using artificial intelligence are described.

Conclusion

Digitized scoring of tumor biopsies and resection material offers interesting future perspectives to determine patient prognosis and response to therapy. The fact that the TSR method is relatively easy, quick, and cheap, offers great potential for its implementation in routine diagnostics, but this requires high quality validation studies.

Stromal scoring in advanced colon and rectal cancer: Stroma-rich tumors and their association with aggressive phenotypes

Background: Our aim was to explore relevance of the proportion between neoplastic cell component and tumor-associated stroma in order to assess its association with confirmed aggressive phenotypes of right/left colon and rectum cancers in a large series of patients. Methods: The quantification of stroma component was performed in patients diagnosed with colorectal adenocarcinoma who underwent surgical resection. The analyzed variables were age, gender, anatomical/pathological features, and tumor-stroma proportion. Tumor-stroma proportion was estimated based on slides used in routine pathology for determination of T status and was described as low, with a stromal percentage ?50% or high, with a stromal percentage >50%. The tumor-stroma proportion was estimated by two observers, and the inter-observer agreement was assessed. Results: The sample included 390 colorectal adenocarcinoma patients. Stroma-rich tumors were observed in 53.3% of cases. Well-differentiated tumors had the lowest stromal proportions (p = 0.028). Stroma-poor tumors showed less depth of invasion (p<0.001). High stromal content was observed in association with tumor budding, perineural, angiolymphatic, and lymph node involvement, and distant metastasis (p?0.001). Colorectal adenocarcinoma without lymph node or distant metastasis involvement had lower stromal proportion, while metastatic ones exhibited high stromal content (p <0.001). The inter-rater reliability (concordance) between the estimations of pathologists for tumor-stroma proportions was high (?=0.746). Conclusion: The tumorstroma proportion in colorectal adenocarcinoma was associated with adverse prognostic factors, reflecting the stage of the disease. Stroma-rich tumors showed a significant correlation with advancement of the disease and its aggressiveness. Due to its availability tumor-stroma proportion evaluation has high application potential and can complement current staging system for colorectal adenocarcinoma.

Standardized Assessment of the Tumor-Stroma Ratio in Colorectal Cancer: Interobserver Validation and Reproducibility of a Potential Prognostic Factor

The tumor stroma plays a relevant role in the initiation and evolution of solid tumors. Tumor-stroma ratio (TSR) is a histological feature that expresses the proportion of the stromal component that surrounds cancer cells. In different studies, the TSR represents a potential prognostic factor: a rich stroma in tumor tissue can promote invasion and aggressiveness. The aim of this study was to evaluate the reproducibility and determine the interobserver agreement in the TSR score. The stromal estimate was evaluated in patients diagnosed with colorectal adenocarcinoma (CRA), who underwent surgical resection. We also evaluated age, gender, and other anatomopathological features. Tumor-stroma ratio was calculated based on the slide used in routine diagnostic pathology to determine the T-status. Stromal percentages were separated into 2 categories: ⩽50%—low stroma and >50%—high stroma. The interobserver agreement in the TSR scoring was evaluated among 4 pathologists at different stages of professional experience, using 2 different ways to learn the scoring system. In total, 98 patients were included in this study; 54.1% were male, with a mean age of 61.9 years. Localized disease was diagnosed in 60.2% of patients. Stromal-poor CRA was predominant. The concordance between the TSR percentages of the 4 pathologists was substantial (Kappa > 0.6). There was greater agreement among pathologists for stromal-poor tumors. Substantial agreement and high reproducibility were observed in the determination of TSR score. The TSR score is feasible, suggesting that the presented methodology can be used to facilitate the determination of the stromal proportion of potential prognostic factor.

Deep Convolutional Neural Network-Based Lymph Node Metastasis Prediction for Colon Cancer Using Histopathological Images

Background

Human evaluation of pathological slides cannot accurately predict lymph node metastasis (LNM), although accurate prediction is essential to determine treatment and follow-up strategies for colon cancer. We aimed to develop accurate histopathological features for LNM in colon cancer.

Methods

We developed a deep convolutional neural network model to distinguish the cancer tissue component of colon cancer using data from the tissue bank of the National Center for Tumor Diseases and the pathology archive at the University Medical Center Mannheim, Germany. This model was applied to whole-slide pathological images of colon cancer patients from The Cancer Genome Atlas (TCGA). The predictive value of the peri-tumoral stroma (PTS) score for LNM was assessed.

Results

A total of 164 patients with stages I, II, and III colon cancer from TCGA were analyzed. The mean PTS score was 0.380 (± SD = 0.285), and significantly higher PTS scores were observed in patients in the LNM-positive group than those in the LNM-negative group (P < 0.001). In the univariate analyses, the PTS scores for the LNM-positive group were significantly higher than those for the LNM-negative group (P < 0.001). Further, the PTS scores in lymphatic invasion and any one of perineural, lymphatic, or venous invasion were significantly increased in the LNM-positive group (P < 0.001 and P < 0.001).

Conclusion

We established the PTS score, a simplified reproducible parameter, for predicting LNM in colon cancer using computer-based analysis that could be used to guide treatment decisions. These findings warrant further confirmation through large-scale prospective clinical trials.

The Proportion of Tumour-Stroma in Metastatic Lymph Nodes is An Accurately Prognostic Indicator of Poor Survival for Advanced-Stage Colon Cancers

The importance of tumour microenvironment in tumour behaviour has now become clearer. This study aimed to determine the prognostic effect of the proportion of tumour-stroma (PTS) in metastatic lymph nodes of advanced-stage colon cancers (CCs). We investigated PTS in positive lymph nodes of stage III-IV CC patients who underwent surgical treatment between 2004 and 2014. We used a standard approach in methodology. PTS was significantly associated with prognostic factors in the metastatic lymph nodes (perineural invasion [p = 0.031], lymphatic invasion [p = 0.032], invasive margin [p = 0.043], advanced pT [p = 0.020], and margin involvement [p = 0.034]). In addition, the correlations between PTS estimates (R = 0.704 to 0.617, p < 0.001), the reproducibility of the research (Κappa = 0.72-0.68) and the usefulness of the cut-off value (ROC: 50.33%; AUC = 0.752 [0.667-0.857]) were successful. In univariate analysis, 5-year survival was poor for RFS (p < 0.001), OS (p = 0.001) and LR (p = 0.013) in high PTS patients. Multivariate analysis confirmed that high PTS was an independent worse parameter for RFS (HR = 1.32, 95% CI: 1.17-2.55, p = 0.001) and OS (HR = 1.37, 95% CI: 1.25-1 - 2.56, p = 0.009). In this study, we showed that high PTS in metastatic lymph nodes was a successful prognostic marker for advanced-stage CCs. Also, the standard approach we used for the methodology was successful.

Tumour Budding and Tumour Stroma Ratio are Reliable Predictors for Death and Recurrence in Elderly Stage I Colon Cancer Patients

AIM

Tumour budding (BD) and Tumour stroma ratio (TSR) are considered valuable survival parameters for colon cancer (CC), but it is still unclear whether these parameters predict a poor prognosis. This study aimed to determine the survival effect of TB and TSR in elderly stage I CC patients.

METHODS

We evaluated these parameters in eighty-eight stage I CC patients who underwent surgical treatment alone between 1998 and 2015. The relationship between these parameters and age was investigated. Model A was used for methodology.

RESULTS

In elderly patients (≥68), these parameters obtained more successful results for following analysis: relationship between prognostic factors [lymphatic invasion (BD, p = 0.003; TSR, p = 0.003), perineural invasion (BD, p = 0.016; TSR, p = 0.004), tumour deposites (BD, p = 0.005; TSR, p = 0.033), MSI (BD, p=0.031; TSR, p=0.012), etc.], correlation of estimates (BD, r=0.724; TSR, r=0.703), and reproducibility of study (BD [Kappa (ĸ)]=0.53-0.75; TSR (ĸ) =0.56-0.71). Also, the cut-off values were useful for BD (area of under ROC (AUC)=0.816 [0.707-0.925]) and TSR (AUC=0.810 [0.697-0.924]). In univariate analysis, in elderly patients, these parameters had a poor 5-year survival for RFS (p<0.001[BD], p=0.001[TSR]), OS (p<0.001[BD], p=0.005[TSR]), and LR (p=0.008[BD], p=0.034[TSR]). Multivariate analysis confirmed that these parameters are independent worse predictors for RFS (BD: 1.42[HR], p=0.002; TSR: 1.50[HR], p=0.001), OS (BD: 1.38[HR], p=0.014; TSR: 1.42[HR], p=0.005), and LR (BD: 1.61[HR], p=0.034) in elderly patients.

CONCLUSIONS

Our results confirm that BD and TSR are reliable indexes for poor survival in elderly stage I CC patients. We recommend using model A for successful results and standardization.

Uniform Noting for International Application of the Tumor-Stroma Ratio as an Easy Diagnostic Tool: Protocol for a Multicenter Prospective Cohort Study

Background

Colon cancer treatment is dependent on the stage at diagnosis. The current Tumor-Node-Metastasis (TNM) staging for the selection of patients for adjuvant chemotherapy needs additional prognostic and predictive biomarkers. Better decision making for chemotherapy will result in reducing over- and undertreatment. We developed a new, easy-to-apply, practice-changing method to select colon cancer patients for adjuvant chemotherapy: the tumor-stroma ratio (TSR). The TSR distinguishes within stage II-III patients who will likely benefit from adjuvant chemotherapy and those who will not.

Objective

The aim of the study was to add, in addition to the TNM classification, the TSR to current routine pathology evaluation. Pathologists will be instructed for scoring the TSR in combination with a quality assessment program. An international multicenter study will validate the parameter prospectively.

Methods

The study is designed for future implementation of the TSR to the current TNM guidelines, using routinely Haematoxylin- and Eosin-stained tumor tissue sections. In part 1 of the study, an electronic learning (e-learning) module with a quality assessment program using the European Society of Pathology framework will be developed. This module will be used to assess the reliability and reproducibility of the TSR, conducted by national and international pathologists. Part 2 will involve the validation of the TSR in a prospective cohort of colon cancer p-stage II-III patients in a multicenter setting. In total, 1500 patients will be included.

Results

The results of part 1 will be expected in the first half of 2019. For part 2, the inclusion of patients in the prospective study, which started at the end of 2018, will take 3 years with an additional follow-up after another 3 years.

Conclusions

The main endpoints of this study are as follows: in part 1, trained (international) pathologists who are able to reliably score the TSR, resulting in low intra- and interobserver variation; in part 2, confirmation of significant survival differences for patients with a stroma-high tumor versus patients with a stroma-low tumor. On the basis of these findings, a modification in current treatment guidelines will be suggested.

Trial Registration

Netherlands Trial Register NTR7270; https://www.trialregister.nl/trial/7072

International Registered Report Identifier (IRRID)

DERR1-10.2196/13464

Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer

PURPOSE

Tumor-stroma ratio (TSR) serves as an independent prognostic factor in colorectal cancer and other solid malignancies. The recent introduction of digital pathology in routine tissue diagnostics holds opportunities for automated TSR analysis. We investigated the potential of computer-aided quantification of intratumoral stroma in rectal cancer whole-slide images.

METHODS

Histological slides from 129 rectal adenocarcinoma patients were analyzed by two experts who selected a suitable stroma hot-spot and visually assessed TSR. A semi-automatic method based on deep learning was trained to segment all relevant tissue types in rectal cancer histology and subsequently applied to the hot-spots provided by the experts. Patients were assigned to a 'stroma-high' or 'stroma-low' group by both TSR methods (visual and automated). This allowed for prognostic comparison between the two methods in terms of disease-specific and disease-free survival times.

RESULTS

With stroma-low as baseline, automated TSR was found to be prognostic independent of age, gender, pT-stage, lymph node status, tumor grade, and whether adjuvant therapy was given, both for disease-specific survival (hazard ratio = 2.48 (95% confidence interval 1.29-4.78)) and for disease-free survival (hazard ratio = 2.05 (95% confidence interval 1.11-3.78)). Visually assessed TSR did not serve as an independent prognostic factor in multivariate analysis.

CONCLUSIONS

This work shows that TSR is an independent prognosticator in rectal cancer when assessed automatically in user-provided stroma hot-spots. The deep learning-based technology presented here may be a significant aid to pathologists in routine diagnostics.