Tumor-Stroma Ratio is an independent predictor for overall survival and disease free survival in gastric cancer patients

Quantification of Gremlin 1 throughout the tumor stroma using whole slide imaging and its clinicopathological significance in gastric cancer

Gremlin 1 (GREM1) is an antagonist of bone morphogenetic protein (BMP). GREM1 is expressed in the stromal cells of various carcinomas and promotes tumor progression by suppressing BMP signaling. We designed this study to establish an evaluation strategy for GREM1 expression, focusing on the tumor stroma, and to examine its clinicopathological significance in gastric cancer (GC) progression. We employed RNA in situ hybridization (ISH) to evaluate the prognostic value of GREM1 expression in a cohort of 104 surgically resected GC cases and assessed ISH scores according to previous reports. GREM1 expression was observed in tumor stromal cells, including fibroblasts. We defined GREM1-positive cells as those expressing ISH score ≥ 3 and quantified the number of GREM1-positive cells using image analysis software. We examined the relationship between the number of GREM1-positive cells in the tumor stroma and clinicopathological features. The number of GREM1-positive cells per tumor stroma ranged from 0 to 714.7 cells/mm2 (median, 1.65 cells/mm2). We divided the 104 GC cases into GREM1-High and GREM1-Low expression groups based on the abovementioned median value. GREM1-High expression group was significantly associated with a more advanced pT grade, pN grade, lymphatic invasion, and venous invasion. Kaplan-Meier analysis showed significantly poorer survival in the GREM1-High expression group than in the GREM1-Low expression group. These results indicated that GREM1 expression in GC is localized in tumor stromal cells, and that high GREM1 expression in the tumor stroma could be a poor prognostic factor.

Tumor microenvironment characteristics association with clinical outcome in patients with resected intestinal-type gastric cancer

BACKGROUND

Tumor microenvironment (TME) characteristics including tumor stroma ratio (TSR), tumor budding (TB), and tumor-infiltrating lymphocytes (TILs) were examined in resected gastric cancer. These TME features have been shown to indicate metastatic potential in colon cancer, and intestinal-type gastric cancer (IGC) has pathological similarities with that malignancy.

METHODS

TSR, TB, and TILs were quantified in routine histological sections from 493 patients with IGC who underwent radical resection at 2 university hospitals in China from 2010 to 2016. TME variables were dichotomized as follows: TSR (50%), TILs (median), TB per international guidelines (4 buds/0.785mm2), and platelet-lymphocyte ratio (PLR) per survival ROC. Association of TME features with patient clinicopathological characteristics, time-to-recurrence (TTR), and cancer-specific-survival (CSS) were examined using univariate and multivariate analysis, including a relative contribution analysis by Cox regression.

RESULTS

Patients whose tumors showed high TSR or high TB or low TILs were each significantly associated with increased T and N stage, higher histological grade, and poorer TTR and CSS at 5 years. Only TSR and N stage were independently associated with TTR and CSS after adjustment for covariates. PLR was only independently associated with TTR after adjustment for covariates. Among the variables examined, only TSR was significantly associated with both TTR (HR 1.72, 95% CI, 1.14-2.60, P = .01) and CSS (HR 1.62, 95% CI, 1.05-2.51, P = .03) multivariately. Relative contribution to TTR revealed that the top 3 contributors were N stage (45.1%), TSR (22.5%), and PLR (12.9%), while the top 3 contributors to CSS were N stage (59.9%), TSR (14.7%), and PLR (10.9%).

CONCLUSIONS

Among the examined TME features, TSR was the most robust for prognostication and was significantly associated with both TTR and CSS. Furthermore, the relative contribution of TSR to patient TTR and CSS was second only to nodal status.

Oral Papillary Squamous Cell Carcinoma and Oral Squamous Cell Carcinoma: A Histopathological and Immunohistochemical Comparative Study

PURPOSE

The aim of the study is to investigate the immunohistochemical expression of both Alpha smooth muscle actin and Transforming Growth Factor beta and compare their expression in oral papillary squamous cell carcinoma with their expression in different histological grades of oral squamous cell carcinoma. A correlation between these immuno-histochemical expressions and histological findings will then be performed. The research question is "Do the percentages of α-SMA and TGF-β immune-expression in OPSCC differ from that in the conventional OSCC?".

METHODS

This will be achieved by collecting archival blocks of oral papillary squamous cell carcinoma and different grades of oral squamous cell carcinoma, staining the specimens with Transforming Growth Factor beta and alpha smooth muscle actin, then measuring the mean staining index of expression in each group and the area percent of both markers.

RESULTS

Results revealed that transforming growth factor beta expression in the epithelium was high in all cases of well-differentiated squamous cell carcinoma, most oral papillary squamous cell carcinoma, and poorly differentiated oral squamous cell carcinoma. On the other hand, different grades of oral squamous cell carcinoma showed a high staining index of alpha smooth muscle actin expression in the stroma. While cases of oral papillary squamous cell carcinoma were either moderate or low-staining.

CONCLUSIONS

Oral papillary squamous cell carcinoma has a favourable prognosis compared to different histological grades, and the prognosis does not depend only on histological grade but also on other prognostic factors.

Influence of Tumor Stroma on the Aggressiveness of Poorly Cohesive Gastric Carcinoma

Tumor-stroma crosstalk promotes the adaptation of cancer cells to the local microenvironment and sustains their growth. We assessed the quantitative and qualitative impact of intralesional stroma on clinic-pathological features and the prognosis of poorly cohesive gastric cancer (PCGC) variants. Tissue microarrays including 75 PCGC specimens were immunostained for cytokeratin 8/18 and α-smooth muscle actin to assess the relative proportion of neoplastic cells versus stromal components and the cases were subsequently divided into stroma-rich (SR) and stroma-poor (SP) tumors. Stromal status is significantly associated with the depth of tumor invasion. Patient survival rate was found to be higher in the SP compared to the SR tumor group and, hence, abundant stroma was identified as a significant risk factor in univariable analysis but had no independent prognostic impact. We also investigated the mRNA levels of KRT8 and the associated transcriptional signatures using the molecular data of 82 PCGC cases divided into KRT8-high and KRT8-low groups. KRT8-high tumors were enriched in proteins localized in the extracellular compartment and their expression levels correlated with longer survival in the KRT8-high group and shorter overall survival in the KRT8-low group. Comprehensively, we find that relative intralesional stromal content is a marker of aggressiveness in PCGC tumors and that extracellular proteins characterize functionally and clinically different PCGC subgroups.

Feasibility and effectiveness of automatic deep learning network and radiomics models for differentiating tumor stroma ratio in pancreatic ductal adenocarcinoma

OBJECTIVE

This study aims to compare the feasibility and effectiveness of automatic deep learning network and radiomics models in differentiating low tumor stroma ratio (TSR) from high TSR in pancreatic ductal adenocarcinoma (PDAC).

METHODS

A retrospective analysis was conducted on a total of 207 PDAC patients from three centers (training cohort: n = 160; test cohort: n = 47). TSR was assessed on hematoxylin and eosin-stained specimens by experienced pathologists and divided as low TSR and high TSR. Deep learning and radiomics models were developed including ShuffulNetV2, Xception, MobileNetV3, ResNet18, support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), and logistic regression (LR). Additionally, the clinical models were constructed through univariate and multivariate logistic regression. Kaplan-Meier survival analysis and log-rank tests were conducted to compare the overall survival time between different TSR groups.

RESULTS

To differentiate low TSR from high TSR, the deep learning models based on ShuffulNetV2, Xception, MobileNetV3, and ResNet18 achieved AUCs of 0.846, 0.924, 0.930, and 0.941, respectively, outperforming the radiomics models based on SVM, KNN, RF, and LR with AUCs of 0.739, 0.717, 0.763, and 0.756, respectively. Resnet 18 achieved the best predictive performance. The clinical model based on T stage alone performed worse than deep learning models and radiomics models. The survival analysis based on 142 of the 207 patients demonstrated that patients with low TSR had longer overall survival.

CONCLUSIONS

Deep learning models demonstrate feasibility and superiority over radiomics in differentiating TSR in PDAC. The tumor stroma ratio in the PDAC microenvironment plays a significant role in determining prognosis.

CRITICAL RELEVANCE STATEMENT

The objective was to compare the feasibility and effectiveness of automatic deep learning networks and radiomics models in identifying the tumor-stroma ratio in pancreatic ductal adenocarcinoma. Our findings demonstrate deep learning models exhibited superior performance compared to traditional radiomics models.

KEY POINTS

• Deep learning demonstrates better performance than radiomics in differentiating tumor-stroma ratio in pancreatic ductal adenocarcinoma. • The tumor-stroma ratio in the pancreatic ductal adenocarcinoma microenvironment plays a protective role in prognosis. • Preoperative prediction of tumor-stroma ratio contributes to clinical decision-making and guiding precise medicine.

Association of CDX2 and mucin expression with chemotherapeutic benefits in patients with stage II/III gastric cancer

BACKGROUND

Better predictors of patients with stage II/III gastric cancer (GC) most likely to benefit from adjuvant chemotherapy are urgently needed. This study aimed to assess the ability of CDX2 and mucin markers to predict prognosis and fluorouracil-based adjuvant chemotherapy benefits.

METHODS

CDX2 and mucin protein expressions were examined by immunohistochemistry and compared with survival and adjuvant chemotherapy benefits in a prospective evaluation cohort of 782 stage II/III GC patients. Then, the main findings were validated in an independent validation cohort (n = 386) and an external mRNA sequencing dataset (ACRG cohort, n = 193).

RESULTS

In the evaluation cohort, CDX2, CD10, MUC2, MUC5AC, and MUC6 expressions were observed in 59.7%, 26.7%, 27.6%, 55.1%, and 57.7% of patients, respectively. However, only the expression of CDX2 was found to be associated with adjuvant chemotherapy benefits. Most importantly, CDX2-negative patients had a poorer prognosis when treated with surgery only, while the prognosis of CDX2-negative and CDX2-positive patients was similar when receiving postoperative adjuvant chemotherapy. Further analysis revealed that patients with CDX2 negative tumors benefited from chemotherapy (5-year overall survival rates: 60.0% with chemotherapy vs. 23.2% with surgery-only, p < 0.001), whereas patients with CDX2 positive tumors did not (pinteraction  = 0.004). Consistent results were obtained in the validation and ACRG cohorts.

CONCLUSIONS

Negative expression of CDX2 is an independent risk factor for survival in stage II/III GC, but subsequent adjuvant chemotherapy is able to compensate for this unfavorable effect. Therefore, active chemotherapy is more urgent for patients with negative CDX2 expression than for patients with positive CDX2 expression.

Tumour-stroma ratio (TSR) in breast cancer: comparison of scoring core biopsies versus resection specimens

PURPOSE

Tumour-stroma ratio (TSR) is an important prognostic and predictive factor in several tumour types. The aim of this study is to determine whether TSR evaluated in breast cancer core biopsies is representative of the whole tumour.

METHOD

Different TSR scoring methods, their reproducibility, and the association of TSR with clinicopathological characteristics were investigated in 178 breast carcinoma core biopsies and corresponding resection specimens. TSR was assessed by two trained scientists on the most representative H&E-stained digitised slides. Patients were treated primarily with surgery between 2010 and 2021 at Semmelweis University, Budapest.

RESULTS

Ninety-one percent of the tumours were hormone receptor (HR)-positive (luminal-like). Interobserver agreement was highest using 100 × magnification (κ_core = 0.906, κ_{resection specimen} = 0.882). The agreement between TSR of core biopsies and resection specimens of the same patients was moderate (κ = 0.514). Differences between the two types of samples were most frequent in cases with TSR scores close to the 50% cut-off point. TSR was strongly correlated with age at diagnosis, pT category, histological type, histological grade, and surrogate molecular subtype. A tendency was identified for more recurrences among stroma-high (SH) tumours (p = 0.07). Significant correlation was detected between the TSR and tumour recurrence in grade 1 HR-positive breast cancer cases (p = 0.03).

CONCLUSIONS

TSR is easy to determine and reproducible on both core biopsies and in resection specimens and is associated with several clinicopathological characteristics of breast cancer. TSR scored on core biopsies is moderately representative for the whole tumour.

Tumor-Stroma Ratio is a Promising Prognostic Classifier in Oropharyngeal Cancer

Tumor-stroma ratio (TSR) has been analyzed in many tumor types. To date, the clinical significance of TSR has not been investigated in oropharyngeal squamous cell carcinoma (OPSCC). We used a recently introduced recommendation for the assessment of TSR in a large cohort of 182 patients with OPSCC treated at the Helsinki University Hospital. The percentage of tumor-associated stroma was estimated in hematoxylin and eosin (HE)-stained sections and categorized into 2 groups: "stroma-high" (>50%) and "stroma-low" (≤50%). In multivariable analysis, TSR had a significant association with patient survival as stroma-high tumors showed worse disease-free survival (hazard ratio [HR] = 3.22, 95% confidence interval [CI] = 1.43-7.26, P = .005), disease-specific survival (HR = 2.48, 95% CI = 1.29-4.74, P = .006), and overall survival (HR = 2.23, 95% CI = 1.29-3.85, P = .004). The prognostic value of TSR was superior to the Tumor-Node-Metastasis classification. In addition, the significant prognostic value of TSR was demonstrated when analyzing human papillomavirus (HPV)-positive and HPV-negative cases separately (P < .05). In conclusion, TSR is a powerful prognostic indicator in OPSCC. It can be assessed quickly without additional costs using standard HE slides. Owing to its simplicity and reproducibility, TSR can be implemented in routine pathology diagnostics and reporting. Patients with stroma-rich tumors have an increased risk of recurrence and cancer-related mortality and may benefit from appropriate intensive treatment strategies with close follow-up.

Tumour stroma ratio is a potential predictor for 5-year disease-free survival in breast cancer

Background

The tumour–stroma ratio (TSR) is identified as a promising prognostic parameter for breast cancer, but the cutoff TSR value is mostly assessed by visual assessment, which lacks objective measurement. The aims of this study were to optimize the cutoff TSR value, and evaluate its prognosis value in patients with breast cancer both as continuous and categorical variables.

Methods

Major clinicopathological and follow-up data were collected for a series of patients with breast cancer. Tissue microarray images stained with cytokeratin immunohistochemistry were evaluated by automated quantitative image analysis algorithms to assess TSR. The potential cutoff point for TSR was optimized using maximally selected rank statistics. The association between TSR and 5-year disease-free survival (5-DFS) was assessed by Cox regression analysis. Kaplan–Meier analysis and log-rank test were used to assess the significance in survival analysis.

Results

The optimal cut-off TSR value was 33.5%. Using this cut-off point, categorical variable analysis found that low TSR (i.e., high stroma, TSR ≤ 33.5%) predicts poor outcomes for 5-DFS (hazard ratio [HR] = 2.82, 95% confidence interval [CI] = 1.81–4.40, P = 0.000). When TSR was considered as a continuous parameter, results showed that increased stroma content was associated with worse 5-DFS (HR = 1.71, 95% CI = 1.34–2.18, P = 0.000). Similar results were also obtained in three molecular subtypes in continuous and categorical variable analyses. Moreover, in the Kaplan–Meier analysis, log-rank test showed that low TSR displayed a worse 5-DFS than high TSR (P = 0.000). Similar results were also obtained in patients with triple-negative breast cancer, human epidermal growth factor receptor 2 (HER2)-positive breast cancer, and luminal–HER2-negative breast cancer.

Conclusion

TSR is an independent predictor for 5-DFS in breast cancer with worse survival outcomes in low TSR. The prognostic value of TSR was also observed in other three molecular subtypes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-10183-5.

Vimentin and Ki-67 immunolabeling in canine gastric carcinomas and their prognostic value

This study evaluated the expression of vimentin and Ki-67 proliferative index (PI) by immunohistochemistry in 30 canine gastric carcinomas (GCs) and a possible association with clinical and pathological features and patient's survival time. Vimentin immunoreactivity was assessed in neoplastic cells (in primary lesions, emboli, and metastases) and tumor-associated stroma (TAS) of canine GCs. Ki-67 PI was quantified in the neoplastic epithelial component. Vimentin immunolabeling in neoplastic cells was found in 30% of the primary lesions, in 82% of the neoplastic emboli, and in 50% of the metastases; in TAS, it was observed in all cases. A mean of 16% of the TAS was immunolabeled for vimentin. High vimentin immunolabeling in the TAS (>16%) was detected in 40% of cases. The average value of Ki-67 PI was 50%, and 80% of the lesions had Ki-67 PI above 20%. Vimentin immunolabeling in neoplastic cells was more frequent in less-differentiated carcinomas (diffuse [29%] and indeterminate types [75%]) than well-differentiated carcinomas (intestinal type [0%], P = .049). No significant differences were observed in vimentin immunolabeling in the TAS or Ki-67 PI according to histological diagnosis, depth of invasion, presence of neoplastic emboli or metastases. However, vimentin immunolabeling in the TAS was positively correlated with Ki-67 PI (r = .394, P = .031). Furthermore, a moderate negative correlation was observed between Ki-67 PI and survival time (r = -0.540). Our results suggest that vimentin and Ki-67 PI have potential for providing prognostic information in cases of canine GCs.

Comprehensive Characterization of Tumor Purity and Its Clinical Implications in Gastric Cancer

Solid tumour tissues are composed of tumour and non-tumour cells, such as stromal cells and immune cells. These non-tumour cells constitute an essential part of the tumour microenvironment (TME), which decrease the tumour purity and play an important role in carcinogenesis, malignancy progression, treatment resistance and prognostic assessment. However, the implications of various purity levels in gastric cancer (GC) remain largely unknown. In the present study, we used an in-silico approach to infer the tumour purity of 2,259 GC samples obtained from our hospital and 12 public datasets based on the transcriptomic data. We systematically evaluated the association of tumour purity with clinical outcomes, biological features, TME characteristics and treatment response in GC. We found that tumour purity might be a patient-specific intrinsic characteristic of GC. Low tumour purity was independently correlated with shorter survival time and faster recurrence and significantly associated with mesenchymal, invasive and metastatic phenotypes. Integrating GC purity into a clinical prognostic nomogram significantly improved predictive validity and reliability. In addition, low tumour purity was strongly associated with immune and stromal cell functions. Fibroblasts, endothelial cells and monocytes were markedly enriched in low-purity tumours, serving as robust indicators of a poor prognosis. Moreover, patients with low GC purity may not benefit more from adjuvant chemotherapy. Our findings highlight that tumour purity confers important clinical, biological, microenvironmental and treatment implications for patients with GC. Therefore, a comprehensive evaluation of tumour purity in individual tumours can provide more insights into the molecular mechanisms of GC, facilitate precise classification and clinical prediction and help to develop more effective individualised treatment strategies.

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

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 Biopsy^TM (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.

CT Radiomics and Machine-Learning Models for Predicting Tumor-Stroma Ratio in Patients With Pancreatic Ductal Adenocarcinoma

Purpose

To develop and validate a machine learning classifier based on multidetector computed tomography (MDCT), for the preoperative prediction of tumor-stroma ratio (TSR) expression in patients with pancreatic ductal adenocarcinoma (PDAC).

Materials and Methods

In this retrospective study, 227 patients with PDAC underwent an MDCT scan and surgical resection. We quantified the TSR by using hematoxylin and eosin staining and extracted 1409 arterial and portal venous phase radiomics features for each patient, respectively. Moreover, we used the least absolute shrinkage and selection operator logistic regression algorithm to reduce the features. The extreme gradient boosting (XGBoost) was developed using a training set consisting of 167 consecutive patients, admitted between December 2016 and December 2017. The model was validated in 60 consecutive patients, admitted between January 2018 and April 2018. We determined the XGBoost classifier performance based on its discriminative ability, calibration, and clinical utility.

Results

We observed low and high TSR in 91 (40.09%) and 136 (59.91%) patients, respectively. A log-rank test revealed significantly longer survival for patients in the TSR-low group than those in the TSR-high group. The prediction model revealed good discrimination in the training (area under the curve [AUC]= 0.93) and moderate discrimination in the validation set (AUC= 0.63). While the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for the training set were 94.06%, 81.82%, 0.89, 0.89, and 0.90, respectively, those for the validation set were 85.71%, 48.00%, 0.70, 0.70, and 0.71, respectively.

Conclusions

The CT radiomics-based XGBoost classifier provides a potentially valuable noninvasive tool to predict TSR in patients with PDAC and optimize risk stratification.

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

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.

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

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.

Semiautomatic analysis of tumor proportion in colon cancer: Lessons from a validation study

The tumor stroma ratio (TSR) is a promising histopathologic prognostic biomarker, which could allow for more accurate risk stratification and improved patient management in colorectal cancer. The purpose of our research was to validate the results of a previous study, which had suggested that not only a low but also a high tumor proportion (TP) might be an independent risk factor for occurrence of distant metastasis and worse overall survival using a semiautomatic image analysis approach with the open-source software ImageJ. We investigated 253 pT3 and pT4 adenocarcinomas of no special type. The previously established thresholds (PES-cut-offs) used to classify the patients (previous 3-tiered-classification) according to the tumor proportion (TP) in a highTP (TP ≥ 54%), a mediumTP (TP < 54% ∩ TP >15%) and a lowTP (TP ≤ 15%) group did not show a significant risk stratification. Even the adjustment of these threshold revealed no significant results. Therefore, a receiver-operating characteristic (ROC) analysis was performed to establish the cut-off with the most significant predictive power and a "new 2-tiered-classification" using this cut-off (40% at MinTP) showed a significantly shorter absence of metastasis for patients with a low TP (p = 0.007). These results confirm that a low TP is associated with an adverse prognosis. This study did not confirm the previous assumption that a high TP might also be a risk factor for occurrence of metastasis. Furthermore, it demonstrates that this semiautomatic technique is not superior to the established method, so that approaches to enhance prognostic techniques should continue.

Gastrointestinal cancer classification and prognostication from histology using deep learning: Systematic review

BACKGROUND

Gastrointestinal cancers account for approximately 20% of all cancer diagnoses and are responsible for 22.5% of cancer deaths worldwide. Artificial intelligence-based diagnostic support systems, in particular convolutional neural network (CNN)-based image analysis tools, have shown great potential in medical computer vision. In this systematic review, we summarise recent studies reporting CNN-based approaches for digital biomarkers for characterization and prognostication of gastrointestinal cancer pathology.

METHODS

Pubmed and Medline were screened for peer-reviewed papers dealing with CNN-based gastrointestinal cancer analyses from histological slides, published between 2015 and 2020.Seven hundred and ninety titles and abstracts were screened, and 58 full-text articles were assessed for eligibility.

RESULTS

Sixteen publications fulfilled our inclusion criteria dealing with tumor or precursor lesion characterization or prognostic and predictive biomarkers: 14 studies on colorectal or rectal cancer, three studies on gastric cancer and none on esophageal cancer. These studies were categorised according to their end-points: polyp characterization, tumor characterization and patient outcome. Regarding the translation into clinical practice, we identified several studies demonstrating generalization of the classifier with external tests and comparisons with pathologists, but none presenting clinical implementation.

CONCLUSIONS

Results of recent studies on CNN-based image analysis in gastrointestinal cancer pathology are promising, but studies were conducted in observational and retrospective settings. Large-scale trials are needed to assess performance and predict clinical usefulness. Furthermore, large-scale trials are required for approval of CNN-based prediction models as medical devices.

Toward a quantitative method for estimating tumour-stroma ratio in breast cancer using polarized light microscopy

The tumour-stroma ratio (TSR) has been explored as a useful source of prognostic information in various cancers, including colorectal, breast, and gastric. Despite research showing potential prognostic utility, its uptake into the clinic has been limited, in part due to challenges associated with subjectivity, reproducibility, and quantification. We have recently proposed a simple, robust, and quantifiable high-contrast method of imaging intra- and peri-tumoural stroma based on polarized light microscopy. Here we report on its use to quantify TSR in human breast cancer using unstained slides from 40 patient samples of invasive ductal carcinoma (IDC). Polarimetric results based on a stromal abundance metric correlated well with pathology designations, showing a statistically significant difference between high- and low-stroma samples as scored by two clinical pathologists. The described polarized light imaging methodology shows promise for use as a quantitative, automatic, and standardizable tool for quantifying TSR, potentially addressing some of the challenges associated with its current estimation.

Proposal of a scoring system for predicting pathological risk based on a semiautomated analysis of whole slide images in oral squamous cell carcinoma

BACKGROUND

The study aimed to evaluate the risk factors based on pathological findings comprehensively in oral squamous cell carcinoma (OSCC) using image analysis.

METHODS

Scanned images of hematoxylin and eosin-, pan-cytokeratin-, CD3-, and CD8-stained slides of OSCC cases from 256 patients were analyzed, and six variables were obtained including the tumor-stroma ratio, tumor budding per tumor bed area, and tumor infiltrating lymphocytes-associated variables. We determined the "score" of all cases based on the variables, and all cases were classified into low-, intermediate-, and high-risk groups.

RESULTS

A significant difference in prognosis was confirmed between the risk groups (p < 0.001), and even when evaluated within different tumor-node-metastasis (TNM) stages, the high-risk groups were associated with poor survival.

CONCLUSIONS

We report our work on a possible descriptive model that can predict prognosis based on pathological and imaging findings regardless of the TNM stage.

The prognostic value of tumor-stromal ratio combined with TNM staging system in esophagus squamous cell carcinoma

Background: Tumor stroma is a crucial component of the tumor environment that interacted with tumor cells and modulated tumor cell proliferation, immune evasion, and metastasis. Tumor-stromal ratio (TSR) has been confirmed as an influential independent prognostic factor for diverse types of cancer, but it was seldom discussed in esophagus squamous cell carcinoma (ESCC). Methods: In present study, pathological sections from the most invasive part of the ESCC of 270 patients were analyzed for their TSR by visual inspection and software. The TSR was combined with the TNM staging system to further explain its predictive value of prognosis. The 57 cases ESCC from TCGA database also were included as an independently validated cohort. Results: Our results indicated that TSR was a robust prognostic factor for ESCC patients. TSR by visual inspection was dependable to reflect the stroma percent of the tumor compared to software calculation. Compared with stroma-low groups, the risk of death increased by 153.1% for patients in the stroma-high group [HR=2.531 (95%CI 1.657-3.867), P<0.001]. The results of ROC analysis in two cohorts indicated that TSNM staging system had better resolving ability with the largest area under the curve [0.698 95%CI (0.635-0.760), 0.691 95%CI (0.555-0.807)], compare to TNM. The novel TSNM staging system revealed strong predictive performance (P<0.001). Conclusion: TSR was a reliable dependent indicator for ESCC prognosis. The TSNM staging system has a better discriminative ability than the conventional TNM staging system, especially for III stage patients.

Gastric cancer nodal tumour-stroma ratios influence prognosis

This study showed that nodal tumour-stroma ratio (NTSR) is an independent prognostic factor for overall and disease-free survival of patients with gastric cancer. Both relative stroma-rich primary tumour-stroma ratio (PTSR) and NTSR were independent negative prognostic factors for overall survival in gastric cancer. This study supports assessment of tumour-stroma ratio in the routine pathological diagnosis of gastric cancer. validated in node-positive disease.

Gastric cancer

Gastric cancer is the fifth most common cancer and the third most common cause of cancer death globally. Risk factors for the condition include Helicobacter pylori infection, age, high salt intake, and diets low in fruit and vegetables. Gastric cancer is diagnosed histologically after endoscopic biopsy and staged using CT, endoscopic ultrasound, PET, and laparoscopy. It is a molecularly and phenotypically highly heterogeneous disease. The main treatment for early gastric cancer is endoscopic resection. Non-early operable gastric cancer is treated with surgery, which should include D2 lymphadenectomy (including lymph node stations in the perigastric mesentery and along the celiac arterial branches). Perioperative or adjuvant chemotherapy improves survival in patients with stage 1B or higher cancers. Advanced gastric cancer is treated with sequential lines of chemotherapy, starting with a platinum and fluoropyrimidine doublet in the first line; median survival is less than 1 year. Targeted therapies licensed to treat gastric cancer include trastuzumab (HER2-positive patients first line), ramucirumab (anti-angiogenic second line), and nivolumab or pembrolizumab (anti-PD-1 third line).

Novel quantitative signature of tumor stromal architecture: polarized light imaging differentiates between myxoid and sclerotic human breast cancer stroma

As a leading cause of death in women, breast cancer is a global health concern for which personalized therapy remains largely unrealized, resulting in over- or under-treatment. Recently, tumor stroma has been shown to carry important prognostic information, both in its relative abundance and morphology, but its current assessment methods are few and suboptimal. Herein, we present a novel stromal architecture signature (SAS) methodology based on polarized light imaging that quantifies patterns of tumor connective tissue. We demonstrate its ability to differentiate between myxoid and sclerotic stroma, two pathology-derived categories associated with significantly different patient outcomes. The results demonstrate a 97% sensitivity and 88% specificity for myxoid stroma identification in a pilot study of 102 regions of interest from human invasive ductal carcinoma breast cancer surgical specimens (20 patients). Additionally, the SAS numerical score is indicative of the wide range of stromal characteristics within these binary classes and highlights ambiguous mixed-morphology regions prone to misclassification. The enabling polarized light microscopy technique is inexpensive, fast, fully automatable, applicable to fresh or embedded tissue without the need for staining and thus potentially translatable into research and/or clinical settings. The SAS metric yields quantifiable and objective stromal characterization with promise for prognosis in many types of cancers beyond breast carcinoma, enabling researchers and clinicians to further investigate the emerging and important role of stromal architectural patterns in solid tumors.

Tumor proportion in colon cancer: results from a semiautomatic image analysis approach

The tumor stroma ratio (TSR) is a promising prognostic biomarker in colon cancer, which could provide additional risk stratification for therapy adaption. The objective of our study was the investigation of the prognostic significance of TSR at different tumor sites in a simple semiautomatic approach with the open-source program ImageJ. We investigated 206 pT3 and pT4 adenocarcinomas of no special type. According to our established thresholds, 31 tumors (15%) were classified as low tumor proportion (TP) (≤ 15% TP), 42 tumors (20%) were classified as high TP (≥ 54% TP), and 133 tumors (65%) were classified as medium TP. High and low TP were associated with an adverse overall survival in comparison to medium TP (p = 0.001 and p = 0.03). Furthermore, the TP was an independent risk factor of occurrence of distant metastasis next to T status, microsatellite status, and tumor budding. The 5-year survival rate was 49% in patients with high TP, 48% in patients with low TP, and 68% in patients with medium TP (p = 0.042, n = 160). Patients with a high TP had less often tumor budding (p = 0.012), lymphovascular invasion (p = 0.049), and less harvested lymph nodes (p = 0.042) in comparison to low TP tumors. The results provide first evidence that a high tumor proportion/low stroma proportion is also associated with an adverse prognosis and that this subgroup might be difficult to identify with other classical histopathologic characteristics that are linked to an adverse prognosis.

The prognostic value of the tumor-stroma ratio is most discriminative in patients with grade III or triple-negative breast cancer

The tumor-stroma ratio (TSR) was evaluated as a promising parameter for breast cancer prognostication in clinically relevant subgroups of patients. The TSR was assessed on hematoxylin and eosin-stained tissue slides of 1,794 breast cancer patients from the Nottingham City Hospital. An independent second cohort of 737 patients from the Netherlands Cancer Institute to Antoni van Leeuwenhoek was used for evaluation. In the Nottingham Breast Cancer series, the TSR was an independent prognostic parameter for recurrence-free survival (RFS; HR 1.35, 95% CI 1.10-1.66, p = 0.004). The interaction term was statistically significant for grade and triple-negative status. Multivariate Cox regression analysis showed a more pronounced effect of the TSR for RFS in grade III tumors (HR 1.89, 95% CI 1.43-2.51, p < 0.001) and triple-negative tumors (HR 1.86, 95% CI 1.10-3.14, p = 0.020). Comparable hazard ratios and confidence intervals were observed for grade and triple-negative status in the ONCOPOOL study. The prognostic value of TSR was not modified by age, tumor size, histology, estrogen receptor status, progesterone receptor status, human epidermal growth factor receptor 2 status or lymph node status. In conclusion, patients with a stroma-high tumor had a worse prognosis compared to patients with a stroma-low tumor. The prognostic value of the TSR is most discriminative in grade III tumors and triple-negative tumors. The TSR was not modified by other clinically relevant parameters making it a potential factor to be included for improved risk stratification.

The tumor-stroma ratio is an independent predictor of survival in patients with 2018 FIGO stage IIIC squamous cell carcinoma of the cervix following primary radical surgery

OBJECTIVE

To determine the value of the tumor-stroma ratio (TSR) while identifying prognostic factors in patients with 2018 International Federation of Gynecology and Obstetrics (FIGO) stage IIIC squamous cell carcinoma of the cervix following primary radical surgery.

METHODS

Three hundred eighty-four patients with node-positive squamous cell carcinoma of the cervix (2018 FIGO stage IIIC) who underwent radical surgery between January 2005 and December 2016 were included in this retrospective study. The TSRs were assessed on hematoxylin and eosin-stained tumor slides and classified as stroma-low (<50% stroma) or stroma-high (≥50% stroma).

RESULTS

Sixty-seven patients were categorized as stroma-high; they had shorter disease-free survival (DFS) and overall survival (OS) periods than did their stroma-low counterparts. On multivariate analysis, a tumor size ≥4 cm, ≥3 metastatic lymph nodes, and stroma-high status were independent predictors of shorter DFS and OS. These factors were incorporated into a prognostic scoring system in which patients were categorized into low- (score 0), intermediate- (score 1), and high-risk (scores 2-3) groups. The scoring system differentiated DFS and OS well (C-index = 0.65, 95% confidence interval, 0.59-0.72; and C-index = 0.65, 95% confidence interval, 0.59-0.72, respectively).

CONCLUSIONS

The TSR is an independent prognostic factor, and our prognostic scoring system that incorporates this parameter exhibits good discriminative ability for both recurrence and survival in patients with 2018 FIGO stage IIIC cervical cancer after radical surgery. The TSR is a potentially novel clinicopathological variable for predicting the prognoses of these patients contingent on the validation of our findings.

Whole-Slide Image Analysis Reveals Quantitative Landscape of Tumor-Immune Microenvironment in Colorectal Cancers

PURPOSE

Despite the well-known prognostic value of the tumor-immune microenvironment (TIME) in colorectal cancers, objective and readily applicable methods for quantifying tumor-infiltrating lymphocytes (TIL) and the tumor-stroma ratio (TSR) are not yet available.

EXPERIMENTAL DESIGN

We established an open-source software-based analytic pipeline for quantifying TILs and the TSR from whole-slide images obtained after CD3 and CD8 IHC staining. Using a random forest classifier, the method separately quantified intraepithelial TILs (iTIL) and stromal TILs (sTIL). We applied this method to discovery and validation cohorts of 578 and 283 stage III or high-risk stage II colorectal cancers patients, respectively, who were subjected to curative surgical resection and oxlaliplatin-based adjuvant chemotherapy.

RESULTS

Automatic quantification of iTILs and sTILs showed a moderate concordance with that obtained after visual inspection by a pathologist. The K-means-based consensus clustering of 197 TIME parameters that showed robustness against interobserver variations caused colorectal cancers to be grouped into five distinctive subgroups, reminiscent of those for consensus molecular subtypes (CMS1-4 and mixed/intermediate group). In accordance with the original CMS report, the CMS4-like subgroup (cluster 4) was significantly associated with a worse 5-year relapse-free survival and proved to be an independent prognostic factor. The clinicopathologic and prognostic features of the TIME subgroups have been validated in an independent validation cohort.

CONCLUSIONS

Machine-learning-based image analysis can be useful for extracting quantitative information about the TIME, using whole-slide histopathologic images. This information can classify colorectal cancers into clinicopathologically relevant subgroups without performing a molecular analysis of the tumors.

Tumour-stroma ratio and 5-year mortality in gastric adenocarcinoma: a systematic review and meta-analysis

Tumour-stroma ratio (TSR) is a novel potential prognostic factor in cancers and based on the proportions of stroma and tumour area. The prognostic value of TSR in gastric cancer is incompletely known. The aim of this study was to estimate prognostic significance of TSR in gastric adenocarcinoma. A search of PubMed (MEDLINE), Web of Science, EMBASE, Cochrane and Scopus databases was performed. A meta-analysis was conducted on five-year survival in gastric cancer patients using inverse variance random-effects methods. The literature search yielded 5329 potential titles, of which a total of seven studies were eligible. Results of six studies including a total of 1779 patients were pooled in the meta-analysis. Only 23 (1.3%) of the patients received neoadjuvant therapy. All six studies had a cut-off of 50% for the proportion of stroma when dividing the patients into low- and high stroma groups. Low TSR (high amount of stroma) was strongly associated with increased five-year mortality (hazard ratio 2.19, 95% CI 1.69–2.85). In conclusion, TSR is a strong prognostic factor in gastric cancer. It could be used to estimate prognosis of gastric cancer patients not receiving neoadjuvant chemotherapy. Further studies including patients receiving neoadjuvant therapy are recommended.

Nuclear shape, architecture and orientation features from H&E images are able to predict recurrence in node-negative gastric adenocarcinoma

BACKGROUND

Identifying intestinal node-negative gastric adenocarcinoma (INGA) patients with high risk of recurrence could help perceive benefit of adjuvant therapy for INGA patients following surgical resection. This study evaluated whether the computer-extracted image features of nuclear shapes, texture, orientation, and tumor architecture on digital images of hematoxylin and eosin stained tissue, could help to predict recurrence in INGA patients.

METHODS

A tissue microarrays cohort of 160 retrospectively INGA cases were digitally scanned, and randomly selected as training cohort (D1 = 60), validation cohort (D2 = 100 and D3 = 100, D2 and D3 are different tumor TMA spots from the same patient), accompanied with immunohistochemistry data cohort (D3' = 100, a duplicate cohort of D3) and negative controls data cohort (D5 = 100, normal adjacent tissues). After nuclear segmentation by watershed-based method, 189 local nuclear features were captured on each TMA core and the top 5 features were selected by Wilcoxon rank sum test within D1. A morphometric-based image classifier (NGAHIC) was composed across the discriminative features and predicted the recurrence in INGA on D2. The intra-tumor heterogeneity was assessed on D3. Manual nuclear atypia grading was conducted on D1 and D2 by two pathologists. The expression of HER2 and Ki67 were detected by immunohistochemistry on D3 and D3', respectively. The association between manual grading and INGA outcome was analysis.

RESULTS

Independent validation results showed the NGAHIC achieved an AUC of 0.76 for recurrence prediction. NGAHIC-positive patients had poorer overall survival (P = 0.017) by univariate survival analysis. Multivariate survival analysis, controlling for T-stage, histology stage, invasion depth, demonstrated NGAHIC-positive was a reproducible prognostic factor for poorer disease-specific survival (HR = 17.24, 95% CI 3.93-75.60, P < 0.001). In contrast, human grading was only prognostic for one reader on D2. Moreover, significant correlations were observed between NGAHIC-positive patients and positivity of HER2 and Ki67 labeling index.

CONCLUSIONS

The NGAHIC could provide precision oncology, personalized cancer management.

The prognostic value of tumor-stroma ratio in tumor-positive axillary lymph nodes of breast cancer patients

The tumor-stroma ratio (TSR) has previously been found to be a strong prognostic parameter in primary breast cancer tumors. Since the presence of tumor cells in lymph nodes is important for clinical decision making, the influence of TSR in the primary breast tumor combined with the TSR in tumor-positive lymph nodes on prognosis was evaluated. Women with invasive breast cancer without distant metastasis who underwent an axillary lymph node dissection between 1985 and 1994 at the Leiden University Medical Center were retrospectively analyzed. TSR assessment was performed on hematoxylin and eosin stained tissue slides. In total, 87 (45.5%) primary tumors were scored as stroma-low and 104 (54.5%) as stroma-high. Patients with a high stromal percentage in the primary tumors had a statistically significant worse relapse free period (RFP) compared to stroma-low tumors (HR 1.97, 95% CI 1.37-2.82, p < 0.001). A total number of 915 lymph nodes were assessed for TSR. In 101 (52.9%) patients, heterogeneity was observed between stroma percentage category in primary tumor and lymph nodes. The combination of TSR of the primary tumor combined with TSR of tumor-positive lymph nodes strengthened each other as independent prognostic parameter for RFP (p = 0.019). Patients with primary tumor stroma-low/lymph nodes stroma-low tumors showed strongly improved RFP rates compared to patients with primary tumor stroma-high/lymph node stroma-high tumors with 10-year percentages of 58 versus 8%, respectively. Assessing the TSR on tumor-positive lymph nodes can provide additional prognostic information. Stromal activation strongly differs between primary tumors and lymph node metastasis.

The prognostic value of tumour-stroma ratio in primary breast cancer with special attention to triple-negative tumours: a review

Purpose

There is a strong need to improve the prognostication of breast cancer patients in order to prevent over- and undertreatment, especially when considering adjuvant chemotherapy. Tumour stroma characteristics might be valuable in predicting disease progression.

Methods

Studies regarding the prognostic value of tumour–stroma ratio (TSR) in breast cancer are evaluated.

Results

A high stromal content is related to a relatively poor prognosis. The most pronounced prognostic effect of this parameter seems to be observed in the triple-negative breast cancer (TNBC) subtype.

Conclusions

TSR assessment might represent a simple, fast and reproducible prognostic factor at no extra costs, and could possibly be incorporated into routine pathological diagnostics. Despite these advantages, a robust clinical validation of this parameter has yet to be established in prospective studies.

Electronic supplementary material

The online version of this article (10.1007/s10549-018-4987-4) contains supplementary material, which is available to authorized users.

Identification of a high-risk subtype of intestinal-type Japanese gastric cancer by quantitative measurement of the luminal tumor proportion

Background

We hypothesized that the relative proportion of tumor (PoT) at the luminal surface can predict gastric cancer (GC) patient survival.

Methods

We measured the luminal PoT in resection specimens from 231 GC patients with stage II/III disease who had surgery at the Kanagawa Cancer Center, Yokohama, Japan. Tissue microarrays were used to assess the extent of immune cell infiltration by CD45 immunohistochemistry. Results were related to histopathological features and patient overall survival (OS).

Results

PoT was significantly lower in diffuse‐type (30%) compared to intestinal‐type GC (41%), P = 0.03. Patients with low PoT intestinal‐type GC survived significantly longer than patients with high PoT intestinal‐type GC (5 years OS: 78% vs 47%, P = 0.0112). Low PoT was an independent favorable prognostic factor in multivariate analysis in intestinal‐type GC. Low PoT was correlated with high content of CD45‐positive immune cells (P = 0.035). There was no relationship between PoT and survival in diffuse‐type GC.

Conclusions

This is the first study to identify a subgroup of patients with stage II/III intestinal‐type GC at high risk of recurrence by measuring PoT at the luminal surface. The relationship between PoT and immune cell content provides an initial insight into potential underlying biological mechanisms.

Tumour-stroma ratio and prognosis in gastric adenocarcinoma

Background

Tumour microenvironment, including tumour–stroma ratio (TSR), might help identifying high-risk cancer patients. However, the significance of TSR in gastric cancer is unclear, especially in the intestinal and diffuse subtypes. The aim of this study was to investigate the tumour–stroma ratio in gastric adenocarcinoma, and its intestinal and diffuse histological subtypes, in relation to prognosis.

Methods

Five hundred and eighty-three gastric adenocarcinoma patients who underwent surgery in Oulu University hospital during years 1983–2016 were included in this retrospective cohort study. TSR was analysed from the slides that were originally used for diagnostic purposes. Patients were divided into stroma-poor (≤50% stroma) and stroma-rich (>50% stroma) groups and TSR was analysed in relation to 5-year mortality and overall mortality.

Results

Patients with stroma-rich tumours had worse 5-year prognosis (HR 1.80, 95% CI 1.41–2.28) compared to stroma-poor tumours. Stratified analysis showed that stroma-rich tumours had worse 5-year prognosis in both intestinal (HR 1.68, 95% CI 1.24–2.27) and diffuse histological types (HR 2.09, 95% CI 1.35–3.23) compared to stroma-poor tumours, respectively.

Conclusions

High proportion of stroma is an independent prognostic factor in both intestinal and diffuse histological subtypes of gastric adenocarcinoma.