Tumor-stroma ratio predicts recurrence in patients with colon cancer treated with neoadjuvant chemotherapy

Are the tumor microenvironment characteristics of pretreatment biopsy specimens of colorectal cancer really effectively predict the efficacy of neoadjuvant therapy: A retrospective multicenter study

More and more studies had pointed out that the tumor microenvironment characteristics based on colorectal cancer (CRC) pretreatment biopsy specimens could effectively predict the efficacy of neoadjuvant therapy, but under hematoxylin and eosin (HE) staining, whether the tumor microenvironment characteristics observed by pathologists could predict the efficacy of neoadjuvant therapy remains to be discussed. We collected 106 CRC patients who received neoadjuvant treatment and surgical resection from 3 hospitals. The number of mitosis, inflammation degree, desmoplastic reaction (DR), necrosis, tumor-stroma ratio (TSR) and tumor budding (TB) of CRC pretreatment biopsy specimens were observed under HE staining, and the degree of tumor pathological remission of CRC surgical specimens after neoadjuvant treatment was evaluated. According to the tumor regression grade (TRG), patients were divided into good-responders (TRG 0-1) and non-responders (TRG 2-3). All data were analyzed with SPSS software (version 23.0) to evaluate the correlation between the number of mitosis, inflammation degree, DR, necrosis, TSR and TB in pretreatment biopsy samples and the treatment effect. In univariate analysis, mitosis (P = .442), inflammation degree (P = .951), DR (P = .186), necrosis (P = .306), TSR (P = .672), and TB (P = .327) were not associated with the response to neoadjuvant therapy. However, we found that for colon cancer, rectal cancer was more likely to benefit from neoadjuvant therapy (P = .024). In addition, we further analyzed the impact of mitosis, inflammation degree, DR, necrosis, TSR and TB on neoadjuvant therapy in rectal cancer, and found that there was no predictive effect. By analyzing the characteristics of tumor microenvironment of CRC pretreatment biopsy specimens under HE staining, such as mitosis, inflammation degree, DR, necrosis, TSR and TB, it was impossible to effectively predict the efficacy of neoadjuvant therapy for CRC.

The tumor-stroma ratio in giant cell tumor of bone: associations with the immune microenvironment and responsiveness to denosumab treatment

BACKGROUND

Currently, there is limited understanding regarding the clinical significance of the tumor-stroma ratio (TSR) in giant cell tumor of bone (GCTB). Hence, we aimed to investigate the distribution of TSR in GCTB and explore its correlation with various clinicopathologic factors, immune microenvironment, survival prognosis, and denosumab treatment responsiveness.

METHODS

We conducted a multicenter cohort study comprising 426 GCTB patients treated at four centers. TSR was evaluated on hematoxylin and eosin-stained and immunofluorescent sections of tumor specimens. Immunohistochemistry was performed to assess CD3+, CD4+, CD8+, CD20+, PD-1+, PD-L1+, and FoxP3+ TIL subtypes as well as Ki-67 expression levels in 426 tissue specimens. These parameters were then analyzed for their correlations with patient outcomes [local recurrence-free survival (LRFS) and overall survival (OS)], clinicopathological features, and denosumab treatment responsiveness.

RESULTS

Low TSR was significantly associated with poor LRFS and OS in both cohorts. Furthermore, TSR was also correlated with multiple clinicopathological features, TIL subtype expression, and denosumab treatment responsiveness. TSR demonstrated similar predictive capabilities as the conventional Campanacci staging system for predicting patients' LRFS and OS.

CONCLUSION

The results of this study provide evidence supporting the use of TSR as a reliable prognostic tool in GCTB and as a predictor of denosumab treatment responsiveness. These findings may aid in developing individualized treatment strategies for GCTB patients in the future.

Construction and validation of a deep learning prognostic model based on digital pathology images of stage III colorectal cancer

BACKGROUND

TNM staging is the main reference standard for prognostic prediction of colorectal cancer (CRC), but the prognosis heterogeneity of patients with the same stage is still large. This study aimed to classify the tumor microenvironment of patients with stage III CRC and quantify the classified tumor tissues based on deep learning to explore the prognostic value of the developed tumor risk signature (TRS).

METHODS

A tissue classification model was developed to identify nine tissues (adipose, background, debris, lymphocytes, mucus, smooth muscle, normal mucosa, stroma, and tumor) in whole-slide images (WSIs) of stage III CRC patients. This model was used to extract tumor tissues from WSIs of 265 stage III CRC patients from The Cancer Genome Atlas and 70 stage III CRC patients from the Sixth Affiliated Hospital of Sun Yat-sen University. We used three different deep learning models for tumor feature extraction and applied a Cox model to establish the TRS. Survival analysis was conducted to explore the prognostic performance of TRS.

RESULTS

The tissue classification model achieved 94.4 % accuracy in identifying nine tissue types. The TRS showed a Harrell's concordance index of 0.736, 0.716, and 0.711 in the internal training, internal validation, and external validation sets. Survival analysis showed that TRS had significant predictive ability (hazard ratio: 3.632, p = 0.03) for prognostic prediction.

CONCLUSION

The TRS is an independent and significant prognostic factor for PFS of stage III CRC patients and it contributes to risk stratification of patients with different clinical stages.

Histopathological tumour microenvironment score independently predicts outcome in primary operable colorectal cancer

Colorectal cancer (CRC) is a heterogenous malignancy and research is focused on identifying novel ways to subtype patients. In this study, a novel classification system, tumour microenvironment score (TMS), was devised based on Klintrup-Mäkinen grade (KMG), tumour stroma percentage (TSP), and tumour budding. TMS was performed using a haematoxylin and eosin (H&E)-stained section from retrospective CRC discovery and validation cohorts (n = 1,030, n = 787). TMS0 patients had high KMG, TMS1 were low for KMG, TSP, and budding, TMS2 were high for budding, or TSP and TMS3 were high for TSP and budding. Scores were assessed for association with survival and clinicopathological characteristics. Mutational landscaping and Templated Oligo-Sequencing (TempO-Seq) profiling were performed to establish differences in the underlying biology of TMS. TMS was independently prognostic in both cohorts (p < 0.001, p < 0.001), with TMS3 predictive of the shortest survival times. TMS3 was associated with adverse clinical features including sidedness, local and distant recurrence, higher T stage, higher N stage, and presence of margin involvement. Gene set enrichment analysis of TempO-Seq data showed higher expression of genes associated with hallmarks of cancer pathways including epithelial to mesenchymal transition (p < 0.001), IL2 STAT5 signalling (p = 0.007), and angiogenesis (p = 0.017) in TMS3. Additionally, enrichment of immunosuppressive immune signatures was associated with TMS3 classification. In conclusion, TMS represents a novel and clinically relevant method for subtyping CRC patients from a single H&E-stained tumour section.

Microenvironment, systemic inflammatory response and tumor markers considering consensus molecular subtypes of colorectal cancer

Introduction: Colorectal carcinomas (CRC) are one of the most frequent malignancies worldwide. Based on gene expression profile analysis, CRCs can be classified into four distinct subtypes also known as the consensus molecular subtypes (CMS), which predict biological behaviour. Besides CMS, several other aspects of tumor microenvironment (TME) and systemic inflammatory response (SIR) influence the outcome of CRC patients. TME and inflammation have important role in the immune (CMS1) and mesenchymal (CMS4) subtypes, however, the relationship between these and systemic inflammation has not been assessed yet. Our objective was to evaluate the connection between CMS, TME and SIR, and to analyze the correlation between these markers and routinely used tumor markers, such as CEA (Carcinoembryonic Antigen) and CA19-9 (Carbohydrate Antigen 19-9). Methods: FFPE (Formalin Fixed Paraffin Embedded) samples of 185 CRC patients were collected. TME was described using tumor-stroma ratio (TSR), Klintrup-Makinen (KM) grade, and Glasgow Microenvironment Score (GMS). CMS classification was performed on tissue microarray using MLH1, PMS2, MSH2 and MSH6, and pan-cytokeratin, CDX2, FRMD6, HTR2B and ZEB1 immunohistochemical stains. Pre-operative tumor marker levels and inflammatory markers [C-reactive protein - CRP, albumin, absolute neutrophil count (ANC), absolute lymphocyte count (ALC), absolute platelet count (APC)] and patient history were retrieved using MedSolution database. Results: Amongst TME-markers, TSR correlated most consistently with adverse clinicopathological features (p < 0.001) and overall survival (p < 0.001). Elevated CRP and modified Glasgow Prognostic Score (mGPS) were associated with worse outcome and aggressive phenotype, similarly to tumor markers CEA and CA19-9. Stroma-Tumor Marker score (STM score), a new combined score of CA19-9 and TSR delivered the second best prognostication after mGPS. Furthermore, CMS4 showed association with TSR and several laboratory markers (albumin and platelet derived factors), but not with other SIR descriptors. CMS did not show any association with CEA and CA19-9 tumor markers. Conclusion: More routinely available TME, SIR and tumor markers alone and in combination deliver reliable prognostic data for choosing the patients with higher risk for propagation. CMS4 is linked with high TSR and poor prognosis, but in overall, CMS-classification showed only limited effect on SIR- and tumor-markers.

Prediction of overall survival after radical gastrectomy using nomograms created by tumor markers

Prediction of prognosis after radical resection of gastric cancer has not been well established. Therefore, we aimed to establish a prognostic model based on a new score system of patients with gastric cancer. A total of 1235 patients who underwent curative gastrectomy at our hospital from October 2015 to April 2017 were included in this study. Univariate and multivariate analyses were used to screen for prognostic risk factors. Construction of the nomogram was based on Cox proportional hazard regression models. The construction of the new score models was analyzed by the receiver operating characteristic curve (ROC curve), calibration curve, and decision curve. Multivariate analysis showed that tumor size, T, N, carcinoembryonic antigen, CA125, and CA19-9 were independent prognostic factors. The new score model had a greater AUC (The area under the ROC curve) than other systems, and the C-index of the nomogram was highly reliable for evaluating the survival of patients with gastric cancer. Based on the tumor markers and other clinical indicators, we developed a precise model to predict the prognosis of patients with gastric cancer after radical surgery. This score system can be helpful to both surgeons and patients.

Significance of Tumor-Stroma Ratio (TSR) in Predicting Outcomes of Malignant Tumors

Background and Objectives: The present study aimed to elucidate the distribution and the prognostic implications of tumor-stroma ratio (TSR) in various malignant tumors through a meta-analysis. Materials and Methods: This meta-analysis included 51 eligible studies with information for overall survival (OS) or disease-free survival (DFS), according to TSR. In addition, subgroup analysis was performed based on criteria for high TSR. Results: The estimated rate of high TSR was 0.605 (95% confidence interval (CI) 0.565-0.644) in overall malignant tumors. The rates of high TSR ranged from 0.276 to 0.865. The highest rate of high TSR was found in endometrial cancer (0.865, 95% CI 0.827-0.895). The estimated high TSR rates of colorectal, esophageal, and stomach cancers were 0.622, 0.529, and 0.448, respectively. In overall cases, patients with high TSR had better OS and DFS than those with low TSR (hazard ratio (HR) 0.631, 95% CI 0.542-0.734, and HR 0.564, 95% CI 0.0.476-0.669, respectively). Significant correlations with OS were found in the breast, cervical, colorectal, esophagus, head and neck, ovary, stomach, and urinary tract cancers. In addition, there were significant correlations of DFS in breast, cervical, colorectal, esophageal, larynx, lung, and stomach cancers. In endometrial cancers, high TSR was significantly correlated with worse OS and DFS. Conclusions: The rate of high TSR was different in various malignant tumors. TSR can be useful for predicting prognosis through a routine microscopic examination of malignant tumors.

Deep learning in digital pathology for personalized treatment plans of cancer patients

Over the past decade, many new cancer treatments have been developed and made available to patients. However, in most cases, these treatments only benefit a specific subgroup of patients, making the selection of treatment for a specific patient an essential but challenging task for oncologists. Although some biomarkers were found to associate with treatment response, manual assessment is time-consuming and subjective. With the rapid developments and expanded implementation of artificial intelligence (AI) in digital pathology, many biomarkers can be quantified automatically from histopathology images. This approach allows for a more efficient and objective assessment of biomarkers, aiding oncologists in formulating personalized treatment plans for cancer patients. This review presents an overview and summary of the recent studies on biomarker quantification and treatment response prediction using hematoxylin-eosin (H&E) stained pathology images. These studies have shown that an AI-based digital pathology approach can be practical and will become increasingly important in improving the selection of cancer treatments for patients.

The Glasgow Microenvironment Score and risk and site of recurrence in TNM I-III colorectal cancer

BACKGROUND

Glasgow Microenvironment Score (GMS) stratifies long-term survival into three groups based on tumour phenotype: peritumoural inflammation (Klintrup-Mäkinen (KM)) and tumour stroma percentage (TSP). However, it is not known if the location of disease recurrence is influenced by the GMS category.

METHODS

Seven hundred and eighty-three TNM I-III colorectal cancers (CRC) were included. GMS (GMS0-high KM; GMS1-low KM, low TSP; GMS2-low KM, high TSP) and cancer-specific survival (CSS), overall survival (OS) and disease recurrence were assessed using Cox regression analysis.

RESULTS

Of the 783 patients, 221 developed CRC recurrence; 65 developed local recurrence + systemic disease. GMS was independent for CSS (HR 1.50, 95% CI 1.17-1.92, p < 0.001) and OS (HR 1.23, 1.05-1.44, p = 0.01). Higher GMS category was associated with T-stage, N-stage, emergency presentation and venous invasion. GMS was independent for local+systemic recurrence (HR 11.53, 95% CI 1.45-91.85, p = 0.04) and distant-only recurrence (HR 3.01, 95% CI 1.59-5.71, p = 0.002). GMS 2 disease did not appear to have statistically better outcomes with adjuvant chemotherapy in high-risk disease.

CONCLUSION

Although confounded by a higher rate of T4 and node-positive disease, GMS 1 and 2 are associated with an increased risk of local and distant recurrence. GMS is an independent poor prognostic indicator for recurrent colorectal cancer. Higher GMS patients may benefit from enhanced postoperative surveillance.

Investigating the role of core needle biopsy in evaluating tumor-stroma ratio (TSR) of invasive breast cancer: a retrospective study

PURPOSE

Tumor-stroma ratio (TSR) of invasive breast carcinoma has gained attention in recent years due to its prognostic significance. Previous studies showed TSR is a potential biomarker for indicating the tumor response to neoadjuvant chemotherapy. However, it is not clear how well TSR evaluation in biopsy specimens might reflect the TSR in resection specimens. We conducted a study to investigate whether biopsy evaluation of TSR can be an alternative method.

METHOD

We collected cases with invasive breast carcinoma of no special type (IBC-NST) from University of Yamanashi hospital between 2011 and 2017 whose biopsy and resection specimens both had a pathologically diagnosis of IBC-NST (n = 146). We conceptualized a method for evaluating TSR in biopsy specimens within a preliminary cohort (n = 50). Within the studied cohort (n = 96), biopsy-based TSR (b-TSR) and resection-based TSR (r-TSR) were scored by two pathologists. We then evaluated our method's validity and performance by measuring interobserver variability between the two pathologists, Spearman's correlation between b-TSR and r-TSR, and the receiver operating characteristics (ROC) analysis for defining stroma-rich and stroma-poor tumors.

RESULTS

Intra-class coefficient between the two pathologists was 0.59. The correlation coefficients between b-TSR and r-TSR in the two pathologists were 0.45 and 0.37. The ROC areas under the curve were 0.7 and 0.67. By considering an r-TSR of < 50% as stroma-rich, the sensitivity and specificity of detecting stroma-rich tumors were 64.1% and 66.7%, respectively, when b-TSR was < 40%.

CONCLUSION

Our current b-TSR evaluation method can provide information about r-TSR and facilitate pre-treatment therapy 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.

The role of neoadjuvant chemotherapy in patients with locally advanced colon cancer: A systematic review and meta-analysis

BACKGROUND

Controversy persists about neoadjuvant chemotherapy (NAC) within the field of locally advanced colon cancer (LACC). The purpose of this study was to assess the existing and latest literature with high quality to determine the role of NAC in various aspects.

METHODS

A comprehensive literature search of the PubMed, Embase, Web of Science, and the Cochrane Library databases was conducted from inception to April 2022. Review Manager 5.3 was applied for meta-analyses with a random-effects model whenever possible.

RESULTS

Overall, 8 studies were included in this systematic review and meta-analysis, comprising 4 randomized controlled trials (RCTs) and 4 retrospective studies involving 40,136 participants. The 3-year overall survival (OS) (HR: 0.90, 95% CI: 0.66-1.23, P = 0.51) and 5-year OS (HR: 0.89, 95% CI: 0.53-1.03, P = 0.53) were comparable between two groups. Mortality in 30 days was found less frequent in the NAC group (OR: 0.43, 95% CI: 0.20-0.91, P = 0.03), whereas no significant differences were detected concerning other perioperative complications, R0 resection, or adverse events. In terms of subgroup analyses for RCTs, less anastomotic leak (OR: 0.51, 95% CI: 0.31-0.86, P = 0.01) and higher R0 resection rate (OR: 2.35, 95% CI: 1.04-5.32, P = 0.04) were observed in the NAC group.

CONCLUSIONS

NAC is safe and feasible for patients with LACC, but no significant survival benefit could be demonstrated. The application of NAC still needs to be prudent until significant evidence supporting the oncological outcomes is presented.

SYSTEMATIC REVIEW REGISTRATION

https://www.crd.york.ac.uk/prospero, identifier (CRD42022333306).

Digital image analysis provides robust tissue microenvironment-based prognosticators in stage I-IV colorectal cancer patients

AIMS

In colorectal cancer (CRC) patients, a promising marker is tumor-stroma ratio (TSR). Quantification issues highlight the importance of precise assessment that might be solved by artificial intelligence (AI)-based digital image analysis systems. Some alternatives have been offered so far, although these platforms are either proprietary developments or require additional programming skills. Our aim was to validate a user-friendly, commercially available software running in everyday computational environment to improve TSR assessment and also to compare the prognostic value of assessing TSR in three distinct regions of interests (ROIs), like hotspot, invasive front and whole tumor. Furthermore, we compared the prognostic power of TSR with newly suggested carcinoma percentage (CP) and carcinoma-stroma percentage (CSP).

METHODS AND RESULTS

Slides of 185 stage I-IV CRC patients with clinical follow up data were scanned and evaluated by a senior pathologist. A machine learning-based digital pathology software was trained to recognize tumoral and stromal compartments. The aforementioned parameters were evaluated in the hotspot, invasive front and whole tumor area, both visually and by machine learning. Patients were classified based on TSR, CP and CSP values. On multivariate analysis, TSR-hotspot was found to be an independent prognostic factor of overall survival (hazard ratio for TSR-hotspot_software: 2.005 (95% confidence interval (CI): 1.146-3.507), p=0.011, for TSR-hostpot_visual: 1.781 (CI: 1.060-2.992) p=0.029). Also, TSR was an independent predictor for distant metastasis and local relapse in most settings. Generally, software performance was comparable to visual evaluation and delivered reliable prognostication in more settings also with CP and CSP values.

CONCLUSIONS

This study presents that software assisted evaluation is a robust prognosticator. Our approach used a less sophisticated and thus easily accessible software without the aid of convolutional neural network; however, it was still effective enough to deliver reliable prognostic information.

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.

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.

The Crohn's-like lymphoid reaction density: a new artificial intelligence quantified prognostic immune index in colon cancer

BACKGROUND

The Crohn's-like lymphoid reaction (CLR) is manifested as peritumoral lymphocytes aggregation in colon cancer, which is a major component of the host immune response to cancer. However, the lack of a unified and objective CLR evaluation standard limits its clinical application. We, therefore, developed a deep learning model for the fully automated CLR density quantification on routine hematoxylin and eosin (HE)-stained whole-slide images (WSIs) and further investigated its prognostic validity for patient stratification.

METHODS

The CLR density was calculated by using a deep learning method on HE-stained WSIs. A training (N = 279) and a validation (N = 194) cohorts were used to evaluate the prognostic value of CLR density for overall survival (OS).

RESULT

The fully automated quantified CLR density was an independent prognostic factor, with high CLR density associated with increased OS in the discovery (HR 0.58, 95% CI 0.38-0.89, P = 0.012) and validation cohort (0.45, 0.23-0.88, 0.020). Integrating CLR density into a Cox model with other risk factors showed improved prognostic capability.

CONCLUSION

We developed a new immune indicator (CLR density) quantified by a deep learning method to evaluate the lymphocytes aggregation in colon cancer. The CLR density was demonstrated its predictive value for OS in two independent cohorts. This approach allows for the objective and standardized quantification while reducing pathologists' workload. Therefore, this fully automated standardized method of CLR evaluation had potential clinical value.

Deep learning-based virtual cytokeratin staining of gastric carcinomas to measure tumor-stroma ratio

The tumor-stroma ratio (TSR) determined by pathologists is subject to intra- and inter-observer variability. We aimed to develop a computational quantification method of TSR using deep learning-based virtual cytokeratin staining algorithms. Patients with 373 advanced (stage III [n = 171] and IV [n = 202]) gastric cancers were analyzed for TSR. Moderate agreement was observed, with a kappa value of 0.623, between deep learning metrics (dTSR) and visual measurement by pathologists (vTSR) and the area under the curve of receiver operating characteristic of 0.907. Moreover, dTSR was significantly associated with the overall survival of the patients (P = 0.0024). In conclusion, we developed a virtual cytokeratin staining and deep learning-based TSR measurement, which may aid in the diagnosis of TSR in gastric cancer.

Tumor-stroma ratio is a crucial histological predictor of occult cervical lymph node metastasis and survival in early-stage (cT1/2N0) oral squamous cell carcinoma

Occult cervical lymph node metastasis is a significant prognostic factor in patients with early-stage (cT1/2N0) oral squamous cell carcinoma (OSCC). The aim of this study was to investigate the potential value of the tumor-stroma ratio (TSR) as a histological predictor of occult cervical metastasis and survival in early-stage OSCC. This retrospective study included 151 patients who underwent excision of the primary lesion and elective neck dissection from 2013 to 2017. The clinicopathological features of the tumor, risk factors associated with occult neck metastasis, and prognostic factors for overall survival (OS) and disease-free survival (DFS) were studied. A significant correlation of TSR (P = 0.009) was found with occult neck metastasis in the multivariate logistic regression model. Multivariate Cox proportional hazards regression analysis showed that the TSR (P = 0.002) and perineural invasion (P = 0.011) were associated with OS. Occult neck metastasis (P = 0.032) was associated with DFS. These findings indicate that assessment of the TSR might be useful in prognostication for early-stage OSCC patients. Moreover, the TSR is effective in allowing an accurate evaluation of the risk of occult neck metastasis, and this may be easily applicable in the routine pathological diagnosis and clinical decision-making for elective neck dissection.

Interventions and Outcomes for Neoadjuvant Treatment of T4 Colon Cancer: A Scoping Review

While adjuvant treatment of colon cancers that penetrate the serosa (T4) have been well-established, neoadjuvant strategies have yet to be formally evaluated. Our objective was to perform a scoping review of eligibility criteria, treatment regimens, and primary outcomes for neoadjuvant approaches to T4 colon cancer. A librarian-led, systematic search of MEDLINE, Embase, Cochrane Library, Web of Science, and CINAHL up to 11 February 2020 was performed. Primary research evaluating neoadjuvant treatment in T4 colon cancer were included. Screening and data abstraction were performed in duplicate; analyses were descriptive or thematic. A total of twenty studies were included, most of which were single-arm, single-center, and retrospective. The primary objectives of the literature to date has been to evaluate treatment feasibility, tumor response, disease-free survival, and overall survival in healthy patients. Conventional XELOX and FOLFOX chemotherapy were the most commonly administered interventions. Rationale for selecting a specific regimen and for treatment eligibility criteria were poorly documented across studies. The current literature on neoadjuvant strategies for T4 colon cancer is overrepresented by single-center, retrospective studies that evaluate treatment feasibility and efficacy in healthy patients. Future studies should prioritize evaluating clear selection criteria and rationale for specific neoadjuvant strategies. Validation of outcomes in multi-center, randomized trials for XELOX and FOLFOX have the most to contribute to the growing evidence for this poorly managed disease.

Prognostic Value of Tumor-Stroma Ratio in Rectal Cancer: A Systematic Review and Meta-analysis

Background

Tumor-stroma ratio (TSR) is a promising new prognostic predictor for patients with rectal cancer (RC). Although several studies focused on this pathologic feature, results from those studies were still inconsistent.

Methods

This research aimed to estimate the prognostic values of TSR for RC. A search of PubMed, EMBASE, and Web of Science was carried out. A meta-analysis was performed on disease-free survival, cancer-specific survival, and overall survival in patients with RC.

Results

The literature search generated 1,072 possible studies, of which a total of 15 studies, involving a total of 5,408 patients, were eventually included in the meta-analysis. Thirteen of the 15 articles set the cutoff for the ratio of stroma at 50%, dividing patients into low-stroma and high-stroma groups. Low TSR (rich-stroma) was significantly associated with poorer survival outcome. (DFS: HR 1.54, 95% CI 1.32–1.79; OS: HR 1.52 95% CI 1.34–1.73; CSS: HR 2.05 95% CI 1.52–2.77).

Conclusion

Present data support TSR to be a risk predictor for poor prognosis in RC patients.

Histogram Analysis of Diffusion-Weighted MR Imaging as a Biomarker to Predict Survival of Surgically Treated Colorectal Cancer Patients

BACKGROUND

Structural abnormality is a well-recognized feature of malignancy. On the other hand, diffusion-weighted MRI (DWI) has been reported as a tool that can reflect tumor biology.

AIMS

The purpose of this study is to apply histogram analysis to DWI to quantify structural abnormality of colorectal cancer, and evaluate its biomarker value.

METHODS

This is a retrospective study of 80 (46 men and 34 women; median age: 68.0 years) colorectal cancer patients who underwent DWI followed by curative surgery at the Chiba University Hospital between 2009 and 2011. Median follow-up time was 62.2 months. Histogram parameters including signal intensity of kurtosis and skewness of the tumor were measured on DWI at b = 1000, and mean apparent diffusion coefficient value (ADC) of the tumor was also measured on ADC map generated by DWIs at b = 0 and 1000. Associations of tumor parameters (kurtosis, skewness, and ADC) with pathological features were analyzed, and these parameters were also compared with overall survival (OS) and relapse-free survival (RFS) using Cox regression and Kaplan-Meier analysis.

RESULTS

ADC of the tumor did not have significant associations with any pathological factors, but kurtosis and skewness of signal intensity in the tumor was significantly different between tumors with distant metastases and those without (4.23 ± 1.31 vs. 3.24 ± 1.32, p = 0.04; 1.09 ± 0.39 vs. 0.57 ± 0.58, p = 0.03). Kurtosis of the tumor was significantly correlated with OS and RFS (p = 0.04, p = 0.03, respectively), and skewness was significantly correlated with OS (p = 0.03) in Cox regression analysis. Higher kurtosis or higher skewness of the tumor was associated with worse OS in Kaplan-Meier analysis (p = 0.01, p = 0.009, log-rank). In subset analysis, there were 50 patients (32 men and 18 women) of lymph node-negative colorectal cancers (≤ stage II); skewness of signal intensity in the tumor was associated with OS using univariate Cox regression analysis (p = 0.04).

CONCLUSIONS

Histogram analysis of DWI can be a prognostic biomarker for colorectal cancer.

Clinicopathological Significances of Tumor-Stroma Ratio (TSR) in Colorectal Cancers: Prognostic Implication of TSR Compared to Hypoxia-Inducible Factor-1α Expression and Microvessel Density

The present study aimed to elucidate the clinicopathological significance and prognostic implications of tumor–stroma ratio (TSR) in colorectal cancers (CRCs). TSRs were investigated in 266 human CRC specimens. The correlations between TSR and clinicopathological characteristics and survival were evaluated. The hypoxia-inducible factor-1α (HIF-1α) immunohistochemical expression of tumor cells and microvessel density (MVD) of stroma were compared between stroma-low and stroma-high subgroups. Results: Stroma-low was found in 185 of 266 CRCs (69.5%). Stroma-low was significantly correlated with less frequent vascular and perineural invasion and distant metastasis than stroma-high. HIF-1α of tumor cells was more highly expressed in the stroma-high subgroup than in the stroma-low subgroup. In addition, MVD was significantly higher in the stroma-high subgroup compared to the stroma-low subgroup. The stroma-low rate was increased considerably in CRCs with a mucinous component and decreased in CRCs with a micropapillary component. There were significant correlations between stroma-low and better overall and recurrence-free survivals. Similar to the literature, we observed that stroma-low was significantly correlated with favorable tumor behaviors and better survival. The microscopic examination of TSR can be useful for predicting the prognosis of CRC patients.

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.

Correlation of the tumour-stroma ratio with diffusion weighted MRI in rectal cancer

OBJECTIVE

This study evaluated the correlation between intratumoural stroma proportion, expressed as tumour-stroma ratio (TSR), and apparent diffusion coefficient (ADC) values in patients with rectal cancer.

METHODS

This multicentre retrospective study included all consecutive patients with rectal cancer, diagnostically confirmed by biopsy and MRI. The training cohort (LUMC, Netherlands) included 33 patients and the validation cohort (VHIO, Spain) 69 patients. Two observers measured the mean and minimum ADCs based on single-slice and whole-volume segmentations. The TSR was determined on diagnostic haematoxylin & eosin stained slides of rectal tumour biopsies. The correlation between TSR and ADC was assessed by Spearman correlation (r_s).

RESULTS

The ADC values between stroma-low and stroma-high tumours were not significantly different. Intra-class correlation (ICC) demonstrated a good level of agreement for the ADC measurements, ranging from 0.84-0.86 for single slice and 0.86-0.90 for the whole-volume protocol. No correlation was observed between the TSR and ADC values, with ADC_meanr_s= -0.162 (p= 0.38) and ADC_minr_s= 0.041 (p= 0.82) for the single-slice and r_s= -0.108 (p= 0.55) and r_s= 0.019 (p= 0.92) for the whole-volume measurements in the training cohort, respectively. Results from the validation cohort were consistent; ADC_meanr_s= -0.022 (p= 0.86) and ADC_minr_s = 0.049 (p= 0.69) for the single-slice and r_s= -0.064 (p= 0.59) and r_s= -0.063 (p= 0.61) for the whole-volume measurements.

CONCLUSIONS

Reproducibility of ADC values is good. Despite positive reports on the correlation between TSR and ADC values in other tumours, this could not be confirmed for rectal cancer.

Artificial intelligence quantified tumour-stroma ratio is an independent predictor for overall survival in resectable colorectal cancer

Background

An artificial intelligence method could accelerate the clinical implementation of tumour-stroma ratio (TSR), which has prognostic relevance in colorectal cancer (CRC). We, therefore, developed a deep learning model for the fully automated TSR quantification on routine haematoxylin and eosin (HE) stained whole-slide images (WSI) and further investigated its prognostic validity for patient stratification.

Methods

We trained a convolutional neural network (CNN) model using transfer learning, with its nine-class tissue classification performance evaluated in two independent test sets. Patch-level segmentation on WSI HE slides was performed using the model, with TSR subsequently derived. A discovery (N=499) and validation cohort (N=315) were used to evaluate the prognostic value of TSR for overall survival (OS).

Findings

The CNN-quantified TSR was a prognostic factor, independently of other clinicopathologic characteristics, with stroma-high associated with reduced OS in the discovery (HR 1.72, 95% CI 1.24-2.37, P=0.001) and validation cohort (2.08, 1.26-3.42, 0.004). Integrating TSR into a Cox model with other risk factors showed improved prognostic capability.

Interpretation

We developed a deep learning model to quantify TSR based on histologic WSI of CRC and demonstrated its prognostic validity for patient stratification for OS in two independent CRC patient cohorts. This fully automatic approach allows for the objective and standardised application while reducing pathologists' workload. Thus, it can potentially be of significant aid in clinical prognosis prediction and decision-making.

Funding

National Key Research and Development Program of China, National Science Fund for Distinguished Young Scholar, and National Science Foundation for Young Scientists of China.

Increased RAB31 Expression in Cancer-Associated Fibroblasts Promotes Colon Cancer Progression Through HGF-MET Signaling

RAB family proteins participate in the dynamic regulation of cellular membrane compartments and are dysregulated in a variety of tumor types, which may alter the biological properties of cancer cells such as proliferation, migration, and invasion. In our previous study, we found that Ras-related protein Rab-31 (RAB31) expression was increased in late-stage colorectal cancer (CRC). The role of RAB31 has never been investigated in CRC. In this study, we found that expression of RAB31 in the tumor stroma but not cancer cells of colon cancer predicted poor survival. RAB31 can be detected in primary cancer-associated fibroblasts (CAFs) and paired normal fibroblasts. Conditioned medium (CM) from RAB31 overexpressing CAFs significantly promoted migration of colon cancer cell lines in vitro and in vivo. This process may be mediated by paracrine action of hepatocyte growth factor (HGF), which was increased in the CM of RAB31-overexpressing CAFs. Blockade of HGF/MET signaling by drug inhibition, knockdown of mesenchymal to epithelial transition factor (MET) in RKO, or antibody neutralization of HGF abolished migration of RKO cells mediated by RAB31 expression in CAFs. We propose that in colon cancer, increased RAB31 expression in CAFs may contribute to tumor progression by regulating the secretion of HGF in the tumor stroma.

Analysis of Spatial Distribution and Prognostic Value of Different Pan Cytokeratin Immunostaining Intensities in Breast Tumor Tissue Sections

Cancer risk prognosis could improve patient survival through early personalized treatment decisions. This is the first systematic analysis of the spatial and prognostic distribution of different pan cytokeratin immunostaining intensities in breast tumors. The prognostic model included 102 breast carcinoma patients, with distant metastasis occurrence as the endpoint. We segmented the full intensity range (0–255) of pan cytokeratin digitized immunostaining into seven discrete narrow grey level ranges: 0–130, 130–160, 160–180, 180–200, 200–220, 220–240, and 240–255. These images were subsequently examined by 33 major (GLCM), fractal and first-order statistics computational analysis features. Interestingly, while moderate intensities were strongly associated with metastasis outcome, high intensities of pan cytokeratin immunostaining provided no prognostic value even after an exhaustive computational analysis. The intense pan cytokeratin immunostaining was also relatively rare, suggesting the low differentiation state of epithelial cells. The observed variability in immunostaining intensities highlighted the intratumoral heterogeneity of the malignant cells and its association with a poor disease outcome. The prognostic importance of the moderate intensity range established by complex computational morphology analyses was supported by simple measurements of its immunostaining area which was associated with favorable disease outcome. This study reveals intratumoral heterogeneity of the pan cytokeratin immunostaining together with the prognostic evaluation and spatial distribution of its discrete intensities.

The important role of the histopathologist in clinical trials: challenges and approaches to tackle them

High-quality histopathology is essential for the success of clinical trials. Histopathologists have a detailed understanding of tumour biology and mechanisms of disease, as well as practical knowledge of optimal tissue handling and logistical service requirements for study delivery, such as biomarker evaluation, tissue acquisition and turnaround times. As such, histopathologist input is essential throughout every stage of research and clinical trials, from concept development and study design to trial delivery, analysis and dissemination of results. Patient recruitment to trials takes place among all healthcare settings, meaning that histopathologists make an invaluable contribution to clinical trials as part of their routine day-to-day work that often goes unrecognised. More complex evaluation of surgical specimens in the neoadjuvant setting and ever-expanding minimum data sets add to the workload of every histopathologist, not just academic pathologists in tertiary centres. This is occurring against a backdrop of increasing workload pressures and a worldwide shortage of histopathologists and biomedical scientists. Providing essential histopathology support for trials at grassroots level requires funding for adequate resources including histopathologist time, education and training, biomedical scientist and administrative support and greater recognition of the contribution made by histopathology. This paper will discuss the many ways in which histopathologists are involved in clinical trials and the challenges faced in meeting the additional demands posed by trial participation and potential ways to address this, with a special emphasis on the UK model and the Cellular-Molecular Pathology Initiative (CM-Path).

Prognostic Significance of the Tumor-Stromal Ratio in Invasive Breast Cancer and a Proposal of a New Ts-TNM Staging System

Background

Previous studies have demonstrated that the tumor-stromal ratio (TSR) was an independent prognostic factor in several types of carcinomas. This study aimed at exploring the prognostic significance of the TSR in invasive breast cancer using immunohistochemistry (IHC)-stained tissue microarrays (TMAs) and integrating the TSR into the traditional tumor-node-metastasis (TNM) staging system.

Methods

The prepared 7 TMAs containing 240 patients with 480 invasive BC specimens were stained with cytokeratin (CK) by the IHC staining method. The ratio of tumor cells and stromal cells was visually assessed. TSR > 1 and TSR ≤ 1 were categorized as the high TSR (low stroma) and low TSR (high stroma) groups, respectively, and the prognostic value of the TSR at 5-year disease-free survival (5-DFS) was analyzed. A new Ts-TNM (tumor stroma-tumor-node-metastasis) staging system was established and assessed.

Results

IHC staining of CK could specifically label tumor cells with clear contrast, making it easy to manually assess TSR. High TSR (low stroma) and low TSR (high stroma) were observed in 52.5% (n = 126) and 47.5 (n = 114) of the cases, according to the division of value 1. A Kaplan-Meier analysis showed that patients in the low TSR group had a worse 5-DFS compared with patients in the high TSR group (P=0.022). Multivariable analysis indicated that the T stage (P=0.014), N status (P < 0.001), histological grade (P < 0.001), estrogen receptor status (P=0.015), and TSR (P=0.011) were independent prognostic factors of invasive BC patients. The new Ts-TNM staging system combining TSR, tumor staging, lymph node status, and metastasis staging was established. The receiver operating characteristic (ROC) curve analysis demonstrated that the ability of the Ts-TNM staging system to predict recurrence was not lower than that of the TNM staging system.

Conclusions

This study confirms that the TSR is a prognostic indicator for invasive breast cancer. The Ts-TNM staging system containing stromal and tumor information may optimize risk stratification for invasive breast cancer.

The Relationship Between Tumor-Stroma Ratio, the Immune Microenvironment, and Survival in Patients With Spinal Chordoma

BACKGROUND

Currently, little is known about the clinical relevance of tumor-stroma ratio (TSR) in chordoma and data discussing the relationship between TSR and immune status of chordoma are lacking.

OBJECTIVE

To characterize TSR distribution in spinal chordoma, and investigated its correlation with clinicopathologic or immunological features of patients and outcome.

METHODS

TSR was assessed visually on hematoxylin and eosin-stained sections from 54 tumor specimens by 2 independent pathologists. Multiplex immunofluorescence was used to quantify the expression levels of microvessel density, Ki-67, Brachyury, and tumor as well as stromal PD-L1. Tumor immunity status including the Immunoscore and densities of tumor-infiltrating lymphocytes (TILs) subtypes were obtained from our published data and reanalyzed.

RESULTS

Bland-Altman plot showed no difference between mean TSR derived from the two observers. TSR was positively associated with stromal PD-L1 expression, the Immunoscore and CD3+ as well as CD4+ TILs density, but negatively correlated with tumor microvessel density, Ki-67 index, surrounding muscle invasion by tumor and number of Foxp3+ and PD-1+ TILs. Low TSR independently predicted poor local recurrence-free survival and overall survival. Moreover, patients with low TSR and low Immunoscore chordoma phenotype were associated with the worst survival. More importantly, combined TSR and Immunoscore accurately reflected prognosis and enhanced the ability of TSR or Immunoscore alone for outcome prediction.

CONCLUSION

These data reveal the significant impact of TSR on tumor progression and immunological response of patients. Subsequent use of agents targeting the stroma compartment may be an effective strategy to treat chordoma especially in combination with immune-based drugs.

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.

How to measure tumour response in rectal cancer? An explanation of discrepancies and suggestions for improvement

Various methods categorize tumour response after neoadjuvant therapy, including down-staging and tumour regression grading. Response categories allow comparison of different treatments within clinical trials and predict outcome. A reproducible response categorization could identify subgroups with high or low risk for the most appropriate subsequent treatments, like watch and wait. Lack of standardization and interpretation difficulties currently limit the usability of these approaches. In this review we describe these difficulties for the evaluation of chemoradiation in rectal cancer. An alternative approach of tumour response is based on patterns of residual disease, including fragmentation. We summarise the evidence behind this alternative method of response categorisation, which explains a number of very relevant clinical discrepancies. These issues include differences between downstaging and tumour regression, high local regrowth in advanced tumours during watchful waiting procedures, the importance of resection margins, the limited value of post-treatment biopsies and the relatively poor outcome of patients with a near complete pathological response. Recognition of these patterns of response can allow meaningful development of novel biomarkers in the future.

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.