Reproducibility and validation of tumour stroma ratio scoring on oesophageal adenocarcinoma biopsies

The proportion of tumour stroma predicts response to treatment of immune checkpoint inhibitor in combination with chemotherapy in patients with stage IIIB-IV non-small cell lung cancer

AIMS

Immunotherapy has brought a new era to cancer treatment, yet we lack dependable predictors for its effectiveness. This study explores the predictive significance of intratumour stroma proportion (iTSP) for treatment success and prognosis in non-small cell lung cancer (NSCLC) patients undergoing treatment with immune check-point inhibitors (ICIs) together with chemotherapy.

METHODS AND RESULTS

We retrospectively collected data from patients with unresectable stage IIIB-IV NSCLC who were treated with first-line ICIs and chemotherapy. Each patient received a confirmed pathological diagnosis, and the pathologist evaluated the iTSP on haematoxylin and eosin (H&E)-stained sections of diagnostic tissue slides. Among the 102 H&E-stained biopsy samples, 61 (59.8%) were categorised as stroma-L (less than 50% iTSP), while 41 (40.2%) were classified as stroma-H (more than 50% iTSP). We observed that the stroma-L group exhibited a significantly better objective response rate (ORR) (72.1 versus 51.2%, P = 0.031) and deeper response depth (DpR) (-50.49 ± 28.79% versus -35.83 ± 29.91%, P = 0.015) compared to the stroma-H group. Furthermore, the stroma-L group showed longer median progression-free survival (PFS) (9.6 versus 6.0 months, P = 0.011) and overall survival (OS) (24.0 versus 12.2 months, P = 0.001) compared to the stroma-H group. Multivariate Cox proportional hazards regression analysis indicated that iTSP was a highly significant prognostic factor for both PFS [hazard ratio (HR) = 1.713; P = 0.030] and OS (HR = 2.225; P = 0.003).

CONCLUSION

Our findings indicate that a lower iTSP corresponds to improved clinical outcomes and greater DpR in individuals with stage IIIB-IV NSCLC treated with first-line ICIs and chemotherapy. The iTSP could potentially serve as a predictive biomarker for ICIs therapy response.

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.

The Glasgow Microenvironment Score: an exemplar of contemporary biomarker evolution in colorectal cancer

Colorectal cancer remains a leading cause of mortality worldwide. Significant variation in response to treatment and survival is evident among patients with similar stage disease. Molecular profiling has highlighted the heterogeneity of colorectal cancer but has had limited impact in daily clinical practice. Biomarkers with robust prognostic and therapeutic relevance are urgently required. Ideally, biomarkers would be derived from H&E sections used for routine pathological staging, have reliable sensitivity and specificity, and require minimal additional training. The biomarker targets would capture key pathological features with proven additive prognostic and clinical utility, such as the local inflammatory response and tumour microenvironment. The Glasgow Microenvironment Score (GMS), first described in 2014, combines assessment of peritumoural inflammation at the invasive margin with quantification of tumour stromal content. Using H&E sections, the Klintrup-Mäkinen (KM) grade is determined by qualitative morphological assessment of the peritumoural lymphocytic infiltrate at the invasive margin and tumour stroma percentage (TSP) calculated in a semi-quantitative manner as a percentage of stroma within the visible field. The resulting three prognostic categories have direct clinical relevance: GMS 0 denotes a tumour with a dense inflammatory infiltrate/high KM grade at the invasive margin and improved survival; GMS 1 represents weak inflammatory response and low TSP associated with intermediate survival; and GMS 2 tumours are typified by a weak inflammatory response, high TSP, and inferior survival. The prognostic capacity of the GMS has been widely validated while its potential to guide chemotherapy has been demonstrated in a large phase 3 trial cohort. Here, we detail its journey from conception through validation to clinical translation and outline the future for this promising and practical biomarker.

A computed tomography-based multitask deep learning model for predicting tumour stroma ratio and treatment outcomes in patients with colorectal cancer: a multicentre cohort study

BACKGROUND

Tumour-stroma interactions, as indicated by tumour-stroma ratio (TSR), offer valuable prognostic stratification information. Current histological assessment of TSR is limited by tissue accessibility and spatial heterogeneity. The authors aimed to develop a multitask deep learning (MDL) model to noninvasively predict TSR and prognosis in colorectal cancer (CRC).

MATERIALS AND METHODS

In this retrospective study including 2268 patients with resected CRC recruited from four centres, the authors developed an MDL model using preoperative computed tomography (CT) images for the simultaneous prediction of TSR and overall survival. Patients in the training cohort ( n =956) and internal validation cohort (IVC, n =240) were randomly selected from centre I. Patients in the external validation cohort 1 (EVC1, n =509), EVC2 ( n =203), and EVC3 ( n =360) were recruited from other three centres. Model performance was evaluated with respect to discrimination and calibration. Furthermore, the authors evaluated whether the model could predict the benefit from adjuvant chemotherapy.

RESULTS

The MDL model demonstrated strong TSR discrimination, yielding areas under the receiver operating curves (AUCs) of 0.855 (95% CI, 0.800-0.910), 0.838 (95% CI, 0.802-0.874), and 0.857 (95% CI, 0.804-0.909) in the three validation cohorts, respectively. The MDL model was also able to predict overall survival and disease-free survival across all cohorts. In multivariable Cox analysis, the MDL score (MDLS) remained an independent prognostic factor after adjusting for clinicopathological variables (all P <0.05). For stage II and stage III disease, patients with a high MDLS benefited from adjuvant chemotherapy [hazard ratio (HR) 0.391 (95% CI, 0.230-0.666), P =0.0003; HR=0.467 (95% CI, 0.331-0.659), P <0.0001, respectively], whereas those with a low MDLS did not.

CONCLUSION

The multitask DL model based on preoperative CT images effectively predicted TSR status and survival in CRC patients, offering valuable guidance for personalized treatment. Prospective studies are needed to confirm its potential to select patients who might benefit from chemotherapy.

Association of Biological Age with Tumor Microenvironment in Patients with Esophageal Adenocarcinoma

INTRODUCTION

Esophageal cancer is the seventh most common cancer worldwide and typically tends to manifest at an older age. Marked heterogeneity in time-dependent functional decline in older adults results in varying grades of clinically manifest patient fitness or frailty. The biological age-related adaptations that accompany functional decline have been shown to modulate the non-malignant cells comprising the tumor microenvironment (TME). In the current work, we studied the association between biological age and TME characteristics in patients with esophageal adenocarcinoma.

METHODS

We comparatively assessed intratumoral histologic stroma quantity, tumor immune cell infiltrate, and blood leukocyte and thrombocyte count in 72 patients stratified over 3 strata of biological age (younger <70 years, fit older ≥70 years, and frail older adults ≥70 years), as defined by a geriatric assessment.

RESULTS

Frailty in older adults was predictive of decreased intratumoral stroma quantity (B = -14.66% stroma, p = 0.022) relative to tumors in chronological-age-matched fit older adults. Moreover, in comparison to younger adults, frail older adults (p = 0.032), but not fit older adults (p = 0.302), demonstrated a lower blood thrombocyte count at the time of diagnosis. Lastly, we found an increased proportion of tumors with a histologic desert TME histotype, comprising low stroma quantity and low immune cell infiltration, in frail older adults.

CONCLUSION

Our results illustrate the stromal-reprogramming effects of biological age and provide a biological underpinning for the clinical relevance of assessing frailty in patients with esophageal adenocarcinoma, further justifying the need for standardized geriatric assessment in geriatric cancer patients.

Prognostic Significance of Tumor-associated Stroma in Nasopharyngeal Carcinoma: A Multicenter Study

Assessment of tumor-associated stroma has shown a reliable prognostic value in recent research. We evaluated the prognostic value of tumor-stroma ratio (TSR) in a large multicenter cohort of nasopharyngeal carcinoma (NPC). We used the conventional hematoxylin and eosin-stained slides of 115 cases of NPC to assess TSR as described in recent guidelines. The amount of tumor-associated stroma was assessed as a percentage and then tumors were classified as stroma-high (>50%) or stroma-low (≤50%). Kaplan-Meier curves, χ 2 test, and Cox regression univariable and multivariable analyses were carried out. A total of 48 (41.7%) tumors were stroma-high and 67 (58.3%) tumors were stroma-low. In the Cox regression multivariable analysis, the tumors categorized as stroma-high were associated with a worse overall survival with a hazard ratio of 2.30 (95% CI: 1.27-4.15, P =0.006) and with poor disease-specific survival (hazard ratio=1.87, 95% CI: 1.07-3.28, P =0.029). The assessment of TSR in NPC is simple and cost-effective, and it has a significant prognostic value. TSR can aid in risk stratification and clinical decision-making in NPC.

Tumour microenvironment influences response to treatment in oesophageal adenocarcinoma

The poor treatment response of oesophageal adenocarcinoma (OAC) leads to low survival rates. Its increasing incidence makes finding more effective treatment a priority. Recent treatment improvements can be attributed to the inclusion of the tumour microenvironment (TME) and immune infiltrates in treatment decisions. OAC TME is largely immunosuppressed and reflects treatment resistance as patients with inflamed TME have better outcomes. Priming the tumour with the appropriate neoadjuvant chemoradiotherapy treatment could lead to higher immune infiltrations and higher expression of immune checkpoints, such as PD-1/PDL-1, CTLA4 or emerging new targets: LAG-3, TIM-3, TIGIT or ICOS. Multiple trials support the addition of immune checkpoint inhibitors to the current standard of care. However, results vary, supporting the need for better response biomarkers based on TME composition. This review explores what is known about OAC TME, the clinical significance of the various cell populations infiltrating it and the emerging therapeutical combination with a focus on immune checkpoints inhibitors.

Deciphering the Morphology of Tumor-Stromal Features in Invasive Breast Cancer Using Artificial Intelligence

Tumor-associated stroma in breast cancer (BC) is complex and exhibits a high degree of heterogeneity. To date, no standardized assessment method has been established. Artificial intelligence (AI) could provide an objective morphologic assessment of tumors and stroma, with the potential to identify new features not discernible by visual microscopy. In this study, we used AI to assess the clinical significance of (1) stroma-to-tumor ratio (S:TR) and (2) the spatial arrangement of stromal cells, tumor cell density, and tumor burden in BC. Whole-slide images of a large cohort (n = 1968) of well-characterized luminal BC cases were examined. Region and cell-level annotation was performed, and supervised deep learning models were applied for automated quantification of tumor and stromal features. S:TR was calculated in terms of surface area and cell count ratio, and the S:TR heterogeneity and spatial distribution were also assessed. Tumor cell density and tumor size were used to estimate tumor burden. Cases were divided into discovery (n = 1027) and test (n = 941) sets for validation of the findings. In the whole cohort, the stroma-to-tumor mean surface area ratio was 0.74, and stromal cell density heterogeneity score was high (0.7/1). BC with high S:TR showed features characteristic of good prognosis and longer patient survival in both the discovery and test sets. Heterogeneous spatial distribution of S:TR areas was predictive of worse outcome. Higher tumor burden was associated with aggressive tumor behavior and shorter survival and was an independent predictor of worse outcome (BC-specific survival; hazard ratio: 1.7, P = .03, 95% CI, 1.04-2.83 and distant metastasis-free survival; hazard ratio: 1.64, P = .04, 95% CI, 1.01-2.62) superior to absolute tumor size. The study concludes that AI provides a tool to assess major and subtle morphologic stromal features in BC with prognostic implications. Tumor burden is more prognostically informative than tumor size.

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.

Prognostic prediction based on histopathologic features of tumor microenvironment in colorectal cancer

Purpose

To automatically quantify colorectal tumor microenvironment (TME) in hematoxylin and eosin stained whole slide images (WSIs), and to develop a TME signature for prognostic prediction in colorectal cancer (CRC).

Methods

A deep learning model based on VGG19 architecture and transfer learning strategy was trained to recognize nine different tissue types in whole slide images of patients with CRC. Seven of the nine tissue types were defined as TME components besides background and debris. Then 13 TME features were calculated based on the areas of TME components. A total of 562 patients with gene expression data, survival information and WSIs were collected from The Cancer Genome Atlas project for further analysis. A TME signature for prognostic prediction was developed and validated using Cox regression method. A prognostic prediction model combined the TME signature and clinical variables was also established. At last, gene-set enrichment analysis was performed to identify the significant TME signature associated pathways by querying Gene Ontology database and Kyoto Encyclopedia of Genes and Genomes database.

Results

The deep learning model achieved an accuracy of 94.2% for tissue type recognition. The developed TME signature was found significantly associated to progression-free survival. The clinical combined model achieved a concordance index of 0.714. Gene-set enrichment analysis revealed the TME signature associated genes were enriched in neuroactive ligand-receptor interaction pathway.

Conclusion

The TME signature was proved to be a prognostic factor and the associated biologic pathways would be beneficial to a better understanding of TME in CRC patients.

Tumor-Stroma Ratio in Basaloid and Conventional Laryngeal Squamous Cell Carcinoma: Prognostic Significance and Concordance in Paired Biopsies and Surgical Samples

Basaloid squamous cell carcinoma (BSCC) is a subtype of squamous cell carcinoma (SCC) associated with a poor prognosis. Tumor–stroma ratio (TSR) has been introduced as a prognostic feature in many solid tumors. TSR was investigated in a series of laryngeal BSCCs and compared with a group of stage-matched conventional SCCs (cSCCs), in both preoperative and surgical specimens, with the intent of ascertaining the more aggressive behavior of BSCC and verifying the presence of stromal-related causes. A series of 14 consecutive laryngeal BSCCs and a control group of 28 stage-matched conventional cSCCs were analyzed. A higher nodal metastasis presence was found in BSCCs (57.1% vs. 28.6%). The recurrence rate was 33.5% and 63.6% in the cSCC and BSCC groups; disease-free survival (DFS) was higher, though not significantly, in patients with cSCC. TSR, large cell nests, and tumor budding showed a moderate to very good agreement, and stroma type a good to very good agreement between biopsies and surgical specimens in the cSCC group. In the BSCC group, agreement was poor to very good for TSR and stroma type, and good to very good for large cell nests and tumor budding. Age was the only feature significant in predicting recurrence in the BSCC group (p = 0.0235). In cSCC, TSR low/stroma rich cases, when evaluated on biopsies or surgical specimens, were associated with lower DFS (p = 0.0036; p = 0.0041, respectively). Laryngeal BSCCs showed a lower DFS than cSCCs, even if statistical significance was not reached. TSR, evaluated in laryngeal biopsies and excised tumors, was prognostic in terms of DFS in cSCC but not in BSCC cases.

Radiomics: A review of current applications and possibilities in the assessment of tumor microenvironment

With the recent success in the application of immunotherapy for treating various advanced cancers, the tumor microenvironment has rapidly become an important field of research. The tumor microenvironment is complex and its characteristics strongly influence disease biology and potentially responses to systemic therapy. Accurate preoperative assessment of tumor microenvironment is of great significance for the formulation of an immunotherapy strategy and evaluation of patient prognosis. As a research hotspot in medical image analysis technology, radiomics has been applied in the auxiliary diagnosis of the tumor microenvironment. This article reviews the current status of radiomics in the elective application on tumor microenvironment and discusses potential prospects.

Development of Tier 2 LC-MRM-MS protein quantification methods for liquid biopsies

In the pursuit of personalized diagnostics and tailored treatments, quantitative protein tests contribute to a more precise definition of health and disease. The development of new quantitative protein tests should be driven by an unmet clinical need and performed in a collaborative effort that involves all stakeholders. With regard to the analytical part, mass spectrometry (MS)-based platforms are an excellent tool for quantification of specific proteins in body fluids, for example focused on cancer. The obtained readouts have great potential in determining tumor aggressiveness to facilitate treatment decisions, and can furthermore be used to monitor patient response. Internationally standardized TNM classifications of malignant tumors are beneficial for diagnosis, however treatment outcome and survival of cancer patients is poorly predicted. To this end, the importance of the tumor microenvironment has endorsed the introduction of the tumor-stroma ratio as a prognostic parameter in solid primary tumor types. Currently, the stromal content of tumor tissues is determined via routine diagnostic pathology slides. With the development of liquid chromatography (LC)-MS methods we aim at quantification of tumor-stroma specific proteins in body fluids. In this mini-review the analytical aspect of this developmental trajectory is further detailed.

Characteristics of tumour stroma in regional lymph node metastases in colorectal cancer patients: a theoretical framework for future diagnostic imaging with FAPI PET/CT

Purpose

The recently developed fibroblast activation protein inhibitor (FAPI) tracer for PET/CT, binding tumour-stromal cancer-associated fibroblasts, is a promising tool for detection of positive lymph nodes. This study provides an overview of features, including sizes and tumour-stromal content, of lymph nodes and their respective lymph node metastases (LNM) in colorectal cancer (CRC), since literature lacks on whether LNMs contain sufficient stroma to potentially allow FAPI-based tumour detection.

Methods

Haematoxylin and eosin-stained tissue slides from 73 stage III colon cancer patients were included. Diameters and areas of all lymph nodes and their LNMs were assessed, the amount of stroma by measuring the stromal compartment area, the conventional and total tumour-stroma ratios (TSR-c and TSR-t, respectively), as well as correlations between these parameters. Also, subgroup analysis using a minimal diameter cut off of 5.0 mm was performed.

Results

In total, 126 lymph nodes were analysed. Although positive correlations were observed between node and LNM for diameter and area (r = 0.852, p < 0.001 and r = 0.960, p < 0.001, respectively), and also between the LNM stromal compartment area and nodal diameter (r = 0.612, p < 0.001), nodal area (r = 0.747, p < 0.001) and LNM area (r = 0.746, p < 0.001), novel insight was that nearly all (98%) LNMs contained stroma, with median TSR-c scores of 35% (IQR 20–60%) and TSR-t of 20% (IQR 10–30%). Moreover, a total of 32 (25%) positive lymph nodes had a diameter of < 5.0 mm.

Conclusion

In LNMs, stroma is abundantly present, independent of size, suggesting a role for FAPI PET/CT in improved lymph node detection in CRC.

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.

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

BACKGROUND

It is critical to develop a reliable and cost-effective prognostic tool for colorectal cancer (CRC) stratification and treatment optimization. Tumor-stroma ratio (TSR) may be a promising indicator of poor prognosis in CRC patients. As a result, we conducted a systematic review on the predictive value of TSR in CRC.

METHODS

This study was carried out according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guideline. An electronic search was completed using commonly used databases PubMed, CENTRAL, Cochrane Central Register of Controlled Trials, and Google scholar till the last search up to May 30, 2021. STATA version 13 was used to analyze the data.

RESULTS

A total of 13 studies [(12 for disease-free survival (DFS) and nine studies for overall survival (OS)] involving 4,857 patients met the inclusion criteria for the systematic review in the present study. In individuals with stage II CRC, stage III CRC, or mixed stage CRC, we observed a significantly higher pooled hazard ratio (HR) in those with a low TSR/greater stromal content (HR, 1.54; 95% CI: 1.20 to 1.88), (HR, 1.90; 95% CI: 1.35 to 2.45), and (HR, 1.70; 95% CI: 1.45 to 1.95), respectively, for predicting DFS. We found that a low TSR ratio had a statistically significant predictive relevance for stage II (HR, 1.43; 95% CI: 1.09 to 1.77) and mixed stages of CRC (HR, 1.65; 95% CI: 1.31 to 2.0) for outcome OS.

CONCLUSION

In patients with CRC, low TSR was found to be a prognostic factor for a worse prognosis (DFS and OS).

Tumor-stroma ratio is associated with Miller-Payne score and pathological response to neoadjuvant chemotherapy in HER2-negative early breast cancer

The tumor-stroma ratio (TSR) has proven to be a strong prognostic factor in breast cancer, demonstrating better survival for patients with stroma-low tumors. Since the role of the TSR as a predictive marker for neoadjuvant chemotherapy outcome is yet unknown, this association was evaluated for HER2-negative breast cancer in the prospective DIRECT and NEOZOTAC trials. The TSR was assessed on 375 hematoxylin and eosin-stained sections of pre-treatment biopsies. Associations between the TSR and chemotherapy response according to the Miller-Payne (MP) grading system, and between the TSR and pathological response were examined using Pearson's chi-square, Cochran-Armitage test for trend and regression analyses. A stroma-low tumor prior to neoadjuvant chemotherapy was significantly associated with a higher MP score (P = .005). This relationship remained significant in the estrogen receptor (ER)-negative subgroup (P = .047). The univariable odds ratio (OR) of a stroma-low tumor on pathological complete response (pCR) was 2.46 (95% CI 1.34-4.51, P = .004), which attenuated to 1.90 (95% CI 0.85-4.25, P = .119) after adjustment for relevant prognostic factors. Subgroup analyses revealed an OR of 5.91 in univariable analyses for ER-negativity (95% CI 1.19-29.48, P = .030) and 1.48 for ER-positivity (95% CI 0.73-3.01, P = .281). In conclusion, a low amount of stroma on pre-treatment biopsies is associated with a higher MP score and pCR rate. Therefore, the TSR is a promising biomarker in predicting neoadjuvant treatment outcome. Incorporating this parameter in routine pathological diagnostics could be worthwhile to prevent overtreatment and undertreatment.

Prognostic features of the tumour microenvironment in oesophageal adenocarcinoma

Oesophageal adenocarcinoma (OAC) is a disease with an incredibly poor survival rate and a complex makeup. The growth and spread of OAC tumours are profoundly influenced by their surrounding microenvironment and the properties of the tumour itself. Constant crosstalk between the tumour and its microenvironment is key to the survival of the tumour and ultimately the death of the patient. The tumour microenvironment (TME) is composed of a complex milieu of cell types including cancer associated fibroblasts (CAFs) which make up the tumour stroma, endothelial cells which line blood and lymphatic vessels and infiltrating immune cell populations. These various cell types and the tumour constantly communicate through environmental cues including fluctuations in pH, hypoxia and the release of mitogens such as cytokines, chemokines and growth factors, many of which help promote malignant progression. Eventually clusters of tumour cells such as tumour buds break away and spread through the lymphatic system to nearby lymph nodes or enter the circulation forming secondary metastasis. Collectively, these factors need to be considered when assessing and treating patients clinically. This review aims to summarise the ways in which these various factors are currently assessed and how they relate to patient treatment and outcome at an individual level.

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.

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.

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.

Association of the tumour stroma percentage in the preoperative biopsies with lymph node metastasis in colorectal cancer

BACKGROUND

Preoperative prediction of lymph node (LN) status is integral to determining the most appropriate treatment strategy for colorectal cancer (CRC). This study aimed to develop and validate a nomogram to predict LN metastasis in CRC preoperatively.

METHODS

A total of 530 patients were enrolled and divided into training and validation cohorts. The tumour stroma percentage (TSP) of the preoperative biopsies was assessed. The risk factors for LN metastasis were selected, and a nomogram was constructed subsequently. The performance of the nomogram was assessed by using the AUROC and the calibration curve, and then validated in the validation cohort.

RESULTS

High TSP was significantly associated with LN metastasis in both the training and validation cohorts. Computed tomography (CT)-reported T stage, CT-reported LN status, preoperative tumour differentiation, carcinoembryonic antigen, carbohydrate antigen 19-9 and TSP were independent predictors of LN metastasis in CRC. A nomogram incorporating the six predictors was constructed. The nomogram yielded good discrimination and calibration, with an AUROC of 0.846 (95% CI: 0.807-0.886) and 0.809 (95% CI: 0.745-0.872) in the training and validation cohorts, respectively.

CONCLUSIONS

Assessment of TSP in the preoperative biopsies provided additional information about the LN status. The nomogram was useful for tailored therapy in CRC preoperatively.

The value of tumor-stroma ratio as predictor of pathologic response after neoadjuvant chemoradiotherapy in esophageal cancer

•

Scoring the tumor-stroma ratio is a simple and reproducible method.

•

Tumor-stroma ratio and response to neoadjuvant chemoradiotherapy are correlated.

•

Stroma-low tumors are likely to respond better to neoadjuvant chemoradiotherapy.

Background and purpose

With currently available techniques, the prediction of pathologic complete response after neoadjuvant chemoradiotherapy is insufficient. The tumor-stroma ratio (TSR) has proven to be a predictor of survival for several types of cancer, including esophageal. The aim of this study was to investigate the value of TSR in predicting pathologic response after neoadjuvant chemoradiotherapy in esophageal cancer patients.

Materials and methods

Patients with esophageal adenocarcinoma or squamous cell carcinoma who received neoadjuvant chemoradiotherapy followed by a resection were selected. Haematoxylin and eosin (H&E) stained sections of diagnostic biopsies were collected and TSR was independently assessed by two investigators. Patients were categorized in stroma-low (≤50% stroma) and stroma-high (>50% stroma) groups for further analyses. The tumor regression grade (TRG) was assessed on H&E stained sections of the resected primary tumor to determine pathologic response.

Results

A total of 94 patients were included in this study, of which 76 patients were categorized as stroma-low and 18 as stroma-high. Forty-two (45%) patients had a major pathologic response (TRG 1–2), whereas 52 (55%) were considered non-responders. After adjustment for gender, tumor type, cT-status and differentiation grade, patients with a stroma-high tumor showed a higher chance of no response compared to patients with a stroma-low tumor (OR 3.57, 95%CI 1.03–12.31, P = 0.04).

Conclusion

TSR showed to have the potential to aid in the prediction of pathologic response in esophageal cancer patients receiving neoadjuvant chemoradiotherapy. Larger validation studies are necessary before implementing this method in daily practice.

Preoperative, biopsy-based assessment of the tumour microenvironment in patients with primary operable colorectal cancer

The tumour microenvironment (TME) is recognised as an important prognostic characteristic and therapeutic target in patients with colorectal cancer (CRC). However, assessment generally utilises surgically resected specimens, precluding neoadjuvant targeting. The present study investigated the feasibility of intra‐epithelial CD3⁺ T‐lymphocyte density and tumour stroma percentage (TSP) assessment using preoperative colonoscopic biopsies from 115 patients who had undergone resection of stages I–III CRC, examining the relationship between biopsy and surgically resected specimen‐based assessment, and the relationship with cancer‐specific survival (CSS). High biopsy CD3⁺ density was associated with high CD3⁺ density in the invasive margin, cancer stroma and intra‐epithelial compartments of surgically resected specimens (area under the curve > 0.62, p < 0.05 for all) and with high Immunoscore. High biopsy TSP predicted high TSP in resected specimens (p = 0.001). Intra‐class correlation coefficient for both measures was >0.7 (p < 0.001), indicating excellent concordance between individuals. Biopsy CD3⁺ density (hazard ratio [HR] 0.23, p = 0.002) and TSP (HR 2.23, p = 0.029) were independently associated with CSS; this was comparable to the prognostic value of full section assessment (HR 0.21, p = 0.004, and HR 2.25, p = 0.033 respectively). These results suggest that assessment of the TME is comparable in biopsy and surgically resected specimens from patients with CRC, and biopsy‐based assessment could allow for stratification prior to surgery or commencement of therapy targeting the TME.

Stromal-derived interleukin 6 drives epithelial-to-mesenchymal transition and therapy resistance in esophageal adenocarcinoma

Esophageal adenocarcinoma (EAC) has a dismal prognosis, and survival benefits of recent multimodality treatments remain small. Cancer-associated fibroblasts (CAFs) are known to contribute to poor outcome by conferring therapy resistance to various cancer types, but this has not been explored in EAC. Importantly, a targeted strategy to circumvent CAF-induced resistance has yet to be identified. By using EAC patient-derived CAFs, organoid cultures, and xenograft models we identified IL-6 as the stromal driver of therapy resistance in EAC. IL-6 activated epithelial-to-mesenchymal transition in cancer cells, which was accompanied by enhanced treatment resistance, migratory capacity, and clonogenicity. Inhibition of IL-6 restored drug sensitivity in patient-derived organoid cultures and cell lines. Analysis of patient gene expression profiles identified ADAM12 as a noninflammation-related serum-borne marker for IL-6-producing CAFs, and serum levels of this marker predicted unfavorable responses to neoadjuvant chemoradiation in EAC patients. These results demonstrate a stromal contribution to therapy resistance in EAC. This signaling can be targeted to resensitize EAC to therapy, and its activity can be measured using serum-borne markers.

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.

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

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

Prediction of pathological response to neoadjuvant treatment in rectal cancer with a two-protein immunohistochemical score derived from stromal gene-profiling

Background

Preoperative chemoradiotherapy followed by surgical mesorectal resection is the standard of care for locally advanced rectal carcinomas. Yet, predicting that patients will respond to treatment remains an unmet clinical challenge.

Experimental design

Using laser-capture microdissection we isolated RNA from stroma and tumour glands from prospective pre-treatment samples (n = 15). Transcriptomic profiles were obtained hybridising PrimeView Affymetrix arrays. We modelled a carcinoma-associated fibroblast-specific genes filtering data using GSE39396.

Results

The analysis of differentially expressed genes of stroma/tumour glands from responder and non-responder patients shows that most changes were associated with the stromal compartment; codifying mainly for extracellular matrix and ribosomal components. We built a carcinoma-associated fibroblast (CAF) specific classifier with genes showing changes in expression according to the tumour regression grade (FN1, COL3A1, COL1A1, MMP2 and IGFBP5). We assessed these five genes at the protein level by means of immunohistochemical staining in a patient's cohort (n = 38). For predictive purposes we used a leave-one-out cross-validated model with a positive predictive value (PPV) of 83.3%. Random Forest identified FN1 and COL3A1 as the best predictors. Rebuilding the leave-one-out cross-validated regression model improved the classification performance with a PPV of 93.3%. An independent cohort was used for classifier validation (n = 36), achieving a PPV of 88.2%. In a multivariate analysis, the two-protein classifier proved to be the only independent predictor of response.

Conclusion

We developed a two-protein immunohistochemical classifier that performs well at predicting the non-response to neoadjuvant treatment in rectal cancer.

Classify epithelium-stroma in histopathological images based on deep transferable network

Recently, the deep learning methods have received more attention in histopathological image analysis. However, the traditional deep learning methods assume that training data and test data have the same distributions, which causes certain limitations in real-world histopathological applications. However, it is costly to recollect a large amount of labeled histology data to train a new neural network for each specified image acquisition procedure even for similar tasks. In this paper, an unsupervised domain adaptation is introduced into a typical deep convolutional neural network (CNN) model to mitigate the repeating of the labels. The unsupervised domain adaptation is implemented by adding two regularisation terms, namely the feature-based adaptation and entropy minimisation, to the object function of a widely used CNN model called the AlexNet. Three independent public epithelium-stroma datasets were used to verify the proposed method. The experimental results have demonstrated that in the epithelium-stroma classification, the proposed method can achieve better performance than the commonly used deep learning methods and some existing deep domain adaptation methods. Therefore, the proposed method can be considered as a better option for the real-world applications of histopathological image analysis because there is no requirement for recollection of large-scale labeled data for every specified domain.

Prognostic value of tumor-stroma ratio combined with the immune status of tumors in invasive breast carcinoma

PURPOSE

Complex interactions occur between cancer cells and cells in the tumor microenvironment. In this study, the prognostic value of the interplay between tumor-stroma ratio (TSR) and the immune status of tumors in breast cancer patients was evaluated.

METHODS

A cohort of 574 breast cancer patients was analyzed. The percentage of tumor stroma was visually estimated on Hematoxylin and Eosin (H&E) stained histological tumor tissue sections. Immunohistochemical staining was performed for classical human leukocyte antigen (HLA) class I, HLA-E, HLA-G, markers for regulatory T (Treg) cells, natural killer (NK) cells and cytotoxic T-lymphocytes (CTLs).

RESULTS

TSR (P < .001) and immune status of tumors (P < .001) were both statistically significant for recurrence free period (RFP) and both independent prognosticators (P < .001) in which tumors with a high stromal content behave more aggressively as well as tumors with a low immune status. Ten years RFP for patients with a stroma-low tumor and high immune status profile was 87% compared to 17% of patients with a stroma-high tumor combined with low immune status profile (P < .001). Classical HLA class I is the most prominent immune marker in the immune status profiles.

CONCLUSIONS

Determination of TSR is a simple, fast and cheap method. The effect on RFP of TSR when combined with immune status of tumors or expression of classical HLA class I is even stronger. Both are promising for further prediction and achievement of tailored treatment for breast cancer patients.

The tumour-stroma ratio in colon cancer: the biological role and its prognostic impact

The tumour microenvironment consists of a complex mixture of non-neoplastic cells, including fibroblasts, immune cells and endothelial cells embedded in the proteins of the extracellular matrix. The tumour microenvironment plays an active role in tumour behaviour. By interacting with cancer cells, it influences disease progression and the metastatic capacity of the tumour. Tumours with a high amount of stroma correspond to poor patient prognosis. The tumour-stroma ratio (TSR) is a strong independent prognostic tool in colon cancer and provides additional value to the current clinically used tumour-node-metastasis classification. The TSR is assessed on conventional haematoxylin and eosin-stained paraffin sections at the invasive front of the tumour. Here we review studies demonstrating the prognostic significance of the TSR in solid epithelial tumours with a focus on colon cancer. Moreover, the biological role of the tumour microenvironment during tumour progression and invasion will be discussed, as well as the attempts to target the tumour stroma for therapeutic purposes. We suggest that the TSR can be implemented with little effort and without additional costs in current routine pathology diagnostics owing to its simplicity and reliability.

Association between tumor-stroma ratio and prognosis in solid tumor patients: a systematic review and meta-analysis

Tumor-related stroma plays an active role in tumor invasion and metastasis. The tumor–stroma ratio (TSR) in the pathologic specimen has drawn increasing attention from the field of predicting tumor prognosis. However, the prognostic value of TSR in solid tumors necessitates further elucidation. We conducted a meta-analysis on 14 studies with 4238 patients through a comprehensive electronic search on databases updated on May 2016 to explore the relationship between TSR and prognosis of solid tumors. The overall hazard ratio showed that rich stroma in tumor tissue was associated with poor overall survival (OS) (14 studies, 4238 patients) and disease-free survival (DFS) (9 studies, 2235 patients) of patients with solid tumors. The effect of low TSR on poor OS was observed among various cancer types, but not in the early stage of cervical caner. A significant relationship between low TSR and poor OS was also observed in the subgroup analyses based on study region, blinding status, and Newcastle–Ottawa Scale (NOS) score. Subgroup analyses indicated that cancer type, clinical stage, study region, blinding status, and NOS score did not affect the prognostic value of TSR for DFS. Moreover, low TSR was significantly correlated with the serious clinical stage, advanced depth of invasion, and positive lymph node metastasis. These findings indicate that a high proportion of stroma in cancer tissue is associated with poor clinical outcomes in cancer patients, and TSR may serve as an independent prognostic factor for solid tumors.

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

Background

Tumour budding (TB) and Tumour Stroma Ratio (TSR) may be rewarding in the treatment stratification of patients with stage II colon cancer. However, lack of standardization may exclude these parameters from being used in a clinical setting. The purpose of this methodologic study was to compare stereology with semi-quantitative estimations of TSR, to investigate the intra-tumoural heterogeneity of TB and TSR, and to assess the intra- and inter-observer agreement.

Methods

Three paraffin embedded tumour blocks, one of them representing the deepest invasive front, were selected from each of 43 patients treated for stage II colon cancer. TSR was estimated in H&E sections semi-quantitatively using conventional microscopy, and stereologically on scanned slides, using the newCAST stereology platform. TB was scored across 10 high power fields at the invasive front in cytokeratin AE1/AE3 stained sections.

Results

Subjective, semi-quantitative estimates of TSR significantly correlated to the stereological estimates, with the best correlation found for sections with the deepest invasive tumour penetration (σ = 0.621, p < 0.001). Inter-observer agreement was moderate to substantial for both TB (Κappa = 0.46–0.73) and TSR (Κappa = 0.70–0.75).

The Intraclass correlation coefficient (ICC) for TSR varied from 0.322 based on stereological hotspot estimation to 0.648 for the semi-quantitative evaluation. For TB, ICC varied from 0.646 based on continuous data to 0.698 based on categorical data (cut-off: 10 buds). Thus, the intra-tumoural heterogeneity for both TB and the semi-quantitative estimation of TSR was low.

Conclusion

We recommend using only one tissue section representing the deepest invasive tumour area for estimation of TSR. For TB we recommend using one tissue section; however due to low representation of high-budding tumours, results must be considered with caution.

Electronic supplementary material

The online version of this article (10.1186/s13000-018-0697-9) contains supplementary material, which is available to authorized users.

Biopsy proportion of tumour predicts pathological tumour response and benefit from chemotherapy in resectable oesophageal carcinoma: results from the UK MRC OE02 trial

Background

Neoadjuvant chemotherapy followed by surgery is the standard of care for UK patients with locally advanced resectable oesophageal carcinoma (OeC). However, not all patients benefit from multimodal treatment and there is a clinical need for biomarkers which can identify chemotherapy responders. This study investigated whether the proportion of tumour cells per tumour area (PoT) measured in the pre-treatment biopsy predicts chemotherapy benefit for OeC patients.

Patients and methods

PoT was quantified using digitized haematoxylin/eosin stained pre-treatment biopsy slides from 281 OeC patients from the UK MRC OE02 trial (141 treated by surgery alone (S); 140 treated by 5-fluorouracil/cisplatin followed by surgery (CS)). The relationship between PoT and clinicopathological data including tumour regression grade (TRG), overall survival and treatment interaction was investigated.

Results

PoT was associated with chemotherapy benefit in a non-linear fashion (test for interaction, P=0.006). Only patients with a biopsy PoT between 40% and 70% received a significant survival benefit from neoadjuvant chemotherapy (N=129; HR (95%CI):1.94 (1.39-2.71), unlike those with lower or higher PoT (PoT<40%, N=39, HR:1.25 (0.66-2.35); PoT>70% (N=28, HR:0.65 (0.36-1.18)). High pre-treatment PoT was related to lack of primary tumour regression (TRG 4 or 5), P=0.0402.

Conclusions

This is the first study to identify in a representative subgroup of OeC patients from a large randomized phase III trial that the proportion of tumour in the pre-chemotherapy biopsy predicts benefit from chemotherapy and may be a clinically useful biomarker for patient treatment stratification.

Proportion of tumour is a novel biomarker which can be measured in the pre-treatment diagnostic biopsy and which may enable the identification of chemotherapy responders and non-responders among patients with oesophageal carcinoma. Proportion of tumour could easily become part of the routine reporting of oesophageal cancer biopsies and may aid in managing patients with borderline resectable cancer.

Tumor-stroma ratio as prognostic factor for survival in rectal adenocarcinoma: A retrospective cohort study

AIM

To evaluate the prognostic value of the tumor-stroma ratio (TSR) in rectal cancer.

METHODS

TSR was determined on hematoxylin and eosin stained histological sections of 154 patients treated for rectal adenocarcinoma without prior neoadjuvant treatment in the period 1996-2006 by two observers to assess reproducibility. Patients were categorized into three categories: TSR-high [carcinoma percentage (CP) ≥ 70%], TSR-intermediate (CP 40%, 50% and 60%) and TSR-low (CP ≤ 30%). The relation between categorized TSR and survival was analyzed using Cox proportional hazards model.

RESULTS

Thirty-six (23.4%) patients were scored as TSR-low, 70 (45.4%) as TSR-intermediate and 48 (31.2%) as TSR-high. TSR had a good interobserver agreement (κ = 0.724, concordance 82.5%). Overall survival (OS) and disease free survival (DFS) were significantly better for patients with a high TSR (P = 0.01 and P = 0.02, respectively). A similar association existed for disease specific survival (P = 0.06). In multivariate analysis, patients without lymph node metastasis and an intermediate TSR had a higher risk of dying from rectal cancer (HR = 5.27, 95%CI: 1.54-18.10), compared to lymph node metastasis negative patients with a high TSR. This group also had a worse DFS (HR = 6.41, 95%CI: 1.84-22.28). An identical association was seen for OS. These relations were not seen in lymph node metastasis positive patients.

CONCLUSION

The TSR has potential as a prognostic factor for survival in surgically treated rectal cancer patients, especially in lymph node negative cases.

Tumor-stroma ratio(TSR) as a potential novel predictor of prognosis in digestive system cancers: A meta-analysis

MAIN PROBLEM

The tumor-stroma ratio (TSR) has been reported as a prognosis predictor in multiple cancers. The aim of this meta-analysis was to investigate the potential value of TSR as a prognostic predictor of cancer in the digestive system.

METHODS

We searched PubMed, Embase, Elsevier and Web of Science. All studies exploring the association of TSR with overall survival (OS) or disease-free survival (DFS), and lymph node metastasis (LNM) were identified.

RESULTS

In total, eight studies were eligible for analysis, and they included 1959 patients. Meta-analysis showed that the low TSR in the tumor could predict poor overall survival (OS) in multiple cancers (pooled Hazard Ratio [HR]: 2.15, 95%CI: 1.80-2.57, P<0.00001, fixed effects). For disease-free survival (DFS), low TSR was also a significant predictor (pooled Hazard Ratio [HR]: 2.31, 95%CI: 1.88-2.83, P<0.00001, fixed effects). In addition, low TSR was correlated with tumor stage.

DISCUSSION

The tumor-stroma ratio (TSR) may potentially serve as a poor prognostic predictor for the metastasis and prognosis of cancer.

Quantitative Image Analysis of Epithelial and Stromal Area in Histological Sections of Colorectal Cancer: An Emerging Diagnostic Tool

In colorectal cancer (CRC), an increase in the stromal (S) area with the reduction of the epithelial (E) parts has been suggested as an indication of tumor progression. Therefore, an automated image method capable of discriminating E and S areas would allow an improved diagnosis. Immunofluorescence staining was performed on paraffin-embedded sections from colorectal tumors (16 samples from patients with liver metastasis and 18 without). Noncancerous tumor adjacent mucosa (n = 5) and normal mucosa (n = 4) were taken as controls. Epithelial cells were identified by an anti-keratin 8 (K8) antibody. Large tissue areas (5–63 mm²/slide) including tumor center, tumor front, and adjacent mucosa were scanned using an automated microscopy system (TissueFAXS). With our newly developed algorithms, we showed that there is more K8-immunoreactive E in the tumor center than in tumor adjacent and normal mucosa. Comparing patients with and without metastasis, the E/S ratio decreased by 20% in the tumor center and by 40% at tumor front in metastatic samples. The reduction of E might be due to a more aggressive phenotype in metastasis patients. The novel software allowed a detailed morphometric analysis of cancer tissue compartments as tools for objective quantitative measurements, reduced analysis time, and increased reproducibility of the data.

Tumor-stroma ratio is a prognostic factor for survival in hepatocellular carcinoma patients after liver resection or transplantation

BACKGROUND

The stromal compartment in several organs seems to play an important role in the initiation, growth, and progression of certain neoplasms. The tumor-stroma ratio (TSR) has been found to be an independent factor for prognosis of several types of carcinomas, but the effect of the TSR on hepatocellular carcinoma (HCC) has not been explored yet. The objective of the study is to evaluate the prognostic importance of TSR in HCC patients after liver resection or transplantation.

METHODS

A total of 300 patients with HCC who underwent liver resection or transplantation were included in this study. TSR was assessed on hematoxylin and eosin-stained sections by 2 independent investigators. Patients were divided into 2 groups: a stroma-rich group (stroma ≥ 50%) and a stroma-poor group (stroma < 50%). Chi-square test, Kaplan-Meier method, and Cox univariable and multivariable regression were used in to analyze the data.

RESULTS

Among the post liver resection patients, the TSR was associated with overall survival (OS) in univariate and multivariate analyses (hazard ratio [HR], 4.35 [95% CI, 2.54-7.47] and HR, 2.55 [95% CI, 1.44-4.52], respectively). Among the post liver transplant patients, the TSR was also associated with OS in univariate and multivariate analyses (HR, 2.92 [95% CI, 1.63-5.23] and HR, 2.76 [95% CI, 1.47-4.85], respectively), and TSR with recurrence-free survival (RFS) in univariate and multivariate analyses (HR, 2.63 [95% CI, 1.42-4.86] and HR, 1.93 [95% CI, 1.03-3.62], respectively). Patients with stroma-poor tumor and who were within the Milan criteria or the UCSF criteria had a better in OS and RFS.

CONCLUSION

We show for the first time that TSR is an independent prognostic factor for HCC patients after liver resection or transplantation. TSR may enable better identification of patients at risk for recurrence in HCC patients after curative treatment and may aid in patient management and development of individualized medicine for treatment of HCC.

Systematic review and meta-analysis of immunohistochemical prognostic biomarkers in resected oesophageal adenocarcinoma

BACKGROUND

Oesophageal adenocarcinoma (OAC) is one of the fastest rising malignancies with continued poor prognosis. Many studies have proposed novel biomarkers but, to date, no immunohistochemical markers of survival after oesophageal resection have entered clinical practice. Here, we systematically review and meta-analyse the published literature, to identify potential biomarkers.

METHODS

Relevant articles were identified via Ovid medline 1946-2013. For inclusion, studies had to conform to REporting recommendations for tumor MARKer (REMARK) prognostic study criteria. The primary end-point was a pooled hazard ratio (HR) and variance, summarising the effect of marker expression on prognosis.

RESULTS

A total of 3059 articles were identified. After exclusion of irrelevant titles and abstracts, 214 articles were reviewed in full. Nine molecules had been examined in more than one study (CD3, CD8, COX-2, EGFR, HER2, Ki67, LgR5, p53 and VEGF) and were meta-analysed. Markers with largest survival effects were COX-2 (HR=2.47, confidence interval (CI)=1.15-3.79), CD3 (HR=0.51, 95% CI=0.32-0.70), CD8 (HR=0.55, CI=0.31-0.80) and EGFR (HR=1.65, 95% CI=1.14-2.16).

DISCUSSION

Current methods have not delivered clinically useful molecular prognostic biomarkers in OAC. We have highlighted the paucity of good-quality robust studies in this field. A genome-to-protein approach would be better suited for the development and subsequent validation of biomarkers. Large collaborative projects with standardised methodology will be required to generate clinically useful biomarkers.

Cancer-associated fibroblasts predict poor outcome and promote periostin-dependent invasion in oesophageal adenocarcinoma

Interactions between cancer cells and cancer-associated fibroblasts (CAFs) play an important role in tumour development and progression. In this study we investigated the functional role of CAFs in oesophageal adenocarcinoma (EAC). We used immunochemistry to analyse a cohort of 183 EAC patients for CAF markers related to disease mortality. We characterized CAFs and normal oesophageal fibroblasts (NOFs) using western blotting, immunofluorescence and gel contraction. Transwell assays, 3D organotypic culture and xenograft models were used to examine the effects on EAC cell function and to dissect molecular mechanisms regulating invasion. Most EACs (93%) contained CAFs with a myofibroblastic (α-SMA-positive) phenotype, which correlated significantly with poor survival [p = 0.016; HR 7. 1 (1.7–29.4)]. Primary CAFs isolated from EACs have a contractile, myofibroblastic phenotype and promote EAC cell invasion in vitro (Transwell assays, p ≤ 0.05; organotypic culture, p < 0.001) and in vivo (p ≤ 0.05). In vitro, this pro-invasive effect is modulated through the matricellular protein periostin. Periostin is secreted by CAFs and acts as a ligand for EAC cell integrins αvβ3 and αvβ5, promoting activation of the PI3kinase–Akt pathway. In patient samples, periostin expression at the tumour cell–stromal interface correlates with poor overall and disease-free survival. Our study highlights the importance of the tumour stroma in EAC progression. Paracrine interaction between CAF-secreted periostin and EAC-expressed integrins results in PI3 kinase–Akt activation and increased tumour cell invasion. Most EACs contain a myofibroblastic CAF-rich stroma; this may explain the aggressive, highly infiltrative nature of the disease, and suggests that stromal targeting may produce therapeutic benefit in EAC patients.

Disorganised stroma determined on pre-treatment breast cancer biopsies is associated with poor response to neoadjuvant chemotherapy: Results from the NEOZOTAC trial

INTRODUCTION

The tumor-associated stroma is of importance for tumor progression and is generally accepted to have a significant influence on patient prognosis. However, little is known regarding specific features of tumor-associated stromal tissues and response to (neoadjuvant) chemotherapy. This study investigated the predictive value of extracellular matrix organization on response to chemotherapy in patients treated in the NEOZOTAC trial.

METHODS

Stromal organisation was analyzed via a simple method using image analysis software on hematoxylin and eosin (H&E)-stained slides from primary tumor biopsies collected as part of the NEOZOTAC trial. Heidenhain's AZAN trichrome-stained slides were also analyzed for comparison of collagen evaluation. Sections were stained for phospho-Smad2 (pS2) in order to determine the relationship of TGF-β signaling with stromal organization.

RESULTS

A statistically significant relationship was observed between stroma consisting of organised collagen and pathological response to neoadjuvant chemotherapy (Odds Ratio 0.276, 95%CI 0.124-0.614, P = 0.002). This parameter was also related to ER-status (P = 0.003), clinical tumor -status (P = 0.041), nodal status (P = 0.029) and pS2 status (P = 0.025). Correlation between stromal organisation determined on H&E-stained and AZAN-stained tissue sections was high (Pearson's correlation coefficient = 0.806).

CONCLUSION

Intratumoral stromal organisation determined using pre-treatment breast cancer biopsies was related to pathological response to chemotherapy. This parameter might play a role in the management of breast cancer for identifying those patients that are likely to benefit from neoadjuvant chemotherapy.

Tumour-microenvironment interactions: role of tumour stroma and proteins produced by cancer-associated fibroblasts in chemotherapy response

BACKGROUND

Cytotoxic chemotherapy improves survival for some, but not all, cancer patients. Non-responders may experience unnecessary toxicity and cancer progression, thus creating an urgent need for biomarkers that can predict the response to chemotherapy. So far, the search for such biomarkers has primarily been focused on the cancer cells and less on their surrounding stroma. This stroma is known to act as a key regulator of tumour progression and, in addition, has been associated with drug delivery and drug efficacy. Fibroblasts represent the major cell type in cancer-associated stroma and they secrete extracellular matrix proteins as well as growth factors. This Medline-based literature review summarises the results from studies on epithelial cancers and aimed at investigating relationships between the quantity and quality of the intra-tumoral stroma, the cancer-associated fibroblasts, the proteins they produce and the concomitant response to chemotherapy. Biomarkers were selected for review that are known to affect cancer-related characteristics and patient prognosis.

RESULTS

The current literature supports the hypothesis that biomarkers derived from the tumour stroma may be useful to predict response to chemotherapy. This notion appears to be related to the overall quantity and cellularity of the intra-tumoural stroma and the predominant constituents of the extracellular matrix.

CONCLUSION

Increasing evidence is emerging showing that tumour-stroma interactions may not only affect tumour progression and patient prognosis, but also the response to chemotherapy. The tumour stroma-derived biomarkers that appear to be most appropriate to determine the patient's response to chemotherapy vary by tumour origin and the availability of pre-treatment tissue. For patients scheduled for adjuvant chemotherapy, the most promising biomarker appears to be the PLAU: SERPINE complex, whereas for patients scheduled for neo-adjuvant chemotherapy the tumour stroma quantity appears to be most relevant.