The proportion of tumour cells is an independent predictor for survival in colorectal cancer patients. Br J Cancer. 2010;102:1519-1523. [PMID: 20407439 PMCID: PMC2869173 DOI: 10.1038/sj.bjc.6605674]

Concomitant expression patterns of CDX2 and SATB2 as prognostic factors in stage III colorectal cancers

CDX2 and SATB2 are often used as biomarkers for identification of colorectal origin in primary or metastatic adenocarcinomas. Loss of CDX2 or SATB2 expression has been associated with poor prognosis in patients with colorectal cancer (CRC). However, little is known regarding clinicopathological features, including prognosis, of CRCs with concomitant loss of CDX2 and SATB2. A total of 431 stage III CRCs were analyzed for their expression status in CDX2 and SATB2 using tissue microarray-based immunohistochemistry and expression status was correlated with clinicopathological variables, molecular alterations, and survival. CDX2-negative (CDX2-) CRCs and SATB2-negative (SATB2-) CRCs were found in 8.1 % and 17.2 % of CRCs, respectively, whereas both CDX2-negative and SATB2-negative (CDX2-/SATB2-) CRCs comprised 3.2 % of the CRCs. On survival analysis, neither CDX2-/SATB2+ nor CDX2+/SABT2- CRCs but CDX2-/SATB2- CRCs were associated with poor prognosis. CDX2-/SATB2- CRCs showed significant associations with tumor subsite of right colon, poor differentiation, decreased expression of CK20, aberrant expression of CK7, CIMP-high, MSI-high, and BRAF mutation. In summary, our results suggest that concomitant loss of CDX2 and SATB2 is a prognostic biomarker but isolated loss of CDX2 or SATB2 is not a prognostic biomarker for stage III CRCs.

The SARIFA biomarker in the context of basic research of lipid-driven cancers

SARIFA was very recently introduced as a histomorphological biomarker with strong prognostic power for colorectal, gastric, prostate, and pancreatic cancer. It is characterized by the direct contact between tumor cells and adipocytes due to a lack of stromal reaction. This can be easily evaluated on routinely available H&E-slides with high interobserver agreement. SARIFA also reflects a specific tumor biology driven by metabolic reprogramming. Tumor cells in SARIFA-positive tumors benefit from direct interaction with adipocytes as an external source of lipids. Numerous studies have shown that lipid metabolism is crucial in carcinogenesis and cancer progression. We found that the interaction between tumor cells and adipocytes was not triggered by obesity, as previously assumed. Instead, we believe that this is due to an immunological mechanism. Knowledge about lipid metabolism in cancer from basic experiments can be transferred to develop strategies targeting this reprogramed metabolism.

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.

Stroma AReactive Invasion Front Areas (SARIFA): a novel histopathologic biomarker in colorectal cancer patients and its association with the luminal tumour proportion

BACKGROUND

Stroma AReactive Invasion Front Areas (SARIFA) is a novel prognostic histopathologic biomarker measured at the invasive front in haematoxylin & eosin (H&E) stained colon and gastric cancer resection specimens. The aim of the current study was to validate the prognostic relevance of SARIFA-status in colorectal cancer (CRC) patients and investigate its association with the luminal proportion of tumour (PoT).

METHODS

We established the SARIFA-status in 164 CRC resection specimens. The relationship between SARIFA-status, clinicopathological characteristics, recurrence-free survival (RFS), cancer-specific survival (CSS), and PoT was investigated.

RESULTS

SARIFA-status was positive in 22.6% of all CRCs. SARIFA-positivity was related to higher pT, pN, pTNM stage and high grade of differentiation. SARIFA-positivity was associated with shorter RFS independent of known prognostic factors analysing all CRCs (RFS: hazard ratio (HR) 2.6, p = 0.032, CSS: HR 2.4, p = 0.05) and shorter RFS and CSS analysing only rectal cancers. SARIFA-positivity, which was measured at the invasive front, was associated with PoT-low (p = 0.009), e.g., higher stroma content, and lower vessel density (p = 0.0059) measured at the luminal tumour surface.

CONCLUSION

Here, we validated the relationship between SARIFA-status and prognosis in CRC patients and provided first evidence for a potential prognostic relevance in the subgroup of rectal cancer patients. Interestingly, CRCs with different SARIFA-status also showed histological differences measurable at the luminal tumour surface. Further studies to better understand the relationship between high luminal intratumoural stroma content and absence of a stroma reaction at the invasive front (SARIFA-positivity) are warranted and may inform future treatment decisions in CRC patients.

Multi-Instance Multi-Task Learning for Joint Clinical Outcome and Genomic Profile Predictions From the Histopathological Images

With the remarkable success of digital histopathology and the deep learning technology, many whole-slide pathological images (WSIs) based deep learning models are designed to help pathologists diagnose human cancers. Recently, rather than predicting categorical variables as in cancer diagnosis, several deep learning studies are also proposed to estimate the continuous variables such as the patients' survival or their transcriptional profile. However, most of the existing studies focus on conducting these predicting tasks separately, which overlooks the useful intrinsic correlation among them that can boost the prediction performance of each individual task. In addition, it is sill challenge to design the WSI-based deep learning models, since a WSI is with huge size but annotated with coarse label. In this study, we propose a general multi-instance multi-task learning framework (HistMIMT) for multi-purpose prediction from WSIs. Specifically, we firstly propose a novel multi-instance learning module (TMICS) considering both common and specific task information across different tasks to generate bag representation for each individual task. Then, a soft-mask based fusion module with channel attention (SFCA) is developed to leverage useful information from the related tasks to help improve the prediction performance on target task. We evaluate our method on three cancer cohorts derived from the Cancer Genome Atlas (TCGA). For each cohort, our multi-purpose prediction tasks range from cancer diagnosis, survival prediction and estimating the transcriptional profile of gene TP53. The experimental results demonstrated that HistMIMT can yield better outcome on all clinical prediction tasks than its competitors.

Unselective Measurement of Tumor-to-Stroma Proportion in Colon Cancer at the Invasion Front-An Elusive Prognostic Factor: Original Patient Data and Review of the Literature

The tumor-to-stroma ratio is a highly debated prognostic factor in the management of several solid tumors and there is no universal agreement on its practicality. In our study, we proposed confirming or dismissing the hypothesis that a simple measurement of stroma quantity is an easy-to-use and strong prognostic tool. We have included 74 consecutive patients with colorectal cancer who underwent primary curative abdominal surgery. The tumors have been grouped into stroma-poor (stroma < 10%), medium-stroma (between 10 and 50%) and stroma-rich (over 50%). The proportion of tumor stroma ranged from 5% to 70% with a median of 25%. Very few, only 6.8% of patients, had stroma-rich tumors, 4% had stroma-poor tumors and 89.2% had tumors with a medium quantity of stroma. The proportion of stroma, at any cut-off, had no statistically significant influence on the disease-specific survival. This can be explained by the low proportion of stroma-rich tumors in our patient group and the inverse correlation between stroma proportion and tumor grade. The real-life proportion of stroma-rich tumors and the complex nature of the stroma-tumor interaction has to be further elucidated.

Predicting 5-year recurrence risk in colorectal cancer: development and validation of a histology-based deep learning approach

BACKGROUND

Accurate estimation of the long-term risk of recurrence in patients with non-metastatic colorectal cancer (CRC) is crucial for clinical management. Histology-based deep learning is expected to provide more abundant information for risk stratification.

METHODS

We developed and validated a weakly supervised deep-learning model for predicting 5-year relapse-free survival (RFS) to stratify patients with different risks based on histological images from three hospitals of 614 cases with non-metastatic CRC. A deep prognostic factor (DL-RRS) was established to stratify patients into high and low-risk group. The areas under the curve (AUCs) were calculated to evaluate the performances of models.

RESULTS

Our proposed model achieves the AUCs of 0.833 (95% CI: 0.736-0.905) and 0.715 (95% CI: 0.647-0.776) on validation cohort and external test cohort, respectively. The 5-year RFS rate was 45.7% for high DL-RRS patients, and 82.5% for low DL-RRS patients respectively in the external test cohort (HR: 3.89, 95% CI: 2.51-6.03, P < 0.001). Adjuvant chemotherapy was associated with improved RFS in Stage II patients with high DL-RRS (HR: 0.15, 95% CI: 0.06-0.38, P < 0.001).

CONCLUSIONS

DL-RRS has a good predictive performance of 5-year recurrence risk in CRC, and will better serve the clinical decision-making.

Prognostic prediction value of the clinical-radiomics tumour-stroma ratio in locally advanced rectal cancer

PURPOSE

To develop and validate a radiomics model based on high-resolution T2WI and a clinical-radiomics model for tumour-stroma ratio (TSR) evaluation with a gold standard of TSR evaluated by rectal specimens without therapeutic interference and further apply them in prognosis prediction of locally advanced rectal cancer (LARC) patients who received neoadjuvant chemoradiotherapy.

METHODS

A total of 178 patients (mean age: 59.35, range 20-85 years; 65 women and 113 men) with rectal cancer who received surgery alone from January 2016 to October 2020 were enrolled and randomly separated at a ratio of 7:3 into training and validation sets. A senior radiologist reviewed after 2 readers manually delineated the whole tumour in consensus on preoperative high-resolution T2WI in the training set. A total of 1046 features were then extracted, and recursive feature elimination embedded with leave-one-out cross validation was applied to select features, with which an MR-TSR evaluation model was built containing 6 filtered features via a support vector machine classifier trained by comparing patients' pathological TSR. Stepwise logistic regression was employed to integrate clinical factors with the radiomics model (Fusion-TSR) in the training set. Later, the MR-TSR and Fusion-TSR models were replicated in the validation set for diagnostic effectiveness evaluation. Subsequently, 243 patients (mean age: 53.74, range 23-74 years; 63 women and 180 men) with LARC from October 2012 to September 2017 who were treated with NCRT prior to surgery and underwent standard pretreatment rectal MR examination were enrolled. The MR-TSR and Fusion-TSR were applied, and the Kaplan-Meier method and log-rank test were used to compare the survival of patients with different MR-TSR and Fusion-TSR. Cox proportional hazards regression was used to calculate the hazard ratio (HR).

RESULTS

Both the MR-TSR and Fusion-TSR models were validated with favourable diagnostic power: the AUC of the MR-TSR was 0.77 (p = 0.01; accuracy = 69.8 %, sensitivity = 88.9 %, specificity = 65.9 %, PPV = 34.8 %, NPV = 96.7 %), while the AUC of the Fusion-TSR was 0.76 (p = 0.014; accuracy = 67.9 %, sensitivity = 88.9 %, specificity = 63.6 %, PPV = 33.3 %, NPV = 96.6 %), outperforming their effectiveness in the training set: the AUC of the MR-TSR was 0.65 (p = 0.035; accuracy = 66.4 %, sensitivity = 61.9 %, specificity = 67.3 %, PPV = 27.7 %, NPV = 90.0 %), while the AUC of the Fusion-TSR was 0.73 (p = 0.001; accuracy = 73.6 %, sensitivity = 71.4 %, specificity = 74.0 %, PPV = 35.73 %, NPV = 92.8 %). With further prognostic analysis, the MR-TSR was validated as a significant prognostic factor for DFS in LARC patients treated with NCRT (p = 0.020, HR = 1.662, 95 % CI = 1.077-2.565), while the Fusion-TSR was a significant prognostic factor for OS (p = 0.005, HR = 2.373, 95 % CI = 1.281-4.396).

CONCLUSIONS

We developed and validated a radiomics TSR and a clinical-radiomics TSR model and successfully applied them to better risk stratification for LARC patients receiving NCRT and for better decision making.

Value of the Tumor-Stroma Ratio and Structural Heterogeneity Measured by a Novel Semiautomatic Image Analysis Technique for Predicting Survival in Patients With Colon Cancer

BACKGROUND

The tumor-stroma ratio and intratumor stromal heterogeneity have been identified as prognostic factors for several carcinomas. Recent advancements in image analysis technologies and their application to medicine have enabled detailed analysis of clinical data beyond human cognition.

OBJECTIVE

This study aimed to investigate the tumor-stroma ratio and intratumor stromal heterogeneity measured using a novel objective and semiautomatic method with image analysis.

DESIGN

A retrospective cohort design.

SETTINGS

Single institution.

PATIENTS

This study included patients who underwent curative colectomy for colon cancer.

MAIN OUTCOME MEASURES

The survival analyses between tumor-stroma ratio or intratumor stromal heterogeneity high and low groups after colectomy were assessed in multivariate analyses.

RESULTS

Two hundred patients were divided into 2 groups based on the median tumor-stroma ratio and intratumor stromal heterogeneity values. The 5-year overall survival and relapse-free survival rates after colectomy significantly differed between the high and low tumor-stroma ratio or intratumor stromal heterogeneity groups. Multivariate analysis identified low tumor-stroma ratio (HR: 1.90, p = 0.03) and high intratumor stromal heterogeneity (HR: 2.44, p = 0.002) as independent poor prognostic factors for relapse-free survival. The tumor-stroma ratio and intratumor stromal heterogeneity correlated with the duration from curative surgery to recurrence. Furthermore, postoperative recurrence within 2 years was predicted with higher accuracy by using the tumor-stroma ratio or intratumor stromal heterogeneity than by using the pathological stage. In a validation cohort, interobserver agreement was assessed by 2 observers, and Cohen's κ coefficient for the tumor-stroma ratio (κ value: 0.70) and intratumor stromal heterogeneity (κ value: 0.60) revealed a substantial interobserver agreement.

LIMITATIONS

This study was limited by its retrospective, single-institution design.

CONCLUSIONS

Tumor-stroma ratio and intratumor stromal heterogeneity calculated using image analysis software have potential as imaging biomarkers for predicting the survival of patients with colon cancer after colectomy. See Video Abstract at http://links.lww.com/DCR/C114 .

VALOR DE LA PROPORCIN DE ESTROMA TUMORAL Y LA HETEROGENEIDAD ESTRUCTURAL MEDIDOS POR UNA NUEVA TCNICA DE ANLISIS DE IMGENES SEMIAUTOMTICA PARA PREDECIR LA SUPERVIVENCIA EN PACIENTES CON CNCER DE COLON

ANTECEDENTES:La proporción de estroma tumoral y la heterogeneidad del estroma intratumoral han sido identificados como factores pronósticos para varios tipos de carcinomas. Los avances recientes en cuanto a las tecnologías de análisis de imágenes y sus aplicaciones en la medicina, han permitido un análisis detallado de los datos clínicos más allá del conocimiento humano.OBJETIVO:Investigar la relación del estroma tumoral y la heterogeneidad del estroma intratumoral calculados mediante un nuevo método objetivo y semiautomático para el análisis de imágenes.DISEÑO:Diseño de cohorte retrospectivo.AJUSTES:Institución única.PACIENTES:Pacientes sometidos a colectomía curativa por cáncer de colon.PRINCIPALES MEDIDAS DE RESULTADO:Los análisis de supervivencia entre la relación del estroma tumoral o la heterogeneidad del estroma intratumoral entre los grupos con valores altos y bajos tras la colectomía, fueron evaluados en análisis multivariados.RESULTADOS:Fueron divididos 200 pacientes en dos grupos basados en la mediana de la proporción con respecto a los valores del estroma tumoral y la heterogeneidad del estroma intratumoral. Las tasas de supervivencia general a los 5 años y de supervivencia libre de recaídas después de la colectomía, difirieron significativamente entre los grupos con índice de estroma tumoral o heterogeneidad del estroma intratumoral altos y bajos. El análisis multivariante identificó una proporción de estroma tumoral baja (cociente de riesgos instantáneos: 1.90, p = 0.03) y una heterogeneidad estromal intratumoral alta (cociente de riesgos instantáneos: 2.44, p = 0.002) como factores independientes de mal pronóstico para la supervivencia libre de recaídas. La proporción de estroma tumoral y la heterogeneidad del estroma intratumoral se correlacionaron con la duración de la recurrencia desde la cirugía.Además, la recurrencia posoperatoria dentro de los 2 años se predijo con mayor precisión mediante el uso del índice de estroma tumoral o la heterogeneidad del estroma intratumoral que mediante el uso del estadio patológico. En una cohorte de validación, la concordancia interobservador fue evaluada por dos observadores, y el coeficiente Kappa de Cohen para la proporción de estroma tumoral y la heterogeneidad estromal intratumoral reveló una concordancia interobservador sustancial (valor Kappa: 0.70, 0.60, respectivamente).LIMITACIONES:Este estudio estuvo limitado por su diseño retrospectivo de una sola institución.CONCLUSIONES:La proporción del estroma tumoral y la heterogeneidad del estroma intratumoral calculadas mediante software de análisis de imágenes tienen potencial como biomarcadores de imagen para predecir la supervivencia de los pacientes con cáncer de colon tras la colectomía. Consulte Video Resumen en http://links.lww.com/DCR/C114 . (Traducción-Dr. Osvaldo Gauto ).

Surformer: An interpretable pattern-perceptive survival transformer for cancer survival prediction from histopathology whole slide images

BACKGROUND AND OBJECTIVE

High-resolution histopathology whole slide images (WSIs) contain abundant valuable information for cancer prognosis. However, most computational pathology methods for survival prediction have weak interpretability and cannot explain the decision-making processes reasonably. To address this issue, we propose a highly interpretable neural network termed pattern-perceptive survival transformer (Surformer) for cancer survival prediction from WSIs.

METHODS

Notably, Surformer can quantify specific histological patterns through bag-level labels without any patch/cell-level auxiliary information. Specifically, the proposed ratio-reserved cross-attention module (RRCA) generates global and local features with the learnable prototypes (pglobal, plocals) as detectors and quantifies the patches correlative to each plocal in the form of ratio factors (rfs). Afterward, multi-head self&cross-attention modules proceed with the computation for feature enhancement against noise. Eventually, the designed disentangling loss function guides multiple local features to focus on distinct patterns, thereby assisting rfs from RRCA in achieving more explicit histological feature quantification.

RESULTS

Extensive experiments on five TCGA datasets illustrate that Surformer outperforms existing state-of-the-art methods. In addition, we highlight its interpretation by visualizing rfs distribution across high-risk and low-risk cohorts and retrieving and analyzing critical histological patterns contributing to the survival prediction.

CONCLUSIONS

Surformer is expected to be exploited as a useful tool for performing histopathology image data-driven analysis and gaining new insights for interpreting the associations between such images and patient survival states.

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.

Prognostic evaluation of cancer associated fibrosis and tumor budding in colorectal cancer

Colorectal carcinoma (CRC) is the second most common cancer and third leading cause of cancer-related deaths worldwide. Although the staging system provides a standardized guidance in treatment regimens, the clinical outcome in patients with colon cancer at the same TNM stage may vary dramatically. Thus, for better predictive accuracy, further prognostic and/or predictive markers are required. Patients who underwent curative surgery for colorectal cancer in past 3 years at a tertiary care hospital were retrospectively included in this cohort study to evaluate the prognostic indicators, tumor-stroma ratio (TSR) and tumor budding (TB) on histopathological sections and correlated them with pTNM staging, histopathological grading, tumor size, and lymphovascular and perineural invasion in patients with colo-rectal cancer. TB was strongly associated with advanced stage of the disease along with lympho-vascular and peri-neural invasion and it can be used as an independent adverse prognostic factor. TSR showed a better sensitivity, specificity, PPV and NPV as compared to TB in patients having poorly differentiated adenocarcinoma than those with moderately or well differentiated.

The prognostic role of tumor budding and tumor-stroma ratio in pulmonary metastasis of colorectal carcinoma

OBJECTIVE

To evaluate the prognostic value of tumor budding and tumor-stroma ratio (TSR) in resected pulmonary metastases of colorectal carcinoma (CRC).

METHODS

In total, 106 pulmonary metastasectomies were performed to 74 patients in two study hospitals during 2000-2020. All relevant clinical data were retrospectively collected. Tumor budding based on the International Tumor Budding Consensus Conference recommendations and TSR in the first resected pulmonary metastases and primary tumors were evaluated from diagnostic hematoxylin-eosin-stained histopathological slides.

RESULTS

60 patients (85.7%) had low tumor budding (≤5 buds/field) and 10 patients (14.3%) had high tumor budding (>5 buds/field) in their first pulmonary metastases of CRC. 5-year overall survival rates of pulmonary metastasectomy in low and high total tumor budding were 28.3% and 37.3% (p = 0.387), respectively. 19 patients (27.1%) had low TSR and 51 patients (72.9%) had high TSR. The 5-year overall survival rates were 32.9% in low and 28.6% in high TSR of first pulmonary metastases (p = 0.746). Tumor budding and TSR did not provide prognostic value in Cox multivariate analysis. Tumor budding and TSR in resected pulmonary metastases were not associated with those of the primary tumor.

CONCLUSION

Tumor budding and TSR in the resected pulmonary metastases of CRC showed no statistically significant prognostic value, however, additional well-powered confirmatory studies are needed.

Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis

MOTIVATION

Spatial proteomics data have been used to map cell states and improve our understanding of tissue organization. More recently, these methods have been extended to study the impact of such organization on disease progression and patient survival. However, to date, the majority of supervised learning methods utilizing these data types did not take full advantage of the spatial information, impacting their performance and utilization.

RESULTS

Taking inspiration from ecology and epidemiology, we developed novel spatial feature extraction methods for use with spatial proteomics data. We used these features to learn prediction models for cancer patient survival. As we show, using the spatial features led to consistent improvement over prior methods that used the spatial proteomics data for the same task. In addition, feature importance analysis revealed new insights about the cell interactions that contribute to patient survival.

AVAILABILITY AND IMPLEMENTATION

The code for this work can be found at gitlab.com/enable-medicine-public/spatsurv.

Expression Profile of Microenvironmental Factors in the Interface Zone of Colorectal Cancer: Histological-Stromal Biomarkers and Cancer Cell-Cancer-Associated Fibroblast-Related Proteins Combined for the Assessment of Tumor Progression

INTRODUCTION

The characterization of tumor microenvironment (TME) related factors and their impact on tumor progression have attracted much interest. We investigated cancer cells and cancer-associated fibroblasts (CAFs) to evaluate biomarkers that are associated with neoplastic progression, observing them in different interface zones of colorectal cancer.

METHODS

On 357 CRC tissue microarrays, using immunohistochemistry, we examined the associations of podoplanin and α-SMA expressed in cancer cells and CAFs and evaluated them in different areas: tumor core, invasive front, tumor budding, tumor-stroma ratio (TSR) scoring, and desmoplastic stroma.

RESULTS

CAFs expressing α-SMA were found in more than 90% of the cases. Podoplanin+ was detected in cancer cells and CAFs, with positivities of 38.6% and 70%, respectively. Higher α-SMA+ CAFs and podoplanin+ cancer cells were observed predominantly at the TSR score area: 94.3% and 64.3% of cases, respectively. The status of podoplanin in CAFs+ was higher in the desmoplastic area (71.6%). Stroma-high tumors showed increased expression of α-SMA and podoplanin in comparison with stroma-low tumors. The status of podoplanin in cancer cells was observed in association with lymphatic invasion and distant metastasis.

CONCLUSION

The substance of the CRC was composed predominantly of the surrounding stroma-α-SMA+ CAFs. Podoplanin expressed in the prognosticator zones was associated with unfavorable pathological features. The combination of histologic and protein-related biomarkers can result in a tool for the stratification of patients with CRC.

Domain-specific transfer learning in the automated scoring of tumor-stroma ratio from histopathological images of colorectal cancer

Tumor-stroma ratio (TSR) is a prognostic factor for many types of solid tumors. In this study, we propose a method for automated estimation of TSR from histopathological images of colorectal cancer. The method is based on convolutional neural networks which were trained to classify colorectal cancer tissue in hematoxylin-eosin stained samples into three classes: stroma, tumor and other. The models were trained using a data set that consists of 1343 whole slide images. Three different training setups were applied with a transfer learning approach using domain-specific data i.e. an external colorectal cancer histopathological data set. The three most accurate models were chosen as a classifier, TSR values were predicted and the results were compared to a visual TSR estimation made by a pathologist. The results suggest that classification accuracy does not improve when domain-specific data are used in the pre-training of the convolutional neural network models in the task at hand. Classification accuracy for stroma, tumor and other reached 96.1% on an independent test set. Among the three classes the best model gained the highest accuracy (99.3%) for class tumor. When TSR was predicted with the best model, the correlation between the predicted values and values estimated by an experienced pathologist was 0.57. Further research is needed to study associations between computationally predicted TSR values and other clinicopathological factors of colorectal cancer and the overall survival of the patients.

Tumor-Stroma Ratio in Colorectal Cancer-Comparison between Human Estimation and Automated Assessment

The tumor-stroma ratio (TSR) has been repeatedly shown to be a prognostic factor for survival prediction of different cancer types. However, an objective and reliable determination of the tumor-stroma ratio remains challenging. We present an easily adaptable deep learning model for accurately segmenting tumor regions in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of colon cancer patients into five distinct classes (tumor, stroma, necrosis, mucus, and background). The tumor-stroma ratio can be determined in the presence of necrotic or mucinous areas. We employ a few-shot model, eventually aiming for the easy adaptability of our approach to related segmentation tasks or other primaries, and compare the results to a well-established state-of-the art approach (U-Net). Both models achieve similar results with an overall accuracy of 86.5% and 86.7%, respectively, indicating that the adaptability does not lead to a significant decrease in accuracy. Moreover, we comprehensively compare with TSR estimates of human observers and examine in detail discrepancies and inter-rater reliability. Adding a second survey for segmentation quality on top of a first survey for TSR estimation, we found that TSR estimations of human observers are not as reliable a ground truth as previously thought.

Autologous dendritic cells loaded with antigens from self-renewing autologous tumor cells as patient-specific therapeutic cancer vaccines

A promising personal immunotherapy is autologous dendritic cells (DC) loaded ex vivo with autologous tumor antigens (ATA) derived from self-renewing autologous cancer cells. DC-ATA are suspended in granulocyte-macrophage colony stimulating factor at the time of each subcutaneous injection. Previously, irradiated autologous tumor cell vaccines have produced encouraging results in 150 cancer patients, but the DC-ATA vaccine demonstrated superiority in single-arm and randomized trials in metastatic melanoma. DC-ATA have been injected into more than 200 patients with melanoma, glioblastoma, and ovarian, hepatocellular, and renal cell cancers. Key observations include: [1] greater than 95% success rates for tumor cell cultures and monocyte collection for dendritic cell production; [2] injections are well-tolerated; [3] the immune response is rapid and includes primarily TH1/TH17 cellular responses; [4] efficacy has been suggested by delayed but durable complete tumor regressions in patients with measurable disease, by progression-free survival in glioblastoma, and by overall survival in melanoma.

Deep learning based tumor-stroma ratio scoring in colon cancer correlates with microscopic assessment

Background

The amount of stroma within the primary tumor is a prognostic parameter for colon cancer patients. This phenomenon can be assessed using the tumor-stroma ratio (TSR), which classifies tumors in stroma-low (≤50% stroma) and stroma-high (>50% stroma). Although the reproducibility for TSR determination is good, improvement might be expected from automation. The aim of this study was to investigate whether the scoring of the TSR in a semi- and fully automated method using deep learning algorithms is feasible.

Methods

A series of 75 colon cancer slides were selected from a trial series of the UNITED study. For the standard determination of the TSR, 3 observers scored the histological slides. Next, the slides were digitized, color normalized, and the stroma percentages were scored using semi- and fully automated deep learning algorithms. Correlations were determined using intraclass correlation coefficients (ICCs) and Spearman rank correlations.

Results

37 (49%) cases were classified as stroma-low and 38 (51%) as stroma-high by visual estimation. A high level of concordance between the 3 observers was reached, with ICCs of 0.91, 0.89, and 0.94 (all P < .001). Between visual and semi-automated assessment the ICC was 0.78 (95% CI 0.23-0.91, P-value 0.005), with a Spearman correlation of 0.88 (P < .001). Spearman correlation coefficients above 0.70 (N=3) were observed for visual estimation versus the fully automated scoring procedures.

Conclusion

Good correlations were observed between standard visual TSR determination and semi- and fully automated TSR scores. At this point, visual examination has the highest observer agreement, but semi-automated scoring could be helpful to support pathologists.

Extramural venous invasion and depth of extramural invasion on preoperative CT as prognostic imaging biomarkers in patients with locally advanced ascending colon cancer

PURPOSE

This study evaluates the prognostic significance of EMVI and DEMI on preoperative CT in patients with ascending colon cancer.

METHODS

This retrospective study included consecutive patients with T3 ascending colon cancer from January 2012 to December 2016 in a tertiary center. Two radiologists independently reviewed EMVI, DEMI, and nodal status on preoperative CT. We assessed the association of age, sex, mucinous adenocarcinoma, EMVI, and DEMI with metastasis on preoperative CT using univariable and multivariable analysis. We also compared disease-free survival (DFS) with and without variables (age, sex, mucinous adenocarcinoma, EMVI, DEMI and adjuvant chemotherapy) using Cox's proportional hazards models. We assessed interobserver agreements on imaging features using the Cohen's weighted kappa.

RESULTS

Of 237 patients [107 men; mean (standard deviation) age, 66 (13) years], 24 had metastases on preoperative CT. Positive EMVI was associated with metastasis (odds ratio 16.9; P < 0.001) on multivariable analysis. Of 194 patients [83 men; 65 (13) years] included for DFS analysis, recurrence was observed in 31 (16%) with median follow-up of 53 months. Positive EMVI [hazard ratio (HR) 4.8; P < 0.001] and DEMI > 5 mm (HR 5.5; P < 0.001) were associated with worse DFS. Interobserver agreements were good (kappa = 0.64-0.67).

CONCLUSION

Positive EMVI and DEMI > 5 mm on preoperative CT were associated with a worse T3 ascending colon cancer prognosis. Thus, these CT findings could be used as imaging biomarkers for T3 ascending colon cancer risk stratification.

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.

Peritumoral tertiary lymphoid structure and tumor stroma percentage predict the prognosis of patients with non-metastatic colorectal cancer

Background

Tertiary lymphoid structures (TLSs) are crucial in promoting and maintaining positive anti-tumor immune responses. The tumor stroma has a powerful immunosuppressive function that could exclude tumor-infiltrating lymphocytes from the tumor beds and lead to a “cold” phenotype. TLSs and tumor stroma percentage (TSP) are significantly associated with the prognosis of patients with certain cancers. However, the exact roles of TLSs and TSP and their intrinsic relationship are still largely unknown in colorectal cancer (CRC).

Methods

TLSs and TSP were assessed using hematoxylin-eosin (H&amp;E) and/or immunohistochemistry (IHC) staining from 114 CRC patients in the training set and 60 CRC patients in the external validation set. The correlation between TILs, TLS and clinicopathological characteristics and their prognostic values were assessed. Finally, we plotted a Nomogram including the TLS, TSP and tumor-node-metastasis (TNM) stage to predict the probability of recurrence-free survival (RFS) at 2- and 5-years in non-metastatic colorectal cancer (nmCRC) patients.

Results

Peritumoral TLS (P-TLS), intratumoral TLS (In-TLS) and high TSP (H-TSP, &gt;50%) were present in 99.1%, 26.3% and 41.2% patients, respectively. H-TSP tumor tends to be associated with lower P-TLS density (P =0.0205). The low P-TLS density (&lt; 0.098/mm2) was significantly associated with reduced RFS (HR=6.597 95% CI: 2.882-15.103, P &lt;0.001) and reduced overall survival (OS) (HR=6.628 95% CI: 2.893-15.183, P &lt; 0.001) of nmCRC patients. In-TLS was not of significance in evaluating the clinical outcomes of nmCRC patients. H-TSP was significantly associated with reduced RFS (HR=0.126 95% CI: 0.048-0.333, P &lt;0.001) and reduced OS (HR=0.125 95% CI: 0.047-0.332, P &lt;0.001) of nmCRC patients. The 5-year RFS of the high P-TLS, low-TLS, H-TSP, and L-TSP groups were 89.7%, 47.2%, 53.2%, and 92.5%, respectively. The P-TLS density, TSP and TNM stage were independent prognosis factors of nmCRC patients. The Nomogram, including the P-TLS density, TSP and TNM stage, outperformed the TNM stage.

Conclusions

High P-TLS density and low TSP (L-TSP) were independent and favorable prognostic factors of nmCRC patients, which might provide new directions for targeted therapy in the CRC tumor microenvironment, especially the tumor immune microenvironment.

A Pathophysiological Approach To Current Biomarkers

Biomarkers are necessary for screening and diagnosing numerous diseases, predicting the prognosis of patients, and following-up treatment and the course of the patient. Everyday new biomarkers are being used in clinics for these purposes. This section will discuss the physiological roles of the various current biomarkers in a healthy person and the pathophysiological mechanisms underlying the release of these biomarkers. This chapter aims to gain a new perspective for evaluating and interpreting the most current biomarkers.

Deep Neural Network Models for Colon Cancer Screening

Simple Summary

Deep learning models have been shown to achieve high performance in diagnosing colon cancer compared to conventional image processing and hand-crafted machine learning methods. Hence, several studies have focused on developing hybrid learning, end-to-end, and transfer learning techniques to reduce manual interaction and for labelling the regions of interest. However, these weak learning techniques do not always provide a clear diagnosis. Therefore, it is necessary to develop a clear explainable learning method that can highlight factors and form the basis of clinical decisions. However, there has been little research carried out employing such transparent approaches. This study discussed the aforementioned models for colon cancer diagnosis.

Abstract

Early detection of colorectal cancer can significantly facilitate clinicians’ decision-making and reduce their workload. This can be achieved using automatic systems with endoscopic and histological images. Recently, the success of deep learning has motivated the development of image- and video-based polyp identification and segmentation. Currently, most diagnostic colonoscopy rooms utilize artificial intelligence methods that are considered to perform well in predicting invasive cancer. Convolutional neural network-based architectures, together with image patches and preprocesses are often widely used. Furthermore, learning transfer and end-to-end learning techniques have been adopted for detection and localization tasks, which improve accuracy and reduce user dependence with limited datasets. However, explainable deep networks that provide transparency, interpretability, reliability, and fairness in clinical diagnostics are preferred. In this review, we summarize the latest advances in such models, with or without transparency, for the prediction of colorectal cancer and also address the knowledge gap in the upcoming technology.

Proposal of an automated tumor-stromal ratio assessment algorithm and a nomogram for prognosis in early-stage invasive breast cancer

BACKGROUND

The tumor-stromal ratio (TSR) has been verified to be a prognostic factor in many solid tumors. In most studies, it was manually assessed on routinely stained H&E slides. This study aimed to assess the TSR using image analysis algorithms developed by the Qupath software, and integrate the TSR into a nomogram for prediction of the survival in invasive breast cancer (BC) patients.

METHODS

A modified TSR assessment algorithm based on the recognition of tumor and stroma tissues was developed using the Qupath software. The TSR of 234 invasive BC specimens in H&E-stained tissue microarrays (TMAs) were assessed with the algorithm and categorized as stroma-low or stroma-high. The consistency of TSR estimation between Qupath prediction and pathologist annotation was analyzed. Univariable and multivariable analyses were applied to select potential risk factors and a nomogram for predicting survival in invasive BC patients was constructed and validated. An extra TMA containing 110 specimens was obtained to validate the conclusion as an independent cohort.

RESULTS

In the discovery cohort, stroma-low and stroma-high were identified in 43.6% and 56.4% cases, respectively. Good concordance was observed between the pathologist annotated and Qupath predicted TSR. The Kaplan-Meier curve showed that stroma-high patients were associated with worse 5-DFS compared to stroma-low patients (p = 0.007). Multivariable analysis identified age, T stage, N status, histological grade, ER status, HER-2 gene, and TSR as potential risk predictors, which were included in the nomogram. The nomogram was well calibrated and showed a favorable predictive value for the recurrence of BC. Kaplan-Meier curves showed that the nomogram had a better risk stratification capability than the TNM staging system. In the external validation of the nomogram, the results were further validated.

CONCLUSIONS

Based on H&E-stained TMAs, this study successfully developed image analysis algorithms for TSR assessment and constructed a nomogram for predicting survival in invasive BC.

Attention-guided sampling for colorectal cancer analysis with digital pathology

Improvements to patient care through the development of automated image analysis in pathology are restricted by the small image patch size that can be processed by convolutional neural networks (CNNs), when compared to the whole-slide image (WSI). Tile-by-tile processing across the entire WSI is slow and inefficient. While this may improve with future computing power, the technique remains vulnerable to noise from uninformative image areas.

We propose a novel attention-inspired algorithm that selects image patches from informative parts of the WSI, first using a sparse randomised grid pattern, then iteratively re-sampling at higher density in regions where a CNN classifies patches as tumour. Subsequent uniform sampling across the enclosing region of interest (ROI) is used to mitigate sampling bias. Benchmarking tests informed the adoption of VGG19 as the main CNN architecture, with 79% classification accuracy. A further CNN was trained to separate false-positive normal epithelium from tumour epithelium, in a novel adaptation of a two-stage model used in brain imaging.

These subsystems were combined in a processing pipeline to generate spatial distributions of classified patches from unseen WSIs. The ROI was predicted with a mean F1 (Dice) score of 86.6% over 100 evaluation WSIs. Several algorithms for evaluating tumour–stroma ratio (TSR) within the ROI were compared, giving a lowest root mean square (RMS) error of 11.3% relative to pathologists’ annotations, against 13.5% for an equivalent tile-by-tile pipeline. Our pipeline processed WSIs between 3.3x and 6.3x faster than tile-by-tile processing.

We propose our attention-based sampling pipeline as a useful tool for pathology researchers, with the further potential for incorporating additional diagnostic calculations.

Tumor Stroma Ratio and Its Significance in Locally Advanced Colorectal Cancer

Colorectal cancer is the third leading cause of cancer-related death, and its incidence is rising in the younger patient population. In the past decade, research has unveiled several processes (underlying tumorigenesis, many of which involve interactions between tumor cells and the surrounding tissue or tumor microenvironment (TME). Interactions between components of the TME are mediated at a sub-microscopic level. However, the endpoint of those interactions results in morphologic changes which can be readily assessed at microscopic examination of biopsy and resection specimens. Among these morphologic changes, alteration to the tumor stroma is a new, important determinant of colorectal cancer progression. Different methodologies to estimate the proportion of tumor stroma relative to tumor cells, or tumor stroma ratio (TSR), have been developed. Subsequent validation has supported the prognostic value, reproducibility and feasibility of TSR in various subgroups of colorectal cancer. In this manuscript, we review the literature surrounding TME in colorectal cancer, with a focus on tumor stroma ratio.

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.

Based on Molecular Subtypes, Immune Characteristics and Genomic Variation to Constructing and Verifying Multi-Gene Prognostic Characteristics of Colorectal Cancer

Background: Colon cancer (COAD) has been identified as being among the most prevalent tumors globally and ranked the third major contributor to cancer-related mortality. COAD is a molecularly heterogeneous disease. There are great differences in clinical manifestations and prognosis among different molecular subtypes.

Methods:379 TCGA-COAD samples were divided into four subtypes: primary proliferative, with collective, crypt-like, and EMT invasion. The differences among the four subtypes were analyzed from the multidimensional perspectives of immunity, genomic variation, and prognosis. The limma package was utilized to identify differentially expressed genes (DEGs) amongst different molecular subtypes. Phenotype-related coexpressed gene modules were identified using WGCNA. The polygenic prognosis model was created utilizing the lasso Cox analysis and verified by time-dependent subject operating characteristics (ROC).

Results: There are some differences in prognosis, TMB and common gene variation, immune score, and immunotherapy/chemotherapy between proliferative and three invasive molecular subtypes. 846 differential genes (DEGs) were obtained by limma packet analysis. Differential gene analysis was utilized to screen the DEGs among distinct subtypes, which were significantly enriched in the pathways related to tumorigenesis and development. Co-expression network analysis found 46 co-expressed genes correlated with proliferative and three invasive phenotypes. Based on differentially co-expressed genes, we developed a prognostic risk model of 8-genes signature, which exhibited strong stability regardless of external and internal validation. RT-PCR experiments proved the expression of eight genes in tumor and normal samples.

Conclusion: We have developed an eight-gene signature prognostic stratification system. Furthermore, we proposed that this classifier can serve as a molecular diagnostic tool to assess the prognosis of colon cancer patients.

Combinatory statuses of tumor stromal percentage and tumor infiltrating lymphocytes as prognostic factors in stage III colorectal cancers

BACKGROUND AND AIM

Tumor stroma and tumor-infiltrating lymphocytes (TILs) are major constituents of the tumor microenvironment, although they have different effects on the prognosis of patients with colorectal cancer (CRC). Combinatory statuses of tumor-stromal percentage (TSP) and TILs are expected to provide more powerful prognostic information but have never been studied in CRCs.

METHODS

Stage III CRCs from patients (n = 487) treated with adjuvant chemotherapy were assessed for their TSP and CD3-TIL or CD8-TIL densities using computer-aided methodology. With cut-off values set at median values for intraepithelial TIL (iTIL) and stromal TIL (sTIL) densities, CRCs were sorted into low and high iTIL or sTIL groups. CRCs were classified into five quintile (Q1-Q5) groups according to their TSP and divided into high TSP (Q5) and low TSP (Q1-4) groups.

RESULTS

The combination of CD8 iTIL density and TSP was found to be an independent prognostic parameter in multivariate survival analysis in terms of cancer-specific survival and recurrence-free survival. CRCs with low CD8 iTIL density and high TSP showed the worst survival. The combinatory status showed more prognostic power than CD8 iTIL density or TSP alone. Multivariate survival analysis in an independent cohort of stage III CRC validated the prognostic power of the combinatory statuses.

CONCLUSIONS

The findings suggest that the combinatory status might serve as a prognostic parameter in stage III CRCs. Further research in a large-scale cohort of patients with stage III CRC is needed to validate the prognostic power of the combinatory status.

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.

A deep learning quantified stroma-immune score to predict survival of patients with stage II-III colorectal cancer

Background

Profound heterogeneity in prognosis has been observed in colorectal cancer (CRC) patients with intermediate levels of disease (stage II–III), advocating the identification of valuable biomarkers that could improve the prognostic stratification. This study aims to develop a deep learning-based pipeline for fully automatic quantification of immune infiltration within the stroma region on immunohistochemical (IHC) whole-slide images (WSIs) and further analyze its prognostic value in CRC.

Methods

Patients from two independent cohorts were divided into three groups: the development group (N = 200), the internal (N = 134), and the external validation group (N = 90). We trained a convolutional neural network for tissue classification of CD3 and CD8 stained WSIs. A scoring system, named stroma-immune score, was established by quantifying the density of CD3⁺ and CD8⁺ T-cells infiltration in the stroma region.

Results

Patients with higher stroma-immune scores had much longer survival. In the development group, 5-year survival rates of the low and high scores were 55.7% and 80.8% (hazard ratio [HR] for high vs. low 0.39, 95% confidence interval [CI] 0.24–0.63, P < 0.001). These results were confirmed in the internal and external validation groups with 5-year survival rates of low and high scores were 57.1% and 78.8%, 63.9% and 88.9%, respectively (internal: HR for high vs. low 0.49, 95% CI 0.28–0.88, P = 0.017; external: HR for high vs. low 0.35, 95% CI 0.15–0.83, P = 0.018). The combination of stroma-immune score and tumor-node-metastasis (TNM) stage showed better discrimination ability for survival prediction than using the TNM stage alone.

Conclusions

We proposed a stroma-immune score via a deep learning-based pipeline to quantify CD3⁺ and CD8⁺ T-cells densities within the stroma region on WSIs of CRC and further predict survival.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02297-w.

Improving the management of early colorectal cancers (eCRC) by using quantitative markers to predict lymph node involvement and thus the need for major resection of pT1 cancers

BACKGROUND

Since implementing the NHS bowel cancer screening programme, the rate of early colorectal cancer (eCRC; pT1) has increased threefold to 17%, but how these lesions should be managed is currently unclear.

AIM

To improve risk stratification of eCRC by developing reproducible quantitative markers to build a multivariate model to predict lymph node metastasis (LNM).

METHODS

Our retrospective cohort of 207 symptomatic pT1 eCRC was assessed for quantitative markers. Associations between categorical data and LNM were performed using χ2 test and Fisher's exact test. Multivariable modelling was performed using logistic regression. Youden's rule gave the cut-point for LNM.

RESULTS

All significant parameters in the univariate analysis were included in a multivariate model; tumour stroma (95% CI 2.3 to 41.0; p=0.002), area of submucosal invasion (95% CI 2.1 to 284.6; p=0.011), poor tumour differentiation (95% CI 2.0 to 358.3; p=0.003) and lymphatic invasion (95% CI 1.3 to 192.6; p=0.028) were predictive of LNM. Youden's rule gave a cut-off of p>5%, capturing 18/19 LNM (94.7%) cases and leading to a resection recommendation for 34% of cases. The model that only included quantitative factors were also significant, capturing 17/19 LNM cases (90%) and leading to resection rate of 35% of cases (72/206).

CONCLUSIONS

In this study, we were able to reduce the potential resection rate of pT1 with the multivariate qualitative and/or quantitative model to 34% or 35% while detecting 95% or 90% of all LNM cases, respectively. While these findings need to be validated, this model could lead to a reduction of the major resection rate in eCRC.

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

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

Stroma AReactive Invasion Front Areas (SARIFA)-A New Easily to Determine Biomarker in Colon Cancer-Results of a Retrospective Study

Simple Summary

Many studies have used histomorphological features to more precisely predict the prognosis of patients with colon cancer, focusing on tumor budding, poorly differentiated clusters, and the tumor–stroma ratio. Here, we introduce SARIFA: Stroma AReactive Invasion Front Area(s). We defined SARIFA as the direct contact between a tumor gland/tumor cell cluster (≥5 cells) and inconspicuous surrounding adipose tissue in the invasion front. SARIFA shows an excellent interobserver reliability and high prognostic value and is thus a promising histomorphological prognostic indicator for adipose-infiltrative adenocarcinomas of the colon.

Abstract

Many studies have used histomorphological features to more precisely predict the prognosis of patients with colon cancer, focusing on tumor budding, poorly differentiated clusters, and the tumor–stroma ratio. Here, we introduce SARIFA: Stroma AReactive Invasion Front Area(s). We defined SARIFA as the direct contact between a tumor gland/tumor cell cluster (≥5 cells) and inconspicuous surrounding adipose tissue in the invasion front. In this retrospective, single-center study, we classified 449 adipose-infiltrative adenocarcinomas (not otherwise specified) from two groups based on SARIFA and found 25% of all tumors to be SARIFA-positive. Kappa values between the two pathologists were good/very good: 0.77 and 0.87. Patients with SARIFA-positive tumors had a significantly shorter colon-cancer-specific survival (p = 0.008, group A), absence of metastasis, and overall survival (p < 0.001, p = 0.003, group B). SARIFA was significantly associated with adverse features such as pT4 stage, lymph node metastasis, tumor budding, and higher tumor grade. Moreover, SARIFA was confirmed as an independent prognostic indicator for colon-cancer-specific survival (p = 0.011, group A). SARIFA assessment was very quick (<1 min). Because of low interobserver variability and good prognostic significance, SARIFA seems to be a promising histomorphological prognostic indicator in adipose-infiltrative adenocarcinomas of the colon. Further studies should validate our results and also determine whether SARIFA is a universal prognostic indicator in solid cancers.

Stromal composition predicts recurrence of early rectal cancer after local excision

AIMS

After local excision of early rectal cancer, definitive lymph node status is not available. An alternative means for accurate assessment of recurrence risk is required to determine the most appropriate subsequent management. Currently used measures are suboptimal. We assess three measures of tumour stromal content to determine their predictive value after local excision in a well-characterised cohort of rectal cancer patients without prior radiotherapy.

METHODS AND RESULTS

A total of 143 patients were included. Haematoxylin and eosin (H&E) sections were scanned for (i) deep neural network (DNN, a machine-learning algorithm) tumour segmentation into compartments including desmoplastic stroma and inflamed stroma; and (ii) digital assessment of tumour stromal fraction (TSR) and optical DNA ploidy analysis. 3' mRNA sequencing was performed to obtain gene expression data from which stromal and immune scores were calculated using the ESTIMATE method. Full results were available for 139 samples and compared with disease-free survival. All three methods were prognostic. Most strongly predictive was a DNN-determined ratio of desmoplastic to inflamed stroma >5.41 (P < 0.0001). A ratio of ESTIMATE stromal to immune score <1.19 was also predictive of disease-free survival (P = 0.00051), as was stromal fraction >36.5% (P = 0.037).

CONCLUSIONS

The DNN-determined ratio of desmoplastic to inflamed ratio is a novel and powerful predictor of disease recurrence in locally excised early rectal cancer. It can be assessed on a single H&E section, so could be applied in routine clinical practice to improve the prognostic information available to patients and clinicians to inform the decision concerning further management.

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

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

Rapid multi-dynamic algorithm for gray image analysis of the stroma percentage on colorectal cancer

Background: Tumor stroma percentage (TSP), as an independent, low-cost prognostic factor, could complement current pathology and act as a more feasible risk factor for prognosis. However, TSP hadn't been applied into TNM staging. Here, the objective of our study was to investigate the prognostic significance of TSP in a robust rapid multi-dynamic approach with the application of MATLAB and threshold Algorithm for Gray Image analysis.

Methods: Using a retrospective collection of 1539 CRC patients comprising three independent cohorts; one SGH cohort (N=996) and two validation cohorts (N =106, N= 437) from 2 institutions. We investigated 996 CRC of no special type. According to our established thresholds, 357 cases (35.84%) were classified as TSP-high and 639 cases (64.16%) as TSP-low. We determined the gray image area as the stromal part of the WSI and calculated the stroma percentage with our proposed method on MATLAB software.

Results: In both TSP-cad(50%) and TSP-cad(median), multivariate analysis showed the TSP-cad was an independent prognostic factor for the vessel invasion and tumor location. For OS, TSP-manual HR=1.512 (95% CI 1.045-2.187); TSP-cad HR=1.443 (95% CI 0.993-2.097) and TSP-cad(median) HR=1.632 (95% CI 1.105-2.410). Fortunately, TSP-manual and TSP-cad were also found independent prognostic factor in all the cohorts. It was found that TSP-cad had slightly higher HR and wider CI than TSP-manual.

Conclusions: Our research showed that TSP was an independent prognostic factor in CRC. Moreover, threshold algorithm for the quantitation of TSP could be established. In conclusion, with this Rapid multi-dynamic threshold Algorithm for Gray Image counting of TSP, which showed a higher accuracy than manual evaluation by pathologists and could be a practical method for CRC to guide clinical decision making.

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.

Deep Learning Prediction of Metastasis in Locally Advanced Colon Cancer Using Binary Histologic Tumor Images

In this study, we developed the Binary ImaGe Colon Metastasis classifier (BIg-CoMet), a semi-guided approach for the stratification of colon cancer patients into two risk groups for the occurrence of distant metastasis, using an InceptionResNetV2-based deep learning model trained on binary images. We enrolled 291 colon cancer patients with pT3 and pT4 adenocarcinomas and converted one cytokeratin-stained representative tumor section per case into a binary image. Image augmentation and dropout layers were incorporated to avoid overfitting. In a validation collective (n = 128), BIg-CoMet was able to discriminate well between patients with and without metastasis (AUC: 0.842, 95% CI: 0.774-0.911). Further, the Kaplan-Meier curves of the metastasis-free survival showed a highly significant worse clinical course for the high-risk group (log-rank test: p < 0.001), and we demonstrated superiority over other established risk factors. A multivariable Cox regression analysis adjusted for confounders supported the use of risk groups as a prognostic factor for the occurrence of metastasis (hazard ratio (HR): 5.4, 95% CI: 2.5-11.7, p < 0.001). BIg-CoMet achieved good performance for both UICC subgroups, especially for UICC III (n = 53), with a positive predictive value of 80%. Our study demonstrates the ability to stratify colon cancer patients via a semi-guided process on images that primarily reflect tumor architecture.

Computerized Assessment of the Tumor-stromal Ratio and Proposal of a Novel Nomogram for Predicting Survival in Invasive Breast Cancer

Background: Various studies have verified the prognostic significance of the tumor-stromal ratio (TSR) in several types of carcinomas using manually assessed H&E stained histologic sections. This study aimed to establish a computerized method to assess the TSR in invasive breast cancer (BC) using immunohistochemistry (IHC)-stained tissue microarrays (TMAs), and integrate the TSR into a novel nomogram for predicting survival.

Methods: IHC-staining of cytokeratin (CK) was performed in 7 prepared TMAs containing 240 patients with 480 invasive BC specimens. The ratio of tumor areas and stromal areas was determined by the computerized method, and categorized as stroma-low and stroma-high groups using the X-tile software. The prognostic value of the TSR at 5-year disease free survival (5-DFS) in each subgroup was analyzed. Univariate and multivariate analyses were performed and a novel nomogram for predicting survival in invasive breast cancer was established and assessed.

Results: The newly developed computerized method could accurately recognize CK-labeled tumor areas and non-labeled stromal areas, and automatically calculate the TSR. Stroma-low and stroma-high accounted for 38.8% (n = 93) and 61.2% (n = 147) of the cases, according to the cut-off value of 55.5% for stroma ratio. The Kaplan-Meier analysis showed that patients in the stroma-high group had a worse 5-DFS compared to patients in the stroma-low group (P = 0.031). Multivariable analysis indicated that the T stage, N status, histological grade, ER status, HER-2 gene, and the TSR were potential risk factors of invasive BC patients, which were included into the nomogram (P < 0.10 for all). The nomogram was well calibrated to predict the probability of 5-DFS and the C-index was 0.817, which was higher than any single predictor. A dynamic nomogram was built for convenient use. The area under the curve (AUC) of the nomogram was 0.870, while that of the TNM staging system was 0.723. The Kaplan-Meier analysis showed that the nomogram had a better risk stratification for invasive BC patients than the TNM staging system.

Conclusions: Based on IHC staining of CK on TMAs, this study successfully developed a computerized method for TSR assessment and established a novel nomogram for predicting survival in invasive BC patients.

Differential and longitudinal immune gene patterns associated with reprogrammed microenvironment and viral mimicry in response to neoadjuvant radiotherapy in rectal cancer

BACKGROUND

Rectal cancers show a highly varied response to neoadjuvant radiotherapy/chemoradiation (RT/CRT) and the impact of the tumor immune microenvironment on this response is poorly understood. Current clinical tumor regression grading systems attempt to measure radiotherapy response but are subject to interobserver variation. An unbiased and unique histopathological quantification method (change in tumor cell density (ΔTCD)) may improve classification of RT/CRT response. Furthermore, immune gene expression profiling (GEP) may identify differences in expression levels of genes relevant to different radiotherapy responses: (1) at baseline between poor and good responders, and (2) longitudinally from preradiotherapy to postradiotherapy samples. Overall, this may inform novel therapeutic RT/CRT combination strategies in rectal cancer.

METHODS

We generated GEPs for 53 patients from biopsies taken prior to preoperative radiotherapy. TCD was used to assess rectal tumor response to neoadjuvant RT/CRT and ΔTCD was subjected to k-means clustering to classify patients into different response categories. Differential gene expression analysis was performed using statistical analysis of microarrays, pathway enrichment analysis and immune cell type analysis using single sample gene set enrichment analysis. Immunohistochemistry was performed to validate specific results. The results were validated using 220 pretreatment samples from publicly available datasets at metalevel of pathway and survival analyses.

RESULTS

ΔTCD scores ranged from 12.4% to -47.7% and stratified patients into three response categories. At baseline, 40 genes were significantly upregulated in poor (n=12) versus good responders (n=21), including myeloid and stromal cell genes. Of several pathways showing significant enrichment at baseline in poor responders, epithelial to mesenchymal transition, coagulation, complement activation and apical junction pathways were validated in external cohorts. Unlike poor responders, good responders showed longitudinal (preradiotherapy vs postradiotherapy samples) upregulation of 198 immune genes, reflecting an increased T-cell-inflamed GEP, type-I interferon and macrophage populations. Longitudinal pathway analysis suggested viral-like pathogen responses occurred in post-treatment resected samples compared with pretreatment biopsies in good responders.

CONCLUSION

This study suggests potentially druggable immune targets in poor responders at baseline and indicates that tumors with a good RT/CRT response reprogrammed from immune "cold" towards an immunologically "hot" phenotype on treatment with radiotherapy.

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.

INFORM: INFrared-based ORganizational Measurements of tumor and its microenvironment to predict patient survival

The structure and organization of a tumor and its microenvironment are often associated with cancer outcomes due to spatially varying molecular composition and signaling. A persistent challenge is to use this physical and chemical spatial organization to understand cancer progression. Here, we present a high-definition infrared imaging-based organizational measurement framework (INFORM) that leverages intrinsic chemical contrast of tissue to label unique components of the tumor and its microenvironment. Using objective and automated computational methods, further, we determine organization characteristics important for prediction. We show that the tumor spatial organization assessed with this framework is predictive of overall survival in colon cancer that adds to capability from clinical variables such as stage and grade, approximately doubling the risk of death in high-risk individuals. Our results open an all-digital avenue for measuring and studying the association between tumor spatial organization and disease progression.

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

Background

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

Methods

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

Results

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

Conclusion

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

The 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.

Developing a Raman spectroscopy-based tool to stratify patient response to pre-operative radiotherapy in rectal cancer

The use of Raman spectroscopy to stratify rectal cancer patient response to pre-operative radiotherapy, using routine pre-treatment biopsy samples.

Does tumor stroma ratio of breast cancer trucut biopsy determine response to neoadjuvant therapy?

Aims and Objectives

We examined the prognostic value of Tumor stroma ratio (TSR) in breast tumor core biopsy (TCB) specimen to determine response to neoadjuvant therapy (NAT) prior to modified radical mastectomy (MRM).

Methods

This was a retrospective analysis of patients with breast cancer who underwent TCB before NAT between August 2016 and July 2018. TSR in TCB was studied independently by 2 pathologists ( VM, VS) defined as stroma rich (TSR≤50%) or stroma poor (TSR>50%). MRM specimen of these patients were subsequently studied .Residual cancer burden (RCB) was calculated using the MD Anderson RCB calculator, categorized as complete (0), good (1) Partial (2) and no response (3). Statistical analysis was done to assess correlation of TSR to RCB.

Results

A total of 62 patients were analyzed. Mean(SD) age was 48(11) years.Twenty eight (45%) and 34 (55%) patients were stroma rich and stroma poor respectively. Twenty six (42%) patients were responders and 36 (58%) non-responders to NAT. Among stroma rich patients, only 3 (10%) were responders (Class 0 &1)and 25 (90%) non-responders(Class2&3)to NAT, among stroma poor patients 23 (68%) responded well and 11 (32%) did not.TSR had a moderate negative correlation with RCB (-0.6). On univariate analysis, only TSR had a significant effect on RCB class (<0.001).

Conclusions

TSR on TCB is a useful prognostic factor to determine response of breast carcinoma patients to neoadjuvant therapy.It is cost effective, simple and quick. Larger multi-centric studies would be useful to study its clinical implications.

Offerhaus G, Siersema PD, Tollenaar RAEM, Hardwick JCH, Hawinkels LJAC, Moons LMG, Lacle MM, Mesker WE, Boonstra JJ. Tumour-stroma ratio has poor prognostic value in non-pedunculated T1 colorectal cancer: A multi-centre case-cohort study

BACKGROUND

Current risk stratification models for early invasive (T1) colorectal cancer are not able to discriminate accurately between prognostic favourable and unfavourable tumours, resulting in over-treatment of a large (>80%) proportion of T1 colorectal cancer patients. The tumour-stroma ratio (TSR), which is a measure for the relative amount of desmoplastic tumour stroma, is reported to be a strong independent prognostic factor in advanced-stage colorectal cancer, with a high stromal content being associated with worse prognosis and survival. We aimed to investigate whether the TSR predicts clinical outcome in patients with non-pedunculated T1 colorectal cancer.

METHODS

Hematoxylin and eosin (H&E)-stained tumour tissue slides from a retrospective multi-centre case cohort of patients with non-pedunculated surgically treated T1 colorectal cancer were assessed for TSR by two independent observers who were blinded for clinical outcomes. The primary end point was adverse outcome, which was defined as the presence of lymph node metastasis in the resection specimen or colorectal cancer recurrence during follow-up.

RESULTS

All 261 patients in the case cohort had H&E slides available for TSR scoring. Of these, 183 were scored as stroma-low, and 78 were scored as stroma-high. There was moderate inter-observer agreement (κ = 0.42). In total, 41 patients had lymph node metastasis, 17 patients had recurrent cancer and five had both. Stroma-high tumours were not associated with an increased risk for an adverse outcome (adjusted hazard ratio = 0.66, 95% confidence interval 0.37-1.18; p = 0.163).

CONCLUSIONS

Our study emphasises that existing prognosticators may not be simply extrapolated to T1 colorectal cancers, even though their prognostic value has been widely validated in more advanced-stage tumours.

Periostin as a key molecule defining desmoplastic environment in colorectal cancer

Categorizing desmoplastic reaction (DR) based on the histological findings of cancer-associated fibroblasts is shown to be a promising novel method to predict prognosis of patients with colorectal cancer (CRC). Periostin (POSTN) in cancer-associated stroma is reportedly associated with poor clinical outcomes. Immunohistochemical staining with an anti-POSTN antibody was performed in 73 patients with pStage III CRC (cohort 1). In addition, to evaluate mRNA and protein expression levels of POSTN, we analyzed paired normal and invasive cancer frozen specimens by quantitative real-time polymerase chain reaction and western blot analysis in 41 patients (cohort 2). In cohort 1, according to the DR categorization, 18, 22, and 33 patients were classified as immature, intermediate, and mature, respectively. High immunoreactivity of POSTN was observed 100%, 68.2%, and 27.3%, respectively (p < 0.0001). The 5-year relapse-free survival rates were 56.8% and 82.7% in high and low POSTN expression subgroups, respectively (p = 0.015). In cohort 2, the POSTN mRNA and protein levels were significantly higher in the immature stroma as compared to the stroma characterized as other DR patterns. POSTN expression was closely associated with DR categorization. POSTN may be a key molecule that contributes to the malignant potential of CRC.