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

Prediction of lymph node metastasis in patients with papillary thyroid cancer based on radiomics analysis and intraoperative frozen section analysis: A retrospective study

INTRODUCTION

To evaluate the diagnostic efficiency among the clinical model, the radiomics model and the nomogram that combined radiomics features, frozen section (FS) analysis and clinical characteristics for the prediction of lymph node (LN) metastasis in patients with papillary thyroid cancer (PTC).

METHODS

A total of 208 patients were randomly divided into two groups randomly with a proportion of 7:3 for the training groups (n = 146) and the validation groups (n = 62). The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for the selection of radiomics features extracted from ultrasound (US) images. Univariate and multivariate logistic analyses were used to select predictors associated with the status of LN. The clinical model, radiomics model and nomogram were subsequently established by logistic regression machine learning. The area under the curve (AUC), sensitivity and specificity were used to evaluate the diagnostic performance of the different models. The Delong test was used to compare the AUC of the three models.

RESULTS

Multivariate analysis indicated that age, size group, Adler grade, ACR score and the psammoma body group were independent predictors of lymph node metastasis (LNM). The results showed that in both the training and validation groups, the nomogram showed better performance than the clinical model, albeit not statistically significant (p > .05), and significantly outperformed the radiomics model (p < .05). However, the nomogram exhibits a slight improvement in sensitivity that could reduce the incidence of false negatives.

CONCLUSION

We propose that the nomogram holds substantial promise as an effective tool for predicting LNM in patients with PTC.

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.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Improved Risk-Stratification Scheme for Mismatch-Repair Proficient Stage II Colorectal Cancers Using the Digital Pathology Biomarker QuantCRC

PURPOSE

There is a need to improve current risk stratification of stage II colorectal cancer to better inform risk of recurrence and guide adjuvant chemotherapy. We sought to examine whether integration of QuantCRC, a digital pathology biomarker utilizing hematoxylin and eosin-stained slides, provides improved risk stratification over current American Society of Clinical Oncology (ASCO) guidelines.

EXPERIMENTAL DESIGN

ASCO and QuantCRC-integrated schemes were applied to a cohort of 398 mismatch-repair proficient (MMRP) stage II colorectal cancers from three large academic medical centers. The ASCO stage II scheme was taken from recent guidelines. The QuantCRC-integrated scheme utilized pT3 versus pT4 and a QuantCRC-derived risk classification. Evaluation of recurrence-free survival (RFS) according to these risk schemes was compared using the log-rank test and HR.

RESULTS

Integration of QuantCRC provides improved risk stratification compared with the ASCO scheme for stage II MMRP colorectal cancers. The QuantCRC-integrated scheme placed more stage II tumors in the low-risk group compared with the ASCO scheme (62.5% vs. 42.2%) without compromising excellent 3-year RFS. The QuantCRC-integrated scheme provided larger HR for both intermediate-risk (2.27; 95% CI, 1.32-3.91; P = 0.003) and high-risk (3.27; 95% CI, 1.42-7.55; P = 0.006) groups compared with ASCO intermediate-risk (1.58; 95% CI, 0.87-2.87; P = 0.1) and high-risk (2.24; 95% CI, 1.09-4.62; P = 0.03) groups. The QuantCRC-integrated risk groups remained prognostic in the subgroup of patients that did not receive any adjuvant chemotherapy.

CONCLUSIONS

Incorporation of QuantCRC into risk stratification provides a powerful predictor of RFS that has potential to guide subsequent treatment and surveillance for stage II MMRP colorectal cancers.

Distill-SODA: Distilling Self-Supervised Vision Transformer for Source-Free Open-Set Domain Adaptation in Computational Pathology

Developing computational pathology models is essential for reducing manual tissue typing from whole slide images, transferring knowledge from the source domain to an unlabeled, shifted target domain, and identifying unseen categories. We propose a practical setting by addressing the above-mentioned challenges in one fell swoop, i.e., source-free open-set domain adaptation. Our methodology focuses on adapting a pre-trained source model to an unlabeled target dataset and encompasses both closed-set and open-set classes. Beyond addressing the semantic shift of unknown classes, our framework also deals with a covariate shift, which manifests as variations in color appearance between source and target tissue samples. Our method hinges on distilling knowledge from a self-supervised vision transformer (ViT), drawing guidance from either robustly pre-trained transformer models or histopathology datasets, including those from the target domain. In pursuit of this, we introduce a novel style-based adversarial data augmentation, serving as hard positives for self-training a ViT, resulting in highly contextualized embeddings. Following this, we cluster semantically akin target images, with the source model offering weak pseudo-labels, albeit with uncertain confidence. To enhance this process, we present the closed-set affinity score (CSAS), aiming to correct the confidence levels of these pseudo-labels and to calculate weighted class prototypes within the contextualized embedding space. Our approach establishes itself as state-of-the-art across three public histopathological datasets for colorectal cancer assessment. Notably, our self-training method seamlessly integrates with open-set detection methods, resulting in enhanced performance in both closed-set and open-set recognition tasks.

Machine learning based local recurrence prediction in colorectal cancer using polarized light imaging

Significance

Current treatment for stage III colorectal cancer (CRC) patients involves surgery that may not be sufficient in many cases, requiring additional adjuvant systemic therapy. Identification of this latter cohort that is likely to recur following surgery is key to better personalized therapy selection, but there is a lack of proper quantitative assessment tools for potential clinical adoption.

Aim

The purpose of this study is to employ Mueller matrix (MM) polarized light microscopy in combination with supervised machine learning (ML) to quantitatively analyze the prognostic value of peri-tumoral collagen in CRC in relation to 5-year local recurrence (LR).

Approach

A simple MM microscope setup was used to image surgical resection samples acquired from stage III CRC patients. Various potential biomarkers of LR were derived from MM elements via decomposition and transformation operations. These were used as features by different supervised ML models to distinguish samples from patients that locally recurred 5 years later from those that did not.

Results

Using the top five most prognostic polarimetric biomarkers ranked by their relevant feature importances, the best-performing XGBoost model achieved a patient-level accuracy of 86%. When the patient pool was further stratified, 96% accuracy was achieved within a tumor-stage-III sub-cohort.

Conclusions

ML-aided polarimetric analysis of collagenous stroma may provide prognostic value toward improving the clinical management of CRC patients.

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.

Results from the UNITED study: a multicenter study validating the prognostic effect of the tumor-stroma ratio in colon cancer

BACKGROUND

The TNM (tumor-node-metastasis) Evaluation Committee of Union for International Cancer Control (UICC) and College of American Pathologists (CAP) recommended to prospectively validate the cost-effective and robust tumor-stroma ratio (TSR) as an independent prognostic parameter, since high intratumor stromal percentages have previously predicted poor patient-related outcomes.

PATIENTS AND METHODS

The 'Uniform Noting for International application of Tumor-stroma ratio as Easy Diagnostic tool' (UNITED) study enrolled patients in 27 participating centers in 12 countries worldwide. The TSR, categorized as stroma-high (>50%) or stroma-low (≤50%), was scored through standardized microscopic assessment by certified pathologists, and effect on disease-free survival (DFS) was evaluated with 3-year median follow-up. Secondary endpoints were benefit assessment of adjuvant chemotherapy (ACT) and overall survival (OS).

RESULTS

A total of 1537 patients were included, with 1388 eligible stage II/III patients curatively operated between 2015 and 2021. DFS was significantly shorter in stroma-high (n = 428) than in stroma-low patients (n = 960) (3-year rates 70% versus 83%; P < 0.001). In multivariate analysis, TSR remained an independent prognosticator for DFS (P < 0.001, hazard ratio 1.49, 95% confidence interval 1.17-1.90). As secondary outcome, DFS was also worse in stage II and III stroma-high patients despite adjuvant treatment (3-year rates stage II 73% versus 92% and stage III 66% versus 80%; P = 0.008 and P = 0.011, respectively). In stage II patients not receiving ACT (n = 322), the TSR outperformed the American Society of Clinical Oncology (ASCO) criteria in identifying patients at risk of events (event rate 21% versus 9%), with a higher discriminatory 3-year DFS rate (stroma-high 80% versus ASCO high risk 91%). A trend toward worse 5-year OS in stroma-high was noticeable (74% versus 83% stroma-low; P = 0.102).

CONCLUSION

The multicenter UNITED study unequivocally validates the TSR as an independent prognosticator, confirming worse outcomes in stroma-high patients. The TSR improved current selection criteria for patients at risk of events, and stroma-high patients potentially experienced chemotherapy resistance. TSR implementation in pathology diagnostics and international guidelines is highly recommended as aid in personalized treatment.

Engineering Tumor Stroma Morphogenesis Using Dynamic Cell-Matrix Spheroid Assembly

The tumor microenvironment consists of resident tumor cells organized within a compositionally diverse, three-dimensional (3D) extracellular matrix (ECM) network that cannot be replicated in vitro using bottom-up synthesis. We report a new self-assembly system to engineer ECM-rich 3D MatriSpheres wherein tumor cells actively organize and concentrate microgram quantities of decellularized ECM dispersions which modulate cell phenotype. 3D colorectal cancer (CRC) MatriSpheres were created using decellularized small intestine submucosa (SIS) as an orthotopic ECM source that had greater proteomic homology to CRC tumor ECM than traditional ECM formulations such as Matrigel. SIS ECM was rapidly concentrated from its environment and assembled into ECM-rich 3D stroma-like regions by mouse and human CRC cell lines within 4-5 days via a mechanism that was rheologically distinct from bulk hydrogel formation. Both ECM organization and transcriptional regulation by 3D ECM cues affected programs of malignancy, lipid metabolism, and immunoregulation that corresponded with an in vivo MC38 tumor cell subpopulation identified via single cell RNA sequencing. This 3D modeling approach stimulates tumor specific tissue morphogenesis that incorporates the complexities of both cancer cell and ECM compartments in a scalable, spontaneous assembly process that may further facilitate precision medicine.

Self-Supervised Learning Reveals Clinically Relevant Histomorphological Patterns for Therapeutic Strategies in Colon Cancer

Self-supervised learning (SSL) automates the extraction and interpretation of histopathology features on unannotated hematoxylin-and-eosin-stained whole-slide images (WSIs). We trained an SSL Barlow Twins-encoder on 435 TCGA colon adenocarcinoma WSIs to extract features from small image patches. Leiden community detection then grouped tiles into histomorphological phenotype clusters (HPCs). HPC reproducibility and predictive ability for overall survival was confirmed in an independent clinical trial cohort (N=1213 WSIs). This unbiased atlas resulted in 47 HPCs displaying unique and sharing clinically significant histomorphological traits, highlighting tissue type, quantity, and architecture, especially in the context of tumor stroma. Through in-depth analysis of these HPCs, including immune landscape and gene set enrichment analysis, and association to clinical outcomes, we shed light on the factors influencing survival and responses to treatments like standard adjuvant chemotherapy and experimental therapies. Further exploration of HPCs may unveil new insights and aid decision-making and personalized treatments for colon cancer patients.

Use of tryptic peptide MALDI mass spectrometry imaging to identify the spatial proteomic landscape of colorectal cancer liver metastases

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide. CRC liver metastases (CRLM) are often resistant to conventional treatments, with high rates of recurrence. Therefore, it is crucial to identify biomarkers for CRLM patients that predict cancer progression. This study utilised matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) in combination with liquid chromatography-tandem mass spectrometry (LC-MS/MS) to spatially map the CRLM tumour proteome. CRLM tissue microarrays (TMAs) of 84 patients were analysed using tryptic peptide MALDI-MSI to spatially monitor peptide abundances across CRLM tissues. Abundance of peptides was compared between tumour vs stroma, male vs female and across three groups of patients based on overall survival (0-3 years, 4-6 years, and 7+ years). Peptides were then characterised and matched using LC-MS/MS. A total of 471 potential peptides were identified by MALDI-MSI. Our results show that two unidentified m/z values (1589.876 and 1092.727) had significantly higher intensities in tumours compared to stroma. Ten m/z values were identified to have correlation with biological sex. Survival analysis identified three peptides (Histone H4, Haemoglobin subunit alpha, and Inosine-5'-monophosphate dehydrogenase 2) and two unidentified m/z values (1305.840 and 1661.060) that were significantly higher in patients with shorter survival (0-3 years relative to 4-6 years and 7+ years). This is the first study using MALDI-MSI, combined with LC-MS/MS, on a large cohort of CRLM patients to identify the spatial proteome in this malignancy. Further, we identify several protein candidates that may be suitable for drug targeting or for future prognostic biomarker development.

Immunohistochemical Expression of Immune Checkpoints; CTLA-4, LAG3, and TIM-3 in Cancer Cells and Tumor-infiltrating Lymphocytes (TILs) in Colorectal Carcinoma

BACKGROUND

Colorectal cancer is considered the third most prevalent cancer in both sexes. Immune checkpoint receptors that regulate T-cell response, stimulation, and development include lymphocyte activating gene 3 (LAG-3), cytotoxic T lymphocyte-associated antigen-4 (CTLA-4), and T-cell immunoglobulin and mucin domain 3 (Tim-3). In addition, they are crucial for the advancement of cancer and tumor immune escape.

OBJECTIVE

This work's aim was to assess the immunohistochemistry expression of Tim-3, CTLA-4, and LAG-3 in cancer cells and tumor-infiltrating lymphocytes (TILs) in colorectal cancer (CRC) and the correlation between these markers and clinicopathological variables and survival data.

METHODS

This study involved 206 CRC specimens processed for CTLA-4, LAG3, and TIM-3 immunohistochemistry and correlated with the clinicopathological and survival parameters of the patients.

RESULTS

High CTLA-4 epithelial expression was highly related to the old age group, large tumor size, low tumor-stroma ratio (TSR), high grade, advanced stage, the presence of distant metastasis (DM), perineural invasion (PNI), necrosis, lymphovascular invasion (LVI), relapse, mortality, overall survival (OS), and disease-free survival (DFS), while negative CTLA-4 TILs expression was highly linked with the presence of gross perforation, low TSR, high tumor budding (TB) score, high grade, advanced stage, the existence of lymph node (LN) metastasis, DM, necrosis, LVI, PNI, DFS, mortality, and OS. Positive LAG-3 TILs expression was highly correlated with large tumor size, gross perforation, low TSR, high TB score, high grade, advanced phase, the presence of LN, necrosis, LVI, PNI, relapse DFS, mortality, and OS. High Tim-3 epithelial expression was extremely linked with low TSR, advanced phase, the presence of LN, LVI, PNI, relapse, DFS, mortality, and OS, while positive Tim-3 TILs expression was related to gross perforation, low TSR, high TB score, advanced stage, the presence of LN, DM, necrosis, relapse, DFS, mortality, and OS.

CONCLUSIONS

The patients' poor prognosis may be related to the immunohistochemistry expression of LAG-3, Tim-3, and CTLA-4 in CRC cancer tissue and TILs. Poor patient consequences can result from the CTLA-4, Tim-3, and LAG-3 co-expression, but CTLA-4 TILs' expression of these proteins may inhibit the growth of tumors.

Establishment of a pathomic-based machine learning model to predict CD276 (B7-H3) expression in colon cancer

CD276 is a promising prognostic indicator and an attractive therapeutic target in various malignancies. However, current methods for CD276 detection are time-consuming and expensive, limiting extensive studies and applications of CD276. We aimed to develop a pathomic model for CD276 prediction from H&E-stained pathological images, and explore the underlying mechanism of the pathomic features by associating the pathomic model with transcription profiles. A dataset of colon adenocarcinoma (COAD) patients was retrieved from the Cancer Genome Atlas (TCGA) database. The dataset was divided into the training and validation sets according to the ratio of 8:2 by a stratified sampling method. Using the gradient boosting machine (GBM) algorithm, we established a pathomic model to predict CD276 expression in COAD. Univariate and multivariate Cox regression analyses were conducted to assess the predictive performance of the pathomic model for overall survival in COAD. Gene Set Enrichment Analysis (GESA) was performed to explore the underlying biological mechanisms of the pathomic model. The pathomic model formed by three pathomic features for CD276 prediction showed an area under the curve (AUC) of 0.833 (95%CI: 0.784-0.882) in the training set and 0.758 (95%CI: 0.637-0.878) in the validation set, respectively. The calibration curves and Hosmer-Lemeshow goodness of fit test showed that the prediction probability of high/low expression of CD276 was in favorable agreement with the real situation in both the training and validation sets (P=0.176 and 0.255, respectively). The DCA curves suggested that the pathomic model acquired high clinical benefit. All the subjects were categorized into high pathomic score (PS) (PS-H) and low PS (PS-L) groups according to the cutoff value of PS. Univariate and multivariate Cox regression analysis indicated that PS was a risk factor for overall survival in COAD. Furthermore, through GESA analysis, we found several immune and inflammatory-related pathways and genes were associated with the pathomic model. We constructed a pathomics-based machine learning model for CD276 prediction directly from H&E-stained images in COAD. Through integrated analysis of the pathomic model and transcriptomics, the interpretability of the pathomic model provide a theoretical basis for further hypothesis and experimental research.

Personalizing adjuvant therapy for patients with colorectal cancer

The current standard-of-care adjuvant treatment for patients with colorectal cancer (CRC) comprises a fluoropyrimidine (5-fluorouracil or capecitabine) as a single agent or in combination with oxaliplatin, for either 3 or 6 months. Selection of therapy depends on conventional histopathological staging procedures, which constitute a blunt tool for patient stratification. Given the relatively marginal survival benefits that patients can derive from adjuvant treatment, improving the safety of chemotherapy regimens and identifying patients most likely to benefit from them is an area of unmet need. Patient stratification should enable distinguishing those at low risk of recurrence and a high chance of cure by surgery from those at higher risk of recurrence who would derive greater absolute benefits from chemotherapy. To this end, genetic analyses have led to the discovery of germline determinants of toxicity from fluoropyrimidines, the identification of patients at high risk of life-threatening toxicity, and enabling dose modulation to improve safety. Thus far, results from analyses of resected tissue to identify mutational or transcriptomic signatures with value as prognostic biomarkers have been rather disappointing. In the past few years, the application of artificial intelligence-driven models to digital images of resected tissue has identified potentially useful algorithms that stratify patients into distinct prognostic groups. Similarly, liquid biopsy approaches involving measurements of circulating tumour DNA after surgery are additionally useful tools to identify patients at high and low risk of tumour recurrence. In this Perspective, we provide an overview of the current landscape of adjuvant therapy for patients with CRC and discuss how new technologies will enable better personalization of therapy in this setting.

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 ).

The Prognostic Value of Micropapillary Pattern in Colon Cancer and Its Role as a High-Risk Feature in Patients With Stage II Disease

BACKGROUND

The association of a micropapillary pattern with oncologic outcomes has not been fully studied in patients with colon cancer.

OBJECTIVE

We evaluated the prognostic value of a micropapillary pattern, especially for patients with stage II colon cancer.

DESIGN

A retrospective comparative cohort study using propensity score matching.

SETTING

This study was conducted at a single tertiary center.

PATIENTS

Patients with primary colon cancer undergoing curative resection from October 2013 to December 2017 were enrolled. Patients were grouped into micropapillary pattern positive or micropapillary pattern negative.

MAIN OUTCOME MEASUREMENTS

Disease-free survival and overall survival.

RESULTS

Of the eligible 2192 patients, 334 (15.2%) were with micropapillary pattern (+). After 1:2 propensity score matching, 668 patients with micropapillary pattern-negative status were selected. The micropapillary pattern-positive group showed significantly worse 3-year disease-free survival (77.6% vs 85.1%, p = 0.007). Three-year overall survival of micropapillary pattern-positive and micropapillary pattern-negative patients did not show a statistically significant difference (88.9% vs 90.4%, p = 0.480). In multivariable analysis, micropapillary pattern-positive was an independent risk factor for poor disease-free survival (HR 1.547, p = 0.008). In the subgroup analysis for 828 patients with stage II disease, 3-year disease-free survival deteriorated significantly in micropapillary pattern-positive patients (82.6% vs 93.0, p < 0.001). Three-year overall survival was 90.1% and 93.9% in patients positive and negative for micropapillary pattern, respectively ( p = 0.082). In the multivariable analysis for patients with stage II disease, micropapillary pattern-positive status was an independent risk factor for poor disease-free survival (HR 2.003, p = 0.031).

LIMITATIONS

Selection bias due to the retrospective nature of the study.

CONCLUSIONS

Micropapillary pattern-positive status may serve as an independent prognostic factor for colon cancer, especially for patients with stage II disease.

VALOR PRONSTICO DEL PATRN MICROPAPILAR Y SU PAPEL COMO CARACTERSTICA DE ALTO RIESGO EN PACIENTES CON CNCER DE COLON EN ESTADO II

ANTECEDENTES:La asociación del patrón micropapilar con los resultados oncológicos no ha sido completamente estudiada en pacientes con cáncer de colon.OBJETIVO:Evaluamos el valor pronóstico del patrón micropapilar, especialmente en pacientes con cáncer de colon en estadio II.DISEÑO:Estudio de cohortes comparativo y retrospectivo que utilize el emparejamiento por puntuación de propensiones.AJUSTE:Estudio realizado en un solo centro terciario.PACIENTES:Se incluyeron los pacientes con cáncer de colon primario sometidos a resección curativa desde octubre de 2013 hasta diciembre de 2017. Los pacientes se agruparon en patrón micropapilar positivo ( + ) o patrón micropapilar negativo ( - ).PRINCIPALES MEDIDAS DE RESULTADO:Sobrevida libre de enfermedad y la sobrevida global.RESULTADOS:De los 2192 pacientes elegibles, 334 (15,2%) tenían patrón micropapilar (+). Después de emparejar el puntaje de propensión 1:2, se seleccionaron 668 pacientes con patrón micropapilar (-). El grupo con patrón micropapilar (+) mostró una sobrevida libre de enfermedad significativamente inferior a los tres años (77,6% frente a 85,1%, p = 0,007). La sobrevida global a los tres años del patrón micropapilar (+) y del patrón micropapilar (-) no mostró una diferencia estadísticamente significativa (88,9 % frente a 90,4%, p = 0,480). En el análisis multivariable, el patrón micropapilar (+) fue un factor de riesgo independiente para una deficiente sobrevida libre de enfermedad (índice de riesgo 1,547, p = 0,008). En el análisis de subgrupos de 828 pacientes con enfermedad en estadio II, la sobrevida libre de enfermedad a los tres años se deterioró significativamente en los pacientes con patrón micropapilar (+) (82,6% frente a 93,0, p < 0,001). La sobrevida global a los tres años fué del 90,1% y del 93,9% en el patrón micropapilar (+) y el patrón micropapilar (-), respectivamente ( p = 0,082). En el análisis multivariable de los pacientes con enfermedad en estadio II, el patrón micropapilar (+) fue un factor de riesgo independiente para una sobrevida libre de enfermedad deficiente (índice de riesgo 2,003, p = 0,031).LIMITACIONES:Sesgo de selección debido a la naturaleza retrospectiva del estudio.CONCLUSIONES:El patrón micropapilar (+) sirve como factor pronóstico independiente para el cáncer de colon, especialmente para pacientes con enfermedad en estadio II. (Traducción-Dr. Xavier Delgadillo ).

Histopathology and levels of proteins in plasma associate with survival after colorectal cancer diagnosis

BACKGROUND

The TNM system is used to assess prognosis after colorectal cancer (CRC) diagnosis. Other prognostic factors reported include histopathological assessments of the tumour, tumour mutations and proteins in the blood. As some of these factors are strongly correlated, it is important to evaluate the independent effects they may have on survival.

METHODS

Tumour samples from 2162 CRC patients were visually assessed for amount of tumour stroma, severity of lymphocytic infiltrate at the tumour margins and the presence of lymphoid follicles. Somatic mutations in the tumour were assessed for 2134 individuals. Pre-surgical levels of 4963 plasma proteins were measured in 128 individuals. The associations between these features and prognosis were inspected by a Cox Proportional Hazards Model (CPH).

RESULTS

Levels of stroma, lymphocytic infiltration and presence of lymphoid follicles all associate with prognosis, along with high tumour mutation burden, high microsatellite instability and TP53 and BRAF mutations. The somatic mutations are correlated with the histopathology and none of the somatic mutations associate with survival in a multivariate analysis. Amount of stroma and lymphocytic infiltration associate with local invasion of tumours. Elevated levels of two plasma proteins, CA-125 and PPP1R1A, associate with a worse prognosis.

CONCLUSIONS

Tumour stroma and lymphocytic infiltration variables are strongly associated with prognosis of CRC and capture the prognostic effects of tumour mutation status. CA-125 and PPP1R1A may be useful prognostic biomarkers in CRC.

Exploring the Complex and Multifaceted Interplay between Melanoma Cells and the Tumor Microenvironment

Malignant melanoma is a very aggressive skin cancer, characterized by a heterogeneous nature and high metastatic potential. The incidence of melanoma is continuously increasing worldwide, and it is one of the most common cancers in young adults. In the past twenty years, our understanding of melanoma biology has increased profoundly, and disease management for patients with disseminated disease has improved due to the emergence of immunotherapy and targeted therapy. However, a significant fraction of patients relapse or do not respond adequately to treatment. This can partly be explained by the complex signaling between the tumor and its microenvironment, giving rise to melanoma phenotypes with different patterns of disease progression. This review focuses on the key aspects and complex relationship between pathogenesis, genetic abnormalities, tumor microenvironment, cellular plasticity, and metabolic reprogramming in melanoma. By acquiring a deeper understanding of the multifaceted features of melanomagenesis, we can reach a point of more individualized and patient-centered disease management and reduced costs of ineffective treatments.

Mueller matrix polarization parameters correlate with local recurrence in patients with stage III colorectal cancer

The peri-tumoural stroma has been explored as a useful source of prognostic information in colorectal cancer. Using Mueller matrix (MM) polarized light microscopy for quantification of unstained histology slides, the current study assesses the prognostic potential of polarimetric characteristics of peri-tumoural collagenous stroma architecture in 38 human stage III colorectal cancer (CRC) patient samples. Specifically, Mueller matrix transformation and polar decomposition parameters were tested for association with 5-year patient local recurrence outcomes. The results show that some of these polarimetric parameters were significantly different (p value < 0.05) for the recurrence versus the no-recurrence patient cohorts (Mann-Whitney U test). MM parameters may thus be prognostically valuable towards improving clinical management/treatment stratification in CRC patients.

Tumor Budding Should Be in Oral Cavity Cancer Reporting: A Retrospective Cohort Study Based on Tumor Microenvironment

The utility of histological grading, which is useful in predicting prognosis in many tumors, is controversial for oral squamous cell carcinoma (OSCC). Therefore, new histopathological parameters should be added to histopathology reports of OSCCs. The study aimed to evaluate the parameters of worst invasion pattern (WPOI) and tumor budding in patients with OSCC, to compare them with other histopathological parameters, clinical data and overall survival, and to evaluate these results within the literature. A total of 73 OSCC cases with excisional biopsies were included in this study. WPOI, tumor budding, cell nest size, tumor-stroma ratio, stromal lymphocyte infiltration and stroma type, as well as classical histopathological parameters, were evaluated on hematoxylin-eosin-stained sections. Perineural invasion, lymph node metastases, advanced stage, presence of more than five buds and single cell invasion pattern in univariate survival analyses are characterized by a shortened overall survival time. While there was no significant difference between WPOI results and survival in the survival analysis, WPOI 5 was associated with more frequent lymph node metastasis and advanced stage at the time of diagnosis compared to WPOI 4. We concluded that tumor budding and single-cell invasion should be considered prognostic histopathologic parameters in OSCC.

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.

Identification of Hypoxia-Associated Signature in Colon Cancer to Assess Tumor Immune Microenvironment and Predict Prognosis Based on 14 Hypoxia-Associated Genes

Purpose

Colon cancer is the main malignant tumor of the digestive tract. Hypoxia is highly related to the occurrence, progression and tumor immune microenvironment (TIME) of cancer. The aim of this study was to identify a hypoxia-associated signature with high accuracy for predicting the prognosis and TIME of colon cancer.

Methods

Download colon cancer data from the GEO and TCGA databases. A novel hypoxia risk model was identified to predict the prognosis of colon cancer patients. Subsequently, GSEA, TIME and mutation analysis were performed in the hypoxia high and low risk score groups. Finally, the signature gene ANKZF1 was selected for functional verification at the cellular level.

Results

A novel hypoxia risk model was identified. The risk score was significantly associated with poorer overall survival in colon cancer, and could be used as an independent prognostic factor for colon cancer. GSEA analysis found that the processes related to stimulate tumor proliferation and anti-apoptosis were significantly enriched in the hypoxia high risk score group. The expression of immunosuppressive cells and most immune checkpoints in the high risk score group was significantly higher than that in the low risk score group. In vitro cell experiments showed that knockdown the expression of ANKZF1 could inhibit the proliferation, migration and invasion of colon cancer cells.

Conclusion

Hypoxia plays an important role in evaluating the TIME and predicting the prognosis of colon cancer.

Prognostic Value of Chromatin Structure Typing in Early-Stage Non-Small Cell Lung Cancer

(1) Background: Chromatin structure typing has been used for prognostic risk stratification among cancer survivors. This study aimed to ascertain the prognostic values of ploidy, nucleotyping, and tumor-stroma ratio (TSR) in predicting disease progression for patients with early-stage non-small cell lung cancer (NSCLC), and to explore whether patients with different nucleotyping profiles can benefit from adjuvant chemotherapy. (2) Methods: DNA ploidy, nucleotyping, and TSR were measured by chromatin structure typing analysis (Matrix Analyser, Room4, Kent, UK). Cox proportional hazard regression models were used to assess the relationships of DNA ploidy, nucleotyping, and TSR with a 5-year disease-free survival (DFS). (3) Results: among 154 early-stage NSCLC patients, 102 were non-diploid, 40 had chromatin heterogeneity, and 126 had a low stroma fraction, respectively. Univariable analysis suggested that non-diploidy was associated with a significantly lower 5-year DFS rate. After combining DNA ploidy and nucleotyping for risk stratification and adjusting for potential confounders, the DNA ploidy and nucleotyping (PN) high-risk group and PN medium-risk group had a 4- (95% CI: 1.497-8.754) and 3-fold (95% CI: 1.196-6.380) increase in the risk of disease progression or mortality within 5 years of follow-up, respectively, compared to the PN low-risk group. In PN high-risk patients, adjuvant therapy was associated with a significantly improved 5-year DFS (HR = 0.214, 95% CI: 0.048-0.957, p = 0.027). (4) Conclusions: the non-diploid DNA status and the combination of ploidy and nucleotyping can be useful prognostic indicators to predict long-term outcomes in early-stage NSCLC patients. Additionally, NSCLC patients with non-diploidy and chromatin homogenous status may benefit from adjuvant therapy.

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.

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.

10 years of extracellular matrix proteomics: Accomplishments, challenges, and future perspectives

The extracellular matrix (ECM) is a complex assembly of hundreds of proteins forming the architectural scaffold of multicellular organisms. In addition to its structural role, the ECM conveys signals orchestrating cellular phenotypes. Alterations of ECM composition, abundance, structure, or mechanics, have been linked to diseases and disorders affecting all physiological systems, including fibrosis and cancer. Deciphering the protein composition of the ECM and how it changes in pathophysiological contexts is thus the first step toward understanding the roles of the ECM in health and disease and toward the development of therapeutic strategies to correct disease-causing ECM alterations. Potentially, the ECM also represents a vast, yet untapped reservoir of disease biomarkers. ECM proteins are characterized by unique biochemical properties that have hindered their study: they are large, heavily and uniquely post-translationally modified, and highly insoluble. Overcoming these challenges, we and others have devised mass-spectrometry-based proteomic approaches to define the ECM composition, or "matrisome", of tissues. This review provides a historical overview of ECM proteomics research and presents the latest advances that now allow the profiling of the ECM of healthy and diseased tissues. The second part highlights recent examples illustrating how ECM proteomics has emerged as a powerful discovery pipeline to identify prognostic cancer biomarkers. The third part discusses remaining challenges limiting our ability to translate findings to clinical application and proposes approaches to overcome them. Last, the review introduces readers to resources available to facilitate the interpretation of ECM proteomics datasets. The ECM was once thought to be impenetrable. MS-based proteomics has proven to be a powerful tool to decode the ECM. In light of the progress made over the past decade, there are reasons to believe that the in-depth exploration of the matrisome is within reach and that we may soon witness the first translational application of ECM proteomics.

Phenotypic subtypes predict outcomes in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths globally. The Colorectal Cancer Subtyping Consortium used the transcriptome-based method to classify CRC according to four molecular subtypes, each showing different genomic alterations and prognoses: CMS1 (microsatellite instable [MSI] immune), CMS2 (canonical), CMS3 (metabolic), and CMS4 (mesenchymal). To expedite the clinical implementation of such methods, easier and preferably tumor phenotype-based methods are needed. In this study, we describe a method to divide patients into four phenotypic subgroups using immunohistochemistry. Moreover, we analyze disease-specific survival (DSS) among different phenotypic subtypes and the associations between the phenotypic subtypes and clinicopathological variables.

METHODS

We categorized 480 surgically treated CRC patients into four phenotypic subtypes (immune, canonical, metabolic, and mesenchymal) using the immunohistochemically determined CD3-CD8 tumor-stroma index, proliferation index, and tumor-stroma percentage. We analyzed survival rates for the phenotypic subtypes in different clinical patient subgroups using the Kaplan-Meier method and Cox regression analysis. Associations between phenotypic subtypes and clinicopathological variables were examined using the chi-square test.

RESULTS

Patients with immune subtype tumors exhibited the best 5-year DSS, while mesenchymal subtype tumors accompanied the worst prognosis. The prognostic value of the canonical subtype showed wide variation among different clinical subgroups. Immune subtype tumors were associated with being female, stage I disease, and a right-side colon location. Metabolic tumors, however, were associated with pT3 and pT4 tumors, and being male. Finally, a mesenchymal subtype associated with stage IV disease, a mucinous histology, and a rectal tumor location.

CONCLUSIONS

Phenotypic subtype predicts patient outcome in CRC. Associations and prognostic values for subtypes resemble the transcriptome-based consensus molecular subtypes (CMS) classification. In our study, the immune subtype stood out with its exceptionally good prognosis. Moreover, the canonical subtype showed wide variability among clinical subgroups. Further studies are needed to investigate the concordance between transcriptome-based classification systems and the phenotypic subtypes.

Evaluating the relevance of surgical margins. Part one: The problems with current methodology

The goal of cancer surgery is to achieve a "clean" microscopic resection, with no residual tumour remaining in the wound. To achieve that goal, the surgeon typically incorporates a measured buffer of grossly normal tissue about the entire circumference of the tumour. Microscopic analysis of the resection boundaries is then performed to determine if all traces of the tumour have been completely removed. This analysis is thought to provide a surrogate indication as to the likelihood for that tumour to recur after surgery. However, it is recognised that tumour recurrence may not occur even when microscopic evidence of tumour has been identified at the resection margins, and recurrence can also occur when conventional histology has considered the tumour to have been completely removed. The explanations for this dichotomy are numerous and include technical and practical limitations of the processing methodology, and also several surgeon-related and tumour-related reasons. Ultimately, the inability to confidently determine when a tumour has been removed sufficiently to prevent recurrence can impact on the ability to provide owners with confident treatment advice. In this article, the authors describe the challenges with defining the true extent of the tumour margin from the perspective of the surgeon, the pathologist and the tumour. The authors also provide an analysis of why our current efforts to ensure that all traces of the local tumour have been successfully removed may provide an imperfect assessment of the risk of recurrence.

Tumour-stroma ratio is a valuable prognostic factor for oral tongue squamous cell carcinoma

OBJECTIVE

The objectives of this study were to estimate the prognostic value of the tumour-stroma ratio (TSR) and tumour budding (TB) in oral tongue squamous cell carcinoma (OTSCC) and to establish a reliable model to predict the outcome of OTSCC patients.

METHODS

A total of 103 patients surgically treated at our hospital were enrolled in this study. Chi-square tests, Kaplan-Meier analyses and Cox proportional hazards regression models were performed for statistical analysis.

RESULTS

Fifty-six patients were categorized as stroma-rich, and 47 patients were categorized as stroma-poor. Only pathological grade was associated with the TSR (p = 0.017). Kaplan-Meier analysis showed that stroma-rich, high-intensity budding and high risk groups were associated with worse prognosis. The Cox regression model showed that the TSR was an independent risk factor for OTSCC patients prognosis, and the high risk group was also related to poor prognosis (p < 0.05). TB was significantly associated with poor prognosis but was not an independent risk factor.

CONCLUSIONS

We found that patients in the stroma-rich group had a worse long-term prognosis. The TSR is an independent risk factor for OTSCC patients' outcome. In addition, a risk model that combined the TSR and TB proved to be valuable for predicting OTSCC patients' outcome.

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

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

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

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

Machine Learning Quantified Tumor-Stroma Ratio Is an Independent Prognosticator in Muscle-Invasive Bladder Cancer

Although the tumor-stroma ratio (TSR) has prognostic value in many cancers, the traditional semi-quantitative visual assessment method has inter-observer variability, making it impossible for clinical practice. We aimed to develop a machine learning (ML) algorithm for accurately quantifying TSR in hematoxylin-and-eosin (H&E)-stained whole slide images (WSI) and further investigate its prognostic effect in patients with muscle-invasive bladder cancer (MIBC). We used an optimal cell classifier previously built based on QuPath open-source software and ML algorithm for quantitative calculation of TSR. We retrospectively analyzed data from two independent cohorts to verify the prognostic significance of ML-based TSR in MIBC patients. WSIs from 133 MIBC patients were used as the discovery set to identify the optimal association of TSR with patient survival outcomes. Furthermore, we performed validation in an independent external cohort consisting of 261 MIBC patients. We demonstrated a significant prognostic association of ML-based TSR with survival outcomes in MIBC patients (p < 0.001 for all comparisons), with higher TSR associated with better prognosis. Uni- and multivariate Cox regression analyses showed that TSR was independently associated with overall survival (p < 0.001 for all analyses) after adjusting for clinicopathological factors including age, gender, and pathologic stage. TSR was found to be a strong prognostic factor that was not redundant with the existing staging system in different subgroup analyses (p < 0.05 for all analyses). Finally, the expression of six genes (DACH1, DEEND2A, NOTCH4, DTWD1, TAF6L, and MARCHF5) were significantly associated with TSR, revealing possible potential biological relevance. In conclusion, we developed an ML algorithm based on WSIs of MIBC patients to accurately quantify TSR and demonstrated its prognostic validity for MIBC patients in two independent cohorts. This objective quantitative method allows application in clinical practice while reducing the workload of pathologists. Thus, it might be of significant aid in promoting precise pathology services in MIBC.

Ex vivo explant model of adenoma and colorectal cancer to explore mechanisms of action and patient response to cancer prevention therapies

Colorectal cancer (CRC) is the second leading cause of cancer death in the UK. Novel therapeutic prevention strategies to inhibit the development and progression of CRC would be invaluable. Potential contenders include low toxicity agents such as dietary-derived agents or repurposed drugs. However, in vitro and in vivo models used in drug development often do not take into account the heterogeneity of tumours or the tumour microenvironment. This limits translation to a clinical setting. Our objectives were to develop an ex vivo method utilizing CRC and adenoma patient-derived explants (PDEs) which facilitates screening of drugs, assessment of toxicity, and efficacy. Our aims were to use a multiplexed immunofluorescence approach to demonstrate the viability of colorectal tissue PDEs, and the ability to assess immune cell composition and interactions. Using clinically achievable concentrations of curcumin, we show a correlation between curcumin-induced tumour and stromal apoptosis (P < .001) in adenomas and cancers; higher stromal content is associated with poorer outcomes. B cell (CD20+ve) and T cell (CD3+ve) density of immune cells within tumour regions in control samples correlated with curcumin-induced tumour apoptosis (P < .001 and P < .05, respectively), suggesting curcumin-induced apoptosis is potentially predicted by baseline measures of immune cells. A decrease in distance between T cells (CD3+ve) and cytokeratin+ve cells was observed, indicating movement of T cells (CD3+ve) towards the tumour margin (P < .001); this change is consistent with an immune environment associated with improved outcomes. Concurrently, an increase in distance between T cells (CD3+ve) and B cells (CD20+ve) was detected following curcumin treatment (P < .001), which may result in a less immunosuppressive tumour milieu. The colorectal tissue PDE model offers significant potential for simultaneously assessing multiple biomarkers in response to drug exposure allowing a greater understanding of mechanisms of action and efficacy in relevant target tissues, that maintain both their structural integrity and immune cell compartments.

Correlation of Immunological and Histopathological Features with Gene Expression-Based Classifiers in Colon Cancer Patients

The purpose of this study was to evaluate the association between four distinct histopathological features: (1) tumor infiltrating lymphocytes, (2) mucinous differentiation, (3) tumor-stroma ratio, plus (4) tumor budding and two gene expression-based classifiers—(1) consensus molecular subtypes (CMS) plus (2) colorectal cancer intrinsic subtypes (CRIS). All four histopathological features were retrospectively scored on hematoxylin and eosin sections of the most invasive part of the primary tumor in 218 stage II and III colon cancer patients from two independent cohorts (AMC-AJCC-90 and AC-ICAM). RNA-based CMS and CRIS assignments were independently obtained for all patients. Contingency tables were constructed and a χ2 test was used to test for statistical significance. Odds ratios with 95% confidence intervals were calculated. The presence of tumor infiltrating lymphocytes and a mucinous phenotype (>50% mucinous surface area) were strongly correlated with CMS1 (p < 0.001 and p = 0.008) and CRIS-A (p = 0.006 and p < 0.001). The presence of mucus (≥ 10%) was associated with CMS3: mucus was present in 64.1% of all CMS3 tumors (p < 0.001). Although a clear association between tumor-stroma ratio and CMS4 was established in this study (p = 0.006), still 32 out of 61 (52.5%) CMS4 tumors were scored as stroma-low, indicating that CMS4 tumors cannot be identified solely based on stromal content. Higher budding counts were seen in CMS4 and CRIS-B tumors (p = 0.045 and p = 0.046). No other associations of the measured parameters were seen for any of the other CRIS subtypes. Our analysis revealed clear associations between histopathologic features and CMS or CRIS subtypes. However, identification of distinct molecular subtypes solely based on histopathology proved to be infeasible. Combining both molecular and morphologic features could potentially improve patient 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.

Polarimetric biomarkers of peri-tumoral stroma can correlate with 5-year survival in patients with left-sided colorectal cancer

Using a novel variant of polarized light microscopy for high-contrast imaging and quantification of unstained histology slides, the current study assesses the prognostic potential of peri-tumoral collagenous stroma architecture in 32 human stage III colorectal cancer (CRC) patient samples. We analyze three distinct polarimetrically-derived images and their associated texture features, explore different unsupervised clustering algorithm models to group the data, and compare the resultant groupings with patient survival. The results demonstrate an appreciable total accuracy of ~ 78% with significant separation (p < 0.05) across all approaches for the binary classification of 5-year patient survival outcomes. Surviving patients preferentially belonged to Cluster 1 irrespective of model approach, suggesting similar stromal microstructural characteristics in this sub-population. The results suggest that polarimetrically-derived stromal biomarkers may possess prognostic value that could improve clinical management/treatment stratification in CRC patients.

Tumor microenvironment involvement in colorectal cancer progression via Wnt/β-catenin pathway: Providing understanding of the complex mechanisms of chemoresistance

Colorectal cancer (CRC) continues to be one of the main causes of death from cancer because patients progress unfavorably due to resistance to current therapies. Dysregulation of the Wnt/β-catenin pathway plays a fundamental role in the genesis and progression of several types of cancer, including CRC. In many subtypes of CRC, hyperactivation of the β-catenin pathway is associated with mutations of the adenomatous polyposis coli gene. However, it can also be associated with other causes. In recent years, studies of the tumor microenvironment (TME) have demonstrated its importance in the development and progression of CRC. In this tumor nest, several cell types, structures, and biomolecules interact with neoplastic cells to pave the way for the spread of the disease. Cross-communications between tumor cells and the TME are then established primarily through paracrine factors, which trigger the activation of numerous signaling pathways. Crucial advances in the field of oncology have been made in the last decade. This Minireview aims to actualize what is known about the central role of the Wnt/β-catenin pathway in CRC chemoresistance and aggressiveness, focusing on cross-communication between CRC cells and the TME. Through this analysis, our main objective was to increase the understanding of this complex disease considering a more global context. Since many treatments for advanced CRC fail due to mechanisms involving chemoresistance, the data here exposed and analyzed are of great interest for the development of novel and effective therapies.

Computational portraits of the tumoral microenvironment in human breast cancer

Breast cancer is the most diagnosed cancer in humans. In recent years, myxoid and proportionated stroma have been described as clinically significant in many cancer subtypes. Here computational portraits of tumor-associated stromata were created from a machine learning (ML) classifier using QuPath to evaluate proportionated stromal area (PSA), myxoid stromal ratio (MSR), and immune stroma proportion (ISP) from whole slide images (WSI). The ML classifier was validated in independent training (n = 40) and validation (n = 109) cohorts finding MSR, PSA, and ISP to be associated with tumor stage, lymph node status, Nottingham grade, stromal differentiation (SD), tumor size, estrogen receptor (ER), progesterone receptor (PR), and receptor tyrosine-protein kinase erbB-2 (HER-2). Overall, MSR correlated better with the clinicopathologic profile than PSA and ISP. High MSR was found to be associated with high tumor stage, low ISP, and high Nottingham histologic score. As a computational biomarker, high MSR was more likely to be associated with luminal B like, Her-2 enriched, and triple-negative biomarker status when compared to luminal A like. The supervised ML superpixel approach demonstrated here can be performed by a trained pathologist to provide a faster and more uniformed approach to the analysis to the tumoral microenvironment (TME). The TME may be relevant for clinical decision-making, determining chemotherapeutic efficacy, and guiding a more overall precision-based breast cancer care.

An Updated Review on EPR-Based Solid Tumor Targeting Nanocarriers for Cancer Treatment

Simple Summary

One of the important efforts in the treatment of cancers is to achieve targeted drug delivery by nanocarriers to be more effective and reduce adverse effects. However, due to the adverse responses of nanocarriers in clinical trials due to the very weak EPR effects, doubts have been raised in this regard. In this study, an attempt has been made to take a critical look at EPR approaches to enable the convergence of previous papers and the EPR critics to reach an appropriate therapeutic path. Although the effectiveness of EPR is highly variable due to the complex microenvironment of the tumor, there is high hope for cancer treatment by describing new strategies to overcome the challenges of EPR effect. Furthermore, in this paper an attempt was made to provide a reliable path for future to develop cancer therapeutics based on EPR effect.

Abstract

The enhanced permeability and retention (EPR) effect in cancer treatment is one of the key mechanisms that enables drug accumulation at the tumor site. However, despite a plethora of virus/inorganic/organic-based nanocarriers designed to rely on the EPR effect to effectively target tumors, most have failed in the clinic. It seems that the non-compliance of research activities with clinical trials, goals unrelated to the EPR effect, and lack of awareness of the impact of solid tumor structure and interactions on the performance of drug nanocarriers have intensified this dissatisfaction. As such, the asymmetric growth and structural complexity of solid tumors, physicochemical properties of drug nanocarriers, EPR analytical combination tools, and EPR description goals should be considered to improve EPR-based cancer therapeutics. This review provides valuable insights into the limitations of the EPR effect in therapeutic efficacy and reports crucial perspectives on how the EPR effect can be modulated to improve the therapeutic effects of nanomedicine.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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.

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.

Serum-based measurements of stromal activation through ADAM12 associate with poor prognosis in colorectal cancer

Background

Recently it has been recognized that stromal markers could be used as a clinically relevant biomarker for therapy response and prognosis. Here, we report on a serum marker for stromal activation, A Disintegrin and Metalloprotease 12 (ADAM12) in colorectal cancer (CRC).

Methods

Using gene expression databases we investigated ADAM12 expression in CRC and delineated the source of ADAM12 expression. The clinical value of ADAM12 was retrospectively assessed in the CAIRO2 trial in metastatic CRC with 235 patients (31% of total cohort), and an independent rectal cancer cohort (n = 20).

Results

ADAM12 is expressed by activated CRC associated fibroblasts. In the CAIRO2 trial cohort, ADAM12 serum levels were prognostic (ADAM12 low versus ADAM12 high; median OS 25.3 vs. 17.1 months, HR 1.48 [95% CI 1.11–1.96], P = 0.007). The prognostic potential was specifically high for metastatic rectal cancer (HR 1.78 [95% CI 1.06–3.00], P = 0.030) and mesenchymal subtype tumors (HR 2.12 [95% CI 1.25–3.60], P = 0.004). ADAM12 also showed potential for predicting recurrence in an exploratory analysis of non-metastatic rectal cancers.

Conclusions

Here we describe a non-invasive marker for activated stroma in CRC which associates with poor outcome, especially for primary cancers located in the rectum.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09436-0.

Monocarboxylate Transporters Are Involved in Extracellular Matrix Remodelling in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a five-year survival rate of <8%. PDAC is characterised by desmoplasia with an abundant extracellular matrix (ECM) rendering current therapies ineffective. Monocarboxylate transporters (MCTs) are key regulators of cellular metabolism and are upregulated in different cancers; however, their role in PDAC desmoplasia is little understood. Here, we investigated MCT and ECM gene expression in primary PDAC patient biopsies using RNA-sequencing data obtained from Gene Expression Omnibus. We generated a hypernetwork model from these data to investigate whether a causal relationship exists between MCTs and ECMs. Our analysis of stromal and epithelial tissues (n = 189) revealed nine differentially expressed MCTs, including the upregulation of SLC16A2/6/10 and the non-coding SLC16A1-AS1, and 502 ECMs, including collagens, laminins, and ECM remodelling enzymes (false discovery rate < 0.05). A causal hypernetwork analysis demonstrated a bidirectional relationship between MCTs and ECMs; four MCT and 255 ECM-related transcripts correlated with 90% of the differentially expressed ECMs (n = 376) and MCTs (n = 7), respectively. The hypernetwork model was robust, established by iterated sampling, direct path analysis, validation by an independent dataset, and random forests. This transcriptomic analysis highlights the role of MCTs in PDAC desmoplasia via associations with ECMs, opening novel treatment pathways to improve patient survival.

A Novel Superpixel Approach to the Tumoral Microenvironment in Colorectal Cancer

Colorectal cancer (CRC) is the most common malignancy of the gastrointestinal tract. The stroma and the tumoral microenvironment (TME) represent ecosystem-like biological networks and are new frontiers in CRC. The present study demonstrates the use of a novel machine learning-based superpixel approach for whole slide images to unravel this biology. Findings of significance include the association of low proportionated stromal area, high immature stromal percentage, and high myxoid stromal ratio (MSR) with worse prognostic outcomes in CRC. Overall, stromal computational markers outperformed all others at predicting clinical outcomes. MSR may be able to prognosticate patients independent of pathological stage, representing an optimal way to effectively prognosticate CRC patients which circumvents the need for more extensive molecular and/or computational profiling. The superpixel approaches to the TME demonstrated here can be performed by a trained pathologist and recorded during synoptic cancer reporting with appropriate quality assurance. Future clinical trials will have the ultimate say in determining whether we can better tailor the need for adjuvant therapy in patients with CRC.

Predictive Role of Tumor-Stroma Ratio for Survival of Patients With Non-Small Cell Lung Cancer: A Meta-Analysis

Background: Role of tumor-stroma ratio (TSR) as a predictor of survival in patients with non-small cell lung cancer (NSCLC) remains not clear. A systematic review and meta-analysis was conducted to summarize current evidence for the role of TSR in NSCLC.

Methods: Relevant cohort studies were retrieved via search of Medline, Embase, and Web of Science databases. The data was combined with a random-effect model by incorporating the between-study heterogeneity. Specifically, subgroup and meta-regression analyses were performed to explore the association between TSR and survival in patients with squamous cell carcinoma (SCC) or adenocarcinoma (AC).

Results: Nine cohort studies with 2031 patients with NSCLC were eligible for the meta-analysis. Pooled results showed that compared to those stroma-poor tumor, patients with stroma rich NSCLC were associated with worse recurrence-free survival (RFS, hazard ratio [HR] = 1.52, 95% confidence interval [CI]: 1.07 to 2.16, p = 0.02) and overall survival (OS, HR = 1.48, 95% CI: 1.20 to 1.82, p < 0.001). Subgroup analyses showed that stroma-rich tumor may be associated with a worse survival of SCC (HR = 1.89 and 1.47 for PFS and OS), but a possibly favorable survival of AC (HR = 0.28 and 0.69 for PFS and OS). Results of meta-regression analysis also showed that higher proportion of patients with SCC was correlated with higher HRs for RFS (Coefficient = 0.012, p = 0.03) and OS (Coefficient = 0.014, p = 0.02) in the included patients, while higher proportion of patients with AC was correlated with lower HRs for RFS (Coefficient = −0.012, p = 0.03) and OS (Coefficient = −0.013, p = 0.04), respectively.

Conclusion: Tumor TSR could be used as a predictor of survival in patients with NSCLC. The relative proportion of patients with SCC/AC in the included NSCLC patients may be an important determinant for the association between TSR and survival in NSCLC. Stroma richness may be a predictor of poor survival in patients with lung SCC, but a predictor of better survival in patients with lung AC.

[Multiplex fluorescent imaging of cancer-associated fibroblasts in colorectal cancer]

BACKGROUND

Cancer-associated fibroblasts (CAFs) are a heterogeneous cell population in the tumor stroma and have important prognostic and clinical significance for solid tumors, including colorectal cancer. The identification of CAF presents difficulties due to the lack of a unique diagnostic marker.

OBJECTIVE

Detection of CAF by multiplex immunohistochemical staining and assessment of their colocalization.

MATERIAL AND METHODS

For multiplex IHC staining specimens of 10 colon adenocarcinomas without neoadjuvant treatment were selected. We used «OPAL 7-COLOR MANUAL IHC KIT» (Akoya Biosciences, USA) with five antibodies (FAP, PDGFRβ, CD31, POD, PCK) for staining and Mantra 2 Quantitative Pathology Imaging System (Akoya Biosciences, USA) for evaluation of results.

RESULTS

CD31 and CAF markers (FAP, PDGFRβ, POD) are expressed fundamentally in different cells (p<0.0001) in all areas of the tumor (apical, central, invasive margin). Pairs FAP+PDGFRβ in all zones demonstrated significantly higher (p<0.0001) square of tandem staining. It shows that these markers are expressed in the same stromal cells (probably CAF). In pair FAP+POD significant colocalization (p=0.011) was detected only in apical zone. We connect this finding rather with active proliferation of population of young fibroblasts in zones of ulceration and granulations than with CAF.

CONCLUSION

We evaluated co-localization of CAF markers (FAP, PDGFRβ, POD) and endothelial cells (CD31) in different zones of colorectal carcinomas. We showed colocalization of CAF markers for pairs FAP+PDGFRβ in all tumor zones and for pair FAP+POD in apical zone.

The Stroma Liquid Biopsy Panel Contains a Stromal-Epithelial Gene Signature Ratio That Is Associated with the Histologic Tumor-Stroma Ratio and Predicts Survival in Colon Cancer

Liquid biopsy has emerged as a novel approach to tumor characterization, offering advantages in sample accessibility and tissue heterogeneity. However, as mutational analysis predominates, the tumor microenvironment has largely remained unacknowledged in liquid biopsy research. The current work provides an explorative transcriptomic characterization of the Stroma Liquid Biopsy^TM (SLB) proteomics panel in colon carcinoma by integrating single-cell and bulk transcriptomics data from publicly available repositories. Expression of SLB genes was significantly enriched in tumors with high histologic stromal content in comparison to tumors with low stromal content (median enrichment score 0.308 vs. 0.222, p = 0.036). In addition, we identified stromal-specific and epithelial-specific expression of the SLB genes, that was subsequently integrated into a gene signature ratio. The stromal-epithelial signature ratio was found to have prognostic significance in a discovery cohort of 359 colon adenocarcinoma patients (OS HR 2.581, 95%CI 1.567-4.251, p < 0.001) and a validation cohort of 229 patients (OS HR 2.590, 95%CI 1.659-4.043, p < 0.001). The framework described here provides transcriptomic evidence for the prognostic significance of the SLB panel constituents in colon carcinoma. Plasma protein levels of the SLB panel may reflect histologic intratumoral stromal content, a poor prognostic tumor characteristic, and hence provide valuable prognostic information in liquid biopsy.