Stroma Percentage Research Articles

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

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

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

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

Tumor Budding, Poorly Differentiated Clusters, and Stroma Percentage in Uterine Endometrioid Carcinomas Are Morphologic Findings Associated With Adverse Clinical Outcomes

Uterine endometrioid carcinoma (UEC) has well-defined morphologic features that carry prognostic significance and guide treatment. In addition to the well-known features, tumor budding (TB), poorly differentiated clusters (PDCs), and amount of stroma within the invasive front are associated with a poor prognostic outcome in many carcinomas. Here, we evaluate TB, PDCs, and the stroma percentage in UEC and correlate these findings with morphologic features known to be associated with a poor clinical outcome and with recurrence-free survival (RFS). We performed a retrospective search of our institution’s EMR for cases of hysterectomy for UEC. We collected clinical data including disease recurrence, death, and data associated with poor patient outcomes. Cases were evaluated for TB, PDCs, and percent stroma by 2 pathologists. Ten 20× fields were examined, and the one with the most PDCs and TB was evaluated. Percent stroma was evaluated in a ×10 field of tumor at the area of deepest invasion. Less than 10% stroma was defined as stroma poor, and &gt;10% stroma was defined as stroma rich. Statistical testing and analysis were conducted, and P-value was set at 0.05. One hundred thirteen cases were evaluated. Decreased RFS was seen with TB (P=0.03), at least 5 PDCs (P=0.01), and stroma-rich tumors (P=0.043). This study shows a statistically significant association between TB presence, at least 5 PDCs, stroma-rich tumors, and decreased RFS in UEC. We conclude that TB, PDCs and stromal evaluation are strong independent prognostic indicators in UEC and provide additional value to the currently used morphologic assessment of UEC.

Phenotypic subtypes predict outcomes in colorectal cancer

BackgroundColorectal 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.MethodsWe 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.ResultsPatients 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.ConclusionsPhenotypic 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.

Complimentary genomic, pathologic, and artificial intelligence analysis on low-grade noninvasive bladder cancer to predict downstream recurrence.

553 Background: Low-grade noninvasive (LGTa) bladder cancer is a relatively quiescent but heterogenous malignancy, characterized by downstream recurrences requiring repeated transurethral resections and frequent surveillance. Investigations to elucidate drivers of recurrence have been sparse, but will help risk-stratify patients with LGTa and allow augmentation of follow up protocols. Methods: Patients with LGTa index tumors were stratified by those with no downstream recurrences (nonrecurrent) vs. those with later recurrences (recurrent). RNA sequencing identified differentially expressed genes (DEGs), deconvoluted for cell-type using xCell. Pathologic analysis was performed by a genitourinary pathologist, then a deep-learning artificial intelligence (AI) platform was leveraged to correlate recurrence risk and recurrence-free survival (RFS) based on deep-learning algorithm of segmented nuclei. Results: Thirty index bladder tumors/patients were identified, 18 (60%) of which had later recurrence (Table). There were 238 DEGs recognized, with recurrent tumors expressing signatures for epithelial mesenchymal transition, myogenesis, TNFα signaling via NFκB, and angiogenesis. Recurrent tumors also demonstrated a higher tissue micoenvironment, stroma, and cancer-associated fibroblast score. Pathologic TME analysis validated these findings, with recurrent tumors demonstrating a higher frequency of inverted growth pattern and a higher median stroma percentage. Finally, the AI-derived signature was predictive of recurrence and risk-stratified the cohort (HR= 5.43 [95% CI 1.1-26.76]) for predicting high vs. low risk of recurrence. Patients in the high risk group had a 87.5% recurrence rate while those in the low risk group had a 28.5% recurrence rate (p&lt;0.01). Conclusions: Using a multi-disciplinary approach, we identified key signatures in recurrent LGTa bladder cancer. Characterization of these factors is a critical first step in the risk-stratification of LGTa tumors, and may allow risk-stratification of surveillance protocols and identification of possible targets for chemoprevention trials. [Table: see text]

Utility of Glasgow Microenvironment Score as a prognostic tool in colorectal carcinoma.

Colorectal carcinoma (CRC) is third most common malignancy in the world. The presence of Lymphocytes particularly at the invasive margin of the tumor have been associated with good immune response indicating better prognosis. The relative tumor stroma is also important in deciding the course of the disease. The Glasgow Microenvironment Score (GMS) comprises of assessment of tumor cell infiltrate using Klintrup-Makinen (KM) grade and tumor stroma percentage. The aim of the present study is to evaluate the utility of GMS score in relation to parameters of adverse histopathological outcome in carcinoma colon that is grading, staging, LVI, PNI and nodal metastasis. Colectomy specimens received over 3 year period were subjected to microscopic evaluation for LVI, PNI, grade, stage & lymph node metastasis. Lymphocytes at the deepest invasive margin of tumor were counted as per KM score in 5 HPF by two independent pathologists . Patients were classified as Low grade (0/1) or high grade (2/3) response. Tumor stroma percentage was calculated as stroma poor (<50%) and stroma rich (>/= 50 %). The GMS was scored by combining the two and scored 0, 1 and 2. Total 37 patients with no prior therapy were included out of which 23 were males and 14 females. 15 patients (40.54%) had GMS of 0, 6 (16.21%) had GMS 1 and 16 (43.24%) had GMS 2. A high GMS was associated with LVI (P = 0.02), PNI (P = 0.01) lymph node metastasis (P = 0.003). However, no significant association was found between GMS with Grade (P = 0.98) and Stage (P = 0.36). Low GMS was associated with good outcome and high GMS with poor outcome. This score can be used for risk stratification, can be of clinical utility and may be applied to pathological descriptions of CRC.

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

BackgroundGlasgow Microenvironment Score (GMS) stratifies long-term survival into three groups based on tumour phenotype: peritumoural inflammation (Klintrup–Mäkinen (KM)) and tumour stroma percentage (TSP). However, it is not known if the location of disease recurrence is influenced by the GMS category.MethodsSeven hundred and eighty-three TNM I–III colorectal cancers (CRC) were included. GMS (GMS0—high KM; GMS1—low KM, low TSP; GMS2—low KM, high TSP) and cancer-specific survival (CSS), overall survival (OS) and disease recurrence were assessed using Cox regression analysis.ResultsOf the 783 patients, 221 developed CRC recurrence; 65 developed local recurrence + systemic disease. GMS was independent for CSS (HR 1.50, 95% CI 1.17–1.92, p < 0.001) and OS (HR 1.23, 1.05–1.44, p = 0.01). Higher GMS category was associated with T-stage, N-stage, emergency presentation and venous invasion. GMS was independent for local+systemic recurrence (HR 11.53, 95% CI 1.45–91.85, p = 0.04) and distant-only recurrence (HR 3.01, 95% CI 1.59–5.71, p = 0.002). GMS 2 disease did not appear to have statistically better outcomes with adjuvant chemotherapy in high-risk disease.ConclusionAlthough confounded by a higher rate of T4 and node-positive disease, GMS 1 and 2 are associated with an increased risk of local and distant recurrence. GMS is an independent poor prognostic indicator for recurrent colorectal cancer. Higher GMS patients may benefit from enhanced postoperative surveillance.

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

BackgroundTertiary lymphoid structures (TLSs) are crucial in promoting and maintaining positive anti-tumor immune responses. The tumor stroma has a powerful immunosuppressive function that could exclude tumor-infiltrating lymphocytes from the tumor beds and lead to a “cold” phenotype. TLSs and tumor stroma percentage (TSP) are significantly associated with the prognosis of patients with certain cancers. However, the exact roles of TLSs and TSP and their intrinsic relationship are still largely unknown in colorectal cancer (CRC).MethodsTLSs and TSP were assessed using hematoxylin-eosin (H&E) and/or immunohistochemistry (IHC) staining from 114 CRC patients in the training set and 60 CRC patients in the external validation set. The correlation between TILs, TLS and clinicopathological characteristics and their prognostic values were assessed. Finally, we plotted a Nomogram including the TLS, TSP and tumor-node-metastasis (TNM) stage to predict the probability of recurrence-free survival (RFS) at 2- and 5-years in non-metastatic colorectal cancer (nmCRC) patients.ResultsPeritumoral TLS (P-TLS), intratumoral TLS (In-TLS) and high TSP (H-TSP, >50%) were present in 99.1%, 26.3% and 41.2% patients, respectively. H-TSP tumor tends to be associated with lower P-TLS density (P =0.0205). The low P-TLS density (< 0.098/mm2) was significantly associated with reduced RFS (HR=6.597 95% CI: 2.882-15.103, P <0.001) and reduced overall survival (OS) (HR=6.628 95% CI: 2.893-15.183, P < 0.001) of nmCRC patients. In-TLS was not of significance in evaluating the clinical outcomes of nmCRC patients. H-TSP was significantly associated with reduced RFS (HR=0.126 95% CI: 0.048-0.333, P <0.001) and reduced OS (HR=0.125 95% CI: 0.047-0.332, P <0.001) of nmCRC patients. The 5-year RFS of the high P-TLS, low-TLS, H-TSP, and L-TSP groups were 89.7%, 47.2%, 53.2%, and 92.5%, respectively. The P-TLS density, TSP and TNM stage were independent prognosis factors of nmCRC patients. The Nomogram, including the P-TLS density, TSP and TNM stage, outperformed the TNM stage.ConclusionsHigh P-TLS density and low TSP (L-TSP) were independent and favorable prognostic factors of nmCRC patients, which might provide new directions for targeted therapy in the CRC tumor microenvironment, especially the tumor immune microenvironment.

311P Differences in stromal component of chordoma are associated with contrast enhancement in MRI and differential gene expression in RNA sequencing

A recent study suggested that stroma percentage may have an important clinical impact on chordoma as it may be associated with tumor progression and treatment response. Considering that radiologic features, such as contrast enhancement and T2 signal intensity, are closely related with tumor component, we hypothesized that stroma and tumor cell component in chordomas may be associated with radiologic features visible on MRI and further associated with different histopathological features and genetic expressions. Therefore, we aimed to investigate whether chordomas with different stroma component show radiologic and genetic differences to obtain a better understanding of the heterogeneity of the disease. A total of 45 patients diagnosed with chordoma were selected between May 2011 and March 2020 from a single institute. Electronic medical records, pathology slides, and pretreatment magnetic resonance imaging (MRI) scans were reviewed. Of the 45 patients, 10 (4 stroma-rich and 6 stroma-poor chordomas) were further analyzed using RNA sequencing. Differential gene expression and gene set analysis were performed. There were 25 (55.6%) stroma-rich and 20 (44.4%) stroma-poor chordomas. No clinical differences were found between the two groups. Radiologically, stroma-rich chordomas showed significant signal enhancement on MRI. Based on the results of RNA sequencing, the upregulated genes in stroma-rich chordomas were cartilage-, collagen/extracellular matrix-, and tumor metastasis/progression-associated genes. Contrarily, tumor suppressor genes were downregulated in stroma-rich chordomas. In conclusion, the stromal component of chordoma differentially affects radiologic features and defines different genetic subgroups of chordoma. However, additional investigations with a larger cohort are needed to verify its clinical significance.

Biological Misinterpretation of Transcriptional Signatures in Tumor Samples Can Unknowingly Undermine Mechanistic Understanding and Faithful Alignment with Preclinical Data.

Precise mechanism-based gene expression signatures (GES) have been developed in appropriate in vitro and in vivo model systems, to identify important cancer-related signaling processes. However, some GESs originally developed to represent specific disease processes, primarily with an epithelial cell focus, are being applied to heterogeneous tumor samples where the expression of the genes in the signature may no longer be epithelial-specific. Therefore, unknowingly, even small changes in tumor stroma percentage can directly influence GESs, undermining the intended mechanistic signaling. Using colorectal cancer as an exemplar, we deployed numerous orthogonal profiling methodologies, including laser capture microdissection, flow cytometry, bulk and multiregional biopsy clinical samples, single-cell RNA sequencing and finally spatial transcriptomics, to perform a comprehensive assessment of the potential for the most widely used GESs to be influenced, or confounded, by stromal content in tumor tissue. To complement this work, we generated a freely-available resource, ConfoundR; https://confoundr.qub.ac.uk/, that enables users to test the extent of stromal influence on an unlimited number of the genes/signatures simultaneously across colorectal, breast, pancreatic, ovarian and prostate cancer datasets. Findings presented here demonstrate the clear potential for misinterpretation of the meaning of GESs, due to widespread stromal influences, which in-turn can undermine faithful alignment between clinical samples and preclinical data/models, particularly cell lines and organoids, or tumor models not fully recapitulating the stromal and immune microenvironment. Efforts to faithfully align preclinical models of disease using phenotypically-designed GESs must ensure that the signatures themselves remain representative of the same biology when applied to clinical samples.

Tumor budding in gastric adenocarcinoma; reflections on tumor microenvironment and programmed death ligand 1 (PD-L1) expression

Introduction: The significance of tumor budding and programmed death ligand 1 (PD-L1) has not been established in gastric adenocarcinoma (GAC). Objectives: This study evaluated tumor budding and PD-L1 expression with regard to tumor microenvironment, clinicopathologic parameters, and overall survival in GAC. Patients and Methods: Totally, 102 GAC cases were assessed immunohistochemically. The associations of tumor budding and PD-L1 with clinicopathologic features, tumor-infiltrating lymphocytes (TILs), tumor stroma percentage (TSP), and overall survival were analyzed. Results: High tumor budding (42.2% of cases) was correlated with distal tumor location, large tumor size, Helicobacter pylori infection, poor differentiation (P = 0.0008, 0.033, 0.011, and 0.005, respectively), lymphovascular invasion, high tumor and nodal stages, and TSP (all P &lt; 0.0001). Tumor budding was highest in the low TILs/high TSP group. PD-L1 expression (43.1% of cases) was correlated with proximal location (p = 0.00021), poor differentiation (P = 0.036), N stage (P = 0.049), high TILs (P &lt; 0.0001), and low tumor budding (P = 0.002). PD-L1 expression was highest in the low tumor budding / high TILs category (P &lt; 0.0001). Cox regression showed that high tumor budding (hazard ratio [HR]: 15.282, P = 0.024, 95% confidence interval [CI]: 1.441–162.069) and positive PD-L1 (HR: 7.502, P = 0.015, 95% CI: 1.469–38.31) were independent prognostic factors for overall survival. Conclusion: Tumor budding is correlated with poor prognostic parameters, whereas PD-L1 expression is inversely correlated with tumor budding. Both are independent predictors of short overall survival. Anti-PD-L1 immunotherapy could be effective in GAC with nodal metastasis, especially cases with high TILs and low tumor budding.

Development and Validation of the Individualized Prognostic Nomograms in Patients With Right- and Left-Sided Colon Cancer.

BackgroundThe overall survival (OS) of patients diagnosed with colon cancer (CC) varied greatly, so did the patients with the same tumor stage. We aimed to design a nomogram that is capable of predicting OS in resected left-sided colon cancers (LSCC) and right-sided colon cancers (RSCC), and thus to stratify patients into different risk groups, respectively.MethodsRecords from a retrospective cohort of 577 patients with complete information were used to construct the nomogram. Univariate and multivariate analyses screened risk factors associated with overall survival. The performance of the nomogram was evaluated with concordance index (c-index), calibration plots, and decision curve analyses for discrimination, accuracy, calibration ability, and clinical net benefits, respectively, which was further compared with the American Joint Committee on Cancer (AJCC) 8th tumor-node-metastasis (TNM) classification. Risk stratification based on nomogram scores was performed with recursive partitioning analysis.ResultsThe LSCC nomogram incorporated carbohydrate antigen 12-5 (CA12-5), age and log odds of positive lymph nodes (LODDS), and RSCC nomogram enrolled tumor stroma percentage (TSP), age and LODDS. Compared with the TNM classification, the LSCC and RSCC nomograms both had a statistically higher C-index (0.837, 95% CI: 0.827–0.846 and 0.780, 95% CI 0.773–0.787, respectively) and more clinical net benefits, respectively. Calibration plots revealed no deviations from reference lines. All results were reproducible in the validation cohort.ConclusionsAn original predictive nomogram was constructed and validated for OS in patients with CC after surgery, which had facilitated physicians to appraise the individual survival of postoperative patients accurately and to identify high-risk patients who were in need of more aggressive treatment and follow-up strategies.

Abstract PO-001: Deep learning-based image analysis of the histological Glasgow Microenvironment Score in patients with colorectal cancer

Abstract Background: Efforts to prognostically stratify colorectal cancer (CRC) patients using histological samples has led to the development of the Glasgow Microenvironment Score (GMS). However, the subjective nature of the GMS assessment criteria makes reproducing the score challenging. Therefore, the aim of the current study was to assess the clinical utility of an image analysis-based approach. Methods: In two cohorts of patients (a test cohort of 297 Stage II/III CRC patients from Norway and a validation cohort of 233 Stage II/III CRC patients from Scotland), one H&amp;E section representing the deepest point of tumour invasion was assessed for Tumour Stroma Percentage (TSP) and the Klintrup-Makinen (KM) local immune grade to produce the GMS. A decision forest-based classifier was built to segment the tumour and stroma compartments, having been trained on a hand annotated subset of the test cohort. A UNET convolutional neural network was trained on the same training set as the decision forest algorithm to segment and quantify the tumour and stroma components. Each section was also annotated for the invasive margin and a manually built, threshold-based lymphocyte detection algorithm applied to quantify the local immune grade. Results: The decision forest classifier was able to stratify the test cohort patients for cancer specific survival (P = 0.009) based on the patients TSP, however failed to stratify patients from the validation cohort (P = 0.055) for TSP without retraining, therefore the deep learning approach was used for this cohort. Individually, the TSP (P = 0.001) generated from the UNET based classifier and the quantification of the local immune grade (P = 0.005), both significantly stratified validation cohort patients for survival. When the individual scores were combined into a digital version of the GMS, not only did the image analysis approach significantly stratify patient survival (P &amp;lt; 0.0001), it also significantly correlated with the manual scores both statistically (Chi Square = 32, P = 1.5 × 10−8) and in terms of survival estimates per patient group (GMS 0 - manual = 159 months vs digital = 159 months; GMS 1 – manual = 136 months vs digital = 139 months ; GMS2 – manual 101 months vs 97 months). To negate the need for any annotation prior to analysis, the lymphocyte detection algorithm was applied to the stroma areas determined by the UNET algorithm. This fully automated digital version of the GMS was able to stratify validation cohort patients into the 3 prognostic groups based on CSS (P &amp;lt; 0.001), however, GMS 0 had a lower survival estimate (automated = 148 vs annotated = 159 months). Conclusion: An H&amp;E based image analysis approach to the GMS, on a single H&amp;E slide, holds prognostic significance for CRC patients and correlates significantly with manual pathological assessment, making the assessment of the GMS more time efficient, reproducible, and clinically relevant. Citation Format: Christopher J. Bigley, John Waller, Alison Bigley, Karin Oien, Joanne Edwards, Antonia Roseweir. Deep learning-based image analysis of the histological Glasgow Microenvironment Score in patients with colorectal cancer [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-001.

Rapid Multi-Dynamic Algorithm for Gray Image Analysis of the Stroma Percentage on Colorectal Cancer

Background: Tumor stroma percentage (TSP), as an independent, low-cost prognostic factor, could complement current pathology and act as a more feasible risk factor for prognosis. The objective of this study was to investigate the prognostic significance of TSP in a robust rapid multi-dynamic approach with the application of MATLAB and threshold Algorithm for Gray Image analysis. Methods: Using a retrospective collection of 1539 colorectal cancer patients comprising three independent cohorts; one SGH cohort (N=996) and two validation cohorts (N =106, N= 437) from 2 institutions. We investigated 996 colorectal cancer of no special type. According to our established thresholds, 357 cases (35.84%) were classified as TSP-high and 639 cases (64.16%) as TSP-low. We determined the gray image area as the stromal part of the WSI and calculated the stroma percentage with our proposed method on MATLAB software. Finding: In both TSP-cad(50%) and TSP-cad(median), multivariate analysis showed the TSP-cad was an independent prognostic factor for the vessel invasion and tumor location. For OS, TSP-manual HR=1.512 (95% CI 1.045-2.187); TSP-cad HR=1.443 (95% CI 0.993-2.097) and TSP-cad(median) HR=1.632 (95% CI 1.105-2.410). Fortunately, TSP-manual and TSP-cad were also found independent prognostic factor in all the cohorts. It was found that TSP-cad had slightly higher HR and wider CI than TSP-manual. The optimal cut-off is less than previous internationally recognized cut-off of 50% in CRC. Interpretation: Using computerised gray image analysis and rapid multi-dynamic algorithms, our research showed that TSP was an independent prognostic factor in CRC. Threshold algorithm for the quantitation of tumor stroma percentage could be established. With this Rapid multi-dynamic threshold Algorithm for Gray Image counting of TSP, which showed a higher accuracy than manual evaluation by pathologists. Funding Statement: This work was supported by the Fudan Medical Device project [20275,DGF501021-01], Independent Research fund of Key Laboratory of Industrial Dust Prevention and Control & Occupational Health and Safety, Ministry of Education (Anhui University of Science and Technology) [NO.EK20201003], National Natural Science Foundation of China [81772526], National Natural Science Foundation of China [82072662], Shanghai Municipal Education Commission-Gaofeng Clinical Medicine [20161425], Shanghai Jiaotong University Medical Cross Fund [YG2017MS28], Three-year Action Plan for Clinical Skills and Clinical Innovation in Shanghai-level Hospitals [SHDC2020CR4022]. Declaration of Interests: The authors have declared no conflicts of interest. Ethics Approval Statement: Written informed consent was obtained from each patient before enrolling in the study. The use of human hematoxylin & eosin(H&E) stained sections of specimens were approved by the Ethical Committee for Clinical Research of Shanghai General Hospital.

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

Background: Tumor stroma percentage (TSP), as an independent, low-cost prognostic factor, could complement current pathology and act as a more feasible risk factor for prognosis. However, TSP hadn't been applied into TNM staging. Here, the objective of our study was to investigate the prognostic significance of TSP in a robust rapid multi-dynamic approach with the application of MATLAB and threshold Algorithm for Gray Image analysis.Methods: Using a retrospective collection of 1539 CRC patients comprising three independent cohorts; one SGH cohort (N=996) and two validation cohorts (N =106, N= 437) from 2 institutions. We investigated 996 CRC of no special type. According to our established thresholds, 357 cases (35.84%) were classified as TSP-high and 639 cases (64.16%) as TSP-low. We determined the gray image area as the stromal part of the WSI and calculated the stroma percentage with our proposed method on MATLAB software.Results: In both TSP-cad(50%) and TSP-cad(median), multivariate analysis showed the TSP-cad was an independent prognostic factor for the vessel invasion and tumor location. For OS, TSP-manual HR=1.512 (95% CI 1.045-2.187); TSP-cad HR=1.443 (95% CI 0.993-2.097) and TSP-cad(median) HR=1.632 (95% CI 1.105-2.410). Fortunately, TSP-manual and TSP-cad were also found independent prognostic factor in all the cohorts. It was found that TSP-cad had slightly higher HR and wider CI than TSP-manual.Conclusions: Our research showed that TSP was an independent prognostic factor in CRC. Moreover, threshold algorithm for the quantitation of TSP could be established. In conclusion, with this Rapid multi-dynamic threshold Algorithm for Gray Image counting of TSP, which showed a higher accuracy than manual evaluation by pathologists and could be a practical method for CRC to guide clinical decision making.

The Glasgow Microenvironment Score associates with prognosis and adjuvant chemotherapy response in colorectal cancer

BackgroundThe Glasgow Microenvironment Score (GMS) combines peritumoural inflammation and tumour stroma percentage to assess interactions between tumour and microenvironment. This was previously demonstrated to associate with colorectal cancer (CRC) prognosis, and now requires validation and assessment of interactions with adjuvant therapy.MethodsTwo cohorts were utilised; 862 TNM I–III CRC validation cohort, and 2912 TNM II–III CRC adjuvant chemotherapy cohort (TransSCOT). Primary endpoints were disease-free survival (DFS) and relapse-free survival (RFS). Exploratory endpoint was adjuvant chemotherapy interaction.ResultsGMS independently associated with DFS (p = 0.001) and RFS (p < 0.001). GMS significantly stratified RFS for both low risk (GMS 0 v GMS 2: HR 3.24 95% CI 1.85–5.68, p < 0.001) and high-risk disease (GMS 0 v GMS 2: HR 2.18 95% CI 1.39–3.41, p = 0.001). In TransSCOT, chemotherapy type (pinteraction = 0.013), but not duration (p = 0.64) was dependent on GMS. Furthermore, GMS 0 significantly associated with improved DFS in patients receiving FOLFOX compared with CAPOX (HR 2.23 95% CI 1.19–4.16, p = 0.012).ConclusionsThis study validates the GMS as a prognostic tool for patients with stage I–III colorectal cancer, independent of TNM, with the ability to stratify both low- and high-risk disease. Furthermore, GMS 0 could be employed to identify a subset of patients that benefit from FOLFOX over CAPOX.

Prognostic effects of histology-based tumour microenvironment scores in resected distal bile duct cancer.

Histology-based tumour microenvironment (TME) scores are useful in predicting the prognosis of gastrointestinal cancer. However, their prognostic roles in distal bile duct cancer (DBDC) have not been previously studied. This study aimed to evaluate the prognostic significance of the TME scores using the Klintrup-Mäkinen (KM) grade, tumour stroma percentage (TSP) and the Glasgow microenvironment score (GMS) in resected DBDC. Eighty-one patients with DBDC who underwent curative resection were enrolled. DBDC was graded according to KM grade, TSP and GMS. A high KM grade was found in 19patients (24%) and a high TSP was found in 47patients (58%). A high TSP was significantly correlated with a low KM grade (P<0.001). The distribution of the GMS, which was developed by combining the KM grade and TSP, was as follows: 0 (n=19, 24%), 1 (n=19, 24%) and 2 (n=43, 52%). A low KM grade, high TSP and high GMS were significantly associated with short overall survival (OS) (P<0.001) and relapse-free survival (RFS) (P<0.001). Furthermore, multivariate analysis showed that a low KM grade [hazard ratio (HR)=3.826; confidence interval (CI)=1.650-8.869; P=0.014], high TSP (HR=2.193; CI=1.173-4.100, P=0.002) and high GMS (HR=7.148; CI=2.811-18.173) were independent prognostic factors for short RFS; a low KM grade (HR=4.324; CI=1.594-11.733) and high GMS (HR=6.332; CI=2.743-14.594) were independent prognostic factors for short OS. Histology-based TME scores, including the KM grade, TSP and GMS, are useful for predicting the survival of patients with resected DBDC.

Diffusion Kurtosis Imaging-A Superior Approach to Assess Tumor-Stroma Ratio in Pancreatic Ductal Adenocarcinoma.

Extensive desmoplastic stroma is a hallmark of pancreatic ductal adenocarcinoma (PDAC) and contributes to tumor progression and to the relative resistance of tumor cells towards (radio) chemotherapy. Thus, therapies that target the stroma are under intense investigation. To allow the stratification of patients who would profit from such therapies, non-invasive methods assessing the stroma content in relation to tumor mass are required. In the current prospective study, we investigated the usefulness of diffusion-weighted magnetic resonance imaging (DW-MRI), a radiologic method that measures the random motion of water molecules in tissue, in the assessment of PDAC lesions, and more specifically in the desmoplastic tumor stroma. We made use of a sophisticated DW-MRI approach, the so-called diffusion kurtosis imaging (DKI), which possesses potential advantages over conventional and widely used monoexponential diffusion-weighted imaging analysis (cDWI). We found that the diffusion constant D from DKI is highly negatively correlated with the percentage of tumor stroma, the latter determined by histology. D performed significantly better than the widely used apparent diffusion coefficient (ADC) from cDWI in distinguishing stroma-rich (>50% stroma percentage) from stroma-poor tumors (≤50% stroma percentage). Moreover, we could prove the potential of the diffusion constant D as a clinically useful imaging parameter for the differentiation of PDAC-lesions from non-neoplastic pancreatic parenchyma. Therefore, the diffusion constant D from DKI could represent a valuable non-invasive imaging biomarker for assessment of stroma content in PDAC, which is applicable for the clinical diagnostic of PDAC.

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

BackgroundPreoperative prediction of lymph node (LN) status is integral to determining the most appropriate treatment strategy for colorectal cancer (CRC). This study aimed to develop and validate a nomogram to predict LN metastasis in CRC preoperatively.MethodsA total of 530 patients were enrolled and divided into training and validation cohorts. The tumour stroma percentage (TSP) of the preoperative biopsies was assessed. The risk factors for LN metastasis were selected, and a nomogram was constructed subsequently. The performance of the nomogram was assessed by using the AUROC and the calibration curve, and then validated in the validation cohort.ResultsHigh TSP was significantly associated with LN metastasis in both the training and validation cohorts. Computed tomography (CT)-reported T stage, CT-reported LN status, preoperative tumour differentiation, carcinoembryonic antigen, carbohydrate antigen 19-9 and TSP were independent predictors of LN metastasis in CRC. A nomogram incorporating the six predictors was constructed. The nomogram yielded good discrimination and calibration, with an AUROC of 0.846 (95% CI: 0.807−0.886) and 0.809 (95% CI: 0.745−0.872) in the training and validation cohorts, respectively.ConclusionsAssessment of TSP in the preoperative biopsies provided additional information about the LN status. The nomogram was useful for tailored therapy in CRC preoperatively.

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

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