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.

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

INTRODUCTION: Esophageal cancer is the seventh most common cancer worldwide and typically tends to manifest at an older age. Marked heterogeneity in time-dependent functional decline in older adults results in varying grades of clinically manifest patient fitness or frailty. The biological age-related adaptations that accompany functional decline have been shown to modulate the non-malignant cells comprising the tumor microenvironment (TME). In the current work, we studied the association between biological age and TME characteristics in patients with esophageal adenocarcinoma. METHODS: We comparatively assessed intratumoral histologic stroma quantity, tumor immune cell infiltrate, and blood leukocyte and thrombocyte count in 72 patients stratified over 3 strata of biological age (younger <70 years, fit older ≥70 years, and frail older adults ≥70 years), as defined by a geriatric assessment. RESULTS: Frailty in older adults was predictive of decreased intratumoral stroma quantity (B = -14.66% stroma, p = 0.022) relative to tumors in chronological-age-matched fit older adults. Moreover, in comparison to younger adults, frail older adults (p = 0.032), but not fit older adults (p = 0.302), demonstrated a lower blood thrombocyte count at the time of diagnosis. Lastly, we found an increased proportion of tumors with a histologic desert TME histotype, comprising low stroma quantity and low immune cell infiltration, in frail older adults. CONCLUSION: Our results illustrate the stromal-reprogramming effects of biological age and provide a biological underpinning for the clinical relevance of assessing frailty in patients with esophageal adenocarcinoma, further justifying the need for standardized geriatric assessment in geriatric cancer patients.

Serum N-Glycosylation RPLC-FD-MS Assay to Assess Colorectal Cancer Surgical Interventions.

A newly developed analytical strategy was applied to profile the total serum N-glycome of 64 colorectal cancer (CRC) patients before and after surgical intervention. In this cohort, it was previously found that serum N-glycome alterations in CRC were associated with patient survival. Here, fluorescent labeling of serum N-glycans was applied using procainamide and followed by sialic acid derivatization specific for α2,6- and α2,3-linkage types via ethyl esterification and amidation, respectively. This strategy allowed efficient separation of specific positional isomers on reversed-phase liquid chromatography-fluorescence detection-mass spectrometry (RPLC-FD-MS) and complemented the previous glycomics data based on matrix-assisted laser desorption/ionization (MALDI)-MS that did not include such separations. The results from comparing pre-operative CRC to post-operative samples were in agreement with studies that identified a decrease in di-antennary structures with core fucosylation and an increase in sialylated tri- and tetra-antennary N-glycans in CRC patient sera. Pre-operative abundances of N-glycans showed good performance for the classification of adenocarcinoma and led to the revisit of the previous MALDI-MS dataset with regard to histological and clinical data. This strategy has the potential to monitor patient profiles before, during, and after clinical events such as treatment, therapy, or surgery and should also be further explored.

Tumour-stroma ratio (TSR) in breast cancer: comparison of scoring core biopsies versus resection specimens.

PURPOSE: Tumour-stroma ratio (TSR) is an important prognostic and predictive factor in several tumour types. The aim of this study is to determine whether TSR evaluated in breast cancer core biopsies is representative of the whole tumour. METHOD: Different TSR scoring methods, their reproducibility, and the association of TSR with clinicopathological characteristics were investigated in 178 breast carcinoma core biopsies and corresponding resection specimens. TSR was assessed by two trained scientists on the most representative H&E-stained digitised slides. Patients were treated primarily with surgery between 2010 and 2021 at Semmelweis University, Budapest. RESULTS: Ninety-one percent of the tumours were hormone receptor (HR)-positive (luminal-like). Interobserver agreement was highest using 100 × magnification (κcore = 0.906, κresection specimen = 0.882). The agreement between TSR of core biopsies and resection specimens of the same patients was moderate (κ = 0.514). Differences between the two types of samples were most frequent in cases with TSR scores close to the 50% cut-off point. TSR was strongly correlated with age at diagnosis, pT category, histological type, histological grade, and surrogate molecular subtype. A tendency was identified for more recurrences among stroma-high (SH) tumours (p = 0.07). Significant correlation was detected between the TSR and tumour recurrence in grade 1 HR-positive breast cancer cases (p = 0.03). CONCLUSIONS: TSR is easy to determine and reproducible on both core biopsies and in resection specimens and is associated with several clinicopathological characteristics of breast cancer. TSR scored on core biopsies is moderately representative for the whole tumour.

Deep learning for multi-class semantic segmentation enables colorectal cancer detection and classification in digital pathology images.

In colorectal cancer (CRC), artificial intelligence (AI) can alleviate the laborious task of characterization and reporting on resected biopsies, including polyps, the numbers of which are increasing as a result of CRC population screening programs ongoing in many countries all around the globe. Here, we present an approach to address two major challenges in the automated assessment of CRC histopathology whole-slide images. We present an AI-based method to segment multiple ([Formula: see text]) tissue compartments in the H &E-stained whole-slide image, which provides a different, more perceptible picture of tissue morphology and composition. We test and compare a panel of state-of-the-art loss functions available for segmentation models, and provide indications about their use in histopathology image segmentation, based on the analysis of (a) a multi-centric cohort of CRC cases from five medical centers in the Netherlands and Germany, and (b) two publicly available datasets on segmentation in CRC. We used the best performing AI model as the basis for a computer-aided diagnosis system that classifies colon biopsies into four main categories that are relevant pathologically. We report the performance of this system on an independent cohort of more than 1000 patients. The results show that with a good segmentation network as a base, a tool can be developed which can support pathologists in the risk stratification of colorectal cancer patients, among other possible uses. We have made the segmentation model available for research use on https://grand-challenge.org/algorithms/colon-tissue-segmentation/ .

LncRNA LITATS1 suppresses TGF-ß-induced EMT and cancer cell plasticity by potentiating TßRI degradation.

Epithelial cells acquire mesenchymal phenotypes through epithelial-mesenchymal transition (EMT) during cancer progression. However, how epithelial cells retain their epithelial traits and prevent malignant transformation is not well understood. Here, we report that the long noncoding RNA LITATS1 (LINC01137, ZC3H12A-DT) is an epithelial gatekeeper in normal epithelial cells and inhibits EMT in breast and non-small cell lung cancer cells. Transcriptome analysis identified LITATS1 as a TGF-ß target gene. LITATS1 expression is reduced in lung adenocarcinoma tissues compared with adjacent normal tissues and correlates with a favorable prognosis in breast and non-small cell lung cancer patients. LITATS1 depletion promotes TGF-ß-induced EMT, migration, and extravasation in cancer cells. Unbiased pathway analysis demonstrated that LITATS1 knockdown potently and selectively potentiates TGF-ß/SMAD signaling. Mechanistically, LITATS1 enhances the polyubiquitination and proteasomal degradation of TGF-ß type I receptor (TßRI). LITATS1 interacts with TßRI and the E3 ligase SMURF2, promoting the cytoplasmic retention of SMURF2. Our findings highlight a protective function of LITATS1 in epithelial integrity maintenance through the attenuation of TGF-ß/SMAD signaling and EMT.

Differential optineurin expression controls TGFß signaling and is a key determinant for metastasis of triple negative breast cancer.

Triple-negative breast cancer (TNBC) is the most challenging breast cancer subtype to treat due to its aggressive characteristics and low response to the existing clinical therapies. Distant metastasis is the main cause of death of TNBC patients. Better understanding of the mechanisms underlying TNBC metastasis may lead to new strategies of early diagnosis and more efficient treatment. In our study, we uncovered that the autophagy receptor optineurin (OPTN) plays an unexpected role in TNBC metastasis. Data mining of publicly available data bases revealed that the mRNA level of OPTN in TNBC patients positively correlates with relapse free and distance metastasis free survival. Importantly, in vitro and in vivo models demonstrated that OPTN suppresses TNBC metastasis. Mechanistically, OPTN inhibited the pro-oncogenic transforming growth factor-ß (TGFß) signaling in TNBC cells by interacting with TGFß type I receptor (TßRI) and promoting its ubiquitination for degradation. Consistent with our experimental findings, the clinical TNBC samples displayed a negative correlation between OPTN mRNA expression and TGFß gene response signature and expression of proto-typic TGFß target genes. Altogether, our study demonstrates that OPTN is a negative regulator for TGFß receptor/SMAD signaling and suppresses metastasis in TNBC.

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

Background: The amount of stroma within the primary tumor is a prognostic parameter for colon cancer patients. This phenomenon can be assessed using the tumor-stroma ratio (TSR), which classifies tumors in stroma-low (≤50% stroma) and stroma-high (>50% stroma). Although the reproducibility for TSR determination is good, improvement might be expected from automation. The aim of this study was to investigate whether the scoring of the TSR in a semi- and fully automated method using deep learning algorithms is feasible. Methods: A series of 75 colon cancer slides were selected from a trial series of the UNITED study. For the standard determination of the TSR, 3 observers scored the histological slides. Next, the slides were digitized, color normalized, and the stroma percentages were scored using semi- and fully automated deep learning algorithms. Correlations were determined using intraclass correlation coefficients (ICCs) and Spearman rank correlations. Results: 37 (49%) cases were classified as stroma-low and 38 (51%) as stroma-high by visual estimation. A high level of concordance between the 3 observers was reached, with ICCs of 0.91, 0.89, and 0.94 (all P < .001). Between visual and semi-automated assessment the ICC was 0.78 (95% CI 0.23-0.91, P-value 0.005), with a Spearman correlation of 0.88 (P < .001). Spearman correlation coefficients above 0.70 (N=3) were observed for visual estimation versus the fully automated scoring procedures. Conclusion: Good correlations were observed between standard visual TSR determination and semi- and fully automated TSR scores. At this point, visual examination has the highest observer agreement, but semi-automated scoring could be helpful to support pathologists.

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

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

Longitudinal Serum Protein Analysis of Women with a High Risk of Developing Breast Cancer Reveals Large Interpatient Versus Small Intrapatient Variations: First Results from the TESTBREAST Study.

The prospective, multicenter TESTBREAST study was initiated with the aim of identifying a novel panel of blood-based protein biomarkers to enable early breast cancer detection for moderate-to-high-risk women. Serum samples were collected every (half) year up until diagnosis. Protein levels were longitudinally measured to determine intrapatient and interpatient variabilities. To this end, protein cluster patterns were evaluated to form a conceptual basis for further clinical analyses. Using a mass spectrometry-based bottom-up proteomics strategy, the protein abundance of 30 samples was analyzed: five sequential serum samples from six high-risk women; three who developed a breast malignancy (cases) and three who did not (controls). Serum samples were chromatographically fractionated and an in-depth serum proteome was acquired. Cluster analyses were applied to indicate differences between and within protein levels in serum samples of individuals. Statistical analyses were performed using ANOVA to select proteins with a high level of clustering. Cluster analyses on 30 serum samples revealed unique patterns of protein clustering for each patient, indicating a greater interpatient than intrapatient variability in protein levels of the longitudinally acquired samples. Moreover, the most distinctive proteins in the cluster analysis were identified. Strong clustering patterns within longitudinal intrapatient samples have demonstrated the importance of identifying small changes in protein levels for individuals over time. This underlines the significance of longitudinal serum measurements, that patients can serve as their own controls, and the relevance of the current study set-up for early detection. The TESTBREAST study will continue its pursuit toward establishing a protein panel for early breast cancer detection.

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.

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

PURPOSE: The recently developed fibroblast activation protein inhibitor (FAPI) tracer for PET/CT, binding tumour-stromal cancer-associated fibroblasts, is a promising tool for detection of positive lymph nodes. This study provides an overview of features, including sizes and tumour-stromal content, of lymph nodes and their respective lymph node metastases (LNM) in colorectal cancer (CRC), since literature lacks on whether LNMs contain sufficient stroma to potentially allow FAPI-based tumour detection. METHODS: Haematoxylin and eosin-stained tissue slides from 73 stage III colon cancer patients were included. Diameters and areas of all lymph nodes and their LNMs were assessed, the amount of stroma by measuring the stromal compartment area, the conventional and total tumour-stroma ratios (TSR-c and TSR-t, respectively), as well as correlations between these parameters. Also, subgroup analysis using a minimal diameter cut off of 5.0 mm was performed. RESULTS: In total, 126 lymph nodes were analysed. Although positive correlations were observed between node and LNM for diameter and area (r = 0.852, p < 0.001 and r = 0.960, p < 0.001, respectively), and also between the LNM stromal compartment area and nodal diameter (r = 0.612, p < 0.001), nodal area (r = 0.747, p < 0.001) and LNM area (r = 0.746, p < 0.001), novel insight was that nearly all (98%) LNMs contained stroma, with median TSR-c scores of 35% (IQR 20-60%) and TSR-t of 20% (IQR 10-30%). Moreover, a total of 32 (25%) positive lymph nodes had a diameter of < 5.0 mm. CONCLUSION: In LNMs, stroma is abundantly present, independent of size, suggesting a role for FAPI PET/CT in improved lymph node detection in CRC.

Standardization of the tumor-stroma ratio scoring method for breast cancer research.

PURPOSE: The tumor-stroma ratio (TSR) has repeatedly proven to be correlated with patient outcomes in breast cancer using large retrospective cohorts. However, studies validating the TSR often show variability in methodology, thereby hampering comparisons and uniform outcomes. METHOD: This paper provides a detailed description of a simple and uniform TSR scoring method using Hematoxylin and Eosin (H&E)-stained core biopsies and resection tissue, specifically focused on breast cancer. Possible histological challenges that can be encountered during scoring including suggestions to overcome them are reported. Moreover, the procedure for TSR estimation in lymph nodes, scoring on digital images and the automatic assessment of the TSR using artificial intelligence are described. CONCLUSION: Digitized scoring of tumor biopsies and resection material offers interesting future perspectives to determine patient prognosis and response to therapy. The fact that the TSR method is relatively easy, quick, and cheap, offers great potential for its implementation in routine diagnostics, but this requires high quality validation studies.

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.

RNF12 is regulated by AKT phosphorylation and promotes TGF-ß driven breast cancer metastasis.

Transforming growth factor-ß (TGF-ß) acts as a pro-metastatic factor in advanced breast cancer. RNF12, an E3 ubiquitin ligase, stimulates TGF-ß signaling by binding to the inhibitory SMAD7 and inducing its proteasomal degradation. How RNF12 activity is regulated and its exact role in cancer is incompletely understood. Here we report that RNF12 was overexpressed in invasive breast cancers and its high expression correlated with poor prognosis. RNF12 promoted breast cancer cell migration, invasion, and experimental metastasis in zebrafish and murine xenograft models. RNF12 levels were positively associated with the phosphorylated AKT/protein kinase B (PKB) levels, and both displayed significant higher levels in the basal-like subtype compared with the levels in luminal-like subtype of breast cancer cells. Mechanistically, AKT-mediated phosphorylation induced the nuclear localization of RNF12, maintained its stability, and accelerated the degradation of SMAD7 mediated by RNF12. Furthermore, we demonstrated that RNF12 and AKT cooperated functionally in breast cancer cell migration. Notably, RNF12 expression strongly correlated with both phosphorylated AKT and phosphorylated SMAD2 levels in breast cancer tissues. Thus, our results uncovered RNF12 as an important determinant in the crosstalk between the TGF-ß and AKT signaling pathways during breast cancer progression.

Combined Assessment of the Tumor-Stroma Ratio and Tumor Immune Cell Infiltrate for Immune Checkpoint Inhibitor Therapy Response Prediction in Colon Cancer.

The best current biomarker strategies for predicting response to immune checkpoint inhibitor (ICI) therapy fail to account for interpatient variability in response rates. The histologic tumor-stroma ratio (TSR) quantifies intratumoral stromal content and was recently found to be predictive of response to neoadjuvant therapy in multiple cancer types. In the current work, we predicted the likelihood of ICI therapy responsivity of 335 therapy-naive colon adenocarcinoma tumors from The Cancer Genome Atlas, using bioinformatics approaches. The TSR was scored on diagnostic tissue slides, and tumor-infiltrating immune cells (TIICs) were inferred from transcriptomic data. Tumors with high stromal content demonstrated increased T regulatory cell infiltration (p = 0.014) but failed to predict ICI therapy response. Consequently, we devised a hybrid tumor microenvironment classification of four stromal categories, based on histological stromal content and transcriptomic-deconvoluted immune cell infiltration, which was associated with previously established transcriptomic and genomic biomarkers for ICI therapy response. By integrating these biomarkers, stroma-low/immune-high tumors were predicted to be most responsive to ICI therapy. The framework described here provides evidence for expansion of current histological TIIC quantification to include the TSR as a novel, easy-to-use biomarker for the prediction of ICI therapy response.

Incidence, timing and risk factors of venous thromboembolic events in patients with pancreatic cancer.

INTRODUCTION: Pancreatic cancer is associated with a high risk of venous thromboembolism (VTE). However, comprehensive data on incidence, timing and relevant determinants of VTE in this particular population are scarce. Current study assesses incidence, timing and predictors of VTE in pancreatic cancer through different phases of disease. METHODS: All pancreatic cancer patients treated in our tertiary referral center between 2013 through 2017 were studied. Occurrence of VTE was evaluated from diagnosis through end of follow-up or death. Relevant determinants of VTE were identified in logistic regression models. Hazard ratios were calculated to evaluate impact of VTE on overall survival. RESULTS: In total, 361 patients were followed for a median period of 43 months; 64 were diagnosed with VTE (18%). Most were tumor related thrombosis (59%), incidental (75%) and occurred after anti-cancer treatment had been stopped (80%), only 1.6% occurred during remission phase. Stage IV pancreatic cancer was a predictor for VTE (hazard ratio (HR) 2.46, 95% confidence interval (CI) 0.9-6.8). Biliary drainage (HR 0.52, 95%CI 0.28-0.98) and tumor resection (HR 0.45, 95%CI 0.45-1.83) were protective factors. VTE was not associated with worse survival (HR 1.3; 95% CI 0.97-1.74). CONCLUSIONS: VTE in pancreatic cancer is disease-stage dependent, with 80% occurring in advanced phases of disease when patients no longer receive active treatment. We speculate that this is the main reason for the absence of a survival effect of VTE in our cohort. These practice-based findings should be taken into account when considering wide-spread introduction of primary thromboprophylaxis in patients with pancreatic cancer.

Tumour-stroma ratio outperforms tumour budding as biomarker in colon cancer: a cohort study.

The tumour-stroma ratio (TSR) and tumour budding (TB) are two high-risk factors with potential to be implemented in the next TNM classification. The aim of the current study was to evaluate the practical application of the two biomarkers based on reproducibility, independency and prognostic value. Patients diagnosed with stage II or III colon cancer who underwent surgery between 2005 and 2016 were included. Both TSR and TB were scored on haematoxylin and eosin-stained tissue sections. The TSR, based on the relative amount of stroma, was scored in increments of 10%. TB was scored following the consensus guidelines; a bud was defined as ≤ 4 tumour cells. For analysis, three categories were used. Cohen's kappa was used for reproducibility. The prognostic value was determined with survival analysis. In total, 246 patients were included. The TSR distribution was N = 137 (56%) stroma-low and N = 109 (44%) stroma-high. The TB distribution was TB-low N = 194 (79%), TB-intermediate N = 35 (14%) and TB-high N = 17 (7%). The reproducibility of the TSR was good (interobserver agreement kappa = 0.83 and intraobserver agreement kappa = 0.82), whereas the inter- and intraobserver agreement for scoring TB was moderate (kappa 0.47 and 0.45, respectively). The survival analysis showed an independent prognostic value for disease-free survival for TSR (HR 1.57; 95% CI 1.01-2.44; p = 0.048) and for TB-high (HR 2.01; 95% CI 1.02-3.96; p = 0.043). Based on current results, we suggest the TSR is a more reliable parameter in daily practice due to better reproducibility and independent prognostic value for disease-free survival.

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

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

Clinical Perspective on Proteomic and Glycomic Biomarkers for Diagnosis, Prognosis, and Prediction of Pancreatic Cancer.

Pancreatic ductal adenocarcinoma (PDAC) is known as a highly aggressive malignant disease. Prognosis for patients is notoriously poor, despite improvements in surgical techniques and new (neo)adjuvant chemotherapy regimens. Early detection of PDAC may increase the overall survival. It is furthermore foreseen that precision medicine will provide improved prognostic stratification and prediction of therapeutic response. In this review, omics-based discovery efforts are presented that aim for novel diagnostic and prognostic biomarkers of PDAC. For this purpose, we systematically evaluated the literature published between 1999 and 2020 with a focus on protein- and protein-glycosylation biomarkers in pancreatic cancer patients. Besides genomic and transcriptomic approaches, mass spectrometry (MS)-based proteomics and glycomics of blood- and tissue-derived samples from PDAC patients have yielded new candidates with biomarker potential. However, for reasons discussed in this review, the validation and clinical translation of these candidate markers has not been successful. Consequently, there has been a change of mindset from initial efforts to identify new unimarkers into the current hypothesis that a combination of biomarkers better suits a diagnostic or prognostic panel. With continuing development of current research methods and available techniques combined with careful study designs, new biomarkers could contribute to improved detection, prognosis, and prediction of pancreatic cancer.