Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers.
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ABSTRACT:
SUBMITTER: Thagaard J
PROVIDER: S-EPMC8235502 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:PurposeObesity is associated with poorer outcomes in patients with hormone receptor-positive breast cancers, but this association is not well established for women with triple-negative breast cancers (TNBC). Here, we investigated the prognostic effects of body mass index (BMI) on clinical outcomes in patients with TNBC.MethodsWe identified 1106 patients with TNBC who met the inclusion criteria and were treated between January 2002 and June 2012. Clinical and biological features were collected to evaluate the relation between BMI and breast cancer-specific survival (BCSS) and overall survival (OS) after controlling for other clinically significant variables.ResultsOf 1106 patients, 656 (59.3%) were normal weight (BMI ?24) and 450 patients (40.7%) were overweight(BMI>24). Median follow-up time was 44.8 months. Breast cancer specific death was observed in 140 patients. After adjusting for clinicopathologic risk factors, overweight was associated with OS (hazard ratio [HR]: 1.46, 95% confidence interval [CI]: 1.04-2.06, P =0.028) but not BCSS (HR: 1.34, 95% CI: 0.90-2.01, P =0.15)in all the patients with TNBC. When stratified with menopausal status, overweight was associated with BCSS and OS (HR: 2.27, 95% CI: 1.11-4.63, P = 0.024 and HR: 2.16, 95% CI: 1.21-3.87, P = 0.010, respectively) in premenopausal women. BMI was not associated with BCSS or OS in postmenopausal women.ConclusionsOverweight is an independent prognostic factor of OS in all women with TNBC, and menopause status may be a mitigating factor. Among premenopausal women, overweight women are at a greater risk of poor prognosis than normal weight women. If validated, these findings should be considered in developing preventive programs.
| S-EPMC4479880 | biostudies-literature
Project description:Having markers feasible for breast cancer subtyping, especially for triple negative breast cancer identification is crucial for improving the treatment outcome of such cancers. Here we explore the role of FOXA1 in characterizing triple negative breast cancers and the driving mechanisms. Through in vitro examination of the expression pattern at both transcriptional and translational levels, patient relapse-free survival analysis, immunohistochemistry staining and prediction power assessment using clinical samples, as well as functional studies, we systematically compared the role of FOXA1 in identifying triple negative and luminal type of breast cancers and explored the mechanisms driving such functionalities. We report that FOXA1 under-expression can lead to increased malignancy and cancer stemness, and is a subtyping marker identifying triple negative breast cancers rather than the luminal subtype by transcriptionally suppressing the expression of SOD2 and IL6. We are the first to systematically address the significance of FOXA1 in triple negative breast cancer identification as a biomarker and elucidate the mechanism at the molecular level, through a sequential bioinformatics analysis and experimental validations both in vitro and in clinics. Our discoveries compliment the current biomarker modalities once verified using larger clinical cohorts and improve the precision on characterizing breast cancer heterogeneity.
| S-EPMC6535797 | biostudies-literature
Project description:PurposeOur aim is to automatically align digital subtraction angiography (DSA) series, recorded before and after endovascular thrombectomy. Such alignment may enable quantification of procedural success.MethodsFirstly, we examine the inherent limitations for image registration, caused by the projective characteristics of DSA imaging, in a representative set of image pairs from thrombectomy procedures. Secondly, we develop and assess various image registration methods (SIFT, ORB). We assess these methods using manually annotated point correspondences for thrombectomy image pairs.ResultsLinear transformations that account for scale differences are effective in aligning DSA sequences. Two anatomical landmarks can be reliably identified for registration using a U-net. Point-based registration using SIFT and ORB proves to be most effective for DSA registration and are applicable to recordings for all patient sub-types. Image-based techniques are less effective and did not refine the results of the best point-based registration method.ConclusionWe developed and assessed an automated image registration approach for cerebral DSA sequences, recorded before and after endovascular thrombectomy. Accurate results were obtained for approximately 85% of our image pairs.
| S-EPMC10770205 | biostudies-literature
Project description:This article presents visual image data and detailed methodology for the use of a new method for quantifying the exudation of siderophores during fungal growth. The data include images showing time series for calibration, fungal exudation, and negative controls, as well as replication accuracy information. In addition, we provide detailed protocols for making CAS assay layer plates, the digital analysis protocol for determining area of color change, and discuss growth media that do and do not work with the layer plate method. The results of these data, their interpretation, and further discussion can be found in Andrews et al., 2016 [1].
| S-EPMC4752732 | biostudies-literature
Project description:We aimed to determine the clinical significance of tumour stroma ratio (TSR) in luminal and triple negative breast cancer (TNBC) using digital image analysis and machine learning algorithms. Automated image analysis using QuPath software was applied to a cohort of 647 breast cancer patients (403 luminal and 244 TNBC) using digital H&E images of tissue microarrays (TMAs). Kaplan-Meier and Cox proportional hazards were used to ascertain relationships with overall survival (OS) and breast cancer specific survival (BCSS). For TNBC, low TSR (high stroma) was associated with poor prognosis for both OS (HR 1.9, CI 1.1-3.3, p = 0.021) and BCSS (HR 2.6, HR 1.3-5.4, p = 0.007) in multivariate models, independent of age, size, grade, sTILs, lymph nodal status and chemotherapy. However, for luminal tumours, low TSR (high stroma) was associated with a favourable prognosis in MVA for OS (HR 0.6, CI 0.4-0.8, p = 0.001) but not for BCSS. TSR is a prognostic factor of most significance in TNBC, but also in luminal breast cancer, and can be reliably assessed using quantitative image analysis of TMAs. Further investigation into the contribution of tumour subtype stromal phenotype may further refine these findings.
| S-EPMC7764351 | biostudies-literature
Project description:BackgroundPathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30-40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60-70% show residual disease (RD). The role of the tumor microenvironment in NAC response in patients with TNBC remains unclear. In this study, we developed a machine learning-based two-step pipeline to distinguish between various histological components in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of TNBC tissue biopsies and to identify histological features that can predict NAC response.MethodsH&E-stained WSIs of treatment-naïve biopsies from 85 patients (51 with pCR and 34 with RD) of the model development cohort and 79 patients (41 with pCR and 38 with RD) of the validation cohort were separated through a stratified eightfold cross-validation strategy for the first step and leave-one-out cross-validation strategy for the second step. A tile-level histology label prediction pipeline and four machine-learning classifiers were used to analyze 468,043 tiles of WSIs. The best-trained classifier used 55 texture features from each tile to produce a probability profile during testing. The predicted histology classes were used to generate a histology classification map of the spatial distributions of different tissue regions. A patient-level NAC response prediction pipeline was trained with features derived from paired histology classification maps. The top graph-based features capturing the relevant spatial information across the different histological classes were provided to the radial basis function kernel support vector machine (rbfSVM) classifier for NAC treatment response prediction.ResultsThe tile-level prediction pipeline achieved 86.72% accuracy for histology class classification, while the patient-level pipeline achieved 83.53% NAC response (pCR vs. RD) prediction accuracy of the model development cohort. The model was validated with an independent cohort with tile histology validation accuracy of 83.59% and NAC prediction accuracy of 81.01%. The histological class pairs with the strongest NAC response predictive ability were tumor and tumor tumor-infiltrating lymphocytes for pCR and microvessel density and polyploid giant cancer cells for RD.ConclusionOur machine learning pipeline can robustly identify clinically relevant histological classes that predict NAC response in TNBC patients and may help guide patient selection for NAC treatment.
| S-EPMC10797728 | biostudies-literature
Project description:BackgroundMultipotent mesenchymal stem cells (MSCs) have the potential to repair and regenerate damaged tissues and are considered as attractive candidates for the development of cell-based regenerative therapies. Currently, there are more than 200 clinical trials involving the use of MSCs for a wide variety of indications. However, variations in their isolation, expansion, and particularly characterization have made the interpretation of study outcomes or the rigorous assessment of therapeutic efficacy difficult. An unbiased characterization of MSCs is of major importance and essential to guaranty that only the most suitable cells will be used. The development of standardized and reproducible assays to predict MSC potency is therefore mandatory. The currently used quantification methodologies for the determination of the trilineage potential of MSCs are usually based on absorbance measurements which are imprecise and prone to errors. We therefore aimed at developing a methodology first offering a standardized way to objectively quantify the trilineage potential of MSC preparations and second allowing to discriminate functional differences between clonally expanded cell populations.MethodMSCs originating from several patients were differentiated into osteoblasts, adipocytes, and chondroblasts for 14, 17, and 21 days. Differentiated cells were then stained with the classical dyes: Alizarin Red S for osteoblasts, Oil Red O for adipocytes, and Alcian Blue 8GX for chondroblasts. Quantification of differentiation was then performed with our newly developed digital image analysis (DIA) tool followed by the classical absorbance measurement. The results from the two techniques were then compared.ResultQuantification based on DIA allowed highly standardized and objective dye quantification with superior sensitivity compared to absorbance measurements. Furthermore, small differences between MSC lines in the differentiation potential were highlighted using DIA whereas no difference was detected using absorbance quantification.ConclusionOur approach represents a novel method that simplifies the laboratory procedures not only for the quantification of histological dyes and the degree of differentiation of MSCs, but also due to its color independence, it can be easily adapted for the quantification of a wide range of staining procedures in histology. The method is easily applicable since it is based on open source software and standard light microscopy.
| S-EPMC6390603 | biostudies-literature
Project description:BackgroundCurrent prognostic gene signatures for breast cancer mainly reflect proliferation status and have limited value in triple-negative (TNBC) cancers. The identification of prognostic signatures from TNBC cohorts was limited in the past due to small sample sizes.Methodology/principal findingsWe assembled all currently publically available TNBC gene expression datasets generated on Affymetrix gene chips. Inter-laboratory variation was minimized by filtering methods for both samples and genes. Supervised analysis was performed to identify prognostic signatures from 394 cases which were subsequently tested on an independent validation cohort (n = 261 cases).Conclusions/significanceUsing two distinct false discovery rate thresholds, 25% and <3.5%, a larger (n = 264 probesets) and a smaller (n = 26 probesets) prognostic gene sets were identified and used as prognostic predictors. Most of these genes were positively associated with poor prognosis and correlated to metagenes for inflammation and angiogenesis. No correlation to other previously published prognostic signatures (recurrence score, genomic grade index, 70-gene signature, wound response signature, 7-gene immune response module, stroma derived prognostic predictor, and a medullary like signature) was observed. In multivariate analyses in the validation cohort the two signatures showed hazard ratios of 4.03 (95% confidence interval [CI] 1.71-9.48; P = 0.001) and 4.08 (95% CI 1.79-9.28; P = 0.001), respectively. The 10-year event-free survival was 70% for the good risk and 20% for the high risk group. The 26-gene signatures had modest predictive value (AUC = 0.588) to predict response to neoadjuvant chemotherapy, however, the combination of a B-cell metagene with the prognostic signatures increased its response predictive value. We identified a 264-gene prognostic signature for TNBC which is unrelated to previously known prognostic signatures.
| S-EPMC3248403 | biostudies-literature
Project description:There are no widely-accepted prognostic markers currently available to predict outcomes in patients with triple-negative breast cancer (TNBC), and no targeted therapies with confirmed benefit. We have used MALDI mass spectrometry imaging (MSI) of tryptic peptides to compare regions of cancer and benign tissue in 10 formalin-fixed, paraffin-embedded sections of TNBC tumors. Proteins were identified by reference to a peptide library constructed by LC-MALDI-MS/MS analyses of the same tissues. The prognostic significance of proteins that distinguished between cancer and benign regions was estimated by Kaplan-Meier analysis of their gene expression from public databases. Among peptides that distinguished between cancer and benign tissue in at least 3 tissues with a ROC area under the curve >0.7, 14 represented proteins identified from the reference library, including proteins not previously associated with breast cancer. Initial network analysis using the STRING database showed no obvious functional relationships except among collagen subunits COL1A1, COL1A2, and COL63A, but manual curation, including the addition of EGFR to the analysis, revealed a unique network connecting 10 of the 14 proteins. Kaplan-Meier survival analysis to examine the relationship between tumor expression of genes encoding the 14 proteins, and recurrence-free survival (RFS) in patients with basal-like TNBC showed that, compared to low expression, high expression of nine of the genes was associated with significantly worse RFS, most with hazard ratios >2. In contrast, in estrogen receptor-positive tumors, high expression of these genes showed only low, or no, association with worse RFS. These proteins are proposed as putative markers of RFS in TNBC, and some may also be considered as possible targets for future therapies.
2019-05-06 | PXD013397 | Pride
Project description:Quantification of cellular antigens and their interactions via antibody-based detection methods are widely used in scientific research. Accurate high-throughput quantitation of these assays using general image analysis software can be time consuming and challenging, particularly when attempted by users with limited image processing and analysis knowledge. To overcome this, we have designed Andy's Algorithms, a series of automated image analysis pipelines for FIJI, that permits rapid, accurate and reproducible batch-processing of 3,3'-diaminobenzidine (DAB) immunohistochemistry, proximity ligation assays (PLAs) and other common assays. Andy's Algorithms incorporates a step-by-step tutorial and optimization pipeline to make batch image analysis simple for the untrained user and adaptable across laboratories. Andy's algorithms provide a simpler, faster, standardized work flow compared to existing programs, while offering equivalent performance and additional features, in a free to use open-source application of FIJI. Andy's Algorithms are available at GitHub, publicly accessed at https://github.com/andlaw1841/Andy-s-Algorithm .
| S-EPMC5691210 | biostudies-literature