Tissue Subtypes Research Articles

Pathological tissue changes in brain tumors affect the pH-sensitivity of the T1-corrected apparent exchange dependent relaxation (AREX) of the amide protons.

Measuring the intracellular pH (pHi) is of interest for brain tumor diagnostics. Common metrics of CEST imaging like the amide proton transfer-weighted (APTw) MTRasym are pHi sensitive and allow differentiating malignant tumor from healthy tissue. Yet, the image contrast also depends on additional magnetization transfer effects and T1. In contrast, the apparent exchange-dependent relaxation (AREX) provides a T1 corrected exchange rate of the amide protons. As AREX still depends on amide proton density, its pHi sensitivity remains ambiguous. Hence, we conducted this study to assess the influence of pathologic tissue changes on the pHi sensitivity of AREX in vivo. Patients with newly diagnosed intra-axial brain tumors were prospectively recruited and underwent conventional MRI, quantitative T1 relaxometry, APT-CEST and 31P-MRS on a 3T MRI scanner. Tumors were segmented into contrast-enhancing tumor (CE), surrounding T2 hyperintensity (T2-H) and contralateral normal appearing white matter (CNAWM). T1 mapping and APT-CEST metrics were correlated with 31P-MRS-derived pHi maps (Pearson's correlation). Without differentiating tissue subtypes, pHi did not only correlate significantly with MTRasym (r= 0.46) but also with T1 (r= 0.49). Conversely, AREX only correlated poorly with pHi (r= 0.17). Analyzing different tissue subtypes separately revealed a tissue dependency of the pHi sensitivity of AREX with a significant correlation (r= 0.6) in CNAWM and no correlation in T2-H or CE (r= -0.11/-0.24). CE showed significantly increased MTRasym, pHi, and T1 compared with CNAWM (p< 0.001). In our study, the pHi sensitivity of AREX was limited to CNAWM. The lack of sensitivity in CE and T2-H is probably attributable to altered amide and water proton concentrations in these tissues. Conversely, the correlation of pHi with MTRasym may be explained by the coincidental contrast increase through increased T1 and amide proton density. Therefore, limited structural deviations from CNAWM might be a perquisite for the use of CEST contrasts as pHi-marker.

Rapid Hyperspectral Photothermal Mid-Infrared Spectroscopic Imaging from Sparse Data for Gynecologic Cancer Tissue Subtyping.

Ovarian cancer detection has traditionally relied on a multistep process that includes biopsy, tissue staining, and morphological analysis by experienced pathologists. While widely practiced, this conventional approach suffers from several drawbacks: it is qualitative, time-intensive, and heavily dependent on the quality of staining. Mid-infrared (MIR) hyperspectral photothermal imaging is a label-free, biochemically quantitative technology that, when combined with machine learning algorithms, can eliminate the need for staining and provide quantitative results comparable to traditional histology. However, this technology is slow. This work presents a novel approach to MIR photothermal imaging that enhances its speed by an order of magnitude. This method resolves the longstanding trade-off between imaging resolution and data collection speed, enabling the reconstruction of high-quality, high-resolution images from undersampled data sets and achieving a 10X improvement in data acquisition time. We assessed the performance of our sparse imaging methodology using a variety of quantitative metrics, including mean squared error (MSE), structural similarity index (SSIM), and tissue subtype classification accuracies, employing both random forest and convolutional neural network (CNN) models, accompanied by Receiver Operating Characteristic (ROC) curves. Our statistically robust analysis, based on data from 100 ovarian cancer patient samples and over 65 million data points, demonstrates the method's capability to produce superior image quality and accurately distinguish between different gynecological tissue types with segmentation accuracy exceeding 95%. Our work demonstrates the feasibility of integrating rapid MIR hyperspectral photothermal imaging with machine learning in enhancing ovarian cancer tissue characterization, paving the way for quantitative, label-free, automated histopathology.

Differential effects of melatonin on adipose tissues under normoestrogenic and estrogen-deficient conditions in rats

In post-menopause, oxidative stress due to the decline of natural antioxidants increases the susceptibility to metabolic syndromes (MetS). Estrogen and melatonin (MEL) share antioxidant properties; however, few studies have reported the impact of estrogen deficiency and MEL treatment on morphology, redox status, and antioxidant defense capacity of diverse adipose tissue (AT) subtypes. To investigate this issue, MEL was administered to ovariectomized (OVX) rats and sham-operated rats for 16 weeks (10 mg/kg). The adipocyte morphology, oxidative stress parameters and antioxidant enzyme activity were evaluated in the visceral retroperitoneal adipose tissue (rVAT), subcutaneous inguinal adipose tissue (iSAT) and brown adipose tissue (BAT). In OVX rats, MEL treatment suppressed rVAT hypertrophy and increased the prevalence of small adipocytes in iSAT, suggesting a better lipid distribution among ATs. MEL treatment increased glutathione reductase and glucose-6-phosphate dehydrogenase activity in iSAT; therefore, restored the glutathione level. In rVAT, MEL increased glutathione peroxidase and glutathione reductase activity. MEL minimized the risks for the development of metabolic abnormalities due to estrogen deficiency. However, under normoestrogenic condition, MEL decreased plasma estradiol levels and uterine mass, raising the concerning of its effect on reproductive functions.

Cervical Cancer Tissue Analysis Using Photothermal Midinfrared Spectroscopic Imaging.

Hyperspectral photothermal mid-infrared spectroscopic imaging (HP-MIRSI) is an emerging technology with promising applications in cervical cancer diagnosis and quantitative, label-free histopathology. This study pioneers the application of HP-MIRSI to the evaluation of clinical cervical cancer tissues, achieving excellent tissue type segmentation accuracy of over 95%. This achievement stems from an integrated approach of optimized data acquisition, computational data reconstruction, and the application of machine learning algorithms. The results are statistically robust, drawing from tissue samples of 98 cervical cancer patients and incorporating over 40 million data points. Traditional cervical cancer diagnosis methods entail biopsy, staining, and visual evaluation by a pathologist. This process is qualitative, subject to variations in staining and subjective interpretations, and requires extensive tissue processing, making it costly and time-consuming. In contrast, our proposed alternative can produce images comparable to those from histological analyses without the need for staining or complex sample preparation. This label-free, quantitative method utilizes biochemical data from HP-MIRSI and employs machine-learning algorithms for the rapid and precise segmentation of cervical tissue subtypes. This approach can potentially transform histopathological analysis by offering a more accurate and label-free alternative to conventional diagnostic processes.

Coordinated inflammation and immune response transcriptional regulation in breast cancer molecular subtypes.

Breast cancer, characterized by its complexity and diversity, presents significant challenges in understanding its underlying biology. In this study, we employed gene co-expression network analysis to investigate the gene composition and functional patterns in breast cancer subtypes and normal breast tissue. Our objective was to elucidate the detailed immunological features distinguishing these tumors at the transcriptional level and to explore their implications for diagnosis and treatment. The analysis identified nine distinct gene module clusters, each representing unique transcriptional signatures within breast cancer subtypes and normal tissue. Interestingly, while some clusters exhibited high similarity in gene composition between normal tissue and certain subtypes, others showed lower similarity and shared traits. These clusters provided insights into the immune responses within breast cancer subtypes, revealing diverse immunological functions, including innate and adaptive immune responses. Our findings contribute to a deeper understanding of the molecular mechanisms underlying breast cancer subtypes and highlight their unique characteristics. The immunological signatures identified in this study hold potential implications for diagnostic and therapeutic strategies. Additionally, the network-based approach introduced herein presents a valuable framework for understanding the complexities of other diseases and elucidating their underlying biology.

Housekeeping protein-coding genes interrogated with tissue and individual variations

Housekeeping protein-coding genes are stably expressed genes in cells and tissues that are thought to be engaged in fundamental cellular biological functions. They are often utilized as normalization references in molecular biology research and are especially important in integrated bioinformatic investigations. Prior studies have examined human housekeeping protein-coding genes by analyzing various gene expression datasets. The inclusion of different tissue types significantly impacted the discovery of housekeeping genes. In this report, we investigated particularly individual human subject expression differences in protein-coding genes across different tissue types. We used GTEx V8 gene expression datasets obtained from more than 16,000 human normal tissue samples. Furthermore, the Gini index is utilized to investigate the expression variations of protein-coding genes between tissue and individual donor subjects. Housekeeping protein-coding genes found using Gini index profiles may vary depending on the tissue subtypes investigated, particularly given the diverse sample size collections across the GTEx tissue subtypes. We subsequently selected major tissues and identified subsets of housekeeping genes with stable expression levels among human donors within those tissues. In this work, we provide alternative sets of housekeeping protein-coding genes that show more consistent expression patterns in human subjects across major solid organs. Weblink: https://hpsv.ibms.sinica.edu.tw.

Mucosa-associated lymphoid tissue lymphoma of the lacrimal gland: A case report and literature review.

The most common subtype of primary lymphoma of the ocular adnexa is the mucosa-associated lymphoid tissue (MALT) subtype. MALT lymphoma of the lacrimal gland is relatively rare among the lacrimal gland tumors, and the early clinical symptoms are atypical, which can easily lead to misdiagnosis and missed diagnosis. Here, we report a case of MALT lymphoma of the lacrimal gland and explore its clinical manifestations, pathological characteristics, management, and pathogenesis, with the aim of helping clinicians gain an in-depth understanding of ocular adnexal MALT lymphoma. A 60-year-old man presented to our hospital with proptosis and diplopia. The right eye deviated and shifted toward the lower part of the nose. Orbital enhanced magnetic resonance imaging suggested a mass with a maximum cross-section of 3.2 × 2.1 cm. T1 weighted image was isointense, and the enhancement was more uniform and obvious. The right orbital mass was treated surgically, and the final pathology report was MALT lymphoma. After the pathological report was released, the patient was transferred to the hematology department for further diagnosis and no further treatment was given eventually. Seven months later, the patient did not complain of discomfort. Whole-body positron emission tomography-computed tomography, superficial lymph node examination and orbital magnetic resonance imaging revealed no abnormal changes. The clinical manifestations of MALT lymphoma are heterogeneous. Imaging examination is important for assessing the size of the tumor and its relationship with adjacent tissues. Postoperative pathological examination may provide further evidence for the evaluation of the patient's surgical efficacy and prognosis. Management of MALT lymphoma of the lacrimal gland requires a multidisciplinary approach involving ophthalmologists, hematologists, and radiotherapists.

ML3CNet: Non-local means-assisted automatic framework for lung cancer subtypes classification using histopathological images

Background and objective:Lung cancer (LC) has a high fatality rate that continuously affects human lives all over the world. Early detection of LC prolongs human life and helps to prevent the disease. Histopathological inspection is a common method to diagnose LC. Visual inspection of histopathological diagnosis necessitates more inspection time, and the decision depends on the subjective perception of clinicians. Usually, machine learning techniques mostly depend on traditional feature extraction which is labor-intensive and may not be appropriate for enormous data. In this work, a convolutional neural network (CNN)-based architecture is proposed for the more effective classification of lung tissue subtypes using histopathological images. Methods:Authors have utilized the first-time nonlocal mean (NLM) filter to suppress the effect of noise from histopathological images. NLM filter efficiently eliminated noise while preserving the edges of images. Then, the obtained denoised images are given as input to the proposed multi-headed lung cancer classification convolutional neural network (ML3CNet). Furthermore, the model quantization technique is utilized to reduce the size of the proposed model for the storage of the data. Reduction in model size requires less memory and speeds up data processing. Results:The effectiveness of the proposed model is compared with the other existing state-of-the-art methods. The proposed ML3CNet achieved an average classification accuracy of 99.72%, sensitivity of 99.66%, precision of 99.64%, specificity of 99.84%, F-1 score of 0.9965, and area under the curve of 0.9978. The quantized accuracy of 98.92% is attained by the proposed model. To validate the applicability of the proposed ML3CNet, it has also been tested on the colon cancer dataset. Conclusion:The findings reveal that the proposed approach can be beneficial to automatically classify LC subtypes that might assist healthcare workers in making decisions more precisely. The proposed model can be implemented on the hardware using Raspberry Pi for practical realization.

Abstract PO4-06-12: Distinction of basal-like and triple-negative basal-like breast cancers utilizing a novel comprehensive single-cell liquid biopsy-based test

Abstract Introduction: Breast cancer is a highly heterogeneous disease and is a leading cause of death for women worldwide. Triple-negative breast cancer (TNBC), the most aggressive form of the disease, is clinically defined by the lack of expression of estrogen receptor (ER) and progesterone receptor (PR), as well as lack of over-expression of human epidermal growth factor receptor-2 (HER2). Approximately 70% of all TNBC cases exhibit basal-like cells, characterized by high basal expression of keratins as well as extensive genomic instability with copy number gains and losses across most chromosomes. In this study we show that novel single cell genomic evaluation coupled with immunofluorescence (IF) protein analysis of CTCs can detect TNBC with basal-like genomic features as well as basal-like tumors with expression of ER and HER2. Materials and Methods: Epic Sciences’ DefineMBC™ liquid biopsy test characterizes CTC proteomics and genomics as well as ctDNA for the treatment of metastatic breast cancer. Blood samples from 813 metastatic breast cancer patients, with either newly diagnosed or progressing disease, with historical tissue subtype [HR(+)HER2(-), HR(+/-)HER2(+), and TNBC] supplied by the referring physician were collected and tested. Up to 5 CTCs were selected and isolated from each sample based on morphology and IF signal. DNA was extracted from individual cells, and whole genome amplified and sequenced at low coverage. Copy number analysis was performed to determine copy number gains and losses across chromosomes, as well as large scale-state transitions (LSTs) and ERBB2 amplification. CTCs characterized with high chromosomal instability (e.g., exhibiting &amp;gt;30 LSTs; 1Q firestorm and 8Q stair step) and high levels of cytokeratin expression (mean fluorescence intensity signal &amp;gt;10K) were categorized as basal-like. Results: By previous metastatic tissue biopsy, 77% were HR(+), 8% were HER2(+), and 12% were TNBC. CTCs categorized in DefineMBC as basal-like subtype (9.8 % of all samples tested) showed to be highly present in patients called as TNBC (79% basal-like). Additionally, 54% of HR(+) and 23% of HER2(+) had basal-like CTCS. Patients characterized with TNBC by metastatic tissue biopsy and by DefineMBC exhibited basal-like cells in 88% of the cases. Conclusion: The results show a novel clinical assay which utilizes CTC protein detection as well as single-cell genomic analysis to identify tumors driven by basal-like cells. Generally, TNBC is classified with negative biomarker expression. We show that CTCs in TNBC display genomically basal-like cells. CTCs from HR(+) and HER2(+) tumors may express basal-like features with preserved ER and HER2 expression. The comprehensive nature of DefineMBC™ utilizing genomic analysis of detected CTCs has provided a new tool to diagnose both basal-like TNBC and basal-like hormone receptor positive and HER2 over-expressing tumors. Citation Format: Alessandra Cunsolo, David Bourdon, Ernest Lam, Giuseppe Di Caro, Nilesh Dharajiya, Timothy Pluard, Lee Schwartzberg. Distinction of basal-like and triple-negative basal-like breast cancers utilizing a novel comprehensive single-cell liquid biopsy-based test [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-06-12.

The role of brown adipose tissue in mediating healthful longevity.

There are two major subtypes of adipose tissue, i.e., white adipose tissue (WAT) and brown adipose tissue (BAT). It has been known for a long time that WAT mediates obesity and impairs healthful longevity. More recently, interest has focused on BAT, which, unlike WAT, actually augments healthful aging. The goal of this review is to examine the role of BAT in mediating healthful longevity. A major role for BAT and its related beige adipose tissue is thermogenesis, as a mechanism to maintain body temperature by producing heat through uncoupling protein 1 (UCP1) or through UCP1-independent thermogenic pathways. Our hypothesis is that healthful longevity is, in part, mediated by BAT. BAT protects against the major causes of impaired healthful longevity, i.e., obesity, diabetes, cardiovascular disorders, cancer, Alzheimer's disease, reduced exercise tolerance, and impaired blood flow. Several genetically engineered mouse models have shown that BAT enhances healthful aging and that their BAT is more potent than wild-type (WT) BAT. For example, when BAT, which increases longevity and exercise performance in mice with disruption of the regulator of G protein signaling 14 (RGS14), is transplanted to WT mice, their exercise capacity is enhanced at 3 days after BAT transplantation, whereas BAT transplantation from WT to WT mice also resulted in increased exercise performance, but only at 8 weeks after transplantation. In view of the ability of BAT to mediate healthful longevity, it is likely that a pharmaceutical analog of BAT will become a novel therapeutic modality.

DBDNMF: A Dual Branch Deep Neural Matrix Factorization method for drug response prediction.

Anti-cancer response of cell lines to drugs is in urgent need for individualized precision medical decision-making in the era of precision medicine. Measurements with wet-experiments is time-consuming and expensive and it is almost impossible for wide ranges of application. The design of computational models that can precisely predict the responses between drugs and cell lines could provide a credible reference for further research. Existing methods of response prediction based on matrix factorization or neural networks have revealed that both linear or nonlinear latent characteristics are applicable and effective for the precise prediction of drug responses. However, the majority of them consider only linear or nonlinear relationships for drug response prediction. Herein, we propose a Dual Branch Deep Neural Matrix Factorization (DBDNMF) method to address the above-mentioned issues. DBDNMF learns the latent representation of drugs and cell lines through flexible inputs and reconstructs the partially observed matrix through a series of hidden neural network layers. Experimental results on the datasets of Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) show that the accuracy of drug prediction exceeds state-of-the-art drug response prediction algorithms, demonstrating its reliability and stability. The hierarchical clustering results show that drugs with similar response levels tend to target similar signaling pathway, and cell lines coming from the same tissue subtype tend to share the same pattern of response, which are consistent with previously published studies.

Abstract 6876: Cyclic immunofluorescent analysis of the tumor microenvironment across human brain cancer subtypes

Abstract Introduction: The tumor microenvironment (TME) has been the focus of a myriad of studies over the last decade and advancements in the field of cancer research have grown as a result. Improvements in the number of biomarkers that can be screened has increased across a variety of methods and the field of spatial profiling has largely adapted to keep pace with this. However, many challenges persist: 1) tissue microarrays can be limited by tumor heterogeneity and 2) newer technology can be cost-prohibitive. Brain tumors are of significant importance, especially those of glial origin, as they swiftly progress and result in a high fatality rate. As such, the need for more diverse therapeutic approaches is growing with many already pioneering new IHC-based strategies. The aim of this study was to address both challenges addressed above in the neurobiology space. Methods: Biomarkers of importance in brain cancer tissues were identified and three distinct panels were designed. The first panel consisted of CD68, CTLA-4, p16ink4A, p14ARF, and p53. The second included PDPN, Calretinin, MAGE-D2, PTEN, and Vimentin. The third panel included IDH2, BRAF, GFAP, VEGF, and BOP1. Staining was achieved using Bethyl Laboratories IHC-validated primary and secondary antibodies and Akoya Opal™ Polaris 7-color IHC kit fluorophores (Akoya Biosciences [NEL861001KT]). Optimization was achieved using FFPE tissue microarrays containing normal neural tissue and brain cancer subtypes. The final optimized order was then tested on whole serial sections of meningioma, astrocytoma, and glioblastoma in six-color mIF. Images were generated using the PhenoImager HT® and subsequently merged and analyzed by Pathomics. Results: Phenotyping and total cell counts for each marker were identified for each serially stained tissue set. Expression levels of MAGE-D2 and BRAF were high across malignant tissues of variable gradations. Conversely, in tissues with high expression of MAGE-D2, p53 levels in these samples were lower. This is consistent with findings that suggested that MAGE-D2 is a potential dissociator of p53 by impairing transcriptional activity. Macrophage expression appeared to correlate with CTLA4 and overall tumor infiltration. Conclusions: Utilizing several unique biomarkers to understand the spatial relationships and phenotypes within the tumor microenvironment is becoming a more common practice among researchers. In using this method of serially staining tissue sections and merging the resulting images, more researchers will have access to spatial information which may have been cost-prohibitive otherwise. Additionally, in utilizing whole sections of difficult-to-treat cancerous tissue rather than microarrays, phenotypic information is available across the entire tumor microenvironment enabling a more comprehensive assessment of the TME. Citation Format: Danielle Fails, Michael Spencer. Cyclic immunofluorescent analysis of the tumor microenvironment across human brain cancer subtypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6876.

Partial-Label Contrastive Representation Learning for Fine-grained Biomarkers Prediction from Histopathology Whole Slide Images.

In the domain of histopathology analysis, existing representation learning methods for biomarkers prediction from whole slide images (WSI) face challenges due to the complexity of tissue subtypes and label noise problems. This paper proposed a novel partial-label contrastive representation learning approach to enhance the discrimination of histopathology image representations for fine-grained biomarkers prediction. We designed a partial-label contrastive clustering (PLCC) module for partial-label disambiguation and a dynamic clustering algorithm to sample the most representative features of each category to the clustering queue during the contrastive learning process. We conducted comprehensive experiments on three gene mutation prediction datasets, including USTC-EGFR, BRCA-HER2, and TCGA-EGFR. The results show that our method outperforms 9 existing methods in terms of Accuracy, AUC, and F1 Score. Specifically, our method achieved an AUC of 0.950 in EGFR mutation subtyping of TCGA-EGFR and an AUC of 0.853 in HER2 0/1+/2+/3+ grading of BRCA-HER2, which demonstrates its superiority in fine-grained biomarkers prediction from histopathology whole slide images. The source code is available at https://github.com/WkEEn/PLCC.

Brain Tumor Detection Using Deep Learning

Brain tumor detection is a critical task in neuroimaging that significantly impacts clinical diagnosis and treatment planning. Conventional methods for tumor detection rely on manual interpretation of medical imaging data, which can be time-consuming and subject to interobserver variability. With the advancements in deep learning, particularly in the realm of convolutional neural networks (CNNs), there has been a growing interest in leveraging these techniques for automated brain tumor detection. This study explores the efficacy of deep learning models in the automated detection and classification of brain tumors from magnetic resonance imaging (MRI) scans. A comprehensive dataset of MRI images representing various tumor types and anatomical regions is employed to train and evaluate the deep learning models. The developed CNN-based architectures are designed to capture intricate patterns and features within the MRI scans, facilitating accurate tumor localization and classification. The research investigates the performance of the deep learning models in distinguishing between healthy brain tissue and different tumor subtypes, including gliomas, meningiomas, and metastatic tumors. Performance metrics such as accuracy, sensitivity, specificity, and area under the curve (AUC) are employed to assess the models' ability to precisely identify tumor regions and provide clinically relevant information to aid healthcare professionals. Furthermore, the study addresses challenges associated with model generalization, interpretability, and scalability for deployment in clinical settings. Strategies for optimizing model robustness, enhancing interpretability, and ensuring seamless integration into existing diagnostic workflows are explored. The findings of this research contribute to the growing body of knowledge regarding the application of deep learning in medical imaging analysis, particularly in the domain of brain tumor detection. The implications of this study extend to the development of more efficient and accurate tools to assist radiologists and clinicians in diagnosing brain tumors, thereby potentially improving patient outcomes and treatment strategies.

Identification of P3H1 as a Predictive Prognostic Biomarker for Bladder Urothelial Carcinoma Based on the Cancer Genome Atlas Database.

The extracellular matrix in the tumor microenvironment are closely related to the development of tumors. This study's primary aim is to study the association between prolyl 3-hydroxylase 1 (P3H1) which mainly expresses collagen in extracellular matrix and the progression and prognosis of bladder cancer (BC). The clinical and transcriptome data were acquired from the cancer genome atlas database. BLCAsubtyping is used to evaluate tissue subtypes of BC. The COX proportional hazards can be used to evaluate the survival process's influencing factors. Immunohistochemistry was used to identify differences in the expression of P3H1 in cancer and paired adjacent tissues. GSEA was used to investigate the underlying biological processes. Finally, ssGSEA, TIMER and pRRophetic were used to study the relationship between P3H1 and immune cell infiltration and drug sensitivity. The expression of P3H1 was substantially higher in highly invasive BC samples than in low invasive BC. P3H1 was an independent predictor of overall survival (HR = 1.12, p = 0.03). P3H1 expression was significantly higher in tumor tissues than adjacent normal tissues in clinical tissue samples, and was significantly higher in highly stage cancer than low stage cancer samples. Samples with high P3H1 expression had a higher level of immune cell infiltration and immune function, as well as a significant correlation with macrophage and dendritic cell infiltration and TGF-beta, Th1 cells, and macrophage regulation (cor >0.3, p <0.05). P3H1 high expression samples were substantially more sensitive to docetaxel, cisplatin, vinblastine, camptothecin, paclitaxel, and other medicines than P3H1 low expression samples. P3H1 is a possible oncogene and an independent predictor of poor prognosis in BC; it also has enhanced sensitivity to docetaxel, cisplatin, vinblastine, camptothecin, paclitaxel, and other medications.

Improved artificial intelligence discrimination of minor histological populations by supplementing with color-adjusted images

Despite the dedicated research of artificial intelligence (AI) for pathological images, the construction of AI applicable to histopathological tissue subtypes, is limited by insufficient dataset collection owing to disease infrequency. Here, we present a solution involving the addition of supplemental tissue array (TA) images that are adjusted to the tonality of the main data using a cycle-consistent generative adversarial network (CycleGAN) to the training data for rare tissue types. F1 scores of rare tissue types that constitute < 1.2% of the training data were significantly increased by improving recall values after adding color-adjusted TA images constituting < 0.65% of total training patches. The detector also enabled the equivalent discrimination of clinical images from two distinct hospitals and the capability was more increased following color-correction of test data before AI identification (F1 score from 45.2 ± 27.1 to 77.1 ± 10.3, p < 0.01). These methods also classified intraoperative frozen sections, while excessive supplementation paradoxically decreased F1 scores. These results identify strategies for building an AI that preserves the imbalance between training data with large differences in actual disease frequencies, which is important for constructing AI for practical histopathological classification.

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The cover image is based on the Original Article Single‐cell RNA sequencing reveals new subtypes of lens superficial tissue in humans by Meng‐Chao Zhu et al., https://doi.org/10.1111/cpr.13477. image

Multimodal imaging of metabolic activities for distinguishing subtypes of breast cancer.

Triple negative breast cancer (TNBC) is a highly aggressive form of cancer. Detecting TNBC early is crucial for improving disease prognosis and optimizing treatment. Unfortunately, conventional imaging techniques fall short in providing a comprehensive differentiation of TNBC subtypes due to their limited sensitivity and inability to capture subcellular details. In this study, we present a multimodal imaging platform that integrates heavy water (D2O)-probed stimulated Raman scattering (DO-SRS), two-photon fluorescence (TPF), and second harmonic generation (SHG) imaging. This platform allows us to directly visualize and quantify the metabolic activities of TNBC subtypes at a subcellular level. By utilizing DO-SRS imaging, we were able to identify distinct levels of de novo lipogenesis, protein synthesis, cytochrome c metabolic heterogeneity, and lipid unsaturation rates in various TNBC subtype tissues. Simultaneously, TPF imaging provided spatial distribution mapping of NAD[P]H and flavin signals in TNBC tissues, revealing a high redox ratio and significant lipid turnover rate in TNBC BL2 (HCC1806) samples. Furthermore, SHG imaging enabled us to observe diverse orientations of collagen fibers in TNBC tissues, with higher anisotropy at the tissue boundary compared to the center. Our multimodal imaging platform offers a highly sensitive and subcellular approach to characterizing not only TNBC, but also other tissue subtypes and cancers.

Topologic Parametric Response Mapping Identifies Tissue Subtypes Associated with Emphysema Progression

Small airways disease (SAD) and emphysema are significant components of chronic obstructive pulmonary disease (COPD), a heterogenous disease where predicting progression is difficult. SAD, a principal cause of airflow obstruction in mild COPD, has been identified as a precursor to emphysema. Parametric Response Mapping (PRM) of chest computed tomography (CT) can help distinguish SAD from emphysema. Specifically, topologic PRM can define local patterns of both diseases to characterize how and in whom COPD progresses. We aimed to determine if distribution of CT-based PRM of functional SAD (fSAD) is associated with emphysema progression. We analyzed paired inspiratory-expiratory chest CT scans at baseline and 5-year follow up in 1495 COPDGene subjects using topological analyses of PRM classifications. By spatially aligning temporal scans, we mapped local emphysema at year five to baseline lobar PRM-derived topological readouts. K-means clustering was applied to all observations. Subjects were subtyped based on predominant PRM cluster assignments and assessed using non-parametric statistical tests to determine differences in PRM values, pulmonary function metrics, and clinical measures. We identified distinct lobar imaging patterns and classified subjects into three radiologic subtypes: emphysema-dominant (ED), fSAD-dominant (FD), and fSAD-transition (FT: transition from healthy lung to fSAD). Relative to year five emphysema, FT showed rapid local emphysema progression (-57.5%±1.1) compared to FD (-49.9%±0.5) and ED (-33.1%±0.4). FT consisted primarily of at-risk subjects (roughly 60%) with normal spirometry. The FT subtype of COPD may allow earlier identification of individuals without spirometrically-defined COPD at-risk for developing emphysema.

Valuing vicinity: Memory attention framework for context-based semantic segmentation in histopathology.

The segmentation of histopathological whole slide images into tumourous and non-tumourous types of tissue is a challenging task that requires the consideration of both local and global spatial contexts to classify tumourous regions precisely. The identification of subtypes of tumour tissue complicates the issue as the sharpness of separation decreases and the pathologist's reasoning is even more guided by spatial context. However, the identification of detailed tissue types is crucial for providing personalized cancer therapies. Due to the high resolution of whole slide images, existing semantic segmentation methods, restricted to isolated image sections, are incapable of processing context information beyond. To take a step towards better context comprehension, we propose a patch neighbour attention mechanism to query the neighbouring tissue context from a patch embedding memory bank and infuse context embeddings into bottleneck hidden feature maps. Our memory attention framework (MAF) mimics a pathologist's annotation procedure - zooming out and considering surrounding tissue context. The framework can be integrated into any encoder-decoder segmentation method. We evaluate the MAF on two public breast cancer and liver cancer data sets and an internal kidney cancer data set using famous segmentation models (U-Net, DeeplabV3) and demonstrate the superiority over other context-integrating algorithms - achieving a substantial improvement of up to 17% on Dice score. The code is publicly available at https://github.com/tio-ikim/valuing-vicinity.