Prognostic value of proliferation in invasive breast cancer: a review

Breast cancer survival prediction using an automated mitosis detection pipeline

Mitotic count (MC) is the most common measure to assess tumor proliferation in breast cancer patients and is highly predictive of patient outcomes. It is, however, subject to inter- and intraobserver variation and reproducibility challenges that may hamper its clinical utility. In past studies, artificial intelligence (AI)-supported MC has been shown to correlate well with traditional MC on glass slides. Considering the potential of AI to improve reproducibility of MC between pathologists, we undertook the next validation step by evaluating the prognostic value of a fully automatic method to detect and count mitoses on whole slide images using a deep learning model. The model was developed in the context of the Mitosis Domain Generalization Challenge 2021 (MIDOG21) grand challenge and was expanded by a novel automatic area selector method to find the optimal mitotic hotspot and calculate the MC per 2 mm2. We employed this method on a breast cancer cohort with long-term follow-up from the University Medical Centre Utrecht (N = 912) and compared predictive values for overall survival of AI-based MC and light-microscopic MC, previously assessed during routine diagnostics. The MIDOG21 model was prognostically comparable to the original MC from the pathology report in uni- and multivariate survival analysis. In conclusion, a fully automated MC AI algorithm was validated in a large cohort of breast cancer with regard to retained prognostic value compared with traditional light-microscopic MC.

Recovering single-cell expression profiles from spatial transcriptomics with scResolve

Many popular spatial transcriptomics techniques lack single-cell resolution. Instead, these methods measure the collective gene expression for each location from a mixture of cells, potentially containing multiple cell types. Here, we developed scResolve, a method for recovering single-cell expression profiles from spatial transcriptomics measurements at multi-cellular resolution. scResolve accurately restores expression profiles of individual cells at their locations, which is unattainable with cell type deconvolution. Applications of scResolve on human breast cancer data and human lung disease data demonstrate that scResolve enables cell-type-specific differential gene expression analysis between different tissue contexts and accurate identification of rare cell populations. The spatially resolved cellular-level expression profiles obtained through scResolve facilitate more flexible and precise spatial analysis that complements raw multi-cellular level analysis.

Emerin deficiency drives MCF7 cells to an invasive phenotype

During metastasis, cancer cells traverse the vasculature by squeezing through very small gaps in the endothelium. Thus, nuclei in metastatic cancer cells must become more malleable to move through these gaps. Our lab showed invasive breast cancer cells have 50% less emerin protein resulting in smaller, misshapen nuclei, and higher metastasis rates than non-cancerous controls. Thus, emerin deficiency was predicted to cause increased nuclear compliance, cell migration, and metastasis. We tested this hypothesis by downregulating emerin in noninvasive MCF7 cells and found emerin knockdown causes smaller, dysmorphic nuclei, resulting in increased impeded cell migration. Emerin reduction in invasive breast cancer cells showed similar results. Supporting the clinical relevance of emerin reduction in cancer progression, our analysis of 192 breast cancer patient samples showed emerin expression inversely correlates with cancer invasiveness. We conclude emerin loss is an important driver of invasive transformation and has utility as a biomarker for tumor progression.

Integrating Spatial and Morphological Characteristics into Melanoma Prognosis: A Computational Approach

In this study, the prognostic value of cellular morphology and spatial configurations in melanoma has been examined, aiming to complement traditional prognostic indicators like mitotic activity and tumor thickness. Through a computational pipeline using machine learning and deep learning methods, we quantified nuclei sizes within different spatial regions and analyzed their prognostic significance using univariate and multivariate Cox models. Nuclei sizes in the invasive band demonstrated a significant hazard ratio (HR) of 1.1 (95% CI: 1.03, 1.18). Similarly, the nuclei sizes of tumor cells and Ki67 S100 co-positive cells in the invasive band achieved HRs of 1.07 (95% CI: 1.02, 1.13) and 1.09 (95% CI: 1.04, 1.16), respectively. Our findings reveal that nuclei sizes, particularly in the invasive band, are potentially prognostic factors. Correlation analyses further demonstrated a meaningful relationship between cellular morphology and tumor progression, notably showing that nuclei size within the invasive band correlates substantially with tumor thickness. These results suggest the potential of integrating spatial and morphological analyses into melanoma prognostication.

MKI67 with arterial hypertension predict a poor survival for prostate cancer patients, a real-life investigation

INTRODUCTION

Prostate cancer is a common urology malignant in males, ranking second globally. The disease is especially severe when diagnosed alongside hypertension. MKI67 is an established marker of neoplastic cell proliferation in humans, but the significance of its prognostic value in patients with prostate cancer and hypertension requires further research.

METHODS

In this retrospective analysis, we evaluated 296 hypertensive prostate cancer patients between March 2, 2012, and November 1, 2015. We used Cox regression models and prediction analysis to assess overall survival. Furthermore, we created a nomogram and verified its accuracy using a calibration curve.

RESULTS

Of all participants, 101 (34.12%) died. Our multi-factor analysis revealed that MKI67 expression was associated with an increased hazard ratio of death (> fivefold) (Hazard Ratio 5.829, 95% CI 3.349-10.138, p value < 0.01) and progression (twofold) (HR 2.059, 95% CI 1.368-3.102, p value < 0.01). Our Lasso analysis model displayed that several factors, including heart failure, smoking, ACS, serum albumin, Gealson score, prognostic nutritional index, MKI67 expression, surgery, and stage were high risks of prostate cancer. To ensure each covariate's contribution to cancer prognosis, we created a Cox model nomogram, which accurately predicted the risk of death (C-statistic of 0.8289) and had a proper calibration plot for risk assessment.

CONCLUSION

MKI67 expression predicts poor outcomes for overall mortality in prostate cancer and hypertension patients. Additionally, our cross-validated multivariate score, which includes MKI67, demonstrated accuracy efficacy of predicting prognosis.

Biomarkers for prognosis of meningioma patients: A systematic review and meta-analysis

Meningioma is the most common primary brain tumor and many studies have evaluated numerous biomarkers for their prognostic value, often with inconsistent results. Currently, no reliable biomarkers are available to predict the survival, recurrence, and progression of meningioma patients in clinical practice. This study aims to evaluate the prognostic value of immunohistochemistry-based (IHC) biomarkers of meningioma patients. A systematic literature search was conducted up to November 2023 on PubMed, CENTRAL, CINAHL Plus, and Scopus databases. Two authors independently reviewed the identified relevant studies, extracted data, and assessed the risk of bias of the studies included. Meta-analyses were performed with the hazard ratio (HR) and 95% confidence interval (CI) of overall survival (OS), recurrence-free survival (RFS), and progression-free survival (PFS). The risk of bias in the included studies was evaluated using the Quality in Prognosis Studies (QUIPS) tool. A total of 100 studies with 16,745 patients were included in this review. As the promising markers to predict OS of meningioma patients, Ki-67/MIB-1 (HR = 1.03, 95%CI 1.02 to 1.05) was identified to associate with poor prognosis of the patients. Overexpression of cyclin A (HR = 4.91, 95%CI 1.38 to 17.44), topoisomerase II α (TOP2A) (HR = 4.90, 95%CI 2.96 to 8.12), p53 (HR = 2.40, 95%CI 1.73 to 3.34), vascular endothelial growth factor (VEGF) (HR = 1.61, 95%CI 1.36 to 1.90), and Ki-67 (HR = 1.33, 95%CI 1.21 to 1.46), were identified also as unfavorable prognostic biomarkers for poor RFS of meningioma patients. Conversely, positive progesterone receptor (PR) and p21 staining were associated with longer RFS and are considered biomarkers of favorable prognosis of meningioma patients (HR = 0.60, 95% CI 0.41 to 0.88 and HR = 1.89, 95%CI 1.11 to 3.20). Additionally, high expression of Ki-67 was identified as a prognosis biomarker for poor PFS of meningioma patients (HR = 1.02, 95%CI 1.00 to 1.04). Although only in single studies, KPNA2, CDK6, Cox-2, MCM7 and PCNA are proposed as additional markers with high expression that are related with poor prognosis of meningioma patients. In conclusion, the results of the meta-analysis demonstrated that PR, cyclin A, TOP2A, p21, p53, VEGF and Ki-67 are either positively or negatively associated with survival of meningioma patients and might be useful biomarkers to assess the prognosis.

Reliability and Variability of Ki-67 Digital Image Analysis Methods for Clinical Diagnostics in Breast Cancer

Ki-67 is a nuclear protein associated with proliferation, and a strong potential biomarker in breast cancer, but is not routinely measured in current clinical management owing to a lack of standardization. Digital image analysis (DIA) is a promising technology that could allow high-throughput analysis and standardization. There is a dearth of data on the clinical reliability as well as intra- and interalgorithmic variability of different DIA methods. In this study, we scored and compared a set of breast cancer cases in which manually counted Ki-67 has already been demonstrated to have prognostic value (n = 278) to 5 DIA methods, namely Aperio ePathology (Lieca Biosystems), Definiens Tissue Studio (Definiens AG), Qupath, an unsupervised immunohistochemical color histogram algorithm, and a deep-learning pipeline piNET. The piNET system achieved high agreement (interclass correlation coefficient: 0.850) and correlation (R = 0.85) with the reference score. The Qupath algorithm exhibited a high degree of reproducibility among all rater instances (interclass correlation coefficient: 0.889). Although piNET performed well against absolute manual counts, none of the tested DIA methods classified common Ki-67 cutoffs with high agreement or reached the clinically relevant Cohen's κ of at least 0.8. The highest agreement achieved was a Cohen's κ statistic of 0.73 for cutoffs 20% and 25% by the piNET system. The main contributors to interalgorithmic variation and poor cutoff characterization included heterogeneous tumor biology, varying algorithm implementation, and setting assignments. It appears that image segmentation is the primary explanation for semiautomated intra-algorithmic variation, which involves significant manual intervention to correct. Automated pipelines, such as piNET, may be crucial in developing robust and reproducible unbiased DIA approaches to accurately quantify Ki-67 for clinical diagnosis in the future.

Tissue Expression of Growth Differentiation Factor 11 in Patients with Breast Cancer

Protein growth differentiation factor 11 (GDF11) plays crucial roles in cellular processes, including differentiation and development; however, its clinical relevance in breast cancer patients is poorly understood. We enrolled 68 breast cancer patients who underwent surgery at our hospital and assessed the expression of GDF11 in tumorous, ductal carcinoma in situ (DCIS), and non-tumorous tissues using immunohistochemical staining, with interpretation based on histochemical scoring (H-score). Our results indicated higher GDF11 expressions in DCIS and normal tissues compared to tumorous tissues. In addition, the GDF11 H-score was lower in the patients with a tumor size ≥ 2 cm, pathologic T3 + T4 stages, AJCC III-IV stages, Ki67 ≥ 14% status, HER2-negative, and specific molecular tumor subtypes. Notably, the patients with triple-negative breast cancer exhibited a loss of GDF11 expression. Spearman correlation analysis revealed associations between GDF11 expression and various clinicopathological characteristics, including tumor size, stage, Ki67, and molecular subtypes. Furthermore, GDF11 expression was positively correlated with mean corpuscular hemoglobin concentration and negatively correlated with neutrophil count, as well as standard deviation and coefficient of variation of red cell distribution width. These findings suggest that a decreased GDF11 expression may play a role in breast cancer pathogenesis.

Artificial Intelligence-Based Mitosis Scoring in Breast Cancer: Clinical Application

In recent years, artificial intelligence (AI) has demonstrated exceptional performance in mitosis identification and quantification. However, the implementation of AI in clinical practice needs to be evaluated against the existing methods. This study is aimed at assessing the optimal method of using AI-based mitotic figure scoring in breast cancer (BC). We utilized whole slide images from a large cohort of BC with extended follow-up comprising a discovery (n = 1715) and a validation (n = 859) set (Nottingham cohort). The Cancer Genome Atlas of breast invasive carcinoma (TCGA-BRCA) cohort (n = 757) was used as an external test set. Employing automated mitosis detection, the mitotic count was assessed using 3 different methods, the mitotic count per tumor area (MCT; calculated by dividing the number of mitotic figures by the total tumor area), the mitotic index (MI; defined as the average number of mitotic figures per 1000 malignant cells), and the mitotic activity index (MAI; defined as the number of mitotic figures in 3 mm2 area within the mitotic hotspot). These automated metrics were evaluated and compared based on their correlation with the well-established visual scoring method of the Nottingham grading system and Ki67 score, clinicopathologic parameters, and patient outcomes. AI-based mitotic scores derived from the 3 methods (MCT, MI, and MAI) were significantly correlated with the clinicopathologic characteristics and patient survival (P < .001). However, the mitotic counts and the derived cutoffs varied significantly between the 3 methods. Only MAI and MCT were positively correlated with the gold standard visual scoring method used in Nottingham grading system (r = 0.8 and r = 0.7, respectively) and Ki67 scores (r = 0.69 and r = 0.55, respectively), and MAI was the only independent predictor of survival (P < .05) in multivariate Cox regression analysis. For clinical applications, the optimum method of scoring mitosis using AI needs to be considered. MAI can provide reliable and reproducible results and can accurately quantify mitotic figures in BC.

Regression-based Deep-Learning predicts molecular biomarkers from pathology slides

Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesize that regression-based DL outperforms classification-based DL. Therefore, we develop and evaluate a self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from 11,671 images of patients across nine cancer types. We test our method for multiple clinically and biologically relevant biomarkers: homologous recombination deficiency score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the predictions' correspondence to regions of known clinical relevance over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology.

Cardiac dysfunction and their determinants in patients treated for breast cancer and lymphoma: A cardio-oncology center experience

OBJECTIVE

Cancer and cardiovascular diseases both have adverse effects on each other. We aim in the current study to investigate cardiac dysfunction including its prevalence, and associated factors in patients treated for breast cancer and lymphoma in a unique cardiac oncology center.

METHODS

A single-center retrospective study included 180 patients with cancer breast and lymphoma who presented and were treated at our oncology center from January 2019 to February 2022.

RESULT

Out of 180 consecutive patients, 155 patients (86 %) were diagnosed with cancer breast and 25 patients (14 %) were diagnosed with lymphoma. Patients with lymphoma were older age, less obese, and showed more prevalence of diabetes mellitus (DM) (P = 0.026, 0.05, and 0.04 respectively). They also showed more post-therapy left ventricular (LV) dilatation and lower values of global longitudinal strain (GLS); however, they did not develop more LV dysfunction compared to cancer breast patients. Moreover, lymphoma patients showed poor in-hospital outcomes (P = 0.04, 0.001, and 0.015 for infection, pericardial effusion, and mortality respectively). Cancer therapy-related cardiac dysfunction (CTRCD) was observed in 41 patients (23 %) of our population. The independent predictors of CTRCD in the current study were DM, low body mass index (BMI), and the use of trastuzumab.

CONCLUSIONS

Some patients treated for breast cancer and lymphoma develop LV dysfunction. Lymphoma patients showed more subclinical LV dysfunction and poor in-hospital outcomes compared to patients with cancer breast. DM, low body mass index (BMI), and the use of trastuzumab were the independent predictors of cardiac dysfunction among our patients.

scResolve: Recovering single cell expression profiles from multi-cellular spatial transcriptomics

Many popular spatial transcriptomics techniques lack single-cell resolution. Instead, these methods measure the collective gene expression for each location from a mixture of cells, potentially containing multiple cell types. Here, we developed scResolve, a method for recovering single-cell expression profiles from spatial transcriptomics measurements at multi-cellular resolution. scResolve accurately restores expression profiles of individual cells at their locations, which is unattainable from cell type deconvolution. Applications of scResolve on human breast cancer data and human lung disease data demonstrate that scResolve enables cell type-specific differential gene expression analysis between different tissue contexts and accurate identification of rare cell populations. The spatially resolved cellular-level expression profiles obtained through scResolve facilitate more flexible and precise spatial analysis that complements raw multi-cellular level analysis.

Molecular signature to predict quality of life and survival with glioblastoma using Multiview omics model

Glioblastoma multiforme (GBM) patients show a variety of signs and symptoms that affect their quality of life (QOL) and self-dependence. Since most existing studies have examined prognostic factors based only on clinical factors, there is a need to consider the value of integrating multi-omics data including gene expression and proteomics with clinical data in identifying significant biomarkers for GBM prognosis. Our research aimed to isolate significant features that differentiate between short-term (≤ 6 months) and long-term (≥ 2 years) GBM survival, and between high Karnofsky performance scores (KPS ≥ 80) and low (KPS ≤ 60), using the iterative random forest (iRF) algorithm. Using the Cancer Genomic Atlas (TCGA) database, we identified 35 molecular features composed of 19 genes and 16 proteins. Our findings propose molecular signatures for predicting GBM prognosis and will improve clinical decisions, GBM management, and drug development.

AI-enabled routine H&E image based prognostic marker for early-stage luminal breast cancer

Breast cancer (BC) grade is a well-established subjective prognostic indicator of tumour aggressiveness. Tumour heterogeneity and subjective assessment result in high degree of variability among observers in BC grading. Here we propose an objective Haematoxylin & Eosin (H&E) image-based prognostic marker for early-stage luminal/Her2-negative BReAst CancEr that we term as the BRACE marker. The proposed BRACE marker is derived from AI based assessment of heterogeneity in BC at a detailed level using the power of deep learning. The prognostic ability of the marker is validated in two well-annotated cohorts (Cohort-A/Nottingham: n = 2122 and Cohort-B/Coventry: n = 311) on early-stage luminal/HER2-negative BC patients treated with endocrine therapy and with long-term follow-up. The BRACE marker is able to stratify patients for both distant metastasis free survival (p = 0.001, C-index: 0.73) and BC specific survival (p < 0.0001, C-index: 0.84) showing comparable prediction accuracy to Nottingham Prognostic Index and Magee scores, which are both derived from manual histopathological assessment, to identify luminal BC patients that may be likely to benefit from adjuvant chemotherapy.

Assessment of nuclear grade-based recurrence risk classification in patients with hormone receptor-positive, human epidermal growth factor receptor 2-negative, node-positive high-risk early breast cancer

BACKGROUND

Histological grade (HG) has been used in the MonrachE trial to select patients with hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative, node-positive high-risk early breast cancer (EBC). Although nuclear grade (NG) is widely used in Japan, it is still unclear whether replacing HG with NG can appropriately select high-risk patients.

METHODS

We retrospectively reviewed 647 patients with HR-positive, HER2-negative, node-positive EBC and classified them into the following four groups: group 1: ≥ 4 positive axillary lymph nodes (pALNs) or 1-3 pALNs and either grade 3 of both grading systems or tumors ≥ 5 cm; group 2: 1-3 pALNs, grade < 3, tumor < 5 cm, and Ki-67 ≥ 20%; group 3: 1-3 pALNs, grade < 3, tumor < 5 cm, and Ki-67 < 20%; and group 4: group 2 or 3 by HG classification but group 1 by NG classification. We compared invasive disease-free survival (IDFS) and distant relapse-free survival (DRFS) among the four groups using the Kaplan-Meier method with the log-rank test.

RESULTS

Group 1 had a significantly worse 5-year IDFS and DRFS than groups 2 and 3 (IDFS 80.8% vs. 89.5%, P = 0.0319, 80.8% vs. 95.5%, P = 0.002; DRFS 85.2% vs. 95.3%, P = 0.0025, 85.2% vs. 98.4%, P < 0.001, respectively). Group 4 also had a significantly worse 5-year IDFS (78.0%) and DRFS (83.6%) than groups 2 and 3.

CONCLUSIONS

NG was useful for stratifying the risk of recurrence in patients with HR-positive, HER2-negative, node-positive EBC and was the appropriate risk assessment for patient groups not considered high-risk by HG classification.

Clinicopathological and prognostic value of TIL and PD L1 in triple negative breast carcinomas

Triple negative breast cancer (TNBC), a highly aggressive subtype of breast cancer, accounts for 15 % of all diagnosed breast cancers. This group, which has the worst clinical outcome, high recurrence rate and poor prognosis, does not benefit from specific treatment. Therefore, there is a need to develop more effective biomarker and therapeutic strategies especially for this group. A positive level of immunity has been found to be associated with patient survival in various organ cancers. More specifically, tumor infiltrating lymphocytes (TIL) have been documented to have strong prognostic value. The programmed cell death 1 (PD 1) protein on the surface of T lymphocytes is activated by the Programmed cell death ligand 1 (PD-L1) protein on the cancer cell surface. PD- L1 is thought to form a pathway that results in suppression of antitumor responses when activated. Patients with breast cancer (BC) who underwent resection without neoadjuvant chemotherapy between 2010 and 2020 were included in this study. Of the 302 BCs examined, 21 constitute the group with TNBC. In our study, the mean age of the Triple positive breast cancer (TPBC) and TNBC groups was similar (55.67 ± 12.61 vs. 53.23 ± 8.21, p = 0.384). There was no significant correlation between TPBC and TNBC and tumor size, lymph node, histological grade, and PD-L1 positivity in the center of the tumor (all p-value >.05). It was observed that tumor stage was higher in patients with TNBC than in patients with TPBC (19 % vs. 1.1 %, p = .002). The Ki 67 proliferation index was found to be higher in patients with TNBC than in patients with TPBC (90.5 % vs. 41.8 %, p .001). Although not statistically significant, clinically, CD 3 and CD 8 immune scores with high tumor margin were higher in patients with TNBC than in patients with TPBC (90.4 % vs, 9.6 % and 85.7 % vs. 14.3 %, respectively). Positive expression of PD-L1 at the tumor margin was significantly higher in patients with TNBC than patients with TPBC (20.3 % vs, 52.4 %, p = .002). By Kaplan-Meier analysis, the survival distribution of CD 3 and CD 8 immunoscore, tumor central and margin PD-L1 values were compared. Mean follow-up was 136.18 months (range, 1 - 144 months); and the 10-year Overall Survival (OS) estimate for the population was 90.9 % (95 % CI, 85.5 - 96.7). In this study, this difference was not statistically significant according to the log-rank test. In this study, we aimed to evaluate the relationship between CD 3, CD 8 T lymphocyte immune score and PD-L1 expression at the tumor center and margin in TNBC, the prognostic value and clinicopathological significance of this relationship.

Compression drives diverse transcriptomic and phenotypic adaptations in melanoma

Physical forces are prominent during tumor progression. However, it is still unclear how they impact and drive the diverse phenotypes found in cancer. Here, we apply an integrative approach to investigate the impact of compression on melanoma cells. We apply bioinformatics to screen for the most significant compression-induced transcriptomic changes and investigate phenotypic responses. We show that compression-induced transcriptomic changes are associated with both improvement and worsening of patient prognoses. Phenotypically, volumetric compression inhibits cell proliferation and cell migration. It also induces organelle stress and intracellular oxidative stress and increases pigmentation in malignant melanoma cells and normal human melanocytes. Finally, cells that have undergone compression become more resistant to cisplatin treatment. Our findings indicate that volumetric compression is a double-edged sword for melanoma progression and drives tumor evolution.

Chemopreventive effect of a milk whey by-product derived from Buffalo (Bubalus bubalis) in protecting from colorectal carcinogenesis

BACKGROUND

Several studies show that natural foods are a source of compounds with anticancer properties that affect the gut microbiota and its metabolites. In the present study, we investigate the effect of a delactosed buffalo milk whey by-product (DMW) on colorectal carcinogenesis.

METHODS

The effect of DMW on colorectal carcinoma (CRC) was investigated in the established mouse model of azoxymethane (AOM)-induced colon carcinoma, which closely resembles the human clinical condition of CRC. The effect of DMW on CRC immortalized cell lines was also evaluated to further identify the antineoplastic mechanism of action.

RESULTS

Pretreatment of AOM-treated mice with DMW significantly (P < 0.05) reduced the percentage of mice bearing both aberrant crypt foci with more than four crypts (which are early precancerous lesions that progress to CRC) and tumors. In addition, DMW completely counteracted the effect of AOM on protein expression of caspase-9, cleaved caspase-3 and poly ADP-ribose polymerase in colonic tissue. Administration of DMW alone (i.e. without AOM) resulted in changes in the composition of the gut microbiota, leading to enrichment or depletion of genera associated with health and disease, respectively. DMW was also able to restore AOM-induced changes in specific genera of the gut microbiota. Specifically, DMW reduced the genera Atopobiaceae, Ruminococcus 1 and Lachnospiraceae XPB1014 and increased the genera Parabacteroides and Candidatus Saccharimonas, which were increased and reduced, respectively, by AOM. Blood levels of butyric acid and cancer diagnostic markers (5-methylcytidine and glycerophosphocholine), which were increased by AOM treatment, were reduced by DMW. Furthermore, DMW exerted cytotoxic effects on two human CRC cell lines (HCT116 and HT29) and these effects were associated with the induction of apoptotic signaling.

CONCLUSIONS

Our results suggest that DMW exerts chemopreventive effects and restores the gut microbiota in AOM-induced CRC, and induces cytotoxic effect on CRC cells. DMW could be an important dietary supplement to support a healthy gut microbiota and reduce the prevalence of CRC in humans. Video Abstract.

Deep learning supported mitoses counting on whole slide images: A pilot study for validating breast cancer grading in the clinical workflow

Introduction

Breast cancer (BC) prognosis is largely influenced by histopathological grade, assessed according to the Nottingham modification of Bloom-Richardson (BR). Mitotic count (MC) is a component of histopathological grading but is prone to subjectivity. This study investigated whether mitoses counting in BC using digital whole slide images (WSI) compares better to light microscopy (LM) when assisted by artificial intelligence (AI), and to which extent differences in digital MC (AI assisted or not) result in BR grade variations.

Methods

Fifty BC patients with paired core biopsies and resections were randomly selected. Component scores for BR grade were extracted from pathology reports. MC was assessed using LM, WSI, and AI. Different modalities (LM-MC, WSI-MC, and AI-MC) were analyzed for correlation with scatterplots and linear regression, and for agreement in final BR with Cohen's κ.

Results

MC modalities strongly correlated in both biopsies and resections: LM-MC and WSI-MC (R² 0.85 and 0.83, respectively), LM-MC and AI-MC (R² 0.85 and 0.95), and WSI-MC and AI-MC (R² 0.77 and 0.83). Agreement in BR between modalities was high in both biopsies and resections: LM-MC and WSI-MC (κ 0.93 and 0.83, respectively), LM-MC and AI-MC (κ 0.89 and 0.83), and WSI-MC and AI-MC (κ 0.96 and 0.73).

Conclusion

This first validation study shows that WSI-MC may compare better to LM-MC when using AI. Agreement between BR grade based on the different mitoses counting modalities was high. These results suggest that mitoses counting on WSI can well be done, and validate the presented AI algorithm for pathologist supervised use in daily practice. Further research is required to advance our knowledge of AI-MC, but it appears at least non-inferior to LM-MC.

MHAttnSurv: Multi-head attention for survival prediction using whole-slide pathology images

Whole slide images (WSI) based survival prediction has attracted increasing interest in pathology. Despite this, extracting prognostic information from WSIs remains a challenging task due to their enormous size and the scarcity of pathologist annotations. Previous studies have utilized multiple instance learning approach to combine information from several randomly sampled patches, but this approach may not be adequate as different visual patterns may contribute unequally to prognosis prediction. In this study, we introduce a multi-head attention mechanism that allows each attention head to independently explore the utility of various visual patterns on a tumor slide, thereby enabling more comprehensive information extraction from WSIs. We evaluated our approach on four cancer types from The Cancer Genome Atlas database. Our model achieved an average c-index of 0.640, outperforming three existing state-of-the-art approaches for WSI-based survival prediction on these datasets. Visualization of attention maps reveals that the attention heads synergistically focus on different morphological patterns, providing additional evidence for the effectiveness of multi-head attention in survival prediction.

Syndecan-4 regulates the HER2-positive breast cancer cell proliferation cells via CK19/AKT signalling

Despite the use of the highly specific anti-HER2 receptor (trastuzumab) therapy, HER2-positive breast cancers account for 20-30% of all breast cancer carcinomas, with HER2 status a challenge to treatment interventions. The heparan sulfate proteoglycans (HSPGs) are prominently expressed in the extracellular matrix (ECM), mediate breast cancer proliferation, development, and metastasis with most studies to date conducted in animal models. This study examined HSPGs in HER2-positive human breast cancer cell lines and their contribution to cancer cell proliferation. The study examined the cells following enhancement (via the addition of heparin) and knockdown (KD; using short interfering RNA, siRNA) of HSPG core proteins. The interaction of HSPG core proteins and AKT signalling molecules was examined to identify any influence of this signalling pathway on cancer cell proliferation. Our findings illustrated the HSPG syndecan-4 (SDC4) core protein significantly regulates cell proliferation with increased BC cell proliferation following heparin addition to cultures and decreased cell number following SDC4 KD. In addition, along with SDC4, significant changes in CK19/AKT signalling were identified as mediators of BC HER2-positive BC cell proliferation. This study provides evidence for a cell growth regulatory axis involving HSPGs/CK19 and AKT that represents a potential molecular target to prevent proliferation of HER2-positive breast cancer cells.

Short term starvation potentiates the efficacy of chemotherapy in triple negative breast cancer via metabolic reprogramming

BACKGROUND

Chemotherapy (CT) is central to the treatment of triple negative breast cancer (TNBC), but drug toxicity and resistance place strong restrictions on treatment regimes. Fasting sensitizes cancer cells to a range of chemotherapeutic agents and also ameliorates CT-associated adverse effects. However, the molecular mechanism(s) by which fasting, or short-term starvation (STS), improves the efficacy of CT is poorly characterized.

METHODS

The differential responses of breast cancer or near normal cell lines to combined STS and CT were assessed by cellular viability and integrity assays (Hoechst and PI staining, MTT or H2DCFDA staining, immunofluorescence), metabolic profiling (Seahorse analysis, metabolomics), gene expression (quantitative real-time PCR) and iRNA-mediated silencing. The clinical significance of the in vitro data was evaluated by bioinformatical integration of transcriptomic data from patient data bases: The Cancer Genome Atlas (TCGA), European Genome-phenome Archive (EGA), Gene Expression Omnibus (GEO) and a TNBC cohort. We further examined the translatability of our findings in vivo by establishing a murine syngeneic orthotopic mammary tumor-bearing model.

RESULTS

We provide mechanistic insights into how preconditioning with STS enhances the susceptibility of breast cancer cells to CT. We showed that combined STS and CT enhanced cell death and increased reactive oxygen species (ROS) levels, in association with higher levels of DNA damage and decreased mRNA levels for the NRF2 targets genes NQO1 and TXNRD1 in TNBC cells compared to near normal cells. ROS enhancement was associated with compromised mitochondrial respiration and changes in the metabolic profile, which have a significant clinical prognostic and predictive value. Furthermore, we validate the safety and efficacy of combined periodic hypocaloric diet and CT in a TNBC mouse model.

CONCLUSIONS

Our in vitro, in vivo and clinical findings provide a robust rationale for clinical trials on the therapeutic benefit of short-term caloric restriction as an adjuvant to CT in triple breast cancer treatment.

Expression, assessment and significance of Ki67 expression in breast cancer: an update

Ki67 expression is one of the most important and cost-effective surrogate markers to assess for tumour cell proliferation in breast cancer (BC). The Ki67 labelling index has prognostic and predictive value in patients with early-stage BC, particularly in the hormone receptor-positive, HER2 (human epidermal growth factor receptor 2)-negative (luminal) tumours. However, many challenges exist in using Ki67 in routine clinical practice and it is still not universally used in the clinical setting. Addressing these challenges can potentially improve the clinical utility of Ki67 in BC. In this article, we review the function, immunohistochemical (IHC) expression, methods for scoring and interpretation of results as well as address several challenges of Ki67 assessment in BC. The prodigious attention associated with use of Ki67 IHC as a prognostic marker in BC resulted in high expectation and overestimation of its performance. However, the realisation of some pitfalls and disadvantages, which are expected with any similar markers, resulted in an increasing criticism of its clinical use. It is time to consider a pragmatic approach and weigh the benefits against the weaknesses and identify factors to achieve the best clinical utility. Here we highlight the strengths of its performance and provide some insights to overcome the existing challenges.

Mitosis domain generalization in histopathology images - The MIDOG challenge

The density of mitotic figures (MF) within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of MF by pathologists is subject to a strong inter-rater bias, limiting its prognostic value. State-of-the-art deep learning methods can support experts but have been observed to strongly deteriorate when applied in a different clinical environment. The variability caused by using different whole slide scanners has been identified as one decisive component in the underlying domain shift. The goal of the MICCAI MIDOG 2021 challenge was the creation of scanner-agnostic MF detection algorithms. The challenge used a training set of 200 cases, split across four scanning systems. As test set, an additional 100 cases split across four scanning systems, including two previously unseen scanners, were provided. In this paper, we evaluate and compare the approaches that were submitted to the challenge and identify methodological factors contributing to better performance. The winning algorithm yielded an F₁ score of 0.748 (CI95: 0.704-0.781), exceeding the performance of six experts on the same task.

Therapeutic targets in cancer treatment: Cell cycle proteins

Cancer has been linked to the uncontrolled proliferation of cells and the overexpression of cell-cycle genes. The cell cycle machinery plays a crucial role in the regulation of the apoptosis to mitosis to growth phase progression. The mechanisms of the cell cycle also play an important role in preventing DNA damage. There are multiple members of the protein kinase family that are involved in the activities of the cell cycle. Essential cyclins effectively regulate cyclin-dependent kinases (CDKs), which are themselves adversely regulated by naturally occurring CDK inhibitors. Despite the fact that various compounds can effectively block the cell cycle kinases and being investigated for their potential to fight cancer. This chapter explains the detail of cell cycle and checkpoint regulators, that are crucial to the malignant cellular process. The known CDKs inhibitors and their mechanism of action in various cancers have also been addressed as a step toward the development of a possibly novel technique for the design of new drugs against cell cycle kinase proteins.

Correlation between conventional ultrasound features combined with contrast-enhanced ultrasound patterns and pathological prognostic factors in malignant non-mass breast lesions

OBJECTIVE

To investigate the correlation between ultrasound performance and prognostic factors in malignant non-mass breast lesions (NMLs).

MATERIALS AND METHODS

This study included 106 malignant NMLs in 104 patients. Different US features and contrast enhancement patterns were evaluated. Prognostic factors, including histological types and grades, axillary lymph node and peritumoral lymphovascular status, estrogen and progesterone receptor status and the expression of HER-2 and Ki-67 were determined. A chi-square test and logistic regression analysis were used to analyse possible associations.

RESULTS

Lesion size (OR: 3.08, p = 0.033) and posterior echo attenuation (OR: 8.38, p < 0.001) were useful in reflecting malignant NMLs containing an invasive carcinoma component. Posterior echo attenuation (OR: 7.51, p = 0.003) and unclear enhancement margin (OR: 6.50, p = 0.018) were often found in tumors with axillary lymph node metastases. Peritumoural lymphovascular invasion mostly exhibited posterior echo attenuation (OR: 3.84, p = 0.049) and unclear enhancement margin (OR: 8.68, p = 0.042) on ultrasound images. Perfusion defect was a comparatively accurate enhancement indicator for negative ER (OR: 2.57, p = 0.041) and PR (OR: 3.04, p = 0.008) expression. Calcifications (OR: 3.03, p = 0.025) and enlarged enhancement area (OR: 5.36, p = 0.033) imply an increased risk of positive HER-2 expression. Similarly, Calcifications (OR: 4.13, p = 0.003) and enlarged enhancement area (OR: 11.05, p < 0.001) were valid predictors of high Ki-67 proliferation index.

CONCLUSION

Ultrasound performance is valuable for non-invasive prediction of prognostic factors in malignant NMLs.

DDX43 mRNA expression and protein levels in relation to clinicopathological profile of breast cancer

BACKGROUND

Breast cancer (BC) is the most often diagnosed cancer in women globally. Cancer cells appear to rely heavily on RNA helicases. DDX43 is one of DEAD- box RNA helicase family members. But, the relationship between clinicopathological, prognostic significance in different BC subtypes and DDX43 expression remains unclear. Therefore, the purpose of this study was to assess the clinicopathological significance of DDX43 protein and mRNA expression in different BC subtypes.

MATERIALS AND METHODS

A total of 80 females newly diagnosed with BC and 20 control females that were age-matched were recruited for this study. DDX43 protein levels were measured by ELISA technique. We used a real-time polymerase chain reaction quantification (real-time PCR) to measure the levels of DDX43 mRNA expression. Levels of DDX43 protein and mRNA expression within BC patients had been compared to those of control subjects and correlated with clinicopathological data.

RESULTS

The mean normalized serum levels of DDX43 protein were slightly higher in control than in both benign and malignant groups, but this result was non-significant. The mean normalized level of DDX43 mRNA expression was higher in the control than in both benign and malignant cases, although the results were not statistically significant and marginally significant, respectively. Moreover, the mean normalized level of DDX43 mRNA expression was significantly higher in benign than in malignant cases. In malignant cases, low DDX43 protein expression was linked to higher nuclear grade and invasive duct carcinoma (IDC), whereas high mRNA expression was linked to the aggressive types of breast cancer such as TNBC, higher tumor and nuclear grades.

CONCLUSION

This study explored the potential of using blood DDX43 mRNA expression or protein levels, or both in clinical settings as a marker of disease progression in human breast cancer. DDX43 mRNA expression proposes a less invasive method for discriminating benign from malignant BC.

Long term trends of breast cancer incidence according to proliferation status

BACKGROUND

Long-term breast cancer incidence trends according to proliferation status are poorly described. We studied time-trends in breast cancer incidence, using mitotic count and Ki-67 as markers of proliferation.

METHODS

Among 83,298 Norwegian women followed for breast cancer occurrence 1961-2012, 2995 incident breast cancers were diagnosed. Ki-67 was assessed using immunohistochemistry on tissue microarrays and mitoses were counted on whole sections. We compared incidence rates according to proliferation status among women born 1886-1928 and 1929-1977, estimating age-specific incidence rate ratios. We performed multiple imputations to account for unknown proliferation status. Mean values of Ki-67 and mitotic counts were calculated, according to age and birth year. We performed separate incidence analyses for HER2⁺ and triple negative breast cancers.

RESULTS

Among women aged 40-69 years, incidence rates of tumours with low-proliferative activity were higher among those born in 1929 or later, compared to before 1929, according to Ki-67 and mitotic count. Incidence rates of tumours with high-proliferative activity were also higher in women born in 1929 or later compared to before 1929 according to Ki-67, but not according to mitotic count. Mean values of Ki-67 and mitotic count varied according to age and birth year. In subtype-specific analyses we found an increase of high-proliferative HER2⁺ tumours according to Ki-67 in women born in 1929 or later, compared to before 1929.

CONCLUSIONS

There has been a temporal increase in both low- and high-proliferative breast cancers.

Validation and real-world clinical application of an artificial intelligence algorithm for breast cancer detection in biopsies

Breast cancer is the most common malignant disease worldwide, with over 2.26 million new cases in 2020. Its diagnosis is determined by a histological review of breast biopsy specimens, which can be labor-intensive, subjective, and error-prone. Artificial Intelligence (AI)-based tools can support cancer detection and classification in breast biopsies ensuring rapid, accurate, and objective diagnosis. We present here the development, external clinical validation, and deployment in routine use of an AI-based quality control solution for breast biopsy review. The underlying AI algorithm is trained to identify 51 different types of clinical and morphological features, and it achieves very high accuracy in a large, multi-site validation study. Specifically, the area under the receiver operating characteristic curves (AUC) for the detection of invasive carcinoma and of ductal carcinoma in situ (DCIS) are 0.99 (specificity and sensitivity of 93.57 and 95.51%, respectively) and 0.98 (specificity and sensitivity of 93.79 and 93.20% respectively), respectively. The AI algorithm differentiates well between subtypes of invasive and different grades of in situ carcinomas with an AUC of 0.97 for invasive ductal carcinoma (IDC) vs. invasive lobular carcinoma (ILC) and AUC of 0.92 for DCIS high grade vs. low grade/atypical ductal hyperplasia, respectively, as well as accurately identifies stromal tumor-infiltrating lymphocytes (TILs) with an AUC of 0.965. Deployment of this AI solution as a real-time quality control solution in clinical routine leads to the identification of cancers initially missed by the reviewing pathologist, demonstrating both clinical utility and accuracy in real-world clinical application.

Deep learning models for histologic grading of breast cancer and association with disease prognosis

Histologic grading of breast cancer involves review and scoring of three well-established morphologic features: mitotic count, nuclear pleomorphism, and tubule formation. Taken together, these features form the basis of the Nottingham Grading System which is used to inform breast cancer characterization and prognosis. In this study, we develop deep learning models to perform histologic scoring of all three components using digitized hematoxylin and eosin-stained slides containing invasive breast carcinoma. We first evaluate model performance using pathologist-based reference standards for each component. To complement this typical approach to evaluation, we further evaluate the deep learning models via prognostic analyses. The individual component models perform at or above published benchmarks for algorithm-based grading approaches, achieving high concordance rates with pathologist grading. Further, prognostic performance using deep learning-based grading is on par with that of pathologists performing review of matched slides. By providing scores for each component feature, the deep-learning based approach also provides the potential to identify the grading components contributing most to prognostic value. This may enable optimized prognostic models, opportunities to improve access to consistent grading, and approaches to better understand the links between histologic features and clinical outcomes in breast cancer.

Image Analysis in Digital Pathology Utilizing Machine Learning and Deep Neural Networks

Detection of regions of interest (ROIs) in whole slide images (WSIs) in a clinical setting is a highly subjective and a labor-intensive task. In this work, recent developments in machine learning and computer vision algorithms are presented to assess their possible usage and performance to enhance and accelerate clinical pathology procedures, such as ROI detection in WSIs. In this context, a state-of-the-art deep learning framework (Detectron2) was trained on two cases linked to the TUPAC16 dataset for object detection and on the JPATHOL dataset for instance segmentation. The predictions were evaluated against competing models and further possible improvements are discussed.

Exosomes in Breast Cancer: Involvement in Tumor Dissemination and Prospects for Liquid Biopsy

In women, breast cancer (BC) is the most commonly diagnosed cancer (24.5%) and the leading cause of cancer death (15.5%). Understanding how this heterogeneous disease develops and the confirm mechanisms behind tumor progression is of utmost importance. Exosomes are long-range message vesicles that mediate communication between cells in physiological conditions but also in pathology, such as breast cancer. In recent years, there has been an exponential rise in the scientific studies reporting the change in morphology and cargo of tumor-derived exosomes. Due to the transfer of biologically active molecules, such as RNA (microRNA, long non-coding RNA, mRNA, etc.) and proteins (transcription factors, enzymes, etc.) into recipient cells, these lipid bilayer 30-150 nm vesicles activate numerous signaling pathways that promote tumor development. In this review, we attempt to shed light on exosomes' involvement in breast cancer pathogenesis (including epithelial-to-mesenchymal transition (EMT), tumor cell proliferation and motility, metastatic processes, angiogenesis stimulation, and immune system repression). Moreover, the potential use of exosomes as promising diagnostic biomarkers for liquid biopsy of breast cancer is also discussed.

High levels of unfolded protein response component CHAC1 associates with cancer progression signatures in malignant breast cancer tissues

PURPOSE

The aberrant mRNA expression of a UPR component Cation transport regulator homolog 1 (CHAC1) has been reported to be associated with poor survival in breast and ovarian cancer patients, however, the expression of CHAC1 at protein levels in malignant breast tissues is underreported. The following study aimed at analyzing CHAC1 protein expression in malignant breast cancer tissues.

METHODS

Evaluation of CHAC1 expression in invasive ductal carcinomas (IDCs) with known ER, PR, and HER2 status was carried out using immunohistochemistry (IHC) with CHAC1 specific antibody. The Human breast cancer tissue microarray (TMA, cat# BR1503f, US Biomax, Inc., Rockville, MD) was used to determine CHAC1 expression. The analysis of CHAC1 IHC was done to determine its expression in terms of molecular subtypes of breast cancer, lymph node status, and proliferation index using Qu-Path software. Survival analysis was studied with a Kaplan-Meier plotter.

RESULTS

Immunohistochemical analysis of CHAC1 in breast cancer tissues showed significant up-regulation of CHAC1 as compared to the adjacent normal and benign tissues. Interestingly, CHAC1 immunostaining revealed high expression in tumor tissues with high proliferation and positive lymph node metastasis suggesting that CHAC1 might have an important role to play in breast cancer progression. Furthermore, high CHAC1 expression is associated with poor overall survival (OS) in large breast cancer patient cohorts.

CONCLUSION

As a higher expression of CHAC1 was observed in tissue cores with high Ki67 index and positive lymph node metastasis it may be concluded that enhanced CHAC1 expression correlates with proliferation and metastasis. The further analysis of breast cancer patients' survival data through KM plot indicated that high CHAC1 expression is associated with a bad prognosis hinting that CHAC1 may have a possible prognostic significance in breast cancer.

Defining the area of mitoses counting in invasive breast cancer using whole slide image

Although counting mitoses is part of breast cancer grading, concordance studies showed low agreement. Refining the criteria for mitotic counting can improve concordance, particularly when using whole slide images (WSIs). This study aims to refine the methodology for optimal mitoses counting on WSI. Digital images of 595 hematoxylin and eosin stained sections were evaluated. Several morphological criteria were investigated and applied to define mitotic hotspots. Reproducibility, representativeness, time, and association with outcome were the criteria used to evaluate the best area size for mitoses counting. Three approaches for scoring mitoses on WSIs (single and multiple annotated rectangles and multiple digital high-power (×40) screen fields (HPSFs)) were evaluated. The relative increase in tumor cell density was the most significant and easiest parameter for identifying hotspots. Counting mitoses in 3 mm2 area was the most representative regarding saturation and concordance levels. Counting in area <2 mm2 resulted in a significant reduction in mitotic count (P = 0.02), whereas counting in area ≥4 mm2 was time-consuming and did not add a significant rise in overall mitotic count (P = 0.08). Using multiple HPSF, following calibration, provided the most reliable, timesaving, and practical method for mitoses counting on WSI. This study provides evidence-based methodology for defining the area and methodology of visual mitoses counting using WSI. Visual mitoses scoring on WSI can be performed reliably by adjusting the number of monitor screens.

Epigenetic alterations at distal enhancers are linked to proliferation in human breast cancer

Aberrant DNA methylation is an early event in breast carcinogenesis and plays a critical role in regulating gene expression. Here, we perform genome-wide expression-methylation Quantitative Trait Loci (emQTL) analysis through the integration of DNA methylation and gene expression to identify disease-driving pathways under epigenetic control. By grouping the emQTLs using biclustering we identify associations representing important biological processes associated with breast cancer pathogenesis including regulation of proliferation and tumor-infiltrating fibroblasts. We report genome-wide loss of enhancer methylation at binding sites of proliferation-driving transcription factors including CEBP-β, FOSL1, and FOSL2 with concomitant high expression of proliferation-related genes in aggressive breast tumors as we confirm with scRNA-seq. The identified emQTL-CpGs and genes were found connected through chromatin loops, indicating that proliferation in breast tumors is under epigenetic regulation by DNA methylation. Interestingly, the associations between enhancer methylation and proliferation-related gene expression were also observed within known subtypes of breast cancer, suggesting a common role of epigenetic regulation of proliferation. Taken together, we show that proliferation in breast cancer is linked to loss of methylation at specific enhancers and transcription factor binding and gene activation through chromatin looping.

MiNuGAN: Dual Segmentation of Mitoses and Nuclei Using Conditional GANs on Multi-center Breast H&E Images

Breast cancer is the second most commonly diagnosed type of cancer among women as of 2021. Grading of histopathological images is used to guide breast cancer treatment decisions and a critical component of this is a mitotic score, which is related to tumor aggressiveness. Manual mitosis counting is an extremely tedious manual task, but automated approaches can be used to overcome inefficiency and subjectivity. In this paper, we propose an automatic mitosis and nuclear segmentation method for a diverse set of H&E breast cancer pathology images. The method is based on a conditional generative adversarial network to segment both mitoses and nuclei at the same time. Architecture optimizations are investigated, including hyper parameters and the addition of a focal loss. The accuracy of the proposed method is investigated using images from multiple centers and scanners, including TUPAC16, ICPR14 and ICPR12 datasets. In TUPAC16, we use 618 carefully annotated images of size 256×256 scanned at 40×. TUPAC16 is used to train the model, and segmentation performance is measured on the test set for both nuclei and mitoses. Results on 200 held-out testing images from the TUPAC16 dataset were mean DSC = 0.784 and 0.721 for nuclear and mitosis, respectively. On 202 ICPR12 images, mitosis segmentation accuracy had a mean DSC = 0.782, indicating the model generalizes well to unseen datasets. For datasets that had mitosis centroid annotations, which included 200 TUPAC16, 202 ICPR12 and 524 ICPR14, a mean F1-score of 0.854 was found indicating high mitosis detection accuracy.

Knockdown of circular RNA septin 9 inhibits the malignant progression of breast cancer by reducing the expression of solute carrier family 1 member 5 in a microRNA-149-5p-dependent manner

Breast cancer (BC) is the most frequently diagnosed cancer in women. Increasing evidence suggests that circular RNA (circRNA) exerts critical functions in BC progression. However, the roles of circRNA septin 9 (circSEPT9) in BC development and the underneath mechanism remain largely unclear so far. In this work, the RNA levels of circSEPT9, microRNA-149-5p (miR-149-5p) and solute carrier family 1 member 5 (SLC1A5) were detected by quantitative real-time polymerase chain reaction. Western blot was performed to check protein expression. Glutamine uptake, cell proliferation and cell apoptosis were investigated by glutamine uptake, cell counting kit-8, cell colony formation, 5-Ethynyl-29-deoxyuridine, flow cytometry analysis or DNA content quantitation assay. The interactions of miR-149-5p with circSEPT9 and SLC1A5 were identified by a dual-luciferase reporter assay. Mouse model assay was carried out to analyze the effect of circSEPT9 on tumor formation in vivo. Results showed that circSEPT9 and SLC1A5 expression were significantly upregulated, while miR-149-5p was downregulated in BC tissues and cells as compared with paracancerous normal breast tissues and human normal breast cells. Knockdown of circSEPT9 or SLC1A5 inhibited glutamine uptake and cell proliferation, but induced cell apoptosis in BC cells. SLC1A5 overexpression relieved circSEPT9 silencing-induced repression of BC cell malignancy. In mechanism, circSEPT9 regulated SLC1A5 expression by sponging miR-149-5p. In support, circSEPT9 knockdown led to delayed tumor tumorigenesis in vivo. In summary, these results indicates that circSEPT9 may act an oncogenic role in BC malignant progression by regulating miR-149-5p/SLC1A5 pathway, providing a novel mechanism responsible for BC development.

Comparison of Ultrasound Features With Maximum Standardized Uptake Value Assessed by 18F-Fluorodeoxyglucose-Positron Emission Tomography/Computed Tomography for Prognosis of Estrogen Receptor+/Human Epithelial Growth Factor Receptor 2- Breast Cancer

ABSTRACT

estrogen receptor (ER)+/human epithelial growth factor receptor 2 (HER2)- breast cancers have less aggressive traits and a favorable prognosis when treated early. Prediction of prognosis for treatment outcome or survival in ER+/HER2- cancer is important. Ultrasound (US) is an effective and easy technique for breast cancer diagnosis and tumor characterization. Positron emission tomography/computed tomography (PET/CT) is widely used for diagnosis, staging, and therapeutic response in cancer evaluation, and a high maximum standardized uptake value (SUVmax) is associated with poor prognosis. The study aim was to compare the prognostic value of US features with that of the SUVmax assessed by PET/CT in ER+/HER- breast cancer patients. We retrospectively identified breast cancer patients in our institutional database who had undergone preoperative US and PET/CT, and 96 patients with invasive ductal carcinoma and ductal carcinoma in situ were included in this study. The US features of mass shape, margin, echo pattern, orientation, posterior features, boundary, and calcification in the mass were analyzed. We then analyzed the US features to look for correlations with SUVmax and associations with margins, boundaries, posterior features, histological grade, and ki-67 expression. High SUVmax was correlated with irregular shape, not-circumscribed margin, posterior acoustic enhancement, echogenic halo, and calcification in the mass (P < 0.05, all). Posterior acoustic enhancement was correlated with high ki-67 expression. Many US features of ER+/HER- breast cancer showed associations with SUVmax. Some US features of ER+/HER- breast cancer were useful for predicting prognosis.

Daily caloric restriction limits tumor growth more effectively than caloric cycling regardless of dietary composition

Cancer incidence increases with age and is a leading cause of death. Caloric restriction (CR) confers benefits on health and survival and delays cancer. However, due to CR's stringency, dietary alternatives offering the same cancer protection have become increasingly attractive. Short cycles of a plant-based diet designed to mimic fasting (FMD) are protective against tumorigenesis without the chronic restriction of calories. Yet, it is unclear whether the fasting time, level of dietary restriction, or nutrient composition is the primary driver behind cancer protection. Using a breast cancer model in mice, we compare the potency of daily CR to that of periodic caloric cycling on FMD or an isocaloric standard laboratory chow against primary tumor growth and metastatic burden. Here, we report that daily CR provides greater protection against tumor growth and metastasis to the lung, which may be in part due to the unique immune signature observed with daily CR.

Assessment of proliferation in breast cancer: cell cycle or mitosis? An observational study

BACKGROUND AND AIMS

Proliferation is an important indicator of breast cancer (BC) prognosis, but is assessed using different approaches. Not all cells in the cell cycle are committed to division. This study aimed to characterise quantitative differences between BC cells in the cell cycle and those in mitosis and assess their relationship with other pathological parameters.

METHODS AND RESULTS

A cohort of BC sections (n = 621) was stained with haematoxylin and eosin and immunohistochemistry for Ki-67. The proportion of mitotic cells and Ki-67-positive cells was assessed in the same areas. The Cancer Genome Atlas (TCGA) BC cohort was used to assess MKI-67 transcriptome level and its association with the mitotic counts. The mean proportion of BC cells in the cell cycle was 24% (range = 1-90%), while the mean proportion of BC cells in mitosis was 5% (range = 0-73%). A low proportion of mitoses to whole cycling cells was associated with low histological grade tumours and the luminal A molecular subtype, while tumours with a high proportion of mitoses to the overall cycling cells were associated with triple-negative subtype, larger tumour size, grade 3 tumours and lymph node metastasis. The high mitosis/low Ki-67-positive cells tumours showed a significant association with variables of poor prognosis, including high-grade and triple-negative subtypes.

CONCLUSION

The proportion of BC cells in the cell cycle and mitosis is variable. We show that not only the number of cells in the cell cycle or mitosis, but also the difference between them, provides valuable information on tumour aggressiveness.

Visual assessment of mitotic figures in breast cancer: a comparative study between light microscopy and whole slide images

BACKGROUND AND AIMS

Visual assessment of mitotic figures in breast cancer (BC) remains a challenge. This is expected to be more pronounced in the digital pathology era. This study aims to refine the criteria of mitotic figure recognition, particularly in whole slide images (WSI).

METHOD AND RESULTS

Haematoxylin and eosin (H&E)-stained BC sections (n = 506) were examined using light microscopy (LM) and WSI. A set of features for identifying mitosis in WSI and to distinguish true figures from mimickers was developed. Changes in the mitotic count between the two platforms was explored. Morphological features of mitoses were recorded separately, including absence of nuclear membrane, chromatin hairy-like projections, shape, cytoplasmic features, mitotic cell size and relationship to surrounding cells. Each mitotic phase has its own mimickers. Fifty-eight per cent of mitoses showed absent hairy-like projection in WSI; however, 89% retained their ragged nuclear border, which distinguished them from mimickers including apoptotic cells, lymphocytes and dark elongated hyperchromatic structures. Mitosis in WSI showed loss of fine details, and there was a 20% average reduction rate of mitotic counts when compared to the same area on LM. Using refined mitosis recognition criteria in WSI resulted in a twofold improvement of interobserver concordance. However, when compared to LM, 19% of cases were underscored in WSIs.

CONCLUSIONS

All morphological features of mitosis should be considered to enable recognition and differentiation from their mimickers, particularly in WSI, to ensure reliable BC grading. Refining mitotic cut-offs per specific area when using WSI, based on the degree of reduction and association with outcome, is warranted.

Clinicopathological and prognostic significance of programmed cell death ligand 1 expression in patients diagnosed with breast cancer: meta-analysis

BACKGROUND

Uncertainty exists regarding the clinical relevance of programmed cell death ligand 1 (PD-L1) expression in breast cancer.

METHODS

A systematic review was performed in accordance with PRISMA guidelines. Observational studies that compared high versus low expression of PD-L1 on breast cancer cells were identified. Log hazard ratios (HRs) for disease-free and overall survival and their standard errors were calculated from Kaplan-Meier curves or Cox regression analyses, and pooled using the inverse-variance method. Dichotomous variables were pooled as odds ratios (ORs) using the Mantel-Haenszel method.

RESULTS

Sixty-five studies with 19 870 patients were included; 14 404 patients were classified as having low and 4975 high PD-L1 expression. High PD-L1 was associated with achieving a pathological complete response following neoadjuvant chemotherapy (OR 3.30, 95 per cent confidence interval 1.19 to 9.16; P < 0.01; I2 = 85 per cent). Low PD-L1 expression was associated with human epidermal growth factor receptor 2 (OR 3.98, 1.81 to 8.75; P < 0.001; I2 = 96 per cent) and luminal (OR 14.93, 6.46 to 34.51; P < 0.001; I2 = 99 per cent) breast cancer subtypes. Those with low PD-L1 had favourable overall survival rates (HR 1.30, 1.05 to 1.61; P = 0.02; I2 = 85 per cent).

CONCLUSION

Breast cancers with high PD-L1 expression are associated with aggressive clinicopathological and immunohistochemical characteristics and are more likely to achieve a pathological complete response following neoadjuvant chemotherapy. These breast cancers are, however, associated with worse overall survival outcomes.

Gigantol Targets MYC for Ubiquitin-proteasomal Degradation and Suppresses Lung Cancer Cell Growth

BACKGROUND

Gigantol is a pharmacologically active bibenzyl compound exerting potential anticancer activities. At non-toxic concentrations, it reduces cancer stem cell properties and tumorigenicity. The mechanisms of the effects of gigantol on cancer cell growth are largely unknown. This study aimed to unravel the molecular profile and identify the prominent molecular mechanism of the effects of gigantol in controlling lung cancer cell proliferation.

MATERIALS AND METHODS

Proteomics and bioinformatics analysis were used accompanied by experimental molecular pharmacology approaches.

RESULTS

Gigantol exhibited antiproliferative effects on human lung cancer cells confirmed by 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide proliferation assay and colony growth assay. The protein profile in response to gigantol treatment associated with regulation of cell proliferation was analyzed to determine the prominent protein targets. Among the significant hub proteins, MYC, an important proto-oncogene and proliferation-promoting transcription factor, was down-regulated with the highest number of protein-protein interactions. MYC down-regulation was confirmed by western blot analysis. The up-stream regulator of MYC, Glycogen synthase kinase 3 beta (GSK3β) was found to be responsible for MYC destabilization mediated by gigantol. Gigantol facilitated GSK3β function and resulted in the increase of MYC-ubiquitin complex as evaluated by immunoprecipitation.

CONCLUSION

Gigantol was found to inhibit lung cancer proliferation through induction of GSK3β-mediated MYC ubiquitin-proteasome degradation. These data suggest gigantol to be a promising candidate for novel strategy in inhibition of lung cancer.

Assessment of 25-Year Survival of Women With Estrogen Receptor-Positive/ERBB2-Negative Breast Cancer Treated With and Without Tamoxifen Therapy: A Secondary Analysis of Data From the Stockholm Tamoxifen Randomized Clinical Trial

IMPORTANCE

Clinically used breast cancer markers, such as tumor size, tumor grade, progesterone receptor (PR) status, and Ki-67 status, are known to be associated with short-term survival, but the association of these markers with long-term (25-year) survival is unclear.

OBJECTIVE

To assess the association of clinically used breast cancer markers with long-term survival and treatment benefit among postmenopausal women with lymph node-negative, estrogen receptor [ER]-positive and ERBB2-negative breast cancer who received tamoxifen therapy.

DESIGN, SETTING, AND PARTICIPANTS

This study was a secondary analysis of data from a subset of 565 women with ER-positive/ERBB2-negative breast cancer who participated in the Stockholm tamoxifen (STO-3) randomized clinical trial. The STO-3 clinical trial was conducted from 1976 to 1990 and comprised 1780 postmenopausal women with lymph node-negative breast cancer who were randomized to receive adjuvant tamoxifen therapy or no endocrine therapy. Complete 25-year follow-up data through December 31, 2016, were obtained from Swedish national registers. Immunohistochemical markers were reannotated in 2014. Data were analyzed from April to December 2020.

INTERVENTIONS

Patients in the original STO-3 clinical trial were randomized to receive 2 years of tamoxifen therapy vs no endocrine therapy. In 1983, patients who received tamoxifen therapy without cancer recurrence during the 2-year treatment and who consented to continued participation in the STO-3 study were further randomized to receive 3 additional years of tamoxifen therapy or no endocrine therapy.

MAIN OUTCOMES AND MEASURES

Distant recurrence-free interval (DRFI) by clinically used breast cancer markers was assessed using Kaplan-Meier and multivariable Cox proportional hazards analyses adjusted for age, period of primary diagnosis, tumor size (T1a and T1b [T1a/b], T1c, and T2), tumor grade (1-3), PR status (positive vs negative), Ki-67 status (low vs medium to high), and STO-3 clinical trial arm (tamoxifen treatment vs no adjuvant treatment). A recursive partitioning analysis was performed to evaluate which markers were able to best estimate long-term DRFI.

RESULTS

The study population comprised 565 postmenopausal women (mean [SD] age, 62.0 [5.3] years) with lymph node-negative, ER-positive/ERBB2-negative breast cancer. A statistically significant difference in long-term DRFI was observed by tumor size (88% for T1a/b vs 76% for T1c vs 63% for T2 tumors; log-rank P < .001) and tumor grade (81% for grade 1 vs 77% for grade 2 vs 65% for grade 3 tumors; log-rank P = .02) but not by PR status or Ki-67 status. Patients with smaller tumors (hazard ratio [HR], 0.31 [95% CI, 0.17-0.55] for T1a/b tumors and 0.58 [95% CI, 0.38-0.88] for T1c tumors) and grade 1 tumors (HR, 0.48; 95% CI, 0.24-0.95) experienced a significant reduction in the long-term risk of distant recurrence compared with patients with larger (T2) tumors and grade 3 tumors, respectively. A significant tamoxifen treatment benefit was observed among patients with larger tumors (HR, 0.53 [95% CI, 0.32-0.89] for T1c tumors and 0.34 [95% CI, 0.16-0.73] for T2 tumors), lower tumor grades (HR, 0.24 [95% CI, 0.07-0.82] for grade 1 tumors and 0.50 [95% CI, 0.31-0.80] for grade 2 tumors), and PR-positive status (HR, 0.38; 95% CI, 0.24-0.62). The recursive partitioning analysis revealed that tumor size was the most important characteristic associated with long-term survival, followed by clinical trial arm among patients with larger tumors.

CONCLUSIONS AND RELEVANCE

This secondary analysis of data from the STO-3 clinical trial indicated that, among the selected subgroup of patients, tumor size followed by tumor grade were the markers most significantly associated with long-term survival. Furthermore, a significant long-term tamoxifen treatment benefit was observed among patients with larger tumors, lower tumor grades, and PR-positive tumors.

Mitotic nuclei analysis in breast cancer histopathology images using deep ensemble classifier

Mitotic nuclei estimation in breast tumour samples has a prognostic significance in analysing tumour aggressiveness and grading system. The automated assessment of mitotic nuclei is challenging because of their high similarity with non-mitotic nuclei and heteromorphic appearance. In this work, we have proposed a new Deep Convolutional Neural Network (CNN) based Heterogeneous Ensemble technique "DHE-Mit-Classifier" for analysis of mitotic nuclei in breast histopathology images. The proposed technique in the first step detects candidate mitotic patches from the histopathological biopsy regions, whereas, in the second step, these patches are classified into mitotic and non-mitotic nuclei using the proposed DHE-Mit-Classifier. For the development of a heterogeneous ensemble, five different deep CNNs are designed and used as base-classifiers. These deep CNNs have varying architectural designs to capture the structural, textural, and morphological properties of the mitotic nuclei. The developed base-classifiers exploit different ideas, including (i) region homogeneity and feature invariance, (ii) asymmetric split-transform-merge, (iii) dilated convolution based multi-scale transformation, (iv) spatial and channel attention, and (v) residual learning. Multi-layer-perceptron is used as a meta-classifier to develop a robust and accurate classifier for providing the final decision. The performance of the proposed ensemble "DHE-Mit-Classifier" is evaluated against state-of-the-art CNNs. The performance evaluation on the test set suggests the superiority of the proposed ensemble with an F-score (0.77), recall (0.71), precision (0.83), and area under the precision-recall curve (0.80). The good generalisation of the proposed ensemble with a considerably high F-score and precision suggests its potential use in the development of an assistance tool for pathologists.

Interobserver concordance in visual assessment of Ki67 immunohistochemistry in surgical excision specimens from patients with lymph node-negative breast cancer

PURPOSE

This study aimed to determine the interobserver concordance of two methods for proliferation assessment in breast cancer using Ki67 immunohistochemistry.

METHODS

Ki67 was independently assessed in randomly selected tumour samples from patients with lymph node-negative breast cancer using two different methods: either cell counting or visual estimation of hot spot areas. For hot spot cell counting, positive and negative cell numbers were recorded for total cell counts of 300-500, 500-800 and 800-1000 cells. Visual estimation involved allocation of a score from 1 to 5 using a visual scale to estimate percentage positivity. Interobserver agreement for hot spot counting was calculated using a two-way fixed effects intraclass correlation model, and by using Cohen's kappa measure for visual assessment. Prognostic concordance between the two methods was also calculated using Cohen's kappa.

RESULTS

Samples from 96 patients were included in this analysis. Interobserver agreement for hot spot cell counting was excellent (> 0.75) across all three cell count ranges, with correlation coefficients of 0.88 (95% CI 0.84-0.92), 0.87 (95% CI 0.82-0.91) and 0.89 (95% CI 0.85-0.92), respectively. Interobserver agreement with visual estimation was greatest for hot spots compared with areas of intermediate or low proliferation, with kappa scores of 0.49, 0.42 and 0.40, respectively. Both assessment methods demonstrated excellent prognostic agreement.

CONCLUSIONS

Interobserver and prognostic concordance in Ki67 immunohistochemistry assessments was high using either hot spot cell counting or visual estimation, further supporting the utility and reproducibility of these cost-efficient methods to assess proliferation.

A Cascade of 2.5D CNN and Bidirectional CLSTM Network for Mitotic Cell Detection in 4D Microscopy Image

Mitosis detection is one of the challenging steps in biomedical imaging research, which can be used to observe the cell behavior. Most of the already existing methods that are applied in detecting mitosis usually contain many nonmitotic events (normal cell and background) in the result (false positives, FPs). In order to address such a problem, in this study, we propose to apply 2.5-dimensional (2.5D) networks called CasDetNet_CLSTM, which can accurately detect mitotic events in 4D microscopic images. This CasDetNet_CLSTM involves a 2.5D faster region-based convolutional neural network (Faster R-CNN) as the first network, and a convolutional long short-term memory (CLSTM) network as the second network. The first network is used to select candidate cells using the information from nearby slices, whereas the second network uses temporal information to eliminate FPs and refine the result of the first network. Our experiment shows that the precision and recall of our networks yield better results than those of other state-of-the-art methods.

Accurate and fast mitotic detection using an anchor-free method based on full-scale connection with recurrent deep layer aggregation in 4D microscopy images

BACKGROUND

To effectively detect and investigate various cell-related diseases, it is essential to understand cell behaviour. The ability to detection mitotic cells is a fundamental step in diagnosing cell-related diseases. Convolutional neural networks (CNNs) have been successfully applied to object detection tasks, however, when applied to mitotic cell detection, most existing methods generate high false-positive rates due to the complex characteristics that differentiate normal cells from mitotic cells. Cell size and orientation variations in each stage make detecting mitotic cells difficult in 2D approaches. Therefore, effective extraction of the spatial and temporal features from mitotic data is an important and challenging task. The computational time required for detection is another major concern for mitotic detection in 4D microscopic images.

RESULTS

In this paper, we propose a backbone feature extraction network named full scale connected recurrent deep layer aggregation (RDLA++) for anchor-free mitotic detection. We utilize a 2.5D method that includes 3D spatial information extracted from several 2D images from neighbouring slices that form a multi-stream input.

CONCLUSIONS

Our proposed technique addresses the scale variation problem and can efficiently extract spatial and temporal features from 4D microscopic images, resulting in improved detection accuracy and reduced computation time compared with those of other state-of-the-art methods.