Uninformed Teacher-Student for hard-samples distillation in weakly supervised mitosis localization

BACKGROUND AND OBJECTIVE

Mitotic activity is a crucial biomarker for diagnosing and predicting outcomes for different types of cancers, particularly breast cancer. However, manual mitosis counting is challenging and time-consuming for pathologists, with moderate reproducibility due to biopsy slide size, low mitotic cell density, and pattern heterogeneity. In recent years, deep learning methods based on convolutional neural networks (CNNs) have been proposed to address these limitations. Nonetheless, these methods have been hampered by the available data labels, which usually consist only of the centroids of mitosis, and by the incoming noise from annotated hard negatives. As a result, complex algorithms with multiple stages are often required to refine the labels at the pixel level and reduce the number of false positives.

METHODS

This article presents a novel weakly supervised approach for mitosis detection that utilizes only image-level labels on histological hematoxylin and eosin (H&E) images, avoiding the need for complex labeling scenarios. Also, an Uninformed Teacher-Student (UTS) pipeline is introduced to detect and distill hard samples by comparing weakly supervised localizations and the annotated centroids, using strong augmentations to enhance uncertainty. Additionally, an automatic proliferation score is proposed that mimicks the pathologist-annotated mitotic activity index (MAI). The proposed approach is evaluated on three publicly available datasets for mitosis detection on breast histology samples, and two datasets for mitotic activity counting in whole-slide images.

RESULTS

The proposed framework achieves competitive performance with relevant prior literature in all the datasets used for evaluation without explicitly using the mitosis location information during training. This approach challenges previous methods that rely on strong mitosis location information and multiple stages to refine false positives. Furthermore, the proposed pipeline for hard-sample distillation demonstrates promising dataset-specific improvements. Concretely, when the annotation has not been thoroughly refined by multiple pathologists, the UTS model offers improvements of up to ∼4% in mitosis localization, thanks to the detection and distillation of uncertain cases. Concerning the mitosis counting task, the proposed automatic proliferation score shows a moderate positive correlation with the MAI annotated by pathologists at the biopsy level on two external datasets.

CONCLUSIONS

The proposed Uninformed Teacher-Student pipeline leverages strong augmentations to distill uncertain samples and measure dissimilarities between predicted and annotated mitosis. Results demonstrate the feasibility of the weakly supervised approach and highlight its potential as an objective evaluation tool for tumor proliferation.

Proliferation and immunohistochemistry for p53, CD25 and CK20 in predicting prognosis of non-muscle invasive papillary urothelial carcinomas

Non-muscle invasive papillary urothelial carcinoma is a prevalent disease with a high recurrence tendency. Good prognostic and reproducible biomarkers for tumor recurrence and disease progression are lacking. Currently, WHO grade and tumor stage are essential in risk stratification and treatment decision-making. Here we present the prognostic value of proliferation markers (Ki67, mitotic activity index (MAI) and PPH3) together with p53, CD25 and CK20 immunohistochemistry (IHC). In this population-based retrospective study, 349 primary non-muscle invasive bladder cancers (NMIBC) were available. MAI and PPH3 were calculated manually according to highly standardized previously described methods, Ki-67 by the semi-automated QPRODIT quantification system, p53 and CD25 by the fully automated digital image analysis program Visipharm® and CK20 with the help of the semi-quantitative immunoreactive score (IRS). Survival analyses with log rank test, as well as univariate and multivariate Cox regression analyses were performed for all investigated variables. Age and multifocality were the only significant variables for tumor recurrence. All investigated variables, except gender, were significantly associated with stage progression. In multivariate analysis, MAI was the only prognostic variable for stage progression (p<0.001).

Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer

BACKGROUND

Pathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30-40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60-70% show residual disease (RD). The role of the tumor microenvironment in NAC response in patients with TNBC remains unclear. In this study, we developed a machine learning-based two-step pipeline to distinguish between various histological components in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of TNBC tissue biopsies and to identify histological features that can predict NAC response.

METHODS

H&E-stained WSIs of treatment-naïve biopsies from 85 patients (51 with pCR and 34 with RD) of the model development cohort and 79 patients (41 with pCR and 38 with RD) of the validation cohort were separated through a stratified eightfold cross-validation strategy for the first step and leave-one-out cross-validation strategy for the second step. A tile-level histology label prediction pipeline and four machine-learning classifiers were used to analyze 468,043 tiles of WSIs. The best-trained classifier used 55 texture features from each tile to produce a probability profile during testing. The predicted histology classes were used to generate a histology classification map of the spatial distributions of different tissue regions. A patient-level NAC response prediction pipeline was trained with features derived from paired histology classification maps. The top graph-based features capturing the relevant spatial information across the different histological classes were provided to the radial basis function kernel support vector machine (rbfSVM) classifier for NAC treatment response prediction.

RESULTS

The tile-level prediction pipeline achieved 86.72% accuracy for histology class classification, while the patient-level pipeline achieved 83.53% NAC response (pCR vs. RD) prediction accuracy of the model development cohort. The model was validated with an independent cohort with tile histology validation accuracy of 83.59% and NAC prediction accuracy of 81.01%. The histological class pairs with the strongest NAC response predictive ability were tumor and tumor tumor-infiltrating lymphocytes for pCR and microvessel density and polyploid giant cancer cells for RD.

CONCLUSION

Our machine learning pipeline can robustly identify clinically relevant histological classes that predict NAC response in TNBC patients and may help guide patient selection for NAC treatment.

Predicting Neoadjuvant Treatment Response in Triple-Negative Breast Cancer Using Machine Learning

BACKGROUND

Neoadjuvant chemotherapy (NAC) is the standard treatment for early-stage triple negative breast cancer (TNBC). The primary endpoint of NAC is a pathological complete response (pCR). NAC results in pCR in only 30-40% of TNBC patients. Tumor-infiltrating lymphocytes (TILs), Ki67 and phosphohistone H3 (pH3) are a few known biomarkers to predict NAC response. Currently, systematic evaluation of the combined value of these biomarkers in predicting NAC response is lacking. In this study, the predictive value of markers derived from H&E and IHC stained biopsy tissue was comprehensively evaluated using a supervised machine learning (ML)-based approach. Identifying predictive biomarkers could help guide therapeutic decisions by enabling precise stratification of TNBC patients into responders and partial or non-responders.

METHODS

Serial sections from core needle biopsies (n = 76) were stained with H&E and immunohistochemically for the Ki67 and pH3 markers, followed by whole-slide image (WSI) generation. The serial section stains in H&E stain, Ki67 and pH3 markers formed WSI triplets for each patient. The resulting WSI triplets were co-registered with H&E WSIs serving as the reference. Separate mask region-based CNN (MRCNN) models were trained with annotated H&E, Ki67 and pH3 images for detecting tumor cells, stromal and intratumoral TILs (sTILs and tTILs), Ki67+, and pH3+ cells. Top image patches with a high density of cells of interest were identified as hotspots. Best classifiers for NAC response prediction were identified by training multiple ML models and evaluating their performance by accuracy, area under curve, and confusion matrix analyses.

RESULTS

Highest prediction accuracy was achieved when hotspot regions were identified by tTIL counts and each hotspot was represented by measures of tTILs, sTILs, tumor cells, Ki67+, and pH3+ features. Regardless of the hotspot selection metric, a complementary use of multiple histological features (tTILs, sTILs) and molecular biomarkers (Ki67 and pH3) resulted in top ranked performance at the patient level.

CONCLUSIONS

Overall, our results emphasize that prediction models for NAC response should be based on biomarkers in combination rather than in isolation. Our study provides compelling evidence to support the use of ML-based models to predict NAC response in patients with TNBC.

Prenatal BRCA1 epimutations contribute significantly to triple-negative breast cancer development

BACKGROUND

Normal cell BRCA1 epimutations have been associated with increased risk of triple-negative breast cancer (TNBC). However, the fraction of TNBCs that may have BRCA1 epimutations as their underlying cause is unknown. Neither are the time of occurrence and the potential inheritance patterns of BRCA1 epimutations established.

METHODS

To address these questions, we analyzed BRCA1 methylation status in breast cancer tissue and matched white blood cells (WBC) from 408 patients with 411 primary breast cancers, including 66 TNBCs, applying a highly sensitive sequencing assay, allowing allele-resolved methylation assessment. Furthermore, to assess the time of origin and the characteristics of normal cell BRCA1 methylation, we analyzed umbilical cord blood of 1260 newborn girls and 200 newborn boys. Finally, we assessed BRCA1 methylation status among 575 mothers and 531 fathers of girls with (n = 102) and without (n = 473) BRCA1 methylation.

RESULTS

We found concordant tumor and mosaic WBC BRCA1 epimutations in 10 out of 66 patients with TNBC and in four out of six patients with estrogen receptor (ER)-low expression (< 10%) tumors (combined: 14 out of 72; 19.4%; 95% CI 11.1-30.5). In contrast, we found concordant WBC and tumor methylation in only three out of 220 patients with 221 ER ≥ 10% tumors and zero out of 114 patients with 116 HER2-positive tumors. Intraindividually, BRCA1 epimutations affected the same allele in normal and tumor cells. Assessing BRCA1 methylation in umbilical WBCs from girls, we found mosaic, predominantly monoallelic BRCA1 epimutations, with qualitative features similar to those in adults, in 113/1260 (9.0%) of individuals, but no correlation to BRCA1 methylation status either in mothers or fathers. A significantly lower fraction of newborn boys carried BRCA1 methylation (9/200; 4.5%) as compared to girls (p = 0.038). Similarly, WBC BRCA1 methylation was found less common among fathers (16/531; 3.0%), as compared to mothers (46/575; 8.0%; p = 0.0003).

CONCLUSIONS

Our findings suggest prenatal BRCA1 epimutations might be the underlying cause of around 20% of TNBC and low-ER expression breast cancers. Such constitutional mosaic BRCA1 methylation likely arise through gender-related mechanisms in utero, independent of Mendelian inheritance.

Homologous Recombination Deficiency Across Subtypes of Primary Breast Cancer

PURPOSE

Homologous recombination deficiency (HRD) is highly prevalent in triple-negative breast cancer (TNBC) and associated with response to PARP inhibition (PARPi). Here, we studied the prevalence of HRD in non-TNBC to assess the potential for PARPi in a wider group of patients with breast cancer.

METHODS

HRD status was established using targeted gene panel sequencing (360 genes) and BRCA1 methylation analysis of pretreatment biopsies from 201 patients with primary breast cancer in the phase II PETREMAC trial (ClinicalTrials.gov identifier: NCT02624973). HRD was defined as mutations in BRCA1, BRCA2, BRIP1, BARD1, or PALB2 and/or promoter methylation of BRCA1 (strict definition; HRD-S). In secondary analyses, a wider definition (HRD-W) was used, examining mutations in 20 additional genes. Furthermore, tumor BRCAness (multiplex ligation-dependent probe amplification), PAM50 subtyping, RAD51 nuclear foci to test functional HRD, tumor-infiltrating lymphocyte (TIL), and PD-L1 analyses were performed.

RESULTS

HRD-S was present in 5% of non-TNBC cases (n = 9 of 169), contrasting 47% of the TNBC tumors (n = 15 of 32). HRD-W was observed in 23% of non-TNBC (n = 39 of 169) and 59% of TNBC cases (n = 19 of 32). Of 58 non-TNBC and 30 TNBC biopsies examined for RAD51 foci, 4 of 4 (100%) non-TNBC and 13 of 14 (93%) TNBC cases classified as HRD-S had RAD51 low scores. In contrast, 4 of 17 (24%) non-TNBC and 15 of 19 (79%) TNBC biopsies classified as HRD-W exhibited RAD51 low scores. Of nine non-TNBC tumors with HRD-S status, only one had a basal-like PAM50 signature. There was a high concordance between HRD-S and either BRCAness, high TIL density, or high PD-L1 expression (each P < .001).

CONCLUSION

The prevalence of HRD in non-TNBC suggests that therapy targeting HRD should be evaluated in a wider breast cancer patient population. Strict HRD criteria should be implemented to increase diagnostic precision with respect to functional HRD.

Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer

Background

Pathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30-40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60-70% show residual disease (RD). The role of the tumor microenvironment (TME) in NAC response in patients with TNBC remains unclear. In this study, we developed a machine learning-based two-step pipeline to distinguish between various histological components in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of TNBC tissue biopsies and to identify histological features that can predict NAC response.

Methods

H&E-stained WSIs of treatment-naïve biopsies from 85 patients (51 with pCR and 34 with RD) were separated through a stratified 8-fold cross validation strategy for the first step and leave one out cross validation strategy for the second step. A tile-level histology label prediction pipeline and four machine learning classifiers were used to analyze 468,043 tiles of WSIs. The best-trained classifier used 55 texture features from each tile to produce a probability profile during testing. The predicted histology classes were used to generate a histology classification map of the spatial distributions of different tissue regions. A patient-level NAC response prediction pipeline was trained with features derived from paired histology classification maps. The top graph-based features capturing the relevant spatial information across the different histological classes were provided to the radial basis function kernel support vector machine (rbfSVM) classifier for NAC treatment response prediction.

Results

The tile-level prediction pipeline achieved 86.72% accuracy for histology class classification, while the patient-level pipeline achieved 83.53% NAC response (pCR vs. RD) prediction accuracy. The histological class pairs with the strongest NAC response predictive ability were tumor and tumor tumor-infiltrating lymphocytes for pCR and microvessel density and polyploid giant cancer cells for RD.

Conclusion

Our machine learning pipeline can robustly identify clinically relevant histological classes that predict NAC response in TNBC patients and may help guide patient selection for NAC treatment.

Importance of endocrine treatment adherence and persistence in breast cancer survivorship: a systematic review

PURPOSE

Adjuvant endocrine treatment is essential for treating luminal subtypes of breast cancer, which constitute 75% of all breast malignancies. However, the detrimental side effects of treatment make it difficult for many patients to complete the guideline-required treatment. Such non-adherence may jeopardize the lifesaving ability of anti-estrogen therapy. In this systematic review, we aimed to assess the consequences of non-adherence and non-persistence from available studies meeting strict statistical and clinical criteria.

METHODS

A systematic literature search was performed using several databases, yielding identification of 2,026 studies. After strict selection, 14 studies were eligible for systematic review. The review included studies that examined endocrine treatment non-adherence (patients not taking treatment as prescribed) or non-persistence (patients stopping treatment prematurely), in terms of the effects on event-free survival or overall survival among women with non-metastatic breast cancer.

RESULTS

We identified 10 studies measuring the effects of endocrine treatment non-adherence and non-persistence on event-free survival. Of these studies, seven showed significantly poorer survival for the non-adherent or non-persistent patient groups, with hazard ratios (HRs) ranging from 1.39 (95% CI, 1.07 to 1.53) to 2.44 (95% CI, 1.89 to 3.14). We identified nine studies measuring the effects of endocrine treatment non-adherence and non-persistence on overall survival. Of these studies, seven demonstrated significantly reduced overall survival in the groups with non-adherence and non-persistence, with HRs ranging from 1.26 (95% CI, 1.11 to 1.43) to 2.18 (95% CI, 1.99 to 2.39).

CONCLUSION

The present systematic review demonstrates that non-adherence and non-persistence to endocrine treatment negatively affect event-free and overall survival. Improved follow-up, with focus on adherence and persistence, is vital for improving health outcomes among patients with non-metastatic breast cancer.

Predicting neoadjuvant treatment response in triple-negative breast cancer using machine learning

Background

Neoadjuvant chemotherapy (NAC) is the standard treatment for early-stage triple negative breast cancer (TNBC). The primary endpoint of NAC is a pathological complete response (pCR). NAC results in pCR in only 30%â€"40% of TNBC patients. Tumor-infiltrating lymphocytes (TILs), Ki67 and phosphohistone H3 (pH3) are a few known biomarkers to predict NAC response. Currently, systematic evaluation of the combined value of these biomarkers in predicting NAC response is lacking. In this study, the predictive value of markers derived from H&E and IHC stained biopsy tissue was comprehensively evaluated using a supervised machine learning (ML)-based approach. Identifying predictive biomarkers could help guide therapeutic decisions by enabling precise stratification of TNBC patients into responders and partial or non-responders.

Methods

Serial sections from core needle biopsies (n=76) were stained with H&E, and immunohistochemically for the Ki67 and pH3 markers, followed by whole slide image (WSI) generation. The resulting WSI triplets were co-registered with H&E WSIs serving as the reference. Separate mask region-based CNN (MRCNN) models were trained with annotated H&E, Ki67 and pH3 images for detecting tumor cells, stromal and intratumoral TILs (sTILs and tTILs), Ki67 + , and pH3 + cells. Top image patches with a high density of cells of interest were identified as hotspots. Best classifiers for NAC response prediction were identified by training multiple ML models, and evaluating their performance by accuracy, area under curve, and confusion matrix analyses.

Results

Highest prediction accuracy was achieved when hotspot regions were identified by tTIL counts and each hotspot was represented by measures of tTILs, sTILs, tumor cells, Ki67 + , and pH3 + features. Regardless of the hotspot selection metric, a complementary use of multiple histological features (tTILs, sTILs) and molecular biomarkers (Ki67 and pH3) resulted in top ranked performance at the patient level.

Conclusions

Overall, our results emphasize that prediction models for NAC response should be based on biomarkers in combination rather than in isolation. Our study provides compelling evidence to support the use of ML-based models to predict NAC response in patients with TNBC.

Impact of KRAS and BRAF mutations on treatment efficacy and survival in high‐grade gastroenteropancreatic neuroendocrine neoplasms

High-grade gastroenteropancreatic neuroendocrine neoplasms (HG GEP-NEN) typically disseminate early. Treatment of metastatic disease has limited benefit and prognosis is generally discouraging. Data on the clinical impact of mutations in HG GEP-NEN are scarce. There is an unmet need for reliable biomarkers to predict treatment outcome and prognosis in metastatic HG GEP-NEN. Patients with metastatic HG GEP-NEN diagnosed at three centres were selected for KRAS-, BRAF mutation and microsatellite instability (MSI) analyses. Results were linked to treatment outcome and overall survival. After pathological re-evaluation, 83 patients met inclusion criteria: 77 (93%) GEP neuroendocrine carcinomas (NEC) and six (7%) GEP neuroendocrine tumours (NET) G3. NEC harboured higher frequency of mutations than NET G3. Colon NEC harboured a particular high frequency of BRAF mutations (63%). Immediate disease progression on first-line chemotherapy was significantly higher for NEC with BRAF mutation (73%) versus wild-type (27%) (p = .016) and for colonic primary (65%) versus other NEC (28%) (p = .011). Colon NEC had a significant shorter PFS compared to other primary sites, a finding independent of BRAF status. Immediate disease progression was particularly frequent for BRAF mutated colon NEC (OR 10.2, p = .007). Surprisingly, BRAF mutation did not influence overall survival. KRAS mutation was associated with inferior overall survival for the whole NEC population (HR 2.02, p = .015), but not for those given first-line chemotherapy. All long-term survivors (>24 m) were double wild-type. Three NEC cases (4.8%) were MSI. Colon NEC with BRAF mutation predicted immediate disease progression on first-line chemotherapy, but did not affect PFS or OS. Benefit of first-line platinum/etoposide treatment seems limited for colon NEC, especially for BRAF mutated cases. KRAS mutations did not influence treatment efficacy nor survival for patients receiving first-line chemotherapy. Both frequency and clinical impact of KRAS/BRAF mutations in digestive NEC differ from prior results on digestive adenocarcinoma.

Automated Prognostic Assessment of Endometrial Hyperplasia for Progression Risk Evaluation Using Artificial Intelligence

Endometrial hyperplasia is a precursor to endometrial cancer, characterized by excessive proliferation of glands that is distinguishable from normal endometrium. Current classifications define 2 types of EH, each with a different risk of progression to endometrial cancer. However, these schemes are based on visual assessments and, therefore, subjective, possibly leading to overtreatment or undertreatment. In this study, we developed an automated artificial intelligence tool (ENDOAPP) for the measurement of morphologic and cytologic features of endometrial tissue using the software Visiopharm. The ENDOAPP was used to extract features from whole-slide images of PAN-CK⁺-stained formalin-fixed paraffin-embedded tissue sections from 388 patients diagnosed with endometrial hyperplasia between 1980 and 2007. Follow-up data were available for all patients (mean = 140 months). The most prognostic features were identified by a logistic regression model and used to assign a low-risk or high-risk progression score. Performance of the ENDOAPP was assessed for the following variables: images from 2 different scanners (Hamamatsu XR and S60) and automated placement of a region of interest versus manual placement by an operator. Then, the performance of the application was compared with that of current classification schemes: WHO94, WHO20, and EIN, and the computerized-morphometric risk classification method: D-score. The most significant prognosticators were percentage stroma and the standard deviation of the lesser diameter of epithelial nuclei. The ENDOAPP had an acceptable discriminative power with an area under the curve of 0.765. Furthermore, strong to moderate agreement was observed between manual operators (intraclass correlation coefficient: 0.828) and scanners (intraclass correlation coefficient: 0.791). Comparison of the prognostic capability of each classification scheme revealed that the ENDOAPP had the highest accuracy of 88%-91% alongside the D-score method (91%). The other classification schemes had an accuracy between 83% and 87%. This study demonstrated the use of computer-aided prognosis to classify progression risk in EH for improved patient treatment.

A Case Series Exploration of Multi-Regional Expression Heterogeneity in Triple-Negative Breast Cancer Patients

Extensive intratumoral heterogeneity (ITH) is believed to contribute to therapeutic failure and tumor recurrence, as treatment-resistant cell clones can survive and expand. However, little is known about ITH in triple-negative breast cancer (TNBC) because of the limited number of single-cell sequencing studies on TNBC. In this study, we explored ITH in TNBC by evaluating gene expression-derived and imaging-derived multi-region differences within the same tumor. We obtained tissue specimens from 10 TNBC patients and conducted RNA sequencing analysis of 2-4 regions per tumor. We developed a novel analysis framework to dissect and characterize different types of variability: between-patients (inter-tumoral heterogeneity), between-patients across regions (inter-tumoral and region heterogeneity), and within-patient, between-regions (regional intratumoral heterogeneity). We performed a Bayesian changepoint analysis to assess and classify regional variability as low (convergent) versus high (divergent) within each patient feature (TNBC and PAM50 subtypes, immune, stroma, tumor counts and tumor infiltrating lymphocytes). Gene expression signatures were categorized into three types of variability: between-patients (108 genes), between-patients across regions (183 genes), and within-patients, between-regions (778 genes). Based on the between-patient gene signature, we identified two distinct patient clusters that differed in menopausal status. Significant intratumoral divergence was observed for PAM50 classification, tumor cell counts, and tumor-infiltrating T cell abundance. Other features examined showed a representation of both divergent and convergent results. Lymph node stage was significantly associated with divergent tumors. Our results show extensive intertumoral heterogeneity and regional ITH in gene expression and image-derived features in TNBC. Our findings also raise concerns regarding gene expression based TNBC subtyping. Future studies are warranted to elucidate the role of regional heterogeneity in TNBC as a driver of treatment resistance.

A spatial attention guided deep learning system for prediction of pathological complete response using breast cancer histopathology images

MOTIVATION

Predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) patients accurately is direly needed for clinical decision making. pCR is also regarded as a strong predictor of overall survival. In this work, we propose a deep learning system to predict pCR to NAC based on serial pathology images stained with hematoxylin and eosin and two immunohistochemical biomarkers (Ki67 and PHH3). To support human prior domain knowledge-based guidance and enhance interpretability of the deep learning system, we introduce a human knowledge-derived spatial attention mechanism to inform deep learning models of informative tissue areas of interest. For each patient, three serial breast tumor tissue sections from biopsy blocks were sectioned, stained in three different stains and integrated. The resulting comprehensive attention information from the image triplets is used to guide our prediction system for prognostic tissue regions.

RESULTS

The experimental dataset consists of 26 419 pathology image patches of 1000×1000 pixels from 73 TNBC patients treated with NAC. Image patches from randomly selected 43 patients are used as a training dataset and images patches from the rest 30 are used as a testing dataset. By the maximum voting from patch-level results, our proposed model achieves a 93% patient-level accuracy, outperforming baselines and other state-of-the-art systems, suggesting its high potential for clinical decision making.

AVAILABILITY AND IMPLEMENTATION

The codes, the documentation and example data are available on an open source at: https://github.com/jkonglab/PCR_Prediction_Serial_WSIs_biomarkers.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Liquid biopsies and patient-reported outcome measures for integrative monitoring of patients with early-stage breast cancer: a study protocol for the longitudinal observational Prospective Breast Cancer Biobanking (PBCB) study

INTRODUCTION

Breast cancer is still the most common malignancy among women worldwide. The Prospective Breast Cancer Biobank (PBCB) collects blood and urine from patients with breast cancer every 6 or 12 months for 11 years from 2011 to 2030 at two university hospitals in Western Norway. The project aims to identify new biomarkers that enable detection of systemic recurrences at the molecular level. As blood represents the biological interface between the primary tumour, the microenvironment and distant metastases, liquid biopsies represent the ideal medium to monitor the patient's cancer biology for identification of patients at high risk of relapse and for early detection systemic relapse.Including patient-reported outcome measures (PROMs) allows for a vast number of possibilities to compare PROM data with biological information, enabling the study of fatigue and Quality of Life in patients with breast cancer.

METHODS AND ANALYSIS

A total of 1455 patients with early-stage breast cancer are enrolled in the PBCB study, which has a one-armed prospective observational design. Participants consent to contribute liquid biopsies (i.e., peripheral blood and urine samples) every 6 or 12 months for 11 years. The liquid biopsies are the basis for detection of circulating tumour cells, circulating tumour DNA (ctDNA), exosomal micro-RNA (miRNA), miRNA in Tumour Educated Platelet and metabolomic profiles. In addition, participants respond to 10 PROM questionnaires collected annually. Moreover, a control group comprising 200 women without cancer aged 25-70 years will provide the same data.

ETHICS AND DISSEMINATION

The general research biobank PBCB was approved by the Ministry of Health and Care Services in 2007, by the Regional Ethics Committee (REK) in 2010 (#2010/1957). The PROM (#2011/2161) and the biomarker study PerMoBreCan (#2015/2010) were approved by REK in 2011 and 2015 respectively. Results will be published in international peer reviewed journals. Deidentified data will be accessible on request.

TRIAL REGISTRATION NUMBER

NCT04488614.

[Psychiatric symptoms and COVID-19: results of a national case register]

BACKGROUND

Psychiatric disorders are associated with a more severe course of COVID-19. COVID-19 can also lead to psychiatric symptoms.

AIM

To gain insight into vulnerabilities and protective factors for the course of COVID-19 in a Dutch (neuro)psychiatric population.

METHOD

Patients were divided into three groups: patients with pre-existent mental disorders without and with new (neuro)psychiatric symptoms (NPS) during COVID-19 and patients without pre-existent mental disorders who developed de novo NPS during COVID-19. We summarize the characteristics of each group and compare the subgroups with inferential statistics.

RESULTS

186 patients were included in the case register. Patients with NPS showed a more severe course of COVID-19. Mortality in patients with NPS was higher in patients with pre-existent mental disorders compared to patients without pre-existent mental disorders. The most frequently reported de novo psychiatric symptoms during COVID-19 were delirium (46-70%), anxiety (53-54%) and insomnia (18-42%).

CONCLUSION

NPS might be an expression of a more severe COVID-19 episode. In patients who developed NPS during COVID-19 we found evidence for a higher mortality risk in patients with pre-existent mental disorders. Extra vigilance for neuropsychiatric symptoms during COVID-19 is warranted.

High-Grade Cervical Intraepithelial Neoplasia (CIN) Associates with Increased Proliferation and Attenuated Immune Signaling

Implementation of high-risk human papilloma virus (HPV) screening and the increasing proportion of HPV vaccinated women in the screening program will reduce the percentage of HPV positive women with oncogenic potential. In search of more specific markers to identify women with high risk of cancer development, we used RNA sequencing to compare the transcriptomic immune-profile of 13 lesions with cervical intraepithelial neoplasia grade 3 (CIN3) or adenocarcinoma in situ (AIS) and 14 normal biopsies from women with detected HPV infections. In CIN3/AIS lesions as compared to normal tissue, 27 differential expressed genes were identified. Transcriptomic analysis revealed significantly higher expression of a number of genes related to proliferation, (CDKN2A, MELK, CDK1, MKI67, CCNB2, BUB1, FOXM1, CDKN3), but significantly lower expression of genes related to a favorable immune response (NCAM1, ARG1, CD160, IL18, CX3CL1). Compared to the RNA sequencing results, good correlation was achieved with relative quantitative PCR analysis for NCAM1 and CDKN2A. Quantification of NCAM1 positive cells with immunohistochemistry showed epithelial reduction of NCAM1 in CIN3/AIS lesions. In conclusion, NCAM1 and CDKN2A are two promising candidates to distinguish whether women are at high risk of developing cervical cancer and in need of frequent follow-up.

A Gene Signature Identifying CIN3 Regression and Cervical Cancer Survival

The purpose of this study was to establish a gene signature that may predict CIN3 regression and that may aid in selecting patients who may safely refrain from conization. Oncomine mRNA data including 398 immune-related genes from 21 lesions with confirmed regression and 28 with persistent CIN3 were compared. L1000 mRNA data from a cervical cancer cohort was available for validation (n = 239). Transcriptomic analyses identified TDO2 (p = 0.004), CCL5 (p < 0.001), CCL3 (p = 0.04), CD38 (p = 0.02), and PRF1 (p = 0.005) as upregulated, and LCK downregulated (p = 0.01) in CIN3 regression as compared to persistent CIN3 lesions. From these, a gene signature predicting CIN3 regression with a sensitivity of 91% (AUC = 0.85) was established. Transcriptomic analyses revealed proliferation as significantly linked to persistent CIN3. Within the cancer cohort, high regression signature score associated with immune activation by Gene Set enrichment Analyses (GSEA) and immune cell infiltration by histopathological evaluation (p < 0.001). Low signature score was associated with poor survival (p = 0.007) and large tumors (p = 0.01). In conclusion, the proposed six-gene signature predicts CIN regression and favorable cervical cancer prognosis and points to common drivers in precursors and cervical cancer lesions.

Spatial Attention-Based Deep Learning System for Breast Cancer Pathological Complete Response Prediction with Serial Histopathology Images in Multiple Stains

In triple negative breast cancer (TNBC) treatment, early prediction of pathological complete response (PCR) from chemotherapy before surgical operations is crucial for optimal treatment planning. We propose a novel deep learning-based system to predict PCR to neoadjuvant chemotherapy for TNBC patients with multi-stained histopathology images of serial tissue sections. By first performing tumor cell detection and recognition in a cell detection module, we produce a set of feature maps that capture cell type, shape, and location information. Next, a newly designed spatial attention module integrates such feature maps with original pathology images in multiple stains for enhanced PCR prediction in a dedicated prediction module. We compare it with baseline models that either use a single-stained slide or have no spatial attention module in place. Our proposed system yields 78.3% and 87.5% of accuracy for patch-, and patient-level PCR prediction, respectively, outperforming all other baseline models. Additionally, the heatmaps generated from the spatial attention module can help pathologists in targeting tissue regions important for disease assessment. Our system presents high efficiency and effectiveness and improves interpretability, making it highly promising for immediate clinical and translational impact.

miRNA normalization enables joint analysis of several datasets to increase sensitivity and to reveal novel miRNAs differentially expressed in breast cancer

Different miRNA profiling protocols and technologies introduce differences in the resulting quantitative expression profiles. These include differences in the presence (and measurability) of certain miRNAs. We present and examine a method based on quantile normalization, Adjusted Quantile Normalization (AQuN), to combine miRNA expression data from multiple studies in breast cancer into a single joint dataset for integrative analysis. By pooling multiple datasets, we obtain increased statistical power, surfacing patterns that do not emerge as statistically significant when separately analyzing these datasets. To merge several datasets, as we do here, one needs to overcome both technical and batch differences between these datasets. We compare several approaches for merging and jointly analyzing miRNA datasets. We investigate the statistical confidence for known results and highlight potential new findings that resulted from the joint analysis using AQuN. In particular, we detect several miRNAs to be differentially expressed in estrogen receptor (ER) positive versus ER negative samples. In addition, we identify new potential biomarkers and therapeutic targets for both clinical groups. As a specific example, using the AQuN-derived dataset we detect hsa-miR-193b-5p to have a statistically significant over-expression in the ER positive group, a phenomenon that was not previously reported. Furthermore, as demonstrated by functional assays in breast cancer cell lines, overexpression of hsa-miR-193b-5p in breast cancer cell lines resulted in decreased cell viability in addition to inducing apoptosis. Together, these observations suggest a novel functional role for this miRNA in breast cancer. Packages implementing AQuN are provided for Python and Matlab: https://github.com/YakhiniGroup/PyAQN.

This work demonstrates a practical approach to the joint-analysis of multiple miRNA expression profiling datasets acquired with different measurement technologies. The use of different platforms in miRNA profiling can lead to major differences in results. In particular, some miRNA species are less amenable to detection and quantification by certain platforms or designs. Our approach, termed AQuN, combines quantile normalization with special attention to missing entities, to normalize miRNA expression across datasets, technologies, designs and platforms. As we show, our proposed approach uncovers patterns of interest that would not have emerged as statistically significant when analyzing the datasets individually or with other standard-practice normalization methods.

Amongst our findings, we noted a previously undocumented miRNA that is significantly over-expressed in samples from estrogen-receptor positive breast cancer patients as compared to samples from estrogen-receptor negative patients. We further investigated this miRNA, hsa-miR-193b-5p, and experimentally show, in cell lines, that its expression level impacts the viability of tumor cells. AQuN is available to the community in the form of Python and Matlab packages. The joint-processed data is also made available for further investigation.

A Multiscale Approach for Whole-Slide Image Segmentation of five Tissue Classes in Urothelial Carcinoma Slides

In pathology labs worldwide, we see an increasing number of tissue samples that need to be assessed without the same increase in the number of pathologists. Computational pathology, where digital scans of histological samples called whole-slide images (WSI) are processed by computational tools, can be of help for the pathologists and is gaining research interests. Most research effort has been given to classify slides as being cancerous or not, localization of cancerous regions, and to the “big-four” in cancer: breast, lung, prostate, and bowel. Urothelial carcinoma, the most common form of bladder cancer, is expensive to follow up due to a high risk of recurrence, and grading systems have a high degree of inter- and intra-observer variability. The tissue samples of urothelial carcinoma contain a mixture of damaged tissue, blood, stroma, muscle, and urothelium, where it is mainly muscle and urothelium that is diagnostically relevant. A coarse segmentation of these tissue types would be useful to i) guide pathologists to the diagnostic relevant areas of the WSI, and ii) use as input in a computer-aided diagnostic (CAD) system. However, little work has been done on segmenting tissue types in WSIs, and on computational pathology for urothelial carcinoma in particular. In this work, we are using convolutional neural networks (CNN) for multiscale tile-wise classification and coarse segmentation, including both context and detail, by using three magnification levels: 25x, 100x, and 400x. 28 models were trained on weakly labeled data from 32 WSIs, where the best model got an F1-score of 96.5% across six classes. The multiscale models were consistently better than the single-scale models, demonstrating the benefit of combining multiple scales. No tissue-class ground-truth for complete WSIs exist, but the best models were used to segment seven unseen WSIs where the results were manually inspected by a pathologist and are considered as very promising.

Effects of replacing PSA with Stockholm3 for diagnosis of clinically significant prostate cancer in a healthcare system - the Stavanger experience

OBJECTIVE

To describe early experience of replacing PSA with Stockholm3 for detection of prostate cancer in primary care.

DESIGN AND METHODS

Longitudinal observations, comparing outcome measures before and after the implementation of Stockholm3.

SETTING

Stavanger region in Norway with about 370,000 inhabitants, 304 general practitioners (GPs) in 97 primary care clinics, and one hospital.

INTERVENTION

GPs were instructed to use Stockholm3 instead of PSA as standard procedure for diagnosis of prostate cancer.

MAIN OUTCOME MEASURES

Proportion of GP clinics that had ordered a Stockholm3 test. Number of men referred to needle biopsy. Distribution of clinically significant prostate cancer (csPC) (Gleason Score ≥7) and clinically non-significant prostate cancer (cnsPC) (Gleason Score 6), in needle biopsies. Estimation of direct healthcare costs.

RESULTS

Stockholm3 was rapidly implemented as 91% (88/97) of the clinics started to use the test within 14 weeks. After including 4784 tested men, the percentage who would have been referred for prostate needle biopsy was 29.0% (1387/4784) if based on PSA level ≥3ng/ml, and 20.8% (995/4784) if based on Stockholm3 Risk Score (p < 0.000001). The proportion of positive biopsies with csPC increased from 42% (98/233) before to 65% (185/285) after the implementation. Correspondingly, the proportion of cnsPC decreased from 58% (135/233) before to 35% (100/285) after the implementation (p < 0.0017). Direct healthcare costs were estimated to be reduced by 23-28% per tested man.

CONCLUSION

Replacing PSA with Stockholm3 for early detection of prostate cancer in primary care is feasible. Implementation of Stockholm3 resulted in reduced number of referrals for needle-biopsy and a higher proportion of clinically significant prostate cancer findings in performed biopsies. Direct healthcare costs decreased. KEY POINTS A change from PSA to Stockholm3 for the diagnosis of prostate cancer in primary care in the Stavanger region in Norway is described and assessed. •Implementation of a new blood-based test for prostate cancer detection in primary care was feasible. A majority of GP clinics started to use the test within three months. •Implementation of the Stockholm3 test was followed by: -a 28% reduction in number of men referred for urological prostate cancer work-up -an increase in the proportion of clinically significant cancer in performed prostate biopsies from 42 to 65% -an estimated reduction in direct health care costs between 23 and 28%.

Digital Image Analysis of Ki-67 Stained Tissue Microarrays and Recurrence in Tamoxifen-Treated Breast Cancer Patients

Purpose

The proliferation marker Ki-67 has been used as a prognostic marker to separate low- and high-risk breast cancer subtypes and guide treatment decisions for adjuvant chemotherapy. The association of Ki-67 with response to tamoxifen therapy is unclear. High-throughput automated scoring of Ki-67 might enable standardization of quantification and definition of clinical cut-off values. We hypothesized that digital image analysis (DIA) of Ki-67 can be used to evaluate proliferation in breast cancer tumors, and that Ki-67 may be associated with tamoxifen resistance in early-stage breast cancer.

Patients and Methods

Here, we apply DIA technology from Visiopharm using a custom designed algorithm for quantifying the expression of Ki-67, in a case–control study nested in the Danish Breast Cancer Group clinical database, consisting of stages I, II, or III breast cancer patients of 35–69 years of age, diagnosed during 1985–2001, in the Jutland peninsula, Denmark. We assessed DIA-Ki-67 score on tissue microarrays (TMAs) from breast cancer patients in a case–control study including 541 ER-positive and 300 ER-negative recurrent cases and their non-recurrent controls, matched on ER-status, cancer stage, menopausal status, year of diagnosis, and county of residence. We used logistic regression to estimate odds ratios and associated 95% confidence intervals to determine the association of Ki-67 expression with recurrence risk, adjusting for matching factors, chemotherapy, type of surgery, receipt of radiation therapy, age category, and comorbidity.

Results

Ki-67 was not associated with increased risk of recurrence in tamoxifen-treated patients (ORadj =0.72, 95% CI 0.54, 0.96) or ER-negative patients (ORadj =0.85, 95% CI 0.54, 1.34).

Conclusion

Our findings suggest that Ki-67 digital image analysis in TMAs is not associated with increased risk of recurrence among tamoxifen-treated ER-positive breast cancer or ER-negative breast cancer patients. Overall, our findings do not support an increased risk of recurrence associated with Ki-67 expression.

Protein Conformational Changes in Breast Cancer Sera Using Infrared Spectroscopic Analysis

Protein structural alterations, including misfolding and aggregation, are a hallmark of several diseases, including cancer. However, the possible clinical application of protein conformational analysis using infrared spectroscopy to detect cancer-associated structural changes in proteins has not been established yet. The present study investigates the applicability of Fourier transform infrared spectroscopy in distinguishing the sera of healthy individuals and breast cancer patients. The cancer-associated alterations in the protein structure were analyzed by fitting the amide I (1600-1700 cm-1) band of experimental curves, as well as by comparing the ratio of the absorbance values at the amide II and amide III bands, assigning those as the infrared spectral signatures. The snapshot of the breast cancer-associated alteration in circulating DNA and RNA was also evaluated by extending the spectral fitting protocol to the complex region of carbohydrates and nucleic acids, 1140-1000 cm-1. The sensitivity and specificity of these signatures, representing the ratio of the α-helix and β-pleated sheet in proteins, were both 90%. Likewise, the ratio of amides II and amide III (I1556/I1295) had a sensitivity and specificity of 100% and 80%, respectively. Thus, infrared spectroscopy can serve as a powerful tool to understand the protein structural alterations besides distinguishing breast cancer and healthy serum samples.

A Quantitative Centrosomal Amplification Score Predicts Local Recurrence of Ductal Carcinoma In Situ

PURPOSE

The purpose of this study is to predict risk of local recurrence (LR) in ductal carcinoma in situ (DCIS) with a new visualization and quantification approach using centrosome amplification (CA), a cancer cell-specific trait widely associated with aggressiveness.

EXPERIMENTAL DESIGN

This first-of-its-kind methodology evaluates the severity and frequency of numerical and structural CA present within DCIS and assigns a quantitative centrosomal amplification score (CAS) to each sample. Analyses were performed in a discovery cohort (DC, n = 133) and a validation cohort (VC, n = 119).

RESULTS

DCIS cases with LR exhibited significantly higher CAS than recurrence-free cases. Higher CAS was associated with a greater risk of developing LR (HR, 6.3 and 4.8 for DC and VC, respectively; P < 0.001). CAS remained an independent predictor of relapse-free survival (HR, 7.4 and 4.5 for DC and VC, respectively; P < 0.001) even after accounting for potentially confounding factors [grade, age, comedo necrosis, and radiotherapy (RT)]. Patient stratification using CAS (P < 0.0001) was superior to that by Van Nuys Prognostic Index (VNPI; HR for CAS = 6.2 vs. HR for VNPI = 1.1). Among patients treated with breast-conserving surgery alone, CAS identified patients likely to benefit from adjuvant RT.

CONCLUSIONS

CAS predicted 10-year LR risk for patients who underwent surgical management alone and identified patients who may be at low risk of recurrence, and for whom adjuvant RT may not be required. CAS demonstrated the highest concordance among the known prognostic models such as VNPI and clinicopathologic variables such as grade, age, and comedo necrosis.

Mitotic activity index and CD25+ lymphocytes predict risk of stage progression in non-muscle invasive bladder cancer

In urothelial cell type non-muscle invasive urinary bladder carcinoma, TNM stage and WHO grade are widely used to classify patients into low and high‑risk groups for prognostic and therapeutic decision-making. However, stage and grade reproducibility and prediction accuracy are wanting. This may lead to suboptimal treatment. We evaluated whether proliferation features, nuclear area of the epithelial cancer cells and the composition of stromal and tumor infiltrating lymphocytes have independent prognostic value. In 183 primary non-muscle invasive bladder cancer patients with long follow-up (median for stage progression cohort: 119 months, range 5-173; median for tumor recurrence cohort: 82, range 3-165) proliferation features Ki67, PPH3 and Mitotic Activity Index (MAI), Mean Nuclear Area (MNA), lymphocyte subsets (CD8+, CD4+, CD25+) and plasma cells (CD138+) were assessed on consecutive sections. Post-resection instillation treatments (none, mitomycin, BCG) were strictly standardized during the intake period. Risk of recurrence was associated with expression of Ki67 (≤ 39 vs. > 39) and Multifocality (p = 0.01). Patients with low Ki67 had a higher recurrence rate than those with high Ki67. Lymphocyte composition did not predict recurrence. Stage progression was strongly associated with high values for MAI (>15) and CD25+ (>0.2%). In a multivariate analysis the combination of MAI and CD25+ was the single most prognostic feature (p<0.001). Validation of these results in additional, independent studies is warranted.

MiR-18a and miR-18b are expressed in the stroma of oestrogen receptor alpha negative breast cancers

BACKGROUND

Previously, we have shown that miR-18a and miR-18b gene expression strongly correlates with high proliferation, oestrogen receptor -negativity (ER^-), cytokeratin 5/6 positivity and basal-like features of breast cancer.

METHODS

We investigated the expression and localization of miR-18a and -18b in formalin fixed paraffin embedded (FFPE) tissue from lymph node negative breast cancers (n = 40), by chromogenic in situ hybridization (CISH). The expression level and in situ localization of miR-18a and -18b was assessed with respect to the presence of tumour infiltrating lymphocytes (TILs) and immunohistochemical markers for ER, CD4, CD8, CD20, CD68, CD138, PAX5 and actin. Furthermore, in two independent breast cancer cohorts (94 and 377 patients) the correlation between miR-18a and -18b expression and the relative quantification of 22 immune cell types obtained from the CIBERSORT tool was assessed.

RESULTS

CISH demonstrated distinct and specific cytoplasmic staining for both miR-18a and miR-18b, particularly in the intratumoural stroma and the stroma surrounding the tumour margin. Staining by immunohistochemistry revealed some degree of overlap of miR-18a and -18b with CD68 (monocytes/macrophages), CD138 (plasma cells) and the presence of high percentages of TILs. CIBERSORT analysis showed a strong correlation between M1-macrophages and CD4+ memory activated T-cells with mir-18a and -18b.

CONCLUSIONS

Our study demonstrates that miR-18a and miR-18b expression is associated with ER- breast tumours that display a high degree of inflammation. This expression is potentially associated specifically with macrophages. These results suggest that miR-18a and miR-18b may play a role in the systemic immunological response in ER^- tumours.

Metabolic consequences of perioperative oral carbohydrates in breast cancer patients - an explorative study

Background

The metabolic consequences of preoperative carbohydrate load in breast cancer patients are not known. The present explorative study investigated the systemic and tumor metabolic changes after preoperative per-oral carbohydrate load and their influence on tumor characteristics and survival.

Methods

The study setting was on university hospital level with primary and secondary care functions in south-west Norway. Serum and tumor tissue were sampled from a population-based cohort of 60 patients with operable breast cancer who were randomized to either per-oral carbohydrate load (preOp™; n = 25) or standard pre-operative fasting (n = 35) before surgery. Magnetic resonance (MR) metabolomics was performed on serum samples from all patients and high-resolution magic angle spinning (HR-MAS) MR analysis on 13 tumor samples available from the fasting group and 16 tumor samples from the carbohydrate group.

Results

Fourteen of 28 metabolites were differently expressed between fasting and carbohydrate groups. Partial least squares discriminant analysis showed a significant difference in the metabolic profile between the fasting and carbohydrate groups, compatible with the endocrine effects of insulin (i.e., increased serum-lactate and pyruvate and decreased ketone bodies and amino acids in the carbohydrate group). Among ER-positive tumors (n = 18), glutathione was significantly elevated in the carbohydrate group compared to the fasting group (p = 0.002), with a positive correlation between preoperative S-insulin levels and the glutathione content in tumors (r = 0.680; p = 0.002). In all tumors (n = 29), glutamate was increased in tumors with high proliferation (t-test; p = 0.009), independent of intervention group. Moreover, there was a positive correlation between tumor size and proliferation markers in the carbohydrate group only. Patients with ER-positive / T2 tumors and high tumor glutathione (≥1.09), high S-lactate (≥56.9), and high S-pyruvate (≥12.5) had inferior clinical outcomes regarding relapse-free survival, breast cancer-specific survival, and overall survival. Moreover, Integrated Pathway Analysis (IPA) in serum revealed activation of five major anabolic metabolic networks contributing to proliferation and growth.

Conclusions

Preoperative carbohydrate load increases systemic levels of lactate and pyruvate and tumor levels of glutathione and glutamate in ER-positive patients. These biological changes may contribute to the inferior clinical outcomes observed in luminal T2 breast cancer patients.

Trial of registration

ClinicalTrials.gov; NCT03886389. Retrospectively registered March 22, 2019.

Detection of disseminated tumor cells in bone marrow predict late recurrences in operable breast cancer patients

BACKGROUND

Operable breast cancer patients may experience late recurrences because of reactivation of dormant tumor cells within the bone marrow (BM). Identification of patients who would benefit from extended therapy is therefore needed.

METHODS

BM samples obtained pre- and post-surgery were previously analysed for presence of disseminated tumor cells (DTC) by a multimarker mRNA quantitative reverse-transcription PCR assay. Updated survival analyses were performed on all patient data (n = 191) and in a subgroup of patients alive and recurrence-free after 5 years (n = 156). DTC data were compared to the mitotic activity index (MAI) of the primary tumors. Median follow-up time was 15.3 years.

RESULTS

Among the 191 patients, 49 (25.65%) experienced systemic relapse, 24 (49%) within 5-18 years after surgery. MAI and pre- and post-operative DTC status had significant prognostic value based on Kaplan-Meier analyses and multiple Cox regression in the overall patient cohort. With exclusion of patients who relapsed or died within 5 years from surgery, only pre-operative DTC detection was an independent prognostic marker of late recurrences. High MAI (≥10) did not predict late recurrences or disease-specific mortality.

CONCLUSION

Pre-operative DTC detection, but not MAI status, predicts late recurrences in operable breast cancer.

Influence of pre-operative oral carbohydrate loading vs. standard fasting on tumor proliferation and clinical outcome in breast cancer patients ─ a randomized trial

Background

Conflicting results have been reported on the influence of carbohydrates in breast cancer.

Objective

To determine the influence of pre-operative per-oral carbohydrate load on proliferation in breast tumors.

Design

Randomized controlled trial.

Setting

University hospital with primary and secondary care functions in South-West Norway.

Patients

Sixty-one patients with operable breast cancer from a population-based cohort.

Intervention

Per-oral carbohydrate load (preOp™) 18 and 2–4 h before surgery (n = 26) or standard pre-operative fasting with free consumption of tap water (n = 35).

Measurements

The primary outcome was post-operative tumor proliferation measured by the mitotic activity index (MAI). The secondary outcomes were changes in the levels of serum insulin, insulin-c-peptide, glucose, IGF-1, and IGFBP3; patients’ well-being, and clinical outcome over a median follow-up of 88 months (range 33–97 months).

Results

In the estrogen receptor (ER) positive subgroup (n = 50), high proliferation (MAI ≥ 10) occurred more often in the carbohydrate group (CH) than in the fasting group (p = 0.038). The CH group was more frequently progesterone receptor (PR) negative (p = 0.014). The CH group had a significant increase in insulin (+ 24.31 mIE/L, 95% CI 15.34 mIE/L to 33.27 mIE/L) and insulin c-peptide (+ 1.39 nM, 95% CI 1.03 nM to 1.77 nM), but reduced IGFBP3 levels (− 0.26 nM; 95% CI − 0.46 nM to − 0.051 nM) compared to the fasting group. CH-intervention ER-positive patients had poorer relapse-free survival (73%) than the fasting group (100%; p = 0.012; HR = 9.3, 95% CI, 1.1 to 77.7). In the ER-positive patients, only tumor size (p = 0.021; HR = 6.07, 95% CI 1.31 to 28.03) and the CH/fasting subgrouping (p = 0.040; HR = 9.30, 95% CI 1.11 to 77.82) had independent prognostic value. The adverse clinical outcome of carbohydrate loading occurred only in T2 patients with relapse-free survival of 100% in the fasting group vs. 33% in the CH group (p = 0.015; HR = inf). The CH group reported less pain on days 5 and 6 than the control group (p <  0.001) but otherwise exhibited no factors related to well-being.

Limitation

Only applicable to T2 tumors in patients with ER-positive breast cancer.

Conclusions

Pre-operative carbohydrate load increases proliferation and PR-negativity in ER-positive patients and worsens clinical outcome in ER-positive T2 patients.

Trial registration

CliniTrials.gov; NCT03886389. Retrospectively registered March 22, 2019.

Validation study of MARCKSL1 as a prognostic factor in lymph node-negative breast cancer patients

Protein expression of Myristoylated alanine-rich C kinase substrate like-1 (MARCKSL1) has been identified as a prognostic factor in lymph-node negative (LN^-) breast cancer patients. We aim to validate MARCKSL1 protein expression as a prognostic marker for distant metastasis-free survival (DMFS) in a new cohort of LN^- breast cancer patients. MARCKSL1 expression was evaluated in 151 operable T_1,2N₀M₀ LN^- breast cancer patients by immunohistochemistry. Median follow-up time was 152 months, range 11–189 months. Results were compared with classical prognosticators (age, tumor diameter, grade, estrogen receptor, and proliferation) using single (Kaplan-Meier) and multivariate (Cox model) survival analysis. Thirteen patients (9%) developed distant metastases. With both single and multiple analysis of all features, MARCKSL1 did not show a significant prognostic value for DMFS (p = 0.498). Of the assessed classical prognosticators, only tumor diameter showed prognostic value (hazard ratio 9.3, 95% confidence interval 2.8–31.0, p <0.001). MARCKSL1 expression could not be confirmed as a prognostic factor in this cohort. Possible reasons include changes in diagnostic and treatment guidelines between the discovery and validation cohorts. Further studies are needed to reveal the potential biological role of this protein in breast cancer.

Assessing the prognostic value of PAX2 and PTEN in endometrial carcinogenesis

In order to avoid the consequences of over- and under-treatment of endometrial hyperplasia, diagnostic accuracy and progression risk assessment must be improved. The aim of this study was to assess whether PAX2 or PTEN expression could predict progression-free survival in endometrial intraepithelial neoplasia (EIN) and endometrial endometrioid carcinoma (EEC). Immunohistochemistry for detection of PAX2 and PTEN was performed on 348 endometrial samples; 75 proliferative endometrium (PE), 36 EIN and 237 EEC. Cases classified as PTEN null (1 or more glands negatively stained) were more prevalent in EEC than in PE and EIN (64% EEC vs 11% PE/EIN). A progressive decrease in PAX2 expression was observed from PE to EIN to EEC. Long-term clinical follow-up (6-310 months, median: 126) was available for 62 PE cases, all 36 EIN cases and 178 EEC cases. No patients with PE demonstrated progression to EIN or EEC. Progression of disease was observed in 10 (28%) EIN patients. These patients had significantly lower PAX2 expression than those that regressed (P = 0.005). Progression-free survival analysis revealed that EIN patients with a high-risk PAX2 expression score (H-score ≤75) had a higher probability of progression of disease in comparison to those with a low-risk score (H-score >75). PAX2 expression was not prognostic in EEC nor was PTEN status of prognostic value in either EIN or EEC. PAX2 expression analysis by means of H-score has prognostic potential for the identification of high-risk progression cases in EIN but needs to be validated in a larger cohort.

Serum concentrations of active tamoxifen metabolites predict long-term survival in adjuvantly treated breast cancer patients

Background

Controversies exist as to whether the genetic polymorphisms of the enzymes responsible for the metabolism of tamoxifen can predict breast cancer outcome in patients using adjuvant tamoxifen. Direct measurement of concentrations of active tamoxifen metabolites in serum may be a more biological plausible and robust approach. We have investigated the association between CYP2D6 genotypes, serum concentrations of active tamoxifen metabolites, and long-term outcome in tamoxifen treated breast cancer patients.

Methods

From an original observational study comprising 817 breast cancer patients, 99 women with operable breast cancer were retrospectively included in the present study. This cohort of patients were adjuvantly treated with tamoxifen, had provided serum samples suitable for measuring tamoxifen metabolites, and were relapse-free at 3 years after the primary treatment commenced. The median follow-up time from this entry point to breast cancer death was 13.9 years. Patients were CYP2D6 genotyped and grouped into four CYP2D6 phenotype groups (Ultra rapid, extensive, intermediate, and poor metabolizers). Tamoxifen and nine metabolites were quantified in serum (n = 86) and compared with CYP2D6 phenotype groups and outcome.

Results

Breast cancer patients with low concentrations of Z-4-hydroxy-tamoxifen (Z-4OHtam; ≤ 3.26 nM) had a breast cancer-specific survival (BCSS) of 60% compared to 84% in patients with Z-4OHtam concentrations > 3.26 nM (p = 0.020, log-rank hazard ratio (HR) = 3.56, 95% confidence interval (CI) = 1.14–11.07). For patients with Z-4-hydroxy-N-desmethyl-tamoxifen (Z-endoxifen) levels ≤ 9.00 nM BCSS was 57% compared to 84% for patients with concentrations > 9.00 nM (p = 0.029, HR = 3.73, 95% CI = 1.05–13.22). Low concentrations of Z-4OHtam and Z-endoxifen were associated with poorer survival also after adjusting for clinically relevant variables (HR = 4.27, 95% CI = 1.35–13.58, and HR = 3.70, 95% CI = 1.03–13.25, respectively). Overall survival analysis showed similar survival differences for both active metabolites. The Antiestrogen Activity Score showed comparable effects, but did not improve the prognostic information.

Conclusions

Patients with Z-4OHtam and Z-endoxifen concentrations lower than 3.26 nM or 9.00 nM, respectively, showed an adverse outcome. Our results suggest that direct measurement of active tamoxifen metabolite concentrations could be of clinical value. Validation in larger study cohorts is warranted.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-017-0916-4) contains supplementary material, which is available to authorized users.

Abstract P2-09-11: Metabolite-guided long-term prediction of outcome in tamoxifen treated breast cancer patients

Background

The genetic polymorphisms of CYP2D6 determine its enzymatic activity thereby the pharmacokinetic velocity by which tamoxifen (tam) is converted into active metabolites. Combined analyses of tam metabolites compared to CYP2D6 type and long-term survival could allow prediction of tam response and personalization of therapies. The clinical importance of defining such subgroups in the era of the new long-term (10-year) tam treatment paradigm is actualized.

Material and Methods

From May 1995 to December 1998, patients were included in an observational micro-metastasis study in the Oslo region and treated according to the national guidelines at the time (1). Serum samples were drawn at 3-year follow-up from 356 relapse-free patients, 106 of these were treated with tamoxifen. The median follow up time for breast cancer death was 16.8 years (3.5-19.4).

Serum samples were processed using protein precipitation with acetonitrile. An Aquity UPLC system was used to chromatographically separate 10 tam metabolites using a BEH C18 Phenyl column (100 x 2.1 mm, 1.7 μm particle size) that was developed with a water-methanol gradient containing 0.1% formic acid. The LC system was coupled to a Xevo TQ-S tandem mass spectrometer equipped with an atmospheric pressure photoionization source. The method was validated with respect to linearity, imprecision, accuracy, and functional sensitivity according to FDA guidelines.

Results

The new LC-MS/MS method separated the active Z-isomers of 4OHtam and 4OHNDtam (endoxifen) from its inactive Z'-isomers and E-isomers. Imprecision (intra and inter-day CV %) was within 10 % for target concentrations for all metabolites and accuracies were in the range 95-106%. The method was validated with serum samples from 42 breast cancer patients using 20 mg of tamoxifen.

The endoxifen concentrations ranged from 0 to 90 nM, with a median value of 25 nM. The previous observed endoxifen level of 10 nM in poor metabolizers (2) was used as cut-off for the grouping of patients. The nil endoxifen (NE) group (&lt; 0.1nM, n=14) or low-endoxifene (LE) group (0.1-10 nM, n=8) were grouped together. Univariate survival analysis did not show a significant association between breast cancer specific survival and endoxifen levels. (p=0.15; logrank and p=0.18;Breslow). However, for the period beyond 10-years of follow-up the breast cancer survival differed between the high endoxifene (HE) group and the NE+LE groups. For patients surviving the first 10 years the breast cancer specific survival was 94.2% vs. 77.8% for the HE and NE+LE groups respectively (p=0.020, logrank and p=0.017, Breslow, HR=4.5, CI 95=1.1-17.9). In the multivariate analysis endoxifen ≤/&gt; 10 nM remained the only factor in the final model.

Discussion

We developed a new accurate and precise LC-MS/MS method for the measurement of 10 tamoxifen metabolites. Importantly, the method separates active and inactive isomers of 4OHtam and 4OHNDtam/endoxifen. Despite the low number of patients, we observed a poorer long-term survival beyond 10 years in patients with nil or low serum concentration of endoxifen. A comprehensive analysis is presented addressing the relationship between genotyped based and metabolite based prediction of long-term outcome in tamoxifen treated breast cancer patients.

Ref:

1. Wiedswang G, et al. Clin Ca Res; 2004

2. Mürdter TE, et al. Clin Pharmacol Ther; 2011.

Citation Format: Helland T, Henne N, Bifulco E, Hustad SS, Kristensen VN, Lash T, Borgen E, Janssen EAM, Lien EA, Naume B, Mellgren G, Søiland H. Metabolite-guided long-term prediction of outcome in tamoxifen treated breast cancer patients [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P2-09-11.

Quality assurance of human papillomavirus (HPV) testing in the implementation of HPV primary screening in Norway: an inter-laboratory reproducibility study

Background

Human papillomavirus (HPV) testing as primary screening for cervical cancer is currently being implemented in Norway in a randomized controlled fashion, involving three laboratories. As part of the quality assurance programme of the implementation, an evaluation of the inter-laboratory reproducibility of the HPV test was initiated, to ensure satisfactory HPV test reliability in all three laboratories.

Methods

The HPV test used is the cobas 4800 HPV Test, detecting 14 high-risk types with individual HPV genotype results for HPV16 and HPV18. In addition to the three laboratories involved in the implementation, the Norwegian HPV reference laboratory was included as a fourth comparative laboratory. A stratified sample of 500 cervical liquid based cytology (LBC) samples was used in the evaluation, with an aim towards a high-risk HPV positivity of ~25%. Samples were collected at one laboratory, anonymized, aliquoted, and distributed to the other laboratories.

Results

Comparison of the test results of all four laboratories revealed a 95.6% agreement, an 86.3% positive agreement and a kappa value of 0.94 (95% CI 0.92–0.97). For negative cytology specimens, there was a 95.8% overall agreement, a 67.4% positive agreement, and a kappa value of 0.88 (95% CI 0.80–0.93). For abnormal cytology specimens, there was a 95.8% overall agreement, a 95.5% positive agreement, and a kappa value of 0.86 (95% CI 0.71–0.97).

Conclusions

The study showed a high inter-laboratory reproducibility of HPV testing, implying satisfactory user performance and reliability in the laboratories involved in the implementation project. This is important knowledge and we recommend similar studies always to be performed prior to the introduction of new screening routines.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-016-2028-7) contains supplementary material, which is available to authorized users.

Low Protein A20 in Minor Salivary Glands is Associated with Lymphoma in Primary Sjögren's Syndrome

Patients with primary Sjögren's syndrome (pSS) have an increased risk of developing lymphomas, particularly the subtype mucosa-associated lymphoid tissue (MALT) lymphoma. Chronic antigen stimulation and increased activation of nuclear factor-κB (NF-κB) are important factors for the pathogenesis of MALT lymphomas. Protein A20 is an inhibitor of NF-κB. A recent study of pSS-associated MALT lymphomas identified potential functional abnormalities in the TNFAIP3 gene, which encodes protein A20. The present study aimed to assess protein A20 by immunohistochemistry (IHC) in minor salivary glands (MSGs) and lymphoma tissue sections of patients with pSS and investigate a potential association with lymphoma development. Protein A20 staining in lymphocytes was scored in four categories (0 = negative, 1 = weak, 2 = moderate and 3 = strong). For statistical purposes, these scores were simplified into negative (scores 0-1) and positive (scores 2-3). We investigated associations between protein A20-staining, focus scores, germinal centre (GC)-like structures and monoclonal B-cell infiltration in MSGs. MSG protein A20 staining was weaker in pSS patients with lymphomas than in those without lymphomas (P = 0.01). Weak protein A20 staining was also highly associated with a lack of GC formation (P < 0.01). Finally, weaker A20 staining was observed in the majority of pSS-associated MALT lymphoma tissues. In conclusion, we found absent or weak protein A20 immunoreactivity in MSGs of patients with pSS with lymphomas. This finding indicates that protein A20 downregulation in lymphocytes might be a mechanism underlying lymphoma genesis in patients with pSS.

Epigenome-wide DNA methylation patterns associated with fatigue in primary Sjögren's syndrome

OBJECTIVE

Chronic fatigue is a common, disabling and poorly understood phenomenon. Recent studies indicate that epigenetic mechanisms may be involved in the expression of fatigue, a prominent feature of primary SS (pSS). The aim of this study was to investigate whether DNA methylation profiles of whole blood are associated with fatigue in patients with pSS.

METHODS

Forty-eight pSS patients with high (n = 24) or low (n = 24) fatigue as measured by a visual analogue scale were included. Genome-wide DNA methylation was investigated using the Illumina HumanMethylation450 BeadChip array. After quality control, a total of 383 358 Cytosine-phosphate-Guanine (CpG) sites remained for further analysis. Age, sex and differential cell count estimates were included as covariates in the association model. A false discovery rate-corrected P < 0.05 was considered significant, and a cut-off of 3% average difference in methylation levels between high- and low-fatigue patients was applied.

RESULTS

A total of 251 differentially methylated CpG sites were associated with fatigue. The CpG site with the most pronounced hypomethylation in pSS high fatigue annotated to the SBF2-antisense RNA1 gene. The most distinct hypermethylation was observed at a CpG site annotated to the lymphotoxin alpha gene. Functional pathway analysis of genes with differently methylated CpG sites in subjects with high vs low fatigue revealed enrichment in several pathways associated with innate and adaptive immunity.

CONCLUSION

Some genes involved in regulation of the immune system and in inflammation are differently methylated in pSS patients with high vs low fatigue. These findings point to functional networks that may underlie fatigue. Epigenetic changes could constitute a fatigue-regulating mechanism in pSS.

The Role of MicroRNAs as Predictors of Response to Tamoxifen Treatment in Breast Cancer Patients

Endocrine therapy is a key treatment strategy to control or eradicate hormone-responsive breast cancer. However, resistance to endocrine therapy leads to breast cancer relapse. The recent extension of adjuvant tamoxifen treatment up to 10 years actualizes the need for identifying biological markers that may be used to monitor predictors of treatment response. MicroRNAs are promising biomarkers that may fill the gap between preclinical knowledge and clinical observations regarding endocrine resistance. MicroRNAs regulate gene expression by posttranscriptional repression or degradation of mRNA, most often leading to gene silencing. MicroRNAs have been identified directly in the primary tumor, but also in the circulation of breast cancer patients. The few available studies investigating microRNA in patients suggest that seven microRNAs (miR-10a, miR-26, miR-30c, miR-126a, miR-210, miR-342 and miR-519a) play a role in tamoxifen resistance. Ingenuity Pathway Analysis (IPA) reveals that these seven microRNAs interact more readily with estrogen receptor (ER)-independent pathways than ER-related signaling pathways. Some of these pathways are targetable (e.g., PIK3CA), suggesting that microRNAs as biomarkers of endocrine resistance may have clinical value. Validation of the role of these candidate microRNAs in large prospective studies is warranted.

Prognostic value of gene signatures and proliferation in lymph-node-negative breast cancer

Introduction

The overall survival rate is good for lymph-node-negative breast cancer patients, but they still suffer from serious over- and some undertreatments. Prognostic and predictive gene signatures for node-negative breast cancer have a high number of genes related to proliferation. The prognostic value of gene sets from commercial gene-expression assays were compared with proliferation markers.

Methods

Illumina WG6 mRNA microarray analysis was used to examine 94 fresh-frozen tumour samples from node-negative breast cancer patients. The patients were divided into low- and high-risk groups for distant metastasis based on the MammaPrint-related genes, and into low-, intermediate- and high-risk groups based on the recurrence score algorithm with genes included in Oncotype DX. These data were then compared to proliferation status, as measured by the mitotic activity index, the expressions of phosphohistone H3 (PPH3), and Ki67.

Results

Kaplan-Meier survival analysis for distant-metastasis-free survival revealed that patients with weak and strong PPH3 expressions had 14-year survival rates of 87% (n = 45), and 65% (n = 49, p = 0.014), respectively. Analysis of the MammaPrint classification resulted in 14-year survival rates of 80% (n = 45) and 71% (n = 49, p = 0.287) for patients with low and high risks of recurrence, respectively. The Oncotype DX categorization yielded 14-year survival rates of 83% (n = 18), 79% (n = 42) and 68% (n = 34) for those in the low-, intermediate- and high-risk groups, respectively (p = 0.52). Supervised hierarchical cluster analysis for distant-metastasis-free survival in the subgroup of patients with strong PPH3 expression revealed that the genes involved in Notch signalling and cell adhesion were expressed at higher levels in those patients with distant metastasis.

Conclusion

This pilot study indicates that proliferation has greater prognostic value than the expressions of either MammaPrint- or Oncotype-DX-related genes. Furthermore, in the subgroup of patients with high proliferation, Notch signalling pathway genes appear to be expressed at higher levels in patients who develop distant metastasis.

The prognostic value of mitotic activity index (MAI), phosphohistone H3 (PPH3), cyclin B1, cyclin A, and Ki67, alone and in combinations, in node-negative premenopausal breast cancer

Proliferation, either as the main common denominator in genetic profiles, or in the form of single factors such as Ki67, is recommended for clinical use especially in estrogen receptor-positive (ER) patients. However, due to high costs of genetic profiles and lack of reproducibility for Ki67, studies on other proliferation factors are warranted. The aim of the present study was to evaluate the prognostic value of the proliferation factors mitotic activity index (MAI), phosphohistone H3 (PPH3), cyclin B1, cyclin A and Ki67, alone and in combinations. In 222 consecutive premenopausal node-negative breast cancer patients (87% without adjuvant medical treatment), MAI was assessed on whole tissue sections (predefined cut-off ≥10 mitoses), and PPH3, cyclin B1, cyclin A, and Ki67 on tissue microarray (predefined cut-offs 7th decile). In univariable analysis (high versus low) the strongest prognostic proliferation factor for 10-year distant disease-free survival was MAI (Hazard Ratio (HR)=3.3, 95% Confidence Interval (CI): 1.8-6.1), followed by PPH3, cyclin A, Ki67, and cyclin B1. A combination variable, with patients with MAI and/or cyclin A high defined as high-risk, had even stronger prognostic value (HR=4.2, 95%CI: 2.2-7). When stratifying for ER-status, MAI was a significant prognostic factor in ER-positive patients only (HR=7.0, 95%CI: 3.1-16). Stratified for histological grade, MAI added prognostic value in grade 2 (HR=7.2, 95%CI: 3.1-38) and grade 1 patients. In multivariable analysis including HER2, age, adjuvant medical treatment, ER, and one proliferation factor at a time, only MAI (HR=2.7, 95%CI: 1.1-6.7), and cyclin A (HR=2.7, 95%CI: 1.2-6.0) remained independently prognostic. In conclusion this study confirms the strong prognostic value of all proliferation factors, especially MAI and cyclin A, in all patients, and more specifically in ER-positive patients, and patients with histological grade 2 and 1. Additionally, by combining two proliferation factors, an even stronger prognostic value may be found.

Validation of expression patterns for nine miRNAs in 204 lymph-node negative breast cancers

Introduction

Although lymph node negative (LN-) breast cancer patients have a good 10-years survival (∼85%), most of them still receive adjuvant therapy, while only some benefit from this. More accurate prognostication of LN- breast cancer patient may reduce over- and under-treatment. Until now proliferation is the strongest prognostic factor for LN- breast cancer patients. The small molecule microRNA (miRNA) has opened a new window for prognostic markers, therapeutic targets and/or therapeutic components. Previously it has been shown that miR-18a/b, miR-25, miR-29c and miR-106b correlate to high proliferation.

Methods

The current study validates nine miRNAs (miR-18a/b miR-25, miR-29c, miR-106b, miR375, miR-424, miR-505 and let-7b) significantly correlated with established prognostic breast cancer biomarkers. Total RNA was isolated from 204 formaldehyde-fixed paraffin embedded (FFPE) LN- breast cancers and analyzed with quantitative real-time Polymerase Chain Reaction (qPCR). Independent T-test was used to detect significant correlation between miRNA expression level and the different clinicopathological features for breast cancer.

Results

Strong and significant associations were observed for high expression of miR-18a/b, miR-106b, miR-25 and miR-505 to high proliferation, oestrogen receptor negativity and cytokeratin 5/6 positivity. High expression of let-7b, miR-29c and miR-375 was detected in more differentiated tumours. Kaplan-Meier survival analysis showed that patients with high miR-106b expression had an 81% survival rate vs. 95% (P = 0.004) for patients with low expression.

Conclusion

High expression of miR-18a/b are strongly associated with basal-like breast cancer features, while miR-106b can identify a group with higher risk for developing distant metastases in the subgroup of Her2 negatives. Furthermore miR-106b can identify a group of patients with 100% survival within the otherwise considered high risk group of patients with high proliferation. Using miR-106b as a biomarker in conjunction to mitotic activity index could thereby possibly save 18% of the patients with high proliferation from overtreatment.

Consistent condom use increases the regression rate of cervical intraepithelial neoplasia 2-3

Objective

Cervical intraepithelial neoplasia grades 2-3 (CIN2-3) are usually treated by cone excision, although only 30% progress to cancer and 6–50% regress spontaneously. The aim of this study was to examine the influence of clinical factors like smoking habits, number of lifetime sexual partners, age at first sexual intercourse, sexual activity span and hormonal versus non-hormonal contraception type on the regression rate of CIN2-3.

Methods

In this prospective population-based cohort study 170 women aged 25–40 with abnormal cytology and colposcopy-directed biopsies showing first time onset CIN2-3 were consecutively included. The interval between biopsy and cone excision was standardized to minimum 12 weeks. Regression was defined as ≤CIN1 in the cone biopsy.

Results

The regression rate was 22%. Consistent condom use, defined as those women whose partners used condoms for all instances of sexual intercourse, was infrequent (n = 20, 12%). In univariate analysis consistent condom use, hormonal contraception and age at first sexual intercourse significantly predicted regression. In a multivariate analysis only consistent condom use remained as an independent predictor of regression (regression rate 55%, p = 0.001, hazard ratio = 4.4).

Conclusion

Consistent condom use between punch biopsy and cone excision in first-time onset CIN2-3 patients significantly increases the regression rate.

The prognostic value of MARCKS-like 1 in lymph node-negative breast cancer

There is a need for new biomarkers to more correctly identify node-negative breast cancer patients with a good or bad prognosis. Myristoylated alanine-rich C kinase substrate like-1 (MARCKSL1) is a membrane-bound protein that is associated with cell spreading, integrin activation and exocytosis. Three hundred and five operable T(1,2)N(0)M(0) lymph node-negative breast cancer patients (median follow-up time 121 months, range 10-178 months) were evaluated for MARCKSL1 expression by immunohistochemistry and quantitative real-time PCR. The results were compared with classical prognosticators (age, tumor diameter, grade, estrogen receptor, and proliferation), using single (Kaplan-Meier) and multivariate survival analysis (Cox model). Forty-seven patients (15 %) developed distant metastases. With single and multivariate analysis of all features, MARCKSL1 protein expression was the strongest prognosticator (P < 0.001, HR = 5.1, 95 % CI = 2.7-9.8). Patients with high MARCKSL1 expression (n = 23) showed a 44 % survival versus 88 % in patients with low expression at 15-year follow-up. mRNA expression of MARCKSL1 in formalin fixed paraffin-embedded tissue was also prognostic (P = 0.002, HR = 3.6, 95 % CI = 1.5-8.3). However, the prognostic effect of high and low was opposite from the protein expression, i.e., low expression (relative expression ≤ 0.0264, n = 76) showed a 79 % survival versus 92 % in those with high expression of MARCKSL1 mRNA. Multivariate analysis of all features with distant metastases free survival as the end-point showed that the combination of MARCKSL1 protein and phosphohistone H3 (PPH3) has the strongest independent prognostic value. Patients with high expression (≥13) of PPH3 and high MARCKSL1 protein had 45 % survival versus 78 % survival for patients with low MARCKSL1 protein expression and high expression (≥13) of PPH3. In conclusion, MARCKSL1 has strong prognostic value in lymph node-negative breast cancer patients, especially in those with high proliferation.

Dietary supplementation of krill oil attenuates inflammation and oxidative stress in experimental ulcerative colitis in rats

OBJECTIVE

To evaluate the effects of krill oil (KO) on inflammation and redox status in dextran sulfate sodium (DSS)-induced colitis in rats.

MATERIALS AND METHODS

Thirty male Wistar rats were divided into three groups: Control, DSS, and DSS + KO 5% in a 4-week diet study. Colitis was induced by 5% DSS in the drinking water the last week of the experiment. Weight and disease activity index (DAI), colon length, histological combined score (HCS), colon levels of selected cytokines and prostaglandins, markers of protein oxidative damage, fatty acid profile, and expression of selected genes were measured.

RESULTS

Rats in the DSS group increased their DAI and HCS compared with healthy controls. The colon length was significantly preserved after KO diet. Tumor necrosis factor (TNF)-α and interleukin (IL)-1β were elevated in the DSS group compared with controls. Cytokines and HCS were nonsignificantly lower in the KO versus the DSS group. Prostaglandin (PG)E(3) increased significantly in the KO versus the other groups. Peroxisome proliferator-activated receptor (PPAR)-γ expression was nonsignificantly increased while PPAR-γ coactivator 1α (Pparg1α) expression increased significantly after KO. The levels of protein oxidation markers decreased significantly.

CONCLUSIONS

KO showed protective potential against DSS colitis based on the preservation of colon length, reduction of oxidative markers and the consistent beneficial changes of HCS, cytokine, and (PG)E(3) levels, as well as PPAR-γ and Pparg1α expression compared with DSS alone. These findings indicate an anti-inflammatory and a protein antioxidant effect of KO.

Biomarkers and microsatellite instability analysis of curettings can predict the behavior of FIGO stage I endometrial endometrioid adenocarcinoma

The prognostic value of molecular biomarkers, microsatellite instability, DNA ploidy and morphometric mean shortest nuclear axis in endometrial cancer is conflicting, possibly due to the fact that different studies have used mixtures of histotypes, FIGO stages and different non-standardized non-automated methods. We have evaluated the prognostic value of classical prognostic factors, molecular biomarkers, microsatellite instability, DNA ploidy and morphometric mean shortest nuclear axis in a population-based cohort of FIGO stage I endometrial endometrioid adenocarcinomas. Curettings of 224 FIGO stage I endometrial endometrioid adenocarcinoma patients were reviewed. Clinical information, including follow-up, was obtained from the patients' charts. Microsatellite instability and morphometric mean shortest nuclear axis were obtained in whole tissue sections and molecular biomarkers using tissue microarrays. DNA ploidy was analyzed by image cytometry. Univariate (Kaplan-Meier method) and multivariate (Cox model) survival analysis was performed. With median follow-up of 66 months (1-209), 14 (6%) patients developed metastases. Age, microsatellite instability, molecular biomarkers (p16, p21, p27, p53 and survivin) and morphometric mean shortest nuclear axis had prognostic value. With multivariate analysis, combined survivin, p21 and microsatellite instability overshadowed all other variables. Patients in which any of these features had favorable values had an excellent prognosis, in contrast to those with either high survivin or low p21 (97 vs 78% survival, P<0.0001, hazard ratio=7.8). Combined high survivin and low p21 values and microsatellite instability high identified a small subgroup with an especially poor prognosis (survival rate 57%, P=0.01, hazard ratio=5.6). We conclude that low p21 and high survivin expression are poor prognosis indicators in FIGO stage I endometrial endometrioid adenocarcinoma, especially when high microsatellite instability occurs.

A protein profile study to discriminate CIN lesions from normal cervical epithelium

Background

Cervical intraepithelial neoplasia (CIN), a frequently encountered disease caused by Human Papilloma Virus (HPV) is often diagnosed in formaldehyde-fixed paraffin embedded (FFPE) punch biopsies. Since it is known that this procedure strongly affects the water-soluble proteins contained in the cervical tissue we decided to investigate whether a water-soluble protein-saving biopsy processing method can be used to support the diagnosis of normal and CIN.

Methods

Cervical punch biopsies from 55 women were incubated for 24 h at 4°C in RPMI1640 medium for protein analysis prior to usual FFPE processing and p16 and Ki67-supported histologic consensus diagnosis was assessed. The biopsy supernatants were subjected to surface-enhanced laser desorption-ionization time of flight mass spectrometry (SELDI-TOF MS) for identifying differentially expressed proteins. Binary logistic regression and classification and regression trees (CART) were used to develop a classification model.

Results

The age of the patients ranged from 26 to 40 years (median 29.7). The consensus diagnoses were normal cervical tissue (n = 10) and CIN2-3 (n = 45). The mean protein concentration was 1.00 and 1.09 mg/ml in the normal and CIN2-3 group, respectively. The peak detection and clustering process resulted in 40 protein peaks. Many of these peaks differed between the two groups, but only three had independent discriminating power. The overall classification results were 88%.

Conclusions

Water-soluble proteins sampled from punch biopsies are promising to assist the diagnosis of normal and CIN2-3.

High frequency microsatellite instability has a prognostic value in endometrial endometrioid adenocarcinoma, but only in FIGO stage 1 cases

OBJECTIVES

To analyze the prognostic value of microsatellite instability (MSI) in a population-based study of FIGO stage 1-4 endometrial endometrioid adenocarcinomas.

STUDY DESIGN

Survival analysis in 273 patients of MSI status and clinico-pathologic features. Using a highly sensitive pentaplex polymerase chain reaction to establish MSI status, cases were divided into microsatellite stable (MSS), MSI-low (MSI-L, 1 marker positive) and MSI-high (MSI-H, 2-5 markers positive).

RESULTS

After 61 months median follow-up (1-209), 34 (12.5%) of the patients developed metastases but only 6.4% of the FIGO-1. MSI (especially as MSI-H versus MSS/MSI-Lcombined) was prognostic in FIGO-1 but not in FIGO2-4. The 5 and 10 year recurrence-free survival rates were 98% and 95% in the MSS/MSI-L versus 85% and 73% in the MSI-H patients (P = 0.005).

CONCLUSIONS

MSI-H status assessed by pentaplex polymerase chain reaction is an indicator of poor prognosis in FIGO 1, but not in FIGO 2-4 endometrial endometrioid adenocarcinomas.

Mitotic activity and bone marrow micrometastases have independent prognostic value in node positive breast cancer patients

The purpose of this article is to investigate the prognostic value of the mitotic activity index (MAI) and the presence of disseminated tumor cells (DTCs) in bone marrow (BM), in clinically operable breast cancer patients. We compared routinely assessed MAI, classic prognosticators and BM DTCs, detected by a real-time RT-PCR multimarker assay including cytokeratin 19, mammaglobin A and TWIST1 mRNA, in 179 consecutive patients with operable breast cancer. Over a median follow-up of 96 months (range: 1-126 months), 31 (17.3%) patients experienced a systemic relapse and 26 (14.5%) died of breast cancer-related causes. MAI (≥ 10) was strongly associated with breast cancer-related death in lymph node (LN)-negative patients (hazard ratio (HR): 7.0, confidence interval (CI) 1.74-27.9), whereas both BM DTC-status (HR: 3.3, CI 1.25-8.52) and MAI (HR: 3.1, CI 1.08-8.8) were significant in LN-positive patients. With multivariate Cox regression, MAI was the only significant predictor of breast cancer-specific survival (HR 7.0, CI 1.7-27.9) in LN-negative patients. In LN-positive patients, both BM DTC-status and MAI were strong independent predictors of breast cancer-specific survival (HR 3.3, CI 1.25-8.49 and HR 3.1, CI 1.1-8.9), respectively. Where, however, MAI and BM DTC-status as single parameters were replaced by a combination of these, this showed to be the most significant prognostic marker in both LN-negative (HR 7.7, CI 1.2-50) and LN-positive (HR 6.0, CI 1.4 to 26.4) patients with regard to breast cancer-specific survival. A combination of MAI and BM DTC detection identified both LN-negative and LN-positive breast cancer patients with poor prognosis.

D2-40/p63 defined lymph vessel invasion has additional prognostic value in highly proliferating operable node negative breast cancer patients

Phosphohistone H3 assessed proliferation has strong prognostic value. Lymph vessel invasion by D2-40 is also prognostic, but D2-40+ myoepithelial expression in small ducts completely filled by solid-pattern ductal carcinoma in situ can mimic lymphovascular invasion. As myoepithelial cells are also p63 positive, we have investigated whether lymph vessel invasion identified by combined D2-40/p63 is stronger prognostically than by D2-40 alone, and whether it has independent prognostic value to phosphohistone H3. In 240 operable T(1-2)N(0)M(0) node negative invasive breast cancer patients <71 years, phosphohistone H3 was determined by quantitative immunohistochemistry and lymph vessel invasion by D2-40/p63 double immunostaining. Correlation analysis between the clinico-pathologic factors and lymph vessel invasion, and univariate and multivariate prognostic survival analysis were performed. With median 117 (range: 12-192) months follow-up, 36 patients (15%) developed and 28 (12%) died of distant metastases. Ten of the 61 patients (16%) with cancer cells surrounded by D2-40 were p63 positive and none of these 'false lymph vessel invasion' recurred. D2-40+/p63- lymph vessel invasion occurred in 51/239 (21%) cases and correlated with grade, mitotic activity index, phosphohistone H3, ER, cytokeratin14, and HER2. D2-40+/p63- lymph vessel invasion was strongly prognostic, but far more in women ≥55 than those <55 years (P<0.0001 and 0.04). With multivariate analysis, phosphohistone H3 proliferation was the strongest single prognosticator. Lymph vessel invasion had additional prognostic value to phosphohistone H3 only in women ≥55. This group of patients, without/with lymph vessel invasion, had 10-year survival rates of 83 and 50%, respectively (hazard ratio-lymph vessel invasion=3.0, P=0.04; hazard ratio-phosphohistone H3=6.9, P=0.002). Where age was <55 years, only phosphohistone H3 had independent prognostic value. Combinations of other features had no additional value. In conclusion, T(1-2)N(0)M(0) invasive breast cancer patients ≥55 years with phosphohistone H3≥13, D2-40+/p63- defined lymph vessel invasion identifies a subgroup with a high risk of distant metastases.

Molecular biomarkers in endometrial hyperplasias predict cancer progression

OBJECTIVE

The purpose of this study was to assess the value of the 2003 World Health Organization (WHO) and endometrial intraepithelial neoplasia (EIN) classifications, D-score, and molecular biomarkers in endometrial hyperplasia (EH) for cancer progression.

STUDY DESIGN

We conducted a review of 307 endometrial hyperplasias for WHO and EIN classifications and an analysis of biomarkers, D-score, and cancer progression-free survival.

RESULTS

The WHO, EIN, D-score, and many biomarkers were prognostic; 7.2% of the samples progressed to cancer. The WHO and EIN classifications correlated weakly with CK5/6 and p16. The D-score was strongest prognostically. When >1, it had the lowest false-negative progression rate of all features analyzed. COX2 negativity was the only other independent multivariate cancer progression predictor in endometrial hyperplasia, but only in cases with D-score <1. Eight of 13 cases (61%), with a combined D-score of <1 and negative COX2 progressed, which contrasted with 3 of 139 of all other cases (2.8%) (P < .0001; hazard ratio, 53.0). The biomarkers did not strengthen the prognostic value of the WHO or EIN classification.

CONCLUSION

Combined D-score <1 and COX2 negativity strongly predict cancer progression in endometrial hyperplasias.