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.

The Ki67 dilemma: investigating prognostic cut-offs and reproducibility for automated Ki67 scoring in breast cancer.

PURPOSE: Quantification of Ki67 in breast cancer is a well-established prognostic and predictive marker, but inter-laboratory variability has hampered its clinical usefulness. This study compares the prognostic value and reproducibility of Ki67 scoring using four automated, digital image analysis (DIA) methods and two manual methods. METHODS: The study cohort consisted of 367 patients diagnosed between 1990 and 2004, with hormone receptor positive, HER2 negative, lymph node negative breast cancer. Manual scoring of Ki67 was performed using predefined criteria. DIA Ki67 scoring was performed using QuPath and Visiopharm® platforms. Reproducibility was assessed by the intraclass correlation coefficient (ICC). ROC curve survival analysis identified optimal cutoff values in addition to recommendations by the International Ki67 Working Group and Norwegian Guidelines. Kaplan-Meier curves, log-rank test and Cox regression analysis assessed the association between Ki67 scoring and distant metastasis (DM) free survival. RESULTS: The manual hotspot and global scoring methods showed good agreement when compared to their counterpart DIA methods (ICC > 0.780), and good to excellent agreement between different DIA hotspot scoring platforms (ICC 0.781-0.906). Different Ki67 cutoffs demonstrate significant DM-free survival (p < 0.05). DIA scoring had greater prognostic value for DM-free survival using a 14% cutoff (HR 3.054-4.077) than manual scoring (HR 2.012-2.056). The use of a single cutoff for all scoring methods affected the distribution of prediction outcomes (e.g. false positives and negatives). CONCLUSION: This study demonstrates that DIA scoring of Ki67 is superior to manual methods, but further study is required to standardize automated, DIA scoring and definition of a clinical cut-off.

Quantitative proteomics reveals serum proteome alterations during metastatic disease progression in breast cancer patients.

BACKGROUND: Tumor recurrence and metastatic progression remains the leading cause for breast cancer related mortalities. However, the proteomes of patient- matched primary breast cancer (BC) and metastatic lesions have not yet been identified, due to the lack of clinically annotated longitudinal samples. In this study, we evaluated the global-proteomic landscape of BC patients with and without distant metastasis as well as compared the proteome of distant metastatic disease with its corresponding primary BC, within the same patient. METHODS: We performed mass spectrometry-based proteome profiling of 73 serum samples from 51 BC patients. Among the 51 patients with BC, 29 remained metastasis-free (henceforth called non-progressors), and 22 developed metastases (henceforth called progressors). For the 22 progressors, we obtained two samples: one collected within a year of diagnosis, and the other collected within a year before the diagnosis of metastatic disease. MS data were analyzed using intensity-based absolute quantification and normalized before differential expression analysis. Significantly differentially expressed proteins (DEPs; absolute fold-change ≥ 1.5, P-value < 0.05 and 30% abundance per clinical group) were subjected to pathway analyses. RESULTS: We identified 967 proteins among 73 serum samples from patients with BC. Among these, 39 proteins were altered in serum samples at diagnosis, between progressors and non-progressors. Among these, 4 proteins were further altered when the progressors developed distant metastasis. In addition, within progressors, 20 proteins were altered in serum collected at diagnosis versus at the onset of metastasis. Pathway analysis showed that these proteins encoded pathways that describe metastasis, including epithelial-mesenchymal transition and focal adhesion that are hallmarks of metastatic cascade. CONCLUSIONS: Our results highlight the importance of examining matched samples from distant metastasis with primary BC samples collected at diagnosis to unravel subset of proteins that could be involved in BC progression in serum. This study sets the foundation for additional future investigations that could position these proteins as non-invasive markers for clinically monitoring breast cancer progression in patients.

No signs of mast cell involvement in long-COVID: A case-control study.

Long-COVID caused by SARS-CoV-2 infection has significant and increasing effects on human health worldwide. Although a unifying molecular or biological explanation is lacking, several pathophysiological mechanisms have been proposed. Involvement of mast cells-evolutionary old "multipurpose" innate immune cells-was reported recently in studies of acute infection and post-acute-COVID-19 syndrome. Mast cell activity has been suggested in long-COVID. In this case-control study, we compared data from 24 individuals with long-COVID (according to the NICE criteria) and 24 age- and sex-matched healthy individuals with a history of SARS-CoV-2 infection without developing sequelae. Serum levels of the proteases beta-tryptase (TPSB2) and carboxypeptidase (CPA3), which are mast cell specific, were measured using immunoassays. The values were compared between the two groups and correlated to measures of physical exertional intolerance. TPSB2 and CPA3 levels were median (range) 26.9 (2.0-1000) and 5.8 (1.5-14.0) ng/mL, respectively, in the long-COVID group. The corresponding values in the control group were 10.9 (2.0-1000) (p = 0.93) and 5.3 (3.5-12.9) ng/mL (p = 0.82). No significant correlations between TPSB2 or CPA3 levels and scores on the ten physical subscales of SF-36, 3.1-3.10 were revealed. We found no significant differences in the levels of mast cell activation markers TPSB2 and CPA3 between the long-COVID and control groups and no correlations with proxy markers of exercise intolerance. Mast cell activation does not appear to be part of long-term pathogenesis of long-COVID, at least in the majority of patients.

Nottingham prognostic x (NPx): a risk stratification tool in ER-positive HER2-negative breast cancer: a validation study.

AIMS: In this study, we validate the use of Nottingham Prognostic x (NPx), consisting of tumour size, tumour grade, progesterone receptor (PR) and Ki67 in luminal BC. MATERIALS AND METHODS: Two large cohorts of luminal early-stage BC (n = 2864) were included. PR and Ki67 expression were assessed using full-face resection samples using immunohistochemistry. NPx was calculated and correlated with clinical variables and outcome, together with Oncotype DX recurrence score (RS), that is frequently used as a risk stratifier in luminal BC. RESULTS: In the whole cohort, 38% of patients were classified as high risk using NPx which showed significant association with parameters characteristics of aggressive tumour behaviour and shorter survival (P < 0.0001). NPx classified the moderate Nottingham Prognostic Index (NPI) risk group (n = 1812) into two distinct prognostic subgroups. Of the 82% low-risk group, only 3.8% developed events. Contrasting this, 14% of the high-risk patients developed events during follow-up. A strong association was observed between NPx and Oncotype Dx RS (P < 0.0001), where 66% of patients with intermediate risk RS who had subsequent distant metastases also had a high-risk NPx. CONCLUSION: NPx is a reliable prognostic index in patients with luminal early-stage BC, and in selected patients may be used to guide adjuvant chemotherapy recommendations.

Equipping computational pathology systems with artifact processing pipelines: a showcase for computation and performance trade-offs.

BACKGROUND: Histopathology is a gold standard for cancer diagnosis. It involves extracting tissue specimens from suspicious areas to prepare a glass slide for a microscopic examination. However, histological tissue processing procedures result in the introduction of artifacts, which are ultimately transferred to the digitized version of glass slides, known as whole slide images (WSIs). Artifacts are diagnostically irrelevant areas and may result in wrong predictions from deep learning (DL) algorithms. Therefore, detecting and excluding artifacts in the computational pathology (CPATH) system is essential for reliable automated diagnosis. METHODS: In this paper, we propose a mixture of experts (MoE) scheme for detecting five notable artifacts, including damaged tissue, blur, folded tissue, air bubbles, and histologically irrelevant blood from WSIs. First, we train independent binary DL models as experts to capture particular artifact morphology. Then, we ensemble their predictions using a fusion mechanism. We apply probabilistic thresholding over the final probability distribution to improve the sensitivity of the MoE. We developed four DL pipelines to evaluate computational and performance trade-offs. These include two MoEs and two multiclass models of state-of-the-art deep convolutional neural networks (DCNNs) and vision transformers (ViTs). These DL pipelines are quantitatively and qualitatively evaluated on external and out-of-distribution (OoD) data to assess generalizability and robustness for artifact detection application. RESULTS: We extensively evaluated the proposed MoE and multiclass models. DCNNs-based MoE and ViTs-based MoE schemes outperformed simpler multiclass models and were tested on datasets from different hospitals and cancer types, where MoE using (MobileNet) DCNNs yielded the best results. The proposed MoE yields 86.15 % F1 and 97.93% sensitivity scores on unseen data, retaining less computational cost for inference than MoE using ViTs. This best performance of MoEs comes with relatively higher computational trade-offs than multiclass models. Furthermore, we apply post-processing to create an artifact segmentation mask, a potential artifact-free RoI map, a quality report, and an artifact-refined WSI for further computational analysis. During the qualitative evaluation, field experts assessed the predictive performance of MoEs over OoD WSIs. They rated artifact detection and artifact-free area preservation, where the highest agreement translated to a Cohen Kappa of 0.82, indicating substantial agreement for the overall diagnostic usability of the DCNN-based MoE scheme. CONCLUSIONS: The proposed artifact detection pipeline will not only ensure reliable CPATH predictions but may also provide quality control. In this work, the best-performing pipeline for artifact detection is MoE with DCNNs. Our detailed experiments show that there is always a trade-off between performance and computational complexity, and no straightforward DL solution equally suits all types of data and applications. The code and HistoArtifacts dataset can be found online at Github and Zenodo , respectively.

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 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.

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.

Centrosome amplification: a quantifiable cancer cell trait with prognostic value in solid malignancies.

Numerical and/or structural centrosome amplification (CA) is a hallmark of cancers that is often associated with the aberrant tumor karyotypes and poor clinical outcomes. Mechanistically, CA compromises mitotic fidelity and leads to chromosome instability (CIN), which underlies tumor initiation and progression. Recent technological advances in microscopy and image analysis platforms have enabled better-than-ever detection and quantification of centrosomal aberrancies in cancer. Numerous studies have thenceforth correlated the presence and the degree of CA with indicators of poor prognosis such as higher tumor grade and ability to recur and metastasize. We have pioneered a novel semi-automated pipeline that integrates immunofluorescence confocal microscopy with digital image analysis to yield a quantitative centrosome amplification score (CAS), which is a summation of the severity and frequency of structural and numerical centrosome aberrations in tumor samples. Recent studies in breast cancer show that CA increases across the disease progression continuum, while normal breast tissue exhibited the lowest CA, followed by cancer-adjacent apparently normal, ductal carcinoma in situ and invasive tumors, which showed the highest CA. This finding strengthens the notion that CA could be evolutionarily favored and can promote tumor progression and metastasis. In this review, we discuss the prevalence, extent, and severity of CA in various solid cancer types, the utility of quantifying amplified centrosomes as an independent prognostic marker. We also highlight the clinical feasibility of a CA-based risk score for predicting recurrence, metastasis, and overall prognosis in patients with solid cancers.

Improving public cancer care by implementing precision medicine in Norway: IMPRESS-Norway.

BACKGROUND: Matching treatment based on tumour molecular characteristics has revolutionized the treatment of some cancers and has given hope to many patients. Although personalized cancer care is an old concept, renewed attention has arisen due to recent advancements in cancer diagnostics including access to high-throughput sequencing of tumour tissue. Targeted therapies interfering with cancer specific pathways have been developed and approved for subgroups of patients. These drugs might just as well be efficient in other diagnostic subgroups, not investigated in pharma-led clinical studies, but their potential use on new indications is never explored due to limited number of patients. METHODS: In this national, investigator-initiated, prospective, open-label, non-randomized combined basket- and umbrella-trial, patients are enrolled in multiple parallel cohorts. Each cohort is defined by the patient's tumour type, molecular profile of the tumour, and study drug. Treatment outcome in each cohort is monitored by using a Simon two-stage-like 'admissible' monitoring plan to identify evidence of clinical activity. All drugs available in IMPRESS-Norway have regulatory approval and are funded by pharmaceutical companies. Molecular diagnostics are funded by the public health care system. DISCUSSION: Precision oncology means to stratify treatment based on specific patient characteristics and the molecular profile of the tumor. Use of targeted drugs is currently restricted to specific biomarker-defined subgroups of patients according to their market authorization. However, other cancer patients might also benefit of treatment with these drugs if the same biomarker is present. The emerging technologies in molecular diagnostics are now being implemented in Norway and it is publicly reimbursed, thus more cancer patients will have a more comprehensive genomic profiling of their tumour. Patients with actionable genomic alterations in their tumour may have the possibility to try precision cancer drugs through IMPRESS-Norway, if standard treatment is no longer an option, and the drugs are available in the study. This might benefit some patients. In addition, it is a good example of a public-private collaboration to establish a national infrastructure for precision oncology. Trial registrations EudraCT: 2020-004414-35, registered 02/19/2021; ClinicalTrial.gov: NCT04817956, registered 03/26/2021.

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.

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.

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.

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.

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%.

Drug monitoring of tamoxifen metabolites predicts vaginal dryness and verifies a low discontinuation rate from the Norwegian Prescription Database.

PURPOSE: Tamoxifen is an important targeted endocrine therapy in breast cancer. However, side effects and early discontinuation of tamoxifen remains a barrier for obtaining the improved outcome benefits of long-term tamoxifen treatment. Biomarkers predictive of tamoxifen side effects remain unidentified. The objective of this prospective population-based study was to investigate the value of tamoxifen metabolite concentrations as biomarkers for side effects. A second objective was to assess the validity of discontinuation rates obtained through pharmacy records with the use of tamoxifen drug monitoring. METHODS: Longitudinal serum samples, patient-reported outcome measures and pharmacy records from 220 breast cancer patients were obtained over a 6-year period. Serum concentrations of tamoxifen metabolites were measured by LC-MS/MS. Associations between metabolite concentrations and side effects were analyzed by logistic regression and cross table analyses. To determine the validity of pharmacy records we compared longitudinal tamoxifen concentrations to discontinuation rates obtained through the Norwegian Prescription database (NorPD). Multivariable Cox regression models were performed to identify predictors of discontinuation. RESULTS: At the 2nd year of follow-up, a significant association between vaginal dryness and high concentrations of tamoxifen, Z-4'-OHtam and tam-NoX was identified. NorPD showed a tamoxifen-discontinuation rate of 17.9% at 5 years and drug monitoring demonstrated similar rates. Nausea, vaginal dryness and chemotherapy-naive status were significant risk factors for tamoxifen discontinuation. CONCLUSIONS: This real-world data study suggests that measurements of tamoxifen metabolite concentrations may be predictive of vaginal dryness in breast cancer patients and verifies NorPD as a reliable source of adherence data.

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.

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.