The effect of the stromal component of breast tumours on prediction of clinical outcome using gene expression microarray analysis

INTRODUCTION: The aim of this study was to examine the effect of the cellular composition of biopsies on the error rates of multigene predictors of response of breast tumours to neoadjuvant adriamycin and cyclophosphamide (AC) chemotherapy. MATERIALS AND METHODS: Core biopsies were taken from primary breast tumours of 43 patients prior to AC, and subsequent clinical response was recorded. Post-chemotherapy (day 21) samples were available for 16 of these samples. Frozen sections of each core were used to estimate the proportion of invasive cancer and other tissue components at three levels. Transcriptional profiling was performed using a cDNA array containing 4,600 elements. RESULTS: Twenty-three (53%) patients demonstrated a 'good' and 20 (47%) a 'poor' clinical response. The percentage invasive tumour in core biopsies collected from these patients varied markedly. Despite this, agglomerative clustering of sample expression profiles showed that almost all biopsies from the same tumour aggregated as nearest neighbours. SAM (significance analysis of microarrays) regression analysis identified 144 genes which distinguished high- and low-percentage invasive tumour biopsies at a false discovery rate of not more than 5%. The misclassification error of prediction of clinical response using microarray data from pre-treatment biopsies (on leave-one-out cross-validation) was 28%. When prediction was performed on subsets of samples which were more homogeneous in their proportions of malignant and stromal cells, the misclassification error was considerably lower (8%–13%, p < 0.05 on permutation). CONCLUSION: The non-tumour content of breast cancer samples has a significant effect on gene expression profiles. Consideration of this factor improves accuracy of response prediction by expression array profiling. Future gene expression array prediction studies should be planned taking this into account

Self-reported sleep duration and quality and cardiovascular disease and mortality: A dose-response meta-analysis

Background-There is growing evidence that sleep duration and quality may be associated with cardiovascular harm and mortality. Methods and Results-We conducted a systematic review, meta-analysis, and spline analysis of prospective cohort studies that evaluate the association between sleep duration and quality and cardiovascular outcomes. We searched MEDLINE and EMBASE for these studies and extracted data from identified studies. We utilized linear and nonlinear dose-response meta-analysis models and used DerSimonian-Laird random-effects meta-analysis models of risk ratios, with inverse variance weighting, and the I2statistic to quantify heterogeneity. Seventy-four studies including 3 340 684 participants with 242 240 deaths among 2 564 029 participants who reported death events were reviewed. Findings were broadly similar across both linear and nonlinear dose-response models in 30 studies with >1 000 000 participants, and we report results from the linear model. Self-reported duration of sleep >8 hours was associated with a moderate increased risk of all-cause mortality, with risk ratio, 1.14 (1.05-1.25) for 9 hours, risk ratio, 1.30 (1.19-1.42) for 10 hours, and risk ratio, 1.47 (1.33-1.64) for 11 hours. No significant difference was identified for periods of selfreported sleep <7 hours, whereas similar patterns were observed for stroke and cardiovascular disease mortality. Subjective poor sleep quality was associated with coronary heart disease (risk ratio, 1.44; 95% confidence interval, 1.09-1.90), but no difference in mortality and other outcomes. Conclusions-Divergence from the recommended 7 to 8 hours of sleep is associated with a higher risk of mortality and cardiovascular events. Longer duration of sleep may be more associated with adverse outcomes compared with shorter sleep durations

Chemotherapy-Response Monitoring of Breast Cancer Patients Using Quantitative Ultrasound-Based Intra-Tumour Heterogeneities

© 2017 The Author(s). Anti-cancer therapies including chemotherapy aim to induce tumour cell death. Cell death introduces alterations in cell morphology and tissue micro-structures that cause measurable changes in tissue echogenicity. This study investigated the effectiveness of quantitative ultrasound (QUS) parametric imaging to characterize intra-tumour heterogeneity and monitor the pathological response of breast cancer to chemotherapy in a large cohort of patients (n = 100). Results demonstrated that QUS imaging can non-invasively monitor pathological response and outcome of breast cancer patients to chemotherapy early following treatment initiation. Specifically, QUS biomarkers quantifying spatial heterogeneities in size, concentration and spacing of acoustic scatterers could predict treatment responses of patients with cross-validated accuracies of 82 ± 0.7%, 86 ± 0.7% and 85 ± 0.9% and areas under the receiver operating characteristic (ROC) curve of 0.75 ± 0.1, 0.80 ± 0.1 and 0.89 ± 0.1 at 1, 4 and 8 weeks after the start of treatment, respectively. The patients classified as responders and non-responders using QUS biomarkers demonstrated significantly different survivals, in good agreement with clinical and pathological endpoints. The results form a basis for using early predictive information on survival-linked patient response to facilitate adapting standard anti-cancer treatments on an individual patient basis

MiR-34b is associated with clinical outcome in triple-negative breast cancer patients

<p>Abstract</p> <p>Background</p> <p>Breast cancer is the most common malignancy with the highest incidence rates among women worldwide. Triple-negative breast cancer (TNBC) represents the major phenotype of basal-like molecular subtype of breast cancer, characterized by higher incidence in young women and a very poor prognosis. MicroRNAs (miRNAs) are small non-coding RNAs playing significant role in the pathogenesis of many cancers including breast cancer. Therefore, miRNAs are also potential prognostic and/or predictive biomarkers in triple-negative breast cancer patients.</p> <p>Methods</p> <p>Thirty-nine TNBC patients with available formalin-fixed paraffin-embedded (FFPE) tissues were enrolled in the study. MiR-34a, miR-34b, and miR-34c were analyzed using qRT-PCR and correlated to clinico-pathological features of TNBC patients.</p> <p>Results</p> <p>Expression levels of miR-34b significantly correlate with disease free survival (DFS) (<it>p </it>= 0.0020, log-rank test) and overall survival (OS) (<it>p </it>= 0.0008, log-rank test) of TNBC patients. No other significant associations between miR-34a, miR-34b, and miR-34c with available clinical pathological data were observed.</p> <p>Conclusions</p> <p>MiR-34b expression negatively correlates with disease free survival and overall survival in TNBC patients. Thus, miR-34b may present a new promising prognostic biomarker in TNBC patients, but independent validations are necessary.</p

Evaluation of the current knowledge limitations in breast cancer research: a gap analysis

BACKGROUND A gap analysis was conducted to determine which areas of breast cancer research, if targeted by researchers and funding bodies, could produce the greatest impact on patients. METHODS Fifty-six Breast Cancer Campaign grant holders and prominent UK breast cancer researchers participated in a gap analysis of current breast cancer research. Before, during and following the meeting, groups in seven key research areas participated in cycles of presentation, literature review and discussion. Summary papers were prepared by each group and collated into this position paper highlighting the research gaps, with recommendations for action. RESULTS Gaps were identified in all seven themes. General barriers to progress were lack of financial and practical resources, and poor collaboration between disciplines. Critical gaps in each theme included: (1) genetics (knowledge of genetic changes, their effects and interactions); (2) initiation of breast cancer (how developmental signalling pathways cause ductal elongation and branching at the cellular level and influence stem cell dynamics, and how their disruption initiates tumour formation); (3) progression of breast cancer (deciphering the intracellular and extracellular regulators of early progression, tumour growth, angiogenesis and metastasis); (4) therapies and targets (understanding who develops advanced disease); (5) disease markers (incorporating intelligent trial design into all studies to ensure new treatments are tested in patient groups stratified using biomarkers); (6) prevention (strategies to prevent oestrogen-receptor negative tumours and the long-term effects of chemoprevention for oestrogen-receptor positive tumours); (7) psychosocial aspects of cancer (the use of appropriate psychosocial interventions, and the personal impact of all stages of the disease among patients from a range of ethnic and demographic backgrounds). CONCLUSION Through recommendations to address these gaps with future research, the long-term benefits to patients will include: better estimation of risk in families with breast cancer and strategies to reduce risk; better prediction of drug response and patient prognosis; improved tailoring of treatments to patient subgroups and development of new therapeutic approaches; earlier initiation of treatment; more effective use of resources for screening populations; and an enhanced experience for people with or at risk of breast cancer and their families. The challenge to funding bodies and researchers in all disciplines is to focus on these gaps and to drive advances in knowledge into improvements in patient care

Clinical response after two cycles compared to HER2, Ki-67, p53, and bcl-2 in independently predicting a pathological complete response after preoperative chemotherapy in patients with operable carcinoma of the breast

Introduction To investigate the predictive value of clinical and biological markers for a pathological complete remission after a preoperative dose-dense regimen of doxorubicin and docetaxel, with or without tamoxifen, in primary operable breast cancer. Methods Patients with a histologically confirmed diagnosis of previously untreated, operable, and measurable primary breast cancer (tumour (T), nodes (N) and metastases (M) score: T2-3(>= 3 cm) N0-2 M0) were treated in a prospectively randomised trial with four cycles of dose-dense (bi-weekly) doxorubicin and docetaxel (ddAT) chemotherapy, with or without tamoxifen, prior to surgery. Clinical and pathological parameters (menopausal status, clinical tumour size and nodal status, grade, and clinical response after two cycles) and a panel of biomarkers (oestrogen and progesterone receptors, Ki-67, human epidermal growth factor receptor 2 (HER2), p53, bcl-2, all detected by immunohistochemistry) were correlated with the detection of a pathological complete response (pCR). Results A pCR was observed in 9.7% in 248 patients randomised in the study and in 8.6% in the subset of 196 patients with available tumour tissue. Clinically negative axillary lymph nodes, poor tumour differentiation, negative oestrogen receptor status, negative progesterone receptor status, and loss of bcl-2 were significantly predictive for a pCR in a univariate logistic regression model, whereas in a multivariate analysis only the clinical nodal status and hormonal receptor status provided significantly independent information. Backward stepwise logistic regression revealed a response after two cycles, with hormone receptor status and lymph-node status as significant predictors. Patients with a low percentage of cells stained positive for Ki-67 showed a better response when treated with tamoxifen, whereas patients with a high percentage of Ki-67 positive cells did not have an additional benefit when treated with tamoxifen. Tumours overexpressing HER2 showed a similar response to that in HER2-negative patients when treated without tamoxifen, but when HER2-positive tumours were treated with tamoxifen, no pCR was observed. Conclusion Reliable prediction of a pathological complete response after preoperative chemotherapy is not possible with clinical and biological factors routinely determined before start of treatment. The response after two cycles of chemotherapy is a strong but dependent predictor. The only independent factor in this subset of patients was bcl-2. Trial registration number NCT0054382

Ki67 proliferation in core biopsies versus surgical samples - a model for neo-adjuvant breast cancer studies

Background: An increasing number of neo-adjuvant breast cancer studies are being conducted and a novel model for tumor biological studies, the "window-of-opportunity" model, has revealed several advantages. Change in tumor cell proliferation, estimated by Ki67-expression in pre-therapeutic core biopsies versus post-therapeutic surgical samples is often the primary end-point. The aim of the present study was to investigate potential differences in proliferation scores between core biopsies and surgical samples when patients have not received any intervening anti-cancer treatment. Also, a lack of consensus concerning Ki67 assessment may raise problems in the comparison of neo-adjuvant studies. Thus, the secondary aim was to present a novel model for Ki67 assessment. Methods: Fifty consecutive breast cancer cases with both a core biopsy and a surgical sample available, without intervening neo-adjuvant therapy, were collected and tumor proliferation (Ki67, MIB1 antibody) was assessed immunohistochemically. A theoretical model for the assessment of Ki67 was constructed based on sequential testing of the null hypothesis 20% Ki67-positive cells versus the two-sided alternative more or less than 20% positive cells.. Results: Assessment of Ki67 in 200 tumor cells showed an absolute average proliferation difference of 3.9% between core biopsies and surgical samples (p = 0.046, paired t-test) with the core biopsies being the more proliferative sample type. A corresponding analysis on the log-scale showed the average relative decrease from the biopsy to the surgical specimen to be 19% (p = 0.063, paired t-test on the log-scale). The difference was significant when using the more robust Wilcoxon matched-pairs signed-ranks test (p = 0.029). After dichotomization at 20%, 12 of the 50 sample pairs had discrepant proliferation status, 10 showed high Ki67 in the core biopsy compared to two in the surgical specimen (p = 0.039, McNemar's test). None of the corresponding results for 1000 tumor cells were significant - average absolute difference 2.2% and geometric mean of the ratios 0.85 (p = 0.19 and p = 0.18, respectively, paired t-tests, p = 0.057, Wilcoxon's test) and an equal number of discordant cases after dichotomization. Comparing proliferation values for the initial 200 versus the final 800 cancer cells showed significant absolute differences for both core biopsies and surgical samples 5.3% and 3.2%, respectively (p < 0.0001, paired t-test). Conclusions: A significant difference between core biopsy and surgical sample proliferation values was observed despite no intervening therapy. Future neo-adjuvant breast cancer studies may have to take this into consideration

Response of Estrogen Receptor-Positive Breast Cancer Tumorspheres to Antiestrogen Treatments

Estrogen signaling plays a critical role in the pathogenesis of breast cancer. Because the majority of breast carcinomas express the estrogen receptor ERα, endocrine therapy that impedes estrogen-ER signaling reduces breast cancer mortality and has become a mainstay of breast cancer treatment. However, patients remain at continued risk of relapse for many years after endocrine treatment. It has been proposed that cancer recurrence may be attributed to cancer stem cells (CSCs)/tumor-initiating cells (TICs). Previous studies in breast cancer have shown that such cells can be enriched and propagated in vitro by culturing the cells in suspension as mammospheres/tumorspheres. Here we established tumorspheres from ERα-positive human breast cancer cell line MCF7 and investigated their response to antiestrogens Tamoxifen and Fulvestrant. The tumorsphere cells express lower levels of ERα and are more tumorigenic in xenograft assays than the parental cells. Both 4-hydroxytamoxifen (4-OHT) and Fulvestrant attenuate tumorsphere cell proliferation, but only 4-OHT at high concentrations interferes with sphere formation. However, treated tumorsphere cells retain the self-renewal capacity. Upon withdrawal of antiestrogens, the treated cells resume tumorsphere formation and their tumorigenic potential remains undamaged. Depletion of ERα shows that ERα is dispensable for tumorsphere formation and xenograft tumor growth in mice. Surprisingly, ERα-depleted tumorspheres display heightened sensitivity to 4-OHT and their sphere-forming capacity is diminished after the drug is removed. These results imply that 4-OHT may inhibit cellular targets besides ERα that are essential for tumorsphere growth, and provide a potential strategy to sensitize tumorspheres to endocrine treatment

An algorithm for classifying tumors based on genomic aberrations and selecting representative tumor models

<p>Abstract</p> <p>Background</p> <p>Cancer is a heterogeneous disease caused by genomic aberrations and characterized by significant variability in clinical outcomes and response to therapies. Several subtypes of common cancers have been identified based on alterations of individual cancer genes, such as HER2, EGFR, and others. However, cancer is a complex disease driven by the interaction of multiple genes, so the copy number status of individual genes is not sufficient to define cancer subtypes and predict responses to treatments. A classification based on genome-wide copy number patterns would be better suited for this purpose.</p> <p>Method</p> <p>To develop a more comprehensive cancer taxonomy based on genome-wide patterns of copy number abnormalities, we designed an unsupervised classification algorithm that identifies genomic subgroups of tumors. This algorithm is based on a modified genomic Non-negative Matrix Factorization (gNMF) algorithm and includes several additional components, namely a pilot hierarchical clustering procedure to determine the number of clusters, a multiple random initiation scheme, a new stop criterion for the core gNMF, as well as a 10-fold cross-validation stability test for quality assessment.</p> <p>Result</p> <p>We applied our algorithm to identify genomic subgroups of three major cancer types: non-small cell lung carcinoma (NSCLC), colorectal cancer (CRC), and malignant melanoma. High-density SNP array datasets for patient tumors and established cell lines were used to define genomic subclasses of the diseases and identify cell lines representative of each genomic subtype. The algorithm was compared with several traditional clustering methods and showed improved performance. To validate our genomic taxonomy of NSCLC, we correlated the genomic classification with disease outcomes. Overall survival time and time to recurrence were shown to differ significantly between the genomic subtypes.</p> <p>Conclusions</p> <p>We developed an algorithm for cancer classification based on genome-wide patterns of copy number aberrations and demonstrated its superiority to existing clustering methods. The algorithm was applied to define genomic subgroups of three cancer types and identify cell lines representative of these subgroups. Our data enabled the assembly of representative cell line panels for testing drug candidates.</p

Revisiting the technical validation of tumour biomarker assays: how to open a Pandora's box

A tumour biomarker is a characteristic that is objectively measured and evaluated in tumour samples as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. The development of a biomarker contemplates distinct phases, including discovery by hypothesis-generating preclinical or exploratory studies, development and qualification of the assay for the identification of the biomarker in clinical samples, and validation of its clinical significance. Although guidelines for the development and validation of biomarkers are available, their implementation is challenging, owing to the diversity of biomarkers being developed. The term 'validation' undoubtedly has several meanings; however, in the context of biomarker research, a test may be considered valid if it is 'fit for purpose'. In the process of validation of a biomarker assay, a key point is the validation of the methodology. Here we discuss the challenges for the technical validation of immunohistochemical and gene expression assays to detect tumour biomarkers and provide suggestions of pragmatic solutions to address these challenges