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Purpose: Endocrine therapies, such as tamoxifen, are commonly given to most patients with estrogen receptor (ERα)–positive breast carcinoma but are not indicated for persons with ERα-negative cancer. The factors responsible for response to tamoxifen in 5% to 10% of patients with ERα-negative tumors are not clear. The aim of the present study was to elucidate the biology and prognostic role of the second ER, ERβ, in patients treated with adjuvant tamoxifen.Experimental Design: We investigated ERβ by immunohistochemistry in 353 stage II primary breast tumors from patients treated with 2 years adjuvant tamoxifen, and generated gene expression profiles for a representative subset of 88 tumors.Results: ERβ was associated with increased survival (distant disease-free survival, P = 0.01; overall survival, P = 0.22), and in particular within ERα-negative patients (P = 0.003; P = 0.04), but not in the ERα-positive subgroup (P = 0.49; P = 0.88). Lack of ERβ conferred early relapse (hazard ratio, 14; 95% confidence interval, 1.8-106; P = 0.01) within the ERα-negative subgroup even after adjustment for other markers. ERα was an independent marker only within the ERβ-negative tumors (hazard ratio, 0.44; 95% confidence interval, 0.21-0.89; P = 0.02). An ERβ gene expression profile was identified and was markedly different from the ERα signature.Conclusion: Expression of ERβ is an independent marker for favorable prognosis after adjuvant tamoxifen treatment in ERα-negative breast cancer patients and involves a gene expression program distinct from ERα. These results may be highly clinically significant, because in the United States alone, ∼10,000 women are diagnosed annually with ERα-negative/ERβ-positive breast carcinoma and may benefit from adjuvant tamoxifen.

The prognostic and treatment-predictive markers currently in use for breast cancer are commonly based on the protein levels of individual genes (e.g., steroid receptors) or aspects of the tumor phenotype, such as histological grade and percentage of cells in the DNA synthesis phase of the cell cycle. Microarrays have previously been used to classify binary classes in breast cancer such as estrogen receptor (ER)-α status. To test whether the properties and specific values of conventional prognostic markers are encoded within tumor gene expression profiles, we have analyzed 48 well-characterized primary tumors from lymph node-negative breast cancer patients using 6728-element cDNA microarrays. In the present study, we used artificial neural networks trained with tumor gene expression data to predict the ER protein values on a continuous scale. Furthermore, we determined a gene expression profile-directed threshold for ER protein level to redefine the cutoff between ER-positive and ER-negative classes that may be more biologically relevant. With a similar approach, we studied the prediction of other prognostic parameters such as percentage cells in the S phase of the cell cycle (SPF), histological grade, DNA ploidy status, and progesterone receptor status. Interestingly, there was a consistent reciprocal relationship in expression levels of the genes important for both ER and SPF prediction. This and similar studies may be used to increase our understanding of the biology underlying these markers as well as to improve the currently available prognostic markers for breast cancer.

Analysis + 1 Figure and 3 Tables

Supplementary Materials and Methods, along with Supplementary Figure S1

Dealing with missing data in data analysis is inevitable. Although powerful imputation methods that address this problem exist, there is still much room for improvement. In this study, we examined single imputation based on deep autoencoders, motivated by the apparent success of deep learning to efficiently extract useful dataset features. We have developed a consistent framework for both training and imputation. Moreover, we benchmarked the results against state-of-the-art imputation methods on different data sizes and characteristics. The work was not limited to the one-type variable dataset; we also imputed missing data with multi-type variables, e.g., a combination of binary, categorical, and continuous attributes. To evaluate the imputation methods, we randomly corrupted the complete data, with varying degrees of corruption, and then compared the imputed and original values. In all experiments, the developed autoencoder obtained the smallest error for all ranges of initial data corruption.

Background Individual patients differ in their psychological response when receiving a cancer diagnosis, in this case breast cancer. Given the same disease burden, some patients master the situation well, while others experience a great deal of stress, depression and lowered quality of life. Patients with high psychological resilience are likely to experience fewer stress reactions and better adapt to and manage the life threat and the demanding treatment that follows the diagnosis. If this phenomenon of mastering difficult situations is reflected also in biomolecular processes is not much studied, nor has its capacity for impacting the cancer prognosis been addressed. This project specifically aims, for the first time, to investigate how a breast cancer patient’s psychological resilience is coupled to biomolecular parameters using advanced “omics” and, as a secondary aim, whether it relates to prognosis and quality of life one year after diagnosis. Method The study population consists of newly diagnosed breast cancer patients enrolled in the Sweden Cancerome Analysis Network – Breast (SCAN-B) at four hospitals in Sweden. At the time of cancer diagnosis, the patient fills out the standardized method to measure psychological resilience, the “Connor-Davidson Resilience scale” (CD-RISC), the quality of life measure SF-36, as well as providing social and socioeconomic variables. In addition, one blood sample is collected. At the one-year follow-up, the patient will be subjected to the same assessments, and we also collect information regarding smoking, exercise habits, and BMI, as well as patients’ trust in the treatment and their satisfaction with the care and treatment. Discussion This explorative hypothesis-generating project will pave the way for larger validation studies, potentially leading to a standardized method of measuring psychological resilience as an important parameter in cancer care. Revealing the body-mind interaction, in terms of psychological resilience and quality of life, will herald the development of truly personalized psychosocial care and cancer intervention treatment strategies. Trial registration This is a retrospectively registered trial at ClinicalTrials.gov, ID: NCT03430492 on February 6, 2018. Electronic supplementary material The online version of this article (10.1186/s12885-018-4669-y) contains supplementary material, which is available to authorized users.

Background Sentinel lymph node biopsy (SLNB) is standard staging procedure for nodal status in breast cancer, but lacks therapeutic benefit for patients with benign sentinel nodes. For patients with positive sentinel nodes, individualized surgical strategies are applied depending on the extent of nodal involvement. Preoperative prediction of nodal status is thus important for individualizing axillary surgery avoiding unnecessary surgery. We aimed to predict nodal status in clinically node-negative breast cancer and identify candidates for SLNB omission by including patient-related and pathological characteristics into artificial neural network (ANN) models. Methods Patients with primary breast cancer were consecutively included between January 1, 2009 and December 31, 2012 in a prospectively maintained pathology database. Clinical- and radiological data were extracted from patient’s files and only clinically node-negative patients constituted the final study cohort. ANN-based models for nodal prediction were constructed including 15 risk variables for nodal status. Area under the receiver operating characteristic curve (AUC) and Hosmer-Lemeshow goodness-of-fit test (HL) were used to assess performance and calibration of three predictive ANN-based models for no lymph node metastasis (N0), metastases in 1–3 lymph nodes (N1) and metastases in ≥ 4 lymph nodes (N2). Linear regression models for nodal prediction were calculated for comparison. Results Eight hundred patients (N0, n = 514; N1, n = 232; N2, n = 54) were included. Internally validated AUCs for N0 versus N+ was 0.740 (95% CI = 0.723–0.758); median HL was 9.869 (P = 0.274), for N1 versus N0, 0.705 (95% CI = 0.686–0.724; median HL: 7.421; P = 0.492) and for N2 versus N0 and N1, 0.747 (95% CI = 0.728–0.765; median HL: 9.220; P = 0.324). Tumor size and vascular invasion were top-ranked predictors of all three end-points, followed by estrogen receptor status and lobular cancer for prediction of N2. For each end-point, ANN models showed better discriminatory performance than multivariable logistic regression models. Accepting a false negative rate (FNR) of 10% for predicting N0 by the ANN model, SLNB could have been abstained in 27.25% of patients with clinically node-negative axilla. Conclusions In this retrospective study, ANN showed promising result as decision-supporting tools for estimating nodal disease. If prospectively validated, patients least likely to have nodal metastasis could be spared SLNB using predictive models. Trial registration Registered in the ISRCTN registry with study ID ISRCTN14341750. Date of registration 23/11/2018. Retrospectively registered. Electronic supplementary material The online version of this article (10.1186/s12885-019-5827-6) contains supplementary material, which is available to authorized users.

Summary Sample mislabeling or misannotation has been a long-standing problem in scientific research, particularly prevalent in large-scale, multi-omic studies due to the complexity of multi-omic workflows. There exists an urgent need for implementing quality controls to automatically screen for and correct sample mislabels or misannotations in multi-omic studies. Here, we describe a crowdsourced precisionFDA NCI-CPTAC Multi-omics Enabled Sample Mislabeling Correction Challenge, which provides a framework for systematic benchmarking and evaluation of mislabel identification and correction methods for integrative proteogenomic studies. The challenge received a large number of submissions from domestic and international data scientists, with highly variable performance observed across the submitted methods. Post-challenge collaboration between the top-performing teams and the challenge organizers has created an open-source software, COSMO, with demonstrated high accuracy and robustness in mislabeling identification and correction in simulated and real multi-omic datasets., Highlights • A community effort to combat sample mislabeling in multi-omic studies • Computational solutions received show a wide range of accuracy • The final collaborative product, COSMO, achieves high performance • Applying COSMO to published datasets demonstrates biological impact of the tool, The bigger picture Sample mislabeling is a well-recognized problem in scientific research, particularly prevalent in large-scale, multi-omic studies, due to the complexity of multi-omic workflows. Here, we describe a crowdsourced precisionFDA NCI-CPTAC Multi-omics Enabled Sample Mislabeling Correction Challenge, which provides a framework for systematic benchmarking and evaluation of mislabel identification and correction methods for integrative proteogenomic studies. Individual solutions submitted by the challenge participants, even those from the same team, show a wide range of accuracy, underscoring the importance of the benchmarking effort. Post-challenge collaboration between the top-performing teams and the challenge organizers has created an open-source software, COSMO, with demonstrated high accuracy and robustness in mislabeling identification and correction in simulated and real multi-omic datasets., A community effort to combat sample mislabeling in multi-omic studies leads to an open-source software, COSMO, with demonstrated high accuracy and robustness in mislabeling identification and correction in simulated and real multi-omic datasets.

Background Adjuvant radiotherapy is the standard of care after breast-conserving surgery for primary breast cancer, despite a majority of patients being over- or under-treated. In contrast to adjuvant endocrine therapy and chemotherapy, no diagnostic tests are in clinical use that can stratify patients for adjuvant radiotherapy. This study presents the development and validation of a targeted gene expression assay to predict the risk of ipsilateral breast tumor recurrence and response to adjuvant radiotherapy after breast-conserving surgery in primary breast cancer. Methods Fresh-frozen primary tumors from 336 patients radically (clear margins) operated on with breast-conserving surgery with or without radiotherapy were collected. Patients were split into a discovery cohort (N = 172) and a validation cohort (N = 164). Genes predicting ipsilateral breast tumor recurrence in an Illumina HT12 v4 whole transcriptome analysis were combined with genes identified in the literature (248 genes in total) to develop a targeted radiosensitivity assay on the Nanostring nCounter platform. Single-sample predictors for ipsilateral breast tumor recurrence based on a k-top scoring pairs algorithm were trained, stratified for estrogen receptor (ER) status and radiotherapy. Two previously published profiles, the radiosensitivity signature of Speers et al., and the 10-gene signature of Eschrich et al., were also included in the targeted panel. Results Derived single-sample predictors were prognostic for ipsilateral breast tumor recurrence in radiotherapy-treated ER+ patients (AUC 0.67, p = 0.01), ER+ patients without radiotherapy (AUC = 0.89, p = 0.02), and radiotherapy-treated ER- patients (AUC = 0.78, p

Summary We explore cell heterogeneity during spontaneous and transcription-factor-driven commitment for network inference in hematopoiesis. Since individual genes display discrete OFF states or a distribution of ON levels, we compute and combine pairwise gene associations from binary and continuous components of gene expression in single cells. Ddit3 emerges as a regulatory node with positive linkage to erythroid regulators and negative association with myeloid determinants. Ddit3 loss impairs erythroid colony output from multipotent cells, while forcing Ddit3 in granulo-monocytic progenitors (GMPs) enhances self-renewal and impedes differentiation. Network analysis of Ddit3-transduced GMPs reveals uncoupling of myeloid networks and strengthening of erythroid linkages. RNA sequencing suggests that Ddit3 acts through development or stabilization of a precursor upstream of GMPs with inherent Meg-E potential. The enrichment of Gata2 target genes in Ddit3-dependent transcriptional responses suggests that Ddit3 functions in an erythroid transcriptional network nucleated by Gata2., Graphical Abstract, Highlights • We present a method for inferring gene regulatory networks (GRNs) from single cells • Lineage cross-antagonism is a key property of GRNs of early lineage commitment • Ddit3 is a regulatory node in erythroid lineage programming • A Ddit3-Gata2 regulatory axis antagonizes myeloid and enables erythroid programs, Pina et al. develop a gene regulatory network inference method using single-cell gene expression data and identify Ddit3 as a regulatory node in erythroid lineage programming. The authors explore this inference and show that Ddit3 can antagonize myeloid programming and enable erythroid signatures and forms a regulatory axis with Gata2.

Immunohistochemical expression of CD40 is seen in 60-70% of diffuse large B-cell lymphoma (DLBCL) and is associated with a superior prognosis. By using gene expression profiling we aimed to further explore the underlying mechanisms for this effect. Ninety-eight immunohistochemically defined CD40 positive or negative DLBCL tumors, 63 and 35 respectively, were examined using spotted 55K oligonucleotide arrays. CD40 expressing tumors were characterized by up-regulated expression of genes encoding proteins involved in cell-matrix interactions: collagens, integrin αV, proteoglycans and proteolytic enzymes, and antigen presentation. Immunohistochemistry confirmed that CD40 positive tumors co-express the proinflammatory proteoglycan biglycan (p = 0.005), which in turn correlates with the amount of infiltrating macrophages and CD4 and CD8 positive T-cells. We postulate that immunohistochemical expression of CD40 mainly reflects the inflammatory status in tumors. A high intratumoral inflammatory reaction may correlate with an increased autologous tumor response, and thereby a better prognosis.

Objective The P190 and P210 BCR/ABL1 fusion genes are mainly associated with different types of hematologic malignancies, but it is presently unclear whether they are functionally different following expression in primitive human hematopoietic cells. Materials and Methods We investigated and systematically compared the effects of retroviral P190 BCR/ABL1 and P210 BCR/ABL1 expression on cell proliferation, differentiation, and global gene expression in human CD34 + cells from cord blood. Results. Expression of either P190 BCR/ABL1 or P210 BCR/ABL1 resulted in expansion of erythroid cells and stimulated erythropoietin-independent burst-forming unit-erythroid colony formation. By using a lentiviral anti−signal transducer and activator of transcription 5 (STAT5) short-hairpin RNA, we found that both P190 BCR/ABL1 - and P210 BCR/ABL1 -induced erythroid cell expansion were STAT5 -dependent. Under in vitro conditions favoring B-cell differentiation, neither P190 nor P210 BCR/ABL1- expressing cells formed detectable levels of CD19-positive cells. Gene expression profiling revealed that P190 BCR/ABL1 and P210 BCR/ABL1 induced almost identical gene expression profiles. Conclusions Our data suggest that the early cellular and transcriptional effects of P190 BCR/ABL1 and P210 BCR/ABL1 expression are very similar when they are expressed in the same human progenitor cell population, and that STAT5 is an important regulator of BCR/ABL1 -induced erythroid cell expansion.

In order to identify genes associated with primary chemotherapy-resistance, gene expression profiles (GEP) in tumour tissue from 37 patients with de novo diffuse large B-cell lymphoma (DLBCL), stage II-IV, either in continuous complete remission (n = 24) or with progressive disease during primary treatment (n = 13), were examined using spotted 55K oligonucleotide arrays. Immunohistochemistry was used for confirmation at the protein level. The top 86 genes that best discriminated between the two cohorts were chosen for further analysis. Only seven of 86 genes were overexpressed in the refractory cohort, e.g. RABGGTB and POLE, both potential targets for drug intervention. Seventy-nine of 86 genes were overexpressed in the cured cohort and mainly coded for proteins expressed in the tumour microenvironment, many of them involved in proteolytic activity and remodelling of extra cellular matrix. Furthermore, major histocompatibility complex class I molecules, CD3D and ICAM1 were overexpressed, indicating an enhanced immunological reaction. Immunohistochemistry confirmed the GEP results. The frequency of tumour infiltrating lymphocytes, macrophages, and reactive cells expressing ICAM-1, lysozyme, cathepsin D, urokinase plasminogen activator receptor, signal transducer and activator of transcription 1, and galectin-3 was higher in the cured cohort. These findings indicate that a reactive microenvironment has an impact on the outcome of chemotherapy in DLBCL.

Purpose: Endocrine therapies, such as tamoxifen, are commonly given to most patients with estrogen receptor (ERα)–positive breast carcinoma but are not indicated for persons with ERα-negative cancer. The factors responsible for response to tamoxifen in 5% to 10% of patients with ERα-negative tumors are not clear. The aim of the present study was to elucidate the biology and prognostic role of the second ER, ERβ, in patients treated with adjuvant tamoxifen. Experimental Design: We investigated ERβ by immunohistochemistry in 353 stage II primary breast tumors from patients treated with 2 years adjuvant tamoxifen, and generated gene expression profiles for a representative subset of 88 tumors. Results: ERβ was associated with increased survival (distant disease-free survival, P = 0.01; overall survival, P = 0.22), and in particular within ERα-negative patients (P = 0.003; P = 0.04), but not in the ERα-positive subgroup (P = 0.49; P = 0.88). Lack of ERβ conferred early relapse (hazard ratio, 14; 95% confidence interval, 1.8-106; P = 0.01) within the ERα-negative subgroup even after adjustment for other markers. ERα was an independent marker only within the ERβ-negative tumors (hazard ratio, 0.44; 95% confidence interval, 0.21-0.89; P = 0.02). An ERβ gene expression profile was identified and was markedly different from the ERα signature. Conclusion: Expression of ERβ is an independent marker for favorable prognosis after adjuvant tamoxifen treatment in ERα-negative breast cancer patients and involves a gene expression program distinct from ERα. These results may be highly clinically significant, because in the United States alone, ∼10,000 women are diagnosed annually with ERα-negative/ERβ-positive breast carcinoma and may benefit from adjuvant tamoxifen.

We investigate a new method to place patients into risk groups in censored survival data. Properties such as median survival time, and end survival rate, are implicitly improved by optimizing the area under the survival curve. Artificial neural networks (ANN) are trained to either maximize or minimize this area using a genetic algorithm, and combined into an ensemble to predict one of low, intermediate, or high risk groups. Estimated patient risk can influence treatment choices, and is important for study stratification. A common approach is to sort the patients according to a prognostic index and then group them along the quartile limits. The Cox proportional hazards model (Cox) is one example of this approach. Another method of doing risk grouping is recursive partitioning (Rpart), which constructs a decision tree where each branch point maximizes the statistical separation between the groups. ANN, Cox, and Rpart are compared on five publicly available data sets with varying properties. Cross-validation, as well as separate test sets, are used to validate the models. Results on the test sets show comparable performance, except for the smallest data set where Rpart's predicted risk groups turn out to be inverted, an example of crossing survival curves. Cross-validation shows that all three models exhibit crossing of some survival curves on this small data set but that the ANN model manages the best separation of groups in terms of median survival time before such crossings. The conclusion is that optimizing the area under the survival curve is a viable approach to identify risk groups. Training ANNs to optimize this area combines two key strengths from both prognostic indices and Rpart. First, a desired minimum group size can be specified, as for a prognostic index. Second, the ability to utilize non-linear effects among the covariates, which Rpart is also able to do.

A large proportion of breast cancer patients are treated with adjuvant chemotherapy after the primary operation, but some will recur in spite of this treatment. In order to achieve an improved and more individualised therapy, our knowledge in mechanisms for drug resistance needs to be increased. We have investigated to what extent cDNA microarray measurements could distinguish the likelihood of recurrences after adjuvant CMF (cyclophosphamide, methotrexate and 5-fluorouracil) treatment of premenopausal, lymph node positive breast cancer patients, and have also compared this with the corresponding performance when using conventional clinical variables. We tried several gene selection strategies, and built classifiers using the resulting gene lists. The best performing classifier with odds ratio (OR) = 6.5 (95% confidence interval (CI) = 1.4-62) did not outperform corresponding classifiers based on clinical variables. For the clinical variables, calibrated on the samples, either using all the clinical parameters or the Nottingham Prognostic Index (NPI) parameters, the areas under the receiver operating characteristics (ROC) curve were 0.78 and 0.79, respectively. The ORs at 90% sensitivity were 15 (95% CI = 3.1-140) and 10 (95% CI = 2.1-97), respectively. Our data have provided evidence for a comparable prediction of clinical outcome in CMF-treated breast cancer patients using conventional clinical variables and gene expression based markers. (c) 2006 Elsevier Ltd. All rights reserved.

Global expression profiles of a consecutive series of 121 childhood acute leukemias (87 B lineage acute lymphoblastic leukemias, 11 T cell acute lymphoblastic leukemias, and 23 acute myeloid leukemias), six normal bone marrows, and 10 normal hematopoietic subpopulations of different lineages and maturations were ascertained by using 27K cDNA microarrays. Unsupervised analyses revealed segregation according to lineages and primary genetic changes, i.e., TCF3 ( E2A )/ PBX1 , IGH @/ MYC , ETV6 ( TEL )/ RUNX1 ( AML1 ), 11q23 / MLL , and hyperdiploidy (>50 chromosomes). Supervised discriminatory analyses were used to identify differentially expressed genes correlating with lineage and primary genetic change. The gene-expression profiles of normal hematopoietic cells were also studied. By using principal component analyses (PCA), a differentiation axis was exposed, reflecting lineages and maturation stages of normal hematopoietic cells. By applying the three principal components obtained from PCA of the normal cells on the leukemic samples, similarities between malignant and normal cell lineages and maturations were investigated. Apart from showing that leukemias segregate according to lineage and genetic subtype, we provide an extensive study of the genes correlating with primary genetic changes. We also investigated the expression pattern of these genes in normal hematopoietic cells of different lineages and maturations, identifying genes preferentially expressed by the leukemic cells, suggesting an ectopic activation of a large number of genes, likely to reflect regulatory networks of pathogenetic importance that also may provide attractive targets for future directed therapies.

Hematologic malignancies are characterized by fusion genes of biological/clinical importance. Immortalized cell lines with such aberrations are today widely used to model different aspects of leukemogenesis. Using cDNA microarrays, we determined the gene expression profiles of 40 cell lines as well as of primary leukemias harboring 11q23/MLL rearrangements, t(1;19)[TCF3/PBX1], t(12;21)[ETV6/RUNX1], t(8;21)[RUNX1/CBFA2T1], t(8;14)[IGH@/MYC], t(8;14)[TRA@/MYC], t(9;22)[BCR/ABL1], t(10;11)[PICALM/MLLT10], t(15;17)[PML/RARA], or inv(16)[CBFB/MYH11]. Unsupervised classification revealed that hematopoietic cell lines of diverse origin, but with the same primary genetic changes, segregated together, suggesting that pathogenetically important regulatory networks remain conserved despite numerous passages. Moreover, primary leukemias cosegregated with cell lines carrying identical genetic rearrangements, further supporting that critical regulatory pathways remain intact in hematopoietic cell lines. Transcriptional signatures correlating with clinical subtypes/primary genetic changes were identified and annotated based on their biological/molecular properties and chromosomal localization. Furthermore, the expression profile of tyrosine kinase-encoding genes was investigated, identifying several differentially expressed members, segregating with primary genetic changes, which may be targeted with tyrosine kinase inhibitors. The identified conserved signatures are likely to reflect regulatory networks of importance for the transforming abilities of the primary genetic changes and offer important pathogenetic insights as well as a number of targets for future rational drug design.

We compared the power of gene expression measurements with that of conventional prognostic markers, i.e., clinical, histopathological, and cell biological parameters, for predicting distant metastases in breast cancer patients using both established prognostic indices (e.g., the Nottingham Prognostic Index (NPI)) and novel combinations of conventional markers. We used publicly available data on 97 patients, and the performance of metastasis prediction was represented by receiver operating characteristic (ROC) areas and Kaplan-Meier plots. The gene expression profiler did not perform noticeably better than indices constructed from the clinical variables, e.g., the well established NPI. When analysing separately subgroups, according to the oestrogen receptor (ER) status both approaches could predict clinical outcome more easily for the ER-positive than for the ER-negative cohort. Given the time it may take before microarray processing is used worldwide, particularly due to the costs and the lack of standards, it is important to pursue research using conventional markers. Our analysis suggests that it might be possible to improve the combination of different conventional prognostic markers into one prognostic index.

Evolution equations for multiplicities in QCD cascades can, both in the parton and dipole picture, be used to estimate corrections beyond the formal accuracy of the modified leading log approximation (MLLA). The differences between the two pictures, and other uncertainties beyond first order MLLA corrections, are here investigated in some detail. For example, I discuss how some colour suppressed energy conservation corrections, which cannot be determined without better knowledge of non-perturbative QCD, are related to the question of colour reconnection.

While the hard phase of the strong interaction is well described by perturbative QCD, the soft hadronization phase is less understood. Benefiting from the high statistics from e+e- experiments at the Z0 resonance, it is possible to impose strong two-jet cuts on the data without loosing the statistical significance. In these events perturbative activity is suppressed and hadronization effects can be more prominent. We show that after proper event cuts a set of observables are sensitive to differences in the hadronization models. These observables can thus be important tools for a more detailed study of the hadronization mechanism., 18 pages, 6 ps figures

Mantle cell lymphoma (MCL) is an aggressive B cell lymphoma, where survival has been remarkably improved by use of protocols including high dose cytarabine, rituximab and autologous stem cell transplantation, such as the Nordic MCL2/3 protocols. In 2008, a MCL international prognostic index (MIPI) was created to enable stratification of the clinical diverse MCL patients into three risk groups. So far, use of the MIPI in clinical routine has been limited, as it has been shown that it inadequately separates low and intermediate risk group patients. To improve outcome and minimize treatment-related morbidity, additional parameters need to be evaluated to enable risk-adapted treatment selection. We have investigated the individual prognostic role of the MIPI and molecular markers including SOX11, TP53 (p53), MKI67 (Ki-67) and CCND1 (cyclin D1). Furthermore, we explored the possibility of creating an improved prognostic tool by combining the MIPI with information on molecular markers. SOX11 was shown to significantly add prognostic information to the MIPI, but in multivariate analysis TP53 was the only significant independent molecular marker. Based on these findings, we propose that TP53 and SOX11 should routinely be assessed and that a combined TP53/MIPI score may be used to guide treatment decisions.

Molecular mechanisms employed by individual multipotent cells at the point of lineage commitment remain largely uncharacterized. Current paradigms span from instructive to noise-driven mechanisms. Of considerable interest is also whether commitment involves a limited set of genes or the entire transcriptional program, and to what extent gene expression configures multiple trajectories into commitment. Importantly, the transient nature of the commitment transition confounds the experimental capture of committing cells. We develop a computational framework that simulates stochastic commitment events, and affords mechanistic exploration of the fate transition. We use a combined modeling approach guided by gene expression classifier methods that infers a time-series of stochastic commitment events from experimental growth characteristics and gene expression profiling of individual hematopoietic cells captured immediately before and after commitment. We define putative regulators of commitment and probabilistic rules of transition through machine learning methods, and employ clustering and correlation analyses to interrogate gene regulatory interactions in multipotent cells. Against this background, we develop a Monte Carlo time-series stochastic model of transcription where the parameters governing promoter status, mRNA production and mRNA decay in multipotent cells are fitted to experimental static gene expression distributions. Monte Carlo time is converted to physical time using cell culture kinetic data. Probability of commitment in time is a function of gene expression as defined by a logistic regression model obtained from experimental single-cell expression data. Our approach should be applicable to similar differentiating systems where single cell data is available. Within our system, we identify robust model solutions for the multipotent population within physiologically reasonable values and explore model predictions with regard to molecular scenarios of entry into commitment. The model suggests distinct dependencies of different commitment-associated genes on mRNA dynamics and promoter activity, which globally influence the probability of lineage commitment., Author Summary Stem cells have the capacity to both self-renew and differentiate into specialized cell lineages, thus sustaining tissue formation during embryonic development and permitting tissue homeostasis throughout adult life. Previous studies have suggested that stem cell commitment to a specific lineage may constitute a discrete event of stochastic activation of a small number of key regulator genes. Experimental exploration of this question is challenging, in face of the elusive nature of the commitment transition and due to considerable gene expression heterogeneity between cells. Here, we implement a computational model that simulates gene expression variation through time and affords the capture of in silico commitment events. This model integrates statistical analysis of experimental single-cell gene expression data with dynamical modeling methods to implement a mechanistic framework for stochastic regulation of gene transcription and a probabilistic approach for the commitment rules. Applied to blood cells, our method identifies potential commitment-associated genes, explores how their expression patterns can define alternative commitment regimes, and suggests how differences in regulation of gene expression dynamics can impact the frequency of commitment.

OBJECTIVE: The concordance index (c-index) is the standard way of evaluating the performance of prognostic models in the presence of censored data. Constructing prognostic models using artificial neural networks (ANNs) is commonly done by training on error functions which are modified versions of the c-index. Our objective was to demonstrate the capability of training directly on the c-index and to evaluate our approach compared to the Cox proportional hazards model. METHOD: We constructed a prognostic model using an ensemble of ANNs which were trained using a genetic algorithm. The individual networks were trained on a non-linear artificial data set divided into a training and test set both of size 2000, where 50% of the data was censored. The ANNs were also trained on a data set consisting of 4042 patients treated for breast cancer spread over five different medical studies, 2/3 used for training and 1/3 used as a test set. A Cox model was also constructed on the same data in both cases. The two models' c-indices on the test sets were then compared. The ranking performance of the models is additionally presented visually using modified scatter plots. RESULTS: Cross validation on the cancer training set did not indicate any non-linear effects between the covariates. An ensemble of 30 ANNs with one hidden neuron was therefore used. The ANN model had almost the same c-index score as the Cox model (c-index=0.70 and 0.71, respectively) on the cancer test set. Both models identified similarly sized low risk groups with at most 10% false positives, 49 for the ANN model and 60 for the Cox model, but repeated bootstrap runs indicate that the difference was not significant. A significant difference could however be seen when applied on the non-linear synthetic data set. In that case the ANN ensemble managed to achieve a c-index score of 0.90 whereas the Cox model failed to distinguish itself from the random case (c-index=0.49). CONCLUSIONS: We have found empirical evidence that ensembles of ANN models can be optimized directly on the c-index. Comparison with a Cox model indicates that near identical performance is achieved on a real cancer data set while on a non-linear data set the ANN model is clearly superior.

Background Childhood leukemia is characterized by the presence of balanced chromosomal translocations or by other structural or numerical chromosomal changes. It is well know that leukemias with specific molecular abnormalities display profoundly different global gene expression profiles. However, it is largely unknown whether such subtype-specific leukemic signatures are unique or if they are active also in non-hematopoietic normal tissues or in other human cancer types. Methods Using gene set enrichment analysis, we systematically explored whether the transcriptional programs in childhood acute lymphoblastic leukemia (ALL) and myeloid leukemia (AML) were significantly similar to those in different flow-sorted subpopulations of normal hematopoietic cells (n = 8), normal non-hematopoietic tissues (n = 22) or human cancer tissues (n = 13). Results This study revealed that e.g., the t(12;21) [ETV6-RUNX1] subtype of ALL and the t(15;17) [PML-RARA] subtype of AML had transcriptional programs similar to those in normal Pro-B cells and promyelocytes, respectively. Moreover, the 11q23/MLL subtype of ALL showed similarities with non-hematopoietic tissues. Strikingly however, most of the transcriptional programs in the other leukemic subtypes lacked significant similarity to ~100 gene sets derived from normal and malignant tissues. Conclusions This study demonstrates, for the first time, that the expression profiles of childhood leukemia are largely unique, with limited similarities to transcriptional programs active in normal hematopoietic cells, non-hematopoietic normal tissues or the most common forms of human cancer. In addition to providing important pathogenetic insights, these findings should facilitate the identification of candidate genes or transcriptional programs that can be used as unique targets in leukemia.

Global gene expression profiling of highly purified 5q-deleted CD34+CD38−Thy1+ cells in 5q− myelodysplastic syndromes (MDSs) supported that they might originate from and outcompete normal CD34+CD38−Thy1+ hematopoietic stem cells. Few but distinct differences in gene expression distinguished MDS and normal stem cells. Expression of BMI1, encoding a critical regulator of self-renewal, was up-regulated in 5q− stem cells. Whereas multiple previous MDS genetic screens failed to identify altered expression of the gene encoding the myeloid transcription factor CEBPA, stage-specific and extensive down-regulation of CEBPA was specifically observed in MDS progenitors. These studies establish the importance of molecular characterization of distinct stages of cancer stem and progenitor cells to enhance the resolution of stage-specific dysregulated gene expression.

Gene expression analyses were performed on 121 consecutive childhood leukemias (87 B-lineage acute lymphoblastic leukemias (ALLs), 11 T-cell ALLs and 23 acute myeloid leukemias (AMLs)), investigated during an 8-year period at a single center. The supervised learning algorithm k-nearest neighbor was utilized to build gene expression predictors that could classify the ALLs/AMLs according to clinically important subtypes with high accuracy. Validation experiments in an independent data set verified the high prediction accuracies of our classifiers. B-lineage ALLs with uncharacteristic cytogenetic aberrations or with a normal karyotype displayed heterogeneous gene expression profiles, resulting in low prediction accuracies. Minimal residual disease status (MRD) in T-cell ALLs with a high (>0.1%) MRD at day 29 could be classified with 100% accuracy already at the time of diagnosis. In pediatric leukemias with uncharacteristic cytogenetic aberrations or with a normal karyotype, unsupervised analysis identified two novel subgroups: one consisting mainly of cases remaining in complete remission (CR) and one containing a few patients in CR and all but one of the patients who relapsed. This study of a consecutive series of childhood leukemias confirms and extends further previous reports demonstrating that global gene expression profiling provides a valuable tool for genetic and clinical classification of childhood leukemias.

Background Murray's Law states that, when a parent blood vessel branches into daughter vessels, the cube of the radius of the parent vessel is equal to the sum of the cubes of the radii of daughter blood vessels. Murray derived this law by defining a cost function that is the sum of the energy cost of the blood in a vessel and the energy cost of pumping blood through the vessel. The cost is minimized when vessel radii are consistent with Murray's Law. This law has also been derived from the hypothesis that the shear force of moving blood on the inner walls of vessels is constant throughout the vascular system. However, this derivation, like Murray's earlier derivation, is based on the assumption of constant blood flow. Methods To determine the implications of the constant shear force hypothesis and to extend Murray's energy cost minimization to the pulsatile arterial system, a model of pulsatile flow in an elastic tube is analyzed. A new and exact solution for flow velocity, blood flow rate and shear force is derived. Results For medium and small arteries with pulsatile flow, Murray's energy minimization leads to Murray's Law. Furthermore, the hypothesis that the maximum shear force during the cycle of pulsatile flow is constant throughout the arterial system implies that Murray's Law is approximately true. The approximation is good for all but the largest vessels (aorta and its major branches) of the arterial system. Conclusion A cellular mechanism that senses shear force at the inner wall of a blood vessel and triggers remodeling that increases the circumference of the wall when a shear force threshold is exceeded would result in the observed scaling of vessel radii described by Murray's Law.

We have presented an ANN-based method for classification of gene expression data. This method was successfully applied to the example of classifying SRBCTs into distinct diagnostic categories. The key components of this classification procedure are PCA for dimensional reduction and cross-validation to optimize the training of the classifiers. In addition, we described a way to rank the genes according to their importance for the classification. Random permutation tests were introduced to assess the significance of the classification results. There are other ANN methods that have been used to classify gene expression data (Selaru et al., 2002).

We applied 27k spotted cDNA microarray slides to assess gene expression profiles in 26 samples from 24 patients with synovial sarcomas (SS). The data were analyzed in relation to histopathologic type, cytogenetic aberrations, gene fusion type and development of distant metastases. Supervised analysis based on gene fusion type in 12 SS with SS18/SSX1 and 9 with SS18/SSX2 revealed significant differences in gene expression profiles. Among the discriminators were several genes that have previously been found to be upregulated in SS, including AXL, ZIC2, SPAG7, AGRN, FOXC1, NCAM1 and multiple metallothioneins. Histopathology and degree of cytogenetic complexity did not significantly influence expression, whereas a genetic signature that related to development of metastases could be discerned, albeit with a high false-positive rate. In conclusion, our findings demonstrate differentially expressed genes for the 2 major gene fusion variants in SS, SS18/SSX1 and SS18/SSX2, and thereby suggest that these result in different downstream effects.

Soft-tissue sarcomas (STSs) constitute more than 30 histologic entities. In addition, within each entity, tumors are often heterogeneous in macroscopic features, genetic alterations, microscopic appearance, and clinical course. Therefore, there has been concern about whether a single tumor sample can provide a gene expression profile representative of the entire tumor. We used 27-k cDNA microarray slides to assess the importance of intratumor versus intertumor heterogeneity of the gene expression profiles of 2 morphologically heterogeneous STSs. Multiple pieces of tumor (8 and 10 pieces) were obtained from a myxoid variant of malignant fibrous histiocytoma (MFH) and a leiomyosarcoma (LMS), respectively, and the expression patterns were compared with single tumor samples from 20 MFHs and 16 LMSs. Hierarchical clustering analysis of the expression profiles showed that samples from the same tumor clustered together. The average intratumor distance was considerably shorter than the average intertumor distance in both LMS and MFH. In addition, tumor subclusters that distinguished different macroscopic parts of the tumor could be discerned. We concluded that intratumor variability exists but that accurate gene expression profiling also could be obtained using single samples from a large STS. V C 2005 Wiley-Liss, Inc.

Gene expression profiling of tumors using DNA microarrays is a promising method for predicting prognosis and treatment response in cancer patients. It was recently reported that expression profiles of sporadic breast cancers could be used to predict disease recurrence better than currently available clinical and histopathological prognostic factors. Having observed an overlap in those data between the genes that predict outcome and those that predict estrogen receptor-α status, we examined their predictive power in an independent data set. We conclude that it may be important to define prognostic expression profiles separately for estrogen receptor-α-positive and estrogen receptor-α-negative tumors.

We present a simple model for the arterial part of the cardiovascular system, based on Poiseuille flow constrained by the power dissipated into the cells lining the vessels. This, together with the assumption of a volume-filling network, leads to correct predictions for the evolution of vessel radii, vessel lengths and blood pressure in the human arterial system. The model can also be used to find exponents for allometric scaling, and gives good agreement with data on mammals.

Semi-exclusive processes like \gamma p \to \pi^+ Y are closely analogous to DIS, ep \to eX, in the limit where the momentum transfer |t| to the pion and the mass of the inclusive system Y are large but still much smaller than the total CM energy. We apply Bloom-Gilman duality to this semi-exclusive process. The energy dependence of the \gamma p \to \pi^+ n cross section given by semi-local duality agrees with data for moderate values of |t|, but its normalization is underestimated by about two orders of magnitude. This indicates that rather high momentum transfers are required for the validity of PQCD in the hard subprocess \gamma u \to \pi^+ d. In the case of Compton scattering \gamma p \to \gamma p the analogous discrepancy is about one order of magnitude. In electroproduction the virtuality of the incoming photon can be used to directly measure the hardness of the subprocess., Comment: 4 pages, 1 figure

PYTHIA version 6 represents a merger of the PYTHIA 5, JETSET 7 and SPYTHIA programs, with many improvements. It can be used to generate high-energy-physics `events', i.e. sets of outgoing particles produced in the interactions between two incoming particles. The objective is to provide as accurate as possible a representation of event properties in a wide range of reactions. The underlying physics is not understood well enough to give an exact description; the programs therefore contain a combination of analytical results and various models. The emphasis in this article is on new aspects, but a few words of general introduction are included. Further documentation is available on the web., Comment: 1 + 27 pages, submitted to Computer Physics Communications

Evolution equations for multiplicities in QCD cascades can, both in the parton and dipole picture, be used to estimate corrections beyond the formal accuracy of the modified leading log approximation (MLLA). The differences between the two pictures, and other uncertainties beyond first order MLLA corrections, are here investigated in some detail. For example, I discuss how some colour suppressed corrections, which cannot be determined without better knowledge of non-perturbative QCD, are related to the question of colour reconnection. A generalized evolution equation for the dipole cascade is also presented., 17 pages, 3 ps figures

I discuss a Monte Carlo algorithm for hadronization in the Lund string fragmentation picture, which conserves energy and momentum exactly as is proposed by the model. An embryo to this Monte Carlo is used to calculate the total decay density of a string. This is found to reach its predicted asymptotic behaviour already at a string energy of about six hadron masses., 14 pages, 4 ps figures

A thorough verification of the distinct differences in the properties of quark and gluon jets is considered as one of the most instructive tests of the basic ideas of QCD. In the real life experiments such a comparison appears to be quite a delicate task and various subtle issues require further theoretical efforts. In this paper we discuss in detail the possibility to extract the theoretically adequate information from the particle multiplicity patterns in three-jet events in e+e- annihilation., 10+1 pages, 3 figures. References added

We propose event cuts in deep inelastic scattering, suitable for an examination of quark fragmentation universality. We compare the current hemisphere of the Breit frame with hemispheres in e+e- annihilation events, using Monte Carlo simulations. The agreement between the two processes is improved after the suggested events cuts. A method to study the scale evolution in quark hemispheres using data from fixed energy e+e- experiments is presented. This makes it possible to use the high statistics from LEP1 also at scales below the Z mass. We also discuss observables which are sensitive to the dynamics of regions closer to the remnant. The observables probes the relatively clean region on the current side of the hardest emission in the event, and can be used to distinguish between different assumptions about remnant effects and mechanisms for the parton evolution., 16 pages, 7 ps figures

An improved version of the ``pop-corn'' model for baryon production in quark and gluon jets is presented. With a reduced number of parameters the model reproduces well both production rates for different baryon species and baryon momentum distributions. Predictions are presented for a set of baryon-antibaryon correlations., Comment: 22 pages, 11 Postscript figures

Background For 2-dye microarray platforms, some missing values may arise from an un-measurably low RNA expression in one channel only. Information of such "one-channel depletion" is so far not included in algorithms for imputation of missing values. Results Calculating the mean deviation between imputed values and duplicate controls in five datasets, we show that KNN-based imputation gives a systematic bias of the imputed expression values of one-channel depleted spots. Evaluating the correction of this bias by cross-validation showed that the mean square deviation between imputed values and duplicates were reduced up to 51%, depending on dataset. Conclusion By including more information in the imputation step, we more accurately estimate missing expression values.

Background: The aim of this study was to identify possible differences in gene expression profiles (GEP) between two clinically very different patient populations, cured versus primary chemotherapy-refractory diffuse large B-cell lymphoma (DLBCL). Material and methods: Tumor samples from 37 patients with de novo DLBCL, stage II-IV, either in continuous primary complete remission (n=24) or with progressive disease during primary treatment (n=13) were selected from a larger study cohort and examined with respect to GEP using spotted 55K oligonucleotide arrays. Formalin fixed paraffin-embedded tissue for immunohistochemical analysis was available in 33 of 37 cases, and was used for confirmation on protein level. Results: The top 86 genes with the greatest statistical ability of discriminating the two patient groups were chosen for further analysis. Seventy-nine were over-expressed in the cured cohort, many of them coding for proteins expressed in the tumor microenvironment: CD68, several proteolytic enzymes and proteins involved in remodelling of extra cellular matrix and inflammation. Furthermore, MHC class I molecules, CD3δ, NK transcript 4, and ICAM-1 were overexpressed indicating an enhanced immunological reaction. Immunohistochemistry showed that the frequency of cells expressing CD68, defining the macrophage population, was higher in the cured cohort and that expression of lysozyme, cathepsin D, UPAR, and ICAM-1, was mainly seen within the reactive cells. Tumor infiltrating CD8+ T-cells were more frequent within the cured cohort, corresponding to the increased MHC class I expression seen within this group. Conclusions: In DLBCL, genes coding for antigens present in the tumor microenvironment are differentially expressed in patients with cured vs. chemotherapy-refractory disease. Our findings suggest that a reactive microenvironment, including tumor infiltrating T-cells and macrophages, may have impact on outcome of chemotherapy in DLBCL.

Background Soft tissue sarcoma (STS) diagnosis is challenging because of a multitude of histopathological subtypes, different genetic characteristics, and frequent intratumoral pleomorphism. One-third of STS metastasize and current risk-stratification is suboptimal, therefore, novel diagnostic and prognostic markers would be clinically valuable. We assessed the diagnostic and prognostic value of array-based gene expression profiles using 27 k cDNA microarrays in 177, mainly high-grade, STS of 13 histopathological subtypes. Results Unsupervised analysis resulted in two major clusters - one mainly containing STS characterized by type-specific genetic alterations and the other with a predominance of genetically complex and pleomorphic STS. Synovial sarcomas, myxoid/round-cell liposarcomas, and gastrointestinal stromal tumors clustered tightly within the former cluster and discriminatory signatures for these were characterized by developmental genes from the EGFR, FGFR, Wnt, Notch, Hedgehog, RAR and KIT signaling pathways. The more pleomorphic STS subtypes, e.g. leiomyosarcoma, malignant fibrous histiocytoma/undifferentiated pleomorphic sarcoma and dedifferentiated/pleomorphic liposarcoma, were part of the latter cluster and were characterized by relatively heterogeneous profiles, although subclusters herein were identified. A prognostic signature partly characterized by hypoxia-related genes was identified among 89 genetically complex pleomorphic primary STS and could, in a multivariate analysis including established prognostic markers, independently predict the risk of metastasis with a hazard ratio of 2.2 (P = 0.04). Conclusion Diagnostic gene expression profiles linking signaling pathways to the different STS subtypes were demonstrated and a hypoxia-induced metastatic profile was identified in the pleomorphic, high-grade STS. These findings verify diagnostic utility and application of expression data for improved selection of high-risk STS patients.