Facilitating clinically relevant skin tumor diagnostics with spectroscopy-driven machine learning.

In the dawning era of artificial intelligence (AI), health care stands to undergo a significant transformation with the increasing digitalization of patient data. Digital imaging, in particular, will serve as an important platform for AI to aid decision making and diagnostics. A growing number of studies demonstrate the potential of automatic pre-surgical skin tumor delineation, which could have tremendous impact on clinical practice. However, current methods rely on having ground truth images in which tumor borders are already identified, which is not clinically possible. We report a novel approach where hyperspectral images provide spectra from small regions representing healthy tissue and tumor, which are used to generate prediction maps using artificial neural networks (ANNs), after which a segmentation algorithm automatically identifies the tumor borders. This circumvents the need for ground truth images, since an ANN model is trained with data from each individual patient, representing a more clinically relevant approach.

A community effort to identify and correct mislabeled samples in proteogenomic studies.

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.

Artificial neural network models to predict nodal status in clinically node-negative breast cancer.

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.

A multicenter study investigating the molecular fingerprint of psychological resilience in breast cancer patients: study protocol of the SCAN-B resilience study.

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.

Identification and validation of single-sample breast cancer radiosensitivity gene expression predictors.

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 < 0.001). Among ER+ patients, radiotherapy had an excellent effect on tumors classified as radiosensitive (p < 0.001), while radiotherapy had no effect on tumors classified as radioresistant (p = 0.36) and there was a high risk of ipsilateral breast tumor recurrence (55% at 10 years). Our single-sample predictors developed in ER+ tumors and the radiosensitivity signature correlated with proliferation, while single-sample predictors developed in ER- tumors correlated with immune response. The 10-gene signature negatively correlated with both proliferation and immune response. CONCLUSIONS: Our targeted single-sample predictors were prognostic for ipsilateral breast tumor recurrence and have the potential to stratify patients for adjuvant radiotherapy. The correlation of models with biology may explain the different performance in subgroups of breast cancer.

Finding Risk Groups by Optimizing Artificial Neural Networks on the Area under the Survival Curve Using Genetic Algorithms.

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.

Single-Cell Network Analysis Identifies DDIT3 as a Nodal Lineage Regulator in Hematopoiesis.

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.

SOX11 and TP53 add prognostic information to MIPI in a homogenously treated cohort of mantle cell lymphoma--a Nordic Lymphoma Group study.

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.

Transcriptional regulation of lineage commitment--a stochastic model of cell fate 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.

Training artificial neural networks directly on the concordance index for censored data using genetic algorithms.

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.

Gene expression profiling indicates that immunohistochemical expression of CD40 is a marker of an inflammatory reaction in the tumor stroma of diffuse large B-cell lymphoma.

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.

Identification of uniquely expressed transcription factors in highly purified B-cell lymphoma samples.

Transcription factors (TFs) are critical for B-cell differentiation, affecting gene expression both by repression and transcriptional activation. Still, this information is not used for classification of B-cell lymphomas (BCLs). Traditionally, BCLs are diagnosed based on a phenotypic resemblance to normal B-cells; assessed by immunohistochemistry or flow cytometry, by using a handful of phenotypic markers. In the last decade, diagnostic and prognostic evaluation has been facilitated by global gene expression profiling (GEP), providing a new powerful means for the classification, prediction of survival, and response to treatment of lymphomas. However, most GEP studies have typically been performed on whole tissue samples, containing varying degrees of tumor cell content, which results in uncertainties in data analysis. In this study, global GEP analyses were performed on highly purified, flow-cytometry sorted tumor-cells from eight subgroups of BCLs. This enabled identification of TFs that can be uniquely associated to the tumor cells of chronic lymphocytic leukemia (CLL), diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), hairy cell leukemia (HCL), and mantle cell lymphoma (MCL). The identified transcription factors influence both the global and specific gene expression of the BCLs and have possible implications for diagnosis and treatment.

Gene expression signatures in childhood acute leukemias are largely unique and distinct from those of normal tissues and other malignancies.

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 approximately 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.

Expression of P190 and P210 BCR/ABL1 in normal human CD34(+) cells induces similar gene expression profiles and results in a STAT5-dependent expansion of the erythroid lineage.

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.

Genes associated with the tumour microenvironment are differentially expressed in cured versus primary chemotherapy-refractory diffuse large B-cell lymphoma.

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.

Accounting for one-channel depletion improves missing value imputation in 2-dye microarray data.

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.

The molecular signature of MDS stem cells supports a stem-cell origin of 5q myelodysplastic syndromes.

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.

Estrogen receptor beta expression is associated with tamoxifen response in ERalpha-negative breast carcinoma.

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

Diagnostic and prognostic gene expression signatures in 177 soft tissue sarcomas: hypoxia-induced transcription profile signifies metastatic potential.

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.

Gene expression profilers and conventional clinical markers to predict distant recurrences for premenopausal breast cancer patients after adjuvant chemotherapy.

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.