The Effectiveness of Three Regions in Mitochondrial Genome for Aphid DNA Barcoding: A Case in Lachininae

Background

The mitochondrial gene COI has been widely used by taxonomists as a standard DNA barcode sequence for the identification of many animal species. However, the COI region is of limited use for identifying certain species and is not efficiently amplified by PCR in all animal taxa. To evaluate the utility of COI as a DNA barcode and to identify other barcode genes, we chose the aphid subfamily Lachninae (Hemiptera: Aphididae) as the focus of our study. We compared the results obtained using COI with two other mitochondrial genes, COII and Cytb. In addition, we propose a new method to improve the efficiency of species identification using DNA barcoding.

Methodology/Principal Findings

Three mitochondrial genes (COI, COII and Cytb) were sequenced and were used in the identification of over 80 species of Lachninae. The COI and COII genes demonstrated a greater PCR amplification efficiency than Cytb. Species identification using COII sequences had a higher frequency of success (96.9% in “best match” and 90.8% in “best close match”) and yielded lower intra- and higher interspecific genetic divergence values than the other two markers. The use of “tag barcodes” is a new approach that involves attaching a species-specific tag to the standard DNA barcode. With this method, the “barcoding overlap” can be nearly eliminated. As a result, we were able to increase the identification success rate from 83.9% to 95.2% by using COI and the “best close match” technique.

Conclusions/Significance

A COII-based identification system should be more effective in identifying lachnine species than COI or Cytb. However, the Cytb gene is an effective marker for the study of aphid population genetics due to its high sequence diversity. Furthermore, the use of “tag barcodes” can improve the accuracy of DNA barcoding identification by reducing or removing the overlap between intra- and inter-specific genetic divergence values.     

Integrated analysis of multiple microarray datasets identifies a reproducible survival predictor in ovarian cancer

Background

Public data integration may help overcome challenges in clinical implementation of microarray profiles. We integrated several ovarian cancer datasets to identify a reproducible predictor of survival.

Methodology/Principal Findings

Four microarray datasets from different institutions comprising 265 advanced stage tumors were uniformly reprocessed into a single training dataset, also adjusting for inter-laboratory variation (“batch-effect”). Supervised principal component survival analysis was employed to identify prognostic models. Models were independently validated in a 61-patient cohort using a custom array genechip and a publicly available 229-array dataset. Molecular correspondence of high- and low-risk outcome groups between training and validation datasets was demonstrated using Subclass Mapping. Previously established molecular phenotypes in the 2^nd validation set were correlated with high and low-risk outcome groups. Functional representational and pathway analysis was used to explore gene networks associated with high and low risk phenotypes. A 19-gene model showed optimal performance in the training set (median OS 31 and 78 months, p<0.01), 1^st validation set (median OS 32 months versus not-yet-reached, p = 0.026) and 2^nd validation set (median OS 43 versus 61 months, p = 0.013) maintaining independent prognostic power in multivariate analysis. There was strong molecular correspondence of the respective high- and low-risk tumors between training and 1^st validation set. Low and high-risk tumors were enriched for favorable and unfavorable molecular subtypes and pathways, previously defined in the public 2^nd validation set.

Conclusions/Significance

Integration of previously generated cancer microarray datasets may lead to robust and widely applicable survival predictors. These predictors are not simply a compilation of prognostic genes but appear to track true molecular phenotypes of good- and poor-outcome.     

BOLD Correlates of Trial-by-Trial Reaction Time Variability in Gray and White Matter: A Multi-Study fMRI Analysis

Background

Reaction time (RT) is one of the most widely used measures of performance in experimental psychology, yet relatively few fMRI studies have included trial-by-trial differences in RT as a predictor variable in their analyses. Using a multi-study approach, we investigated whether there are brain regions that show a general relationship between trial-by-trial RT variability and activation across a range of cognitive tasks.

Methodology/Principal Findings

The relation between trial-by-trial differences in RT and brain activation was modeled in five different fMRI datasets spanning a range of experimental tasks and stimulus modalities. Three main findings were identified. First, in a widely distributed set of gray and white matter regions, activation was delayed on trials with long RTs relative to short RTs, suggesting delayed initiation of underlying physiological processes. Second, in lateral and medial frontal regions, activation showed a “time-on-task” effect, increasing linearly as a function of RT. Finally, RT variability reliably modulated the BOLD signal not only in gray matter but also in diffuse regions of white matter.

Conclusions/Significance

The results highlight the importance of modeling trial-by-trial RT in fMRI analyses and raise the possibility that RT variability may provide a powerful probe for investigating the previously elusive white matter BOLD signal.     

Missing value imputation in high-dimensional phenomic data: imputable or not,and how?

Background

In modern biomedical research of complex diseases, a large number of demographic and clinical variables, herein called phenomic data, are often collected and missing values (MVs) are inevitable in the data collection process. Since many downstream statistical and bioinformatics methods require complete data matrix, imputation is a common and practical solution. In high-throughput experiments such as microarray experiments, continuous intensities are measured and many mature missing value imputation methods have been developed and widely applied. Numerous methods for missing data imputation of microarray data have been developed. Large phenomic data, however, contain continuous, nominal, binary and ordinal data types, which void application of most methods. Though several methods have been developed in the past few years, not a single complete guideline is proposed with respect to phenomic missing data imputation.

Results

In this paper, we investigated existing imputation methods for phenomic data, proposed a self-training selection (STS) scheme to select the best imputation method and provide a practical guideline for general applications. We introduced a novel concept of “imputability measure” (IM) to identify missing values that are fundamentally inadequate to impute. In addition, we also developed four variations of K-nearest-neighbor (KNN) methods and compared with two existing methods, multivariate imputation by chained equations (MICE) and missForest. The four variations are imputation by variables (KNN-V), by subjects (KNN-S), their weighted hybrid (KNN-H) and an adaptively weighted hybrid (KNN-A). We performed simulations and applied different imputation methods and the STS scheme to three lung disease phenomic datasets to evaluate the methods. An R package “phenomeImpute” is made publicly available.

Conclusions

Simulations and applications to real datasets showed that MICE often did not perform well; KNN-A, KNN-H and random forest were among the top performers although no method universally performed the best. Imputation of missing values with low imputability measures increased imputation errors greatly and could potentially deteriorate downstream analyses. The STS scheme was accurate in selecting the optimal method by evaluating methods in a second layer of missingness simulation. All source files for the simulation and the real data analyses are available on the author’s publication website.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0346-6) contains supplementary material, which is available to authorized users.     

Incidence of sarcoma histotypes and molecular subtypes in a prospective epidemiological study with central pathology review and molecular testing

Background

The exact overall incidence of sarcoma and sarcoma subtypes is not known. The objective of the present population-based study was to determine this incidence in a European region (Rhone-Alpes) of six million inhabitants, based on a central pathological review of the cases.

Methodology/Principal Findings

From March 2005 to February 2007, pathology reports and tumor blocks were prospectively collected from the 158 pathologists of the Rhone-Alpes region. All diagnosed or suspected cases of sarcoma were collected, reviewed centrally, examined for molecular alterations and classified according to the 2002 World Health Organization classification. Of the 1287 patients screened during the study period, 748 met the criteria for inclusion in the study. The overall crude and world age-standardized incidence rates were respectively 6.2 and 4.8 per 100,000/year. Incidence rates for soft tissue, visceral and bone sarcomas were respectively 3.6, 2.0 and 0.6 per 100,000. The most frequent histological subtypes were gastrointestinal stromal tumor (18%; 1.1/100,000), unclassified sarcoma (16%; 1/100,000), liposarcoma (15%; 0.9/100,000) and leiomyosarcoma (11%; 0.7/100,000).

Conclusions/Significance

The observed incidence of sarcomas was higher than expected. This study is the first detailed investigation of the crude incidence of histological and molecular subtypes of sarcomas.     

Multimodality treatment of locally advanced soft-tissue sarcomas of the extremities

Background

Adjuvant radiotherapy (RTE) still has a fundamental role as a post-operative treatment of locally advanced soft tissues sarcomas of the extremities. Moreover the employment of combined modalities in locally advanced soft tissues sarcomas of the extremities allow to maximize the chance of local cure even in difficult presentation cases, and possibly improve survival, especially in high-risk disease patients. In patients with sarcomas of the extremities in which definitive surgery has not been radical (with positive or “close” margins) radiotherapy can improve the results in terms of Disease Free Survival (DFS) and, together with chemotherapy, of Overall Survival (OS). We recommend radiotherapy in case of deep tumor location, inadequate surgical margins and grade 3 tumour; for positive or “marginal (or close)” excision (that means inadequate surgery) or in selected patients with a bad prognosis, we believe that a multidisciplinary approach can be preferable.

Introduction

Adjuvant radiotherapy (RTE) still has a fundamental role as a post-operative treatment. In patients with sarcomas of the extremities in whom definitive surgery has been or not radical (positive or “close” margins), radiotherapy with chemotherapy can improve the results in terms of Disease Free Survival (DFS) and Overall Survival (OS), while RTE alone seems to improve local control.

Materials and methods

From 1/2000 to 12/2005 we treated 34 patients affected by locally advanced sarcomas of the upper or lower extremities with radiotherapy (doses ranging from 54 to 66 Gy) and chemotherapy in 18/34 with an adjuvant scheme that consisted in Epirubicine (120 mg/m²) plus Ifosfamide (7000–9000 mg/m²).

Results

Disease Free Survival (DFS) and the Overall Survival (OS) rates were 76% and 82%, respectively. Eighteen patients developed one or more long-term side effects. Most of these complications were mild: all patients experienced only erithema, edema, local sclerosis or moderate pain.

Conclusion

Radiotherapy has an important role as a post-operative treatment also when surgery was non-radical. It improves local control more in patients with high-grade sarcomas of the extremity with positive or close margins. It is still difficult to assess the role of adjuvant chemotherapy.     

EGFR Inhibition Abrogates Leiomyosarcoma Cell Chemoresistance through Inactivation of Survival Pathways and Impairment of CSC Potential

Background

Tumor cells with stem-like phenotype and properties, known as cancer stem cells (CSC), have been identified in most solid tumors and are presumed to be responsible for driving tumor initiation, chemoresistance, relapse, or metastasis. A subpopulation of cells with increased stem-like potential has also been identified within sarcomas. These cells are endowed with increased tumorigenic potential, chemoresistance, expression of embryonic markers, and side population(SP) phenotype. Leiomyosarcomas (LMS) are soft tissue sarcomas presumably arising from undifferentiated cells of mesenchymal origin, the Mesenchymal Stem Cells (MSC). Frequent recurrence of LMS and chemoresistance of relapsed patients may likely result from the failure to target CSC. Therefore, therapeutic cues coming from the cancer stem cell (CSC) field may drastically improve patient outcome.

Methodology/Principal Findings

We expanded LMS stem-like cells from patient samples in vitro and examined the possibility to counteract LMS malignancy through a stem-like cell effective approach. LMS stem-like cells were in vitro expanded both as “tumor spheres” and as “monolayers” in Mesenchymal Stem Cell (MSC) conditions. LMS stem-like cells displayed MSC phenotype, higher SP fraction, and increased drug-extrusion, extended proliferation potential, self-renewal, and multiple differentiation ability. They were chemoresistant, highly tumorigenic, and faithfully reproduced the patient tumor in mice. Such cells displayed activation of EGFR/AKT/MAPK pathways, suggesting a possibility in overcoming their chemoresistance through EGFR blockade. IRESSA plus Vincristine treatment determined pathway inactivation, impairment of SP phenotype, high cytotoxicity in vitro and strong antitumor activity in stem-like cell-generated patient-like xenografts, targeting both stem-like and differentiated cells.

Conclusions/Significance

EGFR blockade combined with vincristine determines stem-like cell effective antitumor activity in vitro and in vivo against LMS, thus providing a potential therapy for LMS patients.     

The Use of P63 Immunohistochemistry for the Identification of Squamous Cell Carcinoma of the Lung

Introduction

While some targeted agents should not be used in squamous cell carcinomas (SCCs), other agents might preferably target SCCs. In a previous microarray study, one of the top differentially expressed genes between adenocarcinomas (ACs) and SCCs is P63. It is a well-known marker of squamous differentiation, but surprisingly, its expression is not widely used for this purpose. Our goals in this study were (1) to further confirm our microarray data, (2) to analize the value of P63 immunohistochemistry (IHC) in reducing the number of large cell carcinoma (LCC) diagnoses in surgical specimens, and (3) to investigate the potential of P63 IHC to minimize the proportion of “carcinoma NOS (not otherwise specified)” in a prospective series of small tumor samples.

Methods

With these goals in mind, we studied (1) a tissue-microarray comprising 33 ACs and 99 SCCs on which we performed P63 IHC, (2) a series of 20 surgically resected LCCs studied for P63 and TTF-1 IHC, and (3) a prospective cohort of 66 small thoracic samples, including 32 carcinoma NOS, that were further classified by the result of P63 and TTF-1 IHC.

Results

The results in the three independent cohorts were as follows: (1) P63 IHC was differentially expressed in SCCs when compared to ACs (p<0.0001); (2) half of the 20 (50%) LCCs were positive for P63 and were reclassified as SCCs; and (3) all P63 positive cases (34%) were diagnosed as SCCs.

Conclusions

P63 IHC is useful for the identification of lung SCCs.     

Genome-wide expression analysis suggests a crucial role of dysregulation of matrix metalloproteinases pathway in undifferentiated thyroid carcinoma

Background

Thyroid cancer (TC) is the most common malignant cancer of the Endocrine System. Histologically, there are three main subtypes of TC: follicular, papillary and anaplastic. Diagnosing a thyroid tumor subtype with a high level of accuracy and confidence is still a difficult task because genetic, molecular and cellular mechanisms underlying the transition from differentiated to undifferentiated thyroid tumors are not well understood.A genome-wide analysis of these three subtypes of thyroid carcinoma was carried out in order to identify significant differences in expression levels as well as enriched pathways for non-shared molecular and cellular features between subtypes.

Results

Inhibition of matrix metalloproteinases pathway is a major event involved in thyroid cancer progression and its dysregulation may result crucial for invasiveness, migration and metastasis. This pathway is drastically altered in ATC while in FTC and PTC, the most important pathways are related to DNA-repair activation or cell to cell signaling events.

Conclusion

A progression from FTC to PTC and then to ATC was detected and validated on two independent datasets. Moreover, PTX3, COLEC12 and PDGFRA genes were found as possible candidates for biomarkers of ATC while GPR110 could be tested to distinguish PTC over other tumor subtypes. The genome-wide analysis emphasizes the preponderance of pathway-dysregulation mechanisms over simple gene-malfunction as the main mechanism involved in the development of a cancer phenotype.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1372-0) contains supplementary material, which is available to authorized users.     

Prognostic Impact of Jab1, p16, p21, p62, Ki67 and Skp2 in Soft Tissue Sarcomas

Purpose

The purpose of this study is to clarify the prognostic significance of expression of Jab1, p16, p21, p62, Ki67 and Skp2 in soft tissue sarcomas (STS). Optimised treatment of STS requires better identification of high risk patients who will benefit from adjuvant therapy. The prognostic significance of Jab1, p16, p21, p62, Ki67 and Skp2 in STS has not been sufficiently investigated.

Experimental Design

Tissue microarrays from 193 STS patients were constructed from duplicate cores of viable and representative neoplastic tumor areas. Immunohistochemistry was used to evaluate the expression of Jab1, p16, p21, p62, Ki67 and Skp2.

Results

In univariate analyses, high tumor expression of Ki67 (P = 0.007) and Skp2 (P = 0.050) correlated with shorter disease-specific survival (DSS). In subgroup analysis, a correlation between Skp2 and DSS was seen in patients with malignancy grade 1 or 2 (P = 0.027), tumor size >5 cm (P = 0.018), no radiotherapy given (P = 0.029) and no chemotherapy given (P = 0.017). No such relationship was apparent for Jab1, p16, p21 and p62; but p62 showed a positive correlation to malignancy grade (P = 0.019). Ki67 was strongly positively correlated to malignancy grade (P = 0.001). In multivariate analyses, Skp2 was an independent negative prognostic factor for DSS in women (P = 0.009) and in patients without administered chemotherapy or radiotherapy (P = 0.026).

Conclusions

Increased expression of Skp2 in patients with soft tissue sarcomas is an independent negative prognostic factor for disease-specific survival in women and in patients not administered chemotherapy or radiotherapy. Besides, further studies are warranted to explore if adjuvant chemotherapy or radiotherapy improve the poor prognosis of STS with high Skp2 expression.     

Right-hand-side updating for fast computing of genomic breeding values

Background

Since both the number of SNPs (single nucleotide polymorphisms) used in genomic prediction and the number of individuals used in training datasets are rapidly increasing, there is an increasing need to improve the efficiency of genomic prediction models in terms of computing time and memory (RAM) required.

Methods

In this paper, two alternative algorithms for genomic prediction are presented that replace the originally suggested residual updating algorithm, without affecting the estimates. The first alternative algorithm continues to use residual updating, but takes advantage of the characteristic that the predictor variables in the model (i.e. the SNP genotypes) take only three different values, and is therefore termed “improved residual updating”. The second alternative algorithm, here termed “right-hand-side updating” (RHS-updating), extends the idea of improved residual updating across multiple SNPs. The alternative algorithms can be implemented for a range of different genomic predictions models, including random regression BLUP (best linear unbiased prediction) and most Bayesian genomic prediction models. To test the required computing time and RAM, both alternative algorithms were implemented in a Bayesian stochastic search variable selection model.

Results

Compared to the original algorithm, the improved residual updating algorithm reduced CPU time by 35.3 to 43.3%, without changing memory requirements. The RHS-updating algorithm reduced CPU time by 74.5 to 93.0% and memory requirements by 13.1 to 66.4% compared to the original algorithm.

Conclusions

The presented RHS-updating algorithm provides an interesting alternative to reduce both computing time and memory requirements for a range of genomic prediction models.     

Macrophages Mediate a Switch between Canonical and Non-Canonical Wnt Pathways in Canine Mammary Tumors

Objective

According to the current hypothesis, tumor-associated macrophages (TAMs) are “corrupted” by cancer cells and subsequently facilitate, rather than inhibit, tumor metastasis. Because the molecular mechanisms of cancer cell–TAM interactions are complicated and controversial we aimed to better define this phenomenon.

Methods and Results

Using microRNA microarrays, Real-time qPCR and Western blot we showed that co-culture of canine mammary tumor cells with TAMs or treatment with macrophage-conditioned medium inhibited the canonical Wnt pathway and activated the non-canonical Wnt pathway in tumor cells. We also showed that co-culture of TAMs with tumor cells increased expression of canonical Wnt inhibitors in TAMs. Subsequently, we demonstrated macrophage-induced invasive growth patterns and epithelial–mesenchymal transition of tumor cells. Validation of these results in canine mammary carcinoma tissues (n = 50) and xenograft tumors indicated the activation of non-canonical and canonical Wnt pathways in metastatic tumors and non-metastatic malignancies, respectively. Activation of non-canonical Wnt pathway correlated with number of TAMs.

Conclusions

We demonstrated that TAMs mediate a “switch” between canonical and non-canonical Wnt signaling pathways in canine mammary tumors, leading to increased tumor invasion and metastasis.Interestingly, similar changes in neoplastic cells were observed in the presence of macrophage-conditioned medium or live macrophages. These observations indicate that rather than being “corrupted” by cancer cells, TAMs constitutively secrete canonical Wnt inhibitors that decrease tumor proliferation and development, but as a side effect, they induce the non-canonical Wnt pathway, which leads to tumor metastasis.These data challenge the conventional understanding of TAM–cancer cell interactions.     

Distinct Patterns of DNA Damage Response and Apoptosis Correlate with Jak/Stat and PI3Kinase Response Profiles in Human Acute Myelogenous Leukemia

Background

Single cell network profiling (SCNP) utilizing flow cytometry measures alterations in intracellular signaling responses. Here SCNP was used to characterize Acute Myeloid Leukemia (AML) disease subtypes based on survival, DNA damage response and apoptosis pathways.

Methodology and Principal Findings

Thirty four diagnostic non-M3 AML samples from patients with known clinical outcome were treated with a panel of myeloid growth factors and cytokines, as well as with apoptosis-inducing agents. Analysis of induced Jak/Stat and PI3K pathway responses in blasts from individual patient samples identified subgroups with distinct signaling profiles that were not seen in the absence of a modulator. In vitro exposure of patient samples to etoposide, a DNA damaging agent, revealed three distinct “DNA damage response (DDR)/apoptosis” profiles: 1) AML blasts with a defective DDR and failure to undergo apoptosis; 2) AML blasts with proficient DDR and failure to undergo apoptosis; 3) AML blasts with proficiency in both DDR and apoptosis pathways. Notably, AML samples from clinical responders fell within the “DDR/apoptosis” proficient profile and, as well, had low PI3K and Jak/Stat signaling responses. In contrast, samples from clinical non responders had variable signaling profiles often with in vitro apoptotic failure and elevated PI3K pathway activity. Individual patient samples often harbored multiple, distinct, leukemia-associated cell populations identifiable by their surface marker expression, functional performance of signaling pathway in the face of cytokine or growth factor stimulation, as well as their response to apoptosis-inducing agents.

Conclusions and Significance

Characterizing and tracking changes in intracellular pathway profiles in cell subpopulations both at baseline and under therapeutic pressure will likely have important clinical applications, potentially informing the selection of beneficial targeted agents, used either alone or in combination with chemotherapy.     

Tumor Endothelial Inflammation Predicts Clinical Outcome in Diverse Human Cancers

Background

Vascular endothelial cells contribute to the pathogenesis of numerous human diseases by actively regulating the stromal inflammatory response; however, little is known regarding the role of endothelial inflammation in the growth of human tumors and its influence on the prognosis of human cancers.

Methods

Using an experimental model of tumor necrosis factor-alpha (TNF-α)-mediated inflammation, we characterized inflammatory gene expression in immunopurified tumor-associated endothelial cells. These genes formed the basis of a multivariate molecular predictor of overall survival that was trained and validated in four types of human cancer.

Results

We report that expression of experimentally derived tumor endothelial genes distinguished pathologic tissue specimens from normal controls in several human diseases associated with chronic inflammation. We trained these genes in human cancer datasets and defined a six-gene inflammatory signature that predicted significantly reduced overall survival in breast cancer, colon cancer, lung cancer, and glioma. This endothelial-derived signature predicted outcome independently of, but cooperatively with, standard clinical and pathological prognostic factors. Consistent with these findings, conditioned culture media from human endothelial cells stimulated by pro-inflammatory cytokines accelerated the growth of human colon and breast tumors in immunodeficient mice as compared with conditioned media from untreated endothelial cells.

Conclusions

This study provides the first prognostic cancer gene signature derived from an experimental model of tumor-associated endothelial inflammation. These findings support the notion that activation of inflammatory pathways in non-malignant tumor-infiltrating endothelial cells contributes to tumor growth and progression in multiple human cancers. Importantly, these results identify endothelial-derived factors that could serve as potential targets for therapy in diverse human cancers.     

Selecting the quality of mule duck fatty liver based on near-infrared spectroscopy

Background

“Foie gras” is produced predominantly in France and about 90% of the commercialized product is obtained from male mule ducks. The melting rate (percentage of fat released during cooking) is the main criterion used to determine the quality of “foie gras”. However, up to now the melting rate could not be predicted without causing liver damage, which means that selection programs could not use this criterion.

Methods

Fatty liver phenotypes were obtained for a population of over 1400 overfed male mule ducks. The phenotypes were based on two types of near-infrared spectra (on the liver surface and on ground liver) in order to predict the melting rate and liver composition (ash, dry matter, lipid and protein contents). Genetic parameters were computed in multiple traits with a “sire-dam” model and using a Gibbs sampling approach.

Results

The estimates for the genetic parameters show that the measured melting rate and the predicted melting rate obtained with two near-infrared spectrometer devices are genetically the same trait: genetic correlations are very high (ranging from +0.89 to +0.97 depending on the mule duck parental line and the spectrometer) and heritabilities are comparable. The predictions based on the spectra of ground liver samples using a laboratory spectrometer correlate with those based on the surface spectra using a portable spectrometer (from +0.83 to +0.95 for dry matter, lipid and protein content) and are particularly high for the melting rate (higher than +0.95). Although less accurate than the predictions obtained using the spectra of ground liver samples, the phenotypic prediction of the melting rate based on surface spectra is sufficiently accurate to be used by “foie gras” processors.

Conclusions

Near-infrared spectrometry is an efficient tool to select liver quality in breeding programs because animals can be ranked according to their liver melting rate without damaging their livers. Thus, these original results will help breeders to select ducks based on the liver melting rate, a crucial criterion that defines the quality of the liver and for which there was previously no accurate predictor.