Molecular Phenotypes of Acute Respiratory Distress Syndrome in the ROSE Trial Have Differential Outcomes and Gene Expression Patterns That Differ at Baseline and Longitudinally over Time.

Rationale: Two molecular phenotypes have been identified in acute respiratory distress syndrome (ARDS). In the ROSE (Reevaluation of Systemic Early Neuromuscular Blockade) trial of cisatracurium in moderate to severe ARDS, we addressed three unanswered questions: 1) Do the same phenotypes emerge in a more severe ARDS cohort with earlier recruitment; 2) Do phenotypes respond differently to neuromuscular blockade? and 3) What biological pathways most differentiate inflammatory phenotypes?Methods: We performed latent class analysis in ROSE using preenrollment clinical and protein biomarkers. In a subset of patients (n = 134), we sequenced whole-blood RNA using enrollment and Day 2 samples and performed differential gene expression and pathway analyses. Informed by the differential gene expression analysis, we measured additional plasma proteins and evaluated their abundance relative to gene expression amounts.Measurements and Main Results: In ROSE, we identified the hypoinflammatory (60.4%) and hyperinflammatory (39.6%) phenotypes with similar biological and clinical characteristics as prior studies, including higher mortality at Day 90 for the hyperinflammatory phenotype (30.3% vs. 61.6%; P < 0.0001). We observed no treatment interaction between the phenotypes and randomized groups for mortality. The hyperinflammatory phenotype was enriched for genes associated with innate immune response, tissue remodeling, and zinc metabolism at Day 0 and collagen synthesis and neutrophil degranulation at Day 2. Longitudinal changes in gene expression patterns differed dependent on survivorship. For most highly expressed genes, we observed correlations with their corresponding plasma proteins' abundance. However, for the class-defining plasma proteins in the latent class analysis, no correlation was observed with their corresponding genes' expression.Conclusions: The hyperinflammatory and hypoinflammatory phenotypes have different clinical, protein, and dynamic transcriptional characteristics. These findings support the clinical and biological potential of molecular phenotypes to advance precision care in ARDS.

Gene signatures in U-BIOPRED severe asthma for molecular phenotyping and precision medicine: time for clinical use.

INTRODUCTION: The use and generation of gene signatures have been established as a method to define molecular endotypes in complex diseases such as severe asthma. Bioinformatic approaches have now been applied to large omics datasets to define the various co-existing inflammatory and cellular functional pathways driving or characterizing a particular molecular endotype. AREAS COVERED: Molecular phenotypes and endotypes of Type 2 inflammatory pathways and also of non-Type 2 inflammatory pathways, such as IL-6 trans-signaling, IL-17 activation, and IL-22 activation, have been defined in the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes dataset. There has also been the identification of the role of mast cell activation and of macrophage dysfunction in various phenotypes of severe asthma. EXPERT OPINION: Phenotyping on the basis of clinical treatable traits is not sufficient for understanding of mechanisms driving the disease in severe asthma. It is time to consider whether certain patients with severe asthma, such as those non-responsive to current therapies, including Type 2 biologics, would be better served using an approach of molecular endotyping using gene signatures for management purposes rather than the current sole reliance on blood eosinophil counts or exhaled nitric oxide measurements.

Intermediate Molecular Phenotypes to Identify Genetic Markers of Anthracycline-Induced Cardiotoxicity Risk.

Cardiotoxicity due to anthracyclines (CDA) affects cancer patients, but we cannot predict who may suffer from this complication. CDA is a complex trait with a polygenic component that is mainly unidentified. We propose that levels of intermediate molecular phenotypes (IMPs) in the myocardium associated with histopathological damage could explain CDA susceptibility, so variants of genes encoding these IMPs could identify patients susceptible to this complication. Thus, a genetically heterogeneous cohort of mice (n = 165) generated by backcrossing were treated with doxorubicin and docetaxel. We quantified heart fibrosis using an Ariol slide scanner and intramyocardial levels of IMPs using multiplex bead arrays and QPCR. We identified quantitative trait loci linked to IMPs (ipQTLs) and cdaQTLs via linkage analysis. In three cancer patient cohorts, CDA was quantified using echocardiography or Cardiac Magnetic Resonance. CDA behaves as a complex trait in the mouse cohort. IMP levels in the myocardium were associated with CDA. ipQTLs integrated into genetic models with cdaQTLs account for more CDA phenotypic variation than that explained by cda-QTLs alone. Allelic forms of genes encoding IMPs associated with CDA in mice, including AKT1, MAPK14, MAPK8, STAT3, CAS3, and TP53, are genetic determinants of CDA in patients. Two genetic risk scores for pediatric patients (n = 71) and women with breast cancer (n = 420) were generated using machine-learning Least Absolute Shrinkage and Selection Operator (LASSO) regression. Thus, IMPs associated with heart damage identify genetic markers of CDA risk, thereby allowing more personalized patient management.

Impact of Cuproptosis-related markers on clinical status, tumor immune microenvironment and immunotherapy in colorectal cancer: A multi-omic analysis.

Background: Cuproptosis, a novel identified cell death form induced by copper, is characterized by aggregation of lipoylated mitochondrial enzymes and the destabilization of Fe-S cluster proteins. However, the function and potential clinical value of cuproptosis and cuproptosis-related biomarkers in colorectal cancer (CRC) remain largely unknown. Methods: A comprehensive multi-omics (transcriptomics, genomics, and single-cell transcriptome) analysis was performed for identifying the influence of 16 cuproptosis-related markers on clinical status, molecular functions and tumor microenvironment (TME) in CRC. A novel cuproptosis-related scoring system (CuproScore) based on cuproptosis-related markers was also constructed to predict the prognosis of CRC individuals, TME and the response to immunotherapy. In addition, our transcriptome cohort of 15 paired CRC tissue, tissue-array, and various assays in 4 kinds of CRC cell lines in vitro were applied for verification. Results: Cuproptosis-related markers were closely associated with both clinical prognosis and molecular functions. And the cuproptosis-related molecular phenotypes and scoring system (CuproScore) could distinguish and predict the prognosis of CRC patients, TME, and the response to immunotherapy in both public and our transcriptome cohorts. Besides, the expression, function and clinical significance of these markers were also checked and analyzed in CRC cell lines and CRC tissues in our own cohorts. Conclusions: In conclusion, we indicated that cuproptosis and CPRMs played a significant role in CRC progression and in modeling the TME. Inducing cuproptosis may be a useful tool for tumor therapy in the future.

Determining asthma endotypes and outcomes: Complementing existing clinical practice with modern machine learning.

There is unprecedented opportunity to use machine learning to integrate high-dimensional molecular data with clinical characteristics to accurately diagnose and manage disease. Asthma is a complex and heterogeneous disease and cannot be solely explained by an aberrant type 2 (T2) immune response. Available and emerging multi-omics datasets of asthma show dysregulation of different biological pathways including those linked to T2 mechanisms. While T2-directed biologics have been life changing for many patients, they have not proven effective for many others despite similar biomarker profiles. Thus, there is a great need to close this gap to understand asthma heterogeneity, which can be achieved by harnessing and integrating the rich multi-omics asthma datasets and the corresponding clinical data. This article presents a compendium of machine learning approaches that can be utilized to bridge the gap between predictive biomarkers and actual causal signatures that are validated in clinical trials to ultimately establish true asthma endotypes.

Identification of Circulating lncRNAs Associated with Gallbladder Cancer Risk by Tissue-Based Preselection, Cis-eQTL Validation, and Analysis of Association with Genotype-Based Expression.

Long noncoding RNAs (lncRNAs) play key roles in cell processes and are good candidates for cancer risk prediction. Few studies have investigated the association between individual genotypes and lncRNA expression. Here we integrate three separate datasets with information on lncRNA expression only, both lncRNA expression and genotype, and genotype information only to identify circulating lncRNAs associated with the risk of gallbladder cancer (GBC) using robust linear and logistic regression techniques. In the first dataset, we preselect lncRNAs based on expression changes along the sequence "gallstones → dysplasia → GBC". In the second dataset, we validate associations between genetic variants and serum expression levels of the preselected lncRNAs (cis-lncRNA-eQTLs) and build lncRNA expression prediction models. In the third dataset, we predict serum lncRNA expression based on individual genotypes and assess the association between genotype-based expression and GBC risk. AC084082.3 and LINC00662 showed increasing expression levels (p-value = 0.009), while C22orf34 expression decreased in the sequence from gallstones to GBC (p-value = 0.04). We identified and validated two cis-LINC00662-eQTLs (r2 = 0.26) and three cis-C22orf34-eQTLs (r2 = 0.24). Only LINC00662 showed a genotyped-based serum expression associated with GBC risk (OR = 1.25 per log2 expression unit, 95% CI 1.04-1.52, p-value = 0.02). Our results suggest that preselection of lncRNAs based on tissue samples and exploitation of cis-lncRNA-eQTLs may facilitate the identification of circulating noncoding RNAs linked to cancer risk.

Soluble biological markers in osteoarthritis.

In recent years, markers research has focused on the structural components of cartilage matrix. Specifically, a second generation of degradation markers has been developed against type II collagen neoepitopes generated by specific enzymes. A particular effort has been made to measure the degradation of minor collagens III and X of the cartilage matrix. However, because clinical data, including longitudinal controlled studies, are very scarce, it remains unclear whether they will be useful as an alternative to or in combination with current more established collagen biological markers to assess patients with osteoarthritis (OA). In addition, new approaches using high-throughput technologies allowed to detect new types of markers and improve the knowledge about the metabolic changes linked to OA. The relative advances coming from phenotype research are a first attempt to classify the heterogeneity of OA, and several markers could improve the phenotype characterization. These phenotypes could improve the selection of patients in clinical trials limiting the size of the studies by selecting patients with OA characteristics corresponding to the metabolic pathway targeted by the molecules evaluated. In addition, the inclusion of rapid progressors only in clinical trials would facilitate the demonstration of efficacy of the investigative drug to reduce joint degradation. The combination of selective biochemical markers appears as a promising and cost-effective approach to fulfill this unmet clinical need. Among the various potential roles of biomarkers in OA, their ability to monitor drug efficacy is probably one of the most important, in association with clinical and imaging parameters. Biochemical markers have the unique property to detect changes in joint tissue metabolism within a few weeks.

Prevalence and prognosis of molecular phenotypes in breast cancer patients by age: a population-based retrospective cohort study in western Algeria.

INTRODUCTION: breast cancer is related to age. The young age remains a controversial issue as a prognostic factor and have more aggressive clinical behavior with poor outcome. We aimed for the first time in Algeria to explore on a large cohort of patients the prevalence of the molecular phenotypes and to describe their clinical characteristics and survival. METHODS: medical record of 1140 Algerian patients were analysed and categorized into three age groups: "young" when women were aged below 40 years; "middle-age" when women were aged from 41 to 54 years old and "elder" when women were over 54 years. Baseline categorical variables were analysed using the Chi-square test and survival curves were constructed using Kaplan Meir method. RESULTS: the distribution of the various prognostic factors did not differ significativelly by age groups except for histological types, hormone receptors status and molecular phenotypes. Most patients were luminal A, indeed, young and intermediate age patients were most likely to be luminal A whereas the aged patients were triple negative with the highest mean DFS. Elsewhere young women are considered as human epidermal growth factor receptor 2 (HER2+) or triple negative molecular subtypes involving more rigorous therapeutic monitoring. The high rate of triple negative breast cancer in aged patients may due to genetic predispositions. CONCLUSION: this study sheds light on the histoclinical and molecular characteristics of breast cancer in young patients, which has a good prognosis than their older counterparts. Our results are therefore surprisingly different from what the literature suggests. A further study should understand this uncommon finding.

In utero and childhood exposure to tobacco smoke and multi-layer molecular signatures in children.

BACKGROUND: The adverse health effects of early life exposure to tobacco smoking have been widely reported. In spite of this, the underlying molecular mechanisms of in utero and postnatal exposure to tobacco smoke are only partially understood. Here, we aimed to identify multi-layer molecular signatures associated with exposure to tobacco smoke in these two exposure windows. METHODS: We investigated the associations of maternal smoking during pregnancy and childhood secondhand smoke (SHS) exposure with molecular features measured in 1203 European children (mean age 8.1 years) from the Human Early Life Exposome (HELIX) project. Molecular features, covering 4 layers, included blood DNA methylation and gene and miRNA transcription, plasma proteins, and sera and urinary metabolites. RESULTS: Maternal smoking during pregnancy was associated with DNA methylation changes at 18 loci in child blood. DNA methylation at 5 of these loci was related to expression of the nearby genes. However, the expression of these genes themselves was only weakly associated with maternal smoking. Conversely, childhood SHS was not associated with blood DNA methylation or transcription patterns, but with reduced levels of several serum metabolites and with increased plasma PAI1 (plasminogen activator inhibitor-1), a protein that inhibits fibrinolysis. Some of the in utero and childhood smoking-related molecular marks showed dose-response trends, with stronger effects with higher dose or longer duration of the exposure. CONCLUSION: In this first study covering multi-layer molecular features, pregnancy and childhood exposure to tobacco smoke were associated with distinct molecular phenotypes in children. The persistent and dose-dependent changes in the methylome make CpGs good candidates to develop biomarkers of past exposure. Moreover, compared to methylation, the weak association of maternal smoking in pregnancy with gene expression suggests different reversal rates and a methylation-based memory to past exposures. Finally, certain metabolites and protein markers evidenced potential early biological effects of postnatal SHS, such as fibrinolysis.

Single sample scoring of molecular phenotypes.

BACKGROUND: Gene set scoring provides a useful approach for quantifying concordance between sample transcriptomes and selected molecular signatures. Most methods use information from all samples to score an individual sample, leading to unstable scores in small data sets and introducing biases from sample composition (e.g. varying numbers of samples for different cancer subtypes). To address these issues, we have developed a truly single sample scoring method, and associated R/Bioconductor package singscore ( https://bioconductor.org/packages/singscore ). RESULTS: We use multiple cancer data sets to compare singscore against widely-used methods, including GSVA, z-score, PLAGE, and ssGSEA. Our approach does not depend upon background samples and scores are thus stable regardless of the composition and number of samples being scored. In contrast, scores obtained by GSVA, z-score, PLAGE and ssGSEA can be unstable when less data are available (NS < 25). The singscore method performs as well as the best performing methods in terms of power, recall, false positive rate and computational time, and provides consistently high and balanced performance across all these criteria. To enhance the impact and utility of our method, we have also included a set of functions implementing visual analysis and diagnostics to support the exploration of molecular phenotypes in single samples and across populations of data. CONCLUSIONS: The singscore method described here functions independent of sample composition in gene expression data and thus it provides stable scores, which are particularly useful for small data sets or data integration. Singscore performs well across all performance criteria, and includes a suite of powerful visualization functions to assist in the interpretation of results. This method performs as well as or better than other scoring approaches in terms of its power to distinguish samples with distinct biology and its ability to call true differential gene sets between two conditions. These scores can be used for dimensional reduction of transcriptomic data and the phenotypic landscapes obtained by scoring samples against multiple molecular signatures may provide insights for sample stratification.

A new bioinformatics tool to help assess the significance of BRCA1 variants.

BACKGROUND: Germline pathogenic variants in the breast cancer type 1 susceptibility gene BRCA1 are associated with a 60% lifetime risk for breast and ovarian cancer. This overall risk estimate is for all BRCA1 variants; obviously, not all variants confer the same risk of developing a disease. In cancer patients, loss of BRCA1 function in tumor tissue has been associated with an increased sensitivity to platinum agents and to poly-(ADP-ribose) polymerase (PARP) inhibitors. For clinical management of both at-risk individuals and cancer patients, it would be important that each identified genetic variant be associated with clinical significance. Unfortunately for the vast majority of variants, the clinical impact is unknown. The availability of results from studies assessing the impact of variants on protein function may provide insight of crucial importance. RESULTS AND CONCLUSION: We have collected, curated, and structured the molecular and cellular phenotypic impact of 3654 distinct BRCA1 variants. The data was modeled in triple format, using the variant as a subject, the studied function as the object, and a predicate describing the relation between the two. Each annotation is supported by a fully traceable evidence. The data was captured using standard ontologies to ensure consistency, and enhance searchability and interoperability. We have assessed the extent to which functional defects at the molecular and cellular levels correlate with the clinical interpretation of variants by ClinVar submitters. Approximately 30% of the ClinVar BRCA1 missense variants have some molecular or cellular assay available in the literature. Pathogenic variants (as assigned by ClinVar) have at least some significant functional defect in 94% of testable cases. For benign variants, 77% of ClinVar benign variants, for which neXtProt Cancer variant portal has data, shows either no or mild experimental functional defects. While this does not provide evidence for clinical interpretation of variants, it may provide some guidance for variants of unknown significance, in the absence of more reliable data. The neXtProt Cancer variant portal ( https://www.nextprot.org/portals/breast-cancer ) contains over 6300 observations at the molecular and/or cellular level for BRCA1 variants.

Metabolic Tumor Burden Assessed by Dual Time Point [18F]FDG PET/CT in Locally Advanced Breast Cancer: Relation with Tumor Biology.

PURPOSE: The aim of the study was to investigate the influence of dual time point 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography/x-ray computed tomography (PET/CT) on the standard uptake value (SUV) and volume-based metabolic variables of breast lesions and their relation with biological characteristics and molecular phenotypes. PROCEDURES: Retrospective analysis including 67 patients with locally advanced breast cancer (LABC). All patients underwent a dual time point [18F]FDG PET/CT, 1 h (PET-1) and 3 h (PET-2) after [18F]FDG administration. Tumors were segmented following a three-dimensional methodology. Semiquantitative metabolic variables (SUVmax, SUVmean, and SUVpeak) and volume-based variables (metabolic tumor volume, MTV, and total lesion glycolysis, TLG) were obtained. Biologic prognostic parameters, such as the hormone receptors status, p53, HER2 expression, proliferation rate (Ki-67), and grading were obtained. Molecular phenotypes and risk-classification [low: luminal A, intermediate: luminal B HER2 (-) or luminal B HER2 (+), and high: HER2 pure or triple negative] were established. Relations between clinical and biological variables with the metabolic parameters were studied. The relevance of each metabolic variable in the prediction of phenotype risk was assessed using a multivariate analysis. RESULTS: SUV-based variables and TLG obtained in the PET-1 and PET-2 showed high and significant correlations between them. MTV and SUV variables (SUVmax, SUVmean, and SUVpeak) where only marginally correlated. Significant differences were found between mean SUV variables and TLG obtained in PET-1 and PET-2. High and significant associations were found between metabolic variables obtained in PET-1 and their homonymous in PET-2. Based on that, only relations of PET-1 variables with biological tumor characteristics were explored. SUV variables showed associations with hormone receptors status (p < 0.001 and p = 0.001 for estrogen and progesterone receptor, respectively) and risk-classification according to phenotype (SUVmax, p = 0.003; SUVmean, p = 0.004; SUVpeak, p = 0.003). As to volume-based variables, only TLG showed association with hormone receptors status (estrogen, p < 0.001; progesterone, p = 0.031), risk-classification (p = 0.007), and grade (p = 0.036). Hormone receptor negative tumors, high-grade tumors, and high-risk phenotypes showed higher TLG values. No association was found between the metabolic variables and Ki-67, HER2, or p53 expression. CONCLUSION: Statistical differences were found between mean SUV-based variables and TLG obtained in the dual time point PET/CT. Most of PET-derived parameters showed high association with molecular factors of breast cancer. However, dual time point PET/CT did not offer any added value to the single PET acquisition with respect to the relations with biological variables, based on PET-1 SUV, and volume-based variables were predictors of those obtained in PET-2.

Biomarkers in Pediatric ARDS: Future Directions.

Acute respiratory distress syndrome (ARDS) is common among mechanically ventilated children and accompanies up to 30% of all pediatric intensive care unit deaths. Though ARDS diagnosis is based on clinical criteria, biological markers of acute lung damage have been extensively studied in adults and children. Biomarkers of inflammation, alveolar epithelial and capillary endothelial disruption, disordered coagulation, and associated derangements measured in the circulation and other body fluids, such as bronchoalveolar lavage, have improved our understanding of pathobiology of ARDS. The biochemical signature of ARDS has been increasingly well described in adult populations, and this has led to the identification of molecular phenotypes to augment clinical classifications. However, there is a paucity of data from pediatric ARDS (pARDS) patients. Biomarkers and molecular phenotypes have the potential to identify patients at high risk of poor outcomes, and perhaps inform the development of targeted therapies for specific groups of patients. Additionally, because of the lower incidence of and mortality from ARDS in pediatric patients relative to adults and lack of robust clinical predictors of outcome, there is an ongoing interest in biological markers as surrogate outcome measures. The recent definition of pARDS provides additional impetus for the measurement of established and novel biomarkers in future pediatric studies in order to further characterize this disease process. This chapter will review the currently available literature and discuss potential future directions for investigation into biomarkers in ARDS among children.

Metabolomics evidences plasma and serum biomarkers differentiating two heavy pig breeds.

In pigs, many production traits are known to vary among breeds or lines. These traits can be considered end phenotypes or external traits as they are the final results of complex biological interactions and processes whose fine biological mechanisms are still largely unknown. This study was designed to compare plasma and serum metabolomic profiles between animals of two heavy pig breeds (12 Italian Large White and 12 Italian Duroc), testing indirectly the hypothesis that different genetic backgrounds might be the determining factors of differences observed on the level of metabolites in the analyzed biofluids between breeds. We used a targeted metabolomic approach based on mass spectrometric detection of about 180 metabolites and applied a statistical validation pipeline to identify differences in the metabolomic profiles of the two heavy pig breeds. Blood samples were collected after jugulation at the slaughterhouse and prepared for metabolomics analysis that was carried out using the Biocrates AbsoluteIDQ p180 Kit, covering five different biochemical classes: glycerophospholipids, amino acids, biogenic amines, hexoses and acylcarnitines. A statistical pipeline that included the selection of the most relevant metabolites differentiating the two breeds by sparse Partial Least Squares Discriminant Analysis (sPLS-DA) was coupled with a stability test and significance test determined with leave one out and permutation procedures. sPLS-DA plots clearly separated the pigs of the two investigated breeds. A few metabolites (a total of five metabolites considering the two biofluids) involved in key metabolic pathways largely contributed to these differences between breeds. In particular, a higher level of the sphingomyelins SM (OH) C14:1 (both in plasma and serum), SM (OH) C16:1 (in serum) and SM C16:0 (in serum) were observed in Italian Duroc than in Italian Large White pigs and the inverse was for the biogenic amine kynurenine (in plasma). The level of another biogenic amine (acetylornithine) was higher in Italian Large White than in Italian Duroc pigs in both analysed biofluids. These results provided biomarkers that could be important to understand the biological differences between these two heavy pig breeds. In particular, according to the functional role played by sphingomyelins in obesity-induced inflammatory responses, it could be possible to speculate that a higher level of sphingomyelins in Italian Duroc might be related to the higher interrmuscular fat deposition of this breed compared with the Italian Large White. Additional studies will be needed to evaluate the relevance of these biomarkers for practical applications in pig breeding and nutrition.

Molecular phenotyping and biomarker development: are we on our way towards targeted therapy for severe asthma?

Although different phenotypes of severe asthma can be identified, all are characterized by common symptoms. Due to their heterogeneity, they exhibit differences in pathogenesis, etiology and clinical responses to therapeutic approaches. The identification of distinct molecular phenotypes to define severe asthmatic patients will allow us to better understand the pathophysiology of the disease and thus to more precisely target the treatment for each patient. To achieve this goal, a systematic search for new, reliable and stable biomarkers specific for each phenotype is essential. This review focuses on the current known molecular phenotypes of severe asthma and highlights the need for biomarkers that could (either alone or in combination) be predictive of the treatment outcome.

Basal (18)F-FDG PET/CT as a predictive biomarker of tumor response for neoadjuvant therapy in breast cancer.

PURPOSE: To explore the relation between tumor kinetic assessed by (18)F-FDG PET and final neoadjuvant chemotherapy (NC) response within a molecular phenotype perspective. MATERIAL AND METHODS: Prospective study included 144 women with breast cancer. All patients underwent a dual-time point (18)F-FDG PET/CT previous to NC. The retention index (RI), between SUV-1 and SUV-2 was calculated. Molecular subtypes were re-grouped in low, intermediate and high-risk biological phenotypes. After NC, all residual primary tumor specimens were histopathologically classified in tumor regression grades (TRG) and response groups. The relation between SUV-1, SUV-2 and RI with the TRG and response groups was evaluated in all molecular subtypes and in accordance with the risk categories. RESULTS: Responder's lesions showed significant greater SUVmax compared to non-responders. The RI value did not show any significant relation with response. Attending to molecular phenotypes, statistical differences were observed with greater SUV for responders having high-risk molecular subtypes. CONCLUSION: Glycolytic tumor characteristics showed a significant correlation with NC response and dependence of risk phenotype.

Glycolytic activity in breast cancer using 18F-FDG PET/CT as prognostic predictor: A molecular phenotype approach.

AIM: To explore the relationship between basal (18)F-FDG uptake in breast tumors and survival in patients with breast cancer (BC) using a molecular phenotype approach. MATERIAL AND METHODS: This prospective and multicentre study included 193 women diagnosed with BC. All patients underwent an (18)F-FDG PET/CT prior to treatment. Maximum standardized uptake value (SUVmax) in tumor (T), lymph nodes (N), and the N/T index was obtained in all the cases. Metabolic stage was established. As regards biological prognostic parameters, tumors were classified into molecular sub-types and risk categories. Overall survival (OS) and disease free survival (DFS) were obtained. An analysis was performed on the relationship between semi-quantitative metabolic parameters with molecular phenotypes and risk categories. The effect of molecular sub-type and risk categories in prognosis was analyzed using Kaplan-Meier and univariate and multivariate tests. RESULTS: Statistical differences were found in both SUVT and SUVN, according to the molecular sub-types and risk classifications, with higher semi-quantitative values in more biologically aggressive tumors. No statistical differences were observed with respect to the N/T index. Kaplan-Meier analysis revealed that risk categories were significantly related to DFS and OS. In the multivariate analysis, metabolic stage and risk phenotype showed a significant association with DFS. CONCLUSION: High-risk phenotype category showed a worst prognosis with respect to the other categories with higher SUVmax in primary tumor and lymph nodes.

Molecular Phenotype of Breast Cancer According to Time Since Last Pregnancy in a Large Cohort of Young Women.

BACKGROUND: The increase in breast cancer risk during pregnancy and postpartum is well known; however, the molecular phenotype of breast cancers occurring shortly after pregnancy has not been well studied. Given this, we investigated whether nulliparity and the time interval since pregnancy among parous women affects the breast cancer phenotype in young women. MATERIALS AND METHODS: We examined molecular phenotype in relation to time since pregnancy in a prospective cohort of 707 young women (aged ≤40 years) with breast cancer. Parity was ascertained from study questionnaires. Using tumor histologic grade on central review and biomarker expression, cancers were categorized as luminal A- or B-like, HER2 enriched, and triple negative. RESULTS: Overall, 32% were luminal A-like, 41% were luminal B-like, 9% were HER2 enriched, and 18% were triple negative. Although, numerically, patients diagnosed >5 years after pregnancy had more luminal A-like subtypes than women with shorter intervals since pregnancy, there was no evidence of a relationship between these intervals and molecular subtypes once family history of breast cancer and age at diagnosis were considered. CONCLUSION: Distribution of breast cancer molecular phenotype did not differ significantly among young women by parity or time interval since parturition when important predictors of tumor phenotype such as age and family history were considered. IMPLICATIONS FOR PRACTICE: Distribution of breast cancer molecular phenotype did not differ among parous young women by time interval since pregnancy. The implication of these findings for clinical practice suggests that pregnancy-associated breast cancers may be seen up to 5 years beyond parturition.

Molecular phenotypes associated with anomalous stamen development in Alternanthera philoxeroides.

Alternanthera philoxeroides is a perennial amphibious weed native to South America but has now spread to diverse parts of the world. A. philoxeroides reproduces both sexually and asexually in its native range, but propagates solely through vegetative means in its introduced range. Traits associated with sexual reproduction become degraded for sexual dysfunction, with flowers possessing either pistillate stamens or male-sterile anthers. Degradations of sexual characters for loss of sexuality commonly take place in clonal plants. The underlying molecular-genetic processes remain largely unknown. We compared the gene expression profiles of abnormal stamens with that of normal stamens by RNA-Seq analysis, and identified a large number of differentially expressed genes between abnormal and normal stamens. In accordance with flower morphology, the expression of B-class MADS-box genes (ApAP3, ApTM6, and ApPI) was markedly reduced in pistillate stamens. However, most of the genes involved in meiosis were expressed normally in stamens with male-sterile anthers. In addition to verifying the expression patterns of genes previously known to be related to stamen and pollen grain development, we also identified previously unknown molecular phenotypes associated with sexual dysfunction in A. philoxeroides, that is helpful for dissecting the molecular mechanisms underpinning various male-sterile phenotypes and the molecular processes underlying the transition from sexuality to asexuality in clonal plants.

Molecular phenotype of the foci in multifocal invasive breast carcinomas: intertumoral heterogeneity is related to shorter survival and may influence the choice of therapy.

BACKGROUND: Multiple synchronous, ipsilateral, invasive foci of breast carcinomas are frequent and are associated with a poorer prognosis. Few studies have investigated the prognostic and therapeutic implications of heterogeneity of such foci. METHODS: The authors reviewed the tumor type, grade, and size of all invasive foci in a series of 110 multifocal breast carcinomas documented on large-format slides. Molecular phenotype was determined by immunohistochemistry in tissue microarray blocks using 3 classification systems. The survival of patients who had tumors with microscopic (tumor type and/or grade) heterogeneity and of those who had tumors with phenotypic heterogeneity was compared with the survival of patients who had multifocal homogeneous tumors using Kaplan-Meier curves. The hazard ratio of dying from breast cancer was also calculated. RESULTS: Intertumoral heterogeneity in tumor type and grade was detected in 16 of 110 tumors (14.6%) and in 6 of 110 tumors (5.5%), respectively. The molecular phenotype of invasive tumor foci within the same breast differed in 10% to 12.7% of patients (11-14 of 110 tumors), depending on the classification system used. Patients who had phenotypically heterogeneous, multifocal cancers had a greater risk of dying from disease (HR=2.879; 95%CI=1.084-7.649; P = .034) and had significantly shorter survival (P = .016). Phenotypic differences were most common in patients who had tumors that were homogeneous in terms of tumor type (11 of 18 tumors) and histology grade (14 of 18 tumors). Phenotyping additional tumor foci had the potential to influence the therapeutic decisions in up to 8 patients. CONCLUSIONS: Phenotyping more than 1 invasive focus of multifocal breast carcinomas only if the individual foci deviate microscopically appears to be insufficient, because phenotypic intertumoral heterogeneity may be observed in microscopically identical foci and has potential prognostic and therapeutic consequences.