Determining the stellar masses of submillimetre galaxies: the critical importance of star formation histories

Submillimetre (submm) galaxies are among the most rapidly star-forming and most massive high-redshift galaxies; thus, their properties provide important constraints on galaxy evolution models. However, there is still a debate about their stellar masses and their nature in the context of the general galaxy population. To test the reliability of their stellar mass determinations, we used a sample of simulated submm galaxies for which we derived stellar masses via spectral energy distribution (SED) modelling (with Grasil, Magphys, Hyperz and LePhare) adopting various star formation histories (SFHs). We found that the assumption of SFHs with two independent components leads to the most accurate stellar masses. Exponentially declining SFHs (tau) lead to lower masses (albeit still consistent with the true values), while the assumption of single-burst SFHs results in a significant mass underestimation. Thus, we conclude that studies based on the higher masses inferred from fitting the SEDs of real submm galaxies with double SFHs are most likely to be correct, implying that submm galaxies lie on the high-mass end of the main sequence of star-forming galaxies. This conclusion appears robust to assumptions of whether or not submm galaxies are driven by major mergers, since the suite of simulated galaxies modelled here contains examples of both merging and isolated galaxies. We identified discrepancies between the true and inferred stellar ages (rather than the dust attenuation) as the primary determinant of the success/failure of the mass recovery. Regardless of the choice of SFH, the SED-derived stellar masses exhibit a factor of ~2 scatter around the true value; this scatter is an inherent limitation of the SED modelling due to simplified assumptions. Finally, we found that the contribution of active galactic nuclei does not have any significant impact on the derived stellar masses.Comment: Accepted to A&A. 11 pages, 9 figures, 1 table. V2 main changes: 1) discussion of the stellar age as the main parameter influencing the success of an SED model (Fig. 4, 5, 7); 2) discussion of the age-dust degeneracy (Fig 9); 3) the comparison of real and simulated submm galaxies (Fig 1

Concomitant Radiotherapy and Chemotherapy for High-Risk Nonmelanoma Skin Carcinomas of the Head and Neck

Background. To report on the use and feasibility of a multimodality approach using concomitant radiotherapy and chemotherapy in patients with high-risk nonmelanoma skin carcinoma (NMSC) of the head and neck. Methods. Records of patients with NMSC of the head and neck who received concomitant CRT at the University of North Carolina between 2001 and 2007 were reviewed. Results. Fifteen identified patients had at least one of the following high-risk factors: T4 disease (93%), unresectability (60%), regional nodal involvement (40%), and/or recurrence (47%). Ten patients were treated in the definitive setting and five in the postoperative setting. Platinum based chemotherapy was given in 14 (93%) patients. Ten of fifteen (67%) patients completed all planned chemotherapy treatments, and thirteen patients (87%) completed at least 80% of planned chemotherapy. Mild radiation dermatitis occurred in all patients and reached grade 3 in 13% of patients. No patients experienced grade 4 or 5 toxicity. With a median followup of 31 months in surviving patients, the 2-year actuarial locoregional control and relapse-free survival were 79% and 49%, respectively. Conclusions. Definitive or postoperative chemoradiotherapy for patients with locally advanced or regionally metastasized NMSC of the head and neck appears feasible with acceptable toxicities and favorable locoregional control

SWISS MADE: Standardized WithIn Class Sum of Squares to Evaluate Methodologies and Dataset Elements

Contemporary high dimensional biological assays, such as mRNA expression microarrays, regularly involve multiple data processing steps, such as experimental processing, computational processing, sample selection, or feature selection (i.e. gene selection), prior to deriving any biological conclusions. These steps can dramatically change the interpretation of an experiment. Evaluation of processing steps has received limited attention in the literature. It is not straightforward to evaluate different processing methods and investigators are often unsure of the best method. We present a simple statistical tool, Standardized WithIn class Sum of Squares (SWISS), that allows investigators to compare alternate data processing methods, such as different experimental methods, normalizations, or technologies, on a dataset in terms of how well they cluster a priori biological classes. SWISS uses Euclidean distance to determine which method does a better job of clustering the data elements based on a priori classifications. We apply SWISS to three different gene expression applications. The first application uses four different datasets to compare different experimental methods, normalizations, and gene sets. The second application, using data from the MicroArray Quality Control (MAQC) project, compares different microarray platforms. The third application compares different technologies: a single Agilent two-color microarray versus one lane of RNA-Seq. These applications give an indication of the variety of problems that SWISS can be helpful in solving. The SWISS analysis of one-color versus two-color microarrays provides investigators who use two-color arrays the opportunity to review their results in light of a single-channel analysis, with all of the associated benefits offered by this design. Analysis of the MACQ data shows differential intersite reproducibility by array platform. SWISS also shows that one lane of RNA-Seq clusters data by biological phenotypes as well as a single Agilent two-color microarray

UNMASC: Tumor-only variant calling with unmatched normal controls

Despite years of progress, mutation detection in cancer samples continues to require significant manual review as a final step. Expert review is particularly challenging in cases where tumors are sequenced without matched normal control DNA. Attempts have been made to call somatic point mutations without a matched normal sample by removing well-known germline variants, utilizing unmatched normal controls, and constructing decision rules to classify sequencing errors and private germline variants. With budgetary constraints related to computational and sequencing costs, finding the appropriate number of controls is a crucial step to identifying somatic variants. Our approach utilizes public databases for canonical somatic variants as well as germline variants and leverages information gathered about nearby positions in the normal controls. Drawing from our cohort of targeted capture panel sequencing of tumor and normal samples with varying tumortypes and demographics, these served as a benchmark for our tumor-only variant calling pipeline to observe the relationship between our ability to correctly classify variants against a number of unmatched normals. With our benchmarked samples, approximately ten normal controls were needed to maintain 94% sensitivity, 99% specificity and 76% positive predictive value, far outperforming comparable methods. Our approach, called UNMASC, also serves as a supplement to traditional tumor with matched normal variant calling workflows and can potentially extend to other concerns arising from analyzing next generation sequencing data

Germline Analysis from Tumor–Germline Sequencing Dyads to Identify Clinically Actionable Secondary Findings

To evaluate germline variants in hereditary cancer susceptibility genes among unselected cancer patients undergoing tumor-germline sequencing

LKB1 loss links serine metabolism to DNA methylation and tumorigenesis

Intermediary metabolism generates substrates for chromatin modification, enabling the potential coupling of metabolic and epigenetic states. Here we identify a network linking metabolic and epigenetic alterations that is central to oncogenic transformation downstream of the liver kinase B1 (LKB1, also known as STK11) tumour suppressor, an integrator of nutrient availability, metabolism and growth. By developing genetically engineered mouse models and primary pancreatic epithelial cells, and employing transcriptional, proteomics, and metabolic analyses, we find that oncogenic cooperation between LKB1 loss and KRAS activation is fuelled by pronounced mTOR-dependent induction of the serine-glycine-one-carbon pathway coupled to S-adenosylmethionine generation. At the same time, DNA methyltransferases are upregulated, leading to elevation in DNA methylation with particular enrichment at retrotransposon elements associated with their transcriptional silencing. Correspondingly, LKB1 deficiency sensitizes cells and tumours to inhibition of serine biosynthesis and DNA methylation. Thus, we define a hypermetabolic state that incites changes in the epigenetic landscape to support tumorigenic growth of LKB1-mutant cells, while resulting in potential therapeutic vulnerabilities

SWISS MADE: Standardized WithIn Class Sum of Squares to Evaluate Methodologies and Dataset Elements

Contemporary high dimensional biological assays, such as mRNA expression microarrays, regularly involve multiple data processing steps, such as experimental processing, computational processing, sample selection, or feature selection (i.e. gene selection), prior to deriving any biological conclusions. These steps can dramatically change the interpretation of an experiment. Evaluation of processing steps has received limited attention in the literature. It is not straightforward to evaluate different processing methods and investigators are often unsure of the best method. We present a simple statistical tool, Standardized WithIn class Sum of Squares (SWISS), that allows investigators to compare alternate data processing methods, such as different experimental methods, normalizations, or technologies, on a dataset in terms of how well they cluster a priori biological classes. SWISS uses Euclidean distance to determine which method does a better job of clustering the data elements based on a priori classifications. We apply SWISS to three different gene expression applications. The first application uses four different datasets to compare different experimental methods, normalizations, and gene sets. The second application, using data from the MicroArray Quality Control (MAQC) project, compares different microarray platforms. The third application compares different technologies: a single Agilent two-color microarray versus one lane of RNA-Seq. These applications give an indication of the variety of problems that SWISS can be helpful in solving. The SWISS analysis of one-color versus two-color microarrays provides investigators who use two-color arrays the opportunity to review their results in light of a single-channel analysis, with all of the associated benefits offered by this design. Analysis of the MACQ data shows differential intersite reproducibility by array platform. SWISS also shows that one lane of RNA-Seq clusters data by biological phenotypes as well as a single Agilent two-color microarray

Alterations of LKB1 and KRAS and risk of brain metastasis: Comprehensive characterization by mutation analysis, copy number, and gene expression in non-small-cell lung carcinoma

Brain metastases are one of the most malignant complications of lung cancer and constitute a significant cause of cancer related morbidity and mortality worldwide. Recent years of investigation suggested a role of LKB1 in NSCLC development and progression, in synergy with KRAS alteration. In this study, we systematically analyzed how LKB1 and KRAS alteration, measured by mutation, gene expression (GE) and copy number (CN), are associated with brain metastasis in NSCLC

Prediction of Lung Cancer Histological Types by RT-qPCR Gene Expression in FFPE Specimens

Lung cancer histologic diagnosis is clinically relevant because there are histology-specific treatment indications and contraindications. Histologic diagnosis can be challenging owing to tumor characteristics, and it has been shown to have less-than-ideal agreement among pathologists reviewing the same specimens. Microarray profiling studies using frozen specimens have shown that histologies exhibit different gene expression trends; however, frozen specimens are not amenable to routine clinical application. Herein, we developed a gene expression–based predictor of lung cancer histology for FFPE specimens, which are routinely available in clinical settings. Genes predictive of lung cancer histologies were derived from published cohorts that had been profiled by microarrays. Expression of these genes was measured by quantitative RT-PCR (RT-qPCR) in a cohort of patients with FFPE lung cancer. A histology expression predictor (HEP) was developed using RT-qPCR expression data for adenocarcinoma, carcinoid, small cell carcinoma, and squamous cell carcinoma. In cross-validation, the HEP exhibited mean accuracy of 84% and κ = 0.77. In separate independent validation sets, the HEP was compared with pathologist diagnoses on the same tumor block specimens, and the HEP yielded similar accuracy and precision as the pathologists. The HEP also exhibited good performance in specimens with low tumor cellularity. Therefore, RT-qPCR gene expression from FFPE specimens can be effectively used to predict lung cancer histology

Lung Squamous Cell Carcinoma mRNA Expression Subtypes Are Reproducible, Clinically Important, and Correspond to Normal Cell Types

Lung squamous cell carcinoma (SCC) is clinically and genetically heterogeneous and current diagnostic practices do not adequately substratify this heterogeneity. A robust, biologically-based SCC subclassification may describe this variability and lead to more precise patient prognosis and management. We sought to determine if SCC mRNA expression subtypes exist, are reproducible across multiple patient cohorts, and are clinically relevant