Unusual Vascular Distribution in the Third Segment of the Axillary Artery.

The third segment of the axillary artery (TSAA) is the main vascular supply to the muscles of the upper limb. Numerous studies have reported atypical branching patterns of the TSAA, which can complicate operative interventions involving structures supplied by this segment of the artery. Our current study evaluated a previously undescribed branching pattern in the TSAA, in which the subscapular artery gave rise to an unusual posterior humeral circumflex artery, and a second subscapular artery. In addition, a third variant was found in the origin of the thoracodorsal artery: two collateral horizontal arteries supplying the deep medial surface of the latissimus dorsi muscle. Vascular anatomical variants may affect the classical upper limb interventions requiring modification of the traditional surgical approaches. This case report aims to evaluate these variants from a clinical perspective regarding the management of upper limb trauma, axillary, breast, and muscle flap surgery.

The Role of Tryptophan Metabolites in Musculoskeletal Stem Cell Aging.

Although aging is considered a normal process, there are cellular and molecular changes that occur with aging that may be detrimental to health. Osteoporosis is one of the most common age-related degenerative diseases, and its progression correlates with aging and decreased capacity for stem cell differentiation and proliferation in both men and women. Tryptophan metabolism through the kynurenine pathway appears to be a key factor in promoting bone-aging phenotypes, promoting bone breakdown and interfering with stem cell function and osteogenesis; however, little data is available on the impact of tryptophan metabolites downstream of kynurenine. Here we review available data on the impact of these tryptophan breakdown products on the body in general and, when available, the existing evidence of their impact on bone. A number of tryptophan metabolites (e.g., 3-hydroxykynurenine (3HKYN), kynurenic acid (KYNA) and anthranilic acid (AA)) have a detrimental effect on bone, decreasing bone mineral density (BMD) and increasing fracture risk. Other metabolites (e.g., 3-hydroxyAA, xanthurenic acid (XA), picolinic acid (PIA), quinolinic acid (QA), and NAD+) promote an increase in bone mineral density and are associated with lower fracture risk. Furthermore, the effects of other tryptophan breakdown products (e.g., serotonin) are complex, with either anabolic or catabolic actions on bone depending on their source. The mechanisms involved in the cellular actions of these tryptophan metabolites on bone are not yet fully known and will require further research as they are potential therapeutic targets. The current review is meant as a brief overview of existing English language literature on tryptophan and its metabolites and their effects on stem cells and musculoskeletal systems. The search terms used for a Medline database search were: kynurenine, mesenchymal stem cells, bone loss, tryptophan metabolism, aging, and oxidative stress.

tRFdb: a database for transfer RNA fragments.

We have created tRFdb, the first database of transfer RNA fragments (tRFs), available at http://genome.bioch.virginia.edu/trfdb/. With over 100 small RNA libraries analyzed, the database currently contains the sequences and read counts of the three classes of tRFs for eight species: R. sphaeroides, S. pombe, D. melanogaster, C. elegans, Xenopus, zebra fish, mouse and human, for a total of 12,877 tRFs. The database can be searched by tRF ID or tRF sequence, and the results can be limited by organism. The search results show the genome coordinates and names of the tRNAs the sequence may derive from, and there are links for the sequence of the tRF and parental tRNA, and links for the read counts in all the corresponding small RNA libraries. As a case study for how this database may be used, we have shown that a certain class of tRFs, tRF-1s, is highly upregulated in B-cell malignancies.

Expression of lncRNAs in Low-Grade Gliomas and Glioblastoma Multiforme: An In Silico Analysis.

BACKGROUND: Each year, over 16,000 patients die from malignant brain cancer in the US. Long noncoding RNAs (lncRNAs) have recently been shown to play critical roles in regulating neurogenesis and brain tumor progression. To better understand the role of lncRNAs in brain cancer, we performed a global analysis to identify and characterize all annotated and novel lncRNAs in both grade II and III gliomas as well as grade IV glioblastomas (glioblastoma multiforme [GBM]). METHODS AND FINDINGS: We determined the expression of all lncRNAs in over 650 brain cancer and 70 normal brain tissue RNA sequencing datasets from The Cancer Genome Atlas (TCGA) and other publicly available datasets. We identified 611 induced and 677 repressed lncRNAs in glial tumors relative to normal brains. Hundreds of lncRNAs were specifically expressed in each of the three lower grade glioma (LGG) subtypes (IDH1/2 wt, IDH1/2 mut, and IDH1/2 mut 1p19q codeletion) and the four subtypes of GBMs (classical, mesenchymal, neural, and proneural). Overlap between the subtype-specific lncRNAs in GBMs and LGGs demonstrated similarities between mesenchymal GBMs and IDH1/2 wt LGGs, with 2-fold higher overlap than would be expected by random chance. Using a multivariate Cox regression survival model, we identified 584 and 282 lncRNAs that were associated with a poor and good prognosis, respectively, in GBM patients. We developed a survival algorithm for LGGs based on the expression of 64 lncRNAs that was associated with patient prognosis in a test set (hazard ratio [HR] = 2.168, 95% CI = 1.765-2.807, p < 0.001) and validation set (HR = 1.921, 95% CI = 1.333-2.767, p < 0.001) of patients from TCGA. The main limitations of this study are that further work is needed to investigate the clinical relevance of our findings, and that validation in an independent dataset is needed to determine the robustness of our survival algorithm. CONCLUSIONS: This work identifies a panel of lncRNAs that appear to be prognostic in gliomas and provides a critical resource for future studies examining the role of lncRNAs in brain cancers.

Meta-analysis of tRNA derived RNA fragments reveals that they are evolutionarily conserved and associate with AGO proteins to recognize specific RNA targets.

BACKGROUND: tRFs, 14 to 32 nt long single-stranded RNA derived from mature or precursor tRNAs, are a recently discovered class of small RNA that have been found to be present in diverse organisms at read counts comparable to miRNAs. Currently, there is a debate about their biogenesis and function. RESULTS: This is the first meta-analysis of tRFs. Analysis of more than 50 short RNA libraries has revealed that tRFs are precisely generated fragments present in all domains of life (bacteria to humans), and are not produced by the miRNA biogenesis pathway. Human PAR-CLIP data shows a striking preference for tRF-5s and tRF-3s to associate with AGO1, 3 and 4 rather than AGO2, and analysis of positional T to C mutational frequency indicates these tRFs associate with Argonautes in a manner similar to miRNAs. The reverse complements of canonical seed positions in these sequences match cross-link centered regions, suggesting these tRF-5s and tRF-3s interact with RNAs in the cell. Consistent with these results, human AGO1 CLASH data contains thousands of tRF-5 and tRF-3 reads chimeric with mRNAs. CONCLUSIONS: tRFs are an abundant class of small RNA present in all domains of life whose biogenesis is distinct from miRNAs. In human HEK293 cells tRFs associate with Argonautes 1, 3 and 4 and not Argonaute 2 which is the main effector protein of miRNA function, but otherwise have very similar properties to miRNAs, indicating tRFs may play a major role in RNA silencing.

Characterization of non-monotonic relationships between tumor mutational burden and clinical outcomes.

Potential clinical biomarkers are often assessed with Cox regressions or their ability to differentiate two groups of patients based on a single cutoff. However, both of these approaches assume a monotonic relationship between the potential biomarker and survival. Tumor mutational burden (TMB) is currently being studied as a predictive biomarker for immunotherapy, and a single cutoff is often used to divide patients. In this study we introduce a two-cutoff approach that allows splitting of patients when a non-monotonic relationship is present, and explore the use of neural networks to model more complex relationships of TMB to outcome data. Using real-world data we find that while in most cases the true relationship between TMB and survival appears monotonic, that is not always the case and researchers should be made aware of this possibility.

Multiple-instance learning of somatic mutations for the classification of tumour type and the prediction of microsatellite status.

Large-scale genomic data are well suited to analysis by deep learning algorithms. However, for many genomic datasets, labels are at the level of the sample rather than for individual genomic measures. Machine learning models leveraging these datasets generate predictions by using statically encoded measures that are then aggregated at the sample level. Here we show that a single weakly supervised end-to-end multiple-instance-learning model with multi-headed attention can be trained to encode and aggregate the local sequence context or genomic position of somatic mutations, hence allowing for the modelling of the importance of individual measures for sample-level classification and thus providing enhanced explainability. The model solves synthetic tasks that conventional models fail at, and achieves best-in-class performance for the classification of tumour type and for predicting microsatellite status. By improving the performance of tasks that require aggregate information from genomic datasets, multiple-instance deep learning may generate biological insight.

A structural role for the PHP domain in E. coli DNA polymerase III.

BACKGROUND: In addition to the core catalytic machinery, bacterial replicative DNA polymerases contain a Polymerase and Histidinol Phosphatase (PHP) domain whose function is not entirely understood. The PHP domains of some bacterial replicases are active metal-dependent nucleases that may play a role in proofreading. In E. coli DNA polymerase III, however, the PHP domain has lost several metal-coordinating residues and is likely to be catalytically inactive. RESULTS: Genomic searches show that the loss of metal-coordinating residues in polymerase PHP domains is likely to have coevolved with the presence of a separate proofreading exonuclease that works with the polymerase. Although the E. coli Pol III PHP domain has lost metal-coordinating residues, the structure of the domain has been conserved to a remarkable degree when compared to that of metal-binding PHP domains. This is demonstrated by our ability to restore metal binding with only three point mutations, as confirmed by the metal-bound crystal structure of this mutant determined at 2.9 Å resolution. We also show that Pol III, a large multi-domain protein, unfolds cooperatively and that mutations in the degenerate metal-binding site of the PHP domain decrease the overall stability of Pol III and reduce its activity. CONCLUSIONS: While the presence of a PHP domain in replicative bacterial polymerases is strictly conserved, its ability to coordinate metals and to perform proofreading exonuclease activity is not, suggesting additional non-enzymatic roles for the domain. Our results show that the PHP domain is a major structural element in Pol III and its integrity modulates both the stability and activity of the polymerase.

Probabilistic Mixture Models Improve Calibration of Panel-derived Tumor Mutational Burden in the Context of both Tumor-normal and Tumor-only Sequencing.

Background: Tumor mutational burden (TMB) has been investigated as a biomarker for immune checkpoint blockade (ICB) therapy. Increasingly, TMB is being estimated with gene panel-based assays (as opposed to full exome sequencing) and different gene panels cover overlapping but distinct genomic coordinates, making comparisons across panels difficult. Previous studies have suggested that standardization and calibration to exome-derived TMB be done for each panel to ensure comparability. With TMB cutoffs being developed from panel-based assays, there is a need to understand how to properly estimate exomic TMB values from different panel-based assays. Design: Our approach to calibration of panel-derived TMB to exomic TMB proposes the use of probabilistic mixture models that allow for nonlinear relationships along with heteroscedastic error. We examined various inputs including nonsynonymous, synonymous, and hotspot counts along with genetic ancestry. Using The Cancer Genome Atlas cohort, we generated a tumor-only version of the panel-restricted data by reintroducing private germline variants. Results: We were able to model more accurately the distribution of both tumor-normal and tumor-only data using the proposed probabilistic mixture models as compared with linear regression. Applying a model trained on tumor-normal data to tumor-only input results in biased TMB predictions. Including synonymous mutations resulted in better regression metrics across both data types, but ultimately a model able to dynamically weight the various input mutation types exhibited optimal performance. Including genetic ancestry improved model performance only in the context of tumor-only data, wherein private germline variants are observed. Significance: A probabilistic mixture model better models the nonlinearity and heteroscedasticity of the data as compared with linear regression. Tumor-only panel data are needed to properly calibrate tumor-only panels to exomic TMB. Leveraging the uncertainty of point estimates from these models better informs cohort stratification in terms of TMB.

Genomic Landscapes and Hallmarks of Mutant RAS in Human Cancers.

The RAS family of small GTPases represents the most commonly activated oncogenes in human cancers. To better understand the prevalence of somatic RAS mutations and the compendium of genes that are coaltered in RAS-mutant tumors, we analyzed targeted next-generation sequencing data of 607,863 mutations from 66,372 tumors in 51 cancer types in the AACR Project GENIE Registry. Bayesian hierarchical models were implemented to estimate the cancer-specific prevalence of RAS and non-RAS somatic mutations, to evaluate co-occurrence and mutual exclusivity, and to model the effects of tumor mutation burden and mutational signatures on comutation patterns. These analyses revealed differential RAS prevalence and comutations with non-RAS genes in a cancer lineage-dependent and context-dependent manner, with differences across age, sex, and ethnic groups. Allele-specific RAS co-mutational patterns included an enrichment in NTRK3 and chromatin-regulating gene mutations in KRAS G12C-mutant non-small cell lung cancer. Integrated multiomic analyses of 10,217 tumors from The Cancer Genome Atlas (TCGA) revealed distinct genotype-driven gene expression programs pointing to differential recruitment of cancer hallmarks as well as phenotypic differences and immune surveillance states in the tumor microenvironment of RAS-mutant tumors. The distinct genomic tracks discovered in RAS-mutant tumors reflected differential clinical outcomes in TCGA cohort and in an independent cohort of patients with KRAS G12C-mutant non-small cell lung cancer that received immunotherapy-containing regimens. The RAS genetic architecture points to cancer lineage-specific therapeutic vulnerabilities that can be leveraged for rationally combining RAS-mutant allele-directed therapies with targeted therapies and immunotherapy. SIGNIFICANCE: The complex genomic landscape of RAS-mutant tumors is reflective of selection processes in a cancer lineage-specific and context-dependent manner, highlighting differential therapeutic vulnerabilities that can be clinically translated.

Kynurenine induces an age-related phenotype in bone marrow stromal cells.

Advanced age is one of the important contributing factors for musculoskeletal deterioration. Although the exact mechanism behind this degeneration is unknown, it has been previously established that nutritional signaling plays a vital role in musculoskeletal pathophysiology. Our group established the vital role of the essential amino acid, tryptophan, in aging musculoskeletal health. With advanced age, inflammatory factors activate indoleamine 2,3-dioxygenase (IDO1) and accumulate excessive intermediate tryptophan metabolites such as Kynurenine (KYN). With age, Kynurenine accumulates and suppresses osteogenic differentiation, impairs autophagy, promotes early senescence, and alters cellular bioenergetics of bone marrow stem cells. Recent studies have shown that Kynurenine negatively impacts bone marrow stromal cells (BMSCs) and, consequently, promotes bone loss. Overall, understanding the mechanism behind BMSCs losing their ability for osteogenic differentiation can provide insight into the prevention of osteoporosis and the development of targeted therapies. Therefore, in this article, we review Kynurenine and how it plays a vital role in BMSC dysfunction and bone loss with age.

Statistical heartburn: an attempt to digest four pizza publications from the Cornell Food and Brand Lab.

BACKGROUND: We present the results of a reanalysis of four articles from the Cornell Food and Brand Lab based on data collected from diners at an Italian restaurant buffet. METHOD: We calculated whether the means, standard deviations, and test statistics were compatible with the sample size. Test statistics and p values were recalculated. We also applied deductive logic to see whether the claims made in each article were compatible with the claims made in the others. We have so far been unable to obtain the data from the authors of the four articles. RESULTS: A thorough reading of the articles and careful reanalysis of the results revealed a wide range of problems. The sample sizes for the number of diners in each condition are incongruous both within and between the four articles. In some cases, the degrees of freedom of between-participant test statistics are larger than the sample size, which is impossible. Many of the computed F and t statistics are inconsistent with the reported means and standard deviations. In some cases, the number of possible inconsistencies for a single statistic was such that we were unable to determine which of the components of that statistic were incorrect. Our Appendix reports approximately 150 inconsistencies in these four articles, which we were able to identify from the reported statistics alone. CONCLUSIONS: We hope that our analysis will encourage readers, using and extending the simple methods that we describe, to undertake their own efforts to verify published results, and that such initiatives will improve the accuracy and reproducibility of the scientific literature. We also anticipate that the editors of the journals that published these four articles may wish to consider whether any corrective action is required.

A pan-cancer analysis of prognostic genes.

Numerous studies have identified prognostic genes in individual cancers, but a thorough pan-cancer analysis has not been performed. In addition, previous studies have mostly used microarray data instead of RNA-SEQ, and have not published comprehensive lists of associations with survival. Using recently available RNA-SEQ and clinical data from The Cancer Genome Atlas for 6,495 patients, we have investigated every annotated and expressed gene's association with survival across 16 cancer types. The most statistically significant harmful and protective genes were not shared across cancers, but were enriched in distinct gene sets which were shared across certain groups of cancers. These groups of cancers were independently recapitulated by both unsupervised clustering of Cox coefficients (a measure of association with survival) for individual genes, and for gene programs. This analysis has revealed unappreciated commonalities among cancers which may provide insights into cancer pathogenesis and rationales for co-opting treatments between cancers.

The lncRNA DRAIC/PCAT29 Locus Constitutes a Tumor-Suppressive Nexus.

UNLABELLED: Long noncoding RNAs (lncRNA) are emerging as major regulators of cellular phenotypes and implicated as oncogenes or tumor suppressors. Here, we report a novel tumor-suppressive locus on human chromosome 15q23 that contains two multiexonic lncRNA genes of 100 kb each: DRAIC (LOC145837) and the recently reported PCAT29. The DRAIC lncRNA was identified from RNA-seq data and is downregulated as prostate cancer cells progress from an androgen-dependent (AD) to a castration-resistant (CR) state. Prostate cancers persisting in patients after androgen deprivation therapy (ADT) select for decreased DRAIC expression, and higher levels of DRAIC in prostate cancer are associated with longer disease-free survival (DFS). Androgen induced androgen receptor (AR) binding to the DRAIC locus and repressed DRAIC expression. In contrast, FOXA1 and NKX3-1 are recruited to the DRAIC locus to induce DRAIC, and FOXA1 specifically counters the repression of DRAIC by AR. The decrease of FOXA1 and NKX3-1, and aberrant activation of AR, thus accounts for the decrease of DRAIC during prostate cancer progression to the CR state. Consistent with DRAIC being a good prognostic marker, DRAIC prevents the transformation of cuboidal epithelial cells to fibroblast-like morphology and prevents cellular migration and invasion. A second tumor-suppressive lncRNA PCAT29, located 20 kb downstream of DRAIC, is regulated identically by AR and FOXA1 and also suppresses cellular migration and metastasis. Finally, based on TCGA analysis, DRAIC expression predicts good prognosis in a wide range of malignancies, including bladder cancer, low-grade gliomas, lung adenocarcinoma, stomach adenocarcinoma, renal clear cell carcinoma, hepatocellular carcinoma, skin melanoma, and stomach adenocarcinoma. IMPLICATIONS: This study reveals a novel tumor-suppressive locus encoding two hormone-regulated lncRNAs, DRAIC and PCAT29, that are prognostic for a wide variety of cancer types.