Cell cycle correlated genes dictate the prognostic power of breast cancer gene lists

<p>Abstract</p> <p>Background</p> <p>Numerous gene lists or "classifiers" have been derived from global gene expression data that assign breast cancers to good and poor prognosis groups. A remarkable feature of these molecular signatures is that they have few genes in common, prompting speculation that they may use distinct genes to measure the same pathophysiological process(es), such as proliferation. However, this supposition has not been rigorously tested. If gene-based classifiers function by measuring a minimal number of cellular processes, we hypothesized that the informative genes for these processes could be identified and the data sets could be adjusted for the predictive contributions of those genes. Such adjustment would then attenuate the predictive function of any signature measuring that same process.</p> <p>Results</p> <p>We tested this hypothesis directly using a novel iterative-subtractive approach. We evaluated five gene expression data sets that sample a broad range of breast cancer subtypes. In all data sets, the dominant cluster capable of predicting metastasis was heavily populated by genes that fluctuate in concert with the cell cycle. When six well-characterized classifiers were examined, all contained a higher than expected proportion of genes that correlate with this cluster. Furthermore, when the data sets were globally adjusted for the cell cycle cluster, each classifier lost its ability to assign tumors to appropriate high and low risk groups. In contrast, adjusting for other predictive gene clusters did not impact their performance.</p> <p>Conclusion</p> <p>These data indicate that the discriminative ability of breast cancer classifiers is dependent upon genes that correlate with cell cycle progression.</p

Rod-shape theranostic nanoparticles facilitate antiretroviral drug biodistribution and activity in human immunodeficiency virus susceptible cells and tissues

Human immunodeficiency virus theranostics facilitates the development of long acting (LA) antiretroviral drugs (ARVs) by defining drug-particle cell depots. Optimal drug formulations are made possible based on precise particle composition, structure, shape and size. Through the creation of rod-shaped particles of defined sizes reflective of native LA drugs, theranostic probes can be deployed to measure particle-cell and tissue biodistribution, antiretroviral activities and drug retention. Methods: Herein, we created multimodal rilpivirine (RPV) 177lutetium labeled bismuth sulfide nanorods (177LuBSNRs) then evaluated their structure, morphology, configuration, chemical composition, biological responses and adverse reactions. Particle biodistribution was analyzed by single photon emission computed tomography (SPECT/CT) and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) imaging. Results: Nanoformulated RPV and BSNRs-RPV particles showed comparable physicochemical and cell biological properties. Drug-particle pharmacokinetics (PK) and biodistribution in lymphoid tissue macrophages proved equivalent, one with the other. Rapid particle uptake and tissue distribution were observed, without adverse reactions, in primary blood-derived and tissue macrophages. The latter was seen within the marginal zones of spleen. Conclusions: These data, taken together, support the use of 177LuBSNRs as theranostic probes as a rapid assessment tool for PK LA ARV measurements

Intrinsic bias in breast cancer gene expression data sets

<p>Abstract</p> <p>Background</p> <p>While global breast cancer gene expression data sets have considerable commonality in terms of their data content, the populations that they represent and the data collection methods utilized can be quite disparate. We sought to assess the extent and consequence of these systematic differences with respect to identifying clinically significant prognostic groups.</p> <p>Methods</p> <p>We ascertained how effectively unsupervised clustering employing randomly generated sets of genes could segregate tumors into prognostic groups using four well-characterized breast cancer data sets.</p> <p>Results</p> <p>Using a common set of 5,000 randomly generated lists (70 genes/list), the percentages of clusters with significant differences in metastasis latencies (HR p-value < 0.01) was 62%, 15%, 21% and 0% in the NKI2 (Netherlands Cancer Institute), Wang, TRANSBIG and KJX64/KJ125 data sets, respectively. Among ER positive tumors, the percentages were 38%, 11%, 4% and 0%, respectively. Few random lists were predictive among ER negative tumors in any data set. Clustering was associated with ER status and, after globally adjusting for the effects of ER-α gene expression, the percentages were 25%, 33%, 1% and 0%, respectively. The impact of adjusting for ER status depended on the extent of confounding between ER-α gene expression and markers of proliferation.</p> <p>Conclusion</p> <p>It is highly probable to identify a statistically significant association between a given gene list and prognosis in the NKI2 dataset due to its large sample size and the interrelationship between ER-α expression and markers of proliferation. In most respects, the TRANSBIG data set generated similar outcomes as the NKI2 data set, although its smaller sample size led to fewer statistically significant results.</p

CD4+ Effector T cells Accelerate Alzheimer\u27s Disease in Mice

BACKGROUND: Alzheimer\u27s disease (AD) is a progressive neurodegenerative disorder characterized by pathological deposition of misfolded self-protein amyloid beta (Aβ) which in kind facilitates tau aggregation and neurodegeneration. Neuroinflammation is accepted as a key disease driver caused by innate microglia activation. Recently, adaptive immune alterations have been uncovered that begin early and persist throughout the disease. How these occur and whether they can be harnessed to halt disease progress is unclear. We propose that self-antigens would induct autoreactive effector T cells (Teffs) that drive pro-inflammatory and neurodestructive immunity leading to cognitive impairments. Here, we investigated the role of effector immunity and how it could affect cellular-level disease pathobiology in an AD animal model. METHODS: In this report, we developed and characterized cloned lines of amyloid beta (Aβ) reactive type 1 T helper (Th1) and type 17 Th (Th17) cells to study their role in AD pathogenesis. The cellular phenotype and antigen-specificity of Aβ-specific Th1 and Th17 clones were confirmed using flow cytometry, immunoblot staining and Aβ T cell epitope loaded haplotype-matched major histocompatibility complex II IA RESULTS: The propagated Aβ-Th1 and Aβ-Th17 clones were confirmed stable and long-lived. Treatment of APP/PS1 mice with Aβ reactive Teffs accelerated memory impairment and systemic inflammation, increased amyloid burden, elevated microglia activation, and exacerbated neuroinflammation. Both Th1 and Th17 Aβ-reactive Teffs progressed AD pathology by downregulating anti-inflammatory and immunosuppressive regulatory T cells (Tregs) as recorded in the periphery and within the central nervous system. CONCLUSIONS: These results underscore an important pathological role for CD4+ Teffs in AD progression. We posit that aberrant disease-associated effector T cell immune responses can be controlled. One solution is by Aβ reactive Tregs

Metabolomics 2022 workshop report: state of QA/QC best practices in LC-MS-based untargeted metabolomics, informed through mQACC community engagement initiatives.

IntroductionThe Metabolomics Quality Assurance and Quality Control Consortium (mQACC) organized a workshop during the Metabolomics 2022 conference.ObjectivesThe goal of the workshop was to disseminate recent findings from mQACC community-engagement efforts and to solicit feedback about a living guidance document of QA/QC best practices for untargeted LC-MS metabolomics.MethodsFour QC-related topics were presented.ResultsDuring the discussion, participants expressed the need for detailed guidance on a broad range of QA/QC-related topics accompanied by use-cases.ConclusionsOngoing efforts will continue to identify, catalog, harmonize, and disseminate QA/QC best practices, including outreach activities, to establish and continually update QA/QC guidelines

Metabolomics 2023 workshop report: moving toward consensus on best QA/QC practices in LC-MS-based untargeted metabolomics.

IntroductionDuring the Metabolomics 2023 conference, the Metabolomics Quality Assurance and Quality Control Consortium (mQACC) presented a QA/QC workshop for LC-MS-based untargeted metabolomics.ObjectivesThe Best Practices Working Group disseminated recent findings from community forums and discussed aspects to include in a living guidance document.MethodsPresentations focused on reference materials, data quality review, metabolite identification/annotation and quality assurance.ResultsLive polling results and follow-up discussions offered a broad international perspective on QA/QC practices.ConclusionsCommunity input gathered from this workshop series is being used to shape the living guidance document, a continually evolving QA/QC best practices resource for metabolomics researchers

Hundreds of variants clustered in genomic loci and biological pathways affect human height

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

Zidovudine plus lamivudine in Human T-Lymphotropic Virus type-I-associated myelopathy: a randomised trial

BACKGROUND: No therapies have been proven to persistently improve the outcome of HTLV-I-associated myelopathy. Clinical benefit has been reported with zidovudine and with lamivudine in observational studies. We therefore conducted a randomised, double blind, placebo controlled study of six months combination therapy with these nucleoside analogues in sixteen patients. RESULTS: Primary outcomes were change in HTLV-I proviral load in PBMCs and clinical measures. Secondary endpoints were changes in T-cell subsets and markers of activation and proliferation. Six patients discontinued zidovudine. No significant changes in pain, bladder function, disability score, gait, proviral load or markers of T-cell activation or proliferation were seen between the two arms. Active therapy was associated with an unexplained decrease in CD8 and non-T lymphocyte counts. CONCLUSION: Failure to detect clinical improvement may have been due irreversible nerve damage in these patients with a long clinical history and future studies should target patients presenting earlier. The lack of virological effect but may reflect a lack of activity of these nucleoside analogues against HTLV-I RT in vivo, inadequate intracellular concentrations of the active moiety or the contribution of new cell infection to maintaining proviral load at this stage of infection may be relatively small masking the effects of RT inhibition

Variants in ADRB1 and CYP2C9: Association with Response to Atenolol and Losartan in Marfan Syndrome

Objective: To test whether variants in ADRB1 and CYP2C9 genes identify subgroups of individuals with differential response to treatment for Marfan syndrome through analysis of data from a large, randomized trial. Study design: In a subset of 250 white, non-Hispanic participants with Marfan syndrome in a prior randomized trial of atenolol vs losartan, the common variants rs1801252 and rs1801253 in ADRB1 and rs1799853 and rs1057910 in CYP2C9 were analyzed. The primary outcome was baseline-adjusted annual rate of change in the maximum aortic root diameter z-score over 3 years, assessed using mixed effects models. Results: Among 122 atenolol-assigned participants, the 70 with rs1801253 CC genotype had greater rate of improvement in aortic root z-score compared with 52 participants with CG or GG genotypes (Time × Genotype interaction P = .005, mean annual z-score change ± SE -0.20 ± 0.03 vs -0.09 ± 0.03). Among participants with the CC genotype in both treatment arms, those assigned to atenolol had greater rate of improvement compared with the 71 of the 121 assigned to losartan (interaction P = .002; -0.20 ± 0.02 vs -0.07 ± 0.02; P < .001). There were no differences in atenolol response by rs1801252 genotype or in losartan response by CYP2C9 metabolizer status. Conclusions: In this exploratory study, ADRB1-rs1801253 was associated with atenolol response in children and young adults with Marfan syndrome. If these findings are confirmed in future studies, ADRB1 genotyping has the potential to guide therapy by identifying those who are likely to have greater therapeutic response to atenolol than losartan