The disruption of proteostasis in neurodegenerative diseases

Cells count on surveillance systems to monitor and protect the cellular proteome which, besides being highly heterogeneous, is constantly being challenged by intrinsic and environmental factors. In this context, the proteostasis network (PN) is essential to achieve a stable and functional proteome. Disruption of the PN is associated with aging and can lead to and/or potentiate the occurrence of many neurodegenerative diseases (ND). This not only emphasizes the importance of the PN in health span and aging but also how its modulation can be a potential target for intervention and treatment of human diseases.info:eu-repo/semantics/publishedVersio

A phase II trial of an alternative schedule of palbociclib and embedded serum TK1 analysis

Palbociclib 3-weeks-on/1-week-off, combined with hormonal therapy, is approved for hormone receptor positive (HR+)/HER2-negative (HER2-) advanced/metastatic breast cancer (MBC). Neutropenia is the most frequent adverse event (AE). We aim to determine whether an alternative 5-days-on/2-days-off weekly schedule reduces grade 3 and above neutropenia (G3 + ANC) incidence. In this single-arm phase II trial, patients with HR+/HER2- MBC received palbociclib 125 mg, 5-days-on/2-days-off, plus letrozole or fulvestrant per physician, on a 28-day cycle (C), as their first- or second-line treatment. The primary endpoint was G3 + ANC in the first 29 days (C1). Secondary endpoints included AEs, efficacy, and serum thymidine kinase 1 (sTK1) activity. At data-cutoff, fifty-four patients received a median of 13 cycles (range 2.6-43.5). The rate of G3 + ANC was 21.3% (95% CI: 11.2-36.1%) without G4 in C1, and 40.7% (95% CI: 27.9-54.9%), including 38.9% G3 and 1.8% G4, in all cycles. The clinical benefit rate was 80.4% (95% CI: 66.5-89.7%). The median progression-free survival (mPFS) (95% CI) was 19.75 (12.11-34.89), 33.5 (17.25-not reached [NR]), and 11.96 (10.43-NR) months, in the overall, endocrine sensitive or resistant population, respectively. High sTK1 at baseline, C1 day 15 (C1D15), and C2D1 were independently prognostic for shorter PFS (p = 9.91 × 1

Genomic complexity predicts resistance to endocrine therapy and CDK4/6 inhibition in hormone receptor-positive (HR+)/HER2-negative metastatic breast cancer

PURPOSE: Clinical biomarkers to identify patients unlikely to benefit from CDK4/6 inhibition (CDK4/6i) in combination with endocrine therapy (ET) are lacking. We implemented a comprehensive circulating tumor DNA (ctDNA) analysis to identify genomic features for predicting and monitoring treatment resistance. EXPERIMENTAL DESIGN: ctDNA was isolated from 216 plasma samples collected from 51 patients with hormone receptor-positive (HR+)/HER2-negative (HER2-) metastatic breast cancer (MBC) on a phase II trial of palbociclib combined with letrozole or fulvestrant (NCT03007979). Boosted whole-exome sequencing (WES) was performed at baseline and clinical progression to evaluate genomic alterations, mutational signatures, and blood tumor mutational burden (bTMB). Low-pass whole-genome sequencing was performed at baseline and serial timepoints to assess blood copy-number burden (bCNB). RESULTS: High bTMB and bCNB were associated with lack of clinical benefit and significantly shorter progression-free survival (PFS) compared with patients with low bTMB or low bCNB (all P \u3c 0.05). Dominant APOBEC signatures were detected at baseline exclusively in cases with high bTMB (5/13, 38.5%) versus low bTMB (0/37, 0%; P = 0.0006). Alterations in ESR1 were enriched in samples with high bTMB (P = 0.0005). There was a high correlation between bTMB determined by WES and bTMB determined using a 600-gene panel (R = 0.98). During serial monitoring, an increase in bCNB score preceded radiographic progression in 12 of 18 (66.7%) patients. CONCLUSIONS: Genomic complexity detected by noninvasive profiling of bTMB and bCNB predicted poor outcomes in patients treated with ET and CDK4/6i and identified early disease progression before imaging. Novel treatment strategies including immunotherapy-based combinations should be investigated in this population

Preservation of Ranking Order in the Expression of Human Housekeeping Genes

Housekeeping (HK) genes fulfill the basic needs for a cell to survive and function properly. Their ubiquitous expression, originally thought to be constant, can vary from tissue to tissue, but this variation remains largely uncharacterized and it could not be explained by previously identified properties of HK genes such as short gene length and high GC content. By analyzing microarray expression data for human genes, we uncovered a previously unnoted characteristic of HK gene expression, namely that the ranking order of their expression levels tends to be preserved from one tissue to another. Further analysis by tensor product decomposition and pathway stratification identified three main factors of the observed ranking preservation, namely that, compared to those of non-HK (NHK) genes, the expression levels of HK genes show a greater degree of dispersion (less overlap), stableness (a smaller variation in expression between tissues), and correlation of expression. Our results shed light on regulatory mechanisms of HK gene expression that are probably different for different HK genes or pathways, but are consistent and coordinated in different tissues

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases

A Survey of Bayesian Statistical Approaches for Big Data

The modern era is characterised as an era of information or Big Data. This has motivated a huge literature on new methods for extracting information and insights from these data. A natural question is how these approaches differ from those that were available prior to the advent of Big Data. We present a review of published studies that present Bayesian statistical approaches specifically for Big Data and discuss the reported and perceived benefits of these approaches. We conclude by addressing the question of whether focusing only on improving computational algorithms and infrastructure will be enough to face the challenges of Big Data

Emerging concepts in biomarker discovery; The US-Japan workshop on immunological molecular markers in oncology

Supported by the Office of International Affairs, National Cancer Institute (NCI), the "US-Japan Workshop on Immunological Biomarkers in Oncology" was held in March 2009. The workshop was related to a task force launched by the International Society for the Biological Therapy of Cancer (iSBTc) and the United States Food and Drug Administration (FDA) to identify strategies for biomarker discovery and validation in the field of biotherapy. The effort will culminate on October 28th 2009 in the "iSBTc-FDA-NCI Workshop on Prognostic and Predictive Immunologic Biomarkers in Cancer", which will be held in Washington DC in association with the Annual Meeting. The purposes of the US-Japan workshop were a) to discuss novel approaches to enhance the discovery of predictive and/or prognostic markers in cancer immunotherapy; b) to define the state of the science in biomarker discovery and validation. The participation of Japanese and US scientists provided the opportunity to identify shared or discordant themes across the distinct immune genetic background and the diverse prevalence of disease between the two Nations

Search for neutral MSSM Higgs bosons decaying to a pair of tau leptons in pp collisions

Peer reviewe

Constraints on parton distribution functions and extraction of the strong coupling constant from the inclusive jet cross section in pp collisions at √s=7 TeV

Peer reviewe