STAT3 inhibition induces apoptosis in cancer cells independent of STAT1 or STAT2

Signal transducers and activators of transcription (STATs) were originally discovered as mediators of signal transduction. Persistent aberrant activation of STAT3 is part of the malignant phenotype of hormone-refractory prostate cancer and pancreatic cancer; this is thought to be mediated by homodimers of phosphorylated STAT3, which translocate to the nucleus.  One consequence of persistently-activated STAT3 in malignant cells is that they depend upon it for survival.   STAT3 is observed to heterodimerize with STAT1 and STAT2; however the contributions of STAT3:STAT1  and STAT3:STAT2 heterodimers to the survival of malignant cells have not been investigated in detail. Previously we reported that single-stranded oligonucleotides containing consensus STAT3 binding sequences (13410 and 13411) were more effective for inducing apoptosis in prostate cancer cells than antisense STAT3 oligonucleotides. Control oligonucleotides (scrambled sequences) had no effect. STAT3-inhibiting oligonucleotide 13410, but not scrambled-sequence oligonucleotides, induced apoptosis in pancreatic cancer cells as well.  Here we report that 13410 and derivative olignucleotides induced apoptosis in STAT1-null and STAT2-null fibrosarcoma cell lines U3A and U6A, as well as in the parental fibrosarcoma cell line 2fTGH. The cell lines expressed constitutively-activated STAT3 and depended on its activity for survival.  Forty-eight hr after transfection of 13410 or related oligonucleotides, significant apoptosis was observed in 2fTGH, U3A and U6A cells. Scrambled-sequence oligonucleotides had no effect on survival.  These data indicate that neither STAT1 nor STAT2 play significant roles in the maintenance of these cells, and by extension that STAT3:STAT1 and STAT3:STAT2 heterodimers regulate a different set of genes from STAT3:STAT3 homodimers.  

An automatic deep learning-based workflow for glioblastoma survival prediction using pre-operative multimodal MR images

We proposed a fully automatic workflow for glioblastoma (GBM) survival prediction using deep learning (DL) methods. 285 glioma (210 GBM, 75 low-grade glioma) patients were included. 163 of the GBM patients had overall survival (OS) data. Every patient had four pre-operative MR scans and manually drawn tumor contours. For automatic tumor segmentation, a 3D convolutional neural network (CNN) was trained and validated using 122 glioma patients. The trained model was applied to the remaining 163 GBM patients to generate tumor contours. The handcrafted and DL-based radiomic features were extracted from auto-contours using explicitly designed algorithms and a pre-trained CNN respectively. 163 GBM patients were randomly split into training (n=122) and testing (n=41) sets for survival analysis. Cox regression models with regularization techniques were trained to construct the handcrafted and DL-based signatures. The prognostic power of the two signatures was evaluated and compared. The 3D CNN achieved an average Dice coefficient of 0.85 across 163 GBM patients for tumor segmentation. The handcrafted signature achieved a C-index of 0.64 (95% CI: 0.55-0.73), while the DL-based signature achieved a C-index of 0.67 (95% CI: 0.57-0.77). Unlike the handcrafted signature, the DL-based signature successfully stratified testing patients into two prognostically distinct groups (p-value<0.01, HR=2.80, 95% CI: 1.26-6.24). The proposed 3D CNN generated accurate GBM tumor contours from four MR images. The DL-based signature resulted in better GBM survival prediction, in terms of higher C-index and significant patient stratification, than the handcrafted signature. The proposed automatic radiomic workflow demonstrated the potential of improving patient stratification and survival prediction in GBM patients

Creation of a novel peptide with enhanced nuclear localization in prostate and pancreatic cancer cell lines

<p>Abstract</p> <p>Background</p> <p>For improved uptake of oligonucleotide-based therapy, the oligonucleotides often are coupled to peptides that facilitate entry into cells. To this end, novel cell-penetrating peptides (CPPs) were designed for mediating intracellular uptake of oligonucleotide-based therapeutics. The novel peptides were based on taking advantage of the nuclear localization properties of transcription factors in combination with a peptide that would bind putatively to cell surfaces. It was observed that adding a glutamate peptide to the N-terminus of the nuclear localization signal (NLS) of the Oct6 transcription factor resulted in a novel CPP with better uptake and better nuclear colocalization than any other peptide tested.</p> <p>Results</p> <p>Uptake of the novel peptide Glu-Oct6 by cancer cell lines was rapid (in less than 1 hr, more than 60% of DU-145 cells were positive for FITC), complete (by 4 hr, 99% of cells were positive for FITC), concentration-dependent, temperature-dependent, and inhibited by sodium azide (NaN₃). Substitution of Phe, Tyr, or Asn moieties for the glutamate portion of the novel peptide resulted in abrogation of novel CPP uptake; however none of the substituted peptides inhibited uptake of the novel CPP when coincubated with cells. Live-cell imaging and analysis by imaging flow cytometry revealed that the novel CPP accumulated in nuclei. Finally, the novel CPP was coupled to a carboxyfluorescein-labeled synthetic oligonucleotide, to see if the peptide could ferry a therapeutic payload into cells.</p> <p>Conclusions</p> <p>These studies document the creation of a novel CPP consisting of a glutamate peptide coupled to the N-terminus of the Oct6 NLS; the novel CPP exhibited nuclear colocalization as well as uptake by prostate and pancreatic cancer cell lines.</p

Fall Risk is Not Black and White

Objective: To determine whether previously reported racial differences in fall rates between White and Black/African American is explained by differences in health status and neighborhood characteristics. Design: Prospective cohort Setting: Community Participants: The study included 550 White and 116 Black older adults in the Greater Boston area (mean age: 78 years; 36% men) who were English-speaking, able to walk across a room, and without severe cognitive impairment. Measurements: Falls were prospectively reported using monthly fall calendars. The location of each fall and fall-related injuries were asked during telephone interviews. At baseline, we assessed risk factors for falls, including sociodemographic characteristics, physiologic risk factors, physical activity, and community-level characteristics. Results: Over the mean follow-up of 1,048 days, 1,539 falls occurred (incidence: 806/1,000 person-years). Whites were more likely than Blacks to experience any falls (867 versus 504 falls per 1,000 person-years; RR [95% CI]: 1.77 [1.33, 2.36]), outdoor falls (418 versus 178 falls per 1,000 person-years; 1.78 [1.08, 2.92]), indoor falls (434 versus 320 falls per 1,000 person-years; 1.44 [1.02, 2.05]), and injurious falls (367 versus 205 falls per 1,000 person-years; 1.79 [1.30, 2.46]). With exception of injurious falls, higher fall rates in Whites than Blacks were substantially attenuated with adjustment for risk factors and community-level characteristics: any fall (1.24 [0.81, 1.89]), outdoor fall (1.57 [0.86, 2.88]), indoor fall (1.08 [0.64, 1.81]), and injurious fall (1.77 [1.14, 2.74]). Conclusion: Our findings suggest that the racial differences in fall rates may be largely due to confounding by individual-level and community-level characteristics

Semiautomated Device for Batch Extraction of Metabolites from Tissue Samples

ABSTRACT: Metabolomics has become a mainstream analytical strategy for investigating metabolism. The quality of data derived from these studies is proportional to the consistency of the sample preparation. Although considerable research has been devoted to finding optimal extraction protocols, most of the established methods require extensive sample handling. Manual sample preparation can be highly effective in the hands of skilled technicians, but an automated tool for purifying metabolites from complex biological tissues would be of obvious utility to the field. Here, we introduce the semiautomated metabolite batch extraction device (SAMBED), a new tool designed to simplify metabolomics sample preparation. We discuss SAMBED’s design and show that SAMBED-based extractions are of comparable quality to extracts produced through traditional methods (13 % mean coefficient of variation from SAMBED versus 16 % from manual extractions). Moreover, we show that aqueous SAMBED-based methods ca

Maternal psychological distress in primary care and association with child behavioural outcomes at age three

Observational studies indicate children whose mothers have poor mental health are at increased risk of socio-emotional behavioural difficulties, but it is unknown whether these outcomes vary by the mothers’ mental health recognition and treatment status. To examine this question, we analysed linked longitudinal primary care and research data from 1078 women enrolled in the Born in Bradford cohort. A latent class analysis of treatment status and self-reported distress broadly categorised women as (a) not having a common mental disorder (CMD) that persisted through pregnancy and the first 2 years after delivery (N = 756, 70.1 %), (b) treated for CMD (N = 67, 6.2 %), or (c) untreated (N = 255, 23.7 %). Compared to children of mothers without CMD, 3-year-old children with mothers classified as having untreated CMD had higher standardised factor scores on the Strengths and Difficulties Questionnaire (d = 0.32), as did children with mothers classified as having treated CMD (d = 0.27). Results were only slightly attenuated in adjusted analyses. Children of mothers with CMD may be at risk for socio-emotional and behavioural difficulties. The development of effective treatments for CMD needs to be balanced by greater attempts to identify and treat women. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00787-015-0777-2) contains supplementary material, which is available to authorized users