Subscribe: pubmed: 0016-5085
http://eutils.ncbi.nlm.nih.gov/entrez/eutils/erss.cgi?rss_guid=0pH7X438KGtKnVGINZ3isjYuP_IGQK4CpGCxaRo3PpQ
Added By: Feedage Forager Feedage Grade C rated
Language: English
Tags:
black white  black  cancer  celiac disease  celiac  crc  disease  gluten  hla gluten  patients  subjects celiac  subjects  white 
Rate this Feed
Rate this feedRate this feedRate this feedRate this feedRate this feed
Rate this feed 1 starRate this feed 2 starRate this feed 3 starRate this feed 4 starRate this feed 5 star

Comments (0)

Feed Details and Statistics Feed Statistics
Preview: pubmed: 0016-5085

pubmed: 0016-5085



NCBI: db=pubmed; Term=0016-5085



 



Factors That Contribute to Differences in Survival of Black vs White Patients With Colorectal Cancer.
Related Articles

Factors That Contribute to Differences in Survival of Black vs White Patients With Colorectal Cancer.

Gastroenterology. 2017 Nov 13;:

Authors: Sineshaw HM, Ng K, Flanders WD, Brawley OW, Jemal A

Abstract
BACKGROUND & AIMS: Previous studies reported that black vs white disparities in survival among elderly patients with colorectal cancer (CRC) were due to differences in tumor characteristics (tumor stage, grade, nodal status, and comorbidity) rather than differences in treatment. We sought to determine the sequential contribution of differences in insurance, comorbidity, tumor characteristics, and treatment receipt to the black-white survival disparity among patients with CRC in 18-64 years old.
METHODS: We used data from the National Cancer Database, a hospital-based cancer registry database sponsored by the American College of Surgeons and the American Cancer Society, on non-Hispanic black (black) and non-Hispanic white (white) patients, 18-64 years old, diagnosed from 2004 through 2012 with single or first primary invasive stage I-IV CRC. Blacks were sequentially matched by demographics, insurance, comorbidity, tumor characteristics, and treatment with 5 white partially overlapping subgroups using propensity score and greedy matching algorithm. We used the Kaplan-Meier method to estimate 5-year survival, and Cox proportional hazards models to generate hazard ratios (HR).
RESULTS: The absolute 5-year survival difference between black and white unmatched patients with CRC was 9.2% (57.3% for black patients vs 66.5% for white patients; P < .0001). The absolute difference in survival did not change after patient groups were matched for demographics, but decreased to 4.9% (47% relative decrease [4.3% of 9.2%]) when they were matched for insurance and to 2.3% when they were matched for tumor characteristics (26% relative decrease [2.4% of 9.2%]). Further matching by treatment did not reduce the difference in 5-year survival between black and white patients. In proportional hazards model, insurance and tumor characteristics matching accounted for the 54% and 27% excess risk of death in black patients, respectively.
CONCLUSIONS: In an analysis of data from the National Cancer Database, we found that insurance coverage differences accounted for approximately one-half of the disparity in survival rate between black vs white patients with CRC in 18-64 years old; tumor characteristics accounted for a quarter of the disparity. Affordable health insurance coverage for all populations could substantially reduce differences in survival times of black vs white patients with CRC.

PMID: 29146523 [PubMed - as supplied by publisher]




Germline Genetic Features of Young Individuals with Colorectal Cancer.
Related Articles

Germline Genetic Features of Young Individuals with Colorectal Cancer.

Gastroenterology. 2017 Nov 12;:

Authors: Stoffel EM, Koeppe E, Everett J, Ulintz P, Kiel M, Osborne J, Williams L, Hanson K, Gruber SB, Rozek LS

Abstract
BACKGROUND & AIMS: The incidence of colorectal cancer (CRC) in individuals younger than 50 years old is increasing. We sought to ascertain the proportion of young CRC cases associated with genetic predisposition.
METHODS: We performed a retrospective study of individuals diagnosed with CRC at an age younger than 50 years, evaluated by the clinical genetics service at a single tertiary care cancer center from 1998 through 2015. We collected data on patient histories, tumor phenotypes, and results of germline DNA sequencing. For subjects with uninformative clinical evaluations, germline DNA samples were (re)sequenced using a research-based next-generation sequencing multigene panel. The primary outcome was identification of a pathogenic germline mutation associated with cancer predisposition.
RESULTS: Of 430 young CRC cases, 111 (26%) had a first-degree relative with CRC. Forty-one of the subjects with CRC (10%) had tumors with histologic evidence for mismatch repair deficiency. Of 315 subjects who underwent clinical germline sequencing, 79 had mutations associated with a hereditary cancer syndrome and 21 had variants of uncertain significance. Fifty-six subjects had pathogenic variants associated with Lynch syndrome (25 with mutations in MSH2, 24 with mutations in MLH1, 5 with mutations in MSH6, and 2 with mutations in PMS2) and 10 subjects had pathogenic variants associated with familial adenomatous polyposis. Thirteen subjects had mutations in other cancer-associated genes (8 in MUTYH, 2 in SMAD4, 1 in BRCA1, 1 in TP53, and 1 in CHEK2), all identified through multigene panel tests. Among 117 patients with uninformative clinical evaluations, next-generation sequence analysis using a multigene panel detected actionable germline variants in 6 patients (5%). Only 43 of the 85 subjects with germline mutations associated with a hereditary cancer syndrome (51%) reported a CRC diagnosis in a first-degree relative.
CONCLUSIONS: Approximately 1 in 5 individuals diagnosed with CRC at age younger than 50 years carries a germline mutation associated with cancer; nearly half of these do not have clinical histories typically associated with the identified syndrome. Germline testing with multigene cancer panels should be considered for all young patients with CRC.

PMID: 29146522 [PubMed - as supplied by publisher]




HLA-DQ-Gluten Tetramer Blood Test Accurately Identifies Patients With and Without Celiac Disease in Absence of Gluten Consumption.
Related Articles

HLA-DQ-Gluten Tetramer Blood Test Accurately Identifies Patients With and Without Celiac Disease in Absence of Gluten Consumption.

Gastroenterology. 2017 Nov 13;:

Authors: Sarna VK, Lundin KEA, Mørkrid L, Qiao SW, Sollid LM, Christophersen A

Abstract
BACKGROUND & AIMS: Celiac disease is characterized by HLA-DQ2/8-restricted responses of CD4+ T cells to cereal gluten proteins. A diagnosis of celiac disease based on serologic and histologic evidence and duodenal histology requires patients to be on gluten-containing diets. The growing number of individuals adhering to a gluten-free diet (GFD) without exclusion of celiac disease complicates its detection. HLA-DQ-gluten tetramers can be used to detect gluten-specific T cells in blood of patients with celiac disease, even if they are on a GFD. We investigated whether an HLA-DQ-gluten tetramer-based assay accurately identifies patients with celiac disease.
METHODS: We produced HLA-DQ-gluten tetramers and added them to peripheral blood mononuclear cells isolated from 143 HLA-DQ2.5+ subjects (62 subjects with celiac disease on a GFD, 19 subjects without celiac disease on a GFD [due to self-reported gluten-sensitivity], 10 subjects with celiac disease on a gluten-containing diet, and 52 presumed healthy individuals [controls]). T cells that bound HLA-DQ-gluten tetramers were quantified by flow cytometry. Laboratory tests and flow cytometry gating analyses were performed by researchers blinded to sample type, except for samples from subjects with celiac disease on a gluten-containing diet. Test precision analyses were performed using samples from 10 subjects.
RESULTS: For the HLA-DQ-gluten tetramer-based assay, we combined flow-cytometry variables in a multiple regression model that identified individuals with celiac disease on a GFD with an area under the receiver operating characteristic curve (AUROC) value of 0.96 (95% CI, 0.89-1.00) vs subjects without celiac disease on a GFD. The assay detected individuals with celiac disease on a gluten-containing diet vs controls with an AUROC value of 0.95 (95% CI, 0.90-1.00). Optimized cut-off values identified subjects with celiac disease on a GFD with 97% sensitivity (95% CI, 0.92-1.00) and 95% specificity (95% CI, 0.84-1.00), vs subjects without celiac disease on a GFD. The values identified subjects with celiac disease on a gluten-containing diet with 100% sensitivity (95% CI, 1.00-1.00]) and 90% specificity (95% CI, 0.83-0.98) vs controls. In an analysis of 4 controls with positive results from the HLA-DQ-gluten tetramer test, 2 had unrecognized celiac disease and the remaining 2 had T cells that proliferated in response to gluten antigen in vitro.
CONCLUSIONS: An HLA-DQ-gluten tetramer-based assays that detects gluten-reactive T cells identifies patients with and without celiac disease with a high level of accuracy, regardless of whether the individuals are on a GFD. This test would allow individuals with suspected celiac disease to avoid gluten challenge and duodenal biopsy, but requires validation in a larger study. Clinicaltrials.gov no: NCT02442219.

PMID: 29146521 [PubMed - as supplied by publisher]




Patterns of Resistance-associated Substitutions in Patients With Chronic HCV Infection Following Treatment with Direct-acting Antivirals.
Related Articles

Patterns of Resistance-associated Substitutions in Patients With Chronic HCV Infection Following Treatment with Direct-acting Antivirals.

Gastroenterology. 2017 Nov 13;:

Authors: Dietz J, Susser S, Vermehren J, Peiffer KH, Grammatikos G, Berger A, Ferenci P, Buti M, Müllhaupt B, Hunyady B, Hinrichsen H, Mauss S, Petersen J, Buggisch P, Felten G, Hüppe D, Knecht G, Lutz T, Schott E, Berg C, Spengler U, von Hahn T, Berg T, Zeuzem S, Sarrazin C, European HCV Resistance Study Group

Abstract
BACKGROUND & AIMS: Little is known about substitutions that mediate resistance of HCV to direct-acting antivirals (DAAs), due to the small number of patients with treatment failure in approval studies. It is important to identify resistance patterns to select effective salvage treatments.
METHODS: We performed a comprehensive analysis for resistance-associated substitutions (RASs) in HCV genes (NS3, NS5A, NS5B) targeted by DAAs. We compared NS3, NS5A, and NS5B sequences from 626 patients in Europe with DAA failure with sequences from 2322 DAA-naïve patients, infected with HCV genotypes 1-4. We considered RASs to be relevant if they were associated with DAA failure in patients or conferred a greater than 2-fold changed in susceptibility compared with a reference strain in in vitro replicon assays. Data were collected on pretreatment status, DAA regimen, the treatment initiation date and duration, and virologic response. Patients who received at least 4 weeks antiviral treatment were included in the analysis.
RESULTS: RASs in NS3 associated with simeprevir or paritaprevir failure include R155K and D168E/V. In addition, several RASs were specifically associated with failure of simeprevir (Q80K/R in patients with genotype 1a or 4) or paritaprevir (Y56H in combination with D168V in patients with genotype 1b). Y93H in NS5A was the RAS most frequently associated with failure of daclatasvir, ledipasvir, or ombitasvir in patients with genotype 1b infection, and L31M was associated with failure of daclatasvir or ledipasvir, but not ombitasvir. RASs in NS5A were heterogeneous among patients with HCV genotype 1a or genotype 4 infections. In patients with HCV genotype 3, Y93H was associated with resistance to daclatasvir, but no RASs were associated with ledipasvir failure, pointing to a limited efficacy of ledipasvir in patients with genotype 3. Among patients failed by sofosbuvir-containing regimens, L159F was enriched in patients with genotype 1b (together with C316N) or genotype 3 infection, whereas the RAS S282T was rarely observed.
CONCLUSIONS: We compared RASs in NS3, NS5A, and NS5B among patients failed by DAA therapy. Theses varied with the HCV genotype and subtype, and the different drug classes. These findings might be used to select salvage therapies.

PMID: 29146520 [PubMed - as supplied by publisher]