Identification of novel biomarkers for the prediction of subclinical coronary artery atherosclerosis in patients with rheumatoid arthritis: an exploratory analysis.

Background: Cardiovascular (CV) risk estimation calculators for the general population underperform in patients with rheumatoid arthritis (RA). The purpose of this study was to identify relevant protein biomarkers that could be added to traditional CV risk calculators to improve the capacity of coronary artery calcification (CAC) prediction in individuals with RA. In a second step, we quantify the improvement of this prediction of CAC when these circulating biomarkers are added to standard risk scores.

Accessing a New Dimension in TP53 Biology: Multiplex Long Amplicon Digital PCR to Specifically Detect and Quantitate Individual Transcripts.

, the most commonly-mutated gene in cancer, undergoes complex alternative splicing. Different transcripts play different biological roles, both in normal function and in the progression of diseases such as cancer. The study of alternative RNA splice forms and their use as clinical biomarkers has been hampered by limited specificity and quantitative accuracy of current methods.

Evofosfamide for the treatment of human papillomavirus-negative head and neck squamous cell carcinoma.

Evofosfamide (TH-302) is a clinical-stage hypoxia-activated prodrug of a DNA-crosslinking nitrogen mustard that has potential utility for human papillomavirus (HPV) negative head and neck squamous cell carcinoma (HNSCC), in which tumor hypoxia limits treatment outcome. We report the preclinical efficacy, target engagement, preliminary predictive biomarkers and initial clinical activity of evofosfamide for HPV-negative HNSCC. Evofosfamide was assessed in 22 genomically characterized cell lines and 7 cell line-derived xenograft (CDX), patient-derived xenograft (PDX), orthotopic, and syngeneic tumor models.

A steroid metabolizing gene variant in a polyfactorial model improves risk prediction in a high incidence breast cancer population.

Background: We have combined functional gene polymorphisms with clinical factors to improve prediction and understanding of sporadic breast cancer risk, particularly within a high incidence Caucasian population.

Methods: A polyfactorial risk model (PFRM) was built from both clinical data and functional single nucleotide polymorphism (SNP) gene candidates using multivariate logistic regression analysis on data from 5022 US Caucasian females (1671 breast cancer cases, 3351 controls), validated in an independent set of 1193 women (400 cases, 793 controls), and reassessed in a unique high incidence breast cancer population (165 cases, 173 controls) from Marin County, CA.

Results: The optimized PFRM consisted of 22 SNPs (19 genes, 6 regulating steroid metabolism) and 5 clinical risk factors, and its 5-year and lifetime risk prediction performance proved significantly superior (~ 2-fold) over the Gail model (Breast Cancer Risk Assessment Tool, BCRAT), whether assessed by odds (OR) or positive likelihood (PLR) ratios over increasing model risk levels.

Validation of the power model of ovarian nongrowing follicle depletion associated with aging in women.

Objective: To validate recently proposed models of ovarian nongrowing follicle (NGF) decay associated with aging within the context of an independent data set.

Design: Prospective investigation.

Setting: Academic medical center.

A new model of reproductive aging: the decline in ovarian non-growing follicle number from birth to menopause.

Background: The primary determinant of reproductive age in women is the number of ovarian non-growing (primordial, intermediate and primary) follicles (NGFs). To better characterize the decline in NGF number associated with aging, we have employed modern stereology techniques to determine NGF number in women from birth to menopause.

Methods: Normal human ovaries were collected from 122 women (aged 0-51 years) undergoing elective oophorectomy, organ donation or autopsy.