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. METHODS: A panel of 141 serum and plasma proteins, which represent a broad base of both CV and RA biology, were evaluated and prioritized as candidate biomarkers. Of these, 39 proteins were selected and measured by commercial ELISA or quantitative mass spectroscopy in 561 individuals with RA in whom a measure of CAC and frozen sera were available. The patients were randomly split 50:50 into a training/validation cohort. Discrimination (using area under the receiver operator characteristic curves) and re-classification (through net reclassification improvement and integrated discrimination improvement calculation) analyses were performed first in the training cohort and replicated in the validation cohort, to estimate the increase in prediction accuracy for CAC using the ACA/AHA (American College of Cardiology and the American Heart Association) score with, compared to without, addition of these circulating biomarkers. RESULTS: The model containing ACC/AHA score plus cytokines (osteopontin, cartilage glycoprotein-39, cystatin C, and chemokine (C-C motif) ligand 18) and plus quantitative mass spectroscopy biomarkers (serpin D1, paraoxonase, and clusterin) had a statistically significant positive net reclassifications index and integrated discrimination improvement for the prediction of CAC, using ACC/AHA score without any biomarkers as the reference category. These results were confirmed in the validation cohort. CONCLUSION: In this exploratory analysis, the addition of several circulating CV and RA biomarkers to a standard CV risk calculator yielded significant improvements in discrimination and reclassification for the presence of CAC in individuals with RA.

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

TP53, the most commonly-mutated gene in cancer, undergoes complex alternative splicing. Different TP53 transcripts play different biological roles, both in normal function and in the progression of diseases such as cancer. The study of TP53's alternative RNA splice forms and their use as clinical biomarkers has been hampered by limited specificity and quantitative accuracy of current methods. TP53 RNA splice variants differ at both 5' and 3' ends, but because they have a common central region of 618 bp, the individual TP53 transcripts are impossible to specifically detect and precisely quantitate using standard PCR-based methods or short-read RNA sequencing. Therefore, we devised multiplex probe-based long amplicon droplet digital PCR (ddPCR) assays, which for the first time allow precise end-to-end quantitation of the seven major TP53 transcripts, with amplicons ranging from 0.85 to 1.85 kb. Multiple modifications to standard ddPCR assay procedures were required to enable specific co-amplification of these long transcripts and to overcome issues with secondary structure. Using these assays, we show that several TP53 transcripts are co-expressed in breast cancers, and illustrate the potential for this method to identify novel TP53 transcripts in tumour cells. This capability will facilitate a new level of biological and clinical understanding of the alternatively-spliced TP53 isoforms.

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. Biomarker analysis used RNA sequencing, whole-exome sequencing, and whole-genome CRISPR knockout screens. Five advanced/metastatic HNSCC patients received evofosfamide monotherapy (480 mg/m2 qw × 3 each month) in a phase 2 study. Evofosfamide was potent and highly selective for hypoxic HNSCC cells. Proliferative rate was a predominant evofosfamide sensitivity determinant and a proliferation metagene correlated with activity in CDX models. Evofosfamide showed efficacy as monotherapy and with radiotherapy in PDX models, augmented CTLA-4 blockade in syngeneic tumors, and reduced hypoxia in nodes disseminated from an orthotopic model. Of 5 advanced HNSCC patients treated with evofosfamide, 2 showed partial responses while 3 had stable disease. In conclusion, evofosfamide shows promising efficacy in aggressive HPV-negative HNSCC, with predictive biomarkers in development to support further clinical evaluation in this indication.

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. PATIENT(S): Normal appearing ovaries collected from 52 women (age 28-51 years) undergoing oophorectomy for benign gynecologic indications. INTERVENTION(S): Determining ovarian NGF counts with systematic random sampling rules and a validated fractionator/optical disector technique. The goodness-of-fit of predicted NGF counts based on the power and double Gaussian models and those observed in the validation set was assessed with the calculation of the Akaike information criterion and R(2) values. MAIN OUTCOME MEASURE(S): The goodness-of-fit between observed and expected ovarian NGF counts. RESULT(S): The power model was an excellent fit to the observed data. The average difference between the observed and expected NGF count was 0.161 (95% CI, -0.058, 0.327). In the present study population, the power model was a superior fit to the observed data compared with the double Gaussian model. CONCLUSION(S): This prospective investigation with an independent set of ovarian NGF counts validates the power model as an excellent characterization of the ovarian NGF decline associated with aging.

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. Improved performance of the PFRM in high risk Marin women was due in part to genotype enrichment by a CYP11B2 (-344T/C) variant. CONCLUSIONS AND GENERAL SIGNIFICANCE: Since the optimized PFRM consistently outperformed BCRAT in all Caucasian study populations, it represents an improved personalized risk assessment tool. The finding of higher Marin County risk linked to a CYP11B2 aldosterone synthase SNP associated with essential hypertension offers a new genetic clue to sporadic breast cancer predisposition.

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. After gross pathologic examination, systematic random sampling was utilized to obtain tissue for analysis by the fractionator/optical disector method. Models to describe the resulting decay curve were constructed and evaluated. RESULTS: NGF decay was best described by a simple power function: log (y) = ax(b) + c, where a, b and c are constants and y = NGF count at age x (R(2) = 0.84, Sums of Squares Error = 28.18 on 119 degrees of freedom). This model implies that follicles decay faster with increasing age. CONCLUSIONS: Unlike previous models of ovarian follicle depletion, our model predicts no sudden change in decay rate, but rather a constantly increasing rate. The model not only agrees well with observed ages of menopause in women, but also is more biologically plausible than previous models. Although the model represents a significant improvement compared with earlier attempts, a considerable percentage of the variation in NGF number between women cannot be explained by age alone.