The emergence of modern statistics in agricultural science : Analysis of variance, experimental design and the reshaping of research at Rothamsted Experimental Station, 1919–1933

During the twentieth century statistical methods have transformed research in the experimental and social sciences. Qualitative evidence has largely been replaced by quantitative results and the tools of statistical inference have helped foster a new ideal of objectivity in scientific knowledge. The paper will investigate this transformation by considering the genesis of analysis of variance and experimental design, statistical methods nowadays taught in every elementary course of statistics for the experimental and social sciences. These methods were developed by the mathematician and geneticist R. A. Fisher during the 1920s, while he was working at Rothamsted Experimental Station, where agricultural research was in turn reshaped by Fisher’s methods. Analysis of variance and experimental design required new practices and instruments in field and laboratory research, and imposed a redistribution of expertise among statisticians, experimental scientists and the farm staff. On the other hand the use of statistical methods in agricultural science called for a systematization of information management and made computing an activity integral to the experimental research done at Rothamsted, permanently integrating the statisticians’ tools and expertise into the station research programme. Fisher’s statistical methods did not remain confined within agricultural research and by the end of the 1950s they had come to stay in psychology, sociology, education, chemistry, medicine, engineering, economics, quality control, just to mention a few of the disciplines which adopted them

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

The Somatic Genomic Landscape of Glioblastoma

We describe the landscape of somatic genomic alterations based on multi-dimensional and comprehensive characterization of more than 500 glioblastoma tumors (GBMs). We identify several novel mutated genes as well as complex rearrangements of signature receptors including EGFR and PDGFRA. TERT promoter mutations are shown to correlate with elevated mRNA expression, supporting a role in telomerase reactivation. Correlative analyses confirm that the survival advantage of the proneural subtype is conferred by the G-CIMP phenotype, and MGMT DNA methylation may be a predictive biomarker for treatment response only in classical subtype GBM. Integrative analysis of genomic and proteomic profiles challenges the notion of therapeutic inhibition of a pathway as an alternative to inhibition of the target itself. These data will facilitate the discovery of therapeutic and diagnostic target candidates, the validation of research and clinical observations and the generation of unanticipated hypotheses that can advance our molecular understanding of this lethal cancer

Primary Meningeal Pleomorphic Xanthoastrocytoma With Anaplastic Features: A Report of 2 Cases, One With BRAF(V600E) Mutation and Clinical Response to the BRAF Inhibitor Dabrafenib

Primary meningeal gliomas are rare tumors composed of a heterogeneous group of neoplasms. We present 2 clinically aggressive cases of primary meningeal pleomorphic xanthoastrocytoma that clinically mimicked meningioma. One case presented in the posterior fossa of a 56-year-old woman; the other centered on the left operculum of a 35-year-old woman. These cases showed many of the classic features of pleomorphic xanthoastrocytoma, except that xanthomatous cells were rare and eosinophilic granular bodies were inconspicuous. Both cases exhibited high proliferative indices and superficially invaded the brain. One case harboring a BRAF mutation disseminated to the thecal sac and showed a clinical response to the targeted BRAF inhibitor dabrafenib. These cases seem to represent an unusual primarily extra-axial presentation of pleomorphic xanthoastrocytoma and may account for at least some of the previously reported cases of primary meningeal glioma and/or glial fibrillary acidic protein-immunoreactive meningioma variants. We suggest that BRAF mutation analysis be considered in all meningeal lesions showing atypical histologic or immunohistochemical profiles, particularly those exhibiting glial differentiation, as a diagnostic aid and possible indication for targeted therapy

Pre-diagnosis lipid levels and mortality after obesity-related cancer diagnosis in the Womens Health Initiative cardiovascular disease biomarker cohort.

BACKGROUND: Published studies have demonstrated inconclusive relationships between serum lipid levels and mortality after cancer. METHODS: The primary objective was to evaluate the relationship between fasting lipid levels and mortality after cancer. Data were obtained on baseline lipids and outcomes after cancer from 1263 postmenopausal women diagnosed with 13 obesity-related cancers who were part of the Womens Health Initiative (WHI) lipid biomarkers cohort. Obesity-related cancers included incident invasive cancers of the breast, colorectum, endometrium, esophagus (adenocarcinoma), kidney, liver, gallbladder, pancreas, ovaries, small intestine, thyroid, stomach, as well as multiple myeloma. Baseline lipid measurements included high-density lipoprotein (HDL)-cholesterol, low-density lipoprotein (LDL)-cholesterol, and non-HDL-cholesterol. Outcomes were all cause, cancer-specific, and CVD mortality. Multivariable Cox proportional hazards models were used to measure associations between lipid levels and mortality (all cause, cancer, and CVD) after a cancer diagnosis, with lipids analyzed as continuous variables. RESULTS: Among women with obesity-related cancer, there were 707 deaths, of which 379 (54%) were due to cancer and 113 (16%) were due to CVD. Mean time from blood draw to cancer diagnosis was 5.1 years (range: 0.05-10 years). LDL-C values above the 95th percentile were associated with higher risk of all-cause mortality (p < 0.001), and cancer-specific mortality (p < 0.001), but not mortality due to CVD. Non-HDL-C values above the 65th percentile were associated with higher risk of all-cause mortality (p = 0.01) and mortality due to CVD (p = 0.003), but not cancer-specific mortality (p = 0.37). HDL-C values above the 95th percentile were associated with lower all-cause mortality (p = 0.002), and above the 65th percentile with lower cancer-specific mortality (p = 0.003), but no significant relationship with mortality due to CVD was observed. CONCLUSIONS: The relationship between pre-diagnosis fasting lipid levels and mortality after cancer diagnosis is complex. These results suggest that improved lipid control through lifestyle and anti-lipid medications could have a meaningful impact on outcomes after cancer

Validation of Breast Cancer Risk Models by Race/Ethnicity, Family History and Molecular Subtypes

(1) Background: The purpose of this study is to compare the performance of four breast cancer risk prediction models by race, molecular subtype, family history of breast cancer, age, and BMI. (2) Methods: Using a cohort of women aged 40–84 without prior history of breast cancer who underwent screening mammography from 2006 to 2015, we generated breast cancer risk estimates using the Breast Cancer Risk Assessment tool (BCRAT), BRCAPRO, Breast Cancer Surveillance Consortium (BCSC) and combined BRCAPRO+BCRAT models. Model calibration and discrimination were compared using observed-to-expected ratios (O/E) and the area under the receiver operator curve (AUC) among patients with at least five years of follow-up. (3) Results: We observed comparable discrimination and calibration across models. There was no significant difference in model performance between Black and White women. Model discrimination was poorer for HER2+ and triple-negative subtypes compared with ER/PR+HER2−. The BRCAPRO+BCRAT model displayed improved calibration and discrimination compared to BRCAPRO among women with a family history of breast cancer. Across models, discriminatory accuracy was greater among obese than non-obese women. When defining high risk as a 5-year risk of 1.67% or greater, models demonstrated discordance in 2.9% to 19.7% of patients. (4) Conclusions: Our results can inform the implementation of risk assessment and risk-based screening among women undergoing screening mammography