Dose-response analysis can be carried out using multi-purpose commercial statistical software, but except for a few special cases the analysis easily becomes cumbersome as relevant, non-standard output requires manual programming. The extension package drc for the statistical environment R provides a flexible and versatile infrastructure for dose-response analyses in general. The present version of the package, reflecting extensions and modifications over the last decade, provides a user-friendly interface to specify the model assumptions about the dose-response relationship and comes with a number of extractors for summarizing fitted models and carrying out inference on derived parameters. The aim of the present paper is to provide an overview of state-of-the-art dose-response analysis, both in terms of general concepts that have evolved and matured over the years and by means of concrete examples.

/pdf/dose-response-analysis-using-r-1rjy8qez6o.pdf

Dose-Response Analysis Using R

Circulating tumor DNA (ctDNA) represents a promising biomarker for noninvasive assessment of cancer burden, but existing methods have insufficient sensitivity or patient coverage for broad clinical applicability. Here we introduce CAncer Personalized Profiling by deep Sequencing (CAPP-Seq), an economical and ultrasensitive method for quantifying ctDNA. We implemented CAPP-Seq for non-small cell lung cancer (NSCLC) with a design covering multiple classes of somatic alterations that identified mutations in >95% of tumors. We detected ctDNA in 100% of stage II–IV and 50% of stage I NSCLC patients, with 96% specificity for mutant allele fractions down to ~0.02%. Levels of ctDNA significantly correlated with tumor volume, distinguished between residual disease and treatment-related imaging changes, and provided earlier response assessment than radiographic approaches. Finally, we explored biopsy-free tumor screening and genotyping with CAPP-Seq. We envision that CAPP-Seq could be routinely applied clinically to detect and monitor diverse malignancies, thus facilitating personalized cancer therapy.

/pdf/an-ultrasensitive-method-for-quantitating-circulating-tumor-2noe0yfo6n.pdf

An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage

http://diposit.ub.edu/dspace/bitstream/2445/165519/4/L20_2020_Cell_181_905_OA.pdf

Inhibition of SARS-CoV-2 Infections in Engineered Human Tissues Using Clinical-Grade Soluble Human ACE2.

Structural Basis for Potent Neutralization of Betacoronaviruses by Single-Domain Camelid Antibodies.

Autophagy increases the lifespan of model organisms; however, its role in promoting mammalian longevity is less well-established1,2. Here we report lifespan and healthspan extension in a mouse model with increased basal autophagy. To determine the effects of constitutively increased autophagy on mammalian health, we generated targeted mutant mice with a Phe121Ala mutation in beclin 1 (Becn1F121A/F121A) that decreases its interaction with the negative regulator BCL2. We demonstrate that the interaction between beclin 1 and BCL2 is disrupted in several tissues in Becn1
 F121A/F121A
 knock-in mice in association with higher levels of basal autophagic flux. Compared to wild-type littermates, the lifespan of both male and female knock-in mice is significantly increased. The healthspan of the knock-in mice also improves, as phenotypes such as age-related renal and cardiac pathological changes and spontaneous tumorigenesis are diminished. Moreover, mice deficient in the anti-ageing protein klotho
 3
 have increased beclin 1 and BCL2 interaction and decreased autophagy. These phenotypes, along with premature lethality and infertility, are rescued by the beclin 1(F121A) mutation. Together, our data demonstrate that disruption of the beclin 1–BCL2 complex is an effective mechanism to increase autophagy, prevent premature ageing, improve healthspan and promote longevity in mammals. A knock-in mutation in beclin 1 that increases autophagy in mice extends animal lifespan, improves healthspan, and also rescues early lethality in mice deficient in the anti-ageing protein klotho.

/pdf/disruption-of-the-beclin-1-bcl2-autophagy-regulatory-complex-1wmiq1ydgc.pdf

Disruption of the beclin 1–BCL2 autophagy regulatory complex promotes longevity in mice

Nonlinear regression, like linear regression, assumes that the scatter of data around the ideal curve follows a Gaussian or normal distribution. This assumption leads to the familiar goal of regression: to minimize the sum of the squares of the vertical or Y-value distances between the points and the curve. Outliers can dominate the sum-of-the-squares calculation, and lead to misleading results. However, we know of no practical method for routinely identifying outliers when fitting curves with nonlinear regression. We describe a new method for identifying outliers when fitting data with nonlinear regression. We first fit the data using a robust form of nonlinear regression, based on the assumption that scatter follows a Lorentzian distribution. We devised a new adaptive method that gradually becomes more robust as the method proceeds. To define outliers, we adapted the false discovery rate approach to handling multiple comparisons. We then remove the outliers, and analyze the data using ordinary least-squares regression. Because the method combines robust regression and outlier removal, we call it the ROUT method. When analyzing simulated data, where all scatter is Gaussian, our method detects (falsely) one or more outlier in only about 1–3% of experiments. When analyzing data contaminated with one or several outliers, the ROUT method performs well at outlier identification, with an average False Discovery Rate less than 1%. Our method, which combines a new method of robust nonlinear regression with a new method of outlier identification, identifies outliers from nonlinear curve fits with reasonable power and few false positives.

/pdf/detecting-outliers-when-fitting-data-with-nonlinear-eo9rjg91by.pdf

Harvey J Motulsky

Papers

Detecting outliers when fitting data with nonlinear regression – a new method based on robust nonlinear regression and the false discovery rate