Evolving the human niche

This is the author accepted manuscript. The final version is available from the National Academy of Sciences via the DOI in this record

A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical Data

With the availability of big medical image data, the selection of an adequate training set is becoming more important to address the heterogeneity of different datasets. Simply including all the data does not only incur high processing costs but can even harm the prediction. We formulate the smart and efficient selection of a training dataset from big medical image data as a multi-armed bandit problem, solved by Thompson sampling. Our method assumes that image features are not available at the time of the selection of the samples, and therefore relies only on meta information associated with the images. Our strategy simultaneously exploits data sources with high chances of yielding useful samples and explores new data regions. For our evaluation, we focus on the application of estimating the age from a brain MRI. Our results on 7,250 subjects from 10 datasets show that our approach leads to higher accuracy while only requiring a fraction of the training data.Comment: MICCAI 2017 Proceeding

Integrative analyses identify modulators of response to neoadjuvant aromatase inhibitors in patients with early breast cancer

Introduction Aromatase inhibitors (AIs) are a vital component of estrogen receptor positive (ER+) breast cancer treatment. De novo and acquired resistance, however, is common. The aims of this study were to relate patterns of copy number aberrations to molecular and proliferative response to AIs, to study differences in the patterns of copy number aberrations between breast cancer samples pre- and post-AI neoadjuvant therapy, and to identify putative biomarkers for resistance to neoadjuvant AI therapy using an integrative analysis approach. Methods Samples from 84 patients derived from two neoadjuvant AI therapy trials were subjected to copy number profiling by microarray-based comparative genomic hybridisation (aCGH, n = 84), gene expression profiling (n = 47), matched pre- and post-AI aCGH (n = 19 pairs) and Ki67-based AI-response analysis (n = 39). Results Integrative analysis of these datasets identified a set of nine genes that, when amplified, were associated with a poor response to AIs, and were significantly overexpressed when amplified, including CHKA, LRP5 and SAPS3. Functional validation in vitro, using cell lines with and without amplification of these genes (SUM44, MDA-MB134-VI, T47D and MCF7) and a model of acquired AI-resistance (MCF7-LTED) identified CHKA as a gene that when amplified modulates estrogen receptor (ER)-driven proliferation, ER/estrogen response element (ERE) transactivation, expression of ER-regulated genes and phosphorylation of V-AKT murine thymoma viral oncogene homolog 1 (AKT1). Conclusions These data provide a rationale for investigation of the role of CHKA in further models of de novo and acquired resistance to AIs, and provide proof of concept that integrative genomic analyses can identify biologically relevant modulators of AI response

Neoadjuvant endocrine therapy in primary breast cancer: indications and use as a research tool

Neoadjuvant endocrine therapy has been increasingly employed in clinical practice to improve surgical options for postmenopausal women with bulky hormone receptor-positive breast cancer. Recent studies indicate that tumour response in this setting may predict long-term outcome of patients on adjuvant endocrine therapy, which argues for its broader application in treating hormone receptor-positive disease. From the research perspective, neoadjuvant endocrine therapy provides a unique opportunity for studies of endocrine responsiveness and the development of novel therapeutic agents

Letrozole in the neoadjuvant setting: the P024 trial

Neoadjuvant chemotherapy trials have consistently reported lower response rates in hormone receptor-positive (HR+) breast cancer when compared with HR− cases. Preoperative endocrine therapy has therefore become a logical alternative and has gained considerable momentum from the finding that aromatase inhibitors (AIs) are more effective than tamoxifen for HR+ breast cancer in both the neoadjuvant and adjuvant settings. The most convincing neoadjuvant trial to demonstrate the superiority of an AI versus tamoxifen was the P024 study, a large multinational double-blind trial in postmenopausal women with HR+ breast cancer ineligible for breast-conserving surgery. The overall response rate (ORR) was 55% for letrozole and 36% for tamoxifen (P < 0.001). Significantly more letrozole-treated patients underwent breast-conserving surgery (45 vs. 35%, respectively; P = 0.022). In addition, ORR was significantly higher with letrozole than tamoxifen in the human epidermal growth factor receptor HER1/HER2+ subgroup (P = 0.0004). The clinical efficacy of letrozole in HER2+ breast cancer was confirmed by fluorescent in situ hybridization analysis and was found to be comparable to that of HER2− cases (ORR 71% in both subsets). Biomarker studies confirmed the superiority of letrozole in centrally assessed estrogen receptor-positive (ER+) tumors and found a strong relationship with the degree of ER positivity for both agents. Interestingly, letrozole was effective even in marginally ER+ tumors and, unlike tamoxifen, consistently reduced the expression from estrogen-regulated genes (progesterone receptor and trefoil factor 1). Furthermore, when analyzed by Ki67 immunohistochemistry, letrozole was significantly more effective than tamoxifen in reducing tumor proliferation (P = 0.0009). Thus, neoadjuvant letrozole is safe and superior to tamoxifen in the treatment of postmenopausal women with HR+ locally advanced breast cancer

Roy-Steiner equations for pion-nucleon scattering

Starting from hyperbolic dispersion relations, we derive a closed system of Roy-Steiner equations for pion-nucleon scattering that respects analyticity, unitarity, and crossing symmetry. We work out analytically all kernel functions and unitarity relations required for the lowest partial waves. In order to suppress the dependence on the high-energy regime we also consider once- and twice-subtracted versions of the equations, where we identify the subtraction constants with subthreshold parameters. Assuming Mandelstam analyticity we determine the maximal range of validity of these equations. As a first step towards the solution of the full system we cast the equations for the $\pi\pi\to\bar NN$ partial waves into the form of a Muskhelishvili-Omn\`es problem with finite matching point, which we solve numerically in the single-channel approximation. We investigate in detail the role of individual contributions to our solutions and discuss some consequences for the spectral functions of the nucleon electromagnetic form factors.Comment: 106 pages, 18 figures; version published in JHE