Identification of Cell-Free Circulating MicroRNAs for the Detection of Early Breast Cancer and Molecular Subtyping

Early detection is crucial for achieving a reduction in breast cancer mortality. Analysis of circulating cell-free microRNAs present in the serum of cancer patients has emerged as a promising new noninvasive biomarker for early detection of tumors and for predicting their molecular classifications. The rationale for this study was to identify subtype-specific molecular profiles of cell-free microRNAs for early detection of breast cancer in serum. Fifty-four early-stage breast cancers with 27 age-matched controls were selected for circulating microRNAs evaluation in the serum. The 54 cases were molecularly classified (luminal A, luminal B, luminal B Her2 positive, Her-2, triple negative). NanoString platform was used for digital detection and quantitation of 800 tagged microRNA probes and comparing the overall differences in serum microRNA expression from breast cancer cases with controls. We identified the 42 most significant (P ≤ 0.05, 1.5-fold) differentially expressed circulating microRNAs in each molecular subtype for further study. Of these microRNAs, 19 were significantly differentially expressed in patients presenting with luminal A, eight in the luminal B, ten in luminal B HER 2 positive, and four in the HER2 enriched subtype. AUC is high with suitable sensitivity and specificity. For the triple negative subtype miR-25-3p had the best accuracy. Predictive analysis of the mRNA targets suggests they encode proteins involved in molecular pathways such as cell adhesion, migration, and proliferation. This study identified subtype-specific molecular profiles of cell-free microRNAs suitable for early detection of breast cancer selected by comparison to the microRNA profile in serum for female controls without apparent risk of breast cancer. This molecular profile should be validated using larger cohort studies to confirm the potential of these miRNA for future use as early detection biomarkers that could avoid unnecessary biopsy in patients with a suspicion of breast cancer.Foundation for Research Support of the State of São Paulo (FAPESP process 2015/21082-0) and Public Ministry of Labor Campinas (Research, Prevention, and Education of Occupational Cancer)

Comprehensive molecular landscape of cetuximab resistance in head and neck cancer cell lines

Cetuximab is the sole anti-EGFR monoclonal antibody that is FDA approved to treat head and neck squamous cell carcinoma (HNSCC). However, no predictive biomarkers of cetuximab response are known for HNSCC. Herein, we address the molecular mechanisms underlying cetuximab resistance in an in vitro model. We established a cetuximab resistant model (FaDu), using increased cetuximab concentrations for more than eight months. The resistance and parental cells were evaluated for cell viability and functional assays. Protein expression was analyzed by Western blot and human cell surface panel by lyoplate. The mutational profile and copy number alterations (CNA) were analyzed using whole-exome sequencing (WES) and the NanoString platform. FaDu resistant clones exhibited at least two-fold higher IC50 compared to the parental cell line. WES showed relevant mutations in several cancer-related genes, and the comparative mRNA expression analysis showed 36 differentially expressed genes associated with EGFR tyrosine kinase inhibitors resistance, RAS, MAPK, and mTOR signaling. Importantly, we observed that overexpression of KRAS, RhoA, and CD44 was associated with cetuximab resistance. Protein analysis revealed EGFR phosphorylation inhibition and mTOR increase in resistant cells. Moreover, the resistant cell line demonstrated an aggressive phenotype with a significant increase in adhesion, the number of colonies, and migration rates. Overall, we identified several molecular alterations in the cetuximab resistant cell line that may constitute novel biomarkers of cetuximab response such as mTOR and RhoA overexpression. These findings indicate new strategies to overcome anti-EGFR resistance in HNSCC.This work was supported by Barretos Cancer Hospital and the Public Ministry of Labor Campinas (Research, Prevention, and Education of Occupational Cancer) in Campinas, Brazil, CAPESDFATD (88887.137283/2017-00). INFG is the recipient of a FAPESP Ph.D. fellowship (2017/22305-9)

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

ATLANTIC EPIPHYTES: a data set of vascular and non-vascular epiphyte plants and lichens from the Atlantic Forest

Epiphytes are hyper-diverse and one of the frequently undervalued life forms in plant surveys and biodiversity inventories. Epiphytes of the Atlantic Forest, one of the most endangered ecosystems in the world, have high endemism and radiated recently in the Pliocene. We aimed to (1) compile an extensive Atlantic Forest data set on vascular, non-vascular plants (including hemiepiphytes), and lichen epiphyte species occurrence and abundance; (2) describe the epiphyte distribution in the Atlantic Forest, in order to indicate future sampling efforts. Our work presents the first epiphyte data set with information on abundance and occurrence of epiphyte phorophyte species. All data compiled here come from three main sources provided by the authors: published sources (comprising peer-reviewed articles, books, and theses), unpublished data, and herbarium data. We compiled a data set composed of 2,095 species, from 89,270 holo/hemiepiphyte records, in the Atlantic Forest of Brazil, Argentina, Paraguay, and Uruguay, recorded from 1824 to early 2018. Most of the records were from qualitative data (occurrence only, 88%), well distributed throughout the Atlantic Forest. For quantitative records, the most common sampling method was individual trees (71%), followed by plot sampling (19%), and transect sampling (10%). Angiosperms (81%) were the most frequently registered group, and Bromeliaceae and Orchidaceae were the families with the greatest number of records (27,272 and 21,945, respectively). Ferns and Lycophytes presented fewer records than Angiosperms, and Polypodiaceae were the most recorded family, and more concentrated in the Southern and Southeastern regions. Data on non-vascular plants and lichens were scarce, with a few disjunct records concentrated in the Northeastern region of the Atlantic Forest. For all non-vascular plant records, Lejeuneaceae, a family of liverworts, was the most recorded family. We hope that our effort to organize scattered epiphyte data help advance the knowledge of epiphyte ecology, as well as our understanding of macroecological and biogeographical patterns in the Atlantic Forest. No copyright restrictions are associated with the data set. Please cite this Ecology Data Paper if the data are used in publication and teaching events. © 2019 The Authors. Ecology © 2019 The Ecological Society of Americ

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

FPI-POFBG Angular Movement Sensor Inscribed in CYTOP Fibers with Dynamic Angle Compensator

We present the development and application of an angular movement sensor based on Fabry-Perot interferometer (FPI) in series with a fiber Bragg grating (FBG) inscribed in cyclic transparent optical polymer (CYTOP) using a femtosecond laser. The FPI was fabricated with UV-curable resin between the CYTOP and a silica pigtail, which provides flexibility and the possibility in situ fabrication. In this case, the FPI is an edge-filter for the FBG response, which presents both wavelength shift and optical power attenuation as the bending angle increases. A dynamic compensator for the material's viscoelastic response resulted in a six-fold reduction in the sensor's transient response error. The FPI-POFBG angular movement sensor was validated in knee angle monitoring during gait cycles, where the sensor presented errors as low as 2.5°. This is a low-cost sensor with high flexibility on fabrication and with the capacity of following subtle angle variations, as the ones commonly occurred in gait cycles