Repurposing Cationic Amphiphilic Antihistamines for Cancer Treatment.

EBioMedicine. 2016 Jul;9:130-9

Authors: Ellegaard AM, Dehlendorff C, Vind AC, Anand A, Cederkvist L, Petersen NH, Nylandsted J, Stenvang J, Mellemgaard A, Østerlind K, Friis S, Jäättelä M

Abstract
Non-small cell lung cancer (NSCLC) is one of the deadliest cancers worldwide. In search for new NSCLC treatment options, we screened a cationic amphiphilic drug (CAD) library for cytotoxicity against NSCLC cells and identified several CAD antihistamines as inducers of lysosomal cell death. We then performed a cohort study on the effect of CAD antihistamine use on mortality of patients diagnosed with non-localized cancer in Denmark between 1995 and 2011. The use of the most commonly prescribed CAD antihistamine, loratadine, was associated with significantly reduced all-cause mortality among patients with non-localized NSCLC or any non-localized cancer when compared with use of non-CAD antihistamines and adjusted for potential confounders. Of the less frequently described CAD antihistamines, astemizole showed a similar significant association with reduced mortality as loratadine among patients with any non-localized cancer, and ebastine use showed a similar tendency. The association between CAD antihistamine use and reduced mortality was stronger among patients with records of concurrent chemotherapy than among those without such records. In line with this, sub-micromolar concentrations of loratadine, astemizole and ebastine sensitized NSCLC cells to chemotherapy and reverted multidrug resistance in NSCLC, breast and prostate cancer cells. Thus, CAD antihistamines may improve the efficacy of cancer chemotherapy.

PMID: 27333030 [PubMed - indexed for MEDLINE]

Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.

Nature. 2016 Sep 29;537(7622):698-702

Authors: Olsson A, Venkatasubramanian M, Chaudhri VK, Aronow BJ, Salomonis N, Singh H, Grimes HL

Abstract
Delineating hierarchical cellular states, including rare intermediates and the networks of regulatory genes that orchestrate cell-type specification, are continuing challenges for developmental biology. Single-cell RNA sequencing is greatly accelerating such research, given its power to provide comprehensive descriptions of genomic states and their presumptive regulators. Haematopoietic multipotential progenitor cells, as well as bipotential intermediates, manifest mixed-lineage patterns of gene expression at a single-cell level. Such mixed-lineage states may reflect the molecular priming of different developmental potentials by co-expressed alternative-lineage determinants, namely transcription factors. Although a bistable gene regulatory network has been proposed to regulate the specification of either neutrophils or macrophages, the nature of the transition states manifested in vivo, and the underlying dynamics of the cell-fate determinants, have remained elusive. Here we use single-cell RNA sequencing coupled with a new analytic tool, iterative clustering and guide-gene selection, and clonogenic assays to delineate hierarchical genomic and regulatory states that culminate in neutrophil or macrophage specification in mice. We show that this analysis captured prevalent mixed-lineage intermediates that manifested concurrent expression of haematopoietic stem cell/progenitor and myeloid progenitor cell genes. It also revealed rare metastable intermediates that had collapsed the haematopoietic stem cell/progenitor gene expression programme, instead expressing low levels of the myeloid determinants, Irf8 and Gfi1 (refs 9, 10, 11, 12, 13). Genetic perturbations and chromatin immunoprecipitation followed by sequencing revealed Irf8 and Gfi1 as key components of counteracting myeloid-gene-regulatory networks. Combined loss of these two determinants 'trapped' the metastable intermediate. We propose that mixed-lineage states are obligatory during cell-fate specification, manifest differing frequencies because of their dynamic instability and are dictated by counteracting gene-regulatory networks.

PMID: 27580035 [PubMed - indexed for MEDLINE]

Towards a Semantic Web of Things: A Hybrid Semantic Annotation, Extraction, and Reasoning Framework for Cyber-Physical System.

Sensors (Basel). 2017 Feb 20;17(2):

Authors: Wu Z, Xu Y, Yang Y, Zhang C, Zhu X, Ji Y

Abstract
Web of Things (WoT) facilitates the discovery and interoperability of Internet of Things (IoT) devices in a cyber-physical system (CPS). Moreover, a uniform knowledge representation of physical resources is quite necessary for further composition, collaboration, and decision-making process in CPS. Though several efforts have integrated semantics with WoT, such as knowledge engineering methods based on semantic sensor networks (SSN), it still could not represent the complex relationships between devices when dynamic composition and collaboration occur, and it totally depends on manual construction of a knowledge base with low scalability. In this paper, to addresses these limitations, we propose the semantic Web of Things (SWoT) framework for CPS (SWoT4CPS). SWoT4CPS provides a hybrid solution with both ontological engineering methods by extending SSN and machine learning methods based on an entity linking (EL) model. To testify to the feasibility and performance, we demonstrate the framework by implementing a temperature anomaly diagnosis and automatic control use case in a building automation system. Evaluation results on the EL method show that linking domain knowledge to DBpedia has a relative high accuracy and the time complexity is at a tolerant level. Advantages and disadvantages of SWoT4CPS with future work are also discussed.

PMID: 28230725 [PubMed - in process]

Principles of Systems Biology, No. 14.

Cell Syst. 2017 Feb 22;4(2):140-143

Authors:

Abstract
This month: sage advice from phage to their offspring; systematic analyses of protein quality control, mitochondrial respiration, and woody biomass; a continental-scale experiment; and engineered protein tools galore.

PMID: 28231445 [PubMed - in process]

Biologically Relevant Heterogeneity: Metrics and Practical Insights.

SLAS Discov. 2017 Mar;22(3):213-237

Authors: Gough A, Stern AM, Maier J, Lezon T, Shun TY, Chennubhotla C, Schurdak ME, Haney SA, Taylor DL

Abstract
Heterogeneity is a fundamental property of biological systems at all scales that must be addressed in a wide range of biomedical applications, including basic biomedical research, drug discovery, diagnostics, and the implementation of precision medicine. There are a number of published approaches to characterizing heterogeneity in cells in vitro and in tissue sections. However, there are no generally accepted approaches for the detection and quantitation of heterogeneity that can be applied in a relatively high-throughput workflow. This review and perspective emphasizes the experimental methods that capture multiplexed cell-level data, as well as the need for standard metrics of the spatial, temporal, and population components of heterogeneity. A recommendation is made for the adoption of a set of three heterogeneity indices that can be implemented in any high-throughput workflow to optimize the decision-making process. In addition, a pairwise mutual information method is suggested as an approach to characterizing the spatial features of heterogeneity, especially in tissue-based imaging. Furthermore, metrics for temporal heterogeneity are in the early stages of development. Example studies indicate that the analysis of functional phenotypic heterogeneity can be exploited to guide decisions in the interpretation of biomedical experiments, drug discovery, diagnostics, and the design of optimal therapeutic strategies for individual patients.

PMID: 28231035 [PubMed - in process]

Conceptual Foundations of Systems Biology Explaining Complex Cardiac Diseases.

Healthcare (Basel). 2017 Feb 21;5(1):

Authors: Louridas GE, Lourida KG

Abstract
Systems biology is an important concept that connects molecular biology and genomics with computing science, mathematics and engineering. An endeavor is made in this paper to associate basic conceptual ideas of systems biology with clinical medicine. Complex cardiac diseases are clinical phenotypes generated by integration of genetic, molecular and environmental factors. Basic concepts of systems biology like network construction, modular thinking, biological constraints (downward biological direction) and emergence (upward biological direction) could be applied to clinical medicine. Especially, in the field of cardiology, these concepts can be used to explain complex clinical cardiac phenotypes like chronic heart failure and coronary artery disease. Cardiac diseases are biological complex entities which like other biological phenomena can be explained by a systems biology approach. The above powerful biological tools of systems biology can explain robustness growth and stability during disease process from modulation to phenotype. The purpose of the present review paper is to implement systems biology strategy and incorporate some conceptual issues raised by this approach into the clinical field of complex cardiac diseases. Cardiac disease process and progression can be addressed by the holistic realistic approach of systems biology in order to define in better terms earlier diagnosis and more effective therapy.

PMID: 28230815 [PubMed - in process]

Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks.

Elife. 2017 Feb 23;6:

Authors: Miconi T

Abstract
Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Chaotic recurrent networks, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide learning. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial. Networks endowed with this learning rule can successfully learn nontrivial tasks requiring flexible (context-dependent) associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. The resulting networks replicate complex dynamics previously observed in animal cortex, such as dynamic encoding of task features and selective integration of sensory inputs. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior.

PMID: 28230528 [PubMed - as supplied by publisher]

Research in Reproduction: Challenges, Needs, and Opportunities.

Front Physiol. 2017;8:46

Authors: Ivell R

PMID: 28228729 [PubMed - in process]

Relationship between salivary/pancreatic amylase and body mass index: a systems biology approach.

BMC Med. 2017 Feb 23;15(1):37

Authors: Bonnefond A, Yengo L, Dechaume A, Canouil M, Castelain M, Roger E, Allegaert F, Caiazzo R, Raverdy V, Pigeyre M, Arredouani A, Borys JM, Lévy-Marchal C, Weill J, Roussel R, Balkau B, Marre M, Pattou F, Brousseau T, Froguel P

Abstract
BACKGROUND: Salivary (AMY1) and pancreatic (AMY2) amylases hydrolyze starch. Copy number of AMY1A (encoding AMY1) was reported to be higher in populations with a high-starch diet and reduced in obese people. These results based on quantitative PCR have been challenged recently. We aimed to re-assess the relationship between amylase and adiposity using a systems biology approach.
METHODS: We assessed the association between plasma enzymatic activity of AMY1 or AMY2, and several metabolic traits in almost 4000 French individuals from D.E.S.I.R. longitudinal study. The effect of the number of copies of AMY1A (encoding AMY1) or AMY2A (encoding AMY2) measured through droplet digital PCR was then analyzed on the same parameters in the same study. A Mendelian randomization analysis was also performed. We subsequently assessed the association between AMY1A copy number and obesity risk in two case-control studies (5000 samples in total). Finally, we assessed the association between body mass index (BMI)-related plasma metabolites and AMY1 or AMY2 activity.
RESULTS: We evidenced strong associations between AMY1 or AMY2 activity and lower BMI. However, we found a modest contribution of AMY1A copy number to lower BMI. Mendelian randomization identified a causal negative effect of BMI on AMY1 and AMY2 activities. Yet, we also found a significant negative contribution of AMY1 activity at baseline to the change in BMI during the 9-year follow-up, and a significant contribution of AMY1A copy number to lower obesity risk in children, suggesting a bidirectional relationship between AMY1 activity and adiposity. Metabonomics identified a BMI-independent association between AMY1 activity and lactate, a product of complex carbohydrate fermentation.
CONCLUSIONS: These findings provide new insights into the involvement of amylase in adiposity and starch metabolism.

PMID: 28228143 [PubMed - in process]

Identifying disease network perturbations through regression on gene expression and pathway topology analysis.

Conf Proc IEEE Eng Med Biol Soc. 2016 Aug;2016:5969-5972

Authors: Dimitrakopoulos GN, Balomenos P, Vrahatis AG, Sgarbas K, Bezerianos A, Dimitrakopoulos GN, Balomenos P, Vrahatis AG, Sgarbas K, Bezerianos A, Sgarbas K, Balomenos P, Dimitrakopoulos GN, Bezerianos A, Vrahatis AG

Abstract
In Systems Biology, network-based approaches have been extensively used to effectively study complex diseases. An important challenge is the detection of network perturbations which disrupt regular biological functions as a result of a disease. In this regard, we introduce a network based pathway analysis method which isolates casual interactions with significant regulatory roles within diseased-perturbed pathways. Specifically, we use gene expression data with Random Forest regression models to assess the interactivity strengths of genes within disease-perturbed networks, using KEGG pathway maps as a source of prior-knowledge pertaining to pathway topology. We deliver as output a network with imprinted perturbations corresponding to the biological phenomena arising in a disease-oriented experiment. The efficacy of our approach is demonstrated on a serous papillary ovarian cancer experiment and results highlight the functional roles of high impact interactions and key gene regulators which cause strong perturbations on pathway networks, in accordance with experimentally validated knowledge from recent literature.

PMID: 28227870 [PubMed - in process]

An in silico model of the effects of vitamin D3 on mycobacterium infected macrophage.

Conf Proc IEEE Eng Med Biol Soc. 2016 Aug;2016:1443-1446

Authors: Gough M, May E, Gough M, May E, Gough M, May E

Abstract
Mycobacterium tuberculosis is a global health concern, causing over one million deaths a year. Alveolar macrophages, as the primary host cell of this intracellular bacterium, play an important role in the course of disease. Vitamin D3 is known to have a potent effect on macrophage behavior during infection, modulating the production of pro- and anti-inflammatory cytokines and immune effector molecules. In a vitamin D3 deficient host, the immune systems response to infection is greatly impaired. We used a quantitative systems biology approach to model the intracellular effects of vitamin D3 and compared our simulation output to our in vitro model of mycobacterium infection of macrophages in the presence and absence of Vitamin D3. Our in silico model results agreed with the in vitro assay results of interleukin-10, an anti-inflammatory protein whose production is known to be influenced by vitamin D3. This model will provide a platform for further investigation of the effects of vitamin D3 deficiency on host immune response to infection.

PMID: 28226776 [PubMed - in process]

Representing Documents via Latent Keyphrase Inference.

Proc Int World Wide Web Conf. 2016 Apr;2016:1057-1067

Authors: Liu J, Ren X, Shang J, Cassidy T, Voss CR, Han J

Abstract
Many text mining approaches adopt bag-of-words or n-grams models to represent documents. Looking beyond just the words, i.e., the explicit surface forms, in a document can improve a computer's understanding of text. Being aware of this, researchers have proposed concept-based models that rely on a human-curated knowledge base to incorporate other related concepts in the document representation. But these methods are not desirable when applied to vertical domains (e.g., literature, enterprise, etc.) due to low coverage of in-domain concepts in the general knowledge base and interference from out-of-domain concepts. In this paper, we propose a data-driven model named Latent Keyphrase Inference (LAKI) that represents documents with a vector of closely related domain keyphrases instead of single words or existing concepts in the knowledge base. We show that given a corpus of in-domain documents, topical content units can be learned for each domain keyphrase, which enables a computer to do smart inference to discover latent document keyphrases, going beyond just explicit mentions. Compared with the state-of-art document representation approaches, LAKI fills the gap between bag-of-words and concept-based models by using domain keyphrases as the basic representation unit. It removes dependency on a knowledge base while providing, with keyphrases, readily interpretable representations. When evaluated against 8 other methods on two text mining tasks over two corpora, LAKI outperformed all.

PMID: 28229132 [PubMed - in process]

Optic Disc and Macular Imaging in Blind Eyes from Non-glaucomatous Optic Neuropathy: A Study with Spectral-domain Optical Coherence Tomography.

Neuroophthalmology. 2017 Feb;41(1):1-6

Authors: Hansapinyo L, Cheng AC, Chan NC, Chan CK

Abstract
The purpose of this study was to determine and compare the optic disc and macular thickness measurements using two spectral-domain optical coherence tomography (SD-OCT) instruments in long-standing blind eyes diagnosed with non-glaucomatous optic neuropathies (NGON). A prospective observational case-series design was used. Twelve eyes from 12 NGON patients with no light perception for at least 6 months underwent optic disc and macular imaging with Cirrus HD-OCT and Spectralis OCT. The correlation between the peripapillary retinal nerve fibre layer (PRNFL) and macular ganglion cell layer and inner plexiform layer (GCL+IPL) thicknesses, and between the duration of no light perception (NLP) and PRNFL/GCL+IPL thicknesses were determined using Spearman's correlation analysis. The mean average PRNFL thickness was 55.9 ± 4.8 µm for Cirrus HD-OCT, which was significantly thicker than that measured by Spectralis OCT (31.9 ± 7.4 µm; p < 0.001). The mean central macular thickness on Cirrus HD-OCT was normal, but there was global thinning at the other macular areas. The mean average GCL+IPL thickness on Cirrus HD-OCT was 51.8 ± 5.8 µm. There was a good correlation between average PRNFL thickness and GCL+IPL thickness (r = 0.830, p = 0.002); however, there was no significant correlation between the duration of NLP to the average PRNFL thickness (on either instruments) or GCL+IPL thickness on Cirrus HD-OCT (p > 0.7). These results show that there was residual PRNFL thickness in NGON eyes with NLP, which varied significantly between SD-OCT instruments. The values of the residual PRNFL and GCL+IPL thicknesses in blind eyes (the "floor" effect) may be useful for prognostic purposes for patients with partial optic atrophy.

PMID: 28228830 [PubMed - in process]

Visualizing patient journals by combining vital signs monitoring and natural language processing.

Conf Proc IEEE Eng Med Biol Soc. 2016 Aug;2016:2529-2532

Authors: Vilic A, Petersen JA, Hoppe K, Sorensen HB, Vilic A, Petersen JA, Hoppe K, Sorensen HB, Petersen JA, Vilic A, Hoppe K, Sorensen HB

Abstract
This paper presents a data-driven approach to graphically presenting text-based patient journals while still maintaining all textual information. The system first creates a timeline representation of a patients' physiological condition during an admission, which is assessed by electronically monitoring vital signs and then combining these into Early Warning Scores (EWS). Hereafter, techniques from Natural Language Processing (NLP) are applied on the existing patient journal to extract all entries. Finally, the two methods are combined into an interactive timeline featuring the ability to see drastic changes in the patients' health, and thereby enabling staff to see where in the journal critical events have taken place.

PMID: 28227035 [PubMed - in process]

S2NI: a mobile platform for nutrition monitoring from spoken data.

Conf Proc IEEE Eng Med Biol Soc. 2016 Aug;2016:1991-1994

Authors: Hezarjaribi N, Reynolds CA, Miller DT, Chaytor N, Ghasemzadeh H, Hezarjaribi N, Reynolds CA, Miller DT, Chaytor N, Ghasemzadeh H, Ghasemzadeh H, Reynolds CA, Chaytor N, Hezarjaribi N, Miller DT

Abstract
Diet and physical activity are important lifestyle and behavioral factors in self-management and prevention of many chronic diseases. Mobile sensors such as accelerometers have been used in the past to objectively measure physical activity or detect eating time. Diet monitoring, however, still relies on self-recorded data by end users where individuals use mobile devices for recording nutrition intake by either entering text or taking images. Such approaches have shown low adherence in technology adoption and achieve only moderate accuracy. In this paper, we propose development and validation of Speech-to-Nutrient-Information (S2NI), a comprehensive nutrition monitoring system that combines speech processing, natural language processing, and text mining in a unified platform to extract nutrient information such as calorie intake from spoken data. After converting the voice data to text, we identify food name and portion size information within the text. We then develop a tiered matching algorithm to search the food name in our nutrition database and to accurately compute calorie intake. Due to its pervasive nature and ease of use, S2NI enables users to report their diet routine more frequently and at anytime through their smartphone. We evaluate S2NI using real data collected with 10 participants. Our experimental results show that S2NI achieves 80.6% accuracy in computing calorie intake.

PMID: 28226908 [PubMed - in process]

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Hidden Markov Models in Bioinformatics: SNV Inference from Next Generation Sequence.

Methods Mol Biol. 2017;1552:123-133

Authors: Bian J, Zhou X

Abstract
The rapid development of next generation sequencing (NGS) technology provides a novel avenue for genomic exploration and research. Hidden Markov models (HMMs) have wide applications in pattern recognition as well as Bioinformatics such as transcription factor binding sites and cis-regulatory modules detection. An application of HMM is introduced in this chapter with the in-deep developing of NGS. Single nucleotide variants (SNVs) inferred from NGS are expected to reveal gene mutations in cancer. However, NGS has lower sequence coverage and poor SNV detection capability in the regulatory regions of the genome. A specific HMM is developed for this purpose to infer the genotype for each position on the genome by incorporating the mapping quality of each read and the corresponding base quality on the reads into the emission probability of HMM. The procedure and the implementation of the algorithm is presented in detail for understanding and programming.

PMID: 28224495 [PubMed - in process]

Current Care and Investigational Therapies in Achondroplasia.

Curr Osteoporos Rep. 2017 Feb 21;:

Authors: Unger S, Bonafé L, Gouze E

Abstract
PURPOSE OF REVIEW: The goal of this review is to evaluate the management options for achondroplasia, the most common non-lethal skeletal dysplasia. This disease is characterized by short stature and a variety of complications, some of which can be quite severe.
RECENT FINDINGS: Despite several attempts to standardize care, there is still no widely accepted consensus. This is in part due to absence of concrete data on the incidence of sudden unexplained death in infants with achondroplasia and the best investigation for ascertaining which individuals could benefit from foramen magnum decompression surgery. In this review, we identify the different options of care and management for the various orthopedic, neurologic, and respiratory complications. In parallel, several innovative or drug repositioning therapies are being investigated that would restore bone growth but may also prevent complications. Achondroplasia is the most common non-lethal skeletal dysplasia. It is characterized by short stature and a variety of complications, some of which can be quite severe. Despite several attempts to standardize care, there is still no widely accepted consensus. This is in part due to absence of concrete data on the incidence of sudden unexplained death in infants with achondroplasia and the best investigation for ascertaining which individuals could benefit from foramen magnum decompression surgery. In this review, we identify the different options of care and management for the various orthopedic, neurologic, and respiratory complications. In parallel, several innovative or drug repositioning therapies are being investigated that would restore bone growth but may also prevent complications.

PMID: 28224446 [PubMed - as supplied by publisher]

Nivolumab for previously treated unresectable metastatic anal cancer (NCI9673): a multicentre, single-arm, phase 2 study.

Lancet Oncol. 2017 Feb 17;:

Authors: Morris VK, Salem ME, Nimeiri H, Iqbal S, Singh P, Ciombor K, Polite B, Deming D, Chan E, Wade JL, Xiao L, Bekaii-Saab T, Vence L, Blando J, Mahvash A, Foo WC, Ohaji C, Pasia M, Bland G, Ohinata A, Rogers J, Mehdizadeh A, Banks K, Lanman R, Wolff RA, Streicher H, Allison J, Sharma P, Eng C

Abstract
BACKGROUND: Squamous cell carcinoma of the anal canal (SCCA) is a rare malignancy associated with infection by human papillomavirus (HPV). No consensus treatment approach exists for the treatment of metastatic disease. Because intratumoral HPV oncoproteins upregulate immune checkpoint proteins such as PD-1 to evade immune-mediated cytotoxicity, we did a trial of the anti-PD-1 antibody nivolumab for patients with metastatic SCCA.
METHODS: We did this single-arm, multicentre, phase 2 trial at ten academic centres in the USA. We enrolled patients with treatment-refractory metastatic SCCA, who were given nivolumab every 2 weeks (3 mg/kg). The primary endpoint was response according to Response Evaluation Criteria in Solid Tumors, version 1.1, in the intention-to-treat population. At the time of data cutoff, the study was ongoing, with patients continuing to receive treatment. The study is registered with ClinicalTrials.gov, number NCT02314169.
RESULTS: We screened 39 patients, of whom 37 were enrolled and received at least one dose of nivolumab. Among the 37 patients, nine (24% [95% CI 15-33]) had responses. There were two complete responses and seven partial responses. Grade 3 adverse events were anaemia (n=2), fatigue (n=1), rash (n=1), and hypothyroidism (n=1). No serious adverse events were reported.
INTERPRETATION: To our knowledge, this is the first completed phase 2 trial of immunotherapy for SCCA. Nivolumab is well tolerated and effective as a monotherapy for patients with metastatic SCCA. Immune checkpoint blockade appears to be a promising approach for patients with this orphan disease.
FUNDING: National Cancer Institute/Cancer Therapy Evaluation Program, the HPV and Anal Cancer Foundation, the E B Anal Cancer Fund, The University of Texas MD Anderson Moon Shots Program, and an anonymous philanthropic donor.

PMID: 28223062 [PubMed - as supplied by publisher]

Anal cancer: from an orphan disease to a curable malignancy?

Lancet Oncol. 2017 Feb 17;:

Authors: Cascinu S

PMID: 28223061 [PubMed - as supplied by publisher]