Takshan/R1 huggingface dataset:
Genetic Analysis of 779 Advanced Differentiated and Anaplastic Thyroid Cancers.

Purpose: To define the genetic landscape of advanced differentiated and anaplastic thyroid cancer (ATC) and identify genetic alterations of potential diagnostic, prognostic, and therapeutic significance. Experimental Design: The genetic profiles of 583 advanced differentiated and 196 ATCs generated with targeted next-generation sequencing cancer-associated gene panels MSK-IMPACT and FoundationOne were analyzed. Results: ATC had more genetic alterations per tumor, and pediatric papillary thyroid cancer had fewer genetic alterations per tumor when compared with other thyroid cancer types. DNA mismatch repair deficit and activity of APOBEC cytidine deaminases were identified as mechanisms associated with high mutational burden in a subset of differentiated thyroid cancers and ATCs. Copy number losses and mutations of CDKN2A and CDKN2B, amplification of CCNE1, amplification of receptor tyrosine kinase genes KDR, KIT, and PDGFRA, amplification of immune evasion genes CD274, PDCD1LG2, and JAK2, and activating point mutations in small GTPase RAC1 were associated with ATC. An association of KDR, KIT, and PDGFRA amplification with the sensitivity of thyroid cancer cells to lenvatinib was shown in vitro Three genetically distinct types of ATCs are proposed. Conclusions: This large-scale analysis describes genetic alterations in a cohort of thyroid cancers enriched in advanced cases. Many novel genetic events previously not seen in thyroid cancer were found. Genetic alterations associated with anaplastic transformation were identified. An updated schematic of thyroid cancer genetic evolution is proposed. Clin Cancer Res; 24(13); 3059-68. ©2018 AACR.
Salivary Gland Cancer Patient-Derived Xenografts Enable Characterization of Cancer Stem Cells and New Gene Events Associated with Tumor Progression.

Purpose: Salivary gland cancers (SGC) frequently present with distant metastases many years after diagnosis, suggesting a cancer stem cell (CSC) subpopulation that initiates late recurrences; however, current models are limited both in their availability and suitability to characterize these rare cells. Experimental Design: Patient-derived xenografts (PDX) were generated by engrafting patient tissue onto nude mice from one acinic cell carcinoma (AciCC), four adenoid cystic carcinoma (ACC), and three mucoepidermoid carcinoma (MEC) cases, which were derived from successive relapses from the same MEC patient. Patient and PDX samples were analyzed by RNA-seq and Exome-seq. Sphere formation potential and in vivo tumorigenicity was assessed by sorting for Aldefluor (ALDH) activity and CD44-expressing subpopulations. Results: For successive MEC relapses we found a time-dependent increase in CSCs (ALDH+CD44high), increasing from 0.2% to 4.5% (P=0.033), but more importantly we observed an increase in individual CSC sphere formation and tumorigenic potential. A 50% increase in mutational burden was documented in subsequent MEC tumors, and this was associated with increased expression of tumor-promoting genes (MT1E, LGR5, and LEF1), decreased expression of tumor-suppressor genes (CDKN2B, SIK1, and TP53), and higher expression of CSC-related proteins such as SOX2, MYC, and ALDH1A1. Finally, genomic analyses identified a novel NFIB-MTFR2 fusion in an ACC tumor and confirmed previously reported fusions (NTRK3-ETV6 and MYB-NFIB)Conclusions: Sequential MEC PDX models preserved key patient features and enabled the identification of genetic events putatively contributing to increases in both CSC proportion and intrinsic tumorigenicity, which mirrored the patient's clinical course. Clin Cancer Res; 24(12); 2935-43. ©2018 AACR.
Antitumor activity of the polo-like kinase inhibitor, TAK-960, against preclinical models of colorectal cancer.

BACKGROUND: Polo-like kinase 1 (Plk1) is a serine/threonine kinase that is a key regulator of multiple stages of mitotic progression. Plk1 is upregulated in many tumor types including colorectal cancer (CRC) and portends a poor prognosis. TAK-960 is an ATP-competitive Plk1 inhibitor that has demonstrated efficacy across a broad range of cancer cell lines, including CRC. In this study, we investigated the activity of TAK-960 against a large collection of CRC models including 55 cell lines and 18 patient-derived xenografts. METHODS: Fifty-five CRC cell lines and 18 PDX models were exposed to TAK-960 and evaluated for proliferation (IC50) and Tumor Growth Inhibition Index, respectively. Additionally, 2 KRAS wild type and 2 KRAS mutant PDX models were treated with TAK-960 as single agent or in combination with cetuximab or irinotecan. TAK-960 mechanism of action was elucidated through immunoblotting and cell cycle analysis. RESULTS: CRC cell lines demonstrated a variable anti-proliferative response to TAK-960 with IC50 values ranging from 0.001 to > 0.75 μmol/L. Anti-proliferative effects were sustained after removal of drug. Following TAK-960 treatment a highly variable accumulation of mitotic (indicating cell cycle arrest) and apoptotic markers was observed. Cell cycle analysis demonstrated that TAK-960 treatment induced G2/M arrest and polyploidy. Six out of the eighteen PDX models responded to single agent TAK-960 therapy (TGII< 20). The addition of TAK-960 to standard of care chemotherapy resulted in largely additive antitumor effects. CONCLUSION: TAK-960 is an active anti-proliferative agent against CRC cell lines and PDX models. Collectively, these data suggest that TAK-960 may be of therapeutic benefit alone or in combination with other agents, although future work should focus on the development of predictive biomarkers and hypothesis-driven rational combinations.
Deep learning of mutation-gene-drug relations from the literature.

BACKGROUND: Molecular biomarkers that can predict drug efficacy in cancer patients are crucial components for the advancement of precision medicine. However, identifying these molecular biomarkers remains a laborious and challenging task. Next-generation sequencing of patients and preclinical models have increasingly led to the identification of novel gene-mutation-drug relations, and these results have been reported and published in the scientific literature. RESULTS: Here, we present two new computational methods that utilize all the PubMed articles as domain specific background knowledge to assist in the extraction and curation of gene-mutation-drug relations from the literature. The first method uses the Biomedical Entity Search Tool (BEST) scoring results as some of the features to train the machine learning classifiers. The second method uses not only the BEST scoring results, but also word vectors in a deep convolutional neural network model that are constructed from and trained on numerous documents such as PubMed abstracts and Google News articles. Using the features obtained from both the BEST search engine scores and word vectors, we extract mutation-gene and mutation-drug relations from the literature using machine learning classifiers such as random forest and deep convolutional neural networks. Our methods achieved better results compared with the state-of-the-art methods. We used our proposed features in a simple machine learning model, and obtained F1-scores of 0.96 and 0.82 for mutation-gene and mutation-drug relation classification, respectively. We also developed a deep learning classification model using convolutional neural networks, BEST scores, and the word embeddings that are pre-trained on PubMed or Google News data. Using deep learning, the classification accuracy improved, and F1-scores of 0.96 and 0.86 were obtained for the mutation-gene and mutation-drug relations, respectively. CONCLUSION: We believe that our computational methods described in this research could be used as an important tool in identifying molecular biomarkers that predict drug responses in cancer patients. We also built a database of these mutation-gene-drug relations that were extracted from all the PubMed abstracts. We believe that our database can prove to be a valuable resource for precision medicine researchers.
Professor Decker replies.

Replies to comments by Allen Frances (see record 2016-05673-005) and Ronald W. Pies (see record 2016-05673-006) on the article by Hannah Decker (see record 2016-05673-004). Frances' sophisticated fine-tuning of Decker's dichotomies is most welcome. Nevertheless, the impact of reductionism on an era does persist. As for Pies, Decker wishes she could share Pies' hopes for the future of an integrated psychiatry, but we are in a biological period that shows little evidence of becoming inclusive of the psychological and the social. ( PsycINFO Database Record
Analysis of rural health centres preparedness for the management of diabetic patients in Malawi.

OBJECTIVE: There is limited data on the quality of primary care management for diabetes mellitus across Africa. The study was aimed at assessing the availability of basic supplies for the rapid diagnosis, treatment and management of diabetes in Malawian rural health facilities. This cross-sectional study was conducted in 55 public and private health centers from 19 districts using a structured questionnaire and checklist to interview the pharmacy personnel or officer in-charge of the health centers. We focused on availability of information, diagnosis and treatment materials for diabetes. RESULTS: Of the 55 health facilities surveyed, 21, 23 and 11 were located in the central, southern and northern regions of Malawi, respectively. Overall, 38% (21/55) of the health centres had glucometers, while 24% (13/55) had urine glucose dipsticks. Only 4% (2/55) of the health centres had recommended first-line medicines for treatment of type 1 and type 2 diabetes. No health centre had diabetes patient records and information, education and communication materials. Most rural health centers in Malawi lack basic health commodities for the screening, diagnosis and treatment of diabetes and this impedes on their effective management of growing diabetes burden. Therefore, health care systems need to adequately equip primary care facilities.
Finding new structural and sequence attributes to predict possible disease association of single amino acid polymorphism (SAP).

MOTIVATION: The rapid accumulation of single amino acid polymorphisms (SAPs), also known as non-synonymous single nucleotide polymorphisms (nsSNPs), brings the opportunities and needs to understand and predict their disease association. Currently published attributes are limited, the detailed mechanisms governing the disease association of a SAP remain unclear and thus, further investigation of new attributes and improvement of the prediction are desired. RESULTS: A SAP dataset was compiled from the Swiss-Prot variant pages. We extracted and demonstrated the effectiveness of several new biologically informative attributes including the structural neighbor profiles that describe the SAP's microenvironment, nearby functional sites that measure the structure-based and sequence-based distances between the SAP site and its nearby functional sites, aggregation properties that measure the likelihood of protein aggregation and disordered regions that consider whether the SAP is located in structurally disordered regions. The new attributes provided insights into the mechanisms of the disease association of SAPs. We built a support vector machines (SVMs) classifier employing a carefully selected set of new and previously published attributes. Through a strict protein-level 5-fold cross-validation, we attained an overall accuracy of 82.61%, and an MCC of 0.60. Moreover, a web server was developed to provide a user-friendly interface for biologists. AVAILABILITY: The web server is available at http://sapred.cbi.pku.edu.cn/
Kotai Antibody Builder: automated high-resolution structural modeling of antibodies.

MOTIVATION: Kotai Antibody Builder is a Web service for tertiary structural modeling of antibody variable regions. It consists of three main steps: hybrid template selection by sequence alignment and canonical rules, 3D rendering of alignments and CDR-H3 loop modeling. For the last step, in addition to rule-based heuristics used to build the initial model, a refinement option is available that uses fragment assembly followed by knowledge-based scoring. Using targets from the Second Antibody Modeling Assessment, we demonstrate that Kotai Antibody Builder generates models with an overall accuracy equal to that of the best-performing semi-automated predictors using expert knowledge. AVAILABILITY AND IMPLEMENTATION: Kotai Antibody Builder is available at http://kotaiab.org CONTACT: standley@ifrec.osaka-u.ac.jp.
A Pilot Study of Biomedical Text Comprehension using an Attention-Based Deep Neural Reader: Design and Experimental Analysis.

BACKGROUND: With the development of artificial intelligence (AI) technology centered on deep-learning, the computer has evolved to a point where it can read a given text and answer a question based on the context of the text. Such a specific task is known as the task of machine comprehension. Existing machine comprehension tasks mostly use datasets of general texts, such as news articles or elementary school-level storybooks. However, no attempt has been made to determine whether an up-to-date deep learning-based machine comprehension model can also process scientific literature containing expert-level knowledge, especially in the biomedical domain. OBJECTIVE: This study aims to investigate whether a machine comprehension model can process biomedical articles as well as general texts. Since there is no dataset for the biomedical literature comprehension task, our work includes generating a large-scale question answering dataset using PubMed and manually evaluating the generated dataset. METHODS: We present an attention-based deep neural model tailored to the biomedical domain. To further enhance the performance of our model, we used a pretrained word vector and biomedical entity type embedding. We also developed an ensemble method of combining the results of several independent models to reduce the variance of the answers from the models. RESULTS: The experimental results showed that our proposed deep neural network model outperformed the baseline model by more than 7% on the new dataset. We also evaluated human performance on the new dataset. The human evaluation result showed that our deep neural model outperformed humans in comprehension by 22% on average. CONCLUSIONS: In this work, we introduced a new task of machine comprehension in the biomedical domain using a deep neural model. Since there was no large-scale dataset for training deep neural models in the biomedical domain, we created the new cloze-style datasets Biomedical Knowledge Comprehension Title (BMKC_T) and Biomedical Knowledge Comprehension Last Sentence (BMKC_LS) (together referred to as BioMedical Knowledge Comprehension) using the PubMed corpus. The experimental results showed that the performance of our model is much higher than that of humans. We observed that our model performed consistently better regardless of the degree of difficulty of a text, whereas humans have difficulty when performing biomedical literature comprehension tasks that require expert level knowledge.
ALK Inhibitor Response in Melanomas Expressing EML4-ALK Fusions and Alternate ALK Isoforms.

Oncogenic ALK fusions occur in several types of cancer and can be effectively treated with ALK inhibitors; however, ALK fusions and treatment response have not been characterized in malignant melanomas. Recently, a novel isoform of ALK (ALKATI ) was reported in 11% of melanomas but the response of melanomas expressing ALKATI to ALK inhibition has not been well characterized. We analyzed 45 melanoma patient-derived xenograft models for ALK mRNA and protein expression. ALK expression was identified in 11 of 45 (24.4%) melanomas. Ten melanomas express wild-type (wt) ALK and/or ALKATI and one mucosal melanoma expresses multiple novel EML4-ALK fusion variants. Melanoma cells expressing different ALK variants were tested for response to ALK inhibitors. Whereas the melanoma expressing EML4-ALK were sensitive to ALK inhibitors in vitro and in vivo, the melanomas expressing wt ALK or ALKATI were not sensitive to ALK inhibitors. In addition, a patient with mucosal melanoma expressing ALKATI was treated with an ALK/ROS1/TRK inhibitor (entrectinib) on a phase I trial but did not respond. Our results demonstrate ALK fusions occur in malignant melanomas and respond to targeted therapy, whereas melanomas expressing ALKATI do not respond to ALK inhibitors. Targeting ALK fusions is an effective therapeutic option for a subset of melanoma patients, but additional clinical studies are needed to determine the efficacy of targeted therapies in melanomas expressing wt ALK or ALKATIMol Cancer Ther; 17(1); 222-31. ©2017 AACR.
FGFR1 Expression Levels Predict BGJ398 Sensitivity of FGFR1-Dependent Head and Neck Squamous Cell Cancers.

PURPOSE: FGFR1 copy-number gain (CNG) occurs in head and neck squamous cell cancers (HNSCC) and is used for patient selection in FGFR-specific inhibitor clinical trials. This study explores FGFR1 mRNA and protein levels in HNSCC cell lines, primary tumors, and patient-derived xenografts (PDX) as predictors of sensitivity to the FGFR inhibitor, NVP-BGJ398. EXPERIMENTAL DESIGN: FGFR1 status, expression levels, and BGJ398 sensitive growth were measured in 12 HNSCC cell lines. Primary HNSCCs (n = 353) were assessed for FGFR1 CNG and mRNA levels, and HNSCC TCGA data were interrogated as an independent sample set. HNSCC PDXs (n = 39) were submitted to FGFR1 copy-number detection and mRNA assays to identify putative FGFR1-dependent tumors. RESULTS: Cell line sensitivity to BGJ398 is associated with FGFR1 mRNA and protein levels, not FGFR1 CNG. Thirty-one percent of primary HNSCC tumors expressed FGFR1 mRNA, 18% exhibited FGFR1 CNG, 35% of amplified tumors were also positive for FGFR1 mRNA. This relationship was confirmed with the TCGA dataset. Using high FGFR1 mRNA for selection, 2 HNSCC PDXs were identified, one of which also exhibited FGFR1 CNG. The nonamplified tumor with high mRNA levels exhibited in vivo sensitivity to BGJ398. CONCLUSIONS: FGFR1 expression associates with BGJ398 sensitivity in HNSCC cell lines and predicts tyrosine kinase inhibitor sensitivity in PDXs. Our results support FGFR1 mRNA or protein expression, rather than FGFR1 CNG as a predictive biomarker for the response to FGFR inhibitors in a subset of patients suffering from HNSCC.
Antitumor activity of a potent MEK inhibitor, TAK-733, against colorectal cancer cell lines and patient derived xenografts.

BACKGROUND: CRC is a significant cause of cancer mortality, and new therapies are needed for patients with advanced disease. TAK-733 is a highly potent and selective investigational novel MEK allosteric site inhibitor. MATERIALS AND METHODS: In a preclinical study of TAK-733, a panel of CRC cell lines were exposed to varying concentrations of the agent for 72 hours followed by a sulforhodamine B assay. Twenty patient-derived colorectal cancer xenografts were then treated with TAK-733 in vivo. Tumor growth inhibition index (TGII) was assessed to evaluate the sensitivity of the CRC explants to TAK-733 while linear regression was utilized to investigate the predictive effects of genotype on the TGII of explants. RESULTS: Fifty-four CRC cell lines were exposed to TAK-733, while 42 cell lines were deemed sensitive across a broad range of mutations. Eighty-two percent of the cell lines within the sensitive subset were BRAF or KRAS/NRAS mutant, whereas 80% of the cell lines within the sensitive subset were PIK3CA WT. Twenty patient-derived human tumor CRC explants were then treated with TAK-733. In total, 15 primary human tumor explants were found to be sensitive to TAK-733 (TGII ≤ 20%), including 9 primary human tumor explants that exhibited tumor regression (TGII > 100%). Explants with a BRAF/KRAS/NRAS mutant and PIK3CA wild-type genotype demonstrated increased sensitivity to TAK-733 with a median TGII of -6%. MEK-response gene signatures also correlated with responsiveness to TAK-733 in KRAS-mutant CRC. CONCLUSIONS: The MEK inhibitor TAK-733 demonstrated robust antitumor activity against CRC cell lines and patient-derived tumor explants. While the preclinical activity observed in this study was considerable, single-agent efficacy in the clinic has been limited in CRC, supporting the use of these models in an iterative manner to elucidate resistance mechanisms that can guide rational combination strategies.
Identifying kinase dependency in cancer cells by integrating high-throughput drug screening and kinase inhibition data.

MOTIVATION: Targeted kinase inhibitors have dramatically improved cancer treatment, but kinase dependency for an individual patient or cancer cell can be challenging to predict. Kinase dependency does not always correspond with gene expression and mutation status. High-throughput drug screens are powerful tools for determining kinase dependency, but drug polypharmacology can make results difficult to interpret. RESULTS: We developed Kinase Addiction Ranker (KAR), an algorithm that integrates high-throughput drug screening data, comprehensive kinase inhibition data and gene expression profiles to identify kinase dependency in cancer cells. We applied KAR to predict kinase dependency of 21 lung cancer cell lines and 151 leukemia patient samples using published datasets. We experimentally validated KAR predictions of FGFR and MTOR dependence in lung cancer cell line H1581, showing synergistic reduction in proliferation after combining ponatinib and AZD8055. AVAILABILITY AND IMPLEMENTATION: KAR can be downloaded as a Python function or a MATLAB script along with example inputs and outputs at: http://tanlab.ucdenver.edu/KAR/. CONTACT: aikchoon.tan@ucdenver.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
An integrated bioinformatics analysis to dissect kinase dependency in triple negative breast cancer.

BACKGROUND: Triple-Negative Breast Cancer (TNBC) is an aggressive disease with a poor prognosis. Clinically, TNBC patients have limited treatment options besides chemotherapy. The goal of this study was to determine the kinase dependency in TNBC cell lines and to predict compounds that could inhibit these kinases using integrative bioinformatics analysis. RESULTS: We integrated publicly available gene expression data, high-throughput pharmacological profiling data, and quantitative in vitro kinase binding data to determine the kinase dependency in 12 TNBC cell lines. We employed Kinase Addiction Ranker (KAR), a novel bioinformatics approach, which integrated these data sources to dissect kinase dependency in TNBC cell lines. We then used the kinase dependency predicted by KAR for each TNBC cell line to query K-Map for compounds targeting these kinases. Wevalidated our predictions using published and new experimental data. CONCLUSIONS: In summary, we implemented an integrative bioinformatics analysis that determines kinase dependency in TNBC. Our analysis revealed candidate kinases as potential targets in TNBC for further pharmacological and biological studies.