Malignancy Types Gene Variation Clinical Stage Genomic Information Phenomic Information Developmental State Heredity Status Histology Site Differentiation Status Molecular Entity Types Phenotypic Entity Types Genomic Variation associated with Malignancy
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Malignancy Types Gene Variation Clinical Stage Genomic InformationPhenomic Information Developmental State Heredity Status Histology Site Differentiation.
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Malignancy Types
Gene
Variation
Clinical Stage
Genomic Information Phenomic Information
Developmental State
Heredity Status
Histology
Site
Differentiation Status
Molecular Entity Types Phenotypic Entity Types
Genomic Variation associated with Malignancy
Flow Chart for Manual Annotation Process
Biomedical Literature
Entity Definitions
Annotators (Experts)Manually Annotated Texts
Machine-learning Algorithm
Annotation Ambiguity
Auto-Annotated Texts
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
A point mutation was found at codon 12 (G A).
Variation
Defining biomedical entities
A point mutation was found at codon 12 (G A).
Variation
A point mutation was found at codon 12 Variation.Type Variation.Location
Malignancy type 0.8456 0.8218Clinical Stage 0.8493 0.6492
Site 0.8005 0.6555Histology 0.8310 0.7774
Developmental State 0.8438 0.7500
Normal textMalignancies
PMID: 15316311Morphologic and molecular characterization of renal cell carcinoma in children and young adults.A new WHO classification of renal cell carcinoma has been introduced in 2004. This classificationincludes the recently described renal cell carcinomas with the ASPL-TFE3 gene fusion and carcinomaswith a PRCC -TFE3 gene fusion. Collectively, these tumors have been termed Xp11.2 or TFE3translocation carcinomas, which primarily occur in children and young adults. To further study thecharacteristics of renal cell carcinoma in young patients and to determine their genetic background, 41renal cell carcinomas of patients younger than 22 years were morphologically and geneticallycharacterized. Loss of heterozygosity analysis of the von Hippel - Lindau gene region and screening forVHL gene mutations by direct sequencing were performed in 20 tumors. TFE3 protein overexpression,which correlates with the presence of a TFE3 gene fusion, was assessed by immunohistochemistry.Applying the new WHO classification for renal cell carcinoma, there were 6 clear cell (15 %), 9 papillary(22 %), 2 chromophobe, and 2 collecting duct carcinomas. Eight carcinomas showed translocationcarcinoma morphology (20 %). One carcinoma occurred 4 years after a neuroblastoma. Thirteen tumorscould not be assigned to types specified by the new WHO classification: 10 were grouped as unclassified(24 %), including a unique renal cell carcinoma with prominently vacuolated cytoplasm and WT1expression. Three carcinomas occurred in combination with nephroblastoma. Molecular analysis revealeddeletions at 3p25-26 in one translocation carcinoma, one chromophobe renal cell carcinoma, and onepapillary renal cell carcinoma. There were no VHL mutations. Nuclear TFE3 overexpression was detectedin 6 renal cell carcinomas, all of which showed areas with voluminous cytoplasm and foci of papillaryarchitecture, consistent with a translocation carcinoma phenotype. The large proportion of TFE3 "translocation " carcinomas and "unclassified " carcinomas in the first two decades of life demonstrates thatrenal cell carcinomas in young patients contain genetically and phenotypically distinct tumors with furtherpotential for novel renal cell carcinoma subtypes. The far lower frequency of clear cell carcinomas andVHL alterations compared with adults suggests that renal cell carcinomas in young patients have a uniquegenetic background.
CRF-based Extractor vs. Pattern Matcher
The testing corpus 39 manually annotated MEDLINE abstracts selected 202 malignancy type mentions identified
The pattern matching system 5,555 malignancy types extracted from NCI neoplasm
Developed well-performed automated entity extractors across genomic and phenotypic domains;
Constructed rule-based computational procedure for normalization;
Applied the extractors and normalizers to all MEDLINE abstracts;
Imported the extracted information into a relational database.
Text Mining Applications -- Hypothesizing NB Candidate Genes
Text Mining Applications -- Hypothesizing NB Candidate Genes
Two distinct subtypes of neuroblastoma
Developmental State
BiologyClinical
StageClinical Outcome
Trk Expression
NB Subtype A Younger age DifferentiationLower Stage
FavorableHigh level
expression of NTRK1
NB Subtype B Older age ProliferationHigher Stage
UnfavorableHigh level
expression of NTRK2
Text Mining Applications -- Hypothesizing NB Candidate Genes
Two distinct subtypes of neuroblastoma• Distinct clinical behaviors (favorable vs. unfavorable)• NGF/NTRK1 (TrkA) vs. BDNF/NTRK2 (TrkB) signaling
pathways
Trk Signaling Angiogenesis DifferentiationDrug
ResistanceTumorigenicity
NB Subtype A NTRK1/NGF Inhibits Yes Inhibits Inhibits
NB Subtype B NTRK2/BDNF Promotes No Promotes Promotes
Text Mining Applications -- Hypothesizing NB Candidate Genes
Two distinct subtypes of neuroblastoma• Distinct clinical behaviors (favorable vs. unfavorable)• NGF/NTRK1 (TrkA) vs. BDNF/NTRK2 (TrkB) signaling
pathways• Determine the early response genes differentiating the two
pathways• More precise prognosis and clinical intervention
Text Mining Applications -- Hypothesizing NB Candidate Genes
SH-SY5Y
NTRK1
SH-SY5Y
NTRK2
NGF BDNF
RNA extraction at 0,1.5hrs,4hrs and 12hrs
Affymetrix U133A Expression Array
(RMAexpress normalization, SAM test)
751 differentially expressed genes
Text Mining Applications -- Hypothesizing NB Candidate Genes
Microarray Expression Data Analysis
Gene Set 1: NTRK1, NTRK2
468
Gene Set 2: NTRK2, NTRK1
283
symbol
NALP1
RALY
CDC2L6
RASGRP2
KCNK3
RPS6KA1
SEC61A2
VGF
CACNA1C
TBX3
THRA
B4GALT5
NRXN2
GNB5
RAI2
FRS3
Text Mining Applications -- Hypothesizing NB Candidate Genes
Differentially represented genes in biomedical literature
• NTRK1 vs. NTRK2 pathway differentially associated genes/proteins based on literature
• Preferential association determined by co-occurrence with either receptor 5 times or more over the other
• Assumption: the co-occurrence frequency is reflecting functional correlation
Text Mining Applications -- Hypothesizing NB Candidate Genes
NTRK1/NTRK2 Preferentially Associated Genes in Literature
LitSet 1: NTRK1 Associated Genes
LitSet 2: NTRK2 Associated Genes
157
514
Text Mining Applications -- Hypothesizing NB Candidate Genes
Microarray Expression Data Analysis NTRK1/NTRK2 Associated Genes in Literature
Gene Set 1: NTRK1, NTRK2 NTRK1 Associated Genes
NTRK2 Associated Genes
468
157
514
Gene Set 2: NTRK2, NTRK1
283
18
4
Functional Pathway Analysis
Determine gene enrichment score for six selected functional pathways:
CD -- Cell Death;CGP -- Cell Growth and Proliferation; CCSI -- Cell-to-Cell Signaling and Interaction; CM -- Cell MorphologyNSDF -- Nervous System Development and Function;CAO -- Cellular Assembly and Organization.
Functional Pathway Analysis
Six selected pathways:
CD -- Cell Death; CM -- Cell Morphology; CGP -- Cell Growth and Proliferation; NSDF -- Nervous System Development and Function; CCSI -- Cell-to-Cell Signaling and Interaction; CAO -- Cellular Assembly and Organization.