malignancy types

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

(G A). Variation.InitialState Variation.AlteredState

Data Gathering

Data Classification


Conceptual boundaries Sub-classification of entities


Conceptual boundaries Sub-classification of entities Levels of specificity


Levels of specificity

Gene Entity

GeneProtein kinase (Super family)MAPK (Gene family)MAPK10

Malignancy type Entity

Cancer/TumorCarcinomaLung carcinomaSquamous cell lung carcinoma

Conceptual boundaries Sub-classification of entities Levels of specificity Conceptual overlaps between entities

Symptom: Subjective or objective evidence of disease.

Disease: A specific pathological process with a characteristic set of symptoms.

Arrhythmia vs. Long QT Syndrome


Conceptual boundaries Sub-classification of entities Levels of specificity Conceptual overlaps between entities Domain-specific clarification

Gene entity clarification:

Regulation element -- promoters (eg. TATA box)



Syntactical boundaries Text boundary issues

The K-ras gene……



Syntactical boundaries Text boundary issues (The K-ras gene) Pronoun co-reference (this gene, it, they)



Syntactical boundaries Text boundary issues (The K-ras gene) Co-reference (this gene, it, they) Structural overlap -- entity within entity (same entity type)

MAP kinase kinase kinase



Syntactical boundaries Text boundary issues (The K-ras gene) Pronoun co-reference (this gene, it, they) Structural overlap -- entity within entity (different entity type)

Squamous cell lung carcinoma



Syntactical boundaries Text boundary issues (The K-ras gene) Co-reference (this gene, it, they) Structural overlap -- entity within entity Discontinuous mentions (N- and K-ras )


Ambiguity within an entity type

Semantic ambiguity challenges

CATcatalaseglycine-N-acyltransferase (GLYAT)

Ambiguity within an entity type Ambiguity between entity types


CATGene entityOrganism

Ambiguity within entity types Ambiguity between entity types Gene entity ambiguity

3% of human genes share aliases Huge ambiguity of genes between species (mouse and human) Gene.general, Gene.gene/RNA, Gene.protein


Gene Variation Malignancy Type

GeneRNAProtein

TypeLocationInitial StateAltered State

SiteHistologyClinical StageDifferentiation StatusHeredity Status Developmental StatePhysical MeasurementCellular Process Expressional StatusEnvironmental FactorClinical TreatmentClinical OutcomeResearch SystemResearch MethodologyDrug Effect



http://www.ldc.upenn.edu/mamandel/itre/annotators/onco/definitions.html

Manual Annotation Corpus Release

Jena University Language & Information Engineering Lab: http://www.julielab.de

K Bretonnel Cohen and Lawrence Hunter, BMC Bioinformatics. 2006; 7(Suppl 3): S5.



Summary -- Entity Definition

Developed iterative process for biomedical entity definition;

Defined genomic and phenotypic entities with distinct conceptual and syntactical boundaries in genomic variation of malignancy;

Constructed a manually annotated corpus with 1442 oncology-focused articles.

Named Entity Extractors

Mycn is amplified in neuroblastoma.

Gene Variation type Malignancy type

Automated Extractor Development

Training and testing data 1442 cancer-focused MEDLINE abstracts 70% for training, 30% for testing



Machine-learning algorithm Conditional Random Field (CRF) Sets of Features

Lung cancer is the … of carcinoma deaths worldwide.

MType Mtype



Machine-learning algorithm Conditional Random Fields (CRFs) Sets of Features

Orthographic features (capitalization, punctuation, digit/number/alpha-numeric/symbol);

Character-N-grams (N=2,3,4); Prefix/Suffix: (*oma); Offsite conjuction (3 consecutive word tokens); Domain-specific lexicon (NCI neoplasm list).

Extractor Performance

• Precision: (true positives)/(true positives + false positives) • Recall: (true positives)/(true positives + false negatives)

Entity Precision RecallGene 0.864 0.787

Variation Type 0.8556 0.7990Location 0.8695 0.7722

State-Initial 0.8430 0.8286State-Sub 0.8035 0.7809Overall 0.8541 0.7870

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

ontology Case-insensitive exact string matching applied 85 malignancy type mentions (42.1%) recognized correctly

The malignancy type extractor 190 malignancy type mentions (94.1%) recognized correctly Included all the baseline-identified mentions

The Types of Mentions NOT Identified by Pattern Matching

Mention Types Mention Examples NCI List

Acronyms NB Neuroblastoma

Lexical variants (plural forms) Renal cell carcinomas Renal cell carcinoma

Polymorphic expressionsLung cancer

(tumor/tumour)Lung neoplasm

higher levels of specificity Solid tumor <More specific tumor>

Tumor names with modifiers Translocation carcinoma Carcinoma

Normalization

abdominal neoplasmabdomen neoplasmAbdominal tumourAbdominal neoplasm NOSAbdominal tumorAbdominal NeoplasmsAbdominal NeoplasmNeoplasm, AbdominalNeoplasms, AbdominalNeoplasm of abdomenTumour of abdomenTumor of abdomenABDOMEN TUMOR

Unique Identifier

Normalization

abdominal neoplasmabdomen neoplasmAbdominal tumourAbdominal neoplasm NOSAbdominal tumorAbdominal NeoplasmsAbdominal NeoplasmNeoplasm, AbdominalNeoplasms, AbdominalNeoplasm of abdomenTumour of abdomenTumor of abdomenABDOMEN TUMOR

UMLS metathesaurus Concept Unique Identifier (CUI)19,397 CUIs with 92,414 synonyms

C0000735

Normalization – Computational Procedures

Rule-based algorithm Applied to both entity mentions and vocabulary terms (UMLS

metathesaurus) Case insensitivity (carcinoma/Carcinoma) Space/punctuation removal (lung-cancer/lungcancer) Stemming (neuroblastoma/neuroblastomas)

Applied to mentions only First/last character removal (additional space/punctuation) First/last word removal (translocation lung carcinoma)

Evaluate the accuracy and the priority of the rules 1,000 randomly selected entity mentions Choose the best performed rule combination and sequences

MEDLINE Data Processing

Tagging MEDLINE pre-2006 abstracts 15,433,668 MEDLINE abstracts 9,153,340 redundant and 580,002 distinct malignancy type

mentions ~60% extracted mentions matched to UMLS CUIs 1,642 CPU-hours (2.44 days on a 28-CPU cluster)

Infrastructure construction (postgreSQL Database)

Gene-Malignancy-Evidence Matrix

A1BG Adenocarcinoma 1634938A1BG Adenocarcinoma 2292657A1BG Adenocarcinoma 3566173…… …… ……ABCC1 Lung Carcinoma 11156254ABCC1 Lung Carcinoma 11159731ABCC1 Lung Carcinoma 11172691…… …… ……B3GAT1 Breast Neoplasm 6870377B3GAT1 Breast Neoplasm 9129046B3GAT1 Breast Neoplasm 9701020…… …… ……ERVK6 Stage IV Melanoma of the Skin 9056412ERVK6 Stage IV Melanoma of the Skin 9620301ERVK6 Stage IV Melanoma of the Skin 9640365…… …… ……NFKB1 Colon Carcinoma 12842827NFKB1 Colon Carcinoma 12901803NFKB1 Colon Carcinoma 12934082…… …… ……VIM Gastrointestinal Stromal Tumor 12375611VIM Gastrointestinal Stromal Tumor 12657940VIM Gastrointestinal Stromal Tumor 12673425…… …… ……

Gene Malignancy Evidence

21,493,687 normalized gene symbols (16,875 unique)21,493,687 normalized gene symbols (16,875 unique)




5,398,954 normalized malignancy types (4,166 CUIs)5,398,954 normalized malignancy types (4,166 CUIs)




3,100,773 distinct Gene-Malignancy-Evidence relations3,100,773 distinct Gene-Malignancy-Evidence relations

Ranked by Frequency

6500

6550

6600

6650

6700

6750

6800

6850

Gene-Malignancy Relaions

TP53-CarcinomaESR1-Breast CarcinomaESR1-Breast Neoplasm

Summary -- Extractor Development and Application

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


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


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


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


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


Microarray Expression Data Analysis

Gene Set 1: NTRK1, NTRK2

468


283

symbol

NALP1

RALY

CDC2L6

RASGRP2

KCNK3

RPS6KA1

SEC61A2

VGF

CACNA1C

TBX3

THRA

B4GALT5

NRXN2

GNB5

RAI2

FRS3


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


NTRK1/NTRK2 Preferentially Associated Genes in Literature

LitSet 1: NTRK1 Associated Genes

LitSet 2: NTRK2 Associated Genes

157

514


Microarray Expression Data Analysis NTRK1/NTRK2 Associated Genes in Literature

Gene Set 1: NTRK1, NTRK2 NTRK1 Associated Genes

NTRK2 Associated Genes

468

157

514


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.

Ingenuity Pathway Analysis Tool Kit

Overall Group(N=10,459)

Array Group(N= 751)

Text Group(N= 550)

Overlap Group(N=22)

CD 1979, 18.9% 153, 20.4% 309, 56.2% 12, 54.5%CGP 2251, 21.5% 154, 20.5% 304, 55.3% 3, 13.6%CCSI 1492, 14.3% 57, 9.98% 186, 33.8% 7, 31.8%CM 1068, 10.2% 85, 11.3% 219, 39.8% 7, 31.8%

NSDF 897, 8.58% 108, 19.6% 148, 26.9% 9, 40.9%CAO 755, 7.22% 103, 13.7% 115, 20.9% 11, 50%

Hypergeometric Test P-values

Array Group Text Group Overlap GroupCD 0.152 0.0166 <0.001

CGP 0.746 0.0216 0.728CCSI 0.999 0.0227 0.009CM 0.146 0.0109 0.001

NSDF <0.001 <0.001 <0.001CAO <0.001 <0.001 <0.001

Hypergeometric Test between Array and Overlap Groups

Overlap GroupCD <0.001

CGP 0.728CCSI 0.00940CM 0.0124

NSDF <0.001CAO 0.0117

Multiple-test corrected P-values (Bonferroni step-down)

RT-PCR Experimental Validation

11 out of 22 genes selected for RT-PCR validation:

Symbol Description

CAMK4 calcium/calmodulin-dependent protein kinase IVVSNL1 visinin-like 1TBC1D8 TBC1 domain family, member 8 (with GRAM domain)RPS6KA1 ribosomal protein S6 kinase, 90kDa, polypeptide 1EFNB3 ephrin-B3B3GAT1 beta-1,3-glucuronyltransferase 1 (glucuronosyltransferase P)GNAS GNAS complex locusNEFH neurofilament, heavy polypeptide 200kDaINA internexin neuronal intermediate filament protein, alphaNEFL neurofilament, light polypeptide 68kDaTYRO3 TYRO3 protein tyrosine kinase


EFNB3

0

0.5

1

1.5

2

2.5

TrkA

TrkB

0hr 1.5hr 4hr 12hr

EFNB3 Discussion

EFNB3 (ephrin-B3) belongs to a family of ligands that binds to Eph family receptor tyrosine kinases

Implicated in axon guidance and vertebrate nervous system development

Exhibited growth-suppressive activity against NB cells in vitro

Preferentially and significantly associated with low tumor stage and favorable clinical outcomes in neuroblastoma primary tumors


TYRO3

0

0.2

0.4

0.6

0.8

1

1.2

1.4

TrkA

TrkB

0hr 1.5hr 4hr 12hr

TYRO3 Discussion

Trans-memberane receptor tyrosine kinase activated by GAS6

GAS6 has showed to promote human fetal oligodendrocyte survival without proliferation

GAS6 may also contribute to cell adhesion and immune responses

Further study of GAS6/TYRO3 signaling is needed

Summary -- NB Application

Prioritized array-determined differentially expressed genes by integrating text mining results

Literature-based method showed its capability of enriching functionally relevant genes by pathway analysis

RT-PCR experiments further validated the inferential power of text mining

Conclusion

Created a process for iteratively and precisely defining biomedical semantic types directly from literature

Developed automated entity extractors across genomic and phenotypic domains in malignancy with satisfactory accuracy rates

Applied this computational entity recognition and normalization process to all MEDLINE abstracts

Integrated text mining results with neuroblastoma experimental data to hypothesize candidate genes differentiating neuroblastoma subtypes

Future Directions

Increasing dimensions of Information matrix

Context-based normalization algorithm

Relation extraction with deeper semantic parsing

Acknowledgement

Penn BioIE Team:

Dr. Mark Liberman Dr. Mark Mandel Dr. Ryan McDonald Dr. Fernando Pereira Annotator team

Brodeur Lab:

Dr. Garrett Brodeur Ms. Ruth Ho Dr. Jane Minturn

CHOP NAP Core:

Dr. Eric Rappaport

CHOP Bioinformatics Core:

Dr. Xiaowu Gai Dr. Jim Zhang

White Lab:

Steve Carroll Hawren Fang Kevin Murphy

malignancy types

Documents

entity different entity

biomedical entitiesambiguity

type variation

initialstate variation

ras geneco

ras genepronoun coreference

specific pathological

theystructural overlap