=Paper=
{{Paper
|id=Vol-1747/BT204_ICBO2016
|storemode=property
|title=CancerMine: Knowledge Base Construction for Personalised Cancer Treatment
|pdfUrl=https://ceur-ws.org/Vol-1747/BT204_ICBO2016.pdf
|volume=Vol-1747
|authors=Jake Lever,Martin Jones,Steven Jm Jones
|dblpUrl=https://dblp.org/rec/conf/icbo/LeverJJ16
}}
==CancerMine: Knowledge Base Construction for Personalised Cancer Treatment ==
https://ceur-ws.org/Vol-1747/BT204_ICBO2016.pdf
CancerMine
Knowledge base construction for personalised cancer treatment
Jake Lever Martin Jones Steven JM Jones
Genome Sciences Centre Genome Sciences Centre Genome Sciences Centre
BC Cancer Agency BC Cancer Agency BC Cancer Agency
Vancouver, Canada Vancouver, Canada Vancouver, Canada
jlever@bcgsc.ca mjones@bcgsc.ca sjones@bcgsc.ca
Abstract— Knowledge of the relevant genomic aberrations is commonly locked in the text of associated publications and
that drive a particular cancer type is necessary to accelerate has not been curated into a usable database. Cancer types also
efficient interpretation of genomic data and enable large-scale play an extremely important contextual role in understanding
endeavours in precision medicine. Currently, this field is limited the function of a particular gene. The NOTCH gene can have
by the lack of focused and scalable literature curation tools that oncogenic effects in blood cancers and be tumour suppressive
can reliably capture the required information. Here we present a in head & neck cancers [4]. Therefore it is very important to
knowledge-base of genes that have been described in the link specific genes with a specific form of cancer.
literature as drivers, oncogenes or tumour suppressors with
respect to a specific type of cancer. We have annotated a large Previous work has linked gene mutations with diseases
body of literature which reports oncogenic aberrations using a based on simple distance metrics [5] and used crowdsourcing
custom designed annotation tool. We then applied VERSE, an in- to annotate gene mutation relations [6]. Our approach uses
house relation extraction tool, to catalogue driver mutations and syntactic and semantic information to predict relations between
illustrate the ability to build a useful resource for clinical cancer types and genes to generate a usable knowledge base
interpretation of genomic data for personalised treatment based on a smaller set of expert annotated data.
approaches.
Keywords—relation extraction, oncogenomics, driver mutations II. METHODS
In order to identify sentences that discussed both a human
I. INTRODUCTION gene and a cancer type, word-lists were generated from popular
bioinformatics ontologies. Due to existing named entity
Improvements in sequencing technology now allow for
recognition tools missing some specific cancer types, a custom
investigation of individual cancers in a clinically actionable
word list was created from the UMLS Metathesaurus [7]. All
time frame. These technologies reveal a set of mutations in the
terms and their synonyms of the type Neoplasm (T191) were
genome of an individual patient’s cancer. These mutations may
selected. This list was then manually trimmed to remove very
disable molecular pathways, up-regulate them or dramatically
general cancer terms so that only cancer types remained. The
change their function in the quest for increased tumour growth
NCBI Gene list [8] with all alternative names was used to
and drug resistance. A bioinformatician examining these sets of
create a list of human genes with their synonyms and was
mutations must identify the important changes and highlight
manually trimmed for several gene names that are common
those relevant for clinical decisions.
words in biomedical literature (e.g. MICE). The cancer type
Distinguishing between driver mutations, that are important list contained 12,522 terms and the gene list contained 59,860
in the tumour development, and passenger mutations, that are terms. Both word lists were filtered by a list of common
coincidental mutations, remains a huge challenge in cancer English words. This word list was built from the stop words
research. Large scale projects, including The Cancer Genome from the NLTK toolkit [9], the most frequent 5,000 words
Atlas (TCGA) [1], have shone a light on the mutational based on the Corpus of Contemporary American English [10]
landscapes of a variety of cancer types. However, TCGA by and a stop word list associated with the NCBI gene data.
necessity focuses on only the most common or accessible types Table 1. Examples of annotated sentences used as training data
of cancer and only on primary tumours. Metastatic tumours are for (a) driving, (b) oncogenic and (c) tumour suppressive associations
a hugely important area, causing 90% of cancer-related with PubMed IDs. Gene names are underlined and cancers are bolded.
mortality [2], and are not as well studied. Existing resources
(such as IntOGen [3]) listing known or statistically derived Recent studies reported S100A2 protein is a molecular driver
(a) in TGF-β induced cell invasion and migration in hepatic
driver genes rely on these large-scale projects but miss variants
carcinoma.(PMID:25591983)
which may be exquisitely characterised in smaller scale studies In summary, our work suggests a new direction for
or are associated with incidental findings discussed in the (b) understanding the oncogenic function of TRAF4 in breast
literature. Smaller studies on specific cancer types are an cancer. (PMID:25738361)
important resource for cancer researchers in understanding In present report, the tumor suppressive role of DMTF1 was
driver mutations. However, the information from these studies (c)
studied and confirmed in bladder cancer. (PMID:25965824)
This research was supported by a Vanier Canada Graduate Scholarship
and funding from Compute Canada.
Medical literature was downloaded in XML format from Table 2. Overview of data in CancerMine knowledge base
the MEDLINE database of PubMed citations and the Pubmed
Central Open Access subset. The raw text was extracted from # of analysed sentences 60,464
the files and processed using the Stanford CoreNLP tools [11]. # of gene terms 155,646
Text was split into sentences and tokenized. A sentence that
contained a term from the cancer types word list and a term # of cancer terms 79,290
from the human gene names wordlist was flagged and stored in
# of driver annotations 1,967
a MySQL database.
In order to enrich the dataset for sentences likely discussing # of oncogenic annotations 6,877
important cancer aberrations, the sentences were filtered for # of tumour suppressive annotations 3,075
those containing “driv”, “oncogen” or “tumo(u)r suppress”. In
literature from 2015, 13,765 sentences were extracted and
examples are shown in Table 1. Equal numbers of sentences
for each filter were then prepared for annotation. IV. CONCLUSION
The CancerMine annotation system displays each pair of In conclusion, we presented a full pipeline for identifying
cancer type term and gene name term that appear in the same sentences that discuss a gene and cancer type, annotating a
sentence. The user can then tag the term pair as having a driver, large number of sentences and training a high-quality relation
oncogenic, tumour suppressive or no relation. Driver relations classifier on them. This data is an important resource for
require the sentence to specifically discuss a genomic improved personalised cancer treatment and can be expanded
aberration driving cancer development. Oncogenic relations to address other specific questions relevant to genome
require the text to state that an aberration is involved in interpretation, such as clinical outcome.
oncogenesis while a tumour suppressive relation requires the
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