ZK DrugResist

Automatic Extraction of Drug Resistance Mutations and Expression Level

Changes from Medline Abstracts

Zoya Khalid

and Osman Ugur Sezerman

Department of Biological Sciences and Bioengineering, Sabanci University, Istanbul, Turkey

Department of Biostatistics and Medical Informatics, Acıbadem University, Istanbul, Turkey

Keywords: Drug Resistance, Mutations, Gene Expression, Naive Bayes, Machine Learning.

Abstract: Drugs are small molecules that generally work by binding to its target which is often a protein. This ligand

molecule binding helps in the treatment of various diseases. Major obstacle to treat complex diseases is the

phenomena underlying drug resistance mechanisms which are not fully understood so far. Previously reported

literature has mentioned few of the motives behind this complex mechanism which dominantly include protein

missense mutations and the changes in the expression levels of certain genes. A better understanding of these

mechanisms is getting crucial for the researchers. Retrieving information on these processes can be

challenging as scientific literature has huge pool of data and extracting the required information has always

been a laborious task. We developed an online pipeline ZK DrugResist that automatically extracts PubMed

abstracts of drug resistance paired with either mutation or expression for a given disease. Our classifier

showed 97.7% accuracy with 93.5% recall and 96.5% F-measure. This system saves plenty of time in terms

of data mining and also reduces efforts in retrieving information from online resources.

1 INTRODUCTION

The term drug also referred as dose or medication is

used for treatment of various diseases. There are two

ways to classify drugs, one named as the small

molecule drugs which include proteins, biological

medicinal product and vaccines which further used as

therapeutic agents for the treatment of certain

diseases. The second way of classification is based on

how the drug is administered that is its specific mode

of action following the therapeutic effects. The drug

usually functions by binding to its target which is

often a protein. Proteins are large biomolecules made

up of amino acids. They are also visualized as large

globular structures that have deep groves in it, which

may have buried binding site that is good for

druggability. The drug molecule will then fits in the

binding site and the process is termed as ligand-

molecule binding. In this way the drug performs its

action and helps diagnosing and curing various

diseases (Dean et al., 2005; Michael, 2002; Walsh,

2000).

Sometimes treatment phase has been passed

through an obstacle “drug resistance” generally

meaning the decrease in the efficacy of the drug in

curing a disease. This is the major constraint to treat

complex diseases. The underlying mechanisms are

not very clear but still there are some notions about it.

First theory states that drugs at their certain target

sites are present in a decreased concentration caused

by increased level of expression of drug molecules.

Second involves the modification of drug targets

which affects the protein-ligand binding complex

(Remy et al., 2003). Drug resistance has a strong

impact on disease treatment; it has been observed that

in many of the cases this brings failure in treatment.

This shows that rate of survival is proportional to how

strongly the mechanism of drug resistance is being

overpowered. The survival chances would increase if

the drug resistance could be overcome (Longley and

Johnston, 2005).

The current study focuses on evaluation of

complex phenomena lying behind the drug resistance

mechanism. From the literature it has been found that

one of the major reasons behind this is the protein

alteration which involves amino acid mutations at

certain residue. Theses missense mutations affect the

binding affinity of the protein with the ligand and

hence results in making drug insensitive to the

168

Khalid, Z. and Sezerman, U.

ZK Drugresist - Automatic Extraction of Drug Resistance Mutations and Expression Level Changes from Medline Abstracts.

DOI: 10.5220/0005664501680173

In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 3: BIOINFORMATICS, pages 168-173

ISBN: 978-989-758-170-0

treatment. For example as reported in previous

studies that V299L, T315A, and F317I/L mutations

are resistant against dasatinib while mutations

likeY253F/H, E255K/V, and F359C/V are resistant

for nilotinib, therefore making protein mutations as

an important factor for drug resistance mechanisms

(Chrisanthar et al., 2008; Hochhaus et al., 2011).

Second important factor is the expression based drug

resistance mechanism. The changes in expression

level which either is the overexpression or down-

regulation of certain genes induces enhanced

resistance against various drugs. As one of the studies

reported the overexpression of ANP32C creates

enhanced resistance against FTY720 drug, hence

makes it ineffective to treatment (Buddaseth et al.,

2014).

In order to retrieve and comprehend drug

resistance mechanisms, researchers either has to look

for the online databases or read all freely available

biomedical documents through online sources, which

is of course a very time consuming task. Many

computational biology/bioinformatics studies have

focused in building automated pipelines to extract

information from PubMed abstracts. There are some

databases published in literature that stores different

aspects of drug and gene relationship like BacMet

which focuses on genetic alterations causing

resistance against antibiotics (Pal and Larsoon, 2014).

Moreover there is another tool named Biozyne P-gp

Predictor which is based on SVM classifier that

differentiates the substrates from efflux pumps

(Levatic et al., 2013). Another similar reported

database is CancerDR which focuses on the

identification of the altered genes encoding drug

targets (Kumar et al., 2013). Retrieving information

from such kind of repositories is a laborious task.

Making automated way of information retrieval is one

solution to this. Previously published methods just

focussed on general analytical tasks like mining genes

and protein names or describing relationship of genes

and drug. These methods don’t emphasize on

combining all these information and placing them in

one platform. Some of the tools on information

mining are already been published, for instance Proux

research group reported the syntactic parsing

methodology for information extraction developed by

(Proux et al., 1998). Similarly another method used

statistical based information (Hishiki et al., 1998;

Ohta et al., 1997). In the same way (Cutting and

Kupiec, 1992; Aronson et al., 1994; Humphreys et al.,

1998 ;) also developed servers that used semantic

analysis approach for information extraction.

Thorough review of the literature revealed that there

is another published tool EDGAR (Rindflesch et al.,

2009) that overcomes the limitations of the previously

existing information retrieval methods. This tool

works in building relationship between genes and

drugs relevant to cancer therapy. But unfortunately

this tool is not available online yet and it is also not

mentioned that how much accuracy authors have

achieved in applying natural language processing on

the abstracts. In another study reported by (Bui et al.,

2010) the authors developed the method for

combining drug and mutation level information for

HIV. Again this method is only specific to HIV. Our

proposed method has successfully benchmarked

already existing methods. ZK DrugResist uses

machine learning approach to retrieve drug resistance

information. It provides one platform that gathers

gene names, drug names, abstracts titles, link to the

abstracts categorised by disease type. Our tool

provides the most systematic way of information

extraction for drug resistance abstracts available on

PubMed. In this way it facilitates the researchers in

mining desired information more robust and more

accurate.

The PubMed directory considered as a rich source

of information as it has a huge collection of abstracts.

Despite this fact, automated mining of worthy

information remains a big challenge for researchers.

Our study aims to develop an online tool to

automatically extract all the abstracts from PubMed

related to drug resistance. These abstracts and the

related information are downloaded into a database.

From this all the information about the mutation, gene

and the expression status is processed and displayed

on web. Furthermore the abstracts are also marked as

cancer or other diseases based on the content

provided in the abstract. We used MugeX and

EnzyMiner approach developed by our

computational biology group for implementing this

classifier (Erdogmus and Sezerman, 2007; Yeniterzi

and Sezerman, 2009).

2 METHODOLOGY

Abstracts available online queried by using search

terms: “Drug resistance”, “amino acid mutation at

drug resistance level”, “expression based drug

resistance” and different combinations of these terms

were downloaded from Medline which are many

thousands in number. Out of them only those

abstracts are filtered that has either the drug resistance

and the protein mutations content present together or

drug resistance and the expression level information

present in a document. The downloaded abstracts

were passed through variety of algorithms including

ZK Drugresist - Automatic Extraction of Drug Resistance Mutations and Expression Level Changes from Medline Abstracts

169

tokenization/sentence splitting followed by porter

stemming. These algorithms are applied in order to

break down the abstract into sentences and then into

words making them easy to process.

2.1 Classification Modules

We applied two learning algorithms Naive Bayes and

Rocchio algorithm both uses bag of words approach.

After pre-processing the dataset, we applied our

classifiers which for our case are based on four levels

of classification. First stage is to separate the abstracts

of drug resistance from the other ones. First the

document is processed by tokenization and porter

stemming algorithm. Further we applied TF-IDF

weighting (term frequency inverse document

frequency). It is the product of two statistics Term

Frequency and Inverse Document Frequency, term

frequency deals with the raw calculation of a term in

a document while inverse document frequency deals

with the significance of a word count. We observed

how many times word “Drug Resistance” appears

together in a document, if it is more common we

labelled the document as drug resistance else it is

labelled as others. Following this the next phase

classification picks the drug resistance tagged

abstracts to further classify them as either mutation or

expression. The regular expressions are designed for

this purpose. If regular expression matches any

mutation related information in the document we

marked it as mutation, on the other hand it is marked

as expression based if the content displays the gene

expression level changes for the drug resistance again

TF-IDF is used for this purpose. Third step is to sub-

categorize the abstracts labelled as mutations. The

mutation can be at amino acid level or at the

nucleotide level our tool is interested only to pick the

protein mutations. Those at DNA level are termed as

ambiguous mutations, so this step targets to remove

ambiguous mutations from the actual ones using the

regular expressions defined earlier. The last module

of our classifier is to divide the cancer related articles

with the ones which are showing other diseases of

metabolic, autoimmune and neurodegenerative. The

documents cited by terms cancer, leukaemia and

tumour belongs to cancer class while the rest are

classified in others category. For term frequency we

used TF-IDF as mentioned before. Figure 1

summarizes all the steps involved in ZK DrugResist.

For implementation Perl Regular Expressions

were used, set of patterns were formed describing the

protein mutations, for instance the mutation cited as

L15V, Arg567Leu, Ala399->Asp and some are

mentioned as full sentences substitution of

Methionine with Valine at position 40. Following the

mutations the gene names parallel to mutation stated

in the abstracts were also downloaded and stored in

the database.

2.1.1 Drug Resistance Vs. Others

As mentioned first stage of classification is to clearly

mark the abstracts which are showing drug resistance

mechanism from the others which are irrelevant to

these. The total abstracts downloaded are 701 in

number. For each of the downloaded abstract the

feature vector is constructed. In order to distinguish

them the frequency of each word is counted as a

feature value. These words were then further

processed using tokenization and porter stemming

algorithms. After breaking the abstract into words and

counting the frequency of term “Drug Resistance” it

is marked as either drug or others.

2.1.2 Mutation Vs. Expressions

In the second category we picked these drug

resistance documents and scanned them for the

mutation level information. The documents cited

using overexpression down regulation kind of terms

are marked as expression abstracts while those which

uses amino acid terminology are marked as mutation

by our algorithm.

2.1.3 Protein Vs. DNA

The Perl regular expressions were applied to extract

the amino acid level mutations from each document.

The major hindrance is the mixing of some protein

mutations with the DNA ones. For example the one

letter code amino acid mutation like A456G can

easily be misinterpreted with the nucleotide letters.

We compiled regular expressions for this ambiguity

to be solved. The documents are classified based on

the content information.

2.1.4 Cancer Vs. Others

In the last module of classification those abstracts

which are associated with cancer were separated with

the abstracts which are related to other diseases which

include neurodegenerative, autoimmune and

metabolic disorders.

2.1.5 Gene/Inhibitors

The gene names following the protein mutation are

also extracted from the abstracts. For this purpose the

complete list of official gene names were being

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downloaded from HUGO database

http://www.genenames.org/. Any gene name

mentioned in the abstract is programmed to match

with the list of the genes stored and the results are

displayed on web. We followed MugeX approach for

this module.

2.2 Implementation

All the steps are implemented in The Perl

Programming Language. Strawberry Perl version

5.20 was used. The regular expressions were

compiled using PERL Regular Expression library.

The necessary information from the articles including

Title, Abstract and PubMed ID was downloaded and

stored in XML format into MySQL database. After

this the documents are processed first using

tokenization using Perl module PPI::Tokenizer. Each

sentence is broken into words and further porter

stemming was applied to each document. This

algorithm is used to remove the common words from

English which are actually not contributing in

classification for example “the”, “is”, “are” and

similar words to that. After pre-processing all the

documents, they are passed through Naive Bayes and

Rocchio classifiers in order to achieve four levels

classification. The database is built on Xampp Server;

database tables are stored in MySQL phpmyadmin of

xampp. The web interface was designed in

WordPress using html and PhP. CGI, DBI and DBD

modules of Perl were being used for retrieving the

data from MySQL databases and displaying the

output on the web page.

2.3 Testing

In order to test the classification results training,

testing and k fold cross validation were employed.

For all the four modules of classification 20% of the

abstracts were being used as test set, while remaining

abstracts were considered as training set. We

performed 5 fold cross validation that means the

whole data is being divided into 5 sets out of which 4

are used are training sets and the rest of one is as test

set. These sets are being shuffled 50 times and

average accuracy for both the training and test sets

were being measured.

3 RESULTS

The classifier is tested with the entire pre-processing

algorithms we implemented. Out of huge pool of data

available online on drug resistance mechanisms we

only filtered those which are showing either mutation

level or expression level changes in causing drug

resistance. This makes up to 701 documents in total.

The first module of classification separates out 144

documents as drugs while the other 557 are the ones

in which mutation is mentioned but not at drug level.

These 144 documents are advanced to the second

level of classification. This shows that out of 144

documents, 91 are those belonging to mutation

category, 22 of them are the expression based

resistance abstracts while rest of 31 did not show

either the mutation or the expression based drug

resistance. These 91 abstracts are then picked to

distinguish the protein and DNA level mutations from

each other. The results showed that 65 of them are the

ones which are being labelled as amino acid

mutations while 25 are the other ones. Last module of

classification separates the cancer disease ones with

the other diseases. The calculation shows that 53 are

the drug resistance mutations at cancer level while 12

are the ones which are in other category. We

compared the results of our classifiers and the results

showed that Naïve Bayes classification outperformed

Rocchio algorithm in precision, recall and accuracy

as shown in Table1. The results of Naive Bayes

classifier are listed in Table 2, 3, 4 and 5 respectively.

The graphical representation is illustrated in Figure 1.

ZK DrugResist is a user friendly web application,

every time a user queries to find the mutations or the

expression based drug resistance information, the in-

built program connects to the MySQL database tables

and displayed the output on webpage as shown by one

of the snapshot in Figure 2. The classifier shows 97%

average accuracy on test set for 5 fold cross

validation.

Table 1: Comparison of Naive Bayes and Rocchio

Algorithm.

Naive Bayes Classifier Rocchio Algorithm

Accuracy Recall Precision Accuracy Recall Precision

97% 96.5% 95.9% 90.5% 83.0% 89.4%

Table 2: Classification Results of Drug Resistance vs.

Others in Training and Test Sets.

No of

Abstracts

Accuracy

Measure

Recall F-measure

660 96.4% 95.4% 93.7%

140 96.7% 96.5% 95.9%

ZK Drugresist - Automatic Extraction of Drug Resistance Mutations and Expression Level Changes from Medline Abstracts

171

Table 3: Classification Results of Mutations vs.

Expressions in Training and Test Sets.

No of

Abstracts

Accuracy

Measure

Recall Precision

115 96% 95.4% 93%

28 97% 96.5% 95.9%

Table 4: Classification Results of Protein Mutations vs.

DNA Mutations in Training and Test Sets.

No of

Abstracts

Accuracy

Measure

Recall Precision

72 96% 94% 93%

18 96.7% 96.5% 95.9%

Table 5: Classification Results of Cancer vs. Others in

Training and Test Sets.

No of

Abstracts

Accuracy

Measure

Recall Precision

52 96% 94% 93%

13 96.7% 96.5% 95.9%

Figure 1: Flowchart of Methodology.

4 CONCLUSIONS

In this study we developed an online pipeline ZK

DrugResist to find the PubMed abstracts of drug

resistance combined with protein and expression

level data. Our tool outperformed the already existing

tools. ZK DrugResist is very proficient in mining

drug resistance semantics in an automated way from

Figure 2: Snapshot of ZK DrugResist showing the abstracts

from cancer disease.

literature without comprising the accuracy measure.

It is freely available online and is a self-explanatory

tool aimed to help researchers in finding desired

information on one click. As for the future work we

might extend this tool to also work on full articles

rather just on the abstracts. As in some cases we found

that the desired information is missing in the abstract

but present in the remaining article. This will further

increase our dataset size and may also improve the

accuracy measure. Our tool is available

http://zkdrugresist.sabanciuniv.edu/.

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