dataminingincancerresearch ieee

14 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | FEBRUARY 2010 1556-603X/10/$26.00©2010IEEE

Paulo J.G. Lisboa, Liverpool John Moores University, UK Alfredo Vellido, Technical University of Catalonia, SPAIN Roberto Tagliaferri and Francesco Napolitano, University of Salerno, ITALY Michele Ceccarelli, University of Sannio, ITALY José D. Martín-Guerrero, University of Valencia, SPAINElia Biganzoli, University of Milano, ITALY

Application Notes

I. Introductiondvances in cancer medicine have

traditionally come from detailed

understanding of biological pro-

cesses, later translated into therapeutic

interventions, whose effectiveness is

established by rigorous analysis of

clinical trials. Over the last

two decades the increasing

throughput of data from

microarray screening, spec-

tral imaging and longitudinal

studies are turning the under-

standing of cancer pathology

into as much a data-based as

a biologically and clinically

driven science, with potential to impact

more strongly on evidence-based deci-

sion support moving towards personal-

ized medicine [1].

This article is not intended as a

comprehensive survey of data mining

applications in cancer. Rather, it provides

starting points for further, more targeted,

literature searches, by embarking on a

guided tour of computational intelli-

gence applications in cancer medicine,

structured in increasing order of the

physical scales of biological processes, as

outlined in Table 1.

II. Robust Clustering and Data VisualizationOver the last decade, cancer has become

a data-intensive area of research, with

accelerating developments in data-

acquisition technologies and specific

methodologies, for example, next-generation

sequencing for monitoring genetic

changes in tumor cells as they progress

from normal to invasive [2].

The first step in unrav-

elling these processes is

the study of co-occur-

rences, leading to the

identification of disease

sub-types. This already has

profound implications for the

clinical management of patients,

impacting on one of the most

funda mental precepts of cancer med-

icine – the histological characterisation

of malignancies. Currently this is over-

whelmingly done by measuring the dif-

ferentiation between cancerous cells and

the original normal cells from which

they have evolved, marking them as well

to poorly differentiated, meaning that the

changes are linked with good to poor

prognosis. Yet it is becoming apparent that

the metabolism of cells is a key factor in

uncontrolled cell proliferation, with a

potentially greater influence on disease

progression than cell differentiation alone.

Clustering is the first line analysis

methodology to mine data bases for use-

ful research hypotheses to take onto fur-

ther, more directed, study [3], [4]. From

a mathematical perspective the objects of

study, be they genes, patients, mode of

action drugs or disease sub-types, are

points in a multi-dimensional space

where similarity is defined, typically

trough a measure of the distance between

object pairs. The main classes of cluster-

ing approaches used in data mining for

cancer are partitional, creating a simple

partition of the data, or hierarchical, which

create a tree-structured hierarchy of the

data. Partitional approaches form a very

Digital Object Identifier 10.1109/MCI.2009.935311

Data Mining in Cancer Research

TABLE 1 Overview of healthcare interventions across physiological scales.

BIOLOGICAL PROCESSES

PHYSIOLOGICAL SCALE

INDUSTRIAL SECTOR

APPLICATION DOMAIN

MOLECULAR BIOLOGYGENOME AND PHYSIOME

BIOINFORMATICSPHARMACEUTICS

SUSCEPTIBILITY TO DISEASE

METABOLIC PATHWAYSTARGETS FOR INTERVENTION

CYTOLOGY AND HISTOLOGY

CELL AND TISSUE LEVELS

SYSTEMS BIOLOGYLABORATORY TESTS

DISEASE DIAGNOSIS

MEDICAL IMAGING

ORGAN LEVEL

NEUROINFORMATICS

MEDICAL INFORMATICS

MEDICAL EQUIPMENT & INSTRUMENTATION

PHARMACEUTICS

ANATOMICAL AND PHYSIOLOGICAL MONITORING

ELECTROPHYSIOLOGICAL MEASUREMENT

CLINICAL SIGNSSYSTEM LEVEL DIAGNOSIS, PROGNOSIS

AND SCREENING

OUT-OF-HOSPITAL CARE

LEVEL OF THE INDIVIDUAL

AMBULATORY MONITORING FOR EARLY DIAGNOSIS

LIFESTYLE FACTORSPOPULATION LEVEL PUBLIC HEALTH

INFORMATICSDISEASE PROTECTION AND PREVENTION

© EYEWIRE

A

Authorized licensed use limited to: LIVERPOOL JOHN MOORES UNIVERSITY. Downloaded on February 12, 2010 at 05:38 from IEEE Xplore. Restrictions apply.

FEBRUARY 2010 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 15

heterogeneous class, including techniques

based on Global Optimization, Vector

Quantization, Kernel functions, Fuzzy

sets, and Graph theory [5]. Hierarchical

approaches can be agglomerative or divisive

and are often used as for first-line clus-

tering of high-throughput data. How-

ever, high- dimensional data spaces are

by nature ultra-metric, each data point

appearing to be at the centre of a hollow

sphere, in contrast with the usual intui-

tive picture in low dimensions [6]. This

effect needs to be taken into account

especially with agglomerative methods

to avoid early stage errors that can arise,

resulting in sub-optimal solutions com-

pared with partition algorithms. A third

main class of algorithms is biclustering

that is to say clustering of both objects

and features within a single model [7].

The range of clustering techniques illus-

trates the complexity of the problem.

The fundamental concept underlying

the definition of clusters, “similarity”, is

complex in itself because of its ambigui-

ty. This difficulty is compounded when

the most relevant features are not known

a priori, yet the choice of different fea-

tures usually produces different solutions.

An open research question is feature selec-

tion operating with unsupervised train-

ing. This will normally involve

parametric modeling and the application

of Bayesian methods to derive the dis-

criminative features which best separate

between the clusters [8].

In addition to such intrinsic complex-

ity, purely algorithmic issues may give

rise to different solutions for the same

data set, even for the same algorithm.

This raises the possibility that cluster sta-

bilization methods including global stabil-

ity assessments [10], [11] and consensus

clustering [12], [13] methods may miss the

real complexity of the problem.

An alternative approach to clustering

assessment shifts the focus from the

search of the best solution to the search

of a set of equally plausible, though

different, solutions [14]. Recently, meta-

clustering [15] (see fig 1) has been pro-

posed as a clustering assessment tool. It is

the process of clustering the solutions i.e.,

the cluster partitions themselves, general-

izing the concept of global stability to

local stability (see Fig. 1), that is, the sta-

bility of a solution with respect to a

homogenous subset of the candidates.

The cluster composition then needs

to be explored in detail, for which den-

drograms provide a useful representation,

usually through sequential univariate

splitting of the data space. However, the

interpretation of geometrical relationships

in the data is often best derived from

direct spatial representations. The most

commonly used methods for data visual-

ization are Multi Dimensional Scaling,

Principal Component Analysis (PCA)

and Self-Organizing Maps [16], [17].

Linear methods are particularly inter-

esting since they enable axes projections

to be overlaid on the data. An efficient

methodology for cluster-based linear

visualization is to use a decomposition of

the covariance matrix which explicitly

takes into account cluster membership.

This is the natural tailoring of PCA to the

case where the data are partitioned into

labelled cohorts – an example from breast

cancer proteomics is shown in Fig. 2 [18].

III. Pathway ModelingThe study of the cell functions is mod-

elled from experimental data sets which

are both complex and dynamical [19],

[20]. This has spawned the field of Sys-

tems Biology to understand and describe

how molecular components interact to

manifest the phenotypic behaviour

which is the target of disease interven-

tion [21], illustrated in Fig. 3. In the sys-

tems biology literature there are two

main classes of approaches:

Inverse problems, which involves ❏

mining data from many perturbation

experiments to identify a compatible

structure of the signaling pathway.

Direct problems, to forecast and simu- ❏

late the forward evolution of dynami-

cal systems, using differential equations.

Several methods make the assump-

tion that the expression level of a given

gene can be considered as a random

variable and the mutual relationships

between them can be derived by statisti-

cal dependencies. The resulting system is

embedded in a probabilistic Graphical

Model [22] described for example by a

Bayesian Network [23], a Factor Graph

[24], a Gaussian Graphical Model [25]

or a Mutual Information derived Influ-

ence Network [26].

IV. Cancer Imaging Cancer imaging is a vast field focused

on non-invasive measurement for

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FIGURE 1 An illustration of meta-clustering. Each point on the map represents a clustering solution and close points represent similar solutions. Colors indicate a measure of quality for the solutions and red circles highlight local stability [15].


16 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | FEBRUARY 2010

cancer detection, diagnosis and grading,

and to monitor response to therapy by

measuring tumor size and detecting re-

growth. Having started out as mainly

anatomical measurement for anomaly

detection as an indicator of malignancy,

for instance using ultrasound and Mag-

netic Resonance Imaging (MRI), can-

cer imaging has developed into multiple

modalities of physiological imaging

including spectroscopy, with Magnetic

Resonance Spectroscopy (MRS) and

spatially localized Chemical Shift Imag-

es (CSI), with Positron-Emission

Tomography (PET) and Single-Photon

emission CT (SPECT) clinically widely

used to measure glucose metabolism

and therefore find regions with excep-

tionally high energetic rates, which are

characteristic of tumor growth. An

accessible review of machine learning

applications to detection and diagnosis

of disease may be found in [27].

A recent development is the use of

specific functional imaging for tumor

delineation, that is to say, to measure

the contour during surgical planning

and to identify tumor striations that

may not be apparent clinically even

during re-section. Initial efforts in this

direction applied blind signal separation

to single-voxel spectra to separate infil-

trating tumor mass compared with

necrotic tissue [28]. However, tumors

are not homogeneous, often mixing

different types of tissue and sometimes

with different grades of malignancy.

One way to resolve this mixture is to

segment the tumor composition, which

is called nosological imaging, of which

examples of diagnostic applications in

brain are reported in [29], [30]. A relat-

ed and very important clinical area is

follow-up imaging to determine tumor

progression and to differentiate recur-

rent tumor growth from treatment-in-

duced tissue changes [31], [32].

V. Prognostic OutcomeOutcome modeling usually refers to

the analysis of patterns of mortality and

recurrence in longitudinal cohort stud-

ies, where a specific groups of patients

followed-up for up to 20 years, record-

ing the occurrence of events of interest

which may be cancer specific mortality,

overall mortality or local and distal

recurrence. The value of these studies is

closely linked to the design of the fol-

low-up process, highlighting the need

to involve biostatistical expertise from

the very earliest stages in study design.

It is required to model the likeli-

hood of the event of interest, condi-

tional on the event not taking place at

the start of the time interval and taking

into account the loss of patients to fol-

low-up for reasons other than the event

of interest, termed censorship.

The application in medical statistics of

linear-in-the-parameters models to non-

linear effects in outcome data generally

involves discretising the data space to gen-

erate piecewise linear approximations to

the desired response surfaces. However,

this heuristic approach can have important

consequences in cancer research, making

it difficult to mine the hazard function for

insights into the dynamics of metastatic

spread [33] and inducing a categorisation

of the histological grade of the tumor,

which can give rise to important sub-

jective effects that impact on the choice

of therapy given to the patient [34].

Neural network models fit non-lin-

ear, time dependent estimates of the

hazard function, without the need to

make proportionality assumptions or to

discretize the state-space [35], [36] and

their accuracy has been established by

multi-centre, double-blind, evaluation

[37] and this framework has recently

been extended for estimating the joint

model for more than one risk, e.g. local

and distal recurrence [38].

VI. Conclusion Computational intelligence methodol-

ogy has been widely applied to data

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3FIGURE 2 Two views of cluster-based visualization of 25-d protein expression date from breast cancer histology showing (a) the HER-2 positive cohort distinct from the rest and (b) rotating the 3-d view to illustrate the separation between the projections of the remaining seven clusters [18].


FEBRUARY 2010 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 17

mining in cancer but open questions

remain before we can fully exploit the

potential of molecular biology, to inte-

grate this into routine imaging and

other non-invasive measurements for

screening and early diagnosis, also to

deliver consistency in laboratory mea-

surements especially in histopathology,

and to more accurately model the bal-

ance between benefit and risk of thera-

py for niche groups of patients, down

to the level of the individual.

New data mining methods need to

be developed to handle high-dimen-

sionality and large data volumes, to more

efficiently model the dynamics of deep

molecular pathways with high levels of

noise and small experimental sample

sizes, and also to robustly estimate haz-

ard functions with censored data with

multiple competing risks. Analytical

models need also to be integrated with

clinical expert knowledge and processes,

potentially by mapping into Boolean

filters [39].

The road ahead is long and bumpy.

This article is provided as a sign-post to

the nature of the key methodological

and application challenges in this grow-

ing and exciting field.

AcknowledgmentsThis work was supported in part by the

Biopattern NoE under FP6/2002/IST/1

and IST-2002-508803. Further support

was provided by MICINN research

project TIN2009-13895-C02-01.

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Thyroid Cancer

Normal Thyrocyte

Normal Thyrocyte

Follicular Adenoma

Follicular Carcinoma

Undifferentiated

Carcinoma

05216 3/31/09

© Kanehisa Laboratories

Papillary

Carcinoma

TRK

REDPTC

Ras

Ras

BRAF

BRAF

PPFP

PPARγ

RXR

BRAFMEK ERK

MEK ERK

Thyroid Follicular Epithelial Cell

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+p

+p

+p +p

+p

DNA

ProliferationSurvival

DNA

ProliferationSurvival

DNA

Proliferation

Proliferation

DNA

DNA

DNA

Damage

GenomicInstability

Impaired GICycle Arrest

ReducedApoptosis

MAPK SignalingPathway

p53 SignalingPathway

PPAR SignalingPathway

Ligands

p53

c-Myc

CyclinD1TCF/LEFEACD β-Catenin

AdherensJunction

Wnt SignalingPathway

Downregulation

IncreasedInvasion andCell Motility

Genetic Alterations

Oncogenes: RET/PTC1, RET/PTC3,TRK (TRM3/NTRK1),TRK-T1, T2 (TPR/NTRK1),TRK-T3 (TFG/NTRK1),BRAF, Ras,

PPFP (PAX8/PPARγ), β-Catenin

Tumor Suppressors: p53

FIGURE 3 An excerpt of the papillary thyroid carcinoma pathway from the KEGG database.


18 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | FEBRUARY 2010

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