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Applications of Machine Learning Approaches
Integrating Analytic Methods and Statistics with High
Dimensional Visualizations to Different Problems in
Cancer Diagnosis and Detection
[LIST OFAUTHORS SUBJECT TO DATASETS USED AND WHO
WRITES OR HAS DONE ANALYSIS]
John McCarthy*, Kenneth A. Marx, Philip O’Neil, M.L.
Ujwal, Patrick Hoffman, Alex Gee and Natasha
Markuzon
AnVil, Inc.
25 Corporate Drive
Burlington, MA 01803
*corresponding author
[email protected];
(781) 272-1600 X 460
Abstract
Introduction to Data Analysis by Machine Learning
Overview of Clustering Methods and Cluster Comparison. Clustering is a method
of unsupervised learning. In supervised learning, the object is to learn predetermined
class assignments from other data attributes. For example, given a set of gene expression
data for samples with known diseases, a supervised learning algorithm might learn to
classify disease states based on patterns of gene expression. In unsupervised learning,
there either are no predetermined classes or class assignments are ignored. Cluster
analysis is the process by which data objects are grouped together based on some
relationship defined between objects. It is an attempt to discover novel relationships
within a given dataset independent of a priori knowledge about the data space [1,2]. An
understanding of relationships between objects is inherent in any clustering technique.
This is encoded in a distance measure or distance metric (sometimes called a similarity
measure or dissimilarity measure). Unlike a distance measure, a distance metric is
required to satisfy the triangle inequality. The most commonly used distance metric is
Euclidean distance, which in three dimensions corresponds to the physical distance
between objects in space. The Manhattan distance is a “city block” measure, in contrast
to the Euclidean “shortest distance between two points.” There are several other
alternatives including correlation measures. Generally, each measure defines a
relationship based on certain assumptions about the underlying data and attempts to
capture particular data characteristics [3].
In conjunction with selecting a distance measure, one must also decide on a clustering
algorithm, the procedure by which these n-dimensional objects are grouped together to
form clusters. Classical clustering techniques are divided into two groups, the
partitioning methods and the hierarchical approaches [1, 3-5]. In more recent years, a
third group of techniques that includes probabilistic approaches has emerged [6]. Similar
to the choice of distance metric, the selection of clustering algorithms for specific
datasets poses problems, as each algorithm focuses on certain types of relationships
within any given dataset, some overlapping and others unique.
The partitioning methods, sometimes referred to as iterative relocation algorithms,
construct clusters by first partitioning the data objects into some number of clusters and
then recursively moving objects between clusters until some cluster measure is
minimized. The result is a set of object groups where each object is assigned to only one
cluster.
Hierarchical approaches are based on tree structures where the data objects occupy
the leaves of the tree and the nodes of the tree define the relationship between subtrees or
leaves. These hierarchical methods are defined as either agglomerative (bottom-up) or
divisive (top-down) approaches. Agglomerative techniques start with each object in a
separate cluster and perform a series of successive fusions of clusters into larger clusters.
Divisive methods start with all objects in a single cluster and provide successive
refinements of the clusters into smaller clusters. Agglomerative methods include: nearest
neighbor (single-link method), furthest neighbor (complete-linkage method), centroid
cluster analysis, median cluster analysis, group average method, Ward’s method,
McQuitty’s methods, Lance and Williams flexible method, and others [7]. Divisive
methods include monothetic methods, which are based on the value of a single attribute,
and polythetic, which are based on the values of all attributes. Hierarchical techniques
provide no indicators on the number of clusters that the data should be clustered into.
The tree structure can be cut at various levels and the resulting subtrees determine the
clusters and their number.
Probabilistic techniques provide information as to how well an object belongs to each
cluster rather than just providing the cluster memberships. Given that most data spaces
do not contain well-defined objects, probabilistic techniques provide additional
information about a data space. Examples of probabilistic techniques include the fuzzy
clustering algorithms [6].
Many additional clustering techniques are a mixture of the basic types of methods
discussed above. Over the past five years there have been a significant number of
academic and commercial clustering and classification approaches focused on high
dimensional data, particularly biological and chemical data. Fasulo [4] for example,
describes recent results on clustering, each of which approaches the clustering problem
from a different perspective and with different goals.
A fundamental question that arises repeatedly is which clustering technique is better?
The answer to this question is an important commercial consideration for pharmaceutical
and biotechnology companies when the $ 500-800 million average cost of developing a
successful drug is at stake. It is even more important when the outcome is accurate
clinical detection of a specific cancer in a patient. In drug discovery, the outcome of a
clustering technique can influence decisions about selecting drug targets and chemical
lead compounds. Several investigators, including Schaffer [8], Felders [9], Dietterich [10]
and Cheng [11], have attempted to answer this question. In our view, the assessment of
which clustering technique is better is domain and data dependent, given the incomplete
information on which clusters are usually based. The question of which technique is
better is not the correct question to ask. A number of recent papers have discussed the
pitfalls of current comparative analyses, especially when using public domain datasets
and databases [12].
Combining Contextual Knowledge with Experimental Data in the Mining of
Microarry Gene Expression and Other Molecular Datasets. The availability of
genome-wide expression profiles promises to have a profound impact on the
understanding of basic cellular processes, the diagnosis and treatment, and the efficacy of
designing and delivering targeted therapeutics. Particularly relevant to these objectives is
the ability to cross-reference experimental and analytical results with previously known
biological facts, hypotheses, theories and results. Biological and biomedical literature
databases provide the kind of knowledge warehouses for such extensive crossreferencing. However, the volume of such databases makes the task of cross-referencing
lengthy, tedious and daunting [13].
In order to explain the underlying biological mechanisms and assign “biological
meaning” to clusters of genes obtained by analytical methods, it is necessary to crossreference genes with external information sources. Efforts in this direction are
particularly relevant as to clustering/classification methods which typically rediscover
known associations between genes. It is therefore important to take full advantage of the
existing knowledge about classical cellular pathways, including the metabolic and
signaling pathways, transcription factors, regulatory elements/motifs in sequence or
structure information, and assigned gene functions. Literature databases, which are a rich
source of information can be used to discover and analyze biologically significant
information based on co-citations or co-occurrences of pairs of genes: gene terms or
gene: disease terms in a given scientific paper. Likewise, one can extract biologically
meaningful relationships in the semantic framework of ontologies being specifically
developed to capture such information that use experimental results reported in literature
[14].
One of AnVil’s strengths is our ability to carry out integrated data mining and
visualization analyses on large, complex nonlinear datasets that may have as many as
50,000 data dimensions. Therefore, we have a practical way to overcome the need to
reduce dimensionality early on in addressing any specific problem. One advantage this
mechanism provides is the ability to simultaneously handle large numbers of data
dimensions, enabling us, for example, to add contextual knowledge into already largedimensionality datasets that researchers have to analyze; the contextual knowledge is
simply considered as additional data dimensions. We discuss the distinct advantages of
our technology in greater detail in the following sections.
The Importance of High-dimensional Data Visualization and its Integration with
Analytic Data Mining Techniques. Visualization, data mining, statistics, as well as
mathematical modeling and simulation are all methodologies that can be used to enhance
the discovery process [15]. AnVil’s expertise lies in a combination of analytic data
mining techniques integrated with advanced high-dimensional visualizations (HDVs).
There are numerous visualizations and a good number of valuable taxonomies (See [16]
for an overview of taxonomies). Most information visualization systems focus on tables
of numerical data (rows and columns), such as 2D and 3D scatterplots [17], although
many of the techniques apply to categorical data. Looking at the taxonomies, the
following stand out as high-dimensional visualizations: Matrix of scatterplots [17]; Heat
maps [17]; Height maps [17]; Table lens [18]; Survey plots [19]; Iconographic displays
[20]; Dimensional stacking (general logic diagrams) [21]; parallel coordinates [22]; Pixel
techniques, circle segments [23]; Multidimensional scaling [23]; Sammon plots [24];
Polar charts [17]; RadViz [25]; Principal component analysis [26]; Principal curve
analysis [27]; Grand Tours [28]; Projection pursuit [29]; Kohonen self-organizing maps
[30]. Grinstein et.al., [31] have compared the capabilities of most of these visualizations.
Historically, static displays include histograms, scatterplots, and large numbers of their
extensions. These can be seen in most commercial graphics and statistical packages
(Spotfire, S-PLUS, SPSS, SAS, MATLAB, Clementine, Partek, Visual Insight’s Advisor,
and SGI’s Mineset, to name a few). Most software packages provide limited features that
allow interactive and dynamic querying of data.
HDVs have been limited to research applications and have not been incorporated into
many commercial products. However, HDVs are extremely useful because they provide
insight during the analysis process and guide the user to more targeted queries.
Visualizations fall into two main categories: (1) low-dimensional, which includes
scatterplots, with from 2-9 variables (fields, columns, parameters) and (2) highdimensional, with 100-1000+ variables. Parallel coordinates or a spider chart or radar
display in Microsoft Excel can display up to 100 dimensions, but place a limit on the
number of records that can be interpreted. There are a few visualizations that deal with a
large number (>100) of dimensions quite well: Heatmaps, Heightmaps, Iconographic
Displays, Pixel Displays, Parallel Coordinates, Survey Plots, and RadViz. When more
than 1000 records are displayed, the lines overlap and cannot be distinguished. Of these,
only RadViz is uniquely capable of dealing with ultra–high-dimensional (>10,000
dimensions) datasets, and we discuss it in detail below.
RadViz™ is a visualization and classification tool that uses a spring analogy for
placement of data points and incorporates machine learning feature reduction techniques
as selectable algorithms. 13-15 The “force” that any feature exerts on a sample point is
determined by Hooke’s law: f  kd . The spring constant, k, ranging from 0.0 to1.0 is
the value of the feature for that sample, and d is the distance between the sample point
and the perimeter point on the RadViz circle assigned to that feature-see Figure 1. The
placement of a sample point, as described in Figure 1 is determined by the point where
the total force determined vectorially from all features is 0. The RadViz display combines
the n data dimensions into a single point for the purpose of clustering, but it also
integrates analytic embedded algorithms in order to intelligently select and radially
arrange the dimensional axes. This arrangement is performed through Autolayout, a
unique, proprietary set of algorithmic features based upon the dimensions’ significance
statistics that optimizes clustering by optimizing the distance separating clusters of
points. The default arrangement is to have all features equally spaced around the
perimeter of the circle, but the feature reduction and class discrimination algorithms
arrange the features unevenly in order to increase the separation of different classes of
sample points. The feature reduction technique used in all figures in the present work is
based on the t statistic with Bonferroni correction for multiple tests. The circle is divided
into n equal sectors or “pie slices,” one for each class. Features assigned to each class are
spaced evenly within the sector for that class, counterclockwise in order of significance
(as determined by the t statistic, comparing samples in the class with all other samples).
As an example, for a 3 class problem, features are assigned to class 1 based on the
sample’s t-statistic, comparing class 1 samples with class 2 and 3 samples combined.
Class 2 features are assigned based on the t-statistic comparing class 2 values with class 1
and 3 combined values, and Class 3 features are assigned based on the t-statistic
comparing class 3 values with class 1 and class 2 combined. Occasionally, when large
portions of the perimeter of the circle have no features assigned to them, the data points
would all cluster on one side of the circle, pulled by the unbalanced force of the features
present in other sectors. In this case, a variation of the spring force calculation is used,
where the features present are effectively divided into qualitatively different forces
comprised of high and low k value classes. This is done via requiring k to range from –
1.0 to 1.0. The net effect is to make some of the features pull (high or +k values) and
others ‘push’ (low or –k values) the points to spread them absolutely into the display
space, but maintaining the relative point separations.It should be stated that one can
simply do feature reduction by choosing the top features by t-statistic significance and
then apply those features to a standard classification algorithm. The t-statistic
significance is a standard method for feature reduction in machine learning approaches,
independently of RadViz. The top significance chemicals selected with the t-statistic are
the same as those selected by RadViz. RadViz has this machine learning feature
embedded in it and is responsible for the selections carried out here. The advantage of
RadViz is that one immediately sees a “visual” clustering of the results of the t-statistic
selection. Generally, the amount of visual class separation correlates to the accuracy of
any classifier built from the reduced features. The additional advantage to this
visualization is that sub clusters, outliers and misclassified points can quickly be seen in
the graphical layout. One of the standard techniques to visualize clusters or class labels
is to perform a Principle Component Analysis and show the points in a 2d or 3d scatter
plot using the first few Principle Components as axes. Often this display shows clear
class separation, but the most important features contributing to the PCA are not easily
seen. RadViz is a “visual” classifier that can help one understand important features and
how many features are related.
And RadViz Figure 1. How it works
We have studied the following systems related to cancer detection:
1. GI50 compound 60 cancer cell lines
2. Microarray lung cancer data
3. proteomics MS dataset
4. UNOS
1. Data Mining the NCI Cancer Cell Line Compound GI50 Data Set
using Supervised Learning Techniques
Introduction.
In a data mining study of 8 large chemical structure databases, it was observed
that the NCI Developmental Therapeutics Programs’data set contained by far the largest
number of unique compounds of all the databases (32). The NCI compound data set has
been mined in a series of reports by the intramural NCI Informatics research group of
Weinstein and collaborators. Supervised learning via cluster correlation, principle
component analysis and various neural network techniques have all been applied, as well
as statistical techniques (33,34). Many literature citations have described compound class
subsets, such as: tubulin active compounds (35), pyrimidine biosynthesis inhibitors (36)
and topoisomerase II inhibitors (37), that possess similar mechanisms of action (MOA),
share similar structures or develop similar patterns of drug resistance. Compound
structure classes such as the ellipticine derivatives have also been studied and point to the
validity of the concept that fingerprint patterns of activity in the NCI data set encode
information concerning MOAs and other biological behavior of tested compounds (38).
More recently, gene expression analysis has been added to the data mining activity of the
NCI compound data set (39). Gene expression profiles of the 60 cancer cell lines have
been employed in a method that predicted chemosensitivity, using the GI50 value data,
for a few hundred compound subset of the NCI data set (40). After we completed our
analysis (41), gene expression data on the 60 cancer cell lines was combined with NCI
compound GI50 data and with a 27,000 feature database computed for the NCI
compounds to calculate chemical features similar to those identified in the following
study and as we have presented elsewhere (42).
Here we use microarray based gene expression data to first establish a number of
‘functional’ classes of the 60 cancer cell lines. These functional classes are then used in a
series of 2-Class supervised learning problems, using a subset of 1400 of the NCI
compounds’ GI50 values as the input to a clustering algorithm in the RadViz™ program
(43). At p < .01 significance, RadViz™ identifies two small compound subsets that
accurately classify the cancer cell line classes: melanoma from non-melanoma and
leukemia from non-leukemia, as we have previously reported (41). We then demonstrate
that independent analytic classifiers validate the two small compound subsets we
selected. We found them to both be significantly enriched in quinone compounds of two
distinct subtypes that we relate to the literature.
Specific Methods Used.
For the ~ 4% missing values found in the 1400 compound data set, we tried and
compared two approaches to missing value replacement: 1) record average replacement;
2) multiple imputation using Schafer’s NORM software (44). Using either missing value
replacement method for the starting data set, there was close agreement( always > 90%)
between the NCI compound lists selected in identical 2-Class Problem classifications we
present below. Therefore, in the present study, we used the record average replacement
method for all the data presented.
Clustering of cell lines was done with R-Project software using the hierarchical
clustering algorithm with “average” linkage method and a dissimilarity matrix computed
as 1 – the Pearson correlations of the gene expression data. AnVil Corporation’s
RadViz™ software (45) was used for feature reduction and initial classification of the
cell lines based on the compound GI50 data. The selected features were validated using
several classifiers from Weka 3.1.9 (Waikato Environment for Knowledge Analysis,
University of Waikato, New Zealand). The classifiers used were IB1 (nearest neighbor),
IB3 (3 nearest neighbor), logistic regression, Naïve Bayes Classifier, support vector
machine, and neural network with back propagation. Both ChemOffice 6.0
(CambridgeSoft Corp.) and the NCI website were used to identify compound structures
via their NSC numbers and substructure searching to identify quinone compounds in the
larger data set were carried out using ChemFinder (CambridgeSoft).
Results and Discussion
Identifying functional cancer cell line classes using gene expression data.
Based upon gene expression data, we initially decided to identify cancer cell line classes
that we could use in a subsequent supervised learning approach to stringently select
compound subsets capable of classifying the individual cancer cell classes. In Figure 2,
we present a hierarchical clustering dendrogram using the 1-Pearson distances calculated
from the T-Matrix, comprised of 1376 gene expression values determined for the 60 NCI
cancer cell lines (43). There are five well defined clusters observed. Four of the clusters
in Figure 2 (renal, leukemia, ovarian and colorectal from second left to right) represent
pure cell line classes. In only the melanoma class instance does the class contain two
members of another clinical tumor type, two breast cancer cell lines - MDA-MB-435 and
MDA-N. The 2 breast cancer cell lines behave functionally as melanoma cells and seem
to be related to melanoma cell lines via a neuroendocrine origin as has already been
observed and remarked upon (43). The remaining cell lines in the Figure 2 dendrogram,
those not found in any of the five functional classes, are defined as being in the sixth
class- the non- melanoma, leukemia, renal, ovarian, colorectal class. In the supervised
learning studies that follow, we treat these six functional clusters as the ground truth.
2-Class Cancer Cell Classifications and Validation of Results. High class
number classification problems are difficult to implement where the data are not clearly
separable into distinct classes and we could not successfully carry out a 6-class
classification of the cancer cell line classes based upon the starting GI50 compound data.
For this reason, we chose to implement 3-Class and 2-Class problems utilizing RadViz™
, which combines an analytic class discrimination layout algorithm, employing feature
reduction, with a high dimensional visualization resulting from the algorithm’s output
(25, 45-47). Starting with the small 1400 compounds’ GI50 data set that contained no
missing values for all 60 cell lines, those compounds were selected that were effective in
carrying out the classification at the p < .01 (Bonferroni corrected t statistic) significance
level. The 3 -Class problem at p < .01 significance, for the melanoma, leukemia and nonmelanoma, non-leukemia classes are presented in Figure 3. In contrast to the 6-Class
problem results we obtained (data not shown), the 3-Class problem result in Figure 3 at
the same significance, p < .01, produced clear and accurate class separations of the 60
cancer cell lines. There were 14 compounds selected in the 2-Class problem as being
most effective (lowest GI50 values) against melanoma class cells and 30 compounds
were identified as most effective against the leukemia class cells. Similar classification
results were obtained for the separate 2-Class problems melanoma vs. non-melanoma and
leukemia vs. non-leukemia (data not shown; [41]). For all other possible 2-Class
problems, we found that few to no compounds could be selected at p <.01.
Validating the results we obtained from our RadViz™ methodology for
compound selection in both 2-Class and the 3-Class problems was our next goal. We
utilized 6 analytic classification techniques ( Instance Based 1, Instance based 3, neural
networks, logistic regression and support vector machines), with the same selected
compounds’ GI50 values as a classifier set based upon calculating the frequency of correct
classification and using a 60-fold repetition of the training-test process using the holdone-out method. Well above 90% accuracies were achieved using these compound
subsets (data not shown; see [41]). For the 80 compounds selected as most effective
against leukemia at the p < .01 criterion, the average accuracy achieved by the 6 analytic
algorithms was 99.3%, corresponding to only a 0.7% error rate. Based upon repetitively
selecting 80 compounds randomly, the average level of accuracy was calculated to be
95.7%, corresponding to a 4.3% error rate (data not shown).
It is counterintuitive to achieve an accuracy as high as 95.7% from any 80
compounds randomly selected from the 1400. However, this is because any 80 randomly
selected compounds will always include a small number of the significant compounds.
Therefore, using the RadViz™ selected compounds represented a greater than 6-fold
lowered level of error compared to the randomly selected compounds, thus validating our
selection methodology.
Quinone Compound Subtypes preferentially effective against melanoma.
Next, we decided to examine the chemical identity of the compounds selected as most
effective against melanoma and leukemia. To summarize, for the 14 compounds selected
in Figure 3 as most effective against melanoma, 11 are p-quinones. Of the 11 p-quinones,
all 11 are internal ring quinone structures. We display a representative example of these
structures in Figure 4. These internal ring quinones possess either 2 neighbor aromatic 5
or 6 member fused rings, some of which are heteroatom containing, on either side of the
quinone ring or an aromatic fused ring neighbor on one side and covalent non-H
substitutions off the other side of the quinone. These substitutions all have
electronegative atoms covalently bonded to either or both the o and m positions of the
quinone ring, except for one compound which has an –OH substituent off the adjacent
ring. In 8 of the cases, the internal ring quinones are directly bonded to 2 electronegative
atoms, either heteroatoms contained within the aromatic fused ring or as small covalent
substituents. And in 2 more cases, the internal quinone ring is bonded to 1 electronegative
atom within a neighboring fused ring.
A recent analysis by Blower et.al., (42) simultaneously correlating gene
expression data for the 60 cancer cell lines with GI50 values for a set of 4463 compounds
for which a 27,000 feature set had been calculated. The investigators identified a subclass of compounds containing a benzothiophenedione core structure that were most
highly correlated with the expression patterns of Rab7 and other melanoma specific
genes. There is clearly some overlap between the internal quinone subtype we have
defined in the present study and the benzothiophenedione core structure members. Out of
the 11 internal quinone compounds we identified, 3 are of the benzothiophenedione core
structure class, but they are not amongst the most effective compounds we identified. The
Rab7 gene is a member of the GTP binding protein family involved in the docking of
cellular transport vesicles and is a key regulator of aggregation and fusion of late
endocytic lysosomes (48). In the same study, a number of other genes whose expression
levels highly correlate with the same compounds, expressed proteins involved in other
lysosomal functions, suggesting a link between the quinone oxidation potential, the
proton pump and the electron transport chain. These investigators suggested the
possibility that benzodithiophenedione compounds may act directly as surrogate
oxidizing agents, effectively competing with ubiquinone in the electron transport chain
and thereby disrupting an essential cellular redox process. The effectiveness of any
compound in this type of mechanism would be based upon its redox potential.
Quinone Compound Subtypes preferentially effective against leukemia. There
were 30 compounds selected as most effective against leukemia in the leukemia, nonleukemia 2-Class Problem, of which 8 are structures containing p-quinones. In contrast to
the internal ring quinones comprising the melanoma class, 6 out of the 8 leukemia pquinones were external ring quinones. We display an example of these structures in
Figure 4B. In contrast to the internal ring quinones, these external ring quinones had only
one aromatic fused ring neighbor which had no ring heteroatoms in all cases. Also
different, the quinone was itself at the periphery of the molecule and had no non-H
substituents off the exterior side of the ring at either o or m positions. Thus, the
‘external’ and ‘internal’ quinone rings should possess different electron densities and
redox potentials for the quinoid oxygens. Besides redox potentials, other possible subtype
differences may exist such as: solubility, steric differences relative to metabolic enzyme
active sites, differential cellular adsorption, etc.
Again, the recent analysis by Blower, et.al., (42) discussed above, identified a
sub-class of compounds, comprised of an indolonaphthoquinone core structure. These
compounds were most highly correlated with the expression patterns of LCP1,
lymphocyte cytosolic protein 1 (L-plastin located on chromosome 13), HS1, a
hematopoietic lineage specific gene, and other leukemia specific genes. There is overlap
between the external quinone subtype and the indolonaphthoquinone core structure
members. This overlap between the two studies is somewhat remarkable since we
included no gene expression data in our analysis of the GI50 values, as did the Blower
study (42). This suggests two things. The first is that there is sufficient information
inherent in the compound GI50 values to carry out the basic core discovery presented here,
without the need to include gene expression data in the analysis. The second is that the
class discrimination layout algorithm of RadViz™, used here to select and array
compound’s axes to maximize the cluster separation, is a highly effective data mining
tool.
Uniqueness of Two Quinone Subtypes. In order to ascertain the uniqueness of
the two quinone subsets found effective against melanoma and leukemia, we first
determined the extent of occurrence of p-quinones of all types in our starting data set of
1400 compounds. To do this, we examined the entire data set via substructure searching
using the ChemFinder 6.0 software. We found that the internal and external quinone
subtypes we identified as effective against melanoma and leukemia respectively,
represent a significant fraction, 25 % (10/41) of all the internal quinones and 40 %
(6/15) of all the external quinones in the data set. In addition, we determined that only
one compound, NSC 621179, which is not a quinone but an epoxide, was found to be
effective against both melanoma and leukemia in a 2-Class classification where one class
was both leukemia and melanoma cell lines and the second class was non-melanoma,
non-leukemia cell lines. This result attests to the uniqueness of the specificity of the two
quinone subtype classes.
The NCI data set lists 92 compounds known to fall within one of 6 Mechanism
Of Action (MOA) Classes: alkylating agents, antimitotic agents, topoisomerase I
inhibitors, topoisomerase II inhibitors, RNA/DNA antimetabolites, DNA antimetabolites
(33). We determined that the most effective 14 and 30 compounds against melanoma and
leukemia respectively identified in the 2-Class problems do not fall into clusters with any
one of these 6 MOA compound classes. Using the 14 melanoma and the 30 leukemia
compounds as the two classes, the RadViz™ class discrimination algorithm layed out the
60 cell lines based upon the 2 class compounds’ GI50 values (figure not shown: [42]),
producing 2 well separated compound classes. Then the 92 known MOA compounds
were simply placed upon this optimized RadViz™ display. None of the 92 compounds in
the 6 MOA classes clustered with any of the compounds in either the melanoma or the
leukemia class.
Sub-classification of Leukemia Cell Lines. We next asked the question whether
we could sub-classify either the melanoma or the leukemia cell lines into distinct clinical
sub-classes based upon using our 2 respective compound classes. Therefore, we first
carried out an unsuccessful 3-Class RadViz™ based classification for the melanotic
melanoma, other melanoma and non-melanoma cell classes at p < .05, using the most
effective 14 compounds identified as most effective against all melanomas (data not
shown).We next carried out a 3-Class RadViz™ based leukemia cell sub-classification
for the acute lymphoblastic leukemia (ALL), non-ALL leukemia (other) and nonleukemia cell classes at p < .05. To carry out the sub-classification, we used the most
effective 30 compounds identified for the p < .01 selection criterion as most effective
against all leukemias and this result is presented in Figure 5. Six of the 30 compounds
were most effective against the ALL class; while 12 of the 30 compounds were most
effective against the non-ALL leukemia. In this result, it is clear that there is a separation
of the 2 ALL cell lines (CCRF-CEM and MOLT-4) from the non-ALL leukemia subclass. These two ALL cell lines were also the most closely clustered leukemia cells in the
Figure 2 gene expression based clustering dendrogram. This suggests the interesting
possibility that the chemical identity of the compounds most effective against the 2 ALL
cell lines are linked to the gene functions most responsible for closely clustering these 2
ALL cell lines in Figure 1.
NAD(P)H: quinone oxidoreductase 1 -Quinone substrates and Leukemias
Different redox potentials and enzymatic reactivities are likely to be the key to how these
quinone subtypes differentially affect melanoma and leukemia cells. In addition to the
gene candidates identified as potentially involved in quinone activity in the Blower, et.al,
(42) study, a strong candidate enzyme for this differential reactivity is NAD(P)H:quinone
oxidoreductase 1 (QRI, NQO1, also DT-diaphorase; EC 1.6.99.2). This enzyme is
expressed in normal cells and at high levels in many types of tumors (49). It catalyzes
two electron reduction of a variety of substrates with the most efficient substrates being
quinones (50). The enzyme has been crystallized and the X-ray structures of the
apoenzyme at 1.7-A resolution and its complex with substrate duroquinone (2,5A) are
known (51,52). NAD(P)H:quinone oxidoreductase 1 is a chemoprotective enzyme that
protects cells from oxidative challenge. Antitumor quinones, of the type we have
identified above in the NCI data set, may be bioactivated by this enzyme to forms that are
cytotoxic (50). This catalytic property makes this enzyme an excellent target for enzymedirected drug development (52). Reductive activation is particularly well–suited for
treatment of hypoxic tumors, where the bioreduction of the chemical agent to
hydroquinone cannot be reversed by endogenous oxygen (53). Interestingly, there are a
number of reports that correlate altered forms or alleles of this enzyme with leukemia
(54-56). These reports, associating leukemias with particular aspects of
NAD(P)H:quinone oxidoreductase 1, suggest the enzyme as likely being a significant
factor in why the external quinone subtypes, acting as particularly potent and effective
substrates, exhibit their differential selectivity toward leukemias. We believe that only
through experiments or calculations to determination the redox potentials of the different
quinone compounds, influenced by the type and distribution of substituent groups, will
the exact nature of the compound selectivity exhibited by the subtypes in our study be
known.
2. Microarrays
Analysis of High Throughput Gene Expression Experiments: Effects of
Normalization Methods on Gene Expression Analysis Clustering Results.
Completion of the Human Genome Project has made possible the study of the gene
expression levels of over 30,000 genes [14,15; although a ‘final’ human genome
sequence is scheduled for release in Spring, 2003]. Major technological advances have
made possible the use of DNA microarrays to speed up this analysis. Even though the
first microarray experiment was only published in 1995, by October 2002 a PubMed
query of microarray literature yielded more than 2300 hits, indicating explosive growth in
the use of this powerful technique. DNA microarrays take advantage of the convergence
of a number of technologies and developments including: robotics and miniaturization of
features to the micron scale (currently 20-200 um surface feature sizes for
spotting/printing and immobilizing sequences for hybridization experiments), DNA
amplification by PCR, automated and efficient oligonucleotide synthesis and labeling
chemistries, and sophisticated bioinformatics approaches. It is this latter aspect of the
development of microarray technology that our Phase II proposal addresses.
One significant aspect of analyzing microarray gene expression data is the need for
normalization to remove non-biological sources of variation (noise), in order to make
meaningful comparisons of data from different microarrays. The noise results from
differences in individual chips, labeling chemistry, length of immobilized oligonucleotide
sequence, different optical properties of various data scanners and other sources. The
importance of understanding and controlling these variables has been underscored by the
apparent lack of reproducibility of some published microarray studies. This has led to the
establishment of the MIAME publication guidelines that detail the following
requirements for describing microarray experiments: 1) experimental design, 2) array
design and the name and location of array spots, 3) sample name extraction and labeling,
4) hybridization protocols, 5) image measurement methods, 6) controls used [16-18].
Normalization techniques that have been applied include simple linear scaling, locally
linear transformations, and other nonlinear methods. To some extent, the techniques used
depend on the type of array being used. In 2 channel arrays, for example cDNA
microarrays, the issue is primarily within-chip normalization to correct distortions based
on location and signal intensity. Between-chip normalization is less of an issue for these
arrays because one channel usually contains a reference tissue that is common to all
arrays in the experiment. Between-chip normalization has the potential of introducing
more noise than it eliminates. A number of thorough discussions of normalization
techniques for cDNA arrays have been presented [19,20]. These normalization
approaches include dye swap experiments to correct for differences between the two
channels, using the lowess function to correct for global intensity based differences (i.e.
across all genes on the chip), and using the lowess function locally to account for spatial
and print-tip differences.
For the majority of applications, Affymetrix microarrays are in use. For these arrays,
between-chip normalization is an important issue, and is closely related to the method of
calculation of gene expression value from multiple probes for each gene. Techniques
proposed for calculating expression include the original Affymetrix method of average
difference between perfect match and mismatch probes, the Model Based Expression
Index approach of Li and Wong [21], and the Robust Multichip Average approach of
Irizzary et al [22]. Durbin et al [23] have suggested a variance-stabilizing transformation
to aid microarray analysis. There is the additional consideration of whether to normalize
data based on probe level measurements or expression calculations, and whether to use a
baseline array for comparison or to normalize over the complete set of data. Bolstad et al
[24] present comparisons of some of these techniques. They recommend probe level and
complete data methods in general, and quantile normalization in particular. They also
found that the invariant set normalization approach of Schadt et al [25] using a baseline
array gives results that are comparable to complete data methods. Our experience has
shown that quantile normalization works well even when probe level data are not
available. However, quantile normalization makes the implicit assumption that the data
on all chips have the same distribution. For some datasets this may not be appropriate.
Different normalization and modeling techniques can lead to widely varying
judgments and interpretations of differential gene expression. In this Phase II proposal,
we aim to investigate the effects of different data normalizations on clustering. We will
compare quantile normalization, invariant set normalization, lowess local regression, and
simple linear scaling. We will focus primarily on Affymetrix type arrays, but we will
ensure that the platform we develop supports the adaptation and application of these
techniques to two channel microarrays where appropriate. We will also investigate the
effects of different modeling techniques on clusters. The more successful a technique is
at removing noise, the more likely it is that the clusters generated will be accurate and
will have biological meaning. On the other hand, the quality and stability of clusters
could be a useful measure of the appropriateness of the normalization and modeling
techniques used. Therefore, a goal of this Phase II proposal is to provide users with
decision making tools to decide which normalization approach is optimal or close to
optimal for a given microarray dataset. Also, the normalization tools will be integrated
with the perturbation algorithm output, discussed below, to determine the stability of
clusters from different normalizations. In this way, we can provide users with the identity
of those genes that are most stable within clusters, and those that are unstable and jump
between clusters as a result of different normalizations.
3. Proteomics
4. UNOS
Conclusions
Acknowledgements
AnVil and the authors gratefully acknowledges support from two SBIR Phase I grants
R43 CA94429-01 and R43 CA096179-01 from the National Cancer Institute. Also,
support is acknowledged from ………..X Y Z
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Figure Legends
Figure 1. RadViz figure
Figure 2. Cancer cell line functional class definition using a hierarchical clustering (1Pearson coefficient) dendrogram for 60 cancer cell lines based upon gene expression
data. Five well defined clusters are shown highlighted. We treat the highlighted cell line
clusters as the truth for the purpose of carrying out studies to identify which chemical
compounds are highly significant in their classifying ability
Figure 3. RadViz™ result for the 3-Class problem classification of melanoma, leukemia
and non-melanoma, non-leukemia cancer cell types at the p < .01 criterion. Cell lines are
symbol coded as described in the figure. A total of 14 compounds (bottom of layout)
were most effective against melanoma and they are layed out on the melanoma sector
(counterclockwise from most to least effective). For leukemia, 30 compounds were
identified as most effective and are layed out in that sector. Some 8 compounds were
found to be most effective against non-melanoma, non-leukemia cell lines and are layed
out in that sector.
Figure 4. One example each of the two quinone subtypes selected in Figure 3 are
displayed. A. The most highly effective of the 11 internal quinone subtype compounds
most effective against melanoma is shown. B. The most highly effective of the 6 external
quinone subtype compounds most effective against leukemia is shown
Figure 5. RadViz™ result for the 3-Class Problem classifying the following three
classes: acute lymphoblastic leukemia (ALL), non-ALL leukemia (other-Leukemia) and
non-leukemia cell classes at p < .05. We used as input the 30 compounds identified in the
Figure 3 classification as most effective against all leukemias at the p < .01 selection
criterion. Cell lines are symbol coded as described in the figure. The NSC numbers of the
compounds selected to classify the classes are presented in the order of their ranking from
most effective to least effective moving counterclockwise within each class sector.
0.2
ME_LOXIMVI
PR_PC-3
PR_DU-145
RE_SN12C
0.6
0.0
LC_HOP-92
BR_MDA-MB-231/ATCC
CNS_SF-295
CNS_SNB-19
CNS_U251
BR_BT-549
CNS_SF-268
CNS_SF-539
CNS_SNB-75
BR_HS578T
RE_A498
RE_CAKI-1
RE_ACHN
RE_UO-31
RE_TK-10
RE_RXF-393
RE_786-0
LC_NCI-H226
LC_HOP-62
OV_OVCAR-8
BR_MCF7/ADF-RES
LC_NCI-H23
LC_NCI-H522
LC_NCI-H460
LC_A549/ATCC
LC_EKVX
LE_SR
LE_RPMI-8226
LE_K-562
LE_HL-60
LE_CCRF-CEM
LE_MOLT-4
OV_SK-OV-3
OV_IGROV1
OV_OVCAR-3
OV_OVCAR-4
OV_OVCAR-5
LC_NCI-H322M
BR_MCF7
BR_T-47D
CO_HCT-116
CO_SW-620
CO_HCT-15
CO_KM12
CO_HT29
CO_HCC-2998
CO_COLO205
BR_MDA-MB-435
BR_MDA-N
0.4
ME_SK-MEL-5
ME_MALME-3M
ME_SK-MEL-28
ME_UACC-257
ME_SK-MEL-2
ME_UACC-62
ME_M14
Height
Cluster Dendrogram
1.0
0.8
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4. 658450
5. 602617
6. 690432
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