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Logical Analysis of Diffuse Large B Cell Lymphoma. Gabriela Alexe 1 , Sorin Alexe 1 , David Axelrod 2 , Peter Hammer 1 , and David Weissmann 3 of RUTCOR(1) and Department of Genetics(2), Rutgers University; and Robert Wood Johnson Medical School(3). This Talk. Lymphoma

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Logical analysis of diffuse large b cell lymphoma

Logical Analysis of Diffuse Large B Cell Lymphoma

Gabriela Alexe1, Sorin Alexe1, David Axelrod2, Peter Hammer1, and David Weissmann3

of RUTCOR(1) and Department of Genetics(2), Rutgers University; and Robert Wood Johnson Medical School(3)


This talk
This Talk

RUTCOR

  • Lymphoma

  • Gene Expression Level Analysis

    • cDNA Microarray

    • Applied to Diffuse Large B-Cell Lymphoma

  • Logical Analysis of Data

    • Discretization/Binarization

    • Support Sets

    • Pattern Generation

    • Theories and Models

    • Prediction



Lymphoma1
Lymphoma

RUTCOR

  • Cancer of lymphoid cells

    • Clonal

    • Uncontrolled growth

    • Metastasis

  • Lymphoma

    • Diagnosis

    • Grade


Diffuse large b cell lymphoma dlbcl
Diffuse Large B Cell Lymphoma (DLBCL)

RUTCOR

  • 31% of non-Hodgkin lymphoma cases

  • 50% long-term, disease-free survival

  • Clinical variability

  • Prognosis & therapy

    • IPI

    • Morphology

    • Gene expression






Gene expression profiling
Gene Expression Profiling

Standard

Tumor

RUTCOR

cDNA microarray analysis


Dlbcl cdna microarray analysis
DLBCL & cDNA Microarray Analysis

RUTCOR

  • Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling,Alizadeh et al., Nature, Vol 403, pp 503-511

  • cDNA microarray data -> unsupervised hierarchical agglomerative clustering

    • Germinal center signature: 76% survival at 5 years

    • Activated B cell signature: 16% at 5 years


Dlbcl clustering
DLBCL Clustering

Germinal center genes

Activated B cell genes

RUTCOR

Each case (patient) is a point in N-dimensional space where N = # of genes



Supervised learning classification of dlbcl
Supervised Learning Classification of DLBCL

RUTCOR

  • Diffuse large B-cell lymphoma prediction by gene-expression profiling and supervised machine learningShipp et al., Nature Medicine, vol 8, p 68-74

  • Prognosis of DLBCL

  • Highly correlated genes -> weighted voting algorithm



Logical analysis of data lad
Logical Analysis of Data (LAD)

RUTCOR

  • Non-statistical method based on:

    • Combinatorics

    • Optimization

    • Logic

  • Based on dataset of cases/patients

  • LAD learns patterns characteristic of classes

    • Subsets of patients who are +/- for a condition

  • Collections of patterns are extensible

    • Predictions


The problem approximation of hidden function
The Problem : Approximation of Hidden Function

RUTCOR

Dataset

HiddenFunction

LAD Approximation


Main components of lad
Main Components of LAD

RUTCOR

  • Discretization/Binarization

  • Support Sets

  • Pattern Generation

  • Theories and Models

  • Prediction


Discretization
Discretization

RUTCOR

Separating

Cutpoints

Minimum Set of

SeparatingCutpoints


Cutpoints and support set
Cutpoints and Support Set

RUTCOR

  • Minimization is NP hard

  • Numerous powerful methods

  • Support set:

    • Cutpoints define a grid in which ideally no cell contains both + and – cases

  • Cutpoints simplify data and decrease noise


Patterns
Patterns

RUTCOR

  • Examples:

    • Gene A > 34 & gene B < 24 & gene C < 2

  • Positive and negative patterns

  • Pattern parameters:

    • Degree (# of conditions)

    • Prevalence (# of +/- cases that satisfy it)

    • Homogeneity (proportion of +/- cases among those it covers)

  • Best: low degree, large prevalence, high homogeneity

  • Patterns are extensible!


Pattern generation
Pattern Generation

RUTCOR

  • Generate patterns based on learning set

  • Stipulate control parameters. For example:

    • Degree £ 4

    • + & - prevalences >= 70%

    • + & - homogeneities = 100%

  • All 75 patterns in 1.2 seconds on Pentium IV 1 Gz PC

  • Evaluate set:

    • Average # of patterns covering each observation

    • Accuracy applied to evaluation set


Patterns illustration
Patterns: Illustration

RUTCOR

Negative Pattern

Positive Pattern


Theories approximations of the 2 regions
Theories: Approximations of the 2 Regions

Positive Theory

Negative Theory

RUTCOR

A theory is a set of positive (or negative) patterns such that every positive (or negative) case is covered.


Models
Models

RUTCOR

  • A set of a positive and a negative theory

  • A good model:

    • Small number of features (genes)

    • Patterns are high quality

      • Low degrees

      • High prevalences

      • High homogeneities

    • Number of patterns is small

      • Maximize their biologic interpretability


Theories and models
Theories and Models

RUTCOR

Unexplained Area

Positive Theory

Negative Theory

Model

Positive Area

Discordant Area

Negative Area


Lad prediction
LAD Prediction

RUTCOR

  • A new case: a set of gene expression levels

  • Satisfy some positive & no negative?

  • Satisfy some negative & no positive ?

  • Satisfy some of both?

    • Which more?

  • Does not satisfy any (rare)




Conclusion
Conclusion

RUTCOR

  • Logical Analysis of Data (LAD ): a versatile new classification method here applied to diagnosis and prognosis of lymphoma.

  • LAD genes differ almost entirely from those specified by other studies.

  • Genes not individually correlated with diagnosis or prognosis but highly correlated in combinations of as few as two genes.

  • Patterns suggest biologic pathways

  • LAD provides highly accurateprognosis of DLBCL


Contacts
Contacts

RUTCOR

  • Gabriela Alexe: [email protected]

  • Soren Alexe: [email protected]

  • David Axelrod: [email protected]

  • Peter Hammer: [email protected]

  • David Weissmann: [email protected]


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