Lecture 8 (D. Geman)

7/29/2019 Lecture 8 (D. Geman)

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STATISTICAL LEARNING IN CANCERBIOLOGY: LECTURE 8

Donald Geman, Michael Ochs, Laurent YounesJohns Hopkins Unversity

ENS-Cachan

March 1, 2013


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LECTURE SERIES

Lecture 1: Introduction (DG)

Lecture 2: Cancer Biology (MO)

Lecture 3: Cell Signaling Inference (MO)

Lecture 4: Genetic Variation (DG)

Lecture 5: Massive Testing (LY)

Lecture 6: Biomarker Discovery (LY)

Lecture 7: Phenotype Prediction (DG) Lecture 8: Embedding Mechanism (DG)

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OUTLINE

Results Without Biology

Gene Regulation in Cancer

Reversed Enrichment Analysis

Regulatory Motifs and Predictors

Looking Ahead

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ACCURACY OF RANK-BASED CLASSIFIERS

0.7

0.8

0.9

1

96 97 96 98 98 100

Leukemia 2

0.9

0.95

1

98 98 98 98 100 97

Leukemia 3

0.9

0.95

1

93 97 96 97 98 97

Leukemia 4

0.6

0.8

1

94 93 93 93 93 90

Prostate 1

0.4

0.6

0.8

1

68 77 77 79 74 76

Prostate 2

0.4

0.6

0.8

1

88 94 95 91 1 00 99

Prostate 3

0.4

0.6

0.8

1

69 78 77 82 77 81

Prostate 4

0.4

0.6

0.81

98 98 98 97 99 97

Prostate 5

0.6

0.8

1

86 86 86 88 83 87

Breast 1

0.6

0.8

1

83 81 82 81 78 85

Breast 2

0.6

0.7

0.8

0.9

1

86 89 89 90 90 88

Average

TSP

TSM

SWP

kTSP

SVM

PAM

Figure: Estimated classification accuracy (ten runs of 10-fold CV) for

datasets D13,...,D21. Bottom diagram represents the average of the

accuracies across all data sets.

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SO WHAT?

All about the same (based on cross-validation).

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SO WHAT?


Generally, nothing stands up to cross-study validation, i.e.,

nothing is replicable.

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SO WHAT?




What is missing, both for serious applications and probablyeven for robust performance, is mechanism.

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SO WHAT?





Bring in the biology at the beginning, not just at the end (thecustomary story about the discovered genes).

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SO WHAT?





Bring in the biology at the beginning, not just at the end (thecustomary story about the discovered genes).

TSP was originally motivated by a comment by aboutcomparing protein concentrations.

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TOWARDS MECHANISM

What might be a mechanistic interpretation of the TSP

classifier, where the context consists of only two genes? Example: Obscurin and PRUNE2 are a TSP that perfectly

distinguish between gastrointestinal stromal tumor (GIST)

and Leiomyosarcoma (LMS) (Price et al, PNAS, 2006).

It has been recently shown that both modulate RhoAactivity (which controls many signaling events):

A splice variant of Prune2 is reported to decrease RhoA

activity when over-expressed; Also, Obscurin contains a Rho-GEF binding domain which

helps to activate RhoA.

Hence, providing an explanation, an hypothesized

mechanism, is not straightforward.

Can we say anything of a generic nature?

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OUTLINE





Looking Ahead

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REGULATORY CANCER BIOLOGY

Hundreds of different cell types with different morphology

and biological functions exist in the human body.

Gene regulatory networks orchestrate this diversity by

regulating distinct gene expressions all encoded from thesame genome.

A variety of molecular alterations (e.g. DNA mutation and

epigenetic modification) can ultimately result in profound

modifications of these regulatory networks and resultinggene expression programs, in some cases causing cancer.

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REGULATORY PATTERNS

These complex networks have distinct network motifs,

defined as patterns of inter-connectivity occurring more

frequently within a network than by chance, showing

distinctive structure and organization. The analysis of the biochemical regulatory networks that

control gene expression, organism development, and

cellular signal transduction has revealed prominently

enriched topologies among all possible triads motifsinvolving three nodes.

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REVIEW: ACTIVATION AND REPRESSION

As seen in the lectures on cell signaling, perhaps the twomost generic and elementary regulatory motifs are simply Aactivates B (denoted A B) and A inhibits B (denotedA B).

As examples of inhibition: A may be constutively on and B constutively off after

development. Or perhaps A is a transcription factor or involved in

methylation of B. In the normal phenotype we see A expressed but perhaps A

becomes inactivated in the cancer phenotype, resulting in

the expression of B, and hence an expression reversal from

normal to cancer.

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REVIEW: TRANSCRIPTION FACTORS (TFS)

Transcription factors are proteins that usually bind upstream

of genes and regulate transcription, either by activation orinhibition.

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TF/MIR MOTIFS (I)

Until recently, gene expression regulatory motifs referred to

the molecular circuitry of transcription factors controlling

gene expression.

In recent years, however, the role of additional regulatory

factors has been revealed. MicroRNAs (miRs) a family ofsmall non-coding RNA molecules negatively regulate

gene expression both at the transcriptional and

post-transcriptional level.

These changes control tissue development, stem cellmaintenance, and key cellular processes like cell growth,

differentiation and apoptosis.

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REVIEW: MICRORNAS (MIRS)

miRs mark mRNAs for degradation by binding to the 3

UTR. miR targets can be predicted due to complementary

binding.

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TF/MIR MOTIFS (II)

TFs and miRs share common regulatory properties and

often co-regulate gene expression.

Statistical analyses have shown that modules involving TF,

miR, and other non-TF genes are usually configured in a

feed-forward-loop (FFL) topology, where the miR inhibits

both the TF and the genes this latter regulates.

These TF/miR regulatory motifs are crucial in organizing

the body plan during development, controlling stem cells,

orchestrating epithelial to mesenchymal transition (EMT),and distinguishing tissues.

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FEED-FORWARD LOOP OF TF/MIR PAIRS

SOX9

Inhibition

Activation Inhibition

SOX9

TARGETS

MIR-124

MIR-30-5P

TARGETS

Figure: The miR inhibits both the TF SOX9 and its target genes. In the

example SOX9 activates the transcription of its target genes, while

miR-124 and miR-30-5p contribute to their degradation.

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TF/MIR MOTIFS IN CANCER

Alterations of miR/TF regulatory modules have beenimplicated in cancer pathogenesis and progression. For instance, a motif involving the tumor suppressor p53, the

oncogene c-Myc, miR-34b, and miR-34c has recently been

identified in prostate cancer. Another circuit involving c-Myc, PTEN, E2F1, p21, and the

miR-17-92 cluster has been implicated in lymphoma, breast,

prostate, stomach, colon, pancreatic, lung cancers.

Finally TF/miR regulatory motifs have been also shown to

modulate therapy response. These data underscore the role of regulatory motifs in

cancer, suggesting that they can classify and predict cancer

phenotypes.

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O


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OUTLINE





Looking Ahead

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H L


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HYPOTHESIS-DRIVEN LEARNING

Associate candidates for differential mechanism (e.g.,

regulatory motifs) with multivariate features.

Strategy: map motifs {M} to features gM(X), where M is

an instantiated regulatory pattern (template). However, doing this directly appears very difficult. Again,

the sample size does not support the combinatorial search.

Instead, pass through differential expression on the way to

g(X).

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D E (DE)


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DIFFERENTIAL EXPRESSION (DE)

How well does the expression of a single gene i predictphenotype?

Decision rule: f(xi) = {xi > t} for a threshold t. Measure performance by the area under the ROC curve.

Not surprisingly, TSPs are enriched for genes that are

significantly differentially expressed.

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DE TSP


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DE AND TSP

Let G be the top 100 DE genes (by AUROC).

Let S be the top 100 TSPs (measured by score ij.

Then S is enriched for pairs i,j G (Fisher exact test).

Example: Prostate data (similar results on other datasets):

i or j / G i,j G

(i,j) S 86 14(i,j) /

S 79,368,664 4,936

Cond. Probs. 106 0.003

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GENERAL QUESTION


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GENERAL QUESTION

Let G1, G2 be subsets of genes. Does G1 G imply G2

enriched for DE genes?

G1 G2+ DE Genes miRNA Regulators

- DE miRNAs Gene targets

+ DE TFs Gene targets

+ DE Genes TF Regulators

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PARTIAL RANK SUM TEST


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PARTIAL RANK SUM TEST

Let A B (features).

Rank all elements within B.

Test statistic: The sum of the ranks of the N largest

elements of A. Reduces to Wilcoxon Rank Sum Test ifN = |A|.

P-value computed by monte carlo.

Measure whether members of A are enriched near the very

top of B.

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DE TFS TO GENE TARGETS


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DE TFS TO GENE TARGETS

Genes targeted by DE TFs (AUROC > 0.85) are enriched for

DE (p< 0.005, PRST with N = 100).

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DE GENES TO TF REGULATORS


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DE GENES TO TF REGULATORS

TFs that target DE genes (AUROC > 0.95) are marginally

enriched for DE (p = 0.05, PRST, N = 10).

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DE GENES TO MIRNA REGULATORS


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DE GENES TO MIRNA REGULATORS

miRNAs which target DE genes are enriched for DE (p = 0.04,

PRST, N = 10).

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OUTLINE


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OUTLINE





Looking Ahead

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MOTIVATION


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MOTIVATION

Complex regulatory cross-talks involving microRNAs (miR)

and transcription factors (TF) control key cellular processes

like apoptosis and proliferation, and are perturbed in cancer.

Therefore, design novel prediction algorithms based on

miR/TF molecular circuitry.

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PRELIMINARY EXPERIMENT


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PRELIMINARY EXPERIMENT

Designed to llustrate the impact of embedding theseregulatory motifs into computational learning.

The phenotype is ER status in breast cancer. Whereas this

is not an open problem, it provides a test case in which the

clinical attributes of the phenotypes are well characterized.

The data are expressions of 9,000 genes common to the

breast cancer datasets GSE22220 (Dataset I) and

GSE19783 (Dataset II), training on the first one and

validating on the second one.

Compare the performance of classifiers based on the

relative expression of two genes chosen randomly versus

chosen under simple network-based constraints.

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REGULATORY GRAPH


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REGULATORY GRAPH

Start with experimentally-justified regulatory networks

among genes.

The regulation of miRs by TFs was obtained from the

miRgen 2.0 database.

The list of experimentally validated miR targets, which

includes the TF, was retrieved from the TarBase v5.0

database.

After cross-referencing to the common genes in Dataset I

and II this yields a network of 200 TF, 373 miR, and 2772target genes.

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RANDOM PAIR CLASSIFIERS


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RANDOM PAIR CLASSIFIERS

Consider the predictor based on comparing the expressions

X1 and X2 of two genes g1 and g2.

Training consists in choosing the order which predicts ER+

and estimating accuracy. There are no parameters to

estimate.

Generate a baseline distribution of performance (measured

on Dataset II) using random pairs by sampling 100,000

classifiers out of the

9,0002

4.107 possible pairs.

Compared the results to pairs (classifiers) derived from thenetwork as follows.

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NETWORK-BASED PAIRS


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NETWORK BASED PAIRS

Consider now pairs of genes {g1, g2} both related to a hubs, either a TF or a miR.

Suppose g1 regulates s, which in turn regulates g2.

Example: g1 inhibits s and s activates g2. We might thenexpect X1 to be large and X2 to be small when s is off,

and vice-versa when s is on, so that s acts as a switchregulating the relative expressions of the two genes.

In our dataset, there are about 31,000 (resp., 42,000) such

pairs with a TF hub (resp., miR hub). All were testedagainst the random pairs.

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TEST RESULTS (I)


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TEST RESULTS (I)

Random and network pairs with classification rates above

0.7 are compared.

There were respectively 862 (0.86%), 375 (1.2%) and 389

(0.96%) high-performing classifiers in each of the threecategories (random, TF hub, miR hub).

A Wilcoxon rank-sum test comparing high-performing

random classifiers with either TF or miR hubs has p-values

of 10

14 and 10

26, respectively.

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TEST RESULTS (II)


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TEST RESULTS (II)

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TEST RESULTS (III)


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TEST RESULTS (III)

The top two-gene classifiers in the network class all

involved the ERS1 gene, consistent with the biology of ER

status in breast cancer.

More interesting were the genes paired with ERS1 in the

best classifiers.

For instance POU2F1 (OCT1) is a TF member of the POU

family, which physically interacts with BRCA1 and ER itself,

and that recruits BRCA1 to the ESR1 promoter to control

ER expression. Notably, BRCA1-mutant breast tumors aretypically ER negative.

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FEEDBACK LOOPS (I)


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C OO S ( )

Still more generally, a variety of regulatory feedback loops

have been identified in mammals. For instance, an exampleof a bi-stable loop is shown below.

Molecules A1, A2 (resp. B1, B2) are from the same species,for example two miRNAs (resp., two mRNAs). Letters in

boldface indicate an on state.

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FEEDBACK LOOPS (II)


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( )

Due to the activation and suppression patterns, we might

expect P(XA1 < XA2|Y = 1) P(XA1 < XA2|Y = 2) and

P(XB1 < XB2|Y = 1) P(XB1 < XB2|Y = 2). Thus there are two expression reversals, one between the

two miRNAs and one, in the opposite direction, between the

two mRNAs.

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FEEDBACK LOOPS (III)


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( )

Given both miRNA and mRNA data, we might then build a

classifier based on the these two switches.

For example, the rank discriminant might simply be 2TSP,

the number of reversals observed. It is in this sense that that expression comparisons may

provide an elementary building block for a connection

between rank-based decision rules and potential

mechanism.

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OUTLINE


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Looking Ahead

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OPPORTUNITY KNOCKING


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The potential impact of applied mathematics on cancer

systems biology is enormous.

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But business as usual in the culture of mathematics is not

likely to have an impact, which requires: Collaborative efforts with biologists and doctors. Stepping outside your intellectual comfort zone, e.g.,

learning some biology.

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But business as usual in the culture of mathematics is not

likely to have an impact, which requires: Collaborative efforts with biologists and doctors. Stepping outside your intellectual comfort zone, e.g.,

learning some biology.

Right now is the most exciting and opportunisitic entry point.

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Lecture 8 (D. Geman)

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