IPAM #3: Childhood Sarcoma

Classification by Gene Expression Profiles

Timothy J. TricheCHLA/USC

Clinical Classification ofChildhood Cancer

• Historical: Morphologic diagnosis + clinical data => risk group, protocol eligibility, treatment (eg, group-based treatment)

• Current: Combined (morphology, immunophenotype, genomic defect) => patient-specific group-based treatment

• Future: Patient-specific therapy, based on multi-genic phenotype?

Osteosarcoma

• Five histologic types, no prognostic value

• Weak prognostic features: site, size, age

• No specific, predictive genetic abnormality (RB, p53)

• Clinical stage only significant prognostic indicator at presentation

Osteosarcoma Prognosis

• Pre-resection chemotherapy => major increase in survival

• Improved survival limited to patients with ≥95% tumor kill

• Patients w/ metastases can be salvaged

But, many exceptions occur:– Responders who metastasize & die– Non-responders who survive– Metastatic patients who survive after resection of mets

Thus, predicting outcome & tailoring therapy remains a major problem

Osteosarcoma: Response to Chemo

Before After

Osteosarcoma Survival

• Surgery only: <10%

• Metastases, no surgery: 0%

• Metastases, surgery: ~20%

• Single-agent chemotherapy: <20%

• Conventional chemotherapy: ~44%

• Up-front chemotherapy: ~65%

• Responders: ~80%

• Non-responders: <40%

Multi-gene Analysis by Microarrays

• Single gene abnormalities, even when present, are inadequate alone to:

– Establish a diagnosis

– Identify individual patients risk profile

– Predict clinical course

– Predict response to therapy

– Predict outcome

• Increasing evidence suggests gene expression profiles may favorably address these issues

Gene Expression Analyses

• Scatter Analyses– 1 X 1– Groups

• Outlier Gene Analyses– Up & down regulated from

mean– Identity

• Cluster Analyses– All genes– Various methods

Specimen Handling

A) Cut pilot section of OCT embedded frozen tissue

tumor

non-tumor dissection of tumor

tissue when

possible

puretumor

B) Cut ~12 frozen sections

C) Extract RNA (<5ug total RNA)

D) Synthesis of double-stranded cDNA

E) In-vitro transcription w/ biotinylated nucleotides

F) Size confirmation of cRNA transcripts

G) Fragmentation of cRNA

500 bp

Osteosarcoma: Gross Appearance

Histopathology of Osteosarcoma

Gene Expression: Osteosarcoma

6= primary tumor, 1993

11= first metastasis, 1996

9= second metastasis, 1998

(died 1999)

12

Met 1 vs. met 2: little similarity

Pilot data

Primary vs. 1st MetastasisPrimaryPrimary 11stst Pulmonary Met Pulmonary Met

Differential Gene Expression:Primary vs. Metastatic Osteosarcoma

-50000 0 50000 100000 150000 200000 250000

Ribosomal Protein L30

Osteonectin

Ribosomal Protein L37A

TF SL1

Thymosin

IMP E16

Pinch Protein

CPT1

Cyclin A

Tat-SF1

CAMP PK RII subunit

NGF beta

Tyrosine Phosphatase

PIGA, A

Uncoupling Protein 3

PSA

Ribosomal Protein L32

PSG11

Primary

Metastasis

OsteonectiOsteonectin lost in n lost in

metastasismetastasis

Primary vs. “Metastasis”

Primary, 1993, pre-RxPrimary, 1993, pre-Rx Tibia lesion, 1998, pre-Rx Tibia lesion, 1998, pre-Rx

Gene Expression Data ClusteringGene Expression Data Clustering

Multiple methods workMultiple methods work

PatternPattern TestedTested

New Postulated New Postulated PatternsPatterns

Optimized Set of Optimized Set of PatternsPatterns

Millions of possible patternsGenerate possible patterns:

Scenario analysis Non-numeric

simulations Computational

linguistics Neural networks Linear/non-linear

optimization methodology

Neural net uses data to optimize pattern

Discovery of Discovery of patterns buried patterns buried

in massive in massive datasetdataset

No process knowledgeNo process knowledge

Pattern Pattern recognitionrecognition

New rules developed

Limited set of probable patterns

Iterative Process

Pattern Recognition

Postulated Postulated PatternsPatterns

DataData

Agglomerative vs. Optimizing Hierarchical Clustering

• Both build a tree of clusters, with data points as leaves, & “nearby” data points as siblings.

• Agglomerative method repeatedly finds closest pair and irreversibly groups them. Bottom-up. Binary tree.

• Optimization methods reconsider assignments based on other assignments and their effects on cluster means & variances.

• Minimize sum of squared distances.– Distance measure matters.– Relate to statistical noise models,

co-regulation models & likelihood of fit.

Agglomerative vs. Optimizing Hierarchical Clustering, cont.

• Optimize means, variances, and cluster memberships.

• Currently we optimize top-down, by levels

• Expectation Maximization: soft memberships. K-means: hard.

• Optimize tree topology (fanout) by CV

• SOM also optimizes at one level, and requires low-dimensional grid embedding of cluster means.

• Alternative to data-cluster distances: cliques of low data-data distances. Also has EM-like stat mech algorithms.

Mimir User Interface

Courtesy of Eric Mjolsness, JPL

Data Flow forSarcoma Analysis

data

labelsscoring

classifierssample

clusteringgene

clustering

Pilot Study of Sarcomas17 cases of osteosarcoma and rhabdomyosarcoma

6800 GeneChip analysis

6800 genes yield 14 gene clusters

Reduced mean space yields 4 sample clusters

OSOS

OS, OSERMS

OS, ARMS 1ªERMS X 4

OS x 3ARMS met X

3

Expandable Tree of Variables Characterizing a Tissue Sample

Clinical response

All variables

Subject Conditions Genes

Outcomes Clinical Demographics

Metastasis Survival PathologyTreatment Age, Sex, etc.…

EM (Expectation Maximization) Gene Clustering

A B C F G D J K

= POOR = INTERMEDIATE = FAVORABLE

Sarcoma Dataset: 45 cases of RMS (Alv + Emb) & Osteosarcoma (R + NR)

Working hypothesis:

Gene expression profiling can detect prognostic distinctions among sarcomas independently of conventional clinical or diagnostic criteria

Future Directions

• Analyze larger data set (institutional, COG) to test hypothesis

• Expand to all sarcomas (RMS, non-RMS, OS, ESFTs)

• Identify biologically important genes

• Creation of custom “sarcoma” arrays using oligomers representing these genes

• Long term studies of COG sarcoma patients using arrays in context with current clinical & biology studies

All osteosarcomas

Osteosarcoma vs RMS GenesLog(0steo)

Mean(Osteo)

Log(Rhabdo)

Mean(Rhabdo)

Mean logRhabdo GENE DESCRIPTION

5.68194.82

7.966357.93

2.83 TNNT 1 Troponin T1, skeletal, slow

6.501952.82

8.8716832.71

2.83 M EST M esoderm specific transcript (m ouse) homolog

7.312240.65

9.7731139.89

2.83 IGF2 Insulin-like growth factor 2 (somatomedin A)

6.01162.35

8.288099.00

2.79 FGFR4 Fibroblast growth factor receptor 4

6.662058.88

8.9313677.36

2.71 IGF2 Insulin-like growth factor 2 (somatomedin A)

5.90 -629.94

7.939770.93

2.56 Adrenal-Specific Protein Pg2

5.751225.65

7.713923.54

2.49 Steroid receptor coactivator (SR C-1) mRN A

6.431166.59

8.409229.32

2.44 GB DEF = DNA for cellular retinol binding protein (CRB P) exons 3 and 4

5.64 -890.82

7.483556.00

2.39 RB P1 Cellular retinol-binding protein

8.6312771.76

10.7577271.57

2.36 Insulin-Like Growth Factor 2

8.2512913.76

5.632011.11

2.33 PTN Pleiotrophin (heparin binding growth factor 8, neurite growth-

promoting factor 1) 6.18 -

1124.24 8.01

7048.57 2.33 M uscle acetylcholine receptor alpha-subunit

8.7719340.53

6.291756.00

2.25 M M P2 Matrix metalloproteinase 2 (gelatinase A; collagenase type IV)

7.091659.82

8.5810916.54

1.90 CCND 2 Cyclin D2

6.36 -907.00

7.724836.68

1.84 TNN I1 Troponin I, skeletal, slow

6.62732.18

8.006595.57

1.83 M YL1 Myosin light chain (alkali)

Proposed COG Study of All Sarcomas

Acknowledgements

• CHLA:– Deb Schofield– Jingsong Zhang

• USC:– Jonathan Buckley– Kim Siegmund

• NCCF:– Mark Krailo

• Caltech:– Barbara Wold– Chris Hart

• JPL:– Eric Mjolsness– Tobias Mann– Joe Roden– Ben Bornstein

• UBC:– Poul Sorensen