a novel patient-specific model of glioma growth kinetics

1
Andrew D. Trister 1,2 , Brian Bot 2 , Andrea Hawkins-Daarud 1 , Kellie Fontes 1 , Carly Bridge 1 , Anne L. Baldock 1 , Jason K. Rockhill 1 , Maciej M. Mrugala 1 , Russ R. Rockne 1 , Erich Huang 2 , Kristin R. Swanson 1 ˀ 1 University of Washington, 2 Sage Bionetworks Seattle, WA Measuring growth kinetics A novel patient-specific model of glioma growth kinetics elucidates underlying biology as measured by gene expression microarray Glioblastoma (GBM) is the most common primary brain tumor with universally poor prognosis. A hallmark of this disease is single- cell invasion into normal tissue. The kinetics of this invasion (D) and of cellular proliferation (ρ) can be estimated from routine pretreatment clinical imaging studies (MRI). T1 + gad T2 Distance Glioma Cell Density Distribution of kinetics 84 GBM patients with gene expression included in TCGA also had evaluable pretreatment MRI available for measurement. 10 0.5 10 1 10 1.5 10 2 10 2.5 10 3 10 0.5 10 1 10 1.5 10 2 10 2.5 D (mm2/years) v = 9 mm/yr v = 900 mm/yr ρ/D= 300 mm 2 ρ/D= 0.3 mm 2 SLOW FAST DIFFUSE NODULAR D (mm 2 /years) ρ (1/years) Differential gene expression of GBM patients with varying growth kinetics REMBRANDT includes gene expression microarray data from 475 patients with glioma (grades II-IV). Differential gene of GBM versus lower grade disease reveals 853 genes whose expression are specific for GBM. 9220 853 2074 11 2464 13 4 Oligodendroglioma Mixed glioma Astrocytoma 647 of these genes were also assessed in GBM patients in TCGA. Differential expression of 20 genes in a linear model of ρ are significant with an FDR < 0.05. This work was funded in part by: Moffitt Cancer Center PSOC NIH/NCI U54CA143970 Sage Bionetworks CCSB NIH/NCI U54CA149237 TMEM45A FAM114A1 IGFB5 RRM2 TPX2 PMM2 AAGAB SHCBP1 GYS1 SLC35G2 CALD1 FBXO7 PTBP1 S100A13 PLCL1 SYN3 FERMT1 DYNLL1 ATP5H FN1 Performing differential expression of these genes in a linear model with D demonstrated 37 genes with a significant FDR < 0.05 COL4A1 COL4A2 MSN TUBB6 DPY19L1 CALD1 PODXL C1R NAMPT MXI1 MCAM MBOAT7 TGIF1 SLC35G2 ANKRD46 PTX DYNLL1 FN1 SLC43A3 SCAPER NDUFA4L2 CD248 LRRC32 BATF3 TOB2 MGC2752 ASB13 DNAJC12 AKAP6 CNNM2 PLA2G6 SYN3 SLCO1A2 TDRKH KCNB1 SLITRK5 RASL10A 84 subjects 84 subjects Gene set enrichment analysis with gene lists derived from the differential gene expression calculation revealed: High D tumors are enriched for gene expression related to cell migration, integrin interactions and HIF activation with a relative reduction in genes related to focal-adhesions and extracellular matrix maintenance High ρ tumors are enriched for genes related to resistance to therapy, induction after ionizing radiation and HIF pathway knock-down This work demonstrates the potential to assess underlying differences in biology in a heterogeneous disease from measurements of routine clinical imaging. We have previously shown that patients with high ρ tumors have worse survival, and here we find that these patients also differentially express genes related to treatment resistance and response to radiation. We have also found that patients with high D tumors demonstrate a decrease in gene expression related to focal adhesion and increase in motility genes. Future work will validate these findings in external datasets and in biological specimens. Survival based on gene set Gene set enrichment analysis p = 0.000222 strata bigClust$class=1 bigClust$class=2 Building a classifier from these genes on 466 GBM patients in TCGA reveals two classes of patients with significantly different survival probability. Conclusions 7 13 11 12 8 9 7 13 10 11 12 8 9 References: 1 CH Wang, JK Rockhill, M Mrugala, et al. Cancer Res 2009;69:9133-9140 2 M Szeto, G Chakraborty, J Hadley, et al. Cancer Res 2009;69:4502-4509 p=0.000222 10 Log 2 expression Log 2 expression 1.0 0.8 0.6 0.4 0.2 0 0 1 2 3 4 5 6 7 8 9 10 Survival probability Time (years)

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Page 1: A novel patient-specific model of glioma growth kinetics

Andrew D. Trister1,2, Brian Bot2, Andrea Hawkins-Daarud1, Kellie Fontes1, Carly Bridge1, Anne L. Baldock1, Jason K. Rockhill1, Maciej M. Mrugala1, Russ R. Rockne1, Erich Huang2, Kristin R. Swanson1

ˀ

1University of Washington, 2Sage BionetworksSeattle, WA

Measuring growth kinetics

A novel patient-specific model of glioma growth kinetics elucidates underlying biology as measured by gene expression microarray

Glioblastoma (GBM) is the most common primary brain tumor with universally poor prognosis. A hallmark of this disease is single-cell invasion into normal tissue. The kinetics of this invasion (D) and of cellular proliferation (ρ) can be estimated from routine pretreatment clinical imaging studies (MRI).

T1 + gad

T2 Distance

Glioma Cell Density

Distribution of kinetics

84 GBM patients with gene expression included in TCGA also had evaluable pretreatment MRI available for measurement.

100.5

101

101.5

102

102.5

103

●●●

●●●

●●

●●

● ●

●●

● ●

100.5 101 101.5 102 102.5

D (mm2/years)

rho

(1/y

ears

)

v = 9 mm/yr

v = 900 mm/yr

ρ/D

= 30

0 m

m2

ρ/D

= 0.

3 m

m2

SLOW

FAST

DIFFUSE

NODULAR

D (mm2/years)

ρ (1

/yea

rs)

Differential gene expression of GBM patients with varying growth kinetics

REMBRANDT includes gene expression microarray data from 475 patients with glioma (grades II-IV). Differential gene of GBM versus lower grade disease reveals 853 genes whose expression are specific for GBM.

9220853

2074

112464 13

4

Oligodendroglioma

Mixed gliomaAstro

cyto

ma

647 of these genes were also assessed in GBM patients in TCGA. Differential expression of 20 genes in a linear model of ρ are significant with an FDR < 0.05.

This work was funded in part by: Moffitt Cancer Center PSOC NIH/NCI U54CA143970Sage Bionetworks CCSB NIH/NCI U54CA149237

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TCGA−32−1977−01A

TCGA−08−0356−01A

TCGA−02−0068−01A

TCGA−28−1755−01A

TCGA−28−1751−01A

TCGA−12−1094−01A

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TCGA−14−2554−01A

TCGA−14−1823−01A

TCGA−06−0169−01A

TCGA−14−1402−01A

TCGA−06−0137−01A

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TCGA−02−0023−01B

TCGA−06−0877−01A

TCGA−06−5418−01A

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TCGA−26−1443−01A

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TCGA−02−0039−01A

TCGA−27−2527−01A

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TCGA−41−4097−01A

TCGA−08−0352−01A

TCGA−14−0781−01B

TCGA−06−2562−01A

TCGA−19−2625−01A

TCGA−12−0778−01A

TCGA−06−0140−01A

TCGA−12−0707−01A

TCGA−19−0963−01B

TCGA−12−1093−01A

TCGA−14−0736−01A

TCGA−14−0813−01A

TCGA−08−0360−01A

TCGA−06−0645−01A

TCGA−02−0071−01A

TCGA−02−0085−01A

TCGA−12−0776−01A

TCGA−02−0099−01A

TCGA−19−1389−01A

TCGA−14−1037−01A

TCGA−12−0775−01A

2335_mt

10476_mt

8655_mt

55612_mt

8224_mt

5334_mt

6284_mt

5725_mt

25793_mt

800_mt

80723_mt

2997_mt

79801_mt

79719_mt

5373_mt

22974_mt

6241_mt

3488_mt

92689_mt

55076_mtTMEM45A

FAM114A1

IGFB5

RRM2

TPX2

PMM2

AAGAB

SHCBP1

GYS1

SLC35G2

CALD1

FBXO7

PTBP1

S100A13

PLCL1

SYN3

FERMT1

DYNLL1

ATP5H

FN1

Performing differential expression of these genes in a linear model with D demonstrated 37 genes with a significant FDR < 0.05

TCGA−26−5136−01B

TCGA−06−0130−01A

TCGA−02−0025−01A

TCGA−02−0028−01A

TCGA−02−0027−01A

TCGA−02−0057−01A

TCGA−02−0009−01A

TCGA−02−0043−01A

TCGA−06−1087−01A

TCGA−06−0206−01A

TCGA−06−0644−01A

TCGA−02−0004−01A

TCGA−02−0060−01A

TCGA−06−1800−01A

TCGA−06−0155−01B

TCGA−06−5858−01A

TCGA−28−1755−01A

TCGA−12−0692−01A

TCGA−12−0707−01A

TCGA−16−1062−01A

TCGA−16−1047−01B

TCGA−12−0829−01A

TCGA−06−0148−01A

TCGA−41−3915−01A

TCGA−02−0107−01A

TCGA−12−1092−01B

TCGA−08−0356−01A

TCGA−06−0125−01A

TCGA−06−2564−01A

TCGA−41−2572−01A

TCGA−19−5951−01A

TCGA−32−1977−01A

TCGA−16−1055−01B

TCGA−06−1802−01A

TCGA−14−0866−01B

TCGA−32−1982−01A

TCGA−06−1086−01A

TCGA−28−2514−01A

TCGA−12−0656−01B

TCGA−12−0703−01A

TCGA−06−0686−01A

TCGA−06−0745−01A

TCGA−16−1063−01B

TCGA−08−0358−01A

TCGA−06−0744−01A

TCGA−12−0820−01A

TCGA−06−1801−01A

TCGA−28−1753−01A

TCGA−76−6191−01A

TCGA−08−0353−01A

TCGA−28−1749−01A

TCGA−27−2528−01A

TCGA−27−1835−01A

TCGA−16−1056−01B

TCGA−16−0848−01A

TCGA−12−1600−01A

TCGA−19−1786−01A

TCGA−06−0157−01A

TCGA−19−0957−01C

TCGA−06−0126−01A

TCGA−06−0650−01A

TCGA−32−2494−01A

TCGA−76−4935−01A

TCGA−26−5135−01A

TCGA−02−0051−01A

TCGA−14−0736−01A

TCGA−02−0039−01A

TCGA−19−2625−01A

TCGA−06−0176−01A

TCGA−27−2527−01A

TCGA−32−4209−01A

TCGA−06−1084−01A

TCGA−06−0166−01A

TCGA−14−1402−01A

TCGA−14−1396−01A

TCGA−19−1790−01B

TCGA−08−0360−01A

TCGA−06−0124−01A

TCGA−19−5947−01A

TCGA−06−0139−01A

TCGA−06−0881−01A

TCGA−76−6193−01A

TCGA−76−6282−01A

TCGA−12−0778−01A

TCGA−26−1439−01A

TCGA−32−2638−01A

TCGA−19−5952−01A

TCGA−06−5410−01A

TCGA−06−5412−01A

TCGA−28−2502−01B

TCGA−02−0086−01A

TCGA−28−2513−01A

TCGA−27−2524−01A

TCGA−14−1452−01A

TCGA−28−1760−01A

TCGA−16−1060−01A

TCGA−02−0015−01A

TCGA−06−0154−01A

TCGA−06−0143−01A

TCGA−19−2619−01A

TCGA−32−4211−01A

TCGA−28−2501−01A

TCGA−06−0168−01A

TCGA−02−2470−01A

TCGA−12−1093−01A

TCGA−41−4097−01A

TCGA−19−0955−01A

TCGA−06−0141−01A

TCGA−12−1599−01A

TCGA−14−0789−01A

TCGA−06−0878−01A

TCGA−12−1088−01A

TCGA−26−1438−01A

TCGA−12−1096−01A

TCGA−16−1045−01B

TCGA−12−0776−01A

TCGA−19−5955−01A

TCGA−02−0068−01A

TCGA−27−1832−01A

TCGA−06−0122−01A

TCGA−28−1750−01A

TCGA−06−0156−01A

TCGA−02−0079−01A

TCGA−14−1034−01A

TCGA−14−1037−01A

TCGA−08−0346−01A

TCGA−06−0169−01A

TCGA−08−0345−01A

TCGA−14−1795−01A

TCGA−32−2491−01A

TCGA−87−5896−01A

TCGA−06−2562−01A

TCGA−41−3393−01A

TCGA−19−2623−01A

TCGA−76−4931−01A

TCGA−26−5132−01A

TCGA−32−1987−01A

TCGA−02−0075−01A

TCGA−06−0140−01A

TCGA−02−0070−01A

TCGA−27−1830−01A

TCGA−08−0373−01A

TCGA−08−0352−01A

TCGA−02−0102−01A

TCGA−76−4928−01B

TCGA−12−5299−01A

TCGA−28−5219−01A

TCGA−06−5414−01A

TCGA−02−0064−01A

TCGA−32−1986−01A

TCGA−14−0781−01B

TCGA−06−0192−01B

TCGA−28−1751−01A

TCGA−06−0645−01A

TCGA−12−1094−01A

TCGA−19−1389−01A

TCGA−12−0775−01A

TCGA−14−1794−01A

TCGA−19−1385−01A

TCGA−06−0147−01A

TCGA−12−0688−01A

TCGA−26−1443−01A

TCGA−12−0654−01B

TCGA−06−0750−01A

TCGA−12−0772−01A

TCGA−14−0867−01A

TCGA−12−0780−01A

TCGA−02−0071−01A

TCGA−06−0190−01A

TCGA−06−0211−01A

TCGA−02−0047−01A

TCGA−02−0007−01A

TCGA−06−5859−01A

TCGA−28−2506−01A

TCGA−06−0195−01B

TCGA−06−0240−01A

TCGA−19−1791−01A

TCGA−12−0670−01B

TCGA−06−0133−01A

TCGA−14−1827−01A

TCGA−12−1098−01C

TCGA−19−5960−01A

TCGA−06−1804−01A

TCGA−12−3646−01A

TCGA−06−0237−01A

TCGA−19−1789−01A

TCGA−12−0618−01A

TCGA−08−0359−01A

TCGA−28−5204−01A

TCGA−06−0167−01A

TCGA−06−0160−01A

TCGA−28−1756−01C

TCGA−19−1788−01A

TCGA−14−1458−01A

TCGA−12−0818−01A

TCGA−08−0344−01A

TCGA−16−0849−01A

TCGA−02−0080−01A

TCGA−14−1456−01C

TCGA−06−6389−01A

TCGA−02−0084−01A

TCGA−08−0351−01A

TCGA−15−1444−01A

TCGA−26−1442−01A

TCGA−06−0129−01A

TCGA−02−0014−01A

TCGA−14−4157−01A

TCGA−19−2629−01A

TCGA−12−3651−01A

TCGA−06−0142−01A

TCGA−02−0024−01B

TCGA−02−0011−01B

TCGA−02−0038−01A

TCGA−02−0116−01A

TCGA−02−0021−01A

TCGA−08−0392−01A

TCGA−06−2569−01A

TCGA−14−0871−01A

TCGA−32−1973−01A

TCGA−06−0188−01A

TCGA−06−6391−01A

TCGA−12−1099−01A

TCGA−16−1460−01A

TCGA−08−0350−01A

TCGA−12−0827−01A

TCGA−02−2466−01A

TCGA−19−2624−01A

TCGA−14−0865−01B

TCGA−14−1825−01A

TCGA−06−0875−01A

TCGA−14−1454−01A

TCGA−02−2483−01A

TCGA−76−4934−01A

TCGA−26−5134−01A

TCGA−06−1805−01A

TCGA−27−1836−01A

TCGA−28−1746−01A

TCGA−06−2558−01A

TCGA−14−1455−01A

TCGA−32−2634−01A

TCGA−06−0241−01A

TCGA−06−0174−01A

TCGA−02−0010−01A

TCGA−19−1390−01A

TCGA−02−0069−01A

TCGA−19−0960−01A

TCGA−02−0054−01A

TCGA−02−0058−01A

TCGA−06−2557−01A

TCGA−06−0238−01A

TCGA−06−2566−01A

TCGA−14−3477−01A

TCGA−41−2575−01A

TCGA−14−3476−01B

TCGA−06−0208−01A

TCGA−41−2571−01A

TCGA−41−2573−01A

TCGA−02−0104−01A

TCGA−06−0178−01A

TCGA−06−0221−01A

TCGA−12−1597−01B

TCGA−27−2521−01A

TCGA−06−5417−01A

TCGA−76−4925−01A

TCGA−02−0114−01A

TCGA−08−0385−01A

TCGA−02−0048−01A

TCGA−15−1449−01A

TCGA−15−1447−01A

TCGA−06−0128−01A

TCGA−76−4932−01A

TCGA−26−5133−01A

TCGA−16−0850−01A

TCGA−14−1821−01A

TCGA−02−0026−01B

TCGA−06−2570−01A

TCGA−28−5216−01A

TCGA−28−5209−01A

TCGA−28−5218−01A

TCGA−28−5215−01A

TCGA−26−5139−01A

TCGA−28−2509−01A

TCGA−08−0246−01A

TCGA−06−2561−01A

TCGA−12−3653−01A

TCGA−12−3650−01A

TCGA−06−0219−01A

TCGA−12−3644−01A

TCGA−12−3652−01A

TCGA−02−0083−01A

TCGA−12−5301−01A

TCGA−76−4929−01A

TCGA−14−2555−01B

TCGA−27−1831−01A

TCGA−06−0939−01A

TCGA−02−2486−01A

TCGA−14−1823−01A

TCGA−06−0197−01A

TCGA−06−0201−01A

TCGA−14−2554−01A

TCGA−32−2615−01A

TCGA−06−0187−01A

TCGA−06−0210−01A

TCGA−32−1991−01A

TCGA−06−5408−01A

TCGA−06−5418−01A

TCGA−76−4926−01B

TCGA−19−5950−01A

TCGA−27−2519−01A

TCGA−19−5959−01A

TCGA−16−1048−01B

TCGA−08−0347−01A

TCGA−08−0375−01A

TCGA−19−2631−01A

TCGA−06−0747−01A

TCGA−14−0787−01A

TCGA−08−0244−01A

TCGA−08−0354−01A

TCGA−08−0389−01A

TCGA−12−0616−01A

TCGA−08−0355−01A

TCGA−08−0390−01A

TCGA−06−0213−01A

TCGA−06−0876−01A

TCGA−12−0822−01A

TCGA−12−0828−01A

TCGA−02−0089−01A

TCGA−14−0783−01B

TCGA−14−0786−01B

TCGA−06−0184−01A

TCGA−28−6450−01A

TCGA−06−0145−01A

TCGA−06−5413−01A

TCGA−32−1978−01A

TCGA−06−2563−01A

TCGA−08−0357−01A

TCGA−27−2523−01A

TCGA−19−1386−01A

TCGA−15−0742−01A

TCGA−12−1095−01A

TCGA−14−1401−01A

TCGA−14−1451−01A

TCGA−06−0879−01A

TCGA−26−1440−01A

TCGA−06−0152−01A

TCGA−06−0137−01A

TCGA−12−0657−01A

TCGA−27−1838−01A

TCGA−02−0099−01A

TCGA−28−1757−01A

TCGA−12−0620−01A

TCGA−12−0619−01A

TCGA−02−0059−01A

TCGA−12−3648−01A

TCGA−06−0877−01A

TCGA−27−1837−01A

TCGA−19−5954−01A

TCGA−32−4213−01A

TCGA−06−2559−01A

TCGA−06−0646−01A

TCGA−32−1979−01A

TCGA−06−6390−01A

TCGA−19−1787−01B

TCGA−08−0386−01A

TCGA−41−5651−01A

TCGA−06−5411−01A

TCGA−28−2510−01A

TCGA−32−1980−01A

TCGA−32−4208−01A

TCGA−19−2620−01A

TCGA−06−0132−01A

TCGA−19−5958−01A

TCGA−06−5856−01A

TCGA−12−3649−01A

TCGA−12−0826−01A

TCGA−06−0173−01A

TCGA−06−0138−01A

TCGA−12−0821−01A

TCGA−06−0214−01A

TCGA−06−0158−01A

TCGA−26−1799−01A

TCGA−19−0963−01B

TCGA−12−1090−01A

TCGA−32−2616−01A

TCGA−12−0773−01A

TCGA−32−2498−01A

TCGA−08−0245−01A

TCGA−19−5956−01A

TCGA−32−5222−01A

TCGA−06−5416−01A

TCGA−28−5213−01A

TCGA−28−5214−01A

TCGA−28−5207−01A

TCGA−28−5208−01A

TCGA−02−0033−01A

TCGA−02−0046−01A

TCGA−02−0003−01A

TCGA−02−0052−01A

TCGA−02−0001−01C

TCGA−02−0055−01A

TCGA−02−0034−01A

TCGA−19−1388−01A

TCGA−28−1745−01B

TCGA−12−1089−01A

TCGA−15−1446−01A

TCGA−08−0380−01A

TCGA−32−4210−01A

TCGA−27−1834−01A

TCGA−28−1747−01C

TCGA−16−0846−01A

TCGA−06−0749−01A

TCGA−19−2621−01B

TCGA−27−1833−01A

TCGA−14−0790−01B

TCGA−14−1453−01A

TCGA−14−0817−01A

TCGA−12−5295−01A

TCGA−76−4927−01A

TCGA−02−0113−01A

TCGA−32−1970−01A

TCGA−12−0819−01A

TCGA−27−2518−01A

TCGA−06−0171−01A

TCGA−06−0216−01A

TCGA−06−0127−01A

TCGA−06−2565−01A

TCGA−12−1598−01A

TCGA−08−0349−01A

TCGA−02−0016−01A

TCGA−12−1097−01B

TCGA−06−0743−01A

TCGA−06−0648−01A

TCGA−08−0348−01A

TCGA−76−6285−01A

TCGA−19−4068−01A

TCGA−02−2485−01A

TCGA−06−0649−01B

TCGA−02−0085−01A

TCGA−14−0812−01B

TCGA−28−1752−01A

TCGA−02−0074−01A

TCGA−32−1976−01A

TCGA−12−1602−01A

TCGA−76−6192−01A

TCGA−32−2632−01A

TCGA−06−2567−01A

TCGA−19−1392−01A

TCGA−14−1459−01A

TCGA−27−2526−01A

TCGA−14−1829−01A

TCGA−02−0115−01A

TCGA−16−0861−01A

TCGA−19−0964−01A

TCGA−06−0882−01A

TCGA−81−5910−01A

TCGA−32−4719−01A

TCGA−41−3392−01A

TCGA−19−1387−01A

TCGA−14−0813−01A

TCGA−19−0962−01B

TCGA−12−1091−01A

TCGA−02−0023−01B

TCGA−32−2495−01A

TCGA−06−0185−01A

TCGA−28−5220−01A

TCGA−06−5415−01A

10633_mt26050_mt3745_mt11022_mt6579_mt8224_mt8398_mt54805_mt9472_mt56521_mt79754_mt65996_mt10766_mt55509_mt2615_mt57124_mt56901_mt49855_mt29015_mt2335_mt8655_mt5806_mt157567_mt80723_mt7050_mt79143_mt4162_mt4601_mt10135_mt715_mt5420_mt800_mt23333_mt84617_mt4478_mt1284_mt1282_mtCOL4A1

COL4A2MSNTUBB6DPY19L1CALD1PODXLC1RNAMPTMXI1MCAMMBOAT7TGIF1SLC35G2ANKRD46PTXDYNLL1FN1SLC43A3SCAPERNDUFA4L2CD248LRRC32BATF3TOB2MGC2752ASB13DNAJC12AKAP6CNNM2PLA2G6SYN3SLCO1A2TDRKHKCNB1SLITRK5RASL10A

84 subjects

84 subjects

Gene set enrichment analysis with gene lists derived from the differential gene expression calculation revealed:

High D tumors are enriched for gene expression related to cell migration, integrin interactions and HIF activation with a relative reduction in genes related to focal-adhesions and extracellular matrix maintenance

High ρ tumors are enriched for genes related to resistance to therapy, induction after ionizing radiation and HIF pathway knock-down

This work demonstrates the potential to assess underlying differences in biology in a heterogeneous disease from measurements of routine clinical imaging. We have previously shown that patients with high ρ tumors have worse survival, and here we find that these patients also differentially express genes related to treatment resistance and response to radiation. We have also found that patients with high D tumors demonstrate a decrease in gene expression related to focal adhesion and increase in motility genes.

Future work will validate these findings in external datasets and in biological specimens.

Survival based on gene set

Gene set enrichment analysis

0.0

0.2

0.4

0.6

0.8

1.0

p = 0.000222

stratabigClust$class=1bigClust$class=2

Building a classifier from these genes on 466 GBM patients in TCGA reveals two classes of patients with significantly different survival probability.

Conclusions

0.00.2

0.40.6

0.81.0

−1.0 −0.5 0.0 0.5 1.0

7

13

11

12

8

9

0.00.2

0.40.6

0.81.0

−1.0 −0.5 0.0 0.5 1.0

7

13

10

11

12

8

9

References: 1CH Wang, JK Rockhill, M Mrugala, et al. Cancer Res 2009;69:9133-9140 2M Szeto, G Chakraborty, J Hadley, et al. Cancer Res 2009;69:4502-4509

p=0.000222

10

Log

2 ex

pres

sion

Log

2 ex

pres

sion

1.0

0.8

0.6

0.4

0.2

0

0 1 2 3 4 5 6 7 8 9 10

Survi

val p

roba

bility

Time (years)