Intraoperative-DESI-MS-based Biopsy Analysis Platform for Glioma Diagnosis, Extent of Tumor Cell Infiltration Estimation,

and IDH SubtypingClint Alfaro, Valentina Pirro, Alan K. Jarmusch, Eyas M. Hattab, Aaron A. Cohen-Gadol,

R. Graham Cooks

Improving Health Through Researchindianactsi.org

MSACL 2019 Palm Springs, CA MS Imaging in Clinical Applications

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Background and introductionOur goal: Develop and evaluate an intraoperativeDESI-MS-based tool to assist neurosurgery byproviding rapid diagnostic information

Gliomas

Claes et al. Acta Neuropathol ,2007, 114:443–458

-Surgical resection is primary treatment

-Grade IV five-year survival rate is 5%

-Residual tumor is primary cause of recurrence Intraoperative MS at IUSM

1) Isocitrate dehydrogenase (IDH) mutation status2) Disease status3) Tumor cell percentage (TCP)

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Pirro et al. PNAS, 2017, 114(26): 6700-6705

Desorption electrospray ionization (DESI)

HV power supply

Nebulizer capillary

SurfaceSample

Desorbed ions

Freely moving sample stage in air

Atmospheric inlet of mass spectrometer

Ion transfer line

VSolvent

N2

Spraycapillary

Gas jet

Spray

Takats et al. Science 2004, 306, 471-473

Detects metabolites and lipids directly from fresh tissue

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Workflow of the intraoperative DESI-MS experiment

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1) Tissue Smearing 2) Sample Loading on DESI Source

3) Sample raster under DESI Spray 4) MS Data Acquisition

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Tissue Smearing Sample Loading on DESI Source

Sample raster under DESI Spray MS Data Acquisition

Workflow of the intraoperative DESI-MS experiment

2HG Monitored with MS3 Product Ion Scan scan

2-hydroxyglutarate for assessing isocitrate dehydrogenase mutation status

IDH MutantIDH Wild-type

147

129

O

2HG DESI Signal Correlates with 2HG Concentration

α-ketoglutaric acid

2-hydroxyglutaric acid (2HG)

Mutant IDH

IDH Wild-type

y = 0.0619x - 2.4573R² = 0.986

0

10

20

30

40

0 250 5002H

G D

ESI-

MS

Sign

al

2HG Concentration (ng/mg tissue) from

Adjacent Tissue

IDH Mutant

Alfaro, C.M. et al. J. Neurosurgery 2019

Histopathology

Pathology-based

Region-Of-Interest

(ROI) Selection

Jarmusch et al. PNAS, 2016, 113(6): 1486-1491

Histopathologically Correlated DESI-MS Database was created

m/z 888m/z 885m/z 834

41 Tissue Sections

Grey matter

White matter

Glioma

m/z 281 m/z 303 m/z 788

m/z 834 m/z 885 m/z 888

Grey matter

Glioma

White matter

174

834

888

794

89885

Lipid and metabolite profiling with DESI-MS imaging

Data fusion

X

Y

Grey matter

Glioma

White matter

447 ROIs Selected

Assessing disease status using PCA-LDA

-1

7.5

16

80 140 200m/z

-1

7

15

700 850 1000m/z

Standard normal variate normalized metabolite

and lipid profiles

Met. PC Scores

Lip. PC Scores

Multiply by training set lipid and metabolite

loading matrices

Combined PC Scores

Met. profile

Lip. profile

Multiply by training set fused

loading matrix

Fusion PC ScoresLDA Prediction andClassification

Linear DiscriminantAnalysis

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Grey matter

Glioma

White matter

Data fusion

Project unknowns onto fused PC score space and predict disease state with LDA

Intraoperative (unknown)

sample

SNV

No

rmal

ized

Inte

nsi

ty

Assessment of tumor infiltrationWhat about mixtures (real samples)?Do dynamics of lipid profile reflect tumor infiltration?

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Glioma

White Matter

Grey Matter

Pirro et al. PNAS, 2017, 114(26): 6700-6705

Assess tumor infiltration using the lipid signature of the tissue

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Y = bGMXGM + bWMXWM + bGXG

Regression using PC scores and ternary mixture model

[PC1 PC2] * [%GM %WM %GL]

Glioma

1:1:1 Mix

White Matter

Grey Matter

Pirro et al. PNAS, 2017, 114(26): 6700-6705

Unknown Spectrum

[%GM %WM %GL]

[%GM %WM %GL] = 100%

G

GMWM

Summary of intraoperative patient cohort

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Subjects

58 recruited

6 Clinical Screenfails

3 DESI Outliers

Biopsies 247 Total

Smears

334 Total

62 excluded

272 smears included in

statistics

Gender24 F

25 M

Age (years) 46 ± 16

IDH Status

23 Mutant

25 Wild-Type

1 Unknown

Glioma

Grade

1 Grade I

17 Grade II

6 Grade III

25 Grade IV

794834

885.6

0

10

20

30

700 850 1000

89.0

174.0

0

50

100

80 140 200

85.1

101.1

0

0.1

0.2

0.3

0.4

80 95 110

Biopsy 155

Biopsy 154

888.7

0

105

210

700 850 1000

89.0

174.0

0

20

40

60

80 140 200

85.0

101.0

0

0.1

0.2

80 95 110m/z

Inte

nsi

ty

A

B C D

E F G

147

129

O

147

129

O

Lipids 2HGMetabolites

Biopsy 155: Glioma, High TCP, IDH Mutant

Biopsy 154: Infiltrative margin(white matter), Low TCP, IDH Mutant

Diagnosis of tumor cores and surgical margins-example from a single subject

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IDH Mutation status was classified with 92.4% accuracy

158 smears, 29 subjects

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Decision line = 6.18

Sensitivity 86.6%Specificity 97.5%

Comparison of histopathology diagnoses with DESI-MS diagnoses

Lipid Deconvolution TCP CategoryLow Medium High Total

Histopathology TCP Category

Low 92 23 11 126Medium 15 9 14 38

High 8 20 80 108Total 115 52 105 272

G = GliomaGM = Grey matterWM = White matterI.M. = Infiltrative margin

Low = 0-33% tumor cellsMedium = 34-66% tumor cellsHigh = 67-100% tumor cells

Mixed tissue samples are challenging for our current data and statistical methods14

PCA-LDADiagnosis from

DESI-MS

GI.M. (GM or WM)

Total

Histopathology DiagnosisG 94 43 137

I.M. (GM or WM) 20 115 135

Total 114 158 272

Conclusions

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Intraoperative DESI-MS system was developed and utilized over 4-year period toanalyze brain tissue biopsies from 49 human subjects

IDH Mutation assay performed the best and provided accuracy of 92.4%

Accurate prediction of mixtures was difficult

Few low-TCP samples were misclassified as high-TCP; few high-TCP samples weremisclassified as low-TCP

Acknowledgements

NIH R21EB015722

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