deep maldi for oncology biomarkers study

Innovation with IntegrityInnovation with Integrity

Deep MALDISerum Proteomic ‘Complementary’ Prognostic Testing for Oncology Research

Rohan A. Thakur, Ph.D.Bruker Daltonics, Asia Pacific

For Research Use Only

Special acknowledgement:Dr. H. Roder, Dr. P. Beresford & Dr. J. Gruia-Gray

Innovation with Integrity

Circulating Blood Proteome

Proteins

Prog

nosi

s

Discov

er

y

Dx

Blood Proteome

Research Biomarker Discovery

Complementary Diagnostics

• Profiling abnormal proteins (as a result of aberrant genes) has significant prognostic potential.

• Can identify patient sub-sets, BEFORE initiation of therapy – patients who will benefit and those who won’t.

• Especially useful when receptor mutation or gene copy numbers don’t relate to disease physiology or pathology


Has disease-rich information which can be used to benefit therapy


Advantage of MALDI-TOF

• Laser based, consistent, reproducible, ionization for proteins• SOP Friendly. No dependency on chromatographic

columns (consumables) or mobile phase formulations.

• Ultra-rugged, self-cleaning technology maximizes uptime (easily do 1000 samples a week)• Low cost of ownership. No dependency on

peripherals like auto-samplers,, or chromatography issues such as carry-over.

• Low maintenance. No solvent or gas lines; no peripherals connections or synchronization required.

• Over 2000 units operating globally in clinical microbiology labs and hospitals• Proven technology platform.

• Technician-friendly, bench-top platform• Simple.

.

Proven platform for clinical

microbiology

>2000 units

operating globally

Class IIMedicalDevice



MALDI and Deep Learning


A Perfect Match of Technologies

Deep Learning Basics• Large data sets with good quality • Excels in tasks where the basic unit has very little

meaning in itself, but the combination of such units has a useful meaning

• Biodesix’ clinical applications: classifiers developed for Myelodysplastic syndrome (MDS) and immunotherapy

‘A single MALDI spot can be consistently sampled thousands of times….’Biodesix Deep MALDI patent US20130320203A1

Machine

Learning

Data Input Sources

ModelReady for Prognostic

Testing

Facial recognition is a result of Deep learning


The Diagnostic CortexTM

Deep learning network to design generalizable and performance-tuned tests based on deep data obtained from Bruker MALDI-TOF mass spectrometers

….

Avoid over-fitting by combining multiple base classifiers

(ensemble average)

Feature spaceSubspace ensemble

Many test candidates

Filter mini-classifiers to design goals

Base classifiers/ complex tests

Train Test

Stratified training/test splits (bags)

Abst

ract

ion

Leve

l

BaseClassifieri


Biodesix uses the Diagnostic Cortex to design tests such as the one for Myelodysplastic syndrome (MDS) and immunotherapy


Rapid Prognostic Capability


Blood draw

Separate Serum (Plasma)

Dilute plasma 1:10 with deionized water & spot (1uL)Analyze on MALDI-TOF & process with Biodesix software (TAT for Results <72h)

1234


Unrivaled (MALDI-TOF) Reproducibility NSCLC Profiling via Proprietary Spectral Processing (veristrat®)

• 9 batches of raw MALDI spectra of 100 technical replicates each of a serum sample:• Large intensity variations both

within and across batches

• The same 9 batches of spectra after spectral processing:• Good reproducibility within and

across batches• Median CV of 9.5%

Inte

nsity

, arb

. un

its

m/z

m/z

Inte

nsity

, arb

. un

its

Before Spectral Processing

After Spectral Processing


M Duncan et al., Proceedings of the 61st ASMS Conference on Mass Spectrometry and Allied Topics. 2013. MP 181.

2000Shots

Per Spot


“Deep MALDI”

• A spectrum in the m/z range where standard MALDI shows few, if any, protein peaks

• The same sample and the same m/z range acquired with Deep MALDI technology showing many (lower) abundance protein peaks

Enhanced sensitivity for measuring >1000’s of serum proteins from <10uL

Deep MALDI with deep learning algorithms uncovers information contained in protein peak profiles that can

be used as a prognostic tool in oncology


100K shots per spot


Results


Two prognostic applications for MALDI-TOF MS serum proteomic tests for Oncology

Non-small cell lung cancer (NSCLC)

• VeriStrat®, serum-based test stratifies patients with NSCLC into 2 groups, Good or Poor

• Patients with a VeriStrat Poor status have a more aggressive disease and may be recommended for alternative therapy

• Uses 2,000 shots for each of 3 spots and KNN analysis

1 Vansteenkiste JF, et al. Poster at: The American Society of Clinical Oncology (ASCO); 2012.

• Serum-based test separates patients with MDS into 2 groups, Good or Poor, with vastly different prognosis

• If validated, the test can provide complementary information for physicians to consider in addition to existing scoring systems to aid in planning treatment for patients with MDS

• Uses the UltraFlex Deep MALDI and Deep Learning approach (100K shots per sample)

Roder et al. Abstract #5304, AACR. Biodesix, Boulder, CO.

Myelodysplastic syndrome (MDS)


Conclusion

• Reflex testing for patients with Non-Small Cell Lung Cancer (NSCLC). The Veristrat® test is a LDT.

• BDX004 (part of FOCAL trial) to be submitted for FDA approval.

• Potential applications to prognostic indications for other tumor types (Breast, Ovarian, MDS) or to define a new classifier that stratifies patients who will benefit from immunotherapy

• Deep MALDI-TOF can be easily done on on commercially available platforms• Deep MALDI is significantly different from

SELDI which focused on measuring individual proteins and their amounts in small data sets.

Proven Prognostic Utility of MALDI-TOF for Oncology Research


Liquid biopsies for tumor mutation profiling and host

tumor response

deep maldi for oncology biomarkers study

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