rna capture sequencing enabled liquid biopsy screening

© 2017 Biogazelle. All rights reserved. 1

RNA capture sequencing enabled liquid biopsy screening

Jo Vandesompele, Biogazelle CSO

Revolutionizing next-generation sequencing

Antwerp, Belgium, March 21, 2017


Biogazelle

research & development stage biotech company deploying the power of RNA for next generation diagnostics and therapeutics in the field of oncology and other diseases

Dx (CRO) unit / Tx unit

focus on clinically relevant and challenging samples

http://bgzlle.com/2mIGo2L

presentation download


Unmet needs in oncology

• more effective and less toxic treatments for durable responses• combination therapies

• companion diagnostic tests > the right drug for the right patient

• better laboratory tests• early diagnosis

• monitoring of treatment effectivity

• early detection of relapse or recurrence


presentation download


Liquid biopsies are the holy grail of precision oncology• easy to obtain

• low risk for the patient

• serial profiling > longitudinal studies

• reflects entire tumor load

• full of biomarker potential• cell-free nucleic acids (DNA & RNA)

• circulating tumor cells

• extracellular vesicles

• tumor educated platelets


Active secretion and passive release of RNA into circulation• vesicles• exosomes, microvesicles, apoptotic bodies

• ribonucleoprotein complexes• AGO2, high-density lipoproteins

• extracellular RNA• intercellular communication

• biomarker potential

Wan et al., Nature Reviews Cancer, 2017


exRNA may offer sensitivity advantages

• Clinical Chemistry, 1972

• 10x higher concentration of RNA than DNA in plasma


RNA has great biomarker potential

• dynamic nature (time, location and condition specific)

• diverse• different types: messenger, micro, long non-coding, transfer, ribosomal, piwi,

sn(o)RNA, etc.

• varying abundance levels: 1 copy/cell > 100,000 copies/cell

• structural differences: splicing (incl. circRNA), isomiRs, fusion, SNV, indel, editing

• measurement technologies are state-of-the-art• RNA sequencing (discovery)

• quantitative and digital PCR (verification, validation, clinical-grade test)

• sensitive, high-throughput, large dynamic range


Challenges in mRNA sequencing (of clinical samples)• fragmented/degraded RNA

• abundant unwanted RNA (ribosomal, hemoglobin)

• low input (1 FFPE scroll, fine needle biopsy, 0.2 ml liquid biopsy)

• sense / antisense overlapping transcripts

• solution: probe based cDNA enrichment, RNA capture sequencing


RNA capture sequencing history

• Levin, 2009 467 genes

• Ueno, 2012 913 genes

• Mercer, 2012 2265 loci, 0.77 Mb

• Halvardson, 2013 exome

• Cabanski, 2014 exome on FFPE

• Biogazelle exome on plasma

• higher sensitivity

• higher transcriptome complexity

• fusion genes, variants


mRNA capture sequencing

fragmented RNA

random primed ds cDNA

adaptor ligation

PCR

2x capture

PCR

cleanup & quant

• 3 days of work; optimized conditions

• 21,415 genes - 214,122 exons – 425,437 probes

• 20M reads FFPE, 15M reads plasma

• +100 cases so far• colon, lung, ovarium, breast, esophageal


Colon & lung cancer samplessensitivity – detected genes

• matching tumor FFPE, plasma and serum (n=2)

• detected genes with FPKM>=1

• FFPE > 12,000 genes

• plasma > 8500 genes

• serum > 7500 genes


Colon & lung cancer samplesstrandedness, read distribution, coverage

• >99% strandedness

• coding regions enriched

• whole transcripts are covered


Colon & lung cancer samplessequencing depth

• at ~15 M saturation is reached and sequencing deeper will not lead to (many) more genes (representative plasma sample)

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Colon & lung cancer samplesreproducibility

• high reproducibility in plasma

• low abundant genes more variable in serum


Colon vs lung cancer plasmaspectral map

samples separate by cancer typeon the second principal component

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crc_p

nsclc_p


Colon vs lung cancer plasma

• PI3K-AKT signaling pathway

• cell cycle

• osteoclast differentiation

• oxidative phosphorylation

• focal adhesion

top 5 enriched significant KEGG pathways upon GSEA


Esophageal cancer plasma titrationexperimental design –platelet-free / platelet-poor plasma

cancer – male : 100%

healthy – female : 0%

50% cancer

10% cancer

2% cancer

2x replicates for each sample

FFPE from cancer tissue : 1x


Platelet-poor plasma (PPP) shows best reproducibility

PFP

PPP

0% cancer 2% cancer 10% cancer 50% cancer 100% cancer


Titration series to determine consistency of RNA abundance signal • if healthy > cancer then 0 > 2 > 10 > 50 > 100

• if cancer > healthy then 100 > 50 > 10 > 2 > 0

• bin the fold-changes • healthy vs. cancer

• determine fraction per bin that is titrating• showing correct order

100% cancer

healthy : 0%

50% cancer

10% cancer

2% cancer


Excellent titration response

platelet-free platelet-poor

titr

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g ge

ne fr

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Ovarian cancer longitudinal studyexperimental design – platelet-free plasma

FFPE tumor

patient 15

patient 17

diagnosisbefore treatment during treatment post-surgery relapse

15.1 15.2 15. 3 15.4 15.615.5

17.1 17.2 17. 3 17.4 17.617.5


RNA seq quality control

gene body coverage cumulative gene diversity


Plasma samples tend to cluster by time point

during treatment

before treatment

patient 15: relapse

cluster dendogram


Plasma samples tend to cluster by time point• samples after 1st chemotherapy cycle stand out

hemolytic sample


Changes upon 1st chemo treatment

patient 15

patient 17

diagnosis after 1 chemo

15.1 15.2

17.1 17.2

• low power, but reproducible changes


Changes upon 1st chemo treatment

• GSEA : lysome, lipid metabolism, ECM-receptor• lysosome

• glycerophospholipid metabolism

• alpha-linolenic acid metabolism

• linoleic acid metabolism

• ether lipid metabolism

• ECM-receptor interaction

• not clear yet what these changes effectively mean

• intriguing and first time that mRNA profiles in plasma provide insights


RNA seq variant calling pipeline

raw RNA seq reads(FASTQ)

(paired-end) RNA sequencing

map to referenceSTAR 2-pass

alignment (BAM)

basecalling, demultiplexing, QC, trimming, adaptor removal

remove duplicate reads & sort

analysis-ready reads

variant callingreads mpileup

VarScan

raw variants (VCF)

SNVs InDels

filtered variants

SNVs InDels

variant filtering(strand bias, base

quality, depth, allelic ratio, …)

variant annotationfunctional/genic annotation

dbSNP/COSMIC/CIViCRNAediting

HGVS/SIFT-PolyPhen

report annotated variants (VCF & CSV)

SNVs InDels

further evaluation, candidate selection, troubleshooting, …

fusion detectionFusionCatcher


Testing of the RNA seq variant pipeline

• HCC1143 DNA exome sequencing vs polyA+ RNA sequencing

• variant positions covered > 4 reads

• good concordance

19,447 2911 4270

• mono-allelic expression

• low coverage

• RNA editing

• low coverage


Testing of the RNA seq variant pipeline

• 16 matched tumor / normal FFPE from diagnostic cases with known mutation

• 15/16 correctly called• myxoid liposarcoma: FUS-DDIT3 fusion• rhabdomyosarcoma: FOXO1-PAX3 fusion• synovial sarcoma: SS18-SSX2 fusion• Ewing sarcoma: ESWR1-FLI1 fusion• colon cancer: KRAS SNV (n=2)• melanoma: BRAF SNV (n=2)• lung cancer: EGFR SNV + EGFR deletion of 15 bp • GIST: PDGFRA SNV + KIT deletion of 21 bp


RNA editing event

• variant present in colon cancer FFPE RNA and plasma RNA, not in FFPE DNA


Therapy response monitoring

FCGR2A mutation present in FFPE and plasma at diagnosisdisappears during treatment and pops up again at time point 4 -> sign of relapse?

15.1 15.2 15. 3 15.4 15.615.5


Platelet-free plasma has very high PCR duplicate levels• ok for gene expression; problematic for variant analyses

• ~1000 variants PFP; ~5000 variants PPP

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T1 T2 T3 T4 T5 T6 FFPE# reads # uniquely mapped # non-duplicate, uniquely mapped


Cumulative unique read coverage

• while better coverage of variants in PPP, due to PCR duplicates, only median coverage of 15 unique reads per variant

• envisaged improvements

• UMI (Fu et al., 2014)

• larger plasma volume

• platelet-rich plasma

• precision vs recall

FFPE

platelet-poor plasma

platelet-freeplasma


More unique reads > more variants

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# R

eads

# Variants

Patient 15

Patient 17

Lineair (Patient 15)

Lineair (Patient 17)

platelet-free plasma


Cancer plasma mixed 1:1 (v/v%) with plasma from healthy individual• AB heterozygous variants (cancer) mixed with BB variants (healthy)

• semiquantitative allele ratio determination

100% cancer50/50

alle

le r

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Conclusions – high level

• RNA is a fascinating molecule with a lot of biomarker potential

• liquid biopsies are emerging as holy grail for precision medicine

• novel application of mRNA capture sequencing in body fluids


Conclusions - details

• plasma > serum

• platelet poor > platelet free plasma

• gene expression: ~10,000 genes reproducibly detected

• variants: ~5000 detected

• PCR duplicates require attention in variant calling• UMI, larger plasma volumes, platelets,…

• many biological questions remain• function circulating RNA, where does it come from, different cargo in different

plasma fractions (RNP, EV, platelet)


Acknowledgements (A-Z)• Carolina Fierro

• Manuel Luypaert

• Nele Nijs

• Pieter Mestdagh

• Sandra Steyaert

• Thomas Piofczyk

• Gary Schroth

• Scott Kuersten

• Anneleen Decock

• Annouck Philippron

• An Hendrix

• David Creytens

• Glen Vergauwen

• Isabelle Rottiers

• Jo Van Dorpe

• Joni Van der Meulen

• Kimberly Verniers

• Olivier De Wever

• Pieter Mestdagh


rna capture sequencing enabled liquid biopsy screening

Health & Medicine