Exploiting extracellular body fluid RNA for precision medicine purposes

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Exploiting extracellular body fluid RNA for precision medicine purposes Jo Vandesompele,

2 Beyond DNA in plasma from cancer patients inside oncology DNA only informs on the cancer cells, not on the stroma nor the host on-target therapeutic activation is not reflected by changes in the DNA outside oncology, (sequence and copy number of) DNA is uninformative plasma RNA concentration is 10x higher sensitivity advantages 0.2 ml (RNA) vs. 2 ml (DNA) liquid biopsy all human body fluids contain RNA reasons not to study RNA: fragmented/degraded, pre-analytical variables, technically more challenging, many unanswered questions, pioneer 2018 Biogazelle. All rights reserved. 2

4 All human body fluids contain exrna ascites (peritoneal fluid) urine plasma serum stool supernatant synovial fluid sputum aqueous humor vitreous humor lacrimal fluid (tears) breast milk amniotic fluid bronchial lavage fluid cerebrospinal fluid gastric fluid nasal wash saliva seminal fluid throat swab vaginal fluid bile The Human Body Fluid exrna Atlas 2018 Biogazelle. All rights reserved. 4

5 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), isoforms, 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 insightful and robust data-analysis differential gene expression, function prediction, pathway activity, deconvolution 2018 Biogazelle. All rights reserved. 5

Active secretion and passive release of RNA into circulation vesicles exosomes, microvesicles, apoptotic bodies ribonucleoprotein complexes AGO2, high-density lipoproteins platelets

6 Active secretion and passive release of RNA into circulation vesicles exosomes, microvesicles, apoptotic bodies ribonucleoprotein complexes AGO2, high-density lipoproteins platelets intercellular communication biomarker potential abundance levels structural information (fusion, mutation, ) Wan et al., Nature Reviews Cancer, Biogazelle. All rights reserved. 6

7 Validated RNA seq workflows for any exrna type cell-free RNA extracellular RNA long RNAs 4 polyadenylated RNA 2 mrna 1 lncrna small RNA 3 microrna non-polyadenylated RNA lncrna rrna, histone RNAs mrna capture sequencing 1 3 end sequencing 2 small RNA sequencing 3 (total RNA sequencing) Biogazelle. All rights reserved. 7

8 Challenges in mrna sequencing body fluids fragmented/degraded RNA abundant unwanted RNA (ribosomal, hemoglobin) low input sense / antisense overlapping transcripts solution: probe based cdna enrichment, RNA capture sequencing 2018 Biogazelle. All rights reserved. 8

9 RNA capture sequencing history Levin, 2009 Ueno, 2012 Mercer, 2012 Halvardson, 2013 Cabanski, 2014 Biogazelle 467 genes 913 genes 2265 loci, 0.77 Mb exome exome on FFPE exome on plasma higher sensitivity higher transcriptome complexity fusion genes, variants 2018 Biogazelle. All rights reserved. 9

10 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 mrnas 214,000 exons 425,000 probes 20M reads FFPE, 15M reads plasma +300 cases so far colon, lung, ovarium, breast, esophageal, pancreas custom probe set (452,000) for 52,000 human long non-coding RNAs (LNCipedia 4.0) 2018 Biogazelle. All rights reserved. 10

11 Colon & lung cancer samples sensitivity 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 2018 Biogazelle. All rights reserved. 11

Colon & lung cancer samples strandedness, read

regions enriched whole transcripts are covered

15 Colon vs lung cancer plasma top 5 enriched significant KEGG pathways upon GSEA PI3K-AKT signaling pathway cell cycle oxidative phosphorylation focal adhesion osteoclast differentiation 2018 Biogazelle. All rights reserved. 15

16 Esophageal cancer plasma titration experimental design platelet-free / platelet-poor plasma cancer male : 100% 50% cancer 10% cancer 2x replicates for each sample 2% cancer healthy female : 0% 2018 Biogazelle. All rights reserved. 16

18 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 50% cancer 10% cancer 2% cancer healthy : 0% 2018 Biogazelle. All rights reserved. 18

19 Excellent titration response 25% lower expression is detected in 80% of the cases platelet-free platelet-poor titrating gene fraction titrating gene fraction 2018 Biogazelle. All rights reserved. 19

20 Ovarian cancer longitudinal study experimental design platelet-free plasma diagnosis before treatment during treatment post-surgery relapse FFPE tumor patient patient Biogazelle. All rights reserved. 20

23 Therapy response monitoring using somatic mutations in RNA FCGR2A mutation present in FFPE and plasma at diagnosis disappears during treatment and pops up again at time point 4 -> sign of relapse? Biogazelle. All rights reserved. 23

Cancer specific RNA editing event seen in plasma variant present in colon cancer

25 Colon cancer longitudinal study platelet-free/poor plasma metastatic setting (anti-vegf or anti-egfr + chemo) diagnosis before treatment 2 weeks after start treatment 1st CT scan 1 month after CT scan 2 months after CT scan patient G patient H patient J 2018 Biogazelle. All rights reserved. 25

26 Large fold differences large fold changes (CT scan vs diagnosis) POLB is the most upregulated gene involved in mismatch repair upregulated upon chemotherapy mutated in colon cancer 2018 Biogazelle. All rights reserved. 26

28 Plasma holds signal of stroma and host gene set enrichment analysis DNA mismatch repair (anti-)proliferation T-cell stimulation inflammatory response t2 t3 t4 tumor stroma immune system organ toxicity 2018 Biogazelle. All rights reserved. 28

Deconvolution of gene expression data tissue/plasma gene expression profiles

31 Deconvolution of body fluid RNA data GTEx based annotation of cell-free RNA example of 2 genes detected in plasma; each colored box indicates gene expression in a normal tissue gene 1: high expression in multiple tissues, likely the plasma signal originates from different tissues gene 2: increased likelihood of plasma signal originating from that particular tissue 2018 Biogazelle. All rights reserved. 31

32 Ovary signal persists in ovarian cancer relapse patient fractional contribution Ovary (%) pre-treatment patient 1 patient patient 17 disease-free fractional contribution Ovary (%) pre-treatment patient 15 relapse 2018 Biogazelle. All rights reserved. 32

33 Prostate cancer plasma is different from healthy plasma plasma plasma higher fraction of prostate epithelial cells higher fraction of early erythroid cells fraction Bronchial Epithelial Cells healthy prostate cancer healthy prostate cancer % prostate epithelial cells fraction Early Erythroid Cells % early erythroid cells Biogazelle. All rights reserved. 33

34 Plasma contains signals from more cell types compared to brain tissue 15 brain 8 plasma Plasma tissue fraction (%) 10 5 tissue fraction (%) 6 4 AdrenalCortex Adrenalgland Amygdala Appendix AtrioventricularNode BDCA4._DentriticCells Bonemarrow CardiacMyocytes Caudatenucleus CD105._Endothelial CD14._Monocytes CD19._BCells.neg._sel.. CD34. CD56._NKCells CD8._Tcells CiliaryGanglion CingulateCortex colon Colorectaladenocarcinoma Fetalbrain Fetalliver Fetallung FetalThyroid GlobusPallidus Hypothalamus Kidney Leukemia_chronicMyelogenousK.562 Leukemia_promyelocytic.HL.60 Leukemialymphoblastic.MOLT.4. Lymphnode Lymphoma_burkitts.Daudi. MedullaOblongata OccipitalLobe Ovary Pancreas ParietalLobe Pituitary Placenta Prostate retina Salivarygland SkeletalMuscle Skin small_intestine SmoothMuscle SubthalamicNucleus Testis TestisGermCell TestisLeydigCell TestisSeminiferousTubule Thymus Thyroid Tongue Tonsil Trachea TrigeminalGanglion Uterus UterusCorpus WholeBlood PancreaticIslet TestisIntersitial CD4._Tcells Lung pineal_day BronchialEpithelialCells Liver Adipocyte Lymphoma_burkitts.Raji. X721_B_lymphoblasts OlfactoryBulb Thalamus CD71._EarlyErythroid CD33._Myeloid pineal_night Wholebrain Heart Spinalcord Pons DorsalRootGanglion Cerebellum CerebellumPeduncles PrefrontalCortex TemporalLobe SuperiorCervicalGanglion 2 0 Caudatenucleus CD33._Myeloid CD4._Tcells CD56._NKCells CerebellumPeduncles CiliaryGanglion CingulateCortex colon FetalThyroid GlobusPallidus Hypothalamus Leukemia_chronicMyelogenousK.562 Leukemia_promyelocytic.HL.60 Leukemialymphoblastic.MOLT.4. Lung Lymphnode Lymphoma_burkitts.Daudi. MedullaOblongata OccipitalLobe PancreaticIslet ParietalLobe Pons retina small_intestine SubthalamicNucleus TemporalLobe TestisGermCell Thalamus Thymus Tonsil Trachea TrigeminalGanglion Adipocyte CardiacMyocytes CD8._Tcells Appendix Wholebrain Lymphoma_burkitts.Raji. CD71._EarlyErythroid TestisSeminiferousTubule OlfactoryBulb BronchialEpithelialCells Pancreas Spinalcord Uterus Thyroid AtrioventricularNode Heart Pituitary Adrenalgland DorsalRootGanglion Placenta TestisLeydigCell Tongue UterusCorpus Amygdala Prostate X721_B_lymphoblasts Ovary pineal_day Colorectaladenocarcinoma Cerebellum Salivarygland Liver Testis Fetalbrain Kidney Skin CD34. BDCA4._DentriticCells AdrenalCortex SmoothMuscle SuperiorCervicalGanglion pineal_night CD14._Monocytes PrefrontalCortex TestisIntersitial SkeletalMuscle Fetallung CD19._BCells.neg._sel.. WholeBlood CD105._Endothelial Fetalliver Bonemarrow 0 mainly neuronal cell types (blue) bone marrow, liver, blood cells, skin, brain, testis, 2018 Biogazelle. All rights reserved. 34

35 Biologically meaningful differences between different fluids urine plasma Kidney Fetal liver Liver CD33 Myeloid Pancreas Skeletal Muscle Pancreat icislet Fetal lung B_lymphoblasts TestisIntersitial Bonemarrow Whole Blood CD19 B-Cells CD105 Endothelial Fetal liver CD71 Early Erythroid Ovary BDCA4 Dentritic Cells Testis Intersitial Skeletal Muscle tissue fraction (%) tissue fraction (%) 2018 Biogazelle. All rights reserved. 35

centrifugation steps RNA extraction kits freeze-thaw cycles input volume

36 Preanalytical jungle of exrna analysis blood collection tube serum vs. plasma platelet rich/poor/free plasma time delay to process the tube centrifugation steps RNA extraction kits freeze-thaw cycles input volume donor [credit: Signature Entertainment] 2018 Biogazelle. All rights reserved. 36

37 Blood collection tube 200% difference unique (non-duplicated) read fraction citrate EDTA 30,000, ,000,000 20,000,000 15,000,000 10,000,000 17M 5M M 12.5M 5,000, #Raw reads #Unique reads #Unique,nondup #Raw reads #Unique reads #Unique,nondup PRP PPP PFP P1 P2 PRP PPP PFP P1 P2 prostate cancer; mirneasy serum/plasma; 200 µl; mrna capture seq 2018 Biogazelle. All rights reserved. 37

38 RNA extraction kit affects gene detection sensitivity #genes (>1 FPKM) kit A kit B kit C 0.2 ml 0.4 ml 1 ml healthy control platelet-poor plasma mrna capture seq 2018 Biogazelle. All rights reserved. 38

extraction 9 methods and 1-3 input volumes 5 x 3 x 5 x RNA 2 x freeze/thaw reproducibility biomarker study variance

39 exrna QC study design phase 1 single factor pre-experiments phase 2 almost full factorial experiment phase 3 post-experiments exrna g-force for PRP 5 centrifugation speeds (no sequencing) time-to-process 3-5 time delays RNA extraction 9 methods and 1-3 input volumes 5 x 3 x 5 x RNA 2 x freeze/thaw reproducibility biomarker study variance component blood collection tube 9 tube types 350 exrna samples mrna + small RNA seq 2018 Biogazelle. All rights reserved. 39

40 exrna QC study goals inform the (relatively young) liquid biopsy research field about the impact of pre-analytical variables on data output provide guidelines for standard operating procedures create a database where every researcher can explore the behavior of his/her gene of interest in relation to changing variables 2018 Biogazelle. All rights reserved. 40

41 Conclusions (1) all human body fluids contain RNA, likely reflecting health and specific disease states Biogazelle developed and benchmarked laboratory methods for exrna analysis unbiased discovery (sequencing) targeted validation (quantitative / digital PCR) standardization of pre-analytical steps is key to success 2018 Biogazelle. All rights reserved. 41

42 Conclusions (2) RNA can be exploited for precision medicine purposes diversity in RNA types and abundance embedded structural information: alternative splicing, circular RNA, SNPs, fusion genes, somatic mutations, RNA editing insightful and robust data-analyses (n=1): gene-set-enrichment analysis / signal deconvolution 2018 Biogazelle. All rights reserved. 42

Acknowledgements (A>Z) Carolina Fierro Manuel Luypaert Nele Nijs Pieter Mestdagh Sandra Steyaert Thomas Piofczyk Scott Kuersten Gary Schroth David Creytens Anneleen Decock Olivier De Wever Bert

43 Acknowledgements (A>Z) Carolina Fierro Manuel Luypaert Nele Nijs Pieter Mestdagh Sandra Steyaert Thomas Piofczyk Scott Kuersten Gary Schroth David Creytens Anneleen Decock Olivier De Wever Bert Dhondt An Hendrix Pieter Mestdagh Piet Ost Annouck Philippron Isabelle Rottiers Jo Van Dorpe Joni Van der Meulen Glen Vergauwen Kimberly Verniers Ken Op de Beeck Patrick Pauwels Marc Peeters Laure Sorber 2018 Biogazelle. All rights reserved. 43

RNA capture sequencing enabled liquid biopsy screening

RNA capture sequencing enabled liquid biopsy screening Jo Vandesompele, Biogazelle CSO LKI symposium Liquid Biopsies & Cancer Leuven, Belgium, August 30, 2017 2017 Biogazelle. All rights reserved. 1 Acknowledgements