Exploiting microRNAs for precision oncology
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This document summarizes a study that aimed to identify prognostic serum microRNA profiles in neuroblastoma. The study performed microRNA profiling on serum samples from different neuroblastoma patient risk groups. Ten microRNAs were found to best discriminate between survival groups. A validation set of over 120 patients was then profiled for these 10 microRNAs, finding they could separate high-risk deceased from low-risk survivor patients. This suggests serum microRNA profiling may help identify ultra-high risk neuroblastoma patients. The document also discusses microRNA biomarker potential in general and optimization of microRNA sequencing workflows from serum samples.
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Exploiting microRNAs for precision oncology
- 1. Exploiting microRNAs for precision oncology March 6, 2017 Jo Vandesompele, Cancer Research Institute Ghent
- 2. PDF version of presentation and most references are available on https://goo.gl/70kyab
- 3. • 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 Unmet needs in oncology
- 4. • easy to obtain • low risk for the patient • serial profiling > longitudinal studies • reflects entire tumor load • full of biomarker potential – cell-free nucleic acids – circulating tumor cells – extracellular vesicles – tumor educated platelets Liquid biopsies are the holy grail of precision oncology
- 5. Liquid biopsies are the holy grail of precision oncology
- 6. Active secretion and passive release of RNA into circulation Wan et al., Nature Reviews Cancer, 2017
- 7. • 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, isoforms, fusion, mutations • 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 RNA has great biomarker potential
- 8. The majority of human genes do not code for proteins protein coding mRNA non-coding miRNA long non-coding RNA 21000 63000 2500 • ncRNA have exquisite condition specific expression patterns • attractive intellectual property landscape
- 9. MicroRNAs fine-tune gene expression • 21-23 nt long negative regulators of gene expression • predominantly bind 3’UTR of mRNA • translational inhibition • mRNA degradation miRNA gene nucleus cytoplasm ORF DICER Pri-miRNA Pre-miRNA miRNA-miRNA* duplex mature miRNA Unwind miRISC assembly Imperfect complementarity
- 10. MicroRNAs play a role in all the hallmarks of cancer Bertoli et al., Theranostics, 2015
- 11. • miRs undergo (epi)genetic alterations – deletion (e.g. miR-15/16 in CLL) – amplification (e.g. miR-17-92) – mutation, methylation, etc. – sponge titration (lncRNAs) • miRNA biogenesis pathway alterations – mutations in Drosha, Dicer, … • mRNA target genes – create new miR target recognition sites – disrupt miR binding sites – alternative splicing / differential UTR usage MicroRNAs are genetically altered in cancer
- 12. • high degree of homology between family members • small differences in expression level among conditions • low abundance (e.g. in body fluids) • isomiR sequence variants MicroRNA quantification challenges
- 13. Keeping track of microRNA annotation changes • www.mirbasetracker.org (Van Peer et al., Database, 2014) • e.g. hsa-miR-422b
- 14. • comparison of 11 commercial microRNA gene expression technologies (qPCR, microarrays, sequencing) • novel objective and robust performance metrics • framework for platform comparison, incl. set of standardized samples • Mestdagh et al., Nature Methods, 2014 miRNA quality control study
- 16. • each platform has its own strengths and weaknesses • selection of an optimal platform in part depends on the application and goals of the study – low input amount studies (e.g. serum/plasma profiling) – discovery vs. validation – isomiRs • recommendation to combine 2 different technologies for discovery and validation • other things to consider: cost, throughput, sample input amount, content size, ease of use, … • TruSeq small RNA sequencing + miScript qPCR miRQC conclusions
- 17. Q F AAAAAAAAA TTTTTTTTTT TTTTTTTTTT stem-loop RT universal RT mature miRNA mature miRNA reverse transcription quantitative PCR F primer R primerprobe reverse transcription quantitative PCR F primer R primer A BTruSeq small RNA seq miScript qPCR • 10 cycle multiplex preamp • lower adaptor concentration • more PCR cycles • Pippin lib size selection • qPCR lib quant
- 18. • RNA input, library prep kit, library purification, read depth, data processing, donor status (healthy vs. diseased), body fluid type (platelet level in plasma) • 500 – 800 miRNAs per 200 µl serum sample (<100 miRQC) with high reproducibility miRNA seq on human serum 5 10 15 5 10 15 sample 9 normalized read count replicate 1 normalizedreadcountreplicate2 R = 0.963 5 10 15 5 10 15 sample 15 normalized read count replicate 1 normalizedreadcountreplicate2 R = 0.968 A numberofdetectedmiRNAs acrossallsamples 0 200 400 600 800 1000 1200 1400 2014−006−001 2014−006−002 2014−006−004 2014−006−006 2014−006−012 2014−006−019 B numberofdetectedmiRNAspersample 0 200 400 600 800 2014−006−008 2014−006−009 2014−006−013 2014−006−015 2014−006−017 C numberofdetectedmiRNAspersample 0 200 400 600 800 15M 25M Sample1 Sample2 Sample3 Sample4 Sample5 Sample6 Sample1 Sample2 Sample3 Sample4 Sample5 data courtesy of Biogazelle
- 19. • optimization of the library prep workflow results in more efficient detection of miRNAs miRNA seq on human serum serum 1 serum 2 miRNAreadsrelativetoSTDprotocol 0 20 40 60 80 100 120 140 serum 1 serum 2 miRNAsdetectedrelativetoSTDprotocol 0 20 40 60 80 100 120 standard protocol optimized protocol 30% more miRNA reads 15% more miRNAs detected data courtesy of Biogazelle
- 20. • www.mi-star.org (Van Peer, De Paepe et al., NAR, 2016) miSTAR target prediction
- 21. miSTAR has better overall performance and equal/better precisionAreaUnderCurve miSTAR
- 22. Case 1: prognostic serum microRNA profiling in neuroblastoma Cian Will Joep Max low risk low risk high risk high risk • most frequent extracranial solid tumor in children • aim: identify ultra-high-risk patients to make them eligible for new experimental drugs
- 23. • full miRNome miScript qPCR profiling (n=2405) of 5 pooled serum samples from 3 different risk groups – low risk survivors – high risk survivors – high risk deceased Experiment design
- 24. • full miRNome miScript qPCR profiling (n=2405) of 5 pooled serum samples from 3 different risk groups – low risk survivors – high risk survivors – high risk deceased • selection of 781 miRs expressed in the pools • individual qPCR profiling of 781 miRs on 200 µl serum – SIOPEN cohort of +120 high/low risk patients • modified global mean normalization (D’haene et al., Methods Mol Biol, 2012) Experiment design
- 25. Top 10 differential microRNAs in serum discriminate survival groups hsa−miR−10b−3p hsa−miR−592 hsa−miR−9−3p HR deceased LR survivors HR deceased HR survivors fase2D12 val1B10 fase2E11 fase2E12 val1C2 fase2A5 fase2D4 fase2D10 fase2B12 fase2A10 fase2E5 val1D4 fase2G4 fase2C11 fase2F3 fase2G3 fase2G6 val1C6 fase2G12 val1E6 val1E7 fase2C12 fase2G8 val1E4 fase2A8 fase2B3 fase2B10 fase2A2 fase2B2 fase2B1 val1B4 fase2F8 fase2E8 fase2A6 val1C11 val1E12 fase2C2 fase2B7 fase2B4 fase2A1 fase2C6 fase2D7 val1A3 fase2B9 fase2D6 val1E2 fase2A7 fase2D1 fase2E4 fase2B5 fase2B8 fase2D2 fase2C1 fase2D5 val1B2 fase2F2 fase2H1 val1E11 hsa−miR−30c−5p hsa−miR−30b−5p hsa−miR−3192 hsa−miR−3679−5p hsa−miR−4747−3p hsa−miR−518a−3p hsa−miR−187−3p hsa−miR−4294 hsa−miR−30d−3p hsa−miR−541−5p fase2D12 fase2C12 fase2B9 fase2B8 fase2F2 fase2C2 val1E12 val1C11 fase2B4 fase2B10 val1A3 fase2B2 fase2E8 fase2C6 fase2A6 fase2G3 fase2C1 val1E4 fase2D6 fase2A1 fase2D1 fase2B5 fase2B7 fase2B3 fase2D2 fase2A8 fase2B1 fase2F4 fase2A2 fase2G2 fase2F8 fase2D7 fase2C3 fase2B6 fase2G11 fase2E3 fase2E9 fase2C4 val1D9 val1D3 fase2H7 fase2H4 fase2H6 fase2F12 fase2A12 fase2G1 val1B6 val1A9 val1C10 fase2H5 val1A1 fase2A3 fase2G9 fase2A9 val1B5 fase2C9 fase2D3 val1D5 fase2A11 fase2A4 fase2C8 fase2E10 fase2G10 fase2D8 fase2E2 hsa−miR−3200−5p hsa−miR−224−5p hsa−miR−375 hsa−miR−124−3p hsa−miR−129−5p hsa−miR−490−5p hsa−miR−218−5p hsa−miR−873−3p hsa−miR−10b−3p hsa−miR−592 hsa−miR−9−3p fase2D12 fase2C12 fase2B9 fase2B8 fase2F2 fase2C2 val1E12 val1C11 fase2B4 fase2B10 val1A3 fase2B2 fase2E8 fase2C6 fase2A6 fase2G3 fase2C1 val1E4 fase2D6 fase2A1 fase2D1 fase2B5 fase2B7 fase2B3 fase2D2 fase2A8 fase2B1 fase2F4 fase2A2 fase2G2 fase2F8 fase2D7 fase2C3 fase2B6 fase2G11 fase2E3 fase2E9 fase2C4 val1D9 val1D3 fase2H7 fase2H4 fase2H6 fase2F12 fase2A12 fase2G1 val1B6 val1A9 val1C10 fase2H5 val1A1 fase2A3 fase2G9 fase2A9 val1B5 fase2C9 fase2D3 val1D5 fase2A11 fase2A4 fase2C8 fase2E10 fase2G10 fase2D8 fase2E2 hsa−miR−3200−5p hsa−miR−224−5p hsa−miR−375 hsa−miR−124−3p hsa−miR−129−5p hsa−miR−490−5p hsa−miR−218−5p hsa−miR−873−3p hsa−miR−10b−3p hsa−miR−592 hsa−miR−9−3p hsa−miR−30d−3p hsa−miR−541−5p fase2D12 val1B10 fase2E11 fase2E12 val1C2 fase2A5 fase2D4 fase2D10 fase2B12 fase2A10 fase2E5 val1D4 fase2G4 fase2C11 fase2F3 fase2G3 fase2G6 val1C6 fase2G12 val1E6 val1E7 fase2C12 fase2G8 val1E4 fase2A8 fase2B3 fase2B10 fase2A2 fase2B2 fase2B1 val1B4 fase2F8 fase2E8 fase2A6 val1C11 val1E12 fase2C2 fase2B7 fase2B4 fase2A1 fase2C6 fase2D7 val1A3 fase2B9 fase2D6 val1E2 fase2A7 fase2D1 fase2E4 fase2B5 fase2B8 fase2D2 fase2C1 fase2D5 val1B2 fase2F2 fase2H1 val1E11 hsa−miR−30c−5p hsa−miR−30b−5p hsa−miR−3192 hsa−miR−3679−5p hsa−miR−4747−3p hsa−miR−518a−3p hsa−miR−187−3p hsa−miR−4294 hsa−miR−30d−3p hsa−miR−541−5p biased towards similarity metric & cluster method has no capacity to predict for an individual
- 26. • idasanutlin is a selective MDM2 inhibitor, releasing TP53 from negative control • before going to clinical phases in human during drug development, preclinical work in animal models is needed (safety, efficacy, biomarkers) • goals – identify liquid biopsy tumor markers for disease monitoring – identify on target drug efficacy markers Case 2: serum miR analysis in a preclinical model of NB Table of Contents (TOC) N H Cl Cl NH O F CN F OHO O RG7388 Journal of Medicinal Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 Isadanutlin* (RG7388)*
- 27. • jugular vein puncture with a lancet (100 µl blood) Verification of miRNA seq on ½ RNA from 50 µl of murine serum
- 28. • optimized TruSeq small RNA sequencing results in massive amount of 5’ tRNA halves Verification of miRNA seq on ½ RNA from 50 µl of murine serum RNA fragment size 22 nt 30 nt readcount
- 29. • regulated process under stress and in cancer tRNAs as source of small non-coding RNAs with various functions Anderson and Ivanov, 2014
- 30. Probe based removal of unwanted small RNA fragments miRNA 5’ tRNA halves 5’ bioƟnylated DNA probe magneƟc streptavidin beads + magneƟc field purified RNA miR Beads RNa purified RNA u control beads RNase H 0 20 40 60 80 100 120 tRNA-gly tRNA-his tRNA-val tRNA-glu relaƟveabundance(%) control beads RNase H B
- 31. Probe based removal of unwanted small RNA fragments probes: 0 16 avg miRNAs: 169 570 tRNA %: 53.44% 3.88% miRNA %: 1.12% 28.33% • 25x enrichment of miRNA, 14x depletion of 5’ tRFs • Van Goethem et al., Scientific Reports, 2016
- 32. Experiment design day 7 engraftment day 21 start treatment day 35 end treatment 2 w 106 SH-SY5Y cells day 1 day 18 day 35day 22 idasanutlin temsirolimus 2 w
- 33. 56 miR indicators of tumor load0 2 0 2 4 6log2(cou 0 2 4 6 0 2 4 6 hsa miR 105 5p hsa miR 1180 3p hsa miR 125b 2 3p hsa miR 1269a hsa miR 1269b hsa miR 1271 5p hsa miR 1301 3p hsa miR 1307 3p hsa miR 1307 5p hsa miR 1468 5p hsa miR 151a 3p hsa miR 16 2 3p hsa miR 182 5p hsa miR 191 3p hsa miR 197 3p hsa miR 199b 5p hsa miR 28 3p hsa miR 301b 3p hsa miR 330 3p hsa miR 339 3p hsa miR 345 5p hsa miR 3605 3p hsa miR 361 3p hsa miR 3615 hsa miR 3909 hsa miR 424 3p hsa miR 432 5p hsa miR 4326 hsa miR 450b 5p hsa miR 454 5p hsa miR 483 3p hsa miR 483 5p hsa miR 500a 3p hsa miR 501 3p hsa miR 505 3p hsa miR 561 5p hsa miR 576 5p hsa miR 589 3p hsa miR 589 5p hsa miR 598 3p hsa miR 6511b 3p hsa miR 654 3p/mmu miR 654 3p -6 10 15 25 days 0.0 2.5 5.0 log2 fold change signifcantly differentialy expressed no yes 24 56 4 B log2 (count before engraftment ) log2(countafterengraftment ) log2 (countnot-engrafted ) DESeq2 • 53 are human specific, 3 are conserved between human and mouse • 5p and 3p arms of the same pre-miR are present • gradual increase of these 56 miRs in tumor-bearing vs. non-engrafted over 4 time points 0 2 4 6 0 2 4 6 log2(countafterengraftment ) 0 2 4 6 0 2 4 6 hsa miR 105 5p hsa miR 1180 3p hsa miR 125b 2 3p hsa miR 1269a hsa miR 1269b hsa miR 1271 5p hsa miR 1301 3p hsa miR 1307 3p hsa miR 1307 5p hsa miR 1468 5p hsa miR 151a 3p hsa miR 16 2 3p hsa miR 182 5p hsa miR 191 3p hsa miR 197 3p hsa miR 199b 5p hsa miR 28 3p hsa miR 301b 3p hsa miR 330 3p hsa miR 339 3p hsa miR 345 5p hsa miR 3605 3p hsa miR 361 3p hsa miR 3615 hsa miR 3909 hsa miR 424 3p hsa miR 432 5p hsa miR 4326 hsa miR 450b 5p hsa miR 454 5p hsa miR 483 3p hsa miR 483 5p hsa miR 500a 3p hsa miR 501 3p hsa miR 505 3p hsa miR 561 5p hsa miR 576 5p hsa miR 589 3p hsa miR 589 5p hsa miR 598 3p hsa miR 6511b 3p hsa miR 654 3p/mmu miR 654 3p hsa miR 660 5p hsa miR 675 3p hsa miR 675 5p hsa miR 767 5p/mmu miR 767 hsa miR 767 5p hsa miR 769 5p hsa miR 7706 hsa miR 873 3p hsa miR 887 3p hsa miR 92b 3p/mmu miR 92b 3p hsa miR 941 -6 10 15 25 days 24 56 4 A C B D log2 (count before engraftment ) log2(countafterengraftment ) log2 (countnot-engrafted )
- 34. 56 serum miRs are proportional to tumor volume tumorweight(g) log2meanexpressoin log2 mean expression tumor weight (g)cumulative proportion of serum miRs in vivo luciferase imaging endpoints
- 35. 56 serum miRs are proportional to tumor volume tumorweight(g) logluciferasesignal log2 mean expression log2 mean expression
- 36. Serum tumor load miRs are high abundant in tumor 0.0 2.5 5.0 7.5 10.0 hsa−miR−92b−3p hsa−miR−151a−3p hsa−miR−28−3p hsa−miR−500a−3p hsa−miR−769−5phsa−miR−941hsa−miR−887−3p hsa−miR−345−5phsa−miR−301b−3phsa−miR−125b−2−3phsa−miR−1307−5p hsa−miR−767−5phsa−miR−589−5p hsa−miR−1307−3p hsa−miR−197−3phsa−miR−7706hsa−miR−21−3phsa−miR−660−5p hsa−miR−873−3p hsa−miR−589−3p hsa−miR−598−3phsa−miR−483−5p hsa−miR−135a−5phsa−miR−450b−5p hsa−miR−339−3phsa−miR−873−5p hsa−miR−3615 hsa−miR−483−3p hsa−miR−105−5phsa−miR−191−3p hsa−miR−330−3p hsa−miR−1468−5p hsa−miR−4326 hsa−miR−3648 hsa−miR−129−2−3p hsa−miR−675−3p hsa−miR−499a−5phsa−miR−455−5p log(Count) Differentially expressed in serum NO YES miRNA Expression in cell_line logcounts tumor miRs ordered according to abundance serum tumor load miR
- 37. 20 out of 56 miRs are higher expressed in human HR NB hsa−miR−1269a hsa−miR−1307−3p hsa−miR−16−2−3p hsa−miR−191−3p hsa−miR−199b−5p hsa−miR−330−3p hsa−miR−339−3p hsa−miR−345−5p hsa−miR−3605−3p hsa−miR−424−3p hsa−miR−432−5p hsa−miR−454−5p hsa−miR−4741 hsa−miR−483−3p hsa−miR−483−5p hsa−miR−500a−3p hsa−miR−501−3p hsa−miR−675−5p hsa−miR−769−5p hsa−miR−92b−3p 0 2 4 0 2 4 6 0 2 4 6 8 0 2 4 0 2 4 0 1 2 3 4 5 0 2 4 6 0.0 2.5 5.0 7.5 0 2 4 6 0 1 2 3 4 5 0 2 4 0 1 2 3 0 1 2 3 4 5 0.0 2.5 5.0 7.5 10.0 0.0 2.5 5.0 7.5 10.0 0 1 2 3 4 0 2 4 6 0 2 4 6 0 1 2 3 4 0 1 2 3 4 NBHR NBHR H S N R NBHR NBHR H S N R NBHR NBHR H S N R NBHR NBHR H S N R log2(relativeexpression) HR neuroblastoma n=5 healthy children n=5 HR neuroblastoma n=5 rabdomyosarcoma n=5 nephroblastoma n=5 sarcoma n=5
- 38. 24 idasanutlin induced human miRs hsa miR 802/mmu miR 802 5p vehicle idasanutlin vehicle idasanutlin vehicle idasanutlin vehicle idasanutlin2 0 2 rescaled log2 (count) 1 2 3 hsa miR 134 5p/mmu miR 134 5p 4 1 2 3 4 hsa miR 34a 5p/mmu miR 34a 5p 1 2 3 4 1dayaŌertreatment 10daysaŌertreatment A B hsa miR 485 3p/mmu miR 485 3p hsa miR 143 5p/mmu miR 143 5p hsa miR 4492 hsa miR 216a 5p/mmu miR 216a 5p hsa miR 636/mmu miR 5126 hsa miR 146b 5p/mmu miR 146b 5p hsa miR 378a 3p/mmu miR 378b hsa miR 365b 5p/mmu miR 365 2 5p hsa miR 6087 hsa miR 490 5p/mmu miR 490 5p hsa miR 10a 5p/mmu miR 10a 5p hsa miR 668 3p/mmu miR 668 3p hsa miR 212 3p/mmu miR 212 3p hsa miR 29c 3p/mmu miR 29c 3p hsa miR 188 5p/mmu miR 188 5p hsa miR 136 3p/mmu miR 136 3p hsa miR 143 3p/mmu miR 143 3p hsa miR 145 3p/mmu miR 145a 3p hsa miR 145 5p/mmu miR 145a 5p hsa miR 490 3p/mmu miR 490 3p -6 10 20 1 3 1 day 11 days 0 0 0 1 day 11 days idasanutlin temsirolimus 15 25 miR-143/145 cluster miR-34a 1dayaftertreatment 10days1dayaftertreatment treatment vs. control + before and after engraftment
- 39. miR-34a-5p & 212-3p are circulating biomarkers for TP53 activation 6 7 −6 11 15 25 day log2(count) no yescontrol hsa−miR−34a−5p/mmu−miR−34a−5p tumortreatment idasanutlin A B 6 7 −6 11 15 25 day log2(count) hsa−miR−212−3p/mmu−miR−212−3p 7.5 8.0 8.5 9.0 control idasanutlin log2(count) hsa−miR−34a−5p/mmu−miR−34a−5p 5 6 7 control idasanutlin log2(count) treatment control idasanutlin hsa−miR−212−3p/mmu−miR−212−3p C D 6 7 −6 11 15 25 day log2(count) no yescontrol hsa−miR−34a−5p/mmu−miR−34a−5p tumortreatment idasanutlin A B 6 7 −6 11 15 25 day log2(count) hsa−miR−212−3p/mmu−miR−212−3p 7.5 8.0 8.5 9.0 control idasanutlin log2(count) hsa−miR−34a−5p/mmu−miR−34a−5p 5 6 7 control idasanutlin log2(count) treatment control idasanutlin hsa−miR−212−3p/mmu−miR−212−3p C D tumorendpointserum
- 40. • tools available to study miRNAs – miRBase Tracker, miSTAR – miRQC, global mean normalization, tRNA depletion • circulating miRNAs are promising biomarkers in neuroblastoma – outcome prediction in high-risk group – tumor load assessment > patient monitoring / diagnosis – target engagement in the tumor Conclusions
- 41. KOTK, STK, FWO, UGent BOF/GOA/IOF, Fournier-Majoie Nationale Loterij, Kinderkankerfonds Acknowledgements https://goo.gl/70kyab