Discovery of mirna cancer biomarkers

1 Discovery of mirna cancer biomarkers Thomas Litman, Ph.D. Head of Biomarker Discovery www.exiqon.com June 7 2007

Agenda 2 micrornas Biogenesis Function LNA Applications in mirna detection CASE STUDIES Breast cancer CUP Methods Results Conclusions mirna signatures

microrna - biogenesis 3 pri-mirna Drosha pre-mirna Exportin-5 Dicer mirna RISC Target recognition 100-1000 s nt Cleaves the pri-mirna (nucleus) ~70 nt partially paired RNA duplex Exports the pre-mirna into the cytoplasm Cleaves the pre-mirna (cytoplasm) ~21 nt partially paired RNA duplex Blocks translation mirna mrna complementarity sirna Wienholds E & Plasterk RHA.FEBS Letters 2005; 579: 5911-5922.

Mode of action: Gene silencing 4 RISC Target recognition Perfect complementarity Partial complementarity Target mrnas 2-8 nt complementarity between mirna and mrna mrna cleavage Translational repression Wienholds E & Plasterk RHA. FEBS Letters 2005; 579: 5911-5922.

Additions to the Central Dogma 5 Genome Imprinting methylation Splicing Regulation by proteins mirna Transcriptome Regulation by RNA Proteome Ribozymes Morten Lindow

microrna publications a growing field 6 Number of mirna publications/year 700 600 500 400 300 200 100 0 mirnas found in mouse and human mirnas classify cancer mirnas implicated in cancer Nobel Prize in Physiology or Medicine to Andrew Z. Fire & Craig C. Mello for their discovery of RNAi May Jan 2000 2001 2002 2003 2004 2005 2006 2007 Year

micrornas a growing family 7 4361 mirnas identified in vertebrates, invertebrates and plants 5000 Number of mirnas in mirbase 4000 3000 2000 1000 0 all species human 475 human mirnas 475 human mirnas 2004 2005 2006 2007 Year

micrornas characteristics and complexity 8 Phylogenetically conserved Constitute 1-4 % of the predicted genes Often localized in fragile chromosomal regions At least one third of all human genes are regulated by mirnas Each mirna has many targets Each 3 UTR may be targeted by many mirnas Complex regulatory networks mir-1 mir-2 mir-3 mir-4 mir-5 mir-6 mir-7 mir-8 mrna-3 UTR mir-1 mir-4 mir-1 mir-7 mir-2 mir-5 mir-1 mir-3 mir-8

micrornas: physiology and disease 9 Development and Differentiation Adipocytes Blood Brain CNS Granulocytes Muscle Stem cells Apoptosis Disease associations Viral infections Diabetes Inflammation Neurological disease Fragile X syndrom Spinal muscular atrophy Waisman syndrome MRX3 Cancer oncomirs: tumor suppressors oncogenes Classification power Diagnostic Prognostic Predictive

mirnas and cancer 10 10 Cancer specific mirnas identified in: Brain Breast Colon Leukaemia Liver Lung Thyroid Pancreas mirna profiles appear better at classifying tumors than mrna profiles mirna profiles classify pre-malignant stages up-regulated oncogenic mirnas down-regulated tumor suppressor mirnas

Classification mirna vs. mrna 11 11 mirna profiles appear superior to mrna for cancer classification mirna mrna mirna profiles cluster the GI samples well (GI, gastrointestinal origin) mrnas do not cluster the GI-samples very well Lu et al. MicroRNA expression profiles classify human cancers. Nature 2005; 439: 834-8

12 12 micrornas Biogenesis Function LNA Applications in mirna detection Agenda Breast cancer CUP Methods Results Conclusions

Locked Nucleic Acid - (LNA ) 13 13 A a bicyclic high affinity RNA mimic with the sugar ring locked in the 3 -endo conformation Increased T m (Tm increases by 2-8ºC per base) Obeys Watson-Crick base-pairing rules Perfect for design of short probes in high stringency assays: Improved mismatch discrimination (SNPs, mirnas) High sensitivity and specificity in hybridizations LNA

LNA probes are high-affinity RNA mimics 14 14 RNA:RNA duplex stable A-helix good base-stacking LNA:RNA duplex K. Bondensgaard et al., Chem. Eur. J. 2000, 6, 2687 M. Petersen et al., J. Am. Chem. Soc. 2002, 124, 5974 Overlay Nearly seamless fit

Mismatch discrimination by LNA probes 15 15 Perfect Match Single Mismatch O O O P O - O O B LNA 8-mer 5 -TGCTGGTG-3 3 -ACGACCAC-5 3 -ACGGCCAC-5 71 C 45 C T m 26 C O O O O P O - B DNA 8-mer 5 -TGCTGGTG-3 35 C 25 C 10 C LNA probes are extremely powerful in mismatch discrimination due to increased Tm

The significance of Tm 16 16

Residual mismatch hybridization at Th 17 17 Signal Usually only one hybridization temperature (Th) can be applied in each experiment. Perfect match signal 50% Residual signal Th At a given hybridization temperature (Th) residual signal may occur from mis-matched targets.

Distributions of Tm and Tm 18 18 If capture probes are not Tmnormalized the distribution of Tms are wide. It is difficult to select a stringent Th. Th Th Distribution of MM-Tm Distribution of PM-Tm If capture probes have low Tms the Tms are smaller. No stringent Th. Distribution of MM-Tm Distribution of PM-Tm Using Tm-normalized capture probes with high Tms ensures a large Tm-span to select a stringent hybridization temp (Th). Th Distribution of MM-Tm Distribution of PM-Tm

LNA - applications 19 19 DNA probe LNA probe Northern Blots 100 50 25 10 5 2.5 100 50 25 10 5 2.5 µg RNA 6 h exp In situ hybridization Microarrays qrt-pcr Luminex pancreas 12 h exp 24 h exp 6 h exp 12 h exp mir171 mir319 mir-217 EtBr

Luminex 100 IS Total System: FlexmiR 20 20

LNA Capture Probe Design 21 21 5 -UGAGGUAGUAGGUUGUAUGGUU-3 actccatcatccaacataccaa-5 Full length DNA capture probe, Tm = 45-75 C Full length LNA capture probe, Tm > 90 C Capture probes are LNA-DNA mixmers Capture probes are normalized to T m =72ºC Hybridized under high stringency conditions

mircury an array of mirna analysis tools 22 22 Sample isolation Profiling Validation I quantification Validation II spatial Functional analysis - Knockdown Functional analysis Upregulation mircury sample prep mircury array mircury qpcr mircury in situ mircury knockdown mircury mirmimmicks detection mircury Northern blots mirbase 9.2 + spike-in ctrls + updates Human Mouse Rat

23 23 micrornas Biogenesis Function LNA Applications in mirna detection Agenda CASE STUDY Breast cancer CUP Methods Results Conclusions

Diagnosis Case story: Breast cancer 24 24 Non-biomarker based Breast self-examination Mammography only screening test that reduces mortality MRI, high-resolution ultrasound Biopsy histology Sentinel lymph node biopsy stage and grade classification Biomarker based Proteins mrna profiles mirna profiles

mirna a promising biomarker 25 25 Cancer patient stratification Capability of classify cancer of unknown primary No of data points needed Stability of target Dynamic range (assay robustness) Prognostic capability Information for treatment selection mirna 1 High High 2-20 High High High High mrna High High 50-15.000 Low Medium-high High High Chromosomal deletion/ amplification Low Low 1-2 Very high On/Off High in specific cases High in specific cases Chromosomal methylation Low Low 1-10 Very high On/Off High in specific cases High in specific cases SNP Low Low 1-10 Very high On/Off Low Low- high when related to drug degradation Protein Low Low 1-2 Very high High in specific cases High in specific cases 1: Recommended supplementary reading: Cummins and Velculescu, Oncogene, 2006, 25, 6220-6227. Implications of micro-rna profiling for cancer diagnosis Low

Biomarker examples Case story: Breast cancer 26 26 Established biomarkers for breast cancer BCL2 (Anti-apoptotic protein) BRCA1/2 (DNA repair) EGFR (Tyrosine kinase receptor) ERBB2* (HER2, tyrosine kinase receptor) ESR* (Estrogen receptor) PGP (P-glycoprotein, drug resistance) PGR (Progesterone receptor) TP53 (TF, tumor suppressor) UPA (Serine protease) *approved by FDA as breast cancer biomarkers

Methods: Samples 27 27 5CM 1CM T1 Biopsies are taken from the primary tumor (T1) and from normal adjacent tissue (1cm and 5cm from the tumor). Source: MAMBIO & John Zibert, KAS Herlev

Methods: mircury microarrays 28 28 Total RNA 1 µg mirna Enzyme 5 - -3 F Adaptor Hybridize Scan

Methods: q RT-PCR 30 30 Total RNA < 10 ng 5 - cdna synthesis mirna -5 mir-specific RT primer Reverse transcriptase PCR amplification and real-time detection Forward primer cdna

Results - Technology 31 31 mircury LNA microarray 120 100 High specificity 80 60 40 20 0 let-7a let-7b let-7c let-7d let-7e let-7f let-7g let-7i let-7 spike-in - 2 1 2 1 1 2 4 number of mismatches hsa-let-7a UGAGGUAGUAGGUUGUAUAGUU hsa-let-7b UGAGGUAGUAGGUUGUGUGGUU hsa-let-7c UGAGGUAGUAGGUUGUAUGGUU hsa-let-7d AGAGGUAGUAGGUUGCAUAGUhsa-let-7e UGAGGUAGGAGGUUGUAUAGUhsa-let-7f UGAGGUAGUAGAUUGUAUAGUU hsa-let-7g UGAGGUAGUAGUUUGUACAGUhsa-let-7i UGAGGUAGUAGUUUGUGCUGU-

Results - Technology 32 32 Reproducibility R spike-in = 0.99 R mirna = 0.97

Results Biology: mirnas in breast cancer 33 33 Log2 fold ratio(tumor/normal) 4 3 2 1 0-1 -2-3 -4-5 -6-7 M-A plot of mirna gene expression Upregulated in breast cancer 5 6 7 8 9 10 11 12 13 14 15 Downregulated in breast cancer Average log2 intensity

Results Biology: mirnas in breast cancer 34 34 Breast cancer specific mirnas 6 5 Log2(Cancer/Normal) 4 3 2 1 0-1 The let-7 family negatively regulates Ras 125a let-7a let-7d 125b 143 10b let-7f 155 213 196a 202 34 136 21-2 -3-4 mir-21: anti-apoptotic

Clear separation of cancer from normal 36 36 T28a T100 T28b Principal Component Analysis (PCA) performed on the mirnas that varied across the analyzed samples PC2 T583 T601 T557b T557a T525 T567b N583 T567a N567 N100 N525 N601 T534 T27 T596 N27 T26 N28a N28b Tumor samples (red) separate from normal tissue (blue) N=11 patients paired samples: tumor vs. normal adjacent tissue N557 N26 N534 N596 PC1

Clustering of mirna expression patterns 37 37 Normal tissue Tumor tissue mir-1 mir-2 mir-3 mir-4 mir-5 mir-6 mir-7 mir-8 mir-9 mir-10 mir-11 mir-12 mir-13 mir-14 mir-15 mir-16

Array data confirmed by 454 sequencing 38 38 Top-25 expressed mirnas (Based on mirnas from five breast cancers) 454 sequencing mircury LNA Array 5 20 5 80% agreement for the top 25 mirnas Many novel mirnas identified

454 - Results 454Reads.qual 39 39 Poly-A tails often have low quality score 35 30 25 20 15 10 5 0 CTCAGCGACCTCGGCTGTCACTCAATCTATTGAAAGTCAGCCCTCGACACAAGGGTTTGTAAAAAAAAAAAAAAAAANAAAAAGGCGGGNGATNTCTCT

454 - Results 40 40 Short and long? How does the distribution of read lengths look like? Length Count 10-19 0 20-29 0 30-39 15 40-49 338 50-59 477 60-69 1160 70-99 11529 80-99 44391 90-99 45295 100-109 23518 110-119 74397 120-129 53557 130-139 30629 140-149 11211 150-159 4675 160-169 1018 170-179 208 180-189 82 190-199 27 200-209 17 210-219 4 220-229 5 230-239 1 240-249 1 250-259 0 260-269 0 270-279 0 280-289 1 count 80000 70000 60000 50000 40000 30000 20000 10000 0 Size distribution - 454 tags 30-39 40-49 50-59 60-69 70-99 80-99 90-99 100-109 110-119 120-129 130-139 140-149 150-159 160-169 170-179 180-189 190-199 200-209 size range (nt)

454 new mirs? 41 41 And now, to the really interesting stuff! How many potentially new mirs can we find? - How do the new mirs map to the genome? - Do they have a potential hairpin precursor? - What are the predicted targets? - And how about the pre-mirs? - Why don t we see any of the old ones? - Are they hiding somewhere or just gone? Do we se other RNA fragments of interest? - trna, mrna, rrna, snrna, PIWIs, anything else? - something for you?

42 42 micrornas Biogenesis Function LNA Applications in mirna detection Agenda CASE STUDY Breast cancer CUP Methods Results Conclusions

Cancer of Unknown Primary (CUP) 43 43 Metastatic cancer Often poorly differentiated cells Around 4-5% of all diagnosed cancers Poor prognosis: median survival 5-6 months 1-year survival rate 25% No early classification method per definition Difficult diagnostics: Time consuming Expensive

The diagnostic vision Case story: CUP 44 44 The Vision Cancer of unknown primary Test set mirna classifier Validation set Identification of origin Directed treatment

Identifying the primary origin Case story: CUP 45 45 Step 1 Step 2 Step 3 Step 4 Step 5 Metastais identified in the lymph Biopsy taken: mirna profile conducted Profile matched normal colon tissue Primary tumor identified in colon Focused and optimized treatment Normal colon profile Metastasis from lymph node Profile of primary tumor CRC: Colon Rectal Cancer

46 46 micrornas Biogenesis Function Agenda LNA Applications in mirna detection Breast cancer CUP Conclusions

Conclusions 47 47 Cancer associated mirnas represent potential biomarkers and perhaps future targets. Workflow: 1. Identify new mirnas by e.g. 454 sequencing 2. High throughput microarray detection of new mirnas 3. Target validation by Q RT-PCR Issues: 1. mirna prediction difficult structure, conservation? 2. Target prediction difficult off-target effects? 3. Only few experimentally verified targets To do: Correlation to mrna and protein data

Acknowledgments Thank you Exiqon Carsten Also Christian Glue Jette Dam Hedegaard Louise Christiansen Michael Hansen Mikkel Nørholm Nana Jacobsen Niels Tolstrup Nina Stahlberg Peter Mouritzen Peter Stein Nielsen Rolf Søkilde Søren M Echwald Søren Møller Søren Rasmussen Søs Ludwigsen Tine Sommer Bisgaard Torsten Bryld Herlev University Hospital Thank you Alastair Hansen Claus Sommer Bisgaard Henrik Flyger Inge Marie Svane Jens Eriksen John Zibert Marianne Fregild Thomas Horn And thank you 48 48