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miRNAs and biomarkers

miRNAs and biomarkers. Gabriella Sozzi. diagnostic microRNAs in lung tumors stratifying lung cancer molecular subtypes ( Landi L. et al) prognostic microRNAS in tumors miRNA expression profiles to predict clinical outcomes of resectable SCLC patients (Nan Bi et al)

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miRNAs and biomarkers

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  1. miRNAs and biomarkers Gabriella Sozzi

  2. diagnostic microRNAs in lung tumors • stratifying lung cancer molecular subtypes ( Landi L. et al) • prognostic microRNAS in tumors • miRNA expression profiles to predict clinical outcomes of resectable SCLC patients (Nan Bi et al) • MicroRNAs associated with survival in malignant pleural mesothelioma patients (Kirschner et al) • diagnostic microRNAs in biological fluids • sputum miRNA expression profiles for the detection of non-small cell lung cancer (Razzak et al) • plasma miRNA test for lung cancer screening (Sozzi et al) • Biomarker-Driven Programs for Lung Cancer Screening • General considerations (Massion P.)

  3. One miRNA … mRNA mRNA mRNA mRNA mRNA small noncoding RNAs that regulate gene expression by binding complementary sequences of target mRNAs and inducing their degradation or translational repression Evolutionary conserved One miRNA has multiple targets microRNA: a new class of biomarkers

  4. Diagnostic/prognostic miRNAs in lung cancer let-7a: target KRAS Diagnostic miRNA signatures Takamizawa et al., 2004 43 miRNAs (let-7a, miR-205, miR-126, miR-21) Yanaihara et al., 2006 miR-205 SCC; miR-21  ADC Lebanony et al., 2009 34 miRNAs  ADC vs. SCC Prognostic miRNAs Landi et al., 2010 ↓ Let-7a miR-155 in ADC let-7a, miR-221, miR-137, miR-372 & miR-182∗ Yu et al., 2008 Takamizawa et al., 2004, Yanaihara et al., 2006 ↓ miR-34a: targets C-MET, BCL2 ↓let-7a, -34a, 34c, 25, -91 Landi et al., 2010 Gallardo et al., 2009

  5. Lung cancer meta-signature miRNAs Urmo Vo˜sa Int. J. Cancer 2013 • Urmo Võsa • Int. J. Cancer 2013 • 20 published miRNA studies • 598 tumor and 528 non-cancerous samples • 15 miRNA metasignature • robust rank aggregation method

  6. microRNA : plasma/serum-based biomarkers for cancer detection? • Blood-based miRNA studies are in their infancy • miRNA remain rather intact and stable in plasma/serum • Simple universally applicable assay for quantification (i.e. qRT-PCR) • In lung cancer plasma/serum levels of miRNAs might have diagnostic (Silva, ERJ 2010; Shen, Lab Invest 2010; Foss, J TO 2011;Boeri PNAS 2011; Bianchi EmboMolMed 2011; Hennessey, PLoS One 2012) andprognostic value (Hu, JCO, 2010). miRNAs have been found packaged in exosomes derived from multivesicular bodies (7) or be exported in the presence of RNA-binding proteins (i.e. Ago-2)(8) or might be exported microvesicles shed during membrane blebbing (9). Once in the extracellular space, these miRNAs could be taken up by other cells, degraded by RNases, or excreted(10).

  7. MO 16.01: Different Micro-RNA Expression In Lung Adenocarcinoma With Molecular Driver Events Lorenza Landi1 Pierluigi Gasparini2, Stefania Carasi 2, Carmelo Tibaldi1 , Luciano Cascione2, Greta Alì3, Armida D’Incecco1, Jessica Salvini1, Gabriele Minuti1 , Antonio Chella3 , Gabriella Fontanini3, Federico Cappuzzo1 and Carlo M.Croce2 1 Istituto Toscano Tumori, Dipartimento di Oncologia, Livorno Italy 2 The Ohio State University, Comprehensive Cancer Center, Department of Molecular Virology, Immunology and Medical Genetics, Columbus, OH, USA 3 Azienda Ospedaliera Universitaria Pisana, Pisa, Italy

  8. Trial Background: • Oncogenic driving mutations identify lung adenocarcinoma with different prognosis and sensitivity to targeted therapy • Recent studies have suggested that miRNAs could be useful for stratifying lung cancer subtypes, however miRNAs deregulation in NSCLC with ALK translocation, EGFR or KRAS mutations is largely unknown • Aim: • Identify miRNA signature differences according to the presence of specific oncogenic driver • Methods: • Retrospective analysis of a cohort of 67 NSCLCs matched with 17 normal lung tissues • RNA was isolated from FFPE using the Recover ALL kit (Ambion) and miRNAlevels were analyzed using the NanoStringmiRNA V2 panel • Data were processed according to manufacture guidelines. We used Limma to test for differential expression analysis data • The miRNAs expression between tissues for all RT-qPCR was analyzed using the parametric t-test (unpaired,2-tailed for validation) MO 16.01: Different Micro-RNA Expression In Lung Adenocarcinoma With Molecular Driver Events - Landi L

  9. Patient Characteristics * other histology included patients with clear cell carcinoma; § EGFR wild type (wt) included patients EGFR wt and KRAS wt and ALK negative; ^ Codon 12 exclusively; ° defined by break-apart FISH assay. MO 16.01: Different Micro-RNA Expression In Lung Adenocarcinoma With Molecular Driver Events - Landi L

  10. Results ALK +ve Normal EGFR WT KRAS mut. EGFR mut hsa-miR-515 family expression in normal versus tumor and according to molecular events Upregulated Downregulated *p < 0.001 MO 16.01: Different Micro-RNA Expression In Lung Adenocarcinoma With Molecular Driver Events - Landi L

  11. Conclusions • miRNAsprofile significantly differs in lung cancer patients with ALK translocation, EGFR mutations and KRAS mutations • Prognostic and predictive role of several miRNAs are currently under investigation • miRNAs expression could represent an useful tool to refine diagnosis of oncogene addicted NSCLC • Targeting miRNAs could represent a potential strategy to modulate sensitivity to biological agents MO 16.01: Different Micro-RNA Expression In Lung Adenocarcinoma With Molecular Driver Events - Landi L

  12. MicroRNA Signature Predicts Survival in Resectable Small Cell Lung Cancer Nan Bi, Jianzhong Cao, Yongmei Song, Jie Shen, Wenyang Liu, Jing Fan, Guogui Sun, Tong Tong, Jie He, Yuankai Shi, Xun Zhang, Ning Lu, Qimin Zhan, and Luhua Wang Cancer Hospital and Cancer Institute, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China

  13. Study Design Training set (n=42) R RNA isolation Testing set (n=40) Patients Tissue Normal Lung (n=3) Micro- array Identifying miRNA signature associated with overall surivival 42 patients miRNA analysis Good prognosis Internal validation qRT- PCR Bad prognosis 40 patients

  14. Results (3)-Training Set (N=42) The expression levels of miR-886-3p and miR-150 are lower in SCLC tumors than those in normal lung tissues. P=0.05 P=0.09 SCLC NL SCLC NL N=3 N=42 N=3 N=42

  15. Results (3)-Training Set (N=42) miRNA signature: 0.545microRNA150 + 0.617microRNA886-3p P=0.02 Low risk (N=21)MST notreached High risk (N=21) MST = 12.6 months D

  16. Results (5)-Test Set (N=40) P=0.005 Low risk (N=20)MST notreached High risk (N=20) MST = 18.9 months

  17. Results (6)-MiRNA Signature Predicts PFS in both Training and Test Groups B A Low risk Low risk High risk High risk Training group (N=42) Test group (N=40)

  18. Results (7)- Multivariate Regression Analysis of MiRNA Signature and Survivals in Test Set (N=40)

  19. Conclusion • A miR-150/miR-886-3p signature was correlated with the survivals in 42 resectable SCLCs and validated independently with another 40 SCLC cases. • The expression levels of both miR-150 and miR-886-3p were lower in tumors than in normal lung tissues, indicating both of them could serve as tumor suppressor genes in SCLC. • MicroRNAs may serve as promising prognostic markers as well as noval therapeutic targets for SCLC. • Larger sample size and function studies are warranted to validate our findings.

  20. MicroRNAs miR-17-5p, miR-21 and miR-210 are associated with survival in malignant pleural mesothelioma patients undergoing extrapleural pneumonectomy Michaela B Kirschner1, Yuen Yee Cheng1, Steven C Kao1,2, Brian C McCaughan3,4, Nico van Zandwijk1, Glen Reid1 1Asbestos Diseases Research Institute, University of Sydney 2Department of Medical Oncology, Sydney Cancer Centre 3Cardiothoracic Surgical Unit, Royal Prince Alfred Hospital Sydney 4The Baird Institute and Sydney Medical School, University of Sydney

  21. Patient characteristics • Patients undergoing EPP in Sydney between 1994 and 2009: • Series previously used to assess NLR and Calretinin (Kao et al, JTO, 2011) • Complete Cohort = 85 • Patients with RNA = 64 Training Set (microarray+RT-qPCR) Test Set (RT-qPCR)

  22. Kaplan-Meier and Multivariate Analysis • Classic prognostic factors (N=48): • Female gender (49.8 mo vs 14.6 mo in males, p=0.019) • Epithelioid histology (18.17 mo vs 12.16 mo in biphasic, p=0.048) • (p=0.005) • (p=0.001) • (p=0.031) 28.2 mo 19.7 mo 24.2 mo • Cox-Regression for each microRNA combined with classic prognostic factors (Histology, age, gender, stage) 13.3 mo 9.4 mo 13.3 mo

  23. Conclusions and Future Directions • Lower expression levels of three microRNAs in tumour tissue are associated with longer survival of patients undergoing EPP • miR-17-5p and miR-21 remain significant in a multivariate model including classic prognostic factors Those microRNAs have the potential to assist in better selection of patients considered for EPP • Validation in independent samples sets is required • Combination of several microRNAs as potential prognostic signature

  24. miRNAs in biological fluids • P2.20-011 | A prospective clinical study evaluating stage dependent sputum micro-RNA expression profiles for the detection of non-small cell lung cancer • Authors:Rene Razzak1, Eric L.R. Bédard1, Julian O. Kim2, Sayf Gazala1, Linghong Guo2, Sunita Ghosh2, Anil A. Joy2, Tirath Nijjar2, Eric Wong1, Wilson H. Roa21University of Alberta, Edmonton, AB/CANADA, 2Cross Cancer Institute, Edmonton, AB/CANADA

  25. Our objective was to utilize an efficient, cost-effective panel consisting of 3 miRNAs (miR-21, miR-210 and miR-372) for prospective validation as a potential means of accurately detecting NSCLC. This panel was selected based on retrospective analysis of 11 miRNAs our group had previously undertaken using separate NSCLC and control cohorts.

  26. 21 early NSCLC (≤ Stage II) patients, 22 advanced NSCLC (≥ Stage III) patients and 10 control subjects were prospectively accrued. A single sputum sample was obtained through spontaneous expectoration from each study participant. • miR-21, miR-210 and miR-372 expression was conducted on each sputum sample and normalized to an endongenous control (U6) relative to a MRC-5 reference sample, using RNA reverse transcription and Quantitative real-time Polymerase Chain Reaction (RT-qPCR). • Statistical evaluation consisted of unsupervised hierarchical cluster analysis of the experimental-normalized miRNA expression profiles using within-group linkage.

  27. Comparing early NSCLC to controls, the use of miR-21, miR-210 and miR372 expression yielded a diagnostic sensitivity of 66.7% and a specificity of 90.0%. Advanced NSCLC patients had an improved sensitivity of 81.8% with the same specificity of 90.0%. The utilization of miR-21, miR-210 and miR372 sputum expression might provide a sensitive and specific means of detecting NSCLC. The potential linkage between their expression and NSCLC stage may account for the higher sensitivity observed in the advanced NSCLC group. Future use of this promising panel on a larger population will be required to establish its potential application as a screening tool.

  28. Plasma miRNA test for lung cancer screening Gabriella Sozzi

  29. 2005 - 2011 Smoking cessation Lung function assessment blood sampling 4,000 Smokers ≥ 50 years + LDCT R > 100,000 biological samples R LDCT every year LDCT every 2 years Pastorino U. et al., Eur J Cancer Prev. 2012

  30. Study Design & Aims • Diagnostic performance of miRNA test (3 levels, H-I-L risk MSC classifier) for lung cancer detection across LDCT and observational arms • Combination of LDCT and plasma miRNA test • Prognostic value of the miRNA assay Sozzi G. et al., in press

  31. Diagnostic and prognostic performance of MSC *SE, SP, PPV and NPV were calculated combining pre-specified MSC High and Intermediate versus Low risk. +P=0.0366, based on the Cochran-Armitage test for trend in the proportion of deaths across strata of MSC risk groups among subjects with lung cancer. º plasma sample obtained 30 months before disease detection. ǂ tumor stage information was not available in one patient.p=0.49 for association of MSC with tumor stage Sozzi G. et al., in press

  32. Time dependency analysis of diagnostic performance of MSC, at 6, 12, 18 and 24 months intervals between blood sampling and lung cancer diagnosis ( according to Heagerty PJ ) Sozzi G. et al., in press

  33. Modulation of the miRNA signatures in plasma samples collected pre-disease, at time of disease and after surgery (disease-free) from 20 pts Pre- Median time 20 months (5-28) Median time 18 months (4-46) At surgery Patient developing a second primary lung cancer At II At surgery Post Patient developing surrenal metastases LDCT + Post II Post

  34. Biomarker-Driven Programs for Lung Cancer Screening Pierre P. Massion, MD Thoracic Program Vanderbilt University Nashville, TN WCLC Oct 30th, 2013

  35. Biomarkers in the natural history of lung cancer Disease non-measurable Lung nodules Diagnosis Behavior Recurrence BM of risk Diagnostic BM BM of Response Screening programs Therapeutics

  36. Early Lung cancer diagnostic biomarkersSullivan-Pepe, JNCI 2001- EDRN

  37. Indeterminate Pulmonary Nodules (6-15 mm) Low probability High Prob Risk model IPN PET or Biopsy Low Prob High Prob Low probability Risk increase Risk reduction Risk model + Biomarkers High Prob IPN PET or Biopsy Low Prob Low Prob Low probability Decrease rate of invasive bx, futile thoracotomy Decrease in cost, radiation and anxiety

  38. How good should the biomarker be? • Better than standard of care. • What are the metrics? • Performance of the test: PPV & NPV • ROC curves (TPR vs FPR). C index comparison • Net reclassification Improvement (NRI) index • Change in decision making. • De-emphasize Sensitivity and Specificity • Irrelevant (except in early phase of marker evaluation) • Not stable across populations • Require dichotomization of marker values (loss of information) • No information on added value • Not actionable metric; PPV or NPV are. • Independent of the prevalence of the cancer. Pecot, CEBP 2012

  39. C4d in plasma samples from early lung cancer Plasma C4d levels (a stable complement split product) in early stage lung cancerPhase 2 N=50 N=50 Ajona et al, JNCI 2013

  40. C4d levels in screening detected lung cancer N=158 N=32 Ajona et al, JNCI 2013

  41. A Blood-Based Proteomic Classifier for the Molecular Characterization of Pulmonary NodulesPhase 3 • Shotgun Proteomic analysis of tumors. • Selected candidate proteins for testing in the blood • Developed 13 multiple reaction monitoring MRM assays. LRP1, BGH3, COIA1, TETN, TSP1, ALDOA, GRP78, ISLR, FRIL, LG3BP, PRDX1, FIBA, GSLG1 • Training and testing algorithm. Li et al. Sci Transl Med. 2013 Oct

  42. 13 MRM predictor of lung cancer among 247 lung nodules 4-30 mm (prev 15%) A negative test implies a >2 fold decrease risk for cancer. High NPV of the test would obviate 1/4 patients with benign nodules from a biopsy Li et al. Sci Transl Med. 2013 Oct

  43. 7 Autoantibody signature Phase 4 CAGE, GBU 4–5, HER2, p53, c-myc, NY-ES0-1 and MUC1 Boyle, Annals of Oncology 2010 Lam, Cancer Prev Res 2011 Chapman Tum. Biol. 2012 Jett, Lung Cancer 2013 in press

  44. 7 Autoantibody signatureEarlyCDT- Lung Oncimmune 189 nodules tested with the 7 AAB test In nodules 8-20 mm, the RR is 4.6 P2.20-006 | Autoantibodies to a panel of lung cancer-associated antigens can provide significant discrimination between malignant and non-malignant lung nodules P. Massion

  45. Personalizing the management of indeterminate pulmonary nodules

  46. Clinical utility of a diagnostic biomarker:study design Positive Biopsy test Outcomes: Early stage Futile Thorac. Survival Decrease cost Negative 3 mo CT F/U Randomize IPNs Biopsy SOC (Guidelines) No test 3 mo CT F/U Randomization of nodules based on the use of a biomarker test. Proves that biomarker “+” affects patients outcome Proves that biomarker test affects patients outcome when compared with unselected use of same Standard Of Care.

  47. Conclusions • Many early detection candidate biomarkers exist • Few are validated or tested in preclinical setting. Priority to validate existing candidates. • We need to de-emphasize Sensitivity and Specificity and emphasize NPV or PPV with change in decision making. • BM should provide knowledge about added value and therefore should be integrated to clinical, laboratory and imaging routine data. • To demonstrate clinical utility requires significant investment in effort and resources towards biomarkers driven clinical trial.

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