Presentation on theme: "MiRNAs and biomarkers Gabriella Sozzi. diagnostic microRNAs in lung tumors stratifying lung cancer molecular subtypes ( Landi L. et al) prognostic."— Presentation transcript:
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) 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.)
mRNA One miRNA 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
Diagnostic/prognostic miRNAs in lung cancer Diagnostic miRNA signatures let-7a: target KRAS Takamizawa et al., miRNAs (let-7a, miR-205, miR-126, miR-21) Yanaihara et al., 2006 miR-205 SCC; miR-21 ADC Lebanony et al., 2009 Prognostic miRNAs let-7a, miR-221, miR-137, miR-372 & miR-182 ∗ Yu et al., 2008 ↓ miR-34a: targets C-MET, BCL2 Gallardo et al., 2009 ↓let-7a, -34a, 34c, 25, miRNAs ADC vs. SCC Landi et al., 2010 ↓ Let-7a miR-155 in ADC Takamizawa et al., 2004, Yanaihara et al., 2006
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
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) and prognostic 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).
Lorenza Landi 1 Pierluigi Gasparini 2, Stefania Carasi 2, Carmelo Tibaldi 1, Luciano Cascione 2, Greta Alì 3, Armida D’Incecco 1, Jessica Salvini 1, Gabriele Minuti 1, Antonio Chella 3, Gabriella Fontanini 3, Federico Cappuzzo 1 and Carlo M.Croce 2 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 MO 16.01: Different Micro-RNA Expression In Lung Adenocarcinoma With Molecular Driver Events
MO 16.01: Different Micro-RNA Expression In Lung Adenocarcinoma With Molecular Driver Events - Landi L 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 miRNA levels were analyzed using the NanoString miRNA 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)
* 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. Patient Characteristics MO 16.01: Different Micro-RNA Expression In Lung Adenocarcinoma With Molecular Driver Events - Landi L
Results hsa-miR-515 family expression in normal versus tumor and according to molecular events *p < MO 16.01: Different Micro-RNA Expression In Lung Adenocarcinoma With Molecular Driver Events - Landi L hsa-miR-515 familyNormalALK pos *EGFR WT*EGFR mut*KRAS mut* miR-520d-5p+hsa-miR-518a-5p+hsa-miR miR-520h miR-548d-3p miR-548q miR Upregulated Downregulated ALK +ve EGFR mut Normal EGFR WT KRAS mut.
miRNAs profile 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 Conclusions MO 16.01: Different Micro-RNA Expression In Lung Adenocarcinoma With Molecular Driver Events - Landi L
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 , China
Study Design R Training set (n=42) Testing set (n=40) Micro- array qRT- PCR Internal validation Identifying miRNA signature associated with overall surivival Good prognosis Bad prognosis RNA isolation Tissue Patients miRNA analysis 42 patients 40 patients Normal Lung (n=3)
Results (3)- Training Set (N=42) NL SCLC P=0.05P=0.09 N=3 N=42 The expression levels of miR-886-3p and miR-150 are lower in SCLC tumors than those in normal lung tissues.
Results (3)- Training Set (N=42) miRNA signature: 0.545microRNA microRNA886-3p Low risk (N=21) MST not reached High risk (N=21) MST = 12.6 months D P=0.02
Results (5)- Test Set (N=40) Low risk (N=20) MST not reached High risk (N=20) MST = 18.9 months P=0.005
Results (6)- MiRNA Signature Predicts PFS in both Training and Test Groups Low risk High risk A Low risk High risk B Test group (N=40)Training group (N=42)
Results (7)- Multivariate Regression Analysis of MiRNA Signature and Survivals in Test Set (N=40) VariableHazard Ratio95%CIP value Overall survival miRNA signature (low risk vs high risk) Age (≥60 vs <60) Gender (female vs male) Smoking status (no smoking vs smoking) Progression-free survival miRNA signature (low risk vs high risk) Age (≥60 vs <60) Gender (female vs male) Smoking status (no smoking vs smoking)
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.
MicroRNAs miR-17-5p, miR-21 and miR-210 are associated with survival in malignant pleural mesothelioma patients undergoing extrapleural pneumonectomy Michaela B Kirschner 1, Yuen Yee Cheng 1, Steven C Kao 1,2, Brian C McCaughan 3,4, Nico van Zandwijk 1, Glen Reid 1 1 Asbestos Diseases Research Institute, University of Sydney 2 Department of Medical Oncology, Sydney Cancer Centre 3 Cardiothoracic Surgical Unit, Royal Prince Alfred Hospital Sydney 4 The Baird Institute and Sydney Medical School, University of Sydney
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 VariablesLong survivors (N=8) Short survivors (N=8) Median Age (range) 51.5 (37 – 64)62 (47 – 70) Gender Male Female Histological Subtype Epithelioid Biphasic Stage I II III IV Induction Chemotherapy Yes No Median survival (mo) 57.2 ( – 90.48)6.4 (1.94 – 8.28) VariablesAll patients (N=48) Median Age (range) 58 ( ) Gender Male Female Histological Subtype Epithelioid Biphasic Stage I II III IV Induction Chemotherapy Yes No Median survival (mo) (0.07 – 81.18) Training Set (microarray+RT-qPCR) Test Set (RT-qPCR)
Kaplan-Meier and Multivariate Analysis Hazard Ratio 95 % CIp-value miR-17-5p Low High – (ref) FactorHazard Ratio 95 % CIp-value miR-21 Low High – (ref) <0.001 FactorHazard Ratio 95 % CIp-value miR-210 Low High – (ref) mo 13.3 mo (p=0.005) 24.2 mo 9.4 mo (p=0.001) 19.7 mo 13.3 mo (p=0.031) 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 mo in biphasic, p=0.048) Cox-Regression for each microRNA combined with classic prognostic factors (Histology, age, gender, stage)
Conclusions and Future Directions 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 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
P | A prospective clinical study evaluating stage dependent sputum micro-RNA expression profiles for the detection of non- small cell lung cancer Authors: Rene Razzak 1, Eric L.R. Bédard 1, Julian O. Kim 2, Sayf Gazala 1, Linghong Guo 2, Sunita Ghosh 2, Anil A. Joy 2, Tirath Nijjar 2, Eric Wong 1, Wilson H. Roa 2 1 University of Alberta, Edmonton, AB/CANADA, 2 Cross Cancer Institute, Edmonton, AB/CANADA miRNAs in biological fluids
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.
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.
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.
Plasma miRNA test for lung cancer screening Gabriella Sozzi
+ LDCT 4,000 Smokers ≥ 50 years Smoking cessation Lung function assessment blood sampling R LDCT every year LDCT every 2 years Pastorino U. et al., Eur J Cancer Prev R > 100,000 biological samples
1000 controls (no disease) 130 samples (13%): haemolyzed 870 controls suitable for analyses: 594 LDCT arm; 276 observational arm 85 lung cancer patients 9 patients (11%) : samples not available 7 patients (9%): haemolyzed samples 69 Lung Cancer patients: 26 pre-diagnosis only; 28 at & pre- diagnosis; 15 at diagnosis only 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
Total MSC (risk of lung cancer) High (H) Intermediate (I)Low (L) All subjects93963 (6.7)159 (16.9)717 (76.4) No lung cancer87032 (3.7)130 (14.9)708 (81.4) Lung cancer6931 (44.9)29 (42.0)9 (13.0) performance * SE=87%, SP=81%, PPV=27%, NPV=99% Lung cancer deaths (63.2)6 (31.6)1 (5.3)º Lung cancer, stage I ǂ 3714 (37.8)19 (51.4)4 (10.8) Lung cancer, stage II- III ǂ 125 (41.7)4 (33.3)3 (25.0) Lung cancer, stage IV ǂ 1911 (57.9)6 (31.6)2 (10.5) * 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 Diagnostic and prognostic performance of MSC Sozzi G. et al., in press
Months from blood sampling to lung cancer detection SESPPPVNPV 683%80%18%99% 1286%81%22%99% 1886%81%23%99% 2487%81%25%99% 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
Pre - At surgery Post At II Post II miRNAs returning at the normal levels after surgery miRNAs remaining deregulated after surgery a p 92a p 28-3p Patient developing a second primary lung cancer Modulation of the miRNA signatures in plasma samples collected pre-disease, at time of disease and after surgery (disease-free) from 20 pts Median time 18 months (4-46) Median time 20 months (5-28) Patient developing surrenal metastases At surgery Post LDCT +
Biomarker-Driven Programs for Lung Cancer Screening Pierre P. Massion, MD Thoracic Program Vanderbilt University Nashville, TN WCLC Oct 30th, 2013
Diagnostic BM BM of risk Disease non-measurable Lung nodules BM of Response Biomarkers in the natural history of lung cancer Recurrence Behavior Diagnosis
Early Lung cancer diagnostic biomarkers Sullivan-Pepe, JNCI EDRN Candidates Phase 1Phase 2Phase 3Phase 4Phase 5 Discovery, PredictionAssay validationRetro-longitudinalProspective screeningCancer Control SERUM/PLASMA MALDI TOF MS profilingxxx Autoantibodiesxxx x Specific antigens /proteinsxx x miRNAx x x DNA methylation Bloodx x Circulating Tumor cellsx TUMOR/airway epith Preinvasive histo/cytology xxx DNA methylationxx x RNA airway signaturexx x MALDI MS profilingxx Chromosome aberrationsx x SPUTUM/EBC DNA Methylation Sputumxx x DNA CN -FISHx x VOCs xx
37 Indeterminate Pulmonary Nodules (6-15 mm) High Prob Low probability Low Prob IPN PET or Biopsy High Prob Low probability Risk model IPN PET or Biopsy Decrease rate of invasive bx, futile thoracotomy Decrease in cost, radiation and anxiety High Prob Risk model + Biomarkers Risk reduction Risk increase Low Prob Low probability Low Prob
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
C4d in plasma samples from early lung cancer Ajona et al, JNCI 2013 Plasma C4d levels (a stable complement split product) in early stage lung cancer Phase 2 N=50
C4d levels in screening detected lung cancer N=32N=158 Ajona et al, JNCI 2013
A Blood-Based Proteomic Classifier for the Molecular Characterization of Pulmonary Nodules Phase 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 Oct
13 MRM predictor of lung cancer among 247 lung nodules 4-30 mm (prev 15%) Li et al. Sci Transl Med Oct 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
Boyle, Annals of Oncology 2010 Lam, Cancer Prev Res 2011 Chapman Tum. Biol Jett, Lung Cancer 2013 in press 7 Autoantibody signature Phase 4 CAGE, GBU 4–5, HER2, p53, c-myc, NY-ES0-1 and MUC1
7 Autoantibody signature EarlyCDT- Lung Oncimmune 44 In nodules 8-20 mm, the RR is 4.6 CasesControls Sensitivity44.2 Profile Specificity88.4 Profile PPV NPV84.3 Prevalence0.23 RR nodules tested with the 7 AAB test P | Autoantibodies to a panel of lung cancer-associated antigens can provide significant discrimination between malignant and non-malignant lung nodules P. Massion
Personalizing the management of indeterminate pulmonary nodules 45
Clinical utility of a diagnostic biomarker: study design Positive Randomize IPNs Biopsy Negative3 mo CT F/U SOC (Guidelines) 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. test No test Biopsy 3 mo CT F/U Outcomes: Early stage Futile Thorac. Survival Decrease cost
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.