Combining gene and mirna expression for early detection of lung cancer from whole blood samples in PAXgene

Transcription

1 Combining gene and mirna expression for early detection of lung cancer from whole blood samples in PAXgene Andrew V. Kossenkov 1, Noor B. Dawany 1, Priyankara Wikramasinghe 1, Celia Chang 1, Sandy Widura 1, Anil Vachani 2, Gerard Criner 3, Thomas Bauer 4, Harvey Pass 5, William Rom 5, Michael K. Showe 1 and Louise C. Showe 1 1 Phila.,PA/US, 2 Phila.US, 3 Phila.US, 4 Newark, DE/US, 5 NY,NY/US presenter: Andrew Kossenkov ATS Denver: May 19, 2015

2 Faculty Disclosures ATS 2015 Denver Louise C. Showe Relevant financial relationships with a commercial interest: Oncocyte Corporation, Research Support, Current

3 Lung Cancer impact diagnosis Most prevalent: ~220,000 cases each year ~ 13% of all cancer diagnoses: 1 in a 1,000 per year Deadly: ~150,000 deaths each year ~27% of all cancer deaths Late diagnosis: >70% stage III and above only 15% with Stage I/IIA Poor survival: 18% overall five-year survival >50% for early stages Approaches to early diagnosis CT scan Bronchial brushing Sputum, plasma, etc. Blood gene/mirna expression

4 Previous Results Study of 2009 PBMC (CPT tubes) 221 samples 29 genes Sensitivity: 91% Specificity: 80% ROC AUC: 0.92 Effect of tumor removal Problems: Differences significant but small Nodules are harder to classify Need more samples PBMC isolation must be on site Showe et al. 2009, Kossenkov et al 2011, 2012

5 CPT vs PAXgene Tubes

6 Samples Stage Diagnosis Pairs Platform Clinic 638 unique blood samples HFGCC NYU Temple U UPenn 512 patients from 5 pairs Venous/Arterial 512 Pre-treatment samples Classifier development Microarrays OpenArrays 63 pairs Pre-post 59 4 Cancers Nodules Cancer Nodule Disease 7 non-cancer ->cancer I II III IV

7 Current progress 345 samples processed and analyzed Illumina HT12v4 mrna arrays and mirnas on ABI OpenArray PCR platform 242 (70%) training, 103 (30%) testing samples To ensure completely independent testing set SVM with forward feature selection Classification algorithm is important Analyzed mrna and mirna separately to develop independent classifiers Preliminary accuracy of using just mrna or just mirna Combined classifier developed Combining coding and non-coding features does improve accuracy

8 Technical challenges PAXgene data has more noise than PBMC Benefits of PAXgene sample collection outweigh the problems Noise can be addressed bioinformatically Sample and assay batch effects Kits change, arrays change source of batch effects to correct Bioinformatics quality control Outliers, signs of batch effect, corrections, what is the best approach Picking the right analysis method Robust classifiers without bias and over-fitting

9 Initial Performance: Mid-point ROC curve 242 training samples 103 test samples Cancer vs all controls Accuracies comparison mrna only: 79% mirna only: 71% mrna+mirna: 83% 125 mrnas + 20 mirnas Sensitivity: 76% Specificity: 88% ROC AUC: 0.88 Sensitivity mrna + mirna AUC= Cancers: n=54 Controls: n= Specificity

10 SVM prediction score Control Sample predicted as Cancer Validation: Individual samples 4 nodules 3 2 Correctly predicted as Cancers Misclassified Correctly predicted as Controls Cancers Controls 4 adenocarcinomas

11 Target Accuracy? Error rate, e Test Classifier Error rate approximation Error rate approximation, lower MAD Error rate approximation, upper MAD e = n Current progress 0.17 Target Number of training samples, n

12 Conclusions and Future Directions PAXgene samples do retain ability to diagnose NSCLC Similar to PBMC, but much easier to collect A combined mrna+mirna classifier is better Information in both mrna and mirna compartments is important Lower accuracy with similar sample #s as PBMC Effect of additional cell types in PAXgene vs. PBMC and multiple collection sites More features needed for classification May be reduced with larger training set, but number is compatible with potential development platforms (Nanostring, PCR-arrays) Analysis of all samples is in process, plus subgroups Analysis of the full sample set, including subgroup comparisons Sample diversity and special cases will be assessed Taking advantage of special cases: pre/post, venous/arterial, timecourse, etc

13 Considerations Sample numbers One-two hundred sample studies no longer sufficient Earlier cancer stages Real need is for early stage diagnosis where chances of survival are better Multiple collection sites Single site does not capture the cross-site variability that needs to be addressed for clinical application Controls collected at the same location as cancers To avoid selection bias in the classification problem Pulmonary patients as controls, not healthy donors Harder to diagnose at-risk population of patients with lung problems and questionable lung nodules

14 Acknowledgements Showe Lab Wistar Genomics Wistar Bioinformatics Collection sites Noor Dawany Michael Showe Andrew Kossenkov Celia Chang Sandy Widura Andrew Kossenkov Priyankara Wickramasinghe Rehman Qureshi Upenn: Steven Albelda, Anil Vachani NYU: William Rom, Harvey Pass HFGCC: Thomas Bauer Temple U: Girard Criner Project supported by: PA DOH Tobacco Settlement CURE Funds to LS