Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Development and Validation of a Radiosensitivity Signature for Breast Cancer A collaboration between the University of Michigan (Drs. Pierce, Speers, and Feng) and PFS Genomics Department of Radiation Oncology University of Michigan Background >6000 women with BCS and node-negative disease EBCTCG, Lancet 2005;366:20872106 Can we identify these patient populations currently? Not well from current clinicopathologic data. Additionally, there are conflicting findings from studies assessing association between intrinsic molecular subtypes and radiosensitivity. There is a clear need to develop and validate molecular signatures to predict who will benefit from intensification or omission of radiotherapy. Hypothesis: • Gene expression profiling data from breast cancer cell lines coupled with intrinsic radiosensitivity information can be used to identify a radiosensitivity signature • This signature can be used to identify patients in these two disparate groups and predict likelihood of recurrence after adjuvant RT treatment in early stage patients Human Breast Cancer Cell Lines Clonogenic survival assay performed on 21 BCC lines to determine intrinsic radiosensitivity 10 basal, 8 luminal, 3 HER2/neu cell lines Affymetrix Microarray profiling focused on RT related gene Spearman’s correlation methodexpression with RT sensitivity as a continuous variable 147 genes significantly associated with RT sensitivity (80 + correlated, 67 - correlated) Gene enrichment analysis of positively and negatively associated radiation resistant genes Significant gene enrichment for genes involved in cell cycle arrest and DNA damage response Expression validation (RNA and protein) Functional validation Training of Signature in Human Breast Cancer Datasets with Recurrence Data 343 pts with early stage, LN- IDC treated with BCS and RT (no adjuvant systemic chemo) with LRF survival data-Random Forest Modeling Clinical Validation in Human Breast Cancer Datasets with Recurrence Data 184 pts with early stage, LN- IDC treated with BCS and RT (no adjuvant systemic chemo) with LRFS data Resistant (SF >50% ) Basal - 4 Luminal- 2 HER2 - 1 Moderately Resistant (SF 49-39% Basal - 3 Luminal -3 HER2 - 0 Sensitive (SF <39%) Basal - 3 Luminal - 3 HER2 - 2 P-value: NS • For each gene (~43,000 probe sets) calculate a correlation coefficient between expression values and SF 2Gy value as a continuous variable 4.0 .80 Surviving Fraction after 2 Gy RAD51A P .70 3.5 • Identify genes that are positively or negatively correlated with clonogenic survival with a Pvalue <0.05 and a FDR of < 1% 3.0 .60 2.5 .50 .40 2.0 .30 1.5 1.0 .20 0 R: -0.92 P value: <0.001 FDR: <0.001 1 2 3 Normalized Gene Expression Expression • Use unsupervised hierarchical clustering to evaluate the identified gene 4 lists 5 Basal B Basal A Luminal HER2 67 genes increased in radioresistant cell lines 147 Genes Correlated with Radiation Sensitivity 80 Genes decreased in radioresistant cell lines -2.0 0 2.0 Training of the Signature in Clinical Dataset 343 patients treated with BCS who received adjuvant radiation therapy: Patient characteristics: • 343 patients with mostly pT1 or pT2 tumors • All patients managed surgically with BCS • 215 patients with LN- disease, 128 patients with LN+ disease • 77% ER + ; 23% ER• Median follow-up was 6.7 years (range, 0.05 to 18.3) • 25% (119 patients) with locoregional recurrence events • 141 patients received systemic therapy (110 received chemotherapy, 8 received hormonal therapy, 23 received both) Servant, Clin Can Res. 2012;18:17041715. Training of the Signature in Clinical Dataset • Genes identified used to train a Random Forest Model • Prognostic value of each gene calculated comparing expression values from recurrent vs. non-recurrent patients • Performance evaluated on each subset of genes using out of bag (OOB) error rate • Best performing gene signature was selected and locked for cross-validation and external validation Uni- and Multivariate Analysis in CrossValidation Clinical Dataset- Local Recurrence Validation of Signature in Clinical Dataset 295 patients treated with BCS or mastectomy who received adjuvant radiation therapy without neo- or adjuvant chemotherapy: Patient characteristics: • 295 patients with pT1 or pT2 tumors • 55% (161 patients) with BCS and 45% (134 patients) with mastectomy, all with axillary LN dissection • LN negative (clinically) • 51% (151 patients) LN-negative; 49% (144 patients) LN-positive • Age < 53 yo • 90 patients received chemotherapy, 20 patients received hormonal therapy; 20 patients with both • 1 patient treated with combined chemo +hormonal therapy • 77% (226 patients) ER + ; 23% (69 patients) ER• Median follow-up was 6.7 years (minimum follow-up was 5 years) van de Vijver, N Engl J Med. 2002 Dec 19;347(25):1999-2009. Sensitivity for recurrence: 85% Negative Predictive Value: 97% Log-rank P-value <0.001 Hazard Ratio: 6.1 (95% CI 4.48- Uni- and Multivariate Analysis in CrossValidation Clinical Dataset- Local Recurrence Rate of distant recurrence as a continuous function of the Recurrence Score®. The continuous function was generated using a piecewise log hazard ratio model. The dashed curves indicate the 95% CI and the rug plot (x-axis) shows the Recurrence Score for individual patients in the study. from Paik et al NEJM 2004 Rate of local-recurrence as a continuous function of the Radiation Signature Score from the random forest model prediction. The continuous function was generated using a Cox stepwise logistic regression model. The dashed curves indicate the 95% CI Conclusions • Genes associated with intrinsic radiation resistance or sensitivity can be identified by combining gene expression data and clonogenic survival data from human breast cancer cell lines • Intrinsic radiation sensitivity is independent of breast cancer subtype • Radiation signature development identifies genes with novel association to radiation resistance • This signature predicts likelihood of response to adjuvant radiotherapy and may be useful in identifying patients who may require treatment intensification Selecting a platform • Easiest options include • qPCR array (like Oncotype) • Nanostring platform (like Prosigna) • focused microarray (like Mammaprint) • However, these options don’t allow for • Assessment of multiple signatures (particularly relevant for tissues from valuable phase III studies) • Flexibility in signature refinement • Discovery • Thus, we decided to go with a clinical-grade highdensity array (one of the highest-throughput assays that can be run on formalin-fixed tissue) Precision genomic technology Human Exon Arrays as a Discovery and Validation Platform ARCHIVED FFPE TISSUE GENETIC MATERIAL GENECHIP TECHNOLOGY GENOME ANALYSIS Long term followup available Measuring activity of genes Genome-wide analysis Cancer progression gene signature • Uses archived FFPE tissues (success with up to 25 year old samples) • Clinical-grade expression assay – CLIA certified lab • Robust technology and comprehensive and in-depth data analysis Abdueva et al., Journal of Molecular Diagnostics 2010, Vergara et al., Frontiers in Genetics 2011, Erho et al., Journal of Oncology 2012 22 Human Exon Array: Derived from ENCODE RNA expression data • 5 million features on array • 1.4 million RNA transcripts • 0.2 million mRNA exons • 0.2 million intronic/anti-sense transcripts • ~ 1 million non-coding RNA transcripts! Publications using this array technology in prostate cancer Initial reports of the Decipher signature in different cohorts • Erho N et al. Discovery and validation of a prostate cancer genomic Mayo classifier that predicts early metastasis following radical prostatectomy. PLoS One. 2013 Jun 24;8(6):e66855. • Karnes RJ et al. Validation of a genomic classifier that predicts Mayo metastasis following radical prostatectomy in an at risk patient population. J Urol. 2013 Dec;190(6):2047-53. • Klein EA et al. A genomic classifier improves prediction of metastatic disease within 5 years after surgery in node-negative high-risk prostate Cleveland cancer patients managed by radical prostatectomy without adjuvant Clinic therapy. Eur Urol. 2014. In press. • Den RB et al. Genomic prostate cancer classifier predicts biochemical failure and metastases in patients after postoperative radiation therapy. TJU Int J Radiat Oncol Biol Phys. 2014 Aug 1;89(5):1038-46 • Additional cohorts being assessed from the University of Michigan, Johns Hopkins, NYU, Moffitt, and the Radiation Therapy Oncology Group (RTOG) GenomeDx Biosciences Confidential 2017-05-23 24 Publications using this array technology in prostate cancer Secondary analyses of the datasets • Prensner JR et al. RNA biomarkers associated with metastatic Michigan progression in prostate cancer: A multi-institutional high-throughput analysis of SChLAP1. Lancet Oncology 2014. Accepted and in press. • Den RB et al. A genomic classifier identifies men with adverse pathology after radical prostatectomy who benefit from adjuvant TJU radiation therapy. Journal of Clinical Oncology 2014. Accepted and in press. • Cooperberg MR et al. Combined Value of Validated Clinical and Genomic Risk Stratification Tools for Predicting Prostate Cancer UCSF Mortality in a High-risk Prostatectomy Cohort. Eur Urol. 2014. Accepted and in press. • Ross AE et al. A genomic classifier predicting metastatic disease progression in men with biochemical recurrence after prostatectomy. Hopkins Prostate Cancer Prostatic Dis. 2014 Mar;17(1):64-9 • Additional paper from the University of Michigan on age-related biological changes in tumors GenomeDx Biosciences Confidential 2017-05-23 25 Publications using this array technology in prostate cancer Clinical utility studies • Badani K et al. Impact of a genomic classifier of metastatic risk on Columbia postoperative treatment recommendations for prostate cancer patients: a report from the DECIDE study group. Oncotarget. 2013 Apr;4(4):600-9 • Badani KK et al. Effect of a genomic classifier test on clinical practice Columbia decisions for patients with high-risk prostate cancer after surgery. BJU Int. 2014. In press. • Nguyen PL et al. Impact of a genomic classifier of metastatic risk on post-prostatectomy treatment recommendations by radiation oncologists and urologists. Urology 2014. In press. Harvard/ Michigan GenomeDx Biosciences Confidential 2017-05-23 26 Publications using this array technology in prostate cancer Validation of laboratory biology studies • Prensner JR et al. The long noncoding RNA SChLAP1 promotes Michigan aggressive prostate cancer and antagonizes the SWI/SNF complex. Nature Genetics 2013 Nov;45(11):1392-8. • Prensner JR et al. The IncRNAs PCGEM1 and PRNCR1 are not Michigan implicated in castration resistant prostate cancer. Oncotarget. 2014 Mar 30;5(6):1434-8. • Hurley PJ et al. Secreted protein, acidic and rich in cysteine-like 1 (SPARCL1) is down regulated in aggressive prostate cancers and is Hopkins prognostic for poor clinical outcome. Proc Natl Acad Sci U S A. 2012 Sep 11;109(37):14977-82. • Additional studies submitted to JNCI (Hopkins), European Urology (Michigan), IJROBP (Michigan) GenomeDx Biosciences Confidential 2017-05-23 27 Discovery of SChLAP1 (a long noncoding RNA) as the top gene associated with metastatic progression in prostate cancer Prensner et al, Nature Genetics, 2013; Prensner et al, Lancet Oncology (accepted), 2014 28 FFPE samples profiled using Exon arrays Tumor Type Prostate Bladder Sarcoma Thyroid Breast Pancreas Kidney n 3,200 300 240 120 72 58 20 Erho, N., et al. Discovery and validation of a prostate cancer genomic classifier that predicts early metastasis following radical prostatectomy. PLoS One. 2013 Jun 24;8(6):e66855. doi: 10.1371/journal.pone.0066855. Print 2013. Erho, N. et al. Transcriptome-wide detection of differentially expressed coding and non-coding transcripts and their clinical significance in prostate cancer. J Oncol. 2012;2012:541353. Epub 2012 Aug 16. Abdueva D, et al. Quantitative expression profiling in formalin-fixed paraffin-embedded samples by affymetrix microarrays. J Mol Diag 2010;12:409-17. Karnes, R.J. et al. Validation of a genomic classifier that predicts metastasis following radical prostatectomy in an at risk patient . population. J Urol. 2013 Dec;190(6):2047-53. Mitra, A.P., et al. Discovery and validation of a novel expression signature for recurrence in high-risk bladder cancer post-cystectomy. J NCI accepted April 2014 Presner, J., et al. The long noncoding RNA SChLAP1 promotes aggressive prostate cancer and antagonizes the SWI/SNF complex. Nat Genetics 2013 45(11):1392-8. Wiseman, S.M. et al. Whole-transcriptome profiling of thyroid nodules identifies expression-based signatures for accurate thyroid cancer diagnosis. J Clin Endocrinol Metab 2013 98(10):4072-9 Knudsen, E.S. Progression of ductal carcinoma in situ to invasive breast cancer is associated with gene expression programs of EMT and myoepithelia.2012 Breast Cancer Res Treat. 133(3):1009-24. 29 Exon arrays used to examine laser capture microdissected* stromal and epithelial cells from DCIS and IBC Knudsen, E.S. Progression of ductal carcinoma in situ to invasive breast cancer is associated with gene expression programs of EMT and myoepithelia.2012 Breast Cancer Res Treat. 133(3):1009-24. *LCM peformed on FFPE specimens Exon arrays profiled using an input of 50 ng of RNA 30 RNA Extraction using the GenomeDx protocol • RNA extraction from formalin-fixed, paraffin-embedded specimens follows a procedure over 3 days to first digest, then extract, then isolate and purify RNA for expression analysis. • All conducted in a CLIA-certified laboratory • Optimized for both blocks or slides • Is now semi-automated RTOG 96-01 RNA Yields/Purity (~20 year old blocks) The RNA extraction/exon array approach is now being used for samples from the following RTOG trials: • 96-01 • 92-02 • 94-08 • 94-13 • 99-02 • 99-10 • 01-26 Conclusions • A reliable molecular tool is needed to personalize radiotherapy for early stage breast cancer patients • We have developed a signature for radiation intensification • We have a platform that allows for validation of existing signatures and development of new ones • Thanks to Ian Kunkler, David Cameron, and John Bartlett, we have established a collaboration that aims to apply this platform to randomized clinical trial samples