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Breast Cancer Molecular Profiles Predict Tumor Response of Neoadjuvant Doxorubicin and Paclitaxel, the I-SPY TRIAL (CALGB 150007/150012, ACRIN 6657) L. J. Esserman ,C. Perou, M. Cheang, L. J. van 't Veer, J. Gray, E. Petricoin, K. Conway, L. Carey, A. DeMichele, D. Berry, N. Hylton I-SPY INVESTIGATORS CALGB INTERSPORE ACRIN NCICB Investigation of Serial studies to Predict Your Therapeutic Response with Imaging and And moLecular analysis I SPY WITH MY LITTLE EYE . . . . . . . A BIOMARKER BEGINING WITH X .... I-SPY 1 Clinical Trial Backbone Layered Imaging/Molecular Biomarker Studies Onto Standard Clinical Care Anthracycline Taxane Surgery & RT Serial MRI Scans Serial Core Biopsies Tam if ER+ Trial Endpoints • Early (ASCO POSTER 529) – MRI response after 1 cycle of chemotherapy • Longest Diameter, Volume • Intermediate • pCR Pathologic Complete Response • RCB Residual Cancer Burden • % change in MR volume • Late • 3 year Recurrence Free Survival • 3 year Overall Survival Residual Cancer Burden RCB = 1.4 x [fcell x (d1 d2)] 0.17 + [dmet x (1 - (1 - ) LN ) / ] 0.17 PRIMARY TUMOR BURDEN PRIMARY TUMOR BURDEN Area (cm x cm) Area (cm x cm) % CANCER CELLULARITY % CANCER CELLULARITY + AXILLARY NODAL BURDEN Number of positive LNs Diameter of largest metastasis (mm) Symmans et al. J Clin Oncol. 2007 Oct 1;25(28):4414-22. Residual Cancer Burden • Integrates several pathologic features – Lymph node status – Extent of Tumor Bed – Tumor size – Tumor cellularity • Output is continuous or 4 discrete categories – RCB 0 – RCB I – RCB II – RCB III pCR, no invasive tumor scattered residual disease moderate tumor burden significant tumor burder Symmans et al JCO 2007 I-SPY 1 Biomarker Platforms Tissue: Core or Surgical H&E,IHC,FISH UNC, Penn Expression Arrays p53 GeneChip UNC, UCSF, NKI UNC CGH 5 GMU ) Serum 4 relative copy number (Log2) Protein Arrays (RPMA) 3 1q 2 Id1 proteins autoantibodies phospho proteins 20q 1 0 -1 -2 1 1p 3 5 7 9 Genome location 11 13 15 17 19 17p 19p 21 X UCSF Total Accrual: 237 Institution Name Accrual University of Pennsylvania Medical Center 36 Georgetown University Hospital 4 University of North Carolina 36 Memorial Sloan Kettering Cancer Center 22 University of Washington 5 University of Alabama at Birmingham Medical Center 51 University of Chicago 2 University of Texas Southwestern 14 University of California San Francisco 66 • 1042 frozen cores from 201 patients • 1301 paraffin cores from 223 patients • 948 serum samples from 158 patients. Results I-SPY: Poor Prognosis Tumors 70 significant prognosis genes NKI 70 Gene Profile “Good” Signature “Poor” Signature 9% 91% Mean Tumor Size= 6.0 Present as clinical mass 55% < Age 50 van´t Veer et al., Nature ,2002 Relationship of pCR and RCB with Early Relapse for all I-SPY Pts No pCR (n=157) Relapse-free Proportion Relapse-free Proportion pCR (n=58) Years since surgery RCB 0 (n=56) RCB I (n=18) RCB II (n=86) RCB III (n=41) Years since surgery pCR and RCB in context of molecular features pCR: IHC vs Molecular Subtypes IHC Distribution (n = 190) pCR (n = 190) HR+HER2HR+HER2+ HR-HER2+ HR-HER2- 48% 12% 12% 28% 10% 32% 50% 33% Gene Profile Distribution Intrinsic Subtypes ( n = 149) Luminal A Luminal B Her2-enriched Basal Normal-like HR = Hormone Receptor 29% 19% 15% 32% 5% P-value pCR (n = 144) P-value 2% 15% 52% 34% 43% <0.0001 pCR Rates: RNA Classifiers Gene Profile Distribution ( n = 149) ROR-S Low 26% Moderate 38% High 37% NKI 70 Good Signature 9% Poor Signature 91% Wound Healing Quiescent 23% Activated 77% p53 Mutation Gene signature Wildtype Mutation 50% 50% pCR (n = 144) P-value 5% 22% 40% 8.8 x 10-4 0% 27% 0.038 6% 30% 0.0049 11% 38% 3.7 x 10-4 pCR Rates: DNA Classifiers DNA Profile Distribution MIP Arrays ( n = 118) No Amplification 17q 86% pCR (n = 144) P-value 15% <0.0001 Amplification 17q 14% 59% P53 Gene Chip (n=181) Any mutation 43% 38% 0.003 missense Zn-binding 17% 75% 0.02 0.0003 null 12% 57% pCR and RCB are VERY significant predictors of early relapse in the context of a poor prognosis profile Among Basal-like Tumors RCB I (n = 2) RCB 0 (n = 16) RCB II (n = 17) RCB III (n= 9) Log-rank P = 5.5 x 10-7 Among NKI-70 High Risk RCB I (n = 10) RCB 0 (n= 35) RCB II (n = 55) RCB III (n = 22) Log-rank P = 5.9 x 10-5 Among Activated-Wound Signature RCB I (n = 5) RCB 0 (n = 33) RCB II (n = 45) RCB III (n= 20) Log-rank P = 4.4 x 10-4 Among p53 Mutation Profile RCB I (n= 4) RCB 0 (n= 27) RCB II (n = 24) RCB III (n = 12) Log-rank P = 4.5 x 10-7 Published RNA Signatures • Identify good and poor risk subsets • pCR and RCB are highly predictive of outcome in the poor risk subsets of all signatures • Patients in the high and low subsets differ among signatures • A composite molecular signature can be created Integrated score is a good predictor of prognosis Integrated Score: Good Prognosis Distributed across RCB 0-III All do well REGARDLESS of RCB Integrated score poor prognosis patients associate with RCB Integrated Score, Intermediate prognosis P=0.16 p = 0.158 P=1.89e-07 Integrated Score, Poor prognosis p = 1.89e-07 Activated Proteins Provide Clues for Future Targeting • Method: – Reverse Phase Protein Array (RPMA) – All samples laser capture microdissected • Preliminary findings – pts with pCR: increased phosphorylation of 4EBP1, eNOS, cAbl, STAT5, EGFR, AKT (p<0.05) • all within a linked EGFR-AKT-mTOR pathway activation – pts ER+ with poor response: increased phosphorylation of pIRS, pIGFR, p706S (p<0.05). Observations from I-SPY • LABC have high risk biology – Minimum tumor size 3cm, mean size of 6cm – 91% are molecularly high risk as defined by NKI 70 gene profile – Not screen detected: 84% are interval cancers (Lin, Abstract 1503) • Molecular features identify low and high risk subsets – Low risk subsets: low pCR rates, but good outcomes (<5 yrs) – High risk subsets: high pCR rates (28-59%) to std chemo – High risk subsets: response to therapy (pCR, RCB) is highly predictive of early outcome • Residual Cancer Burden (RCB) – More refined way to measure pathologic response – Highly correlated with RFS and OS • MRI Volume change is a non-invasive way to measure pCR – Highly correlated with path CR and RCB: (Hylton, Abstract #529) Next Steps • The molecular data, with the exception of HER2, does not yet tell us how to treat poor responders – Recurrence after pCR limited to HER2+ patients preTrastuzumab (6 of 7) – The I-SPY repository is a resource for such discovery • We should target improvement in pCR/RCB to improve outcomes – I-SPY 2 is an adaptive neoadjuvant trial designed to rapidly screen agents and biomarkers to improve pCR/RCB • Exclude patients with good prognosis profile BACK-UP Quantitative and serial measurement of tumor response by MRI Pre Treatment Complete response Post Treatment Partial response Progressive disease Patients Accrued n=237 Patients Withdrawn n=16 Patients who didn’t have surgery n=6 Patients Available for Analysis n=221 Patients with pathology assessment after Neoadjuvant Therapy n=215 Patients without RCB n=14 Patients with pCR and RCB n=201 Tissue Distribution & Analyses Schema UNC: Dressler Lab 2 Paraffin Cores Tumor UCSF Check for Tumor Presence 2 Frozen Cores Tumor Present Check for Tumor Presence Core Remainder GMU: Liotta/Petricoin Lab UNC: Livasy, Dressler Lab PENN: DeMichele Lab Her2 Protein Over expression Storage Proteomics Initial H&E IHC Initial H&E DNA FISH Her2, TopoII Amplification CGH UCSF: Gray Lab Gene Chip For P53 UNC: Carey/ Dorsey Lab RNA Gene Expression UNC: Perou Lab UCSF: Haqq Lab MDACC: Pusztai/ Symmans Lab NKI: van’t Veer Lab Data uploaded in: NCI caIntegrator NCI: caBIG, Madhavan UCSC Cancer Genomics Browser UCSC: Haussler, Kent, Zhu, Wang Data Integration: NCI caINTEGRATOR Among ROR-S High Risk RCB I (n = 5) RCB 0 (n = 21) RCB II (n = 18) RCB III (n= 7) Log-rank P = 4.3 x 10-9 Integrated score based prognosis classes: ER+/ER- distributions Prognosis (Counts) ER- ER+ Indeterminate Total Good 2 28 1 31 Intermediate 22 38 4 64 Poor 37 10 2 49 Poor RCB 0 13 6 1 20 Poor RCB I 2 0 0 2 Poor RCB II 12 4 1 17 Poor RCB III 8 0 0 8 Questions • Does early response help us to predict early relapse? – Complete Pathologic Response: pCR – Residual Cancer Burden: RCB • How do the molecular signatures impact on the interpretation of pCR and RCB? Integrating Molecular Profiles 60 50 Frequency 40 30 20 10 0 -4 -3 -2 -1 0 1 2 3 4 Integrated score Poor prognosis Intermediate prognosis Good prognosis •Based on NKI-70, ROR-S, Wound Healing Signature,, p53 mutation profile: +1 , 0, -1 based upon score; Sum the scores