What do liquid biopsies offer us for breast cancer patients? Isaac Garcia-Murillas Breast Cancer Now Research Centre, The institute of Cancer Research, London, UK
Molecular Analysis of breast cancer Invasive Expensive Processing takes time Non-invasive assessment Less expensive Rapid purification Whole picture Surrogate when anatomic biopsies are not feasible
Liquid Biopsies Siravegnaet al Nat Rev Clin Oncol, 2017
Circulating Nucleic acids in cancer: cfdna and ctdna Crowley et al Nat Rev Clin Oncol, 2013
Analysing genomic aberrations in ctdna from breast cancer patients De Mattos-Arruda, Mol Oncol 2015
Clinical applications of ctdna analysis in breast cancer Two very different settings of therapy Early breast cancer Adjuvant therapy to treat micro-metastatic disease Advanced breast cancer Metastatic therapy to treat overt metastatic disease More genetically homogeneous More genetically diverse
Clinical applications of ctdna analysis in breast cancer Wan et al Nat Rev Cancer, 2017
Clinical applications of ctdna analysis in breast cancer Prediction of relapse in early breast cancer Patient Stratification based on alterations Tumour heterogeneity Identification of Mechanisms of resistance to therapy Liquid biopsies in immunotherapy trials
Prediction of relapse in early breast cancer Olsson et al EMBO Med, 2015
Prediction of relapse in early breast cancer
Prediction of relapse in early breast cancer ChemoNEAR study design Garcia-Murillas et al STM, 2015
Prediction of relapse in early breast cancer Predicting early relapsebaseline plasma (DFS) Predicting early relapsesingle post-surgery (DFS) Predicting early relapsemutation tracking (DFS) 78% (43/55) ctdna detection in baseline plasma DNA Detection associated with ER negative and high grade Mutation detection in the immediate post-surgical sample identifies early relapse but misses later relapses Dynamic mutation tracking is highly accurate in predicting relapse
Prediction of relapse in early Predicting early relapsesingle post-surgery (OS) breast cancer Predicting early relapsemutation tracking (OS) All patients with ctdna detected in a single postsurgical time point relapsed and died (7/7, 100% specificity), with modest 39% (7/18) sensitivity for relapse. All patients with ctdna detected at any time point in serial sampling relapsed in the followup period (14/14, 100% specificity), with 78% (14/18) sensitivity for relapse. Detection of ctdna in the adjuvant setting has a high predictive value for future relapse and death from breast cancer.
Clinical applications of ctdna analysis in breast cancer Prediction of relapse in early breast cancer Patient Stratification based on alterations Tumour heterogeneity Identification of Mechanisms of resistance to therapy Liquid biopsies in immunotherapy trials
Patient Stratification based on alterations plasmamatch
Patient Stratification based on alterations ABC-BIO Frequency of mutations and amplifications detected in ctdna from plasma samples in ABC-BIO 25 20 15 % 10 5 0 PIK3CA ESR1 AKT1 HER2 mut HER2 amp Mutations and Amplification Fribbens et al, unpublished
Patient Stratification based on alterations plasmamatch 68 3 3 1 0
Clinical applications of ctdna analysis in breast cancer Prediction of relapse in early breast cancer Patient Stratification based on alterations Tumour heterogeneity Identification of Mechanisms of resistance to therapy Liquid biopsies in immunotherapy trials
Tumour heterogeneity De Mattos-Arruda Mol Oncol, 2015
Tumour heterogeneity Genetic heterogeneity as the engine of targeted therapy resistance Mutation frequency Wand et al Nature, 2014 Turner et al Lancet Oncol, 2012
Clinical applications of ctdna analysis in breast cancer Prediction of relapse in early breast cancer Patient Stratification based on alterations Tumour heterogeneity Identification of Mechanisms of resistance to therapy Liquid biopsies in immunotherapy trials
Identification of Mechanisms of resistance to therapy ESR1mutations as important resistance mechanism in patients treated with endocrine therapy Cluster of mutations in amino acids 537-538 in ligandbinding domain(lbd) reported in AI pretreated patients Mutations in ligand binding domain activate ER ligand independent signalling Resistance to aromatase inhibitors Potentially sensitive to ER degraders ESR1 mutations occur in ~20% of endocrine resistant ER positive breast cancer Toy et al Nature Genetics, 2013 Robinson et al Nature Genetics,2013
Identification of Mechanisms of resistance to therapy ESR1mutations in ctdna and resistance to subsequent aromatase inhibitor (AI) ESR1 mutations rarely acquired during adjuvant AI but are commonly selected by therapy for metastatic disease Evidence that mechanisms of resistance to targeted therapy may be different between the treatment of micro-metastatic and overt metastatic cancer. Schiavon et al STM, 2015
Identification of Mechanisms of resistance to therapy SoFEA/PALOMA-3 Does detection of ESR1 mutations in plasma predict for relative sensitivity to fulvestrant vs exemestane? Is CDK4/6 inhibition effective in ESR1 mutant cancers?
Identification of Mechanisms of resistance to therapy SoFEA 39.1% patients (63/161) had ESR1 mutations detected in plasma Detection of ESR1mutations in plasma DNA suggests relative resistance to exemestane, and relative sensitivity to fulvestrant Patients with no ESR1 mutation may derive further benefit from exemestane as well as fulvestrant Fribbens et al JCO, 2015
Identification of Mechanisms of resistance to therapy PALOMA-3 25.3% patients (91/360) had ESR1 mutations detected in plasma Palbociclib and fulvestrant appears equally effective in patients with or without ESR1 mutations Fribbens et al JCO, 2015
Clinical applications of ctdna analysis in breast cancer Prediction of relapse in early breast cancer Patient Stratification based on alterations Tumour heterogeneity Identification of Mechanisms of resistance to therapy Liquid biopsies in immunotherapy trials
Liquid Biopsies on immunotherapy trials C-TRAK TNBC
Final Remarks
The patients that participate on the studies The Royal Marsden Hospital/The Institute of Cancer Research Molecular Oncology Lab:Nick Turner, Alex Pearson, Sarah Hrebien, Matthew Beaney, Claire Swift, Monee Shamsher, Ben O Leary, Iñaki Comino-Mendez, Gaia Schiavon, Charlotte Fribbens, Neha Chopra, Ros Cutts Generations Lab: Katarzyna Tomczyk Histopathology Lab: Frances Daley Acknowledgements