Longitudinal Liquid Biopsy and Mathematical Modeling of Clonal Evolution Forecast Time to Treatment Failure in the PROSPECT-C Phase II Colorectal Cancer Clinical Trial.
Khurum H KhanDavid CunninghamBenjamin WernerGeorgios VlachogiannisInmaculada SpiteriTimon HeideJavier Fernandez MateosAlexandra VatsiouAndrea LampisMahnaz Darvish DamavandiHazel LoteIan Said HuntingfordSomaieh HedayatIan ChauNina TunariuGiulia MentrastiFrancesco TrevisaniSheela RaoGayathri AnandappaDavid WatkinsNaureen StarlingJanet ThomasClare PeckittNasir KhanMassimo RuggeRuwaida BegumBlanka HezelovaAnnette BryantThomas JonesPaula ProszekAngelo Paolo Dei TosJens C HahneMichael HubankChiara BraconiAndrea SottorivaNicola ValeriPublished in: Cancer discovery (2018)
Sequential profiling of plasma cell-free DNA (cfDNA) holds immense promise for early detection of patient progression. However, how to exploit the predictive power of cfDNA as a liquid biopsy in the clinic remains unclear. RAS pathway aberrations can be tracked in cfDNA to monitor resistance to anti-EGFR monoclonal antibodies in patients with metastatic colorectal cancer. In this prospective phase II clinical trial of single-agent cetuximab in RAS wild-type patients, we combine genomic profiling of serial cfDNA and matched sequential tissue biopsies with imaging and mathematical modeling of cancer evolution. We show that a significant proportion of patients defined as RAS wild-type based on diagnostic tissue analysis harbor aberrations in the RAS pathway in pretreatment cfDNA and, in fact, do not benefit from EGFR inhibition. We demonstrate that primary and acquired resistance to cetuximab are often of polyclonal nature, and these dynamics can be observed in tissue and plasma. Furthermore, evolutionary modeling combined with frequent serial sampling of cfDNA allows prediction of the expected time to treatment failure in individual patients. This study demonstrates how integrating frequently sampled longitudinal liquid biopsies with a mathematical framework of tumor evolution allows individualized quantitative forecasting of progression, providing novel opportunities for adaptive personalized therapies.Significance: Liquid biopsies capture spatial and temporal heterogeneity underpinning resistance to anti-EGFR monoclonal antibodies in colorectal cancer. Dense serial sampling is needed to predict the time to treatment failure and generate a window of opportunity for intervention. Cancer Discov; 8(10); 1270-85. ©2018 AACR. See related commentary by Siravegna and Corcoran, p. 1213 This article is highlighted in the In This Issue feature, p. 1195.
Keyphrases
- wild type
- clinical trial
- phase ii
- end stage renal disease
- small cell lung cancer
- ejection fraction
- newly diagnosed
- metastatic colorectal cancer
- randomized controlled trial
- high resolution
- peritoneal dialysis
- primary care
- tyrosine kinase
- machine learning
- epidermal growth factor receptor
- papillary thyroid
- single cell
- dna methylation
- patient reported
- double blind
- cross sectional
- patient reported outcomes
- young adults
- rectal cancer
- combination therapy
- replacement therapy
- locally advanced
- artificial intelligence
- lymph node metastasis