Longitudinal Liquid Biopsy and Mathematical Modeling of Clonal Evolution Forecast Time to Treatment Failure in the PROSPECT-C Phase II Colorectal Cancer Clinical Trial

Abstract

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.

Fig 1. Overview of trial related biopsies and CONSORT diagram describing the PROSPECT-C Trial.

(A) Patients (pts) meeting all the inclusion and no exclusion criteria were required to have pre-treatment CT scan. All pts were also required to have pre-treatment mandatory core biopsy, followed by a core biopsy at 3 months if they had PR. Pts were monitored by CT scans every 3 months until the time of PD and if clinically feasible, they had biopsy of 1 or 2 progressing lesions from PD sites. Plasma samples were collected every 4 weeks until the time of PD. (B) Out of 47 pts initially consented, two were excluded from analysis: one (1015) was found to harbour an NRAS mutation on archival material during the screening process while the other one (1031) rapidly progressed before commencing cetuximab. Two pts in the first cohort (1005-1018) for whom no mutations were detected by ddPCR were also tested by cfDNA NGS. 5 pts in the second cohort were not tested by cfDNA NGS as their progression free survival was between 3 and 6 months thus they were not considered either primary resistant (PFS ≤3 months) nor long-term responders (PFS >6 months). cfDNA= cell-free DNA; ddPCR= digital-droplet PCR; PFS=Progression Free Survival; CT=computed tomography; PD=progressive disease; PR=partial response.

Fig 2. Clinical efficacy outcomes according to mutations in cell-free (cf)DNA, in primary and acquired resistance to anti-EGFR therapy.

(A) Waterfall plot demonstrating changes in tumour burden by RECIST v1.1, according to patients with detectable or undetectable mutations/amplifications in baseline plasma. Asterisks indicate patients who rapidly progressed and died prior to re-staging scan (3 months) (B) Spider plot showing depth and duration of tumor regressions, according to presence or absence of mutations in cfDNA. Progression Free Survival (C) and Overall Survival (D) of patients according to detectable or undetectable baseline mutations/amplifications. (E) Avenio cfDNA NGS results in two patients with primary resistance to cetuximab shows polyclonal resistance. (F) An example of a patient who received 10 months of cetuximab treatment with initial PR (at 3 months) followed by PD on a subsequent CT scan; tracking of plasma mutations on a 4-weekly basis revealed two mutations (KRAS G12D and KRAS Q61H) preceding the changes in CEA (G) An example of a primary progression on the treatment with polyclonal resistance demonstrated by the presence and relative increase in frequency of KRAS G12D and APC mutations along with emergence and relative increase in frequency of KRAS G13D and KRAS Q61H mutations under the selective pressure of anti-EGFR therapy. (H) Heat-map showing results of cfDNA analysis using the Avenio panel in patients with acquired resistance in the PROSPECT-C Trial. Baseline (BL), intermediate [INT (cycle 3)] and progression (PD) cfDNA from patients with PFS >6 months was tested for a panel of 77 cancer related genes. Red boxes indicate presence of mutations in different genes. CEA= carcinoembryonic antigen; CT= computed tomography; ddPCR= digital droplet polymerase chain reaction; RECIST= response evaluation criteria in solid tumours; EGFR= epidermal growth factor receptor; PR= partial response; PFS= Progression Free Survival; VAF= variant allele frequency.

Fig 3. EGFR pathway addiction, and impact of cancer heterogeneity on clinical decisions.

An EGFR amplification was observed in the baseline liver biopsy of a long term responder to cetuximab. Treatment was halted after 16 months due to clinical and minor RECIST v1.1 progression of a non-target lesion (orange circle). Interestingly, after cetuximab was withdrawn a rapid progression of the EGFR amplified metastatic liver lesion originally biopsied was observed along with new liver deposits within 6 weeks (red circles). On the contrary, the metastasis biopsied at time of progression did not show any significant change in volume and showed no EGFR amplification suggesting that this metastasis was not dependent upon EGFR signalling. A rise in APC mutant clones was observed synchronously with the increase in size of the non-target metastasis that led to treatment discontinuation; CEA lagged behind and no RAS pathway mutant clones were detected at any time point.

Fig 4. Concordance between liquid versus tissue biopsies.

(A) Heat map demonstrating validation of mutations/amplifications detected in plasma by ddPCR, in tissue samples obtained at clinically relevant timepoints. For NGS variants detected with confidence are reported with a star (posterior probability < 0.05, see Material and Methods). (B) comparions between limit of detectability of clinically validated assays (i.e. COBAS) and ultra-deep sequencing used in the study showing that most of the KRAS sub-clonal mutations causing cetuximab resistance (solid symbols) were below the detection threshold of standard clinical assays. (C) Example of a patient with RAS pathway intra-tumour heterogeneity between resected primary and synchronous liver metastases. (D) Example of a patient with primary progression and detection of ERBB2 amplification in plasma, further validated with IHC and CISH in the tissue obtained both at baseline and progressive disease. Of note, in this case of non-CEA secreting tumour, baseline biopsy of a supraclavicular lymph node and subsequently of a progressing peritoneal biopsy were conducted and both demonstrated concordance in detection of a (likely clonal) HER2 amplification. (E) A case of a patient with no baseline mutation/amplification and initial clinical benefit with PR to cetuximab but later emergence of c-MET amplification at 2 months (preceding CEA changes) and subsequent increase in fractional abundance at the time of progression. FISH analysis of the tissue at four different time points confirmed the emergence of foci of cMET at PR and PD. (F) NGS of tissue biopsies [archival (n=2), baseline (n=2), PR [n=2 (3 months)] and disease progression [n=1] revealed the emergence of an NRAS G12C mutation and decay in other mutant clones suggestive of a selection bottleneck (cartoon). CEA= carcinoembryonic antigen; CT= computed tomography; RECIST= response evaluation criteria in solid tumours; PD= progressive disease; PR= partial response. NGS= next generation sequencing.

Fig 5. Forecasting waiting time to progression in responders using evolutionary modelling and frequent cfDNA sampling.

(A) From the point of view of therapy, the tumour at baseline can be modelled as comprised of a treatment sensitive population with size n_s and a resistant population with size n_r, usually small. Under treatment, the sensitive decreases at rate λ_s, and the resistant increases at rate λ_r. These dynamics are capture by the equation of N(t). (B) The sum the two populations N(t) corresponds to tumour burden over time and has the characteristic U-shape curve of an initial response (tumour shrinks) followed by relapse (tumour comes back). (C) As CEA measurements are a surrogate for the tumour burden N(t), in this illustrative example we applied the model to the CEA values over the course of cetuximab treatment for patient 1014. (D) Fitting the model to CEA values allowed measuring the n and λ parameters, thus estimating the response and relapse rates in each individual patient. Here response rates λ_s are shown. Unsurprisingly, most progression and stable disease patients showed λ_s = 0 (no response), indicated by the absence of bars. (E) Relapse rates λ_r varied between patients, with stable disease patients showing a trend of slow relapse. Both progression patients and responders showed relatively high relapse. In responders with high relapse rates, the initial frequency of the resistant subpopulation was likely low. (F) The combination of relapse rate and initial frequency of the resistant subclone allowed the stratification of patients, where responders showed low initial frequency of the resistant clone or very slow relapse rates, or both. We note that even for moderately low frequencies of resistant subclones (e.g. 1-10%) rapid growth is sufficient to induce lack of response as the tumour grows back even before the first CT-scan. (G) We applied the total tumour burden model (Equation 1) and the resistant population model (Equation 2) to CEA and cfDNA respectively. In responders the cfDNA preceded tumour burden of several weeks, supporting the predictive value of cfDNAm whereas in non-responders (H) the two curves overlapped. (I) Strikingly, we found a significant correlation between the relapse rate measured from CEA and the relapse rate measured from cfDNA, indicating that measuring evolutionary dynamics from plasma can inform on the expected dynamics that will occur later on at the macro-scale (CEA and RECIST v1.1). (J) We measured the evolutionary parameters (n_r and λ_r) of the resistant population from CEA and cfDNA for responder 1007 and use the evolutionary framework to predict when we expect to observe relapse in the CT-scan under RECIST v1.1 criteria (+20% increase in target lesions diameter). Despite the limited precision of RECIST v1.1 and the infrequency of the CT-scan, both predictions based on cfDNA (the dominant KRAS G12D subclone) and CEA were strikingly accurate. (K) We verified the same predictions for all responders in our cohort for which we had at least CEA and confirm the power of predicting waiting time to relapse using our approach was remarkable. We note that when resistance was polyclonal but only one clone was detected (e.g. patient 1014 – cMET amplified clone), our method allows measuring the contribution of unobserved resistant subclones by studying the difference between predicted time of relapse and RECIST. (L) The predictive power of the mathematical framework applied to cfDNA is illustrated here. In this example we consider a fix initial resistant population n_r=0.0003 (median n_r in our cohort) and vary the relapse rate λ_r between 0.01 and 0.41 (range in our cohort). The time when we observed the mutant alleles in plasma (blue) depends on the sensitivity of the assay. Assuming to profile cfDNA every week, with a sufficient number of positive time points (n=5), we can fit our model to the data and determine the evolutionary parameters, thus allowing for predicting the expected waiting time to RECIST v1.1 progression. The higher the sensitivity, the earlier progression can be predicted, thus creating a window of opportunity for clinicians to take patient-specific treatment decisions (yellow). CEA= carcinoembryonic antigen; RECIST= response evaluation criteria in solid tumours; variant allele frequency=VAF