Early ctDNA dynamics as a surrogate for progression-free survival in advanced breast cancer in the BEECH trial

S Hrebien, V Citi, I Garcia-Murillas, R Cutts, K Fenwick, I Kozarewa, R McEwen, J Ratnayake, R Maudsley, T H Carr, E C de Bruin, G Schiavon, M Oliveira, N Turner, S Hrebien, V Citi, I Garcia-Murillas, R Cutts, K Fenwick, I Kozarewa, R McEwen, J Ratnayake, R Maudsley, T H Carr, E C de Bruin, G Schiavon, M Oliveira, N Turner

Abstract

Background: Dynamic changes in circulating tumour DNA (ctDNA) levels may predict long-term outcome. We utilised samples from a phase I/II randomised trial (BEECH) to assess ctDNA dynamics as a surrogate for progression-free survival (PFS) and early predictor of drug efficacy.

Patients and methods: Patients with estrogen receptor-positive advanced metastatic breast cancer (ER+ mBC) in the BEECH study, paclitaxel plus placebo versus paclitaxel plus AKT inhibitor capivasertib, had plasma samples collected for ctDNA analysis at baseline and at multiple time points in the development cohort (safety run-in, part A) and validation cohort (randomised, part B). Baseline sample ctDNA sequencing identified mutations for longitudinal analysis and mutation-specific digital droplet PCR (ddPCR) assays were utilised to assess change in ctDNA abundance (allele fraction) between baseline and 872 on-treatment samples. Primary objective was to assess whether early suppression of ctDNA, based on pre-defined criteria from the development cohort, independently predicted outcome in the validation cohort.

Results: In the development cohort, suppression of ctDNA was apparent after 8 days of treatment (P = 0.014), with cycle 2 day 1 (4 weeks) identified as the optimal time point to predict PFS from early ctDNA dynamics. In the validation cohort, median PFS was 11.1 months in patients with suppressed ctDNA at 4 weeks and 6.4 months in patients with high ctDNA (hazard ratio = 0.20, 95% confidence interval 0.083-0.50, P < 0.0001). There was no difference in the level of ctDNA suppression between patients randomised to capivasertib or placebo overall (P = 0.904) nor in the PIK3CA mutant subpopulation (P = 0.071). Clonal haematopoiesis of indeterminate potential (CHIP) was evident in 30% (18/59) baseline samples, although CHIP had no effect on tolerance of chemotherapy nor on PFS.

Conclusion: Early on-treatment ctDNA dynamics are a surrogate for PFS. Dynamic ctDNA assessment has the potential to substantially enhance early drug development.

Clinical registration number: NCT01625286.

Keywords: BEECH trial; breast cancer; capivasertib; circulating tumour DNA.

© The Author(s) 2019. Published by Oxford University Press on behalf of the European Society for Medical Oncology.

Figures

Figure 1.
Figure 1.
Overview of BEECH study exploratory analysis. (A) Schema of plasma collection during the BEECH trial for both development part A and validation part B cohorts. Red arrows indicate part A sampling only, blue part B sampling only and purple part A and B shared timepoint sampling. Tracking samples were collected on day 1 of each treatment cycle. (B) CONSORT diagrams of part A and part B exploratory plasma baseline analysis.
Figure 2.
Figure 2.
Identification of optimal circulating tumour DNA (ctDNA) early timepoint for prediction of progression-free survival (PFS) length in the development cohort. (A) Circulating tumour DNA ratios (CDRs) at designated timepoints in the first 4 weeks of study treatment, separated by short and long PFS in study part A. Differing numbers in the long and short groups reflect missed sample collection timepoints in some patients. Long and short PFS was determined by a scan at 12 weeks on study, the time-point of the first scan in part A. C2D1 (CDR28) was the strongest predictive timepoint P = 0.0007, P value Mann–Whitney U test. (B) PFS for development part A patients split by CDR28 suppressed (CDR28<0.25) versus CDR28 high. P value log rank test. (C) Longitudinal tracking of a PIK3CA c.1624G>A (p.E542K) mutation in a patient classified as a long PFS by ctDNA demonstrating successful suppression of ctDNA before rise before progression. (D) Longitudinal tracking of a TP53 c.815T>C (p.V272A) mutation in a patient classified as a short PFS by ctDNA demonstrating failure to suppress ctDNA in the first 4 weeks of treatment.
Figure 3.
Figure 3.
Early ctDNA dynamics are a surrogate for progression-free survival (PFS) in the independent validation cohort. (A) PFS for validation cohort part B split by CDR28 suppressed (CDR28P value log rank test. (B) PFS for high versus suppressed CDR28, stratified for placebo (top) and capivasertib (bottom) treatment arms. (C) Box plot of CDR28 by treatment arm for all patients and CDR28 for treatment arms subdivided by PIK3CA mutation status. Patients with undetectable ctDNA at C2 D1 were assigned an arbitrary low value for statistical analysis. P value from Mann–Whitney U test, not significant across the groups.
Figure 4.
Figure 4.
Circulating tumour DNA (ctDNA) dynamics during treatment and lead-time over clinical progression. (A) Longitudinal tracking of a PIK3CA c.1633G>A (p.E545K) mutation in a patient who demonstrates a clear fall to a sustained nadir and a clear rise before clinical progression. (B) Longitudinal tracking of a PIK3CA c.3140 A>G (p.H1047R) mutation in a patient who demonstrates a fall in ctDNA which then fluctuates at a low level across multiple timepoints. (C) Longitudinal tracking of a PIK3R1 DelCTGAGA (p.L573_R574del) deletion in a patient who demonstrates a clear fall in ctDNA but a gradual rise over subsequent cycles before clinical progression. (D) Distribution of lead-time of calculated molecular progression before confirmed clinical progression (range 0–329 days).
Figure 5.
Figure 5.
Clonal haematopoiesis of indeterminate potential (CHIP) is frequently detected in advanced breast cancer. (A) Clonal haematopoiesis variants detected by sequencing of baseline plasma in part B. (B) Clinical and pathological associations of CHIP detection in baseline plasma. (C) Longitudinal tracking of a PIK3CA c.3140 A>G (p.H1047R) mutation and a DNMT3A CHIP splice site donor c.25236935 C>T variant in the same patient. (D) Progression-free survival by baseline detection of CHIP for the whole study population, capivasertib and placebo treatment arms. P values from log rank test.

References

    1. Dawson SJ, Tsui DW, Murtaza M. et al. Analysis of circulating tumor DNA to monitor metastatic breast cancer. N Engl J Med 2013; 368(13): 1199–1209.
    1. Murtaza M, Dawson SJ, Tsui DW. et al. Non-invasive analysis of acquired resistance to cancer therapy by sequencing of plasma DNA. Nature 2013; 497(7447): 108–112.
    1. Forshew T, Murtaza M, Parkinson C. et al. Noninvasive identification and monitoring of cancer mutations by targeted deep sequencing of plasma DNA. Sci Transl Med 2012; 4: 136ra168.
    1. Bettegowda C, Sausen M, Leary RJ. et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci Transl Med 2014; 6: 224ra224.
    1. Diehl F, Schmidt K, Choti MA. et al. Circulating mutant DNA to assess tumor dynamics. Nat Med 2008; 14(9): 985–990.
    1. Thierry AR, Mouliere F, Gongora C. et al. Origin and quantification of circulating DNA in mice with human colorectal cancer xenografts. Nucleic Acids Res 2010; 38(18): 6159–6175.
    1. Parkinson CA, Gale D, Piskorz AM. et al. Exploratory analysis of TP53 mutations in circulating tumour DNA as biomarkers of treatment response for patients with relapsed high-grade serous ovarian carcinoma: a retrospective study. PLoS Med 2016; 13: e1002198..
    1. Abbosh C, Birkbak NJ, Wilson GA. et al. Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature 2017; 545(7655): 446–451.
    1. Tie J, Kinde I, Wang Y. et al. Circulating tumor DNA as an early marker of therapeutic response in patients with metastatic colorectal cancer. Ann Oncol 2015; 26(8): 1715–1722.
    1. Sanmamed MF, Fernandez-Landazuri S, Rodriguez C. et al. Quantitative cell-free circulating BRAFV600E mutation analysis by use of droplet digital PCR in the follow-up of patients with melanoma being treated with BRAF inhibitors. Clin Chem 2015; 61(1): 297–304.
    1. Hyman DM, Smyth LM, Donoghue MTA. et al. AKT inhibition in solid tumors with AKT1 Mutations. J Clin Oncol 2017; 35(20): 2251–2259.
    1. O'Leary B, Hrebien S, Morden JP. et al. Early circulating tumor DNA dynamics and clonal selection with palbociclib and fulvestrant for breast cancer. Nat Commun 2018; 9: 896.
    1. Ma CX, Bose R, Gao F. et al. Neratinib efficacy and circulating tumor DNA detection of HER2 mutations in HER2 non-amplified metastatic breast cancer. Clin Cancer Res 2017; 23(9): 5687–5695.
    1. Chen YH, Hancock BA, Solzak JP. et al. Next-generation sequencing of circulating tumor DNA to predict recurrence in triple-negative breast cancer patients with residual disease after neoadjuvant chemotherapy. NPJ Breast Cancer 2017; 3: 24..
    1. Cabel L, Riva F, Servois V. et al. Circulating tumor DNA changes for early monitoring of anti-PD1 immunotherapy: a proof-of-concept study. Ann Oncol 2017; 28(8): 1996–2001.
    1. Marchetti A, Palma JF, Felicioni L. et al. Early prediction of response to tyrosine kinase inhibitors by quantification of EGFR mutations in plasma of NSCLC patients. J Thorac Oncol 2015; 10(10): 1437–1443.
    1. Garcia-Murillas I, Schiavon G, Weigelt B. et al. Mutation tracking in circulating tumor DNA predicts relapse in early breast cancer. Sci Transl Med 2015; 7(302): 302ra133.
    1. Frenel JS, Carreira S, Goodall J. et al. Serial next-generation sequencing of circulating cell-free DNA evaluating tumor clone response to molecularly targeted drug administration. Clin Cancer Res 2015; 21(20): 4586–4596.
    1. Xi L, Pham TH, Payabyab EC. et al. Circulating tumor DNA as an early indicator of response to T-cell transfer immunotherapy in metastatic melanoma. Clin Cancer Res 2016; 22(22): 5480–5486.
    1. Cristofanilli M, Budd GT, Ellis MJ. et al. Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N Engl J Med 2004; 351(8): 781–791.
    1. Smerage JB, Barlow WE, Hortobagyi GN. et al. Circulating tumor cells and response to chemotherapy in metastatic breast cancer: SWOG S0500. J Clin Oncol 2014; 32(31): 3483–3489.
    1. Turner NC, Alarcón E, Armstrong AC. et al. BEECH: a dose-finding run-in followed by a randomised phase II study assessing the efficacy of AKT inhibitor capivasertib (AZD5363) combined with paclitaxel in patients with estrogen receptor-positive advanced or metastatic breast cancer, and in a PIK3CA mutant sub-population. Ann Oncol 2019; 30(5): 774–780.
    1. Genovese G, Kahler AK, Handsaker RE. et al. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N Engl J Med 2014; 371(26): 2477–2487.
    1. Jaiswal S, Fontanillas P, Flannick J. et al. Age-related clonal hematopoiesis associated with adverse outcomes. N Engl J Med 2014; 371(26): 2488–2498.
    1. Acuna-Hidalgo R, Sengul H, Steehouwer M. et al. Ultra-sensitive sequencing identifies high prevalence of clonal hematopoiesis-associated mutations throughout adult life. Am J Hum Genet 2017; 101(1): 50–64.
    1. Newman AM, Bratman SV, To J. et al. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat Med 2014; 20(5): 548–554.
    1. Jamal-Hanjani M, Wilson GA, Horswell S. et al. Detection of ubiquitous and heterogeneous mutations in cell-free DNA from patients with early-stage non-small-cell lung cancer. Ann Oncol 2016; 27(5): 862–867.
    1. Jongen-Lavrencic M, Grob T, Hanekamp D. et al. Molecular minimal residual disease in acute myeloid leukemia. N Engl J Med 2018; 378(13): 1189–1199.

Source: PubMed

3
Sottoscrivi