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. 2016 Dec 20;13(12):e1002198.
doi: 10.1371/journal.pmed.1002198. eCollection 2016 Dec.

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

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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

Christine A Parkinson et al. PLoS Med. .

Abstract

Background: Circulating tumour DNA (ctDNA) carrying tumour-specific sequence alterations may provide a minimally invasive means to dynamically assess tumour burden and response to treatment in cancer patients. Somatic TP53 mutations are a defining feature of high-grade serous ovarian carcinoma (HGSOC). We tested whether these mutations could be used as personalised markers to monitor tumour burden and early changes as a predictor of response and time to progression (TTP).

Methods and findings: We performed a retrospective analysis of serial plasma samples collected during routine clinical visits from 40 patients with HGSOC undergoing heterogeneous standard of care treatment. Patient-specific TP53 assays were developed for 31 unique mutations identified in formalin-fixed paraffin-embedded tumour DNA from these patients. These assays were used to quantify ctDNA in 318 plasma samples using microfluidic digital PCR. The TP53 mutant allele fraction (TP53MAF) was compared to serum CA-125, the current gold-standard response marker for HGSOC in blood, as well as to disease volume on computed tomography scans by volumetric analysis. Changes after one cycle of treatment were compared with TTP. The median TP53MAF prior to treatment in 51 relapsed treatment courses was 8% (interquartile range [IQR] 1.2%-22%) compared to 0.7% (IQR 0.3%-2.0%) for seven untreated newly diagnosed stage IIIC/IV patients. TP53MAF correlated with volumetric measurements (Pearson r = 0.59, p < 0.001), and this correlation improved when patients with ascites were excluded (r = 0.82). The ratio of TP53MAF to volume of disease was higher in relapsed patients (0.04% per cm3) than in untreated patients (0.0008% per cm3, p = 0.004). In nearly all relapsed patients with disease volume > 32 cm3, ctDNA was detected at ≥20 amplifiable copies per millilitre of plasma. In 49 treatment courses for relapsed disease, pre-treatment TP53MAF concentration, but not CA-125, was associated with TTP. Response to chemotherapy was seen earlier with ctDNA, with a median time to nadir of 37 d (IQR 28-54) compared with a median time to nadir of 84 d (IQR 42-116) for CA-125. In 32 relapsed treatment courses evaluable for response after one cycle of chemotherapy, a decrease in TP53MAF of >60% was an independent predictor of TTP in multivariable analysis (hazard ratio 0.22, 95% CI 0.07-0.67, p = 0.008). Conversely, a decrease in TP53MAF of ≤60% was associated with poor response and identified cases with TTP < 6 mo with 71% sensitivity (95% CI 42%-92%) and 88% specificity (95% CI 64%-99%). Specificity was improved when patients with recent drainage of ascites were excluded. Ascites drainage led to a reduction of TP53MAF concentration. The limitations of this study include retrospective design, small sample size, and heterogeneity of treatment within the cohort.

Conclusions: In this retrospective study, we demonstrated that ctDNA is correlated with volume of disease at the start of treatment in women with HGSOC and that a decrease of ≤60% in TP53MAF after one cycle of chemotherapy was associated with shorter TTP. These results provide evidence that ctDNA has the potential to be a highly specific early molecular response marker in HGSOC and warrants further investigation in larger cohorts receiving uniform treatment.

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Conflict of interest statement

We have read the journal's policy and the authors of this manuscript have the following competing interests: DG, NR and JDB are co-founders, shareholders and officers/consultants of Inivata Ltd, a cancer genomics company that commercialises ctDNA analysis.

Figures

Fig 1
Fig 1. Schema of workflow for circulating tumour DNA analysis.
ctDNA, circulating tumour DNA; FFPE, formalin-fixed paraffin-embedded; HGSOC, high-grade serous ovarian carcinoma; PD, progressive disease; SD, stable disease.
Fig 2
Fig 2. Comparison of TP53 mutant allele fraction to tumour volume.
(A) Example of volumetric analysis of high-grade serous ovarian cancer with relapsed disease in abdominal lymph nodes. Left panel shows cross-sectional view. Right panel shows 3-D reconstruction to show disease volume. Green shading indicates regions of interest for volume measurements. Lymph node masses are indicated by arrowheads and labelled as follows: AC, aorto-caval; CI, common iliac; PA, para-aortic; RC, retro-caval. (B) Ranked total volume of tumour at start of treatment course. Filled circles indicate cases with TP53MAC ≥ 20 AC/ml. Arrow indicates tumour volume of 32 cm3. (C and D) Linear regression analysis of TP53MAF and CA-125 with tumour volume in 22 relapsed events without ascites. Grey shading shows 95% confidence intervals. (E and F) Comparison of TP53MAF and CA-125 values adjusted for tumour volume between relapsed and newly diagnosed patients before treatment. (G) Time to progression analysis for relapsed patients with greater or less than the median pre-treatment TP53MAF. ctDNA, circulating tumour DNA; HR, hazard ratio; ND, not detected; TP53MAF, TP53 mutant allele fraction.
Fig 3
Fig 3. Circulating tumour DNA and CA-125 kinetics during chemotherapy.
(A) TP53MAF and CA-125 kinetics from start of treatment, normalised to the pre-treatment levels. Asterisk denotes one treatment course where the patient developed new brain metastases. Yellow circles and blue boxes indicate nadir points. (B) Box plots show time to nadir following start of chemotherapy for CA-125 and TP53MAF. (C–F) Illustrative cases of TP53MAF and CA-125 kinetics. (C) Faster time to nadir and greater dynamic range of TP53MAF compared with CA-125. (D) Discrepant TP53MAF and CA-125 kinetics. This patient commenced on third-line chemotherapy and had a minor response on CT (stable disease by RECIST). CA-125 fell slightly whilst TP53MAF increased. After cycle 4, the patient developed new headaches, and a CT scan showed new brain metastases (marked by asterisk). (E) Discrepant TP53MAF and CA-125 kinetics. This patient commenced third-line chemotherapy, and the TP53MAF and CA-125 values diverged. CT scan showed progressive disease, in keeping with rise of TP53MAF. (F) The effect of ascitic drainage on plasma TP53MAF levels. This patient had an ascitic drain (at time = 4 d) before starting chemotherapy, with a ctDNA sample taken before (time = 0 d) and after (time = 29 d) the ascitic drain. Following drainage of 8 l of ascites, and before start of any further treatment, TP53MAF fell from 7.5% to 3.3%. CA-125 decreased from 86 IU/ml to 46 IU/ml. This patient had small-volume (1 cm3) solid disease and large-volume ascites. CA-125, cancer antigen 125; ctDNA, circulating tumour DNA; PD, progressive disease; PLD, pegylated liposomal doxorubicin; PR, partial response; SD, stable disease; TP53MAF, TP53 mutant allele fraction.
Fig 4
Fig 4. ROC curves and Kaplan-Meier plots for change in circulating tumour DNA after one cycle of chemotherapy, including and excluding courses with recent ascitic drains.
(A) ROC plot identifies 60% decrease in TP53MAF as the most accurate threshold for predicting 6-mo TTP in all patients. (B) Kaplan-Meier curve showing TTP for patients with decrease of ≤60% or >60% after one cycle of chemotherapy. (C) ROC plot identifies a 60% decrease in TP53MAF as the most accurate threshold for predicting 6-mo TTP in patients without ascitic drains. (D) Kaplan-Meier curve for TP53MAF decrease after one cycle of chemotherapy to predict 6-mo progression-free survival in patients without ascitic drains. ctDNA, circulating tumour DNA; HR, hazard ratio; ROC, receiver operating characteristic; TP53MAF, TP53 mutant allele fraction; TTP, time to progression.

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