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ctDNA를 사용하여 TRACERx에서 초기 폐암 전이성 전파 추적

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ctDNA를 사용하여 TRACERx에서 초기 폐암 전이성 전파 추적

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순환 종양 DNA(ctDNA)는 완치 목적 치료 후에도 지속되는 잔여 종양 세포를 검출하고 프로파일링하는 데 사용될 수 있습니다1. 초기 단계 비소세포폐암(NSCLC)에서 재발의 계통발생적 바이오마커로서 ctDNA의 역할을 결정하려면 세로 혈장 샘플링과 확장된 추적 조사를 통합한 대규모 환자 코호트에 대한 연구가 필요합니다. 여기에서 우리는 TRACERx 연구에 등록한 197명의 환자로부터 수집한 1,069개의 혈장 샘플에 걸쳐 절제된 NSCLC 조직에서 확인된 200개의 돌연변이의 중앙값을 추적하는 ctDNA 방법을 개발했습니다2. 수술 전 ctDNA 검출이 부족하여 생물학적으로 무통성인 폐 선암종이 좋은 임상 결과로 구별되었습니다. 수술 후 혈장 분석은 표준 치료 방사선학적 감시 및 세포독성 보조제 요법의 투여 맥락 내에서 해석되었습니다. 수술 후 120일 이내에 수집된 혈장 샘플에 대한 랜드마크 분석 결과, 임상적 재발을 경험한 전체 환자의 49%를 포함해 25%의 환자에서 ctDNA가 검출된 것으로 나타났습니다. 3~6개월간 ctDNA 감시를 통해 랜드마크 음성 환자의 추가 20%에서 질병 재발이 임박한 것으로 확인되었습니다. 우리는 낮은 ctDNA 수준에서 서브클론 구조를 비침습적으로 추적하기 위한 생물정보학 도구(ECLIPSE)를 개발했습니다. ECLIPSE는 불량한 임상 결과와 관련된 다클론성 전이성 파종 환자를 식별했습니다. 수술 전 혈장에서 서브클론 암 세포 분율을 측정함으로써 우리는 미래 전이를 뿌릴 서브클론이 비전이성 서브클론에 비해 훨씬 더 확장되었음을 발견했습니다. 우리의 연구 결과는 (신)보조 시험의 발전을 뒷받침하고 낮은 ctDNA 수준의 액체 생검을 사용하여 전이성 전파 과정에 대한 통찰력을 제공할 것입니다.

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이 연구 중에 사용되거나 분석된 cfDNA 시퀀싱 파일, RNA-seq 데이터 및 다중 영역 종양 엑솜 시퀀싱 데이터(각 경우 TRACERx 연구의 경우)는 유럽 생물정보학 연구소(European Bioinformatics Institute)가 주최하는 유럽 게놈-페놈 아카이브(EGA)에 기탁되었습니다. (EBI) 및 게놈 규제 센터(CRG)는 가입 코드 EGAS00001006494, EGAS00001006517 및 EGAS00001006494에 따라 데이터 및 상업적 파트너십 계약의 특성으로 인해 접근이 통제됩니다. 액세스 신청 방법에 대한 자세한 내용은 링크된 페이지에서 확인할 수 있습니다.

ECLIPSE는 학술적 비상업적 연구 목적으로만 사용할 수 있는 github(https://github.com/amf71/ECLIPSE)에서 설치할 수 있는 R 패키지로 제공됩니다. 이 백서의 수치를 생성하는 데 사용된 코드는 요청 시 제공됩니다.

Moding, EJ, Nabet, BY, Alizadeh, AA & Diehn, M. 고형 종양의 액체 잔존물 검출: 순환 종양 DNA 최소 잔존 질환. 암 발견. 11, 2968~2986(2021).

기사 CAS PubMed PubMed Central Google Scholar

 0.1 threshold meaning that they were deemed negative for ctDNA. B. Postoperative caller P values observed in n = 5 patients who had relapse of their NSCLC. 1 of 13 calls was made between caller P values of 0.1 and 0.01, the remaining 12 calls were made at a caller P value less than 0.01. C. Preoperative ctDNA calls from pilot cohort; 7 patients had positive ctDNA in plasma prior to surgery, all calls were made at caller P values < 0.01. D. In-silico simulation analysis to assess MRD caller specificity. 3157 mock MRD panels were generated within the evaluable pilot patient libraries and MRD caller P values were assessed. At a caller P value < 0.1 threshold, 121/3157 simulated mock panels were ctDNA positive (in-silico specificity of 96.2%); at a caller P value threshold < 0.01, 22/3157 simulated mock panels were ctDNA positive (in-silico specificity of 99.3%). E-F. Analytical validation of 50 variant MRD detection panels. E. Fragmented DNA with a known single nucleotide polymorphism (SNP) profile was spiked into a second background of fragmented DNA with a different SNP profile and a patient-specific panel targeted 50 alternate positions present in spiked-in DNA. 559 data points were generated across different DNA input quantities indicated, to establish the limit of detection plots. The Y axis and centre of the error bars demonstrate sensitivity (defined as the proportion of all repeats that resulted in MRD detection using a caller P value of 0.01). The confidence intervals on the plot are Clopper-Pearson confidence intervals (95% CIs). The X axis shows the quantity of variant germline DNA that was spiked into each repeat expressed as a percentage of total DNA in that sample. F. Circulating tumour DNA samples with high variant allele fractions were spiked into a different cell-free DNA background. Variant positions in ctDNA were targeted with a 50 variant panel; 100 data points were generated across the DNA input quantities indicated. Axes and error bars are the same as (E). G. Data from analyses of 48 blank samples donated by 24 healthy participants, caller P values are displayed. H. Barplots demonstrating the intended allele frequencies and the measured allele frequencies in the different spike-ins presented in part (E) and part (F) only data from variant DNA positive samples are presented. The colours of the barplot represent different DNA input masses as shown by the legend. The error bars on the plot represent the mean value of all positive spike-in samples +/− standard deviation of the values. Where the error bar is absent, this is because at this spike-in level and DNA input mass, only one positive sample was observed. Where the error bar led to an observed mean AF less than 0, the error bar was stopped at 0 for visualization purposes (the 0.05% spike-in, 2 ng input mass case). The horizontal dashed lines correspond to 0.1%, 0.05%, and 0.01% spike-in categories. Each data point is represented on the plots by a circle. n = 369 variant DNA positive samples displayed in LOD1 barchart, n = 93 variant DNA positive samples displayed in LOD2 barchart. I. Comparison between the content of cell-free DNA input into ddPCR reactions (yellow) and AMP PCR reactions (blue). Hinges correspond to first and third quartiles, whiskers extend to the largest/smallest value no further than 1.5x the interquartile range. Centre lines represent medians. Each dot on the plot represents a data point, lines connect paired samples from the same patient. Significantly more cell-free DNA was input into ddPCR reactions (paired two-sided Wilcoxon-test P = 0.01366). J. Orthogonal comparison between ctDNA detection based on AMP panels used in TRACERx and ddPCR against a single clonal variant. ddPCR ctDNA positive call threshold was two mutant droplets (bottom table) and one mutant droplet (top table). Percentage positive agreement (PPA) and percentage negative agreement (NPA) using ddPCR as the comparator is displayed in the table. Two-sided Fisher's test P values are demonstrated under the cross tables. K. A 300 mutation patient-specific panel was designed and applied to 10 ng DNA samples containing spike-in variant levels from 0% to 0.1%. In silico sub-sampling of the 300 mutations was performed (3 x 200 mutation in silico panels, 3x 100 mutation in silico panels and 3x 50 mutation in silico panels, see methods) and sensitivities are categorized by the number of mutations targeted by the panel./p>0.1% clonal ctDNA level & >=10 ng DNA input (high subclone sensitivity samples) with ECLIPSE and those measured with multi-region tissue sequencing (M-seq) at surgery (N = 71 patients and 684 subclones included). B. Copy number unaware CCFs calculated only using VAFs (methods) compared to tissue CCF from M-seq. All preoperative samples with phylogenetic data, >0.1% clonal ctDNA level & >=10 ng DNA input (high subclone sensitivity samples) were included (N = 71 patients and 684 subclones included). C. A scatter plot demonstrating the relationship between clonal ctDNA level and the proportion of multi-region tumour exome (M-seq) defined subclones detected by ECLIPSE based on varying subclonal cancer cell fractions as indicated, loess lines are fitted to the plots, n = 117 ctDNA positive preoperative samples. D. A comparison of preoperative plasma CCFs and the average CCFs across all tissue regions sampled at surgery for clones that were unique to one tumour tissue region and for clones that were distributed across more than two tumour tissue regions. N = 71 patients and 684 subclones included. A Wilcoxon-test was used to compare groups. E. A comparison of preoperative plasma CCFs and the average CCFs across all tissue regions sampled at surgery for clones that were unique to one tumour tissue region separated between small (<20 cm3), medium (>20 cm3 & <100 cm3), and large (>100 cm3) tumours as measured on preoperative PET/CT scans. N = 71 patients and 684 subclones included. A Wilcoxon-test was used to compare groups. F. A comparison of detection rates in preoperative plasma for 20% CCF subclones across a range of clonal ctDNA levels split by whether the subclones were spread across multiple primary tumour tissue regions or were limited to only a single primary tumour tissue region. 1924 subclones were assessed in 197 preoperative plasma samples. G. A map of tumour clones with areas of multi-regional tissue sampling indicated and clones which are over- and undersampled highlighted. Most of the undersampled clones are in fact not in the sampled areas creating a bias towards oversampling in clones which we are able to detect, an effect also called the ‘winner's curse’. H. A ROC curve describing the sensitivity and specificity of detecting clonal illusion mutations using plasma-based CCFs with 95% confidence intervals generated using bootstrapping across 500-fold cross-validation (N = 71 tumours)./p>