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Monitoraggio della diffusione metastatica precoce del cancro polmonare in TRACERx utilizzando ctDNA

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Monitoraggio della diffusione metastatica precoce del cancro polmonare in TRACERx utilizzando ctDNA

Nature volume 616, pages

Natura volume 616, pagine 553–562 (2023) Citare questo articolo

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Il DNA tumorale circolante (ctDNA) può essere utilizzato per rilevare e profilare le cellule tumorali residue che persistono dopo una terapia con intento curativo1. Lo studio di ampie coorti di pazienti che incorporino il campionamento longitudinale del plasma e un follow-up esteso è necessario per determinare il ruolo del ctDNA come biomarker filogenetico di recidiva nel carcinoma polmonare non a piccole cellule (NSCLC) in stadio iniziale. Qui abbiamo sviluppato metodi ctDNA che tracciano una mediana di 200 mutazioni identificate nel tessuto NSCLC resecato su 1.069 campioni di plasma raccolti da 197 pazienti arruolati nello studio TRACERx2. La mancanza di rilevamento preoperatorio del ctDNA ha distinto l'adenocarcinoma polmonare biologicamente indolente con un buon esito clinico. Le analisi del plasma postoperatorio sono state interpretate nel contesto della sorveglianza radiologica standard di cura e della somministrazione di terapia adiuvante citotossica. Analisi di riferimento dei campioni di plasma raccolti entro 120 giorni dall'intervento chirurgico hanno rivelato la rilevazione del ctDNA nel 25% dei pazienti, compreso il 49% di tutti i pazienti che hanno manifestato recidiva clinica; La sorveglianza del ctDNA da 3 a 6 mesi ha identificato una recidiva imminente della malattia in un ulteriore 20% dei pazienti con test di riferimento negativo. Abbiamo sviluppato uno strumento bioinformatico (ECLIPSE) per il tracciamento non invasivo dell'architettura subclonale a bassi livelli di ctDNA. ECLIPSE ha identificato pazienti con disseminazione metastatica policlonale, associata a un esito clinico sfavorevole. Misurando le frazioni di cellule tumorali dei subcloni nel plasma preoperatorio, abbiamo scoperto che i subcloni che seminavano metastasi future erano significativamente più espansi rispetto ai subcloni non metastatici. I nostri risultati supporteranno i progressi degli studi (neo)adiuvanti e forniranno approfondimenti sul processo di diffusione metastatica utilizzando biopsia liquida a basso livello di ctDNA.

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Natura Open Access 12 aprile 2023

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I file di sequenziamento del cfDNA, i dati di RNA-seq e i dati di sequenziamento dell'esoma tumorale multiregione (in ogni caso provenienti dallo studio TRACERx) utilizzati o analizzati durante questo studio sono stati depositati presso l'Archivio europeo genoma-fenomeno (EGA), ospitato dall'Istituto europeo di bioinformatica (EBI) e il Centro per la regolazione genomica (CRG) con i codici di accesso EGAS00001006494, EGAS00001006517 e EGAS00001006494 ed è soggetto ad accesso controllato a causa della natura dei dati e degli accordi di partnership commerciale. I dettagli su come richiedere l'accesso sono disponibili nella pagina collegata.

ECLIPSE è disponibile come pacchetto R da installare da github (https://github.com/amf71/ECLIPSE), disponibile solo per scopi di ricerca accademica non commerciale. Il codice utilizzato per produrre le figure in questo documento è disponibile su richiesta.

Moding, EJ, Nabet, BY, Alizadeh, AA & Diehn, M. Rilevazione di residui liquidi di tumori solidi: malattia residua minima del DNA tumorale circolante. Scov. 11, 2968–2986 (2021).

Articolo 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>