Longitudinal monitoring of disease burden and response using ctDNA from dried blood spots in xenograft models
2022; Springer Nature; Volume: 14; Issue: 8 Linguagem: Inglês
10.15252/emmm.202215729
ISSN1757-4684
AutoresCarolin M. Sauer, Katrin Heider, Jelena Belic, Samantha E. Boyle, James Hall, Dominique‐Laurent Couturier, Angela An, Aadhitthya Vijayaraghavan, Marika Reinius, Karen Hosking, Maria Vias, Nitzan Rosenfeld, James D. Brenton,
Tópico(s)Single-cell and spatial transcriptomics
ResumoReport13 June 2022Open Access Source DataTransparent process Longitudinal monitoring of disease burden and response using ctDNA from dried blood spots in xenograft models Carolin M Sauer Corresponding Author Carolin M Sauer [email protected] orcid.org/0000-0003-2168-6630 Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK Cancer Research UK Major Centre–Cambridge, University of Cambridge, Cambridge, UK Contribution: Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review & editing Search for more papers by this author Katrin Heider Katrin Heider orcid.org/0000-0003-4035-1668 Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK Cancer Research UK Major Centre–Cambridge, University of Cambridge, Cambridge, UK Contribution: Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review & editing Search for more papers by this author Jelena Belic Jelena Belic Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK Cancer Research UK Major Centre–Cambridge, University of Cambridge, Cambridge, UK Contribution: Resources, Data curation, Investigation Search for more papers by this author Samantha E Boyle Samantha E Boyle Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK Cancer Research UK Major Centre–Cambridge, University of Cambridge, Cambridge, UK Contribution: Resources, Investigation, Methodology Search for more papers by this author James A Hall James A Hall Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK Cancer Research UK Major Centre–Cambridge, University of Cambridge, Cambridge, UK Contribution: Resources, Investigation, Methodology Search for more papers by this author Dominique-Laurent Couturier Dominique-Laurent Couturier Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK Contribution: Resources, Formal analysis, Writing - review & editing Search for more papers by this author Angela An Angela An Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK Cancer Research UK Major Centre–Cambridge, University of Cambridge, Cambridge, UK Contribution: Resources, Investigation, Writing - review & editing Search for more papers by this author Aadhitthya Vijayaraghavan Aadhitthya Vijayaraghavan Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK Cancer Research UK Major Centre–Cambridge, University of Cambridge, Cambridge, UK Contribution: Resources, Data curation, Software, Formal analysis, Methodology Search for more papers by this author Marika AV Reinius Marika AV Reinius orcid.org/0000-0002-9778-3317 Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK Cancer Research UK Major Centre–Cambridge, University of Cambridge, Cambridge, UK Contribution: Data curation, Writing - review & editing Search for more papers by this author Karen Hosking Karen Hosking Cancer Research UK Major Centre–Cambridge, University of Cambridge, Cambridge, UK Contribution: Data curation Search for more papers by this author Maria Vias Maria Vias orcid.org/0000-0003-4955-0102 Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK Cancer Research UK Major Centre–Cambridge, University of Cambridge, Cambridge, UK Contribution: Resources, Supervision, Investigation, Methodology, Project administration, Writing - review & editing Search for more papers by this author Nitzan Rosenfeld Corresponding Author Nitzan Rosenfeld [email protected] orcid.org/0000-0002-2825-4788 Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK Cancer Research UK Major Centre–Cambridge, University of Cambridge, Cambridge, UK Contribution: Conceptualization, Supervision, Funding acquisition, Writing - original draft, Project administration, Writing - review & editing Search for more papers by this author James D Brenton Corresponding Author James D Brenton [email protected] orcid.org/0000-0002-5738-6683 Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK Cancer Research UK Major Centre–Cambridge, University of Cambridge, Cambridge, UK Cambridge University Hospitals NHS Foundation Trust and National Institute for Health Research Cambridge Biomedical Research Centre, Addenbrooke's Hospital, Cambridge, UK Department of Oncology, University of Cambridge, Cambridge, UK Contribution: Conceptualization, Supervision, Funding acquisition, Writing - original draft, Project administration, Writing - review & editing Search for more papers by this author Carolin M Sauer Corresponding Author Carolin M Sauer [email protected] orcid.org/0000-0003-2168-6630 Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK Cancer Research UK Major Centre–Cambridge, University of Cambridge, Cambridge, UK Contribution: Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review & editing Search for more papers by this author Katrin Heider Katrin Heider orcid.org/0000-0003-4035-1668 Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK Cancer Research UK Major Centre–Cambridge, University of Cambridge, Cambridge, UK Contribution: Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review & editing Search for more papers by this author Jelena Belic Jelena Belic Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK Cancer Research UK Major Centre–Cambridge, University of Cambridge, Cambridge, UK Contribution: Resources, Data curation, Investigation Search for more papers by this author Samantha E Boyle Samantha E Boyle Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK Cancer Research UK Major Centre–Cambridge, University of Cambridge, Cambridge, UK Contribution: Resources, Investigation, Methodology Search for more papers by this author James A Hall James A Hall Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK Cancer Research UK Major Centre–Cambridge, University of Cambridge, Cambridge, UK Contribution: Resources, Investigation, Methodology Search for more papers by this author Dominique-Laurent Couturier Dominique-Laurent Couturier Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK Contribution: Resources, Formal analysis, Writing - review & editing Search for more papers by this author Angela An Angela An Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK Cancer Research UK Major Centre–Cambridge, University of Cambridge, Cambridge, UK Contribution: Resources, Investigation, Writing - review & editing Search for more papers by this author Aadhitthya Vijayaraghavan Aadhitthya Vijayaraghavan Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK Cancer Research UK Major Centre–Cambridge, University of Cambridge, Cambridge, UK Contribution: Resources, Data curation, Software, Formal analysis, Methodology Search for more papers by this author Marika AV Reinius Marika AV Reinius orcid.org/0000-0002-9778-3317 Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK Cancer Research UK Major Centre–Cambridge, University of Cambridge, Cambridge, UK Contribution: Data curation, Writing - review & editing Search for more papers by this author Karen Hosking Karen Hosking Cancer Research UK Major Centre–Cambridge, University of Cambridge, Cambridge, UK Contribution: Data curation Search for more papers by this author Maria Vias Maria Vias orcid.org/0000-0003-4955-0102 Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK Cancer Research UK Major Centre–Cambridge, University of Cambridge, Cambridge, UK Contribution: Resources, Supervision, Investigation, Methodology, Project administration, Writing - review & editing Search for more papers by this author Nitzan Rosenfeld Corresponding Author Nitzan Rosenfeld [email protected] orcid.org/0000-0002-2825-4788 Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK Cancer Research UK Major Centre–Cambridge, University of Cambridge, Cambridge, UK Contribution: Conceptualization, Supervision, Funding acquisition, Writing - original draft, Project administration, Writing - review & editing Search for more papers by this author James D Brenton Corresponding Author James D Brenton [email protected] orcid.org/0000-0002-5738-6683 Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK Cancer Research UK Major Centre–Cambridge, University of Cambridge, Cambridge, UK Cambridge University Hospitals NHS Foundation Trust and National Institute for Health Research Cambridge Biomedical Research Centre, Addenbrooke's Hospital, Cambridge, UK Department of Oncology, University of Cambridge, Cambridge, UK Contribution: Conceptualization, Supervision, Funding acquisition, Writing - original draft, Project administration, Writing - review & editing Search for more papers by this author Author Information Carolin M Sauer *,1,2,†, Katrin Heider1,2,†, Jelena Belic1,2, Samantha E Boyle1,2, James A Hall1,2, Dominique-Laurent Couturier1,3, Angela An1,2, Aadhitthya Vijayaraghavan1,2, Marika AV Reinius1,2, Karen Hosking2, Maria Vias1,2, Nitzan Rosenfeld *,1,2,‡ and James D Brenton *,1,2,4,5,‡ 1Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK 2Cancer Research UK Major Centre–Cambridge, University of Cambridge, Cambridge, UK 3Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK 4Cambridge University Hospitals NHS Foundation Trust and National Institute for Health Research Cambridge Biomedical Research Centre, Addenbrooke's Hospital, Cambridge, UK 5Department of Oncology, University of Cambridge, Cambridge, UK † These authors contributed equally to this work as co-first authors ‡ These authors contributed equally to this work as co-senior authors *Corresponding author. Tel: +44 1223769823; E-mail: [email protected] *Corresponding author. Tel: +44 1223769769; E-mail: [email protected] *Corresponding author. Tel: +44 1223769761; E-mail: [email protected] EMBO Mol Med (2022)14:e15729https://doi.org/10.15252/emmm.202215729 See also: I Heidrich & K Pantell (August 2022) PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Whole-genome sequencing (WGS) of circulating tumour DNA (ctDNA) is now a clinically important biomarker for predicting therapy response, disease burden and disease progression. However, the translation of ctDNA monitoring into vital preclinical PDX models has not been possible owing to low circulating blood volumes in small rodents. Here, we describe the longitudinal detection and monitoring of ctDNA from minute volumes of blood in PDX mice. We developed a xenograft Tumour Fraction (xTF) metric using shallow WGS of dried blood spots (DBS), and demonstrate its application to quantify disease burden, monitor treatment response and predict disease outcome in a preclinical study of PDX mice. Further, we show how our DBS-based ctDNA assay can be used to detect gene-specific copy number changes and examine the copy number landscape over time. Use of sequential DBS ctDNA assays could transform future trial designs in both mice and patients by enabling increased sampling and molecular monitoring. Synopsis A novel approach is developed for longitudinal monitoring of tumour burden in patient-derived xenograft (PDX) models using dried blood spots from minute volumes of blood. Circulating tumour DNA (ctDNA) can be detected in minute volumes of blood (~ 50 µl) collected as dried blood spots (DBS) from the tail vein in PDX mice. The xenograft Tumour Fraction (xTF) is calculated using species-specific alignment of reads obtained from shallow whole-genome sequencing of DBS samples. The xTF metric allows accurate monitoring of disease progression over time. The xTF rate of change during the first 30 days of treatment is predictive of disease outcome in PDX mice. The paper explained Problem Whole-genome sequencing (WGS) of circulating tumour DNA (ctDNA) has enabled non-invasive disease stratification and monitoring of disease progression and response in the clinic. Patient-derived xenograft (PDX) mice are frequently used as models to study new treatment approaches for human cancers. However, WGS-based ctDNA assays have not been possible in small rodents owing to constraints on the volume of blood that can be sampled. Result We developed shallow WGS (sWGS) of ctDNA from serial and minimally invasive dried blood spot (DBS) samples. We show that copy number changes are detected over multiple time points and DBS ctDNA recapitulates the biological features of ctDNA in patients. Sequential DBS ctDNA accurately predicts treatment response and disease outcome in PDX mouse models. Impact Our approach enables minimally invasive sampling and sWGS-based detection of ctDNA over time from minute volumes of whole blood (~ 50 μl) in preclinical animal models. It strongly conforms with the 3Rs of animal welfare and has the potential to revolutionise study design in both small rodents and patients. Introduction Liquid biopsies are routinely used in the clinic to sensitively detect and quantify disease burden, and have critical roles for therapeutic decision making in precision medicine (Wan et al, 2017; Cohen et al, 2018; Heitzer et al, 2019; Rothwell et al, 2019; Kilgour et al, 2020; Deveson et al, 2021). Plasma circulating tumour DNA (ctDNA) is the most widely studied circulating analyte for disease monitoring and molecular genotyping of tumours (Cescon et al, 2020; Kilgour et al, 2020). Technical advances in next generation sequencing (NGS) now achieve unprecedented sensitivities for the detection of ctDNA using 6–10 ml of whole blood (Deveson et al, 2021; Rolfo et al, 2021). To enable very accurate monitoring of disease burden and progression, several whole-genome sequencing (WGS)-based strategies have been developed detecting combinations of single-nucleotide variants, small insertions/deletions and somatic copy number aberrations (SCNAs) (Adalsteinsson et al, 2017; Chen & Zhao, 2019; Wan et al, 2020; Zviran et al, 2020; Abbosh & Swanton, 2021; Paracchini et al, 2021). In addition, deriving other biochemical features of ctDNA from WGS, including fragment size and chromosome accessibility, can further enhance detection sensitivity and infer biological information about tumour site of origin (Mouliere et al, 2018; Cristiano et al, 2019; Ulz et al, 2019; Keller et al, 2021; preprint: Markus et al, 2021). Modelling therapeutic response in mice bearing patient-derived xenografts (PDX) is a critical step to test treatment regimens and pharmacogenomics during drug development (Williams, 2018; Ireson et al, 2019). However, WGS-based ctDNA assays cannot be used in small rodents as the circulating blood volume of a mouse is only ~ 1.5–2.5 ml. Consequently, detailed ctDNA assays can only be obtained from terminal bleeding of mice, preventing longitudinal analyses and more efficient therapeutic study designs. Manual measurements of tumour volumes in subcutaneous models are the commonest surrogate to estimate treatment response and disease burden (Pearson et al, 2016; Ice et al, 2019). These measures are often poorly reproducible and can be biased by treatment-induced tissue necrosis and oedema. Using imaging as an alternative to estimate response in PDXs is more time-consuming, requires general anaesthesia and may also need the introduction of in vivo reporter genes (Weissleder, 2002; Koessinger et al, 2020). Therefore, bringing WGS-based ctDNA assays into mice would have two major benefits. Firstly, more efficient and accurate serial measurements across multiple animals, and secondly, the direct translation of biological and biochemical observations from mouse ctDNA studies into patient studies and vice versa. We recently illustrated the detection of ctDNA in dried blood spots (DBS) from minute volumes of whole blood using a size selection approach to enrich for cell-free DNA (cfDNA) (Heider et al, 2020b). Using a modified approach in PDX mice, we now demonstrate that shallow WGS (sWGS) of DBS from 50 µl of whole blood can be used for serial ctDNA measurements, longitudinal disease monitoring and copy number analyses in preclinical studies. The work presented here provides important proof-of-principle data and further supports the application and feasibility of DBS-based ctDNA sampling both in preclinical and clinical studies. Results Development and validation of the xTF metric from DBS To detect and accurately quantify ctDNA from minute volumes of blood in preclinical PDX studies, we developed a xenograft Tumour Fraction (xTF) metric, which is estimated from shallow whole-genome sequencing (sWGS) of DBS samples (Fig 1A). Briefly, 50 µl of blood is collected from the tail vein, deposited onto a filter card, and left to air dry. DNA is extracted, contaminating genomic DNA is removed (Heider et al, 2020b) and subsequently sequenced at low coverage following library preparation. Human- and mouse-specific reads are identified using Xenomapper (Wakefield, 2016), and the xTF is calculated as the ratio of human-specific reads divided by total reads (human and mouse-specific reads) per sample (see Methods). Figure 1. The xTF metric is highly specific and sensitive to detect and quantify ctDNA from dried blood spots Workflow of the dried blood spot (DBS)-based xenograft Tumour Fraction (xTF). DBS are generated by collecting and depositing 50 µl of blood from the tail vein of the mouse onto FTA filter cards. DNA is extracted from blood spots, processed and sequenced as described previously (Heider et al, 2020b). Human-specific reads and mouse-specific reads were separated into species-specific bam files using Xenomapper (Wakefield, 2016). The xTF is then calculated by dividing the number of human-specific reads by the total number of human and mouse-specific reads in a given sample. Comparison of xTF values obtained from healthy non-tumour-bearing mice DBS (n = 10, from 5 individual mice) and PDX DBS (n = 91, from 35 individual mice at day 1, 16 or 29) samples (Welch t-test, P < 2.2 × 10−16). Sensitivity testing using the Mann–Whitney U Wilcoxon test (Wilcoxon test, P = 2.5 × 10−7) showed similar results. Mean ± SD are indicated in red. xTF dilution series. Dilution xTFs (0.01, 0.02, 0.05, 0.07, 0.1, 0.15 and 0.25) were computationally generated by mixing blood spot sequencing data obtained from five ovarian cancer patients and a healthy control mouse. Each dilution therefore contains five biological replicates. The generated dilution series was analysed using Xenomapper and resulting xTF values were compared with the dilution xTFs (Spearman correlation R = 0.99, P < 2.2 × 10−16). Boxplots indicate first quartiles, medians (vertical line) and third quartiles. Whiskers indicate minima and maxima. Fragment length distributions of human- (pink) and mouse- (blue) specific reads from a DBS sample. Two vertical lines indicate 146 and 166 bp, the observed peaks for ctDNA and cfDNA, respectively. Example of an absolute copy number (ACN) profile successfully generated from human-specific reads from a DBS collected from a PDX mouse of patient line 828. Matching ACN profile generated from sWGS of PDX tumour tissue. Appendix Figs S2 and S3 show representative ACN profiles for all four patient lines and the correlation of each copy number bin for the DBS and tissue sample pairs. Correlation of Pearson correlation estimates (comparing ACN bins between tumour tissue and DBS) and xTFs from DBS samples (Spearman R = 0.64, P < 2.2 × 10−16). Download figure Download PowerPoint To test both the specificity and sensitivity of the xTF metric, we established a preclinical study using PDX mice derived from four high-grade serous ovarian cancer (HGSOC) patients (see next section). We collected a total of 10 DBS samples from five healthy non-tumour-bearing mice and 91 DBS samples from 35 tumour-bearing PDX mice. Reads from healthy control mice showed < 0.1% assignment as human-specific sequences (false-positive background). In addition, healthy control mice had significantly lower xTF values compared to tumour-bearing PDX mice, independent of tumour size and disease burden, indicating the high specificity of the xTF metric (Welch t-test, P = 2.2 × 10−16, Fig 1B). To confirm the linearity and sensitivity of our approach, we prepared an in silico 7-point dilution series (see Methods) by combining sequencing reads from a healthy mouse DBS and DBS samples collected from five independent ovarian cancer patients at different ratios. We were able to accurately detect human reads for all seven dilution points, and observed a strong correlation between measured xTFs and spiked-in human reads at human:mouse proportions of 1–25% (Spearman’s R = 0.99, P < 2.2 × 10−16, Fig 1C). Next, we examined the fragment size distributions of human- and mouse-specific reads from sWGS of DBS samples. In human plasma samples, ctDNA has a modal size of approximately 145 bp, which is shorter than cfDNA with a prominent mode of approximately 165 bp (Jahr et al, 2001; Underhill et al, 2016; Mouliere et al, 2018). These fragment size properties were recapitulated in the human- and mouse-specific reads from DBS samples (Fig 1D). By contrast, human-specific reads incorrectly identified in non-tumour-bearing control mice (false-positive background; see Fig 1B) displayed significantly smaller fragment sizes, with the majority of fragment sizes < 50 bp (Appendix Fig 1) suggesting non-specific alignment. Given the high specificity and sensitivity of our approach, we were able to derive absolute copy number (ACN) data from as little as 500,000 human-specific DBS reads using QDNAseq (Scheinin et al, 2014) followed by Rascal (Sauer et al, 2021) (Fig 1E). The observed absolute somatic copy number aberrations (SCNAs) (Fig 1F) and their extent were strongly correlated with sWGS of PDX tumour tissues from the same patient (Appendix Figs S2 and S3A–D). Unsurprisingly, the ability to accurately detect SCNAs in DBS strongly correlated with increasing xTF values (Fig 1G). No correlations were observed when comparing blood spot ACN profiles from healthy non-tumour-bearing mice to any of the four patient tumour tissues (Appendix Fig S3E–G). Using the definitions of copy number gains and losses outlined by the Catalogue of Somatic Mutations In Cancer (COSMIC), amplifications of driver SCNAs were detectable in blood spot samples with xTFs ranging from 0.6–54.4% (Appendix Figs S2 and S3H). The xTF allows accurate monitoring of disease progression We next investigated whether the DBS-based xTF assay could be used for longitudinal monitoring of disease progression and treatment response. An overview of our preclinical PDX study is shown in Fig 2A. The PDX models were selected from four patients with different clinical responses to platinum-based chemotherapy and distinct copy number signatures (Macintyre et al, 2018) for homologous recombination deficiency (HRD) that are predictive of sensitivity to carboplatin (Fig EV1). All PDXs were derived from tumour samples prior to systemic therapy and histological and molecular features were shown to be highly similar to the primary tumour (Appendix Figs S4 and S5). PDX mice were treated with either 50 mg/kg carboplatin or control on day 1 and 8. Tumour volumes were measured weekly, and blood spots were collected on day 1 (prior to treatment start), day 16 and 29 (Fig 2A). Figure 2. The DBS-based xTF allows longitudinal monitoring of disease progression and treatment response in preclinical studies Preclinical PDX study overview. HGSOC patients underwent surgery and standard-of-care chemotherapy with carboplatin and paclitaxel. Disease progression was monitored over time using the CA-125 biomarker, CT scans, as well as ctDNA where available. The treatment-naïve surgical tumour or biopsy specimens were engrafted into NSG mice. Second or third generation PDX mice were then treated with either carboplatin or vehicle control via tail vein injection on day 1 and day 8. Tumour volumes were measured weekly, and blood spots were collected on day 1 (prior to treatment initiation), day 16 and 29. xTF change from baseline during the first 29 days since start of treatment for each PDX patient line. xTFs were normalised to baseline (day 1) xTF values for each mouse (dashed lines). Carboplatin-treated mice are shown in purple, control mice are shown in teal. Bold lines show the linear-model fitted across all mice within the same treatment and patient group. Horizontal dashed lines at y = 1 indicate normalised baseline. Fraction of blood spot samples in which putative driver amplifications were detected over time. The fraction of samples with detected gene amplifications decreases in the carboplatin-treated group, while increasing in the control group over time. Correlation between xTF values and tumour volumes of the nearest matched time point for both untreated (Pearson’s R = 0.45, P = 0.0002), and carboplatin-treated (Pearson’s R = 0.056, P = 0.78) PDX mice. Source data are available online for this figure. Source Data for Figure 2 [emmm202215729-sup-0003-SDataFig2.csv] Download figure Download PowerPoint Click here to expand this figure. Figure EV1. Clinical treatment response, surgery outcome, time until progression and copy number signatures for preclinical study patients A–D. CA-125 values (black line), treatment response assessments estimated via CT scans (vertical grey dashed lines), and ctDNA data, where available (red dashed line), for HGSOC patients 600, 771, 828 and 831, respectively, over time. Surgery and additional treatment regimens are indicated by a pink vertical line and shaded boxes, respectively (CR, Complete Response; PR, Partial Response; SD, Stable Disease; PD, Progressive Disease). E. Stacked bar plots showing copy number signature activities for first generation PDX tissues derived from the four patients (patient 600, 771, 828 and 831) used in the preclinical HGSOC study. Download figure Download PowerPoint We observed a progressive increase in xTF in all 17 untreated PDX control mice. In contrast, the 18 mice that were treated with carboplatin showed PDX-specific decreases in xTFs from DBS samples collected at day 16 and 29 in comparison to pretreatment (day 1) samples (Fig 2B). Similarly, the fraction of samples in which we were able to detect human gene-level amplifications from DBS reads (e.g. MYC and MCM10 amplifications in patients 828 and 771, respectively) increased in untreated and decreased in carboplatin-treated mice over time (Fig 2C). When correlating xTF values to tumour volumes obtained from weekly tumour measurements, we found that xTFs increased with increasing tumour volumes and thus disease burden (Pearson’s R = 0.48, P = 1.2 × 10−6, Appendix Fig S6A). This correlation was strongly observed in all untreated mice (Pearson’s R = 0.45, P = 0.0002, Fig 2D), but not in all treated mice (Pearson’s R = 0.056, P = 0.78, Fig 2D), mostly related to responses in PDX mice from patient line 831 (Appendix Fig S6B). This could be due to treatment-induced tissue necrosis and oedema biasing manual tumour volume measures, and suggests that ctDNA measures could offer a more accurate readout of initial treatment response (during the first 30 days) as less prone to confounding factors on manual size measurements. The xTF rate of change is predictive of disease outcome Early dynamic change in ctDNA can predict progression-free survival and provide real-time assessment of treatment efficacy (O’Leary et al, 2018). Similar predictive measures in mice could also improve the efficiency of PDX study designs. All four PDX lines in our cohort were from patients with platinum-sensitive disease, and PDX 828 and 831 were predicted to have the best response to carboplatin treatment owing to somatic and germline BRCA1 mutations, respectively (Figs EV1 and EV2). PDX 600 and 771 had less marked HRD signatures (Figs EV1E and EV2). Clinical progression-free survival (PFS) and overall survival (OS) (Fig EV2) could not be used as response predictors as the four patients have important differences in prognostic variables for stage and residual disease after surgery (Fig EV1). Click here to expand this figure. Figure EV2. Overview of preclinical patients and PDX lines Overview of clinical timelines until first disease progression in the four HGSOC patients. Table
Referência(s)