Beneficios de la aplicación

treatment ctDNA levels are at their highest. More recently, focus has shifted to analysis of ctDNA in the detection of minimal residual disease, and in patients with early stage cancer. This is more challenging given the low number of mutant fragments available for analysis. Mair et al. and Mouliere et al. used paired-end sWGS to study fragmentation patterns of cfDNA (12–14). Mouliere et al. subsequently tested whether size selection could be used to enrich for tumour-specific ctDNA fragments to improve assay sensitivity (13,14).

5.3.1: Mair et al., Cancer Research, 2019 (12)

5.3.1.1: Aims

The aim of this study was to analyse human ctDNA in a patient-derived rat xenograft model of glioblastoma (GBM) (12). sWGS was performed to analyse fragmentation lengths of human (tumour) and rat (host) DNA. As GBM is known to have low concentrations of ctDNA, tumour mitochondrial DNA (tmtDNA) was also analysed in rat plasma, urine and

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cerebrospinal fluid (CSF) by dPCR using human and rat-specific assays. tmtDNA may potentially be a more sensitive marker than ctDNA as there are 102_-105 _{copies of the}

16.5kb mitochondrial genome in each cancer cell (129).

5.3.1.2: Results

sWGS from the xenograft model showed that human tumour-specific and CSF DNA fragments centred around 145bp in length, compared to 167bp for rat DNA (Figure 5.6). Human and rat mitochondrial DNA showed a peak <100bp.

Tumour mitochondrial DNA was detected in plasma in 82% of rats, compared to 24% for ctDNA, at ~190-fold higher levels, indicating the potential of using tmtDNA to enhance detection in GBM. tmtDNA was also detected in CSF and urine. ctDNA levels in CSF were 5- 8-fold higher than in plasma, possibly due to higher background levels of host DNA in plasma.

5.3.2: Mouliere et al., bioRxiv, 2017; Mouliere et al., Science

Translational Medicine, 2018 (13,14)

5.3.2.1: Aims

Mouliere et al. tested whether size selection of 90bp-150bp fragments could enrich for ctDNA. This data, published initially as a bioRxiv pre-print (13), was followed by further analysis of fragmentation patterns in 344 plasma samples from 200 patients with 18 different cancer types, and 65 presumed healthy controls (14).

5.3.2.2: Results

In the bioRxiv pre-print, in vitro size selection was performed on plasma from 13 relapsed HGSOC patients using a PippinHT 3% agarose cassette, and analysed by TAm-Seq and sWGS. Results demonstrated that tumour-specific DNA could be enriched up to 11-fold. t-MAD (trimmed Mean Absolute Deviation from copy-number neutrality) was developed to analyse sWGS data to measure the median deviation from the copy number neutral state, and quantitatively assess SCNAs on a genome-wide scale. Size selection resulted in a median 1.5-fold increase in t-MAD score across all samples, and a median 2.9-fold increase

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Figure 5.6: Fragmentation patterns of plasma DNA in rat xenograft model of glioblastoma. (A) sWGS reads aligned to human (tumour) or rat (host) genomic reference sequences (B) SCNAs identified in tumour, CSF and plasma by alignment to human and rat genome. (C,D) Size distribution of human ctDNA (red) and rat DNA (blue) in two rat GBM models. Vertical line: 167bp. (E) Size distribution of CSF DNA. (F) Size distribution of mitochondrial DNA (tmtDNA) in plasma from

xenografted rat. Human tmtDNA (purple); Rat tmtDNA (green). wtDNA: wild-type DNA. Figure and Figure Legend adapted from Mair et al. (12)

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in the majority of samples collected post-treatment where ctDNA levels were low. Two samples were highly degraded with no observed 167bp peak, and size selection resulted in a decrease in t-MAD score.

In Mouliere et al. (Science Translational Medicine), sWGS showed different fragmention patterns in patients with different cancer types and healthy controls (Figure 5.7). Longer fragments were observed in healthy controls and in patients with ‘low ctDNA’ cancers (renal, glioblastoma, bladder, pancreatic). ‘High ctDNA’ cancers [melanoma, breast, ovarian, lung, colorectal and cholangiocarcinoma (ChC)], showed an increased proportion of fragments <150bp, and an enrichment of 250bp-320bp fragments.

In silico and in vitro size selection was next used to enhance detection of ctDNA. t-MAD analysis of 45 pre- and post-treatment plasma samples from 35 HGSOC patients showed a mean 2.5-fold increase in 98% samples following in vitro size selection. In addition, SCNAs were detected which were not previously observed in pre-treatment samples (Figure 5.8), including in clinically-relevant genes, including NF1, TERT and MYC. Comparison of t-MAD with dPCR and WES data showed high correlation (Pearson, r = 0.80) between t-MAD and mutant AF in samples above the detection threshold (0.015, based on highest t-MAD score in control samples) and AF>0.025. A spike-in dilution series showed linearity of t-MAD and mutant fraction down to ~0.01 AF. t-MAD also correlated with tumour volume by RECIST v1.1 in analysis of 35 patients (Pearson, r = 0.6).

Machine learning algorithms were used to classify ‘healthy’ and ‘cancer’ samples by analysing cfDNA fragmentation features in sWGS data. Features used included the proportion (P) of fragments of specific size ranges, the ratio of fragments in different size ranges, and the amplitude of the 10bp periodicity oscillations below 150bp. Random Forest (RF) and Logistic Regression (LR) models were trained on 153 samples, and cross-validated on 2 independent datasets of [1] 94 samples, and [2] 83 samples from ‘low ctDNA’ cancers. The RF model was most predictive, using t-MAD, 10bp amplitude, P(160 to 180), P(180 to 220) and P(250 to 320) features, with an AUC of 0.994 in analysis of the 94 validation cohort, and 0.914 in ‘low ctDNA’ samples. It correctly classified cancer in 94% of samples from ‘high ctDNA’ cancers, and 65% of ‘low ctDNA’ cancers. Using just four fragmentation features without t-MAD resulted in an AUC of 0.989 (‘high ctDNA’) and 0.891 (‘low

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Figure 5.7: Plasma fragmentation patterns on a pan-cancer scale. Proportion of fragments <150bp by cancer type. ChC: Cholangiocarcinoma. ‘Other’: Cancer types represented by <4 individuals. Red lines: median proportion for each cancer type. (Kruskal-Wallis, *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001 versus healthy and ‘low ctDNA’ cancers.. Figure and Figure Legend from Mouliere et al. (14)

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Figure 5.8: Enhancing tumour fraction from plasma sequencing with size selection. (A) Plasma samples from HGSOC patients analysed without size selection, or using either in silico or in vitro size selection. (B) Accuracy of 90-150bp in vitro or in silico size selection demonstrated on 20 healthy controls. Green: Before size selection; Blue: After in silico size selection; Orange: After in vitro size selection. Vertical lines: 90bp, 150bp. (C) SCNA analysis of pre- treatment plasma from HGSOC patient (D) SCNA analysis of non-size selected plasma 3 weeks post-treatment (E) SCNA analysis of same plasma in (D) with 90bp-150bp in vitro size selection; Blue: amplifications; Orange: deletions: Grey: copy number neutral regions. Figures and Figure Legend from Mouliere et al. (14)

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5.3.3: Discussion

In Mair et al., a rat xenograft model of glioblastoma was used to study fragmentation patterns of human tumour-specific ctDNA, and rat cfDNA, demonstrating that the ctDNA had shorter fragments, around 147bp in length. This is in agreement with previous observations that tumour-specific DNA has a different size profile (37,38,40). Following these findings, Mouliere et al. used size selection to enrich for tumour-specific DNA, enabling the detection of clinically-relevant SCNAs that had not previously been observed. Analysis of fragment sizes on a pan-cancer scale showed that plasma from ‘high ctDNA’ cancers showed an increased proportion of fragments <150bp, compared to healthy controls and ‘low ctDNA’ cancers. The ranking order was very similar to that observed by Bettegowda et al. who analysed the number of mutant fragment across different cancer types (42). Importantly, unlike the Bettagowda study, no prior knowledge of specific mutations was required to generate this data.

Two novel approaches were developed to analyse sWGS data. t-MAD was used to analyse sWGS data to quantitatively assess copy number data and levels of enrichment on a genome-wide scale. Results showed that t-MAD correlated with tumour volume and mutant allele fraction down to 0.01. Secondly, machine learning was used to build a model incorporating t-MAD scores and size fragmentation features to predict the presence of ctDNA, with the RF model shown to be the most predictive. These studies should be repeated on a larger scale and require further analysis to determine if other biological features of cfDNA can be incorporated to enhance detection in early stage disease or MRD. The ultimate goal for non-invasive cancer diagnostics is to enable cancer to be detected earlier, when patients can be treated with curative intent and improve survival. Size selection and use of machine learning may potentially help in the earlier detection of cancer or MRD. Several studies have used sensitive methods to study whether ctDNA can be a prognostic biomarker in these cohorts. Garcia-Murillas et al. used dPCR to analyse ctDNA to detect MRD in a prospective cohort of early-stage breast cancer patients receiving neoadjuvant chemotherapy (130). Analysis of post-surgical plasma specimens showed that detection of ctDNA was prognostic, predicting relapse with high accuracy in a single post-surgical plasma or in serial follow-up samples (HR: 25.1; CI: 4.08-130.5; HR: 12.0; CI: 3.36-43.07; log-rank p < 0.0001, respectively). Chaudhuri et al. demonstrated that CAPP-Seq could be used to detect MRD in the first post-treatment plasma sample in 94% of

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Stage I-III NSCLC patients treated with curative intent who went on to relapse (131). Tie et al. used Safe-SeqS to detect ctDNA in 7.9% of Stage II colon cancer patients not treated with chemotherapy, 79% of whom relapsed within a median of 27 months. In contrast, only 9.8% of patients with no detectable ctDNA subsequently relapsed (HR 18; 95% CI: 7.9 – 40; p < 0.001) (132). Phallen et al. used TEC-Seq (targeted error correction sequencing), involving deep sequencing of a 58-gene panel, to detect ctDNA in 71%, 59%, 59% and 68% of patients with Stage I-II colorectal, breast, lung or ovarian cancer, respectively (133). More recently, Cohen et al. developed CancerSEEK, a multi-analyte blood test designed to detect both ctDNA, using a 61-gene panel, and 8 clinically-used protein biomarkers to analyse patients with eight common cancer types (ovarian, liver, stomach, pancreatic, oesophageal, colorectal, lung, breast cancer) (134). Analysis of 1005 Stage I-III cancer patients showed a median sensitivity of 70% (p < 10−96 _{one-sided binomial test; range: 33%-}

98% in different cancers types), with a specificity >99%. The unique aspect of this test is the use of multiple analytes to enhance detection of cancer. This multi-modal approach shows promise, and should be explored further to enable early detection of cancer.

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Chapter 6: Summary

This thesis represents nearly 10 years of research investigating the diagnostic potential of circulating tumour DNA, and assessing the hypothesis that ctDNA is a clinically useful biomarker able to monitor tumour dynamics, correlate with disease burden, and be used to guide treatment. When I initiated this work in 2009, ctDNA was known to be present in the plasma of cancer patients, but its clinical relevance had not been fully ascertained.

In Parkinson et al., I was able to demonstrate for the first time that ctDNA levels correlated with tumour burden in high-grade serous ovarian cancer (1). Using patient-specific digital PCR assays, mutant TP53 ctDNA levels were monitored over time. Response to

chemotherapy was seen earlier with ctDNA than CA-125, and pre-treatment TP53MAF was shown to be associated with time to progression. These studies demonstrate the potential of ctDNA in HGSOC as an early response marker. Additional dPCR analysis of specimens from a HGSOC patient with high clonal expansion demonstrated an NF1 deletion was already present in subclonal populations prior to treatment (2).

The development of TAm-Seq, an amplicon-based sequencing assay, demonstrated for the first time that next generation sequencing could be used to non-invasively identify low- frequency mutations in cfDNA, and be used to monitor multiple mutations in parallel (3). This opened up the possibility to use NGS for the detection and monitoring of ctDNA. TAm- Seq was subsequently used to analyse plasma specimens from ovarian, breast and lung cancer patients and demonstrate its ability to monitor tumour dynamics (3,10,11). Murtaza et al. provided the first demonstration that exome sequencing could be used to identify potential mechanisms of resistance in plasma (4). Analysis of multiple specimens from an ER+ve, HER2+ve breast cancer patient demonstrated that plasma DNA can non-invasively reflect the tumour genome, and be used to study clonal evolution (5). Furthermore, Dawson et al. were able to demonstrate that ctDNA had greater correlation with tumour burden than CTCs and CA15-3, and often provided the earliest measure of treatment response in patients with metastatic breast cancer (10).

To enable patients to have access to TAm-Seq technology, I , together with my colleagues, co-founded Inivata, which analyses ctDNA to improve patient healthcare in oncology. I led the development and analytical validation of enhanced TAm-Seq™ technology to

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0.25%-0.33% AF, with mutant alleles detected down to 0.02% AF (7).Remon et al. demonstrated the clinical utility of this assay in the first prospective study of a cohort of EGFR-mutant NSCLC patients treated with osimertinib based on plasma profiling alone (9). Results showed good response rates, with 62.5% with partial response, comparable with tissue-based testing. Further enhancements led to the development of the InVisionFirst- Lung™ assay, able to perform comprehensive genomic profiling of ctDNA, with detection of ALK and ROS1 gene fusions in addition to SNVs, indels and SCNAs (8). This assay has

received a final Local Coverage Determination by Palmetto GBA to be used as a plasma- based test for patients with Stage IIIB/IV NSCLC. This is a major milestone in getting the assay reimbursed for use in the US, and only the second NGS ctDNA assay to have achieved this status.

Further work evaluated different pre-analytic factors that may affect ctDNA levels in plasma collected in different blood tubes and using different processing conditions (6). In addition, analysis of cfDNA fragmentation patterns in a pan-cancer study and in a rat xenograft model demonstrated that tumour-specific DNA is shorter than cfDNA (12–14). Furthermore, size selection and use of machine learning algorithms, incorporating size fragmentation features, enhanced detection of ctDNA and identified clinically-relevant SCNAs.

This has been an exciting decade to be involved in ctDNA research. In this time, I have seen it advance from academic research to clinical implementation for patient benefit.

Encouragingly, the first companion diagnostic based on plasma profiling has now been approved for use to guide treatment with erlotinib and osimertinib for patients with advanced NSCLC. The challenge is now to improve the sensitivity of detection to enable early diagnosis of disease, at a stage when patients can be treated with curative intent, whilst ensuring assays have high specificity to limit detection of false positives. Studies by Cohen et al. demonstrate improved sensitivity of detection by incorporating both ctDNA and protein biomarker analysis (134). The use of multi-analyte assays, incorporating a combination of ctDNA and methylation, mitochondrial DNA, RNA, CTCs, exosomes, tumour- educated platelets (135) and/or protein biomarkers for example, may hopefully go some way towards achieving the ultimate goal of detecting cancer early and improving survival rates.

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