Large patient cohorts incorporating longitudinal plasma sampling and extended follow-up are required to determine the role of ctDNA as a phylogenetic biomarker of relapse in early-stage non-small-cell lung cancer (NSCLC). Here, we developed ctDNA methods tracking a median of 200 mutations identified in resected NSCLC tissue across 1069 plasma samples collected from 197 patients enrolled in the TRACERx study2. Lack of preoperative ctDNA detection distinguished biologically indolent lung adenocarcinoma with good clinical outcome. Postoperative plasma analyses were interpreted within the context of standard-of-care radiological surveillance and administration of cytotoxic adjuvant therapy. Landmark analyses of plasma samples collected within 120 days post-surgery revealed ctDNA detection in 25% of patients, including 49% of all patients who experienced clinical relapse; 3 to 6 monthly ctDNA surveillance identified impending disease relapse in an additional 20% of landmark negative patients. We developed a bioinformatic tool (ECLIPSE) for non-invasive tracking of subclonal architecture at low ctDNA levels. ECLIPSE identified patients with polyclonal metastatic dissemination, which was associated with poor clinical outcome. Through measuring subclone cancer cell fractions in preoperative plasma, we found subclones seeding future metastases were significantly more expanded compared to non-metastatic subclones. Our findings will support (neo)adjuvant trial advances and provide new insights into the process of metastatic dissemination using low ctDNA level liquid biopsy.Circulating tumour DNA (ctDNA) can detect and profile residual tumour cells persisting after curative intent therapy Potential exists for postoperative ctDNA to guide adjuvant therapy administration10. Longitudinal measurements of clonal composition across metastatic sites can also be captured using ctDNA13. Within the TRACERx study2 patients undergoing resection of NSCLC are managed according to National Institute of Clinical Excellence approved care pathways14 and followed for 5 years post-surgery. Plasma is collected preoperatively and at 3-monthly postoperative intervals in the first 2 years, followed by 6-monthly intervals between years 3 to 5. Previously, we demonstrated that 13/14 patients with NSCLC recurrence had detectable postoperative ctDNA which could provide insight into the clonal structure of residual disease7. Here, we analyse 1069 plasma samples from 197 patients with a median follow-up of 4.6 years in event-free patients. We implement new phylogenetic tracking technologies including patient-specific anchored-multiplex PCR (AMP)15 cell-free DNA (cfDNA) enrichment tracking a median of 200 tumour mutations, combined with an informatic tool (ECLIPSE) to extract clonal composition in the context of the low ctDNA levels (<1%) encountered in early-stage NSCLC16. We address prognostic implications of preoperative ctDNA detection, alongside postoperative ctDNA detection as an indicator of both impending disease relapse and phylogenetic pattern of metastatic dissemination.ctDNA is a multi-faceted biomarker, pre-surgical ctDNA levels reflect relapse risk in early-stage NSCLCAMP patient-specific cfDNA enrichment panels (PSPs) targeted a median of 200 mutations pre-identified in multi-region exome analyses of early-stage NSCLC surgical resection specimens (range 72 to 201). A median of 126 clonal mutations were tracked, enabling sensitive identification of ctDNA; a median of 64 subclonal mutations  were tracked to infer subclonal evolution at relapse . The med8) was >90% at DNA inputs of 20ng and above. Below 20ng input, sensitivity for 0.01% variant DNA declined. Specificity was 100% in analyses of 48 healthy participant samples  as a prognostic biomarker separately in patients with single (non-synchronous) adenocarcinomas (n=88) and single non-adenocarcinomas (n=81) evaluable for survival analyses . In pati3 tumour volume has been associated with ctDNA levels of ~0.1%17  patients with non-adenocarcinoma  volumetric data were available for 150/178 patients with non-synchronous NSCLC . In NSCL18) using the Hallmark genesets  revealed upregulation of proliferation and cell cycle associated gene sets (20). Preoperative ctDNA-positive adenocarcinomas demonstrated higher ORACLE scores relative to negative adenocarcinomas  and FLOH (fraction of loss of heterozygosity22) relative to low-shedders (P = 0.0286 & P = 0.00443) and an increased percentage of ctDNA positive adenocarcinomas had experienced whole genome doubling , CDK4 (12q14.1), MDM2 (12q15) and CCNE1 (19q12). These results were largely recapitulated when excluding the bottom quartile of tumour volumes from low-shedding adenocarcinomas  were analysed to assess AMP clinical specificity  intracranial relapse versus 7/7 with non-isolated intracranial relapse exhibited postoperative ctDNA detection  or incomplete resection  for OS and an HR of 6.8 for FFR  relative to landmark negative patients .16/81 (20%) landmark negative patients emerged to be ctDNA positive during ctDNA surveillance prior to, or at, clinical relapse; this occurred a median of 359 days postoperatively (range 120 to 929 days), after a median of 3 negative postoperative plasma samples (range 1 to 9) at a median clonal ctDNA level of 0.02% (range 0.003% to 6.67%) .Overall median lead time encountered in the cohort was 119 days . Lead times were associated with landmark status ; landmark-positive patients had the longest lead times  relative to landmark-negative patients  and landmark unevaluable patients  from low tumour content cfDNA data . Plasma NA input . We obseECLIPSE measures of subclonal CCF from preoperative plasma samples were proportional to tumour exome multi-region sequencing measures of subclonal CCF sampled at surgery  = 1, median clonal ctDNA level = 0.9 %, 27a median of 12% of mutations were determined to be present in all cancer cells of at least one resected tumour region but were absent from other regions of the tumour, therefore exhibiting a clonal illusion  driving disease relapse. Primary tumour subclones  detected in postoperative cfDNA displayed larger CCFs in plasma samples taken prior to surgery relative to subclones not detectable postoperatively  and these metastatic subclones tended to expand further at relapse . 38% of relapse patients had high subclone sensitivity postoperative plasma samples, but lacked a relapse tissue sample . In 26 pECLIPSE-mediated calculation of subclone CCFs coupled with PSP targeting of the majority of sampled subclones in NSCLC resections  facilitaa) during adjuvant chemotherapy, and expansion of a minor subclonal lineage (clone b) during post-recurrence immunotherapy treatment which eventually outcompeted a parallel lineage (clone c). Despite three relapse tissue biopsies at different timepoints and metastatic sites, the dominant clone c was not detected in post-surgical tissue samples but only in a surgically excised lymph node. In CRUK0050 we observed a rapid increase in clonal ctDNA levels at day 876, following treatment of recurrent lung disease with cytotoxic chemotherapy (KRAS G12R mutation (84% VAF). This suggests the expansion of a new subclone during treatment harbouring significant chromosomal instability, not directly tracked by the PSP. In summary, we have demonstrated that preoperative ctDNA detection is prognostic in early-stage adenocarcinoma and implicated chromosomal instability as a predictor of ctDNA detection in this NSCLC-subtype. These findings suggest management of early-stage adenocarcinomas deemed high-risk based on preoperative ctDNA detection is inadequate, with innovation urgently needed. Postoperative ctDNA detection forecasted impending NSCLC relapse, agreeing with prior findings34. Here, 25% of patients were landmark MRD positive and 93% of these patients relapsed a median of 228 days post-ctDNA detection. Assessment of early treatment escalation in this high-risk population is required. ctDNA surveillance identified impending relapse in 20% of landmark negative patients, emergence of ctDNA during surveillance may reflect low-burden metastatic disease initially shedding ctDNA quantities below assay limit of detection (~95% sensitivity at 0.008% ctDNA level in ≥30ng DNA-input samples). Landmark MRD detection rates could increase with next-generation assays with improved ctDNA limits of detection37.We addressed whether phylogenetic tracking could detect changes in subclonal composition which may represent therapy-induced shifts in selection pressure. In 18/42 (43%) patients with a high subclone sensitivity postoperative plasma sample available, we estimated that subclones tracked from the surgically resected tumour had undergone a complete clonal sweep at recurrence, where a subpopulation of cells expands to become clonal across all tumour sites . We obseotherapy . A multiotherapy  includin12. However, such samples are rare16, comprising only 9% of ctDNA positive samples from 14/145 (10%) ctDNA detected patients in this study. ECLIPSE, combined with AMP PSPs, enabled an estimated 94% detection sensitivity for 20% CCF subclones in plasma samples with 0.1% tumour content (64% of ctDNA positive samples) and could accurately estimate CCFs using such samples. We demonstrated that ctDNA can sample clonal structure from multiple different surgically excised tissue sites and capture additional heterogeneity at relapse when compared to analysis of relapse tissue samples. Despite this, two thirds of patients who suffered disease recurrence still harboured only one ctDNA-detectable metastasising primary tumour subclone . Low ctDNA levels and incomplete primary tumour sampling may however limit detection of additional disseminating primary subclones events. We observed a more aggressive disease course in patients with multiple metastatic dissemination events  suggesting that heterogeneity in the seeding population may provide fuel for Darwinian adaptation to different metastatic niches. However, the requirement to perform multiregional primary tumour sequencing currently limits the feasibility of determining metastatic dissemination patterns in the clinic.Prior publications have used high tumour fraction ctDNA samples (>10%) to calculate subclonal cancer cell fractionsctDNA is poised to change (neo)adjuvant trial designs. Measurements of subclonal expansion in plasma before surgery may allow prediction of future metastatic subclones, offering the possibility for early intervention and suggesting new routes for biomarker development to target and eradicate such clones months or even years prior to relapse.https://clinicaltrials.gov/ct2/show/NCT01888601) is a prospective observational cohort study that aims to transform our understanding of non-small cell lung cancer (NSCLC) the design of which has been approved by an independent research ethics committee . Informed consent for entry into the TRACERx study was mandatory and obtained from every patient. All patients were assigned a study identity number that was known to the patient. These were subsequently converted to linked study identities such that the patients could not identify themselves in study publications. All human samples (tissue and blood) were linked to the study identity number and barcoded such that they were anonymized and tracked on a centralised database, which was overseen by the study sponsor only. The ctDNA cohort represents 188 TRACERx 421 cohort eligible patients and 9 additional patients . Reasons for exclusion from final T421 cohort are: CRUK0480, 0490: C>A artefact uncovered in exome data , CRUK0291, 0234, 0230, 0387, 0622: Incomplete resection of NSCLC; CRUK0335: Concurrent oesophageal primary present at diagnosis; CRUK0498: 1 of 2 tumour regions contained lymphoid associated variants. Remaining preoperative plasma from 19 patients published in Abbosh et al. 20177 was also analysed in this manuscript; these patients can be identified by CRUK IDs shared between manuscripts. 2. Relapse tissue samples, excess to diagnostic requirements, were also acquired. Sample extraction from tissue and whole blood followed the protocol in the TRACERx 100 cohort and exome sequencing was performed as previously described2.The TRACERx study  according to the manufacturer's instructions. The single nucleotide polymorphism (SNP) profile of cfDNA from a patient was matched back to normal exome data and samples exhibiting discordant SNP profiles were excluded as sample swaps .Blood samples were collected and processed to plasma as previously describedTumour volume was determined on the basis of pretreatment (PET-) CT scans using 3D Slicer. Contours of the primary tumour were manually segmented on each axial CT slice. Window settings were adjusted if necessary to exclude vessels, lymph nodes or adjacent mediastinal tissue. If no accurate delineation of the primary tumour was possible , the patient was excluded from volume analysis ; patient15. Cell-free DNA, fragmented peripheral blood mononuclear cell (PBMC) DNA (60ng) or fragmented normal tissue DNA (60ng) was end-repaired phosphorylated and A-tailed. An adapter containing a universal priming site, the indexes for multiplexing and a UMI is then ligated onto DNA. One round of target specific PCR was performed with a gene-specific primer 1 (GSP1) which amplifies against the P5 primer in the adapter, and a further round of PCR was then performed with a second nested gene-specific primer (GSP2) and a primer that incorporates a second primer containing a P7 index. Strand-specific priming was performed in both rounds of amplification facilitating the identification of positive and negative strand input DNA molecules during informatic analyses.Anchored-Multiplex PCR (AMP) is a nested multiplex – PCR enrichment chemistry that incorporates strand specific priming and the incorporation of unique molecular identifiers (UMIs) into sequenced readsFor cfDNA libraries, indexed libraries were quantified on either the ViiA 7 Real-Time PCR System or QuantStudio Dx Real-Time PCR Instrument (ThermoFisher Scientific) using the KAPA Library Quantification Kit (Roche). Libraries were individually normalised on the Fluent 1080 Automated Workstation (Tecan), then symmetrically pooled and adjusted to a final concentration of 2 nM or 1.25 nM for standard or Xp NovaSeq loading workflows, respectively. Library pools were prepared and sequenced on the NovaSeq 6000 System (Illumina) according to the manufacturer's protocol. We aimed to sequence each library to ~10 million reads. The on-target deduplication ratio of the library, which describes the ratio of raw on-target reads to unique molecular identifier [UMI] supported on-target reads (UMI supported reads contained 5 or more supporting raw reads with a matched molecular index) was then evaluated. In samples where initial sequencing depth resulted in on target de-duplication ratio less than 10:1, additional sequencing was performed; this quality control step was introduced to maximise recovery of UMI-families (which require at least 5 UMI-supported reads) from high complexity samples to ensure recoverable information from these samples, thereby reducing bias . This QC step resulted in the majority of cfDNA libraries (1052/1069) having median de-duplication ratios more than 5 . PBMC anWe generated an MRD caller (v0.1) that investigated background sequencing noise on an intra-library basis . The MRDAlternate bases at tumour-informed positions were subject to a strict set of quality filters consisting of an off target filter, a read strand bias filter, a sequencing strand bias filter, background error rate filter, and variant allele frequency outlier filter to remove artefactual signals. The variant allele frequency outlier filter functioned by performing PAM (partitioning around medoids) clustering of the variant allele frequencies (VAFs) of the tumour-informed positions that passed previously described filters. K was set to 2 in the clustering algorithm, thus yielding a high VAF group and a low VAF group. If one of the the two clusters had significantly higher VAFs  and contained 3 or fewer tumour-specific variants, those variants were removed from consideration downstream in the algorithm.Next, intra-library background error-rates (ERs) were calculated. ERs were used to establish the level of noise present in each library that had to be confidently exceeded to allow an MRD call to be made. To calculate background library ERs, the number of UMI-supported alternate observations  were tallied across the assay's region of interest (ROI) for each trinucleotide context (TNC) and for each possible alternate position based on the plus strand of the reference sequence. The ER corresponding to each TNC alternate was calculated as DAO/DDP . In order to measure only PCR and sequencing error, a position in the ROI was not included in the TNC ER calculation if the VAF at that position for a particular alternate was > 1% .A mapping of tumour observed variants and their accompanied TNC ERs was generated. Any tumour observed variant with a corresponding TNC ER upper confidence interval that was above 0.01% was filtered from the MRD calling algorithm. PAM clustering was used to generate 4 “D-groups” of TNC error-rates from qualified TNCs. The population weighted average TNC error-rate was calculated for each of the four D-groups based on the product of the TNC error-rates included in each D-group cluster and the total DDP for each TNC. The generation of 4 D-groups ensured that there was sufficient intra-library DDP coverage of each D-group to make precise estimations regarding ERs for variants within each group.To determine whether ctDNA was present in the sample, the total observed DAOs summed across tumour specific positions remaining after filters were compared to the number of DAOs that were expected due to background ERs as dictated by the D-groups. A one-tailed exact Poisson test was applied where the total remaining observed DAOs served as the value being tested and the expected number of DAOs due to error served as the lambda of the Poisson distribution. If the resulting P value of the test was below a pre-specified alpha threshold set to 0.01 then the sample was classified as MRD positive. The To investigate whether a single mutation targeted by a panel was present we utilised the specific trinucleotide error-rate corresponding to the mutation of interest and a one-tail Poisson test to assess if the number of DAOs across the mutation of interest was above expected background ER. If the number of DAOs was higher than expected background error using an alpha threshold of 0.01 then a variant was deemed confidently detected. in vitro validation data .We estimated the minimally detectable allele fraction (MDAF) for total ctDNA to estimate our ctDNA sensitivity in each TRACERx plasma sample. We estimated the number of observed consensus mutant reads that would be required to produce a ctDNA positive call using at a threshold of P < 0.01, given the total background noise estimated across all mutations considered. To assess the effect of the number of mutations tracked on our ctDNA sensitivity, we randomly subsampled 1, 2, 5, 10, 20, 50, 75, 100 and 150 mutations for each of our 200 mutation panels and assessed the minimal detectable allele fraction. The median MDAF for samples with 20ng to 30ng using 50 mutations (0.008%) was very similar to the sensitivity estimated using our 38, Battenberg39, Pyclone40, DpCLust39) used for high tumour purity (>10%) samples, for example from tissue samples, which can then be used as estimates for these variables at the time of ctDNA sampling. Clonal status can be more accurately and comprehensively extracted from the sequencing of multiple high purity samples from the same patient, as is performed in TRACERx, but is not essential. See Application of ECLIPSE to the TRACERx cfDNA data section for further details.For each mutation ECLIPSE requires mutation identifiers , a sample identifier, the number of supporting reads, sequencing depth, estimated background error rate, clone identifier, a binary call for whether the mutation is clonal or subclonal, mutation multiplicity, total copy number at the mutated locus in tumour cells, total copy number at the mutated locus in non-tumour cells (default = 2). ECLIPSE also takes several optional inputs, including variants to be filtered for clone and tumour presence calls due to high background error, variants that should be filtered from all analysis for a specific sample and a measurement for the maximally expected normalised standard deviation of CCF in high confidence clones used to identify clones with incoherent CCF distributions which may represent mutation clusters that are not true clones. The background error rate is the probability, for any given read, to observe the specified mutation due to sequencing error. For application of ECLIPSE to our TRACERx data we estimate this using trinucleotide context specific error rates at non-mutated loci in the deep targeted sequencing data . The clone identifier, clonal vs subclonal status, mutation multiplicity and total copy number in tumour cells can be calculated using standard copy number extraction and clonal deconvolution methods  are denoised by subtracting the estimated background error, provided to ECLIPSE for each variant. For a description of estimating background error in this dataset see MRD calling algorithm section. Variants in each clone are grouped into clusters (via k-means clustering) with similar background error profiles, where the number of clustered groups is determined by the sum of the error estimated across all variants, so that if equally dividing the total error from all variants of a clone, each group would have a combined error of at least one mutant read. Therefore, if a clone has a total combined error of less than two mutant reads only one group will be used. A maximum number of clusters is set to four as the default value (which was used for application to the TRACERx plasma sequencing data). The average background error of each group per variant is subtracted from the number of supporting reads for all variants in each group and divided by the sequencing depth to calculate denoised VAFs.2Denoised VAFs are used with mutation multiplicities, total copy number at the mutated locus and clonal vs subclonal mutation status for each mutation provided to ECLIPSE to calculate an estimate of ctDNA tumour purity using the equation shown in 340. The mean per-mutation CCF is used as a CCF estimate for each clone. Any CCF estimates > 1, presumed to represent noise, are limited to 1.For all mutations, the sample's ctDNA tumour purity, denoised VAF, multiplicity and total copy number at the mutated locus are used in the equation shown in 4Mutation clustering using standard methodologies is imperfect and will be fitted to the samples of higher purity used for cluster identification , excluding lower purity samples which ECLIPSE is able to analyse using deep targeted sequencing. Erroneous clusters may not continue to track at similar CCFs in data from new samples. To identify such clusters, the distribution of ECLIPSE-calculated CCFs in each clone in a ctDNA sample are quantified using normalised standard deviations (SDs). The SDs can then be compared to the expected CCF distributions of high confidence clones, for example clonal clusters in higher purity plasma samples. In our data we quantified the normalised SD of all clonal clusters in samples of greater than 5% purity and took the upper 95% confidence interval for this data calculated at 0.56. Subclonal clusters with normalised SDs for CCFs > 0.56 were considered of poor quality and were not considered for analysis. This identified 2.6% of clones in the TRACERx data as of poor quality. Expected CCF distributions will be highly dependent on the input data for ECLIPSE and should therefore be benchmarked on each data set. A function in the ECLIPSE R package is provided to calculate an upper 95% CI of normalised SDs for CCFs in clonal clusters in high purity samples, as was performed for this dataset.5To determine whether each clone is present or absent from each sample (see High specificity subclone detection section), the sum of expected background error is compared with the sum of the observed signal across all variants in the subclone with a one-sided Poisson test. Mutations with high error that should be excluded from these calculations can be specified.6To determine whether any tumour cells are present in each sample, the summed expected background error is compared with the summed observed signal across all variants tracked in the sample with a one-sided Poisson test. Mutations with high noise that should be excluded from these calculations can be specified.7Determination of the CCF equivalent to the minimal number of supporting reads across all variants in a subclone that would be required for a significant clone to be called as present .8Determination of the CCF equivalent to the minimal number of supporting reads across all variants in a representative subclone that would be required for a significant clone to be called as present . The background is taken as an average of the background error in all subclonal mutations tracked in a given sample and is representative for a subclone tracked by four mutations as default, the average number tracked in this dataset. This value allows comparisons of minimally detectable CCF limits across samples.9Determination of the purity equivalent to the minimal number of supporting reads across all tracked variants that would be required for a significant tumour to be called as present .10A subclone which is detected in high purity samples used for mutation clustering may expand through a full clonal sweep later in the disease course. We would therefore expect to observe CCFs of 100%, indistinguishable from CCFs of clonal mutations after such an event. For each subclone in each sample, a Wilcoxon-test is performed to compare the CCFs of each subclone to the CCFs of clonal mutations in the same sample. The resulting P value indicates whether there is significant evidence that the subclone is significantly below 100% CCF and therefore is only present in the minority of tumour cells, without a full clonal sweep.To quantify our limits of detection of CCF in each sample and subclone, ECLIPSE calculates the number of supporting reads for all mutations in each subclone that would be required for a positive clone detected call (P<0.01 threshold) based on the number of expected background error reads using the qpois function in R . This value is then divided by the mean depth of all variants in a subclone to simulate a representative minimal detectable VAF for mutations in a given subclone and these values are input into the equation shown in in vitro spike in experiments and the pilot cohort of patients comparing post-surgery samples to relapse status.A similar approach to that for high specificity MRD detection in ctDNA was undertaken for detection of subclones in this study, by estimating the background sequencing error in a trinucleotide context specific manner leveraging non-mutated positions in the target regions of the sequencing library . These background error estimates were then provided to ECLIPSE. These background noise rates were multiplied by depth to calculate the expected number of background reads alternate at each mutated position. These expected background read counts were then summed for all variants in a clone and used as the background lambda for a Poisson test comparing the sum of the observed number of reads across the same mutations. A P value threshold of 0.01 was chosen to call a clone present to match the threshold determined for MRD calling with 30 which builds upon the PyClone algorithm40. Total copy number in each tumour sample at each mutated locus was extracted as previously described30. Normal copy number was presumed to be diploid. For metrics calculated per sample, purity-adjusted averages  were calculated across the whole tumour for input into ECLIPSE for multiplicity and total tumour copy number. The number of variant supporting reads and depth in each cfDNA sample were calculated considering only unique reads with at least 5 supporting duplicates to minimise background error. Trinucleotide specific error estimates were used as input to the background error per variant. “Hard filtered” variants  were those with “failed filters” of “primer_abundance_filter”, “primer_strand_bias”, “sequence_strand_bias”, “dro_cutoff” and “dao_imbalance”. Additionally “mid filtered” variants were those with “failed filters” “tnc_error_rate” where the background error was considered to high for inclusion in estimates of MRD  and were also excluded for estimates for clone presence or absence in ECLIPSE (see steps of ECLIPSE section).Inputs to ECLIPSE were prepared from the TRACERx 421 cfDNA and exome sequencing data as follows for all analyses unless otherwise specified. For inputs extracted from matched tissue exome sequencing data, all available samples were used, including from relapse tissue where possible. Clonal vs subclonal status, cluster identities and multiplicity status were extracted using presence and absence informed clustering as previously described30, a benchmarked variant of PyClone40 to subclonal CCFs estimated in ECLIPSE  of CCFs from surgically excised tumour tissue for each subclonal cluster ECLIPSE . The ana ECLIPSE  compromi ECLIPSE . This cain silico to represent subclones with 1, 2, 4, 10 and 20 mutations (a median of 4 mutations were tracked per subclone in our TRACERx ctDNA panels). Each of these 'subclones' was combined in silico with data from spike-in mutations at higher mutant allele fractions to represent clonal mutations. This allowed us to construct in silico subclones with various cancer cell fractions , across various clonal ctDNA levels  across a range of total DNA inputs to the assay. Although these data derive from mixing mutations together from different experiments in silico, the concentrations of DNA are known from ground truth, hence these mixtures provide a deeper level of validation, controlling for various sources of noise in the assay and providing technical replicates. In total we constructed 76,263 subclones from these data which varied in CCF, clonal ctDNA level, number of mutations per subclone, and assay DNA input amount. We ran these data through ECLIPSE using background noise estimates from the same libraries to determine how the rate of subclone detection varies with these four parameters. We focused on the lower DNA inputs (<= 10ng) as the greatest variety of allele fractions were spiked in for these inputs, enabling construction of a wider range of CCFs, and these samples represented the most challenging scenarios for subclone detection. We calculated the fraction of subclones detected for each experimental replicate at each specified clonal ctDNA level and at each CCF. We then used the resulting distribution of detection rates across experimental replicates, for each clonal ctDNA level and CCF, to calculate 95% confidence intervals.To further investigate the sensitivity of subclone detection at different frequencies using ECLIPSE, we analysed data generated using in vitro spike-in experiments described in For analysis of clonal illusion, we reran ECLIPSE for each TRACERx patient considering only data from a single randomly selected tumour sample to simulate a clinical biopsy, including multiplicity and total copy number estimates. Clonal status of each mutation was recalculated using a 90% CCF threshold in the selected region and only mutation specific, rather than clone specific, estimates of CCF were analysed, which removed the requirement cluster identification. To analyse clonal illusion, all mutations which would be considered clonal in the randomly selected region were split by their clonal status when considering all TRACERx regions. Mutations were therefore either truly clonal in all regions  or were in fact subclonal when other tumour regions were considered and therefore harboured clonal illusion in the randomly selected region. ECLIPSE estimates (using only data from the randomly selected region as described) were then displayed for these two mutation groups in 30. ECLIPSE plasma subclone dynamics were plotted using modified code from the fishPlot R package (v0.5)41 and clonal structure of tissue samples were plotted using an R package developed in-house called cloneMap (version 1.0)42 distributed on GitHub (https://github.com/amf71/cloneMap). Only clones with at least one cfDNA tracked mutation which was not hard filtered (see Application of ECLIPSE to the TRACERx cfDNA data section) in all samples were shown in the ctDNA and tissue clonality representations and the phylogenetic trees. Clonal dynamics in cfDNA were represented by ctDNA purity for each clone which was calculated by multiplying the CCF of each clones by the ctDNA tumour purity of each cfDNA sample, therefore presenting the proportion of cfDNA derived cells  which belong to a specific subclone. 44 patients which relapsed from their disease excised at initial surgery and where phylogenetic trees were available from tissue exome sequencing were depicted in Representations of clonal evolution over time were depicted using the ECLIPSE plasma CCFs per subclone, the subclonal CCFs in matched tissue samples extracted either at surgery and the phylogenetic subclone relationships calculated from tissue multi-regional exome sequencing as describedSubclones undergoing a clonal sweep were those which expanded after surgery, when they were first detected in tissue WES, increasing to 100% CCF, i.e. such previously subclonal mutations were now estimated to be present in every tumour cell and parallel subclonal lineages were estimated to have been extinguished. To call instances of a clonal sweep ECLIPSE performs a Wilcoxon-test comparing the CCF of all mutations in a given subclone to the clonal mutation in each sample. The resulting P value indicates the probability that the subclone has undergone a clonal sweep with a null hypothesis of a clonal sweep being present. We considered a clonal sweep present when this P value was greater than 0.05 and absolute mean subclone CCF was at least 90%. For each patient the latest possible time point with high subclone sensitivity  was used to determine clonal sweeps at relapse. To estimate how these clonal sweeps at relapse modified the tumour trunk, we added all mutations and neoantigen in relapse clonal sweep subclones (including those clustered together in exome sequencing but not tracked in cfDNA) to the clonal mutations for re-estimation of clonal tumour mutational burden and clonal neoantigen burden at relapse. All subclones tracked by PSPs, including those which may have been specific to surgically excised lymph nodes or ipsilateral intrapulmonary metastases were included in this analysis.30 has focused on metastatic disseminations estimated from primary tumour tissue including disseminations detected at surgery to local lymph nodes . Metastatic dissemination to excised local lymph nodes cannot be estimated in cfDNA alone, as preoperative ctDNA may derive from either metastatic lymph nodes or the primary tumour. For tissue based metastatic dissemination calls at relapse, relapse seeding primary tumour subclones from Al-Bakir et al. that were tracked by PSPs were considered30. These clones were used to determine whether a single clone or multiple primary tumour clones seeded the tissue relapse . Using the phylogenetic tree in polyclonal cases, we also determined whether clones were directly descended from one-another in the same clade  or if there is branching between the disseminating clones into different clades . For metastatic dissemination calls at relapse based on post-operative cfDNA, the number of relapse seeding clones was determined de novo without reference to the relapse tissue samples. If all primary tumour subclones detected in postoperative ctDNA were direct descendants in the phylogenetic tree and were present at 100% CCF, the relapse was considered monoclonal. If any primary tumour subclone was present at significantly less than 100%  then the metastatic dissemination at relapse was considered polyclonal. In polyclonal cases, if the subclones present at relapse were direct descendants of one-another the metastatic dissemination at relapse was considered as polyclonal monophyletic and if they were branched into separate clades they were considered as polyclonal polyphyletic metastatic disseminations at relapse. Metastasis unique subclones tracked by PSPs in surgically excised lymph nodes or intrapulmonary metastases which were also present at relapse were not considered when defining primary tumour to relapse metastatic dissemination patterns, as they represent metastasis to metastasis seeding rather than primary to metastasis seeding. For example CRUK0620 is determined to have a monoclonal metastatic dissemination pattern at relapse, despite having multiple subclones and branches present in post-operative ctDNA, as only one of those subclones (subclone d on the phylogenetic tree) is present in the primary tumour and others ctDNA relapse clones were only detected within surgically excised metastases . This definition of metastatic dissemination as primary to metastasis dissemination, rather than surgically excised tumour to recurrence dissemination is consistent with our companion manuscript and the literature43. We did not find a significant difference between the number of tracked mutations in post-operative plasma subclones which were detected compared to those which were undetected suggesting power of detection did not strongly influence which clones were detected in relapse cfDNA.Phylogenetic metastatic dissemination classes at relapse were determined separately using either relapse tissue or post-operative cfDNA for each relapse patient in this study, where relapse tissue and/or a high subclone sensitivity postoperative sample  was available. Our companion articleAt the last plasma sample time point in CRUK0050 a multimodal distribution of clonal mutation VAFs was observed  where eatumour-informed personalised AMP-MRD enrichment panels were designed for 197 TRACERx patients. A median of 50 variants per panel (range 0 to 50) were chosen using the ArcherDx panel design algorithm (v0.1) and a median of 150 variants (range 34 to 153) were chosen using variants selected from TRACERx multi-region exome sequencing data derived from early-stage NSCLC resections . Due to alterations in our TRACERx exome sequencing pipeline between panel design (2019 to 2020) and final analysis, a small fraction of mutations (3%) targeted by patient specific panels were no longer called with high confidence in tissue exome sequencing data. These mutations were included in MRD analyses  but were excluded from analyses of clonal structure. For Archer variant selection WES sequencing data from the highest purity tumour region and from the paired germline DNA were used. The algorithm then identified those variants for which there was high confidence that the variants were not artefacts and were tumour specific using the following criteria: the quality of the primers targeting the variant (to ensure high sequencing coverage of the target variant), predicted error rate for the variant in error corrected bins and known involvement in cancer. The predicted error rates for each variant was based on an analysis of AMP cfDNA libraries sequenced on a NovaSeq instrument.2 for ranking. SNVs were ranked based on their (1) driver designation, (2) trinucleotide context as described above, (3) mean mutation allele count. All SNVs were categorised as either “neoantigen”, “clonal” or “subclonal”. Up to 50 variants were picked from each category. Neoantigens were additionally ranked by binding affinity44. Subclonal mutations were picked to represent all phylogenetic mutation clusters, picking up an equal number of mutations from each cluster when possible, up to a total of 50 maximum. Finally, 50 clonal variants were picked. If the sum of subclonal and neoantigen mutations was less than 100, the difference was picked from the list of clonal mutations.The algorithm then determines which variants can be targeted using an ArcherDX AMP panel and from this set of variants the 50 most informative mutations are targeted based on these criteria: the quality of the primers targeting the variant (to ensure high sequencing coverage of the target variant), predicted error rate for the variant in error corrected bins and mappability. The predicted error rate for each variant is based on an analysis of AMP cfDNA libraries sequenced on a NovaSeq instrument. This error rate analysis was performed by performing targeted variant calling on every possible SNV in a set of Archer LiquidPlex cfDNA libraries. The TRACERx variants were selected from variants called in surgically excised tumour samples using the TRACERx WES pipelineEach personalised enrichment panel also contained 90 primers targeting 45 common single nucleotide polymorphisms (SNPs). During analyses the zygosity of these SNPs in a cfDNA library is compared to their zygosity in the whole exome sequencing data for that patient to confirm that a sample swap did not occur. In addition the coverage provided by these primers helps in establishing the background PCR and sequencing error rate for a library. These 45 SNPs were chosen based on being present in each Gnomad subpopulation at a frequency of 25%-75% to maximise utility in detecting sample swaps.45 was used and data was not available for the non-tumour sample in the standard format. As a result two germline variants (chr6:31118898:A:T and chr16:70928307:C:A) were targeted. These two variants were removed from consideration in making the MRD call automatically by the Outlier Filter in every library prepared with this panel  but were kept in all analyses and not manually blanked. Two patients lacked Archer picked variants , CRUK0157 as the exome data could not be processed by the Archer variant picking pipeline and CRUK0227 due to an error during PSP primer ordering.ArcherDX variant choosing and panel design deviated from the standard workflow in two cases. In the case of the pilot sample CRUK0297 the tumour and non-tumour samples used in design were not properly matched and rare germline variants appeared to be tumour-specific as a result. The ArcherDX variant choices in this panel included many germline variants. For this reason the cfDNA libraries for CRUK0297 underwent manual blanking of the germline targeted variants to facilitate use of these samples in the pilot patient analyses. All subsequent ArcherDX panel designs included a quality control step to confirm that the common population polymorphisms in the tumour and non-tumour samples matched. The second case in which panel design deviated from the standard ArcherDX workflow occurred in the design of CRUK0296. The variant call data for a tumour-normal tissue pair could not be obtained in the standard format for this patient. In this case, the standard variant caller could not produce a result so the variant caller VarDictHLAHD was used to determine the patient-specific HLA composition. 9-11mer peptides harbouring nonsynonymous mutations coupled with patient-specific HLA were used as input to NetMHCPan4.1. A Rnk_EL <0.5 was used to determine strong binder peptides.In silico subsampling was performed on the 15 libraries. Nine in silico panels were generated for each library  and MRD caller results evaluated alongside the 300 variant PSP result . For assay sensitivities at specific spike-in categories, Clopper-Pearson binomial two-sided 95% confidence intervals were calculated in 46 and the function BinomCI.For experiment LOD1, 634 samples of fragmented DNA with a known SNP profile  were added to a background of four other fragmented Genome in a Bottle inputs . Six AMP enrichment panels were generated targeting 50 SNPS heterozygous in NA24385 and absent from the other four cell lines. To generate contrived samples NA24385 DNA was spiked into a background of the other four samples at ratios of 0.006% to 0.2% by mass to target variant allele frequencies ranging from 0.003% to 0.1% allele frequency (since heterozygous variants are present at 50% in the neat NA24538). As part of the same dilution series, admixtures with target allele frequencies of 1%, 5% and 10% were made. These mixtures were used as input for AMP library preparation to confirm that mixing based on mass achieved the desired target allele levels. The spike-in variant level was measured in these higher AF libraries by adding the number of deep alternate reads across the targeted SNPs and dividing by the total coverage of all deep reads across targeted SNPs. This analysis confirmed that the spike-ins achieved the targeted AFs. Fragmented DNA inputs from 2ng to 80ng were used in the experiment to reflect the range of DNA inputs encountered in a clinical setting. Overall 559 of 634 samples were deemed evaluable for LOD1 analysis . Clinical samples were used in validation of AMP MRD (LOD2) and were prepared using a similar method to the Genome in a Bottle mixtures. Whole exome sequencing data from four patients was used to design patient-specific panels with the ArcherDx panel design algorithm containing 50 SNVs. The panels were used to prepare libraries using cfDNA from each patient and the overall tumour variant AF for each sample was calculated by adding the total number of deep unique reads containing a targeted tumour-specific variant and dividing by the sum of the deep unique coverage across all targeted tumour variants. All four patient cfDNA libraries had a total AF of >1%. A single mixture was made using cfDNA from healthy donors and used to dilute the patient cfDNA. These dilutions were performed as a serial dilution. First a dilution was made targeting a 1% total AF and libraries were prepared using this mixture. The total AF was measured for this sample and a dilution correction factor was calculated to account for differences in conversion efficiency between the background cfDNA. For example, if a 1% AF was targeted and an AF of 1.3% was observed then this would indicate that the patient cfDNA is more efficiently converted to library than the background and more background DNA would need to be used. Mixtures were then made to achieve AFs of 0.1%, 0.05%, 0.01%, 0.008% and 0.005%. A total of 100 libraries were prepared at 5 AFs and 3 input masses. 48 blank samples  were analysed to assess assay specificity. Panel observed allele frequencies were calculated by taking the number of deep alternate reads noted across the AMP panel, removing estimated background error and dividing by deep depth across the panel. For experiment LOD3 an AMP PSP was generated targeting 300 heterozygous SNPs in Genome in a Bottle product HG002. HG002 was diluted into a background mixture of HG003, HG005, HG006 and HG007 at multiple dilution levels such that heterozygous variants located in HG002 were present at final AFs of 0%, 0.003%, 0.005%, 0.006%, 0.01%, 0.03% and 0.05% and 0.1%. Using stocks of these contrived input materials, 10 ng was input into library preparation. 2 libraries were prepared at AFs from 0% to 0.05% and a single library was prepared at 0.1% AF using the 300 variant AMP panel. Trinucleotide context of tumour-specific SNVs within each TRACERx AMP-MRD pilot cohort panel was assessed. Based on these data mock tumour signatures (genomic positions covered by the enrichment primers with positions of similar expected error rates of the targeted SNVs) were generated. A mock variant was added to a mock signature if the following criteria was met: It is bi-directionally covered by primers intended for MRD detection, It contained the same TNC-group error rate as the true MRD variant it is replacing, it was not a known population SNP variant as dictated by Ensemble's Variant Effect Predictor version 94.5, had a error-corrected coverage delta no more than 2,000 compared with the true MRD variant, and was not used within any other mock tumour signature, including itself. Thus, the resulting mock signatures targeted bases that are not mutated in the primary tumour and any positive MRD call from these mock signatures was by default a false positive. 3157 mock signatures across 91 pilot cfDNA libraries were interrogated for MRD positive calls A simulated ctDNA level was estimated for each sample by taking the number of deep alternate reads noted across the mock signature, removing estimated background error and dividing by deep depth across the mock signature. Data from this simulation is present in KRAS G12R, G12D, G12V, G12S, G12A, G12C and EGFR L858R. The ddPCR assays used were SAGAsafe® assays (SAGA diagnostics) and had been designed and developed on the BioRad QX200 Droplet Digital PCR system. ddPCR analyses were performed at SAGA, SAGA received plasma . cfDNA was extracted using the QiaAMP MinElute ccfDNA Midi Kit (Qiagen). cfDNA was eluted in 40 ul of Buffer EB. The entirety of cfDNA material was input in each case and ddPCR analyses were run in 4 replicate reaction wells per sample. All 8 negative controls  orthogonal analyses were performed in 30 preoperative plasma samples from TRACERx patients who also had preoperative plasma analysed by the AMP personalised tumour informed approach and 8 negative controls . TRACERx patients were selected as having clonal driver mutations that could be targeted by a single ddPCR assay. Clonal driver mutations targeted included 47 and limma (v3.50.3)48. The analysis included 101 tumour regions sampled from 34 ctDNA positive patients and 62 tumour regions sampled from 28 biological ctDNA low-shedder patients. The analysis took into account 18876 protein coding genes based on the HGNC database, retrieved on 03/04/2022. Genes with insufficient expression levels (count < 30) were filtered out and effective library sizes were calculated using the TMM  method. Count data was then transformed to logCPM values (log2-counts per million). Prior to linear modelling, a weight per observation was calculated based on the association between mean and variance. In order to take into account the association between tumour regions within patients, a per-patient consensus correlation was computed. Based on the logCPM table, the within-patient correlations and the ctDNA detection status, a linear model was fitted. A contrast matrix comparing ctDNA positives and biological ctDNA low-shedders was constructed alongside with the associated coefficients and standard errors, and the empirical Bayes method (eBayes function from limma v3.50.348) was used to calculate the moderated t-statistics of differential expression. The resulting gene-level, two-tailed P values were adjusted for multiple testing using the Benjamini-Hochberg (FDR) method. Genes were noted as significantly differentially enriched if their adjusted P value was below 0.05.Gene level transcription analysis used edgeR (v3.36.0)49. The resulting P values were FDR-corrected and an adjusted P value cutoff of 0.05 was employed.The set of significantly overexpressed genes per detection category  was used to calculate Reactome pathway enrichment (ReactomePA v1.38.0)18. Fold change of GSVA enrichment scores comparing 101 tumour regions from 34 ctDNA positives and 62 tumour regions from 28 biological ctDNA low-shedders was calculated using the estimated marginal means (rstatix v0.7.1)50 method, using a linear mixed-effects (lmerTest v3.1-3)51 model to take into account the patient-tumour region associations, treating detection status as fixed effect and patient ID as random effect. The resulting pathway-level P values were FDR-corrected for multiple testing.Additionally, pathway enrichment with respect to the Hallmark Gene Sets from the msigDB database was investigated. Pathway enrichment analysis was carried out on logCPM data including 17815 protein-coding genes using Gene Set Variation Analysis (GSVA v1.42.0)52. A Fisher's exact test was conducted in a two-tailed manner to compare the number of ctDNA positives and ctDNA low-shedders carrying alterations in the frequently mutated genes. The resulting P values were corrected using the FDR method.Driver mutations in 181 genes from 70 ctDNA positive and biological ctDNA low-shedder patients  were included in the analysis. Clonality was determined based as part of the TRACERx WES pipeline. If a patient carried multiple mutations in the same gene with differing clonality, the clonal state was kept. In the gene-level analysis, the top 14 frequently mutated genes were considered. Genes were assigned to pathways as described by Sanchez-Vega et. al23, which takes one sample per patient as input. To investigate genomic regions of recurrent gains and losses, we constructed the single sample copy number profile for each tumour by selecting the maximum (for gains) or minimum (for losses) ploidy-corrected total copy number per segment across the genome. The GISTIC score difference of 0.5 was used as a threshold for significance cutoff. Cytobands were overlapped with output from GISTIC2.0 to get a mean GISTIC score for each cytoband. FLOH and wGII were analysed on the region level . Comparing the chromosomal instability metrics between ctDNA positive and biological ctDNA low-shedder adenocarcinomas and non-adenocarcinomas was performed using a linear mixed model, taking into account the within-sample associations. Pairwise comparisons were made using the estimated marginal means method and P values were FDR-adjusted. Tumour regions were considered whole genome doubled (WGD) if the fraction of the genome with major allele >= copy number 2 was >50% as per previous publications53. Tumours were considered to have WGD if any single region harboured a WGD event. WGD data was available for 63 lung adenocarcinoma patients .Copy number data including allele-specific copy numbers and purity estimates were derived from the TRACERx WES pipeline and were available for 245 tumour and lymph node regions collected from 63 ctDNA positive and biological ctDNA low-shedder patients (166 regions from 35 ctDNA positives and 79 regions from 28 ctDNA negatives). Cytoband analysis was conducted using GISTIC 2.0Using 245 regions from 63 patients (166 regions from 35 ctDNA positives and 79 regions from 28 ctDNA negatives), we performed an estimated marginal means analysis incorporating a linear mixed model approach to account for the within-sample associations. The analysis compared ctDNA positives with biological ctDNA low-shedders.20, including 196 tumour and lymph node regions from 77 ctDNA positive and ctDNA low-shedder patients . Pairwise comparisons between the ctDNA shedders and ctDNA low-shedders were made using the estimated marginal means method with a linear mixed-effects model to account for the within-patient associations between tumour regions.ORACLE scores were calculated by using the method developed by Biswas et al54, and the corresponding P values were computed using the exact method. Venn diagram visualisations were created using eulerr (v6.1.1)55 and ggplotify (v0.1.0)56.Biological low-shedder samples were excluded from the volume-adjusted analysis if their size fell in the lowest quartile size range (< 6042.544 mm3). Transcriptomic and GISTIC analyses were repeated using the volume-adjusted dataset as described above. Taking into account the significantly overexpressed genes and significant cytobands in both datasets, Venn-diagrams were constructed for comparison and Jaccard Similarity Index was calculated to assess the statistical significance of the overlap. The similarity coefficient calculations were performed using the jaccard R package (version 0.1.0)27, or (in the absence of PyClone data) that were present in every primary tumour tissue region analysed (ITH state = 1), and that were unfiltered by the MRD caller, were used in clonal mutation ctDNA level estimations. For each mutation the MRD caller estimated trinucleotide error rate associated with that mutation and the coverage of that mutation was used to estimate the number of expected error controlled reads we would observe due to error. Clonal mutation ctDNA level was then summarised as the total number of error-corrected reads across selected mutations, minus the expected error across these positions (rounded down to the nearest whole integer) divided by total clonal deep coverage. If the clonal ctDNA level was <0% , it was assigned 0%. In two ctDNA positive samples, clonal ctDNA levels were measured at 0% due to mutations driving ctDNA positive status not being assigned a clonal status by the TRACERx pipeline .Mutations that were defined as clonal, either by PyClone clustering as described in our companion manuscript7. In this test set 6 of 8 (75%) adenocarcinomas evaluated had mean clonal mutation levels above the lower 95% confidence interval of the model estimation. We calculated minimal detectable clonal ctDNA level (MDCL) in the 47 ctDNA negative adenocarcinomas by taking the minimum deep alternate observations needed to make a call in patient cfDNA samples and subtracting the estimated deep alternate reads that would occur due to noise in the panel (rounded down to the nearest whole integer). The resulting number was the number of clonal deep alternate reads needed to make a ctDNA positive call . We divided this number by the clonal deep depth across the panel to calculate the minimum clonal ctDNA level that must be exceeded to make a call and called this value MDCL. Using the above linear model, we classified cases as probable technical negatives if the lower 95% confidence interval for predicted clonal ctDNA level was below MDCL and as probable low-shedders if the lower 95% confidence interval for predicted clonal ctDNA level resulting was above MDCL.We generated a linear regression model  where log-10 transformed tumour volume and histology was used to predict log-10 transformed clonal ctDNA level in 96 ctDNA positive non-pilot NSCLCs analysed in this cohort. We used this model to predict clonal ctDNA levels in 47 evaluable adenocarcinomas negative for ctDNA. We tested the capability of this model to predict clonal mutation levels in 8 independent ctDNA positive adenocarcinomas with volume data available analysed in our prior work using a separate assay2), freedom from recurrence  and post-relapse survival (time from recurrence to death from any cause) analyses were performed. 169/187 non-pilot cohort patients were evaluated for 57, survivalAnalysis(v0.3.0)58 and survminer(v0.4.9)59 were used to generate hazard ratios, forest plots, 1- and 2-year survival data and cox regression models in the manuscript. Differences in overall survival between metastatic dissemination classes at relapse were analysed using cox proportional hazard models from either the date of study registration and from the date of MRD detection. A multivariable cox proportional hazard model including maximum relapse ctDNA level, which is known to co-correlate with tumour burden and power for subclone detection, was used to account for this confounder relative to overall survival from the date of study registration.Overall survival , patients with no ctDNA sampling before clinical recurrence  and pilot-patients (n=5) from these analyses.60. For I/O operations and general data manipulations, the R packages tidyverse (v1.3.2)61, data.table (v1.14.6)62, readxl (v1.4.1)63, fst (0.9.8)64, and qusage (v2.28.0)67 were used. All statistical tests were two-sided unless otherwise stated. For assay performance analyses, positive predictive value was calculated as all true positive results divided by the sum of true positive and false positive results; negative predictive value was calculated as all true negative results divided by the sum of false negative plus true negative results; sensitivity was calculated as true positive results divided by the sum of true positive and false negative results; specificity as true negatives divided by the sum of true negatives and false positives. For generation of heatmaps the R package ComplexHeatmap(v2.11.1)68 was used. For general visualisation purposes, R packages ggplot2 (v3.3.5)69, ggpubr (v0.4)70, ggrepel (v0.9.2)71, ggbeeswarm (v.0.6.0)72, scales (v1.2.1.)73, ggforce (v0.4.1)74, and cowplot (v1.1.1)75 were used. For plotting paired data ggpubr(v0.4)70 was used.No statistical methods were used to predetermine sample size. Analysis was performed in the R statistical environment version 4.1.2Supplementary Data GuideSupplementary NoteSupplementary Table 1


B646L (p72) gene was conducted for diagnostic confirmation and molecular characterization of ASFV. Phylogenetic analysis to study the relatedness of current ASFV with those that caused previous outbreaks in Tanzania and representatives of all known 24 ASFV was performed using the Maximum Composite Likelihood model with 1000 bootstrap replications in MEGA 6.0.African swine fever (ASF) is a highly fatal viral hemorrhagic disease of domestic pigs that threatens livelihoods and food security. In Africa, ASF virus (ASFV) circulates in sylvatic (transmission between warthogs and soft argasid ticks) and domestic (transmission between domestic pigs) cycles, with outbreaks resulting from ASFV spill-over from sylvatic cycle. A number of outbreaks were reported in different parts of Tanzania between 2015 and 2017. The present study investigated ASFV transmission patterns through viral DNA sequencing and phylogenetic analysis. A total of 3120 tissue samples were collected from 2396 domestic pigs during outbreaks at different locations in Tanzania between 2015 and 2017. Partial sequencing of the ASFV was confirmed to cause disease in sampled domestic pigs. ASFV genotypes II, IX, and X were detected from reported outbreaks in 2015–2017. The current ASFV isolates were similar to those recently documented in the previous studies in Tanzania. The similarities of these isolates suggests for continuous circulation of ASFV with virus maintenance within the domestic pigs.B646L (p72) gene sequencing. The similarities of current isolates to previously isolated Tanzanian isolates and pattern of disease spread suggest for continuous circulation of ASF with virus’ maintenance in the domestic pigs. Although certain viral genotypes seem to be geographically restricted into certain zones within Tanzania, genotype II seems to expand its geographical range northwards with the likelihood of spreading to other states of the East African Community. The spread of ASFV is due to breach of quarantine and transportation of infected pigs via major highways. Appropriate control measures including zoosanitary measures and quarantine enforcement are recommended to prevent ASF domestic circulation in Tanzania.Genetic analysis confirmed the circulation of ASFV genotypes II, IX, and X by partial  Asfivirus genus and a sole member of the Asfarviridae family [Ornithodoros act as vectors of ASFV and contribute to viral maintenance within the sylvatic cycle as well as in transmitting the virus to domestic pigs [African swine fever (ASF) is a contagious viral hemorrhagic disease of pigs affecting domestic pigs and wild pigs . Africane family . The ASFe family . Warthogtic pigs . Transmitic pigs . Once AStic pigs .B646L) gene, 24 genotypes of ASFV have been identified [The existence of the sylvatic cycle contributes to a rich genetic diversity of ASFV. Based on partial amplification and sequence analysis of the p72 , anorexia, staggering gait, shivering and cutaneous congestion particularly on the outer side of the pinna, belly, limbs and genitalia Fig. a. Pigs wIn total, 3120 tissues samples collected from different parts of Tanzania were screened using ASF diagnostic PCR. Analysis of collected tissue samples confirmed the presence of ASFV in 2170 tissue samples  gene of ASFV Northeastern Tanzanian with 2008 ASFV outbreak in Kenya show only three nucleotide substitution  [In the present study, we found that ASFV genotype X circulated in Northeastern Tanzania, similar to other ASFV genotypes that have been previously described in the area , 22. In →T, A→G) . The Nor→T, A→G) , 19, 24.The ASFV genotype IX was confirmed to cause ASF outbreaks in Northwestern Tanzania. It was observed that ASFV genotype IX is restricted to Northwestern Tanzania, as it was 100% similar to ASFV isolates that caused ASF outbreaks in 2005 in Mwanza, Tanzania. Additionally, phylogenetic analysis revealed that the ASFV IX isolates were closely related to ASFV characterized in Uganda (2003), Kenya (2008) and Democratic Republic of Congo (2009) , 27. ASFThe isolation of ASFV from domestic pigs reports the circulation of these viral genotypes in the domestic pig population in Tanzania. However, this study points up for further isolation and epidemiological investigation in order to fully understand the variations, extent and potential sources of current ASF outbreaks in the region. The occurrence and spread of ASF between different parts of Tanzania is likely due to breach of quarantine imposed in areas affected with ASF. It is mostly likely that pig traders smuggle and transport pigs or pig meat from areas affected with ASF, where the prices are lower, into unaffected areas. Poor biosecurity measures in affected farms and slaughter slabs and swill feeding increase the likelihood of ASFV spread at a given locality, as has been previously described , 22. TraThis study confirmed that ASFV genotype II, IX, and X were responsible for the reported outbreaks between 2015 and 2017. The similarities of the current Tanzanian ASFV isolates with those recently documented in the previous studies and pattern of spread in adjacent location during outbreaks suggest the continuous circulation of ASF with the virus’s maintenance within the domestic cycle. Although certain viral genotypes seem to be geographically restricted into certain zones within Tanzania, genotype II seems to expand its geographical range northwards with the likelihood of spreading to other states of the East African Community. The spread of ASFV was mapped along major highways in Tanzania; this is likely due to the uncontrolled movement of pigs from affected to unaffected areas, breach of quarantine and poor zoosanitary measures. This study recommends continuous virus isolation and investigation to understand the epidemiology of ASFV in Tanzania and neighboring countries for local and inter-regional effective control and prevention interventions.Samples were collected from domestic pigs following reports of suspected ASF outbreaks in different locations within Tanzania between 2015 and 2017. Samples were collected from Mwanza, Manyoni, Kigoma, and Bukoba districts in the year 2015, Babati, Ngara, Magu, Mbeya Municipality, Rungwe and Mbarali districts in the year 2016 and Kalambo, Ileje, Mbozi, Kongwa, Dodoma, Mpwapwa, Gairo, Mbagala, Mvomero, Morogoro Municipality and Kibaha districts in the year 2017 as indicated in Table g for five minutes at 4 °C. The tissue supernatant was transferred into a cryovial and stored at -80 °C until DNA extraction.A total of 3120 tissue samples were collected from 2396 domestic pigs that died from a hemorrhagic disease typical of ASF. Epidemiological information from these farms with outbreaks were collected. Clinical observation of pigs was performed prior to sampling. Tissue samples including spleen, mesenteric lymph nodes, lungs and kidney were collected from dead domestic pigs from suspected ASF. Tissues were temporarily stored at -20 °C before they were transported in ice cool boxes to the laboratory. Approximately, 1 g of each tissue sample was homogenized in 3 mL of sterile phosphate-buffered saline (PBS), followed by centrifugation of the homogenate at 6000 B646L (p72) gene as previously described by Aguero et al. [Aliquots (100 µL) of each of the homogenized tissue samples from the same pig were pooled before conducting DNA extraction. DNA was extracted from the supernatant of pooled homogenized tissues using a QIAamp nucleic acid extraction kit , following the manufacturer’s instructions. The presence of ASFV DNA was detected by polymerase chain reaction (PCR) using the ASF diagnostic primer set PPA1 (5′-AGT TAT GGG AAA CCC GAC CC-3′) and PPA2 (5′-CCC TGA ATC GGA GCA TCC T-3′) that partially amplify the o et al. .B646L (p72) gene using primers p72U (5′-GGC ACA AGT TCG GAC ATG T-3′) and p72D (5′-GTA CTG TAA CGC AGC ACA G-3′) as previously described by Bastos et al. [Genetic characterization of ASFV was conducted in samples confirmed with ASFV by partial nucleotide amplification of the s et al. . AfterwaB646L p7 gene usis et al. .


LSL-KrasG12D;LSL-Trp53R172H;Pdx-1-Cre (KPC) transgenic mouse model of pancreatic ductal adenocarcinoma (PDAC).There is a lack of well-established clinical tools for predicting dendritic cell (DC) vaccination response of pancreatic ductal adenocarcinoma (PDAC). DC vaccine treatment efficiency was demonstrated using histological analysis in pre-clinical studies; however, its usage was limited due to invasiveness. In this study, we aimed to investigate the potential of MRI texture features for detection of early immunotherapeutic response as well as overall survival (OS) of PDAC subjects following dendritic cell (DC) vaccine treatment in KPC mice were treated with DC vaccines, and tumor growth was dynamically monitored. A total of a hundred and fifty-two image features of T2-weighted MRI images were analyzed using a kernel-based support vector machine model to detect treatment effects following the first and third weeks of the treatment. Moreover, univariate analysis was performed to describe the association between MRI texture and survival of KPC mice as well as histological tumor biomarkers.r = 0.78, p < 0.001). Additionally, tissue-specific MRI texture features showed positive association with fibrosis percentage , CK19 positive percentage , and Ki67 positive cells  as histological disease biomarkers.OS for mice in the treatment group was 54.8 ± 22.54 days while the control group had 35.39 ± 17.17 days. A subset of three MRI features distinguished treatment effects starting from the first week with increasing accuracy throughout the treatment (75% to 94%). Besides, we observed that short-run emphasis of approximate wavelet coefficients had a positive correlation with the survival of the KPC mice (Our results demonstrate that MRI texture features can be used as imaging biomarkers for early detection of therapeutic response following DC vaccination in the KPC mouse model of PDAC. Besides, MRI texture can be utilized to characterize tumor microenvironment reflected with histology analysis. Pancreatic ductal adenocarcinoma (PDAC) is the 3rd most common cause of cancer-related deaths in the United States with an expected 44,330 deaths in 2018 , p = ), individually. The behavior of the fibrosis percentages with respect to an MRI texture feature (the entropy of the oriented gradient histogram) was described in Fig. r = , p = ). The characteristics of tissues of treated and control mice were presented in Fig. For evaluation of texture analysis in association with pathological findings as to the gold standard of disease analysis, we employed a two-tailed Student T-test to determine MRI texture features that remarkably characterize the histological outcomes. For trichrome staining histology images, three image features identified with Student T-test listed in Table Our study demonstrated that MRI texture features can serve as imaging biomarkers for early detection of immunotherapeutic response after DC vaccine treatment as well as the long-term outcomes in the KPC mouse model of PDAC.The quantitative image features extracted from T2-weighted MRI data can be used to measure the characteristics of tumors in the KPC mouse model. The determined set of three image features distinguished DC vaccinated tumors from untreated tumor tissue starting from the first week of the treatment (75%). The classification accuracy reached 93.75% in the third week of the treatment. Besides, an MRI texture feature was identified during univariate analysis to predict the survival of the KPC mice in control and DC vaccine treatment groups. Additionally, we used noninvasive MRI texture features to describe the molecular characteristics of the tumor tissue obtained with invasive histopathological analysis.18F-FDG-PET/CT images [The prognostic potential of quantitative imaging features for PDAC in the analysis was reported by several studies –32. In aT images . As tumoT images . The ranr = 0.84, p < 0.001), CK19 positive area  and Ki67 positive cell percentage . The selected quantitative imaging features extracted from T2-weighted MRI data were chosen to detect structural changes or patterns of heterogeneity in tumor tissue that occurred during the DC vaccine treatment. These changes were identified at the cellular level using histological biomarkers, and changes in tissue characteristics were also identified using a univariate model generated with texture features of MRI data. Taken together, the results demonstrate that MRI texture provides promising knowledge to describe pancreatic tumor characteristics and detect early treatment, as verified by gold standard biological analysis.In our study, we focused on the investigation of the radiomic analysis method to early detect responses to the tumor tissue changes to DC vaccination. A set of three radiomic features differentiated DC vaccine treatment effects with increasing accuracy during the treatment period Fig. c and d; There were several limitations to our study. First, tumors were segmented using a manual approach which may introduce user bias and requires additional processing time; however, it is a commonly used approach in pre-clinical studies. Implementing an automated approach for tumor segmentation would allow studies with larger cohorts by reducing potential user bias and time consumption. Second, there was a low number of subjects in our experiments although it’s comparable to other pre-clinical studies. The prognostic histological marker Ki67 was significantly decreased in the treated group while no significant difference in survival between untreated control and treated KPC mice. This may be particularly relevant to the limited number of subjects in each group. The number of subjects will be eventually increased with further studies. Finally, we only considered individual treatment methods to monitor the effects of tumor progression and pathological analysis. This could be addressed by collaborating with multiple institutions.MRI texture features can be used as noninvasive imaging biomarkers to early predict therapeutic responses of DC vaccination in addition to the overall survival of the subjects in the KPC mouse model of PDAC.


Core–double shell nylon-ZnO/polypyrrole electrospun nanofibers were fabricated by combining three straightforward methods . The hybrid fibrous organic–inorganic nanocomposite was obtained starting from freestanding nylon 6/6 nanofibers obtained through electrospinning. Nylon meshes were functionalized with a very thin, continuous ZnO film by a sol–gel process and thermally treated in order to increase its crystallinity. Further, the ZnO coated networks were used as a working electrode for the electrochemical deposition of a very thin, homogenous polypyrrole layer. X-ray diffraction measurements were employed for characterizing the ZnO structures while spectroscopic techniques such as FTIR and Raman were employed for describing the polypyrrole layer. An elemental analysis was performed through X-ray microanalysis, confirming the expected double shell structure. A detailed micromorphological characterization through FESEM and TEM assays evidenced the deposition of both organic and inorganic layers. Highly transparent, flexible due to the presence of the polymer core and embedding a semiconducting heterojunction, such materials can be easily tailored and integrated in functional platforms with a wide range of applications. Developing novel flexible materials with versatile properties using industrial scalable, low cost and straightforward fabrication methods is a major priority. Hybrid organic–inorganic composites are a class of materials that received a great interest due to the embedment of both compound features. Many hybrid composite configurations were reported in the literature ,5,6,7,8,Electrospinning is a fashionable technique intensively utilized to fabricate fiber-based composites with controlled features, compositions and functions ,12,13. WZinc oxide (ZnO) is an n-type semiconductor involved in many material designs, especially when morphologically tailored nanostructures are required. The possibility of utilizing multiple preparation routes in order to obtain various shaped nanostructures characteristics that range from biocompatibility to interesting optoelectronic properties, make this material quite interesting for multiple applications. A lot of chemical or electrochemical preparation ways can be identified for obtaining ZnO thin films or nanostructures. Sol–gel synthesis is one such simple ZnO preparation method that uses inexpensive precursors, being an appropriate technique for covering large areas of materials like electrospun nets ,28.Polypyrrole (PPy) is the most used π conjugated conducting polymer with a p-type semiconductor behavior. Properties such as the structure and morphology, conductivity and stability strongly depend on preparation methods and dopants. The material can be rapidly synthesized starting from the monomer by chemical oxidation or electropolymerization. The second method is preferred due to the possibility of a good control of the deposit’s characteristics and improved conductivity. PPy in a web-like configuration is quite difficult to obtain due to its highly insolubility in common solvents and having low mechanical stability. So, in order to obtain fibrous PPy structures by methods like electrospinning it is necessary to combine it with carrier polymers ,30,31, lThe combination of ZnO with PPy was intensively exploited for obtaining materials with improved properties, especially for generating a p–n organic–inorganic heterojunction. Several papers describe the fabrication of electrospun fiber meshes based on ZnO and PPy, the approaches consisting of decorating the PPy based fibers with ZnO nanoparticles  or vice The present work describes the fabrication and characterization of a hybrid organic–inorganic nanocomposite based on electrospun fiber meshes used as flexible substrates functionalized with very thin ZnO and PPy films. The proposed fabrication procedure involves the electrospinning of nylon 6/6 in order to obtain freestanding nanofibers, coating them with a ZnO layer by a sol–gel process and electrochemically depositing a continuous PPy film on the ZnO shell. The core–double shell nylon-ZnO/polypyrrole nanofibers were thoroughly micromorphological, structural and optical characterized, and the formation of ZnO with a hexagonal wurtzite structure and of PPy being demonstrated. Fabrication conditions were chosen as to preserve the high specific surface of the initial electrospun meshes. The described method has multiple advantages: being versatile allows fast fabrication using low cost precursors; the material’s features can be further tailored by changing some experimental parameters in accordance with the targeted application; enables obtaining of a flexible material (given by the presence of nylon core) with high transparency (by optimizing fiber density), which possess the characteristic properties of ZnO and PPy. Due to the material’s properties described ahead and preparation method versatility, such kind of structures can be easily integrated in wearable sensors, actuators, intelligent clothes or other outstanding devices.3COOH)2·2H2O, ACS reagent, ≥98%, Sigma Aldrich, St. Louis, MO, USA; CAS number 5970-45-6), ethanol absolute , lithium perchlorate , pyrrole , and acetonitrile  were used as received.All preparation materials, namely nylon 6/6 , formic acid , zinc acetate dihydrate (Zn(CHw/v) nylon 6/6 in formic acid. The experimental process is a “classic” one, namely: the solution was loaded in a syringe with a 0.4 mm inner diameter metallic needle and pumped  with a feed rate of 0.5 mL/h; the electrospinning was performed by applying 25 kV  between the needle and the collector (a copper wire frame); the collecting time of the nanofibers was chosen such that the meshes have an optimum fiber density; the distance between the syringe tip and collector was ~15 cm and the nanofibers were collected on copper wire frames as freestanding nets; the electrospinning setup was placed into a plexiglass box in order to keep a constant relative humidity at ~20% and a process temperature of ~23 °C.Nylon 6/6 nanofibers  were prepared by electrospinning a precursor solution of 10% (3COOH)2·2H2O in ethanol), letting them dry between the cycles. In the end, the ZnO coated meshes were rinsed with ethanol for remove any precursor traces. The prepared core–shell nanocomposite was thermally treated in the oven for 3 h at 200 °C in order to improve the crystallinity of ZnO , this temperature being selected so that the nylon core is preserved.Further, the nanofibers were functionalized with a very thin ZnO layer by a sol–gel process, making 15 consecutive cycles of immersing/extracting meshes into/from the precursor solution  was applied for 5 min, the deposition time being selected so that the PPy covered only the nanofibers as a thin, homogenous film . After the deposition process, the core–double shell nanocomposite was rinsed with acetonitrile for removing the unreacted monomer and dopant . The PPy electrodeposition was performed using a Parstat 2273 Princeton Applied Research potentiostat. All fabrication steps were schematically represented in The freestanding ZnO coated webs attached on copper wire frames were transferred onto stainless steel frames by mechanical gripping in order to have a good electrical contact for PPy electrochemical deposition. The PPy synthesis was performed using Nylon-ZnO nets attached on these frames as a working electrode (WE), a platinum mesh as a counter electrode (CE) and a commercial saturated calomel electrode (SCE) as reference. The deposition solution was consisted in 0.2 M pyrrole and 0.1 M LiClO2 substrates.All prepared materials were morphologically characterized using a Zeiss Merlin compact field emission scanning electron microscope (FESEM) . The transmission electron (TEM) and high resolution transmission electron (HRTEM) investigations were performed on a Cs probe-corrected JEM ARM 200F microscope  operated at 200 kV in order to estimate the layer thickness. The Zeiss EVO 50XVP scanning electron microscope  equipped with an energy dispersive X-ray analysis (EDX) QUANTAX Bruker 200 accessory  was employed for establishing the elemental composition of fabricated materials. The optical properties were investigated registering the reflectance spectra utilizing a PerkinElmer Lambda 45 UV–VIS spectrophotometer  equipped with an integrating sphere. The formation of ZnO was emphasized by X-ray diffraction (XRD) using a Bruker AXS D8 Advance instrument  with Cu Kα radiation (λ = 0.154 nm) operating at 40 kV and 40 mA. The synthesis of PPy was demonstrated by Fourier transfer infrared spectroscopy (FTIR) using a PerkinElmer Spotlight Spectrum 100 spectrometer  and Raman spectroscopy employing a BRUKER-RFS27 FT-Raman spectrometer  with 633 nm/17 mW and 325 nm/25 mW laser kits. For a rigorous characterization, the analysis was also performed for as spun nylon mesh. For all subsequent measurements, nylon, nylon-ZnO and nylon-ZnO/PPy meshes were peeled off from copper or stainless steel frames and placed on Si/SiOThe chosen fiber density was in a range that leads to transparent webs, as can be observed from the photographs of nylon, nylon-ZnO and nylon-ZnO/PPy presented in The ZnO structure was analyzed by registering the XRD diffractograms of nylon and nylon-ZnO, which are plotted in 2) versus photon energy , (002) and (110) crystal planes [l planes . These rCore–double shell nylon-ZnO/polypyrrole nanofibers were fabricated by functionalizing freestanding nylon 6/6 meshes obtained through electrospinning with ZnO by a sol–gel process and with polypyrrole by electropolymerization. It is worth mentioning that all synthesis parameters were optimized in order to cover only the nanofibers with very thin organic and inorganic layers, preserving the high active surface of the electrospun meshes. The presence of ZnO before and after polypyrrole deposition was evidenced by an elemental analysis carried out using EDX. The XRD pattern of ZnO coated nets validated the formation of ZnO in the hexagonal wurtzite crystal structure. From its reflectance spectrum a band gap of ~3.3 eV was estimated for ZnO. Both FTIR and Raman spectra confirmed the deposition of polypyrrole on ZnO coated networks. The detailed micromorphological investigation supported the hybrid nanocomposite fabrication mechanism and demonstrated that all deposition steps did not alter the native morphology of nanofiber webs. The very thin ZnO and polypyrrole films fully covered the nanofibers, the thickness of ZnO being estimated of about 10 ± 2 nm, while that of polypyrrole was about 20 ± 5 nm. The XRD and FESEM results were upheld by the TEM and HRTEM measurements. However, the versatility of the fabrication method allowed tailoring the material features according to targeted application. This kind of materials can easily find applications in many fields such as (bio)sensors, actuators, tissue scaffolds, etc., demonstrating their functionality being the topic of future work.