EF-FACS circulating tumor cell detection complements ctDNA-based prognosis in stratification of non-small cell lung cancer patients

EF-FACS circulating tumor cell detection complements ctDNA-based prognosis in stratification of non-small cell lung cancer patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article EF-FACS circulating tumor cell detection complements ctDNA-based prognosis in stratification of non-small cell lung cancer patients Maria Virginia Sanchez-Becerra, Marianne Oulhen, Damien Drubay, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7347165/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Purpose Liquid biopsy provides a real-time dynamic evolution of Non-Small Cell Lung Cancer (NSCLC). Circulating Tumor Cells (CTC) capture tumor heterogeneity enabling single-cell approaches, but their detection requires an enrichment process associated with cell loss. We present an E nrichment- F ree F luorescence A ctivating C ell S orting method (EF-FACS), which permits the detection of a higher proportion of CTCs, and aimed to explore the complementary information of EF-FACS CTCs and ctDNA. Methods EF-FACS is based on fixation, permeabilization and immunostaining (CD45, CD66b, CK, EpCAM, Hoechst) of 12 mL whole blood, to detect and isolate CTCs by FACS. Tumor origin of CTCs is evaluated with a Low-Pass Whole-Genome copy number altered profile. Accuracy was assessed with tumor cells spiked in healthy blood samples. Blood samples of 95 advanced NSCLC patients were collected. 86 paired samples underwent CellSearch and EF-FACS workflows. 85 patients with a ctDNA analysis were included for combined CTC-ctDNA clinical utility. Results EF-FACS was accurate (r = 0.937, p = 1.689e-11). CTC counts were significantly higher by EF-FACS than CellSearch (median CTC counts: 6 vs 0, p < 0.0001). CTCs and ctDNA were found to be independent prognostic biomarkers. CTCs improved the short-term prediction of clinical prognosis factors (p = 0.0212), whereas ctDNA did not (p = 0.537). The combination of CTCs and ctDNA was complementary for enhanced stratification of the risk of death at 1 year in patients with advanced NSCLC. Conclusions EF-FACS is efficient in NSCLC CTC detection, potentially implementable in treating centers using a cytometer. CTCs and ctDNA have a complementary role in prognosis stratification of metastatic NSCLC patients. Health sciences/Biomarkers Biological sciences/Cancer Health sciences/Oncology Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Lung cancer represents a leading cause of cancer mortality worldwide. 1 Patients are generally diagnosed at an advanced disease stage that negatively impacts prognosis and life expectancy. Non-Small Cell Lung Cancer (NSCLC) has become the paradigm of precision medicine, prolonging survival and improving quality of life. Since the discovery of EGFR- mutation as the first predictive biomarker in NSCLC, 2 an increasing number of druggable genomic alterations have been identified. These include ALK, ROS1, KRAS, BRAF, HER2, RET and MET , leading to the development of targeted therapies for all these genomic alterations, which become the standard of care on first- and subsequent treatment lines in patients with metastatic NSCLC. Moreover, immune checkpoint blockers (ICB) have also emerged as a therapeutic strategy for patients not able to receive targeted therapies. Indeed, the development of resistant cell subpopulations within highly heterogeneous cancers still displays a major obstacle to the effective cancer treatment. Hence, the monitoring of disease with sequential tissue biopsies is critical and not feasible in daily practice. Indeed, tissue biopsies frequently underestimates the complexity of the tumor molecular level, thus failing to represent tumor heterogeneity. In this setting, liquid biopsies have become a powerful tool, providing a real-time perspective of cancer evolution, and giving a holistic view of tumor spatial and temporal heterogeneity in a minimally invasive way. 3 Among the different components of liquid biopsies, circulating tumor DNA (ctDNA) became frequently used in clinical practice. Recently, the International Association for the Study of Lung Cancer supports the role of ctDNA as a complement to tissue biopsy at diagnosis, and advocates for a “plasma first” approach to evaluate the mechanisms of resistance in NSCLC harbouring druggable oncogenic alterations. 4 However, ctDNA may have several limitations such as: false negative results, particularly in patients with limited brain metastatic disease, as well as false positive results associated with clonal hematopoiesis or germline variants. Moreover, ctDNA cannot provide transcriptomic and proteomic profiles of cancer cells. Finally, ctDNA fails to capture tumor heterogeneity since it can only be reached by single-cell analysis. 5 , 6 Another component of liquid biopsy, circulating tumor cells (CTCs), are an extremely rare fraction of cells in the bloodstream, deriving from primary tumor and metastasis, including aggressive clones prone to resist therapies. They have proved to be associated with prognosis and progression in NSCLC. 7 – 10 The value of CTC counts as a predictive biomarker of drug sensitivity and pharmacodynamics has been reported in diverse solid tumors. 11 – 13 When regarding NSCLC, our group demonstrated the value of CTCs harboring aberrant ALK copy number in the prediction of crizotinib sensitivity and the levels of chromosomal instability in ROS1 -rearranged NSCLC. 14 – 16 Moreover, in contrast to ctDNA, which solely limits to bulk analysis, CTCs represent a valuable tool in which single-cell analysis can be performed. Recently, we and others have exploited single-CTC genomic sequencing to investigate therapeutic resistance mechanisms and describe tumor heterogeneity. 17 – 22 Despite recent technological advances, the multiomic analysis of CTCs remains extremely challenging because of the rarity and heterogeneity of CTCs that significantly restrains the tumor material for molecular analysis. The dependence of EpCAM expression for CTC detection by the “gold standard” CellSearch technology particularly limits the CTC recognition in NSCLC, due to the high reliance on the Epithelial Mesenchymal Transition (EMT) that associates a loss of EpCAM expression. 23 For patients with stage II-IV NSCLC, only up to 23–27% will yield 2 or more CTCs detected with CellSearch. 8 – 10 , 24 To overcome this constraint, methods such as those based on size, deformability or surface protein expression of CTCs have been developed. 7 , 15 , 16 , 24 – 27 In all these methods, the need for an enrichment process, either by positive or negative selection, before CTC characterisation, considerably impacts the final recovery of extremely rare events. Bypassing this process could open a window of opportunity to increase the quantity of tumor material available to perform wider molecular analysis. In the current study, we present a new detection method for CTCs from advanced NSCLC patients, deprived of the enrichment step, which enables the detection of CTCs in whole blood samples after immunofluorescence staining. Indeed, we show that this method, further called E nrichment- F ree F luorescence A ctivated C ell S orting (EF-FACS) enables higher recovery of NSCLC CTCs compared to CellSearch. We reveal a prognostic prediction for advanced NSCLC, with EF-FACS CTCs performing better in the short term (within the first 6 months), whereas both EF-FACS CTCs and ctDNA have a higher value for prediction in the intermediate term (within the first year after blood collection). MATERIALS AND METHODS Patients Patients with advanced NSCLC from Gustave Roussy (Villejuif, France) were enrolled in this study. The blood samples collection was performed within the STING study (ClinicalTrials.gov identifier: NCT04932525). This trial was conducted in accordance with the Declaration of Helsinki. It was authorized by the French national regulation agency (ANSM) and approved by the Ethics Committee and the institutional review board. Written informed consent was obtained from all patients. Clinical, pathological and molecular data were collected from the electronic medical records. Blood samples (20 mL) at baseline, and optionally at an early timepoint (on days 21–28 of treatment) and at progression, were collected in CellSave tubes ( Menarini Silicon Biosystems ) for CTC analysis and manipulated within the 72 hours from blood collection. Cell line and spiking Lung adenocarcinoma cell line with a deletion in exon 19 of EGFR (PC9) was obtained from the ATCC collection and maintained in RPMI-1640 Medium ( Gibco, Ref: 61870-010 ) supplemented with 1% penicillin/streptomycin ( Gibco by Life technologies, Ref: 15140-122 ) and 10% fetal bovine serum ( SIGMA, Ref: F7524 ) at 37° C in 5% CO 2 . Mycoplasma negativity was regularly confirmed. Blood samples of healthy donors were collected by the public Blood French Establishment ( Établissement Français du Sang ), ensuring anonymization. Pre-established quantities of PC9 cells were directly spiked into healthy donor blood samples by a CytoFLEX SRT cell sorter ( Beckman Coulter Life Sciences ). Each sample from triplicate came from different donors. CellSearch CTCs were enriched and enumerated by using CellSearch ( Menarini Silicon Biosystem ) on 7.5 mL of blood as previously reported. 23 Blood samples were processed within 96 hours and analyzed by two independent experts blinded to clinical outcomes according to the FDA-approved definition for CTCs, namely cells positive for EpCAM, CK and nuclear stain DAPI, but negative for leukocyte marker CD45. CTC enumeration is reported as number per 7.5 mL of blood. EF-FACS (Enrichment-Free Fluorescence-Activated Cell Sorting) Blood was transferred to 50 mL tubes. The components Fixative Buffer and Permeabilizing Buffer (PerFix CTC, Beckman Coulter Life Sciences) were used according to manufacturer protocol and applied to a corresponding volume of blood. Briefly, 100 µL of fixative buffer was added to each 1 mL of blood and incubated 15 minutes at room temperature. Then 6 mL of permeabilizing buffer per 1 mL of blood was added, and, after a 20-minutes incubation, cells were centrifuged at 500 x g for 10 minutes and transferred into the FACS tubes. The antibodies used for the immunofluorescence staining were: 0.625µL of CD45-PC7 ( Beckman Coulter) ; 3µL of CD66b-APC-A750 ( Beckman Coulter) ; 1µL of EpCAM-Alexa Fluor 647 ( Novus Biologicals, Ref: #NBP2-33078AF647 ); 2µL of CK-Alexa Fluor 488 ( CellSignaling, Ref: #4523S ); 1µL of CK7-Alexa Fluor 488 ( Abcam, Ref: #ab208273 ), and 25µL of Hoechst 33342 ( Merck #B2261). This antibody panel was added prior to analysis on the CytoFLEX SRT sorter, equipped with four lasers (405nm, 488nm, 638nm, 561nm) and a 100µm noozle. A sequential gating strategy was used to identify the events of interest, considering a morphologic selection (FSC-A/SSC-A), a positivity for Hoechst-33342, Cytokeratin-7 and pan-cytokeratin; a negativity of CD45 and CD66b (Fig. 2 A). For representative samples, single CTCs and white blood cells were sorted into a 96-well plate and stored at -20° C for genome sequencing. Whole Genome Amplification (WGA), Quality Controls (QC) WGA was performed using the Ampli1 WGA kit ( Menarini Silicon Biosystems, #KI0030 ) following the manufacturer’s instructions. Quality of Ampli1 WGA products was checked using the Ampli1 QC Kit ( Menarini Silicon Biosystems, #KI0027 ) according to manufacturer's instructions. Library preparation and low-pass whole-genome sequencing (LP-WGS) Ampli1 LowPass kit for Illumina ( Menarini Silicon Biosystems, #KI0041 #KI0040 ) was used for preparing LP-WGS libraries from single CTCs, White Blood Cells bulks and germline DNA. Ampli1 LowPass libraries were normalized and sequenced by MiSeq instrument using 150 SR rapid-run mode. Bioinformatics workflow for low-pass whole-genome sequencing Reads were demultiplexed to the sample level using bcl2fastq2 ( https://support.illumina.com/sequencing/sequencing_software/bcl2fastq-conversion-software.html ) v2.19, and their quality assessed with FastQC ( http://www.bioinformatics.babraham.ac.uk/projects/fastqc ) v0.11.1. Read trimming was performed with fastp v0.23.2, 28 discarding low quality (< 20) and uncalled bases, Illumina adapters and ending polyG tracks. Cross-species contamination was performed using Fastq_screen v0.14.3 against default species. 29 Trimmed reads were mapped with BWA-mem2 v2.2.1. 30 PCR and optical duplicates were marked using Picard ( https://broadinstitute.github.io/picard/ ) v3.1.0. Unmapped reads and reads with mate mapped on another chromosome were discarded using samtools v1.18. 31 Depth and coverage metrics were evaluated using mosdepth v0.3.8. 32 All QC results were aggregated using MultiQC v1.19. 33 Copy-number detection, cellularity and global ploidy were assessed using the ichorCNA v0.5.0 package for R v4.1. 34 For a better quality of copy number profiles, a panel of normal using all WGA reference samples was constituted and used as reference for all tumor samples. IchorCNA was used with these custom parameters: expected ploidy between 1 and 4, expected cellularity between 80 and 100%, window size of 500 Kb. CNA profiles quality was evaluated manually, discarding profiles with high noise (high spread) and over-segmentation. Selected CNA profiles aggregation, clustering and heatmap plots were generated using in-house scripts under R v4.1. ctDNA For the CTC and ctDNA paired analyses, the cohort included 85 patients who also had a FoundationOne Liquid CDx analysis performed in routine clinical practice at CTC baseline timepoint. Circulating cell-free DNA extracted from plasma underwent a whole genome shotgun library construction and hybridization-based capture of 324 cancer-related genes, including coding exons and select introns of 309 genes, and select intronic regions or non-coding regions of 15 genes. Hybrid capture selected libraries were sequenced with deep coverage using the NovaSeq 6000 platform. Data were processed with customized analysis pipelines for detection of genomic alterations. Substitutions and insertion and deletion alterations (indels) were reported in 311 genes, copy number alterations were reported in 310 genes, and gene rearrangement in 324 genes. Variant Allele Frequency was presented in percentage, and considered for known oncogenic alterations as reported in OncoKB (available from: www.oncokb.org ) and cBioportal ( www.cbioportal.org ). Statistical analysis The Pearson Correlation Coefficient was calculated to assess the strength of the linear relationship between spiked and recovered cell counts. To compare the performance of CellSearch and EF-FACS in CTC detection on matched samples, we employed the non-parametric Wilcoxon signed rank test for paired data. The main endpoint was the overall survival (OS), defined as the time between the CTC collection and patient death. The secondary endpoint was the progression-free survival (PFS) defined as the time between the CTC collection and the patient progression or death. The prognostic value of EF-FACS-detected CTCs and ctDNA was assessed using Cox multivariable model adjusted for the known prognostic factors (age, gender, presence of metastasis at the CTC collection, ECOG-Performance Status, and treatment with target therapy). These analyses were adjusted for the time between diagnosis and CTC collection to take this period into account. The positivity threshold of the ctDNA was determined from the literature, 35 , 36 considering a positive result when the variant allele fraction (VAF) was ≥ 0.25% in known oncogenic genomic alterations. The positivity threshold of the CTCs was obtained by the multivariable Cox model minimizing the Akaike information criterion (AIC). The proportional hazard assumption was checked using Schoenfeld residuals visualization and trend test. When this assumption was not met, we used a penalized spline to capture the functional form of the time-varying association with the spline function in the time-transform feature of the coxph function (tt). The optimal degrees of freedom were selected automatically based on the cAIC. The additional value of the EF-FACS-detected CTCs and ctDNA to the classical risk factors for the prognostic prediction was assessed from the discrimination performance of the linear predictor of the multivariable Cox model adjusted for the clinical risk factors. This discrimination was measured for the prediction of the prognosis at two clinically relevant timepoints (6 and 12 months) using the area under the time-dependent receiver operating characteristic curve computed using the package timeROC. RESULTS Patients From December 2022 to June 2024 ninety-five patients were enrolled. The study was divided into two cohorts: (i) CellSearch vs EF-FACS cohort, with 86 paired blood samples from 74 patients for the comparison of both methodologies and technical validation of EF-FACS; and (ii) CTC-ctDNA cohort, including 85 patients with a ctDNA analysis at same time of CTC collection. The design of the study is presented in Figure 1. Baseline characteristics are summarized in Table 1. The median age of patients was 66 years old [30-90], including 51% of females and 32% never-smokers. Adenocarcinoma was the most common histological subtype (82%), followed by squamous histology (8%). The remaining patients were diagnosed of No Otherwise Specified (NOS) (4 patients), large cell carcinoma (2 patients), sarcomatoid (1 patient) and undifferentiated carcinoma (2 patients). Oncogene alterations were identified in 46 tumor samples including: 16 classic activating EGFR mutations, 7 ALK fusions, 9 KRAS G12C mutations, 7 RET fusions, 4 MET exon 14 skipping mutations and 3 BRAF V600E mutations. The majority of patients were treatment naïve (73%). 20 patients had blood collection after progression to first line of treatment (21%). At the time of blood collection, 39% patients started target therapy, 36% chemo-immunotherapy, 7% chemotherapy, 7% chemo-radiotherapy, 7% exclusive symptomatic treatment and 4% immunotherapy. Overall, median progression free survival (PFS) was 0.693 years [95% CI; range, 0.534-0.912]. Median overall survival (OS) was not reached ( Supplementary Figure S1) . Enrichment-Free Fluorescence-Activating Cell Sorting CTC detection The EF-FACS workflow includes steps of fixation, permeabilization, and immunostaining of 12 mL blood that enables a phenotypic characterization of CTCs by a new generation flow cytometer (CytoFLEX SRT) and single cell sorting for further molecular analysis. Low-pass whole genome sequencing was performed to confirm malignancy status in a representative selection of sorted single cells (Cytokeratins+, CD45-, CD66B-, Hoechst33342+) . The schematic workflow applied in the study is presented in Figure 2A. A selection of copy number alteration (CNA) profiles of representative patients is shown in Supplementary Figure S2 . Single-CTC CNA profiles demonstrated the intra- and inter-tumor heterogeneity as previously reported. 18,19 To evaluate the accuracy of EF-FACS, a recovery analysis was performed. Pre-established quantities of PC9 cells were directly spiked into healthy donor blood samples using a CytoFLEX SRT sorter. Triplicate blood samples with or without spiked tumor cells underwent an EF-FACS workflow as presented in Figure 2A . The Pearson Correlation Coefficient was equal to 0.937 (p=1.689e-11), indicating a high accuracy in tumor cell detection by EF-FACS, especially at low counts of CTCs. ( Figure 2B and Supplementary Table S1 ). Eighty-six paired blood samples of 74 patients were used for a comparison analysis of EF-FACS with the standard CellSearch ( Figure 2A ). Median number of CTCs detected by CellSearch was 0 [range: 0-31] and for EF-FACS 6 CTCs [range: 0-48]. For EF-FACS analysis 58 out of 86 samples (67.4%) presented at least 5 CTCs per 7.5 mL of blood, while in 26 blood samples 10 or more CTC (30.2%) were found. Contrarily, only 4.6% of samples processed by CellSearch presented 5 or more CTCs. The differences in CTC detection using two parallel methods were highly significant (p<0.0001) ( Figure 2C; Supplementary Table S2 ) showing a superiority of EF-FACS for detecting CTCs in NSCLC patients. This difference was also found on the analysis of 40 paired samples from patients treated by targeted therapy. A focus on patients undergoing targeted therapy was made to explore the potential role of the evolution of CTC counts on sequential blood samples. Thirteen patients had an additional sample collection at an early timepoint (days 21-28 after baseline collection), and 6 patients at progression. There was a median diminution of 7 CTCs [-1; -16] among those patients presenting a Partial Response on first radiologic evaluation at 3 months, while those on Stable disease had a median diminution of 1.5 [-1; +2], and those with progressive disease had a median increase of 9.5 [+1; +37] ( Supplementary Figure S3 ). This tendency must be confirmed in further studies. Association of EF-FACS detected CTCs with prognosis Paired EF-FACS CTC and ctDNA samples were available for 85 patients ( Figure 1 ). Continuous values of CTCs and ctDNA VAF were not correlated ( Supplementary Figure S4 ), suggesting that the EF-FACS CTCs and ctDNA may be two independent prognostic biomarkers. The multivariable Cox model (adjusted for the known risk factors to control the confounding bias, i.e. age, gender, presence of metastasis at the CTC collection, ECOG-Performance Status, treatment with target therapy, and time since diagnosis [TSD] at the CTC collection) revealed that each increase in levels of CTCs decreased the survival probability (HR of 1.04 per increase of 1 CTC, 95%IC [1.00; 1.10]; p=0.025) (Figures 3A, 3B). The proportional hazard assumption did not hold, revealing an “n-shaped” time-varying effect: increasing levels of CTCs were associated with a markedly increased risk only during the first months of follow-up. In contrast, increasing the VAF of oncogenic alterations in ctDNA was not significant, despite having a tendency toward a worse prognosis with increasing levels (HR of 1.81 per increase of 1% of VAF, 95%IC [0.17; 19.5]; p=0.625) ( Figures 3C and 3D ). However, using the cutoff of 0.25%, the detection of ctDNA was associated with a decrease of the survival probability (HR= 4.45, 95%IC [1.63; 12.13]; p=0.004, no deviation to the proportional hazard identified). These results suggest that there is no linear increase in the risk with the ctDNA VAF, explaining that the classical linear assumption of the Cox model failed to capture this relationship. Assuming that a similar relationship may hold for the CTCs, we used the Akaike information criterion (AIC) to determine an optimal cutoff for the CTCs with or without proportional hazard assumptions ( Figure 2D ), suggesting a positivity of the test for a CTC count ≥ 5. As the proportional hazard assumption was not verified for this binarized CTC, we considered the model taking into account the interaction with time as the final model, which presented a better performance than the model considering the continuous CTC (AIC binarized CTC = 218.44; AIC continuous CTC = 223.29). Adjusting the model for both (EF-FACS CTCs and ctDNA) we confirmed that they are independently associated to the risk, and therefore complementary for risk prediction, with an HR of 4.95 ([1.65; 14.87], p=0.004) for the ctDNA, and a time varying HR for the EF-FACS that reached 9 at 3 months to decreases after to 1 ( Supplementary Figure S5 ). These results suggest a short-term high prognostic value of CTC enumeration by EF-FACS in NSCLC. Clinical utility of combined EF-FACS detected CTCs and ctDNA We assessed the discrimination of different risk scores using the time-dependent ROC curves to determine if addition of the EF-FACS-detected CTCs or the ctDNA VAF or both to classic clinical prognostic variables, could help identify patients with a worse prognosis. Classic clinical prognostic variables included in the model: age, gender, metastasis at CTC collection, use of target therapy, ECOG-Performance Status, and delay between CTC isolation and diagnosis. Risk score for EF-FACS-detected CTCs contemplate a threshold of 5 CTCs, while the risk score for ctDNA concerns a VAF threshold of 0.25% for known oncogenic drivers. We considered two periods of time for risk of death prediction: short-term (within the first 6 months after blood collection) (Figure 4A), and intermediate-term (within the 12 months after blood collection) (Figure 4B). We compared four risk scores: (i)“Clinical” risk score, which was our baseline risk score, (ii) “Clinical+CTC” including the clinical factors and the EF-FACS-detected CTCs, (iii) “Clinical+ctDNA” combining the clinical factors and the ctDNA VAF, and (iv)“Clinical+CTC+ctDNA”, which included all factors. Comparing to the Clinical score in a short term, the addition of the CTC had a significant additional value for the prediction of death at a short term (AUC Clinical =0.689, AUC Clinical+CTC =0.818, p=0.0212), while the addition of the ctDNA or the CTC+ctDNA to the clinical score did not (AUC Clinical+ctDNA =0.732, p=0.5370; AUC Clinical+CTC+ctDNA =0.794, p=0.1198). Although the AUC was higher for the Clinical+CTC score, the difference between these 3 scores cannot be statistically confirmed due to restricted sample size ( Figure 4A , second line), suggesting that this performance was mainly driven by the addition of the CTCs. Comparing to the Clinical score, the most significant additive value of prediction of risk in an intermediate term was obtained with the combination of a clinical score with both CTC and ctDNA (AUC Clinical+CTC+ctDNA = 0.829, p=0.0364) ( Figure 4B ). The addition of CTCs to the Clinical+ctDNA score was not statistically significant (p=0.7682), suggesting that this high performance was mainly driven by the ctDNA (AUC Clinical+ctDNA =0.821). Those results highlight the importance of both independent biomarkers, CTCs and ctDNA, in the risk stratification of patients, with each having their advantage according to the disease course. Moreover, the data obtained indicate the significant role of CTC enumeration at baseline to help in the identification of patients with the highest risk of death at short term independently of treatment choice. DISCUSSION Herein, we present a new EF-FACS method for CTC detection that bypasses enrichment and the dependency on EpCAM expression, which are two major constraints of NSCLC CTC study. This method is simple, accurate and particularly sensitive in NSCLC, highlighting its potential for clinical application. We demonstrated that EF-FACS-detected CTCs are an independent prognostic biomarker in NSCLC. They show a high value in risk stratification of NSCLC patients within the first 6 months from CTC collection, allowing the identification of a subset of patients who could potentially benefit from an intensification of treatment. EF-FACS CTCs and ctDNA proved to be independent prognostic biomarkers and CTC count has a complementary value to ctDNA at an intermediate-term monitoring. Historically, the “gold standard” strategy to work on CTCs, was determined by a combination of an enrichment process and CTC detection, and eventually their isolation. Across the last decades, different techniques based on positive selection according to size, deformability or protein expression have been developed to detect CTCs. Among these, CellSearch enumerates CTCs by a positive selection, and has a prognostic value that permitted the FDA approval for breast, 37 , 38 colon 11 and prostate cancer. 12 Nevertheless, technologies limiting CTC capture to EpCAM are not sensitive in patients diagnosed with NSCLC. 23 Otherwise, techniques based on size and deformability of CTCs, like Parsortix, which was previously validated in breast cancer, 39 – 42 or ISET, 14 – 16 , 24 , 26 tend to associate improved detection rates in NSCLC patients. However, they restrain their selection to pre-defined physical characteristics and do not cover the wide spectrum of CTC morphologic heterogeneity. To bypass the constraints mentioned above, our group, along with others, have used the RosetteSep protocol that enables an enrichment on the basis of a negative selection of white blood cells. Indeed, the combination of RosetteSep and FACS facilitates the preservation of the extensive phenotypic heterogeneity of detected and isolated CTCs. 17 , 19 , 43 – 45 Nevertheless, it must be noted that this technique is associated with considerable cell loss. Contrarily, EF-FACS combines red blood cell lysis and permeabilization in one simple step, followed by direct immunostaining, and flow cytometry analysis. The tumoral origin of the detected events can be confirmed by the presence of aberrant copy number profiles in CTCs sorted at single-cell level, as we report in the present work. Our results show that EF-FACS has a high accuracy and sensitivity in NSCLC CTC detection. In fact, it enables a higher detection rate of CTCs (67.4% of patients with 5 or more CTCs) when compared to CellSearch (10.5% of patients with 2 or more CTCs in our cohort, and 23–40% reported in literature). 8 – 10 , 24 Hence, the technique's sensitivity and its simplicity, coupled with its accessibility through FACS technology, makes it a democratizable and broadly implementable option in numerous laboratories. From a clinical point of view, CellSearch CTC counts have been described to be prognostic in advanced NSCLC patients. 8 , 13 Nevertheless, consistent with published studies, the fraction of patients exhibiting a positivity on CTCs remains limited (23–40%). 8–10,24 This fact hinders the clinical utility of CellSearch in NSCLC. EF-FACS presented a higher sensitivity and captured a larger spectrum of patients, revealing a correlation between increasing counts of EF-FACS detected CTCs and a worsening of prognosis. The prognostic significance of the continuous value of CellSearch -detected CTC counts was not possible, as most patients did not yield any CTC. On a prognostic statistical setting, the threshold of positivity was statistically determined at 5 EF-FACS CTCs per 7.5 mL, although the measurement was made with a larger volume of blood (usually 12–14 mL). This value correlated with a significant risk of early death. Its prognostic value along with the sensitivity of the method, opens a window of opportunity for its application in clinical practice. Beyond CTCs, another component of liquid biopsy, ctDNA, has become increasingly used in clinical practice. Among its clinical applications, ctDNA has shown to shorten time to treatment initiation in onco-addicted NSCLC patients. 46 , 47 It has a role in guiding personalized therapeutic strategies, particularly through the evaluation of resistance mechanisms to targeted therapies. 48 Moreover, ctDNA clearance has emerged as a promising biomarker for monitoring treatment response. 49 – 51 Nevertheless, its applications when combined with CTCs have been less explored. Punnoose et al. proposed that decreasing CTC counts may be used as an early indicator of targeted therapy response, and ctDNA could help with the assessment of tumor mutational status. 52 In this sense, our results show that both CTCs and ctDNA VAF were independent biomarkers in our NSCLC cohort. We evaluated the implications of combining CTCs and ctDNA VAF in risk stratification, suggesting that CTCs were not only useful in the short term when combined with clinical prognostic variables, but also in an intermediate term when associated to ctDNA and clinical variables. This highlights the utility of these two independent biomarkers to identify patients with poor prognosis. Similarly, Radovich et al. reported that ctDNA and CTCs were independently associated with disease recurrence in patients with early stage Triple Negative Breast Cancer receiving neoadjuvant treatment. 53 Interestingly, in our study CTCs were significantly associated with short term risk of death, which may reflect an aggressive biological behavior of CTCs that contributes to the dissemination process. An important remark to be considered, is that despite the clinical use of ctDNA, it is limited to genomic analysis, while integral CTCs enable the sequencing of both DNA and RNA, and their immunophenotyping. Indeed, in the present era of antibody-drug conjugates and bi-specific antibodies, with an increasing interest in the evaluation of biomarkers, CTCs provide a real-time image of tumor protein landscape in a minimally invasive way. Furthermore, the isolation of CTCs permits the combination of a phenotypic and genomic characterization at the single-CTC level. This approach has been described to be useful in the evaluation of mechanisms of resistance, as reported by our group in a BRAF and ALK NSCLC patient cohort. 17 – 19 Finally, the application of multi-protein panels, using sensitive flow cytometry, combined with single-cell transcriptomic analysis, could enable the characterization of different potentially druggable subgroups of CTCs. While our study presents the validation and clinical application of this new method, several limitations must be considered. The evaluated cohort is limited and heterogeneous in histology and molecular landscape, and includes patients with a locally advanced disease. Regarding ctDNA analysis, given the enriched population with targetable genomic alterations, we considered the ctDNA VAF values for known onco-drivers, in accordance with previous publications. 54 Finally, despite the fact that we found that EF-FACS was more sensitive and enabled a better patient stratification than CellSearch , a confirmation of our results with a cross validation with an external laboratory should be considered. In conclusion, our study demonstrates that EF-FACS is a relatively simple, democratizable, and sensitive technique for the characterization and isolation of CTCs in the challenging population of NSCLC patients. The complementary use of CTCs and ctDNA enables a prognosis stratification and provides an insight on evolution of the tumor across treatments, in a minimally invasive way. Declarations AUTHORS’ CONTRIBUTIONS MVSB : data curation, investigation, formal analysis, validation, visualization, methodology, writing–original draft, project administration MO : formal analysis, validation, methodology, writing–original draft DD : statistical analysis, validation, writing–original draft and editing JR : conceptualization, resources, manuscript editing MNC : resources, project administration CN : resources, project administration FM : methodology, provided reagents, manuscript editing JMB : methodology, provided reagents, manuscript editing ND : methodology, formal analysis, validation MD : formal analysis AA : technical assistance BJ : formal analysis BL : technical assistance AI : Resources, project administration, supervision, validation, DP : conceptualization, resources BB : conceptualization, resources, supervision, validation, funding acquisition, manuscript editing FF : conceptualization, supervision, validation, funding acquisition, writing–original draft PP : conceptualization, resources, data curation, supervision, methodology, writing–original draft ACKNOWLEDGEMENTS We thank the patients who participated in the trial, the staff members who assisted with the trial, and the team of Precision Medicine Department that supported the trial. This study was supported by the contribution of the “parrainage chercheur-cancer du poumon”. AUTHOR’S DISCLOSURES The authors declare no competing interests. Maria Virginia Sanchez Becerra Research funding: Pharmamar, Abbvie. Jordi Remon Consulting or Advisory Role: Pfizer, Bristol-Myers Squibb, MSD Oncology, Astra/Zeneca, OSE Immunotherapeutics, Janssen Oncology, Genmab, Boehringer ingelheim, Sanofi, Roche/Genentech, Merck Travel, Accommodations, Expenses: Roche/Genentech, Inivata, OSE Immunotherapeutics, OSE Immunotherapeutics, AstraZeneca, MSD Oncology Grants: MSD outside submitted work Leadership role as Secretary of the EORTC lung cancer group. David Planchard Honoraria: AstraZeneca, Bristol-Myers Squibb, Boehringer Ingelheim, Celgene, Eli Lilly, Merck, Merck Sharp and Dohme Oncology, Novartis, Pfizer, prIME Oncology, Roche; Consulting or advisory role: AstraZeneca, Bristol-Myers Squibb, Boehringer Ingelheim, Celgene, Eli Lilly, Merck Sharp and Dohme Oncology, Novartis, Pfizer, prIME Oncology, Roche. Travel, accommodation, expenses: AstraZeneca, Roche, Novartis, prIME Oncology and Pfizer Antoine Italiano Grants from Bayer, MSD, Roche, Merck, and AstraZeneca outside the submitted work. Benjamin Besse Consulting or advisory role: Abbvie, Biontech SE, Beijing Avistone Biotechnology; BristolMyerSqibb, CureVac AG, Pharmamar, Regeneron, Sanofi Aventis, Eli Lilly, Ellipses pharma Ltd, F.Hoffmann-La Roche Ltd, Foghorn Therapeutics Inc., Genmab, Immunocore, Owkin. Speaker: Abbvie, AstraZeneca, BristolMyerSqibb, Daichii Sankyo, Lilly, MSD, Ose Immunotherapeutics, Sanofi Aventis, Servier Steering committee: Astrazeneca, Amgen, Beigene, CureVac AG, GENMAB A/S, Janssen, MSD, Ose Immunotherapeutics, Pharmamar, Sanofi, Takeda Conflict of interest statement MO, DD, MNC, CN, FM, JMB, ND, MD, AA, BJ, BL, PP, FF declare no potential conflicts of interest References Siegel, R. L., Giaquinto, A. N. & Jemal, A. Cancer statistics, 2024. CA Cancer J Clin 74 , 12–49 (2024). Paez, J. G. et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 304 , 1497–1500 (2004). Alix-Panabières, C. & Pantel, K. Advances in liquid biopsy: From exploration to practical application. Cancer Cell 43 , 161–165 (2025). Rolfo, C. et al. Liquid Biopsy for Advanced NSCLC: A Consensus Statement From the International Association for the Study of Lung Cancer. J Thorac Oncol 16 , 1647–1662 (2021). Pascual, J. et al. 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Characteristics of the population of study: whole population, CellSearch vs EF-FACS cohort and CTC-ctDNA cohort. y/o: year old. ECOG-PS: Eastern Cooperative Oncology Group Performance Status. CTC: Circulating Tumor Cells. ctDNA: circulating tumor DNA. VAF: Variant Allele Frequency. CTC counts ( CellSearch and EF-FACS are referred to 7.5 mL blood). Characteristics N(%) Whole population (n=95 pts) CellSearch vs EF-FACS cohort (n=74 pts) CTC-ctDNA cohort (n=85 pts) Age (y/o) [IQR ] 66 (30-90) 64 (30-90) 66 (59-74) Sex -Men -Women 47 (49%) 48 (51%) 38 (51%) 36 (49%) 40 (47%) 45 (53%) Smoking status - Smoker - Non smoker 65 (68%) 30 (31%) 48 (65%) 26 (35%) 57 (67%) 28 (33%) ECOG-PS - 0 - 1 - ≥2 28 (29%) 50 (53%) 17 (18%) 24 (32%) 36 (49%) 14 (19%) 24 (28%) 45 (53%) 16 (19%) Stage (AJCC 8th Edition) - III - IV 7 (7%) 88 (93%) 6 (8%) 68 (92%) 7 (8%) 78 (92%) Histology - Adenocarcinoma - Squamous - Other 78 (82%) 8 (8%) 9 (10%) 59 (80%) 8 (11%) 7 (9%) 72 (85%) 5 (6%) 8 (9%) N lines of treatment at CTC isolation - Treatment naîve - 1 line - ≥2 lines 69 (73%) 20 (21%) 6 (6%) 53 (72%) 16 (22%) 5 (6%) 64 (75%) 13 (15%) 8 (9%) CTC CellSearch Counts Median (range) 0 ≥1 ≥2 ≥5 Not analysed 0 (0;3) 71 (75%) 3 (3%) 2 (2%) 0 21 (22%) 0 (0;3) 71 (96%) 3 (4%) 2 (3%) 0 0 0 (0;3) 61 (72%) 3 (3%) 2 (2%) 0 21 (25%) CTC EF-FACS Counts Median (range) <5 ≥5 6 (0;48) 35 (37%) 60 (63%) 6 (0;48) 26 (35%) 48 (65%) 6 (0;48) 32 (38%) 53 (62%) ctDNA VAF <0,25% ≥0,25% Not analysed 28 (29%) 61 (64%) 6 (6%) 20 (27%) 48 (65%) 6 (8%) 26 (31%) 59 (69%) 0 Additional Declarations No competing interests reported. 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10:29:57","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":20531,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7347165/v1/499dcff95a36f97e3e1590fe.png"},{"id":91845833,"identity":"2457a388-3228-4e73-b151-e59194ff6501","added_by":"auto","created_at":"2025-09-22 10:13:57","extension":"xml","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":142618,"visible":true,"origin":"","legend":"","description":"","filename":"347ad969fc9e4e8fb89546b05bb7d4b31structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7347165/v1/499ed378aa3f25cc007717c1.xml"},{"id":91845834,"identity":"a4509a26-c1ea-40ef-89ab-8986da0055a5","added_by":"auto","created_at":"2025-09-22 10:13:57","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":161373,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7347165/v1/fbfc8f53ac21ecd4086599e4.html"},{"id":91845819,"identity":"941ce227-9828-4c3f-a117-87984b80cab8","added_by":"auto","created_at":"2025-09-22 10:13:57","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":282178,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart of the study. \u003c/strong\u003eNinety-five patients diagnosed with a Non-small Cell Lung Cancer were included in the study. \u003cem\u003eCellSearch\u003c/em\u003e vs EF-FACS cohort includes patients with paired samples undergoing \u003cem\u003eCellSearch\u003c/em\u003e and Enrichment-Free Fluorescence Activating Cell Sorting (EF-FACS)\u003cem\u003e.\u003c/em\u003e CTC-ctDNA cohort included patients with both \u003cem\u003ectDNA \u003c/em\u003eand \u003cem\u003eEF-FACS CTC \u003c/em\u003eanalysis at same timepoints.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7347165/v1/6cd8224543b5209aea979709.jpeg"},{"id":91847321,"identity":"be239743-8776-4ff3-979d-421a308f03b3","added_by":"auto","created_at":"2025-09-22 10:21:57","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":455144,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of the EF-FACS workflow for CTC detection. A. Workflow applied in the NSCLC \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCellSearch\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003evs\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e EF-FACS cohort\u003c/strong\u003e: patient blood samples underwent both \u003cem\u003eCellSearch\u003c/em\u003eand EF-FACS workflows. Confirmation of malignancy of sorted candidates EF-FACS CTCs was made by retrieving Copy Number Alteration profile on Low-Pass Whole Genome Sequencing. Gating strategy to identify CTCs by flow cytometry is shown on the right panel \u003cstrong\u003eB. Pearson correlation (R2) of spiked versus recovered PC9 in healthy donor blood samples undergoing EF-FACS workflow. \u003c/strong\u003eEach point represents one blood sample. \u003cstrong\u003eC. Violin plot representing the comparison of CTCs count/7,5 mL of blood obtained by \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCellSearch\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and EF-FACS from 86 paired blood samples \u003c/strong\u003ein the \u003cem\u003eCellSearch \u003c/em\u003evs\u003cem\u003e \u003c/em\u003eEF-FACS\u003cstrong\u003e \u003c/strong\u003ecohort, confirming significant superiority of EF-FACS in CTC detection (Wilcoxon signed rank test p\u0026lt;0,0001). \u003cstrong\u003eD. Comparison of different thresholds to define the EF-FACS positivity based on the Akaike information criterion (AIC) of the multivariable Cox model adjusted for the classical risk factors (lower is better). \u003c/strong\u003eAnalysis was based on data from CTC-ctDNA cohort.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7347165/v1/c8b7df01256983f0560072e6.jpeg"},{"id":91845820,"identity":"a0bbb44e-2f17-4492-9d34-10748f98f096","added_by":"auto","created_at":"2025-09-22 10:13:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":204025,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpact of increasing EF-FACS CTC counts and increasing ctDNA VAF on survival. \u003c/strong\u003eA. Survival area for increasing CTC counts by EF-FACS adjusted for the classical risk factors (treatment with target therapy [TKI], age, gender, presence of metastasis at the CTC collection, ECOG-Performance Status, and time since diagnosis [TSD] at the CTC collection) using G-computation. B. Forest plot of the multivariable Cox model adjusted for the EF-FACS and classical risk factors. C. Survival area for increasing VAF for oncogenic alterations detected on Next Generation Sequencing of ctDNA adjusted for the classical risk factors using G-computation. D. Forest plot of the multivariable Cox model adjusted for the ctDNA VAF values for oncogenic mutations and classical risk factors\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7347165/v1/2a419a12792c67a12394751f.png"},{"id":91847320,"identity":"ac0cfb45-1dcb-41e1-b2f3-a393a1993b93","added_by":"auto","created_at":"2025-09-22 10:21:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":72968,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvaluation of the performance of the risk of death prediction models. « Clinical » \u003c/strong\u003ecorresponds to a risk model combining prognostic clinical variables: age, gender, metastasis at CTC collection, use of target therapy, delay between CTC isolation and diagnosis and ECOG-Performance Status.\u003cstrong\u003e« CTC » \u003c/strong\u003ecorresponds to the EF-FACS detected CTCs considering a positivity threshold of ≥ 5 CTCs.\u003cstrong\u003e « ctDNA » \u003c/strong\u003econsiders a positivity threshold of Variant Allele Frequency ≥ 0.25% for known oncogenic drivers. White boxes represent the AUC of each prediction model. Blue boxes corresponds to p-values obtained after the comparison of each risk of death prediction model reflected on the correspondant raw and column. Two time frames were considered in the analysis : (A) short term (within the 6 months from CTC collection), (B) intermediate term (within the 12 months from CTC collection).\u003c/p\u003e\n\u003cp\u003eIn a short term, the score based on clinical variables and CTC performed better in risk of death prediction (AUC 0.818, p 0.0212) than the score combining clinical variables and ctDNA (AUC 0.732) or the score combining the clinical variables, CTC and ctDNA (AUC 0.794)\u003c/p\u003e\n\u003cp\u003eIn an intermediate term, the score based on clinical variables, CTC and ctDNA was statistically the most performant (AUC 0.829, p 0.0364), comparing to the score combining clinical and ctDNA (AUC 0.821), or the score based on clinical variables and CTC (AUC 0.759).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7347165/v1/cd692c6fd4215e737d2dfa75.png"},{"id":91848212,"identity":"bc4de72b-1800-4ba3-afc9-e0bc945ccd37","added_by":"auto","created_at":"2025-09-22 10:37:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2549605,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7347165/v1/39a4fdc2-f273-40af-b1d0-168173c41a8a.pdf"},{"id":91845826,"identity":"6a0eb3e2-2025-4130-a71b-0e2484386f64","added_by":"auto","created_at":"2025-09-22 10:13:57","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":738785,"visible":true,"origin":"","legend":"","description":"","filename":"3.SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7347165/v1/7a52918df2dd4300f789d90d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"EF-FACS circulating tumor cell detection complements ctDNA-based prognosis in stratification of non-small cell lung cancer patients","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eLung cancer represents a leading cause of cancer mortality worldwide.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Patients are generally diagnosed at an advanced disease stage that negatively impacts prognosis and life expectancy. Non-Small Cell Lung Cancer (NSCLC) has become the paradigm of precision medicine, prolonging survival and improving quality of life. Since the discovery of \u003cem\u003eEGFR-\u003c/em\u003emutation as the first predictive biomarker in NSCLC, \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e an increasing number of druggable genomic alterations have been identified. These include \u003cem\u003eALK, ROS1, KRAS, BRAF, HER2, RET\u003c/em\u003e and \u003cem\u003eMET\u003c/em\u003e, leading to the development of targeted therapies for all these genomic alterations, which become the standard of care on first- and subsequent treatment lines in patients with metastatic NSCLC. Moreover, immune checkpoint blockers (ICB) have also emerged as a therapeutic strategy for patients not able to receive targeted therapies.\u003c/p\u003e\u003cp\u003eIndeed, the development of resistant cell subpopulations within highly heterogeneous cancers still displays a major obstacle to the effective cancer treatment. Hence, the monitoring of disease with sequential tissue biopsies is critical and not feasible in daily practice. Indeed, tissue biopsies frequently underestimates the complexity of the tumor molecular level, thus failing to represent tumor heterogeneity.\u003c/p\u003e\u003cp\u003eIn this setting, liquid biopsies have become a powerful tool, providing a real-time perspective of cancer evolution, and giving a holistic view of tumor spatial and temporal heterogeneity in a minimally invasive way.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Among the different components of liquid biopsies, circulating tumor DNA (ctDNA) became frequently used in clinical practice. Recently, the International Association for the Study of Lung Cancer supports the role of ctDNA as a complement to tissue biopsy at diagnosis, and advocates for a \u0026ldquo;plasma first\u0026rdquo; approach to evaluate the mechanisms of resistance in NSCLC harbouring druggable oncogenic alterations.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e However, ctDNA may have several limitations such as: false negative results, particularly in patients with limited brain metastatic disease, as well as false positive results associated with clonal hematopoiesis or germline variants. Moreover, ctDNA cannot provide transcriptomic and proteomic profiles of cancer cells. Finally, ctDNA fails to capture tumor heterogeneity since it can only be reached by single-cell analysis.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eAnother component of liquid biopsy, circulating tumor cells (CTCs), are an extremely rare fraction of cells in the bloodstream, deriving from primary tumor and metastasis, including aggressive clones prone to resist therapies. They have proved to be associated with prognosis and progression in NSCLC.\u003csup\u003e\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e The value of CTC counts as a predictive biomarker of drug sensitivity and pharmacodynamics has been reported in diverse solid tumors.\u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e When regarding NSCLC, our group demonstrated the value of CTCs harboring aberrant \u003cem\u003eALK\u003c/em\u003e copy number in the prediction of crizotinib sensitivity and the levels of chromosomal instability in \u003cem\u003eROS1\u003c/em\u003e-rearranged NSCLC.\u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Moreover, in contrast to ctDNA, which solely limits to bulk analysis, CTCs represent a valuable tool in which single-cell analysis can be performed. Recently, we and others have exploited single-CTC genomic sequencing to investigate therapeutic resistance mechanisms and describe tumor heterogeneity.\u003csup\u003e\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eDespite recent technological advances, the multiomic analysis of CTCs remains extremely challenging because of the rarity and heterogeneity of CTCs that significantly restrains the tumor material for molecular analysis. The dependence of EpCAM expression for CTC detection by the \u0026ldquo;gold standard\u0026rdquo; \u003cem\u003eCellSearch\u003c/em\u003e technology particularly limits the CTC recognition in NSCLC, due to the high reliance on the Epithelial Mesenchymal Transition (EMT) that associates a loss of EpCAM expression.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e For patients with stage II-IV NSCLC, only up to 23\u0026ndash;27% will yield 2 or more CTCs detected with \u003cem\u003eCellSearch.\u003c/em\u003e\u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e To overcome this constraint, methods such as those based on size, deformability or surface protein expression of CTCs have been developed.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e In all these methods, the need for an enrichment process, either by positive or negative selection, before CTC characterisation, considerably impacts the final recovery of extremely rare events. Bypassing this process could open a window of opportunity to increase the quantity of tumor material available to perform wider molecular analysis.\u003c/p\u003e\u003cp\u003eIn the current study, we present a new detection method for CTCs from advanced NSCLC patients, deprived of the enrichment step, which enables the detection of CTCs in whole blood samples after immunofluorescence staining. Indeed, we show that this method, further called \u003cb\u003eE\u003c/b\u003enrichment-\u003cb\u003eF\u003c/b\u003eree \u003cb\u003eF\u003c/b\u003eluorescence \u003cb\u003eA\u003c/b\u003ectivated \u003cb\u003eC\u003c/b\u003eell \u003cb\u003eS\u003c/b\u003eorting (EF-FACS) enables higher recovery of NSCLC CTCs compared to \u003cem\u003eCellSearch.\u003c/em\u003e We reveal a prognostic prediction for advanced NSCLC, with EF-FACS CTCs performing better in the short term (within the first 6 months), whereas both EF-FACS CTCs and ctDNA have a higher value for prediction in the intermediate term (within the first year after blood collection).\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePatients\u003c/h2\u003e\u003cp\u003ePatients with advanced NSCLC from Gustave Roussy (Villejuif, France) were enrolled in this study. The blood samples collection was performed within the STING study (ClinicalTrials.gov identifier: NCT04932525). This trial was conducted in accordance with the Declaration of Helsinki. It was authorized by the French national regulation agency (ANSM) and approved by the Ethics Committee and the institutional review board. Written informed consent was obtained from all patients. Clinical, pathological and molecular data were collected from the electronic medical records. Blood samples (20 mL) at baseline, and optionally at an early timepoint (on days 21\u0026ndash;28 of treatment) and at progression, were collected in CellSave tubes (\u003cem\u003eMenarini Silicon Biosystems\u003c/em\u003e) for CTC analysis and manipulated within the 72 hours from blood collection.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCell line and spiking\u003c/h3\u003e\n\u003cp\u003eLung adenocarcinoma cell line with a deletion in exon 19 of \u003cem\u003eEGFR\u003c/em\u003e (PC9) was obtained from the ATCC collection and maintained in RPMI-1640 Medium (\u003cem\u003eGibco, Ref: 61870-010\u003c/em\u003e) supplemented with 1% penicillin/streptomycin (\u003cem\u003eGibco by Life technologies, Ref: 15140-122\u003c/em\u003e) and 10% fetal bovine serum (\u003cem\u003eSIGMA, Ref: F7524\u003c/em\u003e) at 37\u0026deg; C in 5% CO\u003csub\u003e2\u003c/sub\u003e. \u003cem\u003eMycoplasma\u003c/em\u003e negativity was regularly confirmed. Blood samples of healthy donors were collected by the public Blood French Establishment (\u003cem\u003e\u0026Eacute;tablissement Fran\u0026ccedil;ais du Sang\u003c/em\u003e), ensuring anonymization. Pre-established quantities of PC9 cells were directly spiked into healthy donor blood samples by a CytoFLEX SRT cell sorter (\u003cem\u003eBeckman Coulter Life Sciences\u003c/em\u003e). Each sample from triplicate came from different donors.\u003c/p\u003e\n\u003ch3\u003eCellSearch\u003c/h3\u003e\n\u003cp\u003eCTCs were enriched and enumerated by using \u003cem\u003eCellSearch\u003c/em\u003e (\u003cem\u003eMenarini Silicon Biosystem\u003c/em\u003e) on 7.5 mL of blood as previously reported.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Blood samples were processed within 96 hours and analyzed by two independent experts blinded to clinical outcomes according to the FDA-approved definition for CTCs, namely cells positive for EpCAM, CK and nuclear stain DAPI, but negative for leukocyte marker CD45. CTC enumeration is reported as number per 7.5 mL of blood.\u003c/p\u003e\n\u003ch3\u003eEF-FACS (Enrichment-Free Fluorescence-Activated Cell Sorting)\u003c/h3\u003e\n\u003cp\u003eBlood was transferred to 50 mL tubes. The components Fixative Buffer and Permeabilizing Buffer (PerFix CTC, \u003cem\u003eBeckman Coulter Life Sciences)\u003c/em\u003e were used according to manufacturer protocol and applied to a corresponding volume of blood. Briefly, 100 \u0026micro;L of fixative buffer was added to each 1 mL of blood and incubated 15 minutes at room temperature. Then 6 mL of permeabilizing buffer per 1 mL of blood was added, and, after a 20-minutes incubation, cells were centrifuged at 500 x g for 10 minutes and transferred into the FACS tubes. The antibodies used for the immunofluorescence staining were: 0.625\u0026micro;L of CD45-PC7 (\u003cem\u003eBeckman Coulter)\u003c/em\u003e; 3\u0026micro;L of CD66b-APC-A750 (\u003cem\u003eBeckman Coulter)\u003c/em\u003e; 1\u0026micro;L of EpCAM-Alexa Fluor 647 (\u003cem\u003eNovus Biologicals, Ref: #NBP2-33078AF647\u003c/em\u003e); 2\u0026micro;L of CK-Alexa Fluor 488 (\u003cem\u003eCellSignaling, Ref: #4523S\u003c/em\u003e); 1\u0026micro;L of CK7-Alexa Fluor 488 (\u003cem\u003eAbcam, Ref: #ab208273\u003c/em\u003e), and 25\u0026micro;L of Hoechst 33342 (\u003cem\u003eMerck\u003c/em\u003e #B2261). This antibody panel was added prior to analysis on the CytoFLEX SRT sorter, equipped with four lasers (405nm, 488nm, 638nm, 561nm) and a 100\u0026micro;m noozle. A sequential gating strategy was used to identify the events of interest, considering a morphologic selection (FSC-A/SSC-A), a positivity for Hoechst-33342, Cytokeratin-7 and pan-cytokeratin; a negativity of CD45 and CD66b (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). For representative samples, single CTCs and white blood cells were sorted into a 96-well plate and stored at -20\u0026deg; C for genome sequencing.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eWhole Genome Amplification (WGA), Quality Controls (QC)\u003c/h3\u003e\n\u003cp\u003eWGA was performed using the Ampli1 WGA kit (\u003cem\u003eMenarini Silicon Biosystems, #KI0030\u003c/em\u003e) following the manufacturer\u0026rsquo;s instructions. Quality of Ampli1 WGA products was checked using the Ampli1 QC Kit (\u003cem\u003eMenarini Silicon Biosystems, #KI0027\u003c/em\u003e) according to manufacturer's instructions.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eLibrary preparation and low-pass whole-genome sequencing (LP-WGS)\u003c/h2\u003e\u003cp\u003eAmpli1 LowPass kit for Illumina (\u003cem\u003eMenarini Silicon Biosystems, #KI0041 #KI0040\u003c/em\u003e) was used for preparing LP-WGS libraries from single CTCs, White Blood Cells bulks and germline DNA. Ampli1 LowPass libraries were normalized and sequenced by MiSeq instrument using 150 SR rapid-run mode.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eBioinformatics workflow for low-pass whole-genome sequencing\u003c/h3\u003e\n\u003cp\u003eReads were demultiplexed to the sample level using bcl2fastq2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://support.illumina.com/sequencing/sequencing_software/bcl2fastq-conversion-software.html\u003c/span\u003e\u003cspan address=\"https://support.illumina.com/sequencing/sequencing_software/bcl2fastq-conversion-software.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) v2.19, and their quality assessed with FastQC (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioinformatics.babraham.ac.uk/projects/fastqc\u003c/span\u003e\u003cspan address=\"http://www.bioinformatics.babraham.ac.uk/projects/fastqc\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) v0.11.1. Read trimming was performed with fastp v0.23.2,\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e discarding low quality (\u0026lt;\u0026thinsp;20) and uncalled bases, Illumina adapters and ending polyG tracks. Cross-species contamination was performed using Fastq_screen v0.14.3 against default species.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Trimmed reads were mapped with BWA-mem2 v2.2.1.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e PCR and optical duplicates were marked using Picard (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://broadinstitute.github.io/picard/\u003c/span\u003e\u003cspan address=\"https://broadinstitute.github.io/picard/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) v3.1.0. Unmapped reads and reads with mate mapped on another chromosome were discarded using samtools v1.18.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e Depth and coverage metrics were evaluated using mosdepth v0.3.8.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e All QC results were aggregated using MultiQC v1.19.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e Copy-number detection, cellularity and global ploidy were assessed using the ichorCNA v0.5.0 package for R v4.1.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e For a better quality of copy number profiles, a panel of normal using all WGA reference samples was constituted and used as reference for all tumor samples. IchorCNA was used with these custom parameters: expected ploidy between 1 and 4, expected cellularity between 80 and 100%, window size of 500 Kb. CNA profiles quality was evaluated manually, discarding profiles with high noise (high spread) and over-segmentation. Selected CNA profiles aggregation, clustering and heatmap plots were generated using in-house scripts under R v4.1.\u003c/p\u003e\n\u003ch3\u003ectDNA\u003c/h3\u003e\n\u003cp\u003eFor the CTC and ctDNA paired analyses, the cohort included 85 patients who also had a \u003cem\u003eFoundationOne Liquid CDx\u003c/em\u003e analysis performed in routine clinical practice at CTC baseline timepoint. Circulating cell-free DNA extracted from plasma underwent a whole genome shotgun library construction and hybridization-based capture of 324 cancer-related genes, including coding exons and select introns of 309 genes, and select intronic regions or non-coding regions of 15 genes. Hybrid capture selected libraries were sequenced with deep coverage using the \u003cem\u003eNovaSeq 6000\u003c/em\u003e platform. Data were processed with customized analysis pipelines for detection of genomic alterations. Substitutions and insertion and deletion alterations (indels) were reported in 311 genes, copy number alterations were reported in 310 genes, and gene rearrangement in 324 genes. Variant Allele Frequency was presented in percentage, and considered for known oncogenic alterations as reported in OncoKB (available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.oncokb.org\u003c/span\u003e\u003cspan address=\"http://www.oncokb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and cBioportal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.cbioportal.org\u003c/span\u003e\u003cspan address=\"http://www.cbioportal.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe Pearson Correlation Coefficient was calculated to assess the strength of the linear relationship between spiked and recovered cell counts. To compare the performance of \u003cem\u003eCellSearch\u003c/em\u003e and EF-FACS in CTC detection on matched samples, we employed the non-parametric Wilcoxon signed rank test for paired data. The main endpoint was the overall survival (OS), defined as the time between the CTC collection and patient death. The secondary endpoint was the progression-free survival (PFS) defined as the time between the CTC collection and the patient progression or death. The prognostic value of EF-FACS-detected CTCs and ctDNA was assessed using Cox multivariable model adjusted for the known prognostic factors (age, gender, presence of metastasis at the CTC collection, ECOG-Performance Status, and treatment with target therapy). These analyses were adjusted for the time between diagnosis and CTC collection to take this period into account. The positivity threshold of the ctDNA was determined from the literature,\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e considering a positive result when the variant allele fraction (VAF) was \u0026ge;\u0026thinsp;0.25% in known oncogenic genomic alterations. The positivity threshold of the CTCs was obtained by the multivariable Cox model minimizing the Akaike information criterion (AIC). The proportional hazard assumption was checked using Schoenfeld residuals visualization and trend test. When this assumption was not met, we used a penalized spline to capture the functional form of the time-varying association with the spline function in the time-transform feature of the coxph function (tt). The optimal degrees of freedom were selected automatically based on the cAIC. The additional value of the EF-FACS-detected CTCs and ctDNA to the classical risk factors for the prognostic prediction was assessed from the discrimination performance of the linear predictor of the multivariable Cox model adjusted for the clinical risk factors. This discrimination was measured for the prediction of the prognosis at two clinically relevant timepoints (6 and 12 months) using the area under the time-dependent receiver operating characteristic curve computed using the package timeROC.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003ePatients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom December 2022 to June 2024 ninety-five patients were enrolled. The study was divided into two cohorts: (i) \u003cem\u003eCellSearch\u003c/em\u003e vs EF-FACS cohort, with 86 paired blood samples from 74 patients for the comparison of both methodologies and technical validation of EF-FACS; and (ii) CTC-ctDNA cohort, including 85 patients with a ctDNA analysis at same time of CTC collection. The design of the study is presented in \u003cem\u003eFigure 1.\u0026nbsp;\u003c/em\u003eBaseline characteristics are summarized in \u003cem\u003eTable 1.\u003c/em\u003e The median age of patients was 66 years old [30-90], including 51% of females and 32% never-smokers. Adenocarcinoma was the most common histological subtype (82%), followed by squamous histology (8%). The remaining patients were diagnosed of No Otherwise Specified (NOS) (4 patients), large cell carcinoma (2 patients), sarcomatoid (1 patient) and undifferentiated carcinoma (2 patients). Oncogene alterations were identified in 46 tumor samples including: 16 classic activating \u003cem\u003eEGFR\u003c/em\u003e mutations, 7 \u003cem\u003eALK\u003c/em\u003e fusions, 9 \u003cem\u003eKRAS G12C\u003c/em\u003e mutations, 7 \u003cem\u003eRET\u0026nbsp;\u003c/em\u003efusions, 4 \u003cem\u003eMET\u0026nbsp;\u003c/em\u003eexon 14 skipping mutations and 3 \u003cem\u003eBRAF V600E\u003c/em\u003e mutations. The majority of patients were treatment na\u0026iuml;ve (73%). 20 patients had blood collection after progression to first line of treatment (21%). At the time of blood collection, 39% patients started target therapy, 36% chemo-immunotherapy, 7% chemotherapy, 7% chemo-radiotherapy, 7% exclusive symptomatic treatment and 4% immunotherapy. Overall, median progression free survival (PFS) was 0.693 years [95% CI; range, 0.534-0.912]. Median overall survival (OS) was not reached (\u003cem\u003eSupplementary Figure S1)\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnrichment-Free Fluorescence-Activating Cell Sorting CTC detection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe EF-FACS workflow includes steps of fixation, permeabilization, and immunostaining of 12 mL blood that enables a phenotypic characterization of CTCs by a new generation flow cytometer (CytoFLEX SRT) and single cell sorting for further molecular analysis. Low-pass whole genome sequencing was performed to confirm malignancy status in a representative selection of sorted single cells (Cytokeratins+, CD45-, CD66B-, Hoechst33342+)\u003cem\u003e.\u003c/em\u003e The schematic workflow applied in the study is presented in \u003cem\u003eFigure 2A.\u003c/em\u003e A selection of copy number alteration (CNA) profiles of representative patients is shown in \u003cem\u003eSupplementary Figure S2\u003c/em\u003e. Single-CTC CNA profiles demonstrated the intra- and inter-tumor heterogeneity as previously reported.\u003csup\u003e18,19\u003c/sup\u003e\u003cem\u003e\u0026nbsp;\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the accuracy of EF-FACS, a recovery analysis was performed. Pre-established quantities of PC9 cells were directly spiked into healthy donor blood samples using a CytoFLEX SRT sorter. Triplicate blood samples with or without spiked tumor cells underwent an EF-FACS workflow as presented in \u003cem\u003eFigure 2A\u003c/em\u003e. The Pearson Correlation Coefficient was equal to 0.937 (p=1.689e-11), indicating a high accuracy in tumor cell detection by EF-FACS, especially at low counts of CTCs. (\u003cem\u003eFigure 2B\u003c/em\u003e and \u003cem\u003eSupplementary Table S1\u003c/em\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEighty-six paired blood samples of 74 patients were used for a comparison analysis of EF-FACS with the standard \u003cem\u003eCellSearch\u003c/em\u003e (\u003cem\u003eFigure 2A\u003c/em\u003e). Median number of CTCs detected by \u003cem\u003eCellSearch\u003c/em\u003e was 0 [range: 0-31] and for EF-FACS 6 CTCs [range: 0-48]. For EF-FACS analysis 58 out of 86 samples (67.4%) presented at least 5 CTCs per 7.5 mL of blood, while in 26 blood samples 10 or more CTC (30.2%) were found. Contrarily, only 4.6% of samples processed by \u003cem\u003eCellSearch\u003c/em\u003e presented 5 or more CTCs. The differences in CTC detection using two parallel methods were highly significant (p\u0026lt;0.0001) (\u003cem\u003eFigure 2C; Supplementary Table S2\u003c/em\u003e) showing a superiority of EF-FACS for detecting CTCs in NSCLC patients. This difference was also found on the analysis of 40 paired samples from patients treated by targeted therapy.\u003c/p\u003e\n\u003cp\u003eA focus on patients undergoing targeted therapy was made to explore the potential role of the evolution of CTC counts on sequential blood samples. Thirteen patients had an additional sample collection at an early timepoint (days 21-28 after baseline collection), and 6 patients at progression. There was a median diminution of 7 CTCs [-1; -16] among those patients presenting a Partial Response on first radiologic evaluation at 3 months, while those on Stable disease had a median diminution of 1.5 [-1; +2], and those with progressive disease had a median increase of 9.5 [+1; +37] (\u003cem\u003eSupplementary Figure S3\u003c/em\u003e). This tendency must be confirmed in further studies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation of EF-FACS detected CTCs with prognosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePaired EF-FACS CTC and ctDNA samples were available for 85 patients (\u003cem\u003eFigure 1\u003c/em\u003e). Continuous values of CTCs and ctDNA VAF were not correlated (\u003cem\u003eSupplementary Figure\u003c/em\u003e \u003cem\u003eS4\u003c/em\u003e), suggesting that the EF-FACS CTCs and ctDNA may be two independent prognostic biomarkers. The multivariable Cox model (adjusted for the known risk factors to control the confounding bias, i.e. age, gender, presence of metastasis at the CTC collection, ECOG-Performance Status, treatment with target therapy, and time since diagnosis [TSD] at the CTC collection) revealed that each increase in levels of CTCs decreased the survival probability (HR of 1.04 per increase of 1 CTC, 95%IC [1.00; 1.10]; p=0.025) (Figures 3A, 3B). The proportional hazard assumption did not hold, revealing an \u0026ldquo;n-shaped\u0026rdquo; time-varying effect: increasing levels of CTCs were associated with a markedly increased risk only during the first months of follow-up. In contrast, increasing the VAF of oncogenic alterations in ctDNA was not significant, despite having a tendency toward a worse prognosis with increasing levels (HR of 1.81 per increase of 1% of VAF, 95%IC [0.17; 19.5]; p=0.625) (\u003cem\u003eFigures 3C and 3D\u003c/em\u003e). However, using the cutoff of 0.25%, the detection of ctDNA was associated with a decrease of the survival probability (HR= 4.45, 95%IC [1.63; 12.13]; p=0.004, no deviation to the proportional hazard identified). These results suggest that there is no linear increase in the risk with the ctDNA VAF, explaining that the classical linear assumption of the Cox model failed to capture this relationship. Assuming that a similar relationship may hold for the CTCs, we used the Akaike information criterion (AIC) to determine an optimal cutoff for the CTCs with or without proportional hazard assumptions (\u003cem\u003eFigure 2D\u003c/em\u003e), suggesting a positivity of the test for a CTC count \u0026ge; 5. As the proportional hazard assumption was not verified for this binarized CTC, we considered the model taking into account the interaction with time as the final model, which presented a better performance than the model considering the continuous CTC (AIC\u003csub\u003ebinarized CTC\u003c/sub\u003e = 218.44; AIC\u003csub\u003econtinuous CTC\u003c/sub\u003e = 223.29).\u003c/p\u003e\n\u003cp\u003eAdjusting the model for both (EF-FACS CTCs and ctDNA) we confirmed that they are independently associated to the risk, and therefore complementary for risk prediction, with an HR of 4.95 ([1.65; 14.87], p=0.004) for the ctDNA, and a time varying HR for the EF-FACS that reached 9 at 3 months to decreases after to 1 (\u003cem\u003eSupplementary Figure S5\u003c/em\u003e). These results suggest a short-term high prognostic value of CTC enumeration by EF-FACS in NSCLC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical utility of combined EF-FACS detected CTCs and ctDNA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe assessed the discrimination of different risk scores using the time-dependent ROC curves to determine if addition of the EF-FACS-detected CTCs or the ctDNA VAF or both to classic clinical prognostic variables, could help identify patients with a worse prognosis. Classic clinical prognostic variables included in the model: \u0026nbsp;age, gender, metastasis at CTC collection, use of target therapy, ECOG-Performance Status, and delay between CTC isolation and diagnosis. Risk score for EF-FACS-detected CTCs contemplate a threshold of 5 CTCs, while the risk score for ctDNA concerns a VAF threshold of 0.25% for known oncogenic drivers. We considered two periods of time for risk of death prediction: short-term (within the first 6 months after blood collection) (Figure 4A), and intermediate-term (within the 12 months after blood collection) (Figure 4B). We compared four risk scores: (i)\u0026ldquo;Clinical\u0026rdquo; risk score, which was our baseline risk score, (ii) \u0026ldquo;Clinical+CTC\u0026rdquo; including the clinical factors and the EF-FACS-detected CTCs, (iii) \u0026ldquo;Clinical+ctDNA\u0026rdquo; combining the clinical factors and the ctDNA VAF, and (iv)\u0026ldquo;Clinical+CTC+ctDNA\u0026rdquo;, which included all factors. Comparing to the Clinical score in a short term, the addition of the CTC had a significant additional value for the prediction of death at a short term (AUC\u003csub\u003eClinical\u003c/sub\u003e=0.689, AUC\u003csub\u003eClinical+CTC\u003c/sub\u003e=0.818, p=0.0212), while the addition of the ctDNA or the CTC+ctDNA to the clinical score did not (AUC\u003csub\u003eClinical+ctDNA\u003c/sub\u003e=0.732, p=0.5370; AUC\u003csub\u003eClinical+CTC+ctDNA\u003c/sub\u003e=0.794, p=0.1198). Although the AUC was higher for the Clinical+CTC score, the difference between these 3 scores cannot be statistically confirmed due to restricted sample size (\u003cem\u003eFigure 4A\u003c/em\u003e, second line), suggesting that this performance was mainly driven by the addition of the CTCs. Comparing to the Clinical score, the most significant additive value of prediction of risk in an intermediate term was obtained with the combination of a clinical score with both CTC and ctDNA (AUC\u003csub\u003eClinical+CTC+ctDNA\u003c/sub\u003e = 0.829, p=0.0364) (\u003cem\u003eFigure 4B\u003c/em\u003e). The addition of CTCs to the Clinical+ctDNA score was not statistically significant (p=0.7682), suggesting that this high performance was mainly driven by the ctDNA (AUC\u003csub\u003eClinical+ctDNA\u003c/sub\u003e =0.821).\u003c/p\u003e\n\u003cp\u003eThose results highlight the importance of both independent biomarkers, CTCs and ctDNA, in the risk stratification of patients, with each having their advantage according to the disease course. Moreover, the data obtained indicate the significant role of CTC enumeration at baseline to help in the identification of patients with the highest risk of death at short term independently of treatment choice.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eHerein, we present a new EF-FACS method for CTC detection that bypasses enrichment and the dependency on EpCAM expression, which are two major constraints of NSCLC CTC study. This method is simple, accurate and particularly sensitive in NSCLC, highlighting its potential for clinical application. We demonstrated that EF-FACS-detected CTCs are an independent prognostic biomarker in NSCLC. They show a high value in risk stratification of NSCLC patients within the first 6 months from CTC collection, allowing the identification of a subset of patients who could potentially benefit from an intensification of treatment. EF-FACS CTCs and ctDNA proved to be independent prognostic biomarkers and CTC count has a complementary value to ctDNA at an intermediate-term monitoring.\u003c/p\u003e\u003cp\u003eHistorically, the \u0026ldquo;gold standard\u0026rdquo; strategy to work on CTCs, was determined by a combination of an enrichment process and CTC detection, and eventually their isolation. Across the last decades, different techniques based on positive selection according to size, deformability or protein expression have been developed to detect CTCs. Among these, \u003cem\u003eCellSearch\u003c/em\u003e enumerates CTCs by a positive selection, and has a prognostic value that permitted the FDA approval for breast,\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e colon\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e and prostate cancer.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Nevertheless, technologies limiting CTC capture to EpCAM are not sensitive in patients diagnosed with NSCLC.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Otherwise, techniques based on size and deformability of CTCs, like Parsortix, which was previously validated in breast cancer,\u003csup\u003e\u003cspan additionalcitationids=\"CR40 CR41\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e or ISET,\u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e tend to associate improved detection rates in NSCLC patients. However, they restrain their selection to pre-defined physical characteristics and do not cover the wide spectrum of CTC morphologic heterogeneity. To bypass the constraints mentioned above, our group, along with others, have used the RosetteSep protocol that enables an enrichment on the basis of a negative selection of white blood cells. Indeed, the combination of RosetteSep and FACS facilitates the preservation of the extensive phenotypic heterogeneity of detected and isolated CTCs.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e Nevertheless, it must be noted that this technique is associated with considerable cell loss. Contrarily, EF-FACS combines red blood cell lysis and permeabilization in one simple step, followed by direct immunostaining, and flow cytometry analysis. The tumoral origin of the detected events can be confirmed by the presence of aberrant copy number profiles in CTCs sorted at single-cell level, as we report in the present work. Our results show that EF-FACS has a high accuracy and sensitivity in NSCLC CTC detection. In fact, it enables a higher detection rate of CTCs (67.4% of patients with 5 or more CTCs) when compared to \u003cem\u003eCellSearch\u003c/em\u003e (10.5% of patients with 2 or more CTCs in our cohort, and 23\u0026ndash;40% reported in literature).\u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Hence, the technique's sensitivity and its simplicity, coupled with its accessibility through FACS technology, makes it a democratizable and broadly implementable option in numerous laboratories.\u003c/p\u003e\u003cp\u003eFrom a clinical point of view, \u003cem\u003eCellSearch\u003c/em\u003e CTC counts have been described to be prognostic in advanced NSCLC patients.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Nevertheless, consistent with published studies, the fraction of patients exhibiting a positivity on CTCs remains limited (23\u0026ndash;40%).\u003csup\u003e8\u0026ndash;10,24\u003c/sup\u003e This fact hinders the clinical utility of \u003cem\u003eCellSearch\u003c/em\u003e in NSCLC. EF-FACS presented a higher sensitivity and captured a larger spectrum of patients, revealing a correlation between increasing counts of EF-FACS detected CTCs and a worsening of prognosis. The prognostic significance of the continuous value of \u003cem\u003eCellSearch\u003c/em\u003e-detected CTC counts was not possible, as most patients did not yield any CTC. On a prognostic statistical setting, the threshold of positivity was statistically determined at 5 EF-FACS CTCs per 7.5 mL, although the measurement was made with a larger volume of blood (usually 12\u0026ndash;14 mL). This value correlated with a significant risk of early death. Its prognostic value along with the sensitivity of the method, opens a window of opportunity for its application in clinical practice. Beyond CTCs, another component of liquid biopsy, ctDNA, has become increasingly used in clinical practice. Among its clinical applications, ctDNA has shown to shorten time to treatment initiation in onco-addicted NSCLC patients.\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e It has a role in guiding personalized therapeutic strategies, particularly through the evaluation of resistance mechanisms to targeted therapies.\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e Moreover, ctDNA clearance has emerged as a promising biomarker for monitoring treatment response.\u003csup\u003e\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e Nevertheless, its applications when combined with CTCs have been less explored. Punnoose et al. proposed that decreasing CTC counts may be used as an early indicator of targeted therapy response, and ctDNA could help with the assessment of tumor mutational status.\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e In this sense, our results show that both CTCs and ctDNA VAF were independent biomarkers in our NSCLC cohort. We evaluated the implications of combining CTCs and ctDNA VAF in risk stratification, suggesting that CTCs were not only useful in the short term when combined with clinical prognostic variables, but also in an intermediate term when associated to ctDNA and clinical variables. This highlights the utility of these two independent biomarkers to identify patients with poor prognosis. Similarly, Radovich et al. reported that ctDNA and CTCs were independently associated with disease recurrence in patients with early stage Triple Negative Breast Cancer receiving neoadjuvant treatment.\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e Interestingly, in our study CTCs were significantly associated with short term risk of death, which may reflect an aggressive biological behavior of CTCs that contributes to the dissemination process.\u003c/p\u003e\u003cp\u003eAn important remark to be considered, is that despite the clinical use of ctDNA, it is limited to genomic analysis, while integral CTCs enable the sequencing of both DNA and RNA, and their immunophenotyping. Indeed, in the present era of antibody-drug conjugates and bi-specific antibodies, with an increasing interest in the evaluation of biomarkers, CTCs provide a real-time image of tumor protein landscape in a minimally invasive way. Furthermore, the isolation of CTCs permits the combination of a phenotypic and genomic characterization at the single-CTC level. This approach has been described to be useful in the evaluation of mechanisms of resistance, as reported by our group in a \u003cem\u003eBRAF\u003c/em\u003e and \u003cem\u003eALK\u003c/em\u003e NSCLC patient cohort.\u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Finally, the application of multi-protein panels, using sensitive flow cytometry, combined with single-cell transcriptomic analysis, could enable the characterization of different potentially druggable subgroups of CTCs.\u003c/p\u003e\u003cp\u003eWhile our study presents the validation and clinical application of this new method, several limitations must be considered. The evaluated cohort is limited and heterogeneous in histology and molecular landscape, and includes patients with a locally advanced disease. Regarding ctDNA analysis, given the enriched population with targetable genomic alterations, we considered the ctDNA VAF values for known onco-drivers, in accordance with previous publications.\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e Finally, despite the fact that we found that EF-FACS was more sensitive and enabled a better patient stratification than \u003cem\u003eCellSearch\u003c/em\u003e, a confirmation of our results with a cross validation with an external laboratory should be considered.\u003c/p\u003e\u003cp\u003eIn conclusion, our study demonstrates that EF-FACS is a relatively simple, democratizable, and sensitive technique for the characterization and isolation of CTCs in the challenging population of NSCLC patients. The complementary use of CTCs and ctDNA enables a prognosis stratification and provides an insight on evolution of the tumor across treatments, in a minimally invasive way.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAUTHORS’ CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMVSB\u003c/strong\u003e: data curation, investigation, formal analysis, validation, visualization, methodology, writing–original draft, project administration \u003cstrong\u003eMO\u003c/strong\u003e: formal analysis, validation, methodology, writing–original draft \u003cstrong\u003eDD\u003c/strong\u003e: statistical analysis, validation, writing–original draft and editing \u003cstrong\u003eJR\u003c/strong\u003e: conceptualization, resources, manuscript editing \u003cstrong\u003eMNC\u003c/strong\u003e: resources, project administration \u003cstrong\u003eCN\u003c/strong\u003e: resources, project administration \u003cstrong\u003eFM\u003c/strong\u003e: methodology, provided reagents, manuscript editing \u003cstrong\u003eJMB\u003c/strong\u003e: methodology, provided reagents, manuscript editing \u003cstrong\u003eND\u003c/strong\u003e: methodology, formal analysis, validation \u003cstrong\u003eMD\u003c/strong\u003e: formal analysis \u003cstrong\u003eAA\u003c/strong\u003e: technical assistance \u003cstrong\u003eBJ\u003c/strong\u003e: formal analysis \u003cstrong\u003eBL\u003c/strong\u003e: technical assistance \u003cstrong\u003eAI\u003c/strong\u003e: Resources, project administration, supervision, validation, \u003cstrong\u003eDP\u003c/strong\u003e: conceptualization, resources \u003cstrong\u003eBB\u003c/strong\u003e: conceptualization, resources, supervision, validation, funding acquisition, manuscript editing \u003cstrong\u003eFF\u003c/strong\u003e: conceptualization, supervision, validation, funding acquisition, writing–original draft \u003cstrong\u003ePP\u003c/strong\u003e: conceptualization, resources, data curation, supervision, methodology, writing–original draft\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the patients who participated in the trial, the staff members who assisted with the trial, and the team of Precision Medicine Department that supported the trial. This study was supported by the contribution of the “parrainage chercheur-cancer du poumon”.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR’S DISCLOSURES\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaria Virginia Sanchez Becerra\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch funding: Pharmamar, Abbvie.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJordi Remon\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsulting or Advisory Role: Pfizer, Bristol-Myers Squibb, MSD Oncology, Astra/Zeneca, OSE Immunotherapeutics, Janssen Oncology, Genmab, Boehringer ingelheim, Sanofi, Roche/Genentech, Merck\u003c/p\u003e\n\u003cp\u003eTravel, Accommodations, Expenses: Roche/Genentech, Inivata, OSE Immunotherapeutics, OSE Immunotherapeutics, AstraZeneca, MSD Oncology\u003c/p\u003e\n\u003cp\u003eGrants: MSD outside submitted work\u003c/p\u003e\n\u003cp\u003eLeadership role as Secretary of the EORTC lung cancer group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDavid Planchard\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHonoraria: AstraZeneca, Bristol-Myers Squibb, Boehringer Ingelheim, Celgene, Eli Lilly, Merck, Merck Sharp and Dohme Oncology, Novartis, Pfizer, prIME Oncology, Roche;\u003c/p\u003e\n\u003cp\u003eConsulting or advisory role: AstraZeneca, Bristol-Myers Squibb, Boehringer Ingelheim, Celgene, Eli Lilly, Merck Sharp and Dohme Oncology, Novartis, Pfizer, prIME Oncology, Roche.\u003c/p\u003e\n\u003cp\u003eTravel, accommodation, expenses: AstraZeneca, Roche, Novartis, prIME Oncology and Pfizer\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAntoine Italiano\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGrants from Bayer, MSD, Roche, Merck, and AstraZeneca outside the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBenjamin\u0026nbsp;Besse\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsulting or advisory role: Abbvie, Biontech SE, Beijing Avistone Biotechnology; BristolMyerSqibb, CureVac AG, Pharmamar, Regeneron, Sanofi Aventis, Eli Lilly, Ellipses pharma Ltd, F.Hoffmann-La Roche Ltd, Foghorn Therapeutics Inc., Genmab, Immunocore, Owkin.\u003c/p\u003e\n\u003cp\u003eSpeaker: Abbvie, AstraZeneca, BristolMyerSqibb, Daichii Sankyo, Lilly, MSD, Ose Immunotherapeutics, Sanofi Aventis, Servier\u003c/p\u003e\n\u003cp\u003eSteering committee: Astrazeneca, Amgen, Beigene, CureVac AG, GENMAB A/S, Janssen, MSD, Ose Immunotherapeutics, Pharmamar, Sanofi, Takeda\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMO, DD, MNC, CN, FM, JMB, ND, MD, AA, BJ, BL, PP, FF declare no potential conflicts of interest\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel, R. 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M. \u003cem\u003eet al.\u003c/em\u003e Neutrophils escort circulating tumour cells to enable cell cycle progression. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e566\u003c/strong\u003e, 553\u0026ndash;557 (2019).\u003c/li\u003e\n\u003cli\u003eChudziak, J. \u003cem\u003eet al.\u003c/em\u003e Clinical evaluation of a novel microfluidic device for epitope-independent enrichment of circulating tumour cells in patients with small cell lung cancer. \u003cem\u003eAnalyst\u003c/em\u003e \u003cstrong\u003e141\u003c/strong\u003e, 669\u0026ndash;678 (2016).\u003c/li\u003e\n\u003cli\u003eGkountela, S. \u003cem\u003eet al.\u003c/em\u003e Circulating Tumor Cell Clustering Shapes DNA Methylation to Enable Metastasis Seeding. \u003cem\u003eCell\u003c/em\u003e \u003cstrong\u003e176\u003c/strong\u003e, 98-112.e14 (2019).\u003c/li\u003e\n\u003cli\u003eGroen, L. \u003cem\u003eet al.\u003c/em\u003e Transcriptome Profiling of Circulating Tumor Cells to Predict Clinical Outcomes in Metastatic Castration-Resistant Prostate Cancer. \u003cem\u003eInt J Mol Sci\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 9002 (2023).\u003c/li\u003e\n\u003cli\u003eFaugeroux, V. \u003cem\u003eet al.\u003c/em\u003e An Accessible and Unique Insight into Metastasis Mutational Content Through Whole-exome Sequencing of Circulating Tumor Cells in Metastatic Prostate Cancer. \u003cem\u003eEur Urol Oncol\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 498\u0026ndash;508 (2020).\u003c/li\u003e\n\u003cli\u003eFaugeroux, V. \u003cem\u003eet al.\u003c/em\u003e Genetic characterization of a unique neuroendocrine transdifferentiation prostate circulating tumor cell-derived eXplant model. \u003cem\u003eNat Commun\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 1884 (2020).\u003c/li\u003e\n\u003cli\u003eTamminga, M. \u003cem\u003eet al.\u003c/em\u003e Leukapheresis increases circulating tumour cell yield in non-small cell lung cancer, counts related to tumour response and survival. \u003cem\u003eBr J Cancer\u003c/em\u003e \u003cstrong\u003e126\u003c/strong\u003e, 409\u0026ndash;418 (2022).\u003c/li\u003e\n\u003cli\u003eThompson, J. C. \u003cem\u003eet al.\u003c/em\u003e Plasma Genotyping at the Time of Diagnostic Tissue Biopsy Decreases Time-to-Treatment in Patients With Advanced NSCLC-Results From a Prospective Pilot Study. \u003cem\u003eJTO Clin Res Rep\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 100301 (2022).\u003c/li\u003e\n\u003cli\u003eLeighl, N. B. \u003cem\u003eet al.\u003c/em\u003e Clinical Utility of Comprehensive Cell-free DNA Analysis to Identify Genomic Biomarkers in Patients with Newly Diagnosed Metastatic Non-small Cell Lung Cancer. \u003cem\u003eClin Cancer Res\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 4691\u0026ndash;4700 (2019).\u003c/li\u003e\n\u003cli\u003eRemon, J., Steuer, C. E., Ramalingam, S. S. \u0026amp; Felip, E. Osimertinib and other third-generation EGFR TKI in EGFR-mutant NSCLC patients. \u003cem\u003eAnn Oncol\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, i20\u0026ndash;i27 (2018).\u003c/li\u003e\n\u003cli\u003eSoo, R. A. \u003cem\u003eet al.\u003c/em\u003e Early Circulating Tumor DNA Dynamics and Efficacy of Lorlatinib in Patients With Treatment-Naive, Advanced, ALK-Positive NSCLC. \u003cem\u003eJ Thorac Oncol\u003c/em\u003e S1556-0864(23)00580\u0026ndash;4 (2023) doi:10.1016/j.jtho.2023.05.021.\u003c/li\u003e\n\u003cli\u003eErnst, S. M. \u003cem\u003eet al.\u003c/em\u003e Clinical Utility of Circulating Tumor DNA in Patients With Advanced KRASG12C-Mutated NSCLC Treated With Sotorasib. \u003cem\u003eJ Thorac Oncol\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 995\u0026ndash;1006 (2024).\u003c/li\u003e\n\u003cli\u003eMondelo-Mac\u0026iacute;a, P. \u003cem\u003eet al.\u003c/em\u003e Clinical potential of circulating free DNA and circulating tumour cells in patients with metastatic non-small-cell lung cancer treated with pembrolizumab. \u003cem\u003eMol Oncol\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 2923\u0026ndash;2940 (2021).\u003c/li\u003e\n\u003cli\u003ePunnoose, E. A. \u003cem\u003eet al.\u003c/em\u003e Evaluation of circulating tumor cells and circulating tumor DNA in non-small cell lung cancer: association with clinical endpoints in a phase II clinical trial of pertuzumab and erlotinib. \u003cem\u003eClin Cancer Res\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 2391\u0026ndash;2401 (2012).\u003c/li\u003e\n\u003cli\u003eRadovich, M. \u003cem\u003eet al.\u003c/em\u003e Association of Circulating Tumor DNA and Circulating Tumor Cells After Neoadjuvant Chemotherapy With Disease Recurrence in Patients With Triple-Negative Breast Cancer: Preplanned Secondary Analysis of the BRE12-158 Randomized Clinical Trial. \u003cem\u003eJAMA Oncol\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 1410\u0026ndash;1415 (2020).\u003c/li\u003e\n\u003cli\u003eParikh, K. \u003cem\u003eet al.\u003c/em\u003e Impact of EML4-ALK Variants and Co-Occurring TP53 Mutations on Duration of First-Line ALK Tyrosine Kinase Inhibitor Treatment and Overall Survival in ALK Fusion-Positive NSCLC: Real-World Outcomes From the GuardantINFORM database. \u003cem\u003eJ Thorac Oncol\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 1539\u0026ndash;1549 (2024).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Characteristics of the population of study: whole population, \u003cem\u003eCellSearch\u0026nbsp;\u003c/em\u003evs EF-FACS cohort and CTC-ctDNA cohort.\u0026nbsp;\u003c/strong\u003ey/o: year old. ECOG-PS: Eastern Cooperative Oncology Group Performance Status. CTC: Circulating Tumor Cells. ctDNA: circulating tumor DNA. VAF: Variant Allele Frequency. CTC counts (\u003cem\u003eCellSearch\u003c/em\u003e and EF-FACS are referred to 7.5 mL blood).\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"120%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;N(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.8687%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhole population\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(n=95 pts)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.545%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCellSearch \u003cem\u003evs\u003c/em\u003e EF-FACS cohort\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(n=74 pts)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5115%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCTC-ctDNA cohort\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(n=85 pts)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (y/o) [IQR ]\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.8687%;\"\u003e\n \u003cp\u003e66 (30-90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.545%;\"\u003e\n \u003cp\u003e64 (30-90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5115%;\"\u003e\n \u003cp\u003e66 (59-74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e-Men\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e-Women\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.8687%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e47 (49%)\u003c/p\u003e\n \u003cp\u003e48 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.545%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e38 (51%)\u003c/p\u003e\n \u003cp\u003e36 \u0026nbsp;(49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5115%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e40 (47%)\u003c/p\u003e\n \u003cp\u003e45 \u0026nbsp;(53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cem\u003eSmoker\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cem\u003eNon smoker\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.8687%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e65 (68%)\u003c/p\u003e\n \u003cp\u003e30 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.545%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e48 (65%)\u003c/p\u003e\n \u003cp\u003e26 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5115%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e57 (67%)\u003c/p\u003e\n \u003cp\u003e28 \u0026nbsp;(33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eECOG-PS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cem\u003e1\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cem\u003e\u0026ge;2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.8687%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e28 (29%)\u003c/p\u003e\n \u003cp\u003e50 (53%)\u003c/p\u003e\n \u003cp\u003e17 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.545%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e24 (32%)\u003c/p\u003e\n \u003cp\u003e36 (49%)\u003c/p\u003e\n \u003cp\u003e14 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5115%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e24 (28%)\u003c/p\u003e\n \u003cp\u003e45 (53%)\u003c/p\u003e\n \u003cp\u003e16 \u0026nbsp;(19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage (AJCC 8th Edition)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cem\u003eIII\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cem\u003eIV\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.8687%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7 (7%)\u003c/p\u003e\n \u003cp\u003e88 (93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.545%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6 (8%)\u003c/p\u003e\n \u003cp\u003e68 (92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5115%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7 (8%)\u003c/p\u003e\n \u003cp\u003e78 \u0026nbsp;(92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistology\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cem\u003eAdenocarcinoma\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cem\u003eSquamous\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cem\u003eOther\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.8687%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e78 (82%)\u003c/p\u003e\n \u003cp\u003e8 (8%)\u003c/p\u003e\n \u003cp\u003e9 \u0026nbsp; (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.545%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e59 (80%)\u003c/p\u003e\n \u003cp\u003e8 (11%)\u003c/p\u003e\n \u003cp\u003e7 \u0026nbsp; (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5115%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e72 (85%)\u003c/p\u003e\n \u003cp\u003e5 (6%)\u003c/p\u003e\n \u003cp\u003e8 \u0026nbsp;(9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN lines of treatment at CTC isolation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cem\u003eTreatment na\u0026icirc;ve\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cem\u003e1 line\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cem\u003e\u0026ge;2 lines\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.8687%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e69 (73%)\u003c/p\u003e\n \u003cp\u003e20 (21%)\u003c/p\u003e\n \u003cp\u003e6 \u0026nbsp; (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.545%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e53 (72%)\u003c/p\u003e\n \u003cp\u003e16 (22%)\u003c/p\u003e\n \u003cp\u003e5 \u0026nbsp; (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5115%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e64 (75%)\u003c/p\u003e\n \u003cp\u003e13 (15%)\u003c/p\u003e\n \u003cp\u003e8 \u0026nbsp;(9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCTC CellSearch Counts\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eMedian (range)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026ge;1\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026ge;2\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026ge;5\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eNot analysed\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.8687%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0 (0;3)\u003c/p\u003e\n \u003cp\u003e71 (75%)\u003c/p\u003e\n \u003cp\u003e3 (3%) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2 (2%)\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e21 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.545%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0 (0;3)\u003c/p\u003e\n \u003cp\u003e71 (96%)\u003c/p\u003e\n \u003cp\u003e3 (4%)\u003c/p\u003e\n \u003cp\u003e2 (3%)\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5115%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0 (0;3)\u003c/p\u003e\n \u003cp\u003e61 (72%)\u003c/p\u003e\n \u003cp\u003e3 (3%)\u003c/p\u003e\n \u003cp\u003e2 (2%)\u003c/p\u003e\n \u003cp\u003e0 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e21 \u0026nbsp;(25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCTC EF-FACS Counts\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eMedian (range)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;5\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026ge;5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.8687%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6 (0;48)\u003c/p\u003e\n \u003cp\u003e35 (37%)\u003c/p\u003e\n \u003cp\u003e60 (63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.545%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6 (0;48)\u003c/p\u003e\n \u003cp\u003e26 (35%)\u003c/p\u003e\n \u003cp\u003e48 \u0026nbsp;(65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5115%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6 (0;48)\u003c/p\u003e\n \u003cp\u003e32 (38%)\u003c/p\u003e\n \u003cp\u003e53 \u0026nbsp;(62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ectDNA VAF\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0,25%\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026ge;0,25%\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eNot analysed\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.8687%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e28 (29%)\u003c/p\u003e\n \u003cp\u003e61 (64%)\u003c/p\u003e\n \u003cp\u003e6 \u0026nbsp; \u0026nbsp;(6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.545%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e20 (27%)\u003c/p\u003e\n \u003cp\u003e48 (65%)\u003c/p\u003e\n \u003cp\u003e6 \u0026nbsp; \u0026nbsp;(8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5115%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e26 (31%)\u003c/p\u003e\n \u003cp\u003e59 (69%)\u003c/p\u003e\n \u003cp\u003e0 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-precision-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjprecisiononcology","sideBox":"Learn more about [npj Precision Oncology](http://www.nature.com/npjprecisiononcology/)","snPcode":"41698","submissionUrl":"https://submission.springernature.com/new-submission/41698/3","title":"npj Precision Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7347165/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7347165/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eLiquid biopsy provides a real-time dynamic evolution of Non-Small Cell Lung Cancer (NSCLC). Circulating Tumor Cells (CTC) capture tumor heterogeneity enabling single-cell approaches, but their detection requires an enrichment process associated with cell loss. We present an \u003cb\u003eE\u003c/b\u003enrichment-\u003cb\u003eF\u003c/b\u003eree \u003cb\u003eF\u003c/b\u003eluorescence \u003cb\u003eA\u003c/b\u003ectivating \u003cb\u003eC\u003c/b\u003eell \u003cb\u003eS\u003c/b\u003eorting method (EF-FACS), which permits the detection of a higher proportion of CTCs, and aimed to explore the complementary information of EF-FACS CTCs and ctDNA.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eEF-FACS is based on fixation, permeabilization and immunostaining (CD45, CD66b, CK, EpCAM, Hoechst) of 12 mL whole blood, to detect and isolate CTCs by FACS. Tumor origin of CTCs is evaluated with a Low-Pass Whole-Genome copy number altered profile. Accuracy was assessed with tumor cells spiked in healthy blood samples. Blood samples of 95 advanced NSCLC patients were collected. 86 paired samples underwent \u003cem\u003eCellSearch\u003c/em\u003e and EF-FACS workflows. 85 patients with a ctDNA analysis were included for combined CTC-ctDNA clinical utility.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eEF-FACS was accurate (r\u0026thinsp;=\u0026thinsp;0.937, p\u0026thinsp;=\u0026thinsp;1.689e-11). CTC counts were significantly higher by EF-FACS than \u003cem\u003eCellSearch\u003c/em\u003e (median CTC counts: 6 \u003cem\u003evs\u003c/em\u003e 0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). CTCs and ctDNA were found to be independent prognostic biomarkers. CTCs improved the short-term prediction of clinical prognosis factors (p\u0026thinsp;=\u0026thinsp;0.0212), whereas ctDNA did not (p\u0026thinsp;=\u0026thinsp;0.537). The combination of CTCs and ctDNA was complementary for enhanced stratification of the risk of death at 1 year in patients with advanced NSCLC.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eEF-FACS is efficient in NSCLC CTC detection, potentially implementable in treating centers using a cytometer. CTCs and ctDNA have a complementary role in prognosis stratification of metastatic NSCLC patients.\u003c/p\u003e","manuscriptTitle":"EF-FACS circulating tumor cell detection complements ctDNA-based prognosis in stratification of non-small cell lung cancer patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 10:13:52","doi":"10.21203/rs.3.rs-7347165/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-08T00:19:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-19T10:41:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"230070116598138126383244814257220517164","date":"2025-11-29T07:43:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332566502596498878369632198785163305611","date":"2025-10-25T15:42:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-03T22:57:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"130601191545425876054699413406934252878","date":"2025-09-14T16:44:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-12T00:02:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-03T13:39:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-14T15:14:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Precision Oncology","date":"2025-08-11T13:58:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-precision-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjprecisiononcology","sideBox":"Learn more about [npj Precision Oncology](http://www.nature.com/npjprecisiononcology/)","snPcode":"41698","submissionUrl":"https://submission.springernature.com/new-submission/41698/3","title":"npj Precision Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"df8971f0-a43e-46ef-abfd-5b06102af242","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":54901055,"name":"Health sciences/Biomarkers"},{"id":54901056,"name":"Biological sciences/Cancer"},{"id":54901057,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-05-12T14:27:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-22 10:13:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7347165","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7347165","identity":"rs-7347165","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}