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Diagnostic leukapheresis reveals distinct phenotypes of NSCLC circulating tumor cells

Molecular Cancer volume 23, Article number: 93 (2024) Cite this article

Abstract

Background

Circulating tumor cells (CTCs) hold immense promise for unraveling tumor heterogeneity and understanding treatment resistance. However, conventional methods, especially in cancers like non-small cell lung cancer (NSCLC), often yield low CTC numbers, hindering comprehensive analyses. This study addresses this limitation by employing diagnostic leukapheresis (DLA) to cancer patients, enabling the screening of larger blood volumes. To leverage DLA’s full potential, this study introduces a novel approach for CTC enrichment from DLAs.

Methods

DLA was applied to six advanced stage NSCLC patients. For an unbiased CTC enrichment, a two-step approach based on negative depletion of hematopoietic cells was used. Single-cell (sc) whole-transcriptome sequencing was performed, and CTCs were identified based on gene signatures and inferred copy number variations.

Results

Remarkably, this innovative approach led to the identification of unprecedented 3,363 CTC transcriptomes. The extensive heterogeneity among CTCs was unveiled, highlighting distinct phenotypes related to the epithelial-mesenchymal transition (EMT) axis, stemness, immune responsiveness, and metabolism. Comparison with sc transcriptomes from primary NSCLC cells revealed that CTCs encapsulate the heterogeneity of their primary counterparts while maintaining unique CTC-specific phenotypes.

Conclusions

In conclusion, this study pioneers a transformative method for enriching CTCs from DLA, resulting in a substantial increase in CTC numbers. This allowed the creation of the first-ever single-cell whole transcriptome in-depth characterization of the heterogeneity of over 3,300 NSCLC-CTCs. The findings not only confirm the diagnostic value of CTCs in monitoring tumor heterogeneity but also propose a CTC-specific signature that can be exploited for targeted CTC-directed therapies in the future. This comprehensive approach signifies a major leap forward, positioning CTCs as a key player in advancing our understanding of cancer dynamics and paving the way for tailored therapeutic interventions.

Background

Targeted therapy and immune checkpoint inhibitors have achieved remarkable success in treating non-small cell lung cancer (NSCLC); however, lung cancer remains the leading cause of cancer-related mortality globally [1]. While substantial progress has been made in the understanding of genomic-driven resistance through the broader use of next generation sequencing approaches, there is a critical unmet need to elucidate non-genomic-driven resistance mechanisms [2]. This endeavor has been hampered by the accessibility of appropriate tumor material, as the relapsed or progressed tumor sites are often inaccessible without significant biopsy-related risks. Additionally, single site biopsies inadequately represent patient’s intratumor heterogeneity. Accurate comprehension of tumor resistance mechanisms and underlying tumor heterogeneity is essential for developing novel effective therapies, given that tumor heterogeneity is a key driver of therapy resistance, leading to relapse and ultimately to patient death [3].

Circulating tumor cells (CTCs) hold great potential for addressing this challenge, as they can originate from primary tumors and metastases, offering a non-invasive access via the bloodstream [4]. However, current often epithelial cell adhesion molecule (EpCAM)-based detection methods, commonly used on peripheral blood (PB) samples, are limited to a few cancers with higher CTC numbers such as breast and prostate cancer, or very rare cases of patients with exceptionally high CTC numbers. EpCAM’s low expression on NSCLC-CTCs hampers its effectiveness as a marker for enriching CTCs, hindering the application of in-depth single-cell (sc) sequencing techniques crucial for monitoring tumor heterogeneity [4].

Recent reports suggest that diagnostic leukapheresis (DLA) holds potential for increasing CTC numbers [5]. The underlying assumption is that CTCs share density and cell size similarities with mononucleated cells (MNCs), making their collection feasible via leukapheresis [6]. However, the full potential of DLA has been limited thus far, as there is a scarcity of enrichment methods for CTCs from larger blood volumes. Standard enrichment techniques such as FDA-cleared CellSearch® and Parsortix® systems were designed for isolating CTCs from smaller PB samples (7.5- 9 ml PB).

In this study, we present a method for CTC enrichment from a mean of 20 × 108 white blood cells (WBCs) obtained from DLAs (representing a 20-fold increase in WBCs compared to a typical PB sample), leading to an unprecedented number of 3363 sc CTC transcriptomes from six NSCLC patients. Thus, allowing for an in-depth characterization of CTC cell states, including cancer stemness and different metabolic types. We show that our novel DLA-pipeline enables non-invasive longitudinal analyses of CTCs and that it has the potential to transform personalized medicine for patients with metastatic NSCLC and beyond.

Methods

A detailed overview of Materials and Methods can be found in the Supplementary section (Supplementary Material and Methods).

Results

Patient characteristics and enrichment of CTCs from diagnostic leukapheresis

DLA was performed on four NSCLC patients with adenocarcinoma and two NSCLC patients with squamous cell histology. At the time of the DLA, three of those patients had active disease (initial diagnosis or progression from previous treatment), while tumors from the remaining patients were controlled (Supplementary Table 1).

A mean blood volume of 6.0 L per patient was screened during DLA and a mean number of 64.6 × 108 WBCs were collected. MNCs were sufficiently collected via DLA (mean MNC collection efficiency was 41% (range: 26 - 68%, Supplementary Table 2)). As described before, DLAs were generally well tolerated [6]. Blood samples taken before and after DLA indicated minor decreases in peripheral differential blood counts, but all numbers were within the normal range (Supplementary Fig. 1).

For comparison, we also investigated CTCs from a 7.5 ml PB sample taken immediately prior to DLA and from 2 × 108 cells of each DLA, using the “goldstandard”, FDA-cleared CellSearch® system. In our cohort of advanced stage NSCLC patients, CTCs were detected in 2 out of 6 patients (33%), which is consistent with other studies using the EpCAM-based CellSearch® system for detection of CTCs in patients with advanced stage NSCLC [7]. Using DLA increased CTC numbers in these patients (mean CTCs PB 9 (range 0–41) vs. mean CTCs DLA 54 (range 0–216) (Fig. 1B).

Fig. 1
figure 1

Identification and characterization of circulating tumor cells from NSCLC DLA samples. A Graphical overview of the CTC enrichment pipeline from DLAs. B Absolute numbers of CTCs detected by CellSearch® from 7.5 ml of peripheral blood samples, taken prior to DLA procedures and 2 × 108 cells, as well as the extrapolated number of CTCs to be expected in the total DLA product (in grey). Lines connect data from the same patient. C UMAP plot of all cells (n = 9,659) enriched from 6 DLAs from advanced stage NSCLC patients. Color-coding indicates the 14 different clusters. D UMAP plot of all cells colored by their cell types annotated by SingleR analysis (ENCODE reference). Dotted line encircles annotated epithelial cell /CTC clusters. E Heatmap of canonical cell marker. Yellow indicates high expression of a particular gene and purple indicates low expression. F inferCNV analysis [8] of CTCs and reference hematopoietic cells (MEPs, NK cells, GMPs, CMPs, neutrophils and platelets/megakaryocytes). G-I UMAP plot and bar charts of only the CTCs from all DLA samples colored by patient distribution (G) histology (H) and disease state (I). Active disease state active includes DLA performed at initial diagnosis and progression; controlled disease stage includes DLAs at timepoint characterized by stable disease or partial remission. J Heatmap of mean gene expression of canonical genes (epithelial, mesenchymal, cancer stem cell, proliferation, immune response, migration/invasion and hypoxia). K Trajectory analysis of CTCs. Color coding indicates the seven different CTC cluster along the branches L Gene set enrichment analysis using the HALLMARK gene set of DEG comparisons between trajectory endpoint clusters (cluster 1, 5 and 6 between each other) (red: high normalized enrichment score, blue: low normalized enrichment score). MEP Megakaryocyte/erythroid progenitor, NK natural killer, GMP granulocyte-monocyte progenitor, CMP common myeloid progenitor

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Because many NSCLC-CTCs lack expression of epithelial markers such as EpCAM [4], we purposely chose an unbiased approach based on a negative selection of CTCs by targeting hematopoietic cells (HPCs) for CTC enrichment from DLAs prior to sc RNA sequencing (scRNAseq) (Fig. 1A). First leukocytes (CD45+), T-cells (CD3+), endothelial cells (CD31+), monocytes/neutrophils (CD16+) and erythrocytes (CD235a+) were depleted using magnetic beads. Next, the remaining fraction was FACS sorted for live (DAPI−) and CD45− cells. This population was then subjected to whole transcriptome scRNAseq via 10X Genomics technology.

Identification of CTC whole transcriptomes

For identification of CTCs, we pooled sc transcriptomes from all six patients. Shared nearest neighbor modularity clustering revealed 14 distinct clusters containing a total of 9,659 cells (Fig. 1C). High cluster stability and consistency, especially of the CTC cluster, was confirmed by Jaccard Index analysis and Silhouette width (Supplementary Fig. 2A and B, Supplementary Table 3). We utilized reference-based cell type annotation comparing to a reference data set with 'SingleR' and canonical marker gene expression and identified 7 distinct epithelial cell clusters; other identified cell types included megakaryocytes, neutrophils, natural killer (NK) cells, megakaryocyte/erythroid progenitors (MEPs), common myeloid progenitors (CMPs)/pro-myelocytes (CMPs) and granulocyte-monocyte progenitors (GMPs) (Fig. 1D & E, Supplementary Fig. 3A & B, Supplementary Table 4). Trajectory analysis, which enables the study of dynamic changes in gene expression, revealed a separate branch of epithelial cells compared to HPC, confirming a different origin for these cells (Supplementary Fig. 3C & D). Additionally, comparison of differentially expressed genes (DEGs) between HPCs and CTCs indicated substantially higher levels of epithelial markers, including keratins as well as increased expression of cell cycle (CCND1) and proliferation-related genes (SFN) in epithelial cells. S100A2, NQO1, and ID1 were also amongst the top DEGs. These genes are known to be expressed in epithelial cells, including respiratory cells and are associated with cancer progression [9, 10]. In contrast, HPCs exhibited higher expression of genes associated with hematopoietic lineages, such as MPO (myeloperoxidase), DEFA3 (neutrophil defensin 3), and HBA1 and HBA2 (hemoglobin subunits alpha 1 and 2, Supplementary Fig. 3E, Supplementary Table 5). We specifically also investigated endothelial and fibroblast maker genes, as these cells can be found in the peripheral blood. None of the marker genes were identified in either HPC clusters or CTC clusters, with the exception of COL1A1, which was partially expressed in CTC cluster 6 (Supplementary Fig. 3F). Since cells in CTC cluster 6 also expressed EPCAM and KRT19, these cells are of epithelial origin (Fig. 1J). Comparing inferred copy number variations (CNVs) from epithelial cells to reference HPCs revealed notable evidence for CNVs in epithelial cells. Thus, these cells are henceforth referred to as CTCs (Fig. 1F). Overall, a total number of 3,363 NSCLC-CTCs was identified. As a note, inferCNV analyses from CTCs with healthy lung epithelial cells as reference confirmed increased CNV in CTCs (Supplementary Fig. 3G). 

Integrating a publicly available healthy donor PBMC scRNA dataset into the DLA scRNAseq dataset, demonstrated that the seven previously identified distinct CTC clusters prevailed and exclusively consisted of cells from DLA patients, while HPCs from the DLA scRNAseq dataset formed clusters with HPCs from the healthy PBMC dataset (Supplementary Fig. 4). This further corroborates that our pipeline can be used for the specific identification of CTCs.

Notably, the Human Primary Cell Atlas (HPCA) reference within SingleR does not classify megakaryocytes per se, but platelets (Supplementary Fig. 3A). Since most platelets were excluded during enrichment, we tested whether cluster 8 (Fig. 1C) was composed of platelet-coated CTCs. The DEGs between cluster 8 (‘platelets’) and HPCs did not indicate any expression of epithelial marker genes such as keratins or EPCAM, thus contradicting this hypothesis (Supplementary Table 6). Hence, cluster 8 was classified as megakaryocytes.

Characterization of CTCs reveals distinct phenotypes

Surprisingly, CTCs clustered independently of individual patient, primary tumor histology or disease state (Fig. 1G-I), indicating a cellular state-based CTC-intrinsic clustering. To further analyze the different CTC clusters, we performed in-depth characterization of their transcriptomic profiles by analyzing mean gene expression in combination with pathway- and trajectory analyses (Fig. 1J-L, Supplementary Fig. 3H & I, Supplementary Tables 7–10). A heatmap showing the top 10 positive marker genes of each CTC cluster revealed that genes associated with epithelial phenotypes (KRT5, KRT6A), stemness (ALDH1A1) and extracellular matrix remodeling (VTN, MMP7, FBLN1) were distinctive of different CTC clusters (Supplementary Fig. 3I, Supplementary Table 7).

This was further specified by comparison of canonical marker genes demonstrating varied expression among CTC clusters (Fig. 1J). While each cluster showed some expression of different epithelial genes, mesenchymal markers were predominantly expressed in CTC clusters 4, 5 and 6, with CTC cluster 4 exhibiting a cancer stem cell (CSC)-like phenotype and cluster 6 displaying markers of migration and hypoxia. CTC cluster 1 showed immune response marker upregulation and marker indicating increased proliferation. CTC cluster 9, 10 and 13 exhibit diverse phenotypes. They showed enriched epithelial markers, but also some upregulation of makers associated with cancer stemness, proliferation and hypoxia.

To enhance understanding of these phenotypes and their relationships, trajectory analysis was performed, elucidating that CTC clusters 1, 5 and 6 represent the endpoints of three distinct branches (Fig. 1K, Supplementary Fig. 3H). Next, we utilized GSEA to understand the distinctions between the three branches and clusters (Fig. 1L, Supplementary Tables 8–10). In comparison to CTC clusters 5 and 6, CTC cluster 1 enriched fewer genes related to the EMT pathway. However, there was an upregulation of genes associated with mitotic spindles, DNA repair, and E2F targets, coupled with increased activity in cell adhesion via apical junctions in CTC cluster 1. Based on the greater expression of genes involved in the interferon-α/γ-response pathways, CTC cluster 1 could further be characterized as being more immune responsive than CTC cluster 5. The immune response pathways were also more highly expressed in CTC cluster 6 than in CTC cluster 5. GSEA confirmed the mesenchymal phenotype of CTC clusters 5 and 6. Remarkably, despite both CTC clusters exhibiting a mesenchymal-like phenotype in comparison to CTC clusters 1, 10, 9 and 13, they were located at the two ends of the trajectory analysis, indicating substantial heterogeneity of mesenchymal CTCs. According to the GSEA, CTC cluster 5 enriched genes involved in oxidative phosphorylation, adipogenesis and fatty acid metabolism, while CTC cluster 6 encompassed genes related to hypoxia, glycolysis and ROS pathways. Additionally, compared with CTC cluster 1 and 6 cells, CTC cluster 5 cells exhibited decreased expression of genes involved in inflammatory response, interferon response α and γ, indicating a rather immune-evasive phenotype (Fig. 1L).

Taken together, these data indicated a high degree of phenotypic heterogeneity and a variety of CTC phenotypes were revealed: (i) epithelial-like, immune responsive and highly proliferative (CTC cluster 1), (ii) mesenchymal, oxidative phosphorylation, and immune evasive (CTC cluster 5), (iii) mesenchymal, invasive, and glycolytic (CTC cluster 6), and (iv) cancer-stem cell like (CTC cluster 4).

CTCs demonstrate heterogeneity similar to primary tumor cells, while concurrently manifesting CTC-specific phenotypes

To elucidate to which degree CTCs may exhibit distinct phenotypes in comparison to primary tumor cells (PTC), we analyzed the sc CTC-RNAseq dataset alongside an independent scRNAseq dataset from 45 primary NSCLC tumor samples (Supplementary Table 1). We randomly selected 5000 PTCs from the sc PTC-RNAseq dataset set to match the size of the sc CTC-RNAseq dataset, before the two datasets were integrated using the 'harmony' R package for batch effect correction by incorporating NK cells ([11] and Supplementary Material and Methods). This revealed 22 distinct cell clusters in an unsupervised clustering (Fig. 2A and B). As a note, Jaccard Index and Silhouette width indicated good cluster stability and consistency (Supplementary Fig. 2C & D, Supplementary Table 11).

Fig. 2
figure 2

CTCs demonstrate heterogeneity similar to primary tumor cells, while concurrently manifesting CTC-specific phenotypes.  A Graphical overview: the primary tumor cell (PTC) scRNAseq dataset was comprised of scRNA data from n = 45 patients, including n = 42 from [12]. 5000 randomly selected PTCs were taken to match the size of the sc CTC-RNAseq dataset. The 'harmony' R package [11] was used for batch effect correction by incorporating NK cells (see also Supplementary Material and Methods for more information). B UMAP plot of scRNAseq data of CTCs from n = 6 DLAs and a subset of PTCs. C UMAP from B displaying the distribution of DLA CTCs (red) and PTCs (green). D UMAP from B colored by the distribution of NK cells (dark blue), PTCs (grey) and CTC clusters (rest of the colors). E Trajectory analysis of PTCs together with CTCs. Color coding indicates the seven different CTC cluster along the branches. F Unsupervised hierarchical clustering of GSEA analysis with the HALLMARK dataset based on DEGs of individual CTC cluster vs. all PTCs. (red: high normalized enrichment score, blue: low normalized enrichment score). G Heatmap showing the comparison of gene expression in CTC cluster, PTCs and NSCLC cell lines. Red indicates a high expression and blue indicates a low expression

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Remarkably, this analysis indicated the preservation of most CTC clusters (Fig. 2B - D). CTC clusters 12 and 13 from this combined dataset comprised more than 90% CTCs, and CTC cluster 3 and 8 contained more than 75% of CTCs, reinforcing a distinctive phenotype of these CTC clusters (Supplementary Table 12). Notably, only CTC cluster 6 (mesenchymal, invasive and hypoxic) was integrated with PTCs (cluster 2 in Fig. 2B). A trajectory analysis further affirmed the separation of CTC clusters from PTCs, suggesting a potential distinctive phenotype for CTCs (Fig. 2E, Supplementary Fig. 5).

GSEA unveiled enriched pathways encompassing proliferation, translation (G2M checkpoint, DNA repair, E2F and Myc targets) and oxidative phosphorylation, while exhibiting decreased expression in TNFα signaling via NFκB, apoptosis and p53 pathways compared to PTCs (Fig. 2F, Supplementary Table 13). Noteworthy, immune-evasive phenotypes were observed in most CTC clusters, evidenced by reduced enrichment in inflammatory and interferon response pathways. Differential gene expression analysis highlighted genes related to hemoglobin metabolism, with HBB and its partners HBA1 and HBA2 highly expressed in CTCs, emphasizing a CTC-specific function (Fig. 2G). Conversely, genes involved in surfactant metabolism (NAPSA, SFTPB, SFTPA2 and SFTA2) were downregulated in CTCs compared to PTCs. Interestingly, giantin (GOLGB1) which is associated with invasion and was shown to be higher expressed on breast cancer CTCs than on primary breast cancer cells, is also higher expressed in NSCLC-CTCs than primary NSCLC tumor cells (Fig. 2G, Supplementary Table 14).

Overall, these data corroborate the hypothesis that CTCs possess a distinct, immune-evasive phenotype, enabling them to adapt to the unique microenvironment and stresses in the bloodstream. On the other side, mesenchymal CTC clusters 5 and 6 exhibit increased expression of genes associated with EMT, while CTC clusters 9, 10 and 13 presented an overall lower expression of EMT genes compared to PTCs. CTC cluster 6, in particular, showcased a metabolically unique profile with elevated expression of anaerobic glycolysis genes despite oxygen availability in the bloodstream. Together, this also emphasizes the role of CTCs in representing heterogeneity of PTCs, particularly concerning EMT, metabolic disparities, and other relevant pathways.

Discussion

Understanding of tumor heterogeneity and combating therapeutic resistance remain pivotal challenges in cancer treatment. Although CTCs hold great promise, their potential is hindered by their scarcity in standard peripheral blood samples, particularly in low CTC cancers like NSCLC [3, 4]. This is the first study representing a pioneering pipeline for analyzing larger DLA volumes, revealing the full potential of DLA and CTCs as pivotal diagnostic and translational tool in cancer research. Using this pipeline, we identified an unprecedented number of 3,363 CTCs from six NSCLC patients, by far the largest CTC dataset to date. In-depth sc transcriptomes revealed broad functional heterogeneity of CTCs along the EMT axis, including a cancer-stem cell like phenotype and different metabolic states. A comparison with PTCs indicated that CTCs indeed may represent intratumoral heterogeneity, while exhibiting specific CTC-related features.

Confirming prior studies, our findings underscored good tolerability of DLAs. In comparison to other studies based on identification of CTCs by epithelial markers [5, 6], our unbiased approach, focusing on depletion of HPCs and endothelial cells, led to the identification of large numbers of CTC, many of them with low or no EPCAM expression (Fig. 1J). The robustness of our analysis pipeline was confirmed by integration of scRNAseq data from healthy PBMCs with our negatively depleted DLA CTC dataset, which identified the same CTC clusters exclusively in the DLA dataset.

While previous studies showed patient-specific clustering in tissue tumor samples [12, 13], this study surprisingly reveals that NSCLC-CTC clustering is based on the specific tumor phenotype, not donor or clinical characteristics. This suggests that CTCs may be more dedifferentiated than primary tumors, supported by the loss of function of specific genes in CTCs, for instance genes involved in surfactant production (Fig. 2F and G, Supplementary Table 14).

Not surprisingly, CTC phenotypes represented cell states along the EMT continuum. However, only the exceptionally high number of total CTC transcriptomes, achieved through DLAs in combination with our unbiased enrichment, subsequently allowed for an in-depth and robust analysis of these cell states and representation of the full complexity of tumor heterogeneity, a prerequisite for fully understanding and monitor treatment escape and resistance mechanisms. The phenotypic plasticity of CTCs in relation to EMT, immune responsive and metabolic states as well as cancer stem cells also depicts the phenotypic changes that CTCs may endure throughout the metastatic cascade, as well as the plasticity necessary to survive treatment with anti-cancer agents and stress factors in the bloodstream [14].

Furthermore, comparison with PTCs revealed CTC specific phenotypes, such as a generally greater proliferation and translation but also an overall immune-evasive phenotype (lower expression of genes involved in TNFα signaling via NFκB and interferon-α/γ-response), which may be required for CTCs in order to avoid attack by immune cells in the peripheral blood. One such mechanism, the escape of CTCs from NK-cell surveillance by hijacking the HLA-E:CD94-NKG2a checkpoint was recently identified [15]. We further noted the strong upregulation of HBB in CTCs. Although surprising, this finding was described before and may indicate a potential strategy for CTCs to endure oxidative stress [16].

A limitation of this study is a potential underrepresentation of heterotypic CTC cluster, due to the depletion of HPCs as part of CTC cluster. CTC cluster are very rare events, but they may exert greater metastatic potential than single CTCs [17]. Furthermore, the absence of comparison with patient-matched normal (healthy) lung tissue and peripheral blood cells may underrepresent the individual heterogeneity. These questions will be addressed in future studies including longitudinal in-depth analyses of CTCs obtained from DLA.

To summarize, this study unravels distinct CTC phenotypes, illuminating a path towards potential future CTC-directed treatments. The observed heterogeneity in CTCs compared to PTCs underscores the robust potential of CTCs in diagnostic and translational research, which will enhance our understanding of metastatic NSCLC. Altogether, the here proposed DLA – CTC pipeline allows a real-time evaluation of patterns of response and resistance upon immunotherapy and/ or targeted therapies and has the potential to transform personalized medicine for metastatic NSCLC patients and beyond.

Availability of data and materials

The dataset supporting the conclusions of this article are included within the article’s additional files and are also available from the corresponding author on reasonable request.

Abbreviations

BEAM:

Branched expression analysis modeling

CMP:

Common myeloid progenitor

BSA:

Bovine serum albumin

CSC:

Cancer stem cell

CTC:

Circulating tumor cell

CNV:

Copy number variation

DLA:

Diagnostic leukapheresis

DEG:

Differentially expressed gene

DMSO:

Dimethyl sulfoxide

EDTA:

Ethylenediaminetetraacetic acid

EMT:

Epithelial–mesenchymal transition

FBS:

Fetal bovine serum

FDR:

False discovery rate

GSEA:

Gene set enrichment analysis

GMP:

Granulocyte-monocyte progenitor

HPC:

Hematopoietic cells

HPCA:

Human primary cell atlas

MEP:

Megakaryocyte/erythroid progenitors

MNC:

Mononucleated cell

NK:

Natural killer cell

NSCLC:

Non-small cell lung cancer

PB:

Peripheral blood

PBS:

Phosphate buffered saline

PBMC:

Peripheral blood mononuclear cell

PTC:

Primary tumor cell

PC:

Principal component

sc:

Single cell

WBC:

White blood cell

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Acknowledgements

The authors would like to thank Susanne Schlichting at Transfusion Medicine Hamburg for her help in planning DLA logistics and for her excellent technical support; Julia Spötter, Melanie Lachmann, Regine Thiele and Susanne Roscher from FACS Core Facility and everyone from the Single Cell Core Facility at the University Hospital Hamburg Eppendorf (UKE) for technical support and Sina Dietrich from the Pathology Department at the UKE. We thank Marina Vogel and Ursula Fürst at the DKFZ Sequencing Core Facility, and Jan-Philipp Mallm and Katharina Bauer at DKFZ Single Cell Open Lab. We are thankful to everyone at Thorax Clinic Heidelberg, especially the Section Translational Research (STF) and Sabine Wessels for kindly providing us with primary tissue samples. Furthermore, we thank Sarina Heinemann and Andia Louisa Tehrany from the Department of Oncology at the UKE for their technical assistance and support, and Amir Jassim and Eric P. Rahrmann at Cancer Research UK Cambridge Institute for very helpful discussions.

Funding

This work was supported by the Hector Foundation II (no grant number), by the CANCER-ID, an Innovative Medicines Initiative Joint Undertaking under grant agreement n◦ 115749, resources of which are composed of financial contribution from the European Union’s Seventh Framework Program (FP7/2007–2013) and EFPIA companies’ kind contributions; by the Margarete Clemens Stiftung (no grant number); by the Landesforschungsförderung Hamburg LFF GK10, graduate school InTechCanDiTh; by the European Research Council (ERC Starting Grant to SL “758713-ELIMINATE”) and by Bristol Myers Squibb for Study CA209-8RG.

Author information

Author notes

  1. Lisa-Marie Rieckmann, Michael Spohn, Lisa Ruff, Sonja Loges and Melanie Janning contributed equally to this work.

Authors and Affiliations

  1. Department of Personalized Oncology, DKFZ-Hector Cancer Institute, University Medical Center Mannheim, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany

    Lisa-Marie Rieckmann, Lisa Ruff, Roisin O’Reilly, Niklas Beumer, Armin Günther, Lena Willnow, Sonja Loges & Melanie Janning

  2. Division of Personalized Medical Oncology (A420), German Cancer Research Center (DKFZ), Heidelberg, Germany

    Lisa-Marie Rieckmann, Lisa Ruff, Roisin O’Reilly, Niklas Beumer, Armin Günther, Lena Willnow, Sonja Loges & Melanie Janning

  3. German Center for Lung Research (DZL), Heidelberg, Germany

    Lisa-Marie Rieckmann, Lisa Ruff, Roisin O’Reilly, Niklas Beumer, Armin Günther, Lena Willnow, Sonja Loges & Melanie Janning

  4. Department of Personalized Oncology, University Hospital Mannheim, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany

    Lisa-Marie Rieckmann, Lisa Ruff, Roisin O’Reilly, Armin Günther, Lena Willnow, Sonja Loges & Melanie Janning

  5. Bioinformatics Core, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Michael Spohn

  6. Clinic of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Michael Spohn

  7. Research Institute Children’s Cancer Center Hamburg, Hamburg, Germany

    Michael Spohn

  8. Department of Oncology, Hematology and Bone Marrow Transplantation With Section Pneumology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Michael Spohn, Jurek Hille, Janna-Lisa Velthaus-Rusik, Carsten Bokemeyer, Sonja Loges & Melanie Janning

  9. Miltenyi Biotec B.V. & Co. KG, R&D, Bergisch Gladbach, Germany

    David Agorku & Olaf Hardt

  10. Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM GmbH), Heidelberg, Germany

    Lisa Becker, Martin Sprick & Andreas Trumpp

  11. Division of Stem Cells and Cancer, German Cancer Research Center, (DKFZ-ZMBH Alliance), Heidelberg, Germany

    Lisa Becker, Martin Sprick & Andreas Trumpp

  12. III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Alina Borchers & Christian F. Krebs

  13. Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Alina Borchers, Nicola Gagliani & Christian F. Krebs

  14. I. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Jenny Krause & Nicola Gagliani

  15. Department of Tumor Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Jurek Hille, Sabine Riethdorf, Klaus Pantel, Sonja Loges & Melanie Janning

  16. Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Niklas Beumer & Charles D. Imbusch

  17. Faculty of Biosciences, Heidelberg University, Heidelberg, Germany

    Niklas Beumer & Armin Günther

  18. Institute of Pathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Peter Iglauer & Ronald Simon

  19. Institute of Clinical Molecular Biology, Christian-Albrechts-University and University Hospital Schleswig-Holstein, Kiel, Germany

    Sören Franzenburg & Philipp Rosenstiel

  20. Translational Lung Research Center Heidelberg, Member of the German Center for Lung Research (DZL), Department of Thoracic Oncology, Thoraxklinik at University Hospital Heidelberg, Heidelberg, Germany

    Hauke Winter & Michael Thomas

  21. Department of General, Visceral and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Nicola Gagliani

  22. General, Visceral and Pediatric Surgery, University Hospital and Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany

    Nikolas H. Stoecklein

  23. Institute of Transfusion Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Sven Peine

Authors
  1. Lisa-Marie Rieckmann
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  2. Michael Spohn
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  3. Lisa Ruff
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  4. David Agorku
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  5. Lisa Becker
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  6. Alina Borchers
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  7. Jenny Krause
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  8. Roisin O’Reilly
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  9. Jurek Hille
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  10. Janna-Lisa Velthaus-Rusik
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  11. Niklas Beumer
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  12. Armin Günther
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  13. Lena Willnow
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  14. Charles D. Imbusch
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  15. Peter Iglauer
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  16. Ronald Simon
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  17. Sören Franzenburg
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  18. Hauke Winter
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  19. Michael Thomas
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  20. Carsten Bokemeyer
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  21. Nicola Gagliani
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  22. Christian F. Krebs
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  23. Martin Sprick
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  24. Olaf Hardt
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  25. Sabine Riethdorf
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  26. Andreas Trumpp
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  27. Nikolas H. Stoecklein
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  28. Sven Peine
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  29. Philipp Rosenstiel
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  30. Klaus Pantel
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  31. Sonja Loges
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  32. Melanie Janning
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Contributions

MJ and SL designed the overall concept of the study. LMR, MiS, LR, DA, AB, JK, RO, JK, NK, AG, LW, CDI, SF performed experiments and analyzed data, MiS, NK and CDI performed bioinformatic analyses, JLVR, CB, MT, HW, ML, SL diagnosed patients; JLVR, CB, MT, HW, ML, SL, PI and RS provided samples, LB, MaS, AT, DA and OH contributed concept and material for the enrichment of CTC from DLA samples; SP performed DLA; SF, PR, AB, JK, NG and CFK performed sequencing, NHS, SR, KP contributed to the concept of DLA and discussed data and strategy. LMR, LR, MS, MJ, and SL primarily wrote manuscript; LMR, LR, MS, RO, AG and LW prepared figures; All authors discussed the results, figures and commented on the manuscript. All authors read and approved the final version of the manuscript.

Corresponding author

Correspondence to Melanie Janning.

Ethics declarations

Ethics approval and consent to participate

All patients provided written informed consent and the study was approved by the Medical Ethical Committee Hamburg, Germany (PV5392).

Consent for publication

Not applicable.

Competing interests

DA and OH are employees of Miltenyi Biotec B.V. & Co. KG. CB: honoraria and/or advisory board from AstraZeneca, Bayer, Berlin Chemie, BMS, Merck, Roche, Novartis, Sanofi, Lilly/ImClone and travel expenses from BMS, Merck, Pfizer and Sanofi. SL received funding from BerGenBio, Bristol Myers Squibb, Eli Lilly, Roche Pharma and ADC Therapeutics; consulting fees from BerGenBio, BMS, Boehringer Ingelheim, Eli Lilly, Roche Pharma, Medac, Sanofi Aventis, Novartis, AstraZeneca, Pfizer, Takeda, Amgen, Bayer, Janssen and Merck; money for presentations/speakers bureaus from BerGenBio, BMS, Boehringer Ingelheim, Eli Lilly, Roche Pharma, Medac, Sanofi Aventis, Novartis, AstraZeneca, Pfizer, Takeda, Amgen, Bayer, Jannssen and Merck; travel support from BerGenBio, BMS, Boehringer Ingelheim, Eli Lilly, Roche Pharma, Medac, Sanofi Aventis, Novartis, AstraZeneca, Pfizer, Takeda, Amgen, Bayer, Janssen and Merck. MJ: received research support (material) from Miltenyi and funding from Bristol Myers Squibb; speakers honoraria and /or advisory board from Roche, Amgen, AstraZeneca, Novartis, Takeda and travel support from AstraZeneca.

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Rieckmann, LM., Spohn, M., Ruff, L. et al. Diagnostic leukapheresis reveals distinct phenotypes of NSCLC circulating tumor cells. Mol Cancer 23, 93 (2024). https://doi.org/10.1186/s12943-024-01984-2

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  • DOI: https://doi.org/10.1186/s12943-024-01984-2

Keywords

Molecular Cancer

ISSN: 1476-4598