Diagnostic leukapheresis reveals distinct phenotypes of NSCLC circulating tumor cells

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.

Keywords: Circulating tumor cells; Intratumor heterogeneity; Non-small cell lung cancer; Single cell RNA sequencing.

Fig. 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

Fig. 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