Circulating tumor cell (CTC) clusters mediate metastasis at a higher efficiency and are associated with lower overall survival in breast cancer compared to single cells. Combining single-cell RNA sequencing and protein analyses, here we report the profiles of primary tumor cells and lung metastases of triple-negative breast cancer (TNBC). ICAM1 expression increases by 200-fold in the lung metastases of three TNBC patient-derived xenografts (PDXs). Depletion of ICAM1 abrogates lung colonization of TNBC cells by inhibiting homotypic tumor cell-tumor cell cluster formation. Machine learning-based algorithms and mutagenesis analyses identify ICAM1 regions responsible for homophilic ICAM1-ICAM1 interactions, thereby directing homotypic tumor cell clustering, as well as heterotypic tumor-endothelial adhesion for trans-endothelial migration. Moreover, ICAM1 promotes metastasis by activating cellular pathways related to cell cycle and stemness. Finally, blocking ICAM1 interactions significantly inhibits CTC cluster formation, tumor cell transendothelial migration, and lung metastasis. Therefore, ICAM1 can serve as a novel therapeutic target for metastasis initiation of TNBC.

Large numbers of cells are generally required for quantitative global proteome profiling due to surface adsorption losses associated with sample processing. Such bulk measurement obscures important cell-to-cell variability (cell heterogeneity) and makes proteomic profiling impossible for rare cell populations (e.g., circulating tumor cells (CTCs)). Here we report a surfactant-assisted one-pot sample preparation coupled with mass spectrometry (MS) method termed SOP-MS for label-free global single-cell proteomics. SOP-MS capitalizes on the combination of a MS-compatible nonionic surfactant, n-Dodecyl-β-D-maltoside, and hydrophobic surface-based low-bind tubes or multi-well plates for ‘all-in-one’ one-pot sample preparation. This ‘all-in-one’ method including elimination of all sample transfer steps maximally reduces surface adsorption losses for effective processing of single cells, thus improving detection sensitivity for single-cell proteomics. This method allows convenient label-free quantification of hundreds of proteins from single human cells and ~1200 proteins from small tissue sections (close to ~20 cells). When applied to a patient CTC-derived xenograft (PCDX) model at the single-cell resolution, SOP-MS can reveal distinct protein signatures between primary tumor cells and early metastatic lung cells, which are related to the selection pressure of anti-tumor immunity during breast cancer metastasis. The approach paves the way for routine, precise, quantitative single-cell proteomics.

Circulating tumor cells (CTCs) are vital components of liquid biopsies for diagnosis of residual cancer, monitoring of therapy response, and prognosis of recurrence. Scientific dogma focuses on metastasis mediated by single CTCs, but advancement of CTC detection technologies has elucidated multicellular CTC clusters, which are associated with unfavorable clinical outcomes and a 20-to 100-fold greater metastatic potential than single CTCs. While the mechanistic understanding of CTC cluster formation is still in its infancy, multiple cell adhesion molecules and tight junction proteins have been identified that underlie the outperforming attributes of homotypic and heterotypic CTC clusters, such as cell survival, cancer stemness, and immune evasion. Future directions include high-resolution characterization of CTCs at multiomic levels for diagnostic/prognostic evaluations and targeted therapies. Circulating tumor cells form clusters that enhance breast cancer metastasisAlthough metastasis accounts for 90% of solid tumor-related mortality, it currently evades targeted treatments and demands a better understanding. Metastasis is a multistep process during which cancer cells spread from the primary tumor site to distant organs, starting with the primary tumor formation, where tumor cells gradually expand and locally invade the surrounding stroma and tissues, including the blood and lymphatic vessels. At this point, the intravasated tumor cells, now called 'circulating tumor cells' (CTCs) and a vital component of liquid biopsy [1,2], develop adaptive mechanisms that favor their survival in the harsh microenvironment of the circulatory system. CTCs may disseminate to distant parts of the body before they finally extravasate or get trapped within the capillaries in certain organs, form metastatic niches, and regenerate secondary tumors [3][4][5].The presence of CTCs in the blood of patients with cancer was first detected in 1869 by Thomas Ashworth [6], but, because of advances in technology, CTCs have only recently attracted great attention for their key role in tumor metastasis [6,7]. Many studies have shown that CTCs may be used to predict disease progression and prognosis in patients with metastatic cancer [4]. In metastatic cancers such as breast cancer, the currently accepted level of CTCs that correlates with worse overall survival and progression-free survival is five or more CTCs in 7.5 mL of blood [8]. CTC enumeration can be used to better stratify patients with stage IV breast cancer as stage IV aggressive, with more than five CTCs, and stage IV indolent, with fewer than five CTCs [8]. Stage IV indolent disease is associated with significantly longer overall survival, regardless of disease subtype and prior treatment [8]. The presence of CTCs in early breast cancer was also demonstrated to have prognostic impact [9,10]. Thus, CTC analysis in patients with cancer is a minimally invasive, clinically informative method of predicting disease stage and survival that is not dependent on cancer subtype or previous trea...

The clinical relevance of circulating tumor cell clusters (CTC-clusters) in breast cancer (BC) has been mostly studied using the CellSearch®, a marker-dependent method detecting only epithelial-enriched clusters. However, due to epithelial-to-mesenchymal transition, resorting to marker-independent approaches can improve CTC-cluster detection. Blood samples collected from healthy donors and spiked-in with tumor mammospheres, or from BC patients, were processed for CTC-cluster detection with 3 technologies: CellSearch®, CellSieve™ filters, and ScreenCell® filters. In spiked-in samples, the 3 technologies showed similar recovery capability, whereas, in 19 clinical samples processed in parallel with CellSearch® and CellSieve™ filters, filtration allowed us to detect more CTC-clusters than CellSearch® (median number = 7 versus 1, p = 0.0038). Next, samples from 37 early BC (EBC) and 23 metastatic BC (MBC) patients were processed using ScreenCell® filters for attaining both unbiased enrichment and marker-independent identification (based on cytomorphological criteria). At baseline, CTC-clusters were detected in 70% of EBC cases and in 20% of MBC patients (median number = 2, range 0–20, versus 0, range 0–15, p = 0.0015). Marker-independent approaches for CTC-cluster assessment improve detection and show that CTC-clusters are more frequent in EBC than in MBC patients, a novel finding suggesting that dissemination of CTC-clusters is an early event in BC natural history.