The metastatic spread of cancer is achieved by the haematogenous dissemination of circulating tumour cells (CTCs). Generally, however, the temporal dynamics that dictate the generation of metastasis-competent CTCs are largely uncharacterized, and it is often assumed that CTCs are constantly shed from growing tumours or are shed as a consequence of mechanical insults1. Here we observe a striking and unexpected pattern of CTC generation dynamics in both patients with breast cancer and mouse models, highlighting that most spontaneous CTC intravasation events occur during sleep. Further, we demonstrate that rest-phase CTCs are highly prone to metastasize, whereas CTCs generated during the active phase are devoid of metastatic ability. Mechanistically, single-cell RNA sequencing analysis of CTCs reveals a marked upregulation of mitotic genes exclusively during the rest phase in both patients and mouse models, enabling metastasis proficiency. Systemically, we find that key circadian rhythm hormones such as melatonin, testosterone and glucocorticoids dictate CTC generation dynamics, and as a consequence, that insulin directly promotes tumour cell proliferation in vivo, yet in a time-dependent manner. Thus, the spontaneous generation of CTCs with a high proclivity to metastasize does not occur continuously, but it is concentrated within the rest phase of the affected individual, providing a new rationale for time-controlled interrogation and treatment of metastasis-prone cancers.
CTCs are pioneers of the metastatic cascade in several cancer types, including breast cancer1. The factors that regulate spontaneous CTC intravasation in physiological settings are poorly understood, and the general assumption is that CTCs are constantly generated from invasive cancerous tissues2, or generated following mechanical cues such as surgery3 or physical activity4. In patients and in mouse cancer models, the exact timing of the events that characterize metastatic cancer progression, and the principles that dictate intravasation of CTCs and their proclivity to metastasize, are unclear. A better understanding of these processes may result in new approaches for cancer investigation and treatment.
Circadian rhythm and CTC intravasation
We first sought to determine CTC abundance and composition in hospitalized women with progressive breast cancer who had no treatment or were temporarily off-treatment and who consented to donate blood during the active (10:00 am) and rest (4:00 am) phases of the same day, including a total of 30 patients (Fig. 1a). Of these, 21 patients were diagnosed with early breast cancer (no metastasis) and 9 were diagnosed with stage IV metastatic disease at the time of blood sampling (Supplementary Table 1). Strikingly, following antigen-agnostic microfluidic capture of CTCs and confirmation through immunofluorescence staining5, we found most CTCs (78.3%) in samples obtained at night time during the rest phase, including single CTCs, CTC clusters and CTC–white blood cell (WBC) clusters (Fig 1a, Extended Data Fig. 1a and Supplementary Table 1). To test the generality of these findings and to finely characterize the precise timing of the events, we used four different mouse models of breast cancer, including xenografts derived from human breast CTCs (NSG-CDX-BR16), xenografts with established human breast cancer cells (NSG-LM2) or mouse breast cancer cells (NSG-4T1), and an immunocompetent syngeneic breast cancer model (BALB/c-4T1). Following transplantation of breast cancer cells in the mammary fat pad and tumour growth, we examined spontaneous CTC generation over time by means of terminal blood sampling and microfluidic CTC capture. Consistently with patient data, we found most CTC events (99.2% in NSG-LM2, 97% in NSG-CDX-BR16, 93.8% in NSG-4T1, 87% in BALB/c-4T1) to be present in samples obtained through cardiac puncture during the mouse rest phase (corresponding to daylight time, given the inverted circadian rhythm of rodents compared to humans6; Fig. 1b,c and Extended Data Fig. 1a). More precisely, when carrying out a time kinetic analysis of a 24-h time period at intervals of 4 h, we observed a very prominent oscillatory pattern of CTC release, peaking between 4 and 12 h zeitgeber time (ZT; with ZT0 defined as 06:00 am when the lights turn on, and ZT12 defined as 06:00 pm when the lights turn off) corresponding to their rest phase (Fig. 1b and Extended Data Fig. 1b) in mice with analogous tumour burden (Extended Data Fig. 1c,d). When focusing on the two most representative time points for the rest (ZT4) and active (ZT16) phases of the mouse circadian rhythm, we observe marked differences in absolute and normalized CTC counts in all tested models, with a 6- to 88-fold increase for single CTCs, a 12- to 278-fold increase for CTC clusters and an 8- to 34-fold increase for CTC–WBC clusters during ZT4 (Fig. 1c and Extended Data Fig. 1e–g), whereas no changes are observed in the ratio of the various CTCtypes (Extended Data Fig. 1h). Further, we identified highly similar (yet, even more pronounced) oscillatory patterns in CTC abundance when blood samples were obtained directly from the tumour draining vessel (TDV; Fig. 1b and Extended Data Fig. 2a–e). Given these results, the extremely short circulation half-life of CTCs7,8, and their similar clearance rate during different time points of the rest and active phases (Extended Data Fig. 2f,g), we conclude that the main differences in CTC abundance observed during the rest versus the active phase are to be ascribed to differences in intravasation rates. We then attempted to perturb the physiological rhythm of tumour-bearing mice by different means. First,on the one hand, we used well-established methods to shift the normal light/dark (LD) cycle, provoking a jet-lag effect9, and on the other hand, we treated control and jet-lagged mice with melatonin, a key hormone that regulates the sleep cycle (Extended Data Fig. 3a). Strikingly, when analysing blood in all mice at ZT4, we find that jet-lag induction leads to a 38- to 282-fold decrease in single CTCs, a 63- to 484-fold decrease in CTC clusters and a 28- to 219-fold decrease in CTC–WBC clusters compared to those in control mice and with no changes in primary tumour size (Extended Data Fig. 3b–d). Further, when treating tumour-bearing mice with melatonin (daily, 2 h before the start of the rest phase), and exposing them to a jet-lagged or normal LD cycle for a total duration of 22 days, we find a marked melatonin-induced increase in the production of single CTCs, CTC clusters and CTC–WBC clusters in all cases, rescued by the melatonin receptor antagonist luzindole (Extended Data Fig. 3e). Along with the effects of melatonin in increasing CTC production and luzindole in decreasing it without affecting primary tumour size, we observe an augmented or reduced metastatic burden, respectively (Extended Data Fig. 3f,g). We then exposed tumour-bearing mice to altered LD cycles. We used a long-day photoperiod (14:10 LD), as well as two different T-cycles with LD cycles that differ from 24 h (20-t, 10:10 LD and 28-t, 14:14 LD), and tested their effect on CTC generation. We observe a consistent increase in CTC counts during the rest phase in each of these light conditions (Extended Data Fig. 4a–d), suggesting a key role for light exposure, and its consequences, in CTC intravasation. Last, given the oscillatory pattern of CTC intravasation and its relation to the circadian rhythm, we tested whether this pattern was abolished in the context of a syngeneic transplantation of E0771.lmb mouse cancer cells in either wild-type BL/6 mice (BL/6-E0771.lmb) or in Bmal1-homozygous-knockout mice (BL/6-Bmal1−/−-E0771.lmb), the only arrhythmic single-gene-knockout model10 (Fig. 1d). We find that, whereas CTC counts from both cardiac puncture and the TDV follow a typical oscillatory pattern in control BL/6-E0771.lmb mice, oscillation in CTC counts is lost in BL/6-Bmal1−/−-E0771.lmb mice (Fig. 1d and Extended Data Fig. 4e). Of note, BL/6-Bmal1−/−-E0771.lmb mice generally fail to generate CTCs despite identical tumour size and timing of sample collection compared to those for BL/6-E0771.lmb control mice (Extended Data Fig. 4f,g), highlighting that a disruption of the master regulator of the circadian clock results in abolished CTC intravasation. Taken together, these results demonstrate that CTCs are not shed continuously during tumour progression, but the greatest release of single and clustered CTCs is achieved during sleep in both patients with breast cancer and mouse models.
Metastatic ability of CTCs during sleep
We next investigated whether, in addition to their increased production during the rest phase, CTCs that are generated during different phases of the circadian rhythm also harbour a different potential to successfully metastasize. To this end, we used the NSG-LM2 xenograft model, exclusively labelled with either GFP or RFP, and following tumour development, we isolated spontaneously shed CTCs at ZT4 (GFP-labelled) and ZT16 (RFP-labelled) by microfluidics-based capture. With a robotic micromanipulator, we isolated 150 ZT4-generated GFP-labelled CTCs and 150 ZT16-generated RFP-labelled CTCs, simultaneously, each of which composed of 110 single CTCs, 35 CTC clusters and 5 CTC–WBC clusters (representing typical CTC ratios in the NSG-LM2 model), and co-injected them through the tail vein of tumour-free recipient mice at different time points of the circadian rhythm (ZT0, ZT4, ZT12, ZT16) to measure their direct metastatic ability (Fig. 2a). Through in vivo bioluminescence imaging, we find the highest metastatic burden during the rest phase, and in particular at ZT4 (Fig. 2b). To dissect whether these metastases were derived from ZT4 or ZT16 CTCs, we conducted immunohistochemical analysis of the lungs with anti-GFP and anti-RFP antibodies. Remarkably, we find that most metastases derive from ZT4-generated GFP-labelled CTCs (Fig. 2c). These results highlight a substantial contribution of ZT4 CTCs to metastasis formation, as well as a higher proclivity of ZT4 CTCs to form metastases when injected into mice during their rest phase. Next we sought to extend our findings to further models and to precisely quantify the metastatic ability of rest-phase versus active-phase single CTCs, CTC clusters and CTC–WBC clusters, individually. We used both the NSG-CDX-BR16 and NSG-LM2 xenograft models, and following tumour development, we isolated spontaneously shed CTCs by microfluidics-based capture. With a robotic micromanipulator, we then isolated 100 single CTCs, 100 CTCs from CTC clusters and 100 CTCs from CTC–WBC clusters of mice during their rest (ZT4) and active (ZT16) phases, respectively, and injected them through the tail vein of tumour-free recipient mice at ZT12 to measure their direct metastatic ability (Fig. 2d). By means of bioluminescence imaging, we confirm that CTCs obtained during ZT4 exhibit an extraordinary metastasis-forming capacity compared to CTCs that are obtained during ZT16 (Fig. 2e and Extended Data Fig. 5a–f). Of note, when isolated during the rest phase and compared to those in the active phase, CTC clusters and CTC–WBC clusters seem to be endowed with greater metastasis-forming properties than those of single CTCs (Fig. 2e and Extended Data Fig. 5a–f), suggesting that most of the rest-phase-dependent metastatic spread of breast cancer could be ascribed to both homotypic and heterotypic CTC clusters. Together, these results indicate not only that CTC intravasation rates are increased, but also that their metastatic ability is augmented during the rest phase.
Time-dependent gene expression in CTCs
Next we sought to investigate molecular features determining the differential ability of CTCs to seed metastasis during the rest and active phases, respectively. Following mammary fat pad engraftment of BR16 and LM2 breast cancer cells, tumour growth and spontaneous CTC generation, we isolated single CTCs, CTC clusters and CTC–WBC clusters during the rest (ZT4) and active (ZT16) phases of the mouse circadian rhythm, and subjected them individually to single-cell RNA sequencing11,12 (scRNA-seq; Fig. 3a). In total, after filtering for high-quality samples (that is, taking into account the number of expressed genes per cell, the total number of reads per cell and the proportion of reads aligning to mitochondrial genes; see Methods), we obtained a total of 138 CTCs from the NSG-CDX-BR16 model and 108 CTCs from the NSG-LM2 model, representing all types of CTC at ZT4 and ZT16. Using principal component analysis, we find that time point (ZT4 versus ZT16) is a key feature driving variance of gene expression in CTCs (Fig. 3b and Extended Data Fig. 6a), suggesting time-point-driven gene expression changes. Differential gene expression of samples isolated during the rest (ZT4) versus the active (ZT16) phase reveals a set of 121 upregulated genes in ZT4 CTCs (log2[fold change] ≥ 0.5 and false discovery rate ≤ 0.05) and a set of 156 upregulated genes in ZT16 CTCs (log2[fold change] ≤ −0.5 and false discovery rate ≤ 0.05; Fig. 3c and Supplementary Table 2). Of note, we observe that most of the genes defining ZT4 and ZT16 expression signatures are consistently found highly upregulated (that is, fold change) in all types of CTC, yet statistical significance is highest in CTC clusters and CTC–WBC clusters (Extended Data Fig. 6b,c). This isconsistent with a higher variability and higher dropout rate expected in single-cell samples. Gene set enrichment analysis (GSEA) of genes upregulated during ZT4 and ZT16 highlights a highly consistent activity of pathways that support mitosis and cell division during ZT4 (adjusted P value ≤ 0.0001), mirrored by pathways that support ribosomal biogenesis and translation of genes during ZT16 (adjusted P value ≤ 0.0001; Fig. 3d,e, Extended Data Fig. 6d,e and Supplementary Table 3). These findings are consistent with prototypical gene expression timing in eukaryotic cells (that is, comprising recurring ribosome biogenesis and gene translation phases followed by the expression of cell cycle progression genes and the execution of cellular division within a 24-h time frame)13. Of note, in human CTCs isolated from patients with breast cancer during the active (10:00 am) and rest (4:00 am) phases of the same day, we confirm the same pattern of gene expression and pathway activity as observed in mouse models (Fig. 3f and Extended Data Fig. 6e). Gene expression changes ascribed to cell division and translation and inferred at time points ZT4 and ZT16, respectively, are also consistently observed across different time points during the rest and active phases (Fig. 3g and Extended Data Fig. 6f). Given the short half-life of CTCs, we reasoned that oscillatory changes in cellular proliferation could also be visible at the level of the primary tumour when analysed at different times. Accordingly, when staining for the proliferation marker Ki67 in tumours from the NSG-CDX-BR16 and the NSG-LM2 models, along with their CTCs, we find a marked upregulation of Ki67 during the rest phase (Fig. 3h and Extended Data Fig. 7) and consistent with the timing of highest CTC intravasation and expression of mitosis-related genes. Together, molecular gene expression analysis of CTCs from patients and mouse models, isolated during the rest and active phases, highlights very distinct gene expression patterns. During sleep, gene expression is dominated by cell division and mitosis genes, whereas during the active phase, we observe high ribosome biogenesis activity. This oscillatory proliferation timing is observed not only in CTCs but also in the primary tumour, suggesting this as a general phenomenon occurring in breast cancer cells during disease progression.
Regulators of CTC intravasation
Mechanistically, to identify the master regulators of circadian-rhythm-driven CTC generation and proliferation, we took several approaches. We first investigated whether the expression of prototypical circadian clock genes in cancer cells changed between the rest (ZT4) and active (ZT16) phase. Similarly to previous reports highlighting disruption of rhythmicity in the expression of circadian clock genes in cancer14,15, we could not detect differential expression in CTCs or primary tumour cells (Extended Data Fig. 8a–c), whereas we could confirm rhythmicity in non-neoplastic tissues (Extended Data Fig. 8c). We next investigated whether oscillations in CTC counts could be explained by changes in interstitial fluid pressure, differential interplay with immune cells and damage due to different haemolysis rates during different phases of the circadian rhythm. Notably, we found no difference in YAP and TAZ expression levels or localization (as sensors of interstitial pressure), in the abundance of circulating or tumour-infiltrated immune cells, or in apoptotic levels of CTCs during the rest (ZT4) versus active (ZT16) phase (Extended Data Fig. 9a–f). Last, we interrogated our RNA-seq data from CTCs of xenografts and patients to determine the expression levels of receptors for well-known circadian-rhythm-regulated hormones, growth factors or molecules, reasoning that the daily oscillation of their systemic levels could affect cancer cells in a time-dependent fashion. We evaluated the expression of 63 receptors for circadian-rhythm-regulated candidates (Supplementary Table 4), looking for those with a high level of expression in most CTCs and independently of a specific time point (that is, stable expression over time and activity proportional to the levels of their ligand). With these criteria, we find that expression of the glucocorticoid receptor, androgen receptor and insulin receptor is highly represented among single CTCs, CTC clusters and CTC–WBC clusters (Extended Data Fig. 10a), suggesting the involvement of their ligands in time-point-driven CTC generation and proliferation (Fig. 4a). To test this hypothesis, we first treated tumour-bearing mice with either dexamethasone (specific glucocorticoid receptor ligand) or testosterone (the main androgen receptor ligand), both found at high levels in physiological conditions at the onset of the active phase16,17 (that is, when CTC numbers are low). Accordingly, both a single treatment with dexamethasone at 4 mg kg−1 during the rest phase (ZT2) and implantation of a testosterone pellet (slow, continuous release) resulted in a marked reduction in single CTCs, CTC clusters and CTC–WBC clusters when sampled at the peak time during the rest phase (ZT4; Fig. 4b,c and Extended Data Fig. 10b–f). Of note, whereas treatment with dexamethasone or testosterone did not affect primary tumour size (Extended Data Fig. 10c,d), we observed a reduction in the metastatic burden of testosterone-treated mice (Extended Data Fig. 10e), consistent with a prolonged suppression of CTC generation alongside the continuous testosterone release from the pellet. Further, given the well-established link between insulin stimulation and subsequent cell growth and division13,18, we investigated whether insulin oscillations (with insulin being higher during the active phase following glucose intake in physiological conditions19) could also influence the proliferation timing and intravasation of breast cancer cells (that is, whether insulin stimulation during the rest phase could invert the dynamics of CTC release and proliferation). To address this, following tumour development, we treated tumour-bearing mice daily (during the rest phase, at ZT3) for 1 week with 0.7 U kg−1 of insulin and 1 g kg−1 glucose, and quantified CTC abundance during the rest and active phases, respectively. Consistently, we find that insulin treatment during the rest phase decreases CTC intravasation at ZT4, and increases it at ZT16 (Fig. 4d), with no significant changes in primary tumour volume (Extended Data Fig. 10g,h). Of note, treatment with insulin during the rest phase also inverts the proliferation cycle of breast cancer cells (that is, it decreases proliferation during the rest phase and it increases it during the active phase; Fig. 4e). Together, our findings indicate that proliferation and intravasation of breast cancer cells are dictated by daily oscillations in key circadian-rhythm-regulated hormones, whose action influences breast cancer metastasis dynamics.
Our data provide new insights into the processes that dictate the generation of metastasis-competent CTCs. Previous reports have suggested a role of the circadian rhythm in tumorigenesis, with most involving epidemiological studies and linking disruption of the circadian clock to accelerated onset of cancer15,20,21. Yet, dynamics that prospectively govern metastatic disease progression in this context remained poorly characterized. More recent studies using in vivo imaging technologies and interrogation of physiological models have highlighted various mechanisms adopted by cancer cells during the intravasation process22,23,24; however, a detailed understanding of the specific timing of CTC intravasation has been lacking. We find that, in both patients with breast cancer and mouse models, generation of CTCs is highly restricted to the rest phase, and that rest-phase CTCs are endowed with a much greater metastatic proclivity compared to active-phase CTCs. This augmented metastatic ability is conferred by high proliferation rates that occur in a time-dependent manner, and it is influenced by the action of circadian-rhythm-regulated hormones, suggesting the need for time-controlled approaches for the characterization and treatment of breast cancer. These could include the interrogation of clinical samples at highly controlled time points to minimize variability, as well as cancer treatment approaches that are tuned to be maximally effective during sleep.
All patients gave their informed written consent to participate in the study that took place at the University Hospital Basel under the Clinical Research Protocol (number 2020-00014) approved by the Swiss authorities (EKNZ, Ethics Committee northwest/central Switzerland) and in compliance with the Declaration of Helsinki. All patients were hospitalized and were either temporarily off-treatment (patients with stage IV disease) or before operation (patients with stage I–III disease) at the time of blood sampling. A 7.5-ml sample of peripheral blood was collected from patients with breast cancer during the rest (04:00 am) and active (10:00 am) phases of the same day in EDTA vacutainers. The time point for each sample collection was strictly followed.
Human CTC-derived BR16 cells were generated as previously described25 from a patient with hormone-receptor-positive breast cancer at the University Hospital Basel and propagated as suspension cultures in a humidified incubator at 37 °C with 5% O2 and 5% CO2. MDA-MB-231 LM2 human breast cancer cells (obtained from J. Massagué, Memorial Sloan Kettering Cancer Center), E0771.lmb mouse breast cancer cells (obtained from R. Anderson, Olivia Newton-John Cancer Research Institute) and 4T1 mouse breast cancer cells (ATCC) were grown in Dulbecco's modified Eagle medium (Gibco, 11330-057) supplemented with 10% fetal bovine serum (Gibco, 10500064) in a humidified incubator at 37 °C with 20% O2 and 5% CO2. LM2, BR16 and 4T1 cells were transduced with lentiviruses carrying either GFP–luciferase or mCherry–luciferase. Cell lines did not belong to the list of commonly misidentified cell lines (International Cell Line Authentication Committee) and were confirmed negative for mycoplasma contamination. Authentication is not applicable for the human CTC-derived BR16 cell line, the MDA-MB-231 LM2 human breast cancer cell line, and the E0771.lmb mouse breast cancer cell line. 4T1 mouse breast cancer cells were authenticated by Multiplexion GmbH. Finally, for the in vivo mouse immunocompetent experiments, 4T1 and E0771.lmb cells were transduced with lentiviruses carrying CD90.1.
All mouse experiments were carried out according to institutional and cantonal guidelines (approved mouse protocol number 3053, cantonal veterinary office of Basel-City; and approved mouse protocol number 33688, cantonal veterinary office of Zurich). Experimental endpoints that were allowed in our approved licence included tumour-related factors such as a maximum tumour size of 2,800 mm3 or severe ulceration, as well as appearance and behaviour features such as hunching, piloerection or decreased activity. These limits were not exceeded in any of the experiments. Sample size calculations were not predetermined, but the number of animals was chosen to comply with the 3R principles. All mice were randomized before the start of each experiment, but blinding was not carried out. NSG (NOD-scid-Il2rgnull; The Jackson Laboratory), BALB/c (Janvier Labs) and C57BL/6J (The Jackson Laboratory) female mice were kept in pathogen-free conditions, according to institutional guidelines. Bmal1-knockout mice (C57BL/6J background) were purchased and genotyped from the The Jackson Laboratory. Animals were kept in a standard light-cycle photoperiod (12 h light/12 h dark; 12:12 LD) with ZT0 defined as lights on (06.00 am) and ZT12 defined as lights off (06.00 pm). For the 20-t and 28-t cycle studies, animals were kept in 10:10 LD or 14:14 LD cycle conditions, respectively. Orthotopic breast cancer lesions were generated in 8-week-old NSG females following injection with either 1 × 106 LM2-mCherry–luciferase cells (NSG-LM2 model), 1 × 106 BR16-GFP–luciferase cells (NSG-CDX-BR16 model) or 0.5 × 106 4T1-GFP–luciferase cells (NSG-4T1 model) into the mammary fat pad. Similarly, 0.5 × 106 4T1-CD90.1 cells were orthotopically injected into the mammary fat pad of 8-week-old BALB/c female mice (BALB/c-4T1 model). Finally, 1 × 106 E0771.lmb-CD90.1 cells were orthotopically injected into the mammary fat pad of 8-week-old wild-type (BL/6-E0771.lmb model) or Bmal1-knockout (BL/6-Bmal1−/−-E0771.lmb model) mice. In all cases, breast cancer cells were inoculated in 100 μl of 50% Cultrex PathClear Reduced Growth Factor Basement Membrane Extract (R&D Biosystems, 3533-010-02) in phosphate-buffered saline (PBS). Blood draw for CTC analysis, organ dissection and IVIS bioluminescence imaging were carried out during the rest and active phases after 4.5 weeks for NSG-LM2, 4 weeks for NSG-4T1 and BALB/c-4T1, 3 weeks for BL/6-E0771.lmb and BL/6-Bmal1−/−-E0771.lmb and 5–6 months for NSG-CDX-BR16 mice. The time point for each sample collection was strictly followed. All mice were randomized before mouse experiments and blindly selected before injection. Maximal approved tumour volume was never exceeded.
A total of 1 × 106 LM2-mCherry–luciferase cells were orthotopically injected into the mammary fat pad of 8-week-old NSG female mice. Following tumour development, mice were treated with different circadian-regulated hormones on the basis of their pharmacokinetic profiles and the possibility of developing negative regulatory loops following prolonged treatment. For melatonin, treatments started 10 days after the tumour injection, when tumours started growing exponentially and CTCs were not yet detectable in peripheral blood. Mice were treated daily with melatonin (20 mg kg−1; Sigma-Aldrich, M5250-1G) alone or in combination with luzindole (5 mg kg−1; Sigma-Aldrich, L2407). Luzindole treatments were carried out 30 min before melatonin treatment, which was administrated 1.5 h before the onset of the rest phase (ZT0). Blood collection and CTC analysis were carried out at ZT0. For dexamethasone, mice were treated with dexamethasone (4 mg kg−1; Sigma-Aldrich, D1159-500MG) once 2 h before the blood collection (ZT4) to avoid the activation of the negative regulatory loop of the hypothalamus–pituitary–adrenal axis26. For testosterone, mice were injected with testosterone implants (Belma Technologies, T-M/60) 4 days before the tumour injection. Implants were kept until the day of the blood collection (ZT4). For insulin, treatments started 25 days after the tumour cell injection to avoid an effect of insulin on tumour growth. Mice were treated daily with insulin (0.7 U kg−1; Humalog) in parallel with glucose (1 g kg−1; Sigma-Aldrich, G7021) at ZT3. Blood collection and CTC analysis were carried out at ZT4 and ZT16. All treatments were administered as intraperitoneal injections in a final volume of 100 µl.
A total of 1 × 106 LM2-mCherry–luciferase or 0.5 × 106 4T1-GFP–luciferase cells were orthotopically injected into the mammary fat pad of 8-week-old NSG female mice. Jet lag was initiated 1 week after the tumour injection by placing the animals in altered light-cycle conditions with an 8-h light advance every 2–3 days9. Melatonin treatments in jet-lagged mice were administered daily, 1.5 h before the onset of each jet-lagged rest phase. Blood collection and CTC analysis were carried out at the onset of the rest phase.
For patient samples, 7.5 ml of peripheral blood was processed for microfluidic-based CTC capture within 1 h from blood draw. Using the Parsortix Cell Separation System (ANGLE), CTCs were captured in Cell Separation cassettes (GEN3D6.5) and then stained with an antibody cocktail containing EpCAM–AF488 (Cell Signaling Technology, CST5198), HER2–AF488 (BioLegend, 324410), EGFR–FITC (GeneTex, GTX11400) and CD45–BV605 (BioLegend, 304042). For mouse experiments, 0.8 ml of blood was collected through cardiac puncture and processed immediately. For the immunocompromised models, samples were stained only for CD45, as cancer cells were identified on the basis of mCherry or GFP expression. For the immunocompetent models, anti-CD45 staining was carried out in parallel with staining for CD90.1 (OX-7 clone, BioLegend, 202508) to identify WBCs and cancer cells, respectively. The number of captured CTCs, including single CTCs, CTC clusters and CTC–WBC clusters, was determined while cells were still in the cassette. CTCs were then released from the cassette in Dulbecco's PBS (Gibco,14190169) onto ultra-low-attachment plates (Corning, 3471-COR) for further downstream analysis.
Direct metastatic potential assay
A total of 1 × 106 LM2-mCherry–luciferase, LM2-GFP–luciferase or BR16-GFP–luciferase cells were orthotopically injected into the mammary fat pad of 8-week-old NSG female mice. Following tumour development, blood was collected through heart puncture at ZT4 or at ZT16, and run through the Parsortix system, and captured CTCs were released onto ultra-low-attachment plates. Using the CellCelector, an automated single-cell picking system (ALS), single CTCs, CTC clusters and CTC–WBC clusters were individually micromanipulated and then each category was injected into the tail vein of recipient NSG mice. Metastasis onset and growth rate in lungs were non-invasively monitored on a weekly schedule with the IVIS bioluminescence system. The experiment was terminated 4 months post injection of LM2-mCherry–luciferase and LM2-GFP–luciferase cells or 5 months post injection of BR16-GFP–luciferase CTCs.
Immunofluorescence staining and confocal analysis
Dissected organs and primary tumours were fixed in 4% paraformaldehyde at 4 °C overnight. After paraffin embedding, the Thermo Scientific Rotary Microtome Microm HM 355S was used to cut slices of 7 µm in thickness. Following a standard deparaffinization/antigen retrieval protocol, samples were stained for pan-CK (1:65; GeneTex, GTX27753), Ki67 (1:250; Abcam, ab15580), GFP (D5.1; 1:200; Cell Signaling Technology, 2956), mCherry (16D7; 1:300: Invitrogen, M11217) and DAPI (1 µg ml−1; Sigma-Aldrich, D9542-1MG). CTCs were cytocentrifuged onto microscope slides and fixed in 4% paraformaldehyde. Post-fixation, cells were washed with PBS, permeabilized for 5 min in 0.5% TritonX-100/PBS and blocked with 5% BSA in 0.1% Triton/PBS for 1 h before Ki67 (1:250; Abcam, ab15580), TAZ (1:100; BD Biosciences, 560235), YAP (D8H1X; 1:1000; Cell Signaling Technology, 14074) or cleaved caspase 3 (5A1E; 1:100; Cell Signaling Technology, 9664) antibody was added. Immunofluorescence imaging was carried out on a Leica SP5 confocal microscope, and images were taken using the 60× oil lens. All images were analysed by the Fiji image processing software (2.1.0/1.53c).
Mouse tumours were minced into fragments and enzymatically digested for 15 min with 2 mg ml−1 type IV collagenase plus 50 U ml−1 bovine DNase. The digested tumours were mechanically dissociated in C tubes using a GentleMACS device (Miltenyi), and then subjected to red blood cell lysis using ACK Buffer (Lonza) and immediately stained. Whole mouse blood was pelleted and red blood cells were lysed using ACK Buffer and immediately stained. For immunostaining, cells were blocked in a 2% FCS solution containing 2 mM EDTA and FcR blocking reagent (Miltenyi). Mouse peripheral blood cells were stained with the following antibodies: Alexa Fluor 594 anti-mouse Ly-6G (1:500; 1A8 clone, BioLegend, 127602), Alexa Fluor 594 anti-mouse CSF-1R/CD115 (1:200; AFS98 clone, BioLegend, 135520), APC/Cyanine7 anti-CD11b antibody (1:500; M1/70 clone, BioLegend, 101226), Brilliant Violet 421 anti-mouse CD3 (1:200; 17A2 clone, BioLegend, 100228), PE anti-mouse NKp46/CD335 (1:100; 29A1.4 clone, BioLegend, 137647), Alexa Fluor 647 anti-mouse CD49b (1:200; DX5 clone, BioLegend, 103511), Alexa Fluor 488 anti-mouse CD8a (1:200; 53-6.7 clone, BioLegend, 100723), Brilliant Violet 510 anti-mouse CD4 (1:500; GK1.5 clone, BioLegend, 100449), PE anti-mouse CD223/LAG-3 (1:100; C9B7W clone, BioLegend, 125224). Dissociated tumour cells were stained with the above-mentioned antibodies plus CD90.1 (1:500; OX-7 clone, BioLegend, 202508) to distinguish CD90.1-labelled cancer cells from the infiltrating stroma. All samples were processed on an LSR Fortessa device (BD) and further analysed with FlowJo (Tree Star).
Using the CellCelector, single CTCs, CTC clusters and CTC–WBC clusters were collected and immediately transferred into individual tubes (Axygen, 321-032-501) containing 2.5 µl RLT Plus lysis buffer and 1 U SUPERase IN RNase inhibitor (Invitrogen, AM2694). Samples were immediately frozen and kept at −80 °C until further processing. Amplified cDNA was prepared according to the Smart-seq2 protocol. Libraries were prepared using Nextera XT (Illumina) and sequenced on an Illumina NextSeq500 instrument in 75-base-pair single-read mode. This yielded a median raw sequencing depth of 1.64 million reads per sample.
Sequencing reads were quality trimmed with Trim Galore! (v0.6.5, https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/; parameters: –q 20 –length 20) and Cutadapt (v3.4). Quality assessment of RNA-seq data was carried out using FastQC (v0.11.4, https://www.bioinformatics.babraham.ac.uk/projects/fastqc) and FastQ Screen (v0.11.4, https://www.bioinformatics.babraham.ac.uk/projects/fastq_screen/) and visualized with MultiQC (v1.7). Trimmed reads were aligned to human (GRCh38) genome reference using STAR (v.2.7.3a; parameters: --twopassMode Basic --outSAMmapqUnique 60 --sjdbGTFfile) with splice junctions from the human GENCODE annotation (release 35). To eliminate residual contamination from mouse RNA, reads derived from xenograft models were also aligned to the mouse (GRCm38) genome reference using STAR (v.2.7.3a; parameters: --twopassMode Basic --outSAMmapqUnique 60 --sjdbGTFfile), with splice junctions from the mouse GENCODE annotation (release M25) and assigned to either human or mouse using Disambiguate (v1.0.0). Resulting BAM files were sorted by Samtools (v1.10), and the alignment quality was evaluated using RSeQC (v.2.6.4). The gene-level expression counts were computed with featureCounts (v.2.0.1; parameters: -t exon -g gene_id --minOverlap 10 -Q 10) using the human gene annotations from GENCODE (release 35). Genes present with at least 3 reads in 50% of the samples were kept for the analysis. Single-cell samples were retained for further analyses if they had at least 50,000 reads, at least 5,000 genes with non-zero expression and less than 50% of reads mapping to mitochondrial genes. For samples containing more than 1 cell (CTC clusters and CTC–WBC clusters), the minimum number of genes was set to 8,000. Read counts were normalized using the trimmed mean of M-values method implemented in the R/Bioconductor package edgeR (v3.34.1). Quality control and visualization of processed data were carried out with the help of the R/Bioconductor scater package (v1.20.1). After normalization, principal component analysis was conducted using gene expression (log2-normalized counts) of the top 500 genes with the largest biological components according to the getTopHVGs function from the R/Bioconductor package scran (v1.20.1). Selected principal components were associated with technical and biological variables using Pearson correlation. The number of PCs selected was defined by the elbow method.
Differential expression and GSEA
Differential expression was computed with the quasi-likelihood approach from the edgeR R/Bioconductor package (v3.34.1) using robust dispersion estimates. Before differential expression analysis, genes detected in less than 50% of the samples, considering the size of the smallest group of replicates, were removed from the analysis (threshold 5 counts per million). P values were adjusted for multiple comparisons using the Benjamini–Hochberg method. GSEA was conducted with the fast GSEA method implemented in the R/Bioconductor package clusterProfiler (v4.0.5). As input for GSEA, we used a list of genes ranked by fold-change and two gene set collections from the Molecular Signatures Database (MsigDB, v7.4): C2 canonical pathways and C5 GO biological process. Fast GSEA carries out a preliminary estimation of enrichment P values using a permutation test (1,000 permutations) and a secondary estimation of low P values using the multilevel algorithm with a 1 × 10−10 boundary. An adjusted P-value cutoff of 0.0001 was applied to define enriched gene sets. Only gene sets with a size between 10 and 500 genes were included in the analysis. The Jaccard coefficient was computed to measure the similarity between the enriched terms using the genes included in the GSEA leading-edge subset within each gene set. In NSG-LM2 and patients, the GSEA analysis was carried out using only the enriched terms from the NSG-CDX-BR16 model. GSVA was conducted with the R/Bioconductor GSVA package to obtain sample level enrichment scores for the same MSigDB collections evaluated in the GSEA analysis. Differences in enrichment score across the multiple time points were estimated using the moderated F statistic obtained through the empirical Bayes approach implemented in the R/Bioconductor package limma v3.48.3. Time points with fewer than three replicates were removed from this analysis.
Data analysis, statistical testing and visualization were conducted in Graphpad Prism (v.9.1.1), R (version 4.1.0; R Foundation for Statistical Computing) and Bioconductor (v.3.13). Illustrations were created with BioRender. The figure legends describe the statistical approach used for each analysis.
Further information on research design is available in the Nature Research Reporting Summary linked to this paper.
RNA-seq data have been deposited in the Gene Expression Omnibus (GEO, National Center for Biotechnology Information; accession number GSE180097). Processed transcriptomics data, large datasets and other files required for reproducibility are available from the Zenodo data repository (https://doi.org/10.5281/zenodo.6358987). The human reference genome (GRCh38), mouse reference genome (GRCm38), human gene annotation (release 35) and mouse gene annotation (release M25) files were downloaded from GENCODE (https://www.gencodegenes.org). Gene sets were downloaded from the Molecular Signatures Database (MsigDB, v7.4, http://www.gsea-msigdb.org/gsea/msigdb/collections.jsp). All data are available from the corresponding author upon reasonable request. Source data are provided with this paper.
Code related to the data analysis of this study has been deposited to GitHub (https://github.com/TheAcetoLab/diamantopoulou-ctc-dynamics) and archived at Zenodo (https://doi.org/10.5281/zenodo.6484917). Descriptions of how to reproduce the analysis workflows (showing code and R package version numbers) and the figures presented in this paper are available at https://theacetolab.github.io/diamantopoulou-ctc-dynamics.
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We thank all the patients who donated blood for our study, as well as involved clinicians and study nurses. We thank members and collaborators of the laboratory of N.A. for scientific feedback and discussions. We thank J. Massagué (Memorial Sloan Kettering Cancer Center) for donating LM2 breast cancer cells; R. Anderson (Olivia Newton-John Cancer Research Institute) for donating E0771.lmb mouse breast cancer cells; the Genomics Facility Basel (D-BSSE of the ETH Zurich) for generating libraries and carrying out next-generation sequencing; P. Lorentz (University of Basel) for microscopy support; A. Offinger (University of Basel) and her team as well as the EPIC team (ETH Zurich) for support with animal work; M. Sonderegger-Stalder and K. Degener (Cantonal Hospital Basel-Land) for support with clinical samples. Research in the laboratory of N.A. is supported by the European Research Council (101001652), the strategic focus area of Personalized Health and Related Technologies at ETH Zurich (PHRT-541), the Future and Emerging Technologies programme of the European Commission (801159-B2B), the Swiss National Science Foundation (PP00P3_190077), the Swiss Cancer League (KLS-4834-08-2019), the Basel Cancer League (KLbB-4763-02-2019), the two Cantons of Basel through the ETH Zürich (PMB-01-16), the University of Basel and the ETH Zürich. Z.D. is an H2020 Marie Skłodowska-Curie Actions (101028567) Fellow.
N.A. is a co-founder and member of the board of PAGE Therapeutics AG, Switzerland, listed as an inventor in patent applications related to CTCs, a paid consultant for the Swiss Re Group, the Bracco Group, Tethis S.p.A, Thermo Fisher and ANGLE plc, and a Novartis shareholder. C.R. is a co-founder of PAGE Therapeutics AG, Switzerland. All other authors declare no competing interests.
Peer review information
Nature thanks John Hogenesch, Sunitha Nagrath and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Extended data figures and tables
Extended Data Fig. 1 Tumor size and CTCs intravasation rates during different phases of the circadian rhythm.
a, Pie charts displaying the mean percent of total CTCs, single CTCs, CTC clusters and CTC-WBC clusters detected during the rest or active phase in breast cancer patients (n = 30), in NSG-LM2 mice (n = 6), NSG-CDX-BR16 mice (n = 6), NSG-4T1 mice (n = 4) or BALB/c-4T1 mice (n = 6). b, Time kinetic analysis showing mean CTC counts in the NSG-LM2 (n = 3), NSG-4T1 (n = 3) and BALB/c-4T1 (ZT0, ZT4, ZT20 n = 3; ZT12, ZT16 n = 4) breast cancer mouse models over a 24-h time period. Data are presented as mean ± s.e.m. c, Plots showing the size of the primary tumors dissected at different timepoints from NSG-CDX-BR16 mice (ZT0, ZT4, ZT16, ZT20 n = 4; ZT12 n = 3), NSG-LM2 (n = 3), NSG-4T1 mice (n = 3) and BALB/c-4T1 (ZT0, ZT4, ZT20 n = 3; ZT12, ZT16 n = 4) mice. Data are presented as mean ± s.e.m. d, Representative bioluminescence images of lungs from NSG-CDX-BR16, NSG-LM2 and NSG-4T1 mice taken at different timepoints (ZT0, ZT4, ZT12, ZT16, ZT20) (n = 3). e, Box plots showing the distribution of the number of single CTCs (P = 0.0043), CTC clusters (P = 0.0087) and CTC-WBC clusters (P = 0.0130) collected at ZT4 or ZT16 in the NSG-CDX-BR16 mouse model (n = 6). Center lines in the box represent the median; box limits represent first and third quartile; extremes of the whisker lines represent the minimum and maximum observed values. Data are presented as mean ± s.e.m.; * P < 0.05, ** P < 0.01 by two-sided Mann-Whitney test. f, Plots showing the size of primary tumors from NSG-CDX-BR16 (n = 6), NSG-LM2 (n = 6), NSG-4T1 (n = 4) and BALB/c-4T1 (n = 5) mice dissected at ZT4 or ZT16. Data are presented as mean ± s.e.m. g, Plots showing the mean fold change increase of CTC counts isolated at ZT4 or ZT16 from NSG-LM2, NSG-CDX-BR16, NSG-4T1 and BALB/c-4T1 mice. h, Pie charts displaying the mean percentage of single CTCs, CTC clusters and CTC-WBC clusters detected during the rest or active phase in patients (n = 7), NSG-LM2 (n = 6), NSG-CDX-BR16 (n = 6), NSG-4T1 (n = 4) or BALB/c-4T1 (n = 6) mice. For all panels, n represents the number of biologically independent mice
a, Box plots showing the distribution of the number of CTCs collected at ZT4 or ZT16 via cardiac puncture or tumor draining vessel (TDV) in the NSG-CDX-BR16 breast cancer mouse model (n = 4; P = 0.0286 for all). b, Representative bioluminescence images of lungs from NSG-CDX-BR16 mice taken at different timepoints (ZT0, ZT4, ZT12, ZT16, ZT20) over a 24-h time period (n = 4). c, Box plots showing the distribution of the number of CTCs collected at ZT4 or ZT16 via cardiac puncture or TDV in the NSG-LM2 breast cancer mouse model (n = 4; P = 0.0286 for all). d, Representative bioluminescence images of lungs from NSG-LM2 mice taken at ZT4 or ZT16 (n = 4). e, Plot showing the size of primary tumors dissected from NSG-LM2 mice at ZT4 or ZT16 (n = 4). f, Time kinetic analysis showing fold change differences in the number of LM2 cells detected in the circulation after their intravascular inoculation at different time points of the circadian rhythm (ZT0, ZT4, ZT12, ZT16) (n = 3 except ZT4 where n = 4). g, Plots showing the percentage of CTC clearance at different time points of the circadian rhythm (ZT0, ZT4, ZT12, ZT16) 5 min after intravascular inoculation of LM2 cells (n = 3 except ZT4 where n = 4). For panels “e”, “f” and “g”, data are presented as mean ± s.e.m. For panels ‘a’ and “c”, center lines in the box represent the median; box limits represent first and third quartile; extremes of the whisker lines represent the minimum and maximum observed values. * P < 0.05 by two-sided Mann-Whitney test. For all panels, n represents the number of biologically independent mice
a, Illustration of the experimental design for “b” and “e”. b, Box plots showing the mean number of CTCs isolated from NSG-LM2 (n = 5; single CTCs P = 0.0079, CTC clusters P = 0.0079, CTC-WBC clusters P = 0.0317) and NSG-4T1 (n = 4; P = 0.0286 for all) mice that were kept in standard light cycle conditions (12:12, LD) or being jet-lagged. The blood draw was performed at ZT4. c, Plots showing the mean fold change decrease of CTC counts upon jet lag in NSG-LM2 (n = 5) and NSG-4T1 (n = 4) mice shown in “b”. d, Plots showing the size of primary tumors dissected from NSG-LM2 (n = 5) and NSG-4T1 (n = 4) mice shown in “b”. Data are presented as mean ± s.e.m. e, Box plots showing the distribution of the number of CTCs isolated from NSG-LM2 mice that were being jet-lagged (left) or kept in standard light cycle conditions (right) and were treated with melatonin alone or in combination with its antagonist luzindole. The blood draw was performed at ZT4 or ZT0. (n = 4, except control and melatonin-treated mice in combination with luzindole at ZT4 where n = 5; ZT4 P = 0.0159 except CTC-WBC clusters treated with melatonin in combination with lunzindole where P = 0.0317; ZT0 P = 0.0286 except single CTCs treated with melatonin in combination with lunzindole where P = 0.0091). f, Plots showing the size of primary tumors dissected from mice shown in “e”. Data are presented as mean ± s.e.m. g, Representative bioluminescence images of lungs from NSG-LM2 mice that were kept in standard light cycle conditions (12:12, LD) and were treated with melatonin alone or in combination with luzindole. For panels ‘b’ and “e”, center lines in the box represent the median; box limits represent first and third quartile; extremes of the whisker lines represent the minimum and maximum observed values. * P < 0.05, ** P < 0.01 by two-sided Mann-Whitney test. For all panels, n represents the number of biologically independent mice
a, Time kinetic analysis showing mean CTC counts (single CTCs, CTC clusters and CTC-WBC clusters) in the NSG-LM2 mice kept in altered light-dark (LD) cycles (LD 14:10, n = 3; LD 10:10, n = 4, except ZT10 and ZT20 where n = 3; LD 14:14, n = 4, except ZT14 where n = 3). b, Scatter dot plots showing the distribution of the number of single CTCs, CTC clusters and CTC-WBC clusters isolated from NSG-LM2 mice that were kept in altered light cycles (LD 14:10, n = 3; LD 10:10, n = 4; LD 14:14, n = 4; P = 0.0286 for all). * P < 0.05 by two-sided Mann-Whitney test. c, Plots showing the size of primary tumors dissected from NSG-LM2 mice shown in “a”. d, Representative bioluminescence images of lungs from NSG-LM2 mice shown in “a”. e, Graphs showing time kinetic analysis of CTC counts (single CTCs, CTC clusters and CTC-WBC clusters) in the BL/6-EO771.lmb (ZT4, ZT12, ZT16 n = 4; ZT0 n = 3) and BL/6-Bmal1−/−-EO771.lmb (n = 3) breast cancer mouse models collected via tumor draining vessel (TDV) over a 24-h time period. f, Plots showing the size of the primary tumors dissected from BL/6-EO771.lmb and BL/6-Bmal1−/−-EO771.lmb mice shown in Fig. 1d. g, Plotted actograms showing the running activity of the BL/6-EO771.lmb and BL/6-Bmal1−/−-EO771.lmb mice with dark and light areas representing low and high activity, respectively. For all panels, data are presented as mean ± s.e.m. n represents the number of biologically independent mice
a, Representative immunofluorescence images for Pan-CK in lungs of mice injected with single CTCs, CTC clusters and CTC-WBC clusters collected at ZT4 or ZT16 from NSG-LM2 mice (ZT4 n = 3 except CTC-WBC clusters n = 2; ZT16 n = 4 for all). Scale bar = 100 μm. b, Plot showing the size of the metastatic lesions detected in the lungs of mice injected with single CTCs, CTC clusters or CTC-WBC clusters collected at ZT4 or ZT16 of NSG-LM2 mice (ZT4 n = 3 except CTC-WBC clusters n = 2; ZT16 n = 4 for all; P = 0.0007). c, Representative bioluminescence images of bones from mice injected with single CTCs, CTC clusters or CTC-WBC clusters collected at ZT4 or ZT16 from NSG-CDX-BR16 mice. Mice were not injected with CTC-WBC clusters collected during the active phase, due to their rarity. d, Plot showing normalized bioluminescence signal obtained from bones of mice shown in panel “c” (single CTCs n = 4; CTC clusters n = 5; P = 0.006). e, Representative bioluminescence images of livers from mice injected with single CTCs or CTC clusters, collected at ZT4 or ZT16 from NSG-CDX-BR16 mice. Mice were not injected with CTC-WBC clusters collected during the active phase, due to their rarity. f, Plot showing normalized bioluminescence signal obtained from liver of mice shown in panel “e” (n = 5 except single CTCs collected at ZT16 where n = 4; P = 0.0301). For all panels, data are presented as mean ± s.e.m.; unpaired two-sided t-test * P < 0.05, ** P < 0.01, *** P < 0.001. n represents the number of biologically independent mice
Extended Data Fig. 6 Time point of CTC isolation is the main determinant of gene expression heterogeneity in CTCs.
a, Heatmap showing the Pearson’s correlation coefficient of PC1-7 eigenvectors from gene expression with technical and biological variables in BR16-CDX CTCs. P values by two-sided Pearson’s correlation test (*P < 0.01, **P < 0.001, ***P < 0.0001). b, Scatter plot showing the correlation of the fold-change between active and rest phase in single CTC (Y-axis) versus CTC clusters and CTC-WBC (X-axis), using genes with FDR ≤ 0.05 in any of the two sets (two-sided Pearson’s correlation coefficient 0.57, P value ≤ 2.22e-16). Points are colored according to the dataset where they were found with a FDR ≤ 0.05 (both, single CTC or CTC clusters and CTC-WBC clusters). The dashed red line represents the linear regression line using all the points in the plot. c, Bar plot showing the number of differentially expressed genes (absolute log2 fold change ≥ 0.5 and FDR ≤ 0.05) using all the samples (‘All’), using clustered CTCs (CTC clusters and CTC-WBC clusters) and using single CTCs. d, Heatmap showing the pair-wise similarity matrix of enriched gene sets (gene set enrichment analysis (GSEA) adjusted P value ≤ 0.001) using differential expression between CTCs of rest and active phase from NSG-CDX-BR16 mice. Heatmap colors represent the Jaccard similarity coefficient using the set of core genes in each gene set. The heatmap on the right represents the GSEA adjusted P value. e, Plots comparing the normalized enrichment score (NES) and adjusted P value (dot size) obtained using GSEA for gene sets shown in “d”. Gene sets with an adjusted P value ≤ 0.05 in each sample set are highlighted in red. f, GSVA score for translation (yellow, n = 5) and cell division (blue, n = 17) gene sets in CTCs obtained from the NSG-LM2 time-kinetics experiment. Yellow and blue lines represent the average at each time point. Individual points represent the enrichment score for each CTC sample. The white and grey backgrounds represent environmental light (rest period) and dark conditions (active period), respectively. Adjusted F-test P values as obtained from limma are shown for each individual gene set.
Extended Data Fig. 7 The proliferation status of primary tumours changes in a circadian rhythm dependent manner.
a, Representative immunofluorescence images of Ki67 and Pan-CK in primary tumors from NSG-CDX-BR16 mice, dissected at different timepoints (ZT0, ZT4, ZT12, ZT16, ZT20) over a 24-h time period (ZT4, ZT12, ZT16 n = 3; ZT0, ZT20 n = 2; P = 0.0270; scale bar = 100 μm) (left). The plot shows the intensity of Ki67 in tumors of NSG-CDX-BR16 mice during different timepoints (right). Data are presented as mean ± s.e.m.; unpaired two-sided t-test * P < 0.05. b, Representative immunofluorescence images of Ki67 in CTCs collected at ZT4 and ZT16 from NSG-LM2 and NSG-CDX-BR16 mice. Scale bar = 10 μm. c, Plots showing the distribution of Ki67 intensity in single CTCs (NSG-LM2 P = 0.0495; NSG-CDX-BR16 P = 0.0001) and CTC clusters (NSG-LM2 P = 0.0223; NSG-CDX-BR16 P = 0.0045) collected at ZT4 and ZT16 from NSG-LM2 and NSG-CDX-BR16 mice (n = 3). Box center lines in the box represent the median; limits represent first and third quartile; extremes of the whisker lines represent the minimum and maximum observed values. * P < 0.05, ** P < 0.01, *** P < 0.001 by two-sided Mann-Whitney test. For all panels, n represents the number of biologically independent mice
a, Plot showing the expression distribution of core circadian genes in CTCs from NSG-CDX-BR16 mice. The fold change (FC, in log2 scale) and adjusted P value from the global differential expression analysis are shown for each gene. b, Density plot showing the distribution of the average expression (log2 counts per million) of genes in CTCs from NSG-CDX-BR16 mice. Core circadian genes are labeled in the X-axis. c, qPCR for Bmal1 expression in the liver, adrenal glands and primary tumor of NSG-LM2 mice (n = 3 for all the time points of the adreanal glands; n = 3 for all the time points of the liver and tumor, except ZT4 and ZT20 where n = 4). Data are relative to the time point with the lowest expression levels of Bmal1 (ZT16; set as 1) and are presented as mean ± s.e.m. n represents the number of biologically independent mice
Extended Data Fig. 9 Assessment of interestial pressure, immunosurveillance and apoptosis in CTCs at different timepoints of the circadian rhythm.
a, Representative immunofluorescence images of Taz and Yap in CTCs collected at ZT4 and ZT16 from NSG-LM2 and NSG-CDX-BR16 mice. Plots showing the distribution of Taz and Yap intensity in CTCs shown in the same panel (n = 3). Scale bar = 10 μm. b, Plot showing the expression distribution of TEAD genes in CTCs from NSG-CDX-BR16 mice. The fold change (FC, in log2 scale) and adjusted P value from the global differential expression analysis are shown for each gene. c, Gating strategy to determine the frequency of the indicated cell populations in panels “d” and “e”. The percentage values refer to the parental population considered in each panel. d, Plot showing the frequency of white blood cells (WBCs) from peripheral blood (PB) isolated during the rest (n = 8) or active phase (n = 10) from BALB/c-4T1 mice. e, Plot showing the frequency of tumor-infiltrated WBCs isolated during the rest (n = 8) or active phase (n = 10) from BALB/c-4T1 mice. f, Representative immunofluorescence images of cleaved caspase-3 in CTCs collected at ZT4 and ZT16 from the NSG-LM2 and NSG-CDX-BR16 mice. Plots showing the distribution of cleaved caspase-3 intensity in CTCs shown in the same panel (n = 3). Scale bar = 10 μm. For panels “a” and “f”, center lines in the box represent the median; box limits represent first and third quartile; extremes of the whisker lines represent the minimum and maximum observed values. ns: non statistically significant by two-sided Mann-Whitney test. For panels “d” and “e”, data are presented as mean ± s.e.m.; ns: non statistically significant by unpaired two sided t-test. For all panels, n represents the number of biologically independent mice
a, Density plots showing the distribution of the average expression (log2 counts per million) of genes encoding for receptors of circadian-regulated hormones, growth factors or molecules in CTCs from NSG-CDX-BR16 mice, NSG-LM2 mice and patients with breast cancer. Genes for the glucocorticoid receptor, androgen receptor and insulin receptor are labeled in the X-axis. b, Time kinetic plot showing the pharmacokinetic profile of dexamethasone-treated mice (n = 2). c, Plots showing the size of the primary tumors dissected from dexamethasone-treated or control NSG-LM2 mice (n = 4). d, Plots showing the size of the primary tumors dissected from testosterone-treated (n = 5) or control NSG-LM2 mice (n = 4). e, Representative bioluminescence images of lungs from untreated or testosterone-treated NSG-LM2 mice (left). Plot showing normalized bioluminescence signal obtained from lungs of testosterone-treated or control NSG-LM2 mice (n = 3; P = 0.0005). f, Plot showing plasma concentration of testosterone in control and testosterone-treated mice (n = 3; P = 0.0237). g, Plots showing the primary tumors dissected from control or insulin-treated mice at ZT4 and ZT16 (n = 8, except insulin-treated mice at ZT16 where n = 6.). h, Plot showing plasma concentration of insulin in control and insulin treated mice (n = 5; P = 0.0321). For all panels, data are presented as mean ± s.e.m.; For panels “e”, “f” and “h”, * P < 0.05, *** P < 0.001 by unpaired two sided t-test. For all panels, n represents the number of biologically independent mice
Supplementary Table 1 Clinical features of enrolled patients with breast cancer. For each patient, the table shows patient age, initial AJCC stage, date of first diagnosis, ER, PR, HER2 and Ki67 status of the primary tumour. The table also shows the presence of metastatic disease at the time of blood donation, as well as the number of single CTCs, CTC clusters and CTC–WBC clusters detected during the rest (04:00 am) or active period (10:00 am) per 7.5 ml of peripheral blood.
Supplementary Table 2 Genes differentially expressed in CTCs of NSG-CDX-BR16 mice during the rest phase versus active phase. Table listing the differentially expressed genes comparing CTCs obtained in the rest phase (n = 65) versus the active phase (n = 73) of NSG-CDX-BR16 mice. All genes evaluated are included in the table (n = 12,261). Fold change and P values were computed with the quasi-likelihood approach from edgeR using robust dispersion estimates. For fold-change calculation, active-phase samples were used in the denominator.
Supplementary Table 3 GSEA from differentially expressed genes in CTCs of NSG-CDX-BR16 mice during the rest phase versus active phase. Table listing the enriched gene sets (n = 138, adjusted P value < 0.05) in CTCs obtained in the rest versus active phase from NSG-CDX-BR16 mice. The GSEA was carried out using ranking genes as input, according to fold change as shown in Supplementary Table 2. P values were obtained using the fast GSEA method with an eps parameter of 1 ×10−10.
Supplementary Table 4 List of receptors activated by circadian-regulated hormones, growth factors or molecules. Table listing the expression levels of genes for receptors activated by circadian-rhythm-regulated hormones, growth factors or molecules in CTCs obtained from NSG-CDX-BR16 mice. Fold change, P values and FDR were extracted from the global differential expression analysis results and were computed with the quasi-likelihood approach from edgeR using robust dispersion estimates.
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Diamantopoulou, Z., Castro-Giner, F., Schwab, F.D. et al. The metastatic spread of breast cancer accelerates during sleep. Nature 607, 156–162 (2022). https://doi.org/10.1038/s41586-022-04875-y
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