Nature Methods: doi: /nmeth Supplementary Figure 1

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1 Supplementary Figure 1 Finite-element analysis of cell cluster dynamics in different cluster trap architectures. (a) Cluster-Chip (b) Filter (c) A structure identical to the Cluster-Chip except that one of the openings is closed d) A filter formed by two triangular pillars. In all cases except the Cluster-Chip, cluster larger than the opening size passes due to its elastic behavior. The cluster being missed points to the importance of split flow fields simultaneously effecting the cluster in the Cluster-Chip and proves that the capture in the Cluster-Chip is not simply due to filtering. In all simulations, the opening widths and diameters of individual cells are 14 μm.

2 Supplementary Figure 2 Probability of chip-mediated artifact cluster formation from single cells on the Cluster-Chip. The calculated probabilities are based on simplifying assumptions on chip geometry and cell adhesion. We assume a blood sample with 1000 CTCs. In addition, each of the 4096 parallel cluster traps on the Cluster-Chip is assumed to have equal probability of receiving CTCs and whenever multiple CTCs go into the same trap, a CTC-cluster is formed. We used Poisson approximation to calculate probability of forming a CTC-cluster composed of a specific number of cells. Our model confirms that it is extremely unlikely to form artifact CTC clusters on the Cluster-Chip.

3 Supplementary Figure 3 Experimental setup used to quantify the capture efficiency of the Cluster-Chip. A smaller version of the Cluster-Chip is connected to a large microfluidic waste chamber, which permits to accurately identify and analyze non-captured cell clusters in whole blood. Both the chip and waste chamber are imaged using fluorescence microscopy to calculate the cell cluster capture efficiency.

4 Supplementary Figure 4 Distribution of captured small and large clusters on the Cluster-Chip. Normalized distribution of (a) 2-cell clusters (b) 3-cell clusters (c) clusters containing 4 or more cells across the Cluster-Chip. We used simulated whole blood samples spiked with artificial clusters of MDA-MB-231 breast cancer cell line. The Cluster-Chip was operated at 2.5 ml/hr and was on a thermoelectric cooler set at 4 C.

5 Supplementary Figure 5 Characterization of MDA-MB-231 cell cluster size distribution before spiking. We prepare clusters of fluorescently labeled MDA-MB-231 cancer cells and deposit the suspension on an ultra-low attachment culture dish. Using fluorescence microscopy, we count the number of cells within each cluster and then spike into whole blood in order to prepare a simulated sample containing characterized cluster population for quantitative device testing.

6 Supplementary Figure 6 Intact and damaged cell clusters captured using a membrane filter. Fluorescent and brightftield images of intact and damaged MDA-MB-231 cell clusters when captured using a polycarbonate tracketched membrane filter operated under 1.5 psi.

7 Supplementary Figure 7 Viability of cell clusters released from the Cluster-Chip. The Cluster-Chip is operated at 4 C and the clusters were released at 250 ml/hr reverse flow rate (A) Fluorescent images of MDA-MB- 231 clusters treated with two-color viability (green)/cytotoxicity (red) assay. The cells are divided in two batches: Control and cell clusters captured and released from Cluster-Chip (B) Percentage of viable clusters captured and released using Cluster-Chip against the control. Scale bar 100 μm.

8 Supplementary Figure 8 Images of CTC -clusters isolated from a breast cancer patient and released in solution. CTC-clusters isolated from a patient with metastatic breast cancer using the Cluster-Chip operated at 4 C and released with reverse flow. CTC clusters are not fixed and live stained for common breast cancer surface markers. Scale bar 20 μm.

9 Supplementary Figure 9 Correlation between the number of CTCs and CTC clusters in patients. Comparison of number of CTC clusters isolated using the Cluster-Chip and number of single CTCs isolated using CTC-iChip from the same patient at the same timepoint. Among the 19 cases studied, we found no correlation between the numbers of CTC-clusters and that of single CTCs.

10 Supplementary Figure 10 Proliferative index of single CTCs isolated from a breast cancer patient. Representative images of a Ki67-negative (-) and a Ki67-positive (+) CTC stained with antibodies against wide spectrum cytokeratin (CK, red), Ki67 (yellow) and CD45 (green). Nuclei are stained with DAPI (blue). The bar graph shows the mean percentage of Ki67- positive cells isolated from a breast cancer patient across multiple time points. All together, 439 single CTCs were stained and 162 resulted positive for Ki67.

11 Supplementary Figure 11 Heat map showing expression levels of CTC clusters isolated from a patient. Heatmap showing expression levels of representative epithelial-to-mesenchymal transition (EMT) genes in released CTC-clusters and healthy donor white blood cells (WBCs). Epithelial markers E-cadherin (CDH1), EpCAM and Mucin1 (MUC1), as well as mesenchymal markers Vimentin (VIM), Fibronectin1 (FN1), N-cadherin (CDH2) and TWIST1 are shown.

12 Supplementary Table 1 Clinical and molecular tumor characteristics of the patients used for CTC-clusters quantification Patient ID Tumor type Gender Age Molecular type Mutation profile or Prostate- specific antigen levels (ng/ml) BR11 Breast cancer Female 68 Triple- negative breast cancer BRCA carrier BR21 Breast cancer Female 71 Hormone receptor positive breast cancer PIK3CA BR- 29 Breast cancer Female 56 Hormone receptor positive breast cancer PIK3CA BRx- 102 Breast cancer Female 60 Triple- negative breast cancer No mutations found BRx- 104 Breast cancer Female 62 Hormone receptor positive breast cancer Not analyzed BRx- 107 Breast cancer Female 58 Hormone receptor positive breast cancer No mutations found BRx- 108 Breast cancer Female 49 Hormone receptor positive breast cancer No mutations found BRx- 109 Breast cancer Female 42 Hormone receptor positive breast cancer No mutations found BRx- 11 Breast cancer Female 51 Hormone receptor positive breast cancer PIK3CA BRx- 13 Breast cancer Female 60 Hormone receptor positive breast cancer PIK3CA and HRAS BRx- 16 Breast cancer Female 74 Triple- negative breast cancer BRCA carrier BRx- 18 Breast cancer Female 53 HER2 positive breast cancer PIK3CA BRx- 35 Breast cancer Female 54 Hormone receptor positive breast cancer Not analyzed BRx- 38 Breast cancer Female 54 Hormone receptor positive breast cancer PIK3CA BRx- 39 Breast cancer Female 52 Hormone receptor positive breast cancer PIK3CA BRx- 42 Breast cancer Female 54 Hormone receptor positive breast cancer PIK3CA BRx- 53 Breast cancer Female 54 Hormone receptor positive breast cancer PIK3CA BRx- 55 Breast cancer Female 80 Hormone receptor positive breast cancer PIK3CA BRx- 61 Breast cancer Female 55 Hormone receptor positive breast cancer No mutations found Brx- 66 Breast cancer Female 66 Hormone receptor positive breast cancer PIK3CA BRx- 74 Breast cancer Female 72 Hormone receptor positive breast cancer No mutations found Brx- 78 Breast cancer Female 66 Hormone receptor positive breast cancer PIK3CA Brx- 85 Breast cancer Female 56 Triple- negative breast cancer No mutations found BRx- 93 Breast cancer Female 54 Hormone receptor positive breast cancer No mutations found BRx- 94 Breast cancer Female 44 Triple- negative breast cancer No mutations found BRx- 98 Breast cancer Female 75 Hormone receptor positive breast cancer PIK3CA BRx- 99 Breast cancer Female 60 Hormone receptor positive breast cancer PIK3CA Mel- 28 Melanoma Female 40 Metastatic melanoma BRAF Mel- 31 Melanoma Male 63 Metastatic melanoma NRAS

13 Mel- 41 Melanoma Male 72 Metastatic melanoma BRAF Mel- 45 Melanoma Female 56 Metastatic melanoma BRAF Mel- 47 Melanoma Female 75 Metastatic melanoma No mutations found Mel- 48 Melanoma Male 55 Metastatic melanoma BRAF Mel- 50 Melanoma Male 64 Metastatic melanoma No mutations found Mel- 57 Melanoma Male 76 Metastatic melanoma NRAS Mel- 61 Melanoma Male 49 Metastatic melanoma BRAF Mel- 63 Melanoma Female 35 Metastatic melanoma BRAF Mel- 64 Melanoma Female 66 Metastatic uveal melanoma No mutations found Mel- 67 Melanoma Male 62 Metastatic melanoma No mutations found Mel- 69 Melanoma Female 66 Metastatic melanoma BRAF Mel- 72 Melanoma Male 20 Metastatic melanoma No mutations found Mel- 73 Melanoma Male 71 Metastatic melanoma No mutations found Mel- 79 Melanoma Male 61 Metastatic melanoma No mutations found Mel- 82 Melanoma Female 37 Metastatic melanoma BRAF Mel- 83 Melanoma Male 63 Metastatic melanoma No mutations found Mel- 85 Melanoma Male 56 Metastatic uveal melanoma BRAF Mel- 87 Melanoma Male 64 Metastatic melanoma BRAF GU- 04 Prostate cancer Male 82 Castration- resistant prostate cancer PSA GU- 51 Prostate cancer Male 61 Castration- resistant prostate cancer PSA GU- 52 Prostate cancer Male 72 Castration- resistant prostate cancer PSA GU- 112 Prostate cancer Male 84 Castration- resistant prostate cancer PSA 0.53 GU- 121 Prostate cancer Male 72 Castration- resistant prostate cancer PSA GU- 126 Prostate cancer Male 69 Castration- resistant prostate cancer PSA 9.64 GU- 133 Prostate cancer Male 75 Castration- resistant prostate cancer PSA GU- 143 Prostate cancer Male 78 Castration- resistant prostate cancer PSA 2.74 GU- 151 Prostate cancer Male 69 Castration- resistant prostate cancer PSA GU- 156 Prostate cancer Male 73 Castration- resistant prostate cancer PSA 0.2 GU- 157 Prostate cancer Male 78 Castration- resistant prostate cancer PSA GU- 160 Prostate cancer Male 61 Castration- resistant prostate cancer PSA GU- 162 Prostate cancer Male 63 Castration- resistant prostate cancer PSA 0.13