Circulating extracellular vesicles provide valuable protein, but not DNA, biomarkers in metastatic breast cancer

Abstract

Detection of cell-free circulating tumour DNA (ctDNA) and cancer-specific extracellular vesicles (EVs) in patient blood have been widely explored as non-invasive biomarkers for cancer detection and disease follow up. However, most of the protocols used to isolate EVs co-isolate other components and the actual value of EV-associated markers remain unclear. To determine the optimal source of clinically-relevant circulating biomarkers in breast cancer, we applied a size exclusion chromatography (SEC) procedure to analyse separately the content in nucleic acids of EV-enriched and EV-depleted fractions, in comparison to total plasma. Both cellular and mitochondrial DNA (cellDNA and mtDNA) were detected in EV-rich and EV-poor fractions. Analysing specific mutations identified from tumour tissues, we detected tumour-specific cellular alleles in all SEC fractions. However, quantification of ctDNA from total plasma was more sensitive than from any SEC fractions. On the other hand, mtDNA was preferentially enriched in EV fractions from healthy donor, whereas cancer patients displayed more abundant mtDNA in total plasma, and equally distributed in all fractions. In contrast to nucleic acids, using a Multiplexed bead-based EV-analysis assay, we identified three surface proteins enriched in EVs from metastatic breast cancer plasma, suggesting that a small set of EV surface molecules could provide a disease signature. Our findings provide evidence that the detection of DNA within total circulating EVs does not add value as compared to the whole plasma, at least in the metastatic breast cancer patients used here. However, analysis of a subtype of EV-associated proteins may reliably identify cancer patients. These non-invasive biomarkers represent a promising tool for cancer diagnosis and real-time monitoring of treatment efficacy and these results will impact the development of therapeutic approaches using EVs as targets or biomarkers of cancer.

Keywords: DNA; biomarkers; breast cancer; exosomes; extracellular vesicles; liquid biopsy; plasma; surface protein.

FIGURE 1

Cell free genomic DNA is less abundant in EV‐associated fractions than in total plasma. (a). Experimental workflow to separate by Size‐Exclusion Chromatography (SEC) EVs from smaller circulating components in plasma. Western blots (c) and Nanoparticle tracking analyses (b) were performed on ultracentrifugation (UC) pellets to characterize each elution fraction. Downstream DNA analyses (Figure 1 d‐e, Figure 2 and 3) were performed on each pool of fractions (EV‐enriched fractions (F7‐11), intermediate fractions (F12‐17), soluble fractions (F18‐24)) and total plasma in parallel. Surface protein analyses (Figure 4) were performed on EV‐enriched fractions. (b). Particle count in each SEC fraction by nanoparticle tracking analysis. One representative experiment. (c). Protein composition analysis by Western blotting of all SEC fractions showing total proteins, EV‐associated markers (three surface proteins: CD63, CD9 and MHCI, and one soluble cytosolic protein: Syntenin), and an EV‐excluded protein (ApoA1). Top and bottom panels are from two independent experiments, CD9 distribution is shown in both to allow comparison. (d‐e). Quantification of total DNA by fluorometric quantitation using Qubit (d) or amplifiable cellular DNA (cellDNA) using a LINE1 qPCR (e) in EV‐enriched (F7‐11), intermediate (F12‐17) and soluble (F18‐24) fractions versus total plasma in both healthy donors (HD, N = 15, black symbols) and breast cancer patients (BCP, N = 27, orange symbols). Thick black lines show the median with interquartile range. Orange bars highlight differences among BCP, black bars among HD and grey bars between BCP and HD. Only significant P‐values are indicated, see Table S2 for more details on statistical results

FIGURE 2

Quantification of circulating tumour DNA from unprocessed plasma is more sensitive than from EV‐enriched fractions. (a). Tumour DNA detection in plasma SEC fractions through PIK3CA H1047R mutation analysis by ddPCR. We analysed plain healthy donor plasma (upper panels) or healthy plasma spiked with concentrated conditioned media (CCM) collected from HCT116 tumour cells (lower panels). MAF: mutant allele fraction, DP: double positive droplets (WT + PIK3CA MUT), ED: empty droplets. (b). PIK3CA mutation detection by ddPCR in plasma SEC fractions from breast cancer patients. Examples from two different patients with PIK3CA H1047R (P#6) or E545K (P#17) mutations. MAF = mutant allele fraction, DP: double positive droplets (WT + PIK3CA MUT), ED: empty droplets. (c). Number of mutant (MUT), wild‐type (WT) or total copies detected by ddPCR in each elution fraction pools and total plasma (TP) of breast cancer patients (N _Patients = 27, N _Mutations = 29). p_MUT = 0.009, p_WT < 0.0001, p_TOT < 0.0001. (d). Mutant allele fractions (MAF) in each SEC elution pool and total plasma (TP) of breast cancer patients (N _Patients = 27, N _Mutations = 29). Black lines display the means, color lines SD. The number of samples with MAF = 0% correspond to the samples with no mutations detected in (e). (e). Number of known mutations detected versus not detected in each SEC fraction pool and total plasma of breast cancer patients (N _Patients = 27, N _Mutations = 29). The mutations targeted were previously identified from each patient's tumour tissues (see Table S1)

FIGURE 3

Isolation of plasma EVs does not improve the quantification of mitochondrial DNA (mtDNA) for disease detection or monitoring. (a). Amount of mitochondrial DNA (mtDNA), quantified by quantitative PCR targeting the ND5 gene, in SEC fractions and total plasma of healthy donors (HD, N = 15, black circles) and breast cancer patients (BCP, N = 21, orange circles). Thick black lines show the median with interquartile range. Only significant P‐values are indicated, see Table S2 for more details on statistical results. (b‐c). Donor‐specific comparisons of mtDNA levels detected in EV‐enriched, EV‐depleted and total plasma of each healthy donor (b) or breast cancer patient (c). This is another representation of the data displayed in 3A. (d). Amount of mtDNA in SEC fractions of healthy donors as compared to patients classified by disease burden (DB): high (> 2 metastatic sites) or low (≤2 metastatic sites). Only significant P‐values are indicated, see Table S2 for more details on statistical results. (e). Amount of mtDNA in SEC fractions of healthy donors as compared to patients displaying progressive disease (PD) or not (non‐PD). Only significant P‐values are indicated, see Table S2 for more details on statistical results

FIGURE 4

A subset of EV surface proteins reliably identify breast cancer patients. (a). Distribution of the mean fluorescence intensity (MFI), normalized by the MFI of beads alone, for each of the 37 membrane protein markers and two isotype controls, Recombinant Antibody (REA) and mIgG1, in breast cancer patients (n = 9) versus healthy donors (n = 9). The markers are ordered by differential distribution significance (Mann–Whitney U test P‐values). The four markers the most significantly enriched in breast cancer patients are highlighted in bold. (b). Number of samples detected as breast cancer patients using our 4‐surface‐marker classifier. (c). Ratios of the normalized MFI of each marker over their isotype controls. Only markers for which the corresponding control isotype was included in the analysis (mIgG1 and REA) are displayed. The three markers the most significantly enriched in breast cancer patients are highlighted in bold. (d). Normalized MFI of the three markers the most significantly enriched in breast cancer patients versus two markers either less significantly enriched (CD20), or non‐significantly different (CD142), compared to the normalized MFI of the isotype control beads. (e). Distribution of arbitrary indexes calculated by summing the three ratios of the normalized MFI of CD326, CD105 and CD146 for BCP versus HD. The dashed line marks the computed threshold (HD Index mean + 95% CI = 28.86)