Extracellular Vesicle and Particle Biomarkers Define Multiple Human Cancers

Abstract

There is an unmet clinical need for improved tissue and liquid biopsy tools for cancer detection. We investigated the proteomic profile of extracellular vesicles and particles (EVPs) in 426 human samples from tissue explants (TEs), plasma, and other bodily fluids. Among traditional exosome markers, CD9, HSPA8, ALIX, and HSP90AB1 represent pan-EVP markers, while ACTB, MSN, and RAP1B are novel pan-EVP markers. To confirm that EVPs are ideal diagnostic tools, we analyzed proteomes of TE- (n = 151) and plasma-derived (n = 120) EVPs. Comparison of TE EVPs identified proteins (e.g., VCAN, TNC, and THBS2) that distinguish tumors from normal tissues with 90% sensitivity/94% specificity. Machine-learning classification of plasma-derived EVP cargo, including immunoglobulins, revealed 95% sensitivity/90% specificity in detecting cancer. Finally, we defined a panel of tumor-type-specific EVP proteins in TEs and plasma, which can classify tumors of unknown primary origin. Thus, EVP proteins can serve as reliable biomarkers for cancer detection and determining cancer type.

Keywords: biomarkers; cancer; cancer of unknown primary origin; damage-associated molecular patterns; early cancer detection; exomeres; exosomes; extracellular vesicles and particles; liquid biopsy; proteomics.

Figure 1.. Proteomic Characterization of EVPs Obtained from 497 Samples from Seven Different Sources

(A) EVPs from 426 human and 71 murine samples were analyzed by liquid chromatography tandem-mass spectrometry (LC-MS/MS). (B) Centrifugation protocol and workflow for EVP enrichment (left, *sucrose cushion was applied to PaCa samples), representative nanoparticle tracking analysis (middle), and transmission electron microscopy imaging (right) of EVPs from human control (HC) plasma. Scale bar represents 200 nm. (C) Pearson correlation of EVP protein expression among samples types. Larger and darker circles depict a higher correlation between samples. (D) Positivity for 11 conventional exosomal protein markers across different tissue types. The frequency (%) of samples expressing the specified protein is noted in each box. Darker red depicts higher frequency. (E) The frequency (%) of samples from each source positive for the 13 newly defined EVP markers. Proteins found in >50% of all human samples were identified. Annotation as in (D). (F) GO analysis for the 13 common EVP proteins listed in (E). (G) Western blot of EVPs isolated from human cell lines, PaCa TEs, as well as HC and PaCa plasma for conventional and newly identified EVP markers. (H) ExoView analyses performed on EVPs isolated from HC plasma for conventional and newly identified EVP markers. Error bars represent the standard error of the mean. See also Tables S1 and S2.

Figure 2.. PaCa- and LuCa-Specific EVP Protein Cargo in Surgically Removed TEs

(A) Diagram of TE culture method for EVPs isolation from paired TT and AT and matched DT (for LuCa). (B) Top 30 proteins highly represented in PaCa TT compared to AT (top), and LuCa TT compared to AT and DT (bottom). The heatmap is based on proteins found >50% of TT at levels >10-fold higher in TT than AT (FDR <0.05). Colored boxes identify sample pairs. (C) Top 50 proteins never found in AT but found in >50% of PaCa (top), and the two proteins found in 50% of LuCa TT and never found in AT or DT (bottom). See also Tables S3 and S4.

Figure 3.. EVP DAMP Molecules Enriched in PaCa and LuCa

(A and B) EVP proteins enriched in (A) PaCa TT and found in >50% of TT samples; and (B) LuCa TT or AT/DT, found in >50% of TT or AT/DT samples, respectively, with >10-fold difference and FDR <0.05. Paired t test was used to calculate FDR. See also Table S5.

Figure 4.. Identification of Tumor-Associated EVP Signatures in Surgically Removed Tissue Explants from Multiple Tumor Types

(A) Proteins with the highest predictive values in classifying tumor and non-tumor samples by random forest algorithm. (B) Classification error matrix using a random forest classifier of 75% training set and 25% test set, for the 16 proteins from (A) (left), and all 2,240 tissue explant EVP proteins (right). The number of samples identified is noted in each box. See also Table S6.

Figure 5.. Identification and Validation of Tumor-Associated Protein Cargo in Tissue- and Plasma-Derived EVPs

(A and B) Proteins exclusively found in >30% of (A) PaCa or (B) LuCa patient plasma-derived EVP samples but never found in HC plasma-derived EVP samples (left) were matched to explant-derived EVPs (PaCa TT and AT; LuCa TT, AT, and DT; right). The colored boxes identify sample pairs. (C and D) EVP proteins found in >30% (C) of neuroblastoma or (D) osteosarcoma plasma-derived EVPs but never in plasma EVPs from HC (left), and their presence in tumor explant EVPs (right). (E) Top 20 PaCa plasma markers validated by targeted MS in an independent cohort of PaCa and HC. (F) Log2 protein expression of the data presented in (E). p values calculated by Wilcoxon rank-sum test. (G) ELISA validation of CA2, LTF, and CD55 in plasma EVP from 15 PaCa and 15 HC. *p < 0.05, ***p = 0.0001, ****p < 0.0001. p values are calculated by Mann-Whitney U test. Error bars represent median with interquartile range. (H) ELISA validation of RHOV in plasma EVP from 14 LuCa and 7 HC. *p < .05. p value calculated by t test. Error bars represent median with interquartile range.

Figure 6.. Identification of Tumor-Associated EVP Signatures in Plasma From Patients with Various Cancers

(A) EVP proteins with the highest predictive values in classifying tumor and non-tumor plasma samples by random forest algorithm. (B) Classification error matrix using random forest classifier of 75% training set and 25% test set, for the 47 proteins in A (left), and all 372 plasma EVP proteins (right). The number of samples identified is noted in each box. See also Table S7.

Figure 7.. Tumor-Derived EVP Profiles Classify Primary Tumor of Origin

(A) Classification error matrix for 75% training and 25% test sets from tissue explant-derived EVPs. (B) Unsupervised 2D (left) and supervised 3D (right) t-SNE plots representing proteins in (C). (C) Proteins with the highest predictive value in the random forest algorithm based on primary tumor tissue-derived EVPs. Primary tumor tissue (n = 38; colon [n = 3, stage 0 = 1, stage III = 2], lung [n = 14, stage I = 7, stage II = 5, stage III = 2], and pancreas [n = 21, stage I = 1, stage II = 15, stage III = 5]) or tumor-positive draining lymph nodes from melanoma patients (n = 5, stage III = 5) were analyzed. (D) Classification error matrix for the 75% training and 25% test sets from plasma-derived EVPs. (E) Unsupervised 2D (left) and supervised 3D (right) t-SNE plots representing proteins in (F). (F) Proteins with the highest predictive value as determined by random forest algorithm based on plasma-derived EVP differences relative to primary tumor type. Samples included breast cancer (n = 8, stage I = 1, stage II = 2, stage IV = 5), colorectal cancer (n = 3, stage 0 = 1, stage III = 2), lung cancer (n = 12, stage I = 6, stage II = 5, stage III = 1), pancreatic cancer (n = 9, stage 2 = 7, stage 3 = 2), and mesothelioma (n = 15, stage I = 2, stage III = 1, stage IV = 1, not available [NA] = 11). (G) Model illustrating sources of plasma EVPs reflecting a combination of TT-, AT/DT-, and DO-derived EVPs.