Research ArticleCancer

Multiparametric plasma EV profiling facilitates diagnosis of pancreatic malignancy

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Science Translational Medicine  24 May 2017:
Vol. 9, Issue 391, eaal3226
DOI: 10.1126/scitranslmed.aal3226
  • Fig. 1. Working principle of the plasmon sensor chip (NPS) for tEVs.

    (A) EV binding to the nanopore surface via monoclonal antibody (mAb) immobilized on the gold surface causes a spectral shift of light transmitted through the nanopores. (B) The spectral shift of resonance peak in light transmission is measured to quantify the amount of EVs captured on the nanopore surface. a.u., arbitrary unit. (C) Scanning electron micrographs show the periodically arranged nanopore array and EVs captured on the surface. Each nanohole has a diameter of 200 nm and a periodicity of 500 nm.

  • Fig. 2. In vitro profiling of tEV markers on cell line–derived EVs.

    (A) The molecular expression of cancer markers (EGFR, EPCAM, HER2, MUC1, GPC1, WNT2, and GRP94) and EV markers (CD63, RAB5B, and CD9) was characterized on EVs derived from 4 cancer cell lines and 11 PDX cell lines including PDAC, metastatic PDAC (PDAC-MET), and IPMN. (B) Correlation of protein expression between EVs and their parental cell lines (1157-PDAC, 1222-PDAC, 1247-PDAC, and 1494-PDAC). (C) Sensitivity comparison between NPS and the gold standard ELISA. The responses were normalized against the values of highest concentrations.

  • Fig. 3. Molecular profiling of plasma EV for a training cohort.

    (A) Putative cancer markers (EGFR, EPCAM, HER2, and MUC1) and PDAC markers (GPC1, WNT2 and GRP94) were profiled on EVs collected from 22 PDAC patients and 10 healthy controls. (B) ROC curves were calculated for single protein markers as well as for the PDACEV signature combination to determine optimum EV threshold values. AUC, area under the curve. (C) A combined marker panel (EGFR, EPCAM, MUC1, GPC1, and WNT2) was established as a PDACEV signature that showed 100% accuracy for the training cohort in distinguishing PDAC from healthy controls. P value was determined by Mann-Whitney test. ****P < 0.0001. (D) A waterfall plot shows the PDACEV signature signals sorted from high (left) to low (right). Each column represents a different patient sample (red, malignant; blue, benign).

  • Fig. 4. The PDACEV signature differentiation of PDAC patients from pancreatitis and control patient groups.

    (A) Heatmap analysis of EV markers. The PDACEV signature is defined as a combined marker panel of EGFR, EPCAM, MUC1, GPC1, and WNT2. (B to D) The established PDACEV signature signals (B), EV concentrations (C), and single GPC1 signal (D) as measured for plasma EVs collected from 22 PDAC patients, 8 with pancreatitis, 5 with benign cystic tumors, and 8 age-matched controls. Pairwise comparison P values were determined by the Dunn’s multiple comparisons test. *P < 0.05, ***P < 0.001, ****P < 0.0001. n.s., not significant.

  • Fig. 5. Distribution of EV protein marker signals.

    Waterfall plots show EV protein content for each of the different biomarkers sorted from high (left) to low (right). Each column represents a different patient sample (red, PDAC, n = 22; blue, pancreatitis, n = 8; green, age-matched controls and benign cystic tumors, n = 13).

  • Fig. 6. Comparison of EV analyses with conventional clinical metrics.

    Correlation of the PDACEV signature values with serum biomarkers [CA 19-9 (A) and CEA (B)] and the tumor diameter (C) for patients with PDAC. tx, treatment. The dashed red lines indicate the threshold values for positivity (CA 19-9, 37 U/ml; CEA, 5 ng/ml; PDACEV signature, 0.87).

  • Fig. 7. EV analyses for patients with different types of pancreatic diseases.

    The PDACEV signature values were measured for patient cohorts (n = 103) including (i) PDAC without treatment (n = 22), (ii) PDAC treated with neoadjuvant regimen (n = 24), (iii) IPMN (n = 13), (iv) other GI cancers mimicking the symptoms of pancreaticoduodenal cancers (n = 11), (v) pancreatic NET (n = 12), (vi) pancreatitis (n = 8), (vii) benign cystic tumors (n = 5), and (viii) age-matched controls (n = 18).

  • Table 1. Summary of patient cohorts.
    CharacteristicTraining cohortProspective cohortTotal
    MalignantBenignMalignantBenign
    Total cases22108221135
    Subtypes
      PDAC untreated132235
      PDAC neoadjuvant tx92433
      IPMN inter/high-grade1313
      NET1212
      Other cancer1111
      Benign cystic tumor55
      Pancreatitis88
      Controls10818
    Age (years)
      Median6848655763
      Range47–8823–8217–8419–9117–91
    Sex
      Male84471473
      Female141401469
    CA 19-9
      PDAC untreated1,148 (1–6,684)1,006 (1–10,625)1,064 (1–10,625)
      PDAC neoadjuvant tx2,258 (19–17,101)657 (4–7,730)1,175 (4–17,101)
      IPMN10.6 (1–26)10.6 (1–26)
    CEA
      PDAC untreated7.0 (2–12)3.4 (0.6–22.1)5.0 (0.6–22.1)
      PDAC neoadjuvant tx11.0 (1–53)56.4 (0.7–1,003)40 (0.7–1,003)
      IPMN2.1 (0.7–3.3)2.1 (0.7–3.3)
    Stage
      I01010
      II14243
      III5510
      IV161026
    Co-therapies PDAC
      Folfirinox/XRT42125
      Gemcitabine516
      Other22
  • Table 2. Statistical analyses of EV markers for training and prospective cohorts.

    Ninety-five percent CIs are indicated in parentheses. NA, not applicable.

    Biomarker(s)nCutoffAUCTraining cohort (n = 32)Prospective cohort (n = 43)
    Sensitivity (%)Specificity (%)Accuracy (%)Sensitivity (%)Specificity (%)Accuracy (%)
    EGFR10.15
    (0.01–0.24)
    0.90
    (0.79–1)
    731008159
    (36–79)
    76
    (53–92)
    67
    (51–81)
    EPCAM10.28
    (0.01–0.34)
    0.88
    (0.77–0.99)
    731008145
    (24–68)
    95
    (76–100)
    70
    (54–83)
    HER210.13
    (0.03–0.32)
    0.72
    (0.55–0.89)
    59906959
    (36–79)
    85
    (64–97)
    72
    (56–85)
    MUC110.34
    (0.02–0.53)
    0.66
    (0.48–0.84)
    361005636
    (17–59)
    90
    (70–99)
    63
    (47–77)
    GPC110.04
    (0.04–0.68)
    0.48
    (0.28–0.67)
    55605682
    (60–95)
    52
    (30–74)
    67
    (51–81)
    WNT210.18
    (0.09–0.48)
    0.84
    (0.71–0.96)
    77908164
    (41–83)
    76
    (53–92)
    70
    (54–83)
    GRP9410.10
    (0.02–0.46)
    0.73
    (0.55–0.90)
    73707255
    (32–76)
    71
    (48–89)
    63
    (47–77)
    B7-H310.19
    (0.02–0.23)
    0.75
    (0.58–0.93)
    5010059NANANA
    EGFR + EPCAM + HER2 + MUC140.67
    (0.29–0.68)
    0.99
    (0.97–1)
    911009486
    (65–97)
    86
    (64–97)
    86
    (72–95)
    EGFR + EPCAM + GPC1 + WNT240.74
    (0.65–0.84)
    1.010010010082
    (60–95)
    90
    (70–99)
    86
    (72–95)
    EGFR + EPCAM +
    MUC1 + GPC1 +
    WNT2
    50.87
    (0.68–1.00)
    1.010010010086
    (65–97)
    81
    (58–95)
    84
    (69–93)
    EGFR + EPCAM + HER2 + MUC1 + GPC1 + WNT260.89
    (0.73–1.00)
    1.010010010095
    (77–100)
    81
    (58–95)
    88
    (75–96)

Supplementary Materials

  • www.sciencetranslationalmedicine.org/cgi/content/full/9/391/eaal3226/DC1

    Fig. S1. NPS setup.

    Fig. S2. Comparison of PDAC and EV markers on cells and EVs.

    Fig. S3. EV markers in a training cohort.

    Fig. S4. Comparison of EV counts with conventional clinical metrics.

    Fig. S5. NPS signal from validation cohort tEVs.

    Fig. S6. EV counts for patients with different types of pancreatic diseases.

    Fig. S7. Comparison of the PDACEV signature and SSTR2 in different patient cohorts.

    Table S1. Antibodies used in flow cytometry and NPS.

    References (40, 41)

  • Supplementary Material for:

    Multiparametric plasma EV profiling facilitates diagnosis of pancreatic malignancy

    Katherine S. Yang, Hyungsoon Im, Seonki Hong, Ilaria Pergolini, Andres Fernandez del Castillo, Rui Wang, Susan Clardy, Chen-Han Huang, Craig Pille, Soldano Ferrone, Robert Yang, Cesar M. Castro, Hakho Lee, Carlos Fernandez del Castillo, Ralph Weissleder*

    *Corresponding author. Email: rweissleder{at}mgh.harvard.edu

    Published 24 May 2017, Sci. Transl. Med. 9, eaal3226 (2017)
    DOI: 10.1126/scitranslmed.aal3226

    This PDF file includes:

    • Fig. S1. NPS setup.
    • Fig. S2. Comparison of PDAC and EV markers on cells and EVs.
    • Fig. S3. EV markers in a training cohort.
    • Fig. S4. Comparison of EV counts with conventional clinical metrics.
    • Fig. S5. NPS signal from validation cohort tEVs.
    • Fig. S6. EV counts for patients with different types of pancreatic diseases.
    • Fig. S7. Comparison of the PDACEV signature and SSTR2 in different patient cohorts.
    • Table S1. Antibodies used in flow cytometry and NPS.
    • References (40, 41)

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