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Quantitative Analysis of Immune Infiltrates in Primary Melanoma

Robyn D. Gartrell, Douglas K. Marks, Thomas D. Hart, Gen Li, Danielle R. Davari, Alan Wu, Zoë Blake, Yan Lu, Kayleigh N. Askin, Anthea Monod, Camden L. Esancy, Edward C. Stack, Dan Tong Jia, Paul M. Armenta, Yichun Fu, Daisuke Izaki, Bret Taback, Raul Rabadan, Howard L. Kaufman, Charles G. Drake, Basil A. Horst and Yvonne M. Saenger
Robyn D. Gartrell
1Departments of Pediatrics, Pediatric Hematology/Oncology and Medicine, Hematology/Oncology, Columbia University Medical Center/New York Presbyterian, New York, New York.
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Douglas K. Marks
2Department of Medicine, Division of Hematology/Oncology, Columbia University Medical Center/New York Presbyterian, New York, New York.
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Thomas D. Hart
3Columbia University, Columbia College, New York, New York.
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Gen Li
4Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, New York.
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Danielle R. Davari
3Columbia University, Columbia College, New York, New York.
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Alan Wu
5Mailman School of Public Health, Columbia University, New York, New York.
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Zoë Blake
2Department of Medicine, Division of Hematology/Oncology, Columbia University Medical Center/New York Presbyterian, New York, New York.
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Yan Lu
2Department of Medicine, Division of Hematology/Oncology, Columbia University Medical Center/New York Presbyterian, New York, New York.
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Kayleigh N. Askin
3Columbia University, Columbia College, New York, New York.
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Anthea Monod
6Department of Systems Biology, Columbia University, New York, New York.
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Camden L. Esancy
2Department of Medicine, Division of Hematology/Oncology, Columbia University Medical Center/New York Presbyterian, New York, New York.
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Edward C. Stack
7PerkinElmer, Hopkinton, Massachusetts.
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Dan Tong Jia
8Columbia University, College of Physician and Surgeons, New York, New York.
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Paul M. Armenta
8Columbia University, College of Physician and Surgeons, New York, New York.
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Yichun Fu
8Columbia University, College of Physician and Surgeons, New York, New York.
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Daisuke Izaki
3Columbia University, Columbia College, New York, New York.
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Bret Taback
9Department of Surgery, Columbia University Medical Center/New York Presbyterian, New York, New York.
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Raul Rabadan
6Department of Systems Biology, Columbia University, New York, New York.
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Howard L. Kaufman
10Department of Surgery, Rutgers Cancer Institute, New York, New York.
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Charles G. Drake
2Department of Medicine, Division of Hematology/Oncology, Columbia University Medical Center/New York Presbyterian, New York, New York.
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Basil A. Horst
11Department of Dermatopathology, Columbia University Medical Center, New York, New York.
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Yvonne M. Saenger
2Department of Medicine, Division of Hematology/Oncology, Columbia University Medical Center/New York Presbyterian, New York, New York.
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  • For correspondence: yms4@cumc.columbia.edu
DOI: 10.1158/2326-6066.CIR-17-0360 Published April 2018
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  • Figure 1.
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    Figure 1.

    Characterization of the tumor immune microenvironment with qmIF, including evaluation of spatial distribution of CTLs relative to tumor cells and CD68+ macrophages. Processing of slides done in inForm (PerkinElmer). Steps for analysis using inForm represented using a single image from 1 patient. A, Multiplex image of a melanoma stained using qmIF. DAPI (nuclei, blue), SOX10 (tumor, red), CD3 (T cells, cyan), CD8 (CTLs, magenta), CD68 (macrophages, green), Ki67 (proliferation marker, yellow), HLA-DR (activation marker, orange). B, Area from the multiplex image (marked by white inset) zoomed in as DAPI only. C, Cell segmentation of zoomed DAPI image. D–G, Representative analysis steps of the image in A. D, Tissue segmentation. E, Phenotyping showing base phenotypes: macrophages (green), T cells (cyan), tumor (red), and other (blue). F, Scoring with representation of HLA-DR scoring (orange). G, Representative visual example of nearest-neighbor analysis to evaluate the distance between CD3+CD8+ (pink) and SOX10+Ki67+ (yellow). H, Density of CTLs and CD68+ macrophages (n = 104). CD3+CD8+ (far left, P < 0.0001); CD3+CD8+HLA-DR+ (middle left, P < 0.0001); CD68+ (middle right, P < 0.0001); CD68+HLA-DR+ (far right, P < 0.0001). I, Median distance of CTLs to SOX10+Ki67− (red) or SOX10+Ki67+ (blue) grouped (left, P < 0.0001; n = 86). Matched median distance to Ki67− and Ki67+ per patient (right). J, Median distance of CD3+CD8+ to CD68+HLA-DR− (red) or CD68+HLA-DR+ (blue) grouped (left, P < 0.0001; n = 97). Matched median distance to HLA-DR− and HLA-DR+ per patient (right). Macrophages: Mφ. Statistical comparison performed using Mann–Whitney test. *, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001; ****, P ≤ 0.0001. A–G images: white bars = 10 μm.

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    Figure 2.

    Infiltration of CTLs in tumor and stroma and distance of CTLs to nonproliferating tumor cells associates with DSS. Melanoma slides were stained for qmIF with DAPI (blue), SOX10 (red), CD3 (cyan), CD8 (magenta), CD68 (green), Ki67 (yellow), and HLA-DR (orange). Multiplex images of melanoma showing (A) high and (B) low infiltration of CTLs in tumor. Kaplan–Meier (KM) curves were created using classification and regression tree (CART) analysis for each variable shown in C–H. C, High (n = 57) and low (n = 7) density of CD3+CD8+ cells in the stroma (P = 0.0038). D, High (n = 38) and low (n = 26) CD3+CD8+ cells in the tumor (P = 0.0147). E, High (n = 57) and low (n = 7) density of CD3+CD8+HLA-DR+ cells in the stroma (P = 0.0005). F, High (n = 38) and low (n = 26) density of CD3+CD8+HLA-DR+ cells in the tumor (P = 0.0167). G, Far (n = 7) and close (n = 54) distance of CD3+CD8+ cells to SOX10+Ki67− tumor cells (P = 0.0006). H, Far (n = 25) and close (n = 29) distance from CD3+CD8+ cells to proliferating (SOX10+Ki67+) tumor cells (P = 0.0618). Statistical comparison performed using log-rank (Mantel–Cox) test. ns, not significant (P > 0.05); *, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001; ****, P ≤ 0.0001. White bars in A and B, 10 μm.

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    Figure 3.

    High infiltration of CD68+ macrophages in the tumor and stroma and close distance of CTLs to HLA-DR− macrophages associates with poor DSS. A, Multiplex image of a melanoma slide stained using qmIF for DAPI (blue), SOX10 (red), CD3 (cyan), CD8 (magenta), CD68 (green), Ki67 (yellow), and HLA-DR (orange). B, Multiplex image of melanoma showing DAPI (blue) and CD68 (green) for macrophages. KM curves were created using CART analysis for each variable shown in C–H. C, High (n = 7) and low (n = 57) density of CD68+ macrophages in the stroma (P = 0.0006). D, High (n = 55) and low (n = 9) density of CD68+ macrophages in the tumor (P = 0.0426). E, High (n = 18) and low (n = 46) density of CD68+HLA-DR− macrophages in the stroma (P = 0.0013). F, High (n = 10) and low (n = 54) density of CD68+HLA-DR+ macrophages in stroma (P = 0.0637). G, Far (n = 47) and close (n = 14) distance of CTLs to HLA-DR− macrophages (P = 0.0016). H, Far (n = 9) and close (n = 52) distance of CTLs to HLA-DR+ macrophages (P = 0.0388). Statistical comparison performed using log-rank (Mantel–Cox) test. ns: not significant (P > 0.05); *, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001; ****, P ≤ 0.0001). White bars in A and B, 10 μm.

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    Figure 4.

    Close distance of CD68+HLA-DR− macrophages to CTLs associates with poor prognosis in stage II to III melanoma. A, Multiplex image of melanoma with selected region (left) shown again, including only HLA-DR, DAPI, and CD68 stains (right). White arrows: HLA-DR− macrophages. B, ROC curve for distance of CTLs to HLA-DR− macrophages (Mφ; n = 61, AUC = 0.682, P = 0.011) and KM curve using the AUC cutoff (P = 0.0077), far (n = 42), close (n = 19). Statistical comparison performed using log-rank (Mantel–Cox) test.**, P ≤ 0.01. White bars in A and inset, 10 μm.

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    Figure 5.

    Establishing CTL/macrophage ratio in stroma as a biomarker for stage II to III melanoma. CTL/macrophage ratio in the stroma using the ROC curve and the Cox proportional hazards model. A, ROC curve for the CTL/macrophage ratio in the stroma (n = 64, AUC = 0.724, P = 0.026, cutoff = 2.557). B, DSS KM curve using the AUC cutoff (P = 0.0033 in 64 patients, high (n = 33), low (n = 31)). C, Overall survival (OS) KM curve using the AUC cutoff [n = 104, P = 0.0076, high (n = 52), low (n = 52)]. D, Univariable and multivariable Cox analysis of DSS (n = 64) and OS (n = 104) patients. Statistical comparison for DSS and OS performed using log-rank (Mantel–Cox) test. Values in bold are significant at P ≤ 0.05. *, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001; ****, P ≤ 0.0001.

Tables

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  • Table 1.

    Melanoma patient characteristics of the OS cohort

    (n = 104)
    Clinical characteristics
    Gender, n (%)
     Male75 (72.1)
     Female29 (27.9)
    Age, n (range)
     Median74.5 (22–96)
    Location of tumor, n (%)
     Trunk61 (58.7)
     Extremity41 (39.4)
     Unknown2 (1.9)
    Stage, n (%)
     II91 (87.5)
     III13 (12.5)
    Pathologic characteristics
    Depth (mm)
     Median, n (range)2.5 (0.6–26)
    Ulceration, n (%)
     Absent36 (34.6)
     Present65 (62.5)
     Unknown3 (2.9)
    TILs, n (%)
     Absent2 (1.9)
     Non-brisk59 (56.8)
     Brisk33 (31.7)
     Unknown10 (9.6)
    Outcome characteristics
    Patient follow-up (months)
     Median, n (range)45 (4–173)
    OS (months)
     Alive (at least 2 years), n (%)31 (29.8)
     Dead, n (%)73 (70.2)
    DSS (months)
     Alive or NED at death, n (%)42 (40.4)
     Dead with melanoma, n (%)22 (21.2)
     Unknown, n (%)40 (38.4)
    • Abbreviations: DSS, disease-specific survival; NED, no evidence of disease; TILs, tumor-infiltrating lymphocytes.

  • Table 2.

    Pearson correlation matrix

    VariablesDetailsAgeStageLocationDepthUlcerationTILsCD68+ stromaCD8/CD68 stromaCD8+ stroma
    Age≥55 vs. <551−0.3330.026−0.006−0.071−0.008−0.038−0.066−0.051
    StageIII vs. II−0.3331−0.127−0.139−0.215−0.115−0.089−0.238−0.031
    LocationExtremity vs. trunk0.026−0.1271−0.006−0.0510.171−0.039−0.0020.083
    Depth≥2 mm vs. <2 mm−0.006−0.139−0.0061−0.2690.054−0.079−0.060−0.224
    UlcerationPositive vs. negative−0.071−0.215−0.051−0.26910.0050.0260.0440.145
    TILsAbsent, non−brisk, brisk−0.008−0.1150.1710.0540.0051−0.072−0.265−0.504
    CD68+ stromaHigh vs. low−0.038−0.089−0.039−0.0790.026−0.07210.5840.162
    CD8/CD68 stromaLow vs. high−0.066−0.238−0.002−0.0600.044−0.2650.58410.564
    CD8+ stromaLow vs. high−0.051−0.0310.083−0.2240.145−0.5040.1620.5641
    • NOTE: Correlation of CTL/macrophage ratio with TILs (P = 0.044), CD8+ stroma (P < 0.0001), and CD68+ stroma (P < 0.0001). Correlation with depth, ulceration, age, and stage also shown. n = 58; 6 patients were removed due to unknown status for ulceration or TILs.

    • Values in bold are significant at P ≤ 0.05.

Additional Files

  • Figures
  • Tables
  • Supplementary Data

    • Supplemental Table S1-4 and Supplemental Figure S1-S7 - Supplemental Figure 1: Flow chart for database Supplemental Figure 2: Representation of Tissue segmentation Supplemental Figure 3: Representation of Cell Segmentation Supplemental Figure 4: Representation of Phenotyping Supplemental Figure 5: Representation of Scoring Supplemental Figure 6: Supplementary Distance and Survival Data Supplementary ROC Curve and Univariable Cox results Supplemental Figure S7: ROC curve for 2y and 5y DSS
    • Supplementary Table S1 - Demographics for Unanalyzable Cohort
    • Supplementary Table S2 - Demographics for DSS Cohort.
    • Supplementary Table S3 - Supplementary ROC Curve and Univariable Cox results
    • SupplementaryTable S4 - Supplementary Pearson Correlation Matrix
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Cancer Immunology Research: 6 (4)
April 2018
Volume 6, Issue 4
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Quantitative Analysis of Immune Infiltrates in Primary Melanoma
Robyn D. Gartrell, Douglas K. Marks, Thomas D. Hart, Gen Li, Danielle R. Davari, Alan Wu, Zoë Blake, Yan Lu, Kayleigh N. Askin, Anthea Monod, Camden L. Esancy, Edward C. Stack, Dan Tong Jia, Paul M. Armenta, Yichun Fu, Daisuke Izaki, Bret Taback, Raul Rabadan, Howard L. Kaufman, Charles G. Drake, Basil A. Horst and Yvonne M. Saenger
Cancer Immunol Res April 1 2018 (6) (4) 481-493; DOI: 10.1158/2326-6066.CIR-17-0360

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Quantitative Analysis of Immune Infiltrates in Primary Melanoma
Robyn D. Gartrell, Douglas K. Marks, Thomas D. Hart, Gen Li, Danielle R. Davari, Alan Wu, Zoë Blake, Yan Lu, Kayleigh N. Askin, Anthea Monod, Camden L. Esancy, Edward C. Stack, Dan Tong Jia, Paul M. Armenta, Yichun Fu, Daisuke Izaki, Bret Taback, Raul Rabadan, Howard L. Kaufman, Charles G. Drake, Basil A. Horst and Yvonne M. Saenger
Cancer Immunol Res April 1 2018 (6) (4) 481-493; DOI: 10.1158/2326-6066.CIR-17-0360
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