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Peripheral Blood TCR Repertoire Profiling May Facilitate Patient Stratification for Immunotherapy against Melanoma

Sabrina A. Hogan, Anaïs Courtier, Phil F. Cheng, Nicoletta F. Jaberg-Bentele, Simone M. Goldinger, Manuarii Manuel, Solène Perez, Nadia Plantier, Jean-François Mouret, Thi Dan Linh Nguyen-Kim, Marieke I.G. Raaijmakers, Pia Kvistborg, Nicolas Pasqual, John B.A.G. Haanen, Reinhard Dummer and Mitchell P. Levesque
Sabrina A. Hogan
1University Hospital Zurich, University of Zurich, Zurich, Switzerland.
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  • ORCID record for Sabrina A. Hogan
Anaïs Courtier
2ImmunID Technologies, Grenoble, France.
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Phil F. Cheng
1University Hospital Zurich, University of Zurich, Zurich, Switzerland.
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Nicoletta F. Jaberg-Bentele
1University Hospital Zurich, University of Zurich, Zurich, Switzerland.
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Simone M. Goldinger
1University Hospital Zurich, University of Zurich, Zurich, Switzerland.
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Manuarii Manuel
2ImmunID Technologies, Grenoble, France.
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Solène Perez
2ImmunID Technologies, Grenoble, France.
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Nadia Plantier
2ImmunID Technologies, Grenoble, France.
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Jean-François Mouret
2ImmunID Technologies, Grenoble, France.
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Thi Dan Linh Nguyen-Kim
1University Hospital Zurich, University of Zurich, Zurich, Switzerland.
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Marieke I.G. Raaijmakers
1University Hospital Zurich, University of Zurich, Zurich, Switzerland.
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Pia Kvistborg
3Netherlands Cancer Institute, Amsterdam, the Netherlands.
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Nicolas Pasqual
2ImmunID Technologies, Grenoble, France.
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John B.A.G. Haanen
3Netherlands Cancer Institute, Amsterdam, the Netherlands.
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Reinhard Dummer
1University Hospital Zurich, University of Zurich, Zurich, Switzerland.
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Mitchell P. Levesque
1University Hospital Zurich, University of Zurich, Zurich, Switzerland.
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  • For correspondence: Mitchell.Levesque@usz.ch
DOI: 10.1158/2326-6066.CIR-18-0136 Published January 2019
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  • Figure 1.
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    Figure 1.

    Outline of the analysis pipeline.

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    Figure 2.

    Kaplan–Meier plots showing survival in R (responders) and NR (nonresponders) to anti-PD1 treatment. Colored bands represent 95% confidence interval (CI). A, PFS probability, B, OS probability.

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    Figure 3.

    Logistic regression model assessing the relationship between pretreatment clinical parameters (including DE50) and response to therapy. Forest plots and tables represent the β-coefficients of the clinical variables that may affect response to treatment. Blue lines represent the 95% CI. A, Univariate analysis for the anti–CTLA4-treated cohort. *, DE50 is the only factor conserved in the multivariate analysis. B, Univariate analysis for the anti–PD1-treated cohort. *, DE50 is the only factor conserved in the multivariate analysis. In the multivariate analyses, factors were selected using a backward selection process.

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    Figure 4.

    TCR diversity evenness prior to treatment. A, Dot plot showing DE50 at baseline in patients who eventually responded or did not respond to anti-CTLA4 therapy. N = 42. Dashed line, 20.03%. B, Dot plot showing DE50 levels at baseline in patients who eventually responded or did not respond to anti-PD1 therapy. N = 38. Dashed line, 20.4%. P value calculated with the Fisher exact test.

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    Figure 5.

    Cox regression model assessing the correlation of clinical variables with PFS in the anti-PD1 cohort. Forest plots and tables represent the β-coefficients of the clinical variables that may affect PFS. Blue lines represent the 95% CI. A, Univariate analysis. B, Multivariate analysis using backward selection procedure.

Tables

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  • Table 1.

    Patient characteristics for anti-CTLA4 and anti-PD1 cohorts

    Anti-PD1Anti-CTLA4
    Patient characteristicsN (%)N (%)
    Age, years
     <402 (5.3)3 (7.1)
     40–5511 (28.9)14 (33.3)
     56–7014 (36.8)18 (42.9)
     >7011 (28.9)7 (16.7)
    Gender
     Male27 (71.1)23 (54.8)
     Female11 (28.9)19 (45.2)
    PFS
     <3 months9 (23.7)13 (59.1)
     3–9 months11 (28.9)6 (27.3)
     >9 months18 (47.4)3 (13.6)
    RECIST1.1 at ∼12 weeks
     CR1 (2.6)2 (4.8)
     PR11 (28.9)10 (23.8)
     SD12 (31.6)3 (7.1)
     PD14 (36.8)27 (64.3)
    BRAF mutation status
     Wild-type27 (73.0)
     Mutated10 (27.0)
    NRAS mutation status
     Wild-type21 (60.0)
     Mutated14 (40.0)
    Previous treatments
     Chemotherapy3 (7.9)
     Targeted therapy2 (5.3)
     Immunotherapy (ipilimumab)28 (73.7)
     Radiotherapy4 (10.5)
     Interferon1 (2.6)
    Lactate dehydrogenase (LDH/ULN)
     <120 (54.1)27 (67.5)
     ≥117 (45.9)13 (32.5)
    S100
     <0.2 μg/L10 (27.8)
     ≥0.2 μg/L26 (72.2)
    ANC
     <8 g/L35 (92.1)40 (95.2)
     ≥8 g/L3 (7.9)2 (4.8)
    ALC
     <1.5 g/L25 (65.8)28 (66.7)
     ≥1.5 g/L13 (34.2)14 (33.3)
    Leukocytes
     <9.6 g/L34 (89.5)
     ≥9.6 g/L4 (10.5)
    Basophils
     <0.15 g/L20 (52.6)
     ≥0.15 g/L18 (47.4)
    Eosinophils
     <0.7 g/L36 (94.7)
     ≥0.7 g/L2 (5.3)
    • NOTE: N, number of patients in each category. %, representation in percent for each category.

Additional Files

  • Figures
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  • Supplementary Data

    • Figure S1 - Effect of previous treatment on variables tested in our model. (Kruskal-Wallis test)
    • Figure S2 - Lymphocyte to leukocyte ratio in high and low ANC/ALC groups for anti-PD1 treated patients at baseline. (Wilcoxon test)
    • Figure S3 - Baseline DE50 in anti-PD1 treated patients who A) had a PFS above or under 12 months, B) survived more or less than 24 months. Dashed line = 20.4%. P value calculated with Fisher's test.
    • Figure S4 - 3D representation of the TCR diversity. Each peak represents a rearrangement between a given V gene family and a J segment. X axis: TRBV, y axis: TRBJ, z axis relative intensity. The table shows the top 10 rearrangements, listed in decreasing order of contribution to the global repertoire. Contribution is calculated based on the ratio between individual rearrangement intensity and the sum of all rearrangements intensities. A) Immune maps of the patients eventually responding to anti-PD1 therapy (PD and CR). B) Immune maps of the patients who eventually did not respond to anti-PD1 therapy (PD).
    • Supplementary Figure Legends - Figure Legends for Supplemental Figures 1-4
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Cancer Immunology Research: 7 (1)
January 2019
Volume 7, Issue 1
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Peripheral Blood TCR Repertoire Profiling May Facilitate Patient Stratification for Immunotherapy against Melanoma
Sabrina A. Hogan, Anaïs Courtier, Phil F. Cheng, Nicoletta F. Jaberg-Bentele, Simone M. Goldinger, Manuarii Manuel, Solène Perez, Nadia Plantier, Jean-François Mouret, Thi Dan Linh Nguyen-Kim, Marieke I.G. Raaijmakers, Pia Kvistborg, Nicolas Pasqual, John B.A.G. Haanen, Reinhard Dummer and Mitchell P. Levesque
Cancer Immunol Res January 1 2019 (7) (1) 77-85; DOI: 10.1158/2326-6066.CIR-18-0136

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Peripheral Blood TCR Repertoire Profiling May Facilitate Patient Stratification for Immunotherapy against Melanoma
Sabrina A. Hogan, Anaïs Courtier, Phil F. Cheng, Nicoletta F. Jaberg-Bentele, Simone M. Goldinger, Manuarii Manuel, Solène Perez, Nadia Plantier, Jean-François Mouret, Thi Dan Linh Nguyen-Kim, Marieke I.G. Raaijmakers, Pia Kvistborg, Nicolas Pasqual, John B.A.G. Haanen, Reinhard Dummer and Mitchell P. Levesque
Cancer Immunol Res January 1 2019 (7) (1) 77-85; DOI: 10.1158/2326-6066.CIR-18-0136
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