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. 2018 Apr 2;16(1):82.
doi: 10.1186/s12967-018-1452-4.

Baseline antibody profiles predict toxicity in melanoma patients treated with immune checkpoint inhibitors

Michael F Gowen  1 , Keith M Giles  1 , Danny Simpson  2 , Jeremy Tchack  1 , Hua Zhou  3 , Una Moran  1 , Zarmeena Dawood  1 , Anna C Pavlick  4 , Shaohui Hu  5 , Melissa A Wilson  4 , Hua Zhong  6 , Michelle Krogsgaard  7 , Tomas Kirchhoff  2 , Iman Osman  8   9   10
Affiliations

Baseline antibody profiles predict toxicity in melanoma patients treated with immune checkpoint inhibitors

Michael F Gowen et al. J Transl Med. .

Abstract

Background: Immune checkpoint inhibitors (anti-CTLA-4, anti-PD-1, or the combination) enhance anti-tumor immune responses, yielding durable clinical benefit in several cancer types, including melanoma. However, a subset of patients experience immune-related adverse events (irAEs), which can be severe and result in treatment termination. To date, no biomarker exists that can predict development of irAEs.

Methods: We hypothesized that pre-treatment antibody profiles identify a subset of patients who possess a sub-clinical autoimmune phenotype that predisposes them to develop severe irAEs following immune system disinhibition. Using a HuProt human proteome array, we profiled baseline antibody levels in sera from melanoma patients treated with anti-CTLA-4, anti-PD-1, or the combination, and used support vector machine models to identify pre-treatment antibody signatures that predict irAE development.

Results: We identified distinct pre-treatment serum antibody profiles associated with severe irAEs for each therapy group. Support vector machine classifier models identified antibody signatures that could effectively discriminate between toxicity groups with > 90% accuracy, sensitivity, and specificity. Pathway analyses revealed significant enrichment of antibody targets associated with immunity/autoimmunity, including TNFα signaling, toll-like receptor signaling and microRNA biogenesis.

Conclusions: Our results provide the first evidence supporting a predisposition to develop severe irAEs upon immune system disinhibition, which requires further independent validation in a clinical trial setting.

Keywords: Antibodies; Biomarker; Immunotherapy; Melanoma; Toxicity.

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Figures

Fig. 1
Fig. 1
Validation of a proteomic microarray for measurement of serum antibodies. a Intra-chip reproducibility was assessed by comparing probe intensity readings for duplicate spots from 10 independent serum samples/chips. Linear regression analysis was used to determination the correlation between spots within chips. To assess interchip reproducibility, probe intensity readings were assessed in the same 10 serum samples across two distinct microarrays on separate occasions, and linear regression analysis was used to determine the correlation between chips. b Comparison of probe array signal intensities for anti-CTLA-4 antibodies from serum samples (n = 39) from melanoma patients taken before and after anti-CTLA-4 ICI treatment. Top, raw array scans of duplicate anti-CTLA-4 spots for pre- and post-anti-CTLA-4 samples from patient 10-262. Bottom, graph showing combined anti-CTLA-4 array signals (mean ± SD) for all pre- and post-treatment samples. *p < 0.0001
Fig. 2
Fig. 2
Antibodies from baseline sera of melanoma patients are associated with ICI toxicity. a Volcano plot of differential antibody levels from baseline sera comparing none/mild vs. severe toxicity for anti-CTLA-4-treated patients (n = 37). Filtered antibodies are highlighted in blue, and curated antibodies are indicated in red (downregulated with severe toxicity) or purple (upregulated with severe toxicity). b As for a, but comparing no/mild vs. severe toxicity for anti-PD-1-treated patients (n = 27). c As for a, but comparing mild vs. severe toxicity for anti-CTLA-4 and anti-PD-1 combination treated patients (n = 11). d Boxplots showing probe intensities for the 15 most differentially expressed antibodies (DE; based on p values) between sera from antiCTLA-4 patients (n = 37) with no/mild toxicity (blue) vs. those with severe toxicity (orange). Data represent median probe intensities ± sd. e As for d, but for samples comparing no/mild vs. severe toxicity for anti-PD-1-treated patients (n = 27). f As for d, but for samples comparing mild vs. severe toxicity for combination anti-CTLA-4 and anti-PD-1-treated patients (n = 11)
Fig. 3
Fig. 3
Functional significance of toxicity-associated antibodies. a Functional pathway enrichment (WikiPathways) of protein targets from the filtered set of toxicity-associated antibodies from anti-CTLA-4-treated patients. b As for a, but for anti-PD-1-treated patients. c As for a, but for combination-treated patients. d Summary of immune toxicity associations for protein targets of top 15 DE termination-associated antibodies from anti-CTLA-4-treated patients. e As for d, but for anti-PD-1-treated patients. f As for d, but for combination-treated patients
Fig. 4
Fig. 4
Development of classification models to predict immunotherapy toxicity using antibodies from pre-treatment melanoma patient sera. a Scatterplot showing distribution of decision values from support vector machine (SVM) classifier models based on “filtered” antibody (feature) lists for prediction of severe toxicity. Data summarizes training and testing results from 100 repetitions of fivefold cross validation for pre-anti-CTLA-4 samples. Gold circles represent true positives (severe toxicity sample called as severe toxicity) and green crosses represent true negatives (no/mild toxicity sample called as no/mild toxicity). Red circles represent false negatives (severe toxicity sample called as no/mild toxicity) and blue crosses represent false positives (no/mild toxicity called as severe toxicity). b As for a, but summarizing 100 repetitions of fivefold cross validation for anti-PD-1 samples. c As for a, but summarizing 100 repetitions of threefold cross validation for anti-CTLA-4 and anti-PD-1 combination samples. d Summary of accuracy, sensitivity, and specificity cross validation statistics based on SVM models for prediction of toxicity in anti-CTLA-4 samples (no/mild toxicity, n = 30; severe, n = 9). e As for d, but for anti-PD-1 samples (no/mild toxicity, n = 19; severe, n = 9). f As for d, but for combined anti-CTLA-4 and anti-PD-1 samples (mild toxicity, n = 4; severe, n = 7)
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