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. 2023 Jan 11;15(678):eadd8469.
doi: 10.1126/scitranslmed.add8469. Epub 2023 Jan 11.

Posttranslational modifications induce autoantibodies with risk prediction capability in patients with small cell lung cancer

Kristin J Lastwika  1   2 , Andrew Kunihiro  2 , Joell L Solan  2 , Yuzheng Zhang  3 , Lydia R Taverne  2 , David Shelley  2 , Jung-Hyun Rho  2 , Timothy W Randolph  3 , Christopher I Li  2 , Eric L Grogan  4 , Pierre P Massion  5 , Annette L Fitzpatrick  6   7 , David MacPherson  2   8 , A McGarry Houghton  1   8 , Paul D Lampe  2   8
Affiliations

Posttranslational modifications induce autoantibodies with risk prediction capability in patients with small cell lung cancer

Kristin J Lastwika et al. Sci Transl Med. .

Abstract

Small cell lung cancer (SCLC) elicits the generation of autoantibodies that result in unique paraneoplastic neurological syndromes. The mechanistic basis for the formation of such autoantibodies is largely unknown but is key to understanding their etiology. We developed a high-dimensional technique that enables detection of autoantibodies in complex with native antigens directly from patient plasma. Here, we used our platform to screen 1009 human plasma samples for 3600 autoantibody-antigen complexes, finding that plasma from patients with SCLC harbors, on average, fourfold higher disease-specific autoantibody signals compared with plasma from patients with other cancers. Across three independent SCLC cohorts, we identified a set of common but previously unknown autoantibodies that are produced in response to both intracellular and extracellular tumor antigens. We further characterized several disease-specific posttranslational modifications within extracellular proteins targeted by these autoantibodies, including citrullination, isoaspartylation, and cancer-specific glycosylation. Because most patients with SCLC have metastatic disease at diagnosis, we queried whether these autoantibodies could be used for SCLC early detection. We created a risk prediction model using five autoantibodies with an average area under the curve of 0.84 for the three cohorts that improved to 0.96 by incorporating cigarette smoke consumption in pack years. Together, our findings provide an innovative approach to identify circulating autoantibodies in SCLC with mechanistic insight into disease-specific immunogenicity and clinical utility.

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Figures

Figure 1.
Figure 1.. Antigen-complexed autoantibodies are highly upregulated in SCLC.
(A) The mean coefficient signal of upregulated (p<0.05) IgG and IgM autoantibodies in SCLC are higher (one-way ANOVA) than any other cancer type analyzed on antibody-antigen complex arrays. n=98 SCLC and 125 controls; n=47 NSCLC and 47 controls; n=86 pancreatic cancer and 86 controls; n=139 colon cancer and 139 controls; n=121 breast cancers and 121 controls. Box and whisker plots indicate the range of coefficients min to max for each sample. (B to D) IgG and IgM autoantibodies were sequentially discovered in the Cardiovascular Health Study (CHS) pre-diagnostic cohort (B), tested in the Fred Hutch diagnostic cohort (C), and validated in a third cohort from Vanderbilt (D). Blue circles indicate increased IgG against the target Ag; red circles indicate increased IgM against the target Ag; - indicates decreased antibodies against the target Ag; and × indicates no significant difference.
Figure 2.
Figure 2.. Antigens targeted by autoantibodies are upregulated in SCLC.
Representative immunohistochemical (IHC) images are shown of antigens targeted by peripheral autoantibodies on SCLC tissue microarrays (n=45 to 62 cases). Scale bar, 50μM. Bar graph contains positive or negative scoring of tissue microarray cores. Positive scores are further categorized by stage at diagnosis.
Figure 3.
Figure 3.. SCLC AAbs to CD133 target glycosylation motifs.
(A) Shown are immunoblots from H82 cell lysates (Lys) immunoprecipitated (IP) with CD133 antibody or isotype control and probed with anti-CD133 and anti-sLeA antibodies (N=3). Dep is depleted cell lysate after immunoprecipitation. The arrow indicates the expected protein size of CD133 at 133kDa (B) Shown are immunoblots from H82 lysates that were immunoprecipitated with isotype control antibody (CON) or CD133 antibody (N=3). Whole cell lysate (WCL) prior to immunoprecipitation is included to indicate the starting material. Lysates immunoprecipitated with CD133 antibody were then treated according to the manufacturer recommended deglycosylation protocol either with PRIME deglycosylase (+) or without enzyme (−). Immunoblots were probed with anti-CD133 and anti-sLeA antibodies. (C) ELISA quantification of autoantibody concentrations is shown for SCLC patient plasma that bound to CD133 from H82 cell lysates that had or had not been pre-treated with PRIME deglycosylase (n=5 per group). Con, negative control. Data are presented as individual values, the bar represents the mean ± SEM. *p<0.05, **p<0.005, ***p<0.0005 by student’s t test.
Figure 4.
Figure 4.. SCLC AAb can target isoaspartylated post-translational modifications.
(A) Shown are immunoblots from GST-fusion proteins bound to glutathione beads, treated with or without isoaspartylation-inducing buffer (IsoAsp), incubated with pooled plasma from patients with SCLC (n=5 cases) and probed with anti-human IgG and anti-GST antibodies. (B) Multiple immunoblot experiments were quantified (N=3 to 4). Data are presented as individual data points and the bar represents the mean ± SEM (C) The ratio of IgG from SCLC patient plasma bound to IsoAsp compared to wild type (WT) peptides was measured by ELISA (n=4 to 8 cases). Horizontal bars indicate the mean.
Figure 5.
Figure 5.. SCLC AAbs target citrullinated TFRC.
(A) Shown are immunoblots from H82 or H69 whole cell lysates (WCL) probed with TFRC or citruilline antibodies (N=3). GAPDH was used for a loading control. (B) Shown are immunoblots from 3T3 or H82 WCL and H82 lysates immunoprecipitated with TFRC, citrulline (CIT), or isotype control antibody (CON) and probed with anti-TFRC or anti-citrulline antibodies (N=3). (C) The ratio of IgG from SCLC patient plasma bound to citrullinated (Cit) compared to wild type (WT) peptides was measured by ELISA (n=8). (D) IgG from 10 patients with SCLC (case) and 10 controls (ctrl) captured in complex with antigen (AAb-TAA) by anti-TFRC antibody, free from antigen (AAb) by TFRC recombinant protein, or by cit/WT peptides were quantified by ELISA. An anti-TFRC antibody was used a positive control (pos ctrl) for TFRC recombinant protein. Data are presented as individual data points, the horizonal bar as mean ± SEM. *p<0.05 and **p<0.005 by paired sample t tests.
Figure 6.
Figure 6.. Panel autoantibodies can accurately detect SCLC.
(A) Panel autoantibodies were analyzed as a function of the time of blood draw in the CHS cohort (pre-diagnostic, 1 to 2 years prior to diagnosis (dx) or less than 1 year to dx) and Fred Hutch or Vanderbilt cohorts (diagnostic, limited or extensive stage); *p<0.05; **p<0.005, ***p<0.0005 unpaired t-test. (B) Panel autoantibodies are significantly higher in plasma from SCLC cases (n=98) compared to cases from other cancers (n=75 NSCLC, n=86 pancreatic, n=87 colon) or controls (n=175) from all cohorts **p<0.001 unpaired t-test. Data are presented as individual data points, the horizonal bar as mean ± SEM.
Figure 7.
Figure 7.. A risk prediction model can accurately detect SCLC.
(A to C) Receiver operating characteristic (ROC) curves are shown for the 5-autoantibody (AAb, blue dashed line) complex panel identified by maximizing AUC in the Vanderbilt (VB) dataset (A), then fixing the coefficients and testing in the Cardiovascular Health Study (CHS) (B) and Fred Hutch (C) cohorts. ROC curves are also shown for pack years (PY, purple dotted line) and the combination of pack years and 5-AAb panel (black solid line).
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