Purpose Mutations in TP53 induce autoantibody immune responses in a subset of cancer patients, which have been proposed as biomarkers for early detection. Here, we investigate the association of p53-specific autoantibodies with multiple tumor subtypes and determine the association with p53 mutation status and epitope specificity. Experimental design IgG p53 autoantibodies (p53-AAb), were quantified in 412 serum samples using a programmable ELISA assay from patients with serous ovarian, pancreatic adenocarcinoma, and breast cancer. To determine if patients generated mutation-specific autoantibodies we designed a panel of the most relevant 51 p53 point mutant proteins, to be displayed on custom programmable protein microarrays. To determine the epitope specificity we displayed 12 overlapping tiling fragments and 38 N- and C-terminal deletions spanning the length of the wild-type p53 protein. Results We detected p53-AAb with sensitivities of 58.8% (ovarian), 22% (pancreatic), 32% (triple negative breast cancer), and 10.2% (HER2+ breast cancer) at 94% specificity. Sera with p53-AAb contained broadly reactive autoantibodies to 51 displayed p53 mutant proteins, demonstrating a polyclonal response to common epitopes. All p53-AAb displayed broad polyclonal immune response to both continuous and discontinuous epitopes at the N- and C-terminus as well as the DNA-binding domain. Conclusion and clinical relevance In this comprehensive analysis, mutations in tumor p53 induce strong, polyclonal autoantibodies with broadly reactive epitope specificity.

Diagnostic IV, therapeutic IV.

Although several lines of evidence have established the central role of epithelial-to-mesenchymal-transition (EMT) in malignant progression of non-small cell lung cancers (NSCLCs), the molecular events connecting EMT to malignancy remain poorly understood. This study presents evidence that Long Interspersed Nuclear Element-1 (LINE-1) retrotransposon couples EMT programming with malignancy in human bronchial epithelial cells (BEAS-2B). This conclusion is supported by studies showing that: 1) activation of EMT programming by TGF-β1 increases LINE-1 mRNAs and protein; 2) the lung carcinogen benzo(a)pyrene coregulates TGF-β1 and LINE-1 mRNAs, with LINE-1 positioned downstream of TGF-β1 signaling; and, 3) forced expression of LINE-1 in BEAS-2B cells recapitulates EMT programming and induces malignant phenotypes and tumorigenesis in vivo. These findings identify a TGFβ1-LINE-1 axis as a critical effector pathway that can be targeted for the development of precision therapies during malignant progression of intractable NSCLCs.

Background Given the current epidemic of human papillomavirus (HPV)-related oropharyngeal cancer (OPC), a screening strategy is urgently needed. The presence of serum antibodies to HPV16 early (E) antigens is associated with an increased risk for OPC. The purpose of this study was to evaluate the diagnostic accuracy of antibodies to a panel of HPV16 E antigens in screening for OPC. Methods This case-control study included 378 patients with OPC; 153 patients with non-oropharyngeal head and neck cancer (non-OPC); and 782 healthy control subjects. Tumor HPV status was determined by p16 immunohistochemistry and HPV in situ hybridization. HPV16 E antibody levels in serum were identified by ELISA. A trained binary logistic regression model based on the combination of all E antigens was pre-defined and applied to the dataset. We calculated sensitivity/specificity of the assay to distinguish HPV-related OPC from controls. Logistic regression analysis was used to calculate odds ratios (OR) with 95% confidence intervals (CI) for the association of head and neck cancer with antibody status. Results Of the 378 patients with OPC, 348 had p16-positive OPC. HPV16 E antibody levels were significantly higher among patients with p16-positive OPC but not among patients with non-OPC or among controls. Serology showed high sensitivity and specificity for HPV-related OPC (binary classifier: sensitivity, 83% and specificity, 99% for p16-positive OPC). Conclusions A trained binary classification algorithm that incorporates information about multiple E antibodies showed high sensitivity and specificity and may be advantageous for risk stratification in future screening trials.