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.

Background

Many circulating biomarkers have been reported for the diagnosis of breast cancer, but few, if any, have undergone rigorous credentialing using prospective cohorts and blinded evaluation.

Methods

The NCI Early Detection Research Network (EDRN) has created a prospective, multicenter collection of plasma and serum samples from 832 subjects designed to evaluate circulating biomarkers for the detection and diagnosis of breast cancer. These samples are available to investigators who wish to evaluate their biomarkers using a set of blinded samples. The breast cancer reference set is composed of blood samples collected using a standard operating procedure at four U.S. medical centers from 2008 to 2010 from women undergoing either tissue diagnosis for breast cancer or routine screening mammography. The reference set contains samples from women with incident invasive cancer (n = 190), carcinoma in situ (n = 55), benign pathology with atypia (n = 63), benign disease with no atypia (n = 231), and women with no evidence of breast disease by screening mammography (BI-RADS 1 or 2, n = 276). Using a subset of plasma samples (n = 505) from the reference set, we analyzed 90 proteins by multiplexed immunoassays for their potential utility as diagnostic markers.

Results

We found that none of these markers is useful for distinguishing cancer from benign controls. However, elevated CA-125 does appear to be a candidate marker for estrogen receptor-negative cancers.

Conclusions

Markers that can distinguish benign breast conditions from invasive cancer have not yet been found.

Impact

Availability of prospectively collected samples should improve future validation efforts.

Recent studies have revealed the mutational signatures underlying the somatic evolution of cancer, and the prevalences of associated somatic genetic variants. Here we estimate the intensity of positive selection that drives mutations to high frequency in tumors, yielding higher prevalences than expected on the basis of mutation and neutral drift alone. We apply this approach to a sample of 525 head and neck squamous cell carcinoma exomes, producing a rank-ordered list of gene variants by selection intensity. Our results illustrate the complementarity of calculating the intensity of selection on mutations along with tallying the prevalence of individual substitutions in cancer: while many of the most prevalently-altered genes were heavily selected, their relative importance to the cancer phenotype differs from their prevalence and from their P value, with some infrequent variants exhibiting evidence of strong positive selection. Furthermore, we extend our analysis of effect size by quantifying the degree to which mutational processes (such as APOBEC mutagenesis) contributes mutations that are highly selected, driving head and neck squamous cell carcinoma. We calculate the substitutions caused by APOBEC mutagenesis that make the greatest contribution to cancer phenotype among patients. Lastly, we demonstrate via in vitro biochemical experiments that the APOBEC3B protein can deaminate the cytosine bases at two sites whose mutant states are subject to high net realized selection intensities-PIK3CA E545K and E542K. By quantifying the effects of mutations, we deepen the molecular understanding of carcinogenesis in head and neck squamous cell carcinoma.

Neoplasms change over time through a process of cell-level evolution, driven by genetic and epigenetic alterations. However, the ecology of the microenvironment of a neoplastic cell determines which changes provide adaptive benefits. There is widespread recognition of the importance of these evolutionary and ecological processes in cancer, but to date, no system has been proposed for drawing clinically relevant distinctions between how different tumours are evolving. On the basis of a consensus conference of experts in the fields of cancer evolution and cancer ecology, we propose a framework for classifying tumours that is based on four relevant components. These are the diversity of neoplastic cells (intratumoural heterogeneity) and changes over time in that diversity, which make up an evolutionary index (Evo-index), as well as the hazards to neoplastic cell survival and the resources available to neoplastic cells, which make up an ecological index (Eco-index). We review evidence demonstrating the importance of each of these factors and describe multiple methods that can be used to measure them. Development of this classification system holds promise for enabling clinicians to personalize optimal interventions based on the evolvability of the patient's tumour. The Evo- and Eco-indices provide a common lexicon for communicating about how neoplasms change in response to interventions, with potential implications for clinical trials, personalized medicine and basic cancer research.

Access to experimental X-ray diffraction image data is fundamental for validation and reproduction of macromolecular models and indispensable for development of structural biology processing methods. Here, we established a diffraction data publication and dissemination system, Structural Biology Data Grid (SBDG; data.sbgrid.org), to preserve primary experimental data sets that support scientific publications. Data sets are accessible to researchers through a community driven data grid, which facilitates global data access. Our analysis of a pilot collection of crystallographic data sets demonstrates that the information archived by SBDG is sufficient to reprocess data to statistics that meet or exceed the quality of the original published structures. SBDG has extended its services to the entire community and is used to develop support for other types of biomedical data sets. It is anticipated that access to the experimental data sets will enhance the paradigm shift in the community towards a much more dynamic body of continuously improving data analysis.