CancerHERVdb: Human Endogenous Retrovirus (HERV) Expression Database for Human Cancer Accelerates Studies of the Retrovirome and Predictions for HERV-Based Therapies

Abstract

In this study, we sought to create a database summarizing the expression of human endogenous retroviruses (HERVs) in various human cancers. HERVs are suitable therapeutic targets due to their abundance in the human genome, overexpression in various malignancies, and involvement in various cancer pathways. We identified articles on HERVs from PubMed and then prescreened and automatically categorized them using the portable document format (PDF) data extractor (PDE) R package. We discovered 196 primary research articles with HERV expression data from cancer tissues or cancer cell lines. HERV RNA and protein expression was reported in brain, breast, cervical, colorectal, endocrine, gastrointestinal, kidney/renal/pelvis, liver, lung, genital, oral cavity, pharynx, ovary, pancreas, prostate, skin, testicular, urinary/bladder, and uterus cancers, leukemias, lymphomas, and myelomas. Additionally, we discovered reports of HERV RNA-only overexpression in soft tissue cancers including heart, thyroid, bone, and joint cancers. The CancerHERVdb database is hosted in the form of interactive visualizations of the expression data and a summary data table at https://erikstricker.shinyapps.io/cancerHERVdb/. The user can filter the findings according to cancer type, HERV family, HERV gene, or a combination thereof and easily export the results with the corresponding reference list. In our report, we provide examples of potential uses of the CancerHERVdb, such as identification of cancers suitable for off-target treatment with the multiple sclerosis-associated retrovirus (MSRV)-Env-targeting antibody GNbAC1 (now named temelimab) currently in phase 2b clinical trials for multiple sclerosis or the discovery of cancers overexpressing HERV-H long terminal repeat-associating 2 (HHLA2), a newly emerging immune checkpoint. In summary, the CancerHERVdb allows cross-study comparisons, encourages data exploration, and informs about potential off-target effects of HERV-targeting treatments. IMPORTANCE Human endogenous retroviruses (HERVs), which in the past have inserted themselves in various regions of the human genome, are to various degrees activated in virtually every cancer type. While a centralized naming system and resources summarizing HERV levels in cancers are lacking, the CancerHERVdb database provides a consolidated resource for cross-study comparisons, data exploration, and targeted searches of HERV activation. The user can access data extracted from hundreds of articles spanning 25 human cancer categories. Therefore, the CancerHERVdb database can aid in the identification of prognostic and risk markers, drivers of cancer, tumor-specific targets, multicancer spanning signals, and targets for immune therapies. Consequently, the CancerHERVdb database is of direct relevance for clinical as well as basic research.

Keywords: CancerHERVdb; HERV; cancer; database; expression; human endogenous retrovirus.

FIG 1

PRISMA flowchart on how articles were identified, obtained, and assessed. Initially, HERV-related articles were identified through a PubMed search, and full-text articles were obtained using the PubMed-Batch-Download software developed by Bill Greenwald and Texas Medical Center (TMC) library access, filtered using the PDE R package, and in a final step manually evaluated.

FIG 2

Distribution of cancer-related HERV papers by cancer type. A total of 2,735 full-text HERV-related papers were searched for cancer-related keywords. Articles with ≥0.2% of all words being general cancer keywords were considered cancer related. The resulting 655 cancer-related primary research HERV articles were probed for the cancer type that was mentioned most often and divided into one of 26 cancer categories, including nonhuman cancers and a “multiple cancer” category. Nonhuman cancers were included for completeness.

FIG 3

Distribution of cancer-related HERV papers by year. Articles with a single cancer focus were sorted by year of publication and used for visualization. (A) For distribution ratios, gastrointestinal cancer and colorectal cancer were summarized to digestive cancers, leukemia and lymphoma to blood cancer, and lung cancer, uterus cancer, thyroid cancer, myeloma, bone and joint cancer, cervical cancer, soft tissue cancers including heart cancer, urinary bladder cancer, other genital cancer, and other oral cavity and pharynx cancer to other cancer. (B) The seven most studied cancers in HERV-focused articles were evaluated for their publication distribution. Publication density was plotted against the year of publication.

FIG 4

HERV expression by HERV family and cancer type. Background color represents number of different cell lines with expression of HERV (# of pos. CLs). Detection of HERV expression was classified as either protein (P), which includes anti-HERV antibody, or anti-HERV T-cell evidence or RNA, and is depicted by circle or triangle color (Detection). The size of the circle signifies number of cancer patients evaluated, with triangles denoting HERVs evaluated only in cell lines (# human subjects).

FIG 5

CancerHERVdb website screenshots. (A) Home: landing page displaying an interactive version of Fig. 4, which displays data point information on mouse hover and initiates a custom search on mouse click. (B) Table Browser: data table fully browsable and freely downloadable. (C) Custom Search: interface allowing the user to search for HERV expression data by cancer type, HERV family, HERV gene, or a combination thereof. (D) Search by Cancer Type: specific search interface providing summary data for a single or multiple cancer types. Search by HERV Family and Search by HERV Gene have similar interfaces and result displays. The bar graph is interactive, displaying detailed information on mouse hover and initiating a custom search on mouse click.