Building on 18 years of commercially successful research and development in the field of computational immunology, EpiVax has now developed Ancer™, a personalized cancer vaccine platform. Ancer is a high-speed, secure, cloud-based commercial platform for processing cancer/normal protein sets, identifying high-quality patient-specific neo-antigens, and designing personalized cancer vaccines.

EpiVax has established a reputation for innovative cloud-based solutions for biologics and vaccine design. The company’s ISPRI™ and iVAX™ platforms are actively used (>10,000 sequences per month) by global BioPharma companies for biologics and vaccine design. All of EpiVax’s validated vaccine-design tools are integrated into the new Ancer personalized cancer vaccine platform. Ancer is currently being used in several preclinical cancer research and development programs. (Inquiries about research collaborations are welcome, please write svessella [at]

The Ancer™ to Cancer

T cells target and kill a wide range of tumors, establishing a strong rationale for the development of immunotherapeutic approaches to cancer therapy [1]–[6]. Cancer-specific epitopes are comprised of mutated sequences that are not found in the normal human genome. These sequences, also known as neo-antigens or neo-epitopes, are capable of engaging and activating endogenous T cells thereby mediating anti-tumor response. Identification of neo-antigens is now well established for a wide range of tumor types in experimental models and clinical settings, including melanoma, lung and stomach cancer [7]–[12].

Accurate identification of tumor neo-antigens

A range of online tools are now available to identify neo-antigens. A major limitation for most of the on-line tools is the accurate identification of effective neo-antigens. Most published reports describe “hit rates” that are as low as 19-30% using available on-line computational tools [13]–[15]. This poor performance may be due, in part, to the selection of self-epitopes matching regulatory, anergic or deleted T cells along with cancer neo-epitopes.

Selection of self-epitopes can also lead to auto-immune adverse effects if the epitopes are conserved in other organs and weak vaccine response if the epitopes activate Tregs. Some examples of adverse effects reported in the literature include life-threatening diabetic ketoacidosis, colitis, and other conditions that are due to immune cells attacking healthy organs [16]–[28].

Superior identification of true neo-antigens with JanusMatrix, EpiMatrix and iTEM

Ancer analyzes each peptide for all four types of possible responses (Thelper, CTL, Treg, Null), thereby accelerating the process of identifying tumor neo-epitopes to design a vaccine that is unique to each patient’s genetic background and cancer. Ancer identifieds epitopes with a higher degree of accuracy due tothe incorporation of ‘filters’ using EpiVax tools. Top-ranked epitopes selected by Ancer are highly immunogenic in both retrospective and prospective studies [10], [15].

A key attribute of the Ancer system is the JanusMatrix tool which enriches neo-antigen selection for epitopes that stimulate effector T cells, and triages out any epitopes that are highly conserved with self-epitopes (including those not easily found by other pattern matching tools), leading to more accurate selection of class I and class II MHC-restricted neo-epitopes. A second key tool is iTEM, which is used for individualizing T cell epitope selection based on individual patient HLA. EpiMatrix is a proprietary, highly accurate epitope-mapping tool that has been validated in prospective studies [32]–[36]. Epitopes are arranged into strings using VaccineCAD, at tool that has been used by EpiVax in a wide range of published vaccine studies.

The EpiMatrix, iTEM and JanusMatrix tools are integrated into the Ancer platform, which has both an expert interface for individual (one-off) cancer vaccine design and a ‘locked pipeline’ feature for FDA-approved clinical trials. EpiVax plans to promote this tool into as new venture, to be announced in early 2017.

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