A machine-learning platform to predict antibody-stimulating neoantigens from liquid biopsies

I løpet av Q1 2022 har NOI startet å validere prediksjoner fra sin B-celleepitop prediktor, både internt og i samarbeid med Professor Ola Myklebost's laboratorie ved Universitetet i Bergen. I den interne valideringen har NOI benyttet uavhengige testsett for både lineære og konformasjonsmessige B-celleepitoper, i tillegg til testing på godt karakteriserte immunologiske relevante proteiner som "spike" proteinet fra SARD-CoV-2. Resultatene ser svært lovende ut og er klart mer signifikante sammenliknet med tidligere publiserte prediktorer ved "benchmark" testing. Valideringen som benytter pasientprøver fra sarkompasienter ved Universitetet i Bergen pågår. Plasmaprøver fra sarkompasienter og friske kontrolldonorer har blitt profilert ved hjelp av peptid- og proteinarrays for å identifisere tumorspesifikke antistoffresponser. Per nå ser det ikke ut til at målt antistoffrespons binder målproteinene med tumorspesifikke mutasjoner som ble identifisert ved hjelp av NGS. Vi planlegger nå å validere predikeringene i andre krefttyper enn sarkom som har større andel mutasjoner og er dermed mer passende for formålet. NOI har også brukt B-celleepitop prediktoren utviklet i dette prosjektet til å analysere antatte målproteiner fra infeksjonssykdommer og har nylig blitt tildelt prestisjefulle midler fra "The Coalition for Epidemic Preparedness Foundation" (CEPI) verdt cirka 5 millioner USD for å benytte B-celleepitop prediktoren i sammenheng med NEC Immune Profiler for å utvikle en "blueprint" for en pan beta-koronavirusvaksine.

OUTCOMES The project has enabled NOI to: 1. Develop a ML framework for predicting B-cell epitopes in proteins where no prior 3D structure exists, which can be applied to neoantigen prediction and the development of vaccine blueprints against infectious diseases. 2. The results helped NOI to (A) extend the capability of the NIP software to predict non-HLA restricted neoantigens, (B) win an award from CEPI to develop a pan beta-coronavirus vaccine, and (C) encouraged NOI to establish an infectious disease business unit. IMPACTS 1. Enable more cancer patients to benefit from personalized immunotherapies. 2. Enable the development of "liquid biopsy"-based diagnostic methods that can identify cancer patients with early-stage disease helping to save lives and reduce the health and economic societal costs. 3. Facilitate the development of new vaccines to address both emerging and endemic infectious diseases helping reduce the mortality and cost of future pandemics.

There is increasing evidence that neoantigens can be used as generic biomarkers for patient stratification, and represent attractive targets for developing personalized cancer immunotherapies. However, the field has focused on HLA-restricted neoantigens which activate the cellular arm of the immune system (T-cells), and has largely ignored non-HLA-restricted antigens that can activate the humoral arm of the immune system i.e. generate antibodies. Furthermore, all current neoantigen screening and prediction methodologies requires access to tumor biopsy material - which is not always possible to access. The goal of this project is to develop an innovative machine-learning platform named NeoAbScan that can predict non-HLA-restricted neoantigens from next generation sequencing (NGS) data from a patient biopsy, but also from non-invasive liquid biopsies. The outcome will be a tool that can generate a more holistic immune-system-orientated overview of a tumor's neoantigen landscape, which will improve patient stratification, patient monitoring and also broaden the repertoire of targets that can be engineered into personalized cancer vaccines and CAR-T-cells. OncoImmunity will work in collaboration with Ola Myklebost's group at the University of Bergen to validate the software on clinical samples from sarcoma patients. Once the NeoaAbScan software has been validated OncoImmunity will integrate the NeoAbScan platform with its current Immune-Profiler software which can accurately predict HLA-restricted neoantigens from NGS data. We envisage that the integration of the NeoAbScan platform into the Immune-Profiler software will enable OncoImmunity to penetrate the current target market more rapidly and enable the company to target new opportunities in the field of liquid biopsy-based diagnostics and the rapidly growing CAR-T-cell market - that are not directly targetable using the current Immune-Profiler platform.