Bioinformatic Pipeline Design and Assessment for Cancer Neo-Surfaceome Detection

Abstract

Glioblastoma (GBM) remains one of the most aggressive and treatment-resistant brain tumors, with limited systemic therapeutic options and high recurrence rates. New treatment modalities in oncology, including antibody-drug conjugates and CAR-T cells, target the tumor cell surfaceome. However, despite extensive genomic and transcriptomic profiling, actionable surface antigens for targeted immunotherapies remain elusive due to GBM’s high heterogeneity, immunosuppressive microenvironment, and limitations set by the blood-brain barrier. In this study, we present an integrated multi-omics framework for the discovery of tumor-specific surface antigens, collectively termed the neo-surfaceome, arising from somatic single-nucleotide variants, fusion transcripts, and aberrant splicing events.

We restructured our previously developed analysis pipeline into a modular, containerized Nextflow workflow capable of scalable, reproducible RNA-seq and variant analyses across large patient cohorts. This architecture supported seamless process parallelization and enabled a 25-fold reduction in runtime compared to serial execution. To contextualize the functional impact of mutations on protein–protein interactions, we also developed the SURFME Interactome DB database with structurally supported domain–domain interactions. Through performance benchmarks, we ultimately reduced key query times under one second, enabling real-time exploration of variant-disrupted interactions via our Dockerized visualization platform, NSFW (Neo-Surfaceome Feature Workbench). An example query targeting the oncogenic EGFRvIII region revealed potential disruptions with PTK2, RASA1, and VAV2 – proteins strongly implicated in tumorigenic signaling and migration pathways.

Our results highlight the value of a reproducible, extensible pipeline for neo-surfaceome exploration and offer a foundation for future integration of curated structural domain resources. Future improvement plans include incorporating AlphaFold-based domain annotations, expanding variant visualization capabilities, and integrating genome-wide data into a complementary Nextflow workflow. Together, these efforts aim to build a scalable resource for neoantigen discovery and personalized immunotherapeutic development in GBM via data from the Lund Glioblastoma Cohort, and beyond.

Popular Abstract

Glioblastoma is one of the most aggressive and deadly forms of brain cancer in adults, often affecting people over the age of 60. It grows quickly, resists treatment, and nearly always comes back after therapy. While many other cancers have seen huge improvements in treatment and survival chances in recent years, GBM has remained stubbornly untreatable to modern therapy – no new drugs and modern techniques have significantly improved patient survival in the last two decades.

One promising way forward is to look at the proteins on the surface of tumor cells. These proteins can act like "flags" guiding the drugs to the target cancer cells more precisely. In this project, we built on our lab’s expertise in a technique that isolates these surface proteins, including those, that the cell temporarily brings inside via a process called endocytosis, enabling us to pinpoint the most promising targets to carry our cancer cell killing drug inside.

We combined this method with an array of powerful technologies: gene sequencing (RNA-seq), full DNA analysis (whole-genome sequencing), and advanced protein detection (mass spectrometry), and applied on tumor sample from the Lund Glioblastoma Cohort, giving us a better view of what’s really happening on the outside of tumor cells. Then, to make sense of all this data, we also developed a high-performance bioinformatics pipeline, and an interactive database, which helps us understand how mutations might affect surface protein interactions. We even built a lightweight online tool – NSFW, the Neo-Surfaceome Feature Workbench – so researchers can explore these changes visually.

Although this research is still in its early stages, we’re continuing to improve our tools and expand the study to more patients. Our goal is to contribute to the development of more effective personalized treatments for GBM, improving outcomes for people facing this horrible disease.

• author: Horváth, Márton
• supervisor: Mattias Belting
• organization: Degree Projects in Bioinformatics
• course: BINP50 20251
• year: 2025
• type: H2 - Master's Degree (Two Years)
• subject: Biology and Life Sciences
• language: English
• id: 9212609
• date added to LUP: 2025-09-17 16:15:10
• date last changed: 2025-09-17 16:15:10