🧬 Designing Better Cancer Vaccines — UCL-CCC Hackathon 2025 🏆

This repository contains our project developed for the UCL Cancer Collaborative Centre Hackathon 2025, where our team designed an end-to-end computational pipeline to improve personalised cancer vaccine development. Our aim is to identify high-value neoantigen targets that a patient's immune system has not already failed to recognise.

🏆 Result: Winner — UCL Cancer Collaborative Centre Hackathon 2025

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🚀 Project Overview

Cancer vaccines provide a powerful therapeutic avenue, but high non-response rates remain a key challenge. The main bottleneck is the identification and prioritisation of effective neoantigen targets from an enormous search space (≈10¹⁷ peptides). Our solution integrates tumour genomics, patient HLA typing, and TCR repertoire data to generate a ranked list of optimal vaccine candidates.

Key Innovation

We exclude peptides already recognised by exhausted or ineffective T-cell responses, increasing the likelihood of inducing a strong and durable vaccine response.

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🧠 High-Level Pipeline

1. Neoantigen Identification
   Extract mutated peptides (9-mers) present in cancer cells but not in normal tissue.

2. MHC Binding Prediction
   Predict HLA-specific binding affinities to determine surface-presentable peptides.

3. TCR Binding Prediction
   Identify and remove peptides already targeted unsuccessfully by the patient's TCRs.

4. Ranking & Output
   Score the remaining peptides using immunogenicity, presentation likelihood, clonality, conservation, and safety metrics.

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🧬 Data Requirements

Training Data

• Cancer genomes with validated neoantigens
• HLA–peptide binding datasets (IEDB)
• TCR–peptide binding datasets

Patient-Specific Inputs

• Tumour genome sequencing
• Patient HLA genotype
• TCR repertoire sequencing

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🧩 Model Architecture

Our architecture incorporates:

• Neoantigen identification module
• Transformer-based MHC binding prediction
• Structural TCR–peptide interaction modelling (AlphaFold-based)

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📊 Ranking Metrics

• MHC binding affinity
• TCR engagement strength
• Surface presentation probability
• Peptide abundance
• Conservation across cancer subclones
• Phylogenetic clonality
• Cross-reactivity and safety assessment

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📁 Repository Structure

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├── data/
├── models/
├── pipeline/
├── notebooks/
├── results/
└── README.md

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🧑‍🔬 Team TC-AWARE

• Matthew Cowley
• Zhen Wei Yap
• Mohammad Alawwami
• Zarif Shafiei
• Linh Hoang
• Julia Sala-Bayo
• Graham Bonomo-Jackson
• Gleb Gmyzov
• Nick Keatley