Despite major advances in breast cancer therapy, recurrence and metastasis remain key clinical challenges, highlighting the need for innovative treatment strategies. Cancer vaccines have emerged as a promising immunotherapeutic approach; however, conventional tumour antigens often elicit limited immune responses. Recent studies suggest that cryptic and noncanonical peptides, particularly those derived from aberrant translation of noncoding regions such as transposable elements, can generate potent tumour-specific immune responses. These noncoding antigens represent a largely untapped resource for vaccine discovery in breast cancer.
This project aims to systematically identify and prioritise immunogenic noncoding neoantigens through an integrated proteogenomic and immunopeptidomic framework. Leveraging WES/WGS and RNA-seq data from our ongoing translational breast cancer cohort, we will characterise somatic variants, transcriptional dysregulation, and antigen presentation across tumour and normal compartments. All analyses will be consolidated into a reproducible, containerised pipeline to support scalable discovery of novel noncoding neoantigens.
Working under the joint supervision of Dr Sheeba Irshad (Breast Immunology & Cancer Immunotherapy, KCL) and Dr Mohammad M. Karimi (Dark Genome Biology & Bioinformatics, KCL), the student will receive comprehensive training in computational biology, immunology, and translational cancer research. This project will culminate in the development of a data-driven breast cancer vaccine discovery platform, bridging computational predictions with experimental validation to advance personalised cancer immunotherapy.