Design of Personalized mRNA Vaccine for Breast Cancer Patients in Pakistan
Breast Cancer: A Growing Threat to Women in Pakistan
Breast cancer mortality continues to rise in Pakistan. Conventional therapies struggle to reflect the genetic heterogeneity of individual patients. Consequently, we recognized an urgent need for personalized immunotherapy.
Novel Neoantigens Identified by Computational Immunology
Using publicly available whole-exome sequencing data, we analyzed 6,005 missense variants and over 43,000 candidate peptides. From these, we selected seven non‑allergenic neoantigens with strong predicted HLA binding (ΔG ≤ ‑13.0 kcal/mol) and designed a 285‑amino‑acid multi‑epitope antigen incorporating a GM‑CSF adjuvant and helper epitopes. This vaccine can precisely target the individual-specific mutations of each patient.
Predicted Structural Stability and Robust Immune Response
AlphaFold2 modeling and quality metrics such as a ProSA Z‑score of ‑7.14 and ERRAT of 96.59 % confirmed the stability of the designed protein. Throughout a 500 ns molecular dynamics simulation, binding to TLR9 (ΔG = ‑148.8 kcal/mol) and TLR2 (ΔG = ‑16.7 kcal/mol) remained robust, and C‑IMMSIM immune simulations predicted a Th1‑biased cytotoxic T‑cell response. These findings suggest that the immune system is likely to recognize the vaccine effectively.
Personalized Vaccine: A Key to Next‑Generation Cancer Therapy
If this pipeline is experimentally validated, low‑resource settings could rapidly develop personalized mRNA vaccines. Ultimately, it could serve as a pivotal step toward shifting cancer treatment standards to a genomics‑driven, patient‑specific paradigm.
UNLABELLED: Breast cancer is a common cancer type that occurs among women in Pakistan, and the rising incidence and mortality rate underline the need to develop effective, patient-tailored immunotherapies. In this study, we implemented an end-to-end immunoinformatics workflow using publicly available whole-exome sequencing (WES) data deposited under NCBI BioProject PRJNA941166, a cohort-derived resource from the Khyber Pakhtunkhwa region; as a proof of concept, we analyzed all sequencing runs associated with the available case to demonstrate a personalized vaccine design workflow. Somatic variant analysis indicated a high mutational burden, including 6005 missense mutations in genes such as MUC3A and TTN. From > 43,000 candidate mutant peptides, we prioritized seven non-allergenic neoantigens with strong predicted HLA binding (ΔG ≤ - 13.0 kcal/mol). These epitopes were assembled into a 285-amino acid multi-epitope antigen incorporating a GM-CSF adjuvant and helper epitopes. AlphaFold2 modeling and in silico quality assessment supported construct stability (ProSA Z-score - 7.14; ERRAT 96.59%). Across 500 ns molecular dynamics simulations, the vaccine construct remained conformationally stable and showed favorable predicted interactions with innate immune receptors, with strong binding free energies for TLR9 (ΔG = - 148.8 kcal/mol) and TLR2 (ΔG = - 16.7 kcal/mol). Immune simulations using C-IMMSIM suggested a Th1-skewed response characterized by induction of cytotoxic T lymphocytes, memory T-cell formation, and elevated IFN-γ. Although limited to computational predictions and a single publicly available case, the predicted receptor engagement and immunogenicity provide a rationale for preclinical evaluation of this personalized mRNA vaccine design workflow in high-risk populations. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40203-026-00631-6.
This study provides a concrete approach for designing patient‑specific vaccines aimed at reducing the rising breast‑cancer mortality among women in Pakistan. Should personalized mRNA vaccines prove effective, they could enable safer and more efficient cancer prevention and treatment.