Closed-loop genetic algorithms surface the earliest mutation signatures that destabilise the P-53 tumour suppressor as malignant cascades begin. This award-winning research provides early detection insights for cancer screening.
P-53 operates as the "guardian of the genome," halting cell division when DNA damage is detected. Mutations in this gene contribute to over 50% of all human cancers, making it a critical target for early detection strategies.
Traditional approaches focus on already-malignant sequences. This study identifies predictive patterns in pre-malignant mutations, potentially enabling intervention before cancer emerges.
Combining genetic algorithms with self-organizing maps to trace deterministic pathways from healthy to malignant P-53 sequences, revealing early-warning biomarkers for clinical application.
Awarded Best Research Project at UBC Vantage College Capstone Conference for innovative genetic algorithm approaches to identify DNA characteristics leading to P-53 cancerous mutations.
Identify recurring mutation motifs that precede carcinogenic behaviour in the P-53 tumor suppressor gene by simulating mitotic propagation under controlled conditions.
The SOM surfaced six high-risk pentamer motifs (cagcc, agcca, cccag, ccagg, ttttt, ctttt) with an optimal 0.451 silhouette score under a 1×6 matrix configuration.
Curated 25 wild-type and cancerous P-53 DNA strands (2,509 bases each) from the NCBI repository. Pre-processed to remove non-nucleotide characters and aligned pathological/parental pairs.
Spawned a binary tree representing mitotic bifurcation. Each node stores generation index, DNA composition, and malignancy state. Recursion continues to generation 14 to emulate tumour initiation depth.
Depth-first traversal extracts generational paths, evaluates mismatch rates via Levenshtein similarity, and flags the highest-drift ancestors preceding malignant nodes.
Calculated log₄(L) to choose k = 4, transforming each strand into a 1×1024 feature vector representing nucleotide frequency. Result: 73,475 length-adjusted rows.
Applied SOM grids ranging 1×2 to 1×11. Correlation analysis reduced dimensional redundancy before finalising a 1×6 lattice that maximised separation with minimal distortion.
The SOM revealed six distinct mutation clusters with clearly differentiated nucleotide signatures. Clusters enriched in thymine-heavy motifs surfaced consistently in malignant branches, providing early warning indicators for cancer development.
Achieved optimal 0.451 silhouette score with 1×6 grid configuration, indicating well-separated cluster centroids with minimal overlap between mutational trajectories.
Highlighted motifs align with known loss-of-function trajectories for P-53, establishing a computational pipeline to monitor early mutational convergence in other cancer datasets.
Six clusters emerged as reliable precursors to malignant outcomes. Each cluster possesses a signature nucleotide fingerprint, reinforcing that mutation progression is pathway-dependent, not random.
Silhouette scores climbed steadily from 1×2 through 1×6 matrices before dropping sharply. The 1×6 configuration maintains separation without sacrificing interpretability.
Limitations include simulated rather than patient-specific conditions and a relatively small number of base sequences. Framework is portable for expanding datasets.
Scaling this methodology to other tumour suppressor genes could expose similar early-warning mutation signatures, guiding screening pipelines before clinical symptoms manifest.
Genetic algorithms plus SOM clustering expose deterministic shifts toward malignancy. Six pentamer motifs consistently precede malignant conversion, providing high-clarity monitoring targets.
Encoded feature space remains interpretable, enabling rapid clinician dialogue. The motif shortlist now feeds wet-lab validation and computational monitoring pipelines.
Future work will expand datasets with longitudinal patient samples and fuse expression-level data to link mutational motifs with phenotypic impact.
Di Leo, A., et al. (2007). p-53 gene mutations as a predictive marker in advanced breast cancer. Annals of Oncology, 18(6), 997-1003.
Vogelstein, B., Lane, D., & Levine, A. J. (2000). Surfing the p53 network. Nature, 408, 307–310.
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