From Molecular Profiling to Predictive Algorithms: A Conceptual Machine-Learning Framework for Mechanism-Informed Therapy Selection in Multidrug-Resistant Cancer
Abstract
Background. Oncology practice largely selects therapy according to histological classification and population-level response rates rather than the molecular behaviour of an individual tumour. Two convergent resistance mechanisms - dysregulated proliferative signalling (MAPK, PI3K/AKT/mTOR) and evasion of intrinsic apoptosis (BCL-2 family dysregulation) - reinforce one another at the protein level, allowing tumours to circumvent single-pathway interventions.
Proposal. We set out a conceptual machine-learning framework that treats an individual’s proliferative-signalling and apoptosis-resistance status as continuously updated multivariate inputs rather than static single-threshold biomarkers. We specify the framework’s prediction targets, its representation of irregularly sampled longitudinal data, candidate model classes, indicative data requirements, and - for the therapy-recommendation tasks - the causal-inference constraints that distinguish recommending a treatment from predicting an outcome.
Precedent and limits. We review precedent for individual components: machine-learning-guided frontline tyrosine-kinase-inhibitor selection in chronic myeloid leukaemia, transcriptomic-signature-guided adjuvant chemotherapy in pancreatic ductal adenocarcinoma, multi-omic prediction of neoadjuvant response in breast cancer, functional measurement of apoptotic priming by dynamic BH3 profiling, and circulating-tumour-DNA-guided treatment adaptation. To our knowledge, no existing tool integrates proliferative-signalling and apoptosis-resistance status while re-scoring longitudinally across a treatment course. We also examine the mixed interventional record of molecularly guided treatment-assignment trials and state what would need to differ here. We identify an instrumentation gap that constrains the proposal: of the required inputs, only circulating tumour DNA is routinely measurable in series, so the framework is at present longitudinal in one axis and static in the other.
Conclusion. Formalising mechanism-based diagnostics as a continuously updating computational system offers a route toward individualised therapy selection, but its feasibility is contingent on assay development, staged prospective validation, and safety evaluation that the field has not yet undertaken.
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 7 No. 3 (2024): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
12085-12101 |
Published |
May 14, 2024 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Muh’d Saheed Kolawole Abd-Rouf, Peter Oluwasayo Adigun, Emmanuel Olusegun Alalade, Tobi Titus Oyekanmi, Peter Oluwasayo Adigun, Emmanuel Olusegun Alalade, Tobi Titus Oyekanmi, Akinwale John Faniyi, Bolaji Oladapo, Tolulope Esther Awopejo, Olufunmilayo Sofiat Adegoke, Abeeb Jamiu, Ojo Babatunde Michael, Abiodun Obisesan, Sunday Ajala, Michael A. Adekanye, Paris Mikem Yambali, Aisha Bolarinwa Abd-Rouf (%2024). From Molecular Profiling to Predictive Algorithms: A Conceptual Machine-Learning Framework for Mechanism-Informed Therapy Selection in Multidrug-Resistant Cancer. International Journal of Science, Research and Technology , Vol. 7 No. 3 (2024): International Journal of Science, Research and Technology (IJSRAT) , pp. 12085-12101. https://doi.org/10.15662/IJSRAT.2024.0703003 |
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