Foundations of Biological Relativity in Oncology

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Authors: Italo Fabian Gonzalez
Cited by: 0

Abstract

This work introduces a conceptual framework termed Biological Relativity in Oncology, which proposes a shift from viewing cancer as a static, tumor-centered entity toward understanding it as a dynamic, relational biological system. Rather than interpreting oncologic phenomena through absolute measurements (e.g., tumor size, cellularity, or biomarker levels), this framework emphasizes the relative temporal dynamics between competing biological processes within the tumor ecosystem. Inspired by principles from modern physics—particularly the rejection of absolute states in favor of relational descriptions—this model argues that clinical behavior, progression, resistance, and therapeutic failure emerge from imbalances between biological forces, such as proliferation, perfusion, immune activity, hypoxia, clonal expansion, and angiogenesis. Importantly, these forces are not evaluated in isolation, but in terms of their relative rates of change and dominance over time. The framework provides a reinterpretation of functional imaging, tumor microenvironment interactions, and therapeutic sequencing, proposing that many observed clinical outcomes reflect relative system collapse or dominance, rather than intrinsic tumor aggressiveness alone. Clinical examples illustrate how identical tumors may behave differently depending on the dynamic relationships governing their biological context. This work does not disclose computational or mathematical derivations at this stage. Instead, it focuses on establishing a conceptual and interpretive language capable of unifying tumor biology, imaging, and therapy within a single dynamic framework. The model is designed to be compatible with retrospective and prospective validation and is intended as a foundation for translational research exploring personalized, temporally optimized cancer treatment strategies.

Fields of Study & Subjects

Mathematical Biology Tumor Growth Cancer Cells and Metastasis Cancer Genomics and Diagnostics

Publication details

Journal / source
OSF Preprints (OSF Preprints)
Publication date
26 January 2026
Document type
Preprint