Δ-Oncological Relativity: From Absolute Tumors to Relational Cancer Dynamics

Read full text
Authors: Italo Fabian Gonzalez
Cited by: 0

Abstract

This work introduces Δ-Oncological Relativity, a conceptual and theoretical framework that redefines cancer not as an absolute biological entity, but as a dynamic, relational, and time-dependent system, departing from reductionist and tumor-centric models, the framework proposes that oncological phenomena—such as growth, resistance, stability, and therapeutic response—can only be meaningfully understood relative to their biological, microenvironmental, temporal, and clinical contexts. Across four integrated parts, the manuscript develops an ontological, epistemological, and clinically oriented perspective in which tumors and their microenvironments are treated as coupled, co-evolving systems, the framework emphasizes change (Δ), relational reference frames, biological time, and relative stability as central organizing principles, offering coherent explanations for clinical observations that remain paradoxical under absolute models, including therapy-induced resistance, phenotypic plasticity, and context-dependent outcomes. Rather than proposing new mathematical formalisms or computational implementations, this work deliberately focuses on conceptual architecture and narrative integration, providing a unifying language capable of bridging systems biology, evolutionary oncology, and clinical reasoning, Δ-Oncological Relativity does not replace existing oncological knowledge; it reorganizes it within a relational paradigm that supports adaptive, context-aware, and ethically grounded approaches to cancer care. This manuscript is intended as a foundational contribution and the first installment of a broader series exploring the implications of relational dynamics in oncology, with relevance for researchers, clinicians, and theorists interested in the limits of absolute models and the future of dynamic cancer understanding

Fields of Study & Subjects

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

Publication details

Journal / source
OSF Preprints (OSF Preprints)
Publication date
5 February 2026
Document type
Preprint