"Spontaneous Remissions in Terminal-Stage Cancer: Deterministic Decomposition of Regulatory Layers and Operationalization of the NOAH6 Research Framework (Phase 0)"

This repository contains the full manuscript: “Spontaneous Remissions in Terminal-Stage Cancer: Deterministic Decomposition of Regulatory Layers and Operationalization of the NOAH6 Research Framework (Phase 0)” The work presents a deterministic, retrospective analysis of documented spontaneous remission cases in advanced and terminal-stage cancer. Rather than proposing therapeutic mechanisms, the study focuses on reconstructing the structural properties of spontaneous remission as a biological phenomenon. The analysis introduces the NOAH6 framework as an analytical map of five hierarchical regulatory layers (R1–R5) and applies a deterministic coding scheme to heterogeneous case reports. Based on trigger types, temporal patterns, and available biological data, spontaneous remissions are classified into three reproducible dynamic categories (DOK-A, DOK-B, DOK-C). Key contributions of this work include: Demonstration that spontaneous remission is a structured, non-random phenomenon. Identification of consistent dynamic patterns across independent case reports. Evidence that contemporary literature systematically lacks simultaneous measurement of critical regulatory layers (notably R3 and R4). Definition of a minimal Tier-0 variable set that renders the model falsifiable and empirically testable. Transparent documentation of limitations and explicit separation between structural validation and causal validation. This version includes an Addendum (2026) with newly identified PubMed-validated cases (2024–2025) and updated cohort statistics. The inclusion of new data does not modify the framework or conclusions but independently confirms the robustness of the identified structural patterns. The repository is intended as a methodological and analytical contribution to interdisciplinary cancer research, systems biology, and the study of rare biological state transitions. Abstract Spontaneous remission in advanced and terminal-stage cancer remains poorly understood and is commonly treated as an anecdotal or stochastic phenomenon. This study presents a deterministic, retrospective literature analysis (Phase 0) aimed at reconstructing the structural organization of spontaneous remission events rather than proposing therapeutic mechanisms. Using the NOAH6 hierarchical analytical framework, spontaneous remission cases were decomposed into five regulatory layers (R1–R5) and classified into three reproducible dynamic patterns (DOK-A, DOK-B, DOK-C) based on trigger type, temporal characteristics, and available biological signals. A cohort of PubMed-validated spontaneous remission cases was deterministically coded and analyzed. Results demonstrate that spontaneous remissions consistently cluster into structured dynamic categories, while contemporary literature systematically lacks simultaneous measurements of key regulatory layers, particularly the combination of systemic synchronization (R3) and immune reprogramming (R4). This methodological gap prevents mechanistic linkage despite the availability of relevant biomedical technologies. The work does not claim therapeutic causality. Instead, it establishes a falsifiable analytical framework and defines a minimal Tier-0 variable set required to empirically test future hypotheses. The findings support the interpretation of spontaneous remission as a structured, multilayered biological phenomenon and highlight that the primary limitation in current research is methodological fragmentation rather than biological unpredictability.