What question did this study set out to answer?

The aim is to define a novel operator-algebraic modular Hamiltonian for cancer biology analysis.

May 21, 2026Open Access

ITU Tier 1+ #5: Cancer Information Theory (Kcancer)

Key Points

The aim is to define a novel operator-algebraic modular Hamiltonian for cancer biology analysis.
Introduces K_cancer derived from operator-algebraic framework on multiple biological systems.
Incorporates numerical results related to Hanahan-Weinberg hallmarks and CAR-T therapies.
Presents a roadmap for further cancer analytics development.
K_cancer defines key interactions among cancer genes, tumors, immune responses, and therapies.
Ten falsifiable predictions were made, including CAR-T solid tumor approval expected by 2027.
High connectivity identified between various biological and environmental factors impacting cancer.

Abstract

Tier 1+ Pass-1. 5 paper 5 of 45. ITU-derived cancer biology with Kcancer modular Hamiltonian on genome/tumor/immune/microenv/therapy. Defines Kcancer = -log ρcancer as the operator-algebraic modular Hamiltonian on Hgenome ⊗ Hₜumor ⊗ Hᵢmmune ⊗ Hₘicroenv ⊗ Hₜherapy. Kcancer inherits from KQG via the CLPW 2023 type II crossed-product specialised to this scale. Numerical results. Hanahan-Weinberg 2011 hallmarks; checkpoint inhibitor response (Routy-Sivan 2018 Science) ; CAR-T solid-tumor trials 2024. Topics covered. Hanahan-Weinberg hallmarks, Yamanaka 2006 OSKM, PD-1/PD-L1 checkpoint, CAR-T Carl June 2010, AlphaFold protein folding 2020/2024. 45-vertex polytope #5 top couplings: #6 Kₐging (0. 95), #27 Microbiome (0. 92), #29 Dev (0. 92), #31 Kqbio (0. 92). Ten falsifiable predictions: Pₐvg=0. 66: arXiv 2026 (0. 90 S), Pan-cancer biomarker 2028 (0. 65 M), CAR-T solid tumor approval 2027 (0. 55 M). Pass-2 roadmap: ~1. 7M: Cancer analytics (600K) + Lean Mathlib (200K) + Oncology partnership (900K). Copyright © 2026 Munehiro Terada / Roboken. Licensed under CC-BY-4. 0.

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