🔬

Oncology

2
Open Unknowns
6
Cross-Domain Bridges
10
Active Hypotheses

Cross-Domain Bridges

Bridge Hamilton-Jacobi-Bellman control equations provide a principled backbone for adaptive radiotherapy scheduling.

Fields: Control Engineering, Medicine, Oncology

Speculative analogy: Hamilton-Jacobi-Bellman control equations provide a principled backbone for adaptive radiotherapy scheduling....

Bridge Topological Data Analysis (persistent homology, Betti numbers, the Mapper algorithm) classifies the shape of high-dimensional patient data spaces and reveals disease progression trajectories and subtypes that are invisible to distance-based clustering — because the relevant structure is topological (connected components, loops, voids) rather than metric.

Fields: Mathematics, Medicine, Oncology, Computational Biology, Topology

Nicolau et al. (2011) applied the Mapper algorithm (Singh, Mémoli & Carlsson 2007) — which builds a topological skeleton of a point cloud in high-dimensional space — to a breast cancer microarray data...

Bridge The biological effectiveness of ionising radiation — from DNA strand break probability to tumour control — is quantitatively predicted by the Bethe-Bloch stopping power formula: the linear energy transfer (LET) framework bridges quantum electrodynamics track structure to radiobiological effectiveness (RBE) and clinical tumour control probability (TCP) in proton and heavy-ion cancer therapy.

Fields: Medical Physics, Radiation Biology, Oncology, Nuclear Physics, Quantum Electrodynamics

The Bethe-Bloch formula (Bethe 1930, Bloch 1933) gives the mean energy loss per unit path length for a charged particle traversing matter: -dE/dx = (4πe⁴z²N_A Z)/(m_e v² A) × [ln(2m_e v²/I) - ln(1-β...

Bridge Tumor vascular network fragmentation under adaptive therapy maps directly onto percolation-threshold transitions studied in statistical physics.

Fields: Oncology, Statistical Physics, Network Science

When a tumor's blood-supply network is disrupted below its percolation threshold, large-scale connectivity collapses and nutrient delivery fails — the same phase transition that physicists use to mode...

Bridge Kramers-Moyal moment expansions can transfer from stochastic physics to tumor phenotype transition models.

Fields: Statistical Physics, Oncology, Mathematics

Speculative analogy: Kramers-Moyal moment expansions can transfer from stochastic physics to tumor phenotype transition models....

Bridge Markov jump process control can transfer from stochastic systems engineering to cell-state switching therapy design.

Fields: Stochastic Processes, Oncology, Control Engineering

Speculative analogy: Markov jump process control can transfer from stochastic systems engineering to cell-state switching therapy design....

Open Unknowns (2)

Unknown What failure boundaries determine when `b-kramers-moyal-expansion-x-tumor-phenotype-transition-modeling` remains decision-useful? u-estimating-jump-moments-for-tumor-phenotypic-plasticity-models
Unknown What failure boundaries determine when `b-markov-jump-processes-x-cell-state-switching-therapy-design` remains decision-useful? u-therapy-driven-transition-rate-estimation-in-cell-state-markov-models

Active Hypotheses

Hypothesis Active tumour vascular networks can be driven into an "unpercolated active solid" phase by self-propelled cell migration — a fragmentation regime with no classical analogue that makes adaptive therapy more effective than passive percolation models predict. high
Hypothesis APC loss of heterozygosity in intestinal stem cells has a selection coefficient s ~ 0.01-0.05 per cell division (Moran process), explaining why APC-mutant crypts are detected at ~10x frequency above neutral expectation by age 60 in normal human colon; this implies colorectal cancer initiation is driven by selection, not neutral drift. high
Hypothesis Methods transferred from `b-kramers-moyal-expansion-x-tumor-phenotype-transition-modeling` improve target outcomes versus domain-specific baselines at matched cost. high
Hypothesis Methods transferred from `b-markov-jump-processes-x-cell-state-switching-therapy-design` improve target outcomes versus domain-specific baselines at matched cost. high
Hypothesis Pontryagin optimal control-derived adaptive therapy schedules that maintain drug-sensitive clones as evolutionary competitors will double progression-free survival compared to maximum tolerated dose chemotherapy in non-small cell lung cancer with mixed sensitive/ resistant clonal populations. high
Hypothesis Computational protein-protein interface design can produce nanomolar-affinity binders to hub proteins in the PPI network by exploiting their conserved hydrophobic interface hotspots, but off-target toxicity scales with network degree, creating a fundamental therapeutic window constraint imposed by the scale-free network topology. high
Hypothesis Deletion or methylation of CTCF binding sites at TAD boundaries separating proto-oncogenes from their endogenous enhancers will activate those oncogenes proportionally to the Hi-C contact frequency increase between the gene and the newly accessible enhancer — providing a quantitative model for boundary disruption oncogenesis. critical
Hypothesis Topological Data Analysis (Mapper algorithm) applied to TCGA breast cancer gene expression data will identify at least one prognostically significant patient subgroup — defined by topological isolation (a flare or connected component in the Mapper graph) — that is missed by k-means, hierarchical clustering, and PAM50 molecular subtypes, and that shows a statistically significant difference in 10-year overall survival (log-rank p < 0.01) compared to the most similar standard subtype high
Hypothesis Cancer cells that have lost normal cytoskeletal tensegrity architecture have measurably lower mechanical stiffness (Young's modulus) and higher deformability than normal cells of the same tissue type, making atomic force microscopy a viable early detection modality. high
Hypothesis Topoisomerase II inhibitors (doxorubicin, etoposide) kill tumor cells primarily through transcription-coupled double-strand breaks (at highly expressed oncogenes with high TOPO2 density) rather than replication-coupled breaks, explaining why they are active against slowly-dividing tumors and predicting that transcriptomics- based TOPO2 occupancy predicts tumor cell sensitivity. high

Know something about Oncology? Contribute an unknown or hypothesis →

Generated 2026-05-10 · USDR Dashboard