From Protocol to Playbook: Rewriting Cancer Care with Intelligence, Not Inertia. Putting all the Components Together
Why it's time to abandon rigid treatment dogma and embrace a dynamic, data-driven strategy tailored to every patient.
Today, cancer diagnosis and treatment still largely follow a fragmented approach, relying on isolated snapshots of pathology, imaging, or lab tests, and then funneling patients into standardized treatment protocols grounded more in historical dogma than individual complexity. Despite the explosion of medical knowledge, genomic data, and computational power, our clinical frameworks tend to remain static, siloed, and reactive.
It doesn’t have to be this way. With the right architecture, we could replace this piecemeal decision-making with a dynamic, holistic system. One that integrates real-time patient-specific data with population-level insights, genetic profiling, and adaptive strategy modeling. Such a system, powered by AI and rooted in personalized intelligence, would turn treatment from a generic protocol into a strategic, individualized game plan.
As oncology moves steadily from standardized care to precision medicine, the vision of an individualized “game plan” for cancer treatment emerges as both necessary and increasingly feasible. This vision imagines an intelligent, adaptive digital platform that can craft and continuously refine a patient-specific treatment plan, integrating real-time clinical data, genomic profiles, and vast databases of outcomes from comparable patients. It’s more than a dashboard or a decision-support tool: it is an orchestration engine that thinks strategically, learns continuously, and responds adaptively. Let’s explore what such an application might look like and the core components that must interact to make it holistic and effective.
1. Patient-Specific Information Blocks
The cornerstone of the individualized treatment plan is detailed, real-time, patient-specific data. These include:
Demographics and Clinical History: Age, sex, ethnicity, comorbidities, past treatments, allergies, and social determinants of health.
Diagnostic Imaging and Pathology: Radiology scans (CT, MRI, PET), histopathology slides, biomarker expression levels (e.g., PD-L1), and tumor grading/staging.
Molecular and Genetic Profile: Whole-genome sequencing or targeted panels (e.g., BRCA1/2, EGFR, TP53), tumor mutational burden, microsatellite instability, and transcriptomics.
Treatment History and Response Data: What therapies have been tried (surgery, radiation, chemo, immunotherapy), how the tumor responded, what adverse effects occurred, and the duration and durability of remission or progression.
Patient-Reported Outcomes (PROs): Quality of life, side effect burden, preferences, psychological status, and treatment goals.
Real-Time Biometric Data: Wearable sensor data on heart rate, fatigue, activity level, and sleep patterns, indicating recovery or distress between treatments.
This rich tapestry of patient-specific data becomes the dynamic starting point for individualized decision-making.
2. Population-Level Knowledge Blocks
To inform decisions for a specific patient, the application must draw upon vast reservoirs of comparable data:
Large-Scale Clinical Registries: Outcomes from thousands of patients with similar diagnoses, treatments, and profiles, enabling pattern recognition and benchmarking.
Real-World Evidence (RWE): Aggregated data from electronic health records (EHRs), insurance claims, and health apps reflecting off-trial treatment use and patient journeys.
Clinical Trial Databases: Protocols, inclusion/exclusion criteria, endpoints, and results from completed or ongoing trials to identify novel treatment options.
Genomic Databases: Public and proprietary genomic libraries (e.g., The Cancer Genome Atlas, COSMIC) that allow for mutation-outcome correlation.
Guideline Knowledge Bases: NCCN, ESMO, ASCO guidelines integrated into the logic of the application but augmented with real-world nuance and patient-specific adjustments.
The application must be able to query these data lakes dynamically and extract tailored insights.
3. Role of Genetic and Molecular Profiling
Genomic profiling has become a critical pillar in personalizing cancer care:
Targeted Therapy Identification: Matching mutations (e.g., ALK, HER2, BRAF) with available drugs or trials.
Resistance Prediction: Forecasting which mutations confer resistance to therapy (e.g., KRAS mutations negating EGFR inhibitors).
Prognostic Stratification: Using polygenic risk scores or tumor evolution modeling to estimate survival and recurrence risks.
Pathway Activation Analysis: Mapping the signaling networks active in the tumor to suggest combination therapies or synergistic approaches.
The application must translate raw sequencing output into clinically meaningful, actionable intelligence, interfacing with molecular tumor boards as needed.
4. Game Theory and Strategic Modeling
A cancer treatment plan is not a single decision. It’s a series of moves, like a chess game, with the tumor also adapting. Here, game theory provides conceptual tools:
Adaptive Strategy Modeling: Considering how the tumor may evolve or resist treatments and anticipating future moves (e.g., sequencing therapies to avoid resistance traps).
Risk-Reward Balancing: Using Nash equilibria to weigh toxicity against potential benefit, respecting patient preferences.
Stochastic Modeling: Factoring in randomness (e.g., immunotherapy response variability) and using probabilistic simulations to model likely trajectories.
Multi-Agent Systems: Modeling not just the tumor and the physician, but also the immune system, drug metabolism, and environmental factors as interacting agents in a complex game.
These ideas, traditionally theoretical, become powerful when embedded in machine learning models.
5. Artificial Intelligence as the Orchestrator
AI is the binding agent that brings these blocks together into a responsive, learning system:
Predictive Modeling: Deep learning models trained on multimodal data (genomics, images, text) to predict outcomes and personalize regimens.
Reinforcement Learning: Continuously refining treatment sequences based on observed outcomes, akin to how AlphaGo learns optimal strategies.
Natural Language Processing (NLP): Mining unstructured notes, pathology reports, and trial descriptions to surface relevant insights.
Causal Inference Engines: Identifying not just correlations but plausible causes for adverse events or positive responses.
Conversational Interfaces: AI chatbots or assistants helping patients understand their plans and clinicians refine them.
The AI component must be explainable, auditable, and human-in-the-loop, ensuring trust and safety.
6. User Interfaces and Ethical Guardrails
For this application to function in real-world settings:
Physician Dashboard: Visualizations of tumor evolution, therapy sequencing plans, and alerts for emerging clinical trials or resistance signals.
Patient Portal: Plain-language summaries, symptom trackers, decision aids, and secure communication with care teams.
Ethical Oversight: Built-in safeguards for privacy, bias mitigation, and value alignment with patient autonomy and preferences.
Interoperability Infrastructure: APIs and FHIR compliance to allow integration with EHRs, labs, imaging systems, and research databases.
Putting It All Together: A Clinical Vignette
A 58-year-old man is diagnosed with metastatic non-small cell lung cancer. The application immediately imports his clinical history, scans, and biopsy results. It runs his tumor through a genomic panel, identifies an ALK rearrangement, and suggests alectinib as first-line therapy, citing real-world outcomes from similar patients. Based on his cardiovascular comorbidities and genetic risk for hepatotoxicity, it adjusts dosing. The AI projects likely resistance pathways and models future options (e.g., lorlatinib). A wearable detects increased fatigue and reduced activity at week 4, prompting an automatic check-in. His preferences show high value on preserving cognition and minimal hospital visits. His plan shifts subtly toward lower-toxicity maintenance regimens as stability is achieved.
We Need to Migrate From Treatment to Strategy
The future of cancer care lies not just in new drugs but in new architectures of intelligence. A holistic cancer treatment application, driven by AI, informed by genomic and real-world data, shaped by strategic reasoning, can turn reactive care into proactive orchestration. The result is not just personalized medicine, but personalized strategy. And that could mean the difference between hope and uncertainty, between endurance and despair. In this digital oncologist’s assistant, we may find not just a tool, but a partner in the long game of healing.
Why hit subscribe? Because cancer doesn’t follow the rules, and frankly, neither do I. If you're tired of cookie-cutter medicine and want to see what happens when AI, strategy, game theory, scenario building, and a dash of rebellion collide in the oncology world, this is your pit stop. Punch that subscribe button like you're flooring a V12 on the Nürburgring. Because the future of cancer care shouldn’t crawl, it should roar. My objective is to illustrate the possible and then get as many cancer patients on board to advocate for progress. That’s the only way we can effect change. Thanks a Million in advance.
I like the way you think.
To do this we likely have to integrate AI. Patients usually aren't trained in oncology. And our medical oncologists are burdened with a substantial workload already.
One thing I would add to the profile is:
Labs: DEXA, PSA, testosterone, DHT, SHBG, etc.
I upload 15+ documents to AIs for background. One of them is a rough profile document:
Diagnosed in 2018 with Gleason 4+5 T3b/T4 N1M0 SVI, Positive margins, bladder wall invasion.
I am mHSPC GG5 no DNA mutations and I do not have metabolic syndrome. My glucose and insulin levels are low/moderate. I am lean and muscular. Very little bodyfat. All analysis should assume that. I used to have mets but they were eradicated with SBRT in 2023.
6/23/2025 PSA 0.17. Testosterone 232 ng/dl – PSA is overexpressed by androgens. PSADT = N/A (same today as it was when I started aBAT 4 years ago). PSAV = 0.0 ng/mL/Month
Signatera ctDNA MRD 0.0
My goals include: eradication of CSCs, control of mets and eradiation of micro-mets, maintaining HSPC status (avoid CRPC), reduction of MRD (Signatera), athletics.
Molecular profiling of my primary tumor revealed two somatic loss-of-function mutations in the KMT2D gene—a stop-gain variant and a frameshift mutation. These alterations likely compromise KMT2D’s normal function in maintaining genomic stability through epigenetic regulation, rather than directly affecting DNA repair mechanisms. Consequently, tumors deficient in KMT2D may be particularly sensitive to PARP inhibitors, such as olaparib, due to compensatory vulnerabilities within their DNA damage response OSNs. However, the implications for bipolar androgen therapy (BAT) are less clear, since BAT primarily modulates androgen receptor signaling and does not directly intersect with the OSNs affected by KMT2D alterations.
Feature KMT2D Mutations BRCA1/2 or ATM Mutations
Primary Role Epigenetic regulator (histone H3K4 methylation) DNA damage repair (homologous recombination repair, HRR)
Molecular Impact Disrupts enhancer regions, impairing transcriptional regulation of oncogenic OSNs (e.g., PI3K/Akt, EMT) Disrupt DNA repair, leading to genomic instability and accumulation of driver mutations
DNA Repair Link Indirect: Loss increases ROS via FOXO3 suppression, sensitizing cells to DNA damage Direct: Loss of HRR causes synthetic lethality with PARP inhibitors
Therapeutic Targets PARP inhibitors (via ROS-mediated synthetic lethality), PI3K/Akt inhibitors PARP inhibitors (FDA-approved for BRCA1/2), platinum chemotherapy
I have the following SNPs profile:
DNA Variant Table (all of these variants were on the chip)
Important to realize that these are SNPs, not somatic or germline variants.
Gene/Marker Variant/rsID Genotype/Status Estimated Detection Accuracy Mechanistic Theory Evidence Weighting Clinical Significance BAT Relevance
BRCA1 rs189382442 & rs552911643 T/T (biallelic) 25-50% BRCA1 inactivation → HRD → impaired DSB repair → synthetic lethality with BAT + PARPi (A). A
(NCT03522064) Loss-of-function mutations lead to homologous recombination deficiency (HRD), rendering tumors more vulnerable to DNA damage. High sensitivity to BAT-induced DNA damage; enhances synergy with PARP inhibitors.
BRCA2 rs276174802 Loss-of-function 30-60% BRCA2 loss → HRD → BAT-induced replication stress + PARPi → cell death (A). A
(NCT03522064) Biallelic inactivation results in HRD, increasing replication stress and double-strand breaks (DSBs) upon treatment. HRD tumors are highly sensitive to BAT, and this state synergizes with PARP inhibitors (e.g., olaparib).
TP53 rs78378222 (and others, e.g., rs1800372) T/T (pathogenic) 80%-95%(multiple variants detected) TP53 loss → defective DNA repair → BAT amplifies DSBs; PARPi prevents repair (B/C). B/C (COMBAT Trial) Impaired DNA repair that may amplify BAT-induced DNA damage, though associated with a more aggressive cancer phenotype. Enhances the therapeutic window for BAT—especially when combined with PARP inhibitors—by further compromising DNA repair.
AR rs201097725 C/C 95%-98% Enhanced AR signaling under SPA → BAT-induced growth arrest (B/C). B/C (TRANSFORMER Trial) Variants may enhance AR signaling under supraphysiologic androgen (SPA) pulses, potentially increasing susceptibility to growth arrest induced by BAT. High AR dependency generally predicts a good BAT response, though resistance may emerge through AR downregulation.
ATM rs1800056 T/T (homozygous) 40%-60% ATM kinase inactivation → HRD-like state → BAT + PARPi exploit replication stress (B/C). C (Preclinical) A truncating mutation (p.Arg35Ter) leading to ATM loss-of-function, classified as pathogenic per ClinVar. A strong predictor of synergy when combining BAT with PARP inhibitors due to impaired double-strand break repair.
ATM rs587779826 T/T (homozygous) 40%-60% ATM kinase inactivation → HRD-like state → BAT + PARPi exploit replication stress (B/C). C (Preclinical) Likely pathogenic missense variant disrupting ATM kinase activity, also compromising the DNA repair process. Enhances synthetic lethality when BAT-induced DNA damage is paired with PARP inhibition.
ML met indicator: low risk
ML HSPC->CRPC indicator: very low risk