broomva Founder Mode Oncology
Systematic framework for navigating personalized cancer treatment, generalized from Sid Sijbrandij's osteosarcoma case (2022-2026). Transforms the ad-hoc "billionaire with a team" approach into a reproducible methodology using open-source tools and structured decision-making.
The Three-Pillar Framework
Pillar 1: Maximal Diagnostics
Run every available diagnostic modality to build a complete molecular picture. Standard clinical panels miss non-obvious targets.
Minimum diagnostic stack (in priority order):
- Genomics: WGS + WES (tumor/normal paired) — somatic mutations, CNV, structural variants
- Transcriptomics: Bulk RNA-seq + scRNA-seq (tumor + PBMCs) — gene expression, immune landscape, non-obvious targets
- Liquid biopsy: ctDNA (tumor-informed, e.g. Signatera) + methylation-based (e.g. Northstar) — real-time monitoring
- Functional testing: Organoid drug testing + mass response assays — empirical drug sensitivity
- Imaging: Standard (CT/MRI) + novel PET tracers (68Ga-FAP, 68Ga-B7H3) — target validation
- Flow cytometry: B/T cell subsets — immune status tracking
Critical insight: scRNA-seq reveals targets that standard panels miss. In the reference case, scRNA-seq identified FAP overexpression in osteosarcoma — invisible to gene panels and WES — enabling the breakthrough radioligand therapy.
Tissue handling: Always request cryopreserved (flash-frozen) samples alongside FFPE. FFPE destroys RNA quality needed for transcriptomics.
See references/diagnostics-pipeline.md for the complete open-source bioinformatics pipeline.
Pillar 2: Personalized Therapeutic Development
Use diagnostic findings to design patient-specific treatments. Access experimental drugs via FDA expanded access.
Treatment categories (layer compatible modalities):
| Category | Mechanism | Examples |
|---|---|---|
| Checkpoint inhibitors | Remove immune brakes | Dostarlimab (PD-1), Ipilimumab (CTLA-4) |
| Neoantigen vaccines | Train immune recognition | Peptide vaccines (pVACtools), mRNA vaccines |
| Oncolytic viruses | Kill tumor + release antigens | AdaPT-001 (TGF-beta trap) |
| Cell therapies | Direct immune killing | NK cells (SNK-01), CAR-T, MSCs |
| Radioligand therapy | Targeted radiation | 177Lu/225Ac conjugated to tumor-targeting ligand |
| Immune modulators | Amplify response | GM-CSF, Anktiva (IL-15) |
| Targeted therapy | Block specific pathways | XGeva (RANKL), mTOR inhibitors |
Regulatory pathway: FDA Form 3926 (Individual Patient Expanded Access IND) — typically approved within 48 hours. The FDA is faster than hospital IRBs.
See references/treatment-categories.md for detailed treatment logic. See references/regulatory-access.md for expanded access navigation.
Pillar 3: Parallel Treatment
Run compatible therapies simultaneously. Monitor with ctDNA and serial scRNA-seq to measure what works.
Combination logic:
Checkpoint inhibitors → Remove immune brakes (foundation layer)
+ Neoantigen vaccines → Train recognition (synergizes with checkpoint)
+ Oncolytic virus → Kill + release antigens (synergizes with vaccines)
+ Cell therapy → Innate killing (independent mechanism)
+ Radioligand → Targeted kill to specific marker (independent)
+ Immune modulators → Amplify all of the above
Monitoring cadence:
- ctDNA: every 2-4 weeks (real-time response measurement)
- scRNA-seq PBMCs: monthly (immune evolution tracking)
- Imaging (PET/CT/MRI): every 2-3 months
- Flow cytometry: monthly
Success metric: Immune infiltration shift (cold → hot tumor). Reference case: 19% → 89% T cells in tumor microenvironment.
See references/mrd-monitoring.md for liquid biopsy interpretation.
Decision Workflow
1. DIAGNOSE COMPREHENSIVELY
├── Order WGS + WES + RNA-seq + scRNA-seq
├── Establish ctDNA baseline (multiple platforms)
├── Request cryopreserved tissue (not just FFPE)
└── Run functional drug testing (organoids if available)
2. IDENTIFY TARGETS
├── Standard: Known driver mutations → approved targeted therapies
├── Non-obvious: scRNA-seq → overexpressed surface proteins (FAP, B7H3, EphA2)
├── Validate: PET imaging with target-specific tracers (theranostic confirmation)
└── Predict: Neoantigen candidates via pVACseq + MHCflurry
3. DESIGN TREATMENT COMBINATION
├── Foundation: Checkpoint inhibitor (if not contraindicated)
├── Layer: Neoantigen vaccine (peptide or mRNA)
├── Layer: One or more of: oncolytic virus, cell therapy, radioligand
├── Support: Immune modulators, bone protection, etc.
└── Access: FDA Form 3926 for experimental agents
4. MONITOR AND ADAPT
├── ctDNA every 2-4 weeks → detect response or progression early
├── Serial scRNA-seq → track immune landscape evolution
├── Imaging every 2-3 months → structural assessment
└── Adjust: Add/remove therapies based on molecular response
5. MAINTAIN REMISSION
├── Preventive vaccines (mRNA neoantigen, ongoing)
├── Continued monitoring (ctDNA, imaging)
└── Backup: Engineered cell therapies with logic gates (if needed)
Team Structure
| Role | Function | Scaling Alternative |
|---|---|---|
| Care CEO | Orchestrate diagnostics, coordinate institutions | AI agent + case manager |
| Clinical advisory board | Treatment decisions, drug interactions | Tumor board + AI decision support |
| Scientific advisory board | Interpret genomics, design experiments | Bioinformatics platforms |
| Concierge medical service | Logistics, scheduling, access | Patient navigator programs |
Cost Reality
| Approach | Estimated Cost |
|---|---|
| Sid's full approach (2022-2026) | $1M+ |
| Future platform-based personalized oncology | ~$175K (Hershberg projection) |
| Standard pancreatic cancer treatment | $250K+ |
| OpenVaxx DIY mRNA vaccine (materials only) | $4.2K-$13.4K per patient |
| Drug approval (population medicine) | $4.4B |
Structural Biology Layer (AlphaFold + Protein Design)
Structure prediction adds 3D validation on top of the sequence-based pipeline. Four integration points:
1. Neoantigen Vaccine Validation
After pVACseq + MHCflurry rank candidates by sequence, validate top 20-50 with AlphaFold Multimer (peptide + HLA chain). Filter by ipTM >0.5, PAE_interface <10. Re-rank. This catches peptides that score well in 1D but don't physically fit the MHC groove.
2. Radioligand Target Modeling
Retrieve target structure from AlphaFold DB (FAP: AF-Q12884-F1, B7H3: AF-Q5ZPR3-F1). Model ligand binding with Chai/Boltz. Validate that diagnostic (68Ga) and therapeutic (177Lu/225Ac) versions maintain equivalent binding.
3. De Novo Therapeutic Binder Design
When no existing drug fits the target: RFdiffusion (2.8K stars) generates backbone geometries → ProteinMPNN (1.7K stars) designs sequences → ESMFold/AlphaFold2 validates structures. Tier 1 candidates: pLDDT >85, pTM >0.8.
4. Mutation Impact Analysis
For each somatic mutation: predict wildtype vs mutant structures. Surface-altering mutations on expressed proteins → neoantigen candidates. Destabilizing mutations → misfolded protein → immune recognition. ESM-2 embeddings for fast batch screening.
See references/structural-biology.md for the complete pipeline, tool stack, quality thresholds, and key target UniProt IDs.
Key Reference Case Data
- Source: osteosarc.com — 25TB open data (Google Cloud)
- Article: centuryofbio.com/p/sid — "Going Founder Mode on Cancer" by Elliot Hershberg
- Venture fund: evenone.ventures — scaling personalized oncology
- Research repo:
~/broomva/research/founder-mode-cancer/— complete local analysis
References
- Diagnostics Pipeline — Open-source bioinformatics toolchain (BWA → GATK → ASCAT → Scanpy → pVACtools)
- Treatment Categories — Detailed treatment modalities, mechanisms, and combination rationale
- Regulatory Access — FDA expanded access, IRB navigation, tissue access strategies
- MRD Monitoring — Liquid biopsy platforms, interpretation, and cross-platform comparison
- Structural Biology — AlphaFold, RFdiffusion, ProteinMPNN for neoantigen validation and de novo binder design
- Open-Source Tools — GitHub repos, neoantigen vaccine pipelines, analysis tools