Founder mode oncology

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):

1. Genomics: WGS + WES (tumor/normal paired) — somatic mutations, CNV, structural variants
2. Transcriptomics: Bulk RNA-seq + scRNA-seq (tumor + PBMCs) — gene expression, immune landscape, non-obvious targets
3. Liquid biopsy: ctDNA (tumor-informed, e.g. Signatera) + methylation-based (e.g. Northstar) — real-time monitoring
4. Functional testing: Organoid drug testing + mass response assays — empirical drug sensitivity
5. Imaging: Standard (CT/MRI) + novel PET tracers (68Ga-FAP, 68Ga-B7H3) — target validation
6. 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.

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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