Werkwelt

Cure51 deck feedback synthesis — Laura + Amol · April 8, 2026

Combined notes from Laura’s deck-clarity edits and Amol’s investor narrative draft, merged into one tech-VC-facing post.

Laura said

• Reduce text density and open with a clear executive summary before details.
• Lead with the three proof pillars: unique dataset, engaged network, and signed pharma economics.
• Use the “my mom understands it” test for slide clarity before adding AI/process detail.
• Reference slides “Why Cure51?” and “Where new biology emerges: Outliers vs. Controls.” as anchor slides.
• Dutch phrase: Dat is glashelder. (“That is crystal clear.”)

Amol said

Cure51.com monopolizes cancer supersurvivor data to power pharma’s models.

Most of oncology is built on studying why patients die. Cure51 studies why some survive.

Our biobank is ingesting virtually all survivors globally on the deadliest cancers: PDAC, GBM, and Small Cell Lung Cancer — sourced from agreements with 600 apex academic medical centers in 55 countries and characterized at single-cell resolution. This dataset is proprietary, compounding, and structurally irreplicable. Only we have this.

The platform is already generating results. Novel targets have been validated in vitro in pancreatic cancer. A first pharma partnership has been executed on glioblastoma. Small Cell Lung Cancer is now in sequence. $5mm this year from a large EU pharma with upsize to 5x across more targets, plus up to $440mm in tail on that first program.

Backed by Sofinnova and LifeX, we are raising our $20mm Series A to expand indication cohorts, advance validated targets, deepen pharma partnerships, and build a first-in-class therapeutics pipeline rooted in the biology of exceptional survival. The future is in our data.

Synthesis — tight 10-page Series A deck spine

1. Title / one-line thesis: “The irreplicable supersurvivor data platform powering next-generation oncology discovery.”
2. Why now: oncology has failure data but poor survival intelligence; the new winner owns exceptional-survivor data at global scale.
3. The insight: compare outlier survivors vs matched controls (PDAC, GBM, SCLC) at clinical + molecular + single-cell depth.
4. The data moat: ~600 centers / 55 countries, rare-patient access, compounding dataset, structurally hard to recreate.
5. Platform logic: network effects in data ingestion and target confidence; software-like learning loop with biotech monetization.
6. Proof: PDAC targets + validation, GBM pharma program live, SCLC in sequence; initial economics established.
7. Business model: pharma partnerships + internal pipeline + platform expansion.
8. Why we win: first-mover position in rare, fragmented biological assets; relationship moat compounds via execution.
9. Series A use of funds: expand cohorts, advance targets, deepen BD, build therapeutics operating stack.
10. Big outcome: category leader for exceptional-survival biology with durable data advantage.

Slide-writing rules from synthesis

• Keep sequence strict: problem → insight → moat → platform logic → proof → business model → scale story.
• Do not lead with dense mechanism diagrams, a standalone AI slide, giant workflow art, or acronym-heavy opening slides.
• Keep the monopoly logic understandable before scientific depth.

Data requests to harden investor version

1. Exact signed centers and active countries (current count).
2. Exact sample counts by PDAC / GBM / SCLC.
3. Precise scope/wording of the first pharma deal.
4. Targets identified vs targets validated to date.
5. Exact operational definition of “virtually all survivors globally.”
6. Team-slide facts: founder story, scientific leadership, and BD credibility.

Dutch phrase: Dit wordt een scherpe deck. (“This will become a sharp deck.”)