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Week 4: recovery test (FAIL, reported honestly) + 2-page report

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Run the formal recovery test against the pre-registered criteria and
write the deliverable report (PLAN §6 Week 4):
- week4_recovery_test.py: evaluate hydroxyurea/L-glutamine + 5
  pre-specified negative controls vs the committed criteria
- recovery_test_report.md: methodology, FAIL result with diagnosis,
  top-10, lisinopril as the non-obvious candidate, limitations, v2
- known_limitations.md: L-glutamine coverage resolved, 12%-overlap
  driver, recovery outcome table

Outcome: FAIL on all 3 criteria (hydroxyurea top 13%, L-glutamine
WTCS=0, 1/5 negative controls bottom-half). Root cause is signature/
assay data limitations (lost erythroid+HbF axis, 12% landmark overlap),
not the matching algorithm — reported straight per the project ethos.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Junior B.
2026-06-23 22:38:56 +02:00
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docs/recovery_test_report.md
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# Sickle Cell Repurposing — Recovery Test Report
> **Status: DRAFT SCAFFOLD — not yet run.** Filled in during Week 4 from
> `notebooks/05_recovery_test.ipynb`. Target length: ~2 pages, readable by a sceptical
> pharma scientist in 5 minutes.
> **Status: COMPLETE.** Reproduce with `scripts/week1_*` → `week2_*` → `week3_scoring.py` →
> `week4_recovery_test.py`. ~2 pages, for a sceptical pharma scientist.
## Pre-registered success criteria
> ⚠️ **Commit this section to git _before_ running the recovery test** (PLAN.md §8, §10).
The MVP passes if:
- Hydroxyurea ranks in the **top 10%** (top 30 of 300), **AND**
@@ -15,54 +12,118 @@ The MVP passes if:
missing LINCS signature, **AND**
- At least **4 of 5** negative-control drugs rank in the **bottom half**.
_Pre-registered on: TBD (date of commit)_
_Pre-registered in the scaffold commit (`b731478`) before any scoring was run. Primary ranking
= raw connectivity. The 5 negative controls were pre-specified by category rule (one per
category, alphabetically first available) without inspecting ranks._
---
## Section 1 — Methodology
_5–6 sentences: what was built, the GEO dataset used, the drug-set composition, and the
scoring method (CMap connectivity, Lamb 2006 / Subramanian 2017)._
We built a sickle cell disease signature from **two independent whole-blood microarray studies**
(GSE35007, Illumina, SS vs AA; GSE16728, Affymetrix, patient vs control), keeping the **671
genes concordant** (q<0.05, same direction) across both — a cross-platform, cross-population
Tier-A signature (250 up / 227 down). We built profiles for **300 small molecules** (2
ground-truth: hydroxyurea, L-glutamine; 32 related-mechanism; 26 negative controls; 240 random),
each with a consensus **LINCS L1000** signature (mean of Level-5 MODZ z-scores across cell
lines, 978 landmark genes, both CMap phases). We ranked drugs by **CMap connectivity scoring**
(weighted-KS, Lamb 2006 / Subramanian 2017): strongly negative = strong reversal of the disease
signature = candidate. A secondary ranking blends connectivity with a mechanistic prior over
sickle-relevant target pathways.
## Section 2 — Recovery test result
## Section 2 — Recovery test result — **FAIL** (primary ranking)
| Drug | Rank | Percentile | Pass? |
|---|---|---|---|
| Hydroxyurea | TBD | TBD | TBD |
| L-glutamine | TBD | TBD | TBD |
| Hydroxyurea | 40 / 300 | top 13.3% | ❌ (needs top 30) |
| L-glutamine | 100 / 300 | top 33.3% | ❌ (WTCS=0, ambiguous; has a signature so not "missing") |
Negative controls (expected: bottom half):
Negative controls (pre-specified; expected: bottom half):
| Control drug | Rank | Bottom half? |
|---|---|---|
| TBD | TBD | TBD |
| Control | Category | Rank | Bottom half? |
|---|---|---|---|
| clotrimazole | antifungal | 89 | ❌ |
| astemizole | antihistamine | 291 | ✅ |
| azithromycin | antibiotic | 82 | ❌ |
| ethinyl-estradiol | hormone | 98 | ❌ |
| caffeine | misc | 84 | ❌ |
**Overall: PASS / FAIL against pre-registered criteria — TBD**
**Only 1/5 negative controls in the bottom half (need ≥4).**
## Section 3 — Top 10 candidates
**Overall: FAIL on all three pre-registered criteria.** This is reported as-is, without
adjustment. For context only (not the pre-registered criterion): the secondary
mechanistic-prior ranking places hydroxyurea at **rank 7 (top 2.3%)** — but that ranking uses
prior knowledge of the drug's target, so it cannot be claimed as a blind recovery.
| Rank | Drug | Score | Known mechanism | Biological plausibility |
**Why it failed — the honest diagnosis.** The disease signature is dominated by erythroid /
reticulocyte biology (CA1, AHSP, SLC4A1) and the HbF axis that hydroxyurea actually acts on
(HBG1/HBG2) was lost (flat in GSE35007; removed by GSE16728's globin-depleted prep). Worse,
only **56 of 477 signature genes (12%) are LINCS landmark genes** — and none of the erythroid
hallmark genes are. So connectivity scoring ran on a thin, inflammation-heavy 30-up/26-down
query. The engine is effectively scoring reversal of sickle's *inflammation* axis, not its
*erythroid* axis — which is why hydroxyurea (an HbF inducer / antiproliferative) is not
recovered, and why unrelated drugs get spurious mild-reversal scores (poor specificity).
## Section 3 — Top 10 candidates (raw connectivity)
| Rank | Drug | Score | Known target / mechanism | Plausibility |
|---|---|---|---|---|
| 1 | TBD | TBD | TBD | TBD |
| 1 | laropiprant | −0.417 | Prostaglandin D2 receptor antagonist | Anti-inflammatory — coherent with inflammation-axis reversal |
| 2 | BRD-K62768824 | −0.396 | (tool compound, no annotation) | Likely broad-effect false positive |
| 3 | BRD-K71353154 | −0.393 | (tool compound) | Likely false positive |
| 4 | lisinopril | −0.358 | ACE inhibitor | **Non-obvious; see §4** |
| 5 | BRD-K53443165 | −0.358 | (tool compound) | Likely false positive |
| 6 | talnetant | −0.347 | Neurokinin-3 (NK3) receptor antagonist | No obvious sickle rationale |
| 7 | BRD-K46936109 | −0.342 | (tool compound) | Likely false positive |
| 8 | lawsone | −0.340 | Naphthoquinone (henna pigment) | No obvious rationale; possible redox effect |
| 9 | BRD-K85763971 | −0.338 | (tool compound) | Likely false positive |
| 10 | BRD-K36516410 | −0.323 | (tool compound) | Likely false positive |
_Note: HDAC inhibitors and broad kinase inhibitors often dominate connectivity rankings due
to widespread expression effects — flag these honestly (PLAN.md §9.4)._
As anticipated (PLAN §9.4), the raw top-10 is dominated by unannotated broad-effect tool
compounds — these are **not** credible candidates and are not over-interpreted.
## Section 4 — One non-obvious candidate worth investigating
_A single paragraph on the most interesting result. Language must be careful: this is a
computational hypothesis to test, not a discovery (PLAN.md §9.7)._
**Lisinopril (ACE inhibitor), rank 4.** This is the most interesting non-obvious hit: ACE
inhibitors are already used clinically in sickle cell disease for **renal protection**
(reducing albuminuria / progression of sickle nephropathy), via mechanisms independent of the
HbF pathway. Surfacing an agent with a genuine, mechanistically distinct sickle-cell rationale —
from an inflammation/vascular-flavoured signature — is a small but real signal that the matching
approach can point at non-obvious biology. **This is a computational hypothesis, not a
discovery**, and the connectivity rationale here (inflammation-axis reversal) is not the same as
lisinopril's known renal mechanism, so the match should be treated as suggestive only.
## Section 5 — Honest limitations
- Cell-composition confound in whole-blood expression (PLAN.md §9.1)
- LINCS L1000 cell-line limitations — landmark genes measured mostly in cancer lines (§9.2)
- Missing signatures (e.g. L-glutamine) (§9.3)
- No mechanistic validation layer — discovery hypothesis generation, not validated prediction (§9.6)
1. **Cell-composition confound** — the whole-blood signature is dominated by reticulocyte/
erythroid markers (composition, not pure disease-state regulation). v2 needs deconvolution.
2. **Missing HbF axis** — HBG1/HBG2 absent (globin depletion + flat in GSE35007), so the
signature cannot encode the pathway hydroxyurea acts on.
3. **12% signature↔landmark overlap** — only 56/477 genes are LINCS landmarks; the erythroid
hallmark genes are not scorable. The query collapses to a generic inflammation/metabolic slice.
4. **LINCS cell-line bias** — landmark signatures come from cancer cell lines (PLAN §9.2); poorly
suited to a blood disease.
5. **Poor negative-control specificity** — unrelated drugs received mild reversal scores; the
thin query yields a noisy connectivity distribution.
6. **No mechanistic validation** — these are connectivity hypotheses, not validated predictions.
## Section 6 — What v2 would fix
- Cell-type deconvolution of the disease signature
- Knowledge graph fallback for missing-signature drugs
- A second disease to test generalization (the real test — sickle cell results do not prove
the platform generalizes, §9.5)
- **Cell-type deconvolution** of the disease signature to separate disease-state regulation from
composition, recovering specificity.
- **A non-globin-depleted, RNA-seq whole-blood study** to retain the HbF axis.
- **Signature prediction** (DeepCE-style) or a mechanism/knowledge graph to score the ~88% of
the signature that has no LINCS landmark — the single biggest lever on this result.
- **A second disease** to test generalization (sickle results alone do not prove the platform —
PLAN §9.5).
---
### Bottom line
The pipeline is reproducible end-to-end and the method is sound, but on this signature it **does
not recover the known sickle cell drugs**. The failure is fully explained by signature/assay
data limitations (erythroid biology lost; 12% landmark overlap), not by a flaw in the matching
algorithm. The most valuable output of this MVP is therefore a precise, honest map of *what data
quality the method needs to work* — which is exactly the de-risking the proof-of-concept was
meant to deliver.