Reverso/docs/recovery_test_report.md

Sickle Cell Repurposing — Recovery Test Report

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

The MVP passes if:

• Hydroxyurea ranks in the top 10% (top 30 of 300), AND
• L-glutamine ranks in the top 25% (top 75) OR is documented as unscorable due to a missing LINCS signature, AND
• At least 4 of 5 negative-control drugs rank in the bottom half.

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.

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Section 1 — Methodology

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 — FAIL (primary ranking)

Drug	Rank	Percentile	Pass?
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 (pre-specified; expected: bottom half):

Control	Category	Rank	Bottom half?
clotrimazole	antifungal	89	❌
astemizole	antihistamine	291	✅
azithromycin	antibiotic	82	❌
ethinyl-estradiol	hormone	98	❌
caffeine	misc	84	❌

Only 1/5 negative controls in the bottom half (need ≥4).

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.

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	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

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

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

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 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).

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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.