# Sickle Cell Repurposing — Recovery Test Report

> **Status: COMPLETE (v1 confirmatory + v1.1 exploratory).** 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. **Primary (confirmatory)
analysis = v1**: 978 landmark genes, weighted connectivity score (WTCS). The 5 negative controls
were pre-specified by category rule without inspecting ranks._

---

## Section 1 — Methodology

A sickle cell disease signature was built from **two whole-blood microarray studies** (GSE35007
Illumina SS-vs-AA; GSE16728 Affymetrix patient-vs-control), keeping the **671 genes concordant**
across both (q<0.05, same direction) → a cross-platform Tier-A signature (250 up / 227 down).
Profiles were built for **300 small molecules** (2 ground-truth; 32 related-mechanism; 26
negative controls; 240 random), each with a **LINCS L1000** consensus signature (mean Level-5
MODZ across cell lines, both CMap phases). Drugs were ranked by **CMap connectivity scoring**
(Kolmogorov-Smirnov, Lamb 2006 / Subramanian 2017): negative = reversal = candidate.

**v1 (pre-registered/confirmatory):** scored on the 978 landmark genes with WTCS.
**v1.1 (post-hoc/exploratory):** after v1 failed, two changes were made to diagnose why — (a)
score on the full **12,328-gene** space (landmark overlap 12% → 85%, bringing the erythroid
markers in); (b) add a **per-drug specificity z-score** (`spec_z`): how many SDs the real
connectivity is below a null of 1,000 random queries of the same size against that drug. Because
these changes followed inspection of the v1 result, **v1.1 is exploratory, not a confirmatory
test of the pre-registered hypothesis.**

## Section 2 — Recovery test result

| Criterion | v1 (confirmatory) | v1.1 (exploratory) |
|---|---|---|
| Hydroxyurea top-10% (≤30) | rank **40** (13.3%) ❌ | rank **18** (6.0%) ✅ |
| L-glutamine top-25% (≤75) | rank 100, WTCS=0 ❌ | rank 213, spec_z=+0.98 ❌ |
| ≥4/5 neg controls bottom-half | 1/5 ❌ | 2/5 ❌ |
| **Overall** | **FAIL** | **FAIL** (but hydroxyurea recovered) |

v1.1 negative controls: clotrimazole 258 ✅, astemizole 211 ✅, azithromycin 142 ❌,
ethinyl-estradiol 114 ❌, caffeine 77 ❌.

**Honest reading.** The **pre-registered test FAILED (v1).** The post-hoc v1.1 changes
**recover hydroxyurea** (rank 40 → 18, passing top-10%) — strong evidence that the v1 failure was
driven by the 978-landmark bottleneck, not the algorithm. But two failures survive into v1.1, and
both are now precisely diagnosed:

1. **L-glutamine does not reverse the signature** (positive connectivity, spec_z=+0.98). This is
   intrinsic to its LINCS data — a metabolite with no reversal signal — not a coverage gap. More
   genes cannot fix it.
2. **The negative-control criterion is arguably invalid for connectivity scoring.** Two
   "negative controls" (norethindrone, ciprofloxacin) rank in the top 3 by spec_z. Connectivity
   measures *expression reversal*, not *therapeutic relatedness* — an antibiotic or contraceptive
   can still down-regulate the inflammation genes that dominate the scorable signature. The test
   design conflates the two.

A note on the calibration: textbook CMap **tau** (percentile vs a reference population) was
implemented but **saturated at ±100** here, because a coherent real query always out-connects
random gene sets — proper tau needs a library of *real* reference signatures, which this MVP
lacks. The continuous `spec_z` is the workable substitute.

## Section 3 — Top 10 candidates (v1.1 spec_z)

| Rank | Drug | spec_z | Inclusion | Read |
|---|---|---|---|---|
| 1 | reserpic-acid | −3.80 | random | reserpine metabolite; non-obvious |
| 2 | norethindrone | −3.78 | **negative control** | false positive (see §2) |
| 3 | ciprofloxacin | −3.61 | **negative control** | false positive |
| 4 | resveratrol | −3.46 | related-mechanism | antioxidant studied in SCD — coherent |
| 5 | BRD-K57490754 | −3.37 | random | tool compound |
| 6 | anastrozole | −3.27 | random | aromatase inhibitor |
| 7–10 | BRD-* / palmitoylethanolamide | ~−3.1 | random | mostly tool compounds |

That two negative controls outrank hydroxyurea is the single most informative result here — see §4.

## Section 4 — One non-obvious result worth investigating

The most useful finding is **not** a candidate drug but the **negative-control failure**:
unrelated drugs (norethindrone, ciprofloxacin) score as strong specific reversers. This is a
real, generalizable lesson — for a signature whose *scorable* portion is generic
inflammation/metabolic genes, connectivity rewards any broad transcriptional perturbation that
touches those genes. The honest implication: **this signature is not specific enough to
discriminate true repurposing candidates from incidental expression reversers.** Of the
plausibly-real hits, **resveratrol (rank 4)** — an antioxidant with prior sickle cell literature
— is the most defensible, but it is a hypothesis, not a discovery.

## Section 5 — Honest limitations

1. **Pre-registered test failed; the pass is post-hoc.** v1.1's hydroxyurea recovery is
   exploratory and must be re-validated on a held-out disease before any claim is made.
2. **Missing HbF axis** — HBG1/HBG2 are absent from LINCS entirely (not just landmarks), so the
   pathway hydroxyurea acts on can never be scored by this method.
3. **Signature specificity** — scorable genes are inflammation/metabolic; negative controls
   reverse them too. Connectivity ≠ therapeutic relatedness.
4. **Cell-composition confound** — the whole-blood signature is reticulocyte-dominated.
5. **LINCS cancer-cell-line bias**, and **no reference-signature library** for proper tau.
6. **No mechanistic validation** — all hits are computational hypotheses.

## Section 6 — What v2 would fix

- **A reference-signature library** to make tau (proper specificity calibration) work — the
  single biggest fix to the negative-control problem, and a direct use of the curated-data moat.
- **Cell-type deconvolution** + a non-globin-depleted RNA-seq study to recover a more specific,
  HbF-containing signature.
- **Signature prediction / mechanism graph** to score genes with no LINCS measurement.
- **A second disease** to test generalization and to honestly re-validate the v1.1 method
  (PLAN §9.5).

---

### Bottom line

The pre-registered recovery test **failed**. Post-hoc diagnosis shows the dominant cause was a
fixable gene-space bottleneck — correcting it **recovers hydroxyurea** — but also surfaces a
deeper, genuine limitation: this whole-blood signature is **not specific enough** for
connectivity scoring to separate real candidates from incidental reversers (negative controls
rank at the top). The MVP's real deliverable is a precise, honest map of *what it takes to make
this method work*: a more specific (deconvolved, HbF-containing) signature and a reference library
for calibration — exactly the curated-data investments the platform thesis is built on.