# Prompt — Add HPV (head & neck) as a second dataset, with a top-level cancer selector

Add the TCGA HNSC (head & neck) cohort and an HPV-detection objective, behind a new top-level "Cancer / problem" selector. The DSL, engine, airgap, parameters, live view, population, program graph, and result/verdict are SHARED and unchanged — only the *dataset-specific config* swaps (objectives, biology panel, reference sets, diagnostics, post-hoc targets). Read the named code first; don't assume. Do this in two stages.

GUIDING PRINCIPLE: the engine is single. Everything that changes per cancer is config around it.

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## STAGE 1 — Backend: the HNSC dataset + HPV objective
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### 1a. Data pipeline (the main lift)
- FIRST verify the data source: confirm the cBioPortal TCGA HNSC study id and that its clinical data carries **HPV status** (HPV+/HPV−). Mirror the COADREAD provenance approach documented in CLAUDE.md (LFS-resolved media URL). State the study id + HPV field you used.
- Build a second processed cohort alongside the colorectal one: HNSC `expression.parquet` + `clinical`/labels including an **`hpv_status`** label (HPV+ / HPV−), restricted to samples with a called HPV status and complete expression. Put schema constants (an `HNSC_PROCESSED_DIR`, HNSC filenames, the HPV-status column + how +/− is derived) in `data_pipeline/schema.py` — do not hardcode in build. Ensure the build writes `hpv_status` into the labels so `cohort.labels["hpv_status"]` exists after Load.
- LOADING — IMPORTANT: `dsl.Load` (operators.py ~line 62) only schema-resolves the exact string `"processed"`; ANY other string is treated as a *literal directory path*. So do NOT write `Load("processed_hnsc")`. Instead, EITHER (a) extend `Load` to map named sources (`"processed"`→COADREAD dir, `"processed_hnsc"`→`schema.HNSC_PROCESSED_DIR`), OR (b) pass the HNSC directory Path straight to `Load(hnsc_dir)` — Load's existing `else` branch already loads any directory containing clinical.parquet + expression.parquet, so option (b) needs no change to Load.

### 1b. Objective
- Add a binary objective for HPV: spec `{ target: "hpv", metric: "auroc" }`, reusing the SAME orientation-agnostic binary-AUROC machinery as MSI. Concretely: extend `V2Objective.target` (currently `Literal["msi","tmb","none"]`) to include `"hpv"` with `binary=True`; route its `score_vector` through the AUROC path and `worst_score`→0.5 (identical to MSI); add an `HPV_OBJECTIVE` and dispatch `hpv+auroc` in `engine_v2.fitness.objective_from_spec`. No new operators.

### 1c. Data loading per (dataset, target)
- `_prepare_lab_data` (api/app.py ~line 177) is currently keyed by target and always uses `Load("processed")` (colorectal). Generalise it to key by **(dataset, target)**: add a `dataset` argument; for `dataset="hnsc", target="hpv"`, load the HNSC cohort, anonymise its expression, and set `y` = (hpv_status == HPV+). Keep colorectal (`dataset="coadread"`, msi/tmb/none) exactly as-is.

### 1d. API
- `RunRequest` gains a `dataset: str` field (default "coadread"). `_worker` passes it through to `_prepare_lab_data`. Validate dataset+objective combinations (e.g. hpv only valid for hnsc; msi/tmb only for coadread).
- `REFERENCE_SETS` (api/app.py ~line 106) becomes **per-dataset**: colorectal keeps MMR/immune; HNSC gets HPV reference sets — e.g. `"HPV_markers"` = {CDKN2A, …} and a `"cell_cycle"` / E2F-target set (define from a small curated list in one place, like MMR_GENES). `/evaluate` and any diagnostics select the reference sets for the active dataset.
- Diagnostics: the MSI/TMB-rank diagnostics are colorectal-specific — gate them to that dataset; HNSC can have its own (or none) for now.
- Airgap unchanged: anonymise HNSC expression; HPV status is a label (target), never a gene name. Extend the airgap tests to the HNSC path (no gene-symbol leak).

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## STAGE 2 — Frontend: cancer selector + per-dataset config + full "?" coverage
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### 2a. Top-level Cancer / problem selector
- Add a segmented control (same style as the Objective control) ABOVE the Objective row: **Colorectal (TCGA COADREAD)** | **Head & Neck (TCGA HNSC)**. (No side "what swaps" panel — that was mockup annotation only.)
- Drive everything from a **dataset registry**: each entry = `{ label, dataset_id, objectives, biologyPanel, referenceSets, diagnostics, postHocTargets, axisLabels }`. The page renders the selected entry's config around the unchanged engine. Selecting a cancer sets `dataset` on the run request and swaps the objective options, biology panel, reference sets, and diagnostics.

### 2b. HPV biology panel — include the schematic
- Add a "What is HPV? (the biology)" collapsed-by-default disclosure, mirroring the existing "What is MMR?" panel EXACTLY (same collapsible pattern). Embed the HPV reference SVG from repo root **`hpv_reference.svg`** (already created) verbatim, responsive (width 100%, height auto). Show it for the Head & Neck dataset; show the MMR panel for Colorectal.

### 2c. HPV objective + rich "?" explainer
- Add the "HPV detection" objective card for the HNSC dataset (objective spec `{target:"hpv", metric:"auroc"}`), plus "Unsupervised" (same engine).
- Give it the SAME rich click-to-open ParamHelp modal as the other objectives, in plain English (no assumed knowledge). Content: what it optimises ("a score that separates HPV+ from HPV− tumours"); how it's scored (AUROC: 0.5 = coin-flip, 1.0 = perfect; minority ~15–25% HPV+, so AUROC not accuracy); represented as `{target: hpv, metric: AUROC}`; and the honest note that this is *detection/recovery* of a known viral signature (p16/CDKN2A, cell-cycle), not new causation. Use the same intro/footer pattern as the other objective modals.

### 2d. Full "?" coverage — every objective, parameter, result metric, and chart axis
This is a hard requirement, including for HPV:
- **Objectives:** all have rich "?" explainers (above).
- **Parameters:** shared engine — the existing ParamHelp "?" already cover them; no change.
- **Result metrics** (Held-out, Permutation p, Nodes, Genes) and the **verdict / post-hoc** "?" and inline text must be objective-aware and read correctly for HPV (held-out = AUROC separation of HPV+/−; the post-hoc/verdict framing is the supervised one — "real and useful" vs "not reliable" — NOT the unsupervised two-questions framing).
- **Charts:** the fitness-curve y-axis label + its "?" for the HPV objective = "separation (AUROC)" with the AUROC explanation (same as MSI). Ensure the objective-aware label/tooltip records include the `hpv` case.
- All "?" text in plain English explaining the concept, consistent with the rest of the Lab.

### 2e. Reveal & evaluate
- For HNSC, the reference-set toggle shows the HPV sets (HPV_markers / cell_cycle) instead of MMR/immune; overlap + reveal work the same way. Expected win: revealing the winner shows it recovered CDKN2A/p16 + cell-cycle genes.

## CONSTRAINTS
- Colorectal behaviour, airgap tests, and existing runs unchanged.
- The engine / DSL / parameters / live view / population / program graph are identical across datasets — only config swaps.

## Checkpoint
After Stage 1: a CLI/API HPV run on HNSC completes and separates HPV+/− (held-out AUROC should be high). After Stage 2: the cancer selector swaps to Head & Neck, the "What is HPV?" schematic shows, an HPV run gives a strong held-out AUROC with a plain verdict, and reveal recovers p16/CDKN2A + cell-cycle genes. Airgap tests green; colorectal unchanged.