Simulated co-mutation profiles for rare-cancer drug-target prioritisation.

Step 1 / 5

Real cBioPortal pulls

DMG: pooled pediatric cohorts from Gröbner (Nature 2018, n=53) and Petralia (Cell 2020, n=7), giving 60 K27M samples across both the H3.3 and H3.1 histone variants. An earlier derivation queried the wrong HIST1H3B symbol and so contained no H3.1 cases at all; corrected 2026-07-29. All public cBioPortal datasets.

Step 2 / 5

Gaussian copula joint

Per-gene rates estimated from the anchor, then coupled through a latent Gaussian on the shrinkage-stabilised correlation matrix, so the cohort preserves the observed co-mutation structure rather than assuming independence.

Step 3 / 5

Biology-informed shrinkage

At n=60 across 13 genes the empirical correlation matrix is full rank, but it is not trustworthy: several genes carry fewer than ten cases, so their pairwise co-occurrence counts are small and the correlation estimates are high-variance. Ledoit-Wolf shrinkage trades a little bias for a large reduction in that variance, at an intensity of 0.577 on the current anchor. Standard practice shrinks toward the identity, which zeroes out canonical biology, so we shrink toward a sparse literature-derived target of established co-mutation relationships instead. One consequence we state rather than hide: mapping binary correlations onto the latent Gaussian scale does not preserve positive-definiteness, so the latent matrix has to be repaired before it can be sampled from. We use Higham's nearest correlation matrix, which finds the closest valid matrix in Frobenius norm and so repairs the structure that broke rather than attenuating every correlation uniformly. An earlier version blended every off-diagonal correlation 35 percent toward independence, which cut the strongest correlation in the matrix from 0.81 to 0.62; replacing it improved the mean pairwise log-OR residual from 0.955 to 0.842, where lower is closer to the anchor. The fine joint is still regularised, by the shrinkage and by that repair, so the marginals remain the layer to trust.

Step 4 / 5

Co-occurrence audit + bootstrap

The DISCOVER-inspired co-occurrence audit applied to both the anchor and the simulated draws, with the 45 degree agreement plot shipped as Figure 1. Bootstrap CIs computed by resampling the real anchor n with replacement 1,000 times and recomputing each gene frequency on every resample. Intervals are honest about the underlying information.

Step 5 / 5

PubMed retrieval · real CellOracle · DepMap and LINCS proxied

Per subgroup: PubMed retrieval over title-and-abstract embeddings via pgvector, plus a CellOracle in-silico knockout over a DMG base regulatory network built from four real DMG scATAC-seq samples and a 40,000-cell malignant DMG reference. That layer is real model output, precomputed and served as an artifact, covering the 47 transcription-factor regulators that returned signal plus ACVR1 through an explicitly labelled bridge to its downstream TFs. It abstains for any other target rather than substituting a heuristic. The readout is an uncalibrated ordering signal, not a probability, and it is draft, not expert-reviewed. The DepMap dependency and LINCS reversal layers run as labelled heuristic proxies and are declared as such in the report. Every report claim cited at source. Honesty Critic refuses sentences without retrieved evidence.