Clinical-screen redesign · 31 July 2026

A candidate BRCA LGA screen, built to fail safely.

BRCA1/2 large genomic alterations can be detected by NGS in clinical practice. This research prototype now implements a candidate architecture for real-assay verification: sensitive dosage screening, explicit no-calls and independent confirmation.

Research and assay-development software. Not for patient reporting.

Decision

Feasible in principle. Not clinically validated here.

Clinically established approach

Published laboratories and an FDA-reviewed tumor assay prove that NGS can detect BRCA exon-level dosage changes.

!
LGASieve remains research software

The local dataset lacks assay-wide controls and orthogonal truth, so its synthetic metrics cannot become clinical claims.

The correction is not “this cannot be done.” It is: a clinical screen needs broad normalization, calibrated no-call boundaries, assay-matched truth and a confirmation workflow.

01 / Verification

Recursive simulation exposed the weak spots.

Nine development iterations were followed by a larger locked random stream. The final broad-panel model passed its internal gates, then a deliberately harder stress set revealed the remaining failure modes.

Synthetic · completed cases98.92%

Event sensitivity

3,850 / 3,892 · 95% CI 98.54–99.20%
Synthetic · intention to test96.25%

Event sensitivity

3,850 / 4,000; positive no-calls count as missed
Synthetic · completed normals98.49%

Specificity

1,957 / 1,987 · 13/2,000 no-calls; 99.35% normal completion
Hard stress set56.83%

Intention-to-test sensitivity

Mosaic, single-exon, low-depth and transport-shift cases
Claim boundary

These are internal software-development results from in-silico data. Their 95% intervals are conditional Monte Carlo intervals under the fixed simulator and model—not clinical-population or assay uncertainty. They are not clinical sensitivity, specificity, LoD, PPV or NPV.

02 / Method

The architecture separates screening from diagnosis.

The caller is intentionally sensitive at the first layer and conservative at release. That is the pattern used by successful clinical workflows.

01

Normalize broadly

Model BRCA targets against a large, assay-matched control domain instead of normalizing an affected gene against itself.

02

Correct technical structure

Use robust target centers, latent-factor correction, matched references and target-specific callable masks.

03

Screen with explicit states

Return REFLEX_POSITIVE, NEGATIVE_SCREEN, INCOMPLETE_SCREEN or NO_CALL_QC—never silently convert missing evidence into a negative.

04

Confirm independently

Send candidate events to MLPA, exon-dense aCGH, ddPCR or another validated dosage method before patient reporting.

03 / Published evidence

Clinical precedent is real.

Public validations differ in assay, truth unit and reporting policy. Their performance is evidence of feasibility—not a transferable performance claim for LGASieve.

Values are summarized with their original units. See the full report for caveats, confidence intervals and additional comparators.

04 / Supplied evidence

The new data were rerun—without overstating them.

The broad-panel caller correctly refuses the narrow 50-target input. A constrained BRCA1↔BRCA2 fallback was run only to identify review candidates and to measure how much information remains.

141

Incomplete screens

No signal, but four excluded targets prevent a negative interpretation

6

Reflex-positive signals

Unconfirmed screening candidates; truth unknown

3

QC no-calls

Low-depth evidence bundles

0

Truth-paired samples

No local orthogonal BRCA LGA truth

Truth statusUnknown for all six signals

They are screening candidates, not diagnoses. There was zero sample overlap with the three legacy flags, reinforcing the need for broad controls and orthogonal confirmation.

05 / Route to clinical validation

What must happen next.

  1. 1
    Freeze the assay and caller

    Code, panel version, target mask, thresholds, QC rules and confirmation SOP.

  2. 2
    Restore the normalization domain

    Complete panel-wide counts, actual bait design and matched routine controls.

  3. 3
    Build independent truth cohorts

    Single-exon through whole-gene DEL/DUP positives plus verified negatives.

  4. 4
    Measure transport and precision

    Runs, lots, instruments, operators, sites, depth, input and failure cases.

  5. 5
    Run a new, blinded clinical lockbox

    Use an orthogonally truthed cohort and report intention-to-test and completed-case metrics with confidence intervals; the existing 1000G technical lockbox cannot estimate clinical performance.

Full record

Methods, denominators, limitations and downloadable artifacts.

The technical HTML report contains the complete evidence review, algorithm design, recursive simulation history, stress results, supplied-data rerun and frozen validation plan.

Open full report