ctDNA Labeling Engine

kreview evaluates computational fragmentomic features by measuring how accurately they distinguish tumor-derived circulating tumor DNA (ctDNA) from normal apoptotic background DNA.

To do this, it first establishes a rigid "Ground Truth" label matrix utilizing the CtDNALabeler engine (kreview.labels), relying strictly on MSK-IMPACT orthogonal tissue assays.

🔬 The Biological Assumption

Unlike standard genomics which calls somatic variants directly from the blood, Fragmentomics relies on subtle physical signatures—like fragment sizes, nucleosome imprints, and cleavage end-motifs—measured broadly across the entire epigenome.

Because we are evaluating new experimental fragmentomic models, we need incontrovertible proof that the patient sample actually has ctDNA present. For this, we look at Variant Allele Frequency (VAF):

VAF = \frac{\text{Alternate Allele Depth (t\_alt\_count)}}{\text{Total Depth (t\_ref\_count + t\_alt\_count)}}

If a sample contains somatic Single Nucleotide Variants (SNVs) with a high detectable VAF, or massive Copy Number Alterations (CNAs) / Structural Variants (SVs), then the physical tumor footprint in the blood is high.

🏷️ The 5-Tier Labeling Hierarchy

flowchart TD
    classDef pos fill:#e63946,stroke:#b71c1c,color:#fff;
    classDef ppos fill:#f4a261,stroke:#e76f51,color:#fff;
    classDef pneg fill:#a8dadc,stroke:#457b9d,color:#000;
    classDef healthy fill:#2a9d8f,stroke:#264653,color:#fff;
    classDef insuf fill:#6c757d,stroke:#495057,color:#fff;

    S["ACCESS cfDNA Sample"] --> IMPACT{"IMPACT tissue\nsomatic match?"}
    IMPACT -->|Yes| TRUE["True ctDNA+"]:::pos
    IMPACT -->|No| SV_CNA{"SV or CNA\ndetected?"}
    SV_CNA -->|Yes| TRUE
    SV_CNA -->|No| SNV{"Somatic SNVs\n≥ VAF threshold?"}
    SNV -->|Yes| POSS_POS["Possible ctDNA+"]:::ppos
    SNV -->|No| DEPTH{"Total fragments\n≥ 2,000?"}
    DEPTH -->|Yes| POSS_NEG["Possible ctDNA−"]:::pneg
    DEPTH -->|No| INSUF["Insufficient Data"]:::insuf

    HEALTHY["Healthy Volunteer\n(XS1 / XS2)"] --> HN["Healthy Normal"]:::healthy

kreview assigns every sample one of five labels:

1. True ctDNA+

The gold standard. This label is granted only if one of three conditions is met:

• An SNV mutation detected in the blood cfDNA perfectly matches a somatic mutation detected in the patient's matched solid-tissue MSK-IMPACT biopsy.
• A macroscopic structural variant (SV) is positively called.
• Wide-scale somatic Copy Number Alterations (CNAs) are detected.

2. Possible ctDNA+

The silver standard. The sample lacks a matched MSK-IMPACT tissue biopsy (or the biopsy was negative), but the ACCESS assay still detected somatic SNVs passing the configured stringency threshold:

VAF \ge 0.01 \quad \text{and} \quad n_{variants} \ge 1

3. Possible ctDNA−

Symptomatic cancer patients whose blood cfDNA draws generated zero signal: No SNVs, No SVs, No CNAs. While they have cancer, their systemic shedding rate is too low for traditional orthogonal validation.

4. Healthy Normal

True negative controls. Drawn entirely from the MSK XS1 and XS2 healthy volunteer sequencing runs. Their data establishes the baseline apoptotic fragmentation profile.

5. Insufficient Data

Label Distribution

A typical production cohort produces a distribution like: