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;
    classDef demote fill:#d4a5a5,stroke:#c06c6c,color:#000;

    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| CH{"CH-only\ncheck"}
    CH -->|"Non-CH variants > 0"| POSS_POS["Possible ctDNA+"]:::ppos
    CH -->|"ALL variants are CH"| DEMOTED["Demoted → Possible ctDNA−"]:::demote
    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

🧬 Clonal Hematopoiesis (CH) Hotspot Filtering

Clonal hematopoiesis of indeterminate potential (CHIP) is a common age-related phenomenon where hematopoietic stem cells acquire somatic mutations — particularly in genes like DNMT3A, TET2, and ASXL1. These mutations are detectable in cfDNA but originate from the blood lineage, not from a solid tumor.

Without filtering, CH mutations contaminate the labeling pipeline: a sample whose only somatic evidence is a DNMT3A R882H hotspot would be erroneously labeled Possible ctDNA+, even though no tumor-derived DNA is present.

How CH Filtering Works

When --ch-hotspot-maf is provided, the pipeline applies a three-step process:

Step 1: Load CH Hotspot Reference

The CH MAF file (TSV) contains known clonal hematopoiesis hotspot variants. Each row is converted to a (Chromosome, Start_Position, Reference_Allele, Tumor_Seq_Allele2) lookup key:

# Example CH hotspot MAF (tab-separated):
Hugo_Symbol  Chromosome  Start_Position  Reference_Allele  Tumor_Seq_Allele2
DNMT3A       2           25234373        G                 A
TET2         4           106164765       C                 T
ASXL1        20          32434638        G                 T

Step 2: Tag & Count

During SNV summary computation, each somatic variant is checked against the CH hotspot set. Two new counters are produced per sample:

Step 3: CH-Only Demotion

After the initial label assignment, any sample meeting all four of these conditions is demoted from Possible ctDNA+ back to Possible ctDNA−:

IF label == "Possible ctDNA+"
   AND n_non_ch_variants == 0        (ALL variants are CH)
   AND has_impact_match == False     (no IMPACT tissue confirmation)
   AND has_sv == False               (no structural variants)
   AND has_cna == False              (no copy number alterations)
THEN → demote to "Possible ctDNA−"

kreview run \
  --cancer-samplesheet ... \
  --ch-hotspot-maf /path/to/ch_hotspots.maf

📊 Label Distribution

A typical production cohort produces a distribution like:

📈 Continuous VAF Statistics

In addition to discrete labels, kreview computes continuous VAF statistics per sample and stores them in labels.parquet:

These columns are used for diagnostic auditing (e.g. verifying that True ctDNA+ samples have higher VAF than Possible ctDNA+) and are available for future regression modeling.