bio-copy-number-focal-amplification-ecdna

name	bio-copy-number-focal-amplification-ecdna
description	Resolve the architecture of focal oncogene amplifications — extrachromosomal DNA (ecDNA), breakage-fusion-bridge (BFB) cycles, homogeneously staining regions (HSR), and linear amplification — from whole-genome sequencing with AmpliconArchitect, the AmpliconSuite pipeline, and AmpliconClassifier. Covers copy-number seed selection, breakpoint-graph reconstruction, balanced-flow optimization, ecDNA classification, and the limits of depth-only amplification calls. Use when a focal amplification needs structural characterization, when distinguishing ecDNA from chromosomal amplification, suspecting ecDNA-driven oncogene amplification or therapy resistance, or selecting copy-number seeds for amplicon reconstruction.
tool_type	cli
primary_tool	AmpliconArchitect

Version Compatibility

Reference examples tested with: AmpliconSuite-pipeline 1.3+, AmpliconArchitect 1.3+, AmpliconClassifier 1.2+, CNVkit 0.9.10+, Python 3.10+, samtools 1.19+.

Before using code patterns, verify installed versions match. If versions differ:

• CLI: AmpliconSuite-pipeline.py --help, amplicon_classifier.py --help
• AmpliconArchitect needs a $AA_DATA_REPO reference download and a Mosek license (free for academic use); confirm both are configured before running

Verify the reference build — AmpliconArchitect was historically hg19-centric; GRCh38 support and data repos exist but the build must be set explicitly and consistently.

Focal Amplification and ecDNA

"This oncogene is amplified — but how, structurally" -> A depth caller reports "high focal amplification" and stops. The biology depends entirely on the architecture: extrachromosomal DNA (ecDNA) behaves utterly differently from a chromosomal homogeneously staining region. Resolving architecture needs the breakpoint graph, not depth.

• CLI: AmpliconSuite-pipeline.py (end-to-end), AmpliconArchitect (graph reconstruction), AmpliconClassifier (architecture call)
• Input: WGS BAM plus copy-number seeds (high-CN focal regions)

Why Architecture Matters — Four Amplicon Classes

Class	Structure	Behavior	Why it matters
ecDNA	Circular, episomal, no centromere	Hundreds of copies; unequal mitotic segregation; rapid CN adaptation	Drives oncogene overexpression, intratumor heterogeneity, therapy resistance; ~14% of cancers
BFB	Chromosomal, fold-back inversions	Stepwise CN gradient toward telomere	Distinct breakpoint signature; bounded amplification
HSR	Linear, integrated chromosomally	Stable inheritance	Chromosomal — segregates evenly, unlike ecDNA
Linear/simple	Tandem or simple amplification	Modest copy gain	Often passenger-scale; lowest oncogenic concern

ecDNA is the highest-stakes call: because it lacks a centromere it segregates unequally, so copy number can surge under selection — a structural basis for resistance. Depth alone cannot distinguish ecDNA from an HSR; both look like a high-amplitude focal gain.

When to Suspect ecDNA

Signal	Interpretation
Very high focal copy number (CN >> 10) at an oncogene	Consistent with ecDNA; not specific
Amplicon spanning multiple non-contiguous genomic segments	Suggestive — ecDNA often fuses distal regions
Breakpoint graph forms a closed cycle with balanced flow	AmpliconArchitect signature of circular structure
Highly variable per-cell copy number (single-cell / FISH)	Hallmark of unequal ecDNA segregation
Co-amplified enhancers distal to the oncogene	ecDNA can hijack regulatory elements

The AmpliconSuite Workflow

AmpliconArchitect does not call amplifications from scratch — it reconstructs the architecture of the regions it is seeded with. The pipeline is: (1) call copy number and select high-CN focal seeds, (2) AmpliconArchitect builds the breakpoint graph and optimizes a balanced flow, (3) AmpliconClassifier labels each amplicon ecDNA / BFB / HSR / linear.

# End-to-end: AmpliconSuite-pipeline runs CNVkit seeding, AmpliconArchitect, and
# AmpliconClassifier in sequence.
AmpliconSuite-pipeline.py \
    -s sample_id \
    -t 8 \
    --bam tumor.bam \
    --ref GRCh38 \
    --run_AA --run_AC

# Output: per-amplicon breakpoint graphs, cycles files, and an AmpliconClassifier
# table assigning each amplicon an architecture class.

Supplying explicit seeds (recommended when a vetted CNV callset exists):

# Seeds: a BED of high-copy focal regions (e.g. from cnvkit-analysis), filtered to
# CN above the seed threshold and to focal (not arm-level) size.
AmpliconSuite-pipeline.py -s sample_id -t 8 --bam tumor.bam --ref GRCh38 \
    --cnv_bed focal_seeds.bed --run_AA --run_AC

Failure Modes

Garbage copy-number seeds produce garbage amplicons

Trigger: Seeding AmpliconArchitect with a noisy CNV callset, a flat-reference tumor-only callset, or arm-level segments.

Mechanism: AmpliconArchitect reconstructs the architecture of exactly the regions it is seeded with; false high-CN seeds generate spurious amplicons, and arm-level seeds dilute the focal signal.

Symptom: Implausible amplicons at no known oncogene; amplicons spanning whole arms; classifier output dominated by low-confidence calls.

Fix: Seed only vetted, focal, high-CN regions. Build the CNV callset from a proper panel of normals (see cnvkit-analysis); filter to focal size and CN above the seed threshold before passing to AA.

Calling ecDNA from depth alone

Trigger: Labeling a high-amplitude focal gain "ecDNA" without breakpoint-graph evidence.

Mechanism: ecDNA and a chromosomal HSR both present as high focal copy number; only the breakpoint graph (a closed cycle with balanced flow) distinguishes them.

Symptom: ecDNA claimed from a CNVkit/GATK profile; no graph, no cycle.

Fix: Require AmpliconArchitect graph reconstruction and an AmpliconClassifier ecDNA call. Where feasible, confirm with orthogonal evidence — FISH, single-cell copy number (variable per-cell CN), or optical mapping.

Genome-build mismatch

Trigger: BAM aligned to one build, --ref or $AA_DATA_REPO set to another.

Mechanism: Coordinates and the bundled annotation diverge; breakpoints and genes are mis-assigned.

Symptom: Amplicons at wrong loci; AA errors on contig names.

Fix: Set --ref to match the BAM's build and confirm the corresponding $AA_DATA_REPO is installed; AA was historically hg19-centric, so GRCh38 must be explicit.

Short-read limits on complex amplicon resolution

Trigger: Expecting a fully resolved amplicon structure from short-read WGS on a highly rearranged amplicon.

Mechanism: Short reads cannot phase long-range structure or traverse repeats; complex amplicons (many junctions, segmental duplications) are only partially reconstructed.

Symptom: Fragmented breakpoint graph; ambiguous or "unknown" classifier calls on a clearly amplified locus.

Fix: Treat short-read amplicon structure as a hypothesis for the most complex cases; confirm with optical mapping (AmpliconReconstructor) or long-read sequencing.

Inadequate coverage or FFPE input

Trigger: Low-coverage WGS or degraded FFPE DNA.

Mechanism: Breakpoint detection needs sufficient discordant/split-read support; FFPE artifacts add false junctions.

Symptom: Missing junctions; noisy graph; unstable classification.

Fix: Use adequate-coverage WGS (AmpliconArchitect is designed for WGS, not panels/WES); apply FFPE-aware filtering; corroborate junctions across read-pair and split-read evidence.

Reconciliation

Pattern	Likely cause	Action
Depth caller: "amplification"; AA: ecDNA	Architecture only visible in the graph	Trust AA for architecture; depth gives amplitude only
AA ecDNA vs FISH negative	Subclonal ecDNA, or false-positive cycle	Check cell fraction; review graph balanced flow
AA "unknown" on a clear amplicon	Complex structure beyond short-read resolution	Escalate to optical mapping / long-read
BFB vs ecDNA ambiguous	Fold-back and circular signatures overlap	Inspect CN gradient (BFB) vs closed cycle (ecDNA)

Operational rule: A depth caller establishes that a region is amplified and how much; it never establishes the architecture. An ecDNA call requires an AmpliconArchitect breakpoint graph with a closed cycle and an AmpliconClassifier ecDNA label, and ideally orthogonal confirmation (FISH, single-cell, optical mapping). Seeds must be vetted focal high-CN regions, not raw or arm-level calls.

Quantitative Thresholds

Threshold	Value	Source / Rationale
ecDNA prevalence	~14% of cancers	Kim et al 2020; baseline expectation
CN seed threshold	CN >= ~4-5 focal	AmpliconSuite seeding; amplicons, not single-copy gains
Seed size	focal (sub-arm), not whole-arm	Arm-level seeds dilute focal amplicon signal
Assay	whole-genome sequencing	AmpliconArchitect needs genome-wide breakpoint coverage
Confirmation for ecDNA	graph cycle + classifier + orthogonal evidence	Depth alone is insufficient

Common Errors

Error / symptom	Cause	Solution
Amplicons at no known oncogene	Noisy or arm-level seeds	Seed vetted focal high-CN regions only
ecDNA "called" from a CNVkit profile	Depth-only claim, no graph	Run AmpliconArchitect + AmpliconClassifier
AA errors on contig names	Build mismatch	Match --ref and $AA_DATA_REPO to the BAM
AA fails to start	Missing Mosek license / data repo	Configure the academic Mosek license and $AA_DATA_REPO
Fragmented graph on a clear amplicon	Short-read limits / low coverage	Confirm with optical mapping or long reads
Classifier output all low-confidence	Coverage too low or FFPE artifacts	Use adequate-coverage WGS; FFPE-aware filtering

References

• Turner KM et al 2017. Extrachromosomal oncogene amplification drives tumour evolution and genetic heterogeneity. Nature 543:122
• Deshpande V et al 2019. Exploring the landscape of focal amplifications in cancer using AmpliconArchitect. Nat Commun 10:392
• Kim H et al 2020. Extrachromosomal DNA is associated with oncogene amplification and poor outcome across multiple cancers. Nat Genet 52:891
• Luebeck J et al 2024. AmpliconSuite: an end-to-end workflow for analyzing focal amplifications in cancer genomes. bioRxiv (AmpliconSuite-pipeline)
• Luebeck J et al 2020. AmpliconReconstructor integrates NGS and optical mapping to resolve focal amplifications. Nat Commun 11:4374

Related Skills

• copy-number/cnvkit-analysis - Generates the copy-number seeds for amplicon reconstruction
• copy-number/recurrent-cnv - Cohort-level recurrent focal amplification (GISTIC2)
• copy-number/allele-specific-copy-number - Absolute copy number of amplified loci
• copy-number/cnv-annotation - Oncogene annotation of amplified regions
• copy-number/subclonal-copy-number - Subclonal dynamics of ecDNA copy number
• long-read-sequencing/structural-variants - Long-read resolution of complex amplicons