GPTomics/bio-copy-number-subclonal-copy-number

Version Compatibility

Reference examples tested with: R 4.3+ with Battenberg 2.2.10+ and TitanCNA 1.40+, MEDICC2 1.0+, Python 3.10+; impute2/Beagle phasing reference panels.

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

• R: packageVersion('Battenberg') / 'TitanCNA') then ?function
• CLI: medicc2 --help
• Battenberg is GitHub-only (Wedge-lab/battenberg) and needs a 1000 Genomes impute/phasing reference and allele-counter; confirm reference data is installed

Battenberg and TITAN both consume allele-specific data (logR + BAF at heterozygous SNPs); they cannot run on relative copy ratio alone.

Subclonal Copy Number and Tumor Evolution

"This copy number is non-integer — is it noise, or are there subclones" -> A tumor is a mixture of cell populations. When a copy-number change is present in only some cancer cells, bulk sequencing averages it into a non-integer state. A long non-integer segment is not noise — it is a subclonal copy-number alteration, and resolving it reveals the tumor's clonal architecture.

• R: Battenberg (phased clonal + subclonal CN), TitanCNA (HMM mixture of cell populations)
• CLI: medicc2 (whole-genome-doubling-aware copy-number phylogenies)
• Input: allele-specific data — see allele-specific-copy-number for the clonal layer

Clonal vs Subclonal — What the Tools Output

| Concept | Meaning | |---------|---------| | Clonal CNA | Present in all cancer cells; one copy-number state per segment | | Subclonal CNA | Present in a fraction of cancer cells; the segment needs two states plus a fraction | | Cancer cell fraction (CCF) | Fraction of cancer cells carrying the event | | Mirrored subclonal allelic imbalance | Different subclones lose opposite haplotypes of the same region |

Battenberg fits a clonal allele-specific profile (ASCAT internally), then where a segment fits poorly as a single integer state, it models it as a mixture of two states with a subclonal fraction. TITAN uses an HMM whose states span multiple clonal clusters, jointly estimating per-cluster cellular prevalence. Both need haplotype phasing — subclonal allelic imbalance is only resolvable when SNPs are phased.

Tool Selection

| Tool | Model | Best for | Fails when | |------|-------|----------|------------| | Battenberg | Phased clonal fit + per-segment subclonal mixture | WGS, subclonal CN to ~3% of cells, clonal-evolution studies | Low depth/purity; heavy compute; needs phasing reference | | TITAN | HMM mixture across clonal clusters | WGS/WES, joint CN+LOH+subclonal prevalence, few clusters | Many subclones; cluster number must be chosen and swept | | MEDICC2 | WGD-aware minimum-event copy-number phylogeny | Multi-sample / multi-region evolution | Single sample (no tree to build) | | ASCAT/FACETS | Clonal allele-specific only | When subclonal resolution is not needed | Treats subclonal segments as noisy clonal — see allele-specific-copy-number |

Whole-Genome Doubling — Detection and Timing

Whole-genome doubling (WGD) is a discrete, common (~30% of advanced cancers) evolutionary event, and it must be called explicitly because it changes how every copy number is read.

• Detection: A tumor has undergone WGD if more than ~50% of the autosomal genome has a major (more frequent) allele copy number >= 2. WGD tumors have median ploidy ~3.3 versus ~2.1 for non-WGD.
• Relative vs absolute: Depth gives relative copy number; WGD calling needs absolute allele-specific copy number (BAF anchors ploidy). A depth-only profile cannot distinguish a WGD genome from a non-WGD genome — this is the identifiability problem of allele-specific-copy-number in another guise.
• Timing: WGD is timeable relative to point mutations. Mutations that arose before WGD are carried at multiple copies (mutation copy number ~2); mutations after WGD sit at one copy. This dates WGD within the tumor's mutational history.

Calling Subclonal CN with Battenberg

Goal: Fit clonal and subclonal allele-specific copy number genome-wide.

Approach: Generate phased allele counts against a 1000 Genomes reference, run the Battenberg pipeline; segments that fit poorly as one integer state are split into a two-state subclonal mixture with a cellular fraction.

library(Battenberg)

# Battenberg orchestrates allele counting, phasing, ASCAT clonal fit, and the
# subclonal mixture step. Reference data (1000G impute panel) must be installed.
battenberg(
    samplename          = 'tumour_id',
    normalname          = 'normal_id',
    sample_data_file    = 'tumour.bam',
    normal_data_file    = 'normal.bam',
    ismale              = TRUE,
    imputeinfofile      = 'impute_info.txt',
    g1000prefix         = '1000G_loci/1000genomesloci2012_chr',     # SNP loci data
    g1000allelesprefix  = '1000G_alleles/1000genomesAlleles2012_chr', # SNP alleles (WGS)
    problemloci         = 'probloci.txt',
    gccorrectprefix     = 'GC_correction_hg38_chr',
    repliccorrectprefix = 'RT_correction_hg38_chr',
    genomebuild         = 'hg38',                                   # default is hg19
    nthreads            = 8)
# Output *_subclones.txt: per segment, nMaj1/nMin1 (state 1) + frac1, and nMaj2/nMin2 +
# frac2 when the segment is subclonal (two states).

Calling Subclonal CN with TITAN

Goal: Jointly infer copy number, LOH, and the cellular prevalence of clonal clusters.

Approach: TITAN needs both allele counts (het SNPs) and corrected read depth. Load the allele counts; correct tumour/normal read depth for GC and mappability bias; overlay the resulting logR onto the het positions and log-transform; filter; then run the EM and sweep the cluster number — model selection picks the best.

library(TitanCNA)

# Allele counts at het SNPs.
data <- loadAlleleCounts('tumour.allelicCounts.tsv', genomeStyle = 'UCSC')

# Read-depth correction is mandatory: correctReadDepth needs tumour + normal coverage
# WIGs and GC + mappability WIGs. genomeStyle MUST match loadAlleleCounts above
# (default 'NCBI' vs 'UCSC') or getPositionOverlap matches no chromosomes and logR is NA.
cnData <- correctReadDepth('tumour.wig', 'normal.wig', 'gc.wig', 'map.wig',
                           genomeStyle = 'UCSC')
data$logR <- log(2 ^ getPositionOverlap(data$chr, data$posn, cnData))
data <- filterData(data, 1:24, minDepth = 10, maxDepth = 200, map = NULL)

params <- loadDefaultParameters(copyNumber = 8, numberClonalClusters = 2,
                                symmetric = TRUE, data = data)
conv <- runEMclonalCN(data, params, maxiter = 20, txnExpLen = 1e15)
results <- viterbiClonalCN(data, conv)
# Sweep numberClonalClusters (1..5) and compare model fit; the S_Dbw validity index
# or the model log-likelihood selects the cluster number.

Failure Modes

Subclonal call from insufficient depth or purity

Trigger: Calling subclonal CN on shallow WGS or a low-purity tumor.

Mechanism: A subclonal segment's signal is the clonal deviation scaled by the subclone's cell fraction — already small, and below the noise floor at low depth/purity.

Symptom: Many "subclonal" segments with implausibly low fractions; calls not reproducible across reruns or regions.

Fix: Battenberg's ~3%-of-cells sensitivity assumes adequate WGS depth and purity. For low-depth or low-purity samples, treat only clonal CN as reliable and report subclonal calls as exploratory.

Mirrored subclonal allelic imbalance misread

Trigger: A region where different subclones lost opposite haplotypes.

Mechanism: Bulk BAF averages the two opposite losses toward 0.5, so the region can look balanced (clonal, no LOH) when it is in fact subclonally rearranged on both haplotypes.

Symptom: A segment called clonal-balanced that conflicts with multi-region or single-cell data; BAF near 0.5 with an odd logR.

Fix: Phasing (Battenberg) is required to detect mirrored subclonal allelic imbalance. Multi-region or single-cell data resolves it definitively; a single bulk sample can miss it.

WGD not called — every copy number off by a factor

Trigger: Interpreting copy number without first establishing WGD status.

Mechanism: The likelihood surface has near-equal modes at ploidy P and 2P; missing a WGD halves all copy numbers and mis-times every mutation.

Symptom: Copy numbers and mutation copy numbers inconsistent; "subclonal" gains that are actually clonal post-WGD states.

Fix: Call WGD explicitly (>50% of autosomes at major CN >= 2) from absolute allele-specific copy number. Cross-check ploidy against the odd/even CN fraction and clonal-SNV multiplicity before any subclonal interpretation.

Over-interpreting one subclonal segment as a subclone

Trigger: Declaring a distinct tumor subclone from a single subclonal copy-number segment.

Mechanism: A single segment at an intermediate fraction can arise from segmentation error, a mis-fit clonal state, or genuine subclonality — one segment cannot distinguish these.

Symptom: A "subclone" supported by exactly one segment; clonal architecture claims that do not replicate.

Fix: Require multiple concordant subclonal segments at a consistent cell fraction, ideally corroborated by SNV-based subclonal reconstruction (cancer cell fraction clustering) and multi-region sampling.

Single-region sampling misses spatial subclones

Trigger: Inferring clonal architecture from one biopsy of a spatially heterogeneous tumor.

Mechanism: A subclone confined to an unsampled region is invisible; a single region cannot capture branching evolution.

Symptom: Apparently simple clonal architecture contradicted by a second biopsy.

Fix: For evolution and architecture claims, use multi-region sampling and a phylogeny method (MEDICC2 for copy-number trees). Single-region subclonal calls describe that region only.

Reconciliation

| Pattern | Likely cause | Action | |---------|--------------|--------| | Battenberg subclonal vs TITAN clonal | Different mixture models; cluster number | Sweep TITAN clusters; compare cell fractions | | WGD called by one tool, not another | Integer-multiple ploidy ambiguity | Check odd/even CN fraction and SNV multiplicity | | Many low-fraction subclonal segments | Depth/purity too low | Trust only clonal CN; flag subclonal as exploratory | | Subclonal CN vs SNV-based CCF disagree | CN and SNV subclones need not coincide | Integrate both; they answer different questions |

Operational rule: Report subclonal copy number as confident only when (1) depth and purity support it, (2) WGD status is established from absolute allele-specific CN, (3) multiple concordant segments support a subclone at a consistent fraction, and (4) for evolution claims, multi-region data and a copy-number phylogeny are used. A single subclonal segment is a hypothesis, not a subclone.

Quantitative Thresholds

| Threshold | Value | Source / Rationale | |-----------|-------|--------------------| | Battenberg subclonal sensitivity | ~3% of cells | Nik-Zainal 2012; requires adequate WGS depth/purity | | WGD definition | > 50% of autosomes at major CN >= 2 | Bielski 2018; the operational WGD call | | WGD median ploidy | ~3.3 (WGD) vs ~2.1 (non-WGD) | Bielski 2018 pan-cancer | | Pre-WGD mutation copy number | >= ~1.75 | Pre-doubling mutations carried at multiple copies | | TITAN clonal clusters | sweep 1-5, select by fit | Few clusters resolvable from one bulk sample |

Common Errors

| Error / symptom | Cause | Solution | |-----------------|-------|----------| | Battenberg install/run fails | GitHub-only; missing 1000G reference | Install from GitHub; set up the impute reference | | Non-integer segments treated as noise | Subclonal CNA not modeled | Use Battenberg/TITAN, not a clonal-only caller | | All copy numbers half/double expected | WGD not called | Establish WGD from absolute CN; check SNV multiplicity | | Subclones not reproducible | Low depth/purity, single segment | Require depth, concordant segments, multi-region | | Balanced region conflicts with other data | Mirrored subclonal allelic imbalance | Use phased (Battenberg) or single-cell data | | TITAN cluster number arbitrary | Cluster count not swept | Sweep 1-5; select by model fit |

References

• Nik-Zainal S et al 2012. The life history of 21 breast cancers (Battenberg). Cell 149:994
• Ha G et al 2014. TITAN: inference of copy number architectures in clonal cell populations from tumor whole-genome sequence data. Genome Res 24:1881
• Bielski CM et al 2018. Genome doubling shapes the evolution and prognosis of advanced cancers. Nat Genet 50:1189
• Dewhurst SM et al 2014. Tolerance of whole-genome doubling propagates chromosomal instability. Cancer Discov 4:175
• Kaufmann TL et al 2022. MEDICC2: whole-genome doubling aware copy-number phylogenies for cancer evolution. Genome Biol 23:241

Related Skills

• copy-number/allele-specific-copy-number - Clonal allele-specific CN, purity, ploidy
• copy-number/copy-ratio-segmentation - Segmentation feeding subclonal callers
• copy-number/hrd-scoring - Whole-genome-doubling correction for LST
• copy-number/recurrent-cnv - Copy-number signatures including WGD and chromothripsis
• copy-number/cnv-visualization - Visualizing subclonal segments and BAF
• variant-calling/vcf-basics - SNV calls for cancer cell fraction and WGD timing