bio-tumor-fraction-estimation

name	bio-tumor-fraction-estimation
description	Estimates circulating tumor DNA fraction from shallow whole-genome sequencing using ichorCNA. Detects copy number alterations via HMM segmentation and calculates ctDNA percentage. Requires 0.1-1x sWGS coverage. Use when quantifying tumor burden from liquid biopsy or monitoring treatment response.
tool_type	r
primary_tool	ichorCNA

Tumor Fraction Estimation

Estimate ctDNA tumor fraction from shallow whole-genome sequencing.

ichorCNA Overview

ichorCNA (GavinHaLab fork, v0.5.1+) detects copy number alterations and estimates tumor fraction from sWGS (0.1-1x coverage).

Sensitivity: 97-100% detection at >= 3% tumor fraction (2024 validation)

Input Requirements

Requirement	Specification
Data type	sWGS (NOT targeted panel)
Coverage	0.1-1x (0.5x recommended)
Input	BAM files
Output	Tumor fraction, ploidy, CNA segments

Running ichorCNA

library(ichorCNA)

# Step 1: Generate read counts in bins
# Run from command line or use HMMcopy
# readCounter --window 1000000 --quality 20 sample.bam > sample.wig

# Step 2: Run ichorCNA
runIchorCNA(
    WIG = 'sample.wig',
    gcWig = 'gc_hg38_1mb.wig',
    mapWig = 'mappability_hg38_1mb.wig',
    normalPanel = 'pon_median_1mb.rds',
    centromere = 'centromeres_hg38.txt',
    outDir = 'ichor_results/',
    id = 'sample_id',

    # Tumor fraction estimation parameters
    normal = c(0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.99),
    ploidy = c(2, 3),
    maxCN = 5,

    # Subclonality
    estimateScPrevalence = TRUE,
    scStates = c(1, 3),

    # Segmentation
    txnE = 0.9999,
    txnStrength = 10000,

    # Chromosomes
    chrs = paste0('chr', c(1:22, 'X'))
)

Batch Processing

library(ichorCNA)
library(parallel)

process_sample <- function(wig_file, params) {
    sample_id <- basename(wig_file)
    sample_id <- gsub('.wig$', '', sample_id)

    tryCatch({
        runIchorCNA(
            WIG = wig_file,
            gcWig = params$gcWig,
            mapWig = params$mapWig,
            normalPanel = params$normalPanel,
            centromere = params$centromere,
            outDir = params$outDir,
            id = sample_id,
            normal = c(0.5, 0.6, 0.7, 0.8, 0.9  
            ploidy   
            maxCN  
        
        sample  sample_id status  
     error  e 
        sample  sample_id status   error  emessage
    



wig_files  list.files pattern   full.names  
params  
    gcWig  
    mapWig  
    normalPanel  
    centromere  
    outDir  


results  mclapplywig_files process_sample params  params mc.cores  

Parsing Results

parse_ichor_results <- function(results_dir) {
    # Find results files
    param_files <- list.files(results_dir, pattern = '.params.txt$',
                              full.names = TRUE, recursive = TRUE)

    results <- data.frame()

    for (f in param_files) {
        params <- read.table(f, header = TRUE, sep = '\t', stringsAsFactors = FALSE)
        sample_id <- gsub('.params.txt$', '', basename(f))

        results <- rbind(results, data.frame(
            sample = sample_id,
            tumor_fraction    paramsn  
            ploidy  paramsphi
            log_likelihood  paramsloglik
        
    

    results



tf_results  parse_ichor_results
printtf_results

Python Wrapper

import subprocess
import pandas as pd
from pathlib import Path


def run_ichorcna(wig_file, output_dir, gc_wig, map_wig, normal_panel, centromere):
    '''Run ichorCNA from Python.'''
    sample_id = Path(wig_file).stem

    cmd = f'''
    Rscript -e "
    library(ichorCNA)
    runIchorCNA(
        WIG = '{wig_file}',
        gcWig = '{gc_wig}',
        mapWig = '{map_wig}',
        normalPanel = '{normal_panel}',
        centromere = '{centromere}',
        outDir = '{output_dir}',
        id = '{sample_id}',
        normal = c(0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.99),
        ploidy = c(2, 3),
        maxCN = 5
    )
    "
    '''

    subprocess.run(cmd, shell=True, check=True)


def parse_tumor_fraction(params_file):
    '''Parse tumor fraction from ichorCNA output.'''
    df = pd.read_csv(params_file, sep='\t')
    return {
        'tumor_fraction': 1 - df['n'].iloc[0],
        'ploidy': df['phi'].iloc[0],
        'log_likelihood': df['loglik'].iloc[0]
    }

Interpretation

Tumor Fraction	Interpretation
>= 10%	High ctDNA, reliable detection
3-10%	Moderate ctDNA, detectable
< 3%	Low ctDNA, at detection limit
0%	No detectable ctDNA or below LOD

Related Skills

• cfdna-preprocessing - Preprocess BAMs before ichorCNA
• fragment-analysis - Complementary fragmentomics analysis
• ctdna-mutation-detection - Mutation detection from panel data
• copy-number/cnvkit-analysis - CNV concepts