Ascat Cnv For Wgs Tumor Only

Alright, let's dive into the world of ASCAT CNV analysis specifically tailored for Whole Genome Sequencing (WGS) data from tumor-only samples. This is a specialized area within cancer genomics, so buckle up as we unpack the complexities and practical applications.

Understanding ASCAT CNV for Tumor-Only WGS Data

Copy Number Variations (CNVs) are alterations in the DNA of a cell that result in the cell having an abnormal number of copies of one or more sections of the DNA. These variations can include deletions, duplications, and amplifications of specific genomic regions. In the context of cancer, CNVs are often drivers of tumor development and progression, influencing gene expression, cellular signaling, and drug response.

ASCAT (Allele-Specific Copy number Analysis of Tumors) is a widely used algorithm for inferring tumor purity, ploidy, and allele-specific copy number profiles from cancer samples. However, the standard ASCAT algorithm typically requires matched normal samples to accurately distinguish germline variants from somatic copy number alterations. Analyzing tumor-only WGS data presents unique challenges, as we lack a baseline to differentiate inherited variations from those acquired during tumorigenesis. Adapting ASCAT for tumor-only analysis involves specialized techniques and considerations to achieve reliable results.

The Need for Tumor-Only CNV Analysis

While matched normal samples provide the ideal scenario for cancer genomic analysis, they are not always available. Here are some common reasons why tumor-only analysis becomes necessary:

• Retrospective Studies: Biobanks may contain a wealth of tumor samples collected before the routine practice of collecting matched normal samples. Analyzing these archived samples can provide valuable insights into cancer biology and treatment outcomes.
• Cost and Logistics: Obtaining and processing matched normal samples adds to the overall cost and complexity of genomic studies. In large-scale projects, tumor-only analysis can be a more feasible option.
• Specific Tumor Types: In certain tumor types, obtaining a representative normal sample can be difficult or impossible. For example, analyzing circulating tumor DNA (ctDNA) often relies solely on tumor-derived sequences.
• Data Availability: Publicly available datasets may only include tumor data, limiting the scope of analysis to tumor-only methods.

Challenges in Tumor-Only CNV Analysis

Analyzing CNVs in tumor-only samples presents several challenges that must be addressed to ensure accurate and reliable results:

• Distinguishing Germline from Somatic Variants: Without a matched normal sample, it's difficult to differentiate between inherited germline CNVs and somatic CNVs acquired during tumor development. This distinction is crucial for identifying cancer-driving alterations.
• Accurate Estimation of Tumor Purity and Ploidy: Tumor purity (the proportion of tumor cells in the sample) and ploidy (the average number of chromosome sets per cell) are essential parameters for CNV analysis. Estimating these parameters accurately in tumor-only samples is challenging but critical.
• Handling of Germline Heterozygosity: Germline heterozygous single nucleotide polymorphisms (SNPs) are used by ASCAT to infer allele-specific copy numbers. In tumor-only analysis, distinguishing between loss of heterozygosity (LOH) due to somatic events and inherent homozygosity in the germline is difficult.
• Computational Complexity: Adapting ASCAT for tumor-only analysis often requires more sophisticated algorithms and computational approaches, increasing the complexity of the analysis pipeline.

Adapting ASCAT for Tumor-Only WGS Data: A Step-by-Step Guide

Here's a detailed breakdown of how to adapt the ASCAT algorithm for analyzing CNVs in tumor-only WGS data:

1. Data Preprocessing and Alignment:

• Read Alignment: Align the WGS reads to a reference genome (e.g., GRCh38) using a standard aligner like BWA-MEM. Ensure proper handling of read groups and duplicate marking using tools like Picard.
• Base Quality Score Recalibration (BQSR): Perform BQSR using GATK to improve the accuracy of base quality scores, which are crucial for variant calling.
• Coverage Calculation: Calculate the read depth (coverage) across the genome using tools like GATK's DepthOfCoverage or mosdepth. Normalize the coverage to account for GC content bias and mappability issues.

2. Variant Calling and Filtering:

• SNP Calling: Call SNPs using a variant caller like GATK HaplotypeCaller. Apply stringent filtering criteria to remove low-quality variants and potential artifacts.
• Filtering Strategies: Filter variants based on:
  • Quality Score: Remove SNPs with low quality scores (e.g., QUAL < 20).
  • Read Depth: Filter SNPs with insufficient read depth (e.g., DP < 10).
  • Strand Bias: Remove SNPs with significant strand bias (e.g., using Fisher's exact test).
  • Mapping Quality: Filter SNPs with low mapping quality.
• Common Germline Variants: Consider using a panel of normals (PON) approach or a database of common germline variants (e.g., dbSNP) to identify and filter out common germline polymorphisms that are likely not somatic.

3. Generation of ASCAT Input Files:

ASCAT requires two main input files:

• SNP Data: A file containing information on the identified SNPs, including their genomic positions, reference and alternate alleles, and allelic ratios.
• Total Copy Number Data: A file containing the total copy number estimates across the genome, typically derived from the normalized read depth data.

a) SNP Data Generation:

• Allelic Ratio Calculation: Calculate the allelic ratio (B allele frequency, BAF) for each heterozygous SNP. This is the proportion of reads supporting the alternate allele. For tumor-only samples, accurately determining heterozygosity is crucial.
• Accounting for Germline Homozygosity: Without a matched normal, it's difficult to definitively identify germline homozygous SNPs. One strategy is to use population-level allele frequencies from databases like gnomAD. SNPs with very low minor allele frequencies (MAF) are less likely to be heterozygous in the germline.
• Filtering Based on BAF: Filter SNPs with BAF values close to 0 or 1, as these are likely homozygous or subject to allele-specific amplification or deletion.

b) Total Copy Number Data Generation:

• Binning the Genome: Divide the genome into non-overlapping bins of fixed size (e.g., 50kb or 100kb).
• Read Depth Calculation: Calculate the average read depth within each bin.
• Normalization: Normalize the read depth to correct for biases related to GC content, mappability, and replication timing. Several normalization methods are available, including:
  • GC Content Normalization: Correct for the correlation between GC content and read depth.
  • Mappability Normalization: Account for regions of the genome that are difficult to map to.
  • Replication Timing Normalization: Correct for biases related to the timing of DNA replication.
• Segmentation: Segment the normalized read depth data to identify regions of contiguous copy number change. Algorithms like Circular Binary Segmentation (CBS) are commonly used for this purpose.

4. ASCAT Analysis with Modifications for Tumor-Only Data:

• Parameter Tuning: Adapt ASCAT parameters to account for the absence of a matched normal sample. This often involves adjusting the parameters that control the sensitivity of the algorithm to detect copy number changes and the stringency of filtering criteria.
• Ploidy and Purity Estimation: Estimating tumor purity and ploidy is a critical step. Several approaches can be used:
  • Manual Adjustment: Manually adjust the purity and ploidy parameters in ASCAT based on visual inspection of the segmentation results and BAF profiles.
  • External Tools: Use external tools specifically designed for purity and ploidy estimation in tumor-only samples, such as:
    • THetA: A tool that uses allele-specific copy number data to infer tumor heterogeneity and purity.
    • ABSOLUTE: An algorithm that integrates copy number and mutation data to estimate tumor purity and ploidy.
• Iterative Refinement: Perform iterative rounds of ASCAT analysis, adjusting the parameters and filtering criteria based on the results of each iteration. This iterative approach can help to refine the copy number profiles and improve the accuracy of purity and ploidy estimates.
• Accounting for LOH: Loss of heterozygosity (LOH) is a common event in cancer. In tumor-only analysis, it's important to distinguish between LOH due to somatic events and inherent homozygosity in the germline. Examine the BAF profiles in regions of copy number loss to identify potential LOH events.

5. Post-processing and Interpretation:

• CNV Annotation: Annotate the identified CNVs with information on the genes and genomic features that they overlap.
• Functional Analysis: Perform functional analysis to identify the potential impact of the CNVs on gene expression, cellular signaling, and other biological processes.
• Comparison to Existing Data: Compare the CNV profiles to those of other tumors of the same type to identify recurrent alterations and potential therapeutic targets.
• Visualization: Visualize the CNV profiles using genome browsers or custom visualization tools to facilitate interpretation and communication of the results.

Advanced Considerations and Techniques

Beyond the basic steps outlined above, several advanced techniques can further improve the accuracy and reliability of ASCAT analysis in tumor-only WGS data:

• Panel of Normals (PON): Construct a PON from a set of normal samples to identify and filter out common germline variants and systematic biases. While you don't have a matched normal, a PON can help approximate a baseline.
• Machine Learning Approaches: Use machine learning algorithms to classify CNVs as somatic or germline based on features such as allele frequencies, read depth, and genomic context.
• Integrative Analysis: Integrate CNV data with other genomic data types, such as gene expression data and mutation data, to gain a more comprehensive understanding of tumor biology.
• Single-Cell Sequencing: If available, single-cell sequencing data can be used to validate the CNV profiles inferred from bulk WGS data and to identify tumor subclones with distinct copy number alterations.
• Refining Purity Estimates with Mutation Data: Integrating variant allele frequencies (VAFs) of somatic mutations can help refine purity estimates. Higher confidence somatic mutations should exhibit VAFs consistent with the estimated purity and copy number.

Example Workflow using Open-Source Tools

Here's a simplified example of a workflow using open-source tools:

# 1. Alignment (using BWA-MEM)
bwa mem -M -t 8 ref.fa tumor.fastq.gz | samtools view -Sb - | samtools sort -o tumor.sorted.bam

# 2. Duplicate Marking (using Picard)
java -jar picard.jar MarkDuplicates I=tumor.sorted.bam O=tumor.dedup.bam M=metrics.txt

# 3. Base Recalibration (using GATK)
gatk BaseRecalibrator -R ref.fa -I tumor.dedup.bam --known-sites dbsnp.vcf.gz -O recal.table
gatk ApplyBQSR -R ref.fa -I tumor.dedup.bam -bqsr recal.table -O tumor.recal.bam

# 4. Variant Calling (using GATK)
gatk HaplotypeCaller -R ref.fa -I tumor.recal.bam -O tumor.vcf.gz

# 5. Variant Filtering (example - adjust thresholds as needed)
gatk VariantFiltration -R ref.fa -V tumor.vcf.gz -filter "QD < 2.0 || FS > 60.0 || MQ < 40.0" -O filtered_tumor.vcf.gz

# 6. Coverage Calculation (using mosdepth)
mosdepth -n -x output_prefix tumor.recal.bam

# 7. (Python script or R script - example concept) Create ASCAT input files:
#    - Read VCF, calculate BAF, filter SNPs
#    - Read coverage data, normalize, segment

# 8. Run ASCAT (command depends on your ASCAT implementation - this is conceptual)
run_ascat.sh ascat_input_snp.txt ascat_input_cn.txt

# (More scripts/tools) Analyze ASCAT output, annotate CNVs, etc.

Important Notes:

• This is a simplified example. A production-level pipeline would require more sophisticated error handling, logging, and parallelization.
• Adapt the thresholds and parameters used in the filtering steps to your specific dataset and research question.
• Consider using workflow management systems like Nextflow or Snakemake to automate and manage the analysis pipeline.

Limitations and Caveats

While adapting ASCAT for tumor-only WGS data can provide valuable insights, it's important to acknowledge the limitations and caveats:

• Accuracy: The accuracy of CNV calls in tumor-only analysis is generally lower than that achieved with matched normal samples.
• False Positives/Negatives: Tumor-only analysis is more prone to false positive and false negative CNV calls.
• Germline Variant Misclassification: Misclassifying germline variants as somatic CNVs can lead to inaccurate results and misleading interpretations.
• Purity and Ploidy Estimation: Accurate estimation of tumor purity and ploidy is crucial for CNV analysis, and this is more challenging in tumor-only samples.
• Computational Resources: Adapting ASCAT for tumor-only analysis often requires more computational resources than standard analysis.

Conclusion

Analyzing CNVs in tumor-only WGS data using adapted ASCAT methods is a powerful approach for gaining insights into cancer genomics when matched normal samples are unavailable. While this approach presents unique challenges, careful data preprocessing, appropriate parameter tuning, and integration with other genomic data can improve the accuracy and reliability of the results. Remember to interpret the findings cautiously, considering the inherent limitations of tumor-only analysis. As computational methods and algorithms continue to evolve, the accuracy and robustness of tumor-only CNV analysis will undoubtedly improve, further expanding its utility in cancer research and clinical applications.