Somatic variant calling identifies genetic differences in diseased cells compared to a person’s healthy cells. Unlike germline mutations, which are inherited and present in every cell, somatic mutations are acquired after conception and are not passed down to offspring. This computational process analyzes DNA sequencing data to find these specific, acquired alterations. It is a foundational technique in modern biology and medicine, especially in the study and treatment of cancer.
The Process of Identifying Somatic Variants
The standard method for somatic variant calling begins with collecting two samples from one individual: tumor tissue and a separate sample of normal tissue, like blood. This matched-normal sample is indispensable because it provides a baseline of the person’s germline genome. Without it, distinguishing between inherited variants and those acquired somatically would be nearly impossible, as every person’s genome has many differences compared to a standard reference.
Once the samples are collected, DNA is extracted and prepared for Next-Generation Sequencing (NGS). This technology rapidly sequences the DNA from both samples, converting biological material into vast sets of digital data. The output consists of millions of short DNA sequences, or “reads,” from each sample, allowing for a comprehensive view of the genetic landscape.
Next, in a computational step known as alignment, the short reads from both samples are mapped to a standardized human reference genome. This process is like assembling a complex puzzle where the reference genome serves as the picture on the box lid. Each read must be correctly placed, allowing scientists to see how the patient’s DNA compares to the established human sequence.
Finally, specialized software performs the “variant calling.” These algorithms compare the aligned DNA sequences from the tumor sample against those from the normal sample. The software identifies genetic differences—such as single nucleotide changes, insertions, or deletions—that are present in the tumor data but absent from the normal data. These differences are flagged as somatic variants, representing the genetic changes that have occurred specifically within the cancer cells.
Key Challenges in Detection
A challenge in somatic variant calling is tumor purity. Tumor tissue samples are rarely 100% cancer cells; they are complex mixtures that also include healthy, immune, and connective tissue cells. This mixture dilutes the cancer-specific DNA signal, making it more difficult for algorithms to identify somatic variants, much like trying to isolate a single voice in a crowded room.
Tumor heterogeneity adds to this complexity. A single tumor is not a uniform mass of identical cells but a mosaic of different cell populations, each with its own unique set of mutations. This means a particular somatic variant might only exist in a small subset of the tumor cells, resulting in a very weak signal that is difficult to distinguish from background noise.
These factors contribute to the challenge of low variant allele frequency (VAF). The VAF represents the percentage of DNA reads in the aligned data that contain a specific variant. Because of tumor impurity and heterogeneity, a true somatic variant may have a low VAF, creating a statistical challenge. Algorithms must be sensitive enough to detect these low-frequency variants without incorrectly flagging random sequencing errors as real mutations.
The sequencing process itself can introduce errors, known as sequencing artifacts, which can be mistaken for genuine somatic mutations. These can arise from chemical reactions during sample preparation or from errors by the sequencing machine. Sophisticated computational models are required to differentiate these technical artifacts from true biological variants. For example, some algorithms use a “panel of normals” to learn the patterns of common sequencing errors and filter them out.
Clinical and Research Applications
One application of somatic variant calling is in personalized medicine for cancer treatment. By identifying specific mutations responsible for a tumor’s growth, doctors can select targeted therapies. For instance, detecting a BRAF V600E mutation in a melanoma patient can guide the use of specific inhibitors. Similarly, identifying EGFR mutations in lung cancer patients opens the door to targeted drugs, often with fewer side effects than traditional chemotherapy.
The types and patterns of somatic mutations found in a tumor can also provide information for prognosis and diagnosis. Certain mutations are associated with more aggressive forms of cancer, helping clinicians to predict the likely course of the disease. In some cases, the mutational profile can help classify tumors into more specific subtypes that might not be distinguishable by appearance alone.
Somatic variant calling also helps monitor treatment response through liquid biopsies, which analyze tumor DNA in a patient’s bloodstream. This allows clinicians to track a cancer’s evolution over time. This approach can also detect the emergence of new mutations that confer resistance to a therapy, providing an early warning that the treatment is losing its effectiveness and a change in strategy is needed.
On a larger scale, the analysis of somatic variants across thousands of tumor samples is an engine for cancer research. By aggregating and comparing mutational data from large cohorts of patients, scientists can uncover biological pathways commonly disrupted in cancer. This research helps identify new potential drug targets and provides a deeper understanding of how different cancers develop and progress, informing the next generation of treatments.
Interpreting the Results
After the computational process identifies potential somatic variants, interpretation begins. The raw list of genetic changes is first put through a process called annotation, where each variant is cross-referenced with biological databases. This adds layers of information, such as which gene is affected, the potential effect on the gene’s protein, and how frequently the variant appears in population or cancer studies.
A primary goal of interpretation is to distinguish between “driver” and “passenger” mutations. Driver mutations are genetic alterations that contribute to the cancer’s growth, while passenger mutations occur by chance and provide no functional advantage. Identifying the few driver mutations among many passenger mutations is a significant analytical challenge for researchers and clinicians.
The final stage involves classifying variants based on their clinical actionability. This categorization helps determine the practical relevance of each finding. Variants are often sorted into tiers: some may indicate the suitability of an FDA-approved drug, others might suggest eligibility for a clinical trial, and many are classified as variants of unknown significance. This interpretive process transforms raw data into actionable medical insight.