The workflow for assessing Tumor Mutation Burden (TMB) in solid tumors is a detailed process that ensures accurate and personalized insights for cancer treatment. It starts with preparing high-quality tumor samples, followed by DNA sequencing using techniques like Whole-Exome Sequencing (WES) or targeted panels, and includes mutation calling and quality control to maintain data integrity. This approach also involves analyzing TMB profiles across different tumor types, integrating biomarkers like PD-L1, and utilizing visualization tools to interpret mutation patterns effectively. Together, these steps support precision oncology and enhance immunotherapy decision-making.
NGS-based cancer genomic profiling test workflow.( Frampton, G. M. et al,2013)
Accurately assessing TMB starts with high-quality tumor samples that reflect the tumor's unique genetic makeup. To achieve this, biopsies are taken from solid tumors in organs like the lungs, breast, liver, or colon, with a focus on capturing enough tumor cell content to ensure a comprehensive view of the tumor's mutation landscape. Handling these samples with care is vital to prevent contamination from surrounding normal tissue, which, if present in large amounts, can dilute tumor-specific mutations and skew TMB calculations. This meticulous approach ensures that the TMB assessment remains precise and meaningful1.
In clinical applications, formalin-fixed paraffin embedding (FFPE) is commonly used to preserve tumor samples for long-term storage and ease of transport. However, FFPE can sometimes fragment DNA, affecting mutation detection and sequencing accuracy. DNA extracted from FFPE samples may be partially degraded or cross-linked, making precise mutation analysis more challenging. For example, research has shown that FFPE-preserved samples from lung cancer patients often exhibit degraded DNA, which can complicate mutation detection and reduce sequencing accuracy2. To address these issues, careful processing of FFPE samples is required to reduce DNA fragmentation and improve TMB calculation accuracy3. For instance, heat-induced epitope retrieval (HIER) is one technique used to enhance DNA quality, thereby improving the accuracy of downstream analyses, including TMB calculations4.
Advanced techniques, such as laser capture microdissection (LCM), offer a solution to improve sample purity by isolating tumor cells from surrounding non-tumor tissue. LCM allows for precise extraction of tumor cells, significantly reducing contamination risks. For example, in breast cancer studies, LCM has been shown to selectively capture tumor cells from heterogeneous tissue samples, significantly reducing the risk of contamination with normal cells and improving the accuracy of downstream analyses, such as TMB calculations5.
PixCell IIe Laser Capture Microdissection instrument(Espina, V. et al.,2006)
In addition, assessing tumor cellularity, or the proportion of tumor cells in the sample relative to non-tumor cells, is essential before sequencing. Low tumor cellularity can obscure tumor-specific mutation signals, leading to unreliable TMB scores. Pathologists evaluate cellularity levels before further processing, ensuring that samples meet minimum tumor content requirements for accurate TMB assessment. This step is crucial for enhancing TMB reliability and clinical relevance, as TMB is used increasingly to inform immunotherapy decisions.
With the growing role of TMB as a biomarker for immunotherapy, obtaining and processing high-quality samples is essential for accurate results. Techniques such as FFPE preservation, combined with advanced purification methods like LCM and careful cellularity assessment, allow clinicians and researchers to ensure reliable TMB scores, supporting tailored cancer treatments and precision medicine.
After the sample is prepared and the DNA is extracted, the sequencing process kicks off. Typically, one of two main methods is chosen: WES or targeted gene panels. Each method brings its own strengths, tailored to fit the specific goals of the assessment.
WES is a powerful method that looks at all the coding regions in the genome, capturing every mutation within the tumor's genes. This approach helps researchers and doctors find rare and common mutations across a wide range of genes, giving a complete picture of the tumor's unique genetic profile. For instance, in colorectal cancer research, WES has been used to identify rare mutations in the APC and TP53 genes, which are essential for understanding tumor progression and potential therapeutic targets6. Another example is in lung adenocarcinoma, where WES has helped identify mutations in the EGFR gene. These mutations, commonly seen in patients with non-small cell lung cancer, have led to the development of targeted therapies that are now standard treatments for patients with specific EGFR mutations7. WES is especially valuable in research settings where deep insights into a tumor's genetic landscape are essential. Unlike targeted panels, which focus on specific, known genes, WES can uncover novel mutations or emerging patterns that may play crucial roles in cancer progression but might otherwise go undetected.
While WES does come with a higher cost and longer processing time compared to more focused sequencing methods, its depth is unmatched. Most sequencing services require a high depth of coverage—often 100x or more—to accurately capture low-frequency mutations, which contributes to the overall cost. Despite these challenges, WES's detailed data makes it highly beneficial for tumor types with significant genetic diversity. The detailed mutation profiles offered by WES are incredibly valuable for making informed treatment choices or designing personalized therapies tailored to the tumor's full genetic makeup. When a comprehensive view of all mutations is needed, WES proves to be an essential tool in both clinical settings and advanced research.
Targeted gene panels take a focused approach by sequencing a select group of genes known to be critical in cancer and relevant to TMB. These panels are carefully designed to include high-frequency genes and those with a strong influence on tumor biology, offering a faster and more affordable way to assess TMB. Widely used options like FoundationOne CDx, MSK-IMPACT, and Oncomine Comprehensive Assay are available through various sequencing providers, making them a popular choice in clinical settings. For example, FoundationOne CDx, approved by the FDA, covers mutations in 324 genes and has been instrumental in identifying actionable mutations across various cancer types, allowing for personalized treatment options8. Oncomine Comprehensive Assay, another widely used panel, analyzes mutations in around 500 genes, providing a rapid and cost-effective solution for TMB analysis, making it suitable for routine clinical care where quick turnaround times are essential9.
The turnaround time for targeted panels is generally shorter, making them suitable for clinical applications where rapid decision-making is necessary. Targeted panels are also calibrated to capture the types of mutations most predictive of immunotherapy response, thereby balancing precision and efficiency in a clinical context. Furthermore, the use of targeted panels is increasingly supported by health insurance, making them accessible options for patients undergoing TMB assessment.
Additionally, sequencing service providers offer customizable options that cater to various needs, allowing clinicians and researchers to select the most suitable method for their specific requirements. Some sequencing services provide hybrid approaches that combine features of WES and targeted panels, enabling more flexible and cost-effective TMB assessments. For example, some providers offer extended panels that expand beyond 300–500 genes in response to evolving clinical and research needs10.
These services also frequently offer bioinformatics support, which is essential for analyzing sequencing data and calculating TMB accurately. Providers may offer cloud-based platforms or integrated software solutions to streamline the mutation calling process, making it easier for researchers and clinicians to obtain actionable results. Many service providers also ensure compliance with regulatory standards, which is crucial for clinical settings where validated and reproducible data are required.
Overall, DNA sequencing service providers play a vital role in delivering flexible, reliable solutions for TMB assessment, from research-grade WES to clinically optimized targeted gene panels. Their expertise and advanced technology allow for TMB assessments that meet the demands of both cost-effectiveness and high precision in cancer diagnostics and treatment planning.
CD Genomics leverages cutting-edge sequencing technologies to deliver comprehensive genetic insights into solid tumors.
Once sequencing is complete, the next key step is mutation calling—a bioinformatics process that pinpoints the somatic mutations that make up the TMB. This stage is intricate, as it involves separating true tumor-specific mutations from the normal genetic variations, or germline mutations, that are naturally part of the patient's DNA. By filtering out these inherited mutations, mutation calling zeroes in on mutations unique to the tumor, providing a more accurate and meaningful TMB profile. To achieve this, algorithms like MuTect, VarScan, and Strelka are commonly employed. These tools are particularly skilled at identifying somatic mutations by comparing the DNA from the tumor with matched normal DNA from the patient. This comparison helps ensure that only mutations unique to the tumor are included in the TMB calculation, effectively filtering out any background germline mutations that don't contribute to the tumor's mutation load. By zeroing in on these tumor-specific mutations, mutation-calling algorithms produce a more accurate TMB score, providing a clearer and more meaningful picture of the tumor's genetic landscape11.
Overview of the detection of a somatic point mutation using MuTect.( Cibulskis, K. et al,2013)
MuTect, one of the most widely used mutation-calling algorithms, employs Bayesian classifiers to detect even low-frequency mutations within samples that may have limited tumor cell content. This precision is particularly useful in cases with low tumor purity, where the tumor sample may be mixed with normal tissue, as well as in highly heterogeneous tumors where different regions may have different mutation patterns. MuTect's sensitivity and Bayesian framework allow it to accurately detect subtle mutation signals that could otherwise be missed, enhancing the reliability of TMB calculations, especially in clinical contexts where tumor purity cannot always be controlled12.
VarScan and Strelka complement MuTect by refining mutation-calling through their unique approaches. VarScan, for instance, uses a heuristic-based method that can accurately identify mutations even in low-coverage regions, a feature that makes it particularly suited for samples where deep sequencing is limited. Strelka, on the other hand, leverages a combination of Bayesian probability models and statistical testing to identify mutation calls, further improving sensitivity for low-allele frequency variants. This high level of sensitivity makes Strelka a valuable addition to the mutation-calling pipeline, particularly for solid tumors that present with a range of mutation frequencies across different tumor cell populations11.
In addition, advanced bioinformatics pipelines frequently use multiple mutation-calling algorithms to cross-check mutation calls, helping to minimize the risk of false positives. This multi-algorithm strategy boosts the reliability of mutation detection, allowing researchers and clinicians to focus on high-confidence somatic mutations that genuinely reflect the tumor's mutational load. By ensuring that only these verified mutations contribute to the TMB score, this approach strengthens the accuracy and robustness of TMB assessment. This accuracy is vital for solid tumors, which can exhibit diverse mutation landscapes depending on factors like tissue type, environmental exposures, and patient-specific characteristics. Reliable mutation calling provides a foundation for consistent TMB calculations, enabling more effective use of TMB as a predictive biomarker for immunotherapy response in clinical oncology12.
Quality control (QC) is a critical aspect of TMB analysis, as it ensures the reliability and accuracy of the data used to guide clinical decisions. In TMB analysis, QC begins with assessing the quality of the extracted DNA. High DNA quality reduces errors in mutation detection by ensuring that samples have minimal degradation and fragmentation, which could otherwise lead to false-negative or false-positive mutation calls13.
Another critical QC factor is sequencing depth, as adequate coverage depth (typically at least 100x) is necessary to ensure that all relevant regions of the genome are sufficiently represented in sequencing data. High coverage improves the accuracy of mutation detection, particularly in heterogeneous tumor samples where low-frequency mutations may otherwise be missed. Sequencing at lower depths can lead to the underrepresentation of certain mutations, impacting the TMB calculation's precision11.
Bioinformatics QC is equally important, as it involves filtering out sequencing artifacts and low-confidence mutations that could skew TMB calculations. Advanced bioinformatics tools can identify and remove erroneous mutation calls caused by technical artifacts, such as PCR errors or read misalignment. This filtering minimizes false positives and increases the TMB score's accuracy, providing more reliable data for clinical applications12.
To further improve quality control, automated systems and machine learning tools are continually being developed. These technologies streamline the QC process by identifying and correcting errors more efficiently, thus supporting the scalability of TMB analysis for routine clinical use. Machine learning algorithms, for instance, can learn from large datasets to improve the precision of mutation calling and artifact filtering, making TMB assessments faster and more reproducible.
In assessing TMB for solid tumors, data analysis and interpretation are crucial steps that transform raw sequencing data into meaningful insights for cancer treatment. The process starts with identifying somatic mutations—mutations unique to the tumor—while filtering out normal, or germline, mutations found in the patient's DNA. This distinction is essential for calculating TMB accurately, as only tumor-specific mutations contribute to the TMB score, which predicts a tumor's potential response to immunotherapy14.
Once the TMB is calculated, data interpretation involves comparing TMB levels across different tumor types. High TMB scores in tumors like melanoma and non-small cell lung cancer are often associated with better responses to immune checkpoint inhibitors, as a higher mutation count increases the likelihood of generating neoantigens that stimulate an immune response15. This insight helps oncologists make more informed decisions about whether to pursue immunotherapy as a treatment option.
Additionally, interpreting TMB data often includes integrating other biomarkers, such as PD-L1 expression, to enhance prediction accuracy. Combining TMB with PD-L1 status provides a fuller picture of a tumor's immune environment, improving the likelihood of selecting effective, personalized treatments. Advances in visualization tools, such as heatmaps and 3D models, further aid in presenting complex TMB data in clear, actionable formats for clinicians and researchers, ultimately supporting precision oncology efforts.
References:
