The Cancer Genome Atlas (TCGA) is a landmark program that has generated a wealth of data on various types of cancer, including mesothelioma. However, like any large-scale research effort, TCGA has its limitations, and researchers need to be aware of these limitations when using TCGA data for mesothelioma research. In this answer, we will discuss some of the ways to overcome the limitations of TCGA data for mesothelioma research.
Use complementary datasets
One of the main limitations of TCGA data is that it represents a relatively small number of mesothelioma cases. As of the knowledge cutoff of this model in 2021, there were only 87 cases of mesothelioma in TCGA. This small sample size can limit the statistical power of analyses and increase the risk of false positives or negatives.
One way to overcome this limitation is to use complementary datasets. For example, the Mesothelioma Applied Research Foundation (MARF) has created a tissue bank that contains specimens from more than 2,000 mesothelioma patients. These specimens can be used to validate findings from TCGA, as well as to identify new biomarkers and therapeutic targets.
Perform integrative analyses
Another limitation of TCGA data is that it does not capture the full complexity of mesothelioma, which is a heterogeneous disease with multiple subtypes and molecular drivers. TCGA data only provides information on a limited set of molecular features, such as gene expression, copy number alterations, and somatic mutations.
To overcome this limitation, researchers can perform integrative analyses that combine TCGA data with other types of molecular data, such as epigenetic modifications, protein expression, and metabolomics. For example, a recent study used integrative analyses to identify a novel molecular subtype of mesothelioma, which was associated with a distinct clinical outcome and therapeutic response.
Incorporate clinical data
TCGA data is primarily focused on molecular features of mesothelioma, but it does not contain detailed clinical information, such as patient demographics, treatment history, and survival outcomes. This lack of clinical data can limit the ability to translate molecular findings into clinical practice.
To overcome this limitation, researchers can incorporate clinical data from other sources, such as electronic health records, clinical trials, and population-based registries. By integrating molecular and clinical data, researchers can identify molecular features that are associated with specific clinical outcomes, such as response to therapy or survival.
Validate findings in independent cohorts
One of the most important ways to overcome the limitations of TCGA data is to validate findings in independent cohorts. TCGA data provides a valuable resource for hypothesis generation, but it should be seen as a starting point rather than a definitive answer.
To validate findings from TCGA, researchers can use independent cohorts that have similar clinical and molecular characteristics. For example, a recent study used TCGA data to identify a set of genes that was associated with mesothelioma survival. The researchers then validated these findings in an independent cohort of mesothelioma patients, confirming the prognostic value of the gene set.
Use appropriate statistical methods
Finally, it is important to use appropriate statistical methods when analyzing TCGA data. The large number of variables in TCGA data can increase the risk of false positives or negatives if not properly controlled for.
To overcome this limitation, researchers can use statistical methods that are appropriate for high-dimensional data, such as machine learning algorithms, penalized regression models, and network analysis. These methods can help identify molecular features that are truly associated with mesothelioma, while controlling for confounding factors and minimizing the risk of spurious findings.
In conclusion, TCGA data provides a valuable resource for mesothelioma research, but it has its limitations. To overcome these limitations, researchers can use complementary datasets, perform integrative analyses, incorporate clinical data, validate findings in independent cohorts, and use appropriate statistical methods. By doing so, researchers can generate more robust and clinically relevant insights into the molecular mechanisms of mesothelioma, and identify new biomarkers and therapeutic targets for this deadly disease.
