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A detailed analysis of childhood acute myeloid leukemia provides important genetic information for improving outcome predictions in pediatric cancer care.
Acute myeloid leukemia (AML) is a type of cancer affecting the blood and bone marrow1. It occurs when abnormal myeloid cells, which are responsible for producing red blood cells, white blood cells, and platelets, grow and multiply uncontrollably1. These abnormal cells disrupt the production of normal blood cells, leading to symptoms such as fatigue, vulnerability to infections, and easy bruising or bleeding2. While AML is more commonly diagnosed in adults, particularly in individuals over the age of 60, it can also affect children1.
Pediatric AML (pAML), though relatively rare compared to adult cases, poses unique challenges due to its distinct genetic makeup3. Unlike adult cases, pAML has specific genetic abnormalities that impact how the disease develops and responds to treatment3,4. Yet, much of the research in this area has centered on adult cases, leaving a significant knowledge gap concerning the genetic characteristics of pAML and their impact on treatment outcomes3,4,5.
A recent study sought to address this knowledge gap by conducting a comprehensive analysis of the genetic landscape of pAML and its association with clinical outcomes6. The researchers obtained tumor samples from 887 children diagnosed with pAML, ranging from infants to teenagers and young adults. Using advanced sequencing techniques like RNA-sequencing (RNA-seq), they analyzed the genetic material within these cancer samples, with a specific focus on the RNA molecules to identify actively expressed genes. This approach allowed the researchers to detect many different genetic alterations in the cancer cells, such as gene fusions and mutations, many of which were recurrent and class-defining in pAML. For instance, they identified a significant portion of patients with a gene called KMT2Ar that fused or connected with another gene, resulting in a new gene combination that could drive cancer growth. Additionally, mutations or changes in genes like NPM1 and CEBPA were also found to contribute to cancer development by altering a cellular signaling pathway called RAS.
Based on specific genetic alterations and similar gene expression patterns, researchers grouped the pAML cases into different categories. These categories, including both previously recognized classifications such as those outlined by the World Health Organization and newly identified ones, revealed distinct subtypes of pAML with unique characteristics. For example, certain gene fusions were common in infants, while gene mutations like NPM1 and CEBPA were typical in teenagers and young adults, suggesting variations in developmental stages associated with the onset of these subtypes.
Further analysis revealed similar gene expression patterns related to the HOX gene family, which are important for regulating how cells grow and specialize. By examining HOX gene expression, the researchers identified two main clusters within pAML cases: the HOXA group and the HOXB group, each showing distinct mutation profiles, including in genes like FLT3 and WT1. In the HOXA group, FLT3 mutations were mainly characterized by one type (FLT3-ITD), while in the HOXB group, FLT3 mutations were mainly characterized by another type (FLT3-ITD). Additionally, WT1 mutations were found to be more prevalent in the HOXB group compared to the HOXA group. These distinct mutation patterns suggest shared biological processes related to cell development and function within each HOX group.
To analyze how these different subtypes of pAML patients responded to treatment, the researchers used data from clinical trials and found that some subtypes had better outcomes than others. For instance, in a majority of pAML patients, mutations in genes FLT3 and WT1 were tied to worse outcomes, while mutations in CEBPA and NPM1 genes were associated with favorable outcomes. Based on this information, they created a system to predict how well patients with pAML might respond to treatment, taking into account their genetic profile and any remaining cancer after treatment. This predictive system serves as a valuable tool for clinicians in tailoring treatment plans for individual patients.
While RNA sequencing was highlighted as a useful tool for classifying pAML cases into different subtypes, the authors acknowledged the challenge of making this approach universally available in hospitals worldwide due to limited access to advanced sequencing technology and the expertise required for analysis. Therefore, they proposed the development of simple and reliable methods that can be readily accessed by healthcare professionals globally. Additionally, the researchers suggested that exploring common genetic changes in other tumor types could provide further insights into the biology of pAML, aiding in refining its classification system and identifying new therapeutic targets.
Advances in understanding the genetic makeup of pAML and its influence on treatment outcomes marks a big step towards tailoring therapies to individual patients. However, this progress raises concerns about equitable access to such tailored treatments, as they may be costly or unavailable to disadvantaged populations, potentially widening existing disparities in healthcare access. Additionally, using genetic data to create predictive systems may impact how patients and families perceive their outlook and treatment options, with concerns about data privacy and potential discrimination in health insurance or employment based on genetic factors, further complicating the issue. Open communication about the implications of these predictive systems is important to ensure that patients and families make informed decisions about their treatment and care. Individuals might fear being treated unfairly due to their genetic information, leading to anxiety or reluctance to undergo genetic testing. Therefore, ensuring ethical and responsible use of genetic information, along with proper legislation and education to prevent genetic discrimination, is critical to promote equitable access to personalized healthcare for all.
Click here to learn more about the author Taw Meh or contact her at tameh@davidson.edu.
References
1. Khwaja, A. et al. Acute myeloid leukemia. Nature Reviews, 2, 16010. https://www.nature.com/articles/nrdp201610 (2016).
2. Vakiti, A., & Mewawalla, P. Acute myeloid leukemia. StatPearls [Internet]. https://www.ncbi.nlm.nih.gov/books/NBK507875/ (2024).
3. Tarlock, K., & Meshinchi, S. Pediatric acute myeloid leukemia: biology and therapeutic implications of genomic variants. Pediatric clinics of North America, 62, 75–93. https://www.sciencedirect.com/science/article/abs/pii/S0031395514001862?via%3Dihub(2015).
4. Tseng, S. et al. A review of childhood acute myeloid leukemia: diagnosis and novel treatment. Pharmaceuticals (Basel, Switzerland), 16, 1614. https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/38004478/ (2023).
5. Aung, M. et al. Insights into the molecular profiles of adult and pediatric acute myeloid leukemia. Molecular oncology, 15, 2253–2272. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8410545/ (2021).
6. Umeda, M., et al. A new genomic framework to categorize pediatric acute myeloid leukemia. Nature Genetics, 56, 281–293. https://pubmed.ncbi.nlm.nih.gov/38212634/ (2024).
© Copyright 2024 Department of Biology, Davidson College, Davidson, NC 28036.
Great job! Your post highlights an incredible use of RNA-seq and genomic technologies. I’m interested to see how advances in this diagnostic approach alongside advances in treatment options for pAML could provide tailored treatment approaches. As you mentioned, though, the cost will be a big hurdle that researchers will need to overcome for RNA-seq and genomic methods to be applied to diagnosing pAML. Furthermore, it seems like a long way off before disadvantaged portions of the world will be able to access these technologies. It is probably more pertinent to invest in more basic medical treatments which are still inaccessible to some worldwide.
I love the last part of your post! You did a great job identifying the concerns surrounding equity and existing disparities. Even just being part of clinical trials seeking to develop new treatment methods is difficult enough for underserved populations, so actually obtaining the treatment if it’s approved and shown to work would likely be extremely costly and could possibly be delayed by increased demand. Another barrier is the implicit bias that healthcare providers may hold that could cause them to not recommend a patient for a certain treatment. It’s common for POC populations to not be considered for certain diagnoses or treatments simply because of their perceived race. The development of tailored treatments, though certainly beneficial, might not be the exception when it comes to racialized medicine.