I see a future in which precision medicine and personalized care are a reality, and I believe genetics is a fundamental element of that future. To get there, we must break down barriers leading to unstructured clinical data and data silos.
In an interview with Dr. Pawel Buczkowicz, we discuss his research on high-risk childhood cancers and how it led him to address gaps in research and care through software and technology.
I see a future in which precision medicine and personalized care are a reality, and I believe genetics is a fundamental element of that future. To get there, we must break down barriers leading to unstructured clinical data and data silos.
I didn’t initially foresee pursuing a PhD in cancer genetics and molecular pathology during my undergraduate studies. I’ve always had a strong curiosity for science-driven discovery. I’m interested in research across science, from astronomy to biology, theoretical physics, chemistry, and computer science. For one reason or another, I found myself drawn to medicine and healthcare.
I almost studied virology after a co-op placement at a biopharmaceutical company, where I manufactured viruses and viral antigens. Later, during another undergrad co-op at the Hospital for Sick Children, I worked on a project examining gene expression profiles of a childhood brain cancer called medulloblastoma. It was the perfect trifecta of complexity and meaning that got me hooked; genetics, the brain, and cancer are all very interesting and complex. The combination ignited something that led me to dedicate almost a decade to researching the genetics of paediatric brain tumours.
During my research on childhood brain cancer, I focused primarily on a type of brainstem glioma called diffuse intrinsic pontine glioma (DIPG).
I’m particularly proud of the outcomes of this research. My colleagues and I identified two new mutations in DIPG that had never been described in human cancers: one in a gene that codes for the protein histone H3 and one in the ACVR1 gene.
These discoveries, along with a comprehensive analysis of other genetic changes, DNA methylation, RNA-seq, copy number variation, expression array, and histopathology, resulted in the identification of three DIPG subtypes. One of those was renamed “diffuse midline glioma, H3 mutated” and reclassified by the World Health Organization in their international central nervous system tumour classification guidelines. This reclassification changed diagnostic approaches and opened the doors to new treatment opportunities, clinical trials, and further research towards a cure.
In addition to DIPG, I researched several other types of paediactic brain cancers, including medulloblastoma, primitive neuroectodermal tumours (PNET), pleomorphic xanthoastrocytomas (PXA), ependymoma, thalamic gliomas, supratentorial high-grade astrocytomas, and gangliogliomas.
Genetics and next-generation sequencing play a significant role in the understanding of childhood cancers like brainstem glioma. This is because most childhood cancers are caused by dysregulation of developmental pathways and genetic changes that occurred early in development, or were perhaps even inherited.
Genetic similarities between subtypes of childhood cancer can harbour the same mutation much more frequently than cancers in adults. For example, while BRCA1 mutations are present in approximately 3% of breast cancer, and around half of all melanoma cancers in adults have BRAF mutations. In contrast, 85% of DIPG have mutations in the H3F3A gene, while 100% of rhabdomyosarcoma cases show dysregulation of the SMARCB1/INI1 gene.
Adult cancers, which are much more common, occur later in life due to the accumulation of errors in one's DNA. Each cell division can introduce errors, and adult cells have divided many more times by the age of 50 than that of a 5-year-old. Additionally, environmental exposure over decades, such as cigarette smoke, alcohol, UV radiation from the sun, and diet, increase the chance of developing adult cancers. Genetics still play a role, but many other factors contribute to cancer in adults.
Your question highlights the most significant aspect of these communities’ shared struggles. Beyond the personal challenges and uncertainty when a child in a family is ill, the challenges faced by families experiencing childhood cancer and those in the rare disease community stem from the fact that they are both rare. This often means that resources, support initiatives, access to clinical trials, and treatment options are usually concentrated at larger, well-funded academic tertiary care centres. As a result, patients and their caregivers often travel long distances to see specialists with the expertise and networks to provide the necessary care.
Researchers and clinicians encounter similar obstacles due to the rarity of childhood cancers. Funding for studying rare conditions is scarce, and obtaining paediatric cancer specimens for research can be difficult, delaying potential lifesaving discoveries.
In the case of DIPG, several factors made our research efforts feel like we were swimming against the current. First, it was assumed that, because DIPG resembles a type of adult brain cancer under a microscope, it would respond to the same treatment. Second, DIPG occurs in a delicate area of the brain that controls critical functions like breathing and heart rate, preventing surgery or biopsies. Third, once patients were diagnosed with DIPG—usually by MRI alone—they began radiation and chemotherapy treatment. These treatments are designed to kill and damage the cancer cells, so without pretreatment biopsy specimens, I had to be extremely meticulous to be sure any genetic, histological or molecular discoveries were present in the cancer prior to treatment and not caused by it.
The rare nature of the disease compounded these struggles. It was difficult to get enough samples quickly enough to do research at the pace of research programs for adult cancers. You sometimes feel hopeless when young children are dying and you wish you could do more, faster. At the same time, this feeling drove me towards discovering something that can quickly and significantly impact their care.
Yes, my experiences in clinical research set me on my crusade for data standardization, data sharing and interoperability. This ultimately led to the development of PhenoTips as the world’s first complete Genomic Health Record. I noticed significant variability in documentation and workflows, even within a single hospital. I witnessed departments at the same institution using different electronic health record systems, identifying patients using different identifiers, and relying heavily on paper records. Additionally, many electronic record systems were not much better than paper.
Considering healthcare through the genetics lens illuminates how wide-reaching and unique genetics is compared to other medical specialties. Genetics intersects with various fields beyond its impact on disease prevention, diagnosis, and clinical decision-making. Genetics plays a role in prenatal care, paediatrics, rare diseases, oncology, neurology, cardiology, immunology, pharmacogenetics and more. Genetics is unique because it takes a family-orientated approach to healthcare. In addition, there is always a possibility that a clinical symptom or physical finding is not the result of a common condition, but a rare condition, one that you may not even know exists.
Understanding that genetics, genetic testing, and whole genome sequencing would play an ever-growing role in clinical care, I began to realize that the clinical information needed for genomic medicine was lacking in a big way. This is where technology, software, and AI can have a positive impact on healthcare. Giving clinicians the tools in PhenoTips software—integrated with their existing workflows—to provide them with the insights they need to care for their patients is what drives me today.
I see a future in which precision medicine and personalized care are a reality, and I believe genetics is a fundamental element of that future. To get there, we must break down barriers leading to unstructured clinical data and data silos; barriers that were created when policies and healthcare systems were developed without an understanding of heredity and genetics.
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