Cancer Genomics: Driving Precision Medicine
Cancer genomics is transforming oncology, enabling a deeper understanding of cancer at a molecular level. This rapidly evolving field focuses on identifying and categorizing genetic differences and cancer-associated genes. Advancements in sequencing technologies are accelerating this progress through the expansion of available genomic data. Researchers and clinicians can use the ever-increasing datasets to uncover the biomarkers, mutations, and genetic alterations that drive cancer progression. Ultimately, this leads to improvements in cancer diagnosis, treatment, and prevention, paving the way for personalized medicine.
As genomics becomes increasingly integral to oncology research, clinical applications, and screenings, its potential to revolutionize cancer diagnosis and treatment is immense. This blog explores the significant benefits and implications of cancer genomics in modern medicine.
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Benefits of Cancer Genomics in Oncology
Cancer genomics involves analyzing the genome of cancer cells to identify differences that contribute to cancer development and progression. Technologies like Next Generation Sequencing (NGS) and long-read sequencing can help detect specific genetic mutations, chromosomal abnormalities, and other alterations that are biomarkers in different cancers. By understanding these genetic alterations, researchers and clinicians can develop diagnostics to identify specific cancers and targeted therapies to improve patient outcomes.
The Role of Genomics in Cancer Diagnosis
Early and precise detection of cancer is one of the most significant advantages of genomic sequencing. Known biomarkers enable clinicians to identify cancers at earlier stages, which can be crucial for effective treatment. Cancer genomics can not only be used to confirm a symptomatic patient has a particular cancer type and its stage, but also to identify hereditary genetic risk factors during more routine screening.
For example, the presence of BRCA1 and BRCA2 mutations can indicate an increased risk of breast, ovarian, prostate, and pancreatic cancers. Being able to screen for this mutation enables early intervention and monitoring in at-risk individuals[1].
Recent studies have demonstrated the power of genomic data in cancer detection. For example, researchers across various cancer studies have analyzed liquid biopsies, a non-invasive method for extracting circulating tumor DNA (ctDNA) from blood, to identify cancers of different stages in patients[2], [3].
Similarly, a study of diffuse large B-cell lymphoma (DLBCL) — the most common non-Hodgkin lymphoma — also analyzed ctDNA samples and concluded that it was advantageous to screen ctDNA for diagnosis, as well as monitoring treatment response and detecting early relapse[4]. In the study, cancer personalized profiling using deep sequencing (CAPP-seq) was used for targeted sequencing of patient ctDNA for simultaneous genotyping and monitoring[5].
Figure 1. a) Schematic of commonly studied biomarkers from blood plasma liquid biopsies: circulating tumor cells (CTCs), extracellular vesicles (EVs), tumor-educated platelets (TEPs), Cell-free DNA (cfDNA), cell-free RNA (cfRNA), and circulating tumor DNA (ctDNA). b) Alterations observed in tissue tumor DNA and ctDNA: point mutations, insertions/deletions, translocations, copy number variations and epigenomic (DNA methylation) alterations. Adapted from Vlataki, 2023 [2].
Additional cancer genomics studies have determined distinct genetic subtypes of DLBCL. When these subtypes have been considered in various trials, there have been correlations between how patients respond to the therapy and their DLBCL subtype, demonstrating that cancer subtypes need to be considered when evaluating the efficacy of new treatments and should be built into future classification systems. Screening individual patients for associated biomarkers could then help direct the most effective and personalized therapies[6].
Personalized Treatments
By understanding the specific genetic mutations driving a patient’s cancer or subtype, clinicians can select targeted therapies that are more likely to be effective and less likely to cause adverse side effects. There are already many success stories with this approach.
For example, Imatinib (Gleevec) is a targeted therapy for chronic myeloid leukemia (CML). CLM cells contain the abnormal fusion gene BCR-ABL. The BCR-ABL protein is a tyrosine kinase that causes dysregulated cell growth. Imatinib is a tyrosine kinase inhibitor that specifically inhibits BCR-ABL, blocking the cancer cell growth signals[7]. These types of treatments are more effective and less toxic than traditional therapies, as they specifically target cancer cells, leaving healthy tissue unaffected.
Another notable targeted therapy is trastuzumab (Herceptin), a monoclonal antibody specifically used for HER2-positive breast cancer. HER2 (human epidermal growth factor receptor 2) is a protein that can promote the growth of cancer cells, and its overexpression is found in some breast cancers. Trastuzumab specifically targets HER2, inhibiting its action and slowing cancer progression. Patients receiving this targeted therapy have shown significantly improved outcomes compared to those receiving standard chemotherapy[8].
Another success story is the development of epidermal growth factor receptor (EGFR) inhibitors for non-small cell lung cancer (NSCLC). Mutations in the EGFR gene are common in NSCLC, and drugs like erlotinib (Tarceva) target these mutations, and have led to better treatment responses and prolonged survival[9].
Personalized treatments based on genomic data can also reduce the trial-and-error approach often associated with cancer treatment. By selecting therapies that target specific genetic alterations that the patient is known to have, clinicians can avoid unnecessary side effects and improve treatment efficacy. For example, the DLBCL research study provided CAPP-seq data that was used in the treatment decision process for up-front autologous stem cell transplantation. The ctDNA monitoring was shown to be a valuable component in determining the best treatment approach for high-risk patients[4].
Advancements in Precision Medicine
Technological developments, like the advent of NGS and PacBio’s Single-Molecule Sequencing in Real Time (SMRT) method, have been crucial in advancing cancer genomics and precision medicine. They have empowered the comprehensive analysis of cancer genomes, facilitating the discovery of new genetic alterations, cancer subtypes, and potential therapeutic targets.
Short and Long-Read Sequencing Technologies
Short-read NGS technology has made it possible to perform whole genome, whole exome, and targeted sequencing quickly and cost-effectively. But, while massively parallel sequencing (MPS) can be used to sequence large stretches of DNA, it is designed around a fragmentation and amplification approach. For cancer genomics, this can potentially introduce biases when resolving these large stretches, especially in repetitive regions.
The development of long-read technologies such as PacBio HiFi sequencing and Oxford Nanopore ultra-long reads has made sequencing large and repetitive stretches of DNA more reliable and accurate, providing up to 99.9% accuracy, crucial for identifying complex genetic mutations and structural variants. They also provide the ability to collect details of epigenetic modifications, such as DNA methylation and histone modification, and are particularly useful in identifying large genomic rearrangements and other alterations that may otherwise be missed by short-read sequencing.
That said, both short- and long-read sequencing technologies have been instrumental in studying the intricacies of the genome and expanding our understanding of and cancer genomics.
Accelerating Research and Treatment Development
Increasing access to sequencing technologies has allowed for large-scale projects like The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) to generate vast amounts of genomic data[10], [11].
The ICGC data platform by the Accelerating Research in Genomic Oncology (ARGO) project now holds data of mapped genomic alterations from over 50 cancer types[12]. These large-scale studies and the databases they create provide a platform for researchers to identify novel cancer genes and pathways.
Role of Bioinformatics and Big Data
With the increase in available cancer genomics data, however, there is also more demand for computational tools that can help analyze and interpret that data for clinical applications. This is where sophisticated bioinformatics becomes just as crucial as advances in sequencing technology.
Combined with the right expertise, bioinformatics tools can help integrate massive datasets with clinical information. This enables the identification of patterns and correlations between specific mutations and treatment responses, thereby creating connections between clinically relevant mutations and their potential therapeutic targets.
The upsurge in AI developments in recent years has also impacted cancer genomics. New technologies using AI and machine learning methods will become key to interpreting mass cancer genomic datasets[13]. The use of big data analytics helps in understanding the complex interactions within cancer genomes and in developing predictive models for treatment responses. Together, researchers and clinicians can look to use the data to guide the design and development of new treatments and diagnostic tools, allowing use of more precise and personalized treatments in the future.
Challenges and Future Direction
Despite the promising advancements, cancer genomics faces several challenges. The complexity of genomic data, the relatively high cost of sequencing every patient, and the accessibility of genomic testing are still obstacles to navigate through. Additionally, interpreting the clinical significance of genetic variants and managing the vast amounts of data generated remain challenging. This translates to potentially quite a high cost on a per-patient basis.
However, there are new technologies and collaborative research initiatives being developed, aiming to make genomic testing more affordable and accessible. For instance, the 100,000 Genomes Project aims to integrate genomic data into routine clinical practice, making personalized medicine a reality for more patients[14]. Furthermore, the incorporation of AI and machine learning into clinical workflows in the future could be a game changer for overcoming the challenge of interpretability and determining clinical significance of complex cancer genomics data[13].
While datasets are larger than ever before, ethnic minorities tend to be underrepresented. Future directions in cancer genomics would benefit from collecting more ethnically diverse genomic data to expand reference genomes and biomarkers, as is the aim of the human pangenome project[15], thereby reducing the chance of unexpected side effects sometimes seen with otherwise sound treatment strategies.
Further use of multi-omics approaches (genomics, transcriptomics, proteomics, metabolomics) and the integration of data will also help to gain a holistic understanding of cancer biology. Ultimately, it is a range of iterative steps that are pushing cancer genomics towards a future of precision medicine and personalized treatment.
Taking cancer into remission
Genomics is transforming our understanding of cancer, leading to more accurate diagnoses, personalized treatments, and new strategies for early detection and prevention. As the field continues to advance, the potential for cancer genomics to revolutionize oncology grows ever stronger. Having already made significant impacts, cancer genomics holds great promise for finding therapeutic options for more cancers. By exploring the genetic underpinnings of this disease, researchers can develop targeted therapies with minimal side effects, improving patient outcomes.
At Eremid® Genomic Services, we are committed to advancing cancer genomics by making cutting-edge research and technology accessible – including those from Illumina, PacBio, and Oxford Nanopore. We offer high-complexity genomics services through a range of short- and long-read sequencing and microarray technologies, as well as expert bioinformatic analysis. We are a PacBio Certified Service Provider, offering the expertise of a science team with decades of genomics and bioinformatics experience. By leveraging technologies like HiFi sequencing and ONT ultra-long sequencing, we can deliver top-tier clinical genomics services to help drive your research forward.
References
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