Every year, more than 1.8 million people in the United States are diagnosed with cancer. Despite the appearance of new therapies on the market and advances in personalized medicine, more Half a million patients die annually from cancer. only in the United States. Four types (breast, pancreas, colon and rectum, and lung and bronchi) result in the most deaths each year: 42,680 (breast); 51,980 (pancreas); 52,900 (colon and rectum); and 124,730 (lung and bronchi). In addition to heart disease, cancer provides the most deaths in the United States annually. For those under 65 years of age, cancer increases by main cause of death.
Advances in genomic profiling have paved the way to identify different types of cancer, how to connect BRCA1 either BRCA2 to breast cancer, KRAS biomarker for colorectal cancer, CA-125 with ovarian cancer and PCA3 for prostate cancer. In particular, bioinformatics and next generation sequencing (NGS) technologies can accelerate previous timelines for identifying clinically important mutations and provide faster treatment options for patients.
Genomics supports efficacy in treatment selection
These genomic technologies not only help identify cancers, but also offer opportunities to evaluate the potential effectiveness of different treatment options. By analyzing the genomic profile of a particular patient, doctors can see which drugs might offer the most support and which might cause the most harm. For example, if a patient with non-small cell lung cancer has a cancer that is manifested by mutations in the epidermal growth factor receptor (EGFR) gene, treatment with EGFR inhibitors could offer a viable solution. Genomic testing could detect that biomarker and evaluate whether the EGFR inhibitor has potential to treat the genetic change given that causes cancer.
Additionally, understanding a patient’s genomic profile can help a doctor select drugs that are less likely to have an effect. adverse drug reactions for the given patient. Furthermore, such pharmacogenomic information allows physicians adapt the dose of a medication to how quickly the patient can metabolize a medication. The benefits to patients abound: minimized side effects, less trial and error in finding effective medications, potentially fewer doctor’s appointments as a result of getting the right medication sooner, savings from not paying for multiple medications before finding one that works. All of this potentially translates into shorter referral times.
Answers hidden by data
Available genomic analysis tools typically include a large amount of data. In particular, the results include extensive secondary reports. In oncology, reports typically deliver NGS data as raw or secondary data that require specialized interpretation, increasing reliance on bioinformaticians and, as a result, delaying actionable results for patients. Additionally, many oncology workflows often remain fragmented, requiring separate tools and training for the different components needed: variant calling, annotation, and pharmacogenomic interpretation to guide targeted therapies.
TO high need exists for integrated genomic platforms that not only include a complete secondary report but also a Tertiary analysis with integrated pharmacogenomic knowledge and guidelines for therapy.and. This tertiary analysis enables real-time, integrated decisions for precision medicine, so patients can receive answers about viable treatments from their genomic data.
Select an effective genomic profiling technology
Genomic technologies on the market have different value propositions, so oncology researchers, laboratories, and genetic testing companies must carefully consider the unique characteristics of each along with their own needs and budgets when selecting which tool to implement. First, the effectiveness of the technology and the completeness of available genomic profiles vary by tool. Additionally, some platforms unify secondary/tertiary NGS analysis and pharmacogenomic interpretation in a single environment. These options can allow oncology teams to move from raw data to information that guides treatment more quickly, as well as reduce operational costs by eliminating the need for multiple licenses.
Additionally, certain tools require many steps and specific expertise in the bioinformatics domain to use. However, others do not rely on either: for example, certain technologies offer cloud-based workflows (some with as few as three steps) where a user simply uploads the data and then downloads the produced tertiary analysis and recommendations. These options can especially support oncology researchers, molecular pathology laboratories, and genetic testing companies that do not have larger bioinformatics teams on staff. Mitigating the need to employ additional technical experts can help companies further minimize costs.
Ultimately, obtaining effective genomic technology can enable oncology teams to reduce turnaround times for biomarker-based therapeutic decisions, streamline reporting to tumor boards, and improve outcomes for their patients.
Photo: iLexx, Getty Images
Ben Stansfield is Director of Computational Biology at UGenome AIa biotechnology company that focuses on the development of genomics and bioinformatics software for personalized medicine with clinical and research applications.
Komal Sharma is Chief Product Officer at UGenome AI, a biotechnology company focused on developing genomics and bioinformatics software for personalized medicine with clinical and research applications.
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