Innovations in cancer diagnosis and treatment, including precision medicine and companion diagnostics, immuno-oncology therapies, and liquid biopsies that monitor cancer progression, are transforming the oncology landscape. According to the 2022 IQVIA Institute global trends report, precision medicine increasingly dominates in oncology where targeted therapies account for almost all of research, and over 40% of the drug development pipeline is for rare cancers where next-generation biotherapeutics — including cell and gene therapies – are increasingly being deployed.1 Cancer immunotherapies, which use biologic substances from the patient’s body or the laboratory to boost the patient’s immune system, have become first-line treatments in the cancer arsenal.2
Transforming cancer treatment through patient data
As cancer treatment becomes increasingly complex, including biomarkers and genomic testing that play a greater role in diagnosis and treatment decision-making, the amount of information derived from cancer patients’ electronic medical records (EMR) is creating an enormous trove of data.2 More than 13 million EMRs exist for cancer patients in the US alone and an unprecedented amount of data comes from a variety of other sources, such as clinicians, pharmaceutical companies, government and commercial insurers.2 As cancer discoveries continue to yield remarkable diagnostic and treatment advances, the need for robust data analytics to support the most optimum treatment decision-making and outcomes for patients, has grown proportionately.
Given this explosion of EMR data, along with a greater integration of data analytics and artificial intelligence (AI), there are growing opportunities to advance cancer diagnostics, as well as predict and monitor responses to specific therapies, while potentially improving patient outcomes.4,5 Big data, which refers to data derived from routine patient care rather than research, provides a ready source of real-world evidence (RWE), a critical component of determining clinical effectiveness.3 Propelled by the passing of the 21st Century Cures Act, legislation designed to fuel innovation and data interoperability, the Food and Drug Administration (FDA) is required to incorporate RWE into the approval process for cancer treatments and diagnostics.3 RWE can also be used to assess the value of diagnostic tools and treatment interventions in routine practice, as well as help shape bias-free methodologies in clinical trial design.4
The evolving state of diagnostic technologies
Using biomarkers, personalized medicine also incorporates big data to identify which patients will respond to treatment based on the tumor type, as well as predict treatment outcomes and monitor treatment safety and efficacy.5 Traditionally, patient-specific biomarkers in precision medicine have been used to determine therapy based on a “one biomarker, one drug” paradigm, which led to the development of more than 80 molecularly targeted therapies and companion diagnostics for various cancer indications by 2016.6
More recently, researchers' understanding of the need to test multiple biomarkers on individuals, has led to a migration to next-generation sequencing (NGS) cancer testing, which enables the identification of genetic mutations and variants at an unprecedented rate of speed in just one test. The introduction of new genetic testing technologies, that includes NGS as well as multi-gene panels that screen for several genes at once, has dramatically changed the diagnostic landscape.2,5 These innovations have resulted in a shift toward the use of multiple biomarkers and predictive analytics to look at genetic alterations across a variety of cancer subtypes. This has helped to better match the treatment to the patient’s biomarker results and gauge the patient’s response to therapy.3, 6
Liquid biopsies for cancer diagnostics
An even newer paradigm in cancer diagnostics has been the introduction of liquid biopsies, a promising diagnostic technology that analyzes circulating tumor DNA (ctDNA) in solid tumors and hematological malignancies at various time points to detect tumor progression and monitor treatments’ effectiveness.6 Less invasive and potentially more accurate than tissue biopsies, liquid biopsies offer a more complete picture of the cancer’s progression or remission in order to adapt treatments accordingly.6 However, its widespread clinical use has been extremely limited since more advanced analytical technologies are needed for the application of liquid biopsy in clinical practice.7
Challenges of cancer data
As the pace of diagnosis and treatment innovation accelerates, there is an increasing need for a more standardized approach to data analytics in providing the framework for the integration of advances in cancer treatment. This is especially true given the huge amounts of data that are adding increasing complexity and variability to treatments, outcomes, and analytical methods; all of which may prevent clinicians and researchers from drawing critical insights and conclusions from the data.5
While the majority of oncologists are continually seeking better treatments for their patients, the greater stratification of cancers due to newer diagnostic technologies, as well as the proliferation of biomarkers, makes it challenging for physicians to keep up, causing treatment decision-making to be more difficult.3
Opportunities of cancer data clinical insights
At the same time, the growing abundance of patient data and related clinical information provides even greater opportunities for the creation of evidence-based treatment options.2 Data-driven analytic approaches can further promote understanding of disease mechanisms, help define machine learning approaches to predicting patient response, and enhance patient-provider communications to augment the biological and clinical data needed to understand RWE and patient outcomes.3
There is growing awareness among the healthcare industry and oncology community that greater collaboration among the various stakeholders is needed to not just aggregate the data, but apply it to clinical solutions as well.2 Advanced data science collaborations can create digital platforms, using advanced analytics that enable the integration and analysis of research and RWE data. Coupled with the ability to incorporate best practices and the latest research outcomes, data can provide clinicians decision support for providing the right treatment to enhance patient care and outcomes.
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REFERENCES
- Global Oncology Trends, 2022, IQVIA Institute, https://www.iqvia.com/insights/the-iqvia-institute/reports/global-trends-in-r-and-d-2022
- Global Oncology Trends 2019, IQVIA Institute, ©2019 IQVIA, May 2019. https://www.iqvia.com/insights/the-iqvia-institute/reports/global-oncology-trends-2019 Accessed November 25, 2019.
- Data Driven Decisions in Cancer Care: How Using Analytics on EMRs And Biomarkers Will Improve Patient Outcomes,2 McKinsey Cancer Center, 2018. https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/data-driven-decisions-in-cancer-care Accessed November 25, 2019.
- Opportunities for Using Big Data to Advance Cancer Care, Clinical Advances in Hematology & Oncology, December 2018. https://www.hematologyandoncology.net/archives/december-2018/opportunities-for-using-big-data-to-advance-cancer-care/ Accessed November 25, 2019.
- The Potential Use of Big Data in Oncology, Oral Oncology, November 2019. https://www.sciencedirect.com/science/article/pii/S1368837519303021 Accessed November 26, 2019.
- Combination Biomarkers for Precision Oncology, Genetic Engineering and Biotechnology News, May 2019.
- https://www.genengnews.com/sponsored/combination-biomarkers-for-precision-oncology/ Accessed November 27, 2019.
- Recent Advances in Liquid Biopsy for Cancer, Technology Networks, January 2019. https://www.technologynetworks.com/diagnostics/articles/recent-advances-in-liquid-biopsy-for-cancer-314440 Accessed November 27, 2019.
- Bioinformatics, Big Data, and Cancer, National Cancer Institute, March 2019. https://www.cancer.gov/research/nci-role/bioinformatics Accessed November 27, 2019.
