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The Future of Precision Oncology: Bioinformatics Solutions and Challenges

Cancer is a leading cause of death worldwide, accounting for one in every six deaths globally [1]. In particular, advanced and metastatic cancers are notoriously difficult to treat because they are no longer resectable through surgery, which is the mainstay of treatment for most solid cancers [2]. In such cases, alternative therapies, such as chemotherapy, radiotherapy, and immunotherapy, are administered independently or in combination to reduce and potentially remove the tumor. For years, patients diagnosed with the same type of cancer received the same lines of therapy. However, this ‘one-size-fits-all’ approach to cancer treatment often yields drastically different outcomes for each patient and entails severe side effects, reducing the patient’s quality of life. Thus, there is an increased need for the active implementation of precision oncology in clinical practice [3].

Precision oncology is the “use of therapeutics that are expected to confer benefit to a subset of patients whose cancer displays specific molecular or cellular features” [4]. Adopting the precision oncology approach enables oncologists to tailor therapies to the unique biology of the patient’s tumor, which improves the diagnosis, prognosis, and treatment of the disease. Unfortunately, precision oncology has only been adopted to a limited extent in the clinical setting despite its potential benefits. However, advances in sequencing technologies and bioinformatics methods, coupled with sophisticated information systems for managing biological Big Data, may offer unprecedented opportunities for the realization of precision oncology [5].

The fundamental idea behind precision oncology is to develop a highly targeted and personalized treatment based on the molecular characteristics of each patient’s tumor, which is not new [3]. For instance, cancer biomarkers (e.g., mutational cancer driver genes and protein overexpression) are frequently used to make diagnostic and prognostic predictions. The introduction of high-throughput sequencing technologies such as next-generation sequencing (NGS) in cancer research has enabled oncologists to identify and validate a range of genetic alterations, such as single-nucleotide variants (SNVs), copy number variants (CNVs), insertions and deletions (indels), and gene fusions [6]. Today, the expansion of sequencing technologies beyond genomics, including epigenetics, transcriptomics, proteomics, and metabolomics, has led to an exponential rise in the number of targetable tumor-specific molecular alterations identified through molecular profiling [4]. In turn, bioinformatics techniques play a central role in the integration and analysis of molecular profiling results, which, together with other relevant clinical data, such as the patient’s treatment history, comorbidities and complications, radiology imaging scans, and family information, form the basis for therapeutic decision-making by oncologists.

While bioinformatics systems are an essential tool for the large-scale processing of complex clinical data, much work remains to transform the massive volumes of data into real-life treatment recommendations tailored to each patient. An article by Servant et al. (2014) describes an ideal bioinformatics workflow for precision oncology. First, scientists profile patient-derived tumor biopsies (e.g. through microarrays, NGS, immunochemistry, etc.) to assess for mutations and other genetic aberrations. Profiling results are then processed using bioinformatics pipelines to extract relevant information about the tumor, which is then integrated into a shared information system. The system should enable end-users to make queries for real-time data retrieval (e.g. raw and processed data, clinical phenotypes of the patient, etc.) and match treatments to specific targetable molecular alterations. Finally, artificial intelligence (AI) systems should be applied to the bioinformatics pipeline to combine the raw findings from molecular tests with existing clinical data to predict the therapeutic benefits the patient can realistically expect [7].

Several challenges must be addressed to incorporate the bioinformatics workflow into the everyday decision-making by oncologists, namely the lack of adequate technology for the large-scale curation and interpretation of biological Big Data. Unlike other forms of data, biological data is inherently hierarchical and heterogeneous; it exists on multiple levels (e.g. molecules, cells, tissues, organs, whole systems) and in various forms (e.g., sequencing data, clinical data, imaging data, behavioral data) [8]. To handle such complex datasets, oncologists must determine how data parameters will be defined to make the data comparable and interpretable for therapeutic decision-making [9]. Data standardization will therefore be an absolute prerequisite for achieving this goal. Furthermore, to accelerate the mainstream use of precision oncology in clinical care, an integrative database of patient-level oncological data must be established [9]. The database should enable real-time access to longitudinal patient data showing the tumor profile, disease progression, treatment pathways, and health outcomes of comparable patient cases [9]. Healthcare organizations, including hospitals, cancer centers, and clinical laboratories, should make a collective effort to facilitate efficient data sharing across institutions and national borders while maintaining strict standards for data quality, privacy, and security [9].

Although leveraging the full potential of precision oncology requires more significant reform, it is without a doubt that the importance of precision oncology will grow immensely in the coming years. The development of cutting-edge bioinformatics systems for storing, processing, and exchanging molecular data and real-world clinical results will play an integral role in the broad adoption of precision oncology in clinical practice. These technologies may pose new challenges in data analytics and governance, which should be addressed through robust data management practices and ongoing assessment of relevant policies. Nevertheless, bioinformatics solutions to precision oncology will help oncologists better understand their patients’ disease and make more informed decisions, especially in the treatment of rare cancers for which limited therapeutic options and preliminary clinical trial data are available.


  1. World Health Organization. (2022, February 3). Cancer.

  1. Tohme, S., Simmons, R. L., & Tsung, A. (2017). Surgery for cancer: A trigger for metastases. Cancer Research, 77(7), 1548–1552.

  2. Wu, D., Rice, C. M., & Wang, X. (2012). Cancer bioinformatics: a new approach to systems clinical medicine. BMC Bioinformatics, 13, 71.

  3. Malone, E. R., Marc, O., Sabatini, P. J. B., Stockley, T. L., & Siu, L. L. (2020). Molecular profiling for precision cancer therapies. Genome Medicine, 12(8).

  4. Servant, N., Romejon, J., Gestraud, P., La Rosa, P., Lucotte, G., Lair, S., … Hupe, P. (2014). Bioinformatics for precision medicine in oncology: Principles and application to the SHIVA clinical trial. Frontiers in Genetics, 5.

  5. Singer, J., Irmisch, A., Ruscheweyh, H. J., Singer, F., Toussaint, N. C., Levesque, M. P., Stekhoven, D. J., & Beerenwinkel, N. (2019). Bioinformatics for precision oncology. Briefings in Bioinformatics, 20(3), 778-788.

  6. Castaneda, C., Nalley, K., Mannion, C., Bhattacharyya, P., Blake, P., Pecora, A., … Suh, K. S. (2015). Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine. Journal of Clinical Bioinformatics, 5(4).

  7. Li, Y., & Chen, L. (2014). Big biological data: challenges and opportunities. Genomics, Proteomics & Bioinformatics, 12(5), 187–189.

  8. Accenture Life Sciences. (2021, April 23). The future is now: How to drive precision oncology Adoption.


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