Challenges influencing next generation technologies for precision medicine

In this editorial article, we present the perspective of emerging diagnostic technologies and discuss the future direction for precision medicine in oncology. Precision medicine aims to understand the unique properties and dynamics of each patient’s cancer in order to provide the most appropriate and rational treatment. Advances in new molecular technologies promise unprecedented opportunities to grasp the genetic and molecular makeup of individual tumors and thereby render personalized therapeutic actions. Technical or logistical challenges, however, are to be addressed to implement practical precision medicine into clinics.

The concept of precision medicine

Many of traditional medical treatments can be described as “one-size-fits-all” approaches. While successful in many instances, there are also certain groups of patients for which standard approaches are not the best solution. In the oncology arena, a growing number of unique drug targets (overexpressions, amplifications), mutations and resistance mechanisms to anti-cancer drugs have been identified, emphasizing the need better customized therapeutic approaches.^{1, 2} Precision medicine aims to understand the unique properties of each patient’s disease in order to provide the most appropriate and rational treatment.³ Combining generic clinical information with molecular profiling could shed further light on disease conditions and lead to actionable interventions.

The era of precision medicine emerges as national (e.g. Cancer Moonshot⁴) and international research initiatives gain attention and momentum.⁵ A main objective of such ambitious endeavors is to genetically define diseases and health, accrue genetic data for large patient cohorts, and test new drugs for specific mutations. Similar to the Human Genome Project, also an international effort, the catalysts behind precision medicine seek to deliver the “right treatment to the right person at the right time”. In this editorial article, we will emphasize the needs of emerging next generation technologies to drive precision medicine well into the future.

Cutting-edge technologies for precision medicine

Successful completion of the human genome project marked a signifiant inflection point in modern medicine. Molecular pathology has been adapted in clinics to obtain better insight on the molecular information of tumors. Immunofluorescence, molecular imaging, flow cytometry and other molecular profiling strategies are now used to augment the readouts of conventional microscopy. Furthermore, integration with new cutting-edge technologies for functional testing would be crucial for the success of cancer precision medicine.⁶

Single and scant cell molecular analyses

Advances in microdevices and molecular detection have ushered in a new era of single cell analyses. Researchers can now characterize individual cells through various aspects (e.g., protein, RNA, genomic DNA), thereby unveiling cellular heterogeneity currently masked in bulk measurement. A primary example occurs in circulating tumor cell (CTC) analyses. Although the discovery of CTCs dates back to 1869,⁷ little progress has been made due to technical difficulties in harvesting and analyzing rare cells. New microsystems^8–10 for single-cell analysis have revolutionized the field. CTCs are shown to play a critical role in cancer spread, as well as to interrogate both the primary and the metastatic tumor. Ultimately, the combination of genomic and phenotypic data from single cell profiling will improve the degree of precision in cancer staging and real-time monitoring of anticancer treatment.

Extracellular vesicles (EVs) analyses

EVs are membrane-bound vesicles actively shed by mammalian cells into the circulation.¹¹ Their abundance in easily accessible biological fluids (>10⁹ vesicles per mL of blood), high stability and the ability to carry cell-derived biomolecules (e.g. proteins, DNA, RNA) make them attractive as emerging biomarkers.¹² EV analyses reflect a promising non-invasive approach to probe the molecular profiles of tumors; conventional analytical approaches (e.g. flow cytometry, western blotting), however, are not most suitable for EV analysis due to their small sizes (< 200 nm). Therefore, the development of new sensing systems¹³ for high-throughput and sensitive detection of EVs will engage roles of the new markers for cancer diagnosis and treatment monitoring.

Circulating DNA

Somatic mutations, unlike their inherited counterparts, are only contained in affected cells. As such, the presence of DNA fragments circulating in blood (cDNA) could be matched to an increasing library of genetic sequences linked to abnormal mutations driven largely by advances in sequencing technology.¹⁴ cDNA could thus eventually function as specific biomarkers amenable to detection and tracking through serial blood draws.¹⁵ They have inspired academic and commercial efforts given its downstream impact. To realize the full potential of cDNA, concomitant advances in deeper gene sequencing technologies are needed to improve sensitivity challenges that preclude diagnostic confidence at this time. By definition, cDNA arise from lysed (i.e. not intact) cells; they thus provide a snapshot of biological reality. Integration with intact cell analyses would enable function studies using systems biology approaches and tools.

Looking forward

Despite the great promises of precision medicine, obstacles preclude the strategy from readily entering the clinical workflow.

Many drugs targeting mutations and other resistance mechanisms are still under development or being validated in clinical trials. While the dramatic effects of imatinib for chronic myeloid leukemia (CML)¹⁶ inspired current profiling strategies and targeted cancer treatments, most therapeutic options for other cancers have yet to deliver a similar and sustainable impact. Many mutations have been identified through deep sequencing, but the number of targeted therapies matched to those changes is far less. This raises concerns about the impact of novel readouts and the current dearth of actionable interventions. New strategies to enrich for patients who would likely benefit from targeted therapies in the clinical trial are thus necessary.

From an oncology perspective, cancer is not static, but rather dynamic in its progression and response to treatment. Genomic information of archival tissue often provides just a snapshot of the disease. Frequent assessment of patients’ status is crucial to inform treatment strategies. Therefore, the key to success for precision medicine lies in affordable and accessible options for investigators. Expanding attention beyond surgically procured tissues to include ‘next-generation biospecimens’ (e.g. blood, fine-needle aspirates, ascites) would improve research subject accrual and render serial analyses feasible. It is also important to integrate genomic analyses within a systems biology context to better appreciate the various interactions underlying tumor dynamics.

While multi-institutional and international efforts are crucial to obtain clinical information from large cohorts, different lab procedures and analytical approaches often lead to incompatibility of data¹⁷ and low reproducibility. To improve reliability and reproducibility, we need to consider standardization of protocols. This includes sample collection (processing and storing), concurrent clinical data abstraction, biospecimen type, analyses and data organization along with sharing. NIH’s new guideline to improve reproducibility reflects a first step towards such directions.¹⁸

The current biotechnology boom is accompanied by vast and formidable amounts of resulting data. For example, The Cancer Genome Atlas dataset is over 2,500 terabytes and it takes about a month to transfer from one place to another.¹⁹ Even beyond the practicalities of data storage and transfer, lies the matter of actually analyzing them effectively. We must recruit and cultivate next generation experts in bioinformatics and statistics also versed in mathematics/computation and biology. Tapping into the emerging opportunity of artificial intelligence and cloud computing could accelerate progress. Implementing programming and bioinformatics courses during medical and graduate school training could help demystify the field for translational researchers.

Moving forward, novel diagnostic tools that leverage various tissue types and physiological parameters will propel personalized medicine. While expanded analyses will initially challenge bioinformatics, technological advances in data forensics should help elucidate and triage relevancy of the readouts. Serial analyses should better approximate the dynamic states of disease. By doing so, subsequent therapeutic actions would be informed by current, not antiquated data, with high potential for improving clinical response.