Novel Genomic Roadmaps and Their Clinical Translation Ahead.

In our current era, there is no lingering doubt that the promise of precision medicine has been made apparent. Our scientific understanding of genetics and genomics, combined with the operational infrastructure allowing such analysis to easily occur, has transformed the patient–physician discussion in the clinic (1). What was formerly, “We don’t really know what caused the cancer” has turned into “Let’s analyze the cancer and determine its genomic etiology.” But such progress translating molecular understanding of disease into clinical care recommendations has proven fabulously transformative while also frustratingly complex. And an endpoint commonly reached is to explain the unique underpinning of an individual case while realizing such findings don’t really explain other phenotypically similar situations. And from this the next experiment is born. In the current edition of JCEM, however, Suteau and colleagues (2) demonstrate the hopeful power of more broadly exploring the genomic landscape of disease. Though just a starting roadmap, findings such as this generate hope given their likelihood of identifying individualized tumor targets, yet also for their ability to more broadly prove applicable to a whole sector of disease such as advanced thyroid cancer.

Toward this latter endpoint, the Suteau et al. study methodology is worth noting. G protein–coupled receptors (GPCRs) are linked to cancer cell biology, yet have never before been commonly exploited as therapeutic targets in the setting of advanced thyroid cancer. The authors analyzed 17 unique patients with refractory disease, while incorporating their unique RNA expression findings with that of separately available public databases containing parallel data for greater power. The use of RNA expression allows an entire genome to be explored even without an a priori hypothesis as to which gene or pathway might be causative. And the data derived from such an approach are vast, providing the broadest insight into a diverse disease as pathways and common receptors are identified. In this case, their findings provide multiple targets now available for therapeutic trails ahead. With extensive knowledge of receptor targeted compounds already “banked,” such studies also allow for a logical match linking together a down (or up) regulated GPCR with a potential investigational therapeutic.

Importantly, such data also provide even more insight into the clinical world beyond just a novel receptor target. Genomic expression data such as this can also be used for diagnostic purposes as well as individualized prognostication. While targeting the most effective therapeutic based on receptor knowledge can be life saving to a sick patient, broadly applying the understanding of genomic expression to prognostic decisions can impact a much larger population of diseased individuals (3). Within the Suteau investigation, note the authors ability to associate up- and downregulated genes with risk of recurrence as well as risk of death—prognostic data that certainly would influence any clinical follow up strategies or discussions of risk to benefit regarding future therapies. And this is not unique only to thyroid cancer care, as numerous other RNA based expression tests have been developed and are now actively applied to the care of breast, prostate and other malignancies (4, 5).

Yet, as roadmaps open up amazing new avenues for future investigation and application, they can equally hit dead ends. For example, the authors note that the GPCRome seems markedly different between papillary thyroid carcinoma and follicular thyroid carcinoma, despite both malignancies historically grouped together under the umbrella of well-differentiated thyroid cancers (6). Yet, this important GPCRome difference may well provide novel insight as to how to treat each disease more effectively, inclusive of unique targeted interventions or other recommendations that may lead to conservative, nonoperative management. But note that when the GPCRome of follicular thyroid carcinoma was compared to benign follicular thyroid tissue, no unique pattern of expression was identified. Data such as these confirm the complexity of genomic investigation, and stress the likely interaction of genetic with epigenic factors, or perhaps the continual evolution of the malignant microenvironment (7).

Such roadmaps will never be a perfect panacea. They are simply starting points to the scientific journey, logically built and conveying novel findings that no doubt hold great promise. Yet history has proven again and again that such roads can quickly take sharp turns, and open up many more questions than answers. The uniqueness of big data genomic analysis lies in its massive power to highlight both the diversity of phenotypically similar disease, while also defining patterns (or in this case select receptors) common to a large portion of any cohort.

The clinical translation of genomic data are wonderfully exciting to foresee into the decades ahead. We must acknowledge that in only the past 20 years, translational genomics have transformed our clinical discussions from that of conveying, “a well-differentiated thyroid cancer,” to now routinely discussing, “a well-differentiated thyroid cancer with mutations in x and y genes.” And with high likelihood, the very near future is one of telling our patients that they have, “a well-differentiated thyroid cancer with mutations in x and y genes, expressing this unique RNA prognostic pattern, but likely responsive to these two unique GPCR targeted treatments.” And with these insights come more informed discussions that ultimately improve health and longevity for so many.