A Winning Combination Against Drug Resistance


by Ryan Chow

Earlier this year, President Obama announced the Precision Medicine Initiative. Proclaiming that the initiative would “lay the foundation for a new generation of lifesaving discoveries,” the President proposed setting aside $215 million to expedite the clinical translation of personalized genetics research.1 The initiative specifically highlights the development of patient-specific cancer therapies as an immediately promising area for breakthrough research. Accordingly, the National Cancer Institute (NCI) is budgeted to receive $70 million for this specific purpose.

In recent years, several next-generation cancer drugs have been approved for patients harboring certain genetic abnormalities and alterations. For instance, lung cancer patients with activating mutations in EGFR, a gene that regulates cell division, can now undergo erlotinib treatment. Similarly, patients with alterations in ALK, a gene that controls cancer progression, can receive crizotinib and ceritinib therapy.2-4 Such targeted therapeutics are designed to specifically counteract the cancer-promoting effects of these genetic mutations, largely leaving other cellular pathways intact. With their heightened specificity and efficacy profiles, these next-generation drugs have revolutionized the world of cancer therapy, improving patient prognosis and quality of life. It is no wonder, then, that there has been a strong push to fund further research into personalized cancer therapeutics.

But for all of their strengths, these new drugs often have a major caveat: cancers develop resistance to targeted therapeutics within one to two years of initial administration.5 The mechanisms underlying drug resistance have been extensively studied in vitro using established cancer cell lines.6 Although in vitro methods may not always faithfully recapitulate the progression of actual human cancers, these studies have been  critical in identifying secondary “bypass tracks” as facilitators of drug resistance; namely, a cancer may gradually evolve alternate strategies for promoting tumor growth upon pharmacological inhibition of the cancer-initiating mutation. Following this logic, one would hypothesize that combination cancer therapy could potentially overcome drug resistance by blocking both the primary oncogenic pathway as well as the secondary bypass track.

Looking for a way to efficiently identify combination therapies for drug-resistant lung cancers, researchers at Massachusetts General Hospital recently developed a screening platform to interrogate possible secondary bypass tracks within patient tumors that had already become resistant to a targeted therapeutic.7 The results of Crystal et al.’s study were striking: for many of the patient-derived cell lines, the high-throughput drug screen was able to clearly pinpoint the bypass track that the tumors had acquired, thereby uncovering a viable route for further pharmacological intervention. For instance, it was found that co-administration of MET inhibitors could resensitize tumor cells to EGFR inhibitors; importantly, statistical analysis demonstrated that the two inhibitors had synergistic effects that far exceeded the predicted efficacy if each drug was functioning independently.

With this study, the authors were able to identify a diverse range of potent, patient-specific combination therapies that successfully overcame drug resistance. Several intriguing patterns emerged from the aggregate data, revealing commonalities in the mechanisms of acquired resistance. However, one must keep in mind that these patterns, while biologically interesting, are not the key findings of the paper. Rather, the study acts as a proof-of-concept that patient-specific therapeutic strategies can be systematically discovered following the failure of first-line targeted therapies. In that sense, the work of Crystal et al. actually deemphasizes the importance of identifying general trends within the landscape of drug resistance; if we can identify combination therapies on a patient-by-patient level, there is no longer a need to make therapeutic decisions based on population-level correlations.

Paradoxically, their work also serves as a cautionary tale for personalized cancer genomics. Some of the most effective combination therapies that the authors identified through their drug screen could not have been predicted by traditional genetic analysis – that is, the combination therapies targeted pathways that did not possess any genetic alterations. The natural implication is that clinicians must not make treatment decisions based solely on patient genotypes, as the probability of missing important bypass tracks is far from negligible. One would be better off instead performing an entirely unbiased secondary drug screen on a patient-specific basis, in a manner akin to Crystal et al.

In this highly translational work, Crystal et al. have helped set the foundations for the future of personalized cancer medicine. Though their findings have not yet been tested in actual patients, the potential impact to human medicine is clear. Only time will tell if these patient-derived cell models can faithfully capture the complexities of drug resistance and thereby yield clinically effective therapeutic approaches.

Ryan Chow is a junior in Pforzheimer House, studying Human Developmental and Regenerative Biology.

Works Cited

  1. The White House: Office of the Press Secretary. “FACT Sheet: President Obama’s Precision Medicine Initiative.” The White House Briefing Room [Online], January 31, 2015. https://www.whitehouse.gov/the-press-office/2015/01/30/fact-sheet-president-obama-s-precision-medicine-initiative (accessed March 19, 2015).
  2. Tsao, M.S. et al. New England Journal of Medicine 2005, 353(2), 133–144.
  3. Shaw, A.T. et al. New England Journal of Medicine 2014, 370(13), 1189-1197.
  4. Shaw, A.T. et al. New England Journal of Medicine 2014, 371(21), 1963-1971.
  5. Chong, C.R.; Jänne, P.A. Nature Medicine 2013, 19, 1389-1400.
  6. Niederst, M.J.; Engelman, J.A. Science Signaling 2013, 6(294), re6.
  7. Crystal, A.S. et al. Science 2014, 346(6216), 1480-1486.



Categories: Spring 2015

Tagged as: ,

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s