Gazing into Science’s Crystal Ball

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A couple of years ago, I interviewed a patient who suffered from an aggressive form of prostate cancer to discuss his clinical trial experiences. In his search for suitable clinical trials, he had thoroughly studied the scientific details underlying his cancer. In this context, he mentioned how an increasingly popular immunotherapy seemed out of reach for him because it worked well for blood cancers but hit a wall when it came to solid tumors. At that moment, I mentally transported back to a front-row seat at a conference that I had attended several years ago. 

That conference talk was my first encounter with the concept of coating immune cells with receptors that recognize cancer cells. The presenter concluded his slides with a summary of advantages and shortcomings of the chimeric antigen receptor (CAR) T strategy, and I scribbled in my notepad that this “approach seems challenging for solid tumors.” I found it fascinating that this detail came back to me—albeit from the clinic rather than the bench this time. 

People often view basic research as educational exercises where scientists tinker around in the lab, but it is, in fact, the forefront of innovation. Keeping a pulse on basic research allows one to witness the birth of ingenious ideas and the development of cutting-edge technologies, some of which might change people’s lives someday. 

Take the genome editing tool, CRISPR, for example. When this technology made an entrance just over a decade ago, researchers knew right away that this splash would ripple into many spheres of applications.1 So last year, when the first CRISPR-based gene therapy, Casgevy, was approved by the FDA for treating sickle cell disease and transfusion-dependent beta thalassemia, no one batted an eye. Yet, while many expected this momentous win, very few would have guaranteed it in advance. 

That’s because researchers are cautiously optimistic when it comes to new advances in science and technology. Just like a game of snakes and ladders, all ideas start at a level playing field. Some progress or regress rapidly because of unexpected developments. A relatively slow research area might suddenly achieve significant milestones due to a new methodology, or a steadily rising topic can grind to a halt if a prevailing theory is overturned. 

A recent, prime example of this unpredictability is, ironically, a prediction model. When Alpha Fold, an artificial-intelligence (AI) based model debuted about five years ago, it almost instantaneously provided protein biologists with the boost they needed to solve a decades-long problem.2 With protein folding sorted, AI applications unfolded in abundance.   

Today, with increasing crosstalk between interdisciplinary researchers, if a transformative technology props up in one field, it rapidly spreads into other areas as well. Case in point: Scientists use CRISPR to improve the potency of immune cells for treating cancer, and researchers are already looking to identify patient-specific antigens using AI to optimize treatments. 

Although I could not confidently pinpoint the winning strategy for cancer treatments to my interviewee at the time, I believe that every incremental positive result is a step in the right direction. Given all the uncertainty, tracking the progress in basic research might seem like holding an unmarked winning lottery ticket among a thousand others. However, the good news is that no matter which idea gets picked, we all end up winning. 


  1. Jinek M, et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science. 2012;337(6096):816-821
  2. Senior AW, et al. Improved protein structure prediction using potentials from deep learning. Nature. 2020;577:706–710.
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