How Can We Investigate Biomarkers For Lung Cancer Patients More Effectively?

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“May the odds be ever in your favor” is a phrase familiar to most, even if you haven’t read The Hunger Games series. It is a wish reserved for someone embarking on a challenging journey, or even a game, offering them the best chances of winning, not just now, but always. Unfortunately, this phrase – particularly the “always” component – resonates strongly with the lung cancer community; patients are fighting the odds for a chance of survival as researchers and clinicians toil away in search of the best treatment options to improve their outcomes.

Lung cancer is a monster global health issue, causing more deaths than breast, prostate, and colorectal cancer combined. According to the American Cancer Society, 2022 will bear witness to 2.3 million new lung cancer cases worldwide, with over 235,000 adult diagnoses in the United States alone.¹ With a five-year mortality rate of nearly 90 percent, this cancer indication is in dire need of both targeted control interventions and investment in improved early detection and treatment planning. What makes this process so complex is the serious lack of sufficient predictive biomarkers,² turning the lung cancer journey into one in which the patient and physician alike may as well be wearing blindfolds.

The Predictive Biomarker Deficiency

While advances in treatment continue to take place, the overall prognosis for patients diagnosed with lung cancer remains poor. Customarily, markers such as age, performance status, and disease stage have been used to risk-stratify patients and guide therapeutic decisions. These parameters provide some useful information, and molecular and genetic studies have identified several such markers, which appear to play critical roles in tumor growth and affect patient outcomes. For example, the inhibition of the PD-1/PD-L1 checkpoint has emerged as the leading path in the lung cancer biomarker search, but a valid universal biomarker predicting the efficacy of immune checkpoint inhibitors (ICIs) is lacking. The identification of new predictive biomarkers that can anticipate efficacy of or resistance to a specific lung cancer treatment plan is a key priority.

Without sufficient guiding biomarkers, physicians are forced to make their most educated guess as to which treatment plan will be most effective for their patient – a complicated judgment call. It’s not that we’re digging in the wrong spot, we’re just digging in so many different spots that it’s become inefficient and ineffective, and those people who stand to benefit from a solution – patients – simply don’t have the time to wait for hole after hole to be dug and searched.

Physicians confront multiple predicaments occurring simultaneously when choosing a treatment plan for their lung cancer patients, finding themselves in a position of great uncertainty as they face a process that is full of trial and error. With the current “one-size-fits-all” protocol, patients begin either targeted therapies or immunotherapy, alone or in combination with chemotherapy, and if both traditional and available biomarker-informed treatments fail, many patients are referred to clinical trials – as long as it isn’t too late.

Clinical Trial And Error

Clinical trials offer a promising new solution for lung cancer patients and, in some cases, they represent the only hope for survival. But with over 900 trials for this indication currently open and enrolling in the United States alone,³ and no predictive biomarkers to guide physicians and their patients, the guessing game only gets more complicated.

The combination of too many clinical trial options and our serious lack of biomarkers only further decreases our odds of finding a real lung cancer solution. The reality is that clinical trials play an essential role in the cancer care continuum, but despite the progress in research and breakthrough technologies, they are not a viable option for lung cancer patients in need. Why? Because we simply do not have enough clarity on how we can personalize treatment plans, stratify our patients, and transform into a truly individualized approach.

Proteomic Profiling To Help Defy The Odds

When looking for biomarkers, most precision medicine companies focus on the interaction between the therapy and the tumor itself. For example, if there is an anti-PDL1 drug, they will attempt to use PDL1 as a biomarker. This approach is in line with the traditional drug-target identification process – it works well when we look for targets, but it is less effective when we look for biomarkers. This is because the approach neglects the fact that the interaction between the therapy and the tumor is taking place within a very sophisticated biological system – the patient.

We know today that host (patient) response plays a critical role in response determination and resistance mechanisms, forcing us to consider the therapy-tumor-patient interaction. But how do we get an inside look at the building blocks and engines of the biological processes? While DNA and RNA analyses have been the cornerstone in current biomarker discovery, proteins are what provide us with a holistic view of what is taking place biologically inside the patient’s body. Proteins give us deep insight into the complex interplay between the patient, the tumor, and the treatment, increasing the odds of identifying a clinically insightful biomarker platform.  

Utilizing high-throughput protein analysis technology, we can identify proteomic patterns that are predictive of treatment response and provide us with clinically meaningful insights into the active tumor resistance pathways.  At last, we have a blueprint to understanding individual immuno-response, allowing us to truly personalize each patient’s treatment plan. Deciphering this complex interaction requires analysis of thousands of features, not an easy feat, relying on artificial intelligence (AI) and machine learning (ML) technologies. In turn, these technologies allow physicians to assess multidimensional clinical and biological data in a broad range of proteins to successfully measure, monitor, and ultimately improve lung cancer outcomes.

The Game Can Change… In The Patient’s Favor

Finding a successful path for personalized lung cancer treatment isn’t a roll of the dice, and without predictive biomarkers, we cannot improve treatment outcomes for patients. Using proteomic profiling to paint a larger picture, we can better ensure that patients receive the most precise treatment plan in an efficient and effective way, backed by strong clinical data.

Proteomic profiling is proving to be the catalyst in precise, individualized treatment planning, allowing us to make educated decisions instead of guesses and improving every patient’s odds of survival. No more clinical trial and error; no more shots in the dark with people’s lives on the line. It’s time we start focusing our efforts and work together on an approach that can change the face of care for lung cancer patients. Otherwise, the wish of the odds forever being in the patient’s favor is just that – a wish.

References

1. American Cancer Society (2022) Cancer facts & Figures. https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/annual-cancer-facts-and-figures/2022/2022-cancer-facts-and-figures.pdf
2. Dr. Federico Cappuzzo (2021) Special Issue "Biomarkers in Lung Cancer." MDPI Cancers. https://www.mdpi.com/journal/cancers/special_issues/Biomarkers_Lung_Cancer
3. Jason Sager, MD (2021, May 24) NSCLC: Exploring Treatment Challenges and the Potential in Clinical Trials. WebMD. https://www.webmd.com/lung-cancer/features/treatment-challenges-clinical-trials

About The Author:

Ofer Sharon, MD, MBA, CEO of OncoHost, is a physician and entrepreneur with more than 20 years of experience in clinical and commercial product development for startups in the health tech, biotech, and medical device industries. Prior to joining OncoHost, he served multiple roles in global pharmaceutical companies, including as medical director for AstraZeneca (Israel), new technologies scout for Medimmune, and medical director for MSD (Israel). He cofounded several healthcare companies, mainly focusing on bioinformatic and machine learning platforms for clinical deterioration detection and early intervention. Sharon is a major (res.) in the IDF and a medical company commander with specific expertise in disaster relief. He received his MD cum laude from Tel Aviv University, Israel.