Basem Goueli MD/PhD/MBA

I always think of the Jeff Goldblum quote in Jurassic Park where he says "life finds a way" when I think of cancer. However, I simply substitute the word life with cancer in that "cancer finds a way". Indeed, the bane of an oncologist's existence is the heterogeneity of tumors and the numerous resistance mechanisms cancer cells employ. One of the most fundamental questions in Oncology is why we can't cure the vast majority of stage 4 tumors even though we can often eradicate large swaths of tumor cells.

We are witnessing a revolution in cancer therapeutics that is truly bewildering. The number of novel drugs with unique mechanisms of action currently in clinical trial and development is staggering, and will only increase. Much of this has been precipitated by exponential advances in computational biology and precision medicine. To this end, many oncologic breakthroughs in the future will occur at the intersection between these disciplines and the clinic. Thus, understanding the putative clinical relevance and utility of drug development and precision medicine endeavors is essential for related companies, academic researchers, etc. Failure to derive a sophisticated appreciation of how one's work will translate into the clinic can be catastrophic, as you can have "positive related studies" with no clinically applicable "end-game" or return on your investment. Failure of oncologists to develop a strategy for assimilating molecular data and novel agents they will be inundated with in the future will compromise patient care. In this bimonthly editorial series, we strive to ensure readers have clear visibility of the "clinical forest", and not just the associated "trees". We will focus on the present and the future of precision medicine as it relates to the clinic, and provide those in the pharmaceutical industry with an instrumental clinical perspective. We start by introducing the notion of "playing chess against cancer" through treatment cartography, the development of patient specific treatment maps incorporating conventional therapies, clinical trials, molecular studies, etc.

In the incredibly competitive world of drug development, comprehensive precision, and healthtech, every strategic control point is essential. Over the next several months, during this editorial series, we will admittedly push the envelope as we elucidate several strategic control points in precision medicine, healthtech, drug development, clinical trial design, etc. We will weave in and out of these "trees" in a way few are capable of, at breakneck speed, as we elucidate the clinical "forest" comprised of them. As a full-time general hematologist/oncologist specializing in precision medicine, consultant to numerous pharmaceutical companies, CEO and Founder of two AI dependent companies in stealth mode, medical director of two phase 1/2 Xbiotech trials (pancreatic cancer), and having an MD/PhD (biochemistry)/MBA, I hope to offer you a unique perspective at the interface of AI, drug development, clinical trial design, healthtech, and precision medicine. In the last installment of this series, I described playing chess against cancer in the long-term approach to cancer patients. I discussed the refined integration of conventional therapy, precision medicine, and clinical trials via the COMET algorithm in the creation of patient-specific treatment maps, a term I coined treatment cartography. Today we reside in the moment and focus on the immediate care of cancer patients. To this end, whereas the long-term approach to cancer patients is a game of chess, the short-term approach is a game of American football.

In 1945 the volume of human knowledge doubled every 25 years. Now, that number is 12 hours [1]. With our collective computational power rapidly increasing, vast amounts of data and our ability to assimilate it, has seeded unprecedented fertile ground for innovation. Healthtech companies are rapidly sprouting from data ridden soil at exponential rates. Cell free DNA companies, once a rarity, are becoming ubiquitous. The genomics landscape, once dominated by the few, are being inundated by a slew of competitors. Grandiose claims of being able to diagnose 50 different cancers from a single blood sample, or use AI to best dermatologists, radiologists, pathologists, etc., are being made at alarming rates. Accordingly, it’s imperative to know how to assess these claims as fact or fiction, particularly when such claimants may employ “statistical misdirection”. In this addition to “The Insider’s Guide to Translational Medicine” we disarm perpetrators of statistical warfare of their greatest weapons, statistics themselves. To do so we introduce a novel BASIS acronym for analyzing data underlying AI models and new products. Moreover, we introduce a unique harm / inherency / plan / solvency / disadvantage paradigm for developing and assessing business plans, grants, healthtech, genomics companies, etc. We provide a use case for implementation of these thought constructs to assess new entrants in the melanoma detection. Ultimately, I intend to leave you with a rigorous approach to discriminate the good from the bad, and everything in between, in healthtech, multiomics, etc.

Chapter 1 of a 3-chapter Hitchhiker’s guide to cell-free DNA for travelers of all backgrounds and expertise.

Even though I’ve been a hematologist/oncologist for over a decade, I’m horrendous at dealing with death. It still shatters me every time I have to tell a patient they’re going to die of their cancer. It’s supposed to. If it didn’t, I need to find another job.

There have been clinic days where I’ve seen 30 patients, 17 of which had theoretically incurable cancers. Accordingly, I’m on record as saying nobody would be happier than me if my skill set was no longer needed by society.

It’s often forgotten that NO TWO CANCER PATIENTS ARE THE SAME. They have different tumor types with different histology, molecular profiles, stages, comorbidities, etc. Accordingly, in a world so heterogeneous, there are next to no generalities in cancer. However, there is a singular universality, “the earlier the better”. The sooner I know about a patient’s fever, infectious symptoms, etc., the more likely they will do well. The earlier we identify a patient’s cancer the more likely they can be cured.

In this editorial series we’ve moved at breakneck speed as we talked about the long- and short-term approach to cancer care, and how to assess the claims of people in healthtech, genomics, pharmaceuticals, science, etc. [1, 2, 3]. Today we begin to put the entire cell-free DNA cancer screening industry under the microscope. We will assess the grandiose claims of numerous companies that they are the solution and will enable us to get one step closer to putting me out of work. In so doing, we will draw on the lessons conveyed in the prior “Drowning in Data”, “Quarterbacking a Patient’s Cancer Care”, and “Playing Chess Against Cancer” entries in this editorial series, as we take a breathtaking tour of the cell-free DNA world with you as CEO.

Chapter 2 of a Hitchhiker's Guide to Cell-Free DNA.

By the end of 2022, there will be an estimated 609,360 deaths cause by cancer in the US alone [1]. In 2018 there were 17 million new cancer cases and 9.5 million cancer deaths worldwide [2]. By 2040, this is expected to increase to 27.5 million new cancer cases and 16.3 million cancer deaths annually [2]. Currently, only five cancer screening tests are available in the US (breast, colorectal, cervical, lung and prostate), accounting for 42% of annual cancer incidence in people aged 50-90 [3]. Cancer detection before stage IV could reduce cancer-related deaths by >15% within 5 years [3]. Accordingly, the population health cell-free DNA (cfDNA) industry is expected to grow to nearly 30 billion dollars by 2028 [4]. Unsurprisingly, cfDNA will pervade every facet of clinical care, drug development, clinical trials, etc. This has not been lost on the business world as a market that barely existed a little over a decade ago is becoming increasingly saturated. Indeed, there is a new start-up in the cfDNA space daily. The reason for this isn’t just financial, it’s also related to the simplicity of these methodologies and the accessibility of artificial intelligence and machine learning. In the last edition of this series, “The Hitchhiker’s Guide to Cell-Free DNA, Chapter One”, you were introduced as CEO of the fictitious company, Comprehensive Precision. You presented an extensive cfDNA and liquid biopsy primer to your board, and now have to prove your worth. Today you must present your plan to enter the cfDNA market comprised of very talented companies, including Grail, Exact Sciences, Freenome, Guardant Health, etc. Your focus is on population health screening as you intend to put oncologists like me out of a job by identifying cancer before it’s apparent radiographically or symptomatically. Today, in Chapter 2 of “The Hitchhiker’s Guide to Cell-Free DNA” we will perform a cursory market analysis and targeted description of population health cancer screening companies, discuss the associated value chain, and explore methods of entry into the market.

Advances in computational biology, precision medicine, artificial intelligence/machine learning, etc., are launching us into a world where strategic control points in drug discovery, pharmaceuticals, clinical trials, etc., will never be the same. 

In oncology, we do the best we can. We give drugs with 10-20% chances of working because we have nothing else. Drugs are approved, right or wrong, based on mere weeks of overall survival benefit. Clinical trials labeled as novel are merely the result of adding approved drug A to approved drug B and justifying it with a contrived methodology. We often use reasonably effective, but crude metrics, such as PD-L1 expression, mismatch repair (MMR) deficiency, microsatellite instability (MSI) status, and tumor mutation burden (TMB) levels to delineate if patients will respond to immunotherapy. Unfortunately, despite our best intentions, we are often wrong. Cancer cells don’t read a textbook. They don’t care about our predictions or our prognostic models. Accordingly, and I tell this to the people I teach all the time, what the cancer is doing matters far more than what you think it should do. Phenotype is ALWAYS more important than genotype. Nonetheless, it’s imperative we continue to strive to close the gap between expectation and actuality. Numerous companies, academicians, etc., have capitalized on our innate need for control as clinical practitioners and scientists. Artificial Intelligence (AI) and machine learning (ML) based drug response prediction models are being developed at exponential rates. Every week I’m approached by a new company claiming they can accurately identify patients who will respond to a particular drug, and those that will not. Today, we will bring together the short-term and long-term approach to clinical cancer care, AI/ML, multiomics, and cell-free DNA, covered previously in this series.  In this eighth entry to “The Insider’s Guide to Translational Medicine”, we explore the future utility of drug response prediction models in the clinic through a case study of immunotherapy prognostic assays. Specifically, we will discuss Oncocyte and BostonGene, and provide the reader with an incredibly unique perspective UNAVAILABLE ANYWHERE ELSE.