In our modern world, the products of twenty-first century technology are a part of our daily lives. We all carry a “phone” with us that is, in actuality, a powerful minicomputer capable of navigating us to distant cities and finding us the best hotels and places to have dinner, as well as making actual phone calls. These powerful machines can do much more. Almost every day, we see high-resolution images returning to Earth from our latest spaceship to orbit the moon, or from a rover that is patrolling the surface of Mars in search of evidence of previous life on the planet. Rocket scientists actually built a rocket that was able to target and directly hit a fast-moving asteroid, which altered its orbit. Truly, modern technology is a wonderous thing. And yet, patients who develop cancer – particularly advanced forms of cancer characterized by the growth of secondary tumors in sites distant from the primary cancer (metastasis, or stage IV disease) – are treated with drugs that were developed fifty years ago or more. There has been some progress in this area for a small number of patients: most particularly, patients with metastatic melanoma who have dramatic and durable responses to new immunotherapy drugs. However, for the majority of patients that develop metastatic cancer, the outcome of traditional treatment strategies is essentially the same as it was decades ago. As a result of our lack of progress in this critical area, there is still no cure for most patients with metastatic cancer, and this is almost as true today as it was thirty-five years ago.

Interestingly, modern technology is used to diagnose and treat the earlier stages of cancer. State-of-the-art imaging devices are used to locate primary and distance sites of disease with precision. Sophisticated machines that can generate powerful and highly-focused beams of various types of radiation, while at the same time imaging the tumors that are being treated, are used routinely. Cancer surgery has become highly sophisticated with robotic machines such as the “DaVinci instrument” that is now routinely used for prostate cancer surgery. Some new and highly-targeted cancer drugs, as well as immunotherapeutic drugs, are also used in specialized cases (with impressive results) for treatment of patients with invasive cancer. And yet, despite this progress, old fashioned cyto-toxic chemotherapy drugs are commonly used to treat patients with metastatic cancer. The question I attempt to address in my book is this: can we leverage twenty-first century technology to move away from current treatment strategies, which are doomed to fail in patients with metastatic cancer, to newer and better strategies that can be more effective, less toxic, and even tailored to individual patients? And, moreover, does such technology exist?

The answer to the second question is a definitive yes. As a result of the sequencing of the human genome (not to mention the genomes of many other species), coupled with the result of large-scale patient-cancer sequencing efforts carried out by the Cancer Genome Atlas Project and other similar efforts, we have a deep understanding of the molecular fingerprints that characterize and drive human cancer. Due to large-scale efforts carried out by major research consortia, we also have a deep understanding of the genomic features and targeted drug sensitivities of hundreds of cell lines derived from patients with every type of cancer. However, we have so far failed to leverage these large and powerful data sets in ways that allow us to fundamentally change how we treat patients with cancer, especially metastatic cancer.

There are several barriers to the more complete implementation of genomic data for the design of therapeutic strategies. Amongst them is the lack of cancer genomics training for most clinical oncologists, which is compounded by the tight time constraints that most clinicians work under in today’s medical/reimbursement climate. But perhaps the most significant barrier to the full translation of genomic data for patient management (and the one I attempt to address in this book) is the lack of validated biomarkers that act as companion diagnostics for the use of targeted drugs associated with specific genomic features. This is particularly true for making predictions about the therapeutic efficacy of targeted drugs in individual patients rather than groups of patients. To develop and validate genomic biomarkers that can act as companion diagnostics for specific targeted drugs, new approaches need to be developed that fully leverage the many big genomic data sets that are now available.

It has long been true that most people who become biologists, even molecular biologists and geneticists, are not particularly fond of math. Unfortunately, analysis and the use of the kinds of big data sets that result from large scale genomic analyses require high-level mathematics and computer skills. Thus, to truly make significant progress in this field, collaborations must take place between medical oncologists, cancer biologists, computational biologists, data scientists, and statistical mathematicians. This, as it turns out, is a high bar but one we need to tackle head on if we are to do better.

In this book, I attempt to lay the groundwork for a strategy that can be investigated and adopted in order to leverage the functional genomic data that has been derived from patients with cancer and cancer-derived cell lines to predict targeted drug sensitivity. This strategy makes use of modern-day artificial intelligence/machine learning methods that may allow us to predict, on a patient-by-patient basis, which new and novel targeted cancer drugs individual patients will be highly sensitive to. As it happens, targeted drug sensitivity is the key to predicting the efficacy of any drug when administered to patients. Furthermore, targeted drug sensitivity is the lynchpin for the use of effective and well-tolerated targeted drug combinations, which will be essential if we are to use these drugs to make progress in the treatment of metastatic disease. I argue in this book that to accomplish these goals we must make use of the functional genomic and drug sensitivity data that has been derived from hundreds of human cancer-derived cell lines. For these hundreds of cell lines, the mutations and other genomic alterations that drive the cancer, as well as the essence of every gene in the genome and the sensitivity of each cell line to hundreds of targeted drugs, has been empirically determined. Thus, I argue that we, as a community, need to embrace these cell-line-derived data sets and use them to generate predictive approaches for drug sensitivity in individual patients. To do this, we must stop thinking of cell lines as research tools and start thinking of them as patients with cancer, of which we have a deep understanding. Indeed, I devote an entire chapter of the book to this key part of the strategy.  Artificial Intelligence/machine methods are suited to the analysis of large functional genomic data sets, and I believe these methods can be used to develop complex predictive signatures that can act as companion diagnostics that are linked to any number of novel targeted cancer drugs: most particularly, those that are already approved by the FDA. Thus, my goal in this book is to lay out a strategy by which this important goal can be accomplished and, in this way, move us towards the development of more effective targeted drug strategies for patients with metastatic cancer.

Stephen P. Ethier, PhD, is Professor of Pathology and Laboratory Medicine at the Medical University of South Carolina, having previously served as Associate Director for Basic Research and Deputy Director of the Barbara Ann Karmanos Cancer Institute, and Professor of Oncology at Wayne State University, USA. Dr Ethier has spent his entire career studying the biology of breast cancer. In recent years, his work has focused on the genomic alterations that drive breast cancer development and how those genomic changes lead to the expression of malignant phenotypes and drug sensitivities in breast cancer cells. His publications include over 140 peer reviewed scientific papers, and The Lewis Exchange, his first work of fiction.

Using Functional Genomics and Artificial Intelligence to Reverse Engineer Human Cancer Cells is available now at a 25% discount. Enter code PROMO25 to redeem.

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