Cancer Therapy in 2020

Cancer Therapy in 2020

In 2020, an estimated 1.9 million new cancer cases will be diagnosed in the United States, the equivalent of some 5,190 new cases each day. Furthermore, approximately 630,000 Americans are predicted to die of —a staggering 1,720 deaths per day.

As the second most common cause of death in the U.S., exceeded only by heart , cancer places a tremendous burden on individuals, families and our society as a whole. In fact, the Agency for Healthcare Research and Quality estimates that the direct medical costs for cancer in the U.S. in 2015 were an overwhelming $80.2 billion.

Over the last decade, significant advances in research, education, early detection methods and treatment have boosted cancer survival rates while new therapies continue to be developed. The recent introduction of cancer immunotherapies, in particular those based on immune checkpoint inhibitors, has created a paradigm shift in clinical oncology. These drugs work by unleashing the body’s own immune responses to promote elimination of cancer cells.

While traditional therapies such as chemotherapy and radiation are often used as a first-line treatment to fight cancer, immunotherapy has been gaining popularity with the support of promising research, clinical trials and new reimbursement from the Centers for Medicare and Medicaid services (CMS). In fact, a recent analysis shows that hospitals are investing more in cancer immunotherapies, with researchers finding a 199 percent rise in spending on immunotherapies used to treat small cell lung cancer during the first three quarters of 2019, compared to the same period in 2018. Moreover, first-line immunotherapy, either alone or in combination with chemotherapy, is already considered the standard of care for selected patients with non–small cell lung cancer.

While tremendous progress has been made with immunotherapy modalities, today only a small percentage of patients are benefiting from such therapies. A recently published paper estimated that 43.63 percent of U.S. cancer patients are eligible for checkpoint inhibitor immunotherapy; yet, on average, only 12.46 percent are estimated to respond. At the same time, the high cost of immunotherapy ($30,000–300,000 per year for an individual patient) and the risk of developing immune-related adverse events place pressure on the health system to prescribe such therapies only to those patients who are most likely to benefit. However, robust methods to identify appropriate candidates for immunotherapy are still lacking.

From the pharmaceutical industry perspective, the inability to predict which patients will benefit from immunotherapy has resulted in the recent failure of several major clinical trials. For these reasons, identifying appropriate candidates for immunotherapy is critical for maximizing clinical benefit, avoiding unnecessary toxicities and reducing costs.

As we enter the new decade, it is time to rethink the way we approach immunotherapy in order to benefit even more patients and their loved ones, while considering the financial realities on the ground. With the recent rise of artificial intelligence (AI) and machine learning tools to analyze complicated medical data, we now have the opportunity to profile patients earlier on in the immunotherapy treatment process to gain a new level of understanding that will ensure that precious time won’t be lost on a therapy that won’t have a positive impact.

If a patient’s response to immunotherapy can be predicted at the very beginning, treatment costs will be saved and patients can be spared from unnecessary side effects.


Although many cancer treatments initially show positive results, eventual resistance, characterized by tumor relapse or spread, is common. Traditionally, studies investigating tumor aggressiveness and resistance to therapy have focused on tumor-related features such as genetic and epigenetic changes that either accumulate over time or exist from an early stage (PMID 28397828, for example). However, this is only part of the picture. Looking beyond the tumor to see the actual patient, or “host,” adds an important layer of information. It is now becoming clear that the physiological reactions of the host to the treatment, collectively known as the “host response,” ultimately influence tumor behavior, often in favor of tumor growth and spread.

For several years researchers have been investigating how an individual’s unique host response to a variety of cancer therapies may promote therapy resistance and ultimately worsen the outcome, as summarized in recent scientific reviews (PMID 27118493,31645711).

In a practical sense, the host response to any given cancer therapy can be monitored in real time by analyzing a broad range of selected proteins in a series of blood samples; the first sample is collected prior to the commencement of treatment, and the following samples are collected during the early phases of treatment.

The proteomic changes can then be analyzed by machine-learning-based algorithms to detect early signs of resistance to treatment. Therefore, “host response profiling” serves as a predictive tool that determines the likelihood of the treatment’s success and guides the physician in tailoring treatment for individual patients.

Bringing this to life, consider a group of colorectal cancer patients receiving standard-of-care chemotherapy. Host response profiles of individual patients can be determined within the first week after commencing treatment. These profiles reflect the activity of biological processes that fuel tumor progression and, accordingly, are highly predictive of treatment outcome. Indeed, recent research in this direction has demonstrated a correlation between a specifc host response profile and lower survival in chemotherapy-treated colorectal cancer patients (unpublished). By detecting these interactions during the earliest stage of treatment rather than later in the cycle, physicians and oncologists can better plan and adjust treatments that will be effective on the individual level.

With the tools we have in hand, the future of cancer immunotherapy is bright. The American Society of Clinical Oncology (ASCO) has recognized the need for identifying strategies that better predict response to immunotherapy as one of the top nine research priorities to accelerate progress against cancer. Host response analysis takes us a crucial step forward in achieving this goal.

As we head further into 2020, host response profiling for cancer therapy will play a growing role in precision medicine for oncology. It will contribute to the discovery of new novel combination treatments to provide personalized treatment for cancer patients and further improve immunotherapy outcomes.