Precision medicine is here to stay
There have never been more treatment options available to cancer patients than there are today. The progress of precision medicine, most notably in oncology, has caused a paradigm shift in the way patients are treated: by selectively targeting patients’ cancers based on their molecular profile, precision therapies are becoming much more effective, more likely to extend progression-free survival with less damaging side effects, and resulting in a higher quality of life.
There are 631 drugs in later stage development for oncology, and greater than 87% of these drugs are biomarker-driven therapies. The number of targeted therapy and immunotherapy options available at the point of care will continue to increase in the near future, and this shift will continue to dramatically change the way physicians treat cancer. In ten short years, the field has moved from a primarily chemotherapy-driven strategy to significantly more individualized treatments based on patients’ molecular profiles, in keeping with our knowledge that cancer is a genetic disease.
The increase in therapy options, while beneficial to patients, presents unique challenges for managing complexity and cost. While targeted therapies present new opportunities to extend and improve the quality of patients’ lives, acquired resistance to these therapies remains a concern and requires physicians to monitor and potentially make more frequent changes to patient treatment regimens. The frequency of therapy decisions is on the rise, as is the average cost of each therapy. The growing adoption of liquid biopsy to monitor patients’ cancer progression may assist with these decisions, but this diagnostic test comes with its own challenges and cost. Longer survival rates, more frequent therapy regimen changes and more expensive therapies are anticipated to drive a significant increase in oncology spend over the next five years. The increased complexity at the point of care and an increased oncology drug spend are putting pressure on hospitals at a time when they are already challenged to manage costs while providing high-quality care.
Managing the complexity
Oncologists will need to understand and manage the growing body of knowledge around what genomic alterations their patients have, and what the implications of these variants are. Comprehensive knowledge of drug efficacies and contraindications, within specific molecular and clinical contexts, will be required. This molecular knowledge must be considered in the context of the patient’s specific disease histology, and clinical data such as prior treatment, stage and progression of disease are all variables that need to be factored in when the physician is deciding on the treatment strategy for each patient. Today, many health systems utilize a Tumor Board to help sift through the complexity of a cancer treatment strategy. Most Tumor Boards meet periodically and can only review a small number of patients per session; thus, while a Tumor Board is an effective forum, it is not the long-term answer to supporting treatment decisions at the point of care required for millions of cancer patients. The challenge will need to be addressed with far more sophisticated clinical decision support systems than those currently in place. These systems will allow management of the complexity to scale overtime.
Managing the drug spend
Because of the surge in biomarker-driven treatment options, the drug spend on oncology is anticipated to rise from $45 billion in 2018 to $90 billion in 2022. The average cost of chemotherapy – what cancer patients have traditionally received – is $3,000 per patient per month. This cost is relatively inexpensive compared to targeted and immunotherapy treatments, which can run $5,000-15,000/month. A decade ago, only a few genetic aberrations could be targeted, but over the last 2-3 years, many new expensive therapeutics targeting many new genetic aberrations have been approved; these advances will inevitably lead to more physician requests for targeted therapies for their patients.
In challenge lies opportunity
The challenge for health systems will be to manage both patient care and the drug spend, which is growing at a compound annual growth rate (CAGR) of 18%. Many hospitals are changing their payment structures, as they are increasingly incentivized by value-based care payments to keep their patients healthy. They are moving from the traditional fee-for-service model into a shared risk model, becoming Accountable Care Organizations (ACO) or self-insured. This move to shared risk provides hospitals with the opportunity to innovate how they support quality care and manage cost.
Most cancer patients are going to need treatment strategies that move beyond the standard of care. FDA guidelines cover broad molecular patient populations, but decisions are becoming even more patient-specific, and rare variants may be predictive but not supported by large enough randomized trials to be included in guidelines. Therefore, beyond a certain point in most cancer patients’ course of treatment, physicians and payers will not be able to rely solely on guidelines; the options will become off-label treatment (chemotherapy, immunotherapy or targeted therapy) or clinical trial enrollment. Restriction of therapies based on this narrow framework of guidelines excludes a growing number of patients who could, based on available scientific and clinical evidence, benefit from access to targeted therapies once they have exhausted the standard of care options. By ignoring these molecular sub-populations, the quality of care and overall spend is most likely negatively impacted. Most systems in place today to support oncology have been built to support chemotherapy treatment strategies and basic one-to-one biomarker to drug treatments. They lack the ability to incorporate a patient’s whole molecular profile, the evolving evidence on new therapy options, and reimbursement models based on groups of patients with similar molecular profiles.
Decision support for precision oncology
To treat oncology patients effectively and efficiently, physicians will need more comprehensive clinical decision support to help answer the following questions:
• What tests and treatments are appropriate and pre-approved for this patient’s specific cancer type and molecular profile, and which are not?
• What can be approved directly without further review, and which decisions require additional review before they can be made?
• What follow-up tests will be required to monitor treatment efficacy, safety, and/or resistance?
Hospitals will need a comprehensive framework for understanding the possible molecular characteristics of a disease and the ramifications of those characteristics. They will need to offer sufficient diagnostic testing to fully discern each patient’s cancer profile to determine the best course of action. An understanding of not only what a disease-associated gene does, but also the effects of specific gene variants in the context of the patient’s disease will be required.
Labs, which perform the molecular tests, will no longer be able to simply report out the raw data on the existence of a mutation; they now need to perform analyses and interpret the variant data, integrate multiple test results and then connect these test results to therapy options and prognoses. Hospitals and commercial labs are approaching the variant interpretation challenge in a few different ways: crowdsourcing, artificial intelligence, and expert curation of the evidence. While there are pros and cons with each approach, hospitals need to ensure that lab test results are interpreted with the greatest degree of accuracy, as the characterization of a variant as activating or inactivating can have a significant impact on therapy options. Even for FDA-approved drugs, inaccurate characterization of variants may result in a patient receiving approval for a drug to which they are unlikely to respond. Alternatively, a patient with a rare yet sensitizing variant may be excluded from receiving an FDA approved drug because that patient’s specific variant is not listed in the guidelines, despite evidence that the variant may predict sensitivity to the drug. That patient may be put on a less effective therapy which could be more expensive. Association between a variant and a therapy must be based on evidence of a variant’s biological role and any available clinical evidence; further, the patient’s entire molecular profile should be evaluated for variants that confer drug sensitivity and those variants that confer resistance.
Once the evidence on the patient’s molecular profile is gathered from lab tests, and potential therapies based on those test are determined, the complexity doesn’t end there; the molecular factors are only the beginning. The physician needs to understand the underlying clinical attributes that also impact therapy decisions, including prior therapies, stage of disease and state of the patient.
Clinical decision support tools will necessarily have both the ability to incorporate molecular data and will integrate directly with the electronic medical record (EMR).
A shared evidence framework
In the new risk sharing models, an opportunity exists to develop a reimbursement framework based on a shared understanding of the evidence supporting both care and reimbursement decisions. Clinical decision support tools support patient-specific decisions, and evidence-based reimbursement frameworks can use the same evidence to create a structure of rules for groups of patients. These groups of patients can be defined based on the patients’ clinical diagnosis and molecular profile, together with evidence supporting specific treatment strategies. This treatment strategy roadmap would offer appropriate clinical trials and/or evidence-based off-label therapies as part of the patients’ treatment strategies, options previously unavailable from traditional payer models. In the long run, more effective tailored treatment strategies will result in higher quality of care and more optimal drug spend.
Conclusion
Precision medicine is here to stay. As more personalized medicines become available and cancer survivors live longer, providers will face increasing complexity at the point of care, as they have more options to treat individuals over longer time periods. Understanding a patient’s longitudinal journey will become important, as cancers evolve and develop drug resistance. All of these advancements will put pressure on hospitals, which will be increasingly challenged to manage the exponential growth in oncology spend and to ensure that patients are receiving optimal care in a complex environment. To implement an evidence-based reimbursement framework, hospitals will need to invest in new infrastructure to support treatment and reimbursement decisions. The vendors that will be in a position to supply solutions to solve this complexity will have the following characteristics: they will be able to invest the capital necessary to bring together the complexity of the EMR and the molecular data; they will have deep health care knowledge and understand the complexity of the health system environment; they will be agnostic to the drug options available and they will possess strong oncology domain expertise; and finally, they must have the patient at the center of their strategy. Hospitals that seize the opportunity to invest in decision support systems that integrate both genomic and clinical data and provide high-quality, actionable clinical interpretations at the point of care will not only optimize their drug spend, but also provide the best possible outcomes for their patients.
References:
Global Oncology Trends 2017. Report by the QuintilesIMS Institute
Page 11 : Redefinition of Cancer.
• the pipeline of oncology drugs in clinical development has expanded by 45% over the past ten years; 87% of the late stage pipeline are targeted therapies which include small molecule protein kinase inhibitors and biologic monoclonal antibodies.
• the global R&D pipeline for oncology remains robust with 631 late phase therapies, an increase from the number of oncology
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