Patient case: 64-year-old male with cholangiocarcinoma; genetic feature: BRAF (V600E)

Clinical trial: Understanding Evidence-Based Practice Patterns in Advanced NSCLC

Summary: The overarching objective of this study is to close clinical knowledge and performance gaps by providing oncology clinicians with the latest advances and emerging research in the evidence-base and personalized treatment of advanced NSCLC patients. In addition, the research team seeks to meet quality measures relevant to value-based care delivery through IT infrastructure and clinical workflow processes. The research team also hopes to gain insights on clinician practice patterns related to advanced NSCLC, and the correlation between advanced NSCLC patients reported goals of care and advanced NSCLC patients' fit/frailty status and treatment decisions.

Detailed description: Non-small cell lung cancer (NSCLC) accounts for about 25% of all cancer deaths and is by far the leading cause of cancer death among both men and women. Each year, more people die of lung cancer than of colon, breast, and prostate cancers combined. NSCLC mainly occurs in older adults. About two out of three people diagnosed with lung cancer are 65 or older; the average age at the time of diagnosis is 70 years. Despite the many advances over the past few decades related to surgery, radiation therapy and chemotherapy, death rates attributable to lung cancer have remained relatively unchanged. Most patients are diagnosed with distant disease and have a 5 -year survival of only 4%. Today, however, there is renewed optimism that these trends have started to change as recent research advances have led to an explosion in lung cancer genetic and biologic knowledge among NSCLC scientists and expert clinicians. A major focus of recent lung cancer research has been the development and subsequent approval of several immunotherapy and molecular-targeted agents and the identification of related biomarkers to help guide treatment selection for those individuals who harbor specific oncogenic alterations.
  Improvements in advanced NSCLC outcomes have been achieved by the discovery of predictive molecular markers that identify subgroups of patients that may derive benefit from targeted treatment. Multiple molecular markers have been found to have clinical value in advanced NSCLC: EGFR mutations, ALK rearrangements, as well as HER2, MET, B-RAF, RET, and ROS1. In addition, the classification for lung adenocarcinoma was recently revised and now requires morphology, immunohistochemical, and molecular studies, including EGFR and ALK status, as well as ROS1, BRAF, HER2, MET, RET. It is thus important that proper biopsy technique accommodate these crucial tests, that the correct tests are ordered for specific patient populations, and that the results of testing are accurately interpreted to individualize care for patients with targeted agents such as erlotinib, afatinib, crizotinib, and osimertinib. Molecular testing for NSCLC is not ordered routinely due to insufficient knowledge by the multidisciplinary team (medical, surgical, and radiation oncologists) or lack of reimbursement. Several studies have quantified this failure: De Souza et al (2012) showed that no testing for EGFR mutations was observed in 23.5% of patients administered erlotinib, and Pan et al (2013) demonstrated that EGFR testing rates were only 2.3% before 2010, 15.2% in 2010, and still only 32.0% in 2011. Similarly, Spicer et al (2015) demonstrated that 23% of oncologists do not consider EGFR mutation subtypes in making treatment decisions and EGFR mutation testing is not performed in up to 25% of patients. A survey of 133 US medical oncologists found that most (74%) are not very familiar with using biomarkers to tailor therapy. In addition, lung cancer test panels often do not include next generation sequencing, which can greatly streamline the process. Biopsies are often too small or too low quality for adequate testing, necessitating rebiopsy. For example, Ost et al (2013) found that only 21% of patients had a diagnostic evaluation consistent with guidelines.
  National guidelines on molecular testing in NSCLC are available from the NCCN, ASCO, College of American Pathologists, International Association for the Study of Lung Cancer, Association for Molecular Pathology, and others. These guidelines make evidence-based recommendations to better align the latest clinical research with patient care in practice. For example, the NCCN notes that there is a significant association between EGFR mutations—especially exon 19 deletion and exon 21 (L858R, L861) and exon 18 (G719X, G719) mutations—and sensitivity to TKIs, and that the exon 20 insertion mutation may predict resistance to clinically achievable levels of TKIs. The prevalence of EGFR mutations in adenocarcinomas is 10% of Western and up to 50% of Asian patients, with higher EGFR mutation frequency in non-smokers, women, and non-mucinous cancers. KRAS mutations are most common in non-Asians, smokers, and in mucinous adenocarcinoma. In addition, primary resistance to TKI therapy may be associated with KRAS mutation, so KRAS gene sequencing could be useful for the selection of patients as candidates for TKI therapy. ALK fusions have been identified in a subset of patients with NSCLC and represent a unique subset of NSCLC patients for whom ALK inhibitors may represent a very effective therapeutic strategy.
  The current treatment of advanced NSCLC is based on the evaluation of genetic mutations that can guide the personalization of treatment with molecularly targeted agents. The most common genetic variant is the gene that encodes the epidermal growth factor receptor (EGFR) and is found in approximately 15% of Caucasian and more than 50% of Asian patients. Patients harboring EGFR mutations are usually treated with tyrosine kinase inhibitors (TKI), such as gefitinib, erlotinib or afatinib. Similarly, a gain of function of anaplastic lymphoma kinase (ALK) due to a rearrangement with the echinoderm microtubule-associated protein-like 4 (EML4) represents a predictive biomarker of the efficacy of ALK-TKI inhibitors such as crizotinib. However, clinicians may not be adequately informed about newly approved and emerging TKIs.
  It is nearly impossible for the modern-day oncologist to remain current regarding the clinical tsunami of research to personalize NSCLC treatment. This is evidenced by the fact that only 55% -75% of NSCLC patients within the United States are receiving evidence-based care. The tremendous pressures cancer centers and their oncology providers face in quantifiably demonstrating value in the care delivered compounds this problem. Almost instantly, government and commercial payers are demanding a change from pay for quantity to pay for value. In April 2016, CMMI implemented the Oncology Care Model (OCM). The new OCM program is complementary to other value-based payment initiatives in which oncologists may participate, including the Bundled Payment for Care Initiative, Chronic Care Management Program, Transforming Clinical Practices Initiative, Transitional Care Management Program, ACO/Medicare Shared Savings Program, and Medicare Care Choice Model, and others rapidly being introduced by commercial payers. These payment programs are transforming oncology care so that it is more pro-active, coordinated, vigilant and patient focused. At the center of this payment reform is the patient, as the ultimate consumer of health care services. Until recently, patients have been relatively blind to the actual cost and quality of the care they receive. Now, out-of-pocket costs are rising steeply and patients have instant access to a trove of health information as they are forced to become better-educated consumers regarding the costs and likely outcomes of their treatment. A recent JAMA Op-Ed piece receiving significant attention highlights that delivering value-based care requires an understanding of what the patient values. To that end, all the current cancer valued-based models require that oncology providers document their patient's goals of care and that the treatment course is evidence-based and commensurate with patient goals.
  Another significant component of value is ensuring that a patient is "fit" enough for the treatment selected. The priorities of frail patients, whose care is the costliest, are often not noticed nor met. The issue of fit/frailty status in NSCLC is highly relevant, given that the average age of diagnosis is 70. When older adults are ill, they are more prone to hospitalization; higher health care utilization due to cancer toxicities drive up the cost of health care. Older adults have an 11-fold increased incidence of cancer and a 16-fold increased incidence in cancer mortality compared to younger patients. Only 25% of patients over 65 received chemotherapy, posited to be the standard of care. And those who do receive chemotherapy are underrepresented in clinical trials. Comprehensive geriatric assessment (CGA) is recommended to stratify elderly patients with advanced NSCLC to ensure treatment dosing that balances efficacy and toxicity.
  Historically GAs are not routinely performed because they are complex and time-consuming, the optimal tools for administering the GA accurately and efficiently have not been established, many clinicians lack knowledge about how to incorporate GA into decision-making and care of older adults, and integration of a GA into a Health Information System platform has not been adequately studied for feasibility and usage. Hurria and colleagues developed the Cancer Specific Geriatric Assessment (CSGA), a shorter assessment that specifically captures data from seven domains (functional status, comorbid medical conditions, psychological state, cognition, nutritional status, social support, and medications). The CSGA requires nearly 30 minutes to complete which lessens its usefulness in a busy clinic.
  A modified Geriatric Assessment (mGA) tool that utilizes age, functional status as determined by assessment of activities of daily living (ADLs) and instrumental activities of daily living (IADLs), plus comorbidity status was used to develop the Palumbo Frailty Index (FI). The FI categorizes patients into groups of fit, intermediate fit, and frail. In a retrospective analysis of data in 867 older adults with MM, toxicity, treatment discontinuation, and survival rates were correlated with the FI. As a result of this retrospective validation work, fit/frailty status is now being evaluated in the clinical setting by gathering information from a mGA and providing the data to the care provider to guide treatment decisions. Predictors of toxicity in elderly patients include age, tumor/treatment variables, labs, and geriatric assessment variables. The mCGA used in this study includes assessment of activities of daily living (ADLs), instrumental ADLS (IADLs), risk for toxicity using the Cancer and Aging Research Group's (CARG) "Chemotherapy Toxicity Calculator" and additional variables such as age, falls in the past six months, hearing, peripheral neuropathy, creatinine clearance, stage and date of diagnosis.
  The science of value-based cancer care is in its infancy. The association of quality and patient outcomes are still largely a thing of the future—to be informed by longitudinal studies to come that will involve a new generation of better-structured big data. Thus, aligning evidence-based treatment decisions with patient goals and patient's performance/fit status is an imperfect science. There is no better case of such a conundrum than with the advanced NSCLC patient population, where disease is often aggressive, treatment choices are numerous and complicated, patients often have high disease and treatment-related symptom burden, and the overall survival trajectory is sadly among the shortest of all cancer diagnoses.

Eligibility: Inclusion Criteria:
  -  18 years of age or older
  -  Diagnosis of NSCLC
  -  Newly diagnosed, in need of a new line or therapy, or at a treatment decision making timepoint.
  -  Must be able to understand English
  Exclusion Criteria:
  -  Any patient who cannot understand written or spoken English
  -  Any prisoner and.or other vulnerable person

Relevance to patient case: possibly relevant