||5R01CA202956-04 Interpret this number
||Icahn School Of Medicine At Mount Sinai
||Optimizing Treatment of Lung Cancer Patients with Comorbidities
The overall goal of this project is to improve the management of lung cancer patients with comorbidities. Lung
cancer is the leading cause of cancer death in the US. Most patients have serious comorbidities (such as
chronic pulmonary disease, cardiac disease, and chronic kidney disease), related to smoking and aging (mean
age at diagnosis is 70 years). Up to 30% of lung cancer cases are diagnosed at a loco-regional stage, can be
treated with a curative intent, and may experience relatively good long-term survival. However, the risk/benefit
ratio of cancer therapies can be substantially altered in patients with comorbidities because of differences in
toxicity, functional status, life expectancy, and quality of life. Unfortunately, patients with comorbidities are
consistently excluded from randomized controlled trials (RCTs) generating an important gap in knowledge
regarding their management. Lack of data relevant to patients with comorbidities has profound negative
impacts including undertreatment, increased morbidity, and decreased survival. Thus, optimizing the
management of these patients is a major public health priority. In this study, we will use simulation modeling,
an approach complementary to RCTs, to determine the optimal treatment of early stage lung cancer patients
with comorbidities. The Specific Aims are to: 1) enhance and validate the Lung Cancer Policy Model (LCPM) to
simulate the management and subsequent outcomes of patients with early stage lung cancer and specific
comorbidities; 2) determine the optimal management and indications for lobectomy, elective limited resection,
stereotactic body radiotherapy, and other treatments in stage I NSCLC patients with chronic lung and heart
disease as well as by overall burden of comorbidities; 3) determine the optimal indications for adjuvant
chemotherapy in patients with stage II and IIIA NSCLC and chronic lung, heart, or renal disease and by overall
burden of comorbidities; and 4) compare outcomes following different treatment strategies (surgery,
chemotherapy, or chemoradiotherapy) for patients with limited-stage SCLC and chronic lung, heart, or renal
disease. To achieve these Aims, we will use an enhanced version of the LCPM, a well validated mathematical
model of lung cancer progression. In Aim 1, we will use data from several population-based registries to
substantially enhance, calibrate, and validate the LCPM by incorporating functional status, frailty, treatments,
complications of surgery and chemotherapy toxicity, outcomes, survival and quality of life of patients with
comorbidities. Then, we will assess the optimal management, in terms of reducing toxicity and maximizing
survival and quality of life, of patients with early stage lung cancer. Our study is innovative in applying modeling
approaches, mostly used to evaluate cancer screening, to the optimization of lung cancer therapies. The
results of the study will directly inform the management of large numbers of lung cancer patients with
comorbidities, a vulnerable and understudied group that currently experience substantially worse outcomes.
Disparities and Trends in Genetic Testing and Erlotinib Treatment among Metastatic Non-Small Cell Lung Cancer Patients.
, Sheehan D.F.
, Tramontano A.C.
, Kong C.Y.
Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2019 May; 28(5), p. 926-934.