Lung Cancer Risk Assessment &Amp; Etiology
Overview
- Phase
- Not Applicable
- Intervention
- Biospecimen Collection
- Conditions
- Lung Carcinoma
- Sponsor
- City of Hope Medical Center
- Enrollment
- 109
- Locations
- 1
- Primary Endpoint
- Frequency of germline alterations
- Status
- Completed
- Last Updated
- last month
Overview
Brief Summary
This study examines the biological and social determinants that may increase the risk for lung cancer in patients and never-smoking individuals. Biological characteristics of a person can include their genetics and social determinants of a person can include their education, income, and environment, all of which can impact their health. Information collected in this study may help increase early detection of lung cancer.
Detailed Description
PRIMARY OBJECTIVE: I. To assess the frequency of germline cancer susceptibility mutations in affected and unaffected individuals who have a personal or family history suggestive of high lung cancer risk. EXPLORATORY OBJECTIVES: I. To assess the frequency of abnormal radiographic findings in high-risk individuals unaffected with cancer who undergo low-dose computed tomography (CT) screening. II. Use patient survey and medical record data to explore the associations between social determinants of health, biological risk factors, family history and lung cancer incidence. III. To determine the sensitivity and specificity of liquid biopsy for detection of lung cancer in a never-smoking population. OUTLINE: Participants complete a survey over 40-45 minutes at baseline and undergo collection of blood samples at a scheduled clinical or research blood draw. Participants' medical records are also reviewed. Participants not diagnosed with lung cancer (i.e. "unaffected"), undergo a low-dose computerized tomography (CT) scan over 20 minutes.
Investigators
Eligibility Criteria
Inclusion Criteria
- •Have had clinical germline genetic testing and/or have consented or expressed a willingness to consent to the City of Hope (COH) General Research Protocol COH IRB # 07047
- •Age \>= 18 years
- •Willing to have about 30 mL of blood (approximately 2 tablespoons) drawn during the aspiration visit
- •Speak English, Spanish, or Mandarin
- •Lung cancer patients: meeting at least one of the following criteria:
- •Be a never-smoker with lung cancer (excludes atypical/typical carcinoid and small cell tumors)
- •Be diagnosed with lung cancer =\< 50 years of age
- •Have a germline alteration known/suspected to be associated with elevated lung cancer risk
- •Have a strong family history of lung cancer (\>= one first, second or third degree relative with never-smoking lung cancer, \>= three first, second or third degree relative with lung cancer in one lineage \[side of the family\], \>= one first, second or third degree relative diagnosed with lung cancer under the age of
- •Patients may also be considered eligible if they meet the family history criteria in part and have a truncated family structure \[e.g., only one first and one second degree relative with lung cancer in a small family\])
Exclusion Criteria
- •Unable to provide informed consent
- •Patients who fall under the unaffected cohort criteria but received chest imaging (except chest x-ray) within the last year
Arms & Interventions
Observational (survey, biospecimen, medical record, CT)
Participants complete a survey over 40-45 minutes at baseline. Participants' medical records are also reviewed. Participants who have lung cancer but have not undergone treatment and participants not diagnosed with lung cancer (i.e., "unaffected"), undergo collection of blood samples at a scheduled clinical or research blood draw. Participants not diagnosed with lung cancer (i.e. "unaffected"), undergo a low-dose CT scan over 20 minutes.
Intervention: Biospecimen Collection
Observational (survey, biospecimen, medical record, CT)
Participants complete a survey over 40-45 minutes at baseline. Participants' medical records are also reviewed. Participants who have lung cancer but have not undergone treatment and participants not diagnosed with lung cancer (i.e., "unaffected"), undergo collection of blood samples at a scheduled clinical or research blood draw. Participants not diagnosed with lung cancer (i.e. "unaffected"), undergo a low-dose CT scan over 20 minutes.
Intervention: Computed Tomography
Observational (survey, biospecimen, medical record, CT)
Participants complete a survey over 40-45 minutes at baseline. Participants' medical records are also reviewed. Participants who have lung cancer but have not undergone treatment and participants not diagnosed with lung cancer (i.e., "unaffected"), undergo collection of blood samples at a scheduled clinical or research blood draw. Participants not diagnosed with lung cancer (i.e. "unaffected"), undergo a low-dose CT scan over 20 minutes.
Intervention: Electronic Health Record Review
Observational (survey, biospecimen, medical record, CT)
Participants complete a survey over 40-45 minutes at baseline. Participants' medical records are also reviewed. Participants who have lung cancer but have not undergone treatment and participants not diagnosed with lung cancer (i.e., "unaffected"), undergo collection of blood samples at a scheduled clinical or research blood draw. Participants not diagnosed with lung cancer (i.e. "unaffected"), undergo a low-dose CT scan over 20 minutes.
Intervention: Survey Administration
Outcomes
Primary Outcomes
Frequency of germline alterations
Time Frame: Through study completion, an average of 5 years
We will explore the relationships between variables, examining correlations (Pearson or Spearman where appropriate). Summarization of data using univariate distributions of the outcomes of interest (presence of lung cancer and molecular phenotypes) and their bivariate distributions with social determinants of health will be carried out. We will examine whether financial hardship, low levels of education, and traffic proximity are associated with presence of lung cancer and molecular phenotypes by using T-tests to compare means and Fisher's exact test to compare proportions. We will also conduct logistic regression modeling to adjust for relevant covariates like race/ethnicity, age, and sex. If appropriate, we will use standard techniques such as sensitivity analyses or multiple imputation to handle missing data.