Genomic Profiling of Bronchoalveolar Lavage Fluid in Lung Cancer

Author:

Nair Viswam S.123ORCID,Hui Angela Bik-Yu4,Chabon Jacob J.4ORCID,Esfahani Mohammad S.4,Stehr Henning5,Nabet Barzin Y.4ORCID,Zhou Li4ORCID,Chaudhuri Aadel A.6ORCID,Benson Jalen7,Ayers Kelsey7,Bedi Harmeet8ORCID,Ramsey Meghan8,Van Wert Ryan8,Antic Sanja9,Lui Natalie7ORCID,Backhus Leah7,Berry Mark7,Sung Arthur W.8,Massion Pierre P.9,Shrager Joseph B.7,Alizadeh Ash A.41011ORCID,Diehn Maximilian4611ORCID

Affiliation:

1. 1Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington.

2. 2Division of Pulmonary, Critical Care & Sleep Medicine, University of Washington School of Medicine, Seattle, Washington.

3. 3Department of Radiology, Stanford University School of Medicine, Stanford, California.

4. 4Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California.

5. 5Department of Pathology, Stanford University School of Medicine, Stanford, California.

6. 6Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.

7. 7Division of Thoracic Surgery, Stanford University School of Medicine, Stanford, California.

8. 8Division of Pulmonary, Allergy & Critical Care Medicine, Stanford University School of Medicine, Stanford, California.

9. 9Division of Allergy, Pulmonary & Critical Care Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee.

10. 10Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, California.

11. 11Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, California.

Abstract

Abstract Genomic profiling of bronchoalveolar lavage (BAL) samples may be useful for tumor profiling and diagnosis in the clinic. Here, we compared tumor-derived mutations detected in BAL samples from subjects with non–small cell lung cancer (NSCLC) to those detected in matched plasma samples. Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq) was used to genotype DNA purified from BAL, plasma, and tumor samples from patients with NSCLC. The characteristics of cell-free DNA (cfDNA) isolated from BAL fluid were first characterized to optimize the technical approach. Somatic mutations identified in tumor were then compared with those identified in BAL and plasma, and the potential of BAL cfDNA analysis to distinguish lung cancer patients from risk-matched controls was explored. In total, 200 biofluid and tumor samples from 38 cases and 21 controls undergoing BAL for lung cancer evaluation were profiled. More tumor variants were identified in BAL cfDNA than plasma cfDNA in all stages (P < 0.001) and in stage I to II disease only. Four of 21 controls harbored low levels of cancer-associated driver mutations in BAL cfDNA [mean variant allele frequency (VAF) = 0.5%], suggesting the presence of somatic mutations in nonmalignant airway cells. Finally, using a Random Forest model with leave-one-out cross-validation, an exploratory BAL genomic classifier identified lung cancer with 69% sensitivity and 100% specificity in this cohort and detected more cancers than BAL cytology. Detecting tumor-derived mutations by targeted sequencing of BAL cfDNA is technically feasible and appears to be more sensitive than plasma profiling. Further studies are required to define optimal diagnostic applications and clinical utility. Significance: Hybrid-capture, targeted deep sequencing of lung cancer mutational burden in cell-free BAL fluid identifies more tumor-derived mutations with increased allele frequencies compared with plasma cell-free DNA. See related commentary by Rolfo et al., p. 2826

Funder

Fred Hutchinson Cancer Research Center

NCI

NIH Director's New Innovator Award Program

NIH

Publisher

American Association for Cancer Research (AACR)

Subject

Cancer Research,Oncology

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