Shallow whole-genome sequencing of plasma cell-free DNA accurately differentiates small from non-small cell lung carcinoma

Abstract

Background: Accurate lung cancer classification is crucial to guide therapeutic decisions. However, histological subtyping by pathologists requires tumor tissue-a necessity that is often intrinsically associated with procedural difficulties. The analysis of circulating tumor DNA present in minimal-invasive blood samples, referred to as liquid biopsies, could therefore emerge as an attractive alternative. Methods: Concerning adenocarcinoma, squamous cell carcinoma, and small cell carcinoma, our proof of concept study investigates the potential of liquid biopsy-derived copy number alterations, derived from single-end shallow whole-genome sequencing (coverage 0.1-0.5×), across 51 advanced stage lung cancer patients. Results: Genomic abnormality testing reveals anomalies in 86.3% of the liquid biopsies (16/20 for adenocarcinoma, 13/16 for squamous cell, and 15/15 for small cell carcinoma). We demonstrate that copy number profiles from formalin-fixed paraffin-embedded tumor biopsies are well represented by their liquid equivalent. This is especially valid within the small cell carcinoma group, where paired profiles have an average Pearson correlation of 0.86 (95% CI 0.79-0.93). A predictive model trained with public data, derived from 843 tissue biopsies, shows that liquid biopsies exhibit multiple deviations that reflect histological classification. Most notably, distinguishing small from non-small cell lung cancer is characterized by an area under the curve of 0.98 during receiver operating characteristic analysis. Additionally, we investigated how deeper paired-end sequencing, which will eventually become feasible for routine diagnosis, empowers tumor read enrichment by insert size filtering: for all of the 29 resequenced liquid biopsies, the tumor fraction could be increased in silico, thereby "rescuing" three out of five cases with previously undetectable alterations.(Continued on next page)

Longitudinal therapy monitoring of ALK-positive lung cancer by combined copy number and targeted mutation profiling of cell-free DNA

“…At the biologic level, the potential utility of copy number profiling and the t-MAD score for monitoring NSCLC and other tumour entities [ 23 , 38 , [44] , [45] , [46] ] is based on their correlation with the fraction of ctDNA among cfDNA, which makes them a surrogate of tumour burden irrespective of the specific mutations present. In addition, copy number profiling of cfDNA has been used for histological classification of lung tumours [47] and as an early indicator of immunotherapy response [48] . More recently, longitudinal monitoring of genomic copy number from cfDNA in patients with small-cell lung cancer [49] , squamous lung cancer [50] and metastatic castration resistant prostate cancer [51] revealed the utility of this approach for early detection of disease progression and identification of genomic events associated with therapy resistance.…”

Artificial intelligence (AI) and big data in cancer and precision oncology

“…Raman et al (2020) suggests shallow whole genome sequencing for tumour subtyping of advanced lung cancer as an alternative to eliminate invasive tumour histology subtyping. In this study, 86.3% of CNV were detected using NGS and successfully detected the different subtypes to initiate treatment strategies [36] .…”

“…This approach reduces cost of sequencing without compromising the biological data obtained for obtaining accurate clinical insights [34] . Furthermore, shallow sequencing has also been applied to the whole genome for diagnostics in breast cancer [35] , lung cancer [36] and neuroblastoma [37] . For detection of copy number variations (CNV) in breast cancer, the authors implemented shallow whole genome sequencing using FFPE samples.…”

“…In some cancers, like lung cancer, it is fundamental to categorise non-small cell lung cancer from other subtypes such as small cell lung cancer. The subtyping is imperative as it may affect treatment strategies [36] . Histological classification of advanced lung cancers requires invasive and often difficult extraction of tumour samples.…”

WITHDRAWN: Machine Learning Algorithms and Whole Exome Sequencing Data from Breast Cancer Patients in the UK Biobank Predict Survival

“…It has been suggested that shallow whole genome sequencing (sWGS) with low coverage (~ 0.5 X) can be used to pro le cfDNA and ctDNA. Several studies of various cancers [3][4][5][6][7] demonstrated the feasibility of sWGS to identify biomarkers. Based on these studies, we hypothesized that whole exome sequencing (WES) of blood from cancer patients could reveal overall survival prognosis.…”