Cell-free DNA assay for malignancy classification of high-risk lung nodules.

OBJECTIVE: Although low-dose computed tomography has been proven effective to reduce lung cancer-specific mortality, a considerable proportion of surgically resected high-risk lung nodules were still confirmed pathologically benign. There is an unmet need of a novel method for malignancy classification in lung nodules. METHODS: We recruited 307 patients with high-risk lung nodules who underwent curative surgery, and 247 and 60 cases were pathologically confirmed malignant and benign lung lesions, respectively. Plasma samples from each patient were collected before surgery and performed low-depth (5×) whole-genome sequencing. We extracted cell-free DNA characteristics and determined radiomic features. We built models to classify the malignancy using our data and further validated models with 2 independent lung nodule cohorts. RESULTS: Our models using one type of profile were able to distinguish lung cancer and benign lung nodules at an area under the curve metrics of 0.69 to 0.91 in the study cohort. Integrating all the 5 base models using cell-free DNA profiles, the cell-free DNA-based ensemble model achieved an area under the curve of 0.95 (95% CI, 0.92-0.97) in the study cohort and 0.98 (95% CI, 0.96-1.00) in the validation cohort. At a specificity of 95.0%, the sensitivity reached 80.0% in the study cohort. With the same threshold, the specificity and sensitivity had similar performances in both validation cohorts. Furthermore, the performance of area under the curve reached 0.97 in both the study and validation cohorts when considering the radiomic profile. CONCLUSIONS: The cell-free DNA profiles-based method is an efficient noninvasive tool to distinguish malignancies and high-risk but pathologically benign lung nodules.

Testing the generalizability of cfDNA fragmentomic features across different studies for cancer early detection.

cfDNA fragmentomic features have been used in cancer detection models; however, the generalizability of the models needs to be tested. We proposed a type of cfDNA fragmentomic feature named chromosomal arm-level fragment size distribution (ARM-FSD), evaluated and compared its performance and generalizability for lung cancer and pan-cancer detection with existing cfDNA fragmentomic features (as reference) by using cohorts from different institutions. The ARM-FSD lung cancer model outperformed the reference model by ∼10% when being tested by two external cohorts (AUC: 0.97 vs. 0.86; 0.87 vs. 0.76). For pan-cancer detection, the performance of the ARM-FSD based model is consistently higher than the reference (AUC: 0.88 vs. 0.75, 0.98 vs. 0.63) in a pan-cancer and a lung cancer external validation cohort, indicating that ARM-FSD model produces stable performance across multiple cohorts. Our study reveals ARM-FSD based models have a higher generalizability, and highlights the necessity of cross-study validation for predictive model development.

Comprehensive profiling of pathogenic germline large genomic rearrangements in a pan-cancer analysis.

The presence of large genomic rearrangements (LGRs) has been heavily investigated in breast and ovarian cancer. However, correlations between LGRs and cancer types beyond these two have not been extensively profiled, likely due to the highly inefficient methods of detecting these types of alterations. This study utilized next-generation sequencing (NGS) to analyze and classify the germline LGR profile in 17 025 cancer patients across 22 cancer types. We characterized newly identified LGRs based on predicted pathogenicity and took a closer look at genes that acquire both germline and somatic mutations within our samples. The detection method for LGRs was validated using droplet digital polymerase chain reaction (ddPCR) assay of commonly investigated LGR genes. In total, 15 659 samples from across 22 cancer types were retained for analysis after filtering. We observed that, in our cohort, the cancer types with the highest proportion of germline LGRs were ovarian cancer (4.7%), renal cell carcinoma (2.5%), breast cancer (2%), glioma (1.8%) and thyroid carcinoma (1.8%). Annotation of detected germline variants revealed several genes-MSH2, FANCA and PMS2-that contain novel LGRs. We observed co-occurrences between germline LGRs in MSH2 and somatic single nucleotide variants/insertion and deletions (SNVs/InDels) in BRCA2, KTM2B, KDM5A, CHD8, and HNF1A. Furthermore, our analysis showed that samples with pathogenic and likely pathogenic germline LGRs tended to also have higher mutational burden, chromosomal instability, and microsatellite instability ratio compared to samples with pathogenic germline SNVs/InDels. In this study, we demonstrated the prevalence of pathogenic germline LGRs beyond breast and ovarian cancer. The profiles of these pathogenic or likely pathogenic alterations will fuel further investigations and highlight new understanding of LGRs across multiple cancer types.

Exploring potential molecular resistance and clonal evolution in advanced HER2-positive gastric cancer under trastuzumab therapy.

HER2-positive gastric cancer (GC) makes up 15-20% of all GC incidences, and targeted therapy with trastuzumab is the standard of treatment. However, the mechanisms of resistance to trastuzumab are still not fully understood and presents a significant challenge in clinical practice. In this study, whole exome sequencing (WES) was performed on paired tumor tissues before trastuzumab treatment (at baseline) and at progressive disease (PD) in 23 GC patients. Clinicopathological and molecular features that may be associated with primary and/or acquired resistance to trastuzumab were identified. Lauren classification of intestinal type was associated with a more prolonged progression-free survival (PFS) than diffuse type (HR = 0.29, P = 0.019). Patients with low tumor mutation burden (TMB) showed significantly worse PFS, while high chromosome instability (CIN) was correlated with prolonged OS (HR = 0.27; P = 0.044). Patients who responded to treatment had a higher CIN than nonresponders, and a positive trend towards increasing CIN was observed as response improved (P = 0.019). In our cohort, the most common genes to acquire mutations are AURKA, MYC, STK11, and LRP6 with four patients each. We also discovered an association between clonal branching pattern and survival, with an extensive clonal branching pattern being more closely related to a shorter PFS than other branching patterns (HR = 4.71; P = 0.008). We identified potential molecular and clinical factors that provide insight regarding potential association to trastuzumab resistance in advanced HER2-positive GC patients.

Heterogeneity and evolution of tumour immune microenvironment in metastatic gastroesophageal adenocarcinoma.

BACKGROUND: Tumour immune microenvironment heterogeneity is prevalent in numerous cancers and can negatively impact immunotherapy response. Immune heterogeneity and evolution in gastroesophageal adenocarcinoma (GEA) have not been studied in the past. METHODS: Together with a multi-region sampling of normal, primary and metastatic tissues, we performed whole exome sequencing, TCR sequencing as well as immune cell infiltration estimation through deconvolution of gene expression signals. RESULTS: We discovered high TCR repertoire and immune cell infiltration heterogeneity among metastatic sites, while they were homogeneous among primary and normal samples. Metastatic sites shared high levels of abundant TCR clonotypes with blood, indicating immune surveillance via blood. Metastatic sites also had low levels of tumour-eliminating immune cells and were undergoing heavy immunomodulation compared to normal and primary tumour tissues. There was co-evolution of neo-antigen and TCR repertoire, but only in patients with late diverging mutational evolution. Co-evolution of TCR repertoire and immune cell infiltration was seen in all except one patient. CONCLUSIONS: Our findings revealed immune heterogeneity and co-evolution in GEA, which may inform immunotherapy decision-making.