Comparison of actionable events detected in cancer genomes by whole-genome sequencing, in silico whole-exome and mutation panels

Genomic Landscape of Pancreatic Neuroendocrine Tumors and Implications for Clinical Practice

Pancreatic neuroendocrine tumors (pNETs) are the second most prevalent neoplasms of the pancreas with variable prognosis and clinical course. Our knowledge of the genetic alterations in patients with pNETs has expanded in the past decade with the availability of whole-genome sequencing and germline testing. This review will focus on potential clinical applications of the genetic testing in patients with pNETs. For somatic testing, we discuss the commonly prevalent somatic mutations and their impact on prognosis and treatment of patients with pNET. We also highlight the relevant genomic biomarkers that predict response to specific treatments. Previously, germline testing was only recommended for high-risk patients with syndromic features (MEN1, VHL, TSC, and NF1), we review the evolving paradigm of germline testing in pNETs as recent studies have now shown that sporadic-appearing pNETs can also harbor germline variants.

Contemporary Concise Review 2023: Advances in lung cancer and interventional pulmonology

Eligibility criteria for lung cancer screening increasingly need to consider family history of lung cancer, as well as age and smoking status. Lung cancer screening will reveal a multitude of incidental findings, of variable clinical significance, and with a need for clear pathways of management. Pulmonary nodule sampling is enhanced by intra-procedural imaging and cutting-edge robotic technology. Systematic thoracic lymph node sampling has implications for treatment efficacy. Bronchoscopic ablative techniques are feasible for peripheral lung cancers. Bronchoscopic sampling continues to have a high yield for lung cancer molecular characterization. Immunotherapy indications have expanded to include early stage and resectable lung cancer.

Enhancing the quality of panel-based tumor mutation burden assessment: a comprehensive study of real-world and in-silico outcomes

Targeted panel-based tumor mutation burden (TMB) assays are widely employed to guide immunotherapy for patients with solid tumors. However, the accuracy and consistency of this method can be compromised due to the variability in technical details across different laboratories, particularly in terms of panel size, somatic mutation detection and TMB calculation rules. Currently, systematic evaluations of the impact of these technical factors on existing assays and best practice recommendations remain lacking. We assessed the performance of 50 participating panel-based TMB assays involving 38 unique methods using cell line samples. In silico experiments utilizing TCGA MC3 datasets were performed to further dissect the impact of technical factors. Here we show that the panel sizes beyond 1.04 Mb and 389 genes are necessary for the basic discrete accuracy, as determined by over 40,000 synthetic panels. The somatic mutation detection should maintain a reciprocal gap of recall and precision less than 0.179 for reliable psTMB calculation results. The inclusion of synonymous, nonsense and hotspot mutations could enhance the accuracy of panel-based TMB assay. A 5% variant allele frequency cut-off is suitable for TMB assays using tumor samples with at least 20% tumor purity. In conclusion, this multicenter study elucidates the major technical factors as sources of variability in panel-based TMB assays and proposed comprehensive recommendations for the enhancement of accuracy and consistency. These findings will assist clinical laboratories in optimizing the methodological details through bioinformatic experiments to enhance the reliability of panel-based methods.

Computational immunogenomic approaches to predict response to cancer immunotherapies

Cancer immunogenomics is an emerging field that bridges genomics and immunology. The establishment of large-scale genomic collaborative efforts along with the development of new single-cell transcriptomic techniques and multi-omics approaches have enabled characterization of the mutational and transcriptional profiles of many cancer types and helped to identify clinically actionable alterations as well as predictive and prognostic biomarkers. Researchers have developed computational approaches and machine learning algorithms to accurately obtain clinically useful information from genomic and transcriptomic sequencing data from bulk tissue or single cells and explore tumours and their microenvironment. The rapid growth in sequencing and computational approaches has resulted in the unmet need to understand their true potential and limitations in enabling improvements in the management of patients with cancer who are receiving immunotherapies. In this Review, we describe the computational approaches currently available to analyse bulk tissue and single-cell sequencing data from cancer, stromal and immune cells, as well as how best to select the most appropriate tool to address various clinical questions and, ultimately, improve patient outcomes.

Multicentric pilot study to standardize clinical whole exome sequencing (WES) for cancer patients

A growing number of druggable targets and national initiatives for precision oncology necessitate broad genomic profiling for many cancer patients. Whole exome sequencing (WES) offers unbiased analysis of the entire coding sequence, segmentation-based detection of copy number alterations (CNAs), and accurate determination of complex biomarkers including tumor mutational burden (TMB), homologous recombination repair deficiency (HRD), and microsatellite instability (MSI). To assess the inter-institution variability of clinical WES, we performed a comparative pilot study between German Centers of Personalized Medicine (ZPMs) from five participating institutions. Tumor and matched normal DNA from 30 patients were analyzed using custom sequencing protocols and bioinformatic pipelines. Calling of somatic variants was highly concordant with a positive percentage agreement (PPA) between 91 and 95% and a positive predictive value (PPV) between 82 and 95% compared with a three-institution consensus and full agreement for 16 of 17 druggable targets. Explanations for deviations included low VAF or coverage, differing annotations, and different filter protocols. CNAs showed overall agreement in 76% for the genomic sequence with high wet-lab variability. Complex biomarkers correlated strongly between institutions (HRD: 0.79-1, TMB: 0.97-0.99) and all institutions agreed on microsatellite instability. This study will contribute to the development of quality control frameworks for comprehensive genomic profiling and sheds light onto parameters that require stringent standardization.

Whole Genome Sequencing in Advanced Lung Cancer can be Performed Using Diff-Quik Cytology Smears Derived from Endobronchial Ultrasound, Transbronchial Needle Aspiration (EBUS TBNA)

INTRODUCTION

Maximising alternative sample types for genomics in advanced lung cancer is important because bronchoscopic samples may sometimes be insufficient for this purpose. Further, the clinical applications of comprehensive molecular analysis such as whole genome sequencing (WGS) are rapidly developing. Diff-Quik cytology smears from EBUS TBNA is an alternative source of DNA, but its feasibility for WGS has not been previously demonstrated.

METHODS

Diff-Quik smears were collected along with research cell pellets.

RESULTS

Tumour content of smears were compared to research cell pellets from 42 patients, which showed good correlation (Spearman correlation 0.85, P < 0.0001). A subset of eight smears underwent WGS, which presented similar mutation profiles to WGS of the matched cell pellet. DNA yield was predicted using a regression equation of the smears cytology features, which correctly predicted DNA yield > 1500 ng in 7 out of 8 smears.

CONCLUSIONS

WGS of commonly collected Diff-Quik slides is feasible and their DNA yield can be predicted.

A systematic review of computational approaches to understand cancer biology for informed drug repurposing

Cancer is the second leading cause of death globally, trailing only heart disease. In the United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for 2022. Unfortunately, the success rate for new cancer drug development remains less than 10%, making the disease particularly challenging. This low success rate is largely attributed to the complex and poorly understood nature of cancer etiology. Therefore, it is critical to find alternative approaches to understanding cancer biology and developing effective treatments. One such approach is drug repurposing, which offers a shorter drug development timeline and lower costs while increasing the likelihood of success. In this review, we provide a comprehensive analysis of computational approaches for understanding cancer biology, including systems biology, multi-omics, and pathway analysis. Additionally, we examine the use of these methods for drug repurposing in cancer, including the databases and tools that are used for cancer research. Finally, we present case studies of drug repurposing, discussing their limitations and offering recommendations for future research in this area.

Genomic Profiling of Sarcomas: A Promising Weapon in the Therapeutic Arsenal

Sarcomas are rare malignant mesenchymal neoplasms, and the knowledge of tumor biology and genomics is scarce. Chemotherapy is the standard of care in advanced disease, with poor outcomes. Identifying actionable genomic alterations may offer effective salvage therapeutic options when previous lines have failed. Here, we report a retrospective cohort study of sarcoma patients followed at our center and submitted to comprehensive genomic profiling between January 2020 and June 2021. Thirty patients were included, most (96.7%) with reportable genomic alterations. The most common alterations were linked to cell cycle regulation (TP53, CDKN2A/B, and RB1 deletions and CDK4, MDM2, and MYC amplifications). Most patients (96.7%) had microsatellite stability and low tumor mutational burden (≤10 muts/megabase (Mb); median 2 Muts/Mb). Two-thirds of patients had actionable mutations for targeted treatments, including five cases with alterations amenable to targeted therapies with clinical benefit within the patient's tumor type, ten cases with targetable alterations with clinical benefit in other tumor types, and five cases with alterations amenable to targeting with drugs under investigation in a clinical trial setting. A significant proportion of cases in this study had actionable genomic alterations with available targeted drugs. Next-generation sequencing is a feasible option for identifying molecular drivers that can provide therapeutic options for individual patients. Molecular Tumor Boards should be implemented in the clinical practice to discuss genomic findings and inform clinically relevant targeted therapies.