Clinical whole-genome sequencing in cancer diagnosis

Breast cancer stem cells as novel biomarkers

Breast cancer is the most common cancer and the leading cause of mortality worldwide. Despite advancements in detection and treatment, it remains a major cause of cancer-related deaths in women. Breast cancer stem cells (BCSCs) are a crucial group of cells responsible for carcinogenesis, metastasis, medication resistance, and tumor recurrence. Identifying and understanding their molecular pathways is essential for developing effective breast cancer therapy. BCSCs are responsible for tumor genesis, development, metastasis, treatment resistance, and recurrence. Biomarkers are essential tools for identifying high-risk patients, improving diagnostic accuracy, developing follow-up programs, assessing treatment susceptibility, and predicting prognostic outcomes. Stem cell intervention therapy can provide specialized tools for precision therapy. Biomarker analysis in cancer patients is crucial to identify cells associated with disease progression and post-therapeutic relapse. However, negative post-therapeutic impacts can enhance cancer stemness by boosting BCSCs plasticity phenotypes, activating stemness pathways in non-BCSCs, and promoting senescence escape, leading to tumor relapse and metastasis. Despite the advancements in precision medicine, challenges persist in identifying stem cell markers, limiting the number of eligible patients for treatment. The diversity of biomedical research hinders the development of individualization-based preventative, monitoring, and treatment strategies, especially in oncology. Integrating and interpreting clinical and scientific data remains challenging. The development of stem cell-related indicators could significantly improve disease precision, enabling stem cell-targeted therapy and personalized treatment plans, although BCSCs are promising for breast cancer treatment optimization, serving as biomarkers for current therapy modalities. This summary discusses recent advancements in breast cancer stem cell research, including biomarkers, identification methods, molecular mechanisms, and tools for studying their biological origin and lineage development for precision medicine.

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.

Clinical Cytogenetics: Current Practices and Beyond

BACKGROUND

Throughout history, the field of cytogenetics has witnessed significant changes due to the constant evolution of technologies used to assess chromosome number and structure. Similar to the evolution of single nucleotide variant detection from Sanger sequencing to next-generation sequencing, the identification of chromosome alterations has progressed from banding to fluorescence in situ hybridization (FISH) to chromosomal microarrays. More recently, emerging technologies such as optical genome mapping and genome sequencing have made noteworthy contributions to clinical laboratory testing in the field of cytogenetics.

CONTENT

In this review, we journey through some of the most pivotal discoveries that have shaped the development of clinical cytogenetics testing. We also explore the current test offerings, their uses and limitations, and future directions in technology advancements.

SUMMARY

Cytogenetics methods, including banding and targeted assessments like FISH, continue to hold crucial roles in cytogenetic testing. These methods offer a rapid turnaround time, especially for conditions with a known etiology involving recognized cytogenetic aberrations. Additionally, laboratories have the flexibility to now employ higher-throughput methodologies to enhance resolution for cases with greater complexity.

Germline whole genome sequencing in adults with multiple primary tumors

Multiple primary tumors (MPTs) are a harbinger of hereditary cancer syndromes. Affected individuals often fit genetic testing criteria for a number of hereditary cancer genes and undergo multigene panel testing. Other genomic testing options, such as whole exome (WES) and whole genome sequencing (WGS) are available, but the utility of these genomic approaches as a second-tier test for those with uninformative multigene panel testing has not been explored. Here, we report our germline sequencing results from WGS in 9 patients with MPTs who had non-informative multigene panel testing. Following germline WGS, sequence (agnostic or 735 selected genes) and copy number variant (CNV) analysis was performed according to the American College of Medical Genetics (ACMG) standards and guidelines for interpreting sequence variants and reporting CNVs. In this cohort, WGS, as a second-tier test, did not identify additional pathogenic or likely pathogenic variants in cancer predisposition genes. Although we identified a CHEK2 likely pathogenic variant and a MUTYH pathogenic variant, both were previously identified in the multigene panels and were not explanatory for the presented type of tumors. CNV analysis also failed to identify any pathogenic or likely pathogenic variants in cancer predisposition genes. In summary, after multigene panel testing, WGS did not reveal any additional pathogenic variants in patients with MPTs. Our study, based on a small cohort of patients with MPT, suggests that germline gene panel testing may be sufficient to investigate these cases. Future studies with larger sample sizes may further elucidate the additional utility of WGS in MPTs.

Identification of mutation gene prognostic biomarker in multiple myeloma through gene panel exome sequencing and transcriptome analysis in Chinese population

BACKGROUND

The 5-year survival rate of multiple myeloma (MM) in China is less than 40%, with considerable individual heterogeneity. Gene mutations are important predictive biomarkers that influence MM treatment decision. The aim of our study was to uncover the clinical significance of mutated genes in MM in the Chinese population.

METHODS

Targeted exon panel sequencing was performed of 400 genes to detect the gene mutation status in plasma cells from 50 patients with MM. DAVID was used to explore the functions and pathways of mutated genes. Detection of mutant gene expression, prognosis and immune cell infiltration with GSE6477. GEO2R was utilized to identify differentially expressed genes (DEGs). Kaplan-Meier and CIBERSORT were applied to compare survival distributions and evaluate the gene expression associated with immune cell infiltration, respectively.

RESULTS

Mutations of 337 genes were identified in MM. The mutation types included SNP, INS, and DEL, but the dominant mutation type was SNP. Function and pathway analysis of mutant genes were performed to elucidate DNA modifications. We identified a total number of 660 downregulated and 587 upregulated genes from the GSE6477 dataset. Thirty-three common genes were present in both the mutant genes and DEGs. The functions and pathways of the mutated genes were enriched in myeloid cell differentiation, regulation of hemopoiesis, etc. Moreover, we found that the low expression of BCL6, BIRC3, HLA-DQA1, and VCAN was correlated with poor prognosis in MM.

CONCLUSIONS

The mutations and low expression of BCL6, BIRC3, HLA-DQA1, and VCAN were correlated with poor prognosis and immune cell infiltration in MM. This study is the first to reveal the spectrum of mutations in the Chinese population by the use of an NGS panel.

Chromothripsis detection with multiple myeloma patients based on deep graph learning

MOTIVATION

Chromothripsis, associated with poor clinical outcomes, is prognostically vital in multiple myeloma. The catastrophic event is reported to be detectable prior to the progression of multiple myeloma. As a result, chromothripsis detection can contribute to risk estimation and early treatment guidelines for multiple myeloma patients. However, manual diagnosis remains the gold standard approach to detect chromothripsis events with the whole-genome sequencing technology to retrieve both copy number variation (CNV) and structural variation data. Meanwhile, CNV data are much easier to obtain than structural variation data. Hence, in order to reduce the reliance on human experts' efforts and structural variation data extraction, it is necessary to establish a reliable and accurate chromothripsis detection method based on CNV data.

RESULTS

To address those issues, we propose a method to detect chromothripsis solely based on CNV data. With the help of structure learning, the intrinsic relationship-directed acyclic graph of CNV features is inferred to derive a CNV embedding graph (i.e. CNV-DAG). Subsequently, a neural network based on Graph Transformer, local feature extraction, and non-linear feature interaction, is proposed with the embedding graph as the input to distinguish whether the chromothripsis event occurs. Ablation experiments, clustering, and feature importance analysis are also conducted to enable the proposed model to be explained by capturing mechanistic insights.

AVAILABILITY AND IMPLEMENTATION

The source code and data are freely available at https://github.com/luvyfdawnYu/CNV_chromothripsis.

From Samples to Germline and Somatic Sequence Variation: A Focus on Next-Generation Sequencing in Melanoma Research

Next-generation sequencing (NGS) applications have flourished in the last decade, permitting the identification of cancer driver genes and profoundly expanding the possibilities of genomic studies of cancer, including melanoma. Here we aimed to present a technical review across many of the methodological approaches brought by the use of NGS applications with a focus on assessing germline and somatic sequence variation. We provide cautionary notes and discuss key technical details involved in library preparation, the most common problems with the samples, and guidance to circumvent them. We also provide an overview of the sequence-based methods for cancer genomics, exposing the pros and cons of targeted sequencing vs. exome or whole-genome sequencing (WGS), the fundamentals of the most common commercial platforms, and a comparison of throughputs and key applications. Details of the steps and the main software involved in the bioinformatics processing of the sequencing results, from preprocessing to variant prioritization and filtering, are also provided in the context of the full spectrum of genetic variation (SNVs, indels, CNVs, structural variation, and gene fusions). Finally, we put the emphasis on selected bioinformatic pipelines behind (a) short-read WGS identification of small germline and somatic variants, (b) detection of gene fusions from transcriptomes, and (c) de novo assembly of genomes from long-read WGS data. Overall, we provide comprehensive guidance across the main methodological procedures involved in obtaining sequencing results for the most common short- and long-read NGS platforms, highlighting key applications in melanoma research.