Enhancing cancer clonality analysis with integrative genomics

RBM10 Mutation as a Potential Negative Prognostic/Predictive Biomarker to Therapy in Non-Small-Cell Lung Cancer

BACKGROUND

According to WHO, lung cancer is the leading cause of cancer-related death worldwide, but treatment has advanced in the last decade. The widespread use of Next Generation Sequencing has led to the discovery of several pathogenic mutations including RNA binding motif 10 (RBM10), a part of the spliceosome complex that regulates splicing of pre-mRNA.

PATIENTS AND METHODS

Electronic medical records were utilized to create a database of patients (50 patients) seen from 2018-2023 with NSCLC and RBM10 mutations, with appropriate IRB approval. For subgroup analysis, we separated into groups by rapid progression vs stable disease defined as progression-free survival earlier than respective clinical trials.

RESULTS

From the analysis of treatment response the mutated RBM10 population had a median PFS was 6.7 months compared to 13.9 in the wild-type RBM10 population controlled for driver mutations TP53 mutation had a higher representation in the RBM10 mutated rapid progression group than the stable disease group. The ZFHX3 mutation had a higher representation in the RBM10 mutated stable disease group.

CONCLUSIONS

RBM10 mutations were associated with aggressive disease with treatment progression faster than median durations of response. RBM10 mutations with concurrent ZFHX3 and EGFR mutations were associated with more stable disease, while concurrent KRAS and TP53 predicted even more aggressive disease.

Deficiency of the splicing factor RBM10 limits EGFR inhibitor response in EGFR mutant lung cancer

Molecularly targeted cancer therapy has improved outcomes for patients with cancer with targetable oncoproteins, such as mutant EGFR in lung cancer. Yet, the long-term survival of these patients remains limited, because treatment responses are typically incomplete. One potential explanation for the lack of complete and durable responses is that oncogene-driven cancers with activating mutations of EGFR often harbor additional co-occurring genetic alterations. This hypothesis remains untested for most genetic alterations that co-occur with mutant EGFR. Here, we report the functional impact of inactivating genetic alterations of the mRNA splicing factor RNA-binding motif 10 (RBM10) that co-occur with mutant EGFR. RBM10 deficiency decreased EGFR inhibitor efficacy in patient-derived EGFR-mutant tumor models. RBM10 modulated mRNA alternative splicing of the mitochondrial apoptotic regulator Bcl-x to regulate tumor cell apoptosis during treatment. Genetic inactivation of RBM10 diminished EGFR inhibitor–mediated apoptosis by decreasing the ratio of (proapoptotic) Bcl-xS to (antiapoptotic) Bcl-xL isoforms of Bcl-x. RBM10 deficiency was a biomarker of poor response to EGFR inhibitor treatment in clinical samples. Coinhibition of Bcl-xL and mutant EGFR overcame the resistance induced by RBM10 deficiency. This study sheds light on the role of co-occurring genetic alterations and on the effect of splicing factor deficiency on the modulation of sensitivity to targeted kinase inhibitor cancer therapy.

Interrogating Mutant Allele Expression via Customized Reference Genomes to Define Influential Cancer Mutations

Genetic alterations are essential for cancer initiation and progression. However, differentiating mutations that drive the tumor phenotype from mutations that do not affect tumor fitness remains a fundamental challenge in cancer biology. To better understand the impact of a given mutation within cancer, RNA-sequencing data was used to categorize mutations based on their allelic expression. For this purpose, we developed the MAXX (Mutation Allelic Expression Extractor) software, which is highly effective at delineating the allelic expression of both single nucleotide variants and small insertions and deletions. Results from MAXX demonstrated that mutations can be separated into three groups based on their expression of the mutant allele, lack of expression from both alleles, or expression of only the wild-type allele. By taking into consideration the allelic expression patterns of genes that are mutated in PDAC, it was possible to increase the sensitivity of widely used driver mutation detection methods, as well as identify subtypes that have prognostic significance and are associated with sensitivity to select classes of therapeutic agents in cell culture. Thus, differentiating mutations based on their mutant allele expression via MAXX represents a means to parse somatic variants in tumor genomes, helping to elucidate a gene’s respective role in cancer.

Multiple Myeloma Genomics: A Systematic Review

OBJECTIVES

This integrative review describes the genomic variants that have been found to be associated with poor prognosis in patients diagnosed with multiple myeloma (MM). Second, it identifies MM genetic and genomic changes using next-generation sequencing, specifically whole-genome sequencing or exome sequencing.

DATA SOURCE

A search for peer-reviewed articles through PubMed, EBSCOhost, and DePaul WorldCat Libraries Worldwide yielded 33 articles that were included in the final analysis.

CONCLUSION

The most commonly reported genetic changes were KRAS, NRAS, TP53, FAM46C, BRAF, DIS3, ATM, and CCND1. These genetic changes play a role in the pathogenesis of MM, prognostication, and therapeutic targets for novel therapies.

IMPLICATIONS FOR NURSING PRACTICE

MM genetics and genomics are expanding rapidly; oncology nurse clinicians must have basic competencies in genetics and genomics to help patients understand the complexities of genetic and genomic alterations and be able to refer patients to appropriate genomic professionals if needed.

Single-cell Transcriptome Study as Big Data

The rapid growth of single-cell RNA-seq studies (scRNA-seq) demands efficient data storage, processing, and analysis. Big-data technology provides a framework that facilitates the comprehensive discovery of biological signals from inter-institutional scRNA-seq datasets. The strategies to solve the stochastic and heterogeneous single-cell transcriptome signal are discussed in this article. After extensively reviewing the available big-data applications of next-generation sequencing (NGS)-based studies, we propose a workflow that accounts for the unique characteristics of scRNA-seq data and primary objectives of single-cell studies.