Estimating tumor mutational burden from RNA-sequencing without a matched-normal sample

RNA sequence analysis of somatic mutations in aging and Parkinson's Disease

Parkinson's Disease (PD) is an age-related neurodegenerative disorder that has been associated with increased DNA damage. To test if PD is associated with increased somatic mutations, we analyzed RNA-seq data in whole blood from 5 visits of the Parkinson's Progression Markers Initiative for clonally amplified somatic variants. Comprehensive analysis of RNA-sequencing data revealed a total of 5,927 somatic variants (2.4 variants per sample on average). Mutation frequencies were significantly elevated in PD subjects as compared to age-matched controls at the time of the last visit. This was confirmed by RNA analysis of substantia nigra. By contrast, the fraction of carriers with clonal hematopoiesis, was significantly reduced in old PD patients as compared to old healthy controls. These results indicate that while the overall mutation rate is higher in PD, specific clonally amplified mutations are protective against PD, as has been found for Alzheimer's Disease.

Overview on biomarkers for immune oncology drugs

Although immune checkpoint inhibitors (ICIs) are widely used in clinical oncology, less than half of treated cancer patients derive benefit from this therapy. Both tumor- and host-related variables are implicated in response to ICIs. The predictive value of PD-L1 expression is confined only to several cancer types, so this molecule is not an agnostic biomarker. Highly elevated tumor mutation burden (TMB) caused either by excessive carcinogenic exposure or by a deficiency in DNA repair is a reliable indicator for ICI efficacy, as exemplified by tumors with high-level microsatellite instability (MSI-H). Other potentially relevant tumor-related characteristics include gene expression signatures, pattern of tumor infiltration by immune cells, and, perhaps, some immune-response modifying somatic mutations. Host-related factors have not yet been comprehensively considered in relevant clinical trials. Microbiome composition, markers of systemic inflammation [e.g., neutrophil-to-lymphocyte ratio (NLR)], and human leucocyte antigen (HLA) diversity may influence the efficacy of ICIs. Studies on ICI biomarkers are likely to reveal modifiable tumor or host characteristics, which can be utilized to direct the antitumor immune defense. Examples of the latter approach include tumor priming to immune therapy by cytotoxic drugs and elevation of ICI efficacy by microbiome modification.

Identification of diagnostic and prognostic genetic alterations in uveal melanoma using RNA sequencing

Uveal melanoma is a lethal intraocular tumour, in which the presence of various genetic alterations correlates with the risk of metastatic dissemination and survival. Here, we tested the detectability of all key mutations and chromosomal changes from RNA sequencing data in 80 primary uveal melanomas studied by The Cancer Genome Atlas (TCGA) initiative, and in five prospective cases. Whereas unsupervised gene expression profiling strongly indicated the presence of chromosome 3 alterations, it was not reliable in identifying other alterations. Though, the presence of both chromosome 3 and 8q copy number alterations could be successfully inferred from expressed allelic imbalances of heterozygous common single nucleotide polymorphisms. Most mutations were adequately recognised in the RNA by their nucleotide changes (all genes), alternative splicing around the mutation (BAP1) and transcriptome-wide aberrant splicing (SF3B1). Notably, in the TCGA cohort we detected previously unreported mutations in BAP1 (n = 3) and EIF1AX (n = 5), that were missed by the original DNA sequencing. In our prospective cohort, all genetic alterations were successfully identified by combining the described approaches. In conclusion, a transcriptional analysis presents insights into the expressed tumour genotype and its phenotypic consequences and may augment or even substitute DNA-based approaches, with potential applicability in research and clinical practice.

Refined variant calling pipeline on RNA-seq data of breast cancer cell lines without matched-normal samples

OBJECTIVE

RNA-seq delivers valuable insights both to transcriptional patterns and mutational landscapes for transcribed genes. However, as tumour cell lines frequently lack their matched-normal counterpart, variant calling without the paired normal sample is still challenging. In order to exclude variants of common genetic variation without a matched-normal control, filtering strategies need to be developed to identify tumour relevant variants in cell lines.

RESULTS

Here, variants of 29 breast cancer cell lines were called on RNA-seq data via HaplotypeCaller. Low read depth sites, RNA-edit sites, and low complexity regions in coding regions were excluded. Common variants were filtered using 1000 genomes, gnomAD, and dbSNP data. Starting from hundred thousands of single nucleotide variants and small insertions and deletions, about thousand variants remained after filtering for each sample. Extracted variants were validated against the Catalogue of Somatic Mutations in Cancer (COSMIC) for 10 cell lines included in both data sets. Approximately half of the COSMIC variants were successfully called. Importantly, missing variants could mainly be attributed to sites with low read depth. Moreover, filtered variants also included all 10 cancer gene census COSMIC variants, a condensed hallmark variant set.

The clinical application of artificial intelligence in cancer precision treatment

BACKGROUND

Artificial intelligence has made significant contributions to oncology through the availability of high-dimensional datasets and advances in computing and deep learning. Cancer precision medicine aims to optimize therapeutic outcomes and reduce side effects for individual cancer patients. However, a comprehensive review describing the impact of artificial intelligence on cancer precision medicine is lacking.

OBSERVATIONS

By collecting and integrating large volumes of data and applying it to clinical tasks across various algorithms and models, artificial intelligence plays a significant role in cancer precision medicine. Here, we describe the general principles of artificial intelligence, including machine learning and deep learning. We further summarize the latest developments in artificial intelligence applications in cancer precision medicine. In tumor precision treatment, artificial intelligence plays a crucial role in individualizing both conventional and emerging therapies. In specific fields, including target prediction, targeted drug generation, immunotherapy response prediction, neoantigen prediction, and identification of long non-coding RNA, artificial intelligence offers promising perspectives. Finally, we outline the current challenges and ethical issues in the field.

CONCLUSIONS

Recent clinical studies demonstrate that artificial intelligence is involved in cancer precision medicine and has the potential to benefit cancer healthcare, particularly by optimizing conventional therapies, emerging targeted therapies, and individual immunotherapies. This review aims to provide valuable resources to clinicians and researchers and encourage further investigation in this field.

Current status of molecular diagnostics for lung cancer

The management of lung cancer (LC) requires the analysis of a diverse spectrum of molecular targets, including kinase activating mutations in EGFR, ERBB2 (HER2), BRAF and MET oncogenes, KRAS G12C substitutions, and ALK, ROS1, RET and NTRK1-3 gene fusions. Administration of immune checkpoint inhibitors (ICIs) is based on the immunohistochemical (IHC) analysis of PD-L1 expression and determination of tumor mutation burden (TMB). Clinical characteristics of the patients, particularly age, gender and smoking history, significantly influence the probability of finding the above targets: for example, LC in young patients is characterized by high frequency of kinase gene rearrangements, while heavy smokers often have KRAS G12C mutations and/or high TMB. Proper selection of first-line therapy influences overall treatment outcomes, therefore, the majority of these tests need to be completed within no more than 10 working days. Activating events in MAPK signaling pathway are mutually exclusive, hence, fast single-gene testing remains an option for some laboratories. RNA next-generation sequencing (NGS) is capable of detecting the entire repertoire of druggable gene alterations, therefore it is gradually becoming a dominating technology in LC molecular diagnosis.

NCI Cancer Research Data Commons: Cloud-Based Analytic Resources

The NCI's Cloud Resources (CR) are the analytical components of the Cancer Research Data Commons (CRDC) ecosystem. This review describes how the three CRs (Broad Institute FireCloud, Institute for Systems Biology Cancer Gateway in the Cloud, and Seven Bridges Cancer Genomics Cloud) provide access and availability to large, cloud-hosted, multimodal cancer datasets, as well as offer tools and workspaces for performing data analysis where the data resides, without download or storage. In addition, users can upload their own data and tools into their workspaces, allowing researchers to create custom analysis workflows and integrate CRDC-hosted data with their own. See related articles by Brady et al., p. 1384, Wang et al., p. 1388, and Kim et al., p. 1404.

Challenges and Future Directions in the Management of Tumor Mutational Burden-High (TMB-H) Advanced Solid Malignancies

A standardized assessment of Tumor Mutational Burden (TMB) poses challenges across diverse tumor histologies, treatment modalities, and testing platforms, requiring careful consideration to ensure consistency and reproducibility. Despite clinical trials demonstrating favorable responses to immune checkpoint inhibitors (ICIs), not all patients with elevated TMB exhibit benefits, and certain tumors with a normal TMB may respond to ICIs. Therefore, a comprehensive understanding of the intricate interplay between TMB and the tumor microenvironment, as well as genomic features, is crucial to refine its predictive value. Bioinformatics advancements hold potential to improve the precision and cost-effectiveness of TMB assessments, addressing existing challenges. Similarly, integrating TMB with other biomarkers and employing comprehensive, multiomics approaches could further enhance its predictive value. Ongoing collaborative endeavors in research, standardization, and clinical validation are pivotal in harnessing the full potential of TMB as a biomarker in the clinic settings.

Improvement in neoantigen prediction via integration of RNA sequencing data for variant calling

Introduction

Neoantigen-based immunotherapy has emerged as a promising strategy for improving the life expectancy of cancer patients. This therapeutic approach heavily relies on accurate identification of cancer mutations using DNA sequencing (DNAseq) data. However, current workflows tend to provide a large number of neoantigen candidates, of which only a limited number elicit efficient and immunogenic T-cell responses suitable for downstream clinical evaluation. To overcome this limitation and increase the number of high-quality immunogenic neoantigens, we propose integrating RNA sequencing (RNAseq) data into the mutation identification step in the neoantigen prediction workflow.

Methods

In this study, we characterize the mutation profiles identified from DNAseq and/or RNAseq data in tumor tissues of 25 patients with colorectal cancer (CRC). Immunogenicity was then validated by ELISpot assay using long synthesis peptides (sLP).

Results

We detected only 22.4% of variants shared between the two methods. In contrast, RNAseq-derived variants displayed unique features of affinity and immunogenicity. We further established that neoantigen candidates identified by RNAseq data significantly increased the number of highly immunogenic neoantigens (confirmed by ELISpot) that would otherwise be overlooked if relying solely on DNAseq data.

Discussion

This integrative approach holds great potential for improving the selection of neoantigens for personalized cancer immunotherapy, ultimately leading to enhanced treatment outcomes and improved survival rates for cancer patients.