MIXnorm: normalizing RNA-seq data from formalin-fixed paraffin-embedded samples

Optimization of FFPE preparation and identification of gene attributes associated with RNA degradation

Formalin-fixed paraffin-embedded (FFPE) tissues are widely available specimens for clinical studies. However, RNA degradation in FFPE tissues often restricts their utility. In this study, we determined optimal FFPE preparation conditions, including tissue ischemia at 4°C (<48 h) or 25°C for a short time (0.5 h), 48-h fixation at 25°C and sampling from FFPE scrolls instead of sections. Notably, we observed an increase in intronic reads and a significant change in gene rank based on expression level in the FFPE as opposed to fresh-frozen (FF) samples. Additionally, we found that more reads were mapped to genes associated with chemical stimulus in FFPE samples. Furthermore, we demonstrated that more degraded genes in FFPE samples were enriched in genes with short transcripts and high free energy. Besides, we found 40 housekeeping genes exhibited stable expression in FF and FFPE samples across various tissues. Moreover, our study showed that FFPE samples yielded comparable results to FF samples in dimensionality reduction and pathway analyses between case and control samples. Our study established the optimal conditions for FFPE preparation and identified gene attributes associated with degradation, which would provide useful clues for the utility of FFPE tissues in clinical practice and research.

Integrative genomic and transcriptomic analysis in plasmablastic lymphoma identifies disruption of key regulatory pathways

WES coupled with RNA-sequencing of a large PBL cohort reveals genetic drivers of oncogenesis in RTK-RAS, NF-kB, and JAK/STAT signaling.

The mutational landscape and SCNV data emphasize the distinctness of EBV⁺/EBV^– PBL from both DLBCL and multiple myeloma.

Plasmablastic lymphoma (PBL) represents a clinically heterogeneous subtype of aggressive B-cell non-Hodgkin lymphoma. Targeted-sequencing studies and a single-center whole-exome sequencing (WES) study in HIV-positive patients recently revealed several genes associated with PBL pathogenesis; however, the global mutational landscape and transcriptional profile of PBL remain elusive. To inform on disease-associated mutational drivers, mutational patterns, and perturbed pathways in HIV-positive and HIV-negative PBL, we performed WES and transcriptome sequencing (RNA-sequencing) of 33 PBL tumors. Integrative analysis of somatic mutations and gene expression profiles was performed to acquire insights into the divergent genotype–phenotype correlation in Epstein-Barr virus–positive (EBV⁺) and EBV^– PBL. We describe a significant accumulation of mutations in the JAK signal transducer and transcription activator (OSMR, STAT3, PIM1, and SOCS1), as well as receptor tyrosine-kinase RAS (ERBB3, NRAS, PDGFRB, and NTRK) pathways. We provide further evidence of frequent perturbances of NF-κB signaling (NFKB2 and BTK). Induced pathways, identified by RNA-sequencing, closely resemble the mutational profile regarding alterations accentuated in interleukin-6/JAK/STAT signaling, NF-κB activity, and MYC signaling. Moreover, class I major histocompatibility complex–mediated antigen processing and cell cycle regulation were significantly affected by EBV status. An almost exclusive upregulation of phosphatidylinositol 3-kinase/AKT/mTOR signaling in EBV⁺ PBL and a significantly induced expression of NTRK3 in concert with recurrent oncogenic mutations in EBV^– PBL hint at a specific therapeutically targetable mechanism in PBL subgroups. Our characterization of a mutational and transcriptomic landscape in PBL, distinct from that of diffuse large B-cell lymphoma and multiple myeloma, substantiates the pathobiological independence of PBL in the spectrum of B-cell malignancies and thereby refines the taxonomy for aggressive lymphomas.

SMIXnorm: Fast and Accurate RNA-Seq Data Normalization for Formalin-Fixed Paraffin-Embedded Samples

RNA-sequencing (RNA-seq) provides a comprehensive quantification of transcriptomic activities in biological samples. Formalin-Fixed Paraffin-Embedded (FFPE) samples are collected as part of routine clinical procedure, and are the most widely available biological sample format in medical research and patient care. Normalization is an essential step in RNA-seq data analysis. A number of normalization methods, though developed for RNA-seq data from fresh frozen (FF) samples, can be used with FFPE samples as well. The only extant normalization method specifically designed for FFPE RNA-seq data, MIXnorm, which has been shown to outperform the normalization methods, but at the cost of a complex mixture model and a high computational burden. It is therefore important to adapt MIXnorm for simplicity and computational efficiency while maintaining superior performance. Furthermore, it is critical to develop an integrated tool that performs commonly used normalization methods for both FF and FFPE RNA-seq data. We developed a new normalization method for FFPE RNA-seq data, named SMIXnorm, based on a simplified two-component mixture model compared to MIXnorm to facilitate computation. The expression levels of expressed genes are modeled by normal distributions without truncation, and those of non-expressed genes are modeled by zero-inflated Poisson distributions. The maximum likelihood estimates of the model parameters are obtained by a nested Expectation-Maximization algorithm with a less complicated latent variable structure, and closed-form updates are available within each iteration. Real data applications and simulation studies show that SMIXnorm greatly reduces computing time compared to MIXnorm, without sacrificing the performance. More importantly, we developed a web-based tool, RNA-seq Normalization (RSeqNorm), that offers a simple workflow to compute normalized RNA-seq data for both FFPE and FF samples. It includes SMIXnorm and MIXnorm for FFPE RNA-seq data, together with five commonly used normalization methods for FF RNA-seq data. Users can easily upload a raw RNA-seq count matrix and select one of the seven normalization methods to produce a downloadable normalized expression matrix for any downstream analysis. The R package is available at https://github.com/S-YIN/RSEQNORM. The web-based tool, RSeqNorm is available at http://lce.biohpc.swmed.edu/rseqnorm with no restriction to use or redistribute.

A review of the use of next generation sequencing methodologies to identify biomarkers of resistance to CDK4/6 inhibitors in ER+/HER2- breast cancer

The development of cyclin-dependent kinases (CDK) 4 and 6 inhibitors represented a substantial breakthrough in the treatment of estrogen receptor positive (ER+), human epidermal growth factor receptor 2 (HER2) negative metastatic breast cancer. These drugs showed a significant clinical benefit in pivotal clinical trials. However, resistance eventually occurs, leading to disease progression. Next Generation Sequencing methodologies have been employed to investigate predictive biomarkers of response or resistance to CDK4/6 inhibitors. Whole exome and targeted sequencing of solid and liquid biopsies have revealed several possible genomic alterations associated with resistance. Notably, genomic alterations identified by DNA-sequencing did not fully recapitulate the entire landscape of resistance to CDK4/6 inhibitors. Gene expression analysis, such as RNA-Seq methodologies, have provided insights into transcriptional profiles and may need further application. Herein, we report the main findings derived from the use of NGS analysis in the context of resistance to CDK4/6 inhibitors in ER + breast cancer.