Multidimensional biomarker predicts disease control in response to immunotherapy in recurrent or metastatic head and neck squamous-cell carcinoma.

PURPOSE: Anti-PD-1 therapy provides clinical benefit in 40-50% of patients with relapsed and/or metastatic head and neck squamous cell carcinoma (RM-HNSCC). Selection of anti- PD-1 therapy is typically based on patient PD-L1 immunohistochemistry (IHC) which has low specificity for predicting disease control. Therefore, there is a critical need for a clinical biomarker that will predict clinical benefit to anti-PD-1 treatment with high specificity. METHODS: Clinical treatment and outcomes data for 103 RM-HNSCC patients were paired with RNA-sequencing data from formalin-fixed patient samples. Using logistic regression methods, we developed a novel biomarker classifier based on expression patterns in the tumor immune microenvironment to predict disease control with monotherapy PD-1 inhibitors (pembrolizumab and nivolumab). The performance of the biomarker was internally validated using out-of-bag methods. RESULTS: The biomarker significantly predicted disease control (65% in predicted non-progressors vs. 17% in predicted progressors, p < 0.001) and was significantly correlated with overall survival (OS; p = 0.004). In addition, the biomarker outperformed PD-L1 IHC across numerous metrics including sensitivity (0.79 vs 0.64, respectively; p = 0.005) and specificity (0.70 vs 0.61, respectively; p = 0.009). CONCLUSION: This novel assay uses tumor immune microenvironment expression data to predict disease control and OS with high sensitivity and specificity in patients with RM-HNSCC treated with anti-PD-1 monotherapy.

T cell subtype profiling measures exhaustion and predicts anti-PD-1 response.

Anti-PD-1 therapy can provide long, durable benefit to a fraction of patients. The on-label PD-L1 test, however, does not accurately predict response. To build a better biomarker, we created a method called T Cell Subtype Profiling (TCSP) that characterizes the abundance of T cell subtypes (TCSs) in FFPE specimens using five RNA models. These TCS RNA models are created using functional methods, and robustly discriminate between naïve, activated, exhausted, effector memory, and central memory TCSs, without the reliance on non-specific, classical markers. TCSP is analytically valid and corroborates associations between TCSs and clinical outcomes. Multianalyte biomarkers based on TCS estimates predicted response to anti-PD-1 therapy in three different cancers and outperformed the indicated PD-L1 test, as well as Tumor Mutational Burden. Given the utility of TCSP, we investigated the abundance of TCSs in TCGA cancers and created a portal to enable researchers to discover other TCSP-based biomarkers.

Whole-genome sequencing and variant discovery in C. elegans.

Massively parallel sequencing instruments enable rapid and inexpensive DNA sequence data production. Because these instruments are new, their data require characterization with respect to accuracy and utility. To address this, we sequenced a Caernohabditis elegans N2 Bristol strain isolate using the Solexa Sequence Analyzer, and compared the reads to the reference genome to characterize the data and to evaluate coverage and representation. Massively parallel sequencing facilitates strain-to-reference comparison for genome-wide sequence variant discovery. Owing to the short-read-length sequences produced, we developed a revised approach to determine the regions of the genome to which short reads could be uniquely mapped. We then aligned Solexa reads from C. elegans strain CB4858 to the reference, and screened for single-nucleotide polymorphisms (SNPs) and small indels. This study demonstrates the utility of massively parallel short read sequencing for whole genome resequencing and for accurate discovery of genome-wide polymorphisms.

Analytical Performance of an Immunoprofiling Assay Based on RNA Models.

As immuno-oncology drugs grow more popular in the treatment of cancer, better methods are needed to quantify the tumor immune cell component to determine which patients are most likely to benefit from treatment. Methods such as flow cytometry can accurately assess the composition of infiltrating immune cells; however, they show limited use in formalin-fixed, paraffin-embedded (FFPE) specimens. This article describes a novel hybrid-capture RNA sequencing assay, ImmunoPrism, that estimates the relative percentage abundance of eight immune cell types in FFPE solid tumors. Immune health expression models were generated using machine learning methods and used to uniquely identify each immune cell type using the most discriminatively expressed genes. The analytical performance of the assay was assessed using 101 libraries from 40 FFPE and 32 fresh-frozen samples. With defined samples, ImmunoPrism had a precision of ±2.72%, a total error of 2.75%, and a strong correlation (r2 = 0.81; P < 0.001) to flow cytometry. ImmunoPrism had similar performance in dissociated tumor cell samples (total error of 8.12%) and correlated strongly with immunohistochemistry (CD8: r2 = 0.83; P < 0.001) in FFPE samples. Other performance metrics were determined, including limit of detection, reportable range, and reproducibility. The approach used for analytical validation is shared here so that it may serve as a helpful framework for other laboratories when validating future complex RNA-based assays.

Using RNA Sequencing and In Silico Subtraction to Identify Resistance Gene Analog Markers for Lr16 in Wheat.

Leaf rust, caused by Puccinia triticina Eriks., is one of the most widespread diseases of wheat and breeding for resistance is one of the most effective methods of control. Lr16 is a wheat leaf rust resistance gene (R-gene) that provides resistance at both the seedling and adult stages. Simple-sequence repeat (SSR) markers have been used to map Lr16 to the distal end of chromosome 2B. The objectives of this study were to use RNA sequencing (RNA-seq) and in silico subtraction to identify new R-gene analogs (RGAs) and use them as Lr16 markers. RNA was isolated from the susceptible wheat cultivar Thatcher (Tc) and the resistant Tc isolines TcLr10, TcLr16, TcLr21, and sequenced using Illumina technology. Using in silico subtraction, sequences from the resistant Tc isolines were aligned to a Tc reference expressed sequence tag (EST) set. Sequences not aligning to the Tc reference were assembled into contigs and analyzed using BLASTx to determine putative gene functions. Primer pairs were designed for 181 RGA sequences, of which, 137 amplified in at least one of the parents. A mapping population was developed with 165 F2 lines from a cross between the rust-susceptible cultivar Chinese Spring (CS) and TcLr16. Two RGA markers XTaLr16_RGA266585 and XTaLr16_RGA22128 were identified that mapped proximally 1.2 and 23.8 cM from Lr16, respectively. Three SSR markers Xwmc764, Xwmc661, and Xbarc35 mapped between these two RGA markers at distances of 5.0, 10.9, and 16.1 cM from Lr16, respectively. In silico subtraction is an effective technique for isolating RGAs linked to R-genes of interest.

Molecular properties of adult mouse gastric and intestinal epithelial progenitors in their niches.

We have sequenced 36,641 expressed sequence tags from laser capture microdissected adult mouse gastric and small intestinal epithelial progenitors, obtaining 4031 and 3324 unique transcripts, respectively. Using Gene Ontology (GO) terms, each data set was compared with cDNA libraries from intact adult stomach and small intestine. Genes in GO categories enriched in progenitors were filtered against genes in GO categories represented in hematopoietic, neural, and embryonic stem cell transcriptomes and mapped onto transcription factor networks, plus canonical signal transduction and metabolic pathways. Wnt/beta-catenin, phosphoinositide-3/Akt kinase, insulin-like growth factor-1, vascular endothelial growth factor, integrin, and gamma-aminobutyric acid receptor signaling cascades, plus glycerolipid, fatty acid, and amino acid metabolic pathways are among those prominently represented in adult gut progenitors. The results reveal shared as well as distinctive features of adult gut stem cells when compared with other stem cell populations.