Molecular subtyping for pancreatic cancer has made substantial progress in recent years, facilitating the optimization of existing therapeutic approaches to improve clinical outcomes in pancreatic ...cancer. With advances in treatment combinations and choices, it is becoming increasingly important to determine ways to place patients on the best therapies upfront. Although various molecular subtyping systems for pancreatic cancer have been proposed, consensus regarding proposed subtypes, as well as their relative clinical utility, remains largely unknown and presents a natural barrier to wider clinical adoption.
We assess three major subtype classification schemas in the context of results from two clinical trials and by meta-analysis of publicly available expression data to assess statistical criteria of subtype robustness and overall clinical relevance. We then developed a single-sample classifier (SSC) using penalized logistic regression based on the most robust and replicable schema.
We demonstrate that a tumor-intrinsic two-subtype schema is most robust, replicable, and clinically relevant. We developed Purity Independent Subtyping of Tumors (PurIST), a SSC with robust and highly replicable performance on a wide range of platforms and sample types. We show that PurIST subtypes have meaningful associations with patient prognosis and have significant implications for treatment response to FOLIFIRNOX.
The flexibility and utility of PurIST on low-input samples such as tumor biopsies allows it to be used at the time of diagnosis to facilitate the choice of effective therapies for patients with pancreatic ductal adenocarcinoma and should be considered in the context of future clinical trials.
Pancreatic ductal adenocarcinoma (PDAC) remains a lethal disease with a 5-year survival rate of 4%. A key hallmark of PDAC is extensive stromal involvement, which makes capturing precise ...tumor-specific molecular information difficult. Here we have overcome this problem by applying blind source separation to a diverse collection of PDAC gene expression microarray data, including data from primary tumor, metastatic and normal samples. By digitally separating tumor, stromal and normal gene expression, we have identified and validated two tumor subtypes, including a 'basal-like' subtype that has worse outcome and is molecularly similar to basal tumors in bladder and breast cancers. Furthermore, we define 'normal' and 'activated' stromal subtypes, which are independently prognostic. Our results provide new insights into the molecular composition of PDAC, which may be used to tailor therapies or provide decision support in a clinical setting where the choice and timing of therapies are critical.
In the genomic era, the identification of gene signatures associated with disease is of significant interest. Such signatures are often used to predict clinical outcomes in new patients and aid ...clinical decision-making. However, recent studies have shown that gene signatures are often not replicable. This occurrence has practical implications regarding the generalizability and clinical applicability of such signatures. To improve replicability, we introduce a novel approach to select gene signatures from multiple datasets whose effects are consistently nonzero and account for between-study heterogeneity. We build our model upon some rank-based quantities, facilitating integration over different genomic datasets. A high-dimensional penalized generalized linear mixed model is used to select gene signatures and address data heterogeneity. We compare our method to some commonly used strategies that select gene signatures ignoring between-study heterogeneity. We provide asymptotic results justifying the performance of our method and demonstrate its advantage in the presence of heterogeneity through thorough simulation studies. Lastly, we motivate our method through a case study subtyping pancreatic cancer patients from four gene expression studies.
Supplementary materials
for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
Differential transcript usage (DTU) occurs when the relative expression of multiple transcripts arising from the same gene changes between different conditions. Existing approaches to detect DTU ...often rely on computational procedures that can have speed and scalability issues as the number of samples increases. Here we propose a new method, CompDTU, that uses compositional regression to model the relative abundance proportions of each transcript that are of interest in DTU analyses. This procedure leverages fast matrix-based computations that make it ideally suited for DTU analysis with larger sample sizes. This method also allows for the testing of and adjustment for multiple categorical or continuous covariates. Additionally, many existing approaches for DTU ignore quantification uncertainty in the expression estimates for each transcript in RNA-seq data. We extend our CompDTU method to incorporate quantification uncertainty leveraging common output from RNA-seq expression quantification tool in a novel method CompDTUme. Through several power analyses, we show that CompDTU has excellent sensitivity and reduces false positive results relative to existing methods. Additionally, CompDTUme results in further improvements in performance over CompDTU with sufficient sample size for genes with high levels of quantification uncertainty, while also maintaining favorable speed and scalability. We motivate our methods using data from the Cancer Genome Atlas Breast Invasive Carcinoma data set, specifically using RNA-seq data from primary tumors for 740 patients with breast cancer. We show greatly reduced computation time from our new methods as well as the ability to detect several novel genes with significant DTU across different breast cancer subtypes.
Targeting the dysregulated BRAF-MEK-ERK pathway in cancer has increasingly emerged in clinical trial design. Despite clinical responses in specific cancers using inhibitors targeting BRAF and MEK, ...resistance develops often involving nongenomic adaptive bypass mechanisms. Inhibition of MEK1/2 by trametinib in patients with triple-negative breast cancer (TNBC) induced dramatic transcriptional responses, including upregulation of receptor tyrosine kinases (RTK) comparing tumor samples before and after one week of treatment. In preclinical models, MEK inhibition induced genome-wide enhancer formation involving the seeding of BRD4, MED1, H3K27 acetylation, and p300 that drives transcriptional adaptation. Inhibition of the P-TEFb-associated proteins BRD4 and CBP/p300 arrested enhancer seeding and RTK upregulation. BRD4 bromodomain inhibitors overcame trametinib resistance, producing sustained growth inhibition in cells, xenografts, and syngeneic mouse TNBC models. Pharmacologic targeting of P-TEFb members in conjunction with MEK inhibition by trametinib is an effective strategy to durably inhibit epigenomic remodeling required for adaptive resistance.
Widespread transcriptional adaptation to pharmacologic MEK inhibition was observed in TNBC patient tumors. In preclinical models, MEK inhibition induces dramatic genome-wide modulation of chromatin, in the form of
enhancer formation and enhancer remodeling. Pharmacologic targeting of P-TEFb complex members at enhancers is an effective strategy to durably inhibit such adaptation.
.
CALGB (Alliance)/SWOG 80405 was a randomized phase III trial that in first-line patients with metastatic colorectal cancer (mCRC) treated with bevacizumab or cetuximab with chemotherapy. We aimed to ...discover novel mutated genes associated with prognosis and differential response to therapy with the biologics.
Primary tumor DNA from 548 patients was sequenced using FoundationOne. The effect of mutated genes and mutations on overall survival (OS) was tested adjusting for microsatellite instability status,
V600E, all
mutations, arm, sex, and age.
The median number (lower-upper quartile) of mutated genes was 5 (3-7), 5 (3-6) in microsatellite stable and 12.5 (4.5-32) in microsatellite instability-high tumors. Mutated
and
were more frequent in Black (53% and 85%) than White (27% and 65%, respectively) patients while
V600E was less frequent in Black (5%) than White (14%) patients. The median OS in patients with
non-V600E (2.2% of patients) was 31.9 months (95% CI, 15.1 to not applicable NA) similar to that of
wild-type (WT) patients (31.2 months 95% CI, 29.0 to 33.9). Mutated
(10.7% of patients) was associated with improved OS compared with WT
(hazard ratio, 0.57 95% CI, 0.40 to 0.80).
(5.6% of patients) interacted with treatment arms as, in the cetuximab arm, patients with mutated
had a median OS of 11.5 (95% CI, 10.8 to NA) months compared with 30.1 (95% CI, 24.9 to 35.3) months in patients with WT
, whereas in the bevacizumab arm, patients with mutated
had a median OS of 25.0 (95% CI, 14.2 to NA) months compared with 31.3 (95% CI, 29.0 to 34.3) months in patients with WT
.
These results can provide new tools to predict patient outcome and improve therapeutic decisions and trial participation in patient minorities. The molecular alterations identified in this study may direct biomarker-driven studies.
Epigenomics, the study of the human genome and its interactions with proteins and other cellular elements, has become of significant interest in recent years. Such interactions have been shown to ...regulate essential cellular functions and are associated with multiple complex diseases. Therefore, understanding how these interactions may change across conditions is central in biomedical research. Chromatin immunoprecipitation followed by massively parallel sequencing (ChIP‐seq) is one of several techniques to detect local changes in epigenomic activity (peaks). However, existing methods for differential peak calling are not optimized for the diversity in ChIP‐seq signal profiles, are limited to the analysis of two conditions, or cannot classify specific patterns of differential change when multiple patterns exist. To address these limitations, we present a flexible and efficient method for the detection of differential epigenomic activity across multiple conditions. We utilize data from the ENCODE Consortium and show that the presented method, epigraHMM, exhibits superior performance to current tools and it is among the fastest algorithms available, while allowing the classification of combinatorial patterns of differential epigenomic activity and the characterization of chromatin regulatory states.
ZINBA (Zero-Inflated Negative Binomial Algorithm) identifies genomic regions enriched in a variety of ChIP-seq and related next-generation sequencing experiments (DNA-seq), calling both broad and ...narrow modes of enrichment across a range of signal-to-noise ratios. ZINBA models and accounts for factors that co-vary with background or experimental signal, such as G/C content, and identifies enrichment in genomes with complex local copy number variations. ZINBA provides a single unified framework for analyzing DNA-seq experiments in challenging genomic contexts.
Human tissue samples are often mixtures of heterogeneous cell types, which can confound the analyses of gene expression data derived from such tissues. The cell type composition of a tissue sample ...may itself be of interest and is needed for proper analysis of differential gene expression. A variety of computational methods have been developed to estimate cell type proportions using gene‐level expression data. However, RNA isoforms can also be differentially expressed across cell types, and isoform‐level expression could be equally or more informative for determining cell type origin than gene‐level expression. We propose a new computational method, IsoDeconvMM, which estimates cell type fractions using isoform‐level gene expression data. A novel and useful feature of IsoDeconvMM is that it can estimate cell type proportions using only a single gene, though in practice we recommend aggregating estimates of a few dozen genes to obtain more accurate results. We demonstrate the performance of IsoDeconvMM using a unique data set with cell type–specific RNA‐seq data across more than 135 individuals. This data set allows us to evaluate different methods given the biological variation of cell type–specific gene expression data across individuals. We further complement this analysis with additional simulations.
The effects of COMT genetic polymorphism on pain and analgesia are modality – selective and gender – dependent, in both mouse and humanPlease approve Summary as edited.
The enzyme ...catechol-O-methyltransferase (COMT) metabolizes catecholamine neurotransmitters involved in a number of physiological functions, including pain perception. Both human and mouse COMT genes possess functional polymorphisms contributing to interindividual variability in pain phenotypes such as sensitivity to noxious stimuli, severity of clinical pain, and response to pain treatment. In this study, we found that the effects of Comt functional variation in mice are modality specific. Spontaneous inflammatory nociception and thermal nociception behaviors were correlated the most with the presence of the B2 SINE transposon insertion residing in the 3′UTR mRNA region. Similarly, in humans, COMT functional haplotypes were associated with thermal pain perception and with capsaicin-induced pain. Furthermore, COMT genetic variations contributed to pain behaviors in mice and pain ratings in humans in a sex-specific manner. The ancestral Comt variant, without a B2 SINE insertion, was more strongly associated with sensitivity to capsaicin in female vs male mice. In humans, the haplotype coding for low COMT activity increased capsaicin-induced pain perception in women, but not men. These findings reemphasize the fundamental contribution of COMT to pain processes, and provide a fine-grained resolution of this contribution at the genetic level that can be used to guide future studies in the area of pain genetics.
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