Differential transcript usage analysis incorporating quantification uncertainty via compositional measurement error regression modeling

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.

Tumor Intrinsic Subtypes and Gene Expression Signatures in Early-Stage ERBB2/HER2-Positive Breast Cancer: A Pooled Analysis of CALGB 40601, NeoALTTO, and NSABP B-41 Trials

Importance

Biologic features may affect pathologic complete response (pCR) and event-free survival (EFS) after neoadjuvant chemotherapy plus ERBB2/HER2 blockade in ERBB2/HER2-positive early breast cancer (EBC).

Objective

To define the quantitative association between pCR and EFS by intrinsic subtype and by other gene expression signatures in a pooled analysis of 3 phase 3 trials: CALGB 40601, NeoALTTO, and NSABP B-41.

Design, Setting, and Participants

In this retrospective pooled analysis, 1289 patients with EBC received chemotherapy plus either trastuzumab, lapatinib, or the combination, with a combined median follow-up of 5.5 years. Gene expression profiling by RNA sequencing was obtained from 758 samples, and intrinsic subtypes and 618 gene expression signatures were calculated. Data analyses were performed from June 1, 2020, to January 1, 2023.

Main Outcomes and Measures

The association of clinical variables and gene expression biomarkers with pCR and EFS were studied by logistic regression and Cox analyses.

Results

In the pooled analysis, of 758 women, median age was 49 years, 12% were Asian, 6% Black, and 75% were White. Overall, pCR results were associated with EFS in the ERBB2-enriched (hazard ratio [HR], 0.45; 95% CI, 0.29-0.70; P < .001) and basal-like (HR, 0.19; 95% CI, 0.04-0.86; P = .03) subtypes but not in luminal A or B tumors. Dual trastuzumab plus lapatinib blockade over trastuzumab alone had a trend toward EFS benefit in the intention-to-treat population; however, in the ERBB2-enriched subtype there was a significant and independent EFS benefit of trastuzumab plus lapatinib vs trastuzumab alone (HR, 0.47; 95% CI, 0.27-0.83; P = .009). Overall, 275 of 618 gene expression signatures (44.5%) were significantly associated with pCR and 9 of 618 (1.5%) with EFS. The ERBB2/HER2 amplicon and multiple immune signatures were significantly associated with pCR. Luminal-related signatures were associated with lower pCR rates but better EFS, especially among patients with residual disease and independent of hormone receptor status. There was significant adjusted HR for pCR ranging from 0.45 to 0.81 (higher pCR) and 1.21-1.94 (lower pCR rate); significant adjusted HR for EFS ranged from 0.71 to 0.94.

Conclusions and relevance

In patients with ERBB2/HER2-positive EBC, the association between pCR and EFS differed by tumor intrinsic subtype, and the benefit of dual ERBB2/HER2 blockade was limited to ERBB2-enriched tumors. Immune-activated signatures were concordantly associated with higher pCR rates and better EFS, whereas luminal signatures were associated with lower pCR rates.

DNA Mutational Profiling in Patients With Colorectal Cancer Treated With Standard of Care Reveals Differences in Outcome and Racial Distribution of Mutations

PURPOSE

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.

METHODS

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, BRAF V600E, all RAS mutations, arm, sex, and age.

RESULTS

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 KRAS and APC were more frequent in Black (53% and 85%) than White (27% and 65%, respectively) patients while BRAF V600E was less frequent in Black (5%) than White (14%) patients. The median OS in patients with BRAF non-V600E (2.2% of patients) was 31.9 months (95% CI, 15.1 to not applicable [NA]) similar to that of BRAF wild-type (WT) patients (31.2 months [95% CI, 29.0 to 33.9]). Mutated LRP1B (10.7% of patients) was associated with improved OS compared with WT LRP1B (hazard ratio, 0.57 [95% CI, 0.40 to 0.80]). RNF43 (5.6% of patients) interacted with treatment arms as, in the cetuximab arm, patients with mutated RNF43 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 RNF43, whereas in the bevacizumab arm, patients with mutated RNF43 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 RNF43.

CONCLUSION

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.

Efficient computation of high-dimensional penalized generalized linear mixed models by latent factor modeling of the random effects

Modern biomedical datasets are increasingly high-dimensional and exhibit complex correlation structures. Generalized linear mixed models (GLMMs) have long been employed to account for such dependencies. However, proper specification of the fixed and random effects in GLMMs is increasingly difficult in high dimensions, and computational complexity grows with increasing dimension of the random effects. We present a novel reformulation of the GLMM using a factor model decomposition of the random effects, enabling scalable computation of GLMMs in high dimensions by reducing the latent space from a large number of random effects to a smaller set of latent factors. We also extend our prior work to estimate model parameters using a modified Monte Carlo Expectation Conditional Minimization algorithm, allowing us to perform variable selection on both the fixed and random effects simultaneously. We show through simulation that through this factor model decomposition, our method can fit high-dimensional penalized GLMMs faster than comparable methods and more easily scale to larger dimensions not previously seen in existing approaches.

RHOAL57V drives the development of diffuse gastric cancer through IGF1R-PAK1-YAP1 signaling

Cancer-associated mutations in the guanosine triphosphatase (GTPase) RHOA are found at different locations from the mutational hotspots in the structurally and biochemically related RAS. Tyr42-to-Cys (Y42C) and Leu57-to-Val (L57V) substitutions are the two most prevalent RHOA mutations in diffuse gastric cancer (DGC). RHOAY42C exhibits a gain-of-function phenotype and is an oncogenic driver in DGC. Here, we determined how RHOAL57V promotes DGC growth. In mouse gastric organoids with deletion of Cdh1, which encodes the cell adhesion protein E-cadherin, the expression of RHOAL57V, but not of wild-type RHOA, induced an abnormal morphology similar to that of patient-derived DGC organoids. RHOAL57V also exhibited a gain-of-function phenotype and promoted F-actin stress fiber formation and cell migration. RHOAL57V retained interaction with effectors but exhibited impaired RHOA-intrinsic and GAP-catalyzed GTP hydrolysis, which favored formation of the active GTP-bound state. Introduction of missense mutations at KRAS residues analogous to Tyr42 and Leu57 in RHOA did not activate KRAS oncogenic potential, indicating distinct functional effects in otherwise highly related GTPases. Both RHOA mutants stimulated the transcriptional co-activator YAP1 through actin dynamics to promote DGC progression; however, RHOAL57V additionally did so by activating the kinases IGF1R and PAK1, distinct from the FAK-mediated mechanism induced by RHOAY42C. Our results reveal that RHOAL57V and RHOAY42C drive the development of DGC through distinct biochemical and signaling mechanisms.

Association of PIK3CA Mutation With Pathologic Complete Response and Outcome by Hormone Receptor Status and Intrinsic Subtype in Early-Stage ERBB2/HER2-Positive Breast Cancer

Importance

PIK3CA mutations may be associated with outcomes of patients with ERBB2/HER2-positive early breast cancer (EBC).

Objectives

To assess if PIK3CA mutations among patients with ERBB2/HER2-positive EBC are associated with treatment response and outcome, and if these associations vary by hormone receptor (HR) status or intrinsic molecular subtype (IMS).

Design, Setting, and Participants

This cohort study derived data on 184 patients from the phase 3 neoadjuvant Cancer and Leukemia Group B (CALGB) 40601 trial that enrolled patients with ERBB2/HER2-positive EBC in North America between January 1, 2008, and December 31, 2012. Participants received neoadjuvant paclitaxel with trastuzumab, lapatinib, or both. Statistical analysis was performed from March 23, 2022, to March 9, 2023.

Exposures

Gene expression profiling by RNA sequencing with Prediction Analysis of Microarray 50-determined IMS and PIK3CA mutations from whole-exome sequencing were obtained from pretreatment biopsies from 184 of 305 trial participants.

Main Outcomes and Measures

The primary end point was pathologic complete response (pCR) and the secondary end point of event-free survival (EFS). The association of PIK3CA mutations with pCR and EFS by HR status and IMS was estimated using logistic and Cox proportional hazards regression models.

Results

All 184 participants were women, with a median age of 49 years (range 24-75 years). A total of 121 participants (66%) had clinical stage II tumors; 32 (17%) had PIK3CA mutations, most frequently H1047R (38% [12 of 32]) and E545K (22% [7 of 32]). PIK3CA mutations were present in 20 of 102 cases of HR-positive EBC (20%) and 12 of 82 cases HR-negative EBC (15%) and varied by IMS (luminal B, 9 of 25 [36%]; luminal A, 2 of 21 [10%]; and ERBB2/HER2-enriched tumors, 19 of 102 [19%]). Pathologic complete response rates were lower in PIK3CA mutated than PIK3CA wild type in the overall population (34% [11 of 32] vs 49% [74 of 152]; P = .14) and were significantly different among those receiving trastuzumab (30% [7 of 23] vs 54% [63 of 117]; P = .045). At a median follow-up of 9 years, PIK3CA mutations were significantly associated with worse EFS in the overall cohort (hazard ratio, 2.58 [95% CI, 1.24-5.35]; P = .01), which persisted in a multivariable model including pCR, HR status, stage, and IMS (hazard ratio, 2.52 [95% CI, 1.16-5.47]; P = .02). The negative association of PIK3CA mutation was significant in HR-positive (hazard ratio, 3.60 [95% CI, 1.45-8.96]; P = .006) and luminal subtypes (hazard ratio, 4.84 [95% CI, 1.08-21.70]; P = .04), but not in nonluminal and HR-negative tumors.

Conclusions and Relevance

In ERBB2/HER2-positive EBC, PIK3CA mutations were associated with lower pCR rates and independently associated with worse long-term EFS. These findings appear to be associated with PIK3CA mutations in HR-positive and luminal EBC.

Sub-Cluster Identification through Semi-Supervised Optimization of Rare-Cell Silhouettes (SCISSORS) in single-cell RNA-sequencing

MOTIVATION

Single-cell RNA-sequencing (scRNA-seq) has enabled the molecular profiling of thousands to millions of cells simultaneously in biologically heterogenous samples. Currently, the common practice in scRNA-seq is to determine cell type labels through unsupervised clustering and the examination of cluster-specific genes. However, even small differences in analysis and parameter choosing can greatly alter clustering results and thus impose great influence on which cell types are identified. Existing methods largely focus on determining the optimal number of robust clusters, which can be problematic for identifying cells of extremely low abundance due to their subtle contributions toward overall patterns of gene expression.

RESULTS

Here, we present a carefully designed framework, SCISSORS, which accurately profiles subclusters within broad cluster(s) for the identification of rare cell types in scRNA-seq data. SCISSORS employs silhouette scoring for the estimation of heterogeneity of clusters and reveals rare cells in heterogenous clusters by a multi-step semi-supervised reclustering process. Additionally, SCISSORS provides a method for the identification of marker genes of high specificity to the cell type. SCISSORS is wrapped around the popular Seurat R package and can be easily integrated into existing Seurat pipelines.

AVAILABILITY AND IMPLEMENTATION

SCISSORS, including source code and vignettes, are freely available at https://github.com/jr-leary7/SCISSORS.

Prognostic and Predictive Value of Immune-Related Gene Expression Signatures vs Tumor-Infiltrating Lymphocytes in Early-Stage ERBB2/HER2-Positive Breast Cancer: A Correlative Analysis of the CALGB 40601 and PAMELA Trials

Importance

Both tumor-infiltrating lymphocytes (TILs) assessment and immune-related gene expression signatures by RNA profiling predict higher pathologic complete response (pCR) and improved event-free survival (EFS) in patients with early-stage ERBB2/HER2-positive breast cancer. However, whether these 2 measures of immune activation provide similar or additive prognostic value is not known.

Objective

To examine the prognostic ability of TILs and immune-related gene expression signatures, alone and in combination, to predict pCR and EFS in patients with early-stage ERBB2/HER2-positive breast cancer treated in 2 clinical trials.

Design, Setting, and Participants

In this prognostic study, a correlative analysis was performed on the Cancer and Leukemia Group B (CALGB) 40601 trial and the PAMELA trial. In the CALGB 40601 trial, 305 patients were randomly assigned to weekly paclitaxel with trastuzumab, lapatinib, or both for 16 weeks. The primary end point was pCR, with a secondary end point of EFS. In the PAMELA trial, 151 patients received neoadjuvant treatment with trastuzumab and lapatinib for 18 weeks. The primary end point was the ability of the HER2-enriched subtype to predict pCR. The studies were conducted from October 2013 to November 2015 (PAMELA) and from December 2008 to February 2012 (CALGB 40601). Data analyses were performed from June 1, 2020, to January 1, 2022.

Main Outcomes and Measures

Immune-related gene expression profiling by RNA sequencing and TILs were assessed on 230 CALGB 40601 trial pretreatment tumors and 138 PAMELA trial pretreatment tumors. The association of these biomarkers with pCR (CALGB 40601 and PAMELA) and EFS (CALGB 40601) was studied by logistic regression and Cox analyses.

Results

The median age of the patients was 50 years (IQR, 42-50 years), and 305 (100%) were women. Of 202 immune signatures tested, 166 (82.2%) were significantly correlated with TILs. In both trials combined, TILs were significantly associated with pCR (odds ratio, 1.01; 95% CI, 1.01-1.02; P = .02). In addition to TILs, 36 immune signatures were significantly associated with higher pCR rates. Seven of these signatures outperformed TILs for predicting pCR, 6 of which were B-cell related. In a multivariable Cox model adjusted for clinicopathologic factors, including PAM50 intrinsic tumor subtype, the immunoglobulin G signature, but not TILs, was independently associated with EFS (immunoglobulin G signature-adjusted hazard ratio, 0.63; 95% CI, 0.42-0.93; P = .02; TIL-adjusted hazard ratio, 1.00; 95% CI, 0.98-1.02; P = .99).

Conclusions and Relevance

Results of this study suggest that multiple B-cell-related signatures were more strongly associated with pCR and EFS than TILs, which largely represent T cells. When both TILs and gene expression are available, the prognostic value of immune-related signatures appears to be superior.

Abstract PD18-04: Prognostic implications of PIK3CA mutation by hormone receptor status and intrinsic subtype in early stage HER2-positive breast cancer: a correlative analysis from CALGB 40601

Background PIK3CA mutations have been described in 20-25% of early-stage HER2-positive breast tumors [1], and are associated with reduced pathologic complete response (pCR) rate after chemotherapy and anti-HER2 agents [2]. However, the independence of this finding and association with long-term outcomes within HER2+ patients is still largely unknown. Here, we studied the prognostic implications of PIK3CA mutations by hormone receptor (HR) status and intrinsic subtype in patients with early stage HER2+ breast cancer enrolled in CALGB 40601. Method In CALGB 40601, gene expression profiling by RNA sequencing (RNAseq) with PAM50-determined intrinsic subtype and PIK3CA mutations from whole exome sequencing (WES) were obtained from 184/305 (60%) pretreatment core biopsies. We examined the association of PIK3CA mutations with pCR and event free survival (EFS) by HR status and intrinsic subtype using logistic and Cox regression analyses. Results PIK3CA mutations were found in 32 patients (32/184, 17%). The most frequent mutation was H1047R (12/32,38%), followed by E545K (7/32,22%) and E542K (5/32,16%). PIK3CA mutations were present in 20% and 15% of HR-positive and HR-negative BC subpopulations, respectively. Within Luminal-B, Luminal-A and HER2-Enriched breast tumors, PIK3CA mutations occurred in 36%, 10% and 19% respectively. In the overall population there was lower rate of pCR in mutated-PIK3CA patients than wild-type (34% vs 49%). Using only the subset of patients treated with neoadjuvant trastuzumab-based therapy as standard of care (excluding the lapatinib plus paclitaxel arm), we found a statistically significant lower pCR rate among PIK3CA-mutated tumors using logistic regression model (30% vs 54%, OR=0.30, p=0.045). At a median follow-up of 9.1 years, the presence of PIK3CA mutation was significantly associated with worse EFS in the overall study population (HR 2.58, 95% CI 1.24- 5.35, p=0.011). In a multivariable model including pCR status, HR status and intrinsic subtype (HER2-E vs. not), PIK3CA mutation was independently and significantly associated with worse EFS (HR 2.18, 95% CI 1.04- 4.56, p=0.039). The negative impact of PIK3CA mutation on EFS was statistically significant only in patients with HR-positive (HR 3.6, 95% CI 1.45-8.96, p=0.06) and luminal breast tumors (HR 4.84, 95% CI 1.08-21.7, p=0.039), but not in HR-negative and non-luminal subtypes. Conclusion In our study, the presence of PIK3CA mutation was significantly associated with lower pCR rates in patients treated with chemotherapy plus trastuzumab. Moreover, in uni- and multivariable Cox models, PIK3CA mutations were associated with worse long-term survival, which appeared to be driven by HR-positive and luminal HER2-positive breast tumors. References 1. Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumors. Nature 2012;490:61–70. 2. Loibl S, Majewski I, Guarneri V, Nekljudova V, Holmes E, Bria E, et al PIK3CA mutations are associated with reduced pathological complete response rates in primary HER2-positive breast cancer: pooled analysis of 967 patients from five prospective trials investigating lapatinib and trastuzumab. Ann Oncol 2016;27:1519–25.

Citation Format: Paola Zagami, Aranzazu Fernandez-Martinez, Naim U. Rashid, Katherine A Hoadley, Patty Spears, Charles M. Perou, Lisa Carey. Prognostic implications of PIK3CA mutation by hormone receptor status and intrinsic subtype in early stage HER2-positive breast cancer: a correlative analysis from CALGB 40601. [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD18-04.

Functional and biological heterogeneity of KRASQ61 mutations

Missense mutations at the three hotspots in the guanosine triphosphatase (GTPase) RAS-Gly12, Gly13, and Gln61 (commonly known as G12, G13, and Q61, respectively)-occur differentially among the three RAS isoforms. Q61 mutations in KRAS are infrequent and differ markedly in occurrence. Q61H is the predominant mutant (at 57%), followed by Q61R/L/K (collectively 40%), and Q61P and Q61E are the rarest (2 and 1%, respectively). Probability analysis suggested that mutational susceptibility to different DNA base changes cannot account for this distribution. Therefore, we investigated whether these frequencies might be explained by differences in the biochemical, structural, and biological properties of KRASQ61 mutants. Expression of KRASQ61 mutants in NIH 3T3 fibroblasts and RIE-1 epithelial cells caused various alterations in morphology, growth transformation, effector signaling, and metabolism. The relatively rare KRASQ61E mutant stimulated actin stress fiber formation, a phenotype distinct from that of KRASQ61H/R/L/P, which disrupted actin cytoskeletal organization. The crystal structure of KRASQ61E was unexpectedly similar to that of wild-type KRAS, a potential basis for its weak oncogenicity. KRASQ61H/L/R-mutant pancreatic ductal adenocarcinoma (PDAC) cell lines exhibited KRAS-dependent growth and, as observed with KRASG12-mutant PDAC, were susceptible to concurrent inhibition of ERK-MAPK signaling and of autophagy. Our results uncover phenotypic heterogeneity among KRASQ61 mutants and support the potential utility of therapeutic strategies that target KRASQ61 mutant-specific signaling and cellular output.

Estimating cell type composition using isoform expression one gene at a time

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 KRAS-regulated kinome identifies WEE1 and ERK coinhibition as a potential therapeutic strategy in KRAS-mutant pancreatic cancer

Oncogenic KRAS drives cancer growth by activating diverse signaling networks, not all of which have been fully delineated. We set out to establish a system-wide profile of the KRAS-regulated kinase signaling network (kinome) in KRAS-mutant pancreatic ductal adenocarcinoma (PDAC). We knocked down KRAS expression in a panel of six cell lines and then applied multiplexed inhibitor bead/MS to monitor changes in kinase activity and/or expression. We hypothesized that depletion of KRAS would result in downregulation of kinases required for KRAS-mediated transformation and in upregulation of other kinases that could potentially compensate for the deleterious consequences of the loss of KRAS. We identified 15 upregulated and 13 downregulated kinases in common across the panel of cell lines. In agreement with our hypothesis, all 15 of the upregulated kinases have established roles as cancer drivers (e.g., SRC, TGF-β1, ILK), and pharmacological inhibition of one of these upregulated kinases, DDR1, suppressed PDAC growth. Interestingly, 11 of the 13 downregulated kinases have established driver roles in cell cycle progression, particularly in mitosis (e.g., WEE1, Aurora A, PLK1). Consistent with a crucial role for the downregulated kinases in promoting KRAS-driven proliferation, we found that pharmacological inhibition of WEE1 also suppressed PDAC growth. The unexpected paradoxical activation of ERK upon WEE1 inhibition led us to inhibit both WEE1 and ERK concurrently, which caused further potent growth suppression and enhanced apoptotic death compared with WEE1 inhibition alone. We conclude that system-wide delineation of the KRAS-regulated kinome can identify potential therapeutic targets for KRAS-mutant pancreatic cancer.

FOXA1 and adaptive response determinants to HER2 targeted therapy in TBCRC 036

Inhibition of the HER2/ERBB2 receptor is a keystone to treating HER2-positive malignancies, particularly breast cancer, but a significant fraction of HER2-positive (HER2+) breast cancers recur or fail to respond. Anti-HER2 monoclonal antibodies, like trastuzumab or pertuzumab, and ATP active site inhibitors like lapatinib, commonly lack durability because of adaptive changes in the tumor leading to resistance. HER2+ cell line responses to inhibition with lapatinib were analyzed by RNAseq and ChIPseq to characterize transcriptional and epigenetic changes. Motif analysis of lapatinib-responsive genomic regions implicated the pioneer transcription factor FOXA1 as a mediator of adaptive responses. Lapatinib in combination with FOXA1 depletion led to dysregulation of enhancers, impaired adaptive upregulation of HER3, and decreased proliferation. HER2-directed therapy using clinically relevant drugs (trastuzumab with or without lapatinib or pertuzumab) in a 7-day clinical trial designed to examine early pharmacodynamic response to antibody-based anti-HER2 therapy showed reduced FOXA1 expression was coincident with decreased HER2 and HER3 levels, decreased proliferation gene signatures, and increased immune gene signatures. This highlights the importance of the immune response to anti-HER2 antibodies and suggests that inhibiting FOXA1-mediated adaptive responses in combination with HER2 targeting is a potential therapeutic strategy.

Efficient detection and classification of epigenomic changes under multiple conditions

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.

High tumor mutation burden fails to predict immune checkpoint blockade response across all cancer types

BACKGROUND

High tumor mutation burden (TMB-H) has been proposed as a predictive biomarker for response to immune checkpoint blockade (ICB), largely due to the potential for tumor mutations to generate immunogenic neoantigens. Despite recent pan-cancer approval of ICB treatment for any TMB-H tumor, as assessed by the targeted FoundationOne CDx assay in nine tumor types, the utility of this biomarker has not been fully demonstrated across all cancers.

PATIENTS AND METHODS

Data from over 10 000 patient tumors included in The Cancer Genome Atlas were used to compare approaches to determine TMB and identify the correlation between predicted neoantigen load and CD8 T cells. Association of TMB with ICB treatment outcomes was analyzed by both objective response rates (ORRs, N = 1551) and overall survival (OS, N = 1936).

RESULTS

In cancer types where CD8 T-cell levels positively correlated with neoantigen load, such as melanoma, lung, and bladder cancers, TMB-H tumors exhibited a 39.8% ORR to ICB [95% confidence interval (CI) 34.9-44.8], which was significantly higher than that observed in low TMB (TMB-L) tumors [odds ratio (OR) = 4.1, 95% CI 2.9-5.8, P < 2 × 10^-16]. In cancer types that showed no relationship between CD8 T-cell levels and neoantigen load, such as breast cancer, prostate cancer, and glioma, TMB-H tumors failed to achieve a 20% ORR (ORR = 15.3%, 95% CI 9.2-23.4, P = 0.95), and exhibited a significantly lower ORR relative to TMB-L tumors (OR = 0.46, 95% CI 0.24-0.88, P = 0.02). Bulk ORRs were not significantly different between the two categories of tumors (P = 0.10) for patient cohorts assessed. Equivalent results were obtained by analyzing OS and by treating TMB as a continuous variable.

CONCLUSIONS

Our analysis failed to support application of TMB-H as a biomarker for treatment with ICB in all solid cancer types. Further tumor type-specific studies are warranted.

MODEL-BASED FEATURE SELECTION AND CLUSTERING OF RNA-SEQ DATA FOR UNSUPERVISED SUBTYPE DISCOVERY

Clustering is a form of unsupervised learning that aims to uncover latent groups within data based on similarity across a set of features. A common application of this in biomedical research is in delineating novel cancer subtypes from patient gene expression data, given a set of informative genes. However, it is typically unknown a priori what genes may be informative in discriminating between clusters, and what the optimal number of clusters are. Few methods exist for performing unsupervised clustering of RNA-seq samples, and none currently adjust for between-sample global normalization factors, select cluster-discriminatory genes, or account for potential confounding variables during clustering. To address these issues, we propose the Feature Selection and Clustering of RNA-seq (FSCseq): a model-based clustering algorithm that utilizes a finite mixture of regression (FMR) model and the quadratic penalty method with a Smoothly-Clipped Absolute Deviation (SCAD) penalty. The maximization is done by a penalized Classification EM algorithm, allowing us to include normalization factors and confounders in our modeling framework. Given the fitted model, our framework allows for subtype prediction in new patients via posterior probabilities of cluster membership, even in the presence of batch effects. Based on simulations and real data analysis, we show the advantages of our method relative to competing approaches.

Compression of quantification uncertainty for scRNA-seq counts

Motivation

Quantification estimates of gene expression from single-cell RNA-seq (scRNA-seq) data have inherent uncertainty due to reads that map to multiple genes. Many existing scRNA-seq quantification pipelines ignore multi-mapping reads and therefore underestimate expected read counts for many genes. alevin accounts for multi-mapping reads and allows for the generation of ‘inferential replicates’, which reflect quantification uncertainty. Previous methods have shown improved performance when incorporating these replicates into statistical analyses, but storage and use of these replicates increases computation time and memory requirements.

Results

We demonstrate that storing only the mean and variance from a set of inferential replicates (‘compression’) is sufficient to capture gene-level quantification uncertainty, while reducing disk storage to as low as 9% of original storage, and memory usage when loading data to as low as 6%. Using these values, we generate ‘pseudo-inferential’ replicates from a negative binomial distribution and propose a general procedure for incorporating these replicates into a proposed statistical testing framework. When applying this procedure to trajectory-based differential expression analyses, we show false positives are reduced by more than a third for genes with high levels of quantification uncertainty. We additionally extend the Swish method to incorporate pseudo-inferential replicates and demonstrate improvements in computation time and memory usage without any loss in performance. Lastly, we show that discarding multi-mapping reads can result in significant underestimation of counts for functionally important genes in a real dataset.

Availability and implementation

makeInfReps and splitSwish are implemented in the R/Bioconductor fishpond package available at https://bioconductor.org/packages/fishpond. Analyses and simulated datasets can be found in the paper’s GitHub repo at https://github.com/skvanburen/scUncertaintyPaperCode.

Supplementary information

Supplementary data are available at Bioinformatics online.

High-Dimensional Precision Medicine From Patient-Derived Xenografts

The complexity of human cancer often results in significant heterogeneity in response to treatment. Precision medicine offers the potential to improve patient outcomes by leveraging this heterogeneity. Individualized treatment rules (ITRs) formalize precision medicine as maps from the patient covariate space into the space of allowable treatments. The optimal ITR is that which maximizes the mean of a clinical outcome in a population of interest. Patient-derived xenograft (PDX) studies permit the evaluation of multiple treatments within a single tumor, and thus are ideally suited for estimating optimal ITRs. PDX data are characterized by correlated outcomes, a high-dimensional feature space, and a large number of treatments. Here we explore machine learning methods for estimating optimal ITRs from PDX data. We analyze data from a large PDX study to identify biomarkers that are informative for developing personalized treatment recommendations in multiple cancers. We estimate optimal ITRs using regression-based (Q-learning) and direct-search methods (outcome weighted learning). Finally, we implement a superlearner approach to combine multiple estimated ITRs and show that the resulting ITR performs better than any of the input ITRs, mitigating uncertainty regarding user choice. Our results indicate that PDX data are a valuable resource for developing individualized treatment strategies in oncology. Supplementary materials for this article are available online.

Irreversible JNK1-JUN inhibition by JNK-IN-8 sensitizes pancreatic cancer to 5-FU/FOLFOX chemotherapy

Over 55,000 people in the United States are diagnosed with pancreatic ductal adenocarcinoma (PDAC) yearly, and fewer than 20% of these patients survive a year beyond diagnosis. Chemotherapies are considered or used in nearly every PDAC case, but there is limited understanding of the complex signaling responses underlying resistance to these common treatments. Here, we take an unbiased approach to study protein kinase network changes following chemotherapies in patient-derived xenograft (PDX) models of PDAC to facilitate design of rational drug combinations. Proteomics profiling following chemotherapy regimens reveals that activation of JNK-JUN signaling occurs after 5-fluorouracil plus leucovorin (5-FU + LEU) and FOLFOX (5-FU + LEU plus oxaliplatin [OX]), but not after OX alone or gemcitabine. Cell and tumor growth assays with the irreversible inhibitor JNK-IN-8 and genetic manipulations demonstrate that JNK and JUN each contribute to chemoresistance and cancer cell survival after FOLFOX. Active JNK1 and JUN are specifically implicated in these effects, and synergy with JNK-IN-8 is linked to FOLFOX-mediated JUN activation, cell cycle dysregulation, and DNA damage response. This study highlights the potential for JNK-IN-8 as a biological tool and potential combination therapy with FOLFOX in PDAC and reinforces the need to tailor treatment to functional characteristics of individual tumors.

B cell-Derived IL35 Drives STAT3-Dependent CD8+ T-cell Exclusion in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDA) is an aggressive malignancy characterized by a paucity of tumor-proximal CD8+ T cells and resistance to immunotherapeutic interventions. Cancer-associated mechanisms that elicit CD8+ T-cell exclusion and resistance to immunotherapy are not well-known. Here, using a Kras- and p53-driven model of PDA, we describe a mechanism of action for the protumorigenic cytokine IL35 through STAT3 activation in CD8+ T cells. Distinct from its action on CD4+ T cells, IL35 signaling in gp130+CD8+ T cells activated the transcription factor STAT3, which antagonized intratumoral infiltration and effector function of CD8+ T cells via suppression of CXCR3, CCR5, and IFNγ expression. Inhibition of STAT3 signaling in tumor-educated CD8+ T cells improved PDA growth control upon adoptive transfer to tumor-bearing mice. We showed that activation of STAT3 in CD8+ T cells was driven by B cell- but not regulatory T cell-specific production of IL35. We also demonstrated that B cell-specific deletion of IL35 facilitated CD8+ T-cell activation independently of effector or regulatory CD4+ T cells and was sufficient to phenocopy therapeutic anti-IL35 blockade in overcoming resistance to anti-PD-1 immunotherapy. Finally, we identified a circulating IL35+ B-cell subset in patients with PDA and demonstrated that the presence of IL35+ cells predicted increased occurrence of phosphorylated (p)Stat3+CXCR3-CD8+ T cells in tumors and inversely correlated with a cytotoxic T-cell signature in patients. Together, these data identified B cell-mediated IL35/gp130/STAT3 signaling as an important direct link to CD8+ T-cell exclusion and immunotherapy resistance in PDA.

Improved detection of epigenomic marks with mixed-effects hidden Markov models

Chromatin immunoprecipitation followed by next-generation sequencing (ChIP-seq) is a technique to detect genomic regions containing protein-DNA interaction, such as transcription factor binding sites or regions containing histone modifications. One goal of the analysis of ChIP-seq experiments is to identify genomic loci enriched for sequencing reads pertaining to DNA bound to the factor of interest. The accurate identification of such regions aids in the understanding of epigenomic marks and gene regulatory mechanisms. Given the reduction of massively parallel sequencing costs, methods to detect consensus regions of enrichment across multiple samples are of interest. Here, we present a statistical model to detect broad consensus regions of enrichment from ChIP-seq technical or biological replicates through a class of zero-inflated mixed-effects hidden Markov models. We show that the proposed model outperforms existing methods for consensus peak calling in common epigenomic marks by accounting for the excess zeros and sample-specific biases. We apply our method to data from the Encyclopedia of DNA Elements and Roadmap Epigenomics projects and also from an extensive simulation study.

Purity Independent Subtyping of Tumors (PurIST), A Clinically Robust, Single-sample Classifier for Tumor Subtyping in Pancreatic Cancer

PURPOSE

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.

EXPERIMENTAL DESIGN

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.

RESULTS

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.

CONCLUSIONS

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.

Modeling Between-Study Heterogeneity for Improved Replicability in Gene Signature Selection and Clinical Prediction

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 non-zero 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 (pGLMM) 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.

GSK2801, a BAZ2/BRD9 Bromodomain Inhibitor, Synergizes with BET Inhibitors to Induce Apoptosis in Triple-Negative Breast Cancer

Screening of an inhibitor library targeting kinases and epigenetic regulators identified several molecules having antiproliferative synergy with extraterminal domain (BET) bromodomain (BD) inhibitors (JQ1, OTX015) in triple-negative breast cancer (TNBC). GSK2801, an inhibitor of BAZ2A/B BDs, of the imitation switch chromatin remodeling complexes, and BRD9, of the SWI/SNF complex, demonstrated synergy independent of BRD4 control of P-TEFb-mediated pause-release of RNA polymerase II. GSK2801 or RNAi knockdown of BAZ2A/B with JQ1 selectively displaced BRD2 at promoters/enhancers of ETS-regulated genes. Additional displacement of BRD2 from rDNA in the nucleolus coincided with decreased 45S rRNA, revealing a function of BRD2 in regulating RNA polymerase I transcription. In 2D cultures, enhanced displacement of BRD2 from chromatin by combination drug treatment induced senescence. In spheroid cultures, combination treatment induced cleaved caspase-3 and cleaved PARP characteristic of apoptosis in tumor cells. Thus, GSK2801 blocks BRD2-driven transcription in combination with BET inhibitor and induces apoptosis of TNBC. IMPLICATIONS: Synergistic inhibition of BDs encoded in BAZ2A/B, BRD9, and BET proteins induces apoptosis of TNBC by a combinatorial suppression of ribosomal DNA transcription and ETS-regulated genes.

Abstract B34: Novel synergistic combination therapies with BET bromodomain inhibitors in triple-negative breast cancer

Adaptive resistance to targeted cancer therapies is a universal problem in cancer treatment where tumor cells circumvent targeted pathway inhibition to reactivate growth signaling. In our previous work, we observed genome-wide enhancer remodeling following MEK inhibition (MEKi) capable of driving adaptive gene transcription in triple-negative breast cancer (TNBC). Adaptive enhancers were enriched for the BET bromodomain protein BRD4 and cotreatment with MEKi and BET inhibitor (JQ1) could durably suppress TNBC growth in multiple cell lines and preclinical mouse models. There are currently 10 BET inhibitors in clinical trials being tested as single agents across multiple tumor types including TNBC. There is also a growing body of preclinical literature using epigenetic inhibitors to block the adaptive ability of tumor cells in combination with multiple targeted therapies. This led us to screen for inhibitors that synergize with JQ1 to suppress growth of TNBC using a 176-compound library enriched for epigenetic and kinase inhibitors. We performed synergy screens in 6 TNBC cell lines across 6 doses of JQ1 and each library compound. Using the Bliss Independence model to assess synergy, we found that inhibition of MEK, CDK9, Aurora Kinase, CREBBP/P300, and BAZ2A/B was strongly synergistic with JQ1. BRD4, CDK9, and the acetyltransferase CREBBP/P300 are all members of the P-TEFb transcriptional elongation complex. When we performed additional synergy screens against the P300 bromodomain inhibitor CPI-637, we found a significant overlap in synergistic targets with the JQ1 screen including MEK, BET bromodomain proteins, ERK, Aurora Kinase, and CDK9. BAZ2A/B inhibition using the small-molecule inhibitor GSK2801, which targets the bromodomain of BAZ2A/B, synergized significantly stronger with JQ1 across all cell lines compared to CPI637. BAZ2A/B proteins are members of nucleosome remodeling complexes that mediate DNA silencing by aiding in recruitment of histone modifying enzymes. Ongoing studies seek to understand the role of BAZ2A/B and the mechanism of GSK2801 synergy with BET bromodomain inhibition using RNA sequencing and ChIP sequencing experiments. These results define novel targets that synergize with JQ1 to suppress tumor cell growth and illuminate additional mechanisms of transcriptional regulation driven by BET bromodomain proteins in TNBC.

Citation Format: Samantha M. Bevill, Noah Sciaky, Brian T. Golitz, Naim U. Rashid, Jon S. Zawistowski, Gary L. Johnson. Novel synergistic combination therapies with BET bromodomain inhibitors in triple-negative breast cancer [abstract]. In: Proceedings of the AACR Special Conference: Advances in Breast Cancer Research; 2017 Oct 7-10; Hollywood, CA. Philadelphia (PA): AACR; Mol Cancer Res 2018;16(8_Suppl):Abstract nr B34.

Enhancer Remodeling during Adaptive Bypass to MEK Inhibition Is Attenuated by Pharmacologic Targeting of the P-TEFb Complex

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.Significance: 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 de novo enhancer formation and enhancer remodeling. Pharmacologic targeting of P-TEFb complex members at enhancers is an effective strategy to durably inhibit such adaptation. Cancer Discov; 7(3); 302-21. ©2017 AACR.This article is highlighted in the In This Issue feature, p. 235.

A STATISTICAL MODEL TO ASSESS (ALLELE-SPECIFIC) ASSOCIATIONS BETWEEN GENE EXPRESSION AND EPIGENETIC FEATURES USING SEQUENCING DATA

Sequencing techniques have been widely used to assess gene expression (i.e., RNA-seq) or the presence of epigenetic features (e.g., DNase-seq to identify open chromatin regions). In contrast to traditional microarray platforms, sequencing data are typically summarized in the form of discrete counts, and they are able to delineate allele-specific signals, which are not available from microarrays. The presence of epigenetic features are often associated with gene expression, both of which have been shown to be affected by DNA polymorphisms. However, joint models with the flexibility to assess interactions between gene expression, epigenetic features and DNA polymorphisms are currently lacking. In this paper, we develop a statistical model to assess the associations between gene expression and epigenetic features using sequencing data, while explicitly modeling the effects of DNA polymorphisms in either an allele-specific or nonallele-specific manner. We show that in doing so we provide the flexibility to detect associations between gene expression and epigenetic features, as well as conditional associations given DNA polymorphisms. We evaluate the performance of our method using simulations and apply our method to study the association between gene expression and the presence of DNase I Hypersensitive sites (DHSs) in HapMap individuals. Our model can be generalized to exploring the relationships between DNA polymorphisms and any two types of sequencing experiments, a useful feature as the variety of sequencing experiments continue to expand.

Mu Opioid Splice Variant MOR-1K Contributes to the Development of Opioid-Induced Hyperalgesia

BACKGROUND

A subset of the population receiving opioids for the treatment of acute and chronic clinical pain develops a paradoxical increase in pain sensitivity known as opioid-induced hyperalgesia. Given that opioid analgesics are one of few treatments available against clinical pain, it is critical to determine the key molecular mechanisms that drive opioid-induced hyperalgesia in order to reduce its prevalence. Recent evidence implicates a splice variant of the mu opioid receptor known as MOR-1K in the emergence of opioid-induced hyperalgesia. Results from human genetic association and cell signaling studies demonstrate that MOR-1K contributes to decreased opioid analgesic responses and produces increased cellular activity via Gs signaling. Here, we conducted the first study to directly test the role of MOR-1K in opioid-induced hyperalgesia.

METHODS AND RESULTS

In order to examine the role of MOR-1K in opioid-induced hyperalgesia, we first assessed pain responses to mechanical and thermal stimuli prior to, during, and following chronic morphine administration. Results show that genetically diverse mouse strains (C57BL/6J, 129S6, and CXB7/ByJ) exhibited different morphine response profiles with corresponding changes in MOR-1K gene expression patterns. The 129S6 mice exhibited an analgesic response correlating to a measured decrease in MOR-1K gene expression levels, while CXB7/ByJ mice exhibited a hyperalgesic response correlating to a measured increase in MOR-1K gene expression levels. Furthermore, knockdown of MOR-1K in CXB7/ByJ mice via chronic intrathecal siRNA administration not only prevented the development of opioid-induced hyperalgesia, but also unmasked morphine analgesia.

CONCLUSIONS

These findings suggest that MOR-1K is likely a necessary contributor to the development of opioid-induced hyperalgesia. With further research, MOR-1K could be exploited as a target for antagonists that reduce or prevent opioid-induced hyperalgesia.

Trends and predictors of resection of the primary tumor for patients with stage IV colorectal cancer

BACKGROUND AND OBJECTIVES

Over 130,000 patients are diagnosed with colorectal cancer annually, with approximately 20% presenting with unresectable metastatic disease. Recent consensus guidelines recommend against primary tumor resection for asymptomatic patients with unresectable metastases. Our goal was to examine the trends and predictors of surgical resection.

METHODS

Cases of colorectal cancer with synchronous metastases diagnosed between 1988-2010 were identified using the Surveillance, Epidemiology and End Results (SEER) Database. Associations between resection and clinicopathologic variables were sought using univariate and multivariate logistic regression.

RESULTS

Overall, 68% of patients with synchronous metastatic colorectal cancer underwent primary tumor resection. Resection rates were as high as 76% in the earliest time period (1988-1992) and steadily dropped to 60% in the most recent period (2008-2010). Socioeconomic factors associated with resection on univariate analysis included age, race, gender, marital status, insurance status, and geographic region. Clinicopathologic characteristics associated with resection included tumor location, grade, size, and CEA level. In the multivariate model, gender, geographic region, insurance status, tumor location, grade and CEA level were independent predictors of primary tumor resection.

CONCLUSIONS

Surgical resection of the primary site remains common practice for patients with synchronous metastatic colorectal cancer. Treatment disparities are associated with socioeconomic as well as clinicopathologic factors.

Differential and limited expression of mutant alleles in multiple myeloma

Recent work has delineated mutational profiles in multiple myeloma and reported a median of 52 mutations per patient, as well as a set of commonly mutated genes across multiple patients. In this study, we have used deep sequencing of RNA from a subset of these patients to evaluate the proportion of expressed mutations. We find that the majority of previously identified mutations occur within genes with very low or no detectable expression. On average, 27% (range, 11% to 47%) of mutated alleles are found to be expressed, and among mutated genes that are expressed, there often is allele-specific expression where either the mutant or wild-type allele is suppressed. Even in the absence of an overall change in gene expression, the presence of differential allelic expression within malignant cells highlights the important contribution of RNA-sequencing in identifying clinically significant mutational changes relevant to our understanding of myeloma biology and also for therapeutic applications.

Some Statistical Strategies for DAE-seq Data Analysis: Variable Selection and Modeling Dependencies among Observations

In DAE (DNA After Enrichment)-seq experiments, genomic regions related with certain biological processes are enriched/isolated by an assay and are then sequenced on a high-throughput sequencing platform to determine their genomic positions. Statistical analysis of DAE-seq data aims to detect genomic regions with significant aggregations of isolated DNA fragments ("enriched regions") versus all the other regions ("background"). However, many confounding factors may influence DAE-seq signals. In addition, the signals in adjacent genomic regions may exhibit strong correlations, which invalidate the independence assumption employed by many existing methods. To mitigate these issues, we develop a novel Autoregressive Hidden Markov Model (AR-HMM) to account for covariates effects and violations of the independence assumption. We demonstrate that our AR-HMM leads to improved performance in identifying enriched regions in both simulated and real datasets, especially in those in epigenetic datasets with broader regions of DAE-seq signal enrichment. We also introduce a variable selection procedure in the context of the HMM/AR-HMM where the observations are not independent and the mean value of each state-specific emission distribution is modeled by some covariates. We study the theoretical properties of this variable selection procedure and demonstrate its efficacy in simulated and real DAE-seq data. In summary, we develop several practical approaches for DAE-seq data analysis that are also applicable to more general problems in statistics.

Pain modality- and sex-specific effects of COMT genetic functional variants

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.

Cytokine biomarkers and chronic pain: association of genes, transcription, and circulating proteins with temporomandibular disorders and widespread palpation tenderness

For reasons unknown, temporomandibular disorder (TMD) can manifest as localized pain or in conjunction with widespread pain. We evaluated relationships between cytokines and TMD without or with widespread palpation tenderness (TMD-WPT or TMD+WPT, respectively) at protein, transcription factory activity, and gene levels. Additionally, we evaluated the relationship between cytokines and intermediate phenotypes characteristic of TMD and WPT. In a case-control study of 344 females, blood samples were analyzed for levels of 22 cytokines and activity of 48 transcription factors. Intermediate phenotypes were measured by quantitative sensory testing and questionnaires asking about pain, health, and psychological status. Single nucleotide polymorphisms (SNPs) coding cytokines and transcription factors were genotyped. TMD-WPT cases had elevated protein levels of proinflammatory cytokine monocyte chemotactic protein (MCP-1) and antiinflammatory cytokine interleukin (IL)-1ra, whereas TMD+WPT cases had elevated levels of proinflammatory cytokine IL-8. MCP-1, IL-1ra, and IL-8 were differentially associated with experimental pain, self-rated pain, self-rated health, and psychological phenotypes. TMD-WPT and TMD+WPT cases had inhibited transcription activity of the antiinflammatory cytokine transforming growth factor β1 (TGFβ1). Interactions were observed between TGFβ1 and IL-8 SNPs: an additional copy of the TGFβ1 rs2241719 minor T allele was associated with twice the odds of TMD+WPT among individuals homozygous for the IL-8 rs4073 major A allele, and half the odds of TMD+WPT among individuals heterozygous for rs4073. These results demonstrate how pro- and antiinflammatory cytokines contribute to the pathophysiology of TMD and WPT in genetically susceptible people. Furthermore, they identify MCP-1, IL-1ra, IL-8, and TGFβ1 as potential diagnostic markers and therapeutic targets for pain in patients with TMD.

ZINBA integrates local covariates with DNA-seq data to identify broad and narrow regions of enrichment, even within amplified genomic regions

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.

Software website: http://code.google.com/p/zinba/