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 and Cancer-associated Fibroblast Subtypes Independently Predict Outcomes in Pancreatic Cancer.

OBJECTIVE: To assess the utility of tumor-intrinsic and cancer-associated fibroblast (CAF) subtypes of pancreatic ductal adenocarcinoma (PDAC) in predicting response to neoadjuvant therapy (NAT) and overall survival. BACKGROUND: PDAC remains a deadly disease with limited treatment options, and both the tumor as well as the microenvironment play an important role in pathogenesis. Gene expression-based tumor-intrinsic subtypes (classical and basal-like) have been shown to predict outcomes, but tumor microenvironment subtypes are still evolving. METHODS: RNA-sequencing was performed on 114 deidentified resected PDAC tumors. Clinical data were collected by retrospective chart review. Single sample classifiers (SSCs) were used to determine classical and basal-like subtypes as well as tumor-permissive permCAF and tumor-restraining restCAF subtypes. Survival was analyzed using log-rank test. RESULTS: Patients who received NAT had an increase in overall survival (OS), with median survival of 27.9 months compared to 20.1 months for those who did not receive NAT, but the difference did not reach statistical significance (HR 0.64, P=0.076). Either tumor-intrinsic or CAF subtypes alone were associated with OS regardless of NAT or no NAT, and patients with classical or restCAF subtype had the best outcomes. When evaluated together, patients with classical-restCAF subtype had the best OS and basal-permCAF the worst OS (P<0.0001). NAT patients with classical-restCAF subtype demonstrated the longest OS compared to the other groups (P=0.00041). CONCLUSIONS: CAF subtypes have an additive effect over tumor-intrinsic subtypes in predicting survival with or without neoadjuvant FOLFIRINOX in PDAC. Molecular subtyping of both tumor and CAF compartments of PDAC may be important steps in selecting first-line systemic therapy.

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.

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.

Effect of hydrothermal pretreatment on leachate fed Scenedesmus sp. biomass solubilization and biogas production.

The aim of the present study was to assess the effect of hydrothermal pretreatment on the solubilization and anaerobic digestion (AD) of Scenedesmus sp. biomass. At first, the microalgae was cultivated in 5% fresh leachate (FL) to recover nutrients such as nitrogen and phosphorus. Scenedesmus sp. grown in 5% FL obtained 100%, 77% and 97% removal efficiency of ammonium nitrogen (NH4+ - N), total Kjeldahl nitrogen (TKN) and phosphate phosphorous (PO43- -P), respectively. In the following step, the hydrothermal pretreatment of Scenedesmus sp. biomass was carried out at 120, 150 and 170 °C and retention time of 0, 30 and 60 min to evaluate its solubilization and biogas production through AD in batch test. Soluble chemical oxygen demand (sCOD) increased by 260% compared to untreated microalgae at 170 °C for 60 min. In comparison to untreated microalgae, the highest increase in biogas (70%) and methane yield (100%) was observed for 150 °C and 60 min pretreated microalgae as a consequence of hydrothermal pretreatment. Hydrothermal pretreatment has shown effectiveness in enhancing biomass solubilization and increasing biogas yield. Nevertheless, further research at the pilot scale is necessary to thoroughly evaluate the potential and feasibility of hydrothermal pretreatment for full-scale implementation.

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-ß1, 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.

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.

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.

Cisplatin-DNA adduct repair of transcribed genes is controlled by two circadian programs in mouse tissues.

Cisplatin is a major cancer chemotherapeutic drug. It kills cancer cells by damaging their DNA, mainly in the form of Pt-d(GpG) diadducts. However, it also has serious side effects, including nephrotoxicity and hepatotoxicity that limit its usefulness. Chronotherapy is taking circadian time into account during therapy to improve the therapeutic index, by improving efficacy and/or limiting toxicity. To this end, we tested the impact of clock time on excision repair of cisplatin-induced DNA damage at single-nucleotide resolution across the genome in mouse kidney and liver. We found that genome repair is controlled by two circadian programs. Repair of the transcribed strand (TS) of active, circadian-controlled genes is dictated by each gene's phase of transcription, which falls across the circadian cycle with prominent peaks at dawn and dusk. In contrast, repair of the nontranscribed strand (NTS) of all genes, repair of intergenic DNA, and global repair overall peaks at Zeitgeber time ZT08, as basal repair capacity, which is controlled by the circadian clock, peaks at this circadian time. Consequently, the TS and NTS of many genes are repaired out of phase. As most cancers are thought to have defective circadian rhythms, these results suggest that future research on timed dosage of cisplatin could potentially reduce damage to healthy tissue and improve its therapeutic index.

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.

Kinome Profiling Identifies Druggable Targets for Novel Human Cytomegalovirus (HCMV) Antivirals.

Human cytomegalovirus (HCMV) is a significant cause of disease in immune-compromised adults and immune naïve newborns. No vaccine exists to prevent HCMV infection, and current antiviral therapies have toxic side effects that limit the duration and intensity of their use. There is thus an urgent need for new strategies to treat HCMV infection. Repurposing existing drugs as antivirals is an attractive approach to limit the time and cost of new antiviral drug development. Virus-induced changes in infected cells are often driven by changes in cellular kinase activity, which led us to hypothesize that defining the complement of kinases (the kinome), whose abundance or expression is altered during infection would identify existing kinase inhibitors that could be repurposed as new antivirals. To this end, we applied a kinase capture technique, multiplexed kinase inhibitor bead-mass spectrometry (MIB-MS) kinome, to quantitatively measure perturbations in >240 cellular kinases simultaneously in cells infected with a laboratory-adapted (AD169) or clinical (TB40E) HCMV strain. MIB-MS profiling identified time-dependent increases and decreases in MIB binding of multiple kinases including cell cycle kinases, receptor tyrosine kinases, and mitotic kinases. Based on the kinome data, we tested the antiviral effects of kinase inhibitors and other compounds, several of which are in clinical use or development. Using a novel flow cytometry-based assay and a fluorescent reporter virus we identified three compounds that inhibited HCMV replication with IC50 values of <1 µm, and at doses that were not toxic to uninfected cells. The most potent inhibitor of HCMV replication was OTSSP167 (IC50 <1.2 nm), a MELK inhibitor, blocked HCMV early gene expression and viral DNA accumulation, resulting in a >3 log decrease in virus replication. These results show the utility of MIB-MS kinome profiling for identifying existing kinase inhibitors that can potentially be repurposed as novel antiviral drugs.

Mammalian Period represses and de-represses transcription by displacing CLOCK-BMAL1 from promoters in a Cryptochrome-dependent manner.

The mammalian circadian clock is based on a transcription-translation feedback loop (TTFL) consolidated by secondary loops. In the primary TTFL, the circadian locomotor output cycles kaput (CLOCK)-brain and muscle Arnt-like protein-1 (BMAL1) heterodimer acts as the transcriptional activator, and Cryptochrome (CRY) and Period (PER) proteins function as repressors. PER represses by displacing CLOCK-BMAL1 from promoters in a CRY-dependent manner. Interestingly, genes with complex promoters may either be repressed or de-repressed by PER, depending on the particular promoter regulatory elements. Here, using mouse cell lines with defined knockout mutations in clock genes, RNA-seq, ChIP-seq, and reporter gene assays coupled with measurements of DNA-protein interactions in nuclear extracts, we elucidate the dual functions of PER as repressor and de-repressor in a context-dependent manner.

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.

Association between differential gene expression and body mass index among endometrial cancers from The Cancer Genome Atlas Project.

OBJECTIVE: The Cancer Genome Atlas (TCGA) identified four integrated clusters for endometrial cancer (EC): POLE, MSI, CNL and CNH. We evaluated differences in gene expression profiles of obese and non-obese women with EC and examined the association of body mass index (BMI) within the clusters identified in TCGA. METHODS: TCGA RNAseq data was used to identify genes related to increasing BMI among ECs. The POLE, MSI and CNL clusters were composed mostly of endometrioid EC. Patient BMI was compared between these three clusters with one-way ANOVA. Association between gene expression and BMI was also assessed while adjusting for confounding effects of potential confounding factors. p-Values testing the association between gene expression and BMI were adjusted for multiple hypothesis testing over the 20,531 genes considered. RESULTS: Mean BMI was statistically different between the ECs in the CNL (35.8) versus POLE (29.8) cluster (p=0.006) and approached significance for the MSI (33.0) versus CNL (35.8) cluster (p=0.05). 181 genes were significantly up- or down-regulated with increasing BMI in endometrioid EC (q-value<0.01), including LPL, IRS-1, IGFBP4, IGFBP7 and the progesterone receptor. DAVID functional annotation analysis revealed significant enrichment in "cell cycle" (adjusted p-value=1.5E-5) and "DNA metabolic processes" (adjusted p-value=1E-3) for the identified genes. CONCLUSIONS: Obesity related genes were found to be upregulated with increasing BMI among endometrioid ECs. Patients with POLE tumors have the lowest median BMI when compared to MSI and CNL. Given the heterogeneity among endometrioid EC, consideration should be given to abandoning the Type I and II classification of EC tumors.

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.

Evaluation of cell disruption methods for protein and coenzyme Q10 quantification in purple non-sulfur bacteria.

A recent focus has been on the recovery of single-cell protein and other nutritionally valuable bioproducts, such as Coenzyme Q10 (CoQ10) from purple non-sulfur bacteria (PNSB) biomass following wastewater treatment. However, due to PNSB's peculiar cell envelope (e.g., increased membrane cross-section for energy transduction) and relatively smaller cell size compared to well-studied microbial protein sources like yeast and microalgae, the effectiveness of common cell disruption methods for protein quantification from PNSB may differ. Thus, this study examines the efficiency of selected chemical (NaOH and EDTA), mechanical (homogenization and bead milling), physical (thermal and bath/probe sonication), and combined chemical-mechanical/physical treatment techniques on the PNSB cell lysis. PNSB biomass was recovered from the treatment of gas-to-liquid process water. Biomass protein and CoQ10 contents were quantified based on extraction efficiency. Considering single-treatment techniques, bead milling resulted in the best protein yields (p < 0.001), with the other techniques resulting in poor yields. However, the NaOH-assisted sonication (combined chemical/physical treatment technique) resulted in similar protein recovery (p = 1.00) with bead milling, with the former having a better amino acid profile. For example, close to 50% of the amino acids, such as sensitive ones like tryptophan, threonine, cystine, and methionine, were detected in higher concentrations in NaOH-assisted sonication (>10% relative difference) compared to bead-milling due to its less disruptive nature and improved solubility of amino acids in alkaline conditions. Overall, PNSB required more intensive protein extraction techniques than were reported to be effective on other single-cell organisms. NaOH was the preferred chemical for chemical-aided mechanical/physical extraction as EDTA was observed to interfere with the Lowry protein kit, resulting in significantly lower concentrations. However, EDTA was the preferred chemical agent for CoQ10 extraction and quantification. CoQ10 extraction efficiency was also suspected to be adversely influenced by pH and temperature.

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.

Determination of permissive and restraining cancer-associated fibroblast (DeCAF) subtypes.

Cancer-associated fibroblast (CAF) subpopulations in pancreatic ductal adenocarcinoma (PDAC) have been identified using single-cell RNA sequencing (scRNAseq) with divergent characteristics, but their clinical relevance remains unclear. We translate scRNAseq-derived CAF cell-subpopulation-specific marker genes to bulk RNAseq data, and develop a single- sample classifier, DeCAF, for the classification of clinically rest raining and perm issive CAF subtypes. We validate DeCAF in 19 independent bulk transcriptomic datasets across four tumor types (PDAC, mesothelioma, bladder and renal cell carcinoma). DeCAF subtypes have distinct histology features, immune landscapes, and are prognostic and predict response to therapy across cancer types. We demonstrate that DeCAF is clinically replicable and robust for the classification of CAF subtypes in patients for multiple tumor types, providing a better framework for the future development and translation of therapies against permissive CAF subtypes and preservation of restraining CAF subtypes. Significance: We introduce a replicable and robust classifier, DeCAF, that delineates the significance of the role of permissive and restraining CAF subtypes in cancer patients. DeCAF is clinically tractable, prognostic and predictive of treatment response in multiple cancer types and lays the translational groundwork for the preclinical and clinical development of CAF subtype specific therapies.

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.