Comprehensive Signature Research Articles

In vivo CRISPRi screen identified lncRNA portfolio crucial for cutaneous squamous cell carcinoma tumor growth.

Cutaneous squamous cell carcinoma (cSCC) accounts for 20% of all skin cancer deaths globally, making it the second-highest subtype of skin cancer. The prevalence of cSCC in humans, as well as the poor capacity for an efficient prognosis, highlights the need to uncover alternative actors and mechanisms at the foundation of skin cancer development. Significant advances have been made to better understand some key factors in cSCC progression. However, little is known about the role of noncoding RNAs, particularly of a specific category called long noncoding RNA (lncRNA). By performing pseudobulk analysis of single-cell sequencing data from normal and cSCC human skin tissues, we determined a global portfolio of lncRNAs specifically expressed in keratinocyte subpopulations. Integration of CRISPR interference screens in vitro and the xenograft model identified several lncRNAs impacting the growth of cSCC cancer lines both in vitro and in vivo. Among these, we further validated LINC00704 and LINC01116 as proliferation-regulating lncRNAs in cSCC lines and potential biomarkers of cSCC progression. Taken together, our study provides a comprehensive signature of lncRNAs with roles in regulating cSCC progression.

Abstract A083: Genomic Analysis of Structural Variations in Pancreatic Cancer Using Long-Read Sequencing

Abstract Objective: Structural variations (SVs) constitute the greatest source of genomic alterations leading to the oncogenesis of many cancers, including the deadliest pancreatic cancer (PC). However, SVs in PC remain largely undefined due to technological limitations of the traditional short-read sequencing. This study aims to establish a comprehensive signature of genomic SVs in PC. Methods: PacBio whole genome long-read sequencing was employed to investigate the genomic landscape of SVs in eight tissues from four pancreatic cancer patients. A variety of SVs callers, including pbsv, Sniffles, cuteSV, and svim, were comprehensively adopted to improve the accuracy of SVs detection. Many undefined characteristics of germline and somatic SVs were revealed by comparing with multiple previously published databases. Besides, the impacts of structural variations on 3D chromatin organization were analyzed by integrating multi-omics data including Hi-C, RNA seq and ChIP seq. Results: A total of 26,844 and 27,028 non-redundant SVs were identified in human pancreatic cancer and matched para-cancerous tissues, respectively. Notably, among these SVs, 23.97% were novel and had not been previously reported. Germline SVs were further characterized and found enriched in 25 well-known susceptible genes, such as TP63, PVT1, and ABO. Additionally, we proposed a panel of germline SVs with predicted highest pathogenicity, involving STOX1, DSPP, and WRN. Totally, 616 somatic SVs were identified and presented high burdens in patients with lymph node metastasis. We observed that somatic DELs and INSs were significantly more prevalent in repetitive regions than in non-repetitive regions. Interestingly, DELs showed a decrease in the proportion occurring in simple repeat regions but an increase in the proportion occurring in LINE and SINE regions compared to INSs. Moreover, it was observed that deletions within topologically associated domains were associated with local enhanced interactions and disrupted chromatin loops, which might influence H3K27ac modification of histone contributing to enhancer or silencer hijacking. Conclusion: This study provides insights into the genomic signature of SVs in human pancreatic cancer. These findings might shed new light on the complexity of SVs and their pathogenesis by interplaying with chromatin organization. Citation Format: Yongxing Du, Xiaohao Zheng, Yunjie Duan, Chengfeng Wang. Genomic Analysis of Structural Variations in Pancreatic Cancer Using Long-Read Sequencing [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr A083.

A comprehensive signature based on endoplasmic reticulum stress-related genes in predicting prognosis and immunotherapy response in melanoma

Melanoma is considered as one of the most invasion types of skin cancer with high mortality rates. Although combination of immune checkpoint therapy with local surgical excision provide a novel promising therapeutic strategies, the overall prognosis of melanoma patients remains unsatisfactory. Endoplasmic reticulum (ER) stress, a process of protein misfolding and undue accumulation, has been proven to play an indispensable regulatory role in tumor progression and tumor immunity. However, whether the signature based ER genes has predictive value for the prognosis and immunotherapy of melanoma has not been systematically manifested. In this study, the LASSO regression and multivariate Cox regression were applied to construct a novel signature for predicting melanoma prognosis both in the training and testing set. Intriguingly, we found that patients endowed with high- and low-risk scores displayed differences in clinicopathologic classification, immune cell infiltration level, tumor microenvironment, and immune checkpoint treatment response. Subsequently, based on molecular biology experiments, we validated that silencing the expression of RAC1, an ERG composed of the risk signature, could restrain the proliferation and migration, promote apoptosis, as well as increase the expression of PD-1/PD-L1 and CTLA4 in melanoma cells. Taken together, the risk signature was regarded as promising predictors for melanoma prognosis and might provide prospective strategies to ameliorate patients’ response to immunotherapy.

Prospectively defined patterns of APOBEC3A mutagenesis are prevalent in human cancers.

SUMMARYMutational signatures defined by single base substitution (SBS) patterns in cancer have elucidated potential mutagenic processes that contribute to malignancy. Two prevalent mutational patterns in human cancers are attributed to the APOBEC3 cytidine deaminase enzymes. Among the seven human APOBEC3 proteins, APO-BEC3A is a potent deaminase and proposed driver of cancer mutagenesis. In this study, we prospectively examine genome-wide aberrations by expressing human APOBEC3A in avian DT40 cells. From whole-genome sequencing, we detect hundreds to thousands of base substitutions per genome. The APOBEC3A signature includes widespread cytidine mutations and a unique insertion-deletion (indel) signature consisting largely of cytidine deletions. This multi-dimensional APOBEC3A signature is prevalent in human cancer genomes. Our data further reveal replication-associated mutations, the rate of stem-loop and clustered mutations, and deamination of methylated cytidines. This comprehensive signature of APOBEC3A mutagenesis is a tool for future studies and a potential biomarker for APOBEC3 activity in cancer.

SUV39H1 regulates corneal epithelial wound healing via H3K9me3-mediated repression of p27

BackgroundCorneal epithelial wound healing (CEWH) is vital for maintaining the integrity and barrier function of the cornea. Although histone modifications mediating gene expression patterns is fundamental in some other tissues, it remains unclear whether these gene regulation patterns underlie CEWH. Suppressor of variegation 3-9 homolog 1 (SUV39H1) plays a vital role in mediating gene silencing via histone H3 trimethylation of lysine 9 (H3K9me3). This study aims to characterize the comprehensive signature of epigenetic modifiers and determine the role of SUV39H1 in CEWH.MethodsNanoString nCounter technology was used to detect the differentially expressed epigenetic modifiers during CEWH. Bioinformatic analyses were performed to reveal their involvement in this process. After knockdown of SUV39H1 with siRNA transfection, we determined the function of SUV39H1 on cell proliferation and migration in human corneal epithelial cells (HCECs) via MTS, EdU, and wound-healing assay, respectively. Flow cytometry analysis further confirmed the effect of SUV39H1 on the cell cycle of HCECs. Loss-of-function assays for SUV39H1 with siRNA injection or chaetocin assessed the role of SUV39H1 on CEWH in vivo. Quantitative reverse transcription polymerase chain reaction (RT-qPCR) and Western blotting characterized the expression of SUV39H1 and its target genes. Chromatin immunoprecipitation assay was used to evaluate the distributions of H3K9me3 marks at the promoters of SUV39H1 target genes.ResultsWe first identified 92 differentially expressed epigenetic modifiers and revealed their involvement during CEWH. SUV39H1 was confirmed to be upregulated in response to corneal injury. Its downregulation significantly inhibited HCEC proliferation and retarded in vivo CEWH. Furthermore, knockdown of SUV39H1 upregulated the p27 expression level and reduced H3K9me3 marks at p27 promoter in HCECs. In addition, p27 was remarkably downregulated with elevated H3K9me3 marks at its promoter during in vivo CEWH.ConclusionsSUV39H1 plays a critical role in regulating corneal epithelial cell proliferation via H3K9me3-mediated suppression of p27 during CEWH. Our findings suggest that epigenetic modifiers such as SUV39H1 can be potential therapeutic approaches to accelerate corneal repair.

Comprehensive signature analysis of drug metabolism differences in the White, Black and Asian prostate cancer patients.

The drug response sensitivity and related prognosis of prostate cancer varied from races, while the original mechanism remains rarely understood. In this study, the comprehensive signature including transcriptomics, epigenome and single nucleotide polymorphisms (SNPs) of 485 PCa cases- including 415 Whites, 58 Blacks and 12 Asians from the TCGA database were analyzed to investigate the drug metabolism differences between races. We found that Blacks and Whites had a more prominent drug metabolism, cytotoxic therapy resistance, and endocrine therapy resistance than Asians, while Whites were more prominent in drug metabolism, cytotoxic therapy resistance and endocrine therapy resistance than Blacks. Subsequently, the targeted regulation analysis indicated that the racial differences in cytotoxic therapy resistance, endocrine therapy resistance, might originate from drug metabolisms, and 19 drug metabolism-related core genes were confirmed in the multi-omics network for subsequent analysis. Furthermore, we verified that CYP1A1, CYP3A4, CYP2B6, UGT2B17, UGT2B7, UGT1A8, UGT2B11, GAS5, SNHG6, XIST significantly affected antineoplastic drugs sensitivities in PCa cell lines, and these genes also showed good predictive efficiency of drug response and treatment outcomes for PCa in this cohort of patients. These findings revealed a comprehensive signature of drug metabolism differences for the Whites, Blacks and Asians, and it may provide some evidence for making individualized treatment strategies.

FDA-Approved and Emerging Next Generation Predictive Biomarkers for Immune Checkpoint Inhibitors in Cancer Patients.

A patient’s response to immune checkpoint inhibitors (ICIs) is a complex quantitative trait, and determined by multiple intrinsic and extrinsic factors. Three currently FDA-approved predictive biomarkers (progra1mmed cell death ligand-1 (PD-L1); microsatellite instability (MSI); tumor mutational burden (TMB)) are routinely used for patient selection for ICI response in clinical practice. Although clinical utility of these biomarkers has been demonstrated in ample clinical trials, many variables involved in using these biomarkers have poised serious challenges in daily practice. Furthermore, the predicted responders by these three biomarkers only have a small percentage of overlap, suggesting that each biomarker captures different contributing factors to ICI response. Optimized use of currently FDA-approved biomarkers and development of a new generation of predictive biomarkers are urgently needed. In this review, we will first discuss three widely used FDA-approved predictive biomarkers and their optimal use. Secondly, we will review four novel gene signature biomarkers: T-cell inflamed gene expression profile (GEP), T-cell dysfunction and exclusion gene signature (TIDE), melanocytic plasticity signature (MPS) and B-cell focused gene signature. The GEP and TIDE have shown better predictive performance than PD-L1, and PD-L1 or TMB, respectively. The MPS is superior to PD-L1, TMB, and TIDE. The B-cell focused gene signature represents a previously unexplored predictive biomarker to ICI response. Thirdly, we will highlight two combined predictive biomarkers: TMB+GEP and MPS+TIDE. These integrated biomarkers showed improved predictive outcomes compared to a single predictor. Finally, we will present a potential nucleic acid biomarker signature, allowing DNA and RNA biomarkers to be analyzed in one assay. This comprehensive signature could represent a future direction of developing robust predictive biomarkers, particularly for the cold tumors, for ICI response.

Aging drives organ-specific alterations of the inflammatory microenvironment guided by immunomodulatory mediators in mice.

Aging is accompanied by chronic, low-grade systemic inflammation, termed inflammaging, a main driver of age-associated diseases. Such sterile inflammation is typically characterized by elevated levels of pro-inflammatory mediators, such as cytokines, chemokines and reactive oxygen species causing organ damage. Lipid mediators play important roles in the fine-tuning of both the promotion and the resolution of inflammation. Yet, it remains unclear how lipid mediators fit within the concept of inflammaging and how their biosynthesis and function is affected by aging. Here, we provide comprehensive signature profiles of inflammatory markers in organs afflicted with inflammation of young and old C57BL/6 mice. We reveal an organ-specific footprint of inflammation-related cytokines, chemokines and lipid mediators, which are distinctively affected by aging. While some organs are characterized by a pronounced pro-inflammatory microenvironment and impaired resolution during aging, others display elevated levels of pro-resolving mediators or an overall decrease in inflammatory signaling. Our results demonstrate that it proves difficult to establish a unifying concept for alterations of immunomodulatory mediators as consequence of aging and that organ specificity needs to be considered. Moreover, our data imply that inclusion of lipid mediators into the concept of inflammaging provides a comprehensive tool to characterize the inflammatory microenvironment during aging on a broader and yet, more detailed scope.

Development and validation of a novel epigenetic signature for predicting prognosis in colon cancer.

Epigenetic factors play a critical role in carcinogenesis by imparting a distinct feature to the chromatin architecture. The present study aimed to develop a novel epigenetic signature for evaluating the relapse-free survival of colon cancer patients. Public microarray datasets were acquired from the Gene Expression Omnibus databases: GSE39582, GSE17538, GSE33113, and GSE37892 set. Patients from GSE39582 set were randomized 1:1 into training and internal validation series. Patients were divided into high-risk and low-risk groups in training series based on a set of 11 epigenetic factors (p < .001). The good reproducibility for the prognostic value of the epigenetic signature was confirmed in the internal validation series (p < .001), external validation series (a combination of GSE17538 set, GSE33113 set, and GSE37892 set; p = .018), and entire series (p < .001). Furthermore, a nomogram, which integrated the epigenetic signature, pathological stage, and postoperative chemotherapy, was developed based on the GSE39582 set. The time-dependent receiver operating characteristic curve at 1 year demonstrated that the comprehensive signature presented superior prognostic value than the pathological stage. In conclusion, an epigenetic signature, which could be utilized to divide colon cancer patients into two groups with significantly different risk of relapse, was established. This biomarker would aid in identifying patients who require an intensive follow-up and aggressive therapeutic intervention.

Discovering master regulators in hepatocellular carcinoma: one novel MR, SEC14L2 inhibits cancer cells.

Identification of master regulator (MR) genes offers a relatively rapid and efficient way to characterize disease-specific molecular programs. Since strong consensus regarding commonly altered MRs in hepatocellular carcinoma (HCC) is lacking, we generated a compendium of HCC datasets from 21 studies and identified a comprehensive signature consisting of 483 genes commonly deregulated in HCC. We then used reverse engineering of transcriptional networks to identify the MRs that underpin the development and progression of HCC. After cross-validation in different HCC datasets, systematic assessment using patient-derived data confirmed prognostic predictive capacities for most HCC MRs and their corresponding regulons. Our HCC signature covered well-established liver cancer hallmarks, and network analyses revealed coordinated interaction between several MRs. One novel MR, SEC14L2, exerted an anti-proliferative effect in HCC cells and strongly suppressed tumor growth in a mouse model. This study advances our knowledge of transcriptional MRs potentially involved in HCC development and progression that may be targeted by specific interventions.

Machine Learning Unveils Proteotypic Mimicry in Genetically Defined SCN Variants

Background Novel computational algorithms for multi-omics analysis bear great potential to highlight pathomechanisms of monogenic diseases. We recently defined the in-depth proteome of primary human neutrophil granulocytes (PMID 30630937). Here, we ask the question whether proteotypic patterns differ between defined genetic subtypes associated with severe congenital neutropenia (SCN). We focus on two novel genetic variants in constituents of the signal recognition particle (SRPRA and SRP19) and previously reported SCN genotypes SRP54, HAX1, and ELANE. Methods We analyzed proteomes of highly purified neutrophil granulocytes from a total of 26 SCN patients, including 5 with homozygous splice site mutations in SRP19, one patient with a de-novo heterozygous missense mutation in SRPRA (using 5 biological replicates collected months apart) as well as 6 patients with SRP54, 8 with HAX1 and 6 with ELANE mutations. Samples of 70 healthy donors (HD) served as controls. Whole cell proteome analysis was based on data-independent acquisition using a Thermo Fisher QExactive HF mass spectrometer. Data analysis was performed in R and Cytoscape, machine learning approaches included lasso regression and random forest. Results Differential expression analysis in comparison to HD showed in all genotypes overexpression of ribosomes, the translational apparatus, mitochondria, cell-substrate junctions and response to unfolded proteins. Underexpressed proteins showed genotype specific enrichment for granule subsets. Whereas ELANE showed deficiency of primary and secretory granules, HAX1 showed deficiency of specific, tertiary and secretory granules. All SRP genotypes showed markedly reduced abundance of proteins in all granule subsets. Principal component analysis showed clear separation of healthy and diseased proteotypes on the first component, whereas the separation of patient genotypes became clear only using five dimensions. We derived genotype specific proteome signatures by lasso regression, consisting of 26 (minimal specific set) to 128 (comprehensive signature) proteins, and a signature of 48 proteins when joining the SRP genotypes as one group. This signatures allow for perfect separation of the genotypes, demonstrating a clear genotype specific effect on protein abundance levels. We asked the question if the genotypes SRP19 and SRPRA show more similar proteomic profile to SRP54 than the other genotypes (ELANE, HAX1) by training a random forest model on proteome data from SRP54, HAX1, ELANE and HD and subsequently testing if the other SRP samples get classified as SRP54. We observe only few misclassifications using either all proteins (7/10) or using the lasso derived genotype defining proteins (8/10). This strongly supports our hypothesis that mutations in different subunits of the same complex lead to similar proteotype changes, a phenomenon we propose to call "proteotypic mimicry". For a systems biology perspective we selected proteins that were exclusively regulated in the SRP genotypes and restricted a network of interactions to these proteins together with their direct interactors (based on APID level 1). The resulting network contained 464 proteins and 3587 interactions. MCODE analysis identified 16 clusters that were consequently annotated using BINGO enrichment analysis. The SRP specific network shows features of the translational apparatus, the proteasome, the septin complex, splicing and cell-metabolic processes. Further studies to dissect specific pathomechanisms are under way. Conclusion Here we provide for the first time evidence for the correlation between SCN causing genotypes and their corresponding neutrophil proteotypes. In particular, we demonstrate significant overlap of all SRP related proteotypes, indicating a phenomenon we propose to be called "proteotypic mimicry". Studies on similarities and disparities of neutrophil proteotypes will help to raise new hypothesis on distinct cellular dysfunction in defined genetic defects of neutrophil granulocytes. Disclosures No relevant conflicts of interest to declare.

PC03.01 Adjuvant Therapy for Resected NSCLC Harboring EGFR Mutation, Chemotherapy or Targeted Therapy - PROs

Lung cancer is so far the leading cause of cancer death worldwide. Complete surgical resection remains the most effective approach for patients with early-stage non-small cell lung cancer (NSCLC). Patients with completely resected stage I NSCLC could have a 5-year survival of ∼80%, whereas it fails to ∼30%. Since 2003, adjuvant chemotherapy became the standard of care showing 4% absolute 5-year survival benefit compared to surgery alone [1]. Following randomized control trials revealed similar results [2-4], yet limited survival benefit, low therapeutic completed rate plus high toxicities rate may somehow hinder its clinical applicability. In the past decade, targeted therapies had achieved tremendous success in oncogene-driven advanced NSCLC showing both high efficacy and low toxicities [5]. As a matter of course, targeted therapy had its baby steps in early-stage NSCLC. However, no positive results were found in these trials which should probably be blame for large proportion of stage IB patients and incorrect detection methods for EGFR mutation. Besides, conservative trial design without head to head comparison could not fully address toxicities issues caused by chemotherapy. Precise target population seemed to be an essential key point to adjuvant targeted therapies and ADJUVANT as well as EVAN trial came into being [6, 7]. Although overall survival (OS) was not mature enough to measure, significantly prolonged disease-free survival (DFS) was observed in both trials along with better tolerability. Indeed, questioning around adjuvant targeted therapy has never been adjourned such as the optimal duration of targeted therapies, study design and lack of OS data. Ross et al brought up with an informative comment for ADJUVANT trial bringing the concept of MRD (molecular residual disease) into adjuvant treatment [8]. Based on the ctDNA testing after standard of care adjuvant chemotherapy, patients of high risk of recurrence will be given further treatments while observation for the others. It highlighted the significant role of discriminating beneficiaries from adjuvant targeted therapies instead of simply providing more efficient treatment modalities. Previous biomarker-based studies regarding adjuvant treatment among different tumor types [9, 10] had provided inspirational examples, and as well shown clinical feasibility and urgent need for personalized adjuvant treatment after complete surgical resection. For ADJUVANT trial, we established a comprehensive signature of genetic-features (MEDUSA) to guide personalized adjuvant treatment in EGFR-mutant stage II-III NSCLC. Results would be unleashed in upcoming ESMO meeting. Through utilizing multi-omics data, we could predict whether additional treatments, adjuvant chemotherapy only or observation would be adequate for each individual and provide optimal sequential treatments. Further translational researches and corresponding trials regarding resectable NSCLC should decipher the issues. Fortunately, several trials regarding dynamic monitoring postoperative ctDNA or genomic profile of primary cancer to guide sequential treatments are ongoing and the results should be expected. 1. Arriagada R, Bergman B, Dunant A et al. Cisplatin-based adjuvant chemotherapy in patients with completely resected non-small-cell lung cancer. N Engl J Med 2004; 350: 351-360. 2. Winton T, Livingston R, Johnson D et al. Vinorelbine plus cisplatin vs. observation in resected non-small-cell lung cancer. N Engl J Med 2005; 352: 2589-2597. 3. Strauss GM, Herndon JE, 2nd, Maddaus MA et al. Adjuvant paclitaxel plus carboplatin compared with observation in stage IB non-small-cell lung cancer: CALGB 9633 with the Cancer and Leukemia Group B, Radiation Therapy Oncology Group, and North Central Cancer Treatment Group Study Groups. J Clin Oncol 2008; 26: 5043-5051. 4. Douillard JY, Rosell R, De Lena M et al. Adjuvant vinorelbine plus cisplatin versus observation in patients with completely resected stage IB-IIIA non-small-cell lung cancer (Adjuvant Navelbine International Trialist Association [ANITA]): a randomised controlled trial. Lancet Oncol 2006; 7: 719-727. 5. Hirsch FR, Scagliotti GV, Mulshine JL et al. Lung cancer: current therapies and new targeted treatments. Lancet 2017; 389: 299-311. 6. Zhong WZ, Wang Q, Mao WM et al. Gefitinib versus vinorelbine plus cisplatin as adjuvant treatment for stage II-IIIA (N1-N2) EGFR-mutant NSCLC (ADJUVANT/CTONG1104): a randomised, open-label, phase 3 study. Lancet Oncol 2018; 19: 139-148. 7. Yue D, Xu S, Wang Q et al. Erlotinib versus vinorelbine plus cisplatin as adjuvant therapy in Chinese patients with stage IIIA EGFR mutation-positive non-small-cell lung cancer (EVAN): a randomised, open-label, phase 2 trial. Lancet Respir Med 2018; 6: 863-873. 8. Ng TL, Camidge DR. Lung cancer's real adjuvant EGFR targeted therapy questions. Lancet Oncol 2018; 19: 15-17. 9. Sparano JA, Gray RJ, Makower DF et al. Prospective Validation of a 21-Gene Expression Assay in Breast Cancer. N Engl J Med 2015; 373: 2005-2014. 10. Dienstmann R, Salazar R, Tabernero J. Personalizing colon cancer adjuvant therapy: selecting optimal treatments for individual patients. J Clin Oncol 2015; 33: 1787-1796. Adjuvant therapy, EGFR-TKI, Lung cancer

Gene Expression Profiling Reveals that PXR Activation Inhibits Hepatic PPARα Activity and Decreases FGF21 Secretion in Male C57Bl6/J Mice.

The pregnane X receptor (PXR) is the main nuclear receptor regulating the expression of xenobiotic-metabolizing enzymes and is highly expressed in the liver and intestine. Recent studies have highlighted its additional role in lipid homeostasis, however, the mechanisms of these regulations are not fully elucidated. We investigated the transcriptomic signature of PXR activation in the liver of adult wild-type vs. Pxr-/- C57Bl6/J male mice treated with the rodent specific ligand pregnenolone 16α-carbonitrile (PCN). PXR activation increased liver triglyceride accumulation and significantly regulated the expression of 1215 genes, mostly xenobiotic-metabolizing enzymes. Among the down-regulated genes, we identified a strong peroxisome proliferator-activated receptor α (PPARα) signature. Comparison of this signature with a list of fasting-induced PPARα target genes confirmed that PXR activation decreased the expression of more than 25 PPARα target genes, among which was the hepatokine fibroblast growth factor 21 (Fgf21). PXR activation abolished plasmatic levels of FGF21. We provide a comprehensive signature of PXR activation in the liver and identify new PXR target genes that might be involved in the steatogenic effect of PXR. Moreover, we show that PXR activation down-regulates hepatic PPARα activity and FGF21 circulation, which could participate in the pleiotropic role of PXR in energy homeostasis.

Exome and immune cell score analyses reveal great variation within synchronous primary colorectal cancers

BackgroundApproximately 4% of colorectal cancer (CRC) patients have at least two simultaneous cancers in the colon. Due to the shared environment, these synchronous CRCs (SCRCs) provide a unique setting to study colorectal carcinogenesis. Understanding whether these tumours are genetically similar or distinct is essential when designing therapeutic approaches.MethodsWe performed exome sequencing of 47 primary cancers and corresponding normal samples from 23 patients. Additionally, we carried out a comprehensive mutational signature analysis to assess whether tumours had undergone similar mutational processes and the first immune cell score analysis (IS) of SCRC to analyse the interplay between immune cell invasion and mutation profile in both lesions of an individual.ResultsThe tumour pairs shared only few mutations, favouring different mutations in known CRC genes and signalling pathways and displayed variation in their signature content. Two tumour pairs had discordant mismatch repair statuses. In majority of the pairs, IS varied between primaries. Differences were not explained by any clinicopathological variable or mutation burden.ConclusionsThe study shows major diversity within SCRCs. Rather than rely on data from one tumour, our study highlights the need to evaluate both tumours of a synchronous pair for optimised targeted therapy.

A Comprehensive RNA Expression Signature for Cervical Squamous Cell Carcinoma Prognosis

Clinicopathological characteristics alone are not enough to predict the survival of patients with cervical squamous cell carcinoma (CESC) due to clinical heterogeneity. In recent years, many genes and non-coding RNAs have been shown to be oncogenes or tumor-suppressors in CESC cells. This study aimed to develop a comprehensive transcriptomic signature for CESC patient prognosis. Univariate, multivariate, and Least Absolute Shrinkage and Selection Operator penalized Cox regression were used to identify prognostic signatures for CESC patients from transcriptomic data of The Cancer Genome Atlas. A normalized prognostic index (NPI) was formulated as a synthetical index for CESC prognosis. Time-dependent receiver operating characteristic curve analysis was used to compare prognostic signatures. A prognostic transcriptomic signature was identified, including 1 microRNA, 1 long non-coding RNA, and 6 messenger RNAs. Decreased survival was associated with CESC patients being in the high-risk group stratified by NPI. The NPI was an independent predictor for CESC patient prognosis and it outperformed the known clinicopathological characteristics, microRNA-only signature, gene-only signature, and previously identified microRNA and gene signatures. Function and pathway enrichment analysis revealed that the identified prognostic RNAs were mainly involved in angiogenesis. In conclusion, we proposed a transcriptomic signature for CESC prognosis and it may be useful for effective clinical risk management of CESC patients. Moreover, RNAs in the transcriptomic signature provided clues for downstream experimental validation and mechanism exploration.

Abstract 2267: A novel method for classification of microsatellite instability (MSI) using the Oncomine Tumor Mutation Load assay

Abstract Cancer-associated changes in short tandem repeats (STRs) throughout the genome have been shown to be predictive of response to immunotherapy treatment. Traditional assays efficiently rely on a small number of genomic loci to assign an MSI status, but are only effective in a small number of cancer types. Genome-wide profiling of aberrations in STRs has the potential to reveal a comprehensive signature of MSI and could allow for the association of response with samples that are labeled as microsatellite stable (MSS) by traditional assays.To facilitate the discovery of informative repeat sites across the genome, we have extended the Oncomine Tumor Mutation Load assay* to allow for the detection of variants in STRs. Our method addresses challenges of semiconductor sequencing of long STR elements including indel and partial incorporation signal errors. We developed an algorithm to avoid and correct these errors in flowspace, rather than base space, to ensure accurate signal assessment. Our pipeline is able to determine the optimal anchor locations used to identify the exact position of a repeat sequence within a read and then quantify the size, sequence, and number of reads associated with each allele. An additional complication of accurately quantifying the length of an STR is the increased error rates of the polymerase during DNA amplification. By evaluating the similarity of every loci between tumor and normal pairs of an individual using a metric derived from information theory, we are able to account for both the sequencing and PCR associated noise to robustly identify the genome-wide changes. Furthermore by evaluating a set of 24 MSI-High and 24 MSS samples along with their paired normal samples, we created a novel method to identify a highly informative subset of STR loci. Creating a classifier using only these highly informative loci, we are able to recapitulate the traditional MSI assay with 95% accuracy on these initial samples. This general methodology can be further extended to a model where MSI status is predicted using only the tumor sample, without the need for a control from the same individual. Due to the flexibility of this method, we are able to examine an increased number of potentially informative loci and use the total mutation load along with mutation calls in the MMR genes to provide comprehensive, multi-dimensional genomic perspective of samples. While this novel method has been developed and tested on the Oncomine Tumor Mutation Load assay, we have created a novel general approach that can be used on the output of any semiconductor sequencing assay. The extended information derived from individual mutations and the total mutation load, along with STR aberration information allows for a comprehensive evaluation of every sample and promotes the discovery of novel genomic components important for understanding the mechanisms of immune-oncology. *For research use only Citation Format: Aleksandr Pankov, Sameh El-Difrawy, Warren Tom, Jeffrey Conroy, Sean Glenn, Sarabjot Pabla, Carl Morrison, Fiona Hyland, Simon Cawley. A novel method for classification of microsatellite instability (MSI) using the Oncomine Tumor Mutation Load assay [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2267.

Quantagenetics® analysis of laser-induced breakdown spectroscopic data: Rapid and accurate authentication of materials

Many industrial and commercial issues involve authentication of such matters as the manufacturer or geographic source of a material, and quality control of materials, determining whether specific treatments have been properly applied, or if a material is authentic or fraudulent. Often, multiple analytical techniques and tests are used, resulting in expensive and time-consuming testing procedures. Laser-Induced Breakdown Spectroscopy (LIBS) is a rapid laser ablation spectroscopic analytical method. Each LIBS spectrum contains information about the concentration of every element, some isotopic ratios, and the molecular structure of the material, making it a unique and comprehensive signature of the material. Quantagenetics® is a multivariate statistical method based on Bayesian statistics that uses the Euclidian distance between LIBS spectra of materials to classify materials (US Patents 9,063,085 and 8,699,022). The fundamental idea behind Quantagenetics® is that LIBS spectra contain sufficient information to determine the origin and history of materials. This study presents two case studies that illustrate the method. LIBS spectra from 510 Colombian emeralds from 18 mines were classified by mine. Overall, 99.4% of the spectra were correctly classified; the success rate for individual mines ranges from 98.2% to 100%. Some of the mines are separated by distances as little as 200 m, indicating that the method uses the slight but consistent differences in composition to identify the mine of origin accurately. The second study used bars of 17–4 stainless steel from three manufacturers. Each of the three bars was cut into 90 coupons; 30 of each bar received no further treatment, another 30 from each bar received one tempering and hardening treatment, and the final 30 coupons from each bar received a different heat treatment. Using LIBS spectra taken from the coupons, the Quantagenetics® method classified the 270 coupons both by manufacturer (composition) and heat treatment (structure) with an overall success rate of 95.3%. Individual success rates range from 92.4% to 97.6%. These case studies were successful despite having no preconceived knowledge of the materials; artificial intelligence allows the materials to classify themselves without human intervention or bias. Multivariate analysis of LIBS spectra using the Quantagenetics® method has promise to improve quality control and authentication of a wide variety of materials in industrial enterprises.

A comprehensive prognostic signature based on interaction of immune cell infiltration with stromal composition for resected pancreatic ductal adenocarcinoma

A comprehensive prognostic signature based on interaction of immune cell infiltration with stromal composition for resected pancreatic ductal adenocarcinoma

Off-Line Handwritten Signature Recognition by Wavelet Entropy and Neural Network

Handwritten signatures are widely utilized as a form of personal recognition. However, they have the unfortunate shortcoming of being easily abused by those who would fake the identification or intent of an individual which might be very harmful. Therefore, the need for an automatic signature recognition system is crucial. In this paper, a signature recognition approach based on a probabilistic neural network (PNN) and wavelet transform average framing entropy (AFE) is proposed. The system was tested with a wavelet packet (WP) entropy denoted as a WP entropy neural network system (WPENN) and with a discrete wavelet transform (DWT) entropy denoted as a DWT entropy neural network system (DWENN). Our investigation was conducted over several wavelet families and different entropy types. Identification tasks, as well as verification tasks, were investigated for a comprehensive signature system study. Several other methods used in the literature were considered for comparison. Two databases were used for algorithm testing. The best recognition rate result was achieved by WPENN whereby the threshold entropy reached 92%.

A comprehensive prognostic signature based on interaction of immune cell infiltration with stromal composition for resected pancreatic ductal adenocarcinoma

A comprehensive prognostic signature based on interaction of immune cell infiltration with stromal composition for resected pancreatic ductal adenocarcinoma