Whole-transcriptome Sequencing Research Articles

High-throughput molecular assays for inclusion in personalised oncology trials - State-of-the-art and beyond.

In the last decades, the development of high-throughput molecular assays has revolutionised cancer diagnostics, paving the way for the concept of personalised cancer medicine. This progress has been driven by the introduction of such technologies through biomarker-driven oncology trials. In this review, strengths and limitations of various state-of-the-art sequencing technologies, including gene panel sequencing (DNA and RNA), whole-exome/whole-genome sequencing and whole-transcriptome sequencing, are explored, focusing on their ability to identify clinically relevant biomarkers with diagnostic, prognostic and/or predictive impact. This includes the need to assess complex biomarkers, for example microsatellite instability, tumour mutation burden and homologous recombination deficiency, to identify patients suitable for specific therapies, including immunotherapy. Furthermore, the crucial role of biomarker analysis and multidisciplinary molecular tumour boards in selecting patients for trial inclusion is discussed in relation to various trial concepts, including drug repurposing. Recognising that today's exploratory techniques will evolve into tomorrow's routine diagnostics and clinical study inclusion assays, the importance of emerging technologies for multimodal diagnostics, such as proteomics and in vivo drug sensitivity testing, is also discussed. In addition, key regulatory aspects and the importance of patient engagement in all phases of a clinical trial are described. Finally, we propose a set of recommendations for consideration when planning a new precision cancer medicine trial.

Abstract PO2-06-10: Prevalence of genomic alterations in Xerna tumor microenvironment subtypes in triple negative breast cancer patients

Abstract Introduction: Immune checkpoint inhibitors (ICI) are an important therapeutic option for patients with triple negative breast cancer (TNBC). However, identification of patients most likely to respond is challenging. PD-L1 positivity by immunohistochemistry is the standard biomarker used for ICI therapy selection in TNBC. However, other biomarkers, such as analysis of the tumor microenvironment (TME) may be more accurate in predicting response. The XernaTM TME Panel uses RNA sequencing data and machine learning to analyze the TME, utilizing the angiogenic and immunogenic biology of the TME to classify tumors into four TME subtypes. In this study, the distribution of Xerna TME subtypes and associated genomic alterations in TNBC were investigated for their potential use in therapy selection. Methods: A total of 203 TNBC patient samples underwent tumor-normal whole-exome, whole-transcriptome sequencing testing with the OncoExTraTM assay. The whole-transcriptome expression data were analyzed using the Xerna TME Panel to assign each sample to one of four subtypes: Immune Active (IA), Immune Suppressed (IS), Immune Desert (ID) and Angiogenic (A). The IA and IS subtypes both have high immune scores that may be particularly sensitive to ICI therapy. Actionable alterations, defined as those with FDA-approved matched therapies in any cancer, with matched clinical trials, or with evidence in cancer guidelines or the literature for possible matched therapies, were also identified and associations across Xerna subtypes were explored. Results: Approximately half (100 of 203; 49.3%) of the patient samples had high (IA+IS) immune subtypes (Table 1). Targetable alterations associated with an FDA-approved therapy were present in 114 (56.2%) patients. No biomarkers were significantly associated (p < 0.05) with high (IA+IS) versus low (ID+A) immune scores. Biomarkers associated with ICI response, namely mismatch repair gene alterations (MSH2/3/6, MLH1/3, PMS1/2), high tumor mutational burden (TMB-high) and microsatellite instability were detected in only 6 (3.0%), 3 (1.5%) and 1 (0.5%) patient samples respectively, and all but 1, an MSH6 alteration, were in high immune subtype samples. Conclusions: The Xerna TME Panel classified 49.3% of TNBC patient tumors to IA or IS, suggesting they may respond to ICI therapy. Many (56.2%) patient tumors harbored alterations associated with FDA-approved therapies, providing the potential for novel combination therapies. These findings warrant further study and clinical validation in TNBC patients treated with ICI therapy. Table 1. Frequency of actionable biomarkers that were present in at least 10 (5%) TNBC patient samples. Citation Format: Gargi Basu, Janine Lobello, Snehal Thakkar, Jessica Aldrich, Matthew Halbert, Patrick Eimerman, Cynthia Flannery, Nishitha Therala, David Hall, Daniel Pointing, Lea Vohar, Roman Luštrik, Luka Ausec, Mark Uhlik, Seema Iyer, Laura Benjamin, Frederick Baehner. Prevalence of genomic alterations in Xerna tumor microenvironment subtypes in triple negative breast cancer patients [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-06-10.

Exploring the Mechanisms of Yishen Tongluo Decoction on Repairing DNA Damage in Mouse Spermatogonia Cells Based on Whole Transcriptome Sequencing.

The aim of this study was to investigate the potential mechanism through which Yishen Tongluo decoction (YSTL) repairs DNA damage caused by benzo(a)pyrene diol epoxide (BPDE) in mouse spermatocytes (GC-2). The GC-2 cells were divided randomly into the control group, BPDE group, and low-, medium-, and high-dose YSTL groups of YSTL decoction. A comet assay was used to detect the DNA fragment index (DFI) of cells in each group. Based on the DFI results, whole transcriptome sequencing was conducted, followed by trend analysis, gene ontology (GO) enrichment analysis, kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis, and ceRNA network analysis. Compared with the control group, the BPDE group reported a significant increase in the DNA fragmentation index (DFI) (p < .05). Compared with the BPDE group, the low-, high- and medium-dose YSTL groups had a significantly reduced DFI (p < .05). Whole-transcriptome sequencing revealed seven differentially expressed circRNAs, 203 differentially expressed miRNAs, and 3,662 differentially expressed mRNAs between the control group and the BPDE group. There was a total of 12 differentially expressed circRNAs, 204 miRNAs, and 2150 mRNAs between the BPDE group and the traditional Chinese medicine group. The pathways involved include DNA repair pathway, nucleotide excision repair pathway, base excision repair pathway, etc. The ceRNA network reported that Hmga2 was the core protein involved, novel_cir_000117 and mmu-miR-466c-3p were located upstream of Hmga2, and they were regulatory factors associated with Hmga2. Finally, we conclude that YSTL decoction may repair sperm DNA damage caused by BPDE through the novel_cir_000117-mmu-miR-466c-3p-Hmga2 pathway.

RET Alterations Differentiate Molecular Profile of Medullary Thyroid Cancer.

Medullary thyroid cancer (MTC) is a rare cancer originating from parafollicular C cells of the thyroid gland. Therapeutically relevant alterations in MTC are predominantly reported in RET oncogene, and lower-frequency alterations are reported in KRAS and BRAF. Nevertheless, there is an unmet need existing to analyze the MTC in the Indian cohort by using in-depth sequencing techniques that go beyond the identification of known therapeutic biomarkers. Here, we characterize MTC using integrative whole-exome and whole-transcriptome sequencing of 32 MTC tissue samples. We performed clinically relevant variant analysis, molecular pathway analysis, tumor immune-microenvironment analysis, and structural characterization of RET novel mutation. Mutational landscape analysis shows expected RET mutations in 50% of the cases. Furthermore, we observed mutations in known cancer genes like KRAS, HRAS, SF3B1, and BRAF to be altered only in the RET-negative cohort. Pathway analysis showed differential enrichment of mutations in transcriptional deregulation genes in the RET-negative cohort. Furthermore, we observed novel RET kinase domain mutation Y900S showing affinity to RET inhibitors accessed via molecular docking and molecular dynamics simulation. Altogether, this study provides a detailed genomic characterization of patients with MTC of Indian origin, highlighting the possible utility of targeted therapies in this disease.

Discovery and Validation of a 15-Gene Prognostic Signature for Clear Cell Renal Cell Carcinoma.

Develop and validate gene expression-based biomarker associated with recurrent disease to facilitate risk stratification of clear cell renal cell carcinoma (ccRCC). We retrospectively identified 110 patients who underwent radical nephrectomy for ccRCC (discovery cohort). Patients who recurred were matched on the basis of grade/stage to patients without recurrence. Capture whole-transcriptome sequencing was performed on RNA isolated from archival tissue using the Illumina platform. We developed a gene-expression signature to predict recurrence-free survival/disease-free survival (DFS) using a 15-fold lasso and elastic-net regularized linear Cox model. We derived the 31-gene cell cycle progression (mxCCP) score using RNA-seq data for each patient. Kaplan-Meier (KM) curves and multivariable Cox proportional hazard testing were used to validate the independent prognostic impact of the gene-expression signature on DFS, disease-specific survival (DSS), and overall survival (OS) in two validation data sets (combined n = 761). After quality control, the discovery cohort comprised 50 patients with recurrence and 41 patients without, with a median follow-up of 26 and 36 months, respectively. We developed a 15-gene (15G) signature, which was independently associated with worse DFS and DSS (DFS: hazard ratio [HR], 11.08 [95% CI, 4.9 to 25.1]; DSS: HR, 9.67 [95% CI, 3.4 to 27.7]) in a multivariable model adjusting for clinicopathologic parameters (including stage, size, grade, and necrosis [SSIGN] score and Memorial Sloan Kettering Cancer Center nomogram) and mxCCP score. The 15G signature was also independently associated with worse DFS and DSS in both validation data sets (Validation A [n = 382], DFS: HR, 2.6 [95% CI, 1.6 to 4.3]; DSS: HR, 3 [95% CI, 1.4 to 6.1] and Validation B (n = 379), DFS: HR, 2.1 [95% CI, 1.2 to 3.6]; OS: HR, 3 [95% CI, 1.6 to 5.7]) adjusting for clinicopathologic variables and mxCCP score. We developed and validated a novel 15G prognostic signature to improve risk stratification of patients with ccRCC. Pending further validation, this signature has the potential to facilitate optimal treatment allocation.

Whole-Transcriptome Sequencing of Knee Joint Cartilage from Kashin-Beck Disease and Osteoarthritis Patients.

The aim of this study was to provide a comprehensive understanding of similarities and differences in mRNAs, lncRNAs, and circRNAs within cartilage for Kashin-Beck disease (KBD) compared to osteoarthritis (OA). We conducted a comparison of the expression profiles of mRNAs, lncRNAs, and circRNAs via whole-transcriptome sequencing in eight KBD and ten OA individuals. To facilitate functional annotation-enriched analysis for differentially expressed (DE) genes, DE lncRNAs, and DE circRNAs, we employed bioinformatic analysis utilizing Gene Ontology (GO) and KEGG. Additionally, using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), we validated the expression levels of four cartilage-related genes in chondrocytes. We identified a total of 43 DE mRNAs, 1451 DE lncRNAs, and 305 DE circRNAs in KBD cartilage tissue compared to OA (q value < 0.05; |log2FC| > 1). We also performed competing endogenous RNA network analysis, which identified a total of 65 lncRNA-mRNA interactions and 4714 miRNA-circRNA interactions. In particular, we observed that circRNA12218 had binding sites for three miRNAs targeting ACAN, while circRNA12487 had binding sites for seven miRNAs targeting COL2A1. Our results add a novel set of genes and non-coding RNAs that could potentially serve as candidate diagnostic biomarkers or therapeutic targets for KBD patients.

Identification and bioinformatics analysis of lncRNAs in serum of patients with ankylosing spondylitis

ObjectivesThe aim of this study was to explore the long non-coding RNA (lncRNA) expression profiles in serum of patients with ankylosing spondylitis (AS). The role of these lncRNAs in this complex autoimmune situation needs to be evaluated.MethodsWe used high-throughput whole-transcriptome sequencing to generate sequencing data from three patients with AS and three normal controls (NC). Then, we performed bioinformatics analyses to identify the functional and biological processes associated with differentially expressed lncRNAs (DElncRNAs). We confirmed the validity of our RNA-seq data by assessing the expression of eight lncRNAs via quantitative reverse transcription polymerase chain reaction (qRT-PCR) in 20 AS and 20 NC samples. We measured the correlation between the expression levels of lncRNAs and patient clinical index values using the Spearman correlation test.ResultsWe identified 72 significantly upregulated and 73 significantly downregulated lncRNAs in AS patients compared to NC. qRT-PCR was performed to validate the expression of selected DElncRNAs; the results demonstrated that the expression levels of MALAT1:24, NBR2:9, lnc-DLK1-35:13, lnc-LARP1-1:1, lnc-AIPL1-1:7, and lnc-SLC12A7-1:16 were consistent with the sequencing analysis results. Enrichment analysis showed that DElncRNAs mainly participated in the immune and inflammatory responses pathways, such as regulation of protein ubiquitination, major histocompatibility complex class I-mediated antigen processing and presentation, MAPkinase activation, and interleukin-17 signaling pathways. In addition, a competing endogenous RNA network was constructed to determine the interaction among the lncRNAs, microRNAs, and mRNAs based on the confirmed lncRNAs (MALAT1:24 and NBR2:9). We further found the expression of MALAT1:24 and NBR2:9 to be positively correlated with disease severity.ConclusionTaken together, our study presents a comprehensive overview of lncRNAs in the serum of AS patients, thereby contributing novel perspectives on the underlying pathogenic mechanisms of this condition. In addition, our study predicted MALAT1 has the potential to be deeply involved in the pathogenesis of AS.

Abstract LB432: Identification of novel drug targets using the ADAPT biotargeting system on FFPE tissue from patients with glioblastoma multiforme

Abstract Glioblastoma Multiforme (GBM) is one of the most aggressive and lethal brain tumor types with a five-year survival rate of 6.9%. In 2023, more than 14,490 Americans were expected to receive a diagnosis. Although great progress has been achieved in protein target identification and treatment over the last few years, current treatment options are limited, and tumor recurrence occurs at a high rate due to treatment resistance. Aptamers, short single-stranded oligonucleotide ligands with high-affinity binding to molecular targets, are well suited tools for exploring novel targets in highly heterogeneous tumors with complex etiologies like GBM, where clinically relevant biomarkers are limited. In this study, aptamer-based cancer-specific drug target identification was performed by Adaptive Dynamic Artificial Polyligand Targeting (ADAPT) Biotargeting System. In short, a GBM-specific library was enriched through a Systematic Evolution of Ligands by EXponential enrichment (SELEX) process using micro dissected Formalin-Fixed Paraffin-Embedded (FFPE) tissues from GBM patients, along with whole body normal tissue for counterselection. Microdissection of tissue allows the partitioning of different types of cells to focus on specific cells of interest. The enriched libraries have been evaluated on clinical GBM and normal brain control samples through aptamer mediated tissue staining, resulting in preferential binding of enriched libraries compared to controls in the vast majority of GBM patients with little binding to normal brain samples. The final library was subsequently applied in an in-house developed library-based affinity pull-down assay using lysates from FFPE tissue of the same group of GBM patients and normal brain samples, followed by unbiased Mass Spectrometry for protein target identification. Combining the results from this proteomics analysis and data from the internal Whole Transcriptome Sequencing (WTS) database, we were able to identify several targets that are highly expressed in GBM and other cancer lineages. Antibody staining provided further validation of the expression levels and the cellular localization. The targets identified in this study include not only potential novel therapeutic targets, but also well-studied biomarkers and targets with available drugs already in clinical trials, demonstrating the platform has great potential for identifying high affinity binding and selectivity compared to traditional proteomics and other methods in drug target discovery. Citation Format: Xixi Wei, Myunggi An, Radhika Santhanam, Barbara Richardson, Lucas Vu, Justin Saul, Anna Walton, Michael Gee, Daniel Martin, Amanda Hubbard, Alexis Rodin, Matthew Rosenow, Tassilo Hornung. Identification of novel drug targets using the ADAPT biotargeting system on FFPE tissue from patients with glioblastoma multiforme [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB432.

Abstract LB435: Large panel organoid drug testing combined with biomarker analysis: Unveiling prospects for mechanism of action identification and preclinical patient stratification

Abstract Introduction The development of oncology drugs has a persistently high attrition rate of greater than 95%, requiring the development and implementation of novel models and technologies aimed at better predictive abilities during the preclinical phase. Among these, organoid technologies, which involve propagating tumor stem cells in a near-native state, have proven to markedly advance drug testing with enhanced precision. Our previously developed assay-ready organoid technology has paved the way for large panel organoid screening, allowing organoids to augment the efficiency of drug development pipelines, boost patient-derived model usage in preclinical studies, and facilitate accurate predictions of drug action mechanisms. Here, we present a proof-of-concept (POC) large scale organoid experiment, studying the responses of 50 models to the cytotoxic agent paclitaxel, using next generation sequencing data to analyze the drug mechanism of action (MOA) and identify biomarkers that predict drug responses. Methods A panel of 50 organoid models was characterized for paclitaxel response in 9-dose-response curves by measuring ATP levels after a 5-day exposure. Models were genomically profiled by whole-exome sequencing (n = 47) and whole-transcriptome sequencing (n = 44) to analyze the association between organoid pharmacology data and genomic information and elucidate genes and pathways that may be related with, or predictive of, the treatment responses. We performed biomarker analysis on gene expression, mutation, and copy number alteration levels. Results By assessing organoid sensitivity to paclitaxel, we observed no significant indication-specific difference in organoid responses across 7 cancer types. The 50 models were then categorized into responders (AUC &amp;lt; 1.5; n=15) and non-responders (AUC &amp;gt; 2.6; n=14) for biomarker discovery. Differential expression analysis and pathway activation analysis revealed genes and pathways involved in the cell cycle and DNA replication. DNA recombination differentiates responders from non-responders, while genes involved in epithelial cell proliferation, differentiation, and wound healing are associated with drug resistance. Mutation of proto-oncogene RB Transcriptional Corepressor 1, RB1 (a negative regulator of the cell cycle), was found to be a biomarker enriched in non-responders to paclitaxel, corroborating earlier clinical findings. Using the identified profile, we predicted responses for non-tested models in our biobank and validated our approach. Conclusion Large panel organoid drug screening in combination with biomarker analysis brings great opportunities for increasing drug pipeline efficiency. The POC here presented highlights how such a screen can identify MOAs and identify relevant biomarker profiles to be used in clinical trial patient stratification. Citation Format: Linda Xue, Liza Wijler, Marten Hornsveld, Sheng Guo, Leo Price, Marrit Putker, Ludovic Bourre. Large panel organoid drug testing combined with biomarker analysis: Unveiling prospects for mechanism of action identification and preclinical patient stratification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB435.

Integrative analyses of whole-transcriptome sequencing reveals CeRNA regulatory network in pulmonary hypertension treated with FGF21

Noncoding RNAs have been shown to play essential roles in hypoxic pulmonary hypertension (HPH). Our preliminary data showed that HPH is attenuated by fibroblast growth factor 21 (FGF21) administration. Therefore, we further investigated the whole transcriptome RNA expression patterns and interactions in a mice HPH model treated with FGF21. By whole-transcriptome sequencing, differentially expressed mRNAs, miRNAs, lncRNAs, and circRNAs were successfully identified in normoxia (Nx) vs. hypoxia (Hx) and Hx vs. hypoxia + FGF21 (Hx + F21). Differentially expressed mRNAs, miRNAs, lncRNAs, and circRNAs regulated by hypoxia and FGF21 were selected through intersection analysis. Based on prediction databases and sequencing data, differentially co-expressed mRNAs, miRNAs, lncRNAs, and circRNAs were further screened, followed by functional enrichment analysis. MAPK signaling pathway and epigenetic modification were enriched and may play fundamental roles in the therapeutic effects of FGF21. The ceRNA regulatory network of lncRNA–miRNA–mRNA and circRNA–miRNA–mRNA was constructed with miR-7a-5p, miR-449c-5p, miR-676-3p and miR-674-3p as the core. In addition, quantitative real-time PCR experiments were employed to verify the whole-transcriptome sequencing data. The results of luciferase reporter assays highlighted the relationship between miR-449c-5p and XR_878320.1, miR-449c-5p and Stab2, miR-449c-5p and circ_mtcp1, which suggesting that miR-449c-5p may be a key regulator of FGF21 in the treatment of PH. Taken together, this study provides potential biomarkers, pathways, and ceRNA regulatory networks in HPH treated with FGF21 and will provide an experimental basis for the clinical application of FGF21 in PH.

Deciphering the role of non-coding RNAs involved in sorafenib resistance

Sorafenib is an important treatment strategy for advanced hepatocellular carcinoma (HCC). Unfortunately, drug resistance has become a major obstacle in sorafenib application. In this study, whole transcriptome sequencing (WTS) was conducted to compare the paired differences between non-coding RNAs (ncRNAs), including long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), microRNAs (miRNAs), and mRNAs, in sorafenib-resistant and parental cells. The overlap of differentially expressed ncRNAs (DENs) between the SMMC7721/S and Huh7/S cells and their parental cells was determined. 2 upregulated and 3 downregulated lncRNAs, 2 upregulated and 1 downregulated circRNAs, as well as 10 upregulated and 2 downregulated miRNAs, in both SMMC7721/S and Huh7/S cells, attracted more attention. The target genes of these DENs were then identified as the overlaps between the differentially expressed mRNAs achieved using the WTS analysis and the predicted genes of DENs obtained using the “co-localization” or “co-expression,” miRanda, and RNAhybrid analysis. Consequently, the potential regulatory network between overlapping DENs and their target genes in both SMMC7721/S and Huh7/S cells was explored. The “lncRNA-miRNA-mRNA” and “circRNA-miRNA-mRNA” networks were constructed based on the competitive endogenous RNA (ceRNA) theory using the Cytoscape software. In particular, lncRNA MED17-203-miRNA (miR-193a-5p, miR-197-3p, miR-27a-5p, miR-320b, miR-767-3p, miR-767-5p, miR-92a-3p, let-7c-5p)-mRNA,” “circ_0002874-miR-27a-5p-mRNA” and “circ_0078607-miR-320b-mRNA” networks were first introduced in sorafenib-resistant HCC. Furthermore, these networks were most probably connected to the process of metabolic reprogramming, where the activation of the PPAR, HIF-1, Hippo, and TGF-β signaling pathways is governed. Alternatively, the network “circ_0002874-miR-27a-5p-mRNA” was also involved in the regulation of the activation of TGF-β signaling pathways, thus advancing Epithelial-mesenchymal transition (EMT). These findings provide a theoretical basis for exploring the mechanisms underlying sorafenib resistance mediated by metabolic reprogramming and EMT in HCC.

Whole transcriptome sequencing identifies a competitive endogenous RNA network that regulates the immunity of bladder cancer

Several types of non-coding RNAs such as circRNAs, lncRNAs, and miRNAs have been identified to regulate mRNAs through the mechanism known as the competitive endogenous RNA (ceRNA) network. To explore the role of the ceRNA regulatory network in the immune microenvironment of bladder cancer, whole-transcriptome sequencing of bladder tumor and its peritumoral tissues from 38 bladder cancer patients, with a total of 63 samples, was performed to screen differentially expressed circ-, lnc-, mi-, and mRNAs to construct a circ/lnc-mi-mRNA regulatory network with pruning algorithms. We excavated a key immune-related gene BDNF to build the final ceRNA network as hsa-miR-107 sponged by hsa-circ-000211, AC108488.1, and LINC00163. Finally, a meta-analysis of 7 public datasets demonstrated that low expression of BDNF and high expression of hsa-miR-107 were associated with longer survival. Our study identified a ceRNA regulatory network as a potentially new prognostic marker and molecular therapeutic target of bladder cancer.

A radiogenomic multimodal and whole-transcriptome sequencing for preoperative prediction of axillary lymph node metastasis and drug therapeutic response in breast cancer: a retrospective, machine learning and international multicohort study.

Axillary lymph nodes (ALN) status serves as a crucial prognostic indicator in breast cancer (BC). The aim of this study was to construct a radiogenomic multimodal model, based on machine learning and whole-transcriptome sequencing (WTS), to accurately evaluate the risk of ALN metastasis (ALNM), drug therapeutic response and avoid unnecessary axillary surgery in BC patients. In this study, conducted a retrospective analysis of 1078 BC patients from The Cancer Genome Atlas (TCGA), The Cancer Imaging Archive (TCIA), and Foshan cohort. These patients were divided into the TCIA cohort ( N =103), TCIA validation cohort ( N =51), Duke cohort ( N =138), Foshan cohort ( N =106), and TCGA cohort ( N =680). Radiological features were extracted from BC radiological images and differentially expressed gene expression was calibrated using technology. A support vector machine model was employed to screen radiological and genetic features, and a multimodal model was established based on radiogenomic and clinical pathological features to predict ALNM. The accuracy of the model predictions was assessed using the area under the curve (AUC) and the clinical benefit was measured using decision curve analysis. Risk stratification analysis of BC patients was performed by gene set enrichment analysis, differential comparison of immune checkpoint gene expression, and drug sensitivity testing. For the prediction of ALNM, rad-score was able to significantly differentiate between ALN- and ALN+ patients in both the Duke and Foshan cohorts ( P <0.05). Similarly, the gene-score was able to significantly differentiate between ALN- and ALN+ patients in the TCGA cohort ( P <0.05). The radiogenomic multimodal nomogram demonstrated satisfactory performance in the TCIA cohort (AUC 0.82, 95% CI: 0.74-0.91) and the TCIA validation cohort (AUC 0.77, 95% CI: 0.63-0.91). In the risk sub-stratification analysis, there were significant differences in gene pathway enrichment between high and low-risk groups ( P <0.05). Additionally, different risk groups may exhibit varying treatment responses ( P <0.05). Overall, the radiogenomic multimodal model employs multimodal data, including radiological images, genetic, and clinicopathological typing. The radiogenomic multimodal nomogram can precisely predict ALNM and drug therapeutic response in BC patients.

Identification and expression patterns of somatic piRNAs and PIWI genes in Riptortus pedestris (Hemiptera: Alydidae).

RNA interference (RNAi)-based gene silencing is a feasible and sustainable technology for the management of hemipteran pests by double-stranded RNA involvement, including small-interfering RNA, microRNA, and Piwi-interacting RNA (piRNA) pathways, that may help to decrease the usage of chemical insecticides. However, only a few data are available on the somatic piRNAs and their biogenesis genes in Riptortus pedestris, which serves as a significant pest of soybean (Glycine max). In this study, two family members of the PIWI gene were identified and characterized in R. pedestris, containing Argonaute3 (RpAgo3) and Aubergine (RpAub) genes with conserved protein domains, and their clusters were validated by phylogenetic analysis. In addition, they were widely expressed in all developmental stages of the whole body of R. pedestris and had lower expression levels in R. pedestris guts under different rearing conditions based on previous transcriptome sequencing. Furthermore, abundant clean reads were filtered to a total number of 45,998 piRNAs with uridine bias at the first nucleotide (nt) position and 26-32 nt in length by mapping onto the reference genome of R. pedestris according to our previous whole-transcriptome sequencing. Finally, our data revealed that gut bacterial changes were significantly positively or negatively associated with differentially expressed piRNAs among the five comparison groups with Pearson correlation analysis. In conclusion, these findings paved new avenues for the application of RNAi-based biopesticides for broad-spectrum hemipteran pest control.

Integrated Transcriptomic and Proteomic Study of the Mechanism of Action of the Novel Small-Molecule Positive Allosteric Modulator 1 in Targeting PAC1-R for the Treatment of D-Gal-Induced Aging Mice.

Small-molecule positive allosteric modulator 1 (SPAM1), which targets pituitary adenylate cyclase-activating polypeptide receptor 1 (PAC1-R), has been found to have a neuroprotective effect, and the underlying mechanism was explored in this study. First, using a D-galactose (D-gal)-induced aging mouse model, we confirmed that SPAM1 improves the structure of the hippocampal dentate gyrus and restores the number of neurons. Compared with D-gal model mice, SPAM1-treated mice showed up-regulated expression of Sirtuin 6 (SIRT6) and Lamin B1 and down-regulated expression of YinYang 1 (YY1) and p16. A similar tendency was observed in senescent RGC-5 cells induced by long-term culture, indicating that SPAM1 exhibits significant in vitro and in vivo anti-senescence activity in neurons. Then, using whole-transcriptome sequencing and proteomic analysis, we further explored the mechanism behind SPAM1's neuroprotective effects and found that SPAM is involved in the longevity-regulating pathway. Finally, the up-regulation of neurofilament light and medium polypeptides indicated by the proteomics results was further confirmed by Western blotting. These results help to lay a pharmacological network foundation for the use of SPAM1 as a potent anti-aging therapeutic drug to combat neurodegeneration with anti-senescence, neuroprotective, and nerve regeneration activity.

Whole-Transcriptome Sequencing–Based Profiling of the Cutaneous Virome in Patients with Secondary Immunodeficiency

Most viral infections can be self-limited, with no requirement for medical intervention. However, the same viruses can cause severe diseases in patients with compromised immunity due to single-gene diseases, acquired immune deficiency syndrome, or hematologic malignancies or those receiving immunosuppressive drugs. Occasionally, these immunocompromised patients harbor >1 infectious agent, requiring several concomitant diagnostic tests. We have developed, to our knowledge, a previously unreported whole-transcriptome sequencing-based pipeline that allows virome profiling, quantitation, and expression pattern analysis of 926 distinct viruses by sequencing of RNA isolated from a single lesional skin biopsy. This pipeline can also explore host genetics if there is a Mendelian predisposition to infection. We applied this pipeline to 6 Iranian patients with viral-induced skin lesions associated with immune deficiency secondary to HIV, human T-lymphotropic virus 1, chronic lymphocytic leukemia, and post transplant immunosuppression. In 5 cases, definitive human papillomavirus infections were identified, some caused by multiple viral types. In addition to human papillomavirus, coinfection with other viruses (Merkle cell polyomavirus, cytomegalovirus, and human herpesvirus 4) was detected in some lesions. In 1 case, whole-transcriptome sequencing validated the clinical diagnosis of adult T-cell leukemia/lymphoma in a patient with an initial diagnosis of mycosis fungoides/Sézary syndrome. These findings attest to the power of whole-transcriptome sequencing in profiling the cutaneous virome in the context of compromised immunity.

Whole-transcriptome sequencing and bioinformatics analysis of calcified outer-layer tissue from cystic echinococcosis

Whole-transcriptome sequencing and bioinformatics analysis of calcified outer-layer tissue from cystic echinococcosis

Abstract 4972: Bioinformatic analysis of an annotated genomic database is clinically useful in a private cancer center

Abstract Background: Sequencing of patient solid tumor tissue is not a common service provided by community cancer centers. Our community cancer center has collected and sequenced over 5,000 tumor tissue samples from patients with various types of cancer. Since April 2019, we have conducted whole-exome and whole-transcriptome sequencing and stored this data in an internal genomics database. To analyze large patient cohorts, we must first optimize bioinformatic workflows on a smaller scale, which can then be applied to studies of larger populations. Previous studies have shown BRAF mutations are associated with an increased activation of the mitogen-activated protein kinase (MAPK) pathway. We analyzed transcriptomes of a cohort of 30 patients with thyroid cancer, 15 with V600E BRAF mutations and 15 wild-type for BRAF, for MAPK Pathway Activation Scores (MPAS). MPAS serves as a transcriptomic measure of the activation state of the MAPK pathway and has been shown to be a reliable prognostic biomarker of MAPK activity. Method: We conducted a retrospective analysis of data from FFPE tumor samples sent to a commercial CLIA-certified laboratory from April 2019 to October 2023. Samples were profiled by whole exome, whole transcriptome sequencing and immunohistochemistry (IHC). Whole transcriptome sequencing (WTS) was executed using Illumina NovaSeq along with the Agilent SureSelect Human All Exon V7 bait panel. Resulting data were reported in transcripts per million (TPM) using the Salmon expression pipeline. MPAS was calculated using the expression profile of 10 MAPK-associated genes. Results: There was no significant difference in BRAF expression levels or MAPK activation between patients with BRAF mutant and BRAF wild-type thyroid cancer (median MPAS scores: -0.09 vs. 0.11, p=0.9). This analysis workflow will be repeated with a larger patient population of patients with melanoma in the future; however, the consistency of expression between the groups, the rank-order, and the low variability per group suggest that expression changes related to this BRAF mutation may occur upstream or via differential regulation of an indirect pathway. Rank-order statistics and percentiles become more precise as data per tumor type grows. Conclusions: Our community cancer center began building an internal genomics database including whole-exome and whole-transcriptome sequencing since 2019. This resource facilitates analysis of patient samples for tumor mutational burden, gene fusions, transcriptomic data, and other important characteristics which could reveal patterns or trends that inform cancer diagnosis and prognosis of other patients. Analysis of expression data for patients with advanced solid tumors who have mutations in genes of interest could inform on potential targeted treatments in the future. This study represents a single example of the potential of our internal clinically annotated genomics database. Citation Format: Carlos E. Zuazo, Sourat Darabi, David R. Braxton, Phillip Stafford, Michael J. Demeure. Bioinformatic analysis of an annotated genomic database is clinically useful in a private cancer center [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4972.

Abstract 315: Cost-effective, comprehensive detection of gene fusions from DNA and RNA using the UG100 sequencer

Abstract Background: Gene fusions, resulting from chromosomal rearrangements, play a critical role in the oncogenesis of various cancers, often acting as diagnostic markers and therapeutic targets. Their detection is important for understanding tumor biology and guiding personalized treatment strategies. With the evolution of economical sequencing technologies, hypothesis-free gene fusion detection in cancer genomics is becoming more accessible. This study demonstrates the use of the cost-effective Ultima Genomics UG100 platform for a comprehensive analysis of gene fusions, uniquely integrating Whole Genome Sequencing (WGS) and Whole Transcriptome Sequencing (WTS). This integration capitalizes on the strengths of both methods: WGS, which enables a broad analysis of structural variations including extragenic events, and WTS for more functional characterization of expressed gene fusions and abnormal isoforms, offering a synergistic approach that enhances the detection and characterization of genomic rearrangements. Methods: Five cancer cell lines (K562, THP-1, NCI-H660, Kasumi-1, MV4-11) with 29 previously detected gene fusions were sequenced, using both WTS and WGS, in addition to a standard fusion reference sample (Seraseq Fusion RNA Mix, with 16 RNA fusions). The libraries were sequenced on a UG100 sequencer using single ended reads of length ~250bp and analyzed with publicly available software: STAR-Fusion, combined with FusionInspector were used to detect RNA fusions; Manta and GridSS were used to detect DNA structural variations, including translocations. Results: 93% of the documented fusion events were detected in DNA and in RNA, in addition to the 16 evaluated just in RNA (100% detected). Sequence data from both WTS and WGS on the UG100 platform successfully identified a spectrum of gene fusions in both DNA and RNA, thereby confirming the platform's detection capabilities in both types of assays. The structural variation events detected in the WGS were comprised mainly of translocations, but also deletions and duplications. Conclusions: The Ultima Genomics UG100 sequencer has proven to be a cost-effective and efficient tool for the genome-wide discovery and validation of gene fusions, which can be impactful in clinical oncology settings. This study underscores the potential of using an integrated WTS and WGS approach on a single platform to advance our understanding of the genomic complexity of cancer, paving the way for more effective diagnostic and therapeutic strategies. Citation Format: Gila Lithwick-Yanai, Sarah Pollock, Danit Lebanony, Keren Ben Simhon, Sima Benjamin, David Bogumil, Nika Iremadze, Matt Sexton, Peleg Winer, Maya Levy, Shlomit Gilad, Josh Haimes, Giulia Corbet, Jennifer Pavlica, Doron Lipson. Cost-effective, comprehensive detection of gene fusions from DNA and RNA using the UG100 sequencer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 315.

Abstract 6251: Validation and implementation of whole-transcriptome sequencing assay into cancer care: An experience from a cancer center

Abstract Introduction Transcriptome sequencing is a well-established diagnostic tool to characterize and quantify gene expression profiles and to detect fusion transcripts. The utilization of whole-transcriptome sequencing (WTS), in clinical workflows can tremendously help improve diagnosis, risk stratification, and therapy selection. However, there are multiple challenges to widespread clinical implementation of WTS including nucleic acid input, defining appropriate quality control (QC) criteria, tumor specific baseline controls and depth of sequencing. Here, we present a validation study to deploy routine whole transcriptome sequencing with automated analysis in a mid-size academic center. Methods Nucleic acid material was obtained through clinical residual samples in the Center for Genomics and Advanced Technology. RNA samples were extracted using the Purigen Ionic® FFPE to Pure RNA Kit. RNA input sample quantities ranged from 34-100ng. WTS libraries were prepared using the Illumina® RNA Prep with Enrichment (L) Tagmentation kit with the Illumina Exome Panel for FFPE tumor samples. The validation was performed on a set of patient samples with known clinically reported variants from the Illumina TruSight® Tumor 170 assay. A total of 84 true positives with reportable fusions, 5 splicing events, 16 negative samples, and 15 limit of detection (LOD) samples were used. Libraries were prepared in a three-plex format. Sequencing was performed on the Novaseq-6000 system using S2 and S4 flow cells with a target read count of 100 million. All data was processed through the AUGMET informatics platform for demultiplexing, quality control, fusion detection, transcript quantification and determination of alternatively spliced transcript. Results The WTS validation assay demonstrated 98% (n=49/50) sensitivity, with only one false negative, and 100% (n=68/68) specificity. A final review of our data was determined to develop stable QC standards for RNA sequenced libraries to be &amp;gt; 60M reads and &amp;gt; 130bp read length. When analyzing (LOD) for WTS, interestingly, read length was inversely proportional to input and as a result detection of the clinically relevant fusion. Based on our LOD experiments, a minimum input threshold of 20ng of RNA was determined to maintain the sensitivity and specificity. An LOD study for tumor purity was not performed due to the known lack of correlation between tumor purity and fusion transcript quantity. Discussion WTS can detect all clinically reported fusions and splicing events with comparable sensitivity to panel assays. Moreover, simultaneously available transcript quantification and variant calling provide the ability to implement several disease classifiers. This work lays the foundation for integration with our clinically available tumor exome sequencing assay to allow joint exome transcriptome analysis in a single informatics platform. Citation Format: Jillian A. Moran, Edward G. Hughes, Donald C. Green, Shrey Sukhadia, Gregory J. Tsongalis, Samantha F. Allen, Laura Tafe, Brianna Houde, Nevena B. Ognjenovic, Jennifer N. Barbuto, Ivy Riano, Parth Shah. Validation and implementation of whole-transcriptome sequencing assay into cancer care: An experience from a cancer center [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6251.