Multi-omics Biomarker Research Articles

Multiomics biomarkers were not superior to clinical variables for pan-cancer screening

Abstract Background Cancer screening tests are considered pivotal for early diagnosis and survival. However, the efficacy of these tests for improving survival has recently been questioned. This study aims to test if cancer screening could be improved by biomarkers in peripheral blood based on multi-omics data. Methods We utilize multi-omics data from 500,000 participants in the UK Biobank. Machine learning is applied to search for proteins, metabolites, genetic variants, or clinical variables to diagnose cancers collectively and individually. Results Here we show that the overall performance of the potential blood biomarkers do not outperform clinical variables for collective diagnosis. However, we observe promising results for individual cancers in close proximity to peripheral blood, with an Area Under the Curve (AUC) greater than 0.8. Conclusions Our findings suggest that the identification of blood biomarkers for cancer might be complicated by variable overlap between molecular changes in tumor tissues and peripheral blood. This explanation is supported by local proteomics analyses of different tumors, which all show high AUCs, greater than 0.9. Thus, multi-omics biomarkers for the diagnosis of individual cancers may potentially be effective, but not for groups of cancers.

Abstract A009: Integrative multiomic analysis of extracellular vesicle transcriptomics profiling combined with cfDNA methylation reveals improved stratification of low-risk and high- risk prostate cancer patients in urine-based liquid biopsy

Abstract Introduction: Prostate cancer (PCa) ranks as the second most frequently diagnosed cancer and the sixth most prevalent cause of cancer-related mortality in men globally. In this study, we employ a multiomic approach that utilizes both urine cfDNA and extracellular vesicle (EV) RNA-derived analytes to improve the risk stratification of individuals with prostate cancer and compare this to multi-parametric MRI performance as well as risk calculators in the same cohort. Materials and Methods: Urine was collected from 95 individuals comprising 51 benign or low-risk (Grade Group (GG) <=1), 10 intermediate-risk (GG2) and 44 high-risk PCa patients (GG 3-5). EV-RNA and cfDNA were concurrently isolated from each urine specimen to exploit complementary information. We developed an EV-RNA sequencing platform targeting mRNAs and lncRNAs and targeted 50 million reads per sample; and cfDNA methylome profiling reached an equivalent sequencing depth. Expression of EV-RNA and splice variant Differential Transcript Usage (DTU) and cfDNA methylation patterns were analyzed using Bio-Techne’s multiomic platform. Machine learning-based feature selection algorithms identified biomarker signatures from each analyte. Receiver-operator characteristic curves (ROC) were generated utilizing leave-one-out cross-validation of naïve Bayes classifier models to compute the area under the curve (AUC). Individual signatures were integrated to create a multiomic classifier. Results: Differential gene expression (DEx) analysis identified 18 DEx genes between low and high-risk patients with both known and novel implications in PCa. Splice variant analysis unveiled 60 genes previously implicated in PCa with DTU. Over 18,000 differentially methylated bases were detected between high and low-risk PCa patients. Feature selection analysis identified the top ten features of EV-RNA expression, splice variants, and cfDNA methylation, resulting in an integrative multi-analyte signature. The multiomic signature demonstrated an AUC of 0.92, surpassing any individual analyte’s performance, as well as the performance achieved by PSA (AUC = 0.52) and MRI PI RADS(AUC=0.65). Conclusions: While biomarker signatures obtained from each analyte in this study resulted in effective stratification of PCa risk, the multiomic signature further improved the discriminatory power—underscoring the complementary nature of the signals. Thus, a multiomic biomarker discovery strategy leveraging cfDNA and EV cargo exhibits significant potential as a risk assessment tool for high-grade prostate cancer. This approach can facilitate more informed decision-making in the context of biopsy procedures. Citation Format: Sudipto K Chakrabortty, Dulaney Miller, Kyle Manning, Rikky Xing, Jeff Cole, Christopher Benway, Sinead Nguyen, Allan George, Yiyuan Yao, Elena M Cortizas, Christian Ray, Siva Gowrisankar, Seth Yu, Allan Pollack, Sandra M Gaston, Sanoj Punnen, Johan Skog. Integrative multiomic analysis of extracellular vesicle transcriptomics profiling combined with cfDNA methylation reveals improved stratification of low-risk and high- risk prostate cancer patients in urine-based liquid biopsy [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A009.

Identification of bio-markers for insulin resistance and sensitivity through multi-omics analysis

Aims: This study aims to identify multi-omics bio-markers for insulin resistance and sensitivity using machine learning approaches on a dataset integrated from several omics. Methods: The study included 362 patients with Insulin Resistance and Insulin Sensitivity from the Integrative Personal Omics Profiling (iPOP) database. Combining the multi-omics data from the Integrative Human Microbiome Project, this study used machine learning to reveal the relationship between insulin resistance and insulin sensitivity. Results: Of 362 patients 186 were insulin resistance and 176 were insulin sensitivity. 11,585 features were used, including clinical features, RNA transcripts, gut microbiota, cytokines, proteins, and metabolomics. We found 21 features capable of distinguishing insulin resistance from insulin sensitivity using a well-known artificial neural network (ANN) method. The model had an area under the receiver operating characteristic (AUC) of 0.97 in the validation dataset and 0.89 in the test dataset. The ANN model’s performance was compared with Random Forest model. Of the 21 new findings, two metabolites (methyl-uric acid and methylxanthine) are xenobiotics, and three RNA transcripts (SERPINF1, SLC2A2, and CHL1). Conclusion: A small number of multi-omics features identified from 11,585 potential candidates for a machine learning model can accurately predict insulin resistance and sensitivity.

Multidisciplinary lifestyle interventions for neurological disorders during the Silent phase (MINDS) study: a multi-omics randomized controlled trial protocol

IntroductionGiven the prevalence and staggering cost of neurological disorders, there is dire need for effective early detection and intervention tools. Emerging evidence suggests that multidisciplinary lifestyle interventions (MLI) may mitigate the risk and progression of neurological disorders. The objectives of this protocol are (1) to test the impact of MLI on the progression of neurological disorders and (2) to identify multi-omic biomarkers for early stages of neurological disease and the impact of MLIs on these biomarkers.Methods and analysisWe present the Multidisciplinary lifestyle Interventions for Neurological Disorders during the Silent phase (MINDS) protocol, a randomized controlled trial of MLI in neurologically healthy older adults (≥ 50 years old) exhibiting elevated risk for common neurological disorders: stroke, epilepsy, Parkinson’s Disease, or Alzheimer’s disease and related dementias. Participants will be randomly assigned to intervention (n = 100) or control (n = 100) groups. The intervention group will receive 3 months of weekly 2-hour sessions on diet education, yoga, music therapy, and cognitive skills training. The participants’ neurological health and engagement in relevant lifestyle practices will be assessed at regular intervals for 12 months. Neuroimaging and samples for multi-omic analyses will be collected at baseline, and at 3 months and 12 months after enrollment. Primary outcomes will be signs of progression of the neurological disorder risk that qualified them for study enrollment or a clinical diagnosis of the disorder. Secondary and exploratory outcomes will be based on self-reported health and multi-omic data. Data analysis will include between-group and longitudinal within-group analyses.PerspectivesThe MINDS protocol and trial aims to clarify the impact of MLI on the progression of neurological disorder risk or diagnosis in older adults and to identify biomarkers that can be used to confirm MLI efficacy. The ability to validate the impact of MLI on neurological disorder progression based on biomarker data allows the identification of individuals most likely to benefit from such therapies in the early stages of neurological disease.Trial registrationThe trial is registered on the National Institutes of Health (NIH) ClinicalTrials.gov (NCT05984056) site. It was registered on August 2nd, 2023. The trial has full approval of the Cleveland Clinic Internal Review Board.

Quantitative Proteomics and Metabolomics of Culture Medium from Single Human Embryo Reveal Embryo Quality-Related Multiomics Biomarkers.

An effective tool to assess embryo quality in the assisted reproduction clinical practice will enhance successful implantation rates and mitigate high risks of multiple pregnancies. Potential biomarkers secreted into culture medium (CM) during embryo development enable rapid and noninvasive methods of assessing embryo quality. However, small volumes, low biomolecule concentrations, and impurity interference collectively preclude the identification of quality-related biomarkers in single blastocyst CM. Here, we developed a noninvasive trace multiomics approach to screen for potential markers in individual human blastocyst CM. We collected 84 CM samples and divided them into high-quality (HQ) and low-quality (LQ) groups. We evaluated the differentially expressed proteins (DEPs) and metabolites (DEMs) in HQ and LQ CM. A total of 504 proteins and 189 metabolites were detected in individual blastocyst CM. Moreover, 9 DEPs and 32 DEMs were identified in different quality embryo CM. We also categorized HQ embryos into positive implantation (PI) and negative implantation (NI) groups based on ultrasound findings on day 28. We identified 41 DEPs and 4 DEMs associated with clinical implantation outcomes in morphologically HQ embryos using a multiomics analysis approach. This study provides a noninvasive multiomics analysis technique and identifies potential biomarkers for clinical embryo developmental quality assessment.

Identification of novel therapeutic targets for chronic kidney disease and kidney function by integrating multi-omics proteome with transcriptome

BackgroundChronic kidney disease (CKD) is a progressive disease for which there is no effective cure. We aimed to identify potential drug targets for CKD and kidney function by integrating plasma proteome and transcriptome.MethodsWe designed a comprehensive analysis pipeline involving two-sample Mendelian randomization (MR) (for proteins), summary-based MR (SMR) (for mRNA), and colocalization (for coding genes) to identify potential multi-omics biomarkers for CKD and combined the protein–protein interaction, Gene Ontology (GO), and single-cell annotation to explore the potential biological roles. The outcomes included CKD, extensive kidney function phenotypes, and different CKD clinical types (IgA nephropathy, chronic glomerulonephritis, chronic tubulointerstitial nephritis, membranous nephropathy, nephrotic syndrome, and diabetic nephropathy).ResultsLeveraging pQTLs of 3032 proteins from 3 large-scale GWASs and corresponding blood- and tissue-specific eQTLs, we identified 32 proteins associated with CKD, which were validated across diverse CKD datasets, kidney function indicators, and clinical types. Notably, 12 proteins with prior MR support, including fibroblast growth factor 5 (FGF5), isopentenyl-diphosphate delta-isomerase 2 (IDI2), inhibin beta C chain (INHBC), butyrophilin subfamily 3 member A2 (BTN3A2), BTN3A3, uromodulin (UMOD), complement component 4A (C4a), C4b, centrosomal protein of 170 kDa (CEP170), serologically defined colon cancer antigen 8 (SDCCAG8), MHC class I polypeptide-related sequence B (MICB), and liver-expressed antimicrobial peptide 2 (LEAP2), were confirmed. To our knowledge, 20 novel causal proteins have not been previously reported. Five novel proteins, namely, GCKR (OR 1.17, 95% CI 1.10–1.24), IGFBP-5 (OR 0.43, 95% CI 0.29–0.62), sRAGE (OR 1.14, 95% CI 1.07–1.22), GNPTG (OR 0.90, 95% CI 0.86–0.95), and YOD1 (OR 1.39, 95% CI 1.18–1.64,) passed the MR, SMR, and colocalization analysis. The other 15 proteins were also candidate targets (GATM, AIF1L, DQA2, PFKFB2, NFATC1, activin AC, Apo A-IV, MFAP4, DJC10, C2CD2L, TCEA2, HLA-E, PLD3, AIF1, and GMPR1). These proteins interact with each other, and their coding genes were mainly enrichment in immunity-related pathways or presented specificity across tissues, kidney-related tissue cells, and kidney single cells.ConclusionsOur integrated analysis of plasma proteome and transcriptome data identifies 32 potential therapeutic targets for CKD, kidney function, and specific CKD clinical types, offering potential targets for the development of novel immunotherapies, combination therapies, or targeted interventions.

Artificial intelligence-based epigenomic, transcriptomic and histologic signatures of tobacco use in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) biomarker studies rarely employ multi-omic biomarker strategies and pertinent clinicopathologic characteristics to predict mortality. In this study we determine for the first time a combined epigenetic, gene expression, and histology signature that differentiates between patients with different tobacco use history (heavy tobacco use with ≥10 pack years vs. no tobacco use). Using The Cancer Genome Atlas (TCGA) cohort (n = 257) and an internal cohort (n = 40), we identify 3 epigenetic markers (GPR15, GNG12, GDNF) and 13 expression markers (IGHA2, SCG5, RPL3L, NTRK1, CD96, BMP6, TFPI2, EFEMP2, RYR3, DMTN, GPD2, BAALC, and FMO3), which are dysregulated in OSCC patients who were never smokers vs. those who have a ≥ 10 pack year history. While mortality risk prediction based on smoking status and clinicopathologic covariates alone is inaccurate (c-statistic = 0.57), the combined epigenetic/expression and histologic signature has a c-statistic = 0.9409 in predicting 5-year mortality in OSCC patients.

WITHDRAWN: Research on Traditional Chinese Medicine Syndrome Animal Models and Multi-omics Biomarkers in Type II Diabetes: A Review

IntroductionDiabetes, a chronic metabolic disease marked by hyperglycemia, predominantly manifests as Type 2 Diabetes Mellitus (T2DM), representing over 90% of diabetes cases with intricate pathogenesis and varied pathological attributes. Drawing upon Traditional Chinese Medicine (TCM) principles, this article outlines the methodology and evaluation criteria for integrating TCM syndromes into animal models for T2DM while comprehensively examining T2DM pathogenic mechanisms and physiological characteristics from various omics perspectives to pinpoint essential biomarkers. MethodsResearchers conducted comprehensive searches using databases such as Web of Science, Google Scholar, Baidu Scholar and CNKI. Selection criteria were based on content relevance and publication date, resulting in the inclusion of 223 articles, including Chinese and English journal papers, master's and doctoral theses. This comprehensive review aims to highlight the importance of TCM in research related to T2DM, as well as its compatibility with modern technology, providing guidance for future development of targeted drugs and clinical treatment for T2DM. ResultsThis article, based on the fundamental theories of Traditional Chinese Medicine, summarized the methodology and evaluation criteria for constructing 6 types of T2DM animal models: Blood Stasis Syndrome, Kidney Yin Deficiency Syndrome, Spleen Deficiency Syndrome, Yang Deficiency Syndrome, Qi and Yin Deficiency Syndrome, and Yin and Yang Deficiency Syndrome. It comprehensively analyzed the latest research progress on the pathogenic mechanisms and physiological characteristics of T2DM from the perspectives of genomics, proteomics, transcriptomics, metabolomics, and gut microbiomics, providing crucial insights for the discovery of potential biomarkers for T2DM. DiscussionsThe broad use of multi-omics tech aids in T2DM pathogenesis understanding. For comprehensive insight, analyzing genes, proteins, metabolites and microbiota is vital. As a metabolic disorder, T2DM has a prolonged development process, necessitating the selection of different biomarkers for various stages of the disease progression and long-term monitoring of the overall changes in key biomarkers. The development of a “disease-syndrome” animal model is pivotal for the advancement of targeted drug therapies. The crux lies in the accuracy, repeatability, and stability of the model and its assessment criteria. Future investigations should focus on multi-omics analyses across diverse T2DM syndromes to facilitate personalized treatment strategies.

Biomarker development from functional precision medicine datasets via explainable machine learning.

10061 Background: Genomics precision medicine, deployed via tumor panel sequencing, now assists in deploying targeted therapies to cancer patients. Numerous clinical trials have investigated the utility and benefit of genomics precision medicine in multiple cancer indications. Current large-scale studies report actionability rates from ~35% to ~60%, although clinical benefit rates have been shown to be closer to 10%. While this has positively impacted patients in need, the gap between actionability and benefit remains a clinical challenge attributed to multiple factors including the complex, multi-factorial relationship between molecular status and response to therapy. These differences go beyond simple disease states and may be reflective of multiple clinically relevant features including age, sex, and race/ethnicity. Methods: We implemented a functional precision medicine (FPM) program where patients with advanced pediatric cancers were prospectively profiled via high-throughput drug sensitivity testing (DST) of FDA-approved agents on patient-derived tumor cells as well as genomics testing. The objective was to investigate the clinical utility and benefit of FPM guidance in the treatment of pediatric cancer and elucidate the relationship between molecular status of patients’ diseases and treatment responses. We generated DST data (n = 21 patients) and genomic profiling data (n = 20 patients) on pediatric cancer patients in Miami, FL, as well as post-hoc whole exome and transcriptome sequencing data (n = 13 patients) and investigated three specific relationships. Results: We analyzed the relationship between racial/ethnic background and functional response to anti-cancer agents, determining potential differences in response to therapeutic classes. Next, we examined relationships between functional response and cancer type, identifying an unanticipated lack of clustering between disease indications in patients with advanced pediatric cancers. Finally, we applied an explainable machine learning (xML) framework to the functional genomic dataset to develop multi-omics biomarker hypotheses for the chemotherapy agent idarubicin, pinpointing a potential multi-cancer relationship between response to idarubicin and known disease mechanisms in acute myeloid leukemia (AML), the sole indication where idarubicin is approved. We further present additional proof-of-concept studies generating biomarker hypotheses via xML, demonstrating a framework for development of multi-omics biomarkers. Conclusions: We are now expanding our pan-pediatric cancer functional genomics dataset through an NIMHD-funded expansion cohort (NCT05857969, n = 65 patients) to further investigate multi-omics relationships between functional and molecular characteristics and understand the role of race/ethnicity in the complex relationship.

A machine learning algorithm based on multi-omics biomarkers for the detection of tumor microsatellite instability.

1554 Background: Microsatellite instability (MSI) occurs via defects in mismatch repair (MMR) and is characterized by high tumor mutational burden (TMB) and a robust inflammatory response. Accurate MSI-high classification of immune-evasive tumors is vital due to the effectiveness of immune checkpoint inhibitor (ICI) therapy, such as pembrolizumab, which is FDA approved for all MSI-high tumors irrespective of anatomic origin. Detection methods of MSI-status vary widely, including immunohistochemistry (IHC), polymerase chain reaction (PCR), and next-generation sequencing (NGS). We developed a machine learning (ML) model to predict MSI status in patients with solid tumors using comprehensive genomic and immune profiling (CGIP), independent of direct sequencing data from microsatellite sites. Methods: We analyzed samples from 1,838 patients with colorectal cancer (CRC) by CGIP, which included DNA panel testing (523 genes) for pathogenic single nucleotide variants (pSNV) and determination of TMB (mut/Mb), and RNA sequencing (397 genes) for gene expression (GEX). The Boruta algorithm was used to select key genomic and GEX changes associated with MSI status. A distributed gradient boosting algorithm created a predictive model that was trained on 70% of the cohort. The model was tested on the remaining CRC cohort (Test), and in the PanCancer Atlas CRCs (TCGA), endometrial adenocarcinomas (EMCA), and CRC with indeterminate MSI testing by CGIP. Performance of the trained model was assessed using sensitivity, specificity, and positive (PPV) and negative (NPV) predictive value. Results: Feature selection identified 79 genes with pSNVs, 63 GEX changes, and TMB as informative for MSI prediction. The model showed high predictive accuracy in differentiating MSI-H tumors. Of the 39 cases that failed MSI component of CGIP (Indeterminate), 17 had MMR IHC results available, of which 1 demonstrated loss of MLH1/PMS2 and the other 16 showed intact (normal) expression. Testing of this cohort identified 3 cases as MSI-high, at least one of which was confirmed by IHC. No cases with intact MMR protein IHC were identified as MSI-high by the algorithm. Conclusions: Our ML-driven approach accurately assessed MSI status of CRC and EMCA using CGIP data. This approach also identified potential cases with MSI-high status where direct sequencing of microsatellites failed. This study highlights a method to identify patients with potential MSI-high status for orthogonal screening with the MSI component of a test fails. [Table: see text]

Abstract PO1-19-05: COGNITION/-GUIDE – Implementation of Precision Oncology in Early High-Risk Breast Cancer

Abstract Background: The concept of precision oncology is nowadays increasingly implemented into clinical practice but is predominantly restricted to the metastatic setting limiting the benefits to prolongation of progression-free-survival (PFS). Implementation of precision oncology at an earlier disease stage with less tumor burden, heterogeneity and a higher capacity of tumor lesions to inform on the molecular tumor landscape holds the promise to impact cure rates. Methods: The prospective COGNITION/-GUIDE precision oncology platform encompasses the COGNITION diagnostic multi-omics biomarker screening platform (NCT05906407) linked to the multi-arm phase II COGNITION-GUIDE umbrella trial (NCT05332561). Based on a two-step consenting procedure, patients with early breast cancer (eBC) and poor response towards standard-of-care (SOC) neoadjuvant chemotherapy (NACT) are first enrolled into the COGNITION diagnostic platform. Residual bulk tumors are subjected to whole-genome- (WGS)/whole exome- (WES) and RNA-sequencing to enable streamlined detection of a rigorous biomarker-framework for subsequent genomics-guided arm allocation. Pilot analyses revealed that 78% (81/104) of high-risk tumors (TNBC or HER2+ BC with non-pCR or HR+/HER2- BC with non-pCR and CPS-EG score ≥ 3, or ypN+ and CPS-EG score ≥ 2) can be successfully sequenced and discussed within an interdisciplinary molecular tumor board. In 67% (70/104) eligible biomarker-profiles could be identified for potential biomarker-guided therapeutic intervention. In a second step, having completed all standard-of-care neoadjuvant and post-neoadjuvant treatments (except endocrine therapy, which can be administered simultaneously to the experimental therapy), patients can be enrolled into one of 6 biomarker-based arms in the COGNITION-GUIDE phase II trial (atezolizumab, inavolisib, ipatasertib, olaparib, sacituzumab govitecan or trastuzumab/pertuzumab). Primary end point is invasive disease-free survival (IDFS) at 4 years after surgery in the whole study population. Secondary endpoints are overall survival (OS), IDFS in each treatment arm, distant disease-free survival (DDFS) and patient-reported outcomes (PROs). Enrollment in COGNITION-GUIDE started in Q2/2023 and 240 patients will be enrolled in 5 German sites until Q2/2027. A comprehensive biomarker discovery program is integrated on longitudinal tumor tissue and liquid biopsies for exploratory analyses. Further clinical work and accrual will be reported at the meeting. Conclusion: The theranostic COGNITION/-GUIDE platform moves the concept of precision oncology to a potentially curative but still high-risk stage of eBC following SOC NACT. Hence, this novel application in the field of precision oncology harbours the prospect to increase cure rates. Citation Format: Verena Thewes, Luise Strassl, Marc Zapatka, Mario Hlevnjak, Constantin Pixberg, Christian Maurer, Katharina Smetanay, Carlo Fremd, Laura Michel, Sabine Heublein, Celina V. Wagner, Jan-Philip Suppelna, Lars Buschhorn, Albrecht Stenzinger, Renate Haidinger, Eva Schumacher-Wulf, Sibylle Loibl, Pauline Wimberger, Jens-Uwe Blohmer, Peter A. Fasching, Wolfgang Janni, Richard F. Schlenk, Peter Lichter, Andreas Schneeweiss. COGNITION/-GUIDE – Implementation of Precision Oncology in Early High-Risk Breast Cancer [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 PO1-19-05.

Integrating Multi-omics Data for Alzheimer's Disease to Explore Its Biomarkers Via the Hypergraph-Regularized Joint Deep Semi-Non-Negative Matrix Factorization Algorithm.

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder. Its etiology may be associated with genetic, environmental, and lifestyle factors. With the advancement of technology, the integration of genomics, transcriptomics, and imaging data related to AD allows simultaneous exploration of molecular information at different levels and their interaction within the organism. This paper proposes a hypergraph-regularized joint deep semi-non-negative matrix factorization (HR-JDSNMF) algorithm to integrate positron emission tomography (PET), single-nucleotide polymorphism (SNP), and gene expression data for AD. The method employs matrix factorization techniques to nonlinearly decompose the original data at multiple layers, extracting deep features from different omics data, and utilizes hypergraph mining to uncover high-order correlations among the three types of data. Experimental results demonstrate that this approach outperforms several matrix factorization-based algorithms and effectively identifies multi-omics biomarkers for AD. Additionally, single-cell RNA sequencing (scRNA-seq) data for AD were collected, and genes within significant modules were used to categorize different types of cell clusters into high and low-risk cell groups. Finally, the study extensively explores the differences in differentiation and communication between these two cell types. The multi-omics biomarkers unearthed in this study can serve as valuable references for the clinical diagnosis and drug target discovery for AD. The realization of the algorithm in this paper code is available at https://github.com/ShubingKong/HR-JDSNMF .

Abstract LB393: Exosome based multiomics combined with cfDNA methylation reveals complementary signatures in blood based liquid biopsy that carry promise for minimally invasive colorectal cancer screening

Abstract Introduction: Colorectal cancer (CRC) ranks second in cancer morbidity, with approximately 60% of patients being diagnosed at an advanced stage. Here, we apply a multiomic strategy jointly leveraging plasma cell-free DNA (cfDNA) and multiple extracellular vesicle (EV)-derived analytes to enhance CRC diagnostic accuracy. Materials and Methods: Plasma was collected from 96 individuals, 48 CRC patients (stage II-IV) and 48 CRC-negative healthy controls. EVs and cfDNA were isolated from the same plasma sample to look at complementary information within the sample. We developed an EV RNA sequencing platform targeting mRNAs and long non-coding RNAs and sequenced to a depth of 50M reads per sample; cfDNA methylome profiling was sequenced to an equivalent depth. The Olink platform profiled proteins both directly from plasma and from isolated EVs. Expression of EV-derived RNA, splice variants, and proteins in addition to cfDNA methylation patterns were analyzed using Bio-Techne’s multiomic platform. Machine learning-based feature selection algorithms identified biomarker signatures from each analyte. Receiver-operator characteristic curves (ROC) were generated utilizing leave-one-out cross-validation of naïve Bayes classifier models to compute the area under the curve (AUC). Individual signatures were integrated to generate a multiomic classifier. Results: Differential gene expression (DEx) analysis identified ~1000 DEx genes with significant enrichment of CRC-relevant pathways. Splice variant analysis identified several genes previously implicated in CRC with differential isoform usage. Over 1.7 million differentially methylated bases were detected between CRC-positive and CRC-negative cohorts. Segmentation of the genome using out-of-bag reference data yielded ~1100 genomic segments, many of which overlap previously reported CRC biomarkers and identified unique information about the CRC status. Proteomic analysis identified several differentially abundant markers with high discriminatory power. Following feature selection analysis, AUCs obtained from top 10 features of RNA expression, splice variants, cfDNA methylation, and protein abundance with an integrative analysis identified a multi-analyte signature, with an overall AUC of 0.99. Conclusion: While biomarker signatures obtained from each analyte in this study resulted in effective separation of CRC status, the multiomic signature further improved the discriminatory power—underscoring the complementary nature of the signals. Thus, a multiomic biomarker discovery strategy leveraging cfDNA and EV cargo holds tremendous promise for minimally invasive screening and potential detection of early-stage cancers, which is currently under further investigation. Citation Format: Kyle Manning, Dulaney Miller, Yang Yang, Jeff Cole, Shuran Xing, Christopher Benway, Christian Ray, Sudipto Chakrabortty, Brian Haynes, Seth Yu, Johan Skog. Exosome based multiomics combined with cfDNA methylation reveals complementary signatures in blood based liquid biopsy that carry promise for minimally invasive colorectal cancer screening [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 LB393.

Abstract CT138: Evaluating nivolumab (nivo) plus ipilimumab (ipi) in patients with metastatic castration-resistant prostate cancer (mCRPC): Clinical and translational results from the AMADEUS prostate expansion cohort

Abstract Background: Immune checkpoint inhibitors (ICIs) have transformed cancer treatment, offering durable responses for some patients. However, response to ICIs remains limited for many solid tumors, including metastatic castration-resistant prostate cancer (mCRPC). The AMADEUS primary cohort (NCT03651271) and CheckMate 650 (NCT02985957) trials demonstrated promising durable antitumor responses in a subset of mCRPC patients treated with nivo in combination with ipi at 1 mg/kg or 3 mg/kg, respectively. The AMADEUS prostate expansion cohort was initiated to evaluate the safety and efficacy of a higher ipi dose (5 mg/kg) in combination with nivo. Methods: This open-label, multicenter cohort enrolled mCRPC patients with no restriction on the number of prior treatments. The first 10 patients with < 15% tumoral CD8 T cells were randomized 1:1 to receive either ipi at 3 mg/kg (ipi3) or 5 mg/kg (ipi5) IV Q6W for up to 2 doses. In addition, all patients received nivo 1 mg/kg IV Q3W for 4 doses, followed by nivo 480 mg IV Q4W until PD or intolerable toxicity. After a safety pause, 10 additional patients were enrolled to the ipi5 arm. Co-primary endpoints were DCR (CR, PR, or SD ≥ 6 months) and conversion of tumor %CD8 T cells from low (< 15%) to high (≥ 15%). Baseline and on-treatment tumor and blood samples were analyzed for multi-omic biomarkers. This cohort was not powered for formal statistical analysis. Results: DCR was 20% in both ipi3 (N=1/5) and ipi5 (N=3/15) arms. In patients with an on-treatment biopsy, CD8 conversion from low to high occurred in 33% (1/3) of patients in ipi3 and 13% (1/8) in ipi5. However, 50% (4/8) of patients in ipi5 had on-treatment CD8 ≥ 14.8%. Baseline CD8 T cell frequencies and CD8 conversion were not associated with disease control or PSA reduction. The frequency and severity of AEs was consistent with the known safety profile of ipi/nivo, although more patient in ipi5 discontinued treatment owing to an AE (33% vs 0% in ipi3). Both arms showed increased activated (ICOS+, CD38+) and proliferating (Ki67+) CD4 T cells, CD8 T cells, Tregs, inflammatory cytokines (IFNg), chemokines (CXCL9, CXCL10), and soluble PD-1 in circulation. Conclusions: Across both arms, ipi/nivo induced clinical responses and increased %CD8 in a subset of “cold” mCRPC tumors with low CD8 cells. Additionally, the safety profile of ipi 5 mg/kg combined with nivo was consistent with previous trials that tested lower doses of ipi. Further translational analyses may inform which mCRPC patients could benefit from immunotherapy treatment. Trial registration: ClinicalTrials.gov (NCT03651271) Citation Format: Apostolia M. Tsimberidou, Alexandra Drakaki, Danny N. Khalil, Kwame Okrah, Mike Schachter, Shikha Gautam, Diane M. Da Silva, Farah A. Alayli, Christine N. Spencer, Wai Chin Foo, Matthew Adamow, Phillip Wong, Justin Fairchild, Jamie Arnott, Ute Dugan, Silvia Boffo, Lisa Salvador, Christopher R. Cabanski, Sumit K. Subudhi, Padmanee Sharma. Evaluating nivolumab (nivo) plus ipilimumab (ipi) in patients with metastatic castration-resistant prostate cancer (mCRPC): Clinical and translational results from the AMADEUS prostate expansion cohort [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 CT138.

Abstract 4951: Integrative multi-omic machine learning model predicts neoadjuvant immunotherapy response using molecular data and deep learning-derived features from digital pathology

Abstract Background: Over the past decade, there has been an exponential accumulation of digital patient data through molecular profiling and digitization of pathology slides. However, integrating this to advance diagnostics and cancer patient care remains a significant challenge. By integrating information from diverse modalities to capture aspects of the tumor microenvironment (TME) and immune landscape, we aim to improve patient stratification for immunotherapy. Methods: We collected data from baseline biopsies from patients with palpable melanoma stage III (n=195) who received neoadjuvant immunotherapy followed by surgical resection of largest lymph node metastasis. The different modalities included clinical information, whole-genome sequencing (WGS), RNAseq, laboratory tests of blood, haematoxylin and eosin (H&E) images, and PDL1 immunohistochemistry (IHC) images. Cell detection and classification models were trained to identify tumor-infiltrating lymphocytes and tumor cells on H&E whole-slide images (WSI), deriving spatial features to quantitatively describe the tumor-immune interaction. Various biomarkers, including tumor mutational burden, BRAF status, expression of inflammatory gene signatures, systemic neutrophil-to-lymphocyte ratio, and tumor-lymphocyte spatial measures, were collectively integrated using machine learning models (random forest, elastic-net regularized logistic regression, CatBoost, support vector machine (SVM), and an ensemble classifier combined from the other models) within a 5x5-fold nested cross-validation scheme to predict pathologic response. Results: Pathologic response to immunotherapy is associated with multi-omics biomarkers which were integrated using machine learning. The ensemble approach showed that when all modalities were combined a higher AUC (AUC=0.69, 0.61-0.77) could be achieved than for the clinical modality alone. RNA plus clinical emerged as the most predictive standalone modality with an AUC of 0.62 (0.50-0.75). The overall highest AUC was achieved using a SVM (AUC=0.72, 0.66-0.82). Conclusions: Immunotherapy efficacy depends on diverse tumor microenvironment components, which were comprehensively assessed through data integration. A multi-omic predictive model holds promise for refining personalized and effective immunotherapy strategies. Citation Format: Lindsay V. Leek, Vanessa Botha, Vipul A. Baxi, Jonas Teuwen, Hugo M. Horlings, Scott D. Chasalow, Joris van de Haar, Lodewyk F. Wessels, Auditi DebRoy, Emile E. Voest. Integrative multi-omic machine learning model predicts neoadjuvant immunotherapy response using molecular data and deep learning-derived features from digital pathology [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 4951.

Abstract 5182: Exploiting tumor RNA-sequencing data for prediction of immune checkpoint inhibition response

Abstract Immune checkpoint inhibitors (ICI) have become the standard of care as a first- or second-line systemic treatment for patients with various cancer types. Unfortunately, many patients do not respond to this treatment and the occurrence of immune-related adverse events varies widely. There is an urgent need for robust predictive biomarkers for ICI response to identify patients with likely clinical benefit from this costly treatment. Several biomarkers, aimed at predicting response to ICI, have been proposed, including PD-L1 expression, tumor mutation burden (TMB), infiltration of cytotoxic T-cells in the tumor, Microsatellite Instability (MSI) and expression of various immune gene signatures. However, these individual biomarkers have suboptimal performance and multiple complementary omics technologies are required to properly quantify them. Integration of these multi-omic biomarkers has proven valuable in increasing the accuracy and robustness of ICI response prediction.We have developed a computational pipeline that determines the expressed mutation burden (eTMB), MSI status, fraction of infiltrating immune cells and various immune gene expression signatures directly from the RNA-sequencing profile of the tumor. Each biomarker is quantified using a dedicated algorithm that applies machine learning (eTMB and MSI) or computational deconvolution (infiltrating immune cells) and integrates external data sources to maximize performance. Algorithm performance has been validated on large cohorts of tumor RNA-sequencing data with matching gold standard quantification of said biomarkers. As a proof of concept, we applied our pipeline to tumor RNA-sequencing data of 45 gastric cancer patients treated with pembrolizumab. Responders showed significantly higher eTMB scores and were enriched among the MSI-H patients. Fractions of CD8 T-cells and M1 Macrophages, together with interferon gamma and cytotoxic T-cell gene expression signatures, were significantly increased in responders. Notably, integrating these biomarkers into a single prediction model improved prediction of response to checkpoint inhibition therapy compared to predictions based on eTMB or MSI alone. Taken together, our approach enables the quantification of various ICI response biomarkers from a single omics layer (RNA-sequencing) and can contribute to ICI response prediction. Citation Format: Pieter Mestdagh, Pieter-Jan Van Dam, Frederick De Baene, Carolina Fierro, Elise Van Hoof, Emmanuel Rivière, Hanne Dangreau, Reindert Van Cauwenberge, Jo Vandesompele. Exploiting tumor RNA-sequencing data for prediction of immune checkpoint inhibition response [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 5182.

A systematic pan-cancer study on deep learning-based prediction of multi-omic biomarkers from routine pathology images.

The objective of this comprehensive pan-cancer study is to evaluate the potential of deep learning (DL) for molecular profiling of multi-omic biomarkers directly from hematoxylin and eosin (H&E)-stained whole slide images. A total of 12,093 DL models predicting 4031 multi-omic biomarkers across 32 cancer types were trained and validated. The study included a broad range of genetic, transcriptomic, and proteomic biomarkers, as well as established prognostic markers, molecular subtypes, and clinical outcomes. Here we show that 50% of the models achieve an area under the curve (AUC) of 0.644 or higher. The observed AUC for 25% of the models is at least 0.719 and exceeds 0.834 for the top 5%. Molecular profiling with image-based histomorphological features is generally considered feasible for most of the investigated biomarkers and across different cancer types. The performance appears to be independent of tumor purity, sample size, and class ratio (prevalence), suggesting a degree of inherent predictability in histomorphology. The results demonstrate that DL holds promise to predict a wide range of biomarkers across the omics spectrum using only H&E-stained histological slides of solid tumors. This paves the way for accelerating diagnosis and developing more precise treatments for cancer patients.

A pathway-based computational framework for identification of a new modal of multi-omics biomarkers and its application in esophageal cancer

BackgroundThe pathway-based strategy has been recently proposed for identifying biomarkers with the advantages of higher biological interpretability and cross-data robustness than the conventional gene-based strategy. However, its utility in clinical applications has been limited due to the high computational complexity and ill-defined performance. ObjectiveThe current study presents a machine learning-based computational framework using multi-omics data for identifying a new modal of biomarkers, called pathway-derived core biomarkers, which have the advantages of both gene-based and pathway-based biomarkers. MethodsMachine-learning methods and gene-pathway network were integrated to select the pathway-derived core biomarkers. Multiple machine-learning algorithms were used to construct and validate the diagnostic models of the biomarkers based on more than 1400 multi-omics clinical samples of esophageal squamous cell carcinoma (ESCC). ResultsThe results showed that the classifier models based on the new modal biomarkers achieved superior performance in the training datasets with an average AUC/accuracy of 0.98/0.95 and 0.89/0.81 for mRNAs and miRNA, respectively, higher than the currently known classifier models based on the conventional gene-based strategy and pathway-based strategy. In the testing cohorts, the AUC/accuracy increased by 6.1 %/7.3 % than the models based on the native gene-based biomarkers. The improved performance was further confirmed in independent validation cohorts. Specifically, the sensitivity/specificity increased by ∼3 % and the variance significantly decreased by ∼69 % compared with that of the native gene-based biomarkers. Importantly, the pathway-derived core biomarkers also recovered 45 % more previously reported biomarkers than the gene-based biomarkers and are more functionally relevant to the ESCC etiology (involved in 14 versus 7 pathways related with ESCC or other cancer), highlighting the cross-data robustness of this new modal of biomarkers via enhanced functional relevance. ConclusionsThe results demonstrated that the new modal of biomarkers not only have improved predicting performance and robustness, but also exhibit higher functional interpretability thus leading to the potential application in cancer diagnosis.

A 6-month exercise intervention clinical trial in women: effects of physical activity on multi-omics biomarkers and health during the first wave of COVID-19 in Korea

BackgroundCoronavirus disease 2019 (COVID-19) was first reported in December 2019 and the first case in Korea was confirmed on January 20, 2020. Due to the absence of therapeutic agents and vaccines, the Korean government implemented social distancing on February 29, 2020. This study aimed to examine the effect of physical activity (PA) on health through changes in multi-omics biomarkers with a 6-month of exercise intervention during the first wave of COVID-19 in Korea.MethodsTwenty-seven healthy middle-aged women were recruited and 14 subjects completed the exercise intervention. The mean age (± SD) was 46.3 (± 5.33) and the mean BMI (± SD) was 24.9 (± 3.88). A total of three blood and stool samples were collected at enrollment, after period 1, and after period 2 (3-month intervals). The amount of PA was measured with an accelerometer and by questionnaire. Clinical variables were used, including blood pressure, grip strength, flexibility, and blood glucose levels and lipid markers obtained from laboratory tests. The concentration of blood metabolites was measured by targeted metabolomics. Fecal microbiome data were obtained by 16 S rRNA gene amplicon sequencing.ResultsDuring the second half period (period 2), Coronavirus disease 2019 occurred and spread out in Korea, and PA decreased compared with the first half period (period 1) (185.9 ± 168.73 min/week to 102.5 ± 82.30 min/week; p = 0.0101). Blood pressure, hemoglobin A1c (HbA1c), and low-density lipoprotein cholesterol (LDL-C) decreased in period 1 (p < 0.05) and tended to increase again during period 2 (p < 0.05). Forty metabolites were changed significantly during period 1 (FDR p < 0.05), and we found that 6 of them were correlated with changes in blood pressure, HbA1c, and LDL-C via network analysis.ConclusionsOur results may suggest that exercise improves health through changes in biomarkers at multi-omics levels. However, reduced PA due to COVID-19 can adversely affect health, emphasizing the necessity for sustained exercise and support for home-based fitness to maintain health.Trial RegistrationThe trial is retrospectively registered on ClinicalTrials.gov (NCT05927675; June 30, 2023).

P915 Multi-Omics Biomarkers for the Prediction of Response to Biologics in Patients with Inflammatory Bowel Disease

Abstract Background The treatment concept for inflammatory bowel disease (IBD) has been transformed with biologics now recommended as the first-line therapy for moderate-to-severe patients. However, the significant heterogeneity among IBD patients has limited the efficacy of selected biologics based on traditional clinical factors. Therefore, it is imperative to molecularly stratify patients to match them with the most appropriate biologics. In this study, we systematically reviewed baseline omics biomarkers that have the potential to predict response to biological therapies, aiming to facilitate precision medicine in IBD. Methods We conducted a comprehensive literature search using PubMed by which we included studies that explore the role of omics biomarkers in predicting the efficacy of various biologics including anti-TNFα, anti-integrin, anti-IL-12/23P40 and anti-IL-23 P19 in patients with IBD. Results Our review included 110 studies. Of these, 86 studies focused on anti-TNFα, 17 focused on anti-integrin and 7 focused on anti-IL-12/23P40 and/or anti-IL-23P19. These studies investigated multi-levels baseline biomarkers, including genomics, transcriptomics (bulk RNA and sc-RNA sequence), proteomics, microbiome, and metabolomics (derived from serum, urine, or stool). Furthermore, recent studies already focused on integrating multiple omics analysis and showed that the predictive model based on multi-omics data could significantly enhance their performance. Among the available biomarkers, mucosal transcription of OSM (AUROC = 0.83), IL-13RA2 (AUROC = 0.90), and TREM-1 transcription in mucosal biopsy (AUROC = 0.77) as well as in PBMC ( AUROC varies between 0.78 and 0.94) could accurately predict the response to anti-TNFα. The mucosal IL-23A transcription could discriminate responders from non-responders to anti-IL-12/23P40 with an AUROC of 0.90. OSM, biomarkers of intestinal collagen turnover like C4M, IL-17, IL-22, and TNFα in serum also showed significant potential in predicting the response to anti-TNFα, anti-integrin and anti-IL-12/23P40. In addition, single-cell molecular signatures with sc-RNA sequencing provided more profound insights into predicting the response to biologics. The lack of reproducibility in results across different groups may be due to the disparity in patient selection, methodology, and study designs among different investigations. Conclusion Numerous omics markers have shown great potential in predicting the efficacy of biologics. However, it is crucial to explore and validate these novel biomarkers in larger cohorts using harmonized protocols to facilitate their evaluation into actual clinical practice, especially for newer biologics like anti-IL-12/23P40 and anti-IL-23P19.