Biomarkers In Oncology Research Articles

Identification of SLC31A1 as a prognostic biomarker and a target for therapeutics in breast cancer

Copper-induced cell death is regulated through protein lipoylation, which is critical for gene expression and phenotypic regulation. Neverless, the role of Cuproptosis-related genes in breast cancer (BC) remains unknown. This study aimed to construct a prognostic signature based on the expression of Cuproptosis-related genes in order to guide the diagnosis and treatment for BC. Cuproptosis-related genes prognostic signature has ata of 1250 BC tissues and 583 normal breast tissues were obtained from The Cancer Genome Atlas (TCGA), Genotype Tissue Expression (GTEx), and GEO GSE65212. The prognostic signature was established and evaluated with nineteen Cuproptosis-related genes. A series of in silico analyses based on SLC31A1, included expression analysis, independent prognostic analysis, correlation analysis, immune-related analysis and survival analysis. Finally, a series of cell experiments (including quantitative real-time polymerase chain reaction and western blot), and mice experiments were applied to evaluate the impact of SLC31A1 on BC. Cuproptosis-related genes prognostic signature has good predictive promising for survival in BC patients. We discovered that SLC31A1SLC31A1 was overexpressed in BC and was its independent prognostic factor. High expression of the SLC31A1 was correlated with poor prognosis and immune infiltrating of BC. SLC31A1 expression is associated with immune, chemotherapeutic and targeted therapy outcomes in BC. The proliferation, migration, and invasiveness of Her2 + enriched BC cells were decreased by SLC31A1 knockdown, also resulting in a decrease in tumor volume in mouse model. SLC31A1 is a candidate biomarker or therapeutic target in precision oncology, with diagnostic and prognostic significance in BC.

From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology.

Hematoxylin- and eosin-stained whole-slide images (WSIs) are the foundation of diagnosis of cancer. In recent years, development of deep learning-based methods in computational pathology has enabled the prediction of biomarkers directly from WSIs. However, accurately linking tissue phenotype to biomarkers at scale remains a crucial challenge for democratizing complex biomarkers in precision oncology. This protocol describes a practical workflow for solid tumor associative modeling in pathology (STAMP), enabling prediction of biomarkers directly from WSIs by using deep learning. The STAMP workflow is biomarker agnostic and allows for genetic and clinicopathologic tabular data to be included as an additional input, together with histopathology images. The protocol consists of five main stages that have been successfully applied to various research problems: formal problem definition, data preprocessing, modeling, evaluation and clinical translation. The STAMP workflow differentiates itself through its focus on serving as a collaborative framework that can be used by clinicians and engineers alike for setting up research projects in the field of computational pathology. As an example task, we applied STAMP to the prediction of microsatellite instability (MSI) status in colorectal cancer, showing accurate performance for the identification of tumors high in MSI. Moreover, we provide an open-source code base, which has been deployed at several hospitals across the globe to set up computational pathology workflows. The STAMP workflow requires one workday of hands-on computational execution and basic command line knowledge.

The Role of Biomarkers in Personalized Cancer Treatment

Biomarkers are vital instruments in modern medicine, providing information about biological processes and disease progression. Biomarkers in oncology have had a substantial impact on the development of personalised cancer treatments by predicting therapy responses and outcomes. The study investigates the different types of biomarkers, such as diagnostic, prognostic, and predictive biomarkers, and their function in precision medicine. Personalised cancer treatment, which is guided by biomarker testing, improves patient outcomes by adapting medicines to individual genetic profiles. However, obstacles such as biomarker validation, resistance to targeted medicines, and regulatory barriers persist. Overcoming these obstacles will propel future advances in biomarker-driven oncology. Keywords: Biomarkers, personalized medicine, cancer, precision oncology, diagnostic biomarkers.

Longitudinal gut microbiome shifts correlate with clinical outcomes of immune checkpoint inhibitors-treated melanoma: time for microbial biomarkers in oncology?

Longitudinal gut microbiome shifts correlate with clinical outcomes of immune checkpoint inhibitors-treated melanoma: time for microbial biomarkers in oncology?

507P Oncology biomarker recommendations for gastrointestinal cancers as part of ONCOMARKR: The oncology biomarker review project

507P Oncology biomarker recommendations for gastrointestinal cancers as part of ONCOMARKR: The oncology biomarker review project

Relationship of adaptive subtyping and tumour heterogeneity of treatment response to neoadjuvant therapy in hormone receptor–positive HER2-negative early breast cancer: PENELOPE-B.

566 Background: The concept of intrinsic subtyping has been an important step for understanding of breast cancer (BC) as a heterogenous disease. However, these subtypes are not adapted to therapy-induced molecular plasticity. We have evaluated a high-risk luminal BC clinical trial cohort to identify new additional adaptive BC subtypes based on molecular alterations induced during neoadjuvant chemotherapy (NACT). Methods: A total of 1250 ER+/HER2- BC patients (pts) with residual disease after NACT and increased risk (CPS-EG score of ≥3 or 2 with ypN+) were randomized into the PENELOPE-B (NCT01864746) trial to receive palbociclib or placebo. Biomarker analysis was performed in 1411 pre- or postNACT tumor samples including 540 paired samples using the HTG EdgeSeq Oncology Biomarker Panel targeting 2549 genes (HTG Molecular Diagnostics Inc.), with assessment of the absolute assignment of breast cancer intrinsic molecular subtype (AIMS). For a subcohort of 29 pts, we evaluated triplicate samples before and after NACT as well as at the time of metastatic disease. Results: In paired biopsies, a total of 335 genes were significantly different between the pre- and the postNACT cohort. With hierarchical unsupervised clustering of these 335 genes, we characterized five different tumor subtypes with highly significant iDFS survival differences (p<0.0001, n=539). We identified two large groups of tumors with excellent prognosis (adaptive cluster AC1 and AC2, together n=284, 6 events), as well as two groups with a poor prognosis; AC-3 (n=163, 82 events) and AC-4 (n=78, 29 events). The worst prognosis was observed in a small group of tumors with a pre-NACT Basal/HER2E AIMS subtype (n=14, 11 events). AC1 and AC3 are enriched for tumors with an AIMS subtype change from LumB to LumA during NACT, with improved iDFS for AC1. AC2 and AC4 are enriched for tumors with a constant LumA AIMS subtype before and after NACT, with improved iDFS for AC2. The low-risk adaptive subtypes AC1 and AC2 cover a total of 52.7% of pts with a combined event rate of 2.1%. In contrast, the best pretherapeutic conventional AIMS-subtype, preNACT LumA, covers 51.7% of pts, but still has an event rate of 15.4%. This suggests that adaptive subtypes are superior to classical AIMS subtypes for prediction of survival after NACT in luminal BC. Conclusions: Classical approach of intrinsic subtyping relies on constant gene expression, limiting its scope. Adaptive subtyping can complement the classical approach, focusing on those genes that are changed during therapy. Comparison of paired samples before and after therapy is superior to classical subtyping of baseline samples. This approach identified large subgroups of pts with excellent prognosis after NACT despite clinical high risk. This can be an important step to focus on high-risk pts for new neoadjuvant and post-neoadjuvant therapy concepts.

Molecular characterization of growing teratoma syndrome in patients with testicular germ cell tumor.

e17017 Background: Teratomas are unique neoplastic growths that encompass a diverse array of tissue types derived from multiple germ layers, often exhibiting varying degrees of differentiation. Among the phenomena associated with teratomas, the growing teratoma syndrome (GTS) stands out as a rare yet clinically significant occurrence. GTS, characterized by the persistence or regrowth of teratomatous elements despite initial treatment, poses challenges for both diagnosis and management. In this abstract, we present the results of our expression array analysis of teratomas, aiming to uncover novel insights into their molecular composition, heterogeneity, and potential implications for clinical management. Methods: This study included 53 patients with retroperitoneal teratoma (7 chemotherapy naïve and 46 with postchemotherapy teratoma) and 20 patients with viable postchemotherapy germ cell tumor from retroperitoneum that had surgery at National Cancer Institute in Slovakia and for whom formalin-fixed paraffin-embedded (FFPE) tumor tissue was available. Study also includes samples from 5 patients with normal testis as control group. Ten patients (18.9%) in this cohort had growing teratoma syndrome. Analysis was performed by HTG EdgeSeq Oncology Biomarker panel (HTG Molecular Diagnostics, Inc., Tucson, USA). Identified candidate genes associated with GTS were further validated by immunohistochemistry on FFPE. Results: Molecular profiling indicates no differences in expression profile in retroperitoneal teratomas regardless of previous chemotherapy. Expression analysis indicates 1586 genes being differently expressed with high significance ( p < 0.05) in teratomas compared to normal testis and 1571 genes differentially expressed compared postchemotherapy viable tumors. We identified 14 genes being differently expressed in growing teratoma syndrome compared to teratoma without growth (CYP4A22, IGFBP1, CXCL6, LTF, CYP2C8, CFTR, CCL20, PFKFB3, SLC2A3, LIPA, HMOX1, GPNMB, CCL18, DLK1. Pathway analysis revealed MAPK and PI3K−Akt signaling pathways are mostly dysregulated in GTS. Conclusions: This molecular profiling identify genes differentially expressed in teratomas that are potential biomarkers for prediction of teratoma element in postchemotherapy residues in testicular germ cell tumor patients. Moreover, inhibition of identified signaling pathways associated with GTS could represent potentially novel therapeutic targets in case of unresectable disease.

Comprehensive multi-omics analysis provides biological insights and therapeutic strategies for small-cell lung cancer.

Integration of multi-omics analysis into small-cell lung cancer (SCLC) research. In the research of small-cell lung cancer, the integration of multi-omics analysis has become an important research direction. Multi-omics analysis includes the study of genomics, transcriptomics, proteomics, metabolomics, and other levels, which can help us to understand the pathogenesis and development process of diseases more comprehensively as well as develop novel therapeutics and biomarkers for further precision oncology.

Clinical Use of Molecular Biomarkers in Canine and Feline Oncology: Current and Future.

Molecular biomarkers are central to personalised medicine for human cancer patients. It is gaining traction as part of standard veterinary clinical practice for dogs and cats with cancer. Molecular biomarkers can be somatic or germline genomic alterations and can be ascertained from tissues or body fluids using various techniques. This review discusses how these genomic alterations can be determined and the findings used in clinical settings as diagnostic, prognostic, predictive, and screening biomarkers. We showcase the somatic and germline genomic alterations currently available to date for testing dogs and cats in a clinical setting, discussing their utility in each biomarker class. We also look at some emerging molecular biomarkers that are promising for clinical use. Finally, we discuss the hurdles that need to be overcome in going 'bench to bedside', i.e., the translation from discovery of genomic alterations to adoption by veterinary clinicians. As we understand more of the genomics underlying canine and feline tumours, molecular biomarkers will undoubtedly become a mainstay in delivering precision veterinary care to dogs and cats with cancer.

Abstract PO2-24-02: Molecular characterization of primary tumors associated with various CTCs’ subpopulation

Abstract Background: Circulating tumor cells (CTCs) are prognostic in primary breast cancer (BC) patients and represent a heterogeneous population of cells with different phenotypes and biological value. However, molecular characterization of primary tumors associated with various CTCs’ subpopulation is lacking. This study aimed to identify expression of genes associated with the presence of different CTCs subpopulations in primary BC patients using a comprehensive genomics approach. Methods: This prospective ongoing study included 312 patients (pts) with non-metastatic BC enrolled from March 2012 to February 2015. CTCs were detected on day -1 to 0 before surgery. Isolated peripheral blood mononuclear cells (PBMC) were depleted of hematopoietic cells using RossetteSepTM kit negative selection with anti-CD45 antibody. RNA extracted from CD45-depleted (CD45-) PBMC was interrogated for expression of EMT-inducing transcription factors (TWIST1, SNAIL1, SLUG, ZEB1) and epithelial (CK19) gene transcripts by quantitative reverse transcription-PCR. Expressions of gene transcripts in CD45- PBMC of patients were compared to those of CD45- PBMC of 60 healthy donors. Formalin-fixed paraffin-embedded tumor tissue was subject for gene expression analysis performed by HTG EdgeSeq Oncology Biomarker panel (HTG Molecular Diagnostics, Inc., Tucson, USA). Results: CTCs with epithelial phenotype (CTC_EP) were detected in 30 (9.6%) pts, CTC with EMT (epithelial-to-mesenchymal transition) were detected in 54 (17.3%), while blood of 79 (25.3%) pts exhibit at least one type of CTC. Patients with detectable CTC in peripheral blood had inferior disease-free survival compared to patients without detectable CTC (HR = 0.62, 95% CI 0.37-1.06, P=0.048). There was no association between epithelial CTCs and analysed patients/tumour variables. Genes expression analysis identified 7 differentially expressed genes in patients with the CTC_EP phenotype (CHGA, MESP1, COL2A1, MT2A, COMP, MMP7 and ORM2), 200 genes associated with CTC_EMT phenotype (top 10 genes included CHGA, CECR6, SMAD2, HUS1, FGF10, HNF1A, SEC61G, CDK4, CD44 and F3). Analysis identified that low expression of CHGA and MMP7 was consistently associated with any CTC subpopulations. Conclusions: In this study for the first-time revealed gene expressions associated with various CTC subpopulations. We suppose that these genes could represent potential therapeutic target aimed to inhibit metastatic cascade in breast cancer. Citation Format: Michal Mego, Gabriel Minarik, Milan Hucko, Katarina Kalavska, Marian Karaba, Juraj Benca, Tatiana Sedlackova, Lucia Kucerova, Jozef Mardiak, Lubos Klucar, Zuzana Cierna. Molecular characterization of primary tumors associated with various CTCs’ subpopulation [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 PO2-24-02.

Evolutionary screening of precision oncology biomarkers and its applications in prognostic model construction

Evolutionary screening of precision oncology biomarkers and its applications in prognostic model construction

Abstract P19: Comprehensive Benchmark of Somatic Mutation Callers on Cell-Free DNA Next-Generation Sequencing Data in Cancer Liquid Biopsy

Abstract Circulating tumour DNA (ctDNA) has emerged as a rich source of biomarkers in precision oncology. However, ctDNA sequence data is often noisier than that derived from standard tissue biopsies, posing significant challenges for accurate cancer mutation detection. Besides, there is a lack of standardised and unbiased benchmark datasets to evaluate and compare performance of variant callers on ctDNA. To address this issue, we present a novel benchmarking approach and comprehensive benchmark of cancer somatic mutation callers on cell-free DNA (cfDNA) Next Generation Sequencing (NGS) data. To create a large-scale benchmark dataset, we developed a new methodology using real colorectal cancer patients plasma samples exhibiting either ultra-high and an ultra-low levels of ctDNA. This enabled the generation of dilution series of deep Whole Genome Sequencing (WGS, 150x) and the creation of high-quality consensus ground truth calls. We evaluated the performance of 9 SNV callers and 7 short INDEL callers frequently used in academic research, including 2 callers that were specifically designed for cfDNA. We further validated our findings at higher depth of coverage using deep Whole Exome Sequencing (WES, >2,000x) data. We further investigated each caller's features to develop practical guidelines for users depending on their application setting: unguided analysis focusing on overall accuracy vs. guided analysis focusing on sensitivity. Citation Format: Hanaé Carrié, Ngak Leng Sim, Pui Mun Wong, Anna Gan, Yi Ting Lau, Polly Poon, Saranya Thangaraju, Iain Tan, Yoon Sim Yap, Limsoon Wong, Anders Skanderup. Comprehensive Benchmark of Somatic Mutation Callers on Cell-Free DNA Next-Generation Sequencing Data in Cancer Liquid Biopsy [abstract]. In: Proceedings of Frontiers in Cancer Science; 2023 Nov 6-8; Singapore. Philadelphia (PA): AACR; Cancer Res 2024;84(8_Suppl):Abstract nr P19.

Abstract CT231: Identification of a novel agnostic predictive multiomic signature via Elastic Net/Machine Learning in TALAPRO-2 (TP-2), a phase 3 study of talazoparib (TALA) + enzalutamide (ENZA) vs placebo (PBO) + ENZA as first-line (1L) treatment in patients (pts) with metastatic castration-resistant prostate cancer (mCRPC)

Abstract Background: TP-2 (NCT03395197) demonstrated significantly improved radiologic progression-free survival (rPFS) regardless of homologous recombination repair (HRR) gene alteration status for pts with mCRPC who received 1L TALA + ENZA (n=402) vs PBO + ENZA (n=403; Agarwal et al. Lancet. 2023. PMID: 37285865). We explore potential associations of ctDNA genomics and tumor gene expression with efficacy in TP-2. Methods: The analysis was based on the safety population (n=799). Two data sets were used: a FoundationOne®Liquid CDx data set (Azad et al. ASCO 2023, #5056) of prospectively collected/retrospectively analyzed plasma samples (n=681); and a tumor transcriptomic data set generated via HTG’s Oncology Biomarker Panel (with 10 additional genes implicated in PARP inhibitor sensitivity; n=304). Correlations between baseline molecular biomarkers and rPFS were assessed via a Cox proportional hazards model. Multivariate elastic net regression analysis incorporated 151 pts in the TALA + ENZA arm with 2,868 molecular features (ctDNA: 309 genes [only short variant alterations incorporated]; HTG: 2,559 tumor transcripts). Results: Positively predictive candidate tumor biomarkers for TALA + ENZA included androgen response signature and individual androgen receptor (AR) target genes such as PSA, ALDH1A3, and CAMKK2. These expression signatures/transcripts had no/minimal predictive value for PBO + ENZA. A 33-feature (3 genes, 30 transcripts) elastic net signature was identified that was predictive of rPFS with TALA + ENZA: TP53 and AR short variant alteration status were each prognostic and associated with worse rPFS. Expression of multiple AR target genes, including ALDH1A3 (top selected feature) and CAMKK2, was positively associated with rPFS. For TALA + ENZA, this multiomic signature was predictive of rPFS in pts regardless of HRR gene alteration status, and to a lesser extent for PBO + ENZA. None of the 12 HRR genes used for stratification in TP-2 were included in the signature. Conclusions: Our exploratory analysis identified candidate gene expression signatures, including AR pathway elements, potentially associated with differential benefit from TALA + ENZA, reinforcing the potential for exploitable crosstalk between AR and DNA repair pathways. Though validation is necessary, a predictive multiomic signature for benefit from TALA + ENZA regardless of HRR alteration status was identified that included alterations in genes previously implicated in prognosis and expression of multiple AR target transcripts. Strikingly, it did not include any of the 12 HRR genes used in prospective stratification for TP-2, reinforcing the potential benefit for TALA + ENZA beyond HRR-deficient tumors. Citation Format: Glenn Liu, Xinmeng Jasmine Mu, Karim Fizazi, A. Douglas Laird, Nobuaki Matsubara, Steven M. Yip, Jie Pu, Joan Carles, Andre P. Fay, Robert J. Jones, Stefanie Zschäbitz, Josep M. Piulats, Jae Young Joung, Whijae Roh, Jijumon Chelliserry, Nicola Di Santo, Michelle Saul, Arun A. Azad, Neeraj Agarwal. Identification of a novel agnostic predictive multiomic signature via Elastic Net/Machine Learning in TALAPRO-2 (TP-2), a phase 3 study of talazoparib (TALA) + enzalutamide (ENZA) vs placebo (PBO) + ENZA as first-line (1L) treatment in patients (pts) with metastatic castration-resistant prostate cancer (mCRPC) [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 CT231.

Tumor Mutation Burden as a Cornerstone in Precision Oncology Landscapes: Effect of Panel Size and Uncertainty in Cutoffs.

Tumor mutation burden (TMB) has profound implications for personalized cancer therapy, particularly immunotherapy. However, the size of the panel and the cutoff values for an accurate determination of TMB are still controversial. In this study, a pan-cancer analysis was performed on 22 cancer types from The Cancer Genome Atlas. The efficiency of gene panels of different sizes and the effect of cutoff values in accurate TMB determination was assessed on a large cohort using Whole Exome Sequencing data (n = 9929 patients) as the gold standard. Gene panels of four different sizes (i.e., 0.44-2.54 Mb) were selected for comparative analyses. The heterogeneity of TMB within and between cancer types is observed to be very high, and it becomes possible to obtain the exact TMB value as the size of the panel increases. In panels with limited size, it is particularly difficult to recognize patients with low TMB. In addition, the use of a general TMB cutoff can be quite misleading. The optimal cutoff value varies between 5 and 20, depending on the TMB distribution of the different tumor types. The use of comprehensive gene panels and the optimization of TMB cutoff values for different cancer types can make TMB a robust biomarker in precision oncology. Moreover, optimization of TMB can help accelerate translational medicine research, and by extension, delivery of personalized cancer care in the future.

Community cohesion looseness in gene networks reveals individualized drug targets and resistance.

Community cohesion plays a critical role in the determination of an individual's health in social science. Intriguingly, a community structure of gene networks indicates that the concept of community cohesion could be applied between the genes as well to overcome the limitations of single gene-based biomarkers for precision oncology. Here, we develop community cohesion scores which precisely quantify the community ability to retain the interactions between the genes and their cellular functions in each individualized gene network. Using breast cancer as a proof-of-concept study, we measure the community cohesion score profiles of 950 case samples and predict the individualized therapeutic targets in 2-fold. First, we prioritize them by finding druggable genes present in the community with the most and relatively decreased scores in each individual. Then, we pinpoint more individualized therapeutic targets by discovering the genes which greatly contribute to the community cohesion looseness in each individualized gene network. Compared with the previous approaches, the community cohesion scores show at least four times higher performance in predicting effective individualized chemotherapy targets based on drug sensitivity data. Furthermore, the community cohesion scores successfully discover the known breast cancer subtypes and we suggest new targeted therapy targets for triple negative breast cancer (e.g. KIT and GABRP). Lastly, we demonstrate that the community cohesion scores can predict tamoxifen responses in ER+ breast cancer and suggest potential combination therapies (e.g. NAMPT and RXRA inhibitors) to reduce endocrine therapy resistance based on individualized characteristics. Our method opens new perspectives for the biomarker development in precision oncology.

Abstract 2417: Detection and quantification of site-specific DNA methylation from liquid biopsies as a pharmacodynamic biomarker of OTX-2002, a novel MYC-targeting epigenomic mRNA therapeutic

Abstract Omega Therapeutics has developed a novel platform of programmable epigenomic mRNA medicines capable of modifying chromatin state to specifically tune gene expression at the pre-transcriptional level. Epigenomic controllers (ECs) unlock targets that have been historically considered “undruggable,” with one of the most elusive being the MYC oncogene. A direct MYC-targeting anti-cancer agent has previously remained intangible, largely due to the absence of a drug binding pocket and tight autoregulation. A clinical trial is underway (MYCHELANGELO, NCT05497453) to investigate pre-transcriptional inhibition of MYC with OTX-2002 in patients with hepatocellular carcinoma (HCC). OTX-2002, a first-in-class mRNA therapeutic delivered via lipid nanoparticles (LNP), encodes two proteins that durably modify chromatin, in part, through CpG DNA methylation at the MYC locus. Using both liquid and solid biopsy sampling from in vivo studies, we investigated whether target engagement for OTX-2002 could be assessed by MYC methylation. Detection of DNA methylation has long held promise as an oncology biomarker given its functional roles in various cancer types and the potential signal afforded by methylated CpGs. Indeed, DNA methylation has been shown to serve as a robust analyte in liquid biopsy-derived multi-cancer early detection (MCED) and minimal residual disease (MRD) tests that have recently been utilized in the clinic. Many of these platforms are founded on complex models that leverage methylation signals across >1 million CpGs that can span tens of megabases of genomic space. We faced a distinct challenge when compared to these MCED/MRD tests in developing a pharmacodynamic methylation assay for OTX-2002 as the target region consists of just a few kilobases. With a tissue specific LNP delivery system, ultra-high sensitivity was required to identify rare ctDNA events from the larger cfDNA population. To this end, we designed a minimal hybridization/capture panel that targeted ~50 kb of genomic space, effectively allowing for ultra-deep methylation sequencing of MYC. When this technique was paired with enzymatic (EM) conversion for methylation detection and supported by an analysis pipeline focused on epiallele identification, this assay was able to detect methylation down to the theoretical limit in a dilution series of control genomic DNA, 1 in 104 copies of MYC. This degree of sensitivity translated to successful preclinical detection of on-target methylation by OTX-2002 from DNA extracted from plasma samples collected from mice-bearing human HCC xenografts. Overall, we present a non-invasive and exquisitely sensitive method of assessing target engagement and site-specific pharmacodynamic activity of a novel epigenomic medicine that can be directly translated to the clinical setting. Citation Format: Justin Chen, William Senapedis, Stephen Siecinski, Elmer Figueroa, Adam Katz, Samuel Mildrum, Yaoyu E. Wang, Houda Belaghzal, Kayleigh Gallagher, Graeme Hodgson, Charles W. O'Donnell, Thomas G. McCauley. Detection and quantification of site-specific DNA methylation from liquid biopsies as a pharmacodynamic biomarker of OTX-2002, a novel MYC-targeting epigenomic mRNA therapeutic [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 2417.

MicroRNAs, oxidative stress, and genotoxicity as the main inducers in the pathobiology of cancer development.

Cancer is one of the most serious leading causes of death in the world. Many eclectic factors are involved incancer progression including genetic and epigenetic alongside environmental ones. In this account, the performance and fluctuations of microRNAs are significant in cancer diagnosis and treatment, particularly as diagnostic biomarkers in oncology. So, microRNAs manage and control the gene expression after transcription by mRNA degradation, or also they can inhibit their translation. Conspicuously, these molecular structures take part in controlling the cellular, physiological and pathological functions, which many of them can accomplish as tumor inhibitors or oncogenes. Relatively, Oxidative stress is defined as the inequality between the creation of reactive oxygen species (ROS) and the body's ability to detoxify the reactive mediators or repair the resulting injury. ROS and microRNAs have been recognized as main cancer promoters and possible treatment targets. Importantly, genotoxicity has been established as the primary reason for many diseases as well as several malignancies. The procedures have no obvious link with mutagenicity and influence the organization, accuracy of the information, or fragmentation of DNA. Conclusively, mutations in these patterns can lead to carcinogenesis. In this review article, we report the impressive and practical roles of microRNAs, oxidative stress, and genotoxicity in the pathobiology of cancer development in conjunction with their importance as reliable cancer biomarkers and their association with circulating miRNA, exosomes and exosomal miRNAs, RNAremodeling, DNA methylation, and other molecular elements in oncology.

Unlocking the potential of oncology biomarkers: advancements in clinical theranostics.

Cancer biomarkers have revolutionized the field of oncology by providing valuable insights into tumor changes and aiding in screening, diagnosis, prognosis, treatment prediction, and risk assessment. The emergence of "omic" technologies has enabled biomarkers to become reliable and accurate predictors of outcomes during cancer treatment. In this review, we highlight the clinical utility of biomarkers in cancer identification and motivate researchers to establish a personalized/precision approach in oncology. By extending a multidisciplinary technology-based approach, biomarkers offer an alternative to traditional techniques, fulfilling the goal of cancer therapeutics to find a needle in a haystack. We target different forms of cancer to establish a dynamic role of biomarkers in understanding the spectrum of malignancies and their biochemical and molecular characterization, emphasizing their prospective contribution to cancer screening. Biomarkers offer a promising avenue for the early detection of human cancers and the exploration of novel technologies to predict disease severity, facilitating maximum survival and minimum mortality rates. This review provides a comprehensive overview of the potential of biomarkers in oncology and highlights their prospects in advancing cancer diagnosis and treatment.

The Neutrophil-to-Lymphocyte Ratio as a Biomarker in Cutaneous Oncology: A Systematic Review of Evidence beyond Malignant Melanoma.

With the ongoing progress of basic research along with the introduction of new pharmaceutical options spanning almost all therapeutic areas, the need for biomarkers that will be implemented into the personalized medical approach is higher than ever. Their use can be incorporated into clinical practice and can be applied to the classification of disorders and the evaluation of disease severity but also to the monitoring of the progress of therapeutic/pharmaceutical interventions. This systematic review collects the findings of hematologic biomarkers in various cutaneous malignancies, excluding malignant melanoma, to support their potential use in the prognosis but also in the assessment of therapeutic strategies for the specific category of skin disorders.

Abstract B073: A spatially resolved single-cell tumor microenvironment of clinicomolecular subtypes of high-grade serous ovarian cancer

Abstract Background & objective: High grade serous ovarian cancer (HGSC) is the most lethal type of ovarian cancer malignancies. Around 50% of HGSCs are DNA-repair homologous recombination deficient (HRD) and 15% have CCNE1 gene amplification. Previous results have shown that tumor microenvironment (TME) can impact clinical prognosis in HGSC. In this study our objective was to characterize the interplay of the TME with HRD and CCNE1 amplification in HGSC and its association with clinical outcomes. Materials and methods: We collected TME spatial protein expression, genomics, and clinical data from 250 HGSC patients. We determined BRCA1/2 germline and somatic mutation status using targeted sequencing and promoter hyper-methylation. For the tumors without BRCA1/2 mutations, we performed shallow whole genome sequencing, and estimated the CCNE1 amplification and HRD status using shallowHRD and BRCAness copy number profiles. Immune cell infiltration was assessed also using IHC for CD8, CD20, CD68, and CD103. The single-cell spatial landscapes were analyzed for 1000 tumor cores from the tumor center and tumor periphery using cyclic immunofluorescence (tCycIF) imaging for 34 different protein markers. We categorized and spatially analyzed the single cells from the TME to distinct cellular subpopulations using the TRIBUS and scimap software. Results: In our study of the TME using highly multiplexed tissue imaging we identified a total of 4.8 million single cells of them 1.6 million were immune cells belonging to eight biological subpopulations. The immune cell proportion detected in each sample showed high concordance with the IHC estimation by a pathologist. Interestingly, the CD8+, CD20+, CD11c+, and CD15+ high immune cell infiltrations were higher in BRCA1/2 mutated as compared to the CCNE1 amplified tumors. High immune infiltration was associated with prolonged overall survival only in samples debulked before chemotherapy but not in samples debulked after chemotherapy. Using consensus clustering for cell protein expression and cell morphology, we identified five distinct tumoral and stromal cell clusters which showed significant heterogeneity among the tumor genotypes and association with clinical outcomes. Then, after clustering of the spatial single-cell proximity data, we identified 13 distinct recurrent cellular neighborhoods (RCNs). The two RCNs related to the tumor-stroma and tumor-immune interfaces exhibited the highest differences between HRD and CCNE1 samples. Finally, by using random forest machine learning classifiers of the cells and its neighborhoods, and using an approach to minimize overfitting, we were able to detect the features more representative in HRD samples, for example, MHC-I expression in cancer cells and in stromal cells. Conclusion: The integration of multi-omics data revealed distinct TMEs associated with HRD and clinical outcomes; this has the potential to be used to discover biomarkers for precision oncology in HGSCs. Citation Format: Fernando Perez-Villatoro, Lilian van Wagensveld, Aleksandra Shabanova, Siamak Hajizadeh, Julia Casado, Ella Anttila, Maaike A. van der Aa, Roy F.P.M. Kruitwagen, Gabe S. Sonke, Koen K. Van de Vijver, Peter K. Sorger, Hugo M. Horlings, Anniina Färkkilä. A spatially resolved single-cell tumor microenvironment of clinicomolecular subtypes of high-grade serous ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr B073.