Genomic Biomarkers Research Articles

Biomarkers improving genetic and metastatic disease prediction in paraganglioma: insights from a prospective study.

Identifying the risk of malignancy and genetic status in primary paraganglioma or pheochromocytoma (PPGL) is a key challenge. The aim was to assess the diagnostic accuracy of genomic, metabolomic and histopathological biomarkers for predicting metastatic and genetic status. COMETE-TACTIC is a prospective study (NCT02672020) conducted from November 2015 to March 2019 across 16 referral centers. Tumor samples and liquid biopsies from 231 consecutive patients with PPGL were collected. Germline and somatic genetic status were determined by NGS. SDHB, SDHA and CA9 immunohistochemistries were performed on tumor tissues. TERT promoter methylation was assessed by pyrosequencing. Metabolomic profile and circulating miRNAs were measured in liquid biopsies by gas chromatography MS/MS and TaqMan assay quantified by droplet digital PCR, respectively. Tumor analysis outperformed germline analysis for determining genetic status. Positive SDHA and SDHB staining combined with negative CA9 labeling indicated the absence of SDHx and VHL variants. Plasma succinate levels above 4.94µM identified SDHx mutation carriers with 65% sensitivity and 92% specificity (AUC-ROC 0.82, 95%CI 0.70-0.93). Among circulating miRNAs, miR-483-5p was the best classifier of metastatic status (AUC-ROC 0.64, 95%CI 0.52-0.77). A sum of dinucleotide methylation rate of TERT promoter CpGs above 42% predicted metastatic status (AUC-ROC 0.75, 95%CI 0.65-0.85). Multivariate analyses showed that biomarker combinations significantly predicted SDHx status (AUC-ROC 0.99, 95%CI 0.98-1.00) and metastatic potential (AUC-ROC 0.93, 95%CI 0.84-1). Circulating miR-483-5p, plasma succinate, TERT promoter methylation, and SDHB immunostaining are valuable for PPGL risk stratification. Combining biomarkers with clinical data provides excellent diagnostic accuracy for metastatic patients (AUC-ROC 0.97, 95%CI 0.93-1).

Precision oncology platforms: practical strategies for genomic database utilization in cancer treatment.

In recent years, the expansion of molecularly targeted cancer therapies has significantly advanced precision oncology. Parallel developments in next-generation sequencing (NGS) technologies have also improved precision oncology applications, making genomic analysis of tumors more affordable and accessible. Targeted NGS panels now enable the rapid identification of diverse actionable mutations, requiring clinicians to efficiently assess the predictive value of cancer biomarkers for specific treatments. The urgency for timely and accurate decision-making in oncology emphasizes the importance of reliable precision oncology software. Online clinical decision-making tools and associated cancer databases have been designed by consolidating genomic data into standardized, accessible formats. These new platforms are highly integrated and crucial for identifying actionable somatic genomic biomarkers essential for tumor survival, determining corresponding drug targets, and selecting appropriate treatments based on the mutational profile of each patient's tumor. To help oncologists and translational cancer researchers unfamiliar with these tools, we review the utility, accuracy, and comprehensiveness of several commonly used precision medicine software options currently available. Our analysis categorized selected genomic databases based on their primary content, utility, and how well they provide practical guidance for interpreting somatic biomarker data. We identified several comprehensive, mostly open-access platforms that are easy to use for genetic biomarker searches, each with unique features and limitations. Among the precision oncology tools we evaluated, we found MyCancerGenome and OncoKB to be the first choice, offering comprehensive, accurate up-to-date information on the clinical significance of somatic mutations. To illustrate the application of these precision oncology tools in clinical settings, we evaluated three case studies to see how use of the platforms could have influenced treatment planning. Most of the precision oncology software evaluated could be easily streamlined into clinical workflows to provide updated information on approved drugs and clinical trials related the actionable mutations detected. Some platforms were very intuitive and easy to use, while others, often developed in smaller academic settings, were more difficult to navigate and may not be updated consistently. Future enhancements, incorporating artificial intelligence algorithms, are likely to improve integration of the platforms with diverse big data sources, enabling more accurate predictions of potential therapeutic responses.

Identification of Genomic Biomarkers of Disease Progression and Survival in Primary CNS Lymphoma.

The determination of the genetic subtypes of primary CNS lymphoma (PCNSL) and their relationship to differential chemo-immunotherapeutic response has not been established. There is a particular need for genomic biomarkers that identify patients with newly-diagnosed PCNSL at high risk of early progression and death. We applied targeted next-generation sequencing for detection of recurrent single nucleotide variants, copy number alterations and zygosity abnormalities in diagnostic specimens from 78 PCNSL patients treated with a standard methotrexate-based regimen, to identify prognostically significant molecular subgroups. All patients received induction immunochemotherapy and 44 proceeded to dose-intensive consolidation. Genomic aberrations at four loci were associated with 91% of lymphoma progression events and all 15 deaths: (1) chromosome 6p copy-neutral loss of heterozygosity (CN-LOH) or focal homozygous deletion (HD) at 6p21.3, and mutations of tumor suppressor genes (2) BTG1, (3) ETV6 and (4) TP53. Cox regression multivariate analysis demonstrated a high risk of progression in patients with aberrations at these loci. Genomic aberrations at these loci were also associated with significantly shorter survival. Lower expression of HLA-DR was associated with 6p CN-LOH/6p21.3 HD and inferior prognosis. These genomic aberrations identify a high-risk molecular subgroup that may inform risk stratification in PCNSL. Further elucidation of the mechanisms of therapeutic resistance associated with the high-risk genetic phenotype is requisite to facilitate precision medicine and progress in therapy.

Targeting LLT1 as a potential immunotherapy option for cancer patients non-responsive to existing checkpoint therapies in multiple solid tumors

BackgroundHigh levels of LLT1 expression have been found in several cancers, where it interacts with CD161 on NK cells to facilitate tumor immune escape. Targeting LLT1 could potentially relieve this inhibitory signal and enhance anti-tumor responses mediated through NK cells. Using the ‘The Cancer Genome Atlas’ (TCGA) database, we investigated the role of LLT1 in the tumor microenvironment (TME) across various cancers. Identifying such biomarkers could create new therapeutic options for patients in addition to complementing existing immunotherapies.MethodsLLT1 expression was evaluated in 33 cancers using TCGA transcriptome data. Univariate Cox regression analysis was employed to assess the correlation of LLT1 expression with patient survival. The relationship between LLT1 expression with immune infiltrates, immune gene signatures, and cancer genomic biomarkers (TMB, MSI, and MMR) was also investigated. Immunofluorescence studies were conducted to validate LLT1 expression in tumors. Furthermore, using the CRI iAtlas data, we evaluated LLT1 distribution and its correlation with other immune checkpoint genes in patients non-responsive to existing immune checkpoint therapies across multiple solid cancers.ResultsHigh expression of LLT1 was observed in 12 cancers, including BRCA, CHOL, ESCA, GBM, HNSC, KIRC, KIRP, LIHC, LUAD, STAD, SARC, and PCPG. In certain cancers like COAD, KICH, and KIRC, high LLT1 expression was associated with poor prognosis. Further analysis revealed that upregulated LLT1 was associated with an abundance of NK and T cell infiltrates in the TME, as well as exhaustive immune biomarkers, and inversely associated with pro-inflammatory and tumor suppressor signatures. High LLT1 expression is also positively correlated with genomic biomarkers in certain cancers. Immunofluorescence studies confirmed moderate to high LLT1 expression in immune-resistant prostate cancer, glioma, ovarian cancer, and immune-sensitive liver cancer cell lines. An independent assessment of clinical cohorts from CRI iAtlas showed a correlation of upregulated LLT1 with multiple immunosuppressive genes in patients non-responsive to current ICIs.ConclusionsThe biomarker analysis revealed a clear association between elevated LLT1 expression and an immunosuppressive TME in patient cohorts from TCGA and clinical databases. Therefore, this study provides a foundation for utilizing LLT1 as a potential target to improve clinical responses in ICI non-responsive patients with upregulated LLT1.

Clinical, genomic, and proteomic perspectives in the analysis of comorbid conditions in type 2 diabetes mellitus: a retrospective study.

Type-2 Diabetes Mellitus (T2DM) affects millions globally, with escalating rates. It often leads to undiagnosed complications and commonly coexists with other health conditions. This study investigates two types of prevalent comorbidities related to T2DM-the circulatory system (DCM1) and digestive system diseases (DCM2)-using clinical, genomic and proteomic datasets. The aim is to identify new biomarkers by applying existing machine learning (ML) based techniques for early detection, prognosis and diagnosis of these comorbidities. Here, we report a cross-sectional retrospective analysis from a T2DM dataset of T2DM associated concordant comorbidities (diseases with shared pathophysiology and management) from the Diastrat cohort (a T2DM cohort) recruited at the Northern Ireland Centre for Stratified Medicine (NICSM), in Northern Ireland. In the clinical data analysis, we identified that lipidemia was shown to negatively correlate with depression in the DCM1 group while positively correlate with depression in the DCM2 group. In genomic analysis, we identified statistically significant variants rs9844730 in procollagen-lysine (PLOD2), rs73590361 in beta-1,4-N-acetyl- galactosaminyl-transferase (B4GALNT3) and rs964680 in A kinase (PRKA) anchor protein 14 (AKAP14) which appear to differentiate DCM1 and DCM2 groups. In proteomic analysis, we identified 4 statistically significant proteins: natriuretic peptides B (BNP), pro-adrenomedullin (ADM), natriuretic peptides B (NT-proBNP) and discoidin (DCBLD2) that can differentiate DCM1 and DCM2 groups and have built robust ML model using clinical, genomic, and proteomic markers (0.83 receiver operative characteristics curve area, 84% positive predictive value and 83% negative predictive value and a classification accuracy of 83%) for prediction of DCM1 and DCM2 groups. Our study successfully identifies novel clinical, genomic, and proteomic biomarkers that differentiate between circulatory and digestive system comorbidities in Type-2 Diabetes Mellitus patients. The machine learning model we developed demonstrates strong predictive capabilities, providing a promising tool for the early detection, prognosis, and diagnosis of these T2DM-associated comorbidities. These findings have the potential to enhance personalized management strategies for patients with T2DM, ultimately improving clinical outcomes. Further research is needed to validate these biomarkers and integrate them into clinical practice.

USING ARTIFICIAL INTELLIGENCE FOR BIOMARKER ANALYSIS IN CLINICAL DIAGNOSTICS

Introduction. Artificial intelligence (AI) technologies are becoming crucial in clinical diagnostics due to their ability to process and interpret large volumes of data. The implementation of AI for biomarker analysis opens new opportunities in personalized medicine, offering more accurate and individualized approaches to disease diagnosis and treatment. The relevance of this review stems from the need to systematize recent advances in AI application for biomarker analysis, which is critical for early diagnosis and prediction of chronic non-communicable diseases (NCDs). Material and methods. The analysis of peer-reviewed scientific publications and reports from leading research centers over the past five years was conducted. Studies on the application of AI algorithms for analyzing genomic, proteomic, and metabolomic biomarkers were reviewed, including machine learning methods and deep neural networks. Special attention was paid to the integration of multi-marker panels for improving the accuracy of diagnosis and prediction of cardiovascular, digestive, respiratory, endocrine system diseases, as well as oncological and neurodegenerative pathologies. Results. The application of AI has significantly increased the sensitivity and specificity of diagnostics, especially in complex cases requiring analysis of multiple disease parameters. The effectiveness of AI has been demonstrated in early diagnosis of lung, breast, and colorectal cancer, prediction of cardiovascular complications and NCDs progression, including diabetes mellitus and Alzheimer’s disease. AI’s significant contribution to the discovery of new biomarkers, optimization of personalized treatment, and improvement of therapeutic strategies has been noted. Conclusion. The use of AI in biomarker analysis has become a significant breakthrough in medical diagnostics, particularly in oncology, cardiology, and neurodegenerative diseases. The technology allows integration of data about various biomarkers and contributes to creating more accurate models for disease diagnosis and prediction. Further development is associated with technology advancement and overcoming ethical and regulatory barriers, which will expand AI capabilities in clinical practice.

Identifying the best candidate for focal therapy: a comprehensive review.

Despite the evidence supporting the use of focal therapy (FT) in patients with localized prostate cancer (PCa), considerable variability exists in the patient selection criteria across current studies. This study aims to review the most recent evidence concerning the optimal approach to patient selection for FT in PCa. PubMed database was systematically queried for studies reporting patient selection criteria in FT for PCa before December 31, 2023. After excluding non-relevant articles and a quality assessment, data were extracted, and results were described qualitatively. There is no level I evidence regarding the best patient selection approach for FT in patients with PCa. Current international multidisciplinary consensus statements recommend multiparametric magnetic resonance imaging (mpMRI) followed by MRI-targeted and systematic biopsy for all candidates. FT may be considered in clinically localized, intermediate risk (Gleason 3 + 4 and 4 + 3), and preferably unifocal disease. Patients should have an acceptable life expectancy. Those with prostate volume >50 ml and erectile dysfunction should not be excluded from FT. Prostate-specific antigen (PSA) level of < 20 (ideally < 10) ng/mL is recommended. However, the utility of other molecular and genomic biomarkers in patient selection for FT remains unknown. FT may be considered in well-selected patients with localized PCa. This review provides a comprehensive insight regarding the optimal approach for patient selection in FT.

Genomic biomarkers predict response to combined ATR inhibition and radiotherapy.

For decades, chemoradiosensitization with checkpoint kinase inhibitors has been proposed but largely unexplored. A recent study reports the novel ATR kinase inhibitor, RP-3500, synergizes with radiation to control Atm-/- tumors in vivo. RP-3500 did not radiosensitize wild-type or Brca-1 deficient tumors, highlighting the need for a genotype-tailored approach.

P3.13D.05 Genomics and Immune Infiltration Biomarkers for Short-term and Long-Term ES-SCLC Survivors with First-Line Chemoimmunotherapy

P3.13D.05 Genomics and Immune Infiltration Biomarkers for Short-term and Long-Term ES-SCLC Survivors with First-Line Chemoimmunotherapy

Genomic and Serological Rheumatoid Arthritis Biomarkers, MUC5B Promoter Variant, and Interstitial Lung Abnormalities.

Rheumatoid arthritis (RA) has been implicated in interstitial lung disease (ILD) as majority of studies have been comprised of patients with known RA. However, it remains unclear whether an underlying risk for RA in combination with genetic risk for pulmonary fibrosis is associated with radiological markers of early lung injury and fibrosis in broader population samples. Determine whether genetic and serological biomarkers of RA risk in combination with the MUC5B (rs35705950) risk allele (T) are associated with interstitial lung abnormalities (ILA) on computed tomography (CT) scans. Associations of RA-risk HLA-DRB1 alleles (*04:01, *04:08, *04:05, *04:04, *10:01) and serum RA autoantibodies with ILA in the Multi-Ethnic Study of Atherosclerosis (MESA, n=4,018) and COPDGene (n=5,963) cohorts were modeled using logistic regression and adjusted for age, sex, self-reported race and ethnicity, smoking history, body mass index, and principal components of genetic ancestry. The prevalence of an RA risk HLA-DRB1 allele was 16.5% and 21.9% in MESA and COPDGene, respectively. ILA was present in 3.9% and 11% in MESA and COPDGene, respectively. An RA risk HLA-DRB1 allele was not significantly associated with ILA in MESA and COPDGene. In MESA, higher serum levels of IgA rheumatoid factor (RF) and anti-cyclic citrullinated peptide were associated with an odds ratio (OR) for ILA of 1.20 (95% CI 1.07-1.35) and 1.19 (95% CI 1.04-1.37), respectively. Among smokers without baseline ILA, per doubling of IgM RF was associated with an OR for ILA 10 years later of 1.25 (95% CI 1.08-1.43). Associations were not significantly different by MUC5B risk allele status. RA-related HLA-DRB1 alleles were not associated with ILA, whereas higher serum levels of IgM RF among smokers without baseline ILA were associated with subsequent ILA.

Blood-Based Biomarkers in Alzheimer's Disease: Advancing Non-Invasive Diagnostics and Prognostics.

Alzheimer's disease (AD), the most prevalent form of dementia, is expected to rise dramatically in incidence due to the global population aging. Traditional diagnostic approaches, such as cerebrospinal fluid analysis and positron emission tomography, are expensive and invasive, limiting their routine clinical use. Recent advances in blood-based biomarkers, including amyloid-beta, phosphorylated tau, and neurofilament light, offer promising non-invasive alternatives for early AD detection and disease monitoring. This review synthesizes current research on these blood-based biomarkers, highlighting their potential to track AD pathology and enhance diagnostic accuracy. Furthermore, this review uniquely integrates recent findings on protein-protein interaction networks and microRNA pathways, exploring novel combinations of proteomic, genomic, and epigenomic biomarkers that provide new insights into AD's molecular mechanisms. Additionally, we discuss the integration of these biomarkers with advanced neuroimaging techniques, emphasizing their potential to revolutionize AD diagnostics. Although large-scale validation is still needed, these biomarkers represent a critical advancement toward more accessible, cost-effective, and early diagnostic tools for AD.

Tissue-based Profiling Techniques to Achieve Precision Medicine in Cancer: Opportunities and Challenges in Melanoma.

Immunotherapies targeting the programmed cell death 1 (PD-1) and cytotoxic T lymphocyte antigen 4 (CTLA-4) checkpoint receptors have revolutionised the treatment of metastatic melanoma. However, half of treated patients do not respond to or eventually progress on standard therapies and many experience adverse events as a result of drug toxicity. The identification of accurate biomarkers of clinical outcomes are required in order to move away from the one-size-fits-all treatment approach of standard clinical practice, and towards a more personalised approach to enable the administration of the optimal therapy for any given patient and further improve patient outcomes. Recent clinical trials have proven the potential of multi-omics analyses, including genomic, gene expression, and tumour immune profiling, of patients' tumour biopsies, to predict a patient's response to subsequently administered immunotherapies. However, reproducibility of such multi-omics analyses, tissue requirements, and clinical validation have limited the practical application of these approaches in routine clinical workflows. In this review, we discuss several pivotal tissue-based profiling techniques that can be utilised to identify potential genomic, transcriptomic and immune biomarkers predictive of clinical outcomes following treatment with immune checkpoint inhibitors in melanoma. Furthermore, we highlight the key opportunities and challenges associated with the use of each of these techniques. The development and implementation of multimodal predictive models which combine data derived from these various methods is the future for achieving precision medicine for patients with melanoma.

Ensemble transformer-based multiple instance learning to predict pathological subtypes and tumor mutational burden from histopathological whole slide images of endometrial and colorectal cancer

In endometrial cancer (EC) and colorectal cancer (CRC), in addition to microsatellite instability, tumor mutational burden (TMB) has gradually gained attention as a genomic biomarker that can be used clinically to determine which patients may benefit from immune checkpoint inhibitors. High TMB is characterized by a large number of mutated genes, which encode aberrant tumor neoantigens, and implies a better response to immunotherapy. Hence, a part of EC and CRC patients associated with high TMB may have higher chances to receive immunotherapy. TMB measurement was mainly evaluated by whole-exome sequencing or next-generation sequencing, which was costly and difficult to be widely applied in all clinical cases. Therefore, an effective, efficient, low-cost and easily accessible tool is urgently needed to distinguish the TMB status of EC and CRC patients. In this study, we present a deep learning framework, namely Ensemble Transformer-based Multiple Instance Learning with Self-Supervised Learning Vision Transformer feature encoder (ETMIL-SSLViT), to predict pathological subtype and TMB status directly from the H&E stained whole slide images (WSIs) in EC and CRC patients, which is helpful for both pathological classification and cancer treatment planning. Our framework was evaluated on two different cancer cohorts, including an EC cohort with 918 histopathology WSIs from 529 patients and a CRC cohort with 1495 WSIs from 594 patients from The Cancer Genome Atlas. The experimental results show that the proposed methods achieved excellent performance and outperforming seven state-of-the-art (SOTA) methods in cancer subtype classification and TMB prediction on both cancer datasets. Fisher’s exact test further validated that the associations between the predictions of the proposed models and the actual cancer subtype or TMB status are both extremely strong (p<0.001). These promising findings show the potential of our proposed methods to guide personalized treatment decisions by accurately predicting the EC and CRC subtype and the TMB status for effective immunotherapy planning for EC and CRC patients.

Phase 2 trial of regorafenib in recurrent or metastatic adenoid cystic carcinoma.

There is a significant need for effective therapies to treat recurrent/metastatic (R/M) adenoid cystic carcinoma (ACC). This study evaluated the multi-targeted, vascular endothelial growth factor receptor (VEGFR) tyrosine kinase inhibitor (TKI) regorafenib in patients with R/M ACC. Patients with progressive R/M ACC were treated with regorafenib until disease progression, consent withdrawal, or excessive toxicity. The primary endpoints were best overall response (BOR) and 6-month progression-free survival (PFS). Genomic and transcriptomic biomarker analyses were performed in tumors from trial participants. Thirty-eight patients were enrolled, including 7 (18%) patients with prior VEGFR-TKIs. No objective responses were observed. Six-month PFS was 45%, and median PFS was 7.2 months (95%CI 5.2-11.9 months). Presence of either activating NOTCH1 (22%) or KDM6A alterations (24%) was associated with decreased PFS (HR 2.6, 95%CI 1.1-6.1, p=0.03). Bulk RNA sequencing of pre-treatment tumors revealed that regorafenib clinical benefit (CB; PFS≥6 months; n=11) was associated with native enrichment of immune-related signatures. Immune deconvolution revealed a greater degree of macrophage and T-cell infiltration in CB tumors. Tumors from patients with no clinical benefit (NCB; PFS<6 months; n=9) had greater expression of signatures related to cell cycle progression (E2F targets, G2/M checkpoint). The trial failed to meet the pre-specified 6-month PFS and BOR targets. We hypothesize that TKI efficacy may be reliant upon an interplay between kinase inhibition and the ACC immune microenvironment, while programs promoting cell cycle progression may contribute to TKI resistance. These observations suggest that trials evaluating CDK4/6 inhibition plus a VEGFR-TKI should be considered.

Salivary Genomic Biomarkers in Oral Cancer Detection

Salivary Genomic Biomarkers in Oral Cancer Detection

Transcriptome responses to benzo[a]pyrene in liver slices of sub-arctic fish species

Due to the expanding oil-related activities, the arctic and sub-arctic marine environments are increasingly vulnerable to oil-related pollution such as accidental oil spills. These cold-water ecosystems harbor many fish species that are both ecologically and economically important such as the pelagic polar cod (Boreogadus saida), capelin (Mallotus villosus), and benthic long rough dab (Hippoglossoides platessoides). The latter two are much less studied and it is crucial to characterize their responses to oil-related contaminants and develop molecular biomarkers and genomic resources for future monitoring. In this study, liver slice preparation and culture methods were used to characterize the transcriptome responses (using RNA-seq) in capelin and long rough dab to exposures of the polycyclic aromatic hydrocarbon (PAH) compound benzo[a]pyrene (BaP). The liver slice culture and exposure experiments were performed onboard a research vessel in the Barents Sea. Strong up-regulation of genes involved in biotransformation, particularly the aryl hydrocarbon receptor signaling pathway was observed in both species. A comparison of the number of differentially expressed genes (DEGs) with previously published polar cod exposures indicates that the latter responded more strongly (higher number of genes), suggesting higher uptake and bioconcentration of BaP in the fatty liver tissue, although other factors such as differences in clearance rate could potentially affect the responses. This study provides new genomic resources and gene expression biomarkers in capelin and long rough dab, enhancing our understanding of their response mechanism to oil-related contaminants.

Screening for Heart Failure: Biomarkers to Detect Heightened Risk in the General Population.

Heart failure (HF) represents a growing global burden of morbidity and mortality. Identifying individuals at risk for HF development is increasingly important, particularly given the advent of disease-modifying therapies for HF as well as its major risk factors such as obesity actalnd diabetes. We aim to review the key circulating biomarkers associated with future HF which may contribute to HF risk prediction. While current guidelines recommend the use of natriuretic peptides and cardiac troponins in HF risk stratification, there are a diverse array of other emerging protein, metabolic, transcriptomic, and genomic biomarkers of future HF development. These biomarkers not only lend insight into the underlying pathophysiology of HF, which spans inflammation to cardiac fibrosis, but also offer an opportunity to further refine HF risk in addition to established biomarkers. As evolving techniques in molecular biology enable an increased understanding of the complex biologic contributions to HF pathophysiology, there is an important opportunity to construct integrated clinical and multi-omic models to best capture HF risk. Moving forward, future studies should seek to understand the contributions of sex differences, underlying comorbidity burden, and HF subtypes to an individual's HF risk. Further studies are necessary to fully define the clinical utility of biomarker screening approaches to refine HF risk assessment, as well as to link risk assessment directly to preventive strategies for HF.

The Role of Precision Medicine in Autoimmune Diseases

Autoimmune disorders are a broad category of complex, chronic ailments caused by the immune system wrongly targeting the body’s tissues. The variety of over 80 diverse autoimmune illnesses makes effective therapy difficult. Precision medicine, a personalised approach based on individual genetic, environmental, and lifestyle factors, has emerged as a viable treatment method for autoimmune illnesses. Genomic technology and biomarkers are critical in identifying and tailoring therapy options for patients. This study investigates the concept of precision medicine, its applicability in autoimmune illnesses, and the obstacles that come with its implementation. Real-world case studies demonstrate its ability to transform treatment outcomes. The future of autoimmune disease management through precision medicine is dependent on addressing ethical problems, progressing genomic research, and increasing access to these cutting-edge medications. Keywords: Precision medicine, autoimmune diseases, genomics, biomarkers, personalized treatment

913P Characterisation of genomic biomarkers of response to cetuximab versus cisplatin in concomitance with radiotherapy in locally advanced squamous head and neck cancer

913P Characterisation of genomic biomarkers of response to cetuximab versus cisplatin in concomitance with radiotherapy in locally advanced squamous head and neck cancer

Sociocultural code of the health sphere: typology, variability, possibility of impact

Introduction. In the formation of individual approaches to the treatment of diseases, taking into account the characteristics of each patient, the main attention is paid to the characteristics of the human genome and other biomarkers. At the same time, the individual culture of health, the worldview characteristics of patients, as a rule, are not taken into account, which determines the need for further development of the concept of the socio-cultural code of health and determination of the potential for its application. Materials and methods. Critical analysis of domestic and foreign literature, development of a conceptual approach that makes it possible to study the specifics of the attitude of representatives of various social communities to the sphere of health and the possibility of influencing it. Results. The sociocultural code of health is one of the subsystems of the sociocultural code as a whole. The complexity of the social organization makes it possible to single out the levels of the cultural code in accordance with the scale of the social community under consideration: national, ethnic, and corporate socio-cultural code of health. The variability of the socio-cultural code is largely due to the nature of the information space in which social communities are located. The study of indicators of the socio-cultural code by the methods of sociology allows obtaining qualitative and quantitative data characterizing the attitude of society towards the sphere of health. Research limitations. The study is limited by insufficient understanding of the nature of memory and the peculiarities of storing information in it, which limits the possibilities of studying the sociocultural code of the health sector. Conclusion. Taking into account not only predispositions to certain diseases due to the genetic code, but cultural attitudes towards health, prevention and treatment of diseases determined by the socio-cultural code of health, can improve both the quality of treatment and the effectiveness of preventive measures. Emphasis on ethnic, corporate, and occupational cultural characteristics of citizens provides the gain in the effectiveness of educational activities aimed at magnifying the value of health, personal responsibility for it, and raising public awareness about a healthy lifestyle, prevention measures and methods of treatment (as far as possible without resorting to specialists).