Identification Of Predictive Biomarkers Research Articles

Immunotherapy for Early-Stage Triple Negative Breast Cancer: Is Earlier Better?

In this narrative review, we discuss the optimal timing ofimmune checkpoint inhibitors (ICI) in early triple negative breast cancer (TNBC), the landscape of predictive biomarkers for the use ofimmunotherapy, and the mounting literature suggesting a benefit for an early use of ICI. TNBC is associated with a poor prognosis relative to other breast cancer subtypes, and until recently, the treatment of TNBC was limited to cytotoxic chemotherapy. In 2021, the immune-checkpoint inhibitor, pembrolizumab, was approved in combination with neoadjuvant chemotherapy for patients with high-risk early stage TNBC. This approval changed the treatment paradigm of early TNBC concomitantly raised several challenges in clinical practice, pertaining to patient selection, toxicity management, and post-neoadjuvant treatment, among others. The introduction of neoadjuvant chemoimmunotherapy has transformed the treatment landscape for early TNBC. However, several challenges, including patient selection, toxicity management, and the identification of predictive biomarkers, need to be addressed. Future research should focus on refining the timing and duration of immunotherapy, optimizing the chemotherapy partner, and exploring novel predictive biomarkers of response or toxicity.

A Semiautomated Proteomics and Phosphoproteomics Protocol for the Identification of Novel Therapeutic Targets and Predictive Biomarkers in In Vivo Xenograft Models of Pediatric Cancers.

Genomic profiling has identified therapeutic targets for precision treatment of certain cancers, but many patients lack actionable mutations. Additional omics approaches, like proteomics and phosphoproteomics, are essential for comprehensive mapping of cancer-associated molecular phenotypes. In vivo models, such as cell line and patient-derived xenografts (PDX), offer valuable insights into cancer biology and treatment strategies.This chapter presents a semiautomated high-throughput workflow for integrated proteomics and phosphoproteomics analysis on the Kingfish platform coupled with MagReSyn® Zr-IMAC HP. It enhances protein extraction from in vivo xenograft samples and provides better insights into cancers with poor prognosis. The approach successfully identified over 11,000 unique phosphosites and ~6000 proteins in SJSA-1 pediatric osteosarcoma xenografts, demonstrating its efficacy. This workflow is a valuable tool for studying tumor biology and developing precision oncology strategies.

Analytical Study of Breast Cancer and Treatment Techniques

Breast cells have the potential to evolve into a malignant growth known as breast cancer. Intensive research into breast cancer detection aims to gain a deeper understanding of this intricate ailment, which disproportionately impacts women and constitutes a significant global health challenge. Key objectives in breast cancer analysis encompass improving early detection methods, comprehending diverse disease subtypes, developing efficient treatment approaches, and implementing preventive measures. Advances in mammography, ultrasonography, MRI, and innovative imaging techniques are enhancing early detection rates. Molecular and genetic profiling now allows for more precise categorization of breast cancer subtypes, enabling personalized treatment plans based on DNA mutations, protein markers, and gene expression patterns. The identification of prognostic and predictive biomarkers enhances patient outcome predictions, facilitating customized treatments and higher survival rates. Additionally, molecular analysis aids in understanding drug resistance, tumor diversity, and pinpointing specific therapeutic targets. To effectively analyse breast cancer, collaborative research and data exchange are essential, and massive genomic projects are facilitating interdisciplinary collaborations and expediting achievements. We strive for a future where breast cancer is successfully identified, treated, and prevented, thereby improving patient outcomes and lessening the burden of this disease. To that end, we are constantly increasing our knowledge and sharing resources. Results from various machine learning models demonstrate promising accuracy, including CNN (93%), SVM (91%), AlexNet (93.8%), TRF (89.2%), LR (89.0%), and Naive Bayes (90%).

Standard-of-Care Treatment for HER2+ Metastatic Breast Cancer and Emerging Therapeutic Options.

Prior to the advent of the HER2-targeted monoclonal antibody trastuzumab, HER2+ breast cancer (BC) was considered an aggressive disease with a poor prognosis. Over the past 25 years, innovations in molecular biology, pathology, and early therapeutics have transformed the treatment landscape. With the advent of multiple HER2-directed therapies, there have been immense improvements in oncological outcomes in both adjuvant and metastatic settings. Currently, 8 HER2-targeted therapies are approved by the Food and Drug Administration (FDA) for the treatment of early-stage and/or advanced/metastatic disease. Nonetheless, approximately 25% of patients develop recurrent disease or metastasis after HER2-targeted therapy and most patients with HER2+ metastatic breast cancer (MBC) die from their disease. Given the many mechanisms of resistance to HER2-directed therapy, there is a pressing need to further personalize care for patients with HER2+ MBC, by the identification of reliable predictive biomarkers, and the development of novel therapies and combination regimens to overcome therapeutic resistance. Of particular interest are established and novel antibody-drug conjugates, as well as other novel therapeutics and multifaceted approaches to harness the immune system (checkpoint inhibitors, bispecific antibodies, and vaccine therapy). Herein, we discuss standard-of-care treatment of HER2+ MBC, including the management of breast cancer brain metastases (BCBM). Furthermore, we highlight novel treatment approaches for HER2+ MBC, including endeavors to personalize therapy, and discuss ongoing controversies and challenges.

Plasma metabolome analysis for predicting antiviral treatment efficacy in chronic hepatitis B: diagnostic biomarkers and therapeutic insights.

The early and accurate identification of predictive biomarkers for antiviral treatment efficacy remains a significant clinical challenge, particularly in the management of chronic hepatitis B (CHB). This study aimed to assess whether the plasma metabolome could reliably predict the success of antiviral therapy in CHB patients. We conducted a retrospective analysis on 56 treatment-naive CHB patients at the First Affiliated Hospital of Zhejiang University from December 2013 to March 2016. Patients who underwent a 48-week treatment regimen of entecavir (ETV) and interferon-alpha (IFN-α) were randomly assigned to either a discovery cohort (n=29) or a validation cohort (n=27). Based on the outcome of the treatment, patients were classified as HBeAg seroconversion group (High responders, Hrp) or the non-remission group (Low responder, Lrp). Our methodology involved an untargeted analysis of the amine/phenol and carboxylic acid submetabolomes in the CHB patients under treatment, utilizing chemical isotope labeling (CIL) techniques with liquid chromatography-mass spectrometry (LC-MS). Several metabolites were identified as having significant diagnostic potential for distinguishing Hrp from Lrp, with areas under the receiver operating characteristic curve (AUC) exceeding those typical clinical indicators. Notably, four metabolites, namely 2-methyl-3-ketovaleric acid, 2-ketohexanoic acid, 6-oxo-1,4,5,6-tetrahydronicotinic acid, and α-ketoisovaleric acid, demonstrated exceptionally high sensitivity and specificity in both cohorts, nearing 100%. In contrast, the clinical indicators, including HBcAb, log(HBsAg), and HBeAb, demonstrated lower and inconsistent sensitivity and specificity between the discovery and validation cohorts. Using HBcAb as a marker, the sensitivity was 87.5% with 76.9% specificity in the discovery cohort; however, the sensitivity dropped to 46.7% with 91.7% specificity in the validation cohort. Using log(HBsAg), the sensitivity was 84.6% with 69.2% specificity in the discovery cohort, compared to 85.7% sensitivity and 83.3% specificity in the validation cohort. For HBeAb, the separation of Hrp and Lrp had a sensitivity of 87.5% with 69.2% specificity in the discovery cohort, while the validation cohort showed 86.7% sensitivity and 91.7% specificity.

Next-Generation Sequencing (NGS) in lung cancer care: Advantages, applications, and challenges

Lung cancer, a prevalent and lethal malignancy, has witnessed significant therapeutic strides driven by the identification and application of predictive biomarkers. These biomarkers, such as EGFR mutations and ALK rearrangements, guide treatment decisions and improve patient outcomes. The advent of Next-Generation Sequencing (NGS) has revolutionized lung cancer diagnostics and treatment by enabling comprehensive genomic profiling. NGS, a high- -throughput sequencing technology, stands out by simultaneously analyzing multiple genes and addressing the genetic diversity inherent in lung tumors. This article explores the transformative role of NGS in lung cancer, emphasizing its applications in personalized treatment strategies, early detection, immunotherapy selection, and disease monitoring through liquid biopsies. Despite challenges, NGS emerges as a beacon of hope, propelling precision oncology and promising improved patient outcomes. This article underscores the impact of NGS in reshaping the landscape of lung cancer care, guiding therapeutic decisions, and advancing scientific understanding.

Consensus framework for conducting phase I/II clinical trials for children, adolescents, and young adults with pediatric low-grade glioma: Guidelines established by the International Pediatric Low-Grade Glioma Coalition Clinical Trial Working Group.

Within the last few decades, we have witnessed tremendous advancements in the study of pediatric low-grade gliomas (pLGG), leading to a much-improved understanding of their molecular underpinnings. Consequently, we have achieved successful milestones in developing and implementing targeted therapeutic agents for treating these tumors. However, the community continues to face many unknowns when it comes to the most effective clinical implementation of these novel targeted inhibitors or combinations thereof. Questions encompassing optimal dosing strategies, treatment duration, methods for assessing clinical efficacy, and the identification of predictive biomarkers remain unresolved. Here, we offer the consensus of the international pLGG coalition (iPLGGc) clinical trial working group on these important topics and comment on clinical trial design and endpoint rationale. Throughout, we seek to standardize the global approach to early clinical trials (phase I and II) for pLGG, leading to more consistently interpretable results as well as enhancing the pace of novel therapy development and encouraging an increased focus on functional endpoints as well and quality of life for children faced with this disease.

Antibody-drug conjugates in urinary tumors: clinical application, challenge, and perspectives.

Urinary tumors primarily consist of kidney, urothelial, and prostate malignancies, which pose significant treatment challenges, particularly in advanced stages. Antibody-drug conjugates (ADCs) have emerged as a promising therapeutic approach, combining monoclonal antibody specificity with cytotoxic chemotherapeutic payloads. This review highlights recent advancements, opportunities, and challenges in ADC application for urinary tumors. We discuss the FDA-approved ADCs and other novel ADCs under investigation, emphasizing their potential to improve patient outcomes. Furthermore, we explore strategies to address challenges, such as toxicity management, predictive biomarker identification, and resistance mechanisms. Additionally, we examine the integration of ADCs with other treatment modalities, including immune checkpoint inhibitors, targeted therapies, and radiation therapy. By addressing these challenges and exploring innovative approaches, the development of ADCs may significantly enhance therapeutic options and outcomes for patients with advanced urinary tumor.

Identification of CT-based non-invasive radiomic biomarkers for overall survival prediction in oral cavity squamous cell carcinoma

This study addresses the limited non-invasive tools for Oral Cavity Squamous Cell Carcinoma (OSCC) survival prediction by identifying Computed Tomography (CT)-based biomarkers to improve prognosis prediction. A retrospective analysis was conducted on data from 149 OSCC patients, including CT radiomics and clinical information. An ensemble approach involving correlation analysis, score screening, and the Sparse-L1 algorithm was used to select functional features, which were then used to build Cox Proportional Hazards models (CPH). Our CPH achieved a 0.70 concordance index in testing. The model identified two CT-based radiomics features, Gradient-Neighboring-Gray-Tone-Difference-Matrix-Strength (GNS) and normalized-Wavelet-LLL-Gray-Level-Dependence-Matrix-Large-Dependence-High-Gray-Level-Emphasis (HLE), as well as stage and alcohol usage, as survival biomarkers. The GNS group with values above 14 showed a hazard ratio of 0.12 and a 3-year survival rate of about 90%. Conversely, the GNS group with values less than or equal to 14 had a 49% survival rate. For normalized HLE, the high-end group (HLE > − 0.415) had a hazard ratio of 2.41, resulting in a 3-year survival rate of 70%, while the low-end group (HLE ≤ − 0.415) had a 36% survival rate. These findings contribute to our knowledge of how radiomics can be used to predict the outcome so that treatment plans can be tailored for patients people with OSCC to improve their survival.

Predictive biomarker identification of patient-reported outcomes in persons with MS treated with AHSCT

Predictive biomarker identification of patient-reported outcomes in persons with MS treated with AHSCT

The FLT3-like Gene Expression Signature Predicts Response to Quizartinib in Wild-Type FLT3 Acute Myeloid Leukemia: An Analysis of the Pethema Quiwi Trial

Background The identification of predictive biomarkers is crucial for guiding treatment decisions in acute myeloid leukemia (AML). Previously, we identified a FLT3-like gene expression signature in FLT3 wild-type AML patients, which clustered a variable proportion of FLT3 wild-type patients with FLT3-ITD and TKD mutated patients. The QUIWI trial was a randomized, placebo-controlled, phase II study preliminary showing a significant increase in overall survival (OS) in wild-type FLT3 AML patients treated with the FLT3 inhibitor quizartinib (Quiza) plus standard chemotherapy. This preplanned correlative study was designed to assess the value of the FLT3-like signature to predict responses to Quiza. Methods We performed RNA sequencing (RNAseq) analysis on a subset of patients from the clinical trial. RNA was extracted using standard methods, followed by assessment of nucleic acid integrity (TapeStation) and quantification (Qubit). Total mRNA sequencing was performed using polyA RNAseq with TruSeq technology. A total of 206 adequate samples from bone marrow and peripheral blood were sequenced (161 from FLT3-ITD negative enrolled in QUIWI; and 55 from FLT3-ITD positive patients who were screen failure of the QUIWI trial by this reason). The sequences were aligned to the GRCh37 reference genome using the Hisat algorithm. Gene expression quantification was performed using the Bioconductor workflow, and gene expression estimates (FPKM) were obtained. The gene expression estimates were log2 normalized. Finally, those genes mapping to the original FLT3-like signature (595 genes) were selected for downstream analysis. Clustering was based on the hierarchical method, using standard euclidean distance metrics. OS was defined as time from start of screening to death. Event-free survival (EFS) was defined as time from randomization to failure to achieve CR/CRi after 1 or 2 cycles, death in CR/CRi, or relapse (whichever occurred the first). Relapse-free survival was defined as time from randomization to disease relapse or death by any cause. Results Among the total 206 patients, a cluster of 54.37% (N=112) was enriched in FLT3-mutant cases (71.11% of cases, Figure 1A). This subgroup comprised 49.67% of wild-type FLT3 cases (N=80), hereafter FLT3-like patients. In the group of not FLT3-like patients, no differences were identified between the placebo and Quiza group in the total number of deaths (Fisher's p-value, 0.63), EFS (cox p-value, 0.83; HR 1.07 [0.56-2.06]), RFS (cox p-value 0.76; HR 0.88 [0.38-2.01]) and OS (cox p-value, 0.62; HR 1.22 [0.55-2.67]). Among FLT3-like patients, significant differences were identified in the total number of deaths (Fisher's p-value, 0.004), EFS (cox p-value, 0.009; HR 0.45 [0.25-0.82], RFS (cox p-value 0.01, HR 0.37 [0.18-0.79]) and OS (cox p-value 0.01, HR, 0.41 [0.20-0.84]) ( Figure 1B). No statistically significant association was observed between the FLT3-like pattern and the ELN-17 classification: 30.4% were low risk, 40.5% intermediate risk, 29.1% high risk. Conclusion The FLT3-like gene expression signature successfully identified a subset of patients who derived the most benefit from Quiz, while patients without the FLT3-like signature did not demonstrate a benefit compared with placebo. These findings support the use of the FLT3-like signature as a potential biomarker to identify those wild-type FLT3 AML patients who may benefit from Quiz, providing a valuable insight for personalized treatment in AML.

Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis

Background: Endometriosis (EM) is a common gynecological condition in women of reproductive age, with diverse causes and a not yet fully understood pathogenesis. Traditional diagnostics rely on single diagnostic biomarkers and does not integrate a variety of different biomarkers. This study introduces multiple machine learning techniques, enhancing the accuracy of predictive models. A novel diagnostic approach that combines various biomarkers provides a new clinical perspective for improving the diagnostic efficiency of endometriosis, holding significant potential for clinical application.Methods: In this study, GSE51981 was used as a test set, and 11 machine learning algorithms (Lasso, Stepglm, glmBoost, Support Vector Machine, Ridge, Enet, plsRglm, Random Forest, LDA, XGBoost, and NaiveBayes) were employed to construct 113 predictive models for endometriosis. The optimal model was determined based on the AUC values derived from various algorithms. These genes were then evaluated using nine machine learning algorithms (Random Forest, SVM, Gradient Boosting Machine, LASSO, XGB, NNET, Generalized Linear Model, KNN, and Decision Tree) to assess significance scores and identify diagnostic genes for each algorithm. The diagnostic value of these genes was further validated in external datasets from GSE7305, GSE11691, and GSE120103.Results: Analysis of the GSE51981 dataset revealed 62 DEGs. The Stepglm [Both] and plsRglm algorithms identified 30 genes with the most potential using the AUC evaluation. Subsequently, nine machine learning algorithms were applied to select diagnostic genes, leading to the identification of five key diagnostic genes using the LASSO algorithm. The ADAT1 gene exhibited the best single-gene predictive performance, with an AUC of 0.785. A combination of genes (FOS, EPHX1, DLGAP5, PCSK5, and ADAT1) achieves an AUC of 0.836 in the test dataset. Moreover, these genes consistently exhibited an AUC exceeding 0.78 in all validation datasets, demonstrating superior predictive performance. Furthermore, correlation analysis with immune infiltration strengthened their predictive value by demonstrating the close relationship of the diagnostic genes with immune infiltrating cells.Conclusion: A combination of biomarkers consisting of FOS, EPHX1, DLGAP5, PCSK5, and ADAT1 can serve as a diagnostic tool for endometriosis, enhancing diagnostic efficiency. The association of these genes with immune infiltrating cells reveals their potential role in the pathogenesis of endometriosis, providing new insights for early detection and treatment.

Cancer Cachexia: New Insights and Future Directions

Cancer remains a major health problem and is associated with cachexia in up to 80% of cases, leading to decreased survival and quality of life. Cachexia involves complex metabolic disturbances in both protein and energy balance, muscle wasting phenomena, weight loss, systemic inflammation, overall decreased performance status, and tolerability to treatment. The clinical impact of cancer cachexia is very complex, with early detection of cachectic patients and identification of predictive biomarkers being two key factors for improving survival. Thus, a better understanding of the complexity of cancer cachexia phenomena and its main pathophysiological mechanism is much needed. Our review highlights the most important information about cancer cachexia, aiming to disseminate updated research findings about this highly deadly condition.

Unsupervised machine learning models reveal predictive clinical markers of glioblastoma patient survival using white blood cell counts prior to initiating chemoradiation.

Glioblastoma is a malignant brain tumor requiring careful clinical monitoring even after primary management. Personalized medicine has suggested the use of various molecular biomarkers as predictors of patient prognosis or factors utilized for clinical decision-making. However, the accessibility of such molecular testing poses a constraint for various institutes requiring identification of low-cost predictive biomarkers to ensure equitable care. We collected retrospective data from patients seen at Ohio State University, University of Mississippi, Barretos Cancer Hospital (Brazil), and FLENI (Argentina) who were managed for glioblastoma-amounting to 581 patient records documented using REDCap. Patients were evaluated using an unsupervised machine learning approach comprised of dimensionality reduction and eigenvector analysis to visualize the inter-relationship of collected clinical features. We discovered that the serum white blood cell (WBC) count of a patient during baseline planning for treatment was predictive of overall survival with an over 6-month median survival difference between the upper and lower quartiles of WBC count. By utilizing an objective PD-L1 immunohistochemistry quantification algorithm, we were further able to identify an increase in PD-L1 expression in glioblastoma patients with high serum WBC counts. These findings suggest that in a subset of glioblastoma patients the incorporation of WBC count and PD-L1 expression in the brain tumor biopsy as simple biomarkers predicting glioblastoma patient survival. Moreover, machine learning models allow the distillation of complex clinical data sets to uncover novel and meaningful clinical relationships.

Trajectories of immune-related serum proteins and quality of life in patients with pancreatic and other periampullary cancer: the CHAMP study

BackgroundThere is still a profound lack of efficient therapeutic strategies against pancreatic and other periampullary adenocarcinoma. Surgery is seldom possible, leaving palliative chemotherapy the only option for most patients. Chemotherapy treatment is however often accompanied by serious side-effects, and the identification of biomarkers for early prediction of disease and treatment-associated symptoms could help alleviate patient suffering. This study investigated the dynamic interrelationship between immune-related serum proteins, routine biomarkers, and health-related quality of life (HRQoL) factors during chemotherapy treatment of patients enrolled in the prospective, observational study Chemotherapy, Host response And Molecular dynamics in Periampullary cancer (CHAMP).MethodsProximity extension assay was applied to analyse 92 immune-associated proteins in longitudinal serum samples from 75 patients, 18 treated with curative and 57 with palliative intent. HRQoL data were available from all patients at baseline (BL), from 41 patients at three months, and from 23 patients at six months. Information on routine laboratory parameters albumin, CA19-9, CEA and CRP were collected from medical charts.ResultsIn total nine proteins; chemokine (C–C motif) ligand 23 (CCL23), cluster of differentiation 4 (CD4), cluster of differentiation 28 (CD28), decorin (DCN), galectin-1 (Gal-1), granzyme B (GZMB), granzyme H (GZMH), matrix metallopeptidase 7 (MMP7), and monocyte chemotactic protein-1 (MCP-1) were strongly correlated (Spearman’s Rho ≤ -0.6 or ≥ 0.6) with either cognitive functioning (DCN), emotional functioning (DCN, MCP-1), dyspnoea (CD28, GZMB, GZMH) or insomnia (CCL23, CD4, Gal-1, MMP7) during treatment. Associations between routine laboratory parameters (CA 19–9, CA-125, CRP, CEA and albumin) and HRQoL factors were overall weaker. None of the investigated proteins were associated with pain.ConclusionsThis is, to our knowledge, the first study exploring associations between serum biomarkers and HRQoL in patients with pancreatic or other periampullary cancer, and some findings merit further validation. The associations of DCN and MCP-1with impaired cognitive and/or emotional functioning are of particular interest, given their established link to various neurodegenerative conditions. Chemotherapy is known to cause persistent cognitive dysfunction with effects on memory and executive function, referred to as “chemo brain”. It would therefore be of great value to identify biomarkers for early detection and management of this debilitating condition.Trial registrationClinical Trial Registration: NCT03724994.

Whole exome sequencing uncovers HRAS mutations as potential mediators of resistance to metronomic chemotherapy

ObjectivesThe aim of this pilot study is to identify the genetic factors that contribute to the response of metronomic chemotherapy in head and neck squamous cell carcinoma (HNSCC) patients using whole-exome sequencing (WES). This study would facilitate the identification of predictive biomarkers, which would enable personalized treatment strategies and improve treatment outcomes for patients with HNSCC. Materials and MethodsWe have selected patients with recurrent head and neck cancer who underwent metronomic chemotherapy. Sequential tumor biopsies were collected from the patients at different stages of treatment to capture the genomic alterations and tumor evolution during metronomic chemotherapy and sequenced using WES. ResultsWe identified several known HNSCC hallmark genes reported in COSMIC, including KMT2B, NOTCH1, FAT1, TP53, HRAS, CASP8, and CDKN2A. Copy number alteration analysis revealed amplifications and deletions in several oncogenic and tumor suppressor genes. COSMIC Mutational Signature 15 associated with defective DNA mismatch repair was enriched in 73% of HNSCC samples. Further, the comparison of genomic alterations between responders and non-responders identified HRAS gene uniquely mutated in non-responders that could potentially contribute to resistance against metronomic chemotherapy. DiscussionOur findings corroborate the molecular heterogeneity of recurrent HNSCC tumors and establish an association between HRAS mutations and resistance to metronomic chemotherapy, suggesting HRAS as a potential therapeutic target. Combining HRAS inhibitors with metronomic regimens could improve treatment sensitivity in HRAS-mutated HNSCC patients. Further studies are needed to fully elucidate the genomic mechanisms underlying the response to metronomic chemotherapy.

35 A novel therapeutic approach using taurodeoxycholate, a GPCR19R agonist, for atopic dermatitis: a randomized, double-blind, placebo-controlled phase II trial with identification of predictive biomarkers

35 A novel therapeutic approach using taurodeoxycholate, a GPCR19R agonist, for atopic dermatitis: a randomized, double-blind, placebo-controlled phase II trial with identification of predictive biomarkers

Predictive Biomarkers for Immunotherapy in Gastric Cancer: Current Status and Emerging Prospects.

Gastric cancer presents substantial management challenges, and the advent of immunotherapy has ignited renewed hope among patients. Nevertheless, a significant proportion of patients do not respond to immunotherapy, and adverse events associated with immunotherapy also occur on occasion, underscoring the imperative to identify suitable candidates for treatment. Several biomarkers, including programmed death ligand-1 expression, tumor mutation burden, mismatch repair status, Epstein-Barr Virus infection, circulating tumor DNA, and tumor-infiltrating lymphocytes, have demonstrated potential in predicting the effectiveness of immunotherapy in gastric cancer. However, the quest for the optimal predictive biomarker for gastric cancer immunotherapy remains challenging, as each biomarker carries its own limitations. Recently, multi-omics technologies have emerged as promising platforms for discovering novel biomarkers that may help in selecting gastric cancer patients likely to respond to immunotherapy. The identification of reliable predictive biomarkers for immunotherapy in gastric cancer holds the promise of enhancing patient selection and improving treatment outcomes. In this review, we aim to provide an overview of clinically established biomarkers of immunotherapy in gastric cancer. Additionally, we introduce newly reported biomarkers based on multi-omics studies in the context of gastric cancer immunotherapy, thereby contributing to the ongoing efforts to refine patient stratification and treatment strategies.

Identification of predictive biomarkers for endometrial cancer diagnosis and treatment response monitoring using plasma metabolome profiling

BackgroundEndometrial cancer (EMC) is the most common female genital tract malignancy with an increasing prevalence in many countries including Japan, a fact that renders early detection and treatment necessary to protect health and fertility. Although early detection and treatment are necessary to further improve the prognosis of women with endometrial cancer, biomarkers that accurately reflect the pathophysiology of EMC patients are still unclear. Therefore, it is clinically critical to identify biomarkers to assess diagnosis and treatment efficacy to facilitate appropriate treatment and development of new therapies for EMC.MethodsIn this study, wide-targeted plasma metabolome analysis was performed to identify biomarkers for EMC diagnosis and the prediction of treatment responses. The absolute quantification of 628 metabolites in plasma samples from 142 patients with EMC was performed using ultra-high-performance liquid chromatography with tandem mass spectrometry.ResultsThe concentrations of 111 metabolites increased significantly, while the concentrations of 148 metabolites decreased significantly in patients with EMC compared to healthy controls. Specifically, LysoPC and TGs, including unsaturated fatty acids, were reduced in patients with stage IA EMC compared to healthy controls, indicating that these metabolic profiles could be used as early diagnostic markers of EMC. In contrast, blood levels of amino acids such as histidine and tryptophan decreased as the risk of recurrence increased and the stages of EMC advanced. Furthermore, a marked increase in total TG and a decrease in specific TGs and free fatty acids including polyunsaturated fatty acids levels were observed in patients with EMC. These results suggest that the polyunsaturated fatty acids in patients with EMC are crucial for disease progression.ConclusionsOur data identified specific metabolite profiles that reflect the pathogenesis of EMC and showed that these metabolites correlate with the risk of recurrence and disease stage. Analysis of changes in plasma metabolite profiles could be applied for the early diagnosis and monitoring of the course of treatment of EMC patients.

ClinicalOmicsDB: exploring molecular associations of oncology drug responses in clinical trials.

Matching patients to optimal treatment is challenging, in part due to the limited availability of real-world clinical datasets for predictive biomarker identification. The growing integration of omics profiling into clinical trials presents a new opportunity to tackle this challenge. Here, we introduce ClinicalOmicsDB, a web application for exploring molecular associations of oncology drug responses in clinical trials. This database includes transcriptomic data from 40 clinical trial studies, with 5913 patients spanning 11 cancer types. These studies include 67 treatment arms with a variety of chemotherapy, targeted therapy and immunotherapy drugs, and their combinations, which we organize based on an established ontology for easier navigation. The web application provides users with three options to explore molecular associations of oncology drug responses, focusing on studies, treatments or genes, respectively. Gene set analysis further connects treatment response to pathway activity and tumor microenvironment attributes. The user-friendly web interface of ClinicalOmicsDB streamlines interactive analysis. A Rust-based backend speeds up response time, and application programming interfaces and an R package enable programmatic access. We use three case studies to demonstrate the utility of this resource in human cancer studies. ClinicalOmicsDB is freely available at http://trials.linkedomics.org/.