Single Biomarker Research Articles

Where Should I Draw the Line: PET-Driven, Data-Driven, or Manufacturer Cut-Off?

The optimal cut-off for Alzheimer's disease (AD) CSF biomarkers remains controversial. To analyze the performance of cut-off points standardized by three methods: one that optimized the agreement between 11C-Pittsburgh compound B PET (a-PET) and CSF biomarkers (Aβ1-42, pTau, tTau, and Aβ1-42/Aβ1-40 ratio) in our population, called PET-driven; an unbiased cut-off using data from a healthy research cohort, called data-driven, and that provided by the manufacturer. We also compare changes in ATN classification. CSF biomarkers measured by the LUMIPULSE G600II platform and qualitative visualization of amyloid positron emission tomography (a-PET) were performed in all the patients. We established a cut-off for each single biomarker and Aβ1-42/Aβ1-40 ratio that optimized their agreement with a-PET using ROC curves. Sensitivity, Specificity, and Overall Percent of Agreement are assessed using a-PET or clinical diagnosis as gold standard for every cut-off. Also, we established a data-driven cut-off from our cognitively unimpaired cohort. We then analyzed changes in ATN classification. One hundred and ten patients were recruited. Sixty-six (60%) were a-PET positive. PET-driven cut-offs were: pTau > 57, tTau > 362.62, Aβ1-42/Aβ1-40 < 0.069. For a single biomarker, pTau showed the highest accuracy (AUC 0.926). New PET-driven cut-offs classified patients similarly to manufacturer cut-offs (only two patients changed). However, 20 patients (18%) changed when data-driven cut-offs were used. We established our sample's best CSF biomarkers cut-offs using a-PET as the gold standard. These cut-offs categorize better symptomatic subjects than data-driven in ATN classification, but they are very similar to the manufacturer's.

Development and validation of a prognostic prediction model for cervical cancer patients treated with radical radiotherapy: a study based on TCGA database.

Radiotherapy or concurrent chemoradiotherapy is the standard treatment for patients with locally advanced or inoperable cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). However, treatment failure for CESC patients treated with radical radiotherapy still occurs due to local recurrence and distant metastasis. The previous prediction models were focused on all CESC patients, neglecting the prognostic differences under different treatment modalities. Therefore, there is a pressing demand to explore novel biomarkers for the prognosis and sensitivity of radiotherapy in CESC patients treated with radical radiotherapy. As a single biomarker has limited effect in stratifying these patients, our objective was to identify radioresponse-related mRNAs to ameliorate forecast of the prognosis for CESC patients treated with radical radiotherapy. Sample data on CESC patients treated with radical radiotherapy were obtained from The Cancer Genome Atlas (TCGA) database. We randomly separated these patients into a training and test cohorts using a 1:1 ratio. Differential expression analysis was carried out to identify radioresponse-related mRNA sets that were significantly dysregulated between complete response (CR) and radiographic progressive disease (RPD) groups, and univariate Cox regression analyses, least absolute shrinkage and selection operator (LASSO) method and multivariate Cox regression were performed to identify the radioresponse-related signature in the training cohort. we adopted survival analysis to measure the predictive value of the radioresponse-related signature both in the test and entire cohorts. Moreover, we developed a novel nomogram to predict the overall survival (OS) of CESC patients treated with radical radiotherapy. In addition, immune infiltration analysis and Gene Set Enrichment Analysis (GSEA) were conducted to preliminarily explore possible mechanisms. This study included a total of 92 CESC patients subjected to radical radiotherapy. We developed and verified a risk score model based on radioresponse-related mRNA. The radioresponse-related mRNA signature and International Federation of Gynecology and Obstetrics (FIGO) stage were served as independent prognostic factors for CESC patients treated with radical radiotherapy. Moreover, a nomogram integrating radioresponse-related mRNA signature with FIGO stage was established to perform better for predicting 1-, 3-, and 5-year survival rates. Mechanically, the low-risk group under the risk score of this model had a better survival status, and the distribution of CD4 T cells was potentially involved in the regulation of radiotherapy response in CESC, leading to a better survival outcome in the low-risk group. This study presents a new radioresponse-related mRNA signature that shows promising clinical efficacy in predicting the prognosis of CESC patients treated with radical radiotherapy.

An update on endometriosis biomarkers.

Endometriosis is a debilitating gynecologic disorder characterized by chronic pelvic pain, pelvic adhesions and infertility. The gold standard diagnostic modality is histologically by tissue biopsy, although it can be diagnosed empirically if symptoms improve with medical treatment. A delayed diagnosis of endometriosis often leads to a significant impairment in quality of life and work productivity; hence, significant morbidity has been shown to bear a detrimental impact on society and the economy. The ongoing novel investigation into biomarkers for diagnostic or prognostic evaluation of endometriosis may aid in earlier detection, and thereby, improve patient quality-of-life as well as minimize morbidity. Currently, no single biomarker has been validated for endometriosis; however, there are emerging data on the utility of microRNA for diagnosis and prognosis of disease activity. In this brief review, we will identify and categorize the novel biomarkers for endometriosis.

Dynamic bTMB combined with residual ctDNA improves survival prediction in locally advanced NSCLC patients with chemoradiotherapy and consolidation immunotherapy

BackgroundLiquid biopsy-based biomarkers, including circulating tumor DNA (ctDNA) and blood tumor mutational burden (bTMB), are recognized as promising predictors of prognoses and responses to immune checkpoint inhibitors (ICIs), despite insufficient sensitivity of single biomarker detection. This research aims to determine whether the combinatorial utility of longitudinal ctDNA with bTMB analysis could improve the prognostic and predictive effects. MethodsThis prospective two-center cohort trial, consisting of discovery and validation datasets, enrolled unresectable locally advanced non-small-cell lung cancer (LA-NSCLC) patients and assigned them to chemoradiotherapy (CRT) or CRT + consolidation ICI cohorts from 2018 to 2022. Blood specimens were collected pretreatment, 4 weeks post-CRT, and at progression to assess bTMB and ctDNA using 486-gene next-generation sequencing. Dynamic ∆bTMB was calculated as post-CRT bTMB minus baseline bTMB levels. Decision curve analyses were performed to calculate Concordance index (C-index). ResultsOne hundred twenty-eight patients were enrolled. In the discovery dataset (n = 73), patients treated with CRT and consolidation ICI had significantly longer overall survival (OS; median not reached [NR] vs 20.2 months; P < 0.001) and progression-free survival (PFS; median 25.2 vs 11.4 months; P = 0.011) than those without ICI. Longitudinal analysis demonstrated a significant decrease in ctDNA abundance post-CRT (P < 0.001) but a relative increase with disease progression. Post-CRT detectable residual ctDNA correlated with significantly shorter OS (median 18.3 months vs NR; P = 0.001) and PFS (median 7.3 vs 25.2 months; P < 0.001). For patients with residual ctDNA, consolidation ICI brought significantly greater OS (median NR vs 14.8 months; P = 0.005) and PFS (median 13.8 vs 6.2 months; P = 0.028) benefit, but no significant difference for patients with ctDNA clearance. Dynamic ∆bTMB was predictive of prognosis. Patients with residual ctDNA and increased ∆bTMB (∆bTMB > 0) had significantly worse OS (median 9.0 vs 23.0 months vs NR; P < 0.001) and PFS (median 3.4 vs 7.3 vs 25.2 months; P < 0.001). The combinatorial model integrating post-CRT ctDNA with ∆bTMB had optimal predictive effects on OS (C-index = 0.723) and PFS (C-index = 0.693), outperforming individual features. In the independent validation set, we confirmed residual ctDNA predicted poorer PFS (median 50.8 vs 14.3 months; P = 0.026) but identified more consolidation ICI benefit (median NR vs 8.3 months; P = 0.039). The combined model exhibited a stable predictive advantage (C-index = 0.742 for PFS). ConclusionsThe multiparameter assay integrating qualitative residual ctDNA testing with quantitative ∆bTMB dynamics improves patient prognostic risk stratification and efficacy predictions, allowing for personalized consolidation therapy for LA-NSCLC.

Exploratory study of blood biomarkers in patients with post-stroke epilepsy.

In addition to clinical factors, blood-based biomarkers can provide useful information on the risk of developing post-stroke epilepsy (PSE). Our aim was to identify serum biomarkers at stroke onset that could contribute to predicting patients at higher risk of PSE. From a previous study in which 895 acute stroke patients were followed-up, 51 patients developed PSE. We selected 15 patients with PSE and 15 controls without epilepsy. In a biomarker discovery setting, 5 Olink panels of 96 proteins each, were used to determine protein levels. Biomarkers that were down-regulated and overexpressed in PSE patients, and those that showed the strongest interactions with other proteins were validated using an enzyme-linked immunosorbent assay in samples from 50 PSE patients and 50 controls. A ROC curve analysis was used to evaluate the predictive ability of significant biomarkers to develop PSE. Mean age of the PSE discovery cohort was 68.56 ± 15.1, 40% women and baseline NIHSS 12 [IQR 1-25]. Nine proteins were down-expressed: CASP-8, TNFSF-14, STAMBP, ENRAGE, EDA2R, SIRT2, TGF-alpha, OSM and CLEC1B. VEGFa, CD40 and CCL4 showed greatest interactions with the remaining proteins. In the validation analysis, TNFSF-14 was the single biomarker showing statistically significant downregulated levels in PSE patients (p = 0.006) and it showed a good predictive capability to develop PSE (AUC 0.733, 95% CI 0.601-0.865). Protein expression in PSE patients differs from that of non-epileptic stroke patients, suggesting the involvement of several different proteins in post-stroke epileptogenesis. TNFSF-14 emerges as a potential biomarker for predicting PSE.

SERPINA3, FGA, AGP1, ITIH3 and SAA1 as novel biomarkers for eosinophilic granulomatosis with polyangiitis diagnosis and activity assessment.

To identify novel biomarkers for diagnosis and prediction of active eosinophilic granulomatosis with polyangiitis (EGPA) through data-independent acquisition (DIA) analysis. Plasma from 11 EGPA patients and 10 healthy controls (HCs) were analyzed through DIA to identify potential biomarkers. The results were validated in 32 EGPA patients, 24 disease controls (DCs), and 20 HCs using enzyme-linked immunosorbent assay (ELISA). The receiver operating characteristic (ROC) curve was used to assess the diagnostic value of candidate biomarkers. Thirty-five differentially expressed proteins (DEPs) (24 upregulated and 11 downregulated) were screened between EGPA and HC groups. Five proteins, including serine proteinase inhibitor A3 (SERPINA3), alpha-fibrinogen (FGA), alpha-1 acid glycoprotein 1(AGP1), inter-alpha-trypsin inhibitor heavy chain H3 (ITIH3), and serum amyloid A1 (SAA1), were significantly upregulated in EGPA compared with HCs. Apart from SAA1, all proteins were also higher in EGPA patients compared with DCs. Furthermore, a panel of SERPINA3 and SAA1 exhibited potential diagnostic value for EGPA with an area under the curve (AUC) of 0.953, while a panel of SERPINA3, FGA, AGP1, and ITIH3 showed good discriminative power to differentiate EGPA from DCs with AUC of 0.926. Moreover, SERPINA3, FGA, and AGP levels were significantly higher in active EGPA and correlated well with disease activity. A combination of SERPINA3 and AGP1 exhibited an excellent AUC of 0.918 for disease activity assessment. SERPINA3, FGA, AGP1, ITIH3 and SAA1 were identified as potential biomarkers for EGPA diagnosis and disease activity assessment. Among them, as a single biomarker, SERPINA3 has the best diagnostic performance.

A MODERN NONINVASIVE METHODS OF DIAGNOSIS GENITAL ENDOMETRIOSIS ASSOCIATED WITH INFERTILITY

Endometriosis is a disease characterized by an overgrowth of morphologically and functionally endometrium-like tissue outside the uterine cavity. Early diagnosis of endometriosis is very di ffi cult. The gold standard for diagnosis is the surgical method – laparoscopy, performed for infertility, pelvic pain or the presence of pelvic masses. However, this approach is currently being reconsidered, and the search for endometriosis markers for earlier diagnosis of this disease is relevant. The purpose of the literature review was to summarize the current data on promising noninvasive markers of endometriosis determined in various biological media. Materials and methods – summarized a modern data abot perspective noninvasive markes of genital endometriosis associated with infertility, determined diff erence biological fl uid. Results We evaluated more than 30 biomarkers in peritoneal fl uid, serum, urine, and saliva, as well as their combinations in publications that met the selection criteria. Studies have evaluated the diagnostic value and e ffi cacy of endometriosis biomarkers, but the results of these studies have sometimes been inconsistent. We were unable to identify a single biomarker or combination of biomarkers that was unequivocally clinically useful. The most signifi cant changes in biochemical composition were observed in peritoneal fl uid, but it requires invasive intervention to obtain it. Discussion. Saliva and urine studies have shown promising results in terms of diagnostic accuracy, but the evidence was of low quality for introduction into clinical guidelines. A number of serum biomarkers may be useful either for detecting endometriosis at early stages or for diff erentiating ovarian endometrioma from other benign ovarian masses, but the evidence for meaningful conclusions is insu fficient. Conclusion. None of the biomarkers have shown suffi cient accuracy for clinical use outside of the research environment, yet research in this area remains promising.

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.

Gut barrier dysfunction and the risk of ICU-acquired bacteremia- a case–control study

BackgroundImpaired intestinal barrier function can enable passage of enteric microorganisms into the bloodstream and lead to nosocomial bloodstream infections during critical illness. We aimed to determine the relative importance of gut translocation as a source for ICU-acquired enterococcal bacteremia of unknown origin.MethodsWe conducted a nested case–control study in two mixed medical-surgical tertiary ICUs in the Netherlands among patients enrolled between 2011 and 2018. We selected 72 cases with ICU-acquired bacteremia due to enterococci (which are known gastrointestinal tract commensals) and 137 matched controls with bacteremia due to coagulase-negative staphylococci (CoNS) (which are of non-intestinal origin). We measured intestinal fatty acid-binding protein, trefoil factor-3, and citrulline 48 h before bacteremia onset. A composite measure for Gut Barrier Injury (GBI) was calculated as the sum of standardized z-scores for each biomarker plus a clinical gastrointestinal failure score.ResultsNo single biomarker yielded statistically significant differences between cases and controls. Median composite GBI was higher in cases than in controls (0.58, IQR − 0.36–1.69 vs. 0.32, IQR − 0.53–1.57, p = 0.33) and higher composite measures of GBI correlated with higher disease severity and ICU mortality (p < 0.001). In multivariable analysis, higher composite GBI was not significantly associated with increased occurrence of enterococcal bacteremia relative to CoNS bacteremia (adjusted OR 1.12 95% CI 0.93–1.34, p = 0.22).ConclusionsWe could not demonstrate an association between biomarkers of gastrointestinal barrier dysfunction and an increased occurrence of bacteremia due to gut compared to skin flora during critical illness, suggesting against bacterial translocation as a major vector for acquisition of nosocomial bloodstream infections in the ICU.

Enhancing Diabetic Macular Edema Treatment Outcomes: Exploring the ESASO Classification and Structural OCT Biomarkers.

This study assessed the European School of Advanced Studies in Ophthalmology (ESASO) classification's prognostic value for diabetic macular edema (DME) in predicting intravitreal therapy outcomes. In this retrospective, multicenter study, patients aged > 50years with type 1 or 2 diabetes and DME received intravitreal antivascular endothelial growth factor (anti-VEGF) agents (ranibizumab, bevacizumab, and aflibercept) or steroids (dexamethasone). The primary outcome was visual acuity (VA) change post-treatment, termed as functional response, measured 4-6weeks post-third anti-VEGF or 12-16weeks post-steroid injection, stratified by initial DME stage. Of the 560 eyes studied (62% male, mean age 66.7years), 31% were classified as stage 1 (early), 50% stage 2 (advanced), 17% stage 3 (severe), and 2% stage 4 (atrophic). Visual acuity (VA; decimal) improved by 0.12-0.15 decimals in stages 1-2 but only 0.03 decimal in stage 3 (all p < 0.0001) and 0.01 in stage 4 (p = 0.38). Even in eyes with low baseline VA ≤ 0.3, improvements were significant only in stages 1 and 2 (0.12 and 0.17 decimals, respectively). Central subfield thickness (CST) improvement was greatest in stage 3 (-229µm, 37.6%, p < 0.0001), but uncorrelated with VA gains, unlike stages 1 and 2 (respectively: -142µm, 27.4%; - 5µm, 12%; both p < 0.0001). Stage 4 showed no significant CST change. Baseline disorganization of retinal inner layers and focal damage of the ellipsoid zone/external limiting membrane did not influence VA improvement in stages 1 and 2. Treatment patterns varied, with 61% receiving anti-VEGF and 39% dexamethasone, influenced by DME stage, with no significant differences between therapeutic agents. The ESASO classification, which views the retina as a neurovascular unit and integrates multiple biomarkers, surpasses single biomarkers in predicting visual outcomes. Significant functional improvement occurred only in stages 1 and 2, suggesting reversible damage, whereas stages 3 and 4 likely reflect irreversible damage.

Prediction performance comparison of biomarkers for response to immune checkpoint inhibitors in advanced non-small cell lung cancer.

The aim of the present study was to compare the predictive accuracy of PD-L1 immunohistochemistry (IHC), tissue or blood tumor mutation burden (tTMB, bTMB), gene expression profile (GEP), driver gene mutation, and combined biomarkers for immunotherapy response of advanced non-small cell lung cancer (NSCLC). In part 1, clinical trials involved with predictive biomarker exploration for immunotherapy in advanced NSCLC were included. The area under the curve (AUC) of the summary receiver operating characteristic (SROC), sensitivity, specificity, likelihood ratio and predictive value of the biomarkers were evaluated. In part 2, public datasets of immune checkpoint inhibitor (ICI)-treated NSCLC involved with biomarkers were curated (N = 871). Odds ratio (OR) of the positive versus negative biomarker group for objective response rate (ORR) was measured. In part 1, the AUC of combined biomarkers (0.75) was higher than PD-L1 (0.64), tTMB (0.64), bTMB (0.68), GEP (0.67), and driver gene mutation (0.51). Combined biomarkers also had higher specificity, positive likelihood ratio and positive predictive value than single biomarkers. In part 2, the OR of combined biomarkers of PD-L1 plus TMB (PD-L1 cutoff 1%, 0.14; cutoff 50% 0.13) was lower than that of PD-L1 (cutoff 1%, 0.33; cutoff 50% 0.24), tTMB (0.28), bTMB (0.48), EGFR mutation (0.17) and KRAS mutation (0.47), for distinguishing ORR of patients after immunotherapy. Furthermore, positive PD-L1, tTMB-high, wild-type EGFR, and positive PD-L1 plus TMB were associated with prolonged progression-free survival (PFS). Combined biomarkers have superior predictive accuracy than single biomarkers for immunotherapy response of NSCLC. Further investigation is warranted to select optimal biomarkers for various clinical settings.

INFLAMMATION's cognitive impact revealed by a novel "Line of Identity" approach.

Dementia is an "overdetermined" syndrome. Few individuals are demented by any single biomarker, while several may independently explain small fractions of dementia severity. It may be advantageous to identify individuals afflicted by a specific biomarker to guide individualized treatment. We aim to validate a psychometric classifier to identify persons adversely impacted by inflammation and replicate it in a second cohort. Secondary analyses of data collected by the Texas Alzheimer's Research and Care Consortium (TARCC) (N = 3497) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) (N = 1737). Two large, well-characterized multi-center convenience samples. Volunteers with normal cognition (NC), Mild Cognitive Impairment (MCI) or clinical "Alzheimer's Disease (AD)". Participants were assigned to "Afflicted" or "Resilient" classes on the basis of a psychometric classifier derived by confirmatory factor analysis. The groups were contrasted on multiple assessments and biomarkers. The groups were also contrasted regarding 4-year prospective conversions to "AD" from non-demented baseline diagnoses (controls and MCI). The Afflicted groups were predicted to have adverse levels of inflammation-related blood-based biomarkers, greater dementia severity and greater risk of prospective conversion. In ADNI /plasma, 47.1% of subjects were assigned to the Afflicted class. 44.6% of TARCC's subjects were afflicted, 49.5% of non-Hispanic Whites (NHW) and 37.2% of Mexican Americans (MA). There was greater dementia severity in the Afflicted class [by ANOVA: ADNI /F(1) = 686.99, p <0.001; TARCC /F(1) = 1544.01, p <0.001]. "INFLAMMATION" factor composite scores were significantly higher (adverse) in Afflicted subjects [by ANOVA in ADNI /plasma F(1) = 1642.64, p <0.001 and in TARCC /serum F(1) = 3059.96, p <0.001]. Afflicted cases were more likely to convert to AD in the next four years [by Cox's F, ADNI /plasma: F (252, 268) = 3.74 p < 0.001; TARCC /serum: F (160, 134) = 3.03, p < 0.001 (in TARCC's entire sample), F (110, 90) = 4.92, p <0.001 in NHW, and F(50, 44) = 2.13, p = 0.006 in MA]. The proportions converting were similar among afflicted NHW in both cohorts /biofluids but MA exhibited a lower risk (7% in TARCC /serum at 48 months). Our inflammation-specific psychometric classifier selects individuals with pre-specified biomarker profiles and predicts conversion to "AD" across cohorts, biofluids, and ethnicities. This algorithm might be applied to any dementia-related biomarker making the psychometric estimation of individual biomarker effects feasible without biomarker assessment. Our approach also distinguishes individuals resilient to individual biomarker effects allowing for more accurate prediction and precision intervention.

Abstract 5185: A multimodal, spatially defined, nanomechanical signature as predictive biomarker for breast cancer outcome: A 20 year survival study

Abstract Background: With the recent advancement in perioperative treatment protocols, the search for clinical biomarkers that can predict overall response and long-term cancer outcome independent from treatment acquires even more importance. Recently, it has become clear that mechanical properties of the tumor microenvironment in solid tumors play a key role in driving outcome, mediating immune infiltration and activation, and response to therapy. Moreover, a growing body of evidence supports the understanding that mechanical changes induced by a plethora of therapeutic regimens should be leveraged to elicit favorable outcome. Methods: In this work we propose a dual approach to reveal mechanical biomarkers predictive of long-term outcome in solid cancers. Our first approach is based on a newly developed, 60 channel, biomechanical imaging mass cytometry panel to elucidate biomechanical cues that drive tumor aggressiveness and immune infiltration. Our second approach utilizes the AFM-based Automated and Reliable Tissue Diagnostics (ARTIDIS) investigational device to measure mechanical properties of fresh clinical biopsy samples at a nanoscale resolution. Our analysis was performed on over 300 clinical samples, from routine baseline clinical biopsies collected from both the Breast Clinic, University Hospital Basel, and the University of Dundee, in the UK. Patients in these two cohorts were treated with either adjuvant or neoadjuvant approaches, including radiation, chemotherapy, hormonal, and surgical treatment regimens, with individual patient follow up as long as 20 years. Results: We demonstrated with two complimentary technologies that biomechanical spatial patterns drive outcome in breast cancer. Our results include a multi-platform elucidation of the biomechanical phenotypes of epithelial to mesenchymal transitions driven by hypoxic environments. Furthermore, we identified several biomechanical subpopulations of cancer associated fibroblasts, and their role in mediating cancer outcome, in relationship with survival time and several clinical variables (e.g. metastasis, nodes, grade). We mechanistically corroborated these results by evaluating the immune modulatory effect of the identified biomechanical microenvironments in breast cancer. Lastly, we correlated the identified biomechanical microenvironments with the ARTIDIS nanomechanical signature as a clinically integrated, rapid, single biomechanics signature at the nanoscale. Conclusion: Our results further validate the importance of biomechanics in driving tumor response and immune infiltration across a variety of clinical treatment setting. These result support the integration of ARTIDIS nanomechanical signature in clinical settings as a single predictive biomarker able to rapidly and accurately capture biomechanical alterations that drive cancer outcome. Citation Format: Sara Nizzero, Maria Pelaez Soni, Gregory Zaugg, Yitian Xu, Licheng Zhang, Junjun Zheng, Lee B. Jordan, Colin A. Purdie, Philip R. Quinlan, Chandandeep Nagi, Karla A. Sepulveda, Philipp Oertle, Tobias A. Appenzeller, Marko Loparic, Shu-Hsia Chen, Vittorio Cristini, Marija Plodinec, Alastair M. Thompson. A multimodal, spatially defined, nanomechanical signature as predictive biomarker for breast cancer outcome: A 20 year survival study [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 5185.

Abstract 5197: Identification of novel molecular biomarkers for predicting postoperative recurrence of ampulla of Vater carcinoma

Abstract Background: Ampulla of Vater (AoV) carcinoma is a rare tumor that accounts for 20 % of periampullary cancers. Surgical resection is the only treatment that can expect cure of disease, and there is still an unmet need to predict postoperative recurrence. In this study, we investigated the novel molecular biomarkers that can predict the risk of recurrence after surgery with AoV carcinoma. Methods: In this retrospective study, we collected the surgically resected tissue and medical records of patients who were diagnosed with AoV carcinoma at Samsung Medical Center from January 2005 to December 2020. Patients were classified patients according to the initial stage and experience of tumor recurrence. Whole transcriptomic analysis using primary AOV tumor and recurred tumor tissue samples was conducted to identify the molecular biomarker for tumor recurrence. Results: A total of 40 tumor tissue from primary site in non-recurrence group, 22 tumor tissue from primary site in recurrence group, and paired 22 tumor tissue from recurrence site were analyzed. A total of 1005 genes differentially expressed between primary tumor of non-recurrence group and recurrence group were found. Gene set enrichment analysis showed that pathways related to fatty acid metabolism, adipogenesis, oxidative phosphorylation, and hypoxia were upregulated, and pathways associated with interferon gamma response, and mTOR signaling were downregulated in recurrence group. These pathways were significantly affecting the recurrence free survival which was verified with TCGA data, and we further identified some candidate genes including PIM3, IGFBP4, SLC19A1, HIF1A and CXCL11 to predict tumor recurrence . Moreover, we found candidates for single gene level biomarkers to predict early tumor recurrence in N0 stage. Low expression of UQCC3 and high expression of CLCNKB was related to early tumor recurrence. Conclusions: We found the candidates for novel molecular level biomarker to predict postoperative recurrence of AoV carcinoma. In the future, it is necessary to develop a predictive model for the risk of recurrence after surgical resection for AoV carcinoma. Citation Format: Hyemin Kim, Jin Ho Choi, Hyoung-oh Jeong, Aryoung Kim, Se-Hoon Lee, Kwang Hyuck Lee, Kyu Taek Lee, Jong Kyun Lee, Semin Lee, Joo Kyung Park. Identification of novel molecular biomarkers for predicting postoperative recurrence of ampulla of Vater carcinoma [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 5197.

Accurate detection depression cell model with a dual-locked fluorescence probe in response to noradrenaline and HClO

Due to the serious harm of depression to human health and quality of life, an accurate diagnosis of depression is warranted. For the complex etiology of depression, a single biomarker diagnostic method often leads to misdiagnosis. As noradrenaline and HClO are closely related to depression, a “dual-locked” fluorescence probe R-NE-HClO for diagnosing of depression through the simultaneous detection of noradrenaline and HClO was designed and synthesized. Fluorescence of R-NE-HClO can only be restored in the presence of both noradrenaline and HClO. The probe demonstrates excellent selectivity for noradrenaline and HClO and low cytotoxicity in cell imaging experiments. It is to be observed that we successfully applied the probe to accurately detect depressed cells which provides a possible tool for diagnosing depression.

Renal graft function in transplanted patients correlates with CD45RC T cell phenotypic signature.

Biomarkers that could predict the evolution of the graft in transplanted patients and that could allow to adapt the care of the patients would be an invaluable tool. Additionally, certain biomarkers can be target of treatments and help to stratify patients. Potential effective biomarkers have been identified but still need to be confirmed. CD45RC, one of the splicing variants of the CD45 molecule, a tyrosine phosphatase that is critical in negatively or positively regulating the TCR and the BCR signaling, is one marker already described. The frequency of CD8+ T cells expressing high levels of CD45RC before transplantation is increased in patients with an increased risk of acute rejection. However, single biomarkers have limited predictive reliability and the correlation of the expression levels of CD45RC with other cell markers was not reported. In this study, we performed a fluorescent-based high dimensional immunophenotyping of T cells on a cohort of 69 kidney transplant patients either with stable graft function or having experienced acute transplant rejection during the first year after transplantation or at the time of rejection. We identified combinations of markers and cell subsets associated with activation/inflammation or Tregs/tolerance (HLA-DR, PD-1, IFNγ, CD28) as significant biomarkers associated to transplant outcome, and showed the importance of cell segregation based on the CD45RC marker to identify the signature of a stable graft function. Our study highlights potential reliable biomarkers in transplantation to predict and/or monitor easily graft-directed immune responses and adapt immunosuppression treatments to mitigate adverse effects.

Non-Invasive Predictive Biomarkers for Immune-Related Adverse Events Due to Immune Checkpoint Inhibitors.

Immune-related adverse events (irAEs) are the most common complication of immune checkpoint inhibitor (ICI) therapy. With the widespread use of ICIs in patients with solid tumors, up to 40% of the patients develop irAEs within five months of treatment, and 11% develop severe irAEs requiring interventions. A predictive test for irAEs would be a crucial tool for monitoring for complications during and after ICI therapy. We performed an extensive review of potential predictive biomarkers for irAEs in patients who received ICI therapy. Currently, only thyroid-stimulating hormone is utilized in common clinical practice. This is due to the unavailability of commercial tests and unclear predictive values from various studies. Given the lack of single strong predictive biomarkers, some novel approaches using composite scores using genomic, transcriptomics, cytokine levels, or clinical parameters appear appealing. Still, these have yet to be validated and incorporated into clinical practice. Further research conducted to validate the models before implementing them into real-world settings will be of the utmost importance for irAE prediction.

Screening drug-induced liver injury through two independent parameters of lipid droplets and peroxynitrite with a π-extended coumarin-based NIR fluorescent probe

Drug-induced liver injury (DILI) or hepatotoxicity is a significant concern for public health. However, relying on a single biomarker for early DILI diagnosis poses a high risk of false positives. In this study, we reported a near-infrared fluorescent probe (BCOU-S), which enables independent visualization of LDs status and ONOO fluctuations. BCOU-S was constructed by combining a π-extended coumarin core as the NIR fluorophore and a methyl thioether group as the ONOO recognition site. BCOU-S could emit 655 nm NIR fluorescence excited at 518 nm, with a low detection limit of 27 nM and a large Stokes shift of 137 nm. The response mechanism to ONOO was confirmed by molecular orbital density function theory (DFT) and time-dependent DFT (TD-DFT) calculations. BCOU-S monitors LDs status through co-localization, oleic acid (OA) incubation, and starvation induction experiments. It sensitively distinguishes subtle changes in ONOO induced by different drugs in DILI cell models. In the acetaminophen (APAP)-induced DILI model, BCOU-S reveals significant LDs accumulation and increase in ONOO levels, establishing a clear time-effect and dose-effect relationship. Simultaneously monitoring ONOO levels and LDs accumulation, BCOU-S proves to be a more sensitive and effective tool for early DILI prevention, effectively avoiding false positives caused by changes in a single parameter. BCOU-S is a useful tool for further monitoring early DILI development.

Host single nucleotide polymorphisms and biomarkers of neuronal damage and inflammation in people living with HIV

ObjectivesBlood-brain barrier impairment is frequent in people living with human immunodeficiency virus (PLWHIV), affecting the penetration of target cells and antiretrovirals into the central nervous system, through transporters (e.g. ABCB1), leading to neuroinflammation. This study aimed to identify variants of genes encoding transporters able to predict neuroinflammation biomarker levels. MethodsCerebrospinal fluid (CSF) and plasma samples were obtained from PLWHIV. The CSF biomarkers were quantified by commercial assays. Genetic variants were evaluated through real-time polymerase chain reaction (PCR). ResultsA total of 107 PLWHIV (163 samples) were included in the study: 79% were male, median age was 48.5 years, CD4% was 25%, and HIV-associated neurocognitive disorder (HAND) was observed in 17.8%. The ABCB1 2677G>T genetic variant showed a different allelic distribution according to the clinical group (P = 0.026). In linear regression analyses, HIV-related central nervous system disorders, ABCG2 1194+928CC genotype, log viral load, CSF-to-serum albumin ratio, β-1,42 levels, and CSF proteins were retained in the final model as factors independently associated with CSF neopterin levels; CSF proteins and integrase inhibitor use were associated with CSF tau level in the multivariate model. Phospho-tau regression analysis reported the ABCB1 2677GT/TT genotype and CSF proteins as predictors in the final model; sex, protease inhibitors, neopterin, and ABCB1 2677 GT/ TT genotype were predictors in the multivariate regression for β-1,42. ConclusionsFor the first time, pharmacogenetic and clinical features were found to be predictors of neuro-inflammation biomarkers.

Associations of Kidney Tubular Biomarkers With Incident Macroalbuminuria and Sustained Low eGFR in DCCT/EDIC.

Tubulointerstitial injury contributes to diabetic kidney disease (DKD) progression. We tested tubular biomarker associations with DKD development in type 1 diabetes (T1D). We performed a case-cohort study examining associations of tubular biomarkers, measured across seven time points spanning ∼30 years, with incident macroalbuminuria ("severely elevated albuminuria," urinary albumin excretion rate [AER] ≥300 mg/day) and sustained low estimated glomerular filtration rate (eGFR) (persistent eGFR <60 mL/min/1.73 m2) in the Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) study. Biomarkers included KIM-1 and sTNFR1 in serum/plasma, MCP-1 and EGF in urine, and a composite tubular secretion score reflecting secreted solute clearance. We assessed biomarkers using single values, as mean values from consecutive time points, and as change over consecutive time points, each as time-updated exposures. At baseline, mean diabetes duration was 5.9 years, with mean HbA1c 8.9%, eGFR 125 mL/min/1.73 m2, and AER 16 mg/day. There were 4.8 and 3.5 cases per 1,000 person-years of macroalbuminuria and low eGFR, respectively. Assessed according to single biomarker values, KIM-1 was associated with risk of subsequent macroalbuminuria and low eGFR (hazard ratio [HR] per 20% higher biomarker 1.11 [95% CI 1.06, 1.16] and 1.12 [1.04, 1.21], respectively) and sTNFR1 was associated with subsequent macroalbuminuria (1.14 [1.03, 1.25]). Mean KIM-1 and EGF-to-MCP-1 ratio were associated with subsequent low eGFR. In slope analyses, increases in KIM-1 and sTNFR1 were associated with subsequent macroalbuminuria (per 20% biomarker increase, HR 1.81 [1.40, 2.34] and 1.95 [1.18, 3.21]) and low eGFR (2.26 [1.65, 3.09] and 2.94 [1.39, 6.23]). Serial KIM-1 and sTNFR1 are associated with incident macroalbuminuria and sustained low eGFR in T1D.