Combination Of Biomarkers Research Articles

Improved diagnostic markers for invasive pulmonary aspergillosis in COPD patients.

The prevalence of invasive pulmonary aspergillosis (IPA) among patients with chronic obstructive pulmonary disease (COPD) is steadily increasing, leading to high mortality. Although early diagnosis can significantly reduce mortality, the efficacy of current diagnostic methods is limited. Consequently, there is a need for novel approaches for early IPA detection. This retrospective study involved 383 hospitalized COPD patients with GOLD stages III and IV. The IPA group (67 patients) and non-IPA group (316 patients) were identified at the First Affiliated Hospital of Guangzhou Medical University between January 2016 and February 2022. We analyzed common serological indicators in our hospital to identify predictive indicators for the early diagnosis of IPA in COPD patients. The sensitivity and specificity of C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), procalcitonin (PCT), lactate dehydrogenase (LDH), and ceruloplasmin (CER) for diagnosing IPA in COPD patients were as follows: CRP (91.2%, 57.7%), ESR (77.5%, 73.0%), PCT (60.5%, 71.4%), LDH (50.0%, 88.8%), and CER (60.7%, 74.3%). Combinations of biomarkers, such as CRP-ESR, CRP-LDH, ESR-LDH, ESR-CER, and LDH-CER, showed promising diagnostic potential, with larger area under the curve (AUC) values for IPA diagnosis in COPD patients. However, no statistically significant difference was observed between the diagnostic efficacy of single biomarkers and combined biomarkers. Notably, compared to those in the unassisted ventilation group, the patients in the assisted ventilation group (including noninvasive ventilation and tracheal intubation/incision-assisted ventilation group) exhibited significantly greater PCT and LDH levels, while the CER significantly decreased (p=0.021). There were no significant differences in biomarker levels between the ICU group and the non-ICU group. CRP (p<0.01), ESR (p=0.028), PCT (p<0.01), and CER (p<0.01) were positively correlated with hospitalization duration, whereas LDH was not correlated with hospitalization duration. Our study highlights the diagnostic potential of CRP, ESR, PCT, LDH, and CER for IPA in COPD patients. CRP and LDH can also initially predict the need for assisted ventilation, while CRP can initially estimate the length of hospitalization. This study represents the first report of the potential of CER for diagnosing IPA, suggesting its significance for further research.

Performance of Febrile Infant Algorithms by Duration of Fever.

To analyze the performance of commonly used blood tests in febrile infants ≤90 days of age to identify patients at low risk for invasive bacterial infection (bacterial pathogen in blood or cerebrospinal fluid) by duration of fever. We conducted a secondary analysis of a prospective single-center registry that includes all consecutive infants ≤90 days of age with fever without a source evaluated at 1 pediatric emergency department between 2008 and 2021. We defined 3 groups based on caregiver-reported hours of fever (<2, 2-12, and ≥12) and analyzed the performance of the biomarkers and Pediatric Emergency Care Applied Research Network, American Academy of Pediatrics, and Step-by-Step clinical decision rules. We included 2411 infants; 76 (3.0%) were diagnosed with an invasive bacterial infection. The median duration of fever was 4 (interquartile range, 2-12) hours, with 633 (26.3%) patients with fever of <2 hours. The area under the curve was significantly lower in patients with <2 hours for absolute neutrophil count (0.562 vs 0.609 and 0.728) and C-reactive protein (0.568 vs 0.760 and 0.812), but not for procalcitonin (0.749 vs 0.780 and 0.773). Among well-appearing infants older than 21 days and negative urine dipstick with <2 hours of fever, procalcitonin ≥0.14 ng/mL showed a better sensitivity (100% with specificity 53.8%) than that of the combination of biomarkers of Step-by-Step (50.0% and 82.2%), and of the American Academy of Pediatrics and Pediatric Emergency Care Applied Research Network rules (83.3% and 58.3%), respectively. The performance of blood biomarkers, except for procalcitonin, in febrile young infants is lower in fever of very short duration, decreasing the accuracy of the clinical decision rules.

Bayesian and deep-learning models applied to the early detection of ovarian cancer using multiple longitudinal biomarkers.

Ovarian cancer is the most lethal of all gynecological cancers. Cancer Antigen 125 (CA125) is the best-performing ovarian cancer biomarker which however is still not effective as a screening test in the general population. Recent literature reports additional biomarkers with the potential to improve on CA125 for early detection when using longitudinal multimarker models. Our data comprised 180 controls and 44 cases with serum samples sourced from the multimodal arm of UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). Our models were based on Bayesian change-point detection and recurrent neural networks. We obtained a significantly higher performance for CA125-HE4 model using both methodologies (AUC 0.971, sensitivity 96.7% and AUC 0.987, sensitivity 96.7%) with respect to CA125 (AUC 0.949, sensitivity 90.8% and AUC 0.953, sensitivity 92.1%) for Bayesian change-point model (BCP) and recurrent neural networks (RNN) approaches, respectively. One year before diagnosis, the CA125-HE4 model also ranked as the best, whereas at 2 years before diagnosis no multimarker model outperformed CA125. Our study identified and tested different combination of biomarkers using longitudinal multivariable models that outperformed CA125 alone. We showed the potential of multivariable models and candidate biomarkers to increase the detection rate of ovarian cancer.

Development of an Automatic Rule-Based Algorithm for the Detection of Ovarian Cancer Recurrence From Electronic Health Records.

As the onset of cancer recurrence is not explicitly recorded in the electronic health record (EHR), a high volume of manual chart review is required to detect the cancer recurrence. This study aims to develop an automatic rule-based algorithm for detecting ovarian cancer (OC) recurrence on the basis of minimally preprocessed EHR data. The automatic rule-based recurrence detection algorithm (Auto-Recur), using notes on image reading (positron emission tomography-computed tomography [PET-CT], CT, magnetic resonance imaging [MRI]), biomarker (CA125), and treatment information (surgery, chemotherapy, radiotherapy), was developed to detect the first OC recurrence. Auto-Recur contains three single algorithms (images, biomarkers, treatments) and hybrid algorithms (combinations of the single algorithms). The performance of Auto-Recur was assessed using sensitivity, specificity, and accuracy of the recurrence time detected. The recurrence-free survival probabilities were estimated and compared with the retrospective chart review results. The proposed Auto-Recur considerably reduced human resources and time; it saved approximately 1,340 days when scaled to 100,000 patients compared with the conventional retrospective chart review. The hybrid algorithm on the basis of a combination of image, biomarker, and treatment information was the most efficient (sensitivity: 93.4%, specificity: 97.4%) and precisely captured recurrence time (average time error: 8.5 days). The estimated 3-year recurrence-free survival probability (44%) was close to the estimates by the retrospective chart review (45%, log-rank P value = .894). Our rule-based algorithm effectively captured the first OC recurrence from large-scale EHR while closely approximating the recurrence-free survival estimates obtained by conventional retrospective chart reviews. The study findings facilitate large-scale EHR analysis, enhancing clinical research opportunities.

Biological responses in Danio rerio by the disinfectant SDBS in SARS-CoV-2 pandemic

The use of disinfectants, such as Sodium Dodecylbenzene Sulfonic acid salt (SDBS), has grown since the SARS-CoV-2 pandemic, with environmentally unknown consequences. The present study analyzed SDBS effects in the fish species Danio rerio, using a combination of biomarkers. Our data reported that larvae had their total locomotor activity increased when exposed to 1 mg/L of SDBS, but this parameter was decreased in fish exposed to 5 mg/L. A significant increment of erratic movements was reported in fish exposed to 1 and 5 mg/L of SDBS. These concentrations inhibited CYP1A1/CYP1A2, and of GSTs inhibition, suggesting SDBS is not preferentially biotransformed by these routes. Results concerning the antioxidant defense biomarkers (CAT and GPx) showed no straightforward pattern, suggesting SDBS exposure may have resulted in changes in redox balance. Finally, acetylcholinesterase activity increased. In summary, increased use of SDBS in a near future may result in deleterious effects in environmentally exposed fish.

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.

A new biomarker panel for differential diagnosis of cholangiocarcinoma: Results from an exploratory analysis.

Diagnosis of cholangiocarcinoma (CCA) can be challenging due to unclear imaging criteria and difficulty obtaining adequate tissue biopsy. Although serum cancer antigen 19-9 and carcinoembryonic antigen have been proposed as potential diagnostic aids, their use remains limited by insufficient sensitivity and specificity. This exploratory analysis aimed to identify individual- and combinations of serum biomarkers to distinguish CCA from hepatocellular carcinoma (HCC) and chronic liver disease (CLD) controls using samples from a published study. This prospective, multicenter, case-control study included patients aged ≥18 years at high-risk of HCC. Serum and ethylene diamine tetraacetic acid-plasma samples were collected prior to any treatment and confirmed diagnosis of HCC or CCA. Fourteen biomarkers (measured by electrochemiluminescence immunoassays or enzyme-linked immunosorbent assays) were subjected to univariate analysis and 13 included in a multivariate analysis (per selected combinations and exhaustive search). Overall, 55 CCA, 306 HCC, and 733 CLD control samples were analyzed. For distinguishing CCA from HCC, alpha-fetoprotein and matrix metalloproteinase-2 (MMP-2) showed the best individual performance (area under the curve (AUC) 86.6% and 84.4%, respectively); tissue inhibitor of metalloproteinase-1 (TIMP-1) was most able to distinguish CCA from CLD (AUC 94.5%) and from HCC + CLD (AUC 88.6%). The combination of MMP-2 and TIMP-1 was the best-performing two-marker panel, with AUC >90% for all comparisons. MMP-2 and TIMP-1 are promising biomarkers that could support differential diagnosis of CCA. Incorporating these assays into the diagnostic algorithm could provide additional diagnostic information in a non-invasive, rapid manner, and could supplement existing diagnostic methods.

Fatty acid profiles and stable isotope composition of Antarctic macroalgae: a baseline for a combined biomarker approach in food web studies

AbstractThe Western Antarctic Peninsula supports a diverse assemblage of &gt; 100 described macroalgal species that contribute to the base of coastal food webs, but their contribution to local nearshore food webs is still uncertain across larger spatial scales. The analysis of biomarkers, specifically fatty acids and stable isotopes, offers a tool to clarify the trophic role of Antarctic macroalgae. The aim of this study was to describe the fatty acid profiles and stable isotope values of 31 algal species from three divisions (Chlorophyta—1, Ochrophyta—8, Rhodophyta—22) collected at the same sites for both biomarkers. Of these, 13 species had no previously published fatty acid profiles. Most species were rich in polyunsaturated fatty acids (PUFAs), suggesting they are highly nutritious and could be a resource of essential fatty acids for consumers. This was specifically noticeable in the overall low PUFA ratio (∑ω6/∑ω3 ratio), with low ratios being an indicator of high nutritional quality for consumers. Fatty acid profiles of macroalgae grouped strongly by phylogeny (at the levels of division, order, and family), while stable isotope groupings were more driven by the physiological properties of the species. Specifically, some closely related red algal species exhibited very different stable isotope values based on their carbon concentrating mechanisms, with highly 13C-depleted values in several Rhodophyta species. The fact that the two biomarker approaches created different groupings of Antarctic macroalgae collected at the same locations emphasizes that their combined application can be a powerful tool in Antarctic coastal food web studies.

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.

Abstract 1070: Overcoming deficiencies in the early detection of high grade serous ovarian cancer: Evaluating exosomal proteins as novel biomarkers of disease

Abstract Introduction: High-grade serous ovarian cancer (HGSOC) accounts for over 75% of all epithelial ovarian cancer and has a high mortality rate due to a lack of early detection methods. Many biomarkers have been proposed; however, none have been shown to improve detection at an early stage in this patient population. The purpose of this study is to assess whether there are unique extracellular vesicle (EV) protein signatures of early-stage HGSOC that could serve as early detection biomarkers. Methods: 250 serum samples were obtained from a multi-institutional retrospective cohort of patients with all stages of HGSOC and healthy controls. LC-MS/MS and PEA of serum samples from patients with early-stage HGSOC identified differentially expressed EVs proteins in HGSOC versus controls. Tob candidate proteins are validated by ELISA. Results: We have identified the top 10 EVs candidate proteins based on their fold change and statistical significance. When comparing early-stage HGSOC and controls using ELISA, CFH, PCP, and CCNE1 exhibited the highest Area Under the Curve (AUC) values of 0.94, 0.91, and 0.83, respectively. In contrast, MUC16 (also known as CA-125) exhibited an AUC of only 0.42 in whole serum. Furthermore, MUC16 distinguished between controls and both early and advanced-stage HGSOC in EVs, while in whole serum, it was only differentially expressed between controls and advanced-stage disease. Our results demonstrate that the predictive performance of the combined biomarkers surpasses that of any individual biomarker. Further, we integrated the ten aforementioned biomarkers, deriving the optimal combination biomarker by maximizing the corresponding Sum of Harmonic Means (SHUM) function by utilizing data from all 70 patients, results showed the optimal coefficients of the biomarkers, which maximize the diagnostic accuracy for early-stage HGSOC. The overall true positive rate (TPR) of 0.943, false positive rate (FPR) of 0.000, and Mathew's correlation coefficient (MCC) were presented for both the combined biomarker approach and individual biomarkers. The results demonstrated that the predictive performance of the combined biomarkers surpassed that of any individual biomarker. Conclusion: Expression of EV-based protein biomarkers is significantly different in early-stage HGSOC compared to both control and late-stage HGSOC. This work can be readily translated to clinical use and potentially improve the early detection of HGSOC substantially. Citation Format: Michelle Lightfoot, Kalpana Deepa Priya Dorayappan, Lianbo Yu, Colin Hisey, Takahiko Sakaue, Muralidharan Anbalagan, Casey M. Cosgrove, Larry G. Maxwell, Premal Thaker, Beth Y. Karlan, David O’Malley, Raphael E. Pollock, David E. Cohn, Rajan Gogna, Selvendiran Karuppaiyah. Overcoming deficiencies in the early detection of high grade serous ovarian cancer: Evaluating exosomal proteins as novel biomarkers of disease [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 1070.

Abstract 7003: Microfluidic platform for DNA methylation profiling towards early detection of ovarian cancer

Abstract We present a microfluidic platform for molecule-by-molecule detection of heterogeneous epigenetic patterns of rare tumor-derived DNA by highly parallelized digital melt. The platform digitizes DNA molecules in a 4-module, 40,000 1-nL array microfluidic device and observes sequence-dependent fluorescence changes during temperature ramping. The platform is applied to quantify DNA methylation heterogeneity of a biomarker panel within Pap specimens in a small cohort of healthy and ovarian cancer patients, and demonstrates an accuracy of 0.9. DNA hypermethylation of tumor-suppressor genes is an epigenetic phenomena that occurs early in tumorigenesis. Variable methylation patterns are observed in ovarian cancer precursor tissue, and trace amounts of ovarian-tumor-derived DNA can be found in Pap specimens, albeit at low frequencies (&amp;lt;0.01%). This suggests that methylation profiles of DNA extracted from routinely-obtained Pap specimens could contain early cancer biomarkers. However, due to the scarcity of these molecules, current techniques, such as sequencing and digital PCR (dPCR), have technical limitations precluding their ability to profile methylation patterns reliably. Sequencing, although comprehensive, has limited sensitivity, undermining its utility for routine detection of fractions below 0.1%. dPCR achieves high sensitivity, but is limited to known sequences, thus cannot provide comprehensive analysis of molecular heterogeneity. To overcome these limitations, we developed a technique called digital high resolution melt (dHRM), a thermodynamics-based approach to detecting molecular variability by observing the sequence-specific release of a DNA-intercalating dye under a thermal ramp. dHRM can discriminate methylation patterns on CpG-by-CpG basis and detect methylated epialleles at frequencies as low as 0.00005%. Here, DNA from Pap specimens was loaded on a microfluidic chip that digitizes rare target molecules into individual reaction chambers. A thermal-optical platform was developed to perform parallelized dPCR and dHRM. Methylation patterns of a biomarker panel were analyzed to produce a probability score that the Pap specimen contained tumor-derived DNA. 12 healthy and 12 ovarian cancer Pap specimens were assessed for methylation heterogeneity of 9 genes. Optimal methylation density thresholds were determined, and data above each threshold was combined into a single score for each possible combination of biomarkers. In this small cohort, methylation heterogeneity analysis of just 2 loci produced an area under the receiver-operator characteristics curve (AUC) of 0.9. This highly sensitive methylation profiling technology demonstrates promising utility towards early cancer detection. Future work aims to expand the testing cohort for ovarian cancer detection and permit further studies on the impact of methylation heterogeneity on early cancer development. Citation Format: Chrissy O'Hersey, Yang Zhao, Thomas R. Pisanic, Tian-Li Wang, Ie-Ming Shih, Tza-Huei Jeff Wang. Microfluidic platform for DNA methylation profiling towards early detection of ovarian cancer [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 7003.

Abstract 7364: Comprehensive multi-omics analysis for resectable hepatocellular carcinoma uncovers biomarkers to predict microvascular invasion

Abstract Introduction: Microvascular invasion (MVI) is a well-known prognostic factor in predicting cancer relapse after curative resection of resectable hepatocellular carcinoma (HCC).Therefore, there is a clear unmet need for a precision strategy based on cancer-specific multi-omics features to predict the MVI feature at the initial diagnosis of HCC. Methods: Using gene expression profiling for resected human HCC (Discovery cohort, n=240), we identified the transcriptomic signature predicting the MVI feature. Comprehensive analyses were performed using the dataset from the cancer dependency map (DepMap) project, including cancer-specific molecular characterization with multi-omics data, and drug sensitivity with compound screening, and integrative in-silico prediction methods to uncover MVI. The transcriptomic signature predicting the MVI feature were validated in independent translational cohorts (TCGA-LIHC; n=373, KOREA; n=188, TOKYO; n=183, MODENA; n=78, ZHONGSHAN; n=159). Results: The MVI signature with 1028 genes was identified from robust statistical testing from the discovery cohort, and robust validation for the prediction performance of the MVI signature showed significant accuracy in the validation cohort (AUC=0.865, p&amp;lt;0.01). Multi-omics analysis revealed aggressive tumor biology associated with the MVI signature and specified biomarkers. Conclusions: Integrative multi-omics profiling for resectable HCC uncovers biomarkers to predict MVI. A novel combination of biomarkers performs in sorting out the tumor with aggressive tumor biology. A precision strategy to discover resectable tumors beneficial from surgical resection can be established from consecutive clinical trials based on this translational study. Citation Format: Incheon Kang, Woo Young Kwon, Sung Hwan Lee. Comprehensive multi-omics analysis for resectable hepatocellular carcinoma uncovers biomarkers to predict microvascular invasion [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 7364.

Abstract 2351: Genomics annotation and interpretation in somatic oncology using structured data from a clinical knowledgebase

Abstract Accurate and comprehensive interpretation of genomic variants has become a bottleneck in clinical sequencing applications due to the accelerating precision oncology and biomedical information explosion. This motivated us to build Ephesus, a framework enabling curation of predictive, prognostic and diagnostic evidence for clinical biomarkers in cancers. Currently Ephesus is the primary content source for Roche navify Mutation Profiler (nMP). The Ephesus’ data model ensures adherence to best practices in clinical genomic content curation. Variant classification follows the AMP guidelines for somatic variant interpretation. Variant interpretations are derived from international regulatory approvals and clinical practice guidelines (FDA, EMA, TGA, eVIQ, etc.) and recommendations (NCCN, ESMO). The data model is mapped to a set of relational tables. To enforce data integrity and validity, strategies such as data normalization based on standard nomenclatural ontologies, a submit-review-approval workflow, and biological constraints are adopted. Ephesus is deployed as a web application used by curators inside and outside Roche. The UI allows users to conduct edits, filtering, sorting and bulk operations on entities by attributes. In order to minimize manual effort and maximize content coverage, inference rules are applied before ingesting into nMP. Currently Ephesus is developed for data collection, browsing and summarizing primary entities such as biomarkers, evidence items, genes, variants, variant groups, and drugs. The content from the 2023-Aug snapshot contains 11,596 directly curated biomarker profiles (including biomarker combinations), and 6M+ expanded profiles for 41 major cancer types. To evaluate the clinical reporting value of the curated knowledge, ~160k real cancer patient samples of the AACR GENIE project were queried on Ephesus. Compared with three other major knowledgebases, Ephesus/NMP is observed showing the highest performance. Table 1. Percentage of patients or biomarkers with at least one interpretation from each knowledgebase CGI (2022-10-17) CIVIC(2023-09-01) ClinVar(2023-09-09) NMP(2023-08-11) AACR-Genie-v14.0 Total patient % with any interpretation 25% (40,545) 46% (73,835) 81% (130,882) 89% (142,905) 160,965 biomarker % (including combos) with any interpretation 0.03% (256) 0.06% (526) 13.26% (122,424) 73.79% (681,128) 923,093 Citation Format: Jian Li, Lili Niu, Shuba krishna, Fnu Kinshuk, Martin Jones, Carlos Hernandez, Michael Clark, Sandra Balladares. Genomics annotation and interpretation in somatic oncology using structured data from a clinical knowledgebase [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 2351.

Abstract 6104: EDRN Lung Team Project 2 validation of molecular biomarkers for the early detection of lung cancer in the setting of indeterminate pulmonary nodules

Abstract Background: The goal of the Early Detection Research Network (EDRN) Lung Team Project #2 (LTP-2) is to establish a cohort (n= 300) of high-risk people with indeterminate pulmonary nodules (6mm-30mm) to clinically validate the diagnostic accuracy of existing molecular and imaging biomarkers discovered and analytically validated through the EDRN program. These biomarkers comprise signals measured in serum (glycan or cytokine), plasma (protein, miRNA, RNA, or methylated circulating tumor DNA), airway epithelial cells (RNA or DNA), nasal brush cells (RNA or DNA), and/or chest CT images. Methods: Eligible participants had an indeterminate nodule discovered incidentally or through low-dose CT screening within 14 months prior to enrollment and were aged 45-90 years, with 20 pack-years or more cigarette smoking history, free of lung cancer, and willing to provide blood, nasal brushing, and optionally bronchoscopic brush biopsy specimens. Baseline medical and demographic data were collected from each subject. Baseline and years 1 and 2 follow-up CT images were obtained. Each specimen at each respective site was labeled with a 2-D barcode provided by the DMCC and specimen information was entered into the Validation Study Information Management System (VSIMS) developed by the EDRN Data Management and Coordinating Center (DMCC). Aliquots of blinded specimens from selected cases and controls will be dispensed to each EDRN site for biomarker analysis. Analysis plan: Primary endpoint: The positive diagnostic likelihood ratio (DLR+) will be used to measure biomarker performance within values of DLR+ &amp;gt;= 2 or DLR- &amp;lt;= 0.5, a range considered clinically useful for any patients with indeterminate lung nodules. The sample size calculation targeted &amp;gt;80% power to test the null hypotheses that a) a positive biomarker does not alter risk across a range of specificities, and b) a negative biomarker does not alter risk across a range of sensitivities. The EDRN Data Management and Coordinating Center (DMCC) will unblind and complete analysis and report results. Exploratory endpoint: The diagnostic accuracy of combined biomarkers will be calculated using a logistic regression model with flexible functional forms. Results and Discussion: More than 300 subjects were enrolled into the LTP2 cohort including a sufficient number of cases and controls (cohort size n&amp;gt;300) to reach power according to calculated estimates. Chest CT images were collected and are in the process of transfer to a central database. Plasma specimens have been distributed to test sites. RNA and DNA extraction from NBC and AEC is in process and based on results thus far the specimen quality and quantity is more than sufficient from the number of subjects needed to reach power for the primary endpoint and to conduct the planned exploratory analyses. Citation Format: James C. Willey, Eric L. Grogan, Stephen A. Deppen, James G. Herman, Michael N. Kammer, Matthew B. Schabath, Lynn Sorbara, Harvey I. Pass, Jun-Chieh J. Tsay, Ziding Feng, Avrum Spira, Erin L. Crawford, Jackie Dahlgren, Steven M. Dubinett, Kimberly R. Rieger-Christ, Marc E. Lenburg. EDRN Lung Team Project 2 validation of molecular biomarkers for the early detection of lung cancer in the setting of indeterminate pulmonary nodules [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 6104.

Abstract 1066: Colocalization of ovarian cancer-associated biomarkers in tumors, cancer cells and extracellular vesicles

Abstract Introduction: We have previously shown that ovarian cancer can be detected with high specificity and sensitivity by interrogating colocalized membrane-associated cell-surface biomarkers on single tumor-associated extracellular vesicles (EVs). Our blood based Ovarian Cancer Test (OC Test) is composed of 5 cancer-associated biomarkers (BST-2, FOLR1, MUC-1, MUC-16, and sialylated Thomsen-nouveau antigen (sTn)) known to be overexpressed in ovarian cancer relative to healthy tissues. The test employs these 5 biomarkers in 3 combinations to capture and detect EVs from human plasma to distinguish high-grade serous ovarian carcinoma (HGSC) from both benign ovarian tumors and normal samples. Using super-resolution microscopy on ovarian cancer cell lines and EVs and multiplex immunohistochemistry of ovarian benign and tumor tissue, our study aimed to show that the 5 ovarian cancer biomarkers show 1) colocalization on ovarian cancer cells, 2) colocalization on the surface of EVs, and 3) colocalization on cancer cells within ovarian tumor biopsies. Methods: Super-resolution microscopy was employed to visualize stained biomarkers on the cell surface of a collection of ovarian cancer cell lines as well as on the surface of extracellular vesicles released by these cell lines. 58 HGSC (17 Stage I, 30 Stage II, and 10 Stage III) and 17 benign ovarian tumor K2EDTA plasma samples and paired FFPE tissue were sourced from the Ovarian Cancer Research Program (OVCARE, Vancouver BC, Canada). A tumor microarray from identical tissue samples was stained with an Opal multiplex-IHC (mIHC) assay comprised of antibodies to BST2, MUC1, sTn, and pan-cytokeratin and a DAPI counterstain. Machine learning based image analysis was utilized to determine colocalization of the biomarkers at the cellular level within benign and tumor tissue. RNA sequencing was carried out to assess expression levels of the biomarkers in each FFPE tissue sample. OC Tests were run on each plasma sample. Results: Super-resolution microscopy showed colocalization of OC Test combination biomarkers on ovarian cancer cells and EVs derived therefrom. Machine learning-based image analysis of tumor and benign tissue microarrays stained for BST2/MUC1/sTn using mIHC, showed strong evidence for colocalization of these biomarkers on single cells within ovarian tumor tissue, particularly in early-stage tumors, but not on benign tissue. RNA sequencing analysis showed good correlation of biomarker RNA expression levels and with the OC Test results. Conclusions: The results presented here demonstrate that the OC Test biomarkers are colocalized on tumor tissue, ovarian cancer cells, and EVs from ovarian cancer cell lines. RNA and protein expression levels show good correlation with OC Test results. These results provide further evidence that colocalized biomarkers on EVs can be utilized for early detection of cancer. Citation Format: Anthony D. Couvillon, Emily S. Winn-Deen, Shelby Thornton, Shuhong Liu, Michael DeRan, Maciej Pacula, Sanchari Banerjee, Daniel Gusenleitner, Laura T. Bortolin, Jonian Grosha, Timothy Santos-Heiman, Gabrielle Sangiuliano, Kelly M. Biette, Christopher R. Sedlak, Bilal Hamzeh, Delaney M. Byrne, Peter A. Duff, Julie Ho, Dongxia Gao, Amy Jamieson, Lauren T. Cuoco, MacKenzie S. King, Dawn R. Mattoon, Toumy Guettouche, Jessica N. McAlpine, David Huntsman. Colocalization of ovarian cancer-associated biomarkers in tumors, cancer cells and extracellular vesicles [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 1066.

Abstract 2769: Long non coding RNA H19/cell adhesion molecules circuitry as novel target and diagnostic tool for metastatic prostate cancer

Abstract In the complex tumor biology of prostate cancer (PCa), we previously contributed by demonstrating that estrogen receptor and hypoxia signaling promoted transcription of progression-associated genes. Notably, combined treatment, estrogens plus hypoxia, caused a significant down regulation of the long noncoding RNA H19, one of the most sensitive to these pro-tumoral stimuli. Mechanistically, H19 acts as transcriptional repressor: H19 over-expression decreases E-cadherin (CDH1) level causing epithelial-to-mesenchymal transition whereas H19 reduction promotes the expression of E-cadherin and β4 integrin (ITGB4) switching the tumor dissemination program toward an alternative mechanism, specifically the cohesive metastatic phenotype. We focused on this H19/cell adhesion molecules circuitry by investigating its role as: 1.Novel prognostic biomarker at the time of biopsies. eNOS, HIF2a and β4 integrin expression was evaluated by immunohistochemistry, H19 and CDH1 transcripts by Droplet Digital PCR on FFPE-biopsies in a retrospective cohort of PCa patients [n = 30; Gleason score GS≤ 7(3+4) n= 17, GS ≥7(4+3) n= 13; biochemical, local, or metastatic recurrence defining disease progression (n=8/30; time of recurrence: range 2-6 years)]. Importantly, multivariate analysis of combined biomarkers, named “Bioscore”, defined by the presence of at least 3 among HIF-2α, eNOS, CDH1, β4 integrin ≥cut off or H19≤cut off revealed its prognostic value for progression and Progression-Free Survival determined using the ROC analysis (p=0.003) and Kaplan Meier curve (p= 0.005), respectively. 2.Master regulator of metastatic program in vivo and ex vivo PCa experimental models. Upon H19 silencing, mimicking combined estrogens plus hypoxia treatment, luciferase-positive PC-3 and 22Rv1 cells led to increased E-cadherin and β4 integrin expression, enhanced tumor growth and metastasis formation at bone, lung, and liver in PCa mouse models. Further, treatment with H3K27 demethylase inhibitor, GSK-J4, rescued cell adhesion molecules expression by modulating H3K27me3 level at gene promoters, reduced metastasis number and size, and induced cell death in PCa-derived Organotypic Slice Cultures. Additionally, treatment with JQ1 and dBET6 BET protein family inhibitors) markedly reduced both E-cadherin and β4 integrin levels, cell proliferation and in vivo tumor growth. Intriguingly, H19 silencing increased BRD3 and BRD4 levels, implicating H19 in BRD transcription. Overall, this research enhances our understanding of the molecular mechanisms driving aggressive prostate cancer phenotypes, providing a foundation for new diagnostic and therapeutic strategies in prostate cancer management. Citation Format: Valeria Pecci, Aurora Aiello, Sara De Martino, Cristian Ripoli, Dante Rotili, Francesco Pierconti, Francesco Pinto, Claudio Grassi, Carlo Gaetano, Alfredo Pontecorvi, Lidia Strigari, Antonella Farsetti, Simona Nanni. Long non coding RNA H19/cell adhesion molecules circuitry as novel target and diagnostic tool for metastatic prostate cancer [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 2769.

Proteomics landscape and machine learning prediction of long-term response to splenectomy in primary immune thrombocytopenia.

This study aimed to identify key proteomic analytes correlated with response to splenectomy in primary immune thrombocytopenia (ITP). Thirty-four patients were retrospectively collected in the training cohort and 26 were prospectively enrolled as validation cohort. Bone marrow biopsy samples of all participants were collected prior to the splenectomy. A total of 12 modules of proteins were identified by weighted gene co-expression network analysis (WGCNA) method in the developed cohort. The tan module positively correlated with megakaryocyte counts before splenectomy (r = 0.38, p = 0.027), and time to peak platelet level after splenectomy (r = 0.47, p = 0.005). The blue module significantly correlated with response to splenectomy (r = 0.37, p = 0.0031). KEGG pathways analysis found that the PI3K-Akt signalling pathway was predominantly enriched in the tan module, while ribosomal and spliceosome pathways were enriched in the blue module. Machine learning algorithm identified the optimal combination of biomarkers from the blue module in the training cohort, and importantly, cofilin-1 (CFL1) was independently confirmed in the validation cohort. The C-index of CFL1 was >0.7 in both cohorts. Our results highlight the use of bone marrow proteomics analysis for deriving key analytes that predict the response to splenectomy, warranting further exploration of plasma proteomics in this patient population.

Biomarker combination predicting imminent relapse after discontinuation of biological drugs in patients with rheumatoid arthritis in remission.

Compared to conventional disease-modifying antirheumatic drugs (DMARDs), biological DMARDs demonstrate superior efficacy but come with higher costs and increased infection risks. The ability to stop and resume biological DMARD treatment while maintaining remission would significantly alleviate these barriers and anxieties. The objective of this study was to identify biomarkers that can predict an imminent relapse, hopefully enabling the timely resumption of biological DMARDs before relapse occurs. Forty patients with rheumatoid arthritis who had been in remission for more than 12 months were included in the study. The patients discontinued their biological DMARD treatment and were monitored monthly for the next 24 months. Out of the 40 patients, 14 (35%) remained in remission at the end of the 24-month period, while 26 (65%) experienced relapses at different time points. Among the relapse cases, 13 patients experienced early relapse within 6 months, and another 13 patients had late relapse between 6 months and 24 months. Seventy-three cytokines in the sera collected longitudinally from the 13 patients with late relapse were measured by multiplex immunoassay. Using cytokines at two time points, immediately after withdrawal and just before relapse, volcano plot and area under the receiver operating characteristic curves (AUC) were drawn to select cytokines that distinguished imminent relapse. Univariate and multivariate logistic regression analyses were used for the imminent relapse prediction model. IL-6, IL-29, MMP-3, and thymic stromal lymphopoietin (TSLP) were selected as potential biomarkers for imminent relapse prediction. All four cytokines were upregulated at imminent relapse time point. Univariate and multivariate logistic regression showed that a combination model with IL-6, MMP-3, and TSLP yielded an AUC of 0.828 as top predictors of imminent relapse. This methodology allows for the prediction of imminent relapse while patients are in remission, potentially enabling the implementation of on- and off-treatments while maintaining remission. It also helps alleviate patient anxiety regarding the high cost and infection risks associated with biological DMARDs, which are the main obstacles to benefiting from their superb efficacy.

Combination of urinary biomarkers and machine-learning models provided a higher predictive accuracy to predict long-term treatment outcomes of patients with interstitial cystitis/bladder pain syndrome.

To identify predictive factors for satisfactory treatment outcome of the patients with IC/BPS using urine biomarkers and machine-learning models. The IC/BPS patients were prospectively enrolled and provide urine samples. The targeted analytes included inflammatory cytokines, neurotrophins, and oxidative stress biomarkers. The patients with overall subjective symptom improvement of ≥ 50% were considered to have satisfactory results. Binary logistic regression, receiver-operating characteristic (ROC) curve, machine-learning decision tree, and random forest models were used to analyze urinary biomarkers to predict satisfactory results. Altogether, 57.4% of the 291 IC/BPS patients obtained satisfactory results. The patients with satisfactory results had lower levels of baseline urinary inflammatory cytokines and oxidative biomarkers than patients without satisfying results, including interleukin-6, monocyte chemoattractant protein-1 (MCP-1), C-X-C motif chemokine 10 (CXCL10), oxidative stress biomarkers 8-hydroxy-2'-deoxyguanosine (8-OHDG), 8-isoprostane, and total antioxidant capacity (TAC). Logistic regression and multivariable analysis revealed that lower levels of urinary CXCL10, MCP-1, 8-OHDG, and 8-isoprostane were independent factors. The ROC curve revealed that MCP-1 level had best area under curve (AUC: 0.797). In machine-learning decision tree model, combination of urinary C-C motif chemokine 5, 8-isoprostane, TAC, MCP-1, and 8-OHDG could predict satisfactory results (accuracy: 0.81). The random forest model revealed that urinary 8-isoprostance, MCP-1, and 8-OHDG levels had the most important influence on accuracy. Machine learning decision tree model provided a higher accuracy for predicting treatment outcome of patients with IC/BPS than logistic regression, and levels of 8-isoprostance, MCP-1, and 8-OHDG had the most important influence on accuracy.

A Quest for Survival: A Review of the Early Biomarkers of Pancreatic Cancer and the Most Effective Approaches at Present.

Pancreatic cancer (PC) is the most lethal type of cancer; it has the lowest 5-year survival rate among all other types of cancers. More than half of PC cases are diagnosed at an advanced stage due to PC's insidious and non-specific symptoms. Surgery remains the most efficacious treatment option currently available, but only 10-20% of PC cases are resectable upon diagnosis. As of now, the sole biomarker approved by the United States Food and Drug Administration (US-FDA) for PC is carbohydrate antigen 19-9 (CA19-9); however, its use is limited for early diagnosis. An increasing number of studies have investigated a combination of biomarkers. Lately, there has been considerable interest in the application of a liquid biopsy, including the utilization of microRNAs (miRNAs), circulating tumor DNA (ctDNA), and circulating tumor cells (CTCs). Screening for PC is indicated for high-risk patients; studies on new diagnostic models combined with biomarkers for early detection have also shown promising results in terms of the ability of these models and biomarkers to aid clinicians in deciding on whether to start screening. This review seeks to provide a concise overview of the advancements in relation to existing biomarkers and explore novel strategies for the early detection of PC.