Blood-based Signature Research Articles

Early detection of pancreatic cancer by liquid biopsy “PANLIPSY”: a french nation-wide study project

BackgroundPancreatic cancer, predominantly characterized by ductal adenocarcinoma (PDAC) accounts for 90% of cases and is the fourth leading cause of cancer-related deaths globally. Its incidence is notably increasing. This poor prognosis is primarily due to late-stage diagnosis (approximately 70% to 80% of patients are diagnosed at an advanced stage), aggressive tumor biology, and low sensitivity to chemotherapy. Consequently, it is crucial to identify and develop a simple, feasible and reproducible blood-based signature (i.e., combination of biomarkers) for early detection of PDAC.MethodsThe PANLIPSY study is a multi-center, non-interventional prospective clinical trial designed to achieve early detection of PDAC with high specificity and sensitivity, using a combinatorial approach in blood samples. These samples are collected from patients with resectable, borderline or locally advanced, and metastatic stage PDAC within the framework of the French Biological and Clinical Database for PDAC cohort (BACAP 2). All partners of the BACAP consortium are eligible to participate. The study will include 215 PDAC patients, plus 25 patients with benign pancreatic conditions from the PAncreatic Disease Cohort of TOuLouse (PACTOL) cohort, and 115 healthy controls, totaling 355 individuals. Circulating biomarkers will be collected in a total volume of 50 mL of blood, divided into one CellSave tube (10 mL), two CELL-FREE DNA BCT® preservative tubes (18 mL), and five EDTA tubes (22 mL in total). Samples preparation will adhere to the guidelines of the European Liquid Biopsy Society (ELBS). A unique feature of the study is the AI-based comparison of these complementary liquid biopsy biomarkers.Main end-points: i) to define a liquid biopsy signature that includes the most relevant circulating biomarkers, ii) to validate the multi-marker panel in an independent cohort of healthy controls and patients, with resectable PDAC, and iii) to establish a unique liquid biopsy biobank for PDAC study.DiscussionThe PANLIPSY study is a unique prospective non-interventional clinical trial that brings together liquid biopsy experts. The aim is to develop a biological signature for the early detection of PDAC based on AI-assisted detection of circulating biomarkers in blood samples (CTCs, ctDNA, EVs, circulating immune system, circulating cell-free nucleosomes, proteins, and microbiota).Trial registrationClinicalTrials.gov Identifier: NCT06128343 / NCT05824403. Registration dates: June 8,2023 and April 21, 2023.

Indian clinical practice consensus guidelines for the management of oral cavity cancer: Update 2022

The treatment of oral cavity cancer primarily depends on the location, tumor size, and feasibility of organ preservation in patients with negative margins. A liquid biopsy is a minimally invasive approach for detecting blood-based signatures such as circulating tumor cells (CTCs) and circulatory tumor DNA (ctDNA). To assess the presence of occult metastatic disease, sentinel lymph node biopsy (SLNB) is recommended in early-stage oral cavity cancer (OCC). In India, surgery is the preferred choice of treatment for oral cancers. Selected patients, who are medically inoperable or refuse surgery, should be given definitive radiotherapy (RT) as an alternative to surgery. Concurrent chemoradiation (CCRT) may be considered an alternative option to definitive RT in patients with unresectable oral cancer.

P43. Identification of blood-based signatures for spinal tuberculosis: a molecular insight from bench to bedside

P43. Identification of blood-based signatures for spinal tuberculosis: a molecular insight from bench to bedside

Abstract 2017: A liquid biopsy approach to gastric cancer reveals a signature reflective of epithelial to mesenchymal transition

Abstract Comprehensive proteomic profiling of plasmas collected before and after neo-adjuvant treatment for gastric cancer (GC) yielded 522 proteins exhibiting higher pre-treatment levels compared to healthy controls and among them, 345 proteins decreased after the treatment. Increased levels of cytoskeleton-related proteins were manifest in the plasmas of GC patients. Cytoskeletal proteins are known to play integral roles in modulating epithelial plasticity by altering mechanical strength and structural design that is critical for cancer cell motility, epithelial to mesenchymal transition (EMT), metastasis, and invasion. Ingenuity Pathway Analysis of highly expressed circulating proteins identified key oncogenic upstream transcriptional regulators and proteins that promote EMT, including KRAS, MYC and TGFB. Additionally, pathways involved in remodeling of epithelial adherens junctions, actin cytoskeleton signaling and VEGF signaling that also play a key role in EMT were upregulated. Additional datasets from proteomic profiling of multiple (N=6) GC cell lines were intersected with GC patient plasma profiles provided supporting evidence of a cancer cell origin of circulating cytoskeletal features. ACTC1, ACTN1, ACTR3, MYH9, TUBA1A, TUBA4A and TLN1 were highly expressed in both conditioned media released from GC cell lines and plasma samples of GC patients. Our findings are indicative of a blood-based signature of cytoskeletal proteins that is reflective of EMT in the context of gastric cancer. Citation Format: Ali H. Abdel Sater, Yihui Chen, Johannes F. Fahrmann, Ehsan Irajizad, Yining Cai, Fuchung Hsiao, Jody V. Vykoukal, Hiroyuki Katayama, Ricardo A. Léon-Letelier, Rongzhang Dou, Edwin J. Ostrin, Melissa Pizzi, Kohei Yamashita, Matheus Sewastjanow Da Silva, Shumei Song, Jaffer Ajani, Samir Hanash. A liquid biopsy approach to gastric cancer reveals a signature reflective of epithelial to mesenchymal transition [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2017.

Investigating associations between blood metabolites, later life brain imaging measures, and genetic risk for Alzheimer’s disease

BackgroundIdentifying blood-based signatures of brain health and preclinical pathology may offer insights into early disease mechanisms and highlight avenues for intervention. Here, we systematically profiled associations between blood metabolites and whole-brain volume, hippocampal volume, and amyloid-β status among participants of Insight 46—the neuroscience sub-study of the National Survey of Health and Development (NSHD). We additionally explored whether key metabolites were associated with polygenic risk for Alzheimer’s disease (AD).MethodsFollowing quality control, levels of 1019 metabolites—detected with liquid chromatography-mass spectrometry—were available for 1740 participants at age 60–64. Metabolite data were subsequently clustered into modules of co-expressed metabolites using weighted coexpression network analysis. Accompanying MRI and amyloid-PET imaging data were present for 437 participants (age 69–71). Regression analyses tested relationships between metabolite measures—modules and hub metabolites—and imaging outcomes. Hub metabolites were defined as metabolites that were highly connected within significant (pFDR < 0.05) modules or were identified as a hub in a previous analysis on cognitive function in the same cohort. Regression models included adjustments for age, sex, APOE genotype, lipid medication use, childhood cognitive ability, and social factors. Finally, associations were tested between AD polygenic risk scores (PRS), including and excluding the APOE region, and metabolites and modules that significantly associated (pFDR < 0.05) with an imaging outcome (N = 1638).ResultsIn the fully adjusted model, three lipid modules were associated with a brain volume measure (pFDR < 0.05): one enriched in sphingolipids (hippocampal volume: ß = 0.14, 95% CI = [0.055,0.23]), one in several fatty acid pathways (whole-brain volume: ß = − 0.072, 95%CI = [− 0.12, − 0.026]), and another in diacylglycerols and phosphatidylethanolamines (whole-brain volume: ß = − 0.066, 95% CI = [− 0.11, − 0.020]). Twenty-two hub metabolites were associated (pFDR < 0.05) with an imaging outcome (whole-brain volume: 22; hippocampal volume: 4). Some nominal associations were reported for amyloid-β, and with an AD PRS in our genetic analysis, but none survived multiple testing correction.ConclusionsOur findings highlight key metabolites, with functions in membrane integrity and cell signalling, that associated with structural brain measures in later life. Future research should focus on replicating this work and interrogating causality.

Predictive model for BNT162b2 vaccine response in cancer patients based on blood cytokines and growth factors.

Patients with cancer, especially hematological cancer, are at increased risk for breakthrough COVID-19 infection. So far, a predictive biomarker that can assess compromised vaccine-induced anti-SARS-CoV-2 immunity in cancer patients has not been proposed. We employed machine learning approaches to identify a biomarker signature based on blood cytokines, chemokines, and immune- and non-immune-related growth factors linked to vaccine immunogenicity in 199 cancer patients receiving the BNT162b2 vaccine. C-reactive protein (general marker of inflammation), interleukin (IL)-15 (a pro-inflammatory cytokine), IL-18 (interferon-gamma inducing factor), and placental growth factor (an angiogenic cytokine) correctly classified patients with a diminished vaccine response assessed at day 49 with >80% accuracy. Amongst these, CRP showed the highest predictive value for poor response to vaccine administration. Importantly, this unique signature of vaccine response was present at different studied timepoints both before and after vaccination and was not majorly affected by different anti-cancer treatments. We propose a blood-based signature of cytokines and growth factors that can be employed in identifying cancer patients at persistent high risk of COVID-19 despite vaccination with BNT162b2. Our data also suggest that such a signature may reflect the inherent immunological constitution of some cancer patients who are refractive to immunotherapy.

1539. A Nine-Gene Blood-Based Signature Meets the World Health Organization Target Product Profiles for Diagnosis of Active Tuberculosis and Predicting Progression from Latent to Active Disease

Abstract Background As part of its End TB strategy, the WHO has identified the need for non-sputum-based diagnostics that meet target product profiles (TPP) of 90% sensitivity and 70% specificity for diagnosis of Active TB (ATB) and 75% sensitivity and specificity for predicting progression from Latent TB (LTB) to ATB. The successful translation of a 3-gene blood-based signature, identified using diverse datasets, into a prototype point-of-care diagnostic, that meets the WHO TPPs, has demonstrated the power of integrating large amounts of heterogeneous data to identify generalizable disease signatures. We hypothesized that integration of more diverse datasets, comprising patients with ATB or other inflammatory lung diseases, would identify novel, robust signatures, for diagnosing ATB and predicting progression from LTB to ATB, that meet the WHO TPPs. Methods Using multi-cohort analyses, we integrated and analyzed data from 3,615 peripheral blood samples, in 49 publicly available transcriptomic datasets (discovery cohorts), from healthy controls and patients with LTB, ATB, and other diseases (COPD, viral infections, sarcoidosis, etc.). We used data from (1) 3,836 blood samples in 28 retrospective datasets and (2) 360 prospectively collected blood samples, from a household contact study in Moldova, as validation cohorts. Results Using the discovery cohorts we identified a 9-gene signature for diagnosing ATB patients from healthy controls, or individuals with LTB or other diseases. The signature achieved 90% sensitivity and 82% specificity in retrospective validation (Figure 1A) and 90% sensitivity and 69% specificity in the prospective cohort from Moldova (Figure 1B). In a longitudinal cohort of adolescents, the 9-gene signature predicted progression from LTB to ATB up to 1 year prior to sputum conversion with 76% sensitivity and 83% specificity (Figure 1C). Finally, the signature predicted prolonged lung inflammation post-treatment in the Catalysis Treatment Response Cohort (Figure 1D). 9-gene TB signature validates in independent retrospective and prospective cohorts. (A) ROC curves for comparing ATB vs. all other samples (All), or individually comparing ATB vs. Healthy, LTBI or Other Disease (OD) samples in independent retrospective validation and (B) a prospective Moldova cohort. (C) ROC curves comparing progressor and non-progressor samples collected at different time points in the Adolescent Cohort Study (ACS), for the 9-gene signature in solid lines and a previous 3-gene signature in dashed lines, (D) Distribution of the 9-gene score at different time points post-treatment in the Catalysis Treatment Response Cohort Study, for individuals with persistent lung inflammation at 24 weeks compared with those who had clear lungs at 24 weeks. Conclusion Overall, the 9-gene signature meets the WHO TPPs required for the End TB strategy. Disclosures Purvesh Khatri, PhD, Cepheid, Inc.: Advisor/Consultant|Inflammatix, Inc.: Advisor/Consultant|Inflammatix, Inc.: Inflammatix is in negotiations to license the 9-gene signature discussed here.|Inflammatix, Inc.: Stocks/Bonds.

Transcriptomic profiling identifies host-derived biomarker panels for assessing cerebral malaria

Cerebral malaria (CM) is a rare but fatal form of severe malaria sequelae. Host-derived biomarkers have the potential to assist in the early assessment, diagnosis, and prognosis of CM. However, previously reported host-derived CM biomarkers have limited clinical utility despite their ability to discriminate between CM and non-CM conditions, either due to their specificity to other non-malaria conditions or consist of many biomarkers for test implementation. Here, we describe the identification of host-derived transcriptomic signatures that address these limitations using whole-blood transcriptomic profiling, weighted gene co-expression analysis, and random forest classification models. We report three panels; PADI4 + ANGPT2, SLC2A3 + ANGPT2, and SLC2A3 + CA4, that distinguished CM from other malaria manifestations with high accuracy and are poor discriminators of non-malaria conditions that share similar blood-based signatures with severe malaria. Also, these transcripts showed elevated expression in the blood during CM. Our results provide preliminary evidence to support the potential of these biomarker panels in assisting early assessment, diagnosis, and prognosis of CM. Further validations are needed in large and diverse malaria-infected cohorts to establish the clinical utility of these biomarker panels.

MicroRNome analysis generates a blood-based signature for endometriosis

Endometriosis, characterized by endometrial-like tissue outside the uterus, is thought to affect 2–10% of women of reproductive age: representing about 190 million women worldwide. Numerous studies have evaluated the diagnostic value of blood biomarkers but with disappointing results. Thus, the gold standard for diagnosing endometriosis remains laparoscopy. We performed a prospective trial, the ENDO-miRNA study, using both Artificial Intelligence (AI) and Machine Learning (ML), to analyze the current human miRNome to differentiate between patients with and without endometriosis, and to develop a blood-based microRNA (miRNA) diagnostic signature for endometriosis. Here, we present the first blood-based diagnostic signature obtained from a combination of two robust and disruptive technologies merging the intrinsic quality of miRNAs to condense the endometriosis phenotype (and its heterogeneity) with the modeling power of AI. The most accurate signature provides a sensitivity, specificity, and Area Under the Curve (AUC) of 96.8%, 100%, and 98.4%, respectively, and is sufficiently robust and reproducible to replace the gold standard of diagnostic surgery. Such a diagnostic approach for this debilitating disorder could impact recommendations from national and international learned societies.

Abstract 4484: ScRNA-seq of patients with immune-related adverse events and checkpoint inhibitor therapy

Abstract Despite the remarkable success of immune checkpoint inhibitor (ICI) therapy, a significant number of patients develop severe and unpredictable immune-related adverse events (irAEs) affecting a wide variety of organs. Concerns over irAE have led to the exclusion of patients with autoimmune disease from ICI clinical trials. Role of host genetic and immune factors in mediating irAEs remain unclear and it is not clear if the manifestations of irAEs is associated with response to therapy. In this study, we used single cell RNA sequencing (ScRNA-seq) to study the peripheral immune repertoire at a single cell level to identify blood-based signatures predictive of irAEs and response to ICI therapy. We characterized changes in host immune system in 40 patients receiving ICI therapy by performing ScRNA-seq on peripheral blood mononuclear cells (PBMCs) at baseline and post-immunotherapy. In addition, we evaluated serum levels of 40 cytokines/chemokines, antinuclear autoantibodies (ANA) and 124 autoantibodies and performed bulk RNA sequencing on PBMCs at baseline and post immunotherapy in these patients. Our preliminary data analysis identified signatures of autoantibodies and cytokines correlating with response and toxicity. ScRNA-seq data revealed changes in distinct immune cell subsets and gene expression signatures in patient developing irAEs. In this meeting, we will present preliminary ScRNA-seq data combined with immune and genetic correlates of irAEs and response to therapy in our patient cohort. We hope that our studies can help identify blood-based biomarker signatures predictive of irAEs and/or response and reveal novel insights into the mechanisms underlying irAE. Our current genetic data suggests further expanding the genetic studies in larger patient population to identify the role of underlying genetic predisposition to autoimmunity in mediating irAEs. We hope our findings may ultimately help customize therapy, expand use of immunotherapy and prevent toxicities. Citation Format: Shaheen Khan, David E. Gerber. ScRNA-seq of patients with immune-related adverse events and checkpoint inhibitor therapy [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4484.

Ipilimumab or FOLFOX with Nivolumab and Trastuzumab in previously untreated HER2-positive locally advanced or metastatic EsophagoGastric Adenocarcinoma - the randomized phase 2 INTEGA trial (AIO STO 0217)

BackgroundEsophagogastric adenocarcinoma (EGA) currently represents a main cause of cancer related death. Despite an intensified treatment for locally advanced or metastatic EGA with a doublet chemotherapy consisting of a platinum compound and a fluoropyrimidine in combination with trastuzumab for HER2-positive disease or in selected cases with docetaxel, survival remains poor. Recently, immune-oncology based strategies relevantly improved the treatment of different solid tumors and showed some promise in late or later stage trials in EGA. Notably, the combination of immunotherapy with trastuzumab to enhance anti-tumor immunity through activation of innate and adaptive immunity was beneficial in preclinical studies or clinical studies in breast cancer.MethodsThe INTEGA study is an open-label, randomized, multicenter, exploratory phase II trial designed to assess clinical performance, safety and tolerability of ipilimumab or 5-FU/folinic acid and oxaliplatin (FOLFOX) in combination with nivolumab and trastuzumab in patients with previously untreated HER2-positive, locally advanced or metastatic EGA. The primary objective is to determine the clinical performance of ipilimumab or FOLFOX in combination with nivolumab and trastuzumab in terms of overall survival. Secondary objectives are safety and tolerability, efficacy in terms of progression-free survival and objective response rate and blood-based signatures (e.g. immune response or suppression of anti-HER2 resistance) that may correlate with treatment response.DiscussionRecent evidence from the phase II NCT02954536 study (oxaliplatin, capecitabine, trastuzumab and pembrolizumab) suggests the clinical feasibility of combining chemotherapy, trastuzumab and checkpoint inhibition in EGA. However, evidence for a chemotherapy-free regimen is also mounting in HER2-positive disease (NCT02689284) i.e. margetuximab and Pembrolizumab. Both studies excelled with high overall response rates and manageable toxicities. The INTEGA study aims to comparatively assess these results and select a promising new 1st line regimen which then needs to be confirmed in a randomized phase III trial. Further, the translational part of the study might help to better stratify patients and tailor treatment of either arm.Trial registrationNCT03409848 24.01.2018.

Blood-based gene expression profiling to reveal potential response biomarkers for immunotherapy in advanced lung cancer.

e15155 Background: Novel immunotherapies are becoming a viable option for advanced lung cancer treatment. High-throughput gene expression profiling (GEP) has enabled better understanding of patient responsiveness to targeted immunotherapy. However, non-invasive blood-based signatures are needed to monitor and predict response. Methods: To evaluate the predictive and prognostic role of blood-based GEP, we designed a longitudinal study by enrolling 15 patients with advanced lung cancers. A total of 65 samples were collected for RNA sequencing, ~4 blood specimens per patient, before and during anti-PD-1 treatment. We used multiple analyses, including time-course differential gene expression, principal component, lymphocyte compartment deconvolution, and genetic mutation, to search for and assess potential predictive and prognostic aspects. Results: Of 15 patients, 11 were classified as Responders (partial responders) and four were Non-Responders (one stable and three progressive diseases). Our analyses demonstrated: 1) By comparing baseline GEPs from Responders vs. Non-Responders before the first treatment, we identified potential markers (e.g., LY6E is significantly lower expressed in Responders, with Log2 Fold change = -3.44 and p = 1.83E-04) that can be used as predictors of responsiveness of the patients; 2) Immunoglobulin subunits- and T cell receptor complex-related genes were differentially expressed in Responders (DAVID analysis, p = 6.7E-3 and 2.1E-2, respectively), but not in Non-responders; 3) γδ T cells in the lymphocyte compartment were relatively increased in Responders; 4) Despite a different set of genes differentially expressed at different time points, the biggest GEP changes were at ~ week 6, after the second treatment. Additionally, we observed certain genes consistently up- or down-regulated through the whole course of treatment. Furthermore, after the first treatment, genes in the immune response pathway were regulated to different directions in Responders and Non-Responders. For example, interleukin receptor genes, such as IL18R1 and IL18RAP, and CD24 were down-regulated in Responders, but up-regulated in Non-Responders (p = 0.042, 0.023 and 0.044 respectively, t-test for the differential expression in these two groups). Conclusions: The utility of blood GEP to identify predictive and prognostic factors for precision immunotherapy is encouraging. Nevertheless, these results, predictive of the anti-PD-1 clinical response, are preliminary and need to be validated in a larger cohort.

Host transcriptomic signature as alternative test-of-cure in visceral leishmaniasis patients co-infected with HIV

BackgroundVisceral leishmaniasis (VL) treatment in HIV patients very often fails and is followed by high relapse and case-fatality rates. Hence, treatment efficacy assessment is imperative but based on invasive organ aspiration for parasite detection. In the search of a less-invasive alternative and because the host immune response is pivotal for treatment outcome in immunocompromised VL patients, we studied changes in the whole blood transcriptional profile of VL-HIV patients during treatment. MethodsEmbedded in a clinical trial in Northwest Ethiopia, RNA-Seq was performed on whole blood samples of 28 VL-HIV patients before and after completion of a 29-day treatment regimen of AmBisome or AmBisome/miltefosine. Pathway analyses were combined with a machine learning approach to establish a clinically-useful 4-gene set. FindingsDistinct signatures of differentially expressed genes between D0 and D29 were identified for patients who failed treatment and were successfully treated. Pathway analyses in the latter highlighted a downregulation of genes associated with host cellular activity and immunity, and upregulation of antimicrobial peptide activity in phagolysosomes. No signs of disease remission nor pathway enrichment were observed in treatment failure patients. Next, we identified a 4-gene pre-post signature (PRSS33, IL10, SLFN14, HRH4) that could accurately discriminate treatment outcome at end of treatment (D29), displaying an average area-under-the-ROC-curve of 0.95 (CI: 0.75–1.00). InterpretationA simple blood-based signature thus holds significant promise to facilitate treatment efficacy monitoring and provide an alternative test-of-cure to guide patient management in VL-HIV patients. FundingProject funding was provided by the AfricoLeish project, supported by the European Union Seventh Framework Programme (EU FP7).

RNA sequencing of identical twins discordant for autism reveals blood-based signatures implicating immune and transcriptional dysregulation

BackgroundA gap exists in our mechanistic understanding of how genetic and environmental risk factors converge at the molecular level to result in the emergence of autism symptoms. We compared blood-based gene expression signatures in identical twins concordant and discordant for autism spectrum condition (ASC) to differentiate genetic and environmentally driven transcription differences, and establish convergent evidence for biological mechanisms involved in ASC.MethodsGenome-wide gene expression data were generated using RNA-seq on whole blood samples taken from 16 pairs of monozygotic (MZ) twins and seven twin pair members (39 individuals in total), who had been assessed for ASC and autism traits at age 12. Differential expression (DE) analyses were performed between (a) affected and unaffected subjects (N = 36) and (b) within discordant ASC MZ twin pairs (total N = 11) to identify environmental-driven DE. Gene set enrichment and pathway testing was performed on DE gene lists. Finally, an integrative analysis using DNA methylation data aimed to identify genes with consistent evidence for altered regulation in cis.ResultsIn the discordant twin analysis, three genes showed evidence for DE at FDR < 10%: IGHG4, EVI2A and SNORD15B. In the case-control analysis, four DE genes were identified at FDR < 10% including IGHG4, PRR13P5, DEPDC1B, and ZNF501. We find enrichment for DE of genes curated in the SFARI human gene database. Pathways showing evidence of enrichment included those related to immune cell signalling and immune response, transcriptional control and cell cycle/proliferation. Integrative methylomic and transcriptomic analysis identified a number of genes showing suggestive evidence for cis dysregulation.LimitationsIdentical twins stably discordant for ASC are rare, and as such the sample size was limited and constrained to the use of peripheral blood tissue for transcriptomic and methylomic profiling. Given these primary limitations, we focused on transcript-level analysis.ConclusionsUsing a cohort of ASC discordant and concordant MZ twins, we add to the growing body of transcriptomic-based evidence for an immune-based component in the molecular aetiology of ASC. Whilst the sample size was limited, the study demonstrates the utility of the discordant MZ twin design combined with multi-omics integration for maximising the potential to identify disease-associated molecular signals.

Evaluation and Validation of Plasma Proteins Using Two Different Protein Detection Methods for Early Detection of Colorectal Cancer.

Objective: Plasma protein biomarkers could be an efficient alternative for population-based screening for early detection of colorectal cancer (CRC). The objective of this study was to evaluate and validate plasma proteins individually and as a signature for early detection of CRC. Methods: In a three-stage design, proteins were measured firstly by liquid chromatography/multiple reaction monitoring-mass spectrometry (LC/MRM-MS) and later by proximity extension assay (PEA) in a discovery set consisting of 96 newly diagnosed CRC cases and 94 controls free of neoplasms at screening colonoscopy. Two algorithms (one for each measurement method) were derived by Lasso regression and .632+ bootstrap based on 11 proteins that were included in both the LC/MRM-MS and PEA measurements. Additionally, another algorithm was constructed from the same eleven biomarkers plus amphireglin, the most promising protein marker in the PEA measurements that had not been available from the LC/MRM-MS measurements. Lastly the three prediction signatures were validated with PEA in independent samples of participants of screening colonoscopy (CRC (n = 56), advanced adenoma (n = 101), and participants free of neoplasm (n = 102)). Results: The same four proteins were included in all three prediction signatures; mannan binding lectin serine protease 1, osteopontin, serum paraoxonase lactonase 3 and transferrin receptor protein 1, and the third prediction signature additionally included amphiregulin. In the independent validation set from a true screening setting, the five-marker blood-based signature including AREG presented areas under the curves of 0.82 (95% CI, 0.74–0.89), 0.86 (95% CI, 0.77–0.92) and 0.76 (95% CI, 0.64–0.86) for all, early and late stages CRC, respectively. Conclusion: Two different measurement methods consistently identified four protein markers and an algorithm additionally including amphiregulin, a marker measured by PEA only, showed promising performance for detecting early stage CRC in an independent validation in a true screening setting. These proteins may be potential candidates for blood-based tests for early detection of CRC.

A blood-based signature of cerebrospinal fluid A\u03b21\u201342 status

It is increasingly recognized that Alzheimer’s disease (AD) exists before dementia is present and that shifts in amyloid beta occur long before clinical symptoms can be detected. Early detection of these molecular changes is a key aspect for the success of interventions aimed at slowing down rates of cognitive decline. Recent evidence indicates that of the two established methods for measuring amyloid, a decrease in cerebrospinal fluid (CSF) amyloid β1−42 (Aβ1−42) may be an earlier indicator of Alzheimer’s disease risk than measures of amyloid obtained from Positron Emission Tomography (PET). However, CSF collection is highly invasive and expensive. In contrast, blood collection is routinely performed, minimally invasive and cheap. In this work, we develop a blood-based signature that can provide a cheap and minimally invasive estimation of an individual’s CSF amyloid status using a machine learning approach. We show that a Random Forest model derived from plasma analytes can accurately predict subjects as having abnormal (low) CSF Aβ1−42 levels indicative of AD risk (0.84 AUC, 0.78 sensitivity, and 0.73 specificity). Refinement of the modeling indicates that only APOEε4 carrier status and four plasma analytes (CGA, Aβ1−42, Eotaxin 3, APOE) are required to achieve a high level of accuracy. Furthermore, we show across an independent validation cohort that individuals with predicted abnormal CSF Aβ1−42 levels transitioned to an AD diagnosis over 120 months significantly faster than those with predicted normal CSF Aβ1−42 levels and that the resulting model also validates reasonably across PET Aβ1−42 status (0.78 AUC). This is the first study to show that a machine learning approach, using plasma protein levels, age and APOEε4 carrier status, is able to predict CSF Aβ1−42 status, the earliest risk indicator for AD, with high accuracy.

A blood-based transcriptional signature in a novel murine tuberculosis model predicts risk of human tuberculosis progression.

Abstract Tuberculosis (TB) is a highly heterogeneous human disease that develops in a subset of individuals who inhale Mycobacterium tuberculosis (Mtb). Using advanced machine-learning algorithms, we discovered a blood transcriptional signature that identifies Mtb-exposed individuals in the process of progressing to active TB up to 18 months before they exhibit clinical symptoms. This signature may help prevent TB disease by identifying individuals who could benefit from early intervention. However, it fails to identify ~30% of progressors and does not reveal insights into the diverse outcome-governing pathways within the infected lung that underlie TB progression. To develop a tractable system that enables blood-based signatures to be linked to mechanistic pathways within the lung, we pioneered a novel murine TB model that recapitulates key aspects of human Mtb infection. We find that mice infected with a physiologic, ultra-low dose (i.e., 1–3 CFUs) of aerosolized Mtb exhibit a broad range of outcomes, with bacterial burdens ranging from less than 10 to ~106 CFUs within individual lungs. Mice that contain Mtb exhibit well-circumscribed granulomatous structures that share many features with human Mtb granulomas. In addition, we have identified a blood transcriptional signature that distinguishes “controller” and “progressor” mice. Remarkably, this mouse-derived signature is equally as effective as our previously identified human-derived signature at predicting TB risk in humans, confirming the model’s relevance to human disease. This finding enables us to use the tools of the tractable mouse system to address questions central to TB pathogenesis in a clinically relevant model.

A NEW PUTATIVE EARLY BIOMARKER FOR A BLOOD-BASED TIMELY DIAGNOSIS FOR ALZHEIMER’S DISEASE

Although Alzheimer's disease (AD) is commonly regarded as a disease of the brain, it is now recognized that AD is a systemic disease that affects peripheral tissues outside the central nervous system, from the earliest stages of the disease. Given its multifactorial nature, the timely diagnosis of sporadic AD is challenging. There is a clear need for the development of a simple, inexpensive, minimally invasive test for AD to diagnose the disease, ideally at its earliest stages. In the research of blood-based signatures for timely diagnosis of AD, oxidative stress biomarkers have been intensively investigated. Peculiar oxidative-end products can allow the identification of those early oxidative alterations that anticipate the establishment of an overt oxidative stress. In this contest an open isoform of p53 protein (unfolded p53), resulting from nitration to its tyrosine residues, resulted high sensitive to redox environmental alterations and was found peculiarly high expressed in blood cells derived from familiar and sporadic AD. A new conformational specific anti-human p53 antibody has been validated in nearly 300 subjects, including AD, Mild Cognitive Impairment (MCI) patients and age-matched controls, by measuring unfolded p53 with a standardized and reproducible ELISA assay. Unfolded p53 expression resulted higher in plasma of both AD and MCI patients compared to healthy subjects, while in peripheral blood mononuclear cells (PBMCs) unfolded p53 was found increased only in the AD group of patients. Unfolded p53 has been also tested to screen the 4 years follow up patients and it has been useful to predict MCI to AD transition. Although further studies are still necessary for validating unfolded p53 as a potential redox biomarker for AD, the availability of a standardized assay might allow the collection of additional data from different scientific groups. This could be an interesting starting point for a reliable AD-blood-based test that can aid in the timely diagnosis of AD.

Ageing, neurodegeneration and brain rejuvenation.

Although systemic diseases take the biggest toll on human health and well-being, increasingly, a failing brain is the arbiter of a death preceded by a gradual loss of the essence of being. Ageing, which is fundamental to neurodegeneration and dementia, affects every organ in the body and seems to be encoded partly in a blood-based signature. Indeed, factors in the circulation have been shown to modulate ageing and to rejuvenate numerous organs, including the brain. The discovery of such factors, the identification of their origins and a deeper understanding of their functions is ushering in a new era in ageing and dementia research.

Predicting Alzheimer disease from a blood-based biomarker profile: A 54-month follow-up.

We assessed a blood-based signature, which previously demonstrated high accuracy at stratifying individuals with high or low neocortical β-amyloid burden (NAB), to determine whether it could also identify individuals at risk of progression to Alzheimer disease (AD) within 54 months. We generated the blood-based signature for 585 healthy controls (HCs) and 74 participants with mild cognitive impairment (MCI) from the Australian Imaging, Biomarkers and Lifestyle Study who underwent clinical reclassification (blinded to biomarker findings) at 54-month follow-up. The individuals were split into estimated high and low NAB groups based on a cutoff of 1.5 standardized uptake value ratio. We assessed the predictive accuracy of the high and low NAB groupings based on progression to mild cognitive impairment or AD according to clinical reclassification at 54-month follow-up. Twelve percent of HCs with estimated high NAB progressed in comparison to 5% of HCs with estimated low NAB (odds ratio = 2.4). Forty percent of the participants with MCI who had estimated high NAB progressed in comparison to 5% of the participants with MCI who had estimated low NAB (odds ratio = 12.3). These ratios are in line with those reported for Pittsburgh compound B-PET results. Individuals with estimated high NAB had faster rates of memory decline than those with estimated low NAB. These findings suggest that a simple blood-based signature not only provides estimates of NAB but also predicts cognitive decline and disease progression, identifying individuals at risk of progressing toward AD at the prodromal and preclinical stages.