PanelomiX: A threshold-based algorithm to create panels of biomarkers

Virtual patient analysis identifies strategies to improve the performance of predictive biomarkers for PD-1 blockade

Patients with metastatic triple-negative breast cancer (TNBC) show variable responses to PD-1 inhibition. Efficient patient selection by predictive biomarkers would be desirable, but is hindered by the limited performance of existing biomarkers. Here, we leveraged in-silico patient cohorts generated using a quantitative systems pharmacology model of metastatic TNBC, informed by transcriptomic and clinical data, to explore potential ways to improve patient selection. We tested 90 biomarker candidates, including various cellular and molecular species, by a cutoff-based biomarker testing algorithm combined with machine learning-based feature selection. Combinations of pre-treatment biomarkers improved the specificity compared to single biomarkers at the cost of reduced sensitivity. On the other hand, early on-treatment biomarkers, such as the relative change in tumor diameter from baseline measured at two weeks after treatment initiation, achieved remarkably higher sensitivity and specificity. Further, blood-based biomarkers had a comparable ability to tumor- or lymph node-based biomarkers in identifying a subset of responders, potentially suggesting a less invasive way for patient selection.

Blood-Based Biomarkers to Search for Atrial Fibrillation in High-Risk Asymptomatic Individuals and Cryptogenic Stroke Patients

Background

Atrial fibrillation (AF) increases the risk of ischemic stroke in asymptomatic individuals and may be the underlying cause of many cryptogenic strokes. We aimed to test the usefulness of candidate blood-biomarkers related to AF pathophysiology in two prospective cohorts representative of those populations.

Methods

Two hundred seventy-four subjects aged 65–75 years with hypertension and diabetes from the AFRICAT cohort, and 218 cryptogenic stroke patients aged >55 years from the CRYPTO-AF cohort were analyzed. AF was assessed by 4 weeks of monitoring with a wearable Holter device (NuuboTM™). Blood was collected immediately before monitoring started. 10 candidate biomarkers were measured by automated immunoassays (Roche, Penzberg) in the plasma of all patients. Univariate and logistic regression analyses were performed in each cohort separately.

Results

Atrial fibrillation detection rate was 12.4% (AFRICAT cohort) and 22.9% (CRYPTO-AF cohort). 4 biomarkers were significantly increased in asymptomatic individuals with AF [Troponin-T, Angiopoietin-2 (Ang-2), Endocan, and total N-terminal pro-B type natriuretic peptide (NT-proBNP)] and 7 biomarkers showed significantly higher concentrations in cryptogenic stroke patients with AF detection [growth differentiation factor 15, interleukin 6, Troponin-T, Ang-2, Bone morphogenic protein 10, Dickkopf-related protein 3 (DKK-3), and total NT-proBNP]. The models including Ang-2 and total NT-proBNP [AUC 0.764 (0.665–0.863)], and Ang-2 and DKK-3 [AUC = 0.733 (0.654–0.813)], together with age and sex, showed the best performance to detect AF in high-risk asymptomatic individuals, and in cryptogenic stroke patients, respectively.

Conclusion

Blood-biomarkers, in particular, total NT-proBNP, DKK-3, and Ang-2, were associated with AF reflecting two mechanistically different pathways involved in AF pathophysiology (AF stretch and vascular changes). The combination of these biomarkers could be useful in AF screening strategies in the primary care setting and also for searching AF after cryptogenic stroke.

Potential of heart fatty-acid binding protein, neurofilament light, interleukin-10 and S100 calcium-binding protein B in the acute diagnostics and severity assessment of traumatic brain injury

BACKGROUND

There is substantial interest in blood biomarkers as fast and objective diagnostic tools for traumatic brain injury (TBI) in the acute setting.

METHODS

Adult patients (≥18) with TBI of any severity and indications for CT scanning and orthopaedic injury controls were prospectively recruited during 2011-2013 at Turku University Hospital, Finland. The severity of TBI was classified with GCS: GCS 13-15 was classified as mild (mTBI); GCS 9-12 as moderate (moTBI) and GCS 3-8 as severe (sTBI). Serum samples were collected within 24 hours of admission and biomarker levels analysed with high-performance kits. The ability of biomarkers to distinguish between severity of TBI and CT-positive and CT-negative patients was assessed.

RESULTS

Among 189 patients recruited, neurofilament light (NF-L) was obtained from 175 patients with TBI and 40 controls. S100 calcium-binding protein B (S100B), heart fatty-acid binding protein (H-FABP) and interleukin-10 (IL-10) were analysed for 184 patients with TBI and 39 controls. There were statistically significant differences between levels of all biomarkers between the severity classes, but none of the biomarkers distinguished patients with moTBI from patients with sTBI. Patients with mTBI discharged from the ED had lower levels of IL-10 (0.26, IQR=0.21, 0.39 pg/mL), H-FABP (4.15, IQR=2.72, 5.83 ng/mL) and NF-L (8.6, IQR=6.35, 15.98 pg/mL) compared with those admitted to the neurosurgical ward, IL-10 (0.55, IQR=0.31, 1.42 pg/mL), H-FABP (6.022, IQR=4.19, 20.72 ng/mL) and NF-L (13.95, IQR=8.33, 19.93 pg/mL). We observed higher levels of H-FABP and NF-L in older patients with mTBI. None of the biomarkers or their combinations was able to distinguish CT-positive (n=36) or CT-negative (n=58) patients with mTBI from controls.

CONCLUSIONS

S100B, H-FABP, NF-L and IL-10 levels in patients with mTBI were significantly lower than in patients with moTBI and sTBI but alone or in combination, were unable to distinguish patients with mTBI from orthopaedic controls. This suggests these biomarkers cannot be used alone to diagnose mTBI in trauma patients in the acute setting.

A Panel Comprising Serum Amyloid A, White Blood Cells and Nihss for the Triage of Patients at Low Risk of Post-Stroke Infection

Accurate and early prediction of poststroke infections is important to improve antibiotic therapy guidance and/or to avoid unnecessary antibiotic treatment. We hypothesized that the combination of blood biomarkers with clinical parameters could help to optimize risk stratification during hospitalization. In this prospective observational study, blood samples of 283 ischemic stroke patients were collected at hospital admission within 72 h from symptom onset. Among the 283 included patients, 60 developed an infection during the first five days of hospitalization. Performance predictions of blood biomarkers (Serum Amyloid-A (SAA), C-reactive protein, procalcitonin (CRP), white blood cells (WBC), creatinine) and clinical parameters (National Institutes of Health Stroke Scale (NIHSS), age, temperature) for the detection of poststroke infection were evaluated individually using receiver operating characteristics curves. Three machine learning techniques were used for creating panels: Associative Rules Mining, Decision Trees and an internal iterative-threshold based method called PanelomiX. The PanelomiX algorithm showed stable performance when applied to two representative subgroups obtained as splits of the main subgroup. The panel including SAA, WBC and NIHSS had a sensitivity of 97% and a specificity of 45% to identify patients who did not develop an infection. Therefore, it could be used at hospital admission to avoid unnecessary antibiotic (AB) treatment in around half of the patients, and consequently, to reduce AB resistance.

Blood Biomarker Panels for the Early Prediction of Stroke-Associated Complications

Background Acute decompensated heart failure (ADHF) and respiratory tract infections (RTIs) are potentially life-threatening complications in patients experiencing stroke during hospitalization. We aimed to test whether blood biomarker panels might predict these complications early after admission. Methods and Results Nine hundred thirty-eight patients experiencing ischemic stroke were prospectively recruited in the Stroke-Chip study. Post-stroke complications during hospitalization were retrospectively evaluated. Blood samples were drawn within 6 hours after stroke onset, and 14 biomarkers were analyzed by immunoassays. Biomarker values were normalized using log-transformation and Z score. PanelomiX algorithm was used to select panels with the best accuracy for predicting ADHF and RTI. Logistic regression models were constructed with the clinical variables and the biomarker panels. The additional predictive value of the panels compared with the clinical model alone was evaluated by receiver operating characteristic curves. An internal validation through a 10-fold cross-validation with 3 repeats was performed. ADHF and RTI occurred in 19 (2%) and 86 (9.1%) cases, respectively. Three-biomarker panels were developed as predictors: vascular adhesion protein-1 >5.67, NT-proBNP (N-terminal pro-B-type natriuretic peptide) >4.98 and d-dimer >5.38 (sensitivity, 89.5%; specificity, 71.7%) for ADHF; and interleukin-6 >3.97, von Willebrand factor >3.67, and d-dimer >4.58 (sensitivity, 82.6%; specificity, 59.8%) for RTI. Both panels independently predicted stroke complications (panel for ADHF: odds ratio [OR] [95% CI], 10.1 [3-52.2]; panel for RTI: OR, 3.73 [1.95-7.14]) after adjustment by clinical confounders. The addition of the panel to clinical predictors significantly improved areas under the curve of the receiver operating characteristic curves in both cases. Conclusions Blood biomarkers could be useful for the early prediction of ADHF and RTI. Future studies should assess the usefulness of these panels in front of patients experiencing stroke with respiratory symptoms such as dyspnea.

Admission Levels of Interleukin 10 and Amyloid β 1-40 Improve the Outcome Prediction Performance of the Helsinki Computed Tomography Score in Traumatic Brain Injury

Background: Blood biomarkers may enhance outcome prediction performance of head computed tomography scores in traumatic brain injury (TBI).

Objective: To investigate whether admission levels of eight different protein biomarkers can improve the outcome prediction performance of the Helsinki computed tomography score (HCTS) without clinical covariates in TBI.

Materials and methods: Eighty-two patients with computed tomography positive TBIs were included in this study. Plasma levels of β-amyloid isoforms 1–40 (Aβ40) and 1–42 (Aβ42), glial fibrillary acidic protein, heart fatty acid-binding protein, interleukin 10 (IL-10), neurofilament light, S100 calcium-binding protein B, and total tau were measured within 24 h from admission. The patients were divided into favorable (Glasgow Outcome Scale—Extended 5–8, n = 49) and unfavorable (Glasgow Outcome Scale—Extended 1–4, n = 33) groups. The outcome was assessed 6–12 months after injury. An optimal predictive panel was investigated with the sensitivity set at 90–100%.

Results: The HCTS alone yielded a sensitivity of 97.0% (95% CI: 90.9–100) and specificity of 22.4% (95% CI: 10.2–32.7) and partial area under the curve of the receiver operating characteristic of 2.5% (95% CI: 1.1–4.7), in discriminating patients with favorable and unfavorable outcomes. The threshold to detect a patient with unfavorable outcome was an HCTS > 1. The three best individually performing biomarkers in outcome prediction were Aβ40, Aβ42, and neurofilament light. The optimal panel included IL-10, Aβ40, and the HCTS reaching a partial area under the curve of the receiver operating characteristic of 3.4% (95% CI: 1.7–6.2) with a sensitivity of 90.9% (95% CI: 81.8–100) and specificity of 59.2% (95% CI: 40.8–69.4).

Conclusion: Admission plasma levels of IL-10 and Aβ40 significantly improve the prognostication ability of the HCTS after TBI.

Systemic profile of immune factors in an elderly Italian population affected by chronic strongyloidiasis

Background

Strongyloidiasis caused by Strongyloides stercoralis is a soil-transmitted helminthiasis affecting an estimated 370 million people and considered one of the most neglected tropical diseases. Although mostly distributed in tropical and subtropical areas, autochthonous infections have also been documented in north-eastern Italy, even though the transmission presumably stopped decades ago. Because of its peculiar auto-infective cycle, strongyloidiasis can persist lifelong, but the pathophysiological mechanisms associated with the maintenance of such a chronic infection are yet to be fully deciphered.

Methods

Serum levels of 23 immune factors were retrospectively assessed in a subgroup of participants in a randomised clinical trial for the treatment of strongyloidiasis (Strong Treat). Here we included Italian subjects born between 1931 and 1964 and diagnosed with strongyloidiasis between 2013 and 2017 (Ss⁺, n = 32). Serum samples obtained before (BT) and 6 months (6M AT) after ivermectin treatment, as well as from age- and gender-matched uninfected controls (CTRL, n = 34) were analysed.

Results

The assessed immune factors showed a general reduced concertation in Ss⁺ patients and a lack of association with eosinophilia. In our cohort, we did not observe the classical shift towards a type 2 immune response, since Th1 and Th2 cytokines were mostly unaltered. Instead, we observed chemokines as particularly affected by the presence of the parasite, since IL-8, CCL3, CCL4 and CCL5 were significantly reduced in concentration in Ss⁺ subjects compared to CTRL, suggesting that immune cell recruitment to the infection site might be dampened in these patients. This observation was further sustained by a significant increase of CCL4, CCL5 and CCL11 concentrations 6M AT. A significant raised systemic concentration of three growth factors, bFGF, PDGF-BB and IL-7 (haematopoietic growth factor) was also observed post-treatment, indicating a potential involvement in restoring tissue integrity and homeostasis following parasite elimination.

Conclusions

These preliminary data suggest that, in order to survive for such a long period, S. stercoralis might suppress host responses that could otherwise result in its ejection. Our results offer novel insights in the potential mechanisms of disease tolerance that might take place during this chronic infection, including a potential T-cell hypo-responsiveness and a role for chemokines.

Interleukin 10 and Heart Fatty Acid-Binding Protein as Early Outcome Predictors in Patients With Traumatic Brain Injury

Background: Patients with traumatic brain injury (TBI) exhibit a variable and unpredictable outcome. The proteins interleukin 10 (IL-10) and heart fatty acid-binding protein (H-FABP) have shown predictive values for the presence of intracranial lesions. Aim: To evaluate the individual and combined outcome prediction ability of IL-10 and H-FABP, and to compare them to the more studied proteins S100β, glial fibrillary acidic protein (GFAP), and neurofilament light (NF-L), both with and without clinical predictors. Methods: Blood samples from patients with acute TBI (all severities) were collected <24 h post trauma. The outcome was measured >6 months post injury using the Glasgow Outcome Scale Extended (GOSE) score, dichotomizing patients into: (i) those with favorable (GOSE≥5)/unfavorable outcome (GOSE ≤ 4) and complete (GOSE = 8)/incomplete (GOSE ≤ 7) recovery, and (ii) patients with mild TBI (mTBI) and patients with TBIs of all severities. Results: When sensitivity was set at 95-100%, the proteins' individual specificities remained low. H-FABP showed the best specificity (%) and sensitivity (100%) in predicting complete recovery in patients with mTBI. IL-10 had the best specificity (50%) and sensitivity (96%) in identifying patients with favorable outcome in patients with TBIs of all severities. When individual proteins were combined with clinical parameters, a model including H-FABP, NF-L, and ISS yielded a specificity of 56% and a sensitivity of 96% in predicting complete recovery in patients with mTBI. In predicting favorable outcome, a model consisting IL-10, age, and TBI severity reached a specificity of 80% and a sensitivity of 96% in patients with TBIs of all severities. Conclusion: Combining novel TBI biomarkers H-FABP and IL-10 with GFAP, NF-L and S100β and clinical parameters improves outcome prediction models in TBI.

PanelomiX for the Combination of Biomarkers

Proteomics has allowed the discovery and validation of a massive number of biomarkers. However most of them suffer from insufficient specificity and sensitivity and therefore didn't translate to clinical practice. Combining biomarkers with different properties into panels can be an efficient way to bypass these limitations and facilitate the translation of biomarkers into clinical practice.

Screening for Preterm Birth: Potential for a Metabolomics Biomarker Panel

The aim of this preliminary study was to investigate the potential of maternal serum to provide metabolomic biomarker candidates for the prediction of spontaneous preterm birth (SPTB) in asymptomatic pregnant women at 15 and/or 20 weeks’ gestation. Metabolomics LC-MS datasets from serum samples at 15- and 20-weeks’ gestation from a cohort of approximately 50 cases (GA < 37 weeks) and 55 controls (GA > 41weeks) were analysed for candidate biomarkers predictive of SPTB. Lists of the top ranked candidate biomarkers from both multivariate and univariate analyses were produced. At the 20 weeks’ GA time-point these lists had high concordance with each other (85%). A subset of 4 of these features produce a biomarker panel that predicts SPTB with a partial Area Under the Curve (pAUC) of 12.2, a sensitivity of 87.8%, a specificity of 57.7% and a p-value of 0.0013 upon 10-fold cross validation using PanelomiX software. This biomarker panel contained mostly features from groups already associated in the literature with preterm birth and consisted of 4 features from the biological groups of “Bile Acids”, “Prostaglandins”, “Vitamin D and derivatives” and “Fatty Acids and Conjugates”.

Correlation of Blood Biomarkers and Biomarker Panels with Traumatic Findings on Computed Tomography after Traumatic Brain Injury

The aim of the study was to examine the ability of eight protein biomarkers and their combinations in discriminating computed tomography (CT)-negative and CT-positive patients with traumatic brain injury (TBI), utilizing highly sensitive immunoassays in a well-characterized cohort. Blood samples were obtained from 160 patients with acute TBI within 24 h of admission. Levels of β-amyloid isoforms 1-40 (Aβ40) and 1-42 (Aβ42), glial fibrillary acidic protein (GFAP), heart fatty-acid binding protein (H-FABP), interleukin 10 (IL-10), neurofilament light (NF-L), S100 calcium-binding protein B (S100B), and tau were measured. Patients were divided into CT-negative (n = 65) and CT-positive (n = 95), and analyses were conducted separately for TBIs of all severities (Glasgow Coma Scale [GCS] score 3-15) and mild TBIs (mTBIs; GCS 13-15). NF-L, GFAP, and tau were the best in discriminating CT-negative and CT-positive patients, both in patients with mTBI and with all severities. In patients with all severities, area under the curve of the receiver operating characteristic (AUC) was 0.822, 0.817, and 0.781 for GFAP, NF-L, and tau, respectively. In patients with mTBI, AUC was 0.720, 0.689, and 0.676, for GFAP, tau, and NF-L, respectively. The best panel of three biomarkers for discriminating CT-negative and CT-positive patients in the group of all severities was a combination of GFAP+H-FABP+IL-10, with a sensitivity of 100% and specificity of 38.5%. In patients with mTBI, the best panel of three biomarkers was H-FABP+S100B+tau, with a sensitivity of 100% and specificity of 46.4%. Panels of biomarkers outperform individual biomarkers in separating CT-negative and CT-positive patients. Panels consisted mainly of different biomarkers than those that performed best as an individual biomarker.

The Circulating Transcriptome as a Source of Biomarkers for Melanoma

The circulating transcriptome is a valuable source of cancer biomarkers, which, with the exception of microRNAs (miRNAs), remains relatively unexplored. To elucidate which RNAs are present in plasma from melanoma patients and which could be used to distinguish cancer patients from healthy individuals, we used next generation sequencing (NGS), and validation was carried out by qPCR and/or ddPCR. We identified 442 different microRNAs in samples, eleven of which were differentially expressed (p < 0.05). Levels of miR-134-5p and miR-320a-3p were significantly down-regulated (p < 0.001) in melanoma samples (n = 96) compared to healthy controls (n = 28). Differentially expressed protein-encoding mRNA 5'-fragments were enriched for the angiopoietin, p21-activated kinase (PAK), and EIF2 pathways. Levels of ATM1, AMFR, SOS1, and CD109 gene fragments were up-regulated (p < 0.001) in melanoma samples (n = 144) compared to healthy controls (n = 41) (AUC = 0.825). Over 40% of mapped reads were YRNAs, a class of non-coding RNAs that to date has been little explored. Expression levels of RNY3P1, RNY4P1, and RNY4P25 were significantly higher in patients with stage 0 disease than either healthy controls or more advanced stage disease (p < 0.001). In conclusion, we have identified a number of novel RNA biomarkers, which, most importantly, we validated in multi-center retrospective and prospective cohorts, suggesting potential diagnostic use of these RNA species.

Early Levels of Glial Fibrillary Acidic Protein and Neurofilament Light Protein in Predicting the Outcome of Mild Traumatic Brain Injury

The purpose of this study was to correlate the early levels of glial fibrillary acidic protein (GFAP) and neurofilament light protein (NF-L) with outcome in patients with mild traumatic brain injury (mTBI). A total of 107 patients with mTBI (Glasgow Coma Scale ≥13) who had blood samples for GFAP and NF-L available within 24 h of arrival were included. Patients with mTBI were divided into computed tomography (CT)-positive and CT-negative groups. Glasgow Outcome Scale-Extended (GOSE) was used to assess the outcome. Outcomes were defined as complete (GOSE 8) versus incomplete (GOSE <8), and favorable (GOSE 5-8) versus unfavorable (GOSE 1-4). GFAP and NF-L concentrations in blood were measured using ultrasensitive single molecule array technology. Patients with incomplete recovery had significantly higher levels of NF-L compared with those with complete recovery (p = 0.005). The levels of GFAP and NF-L were significantly higher in patients with unfavorable outcome than in patients with favorable outcome (p = 0.002 for GFAP and p < 0.001 for NF-L). For predicting favorable outcome, the area under the receiver operating characteristic curve for GFAP and NF-L was 0.755 and 0.826, respectively. In a multi-variate logistic regression model, the level of NF-L was still a significant predictor for complete recovery (odds ratio [OR] = 1.008; 95% confidence interval [CI], 1.000-1.016). Moreover, the level of NF-L was a significant predictor for complete recovery in CT-positive patients (OR = 1.009; 95% CI, 1.001-1.016). The early levels of GFAP and NF-L are significantly correlated with the outcome in patients with mTBI. The level of NF-L within 24 h from arrival has a significant predictive value in mTBI also in a multi-variate model.

Adipsin, MIP-1b, and IL-8 as CSF Biomarker Panels for ALS Diagnosis

Amyotrophic lateral sclerosis (ALS) is an aggressive neurodegenerative disorder that selectively attacks motor neurons in the brain and spinal cord. Despite important advances in the knowledge of the etiology and progression of the disease, there are still no solid grounds in which a clinician could make an early objective and reliable diagnosis from which patients could benefit. Diagnosis is difficult and basically made by clinical rating scales (ALSRs and El Escorial). The possible finding of biomarkers to aid in the early diagnosis and rate of disease progression could serve for future innovative therapeutic approaches. Recently, it has been suggested that ALS has an important immune component that could represent either the cause or the consequence of the disease. In this report, we analyzed 19 different cytokines and growth factors in the cerebrospinal fluid of 77 ALS patients and 13 controls by decision tree and PanelomiX program. Results showed an increase of Adipsin, MIP-1b, and IL-6, associated with a decrease of IL-8 thresholds, related with ALS patients. This biomarker panel analysis could represent an important aid for diagnosis of ALS alongside the clinical and neurophysiological criteria.

Combining H-FABP and GFAP increases the capacity to differentiate between CT-positive and CT-negative patients with mild traumatic brain injury

Mild traumatic brain injury (mTBI) patients may have trauma-induced brain lesions detectable using CT scans. However, most patients will be CT-negative. There is thus a need for an additional tool to detect patients at risk. Single blood biomarkers, such as S100B and GFAP, have been widely studied in mTBI patients, but to date, none seems to perform well enough. In many different diseases, combining several biomarkers into panels has become increasingly interesting for diagnoses and to enhance classification performance. The present study evaluated 13 proteins individually-H-FABP, MMP-1, MMP-3, MMP-9, VCAM, ICAM, SAA, CRP, GSTP, NKDA, PRDX1, DJ-1 and IL-10-for their capacity to differentiate between patients with and without a brain lesion according to CT results. The best performing proteins were then compared and combined with the S100B and GFAP proteins into a CT-scan triage panel. Patients diagnosed with mTBI, with a Glasgow Coma Scale score of 15 and one additional clinical symptom were enrolled at three different European sites. A blood sample was collected at hospital admission, and a CT scan was performed. Patients were divided into two two-centre cohorts and further dichotomised into CT-positive and CT-negative groups for statistical analysis. Single markers and panels were evaluated using Cohort 1. Four proteins-H-FABP, IL-10, S100B and GFAP-showed significantly higher levels in CT-positive patients. The best-performing biomarker was H-FABP, with a specificity of 32% (95% CI 23-40) and sensitivity reaching 100%. The best-performing two-marker panel for Cohort 1, subsequently validated in Cohort 2, was a combination of H-FABP and GFAP, enhancing specificity to 46% (95% CI 36-55). When adding IL-10 to this panel, specificity reached 52% (95% CI 43-61) with 100% sensitivity. These results showed that proteins combined into panels could be used to efficiently classify CT-positive and CT-negative mTBI patients.

Discovery of Lipidome Alterations Following Traumatic Brain Injury via High-Resolution Metabolomics

Traumatic brain injury (TBI) can occur across wide segments of the population, presenting in a heterogeneous manner that makes diagnosis inconsistent and management challenging. Biomarkers offer the potential to objectively identify injury status, severity, and phenotype by measuring the relative concentrations of endogenous molecules in readily accessible biofluids. Through a data-driven, discovery approach, novel biomarker candidates for TBI were identified in the serum lipidome of adult male Sprague-Dawley rats in the first week following moderate controlled cortical impact (CCI). Serum samples were analyzed in positive and negative modes by ultraperformance liquid chromatography-mass spectrometry (UPLC-MS). A predictive panel for the classification of injured and uninjured sera samples, consisting of 26 dysregulated species belonging to a variety of lipid classes, was developed with a cross-validated accuracy of 85.3% using omniClassifier software to optimize feature selection. Polyunsaturated fatty acids (PUFAs) and PUFA-containing diacylglycerols were found to be upregulated in sera from injured rats, while changes in sphingolipids and other membrane phospholipids were also observed, many of which map to known secondary injury pathways. Overall, the identified biomarker panel offers viable molecular candidates representing lipids that may readily cross the blood-brain barrier (BBB) and aid in the understanding of TBI pathophysiology.

Alignment-Based Prediction of Sites of Metabolism

Prediction of metabolically labile atom positions in a molecule (sites of metabolism) is a key component of the simulation of xenobiotic metabolism as a whole, providing crucial information for the development of safe and effective drugs. In 2008, an exploratory study was published in which sites of metabolism were derived based on molecular shape- and chemical feature-based alignment to a molecule whose site of metabolism (SoM) had been determined by experiments. We present a detailed analysis of the breadth of applicability of alignment-based SoM prediction, including transfer of the approach from a structure- to ligand-based method and extension of the applicability of the models from cytochrome P450 2C9 to all cytochrome P450 isozymes involved in drug metabolism. We evaluate the effect of molecular similarity of the query and reference molecules on the ability of this approach to accurately predict SoMs. In addition, we combine the alignment-based method with a leading chemical reactivity model to take reactivity into account. The combined model yielded superior performance in comparison to the alignment-based approach and the reactivity models with an average area under the receiver operating characteristic curve of 0.85 in cross-validation experiments. In particular, early enrichment was improved, as evidenced by higher BEDROC scores (mean BEDROC = 0.59 for α = 20.0, mean BEDROC = 0.73 for α = 80.5).

Weighted Genetic Risk Scores and Prediction of Weight Gain in Solid Organ Transplant Populations

Background

Polygenic obesity in Solid Organ Transplant (SOT) populations is considered a risk factor for the development of metabolic abnormalities and graft survival. Few studies to date have studied the genetics of weight gain in SOT recipients. We aimed to determine whether weighted genetic risk scores (w-GRS) integrating genetic polymorphisms from GWAS studies (SNP group#1 and SNP group#2) and from Candidate Gene studies (SNP group#3) influence BMI in SOT populations and if they predict ≥10% weight gain (WG) one year after transplantation. To do so, two samples (n_A = 995, n_B = 156) were obtained from naturalistic studies and three w-GRS were constructed and tested for association with BMI over time. Prediction of 10% WG at one year after transplantation was assessed with models containing genetic and clinical factors.

Results

w-GRS were associated with BMI in sample A and B combined (BMI increased by 0.14 and 0.11 units per additional risk allele in SNP group#1 and #2, respectively, p-values<0.008). w-GRS of SNP group#3 showed an effect of 0.01 kg/m² per additional risk allele when combining sample A and B (p-value 0.04). Models with genetic factors performed better than models without in predicting 10% WG at one year after transplantation.

Conclusions

This is the first study in SOT evaluating extensively the association of w-GRS with BMI and the influence of clinical and genetic factors on 10% of WG one year after transplantation, showing the importance of integrating genetic factors in the final model. Genetics of obesity among SOT recipients remains an important issue and can contribute to treatment personalization and prediction of WG after transplantation.

A simple biomarker scoring matrix for early gastric cancer detection

Gastric cancer (GC) is a major cause of death in many parts of the world. While 90% of early GC is curable by resection, only about 5% of patients diagnosed in the late stages survive beyond five years. This provides strong impetus to push for early GC detection through the use of non-invasive biomarkers, before metastatic complications arise. It is also of strong medical interest to identify patients of the diffuse subtype at the earliest time possible, since the disease variant progresses very rapidly and is associated with much higher mortality rate. In this study, we compared quantitatively the gastric fluid proteome of 70 GC patients to 17 individuals with benign gastritis in search of potential biomarkers that aid in GC diagnosis, prognosis and subtype stratification. We report that as much as half of the gastric fluid proteome is subject to regulation in diseased states, and propose a simple biomarker panel scoring matrix for early GC detection with diagnostic sensitivity of 95.7%. We also demonstrate as proof-of-concept that a digitised record generated with SWATH-MS based on 380 protein abundance signatures from the gastric fluid could segregate patients with diffuse-type GC.

Mastitomics, the integrated omics of bovine milk in an experimental model of Streptococcus uberis mastitis: 1. High abundance proteins, acute phase proteins and peptidomics

A peptidomic investigation of milk from an experimental model of Streptococcus uberis mastitis in dairy cows has incorporated a study of milk high abundance and acute phase (APP) proteins as well as analysis of low molecular weight peptide biomarkers. Intramammary infection (IMI) with S. uberis caused a shift in abundance from caseins, β-lactoglobulin and α-lactalbumin to albumin, lactoferrin and IgG with the increase in lactoferrin occurring last. The APP response of haptoglobin, mammary associated serum amyloid A3 and C-reactive protein occurred between 30-48 hours post challenge with peak concentrations of APPs at 72-96 hours post challenge and declined thereafter at a rate resembling the fall in bacterial count rather than the somatic cell count. A peptide biomarker panel for IMI based on capillary electrophoresis and mass spectrometry was developed. It comprised 77 identified peptides (IMI77) composed mainly of casein derived peptides but also including peptides of glycosylation dependent cell adhesion molecule and serum amyloid A. The panel had a biomarker classification score that increased from 36 hour to 81 hour post challenge, significantly differentiating infected from non-infected milk, thus suggesting potential as a peptide biomarker panel of bovine mastitis and specifically that of S. uberis origin. The use of omic technology has shown a multifactorial cross system reaction in high and low abundance proteins and their peptide derivatives with changes of over a thousand fold in analyte levels in response to S. uberis infection.

Identification of metabolomics panels for potential lung cancer screening by analysis of exhaled breath condensate

Exhaled breath condensate (EBC) is one of the less employed biofluids when searching for clinical markers, despite its non-invasive sampling and the potential relationship between its composition and respiratory disease phenotypes such as lung cancer. The advanced stage at which lung cancer is usually detected is the main reason for the high mortality rate of this carcinogenic disease. In this preliminary research, EBC was used as clinical sample to develop a screening tool for lung cancer discrimination from two control groups (with and without risk factor). Three panels of metabolites were configured using the PanelomiX tool to minimize false negatives (specificity) and false positives (sensitivity). The combination of five metabolites led to three panels providing a sensitivity above 77.9%, specificity above 67.5% and the area under the curve (AUC) above 77.5% for the three panels. An additional study was developed as a first approach to study the statistical significance of metabolites at different stages of lung cancer.

Human sweat metabolomics for lung cancer screening

Sweat is one of the less employed biofluids for discovery of markers in spite of its increased application in medicine for detection of drugs or for diagnostic of cystic fibrosis. In this research, human sweat was used as clinical sample to develop a screening tool for lung cancer, which is the carcinogenic disease with the highest mortality rate owing to the advanced stage at which it is usually detected. In this context, a method based on the metabolite analysis of sweat to discriminate between patients with lung cancer versus smokers as control individuals is proposed. The capability of the metabolites identified in sweat to discriminate between both groups of individuals was studied and, among them, a trisaccharide phosphate presented the best independent performance in terms of the specificity/sensitivity pair (80 and 72.7%, respectively). Additionally, two panels of metabolites were configured using the PanelomiX tool as an attempt to reduce false negatives (at least 80% specificity) and false positives (at least 80% sensitivity). The first panel (80% specificity and 69% sensitivity) was composed by suberic acid, a tetrahexose, and a trihexose, while the second panel (69% specificity and 80% sensitivity) included nonanedioic acid, a trihexose, and the monoglyceride MG(22:2). Thus, the combination of the five metabolites led to a single panel providing 80% specificity and 79% sensitivity, reducing the false positive and negative rates to almost 20%. The method was validated by estimation of within-day and between-days variability of the quantitative analysis of the five metabolites.

Combining bioinformatics and MS-based proteomics: clinical implications

Clinical proteomics research aims at i) discovery of protein biomarkers for screening, diagnosis and prognosis of disease, ii) discovery of protein therapeutic targets for improvement of disease prevention, treatment and follow-up, and iii) development of mass spectrometry (MS)-based assays that could be implemented in clinical chemistry, microbiology or hematology laboratories. MS has been increasingly applied in clinical proteomics studies for the identification and quantification of proteins. Bioinformatics plays a key role in the exploitation of MS data in several aspects such as the generation and curation of protein sequence databases, the development of appropriate software for MS data treatment and integration with other omics data and the establishment of adequate standard files for data sharing. In this article, we discuss the main MS approaches and bioinformatics solutions that are currently applied to accomplish the objectives of clinical proteomic research.