The International CTAD Task Force (TF) addressed challenges related to designing clinical trials for agitation in dementia, presenting accomplishments from the two previous TFs on neuropsychiatric symptoms (NPS). In addition, this TF proposed a paradigm shift in NPS assessment and management, presenting Mild Behavioral Impairment (MBI) as a clinical syndrome. MBI is marked by later-life emergent and persistent NPS in dementia-free older persons (ranging from cognitively unimpaired to subjective cognitive decline to mild cognitive impairment), which facilitates earlier detection and better prognostication of Alzheimer's disease (AD). The TF has made the following recommendations for incorporation of NPS into AD preventative trials: (1) clinical trials targeting improvement in MBI symptoms should be undertaken; (2) treatment trials for MBI should be disease specific and confirm the diagnosis of participants using biomarkers; trials should include measures sensitive to cognitive changes in preclinical AD, which can serve as outcome measures, in addition to changes in biomarker levels; (3) as a first step, pharmacotherapeutic trials should address the full MBI complex as well as the specific symptoms/domains that constitute MBI; (4) clinical trials using problemadaptation psychotherapy to target affective MBI should be considered; and (5) MBI should be considered in AD trials of disease modifying therapies. The well-validated and widely-used MBI Checklist (MBI-C) is an appropriate symptom rating scale for these studies, as it was developed specifically to identify and measure MBI in dementia-free persons. Other scales such as the Neuropsychiatric Inventory (NPI) may be used, although administration at two timepoints may be necessary to operationalize the MBI criterion of symptom persistence.

In randomized clinical trials (RCTs) for Alzheimer’s Disease (AD), cerebrospinal fluid (CSF) and positron emission tomography (PET) biomarkers are currently used for the detection and monitoring of AD pathological features. The use of less resource-intensive plasma biomarkers could decrease the burden to study volunteers and limit costs and time for study enrollment. Blood-based markers (BBMs) could thus play an important role in improving the design and the conduct of RCTs on AD. It remains to be determined if the data available on BBMs are strong enough to replace CSF and PET biomarkers as entry criteria and monitoring tools in RCTs.

Blarcamesine (ANAVEX®2-73), a novel oral selective sigma-1 receptor (SIGMAR1) agonist was investigated in a clinical Phase 2a study in Alzheimer’s disease in which blarcamesine resulted in a lower rate of cognitive (MMSE) and functional (ADCS-ADL) decline. Following the positive results of this study, a translational approach led to investigating blarcamesine in an international, double-blind, multicenter, placebo-controlled Phase 2 clinical study of 14-week duration in 132 patients with Parkinson’s disease dementia (PDD). Whole blood transcriptomics analysis (RNAseq) was performed for the PDD study at two timepoints: baseline and Week 14. After quality control filtering, 14,150 genes were retained for analysis. Weighted gene correlation network analysis (WGCNA) was used to identify clusters of genes that show a correlated expression change across patients and timepoints. Thus, a data-driven gene network was generated. Associations between the identified clusters and treatment arms were explored in order to select clusters for which gene expression is significantly associated with the high blarcamesine dose. These associations were assessed using linear mixed effect models with three covariates: dose, patient and timepoint. Significance was assessed with Dunett’s test. Blarcamesine significantly restores functionality in key pathways of Alzheimer’s disease, Parkinson’s disease and Prion diseases. The analysis identified two clusters of genes that are significantly differentially expressed in treated patients and represent compensatory pathways to genes dysregulations induced by neurodegenerative diseases. In both clusters, majority of genes were confirmed to interact (STRING database). Amongst interacting genes, neurodegenerative pathways were identified as overrepresented (p < 0.01). Down-regulation of genes involved with neurodegeneration was observed in the placebo arm, however, was compensated in the blarcamesine arm. This analysis identified a gene network that is differentially expressed in patients treated with blarcamesine after 14 weeks of treatment, compared to placebo. The biological relevance of this gene network was assessed. Pathway analysis confirmed the impact of the treatment on pathways involved in neurodegenerative diseases The identification of a gene network as the blarcamesine response pathway lays the foundation to better understand the mechanism of action at the molecular level of blarcamesine, thus unlocking characterization of responders based on molecular profiling, as well as identification of new indications in the area of neurodegenerative and other disorders.

e14652 Background: Lung cancer is one of the most prevalent types of cancer worldwide: 250,000 new cases are diagnosed yearly in the US, amongst which 80% are non-small cell lung cancers (NSCLC). The optimal biomarker to select patients diagnosed with advanced disease and benefiting the most from immunotherapy is still to be identified. Herein, we used KEM (Knowledge Extraction and Management) explainable Artificial Intelligence (xAI) as a tool that systematically extracts and evaluates all association rules between all variables in a database, thus enabling the identification of subgroups of patients with advanced NSCLC treated with immunotherapy with higher chances of overall survival in the NIVOBIO cohort (Foy et al. Eur J Cancer 2023). We aimed to identify biomarkers linked to optimum response to immunotherapy considering previous lines of therapies. Methods: Data was retrieved from GEO warehouse (GSE161537) and aggregated into a consolidated database totaling 82 patients and 2,568 variables. A two-step analysis plan was then performed. First step relied on KEM xAI: rules between gene expression, number of previous treatment lines and survival were explored: 51,306 rules were generated. 19 rules involving 3 genes were retained using metrics such as Support (number of examples), Confidence (conditional probability) and Lift (relative probability) and focusing on genes with a consistent signal across rules. Second, Cox proportional hazards models were generated using these genes to test the interaction between their expression and the number of previous treatment lines the patient had undergone. Genes with a significant log-rank and interaction p-value were retained. Results: Our analysis identified 2 genes that were significantly associated with overall survival and previous lines of treatment: SOS2 (p < 0.001) and LIFR (p = 0.020) high expression was associated with improved survival among patients with at least two previous treatment lines, whereas it was associated with poor survival for other patients. Consistently, low expression values of these genes were associated with poor survival in patients with less than two previous treatment lines. SOS2 and LIFR gene expression was identified as having a significant interaction with the number of previous treatment lines in the Cox model with a hazard ratio of 0.08 and 0.39 respectively. Conclusions: Our analysis enabled the identification of biomarkers not obviously related to the immune microenvironment and associated with an improved or poor survival depending on when immunotherapy was administered. These findings are laying promising foundations for the development of dynamic biomarkers and their potential translation into therapeutic recommendations after further validation.

1554 Background: Precision oncology aims to guide patient (pts) treatment decisions by matching biological features with available drugs. Extensive genomic analysis allows to identify an actionable alteration in 40-60% of patients. In a recent study of 50 pts with advanced refractory diseases included in PROFILER (NCT01774409), whole exome and fusion transcripts had a limited value over a 90-tumor gene panel (TGP) to increase molecular-based treatment recommendations (MBTR). Herein, we evaluated the feasibility, in the same cohort of pts, of the AI-transcriptional-based therapeutic recommendation-tool OncoKEM to guide treatment recommendations. Methods: 77 fresh frozen (FF) and/or FFPE samples including paired specimens for 53 pts with available RNA-Seq gene expression profiles were included. For each pts, a tumor transcriptional profile (TTP) was generated by identifying differentially expressed genes between the pts tumor and a cohort of matched healthy tissue. A large database of drug transcriptional signatures (DTS) was queried in order to identify a “reversal relationship” between the TTP and a DTS. A total of 205 drugs were ranked, including a subset of 61 FDA and/or EMA approved targeted therapies (aTT). Results: Most common diagnoses were breast cancers (21% of which 63% were TNBC), followed by ovarian cancers (OC, 18%) and soft-tissue sarcomas (STS, 13%). The median number of previous treatment lines was 4 (range: 1 - 10). Among the 77 tumor samples analyzed, 54 (70%) specimens led to the generation of an OncoKEM report, with no differences between FF and FFPE samples (p = 0.85). The overlap between the top 10 proposed drugs between paired FF and FFPE samples was 56% on average. All patients had at least 2 propositions (range: 2-9) of aTT among the top 10 ranked drugs in the Onco KEM reports. Most frequently proposed drugs among the top 10 were palbociclib, talazoparib, infigratinib in TNBC; bosutinib, sapanisertib, SAR125844 in OC; ipilimumab, cabozantinib, sapanisertib in STS. Among the 30 pts (79%) without any MBTR based on TGP/WES/fusion transcript analysis, all had at least 2 proposed aTT in the Onco KEM report (median: 4, range: 2-9). Top ranked drugs were MET (18%), VEGFR (12%), Abl (12%), FGFR (11%), PI3K/AKT/mTOR (11%), PARP (10%) and CDK4/6 inhibitors (7%). Conclusions: AI-transcriptional-based therapeutic recommendation-tool OncoKEM is feasible and has the potential to expand personalized cancer treatment in pts with advanced & refractory diseases without tractable genomic alterations. The clinical relevance assessment is planned in an upcoming clinical trial.

Background The risk of hospitalization or death after influenza infection is higher at the extremes of age and in individuals with comorbidities. We estimated the number of hospitalizations with influenza and characterized the cumulative risk of comorbidities and age on severe outcomes in Mexico and Brazil. Methods We used national hospital discharge data from Brazil (SIH/SUS) from 2010–2018 and Mexico (SAEH) from 2010–2017 to estimate the number of influenza admissions using ICD-10 discharge codes, stratified by age (0–4, 5–17, 18–49, 50–64, and ≥65 years). Duration of hospital stay, admission to the intensive care unit (ICU), and in-hospital case fatality rates (CFRs) defined the severe outcomes. Rates were compared between patients with or without pre-specified comorbidities and by age. Results A total of 327,572 admissions with influenza were recorded in Brazil and 20,613 in Mexico, with peaks period most years. In Brazil, the median hospital stay duration was 3.0 days (interquartile range, 2.0–5.0), ICU admission rate was 3.3% (95% CI, 3.2–3.3%), and in-hospital CFR was 4.6% (95% CI, 4.5–4.7). In Mexico, the median duration of stay was 5.0 days (interquartile range, 3.0–7.0), ICU admission rate was 1.8% (95% CI, 1.6–2.0%), and in-hospital CFR was 6.9% (95% CI, 6.5–7.2). In Brazil, ICU admission and in-hospital CFR were higher in adults aged ≥50 years and increased in the presence of comorbidities, especially cardiovascular disease. In Mexico, comorbidities increased the risk of ICU admission by 1.9 (95% CI, 1.0–3.5) and in-hospital CFR by 13.9 (95% CI, 8.4–22.9) in children 0–4 years. Conclusion The SIH/SUS and SAEH databases can be used to estimate hospital admissions with influenza, and the disease severity. Age and comorbidities, especially cardiovascular disease, are cumulatively associated with more severe outcomes, with differences between countries. This association should be further analyzed in prospective surveillance studies designed to support influenza vaccination strategy decisions.

There is substantial experimental evidence for dysregulation of several microRNA (miRNA) expression levels in Alzheimer’s disease (AD). MiRNAs modulate critical brain intracellular signaling pathways and are associated with AD core pathophysiological mechanisms. First, we conducted a real-time quantitative PCR-based pilot study to identify a set of brain-enriched miRNAs in a monocentric cohort of cognitively normal individuals with subjective memory complaints, a condition associated with increased risk of AD. Second, we investigated the impact of age, sex, and the Apolipoprotein E ε4 ( APOE ε4 ) allele, on the identified miRNA plasma concentrations. In addition, we explored the cross-sectional and longitudinal association of the miRNAs plasma concentrations with regional brain metabolic uptake using amyloid-β (Aβ)-positron emission tomography (Aβ-PET) and 18 F-fluorodeoxyglucose-PET ( 18 F-FDG-PET). We identified a set of six brain-enriched miRNAs—miRNA-125b, miRNA-146a, miRNA-15b, miRNA-148a, miRNA-26b, and miRNA-100. Age, sex, and APOE ε4 allele were not associated with individual miRNA abundance. MiRNA-15b concentrations were significantly lower in the Aβ-PET-positive compared to Aβ-PET-negative individuals. Furthermore, we found a positive effect of the miRNA-15b*time interaction on regional metabolic 18 F-FDG-PET uptake in the left hippocampus. Plasma miRNA-125b concentrations, as well as the miRNA-125b*time interaction (over a 2-year follow-up), were negatively associated with regional Aβ-PET standard uptake value ratio in the right anterior cingulate cortex. At baseline, we found a significantly negative association between plasma miRNA-125b concentrations and 18 F-FDG-PET uptake in specific brain regions. In an asymptomatic at-risk population for AD, we show significant associations between plasma concentrations of miRNA-125b and miRNA-15b with core neuroimaging biomarkers of AD pathophysiology. Our results, coupled with existing experimental evidence, suggest a potential protective anti-Aβ effect of miRNA-15b and a biological link between miRNA-125b and Aβ-independent neurotoxic pathways.

ABSTRACT The practice of precision medicine is defined by the discovery of actionable insights emanating from molecular diagnosis. While serial DNA sequencing may reveal mutations that supervene in causing treatment resistance, the transcriptome has been little explored in the clinic. We report here a case report of a patient with metastatic colon cancer who developed resistance to HER2-targeted therapies with mechanisms that could not be identified by genomic analyses, but which were uncovered by RNA-seq. Serial evaluation of gene expression appears to be a valuable tool for diagnosing a specific mechanisms of treatment resistance, i.e. the resistome, arising from altered gene expression rather than from genomic abnormalities.

Abstract Introduction The search for drugs to treat Alzheimer's disease (AD) has failed to yield effective therapies. Here we report the first genome‐wide search for biomarkers associated with therapeutic response in AD. Blarcamesine (ANAVEX2‐73), a selective sigma‐1 receptor (SIGMAR1) agonist, was studied in a 57‐week Phase 2a trial (NCT02244541). The study was extended for a further 208 weeks (NCT02756858) after meeting its primary safety endpoint. Methods Safety, clinical features, pharmacokinetic, and efficacy, measured by changes in the Mini‐Mental State Examination (MMSE) and the Alzheimer's Disease Cooperative Study‐Activities of Daily Living scale (ADCS‐ADL), were recorded. Whole exome and transcriptome sequences were obtained for 21 patients. The relationship between all available patient data and efficacy outcome measures was analyzed with unsupervised formal concept analysis (FCA), integrated in the Knowledge Extraction and Management (KEM) environment. Results Biomarkers with a significant impact on clinical outcomes were identified at week 57: mean plasma concentration of blarcamesine (slope MMSE:P < .041), genomic variants SIGMAR1 p.Gln2Pro (ΔMMSE:P < .039; ΔADCS‐ADL:P < .063) and COMT p.Leu146fs (ΔMMSE:P < .039; ΔADCS‐ADL:P < .063), and baseline MMSE score (slope MMSE:P < .015). Their combined impact on drug response was confirmed at week 148 with linear mixed effect models. Discussion Confirmatory Phase 2b/3 clinical studies of these patient selection markers are ongoing. This FCA/KEM analysis is a template for the identification of patient selection markers in early therapeutic development for neurologic disorders.

Alzheimer's disease (AD) neuropathology is extremely heterogeneous, and the evolution from preclinical to mild cognitive impairment until dementia is driven by interacting genetic/biological mechanisms not fully captured by current clinical/research criteria. We characterized the heterogeneous "construct" of AD through a cerebrospinal fluid biomarker-guided stratification approach. We analyzed 5 validated pathophysiological cerebrospinal fluid biomarkers (A beta(1-42), t-tau, -p-tau(181), NFL, YKL-40) in 113 participants (healthy controls [N = 20], subjective memory complainers [N = 36], mild cognitive impairment [N = 20], and AD dementia [N = 37], age: 66.7 +/- 10.4, 70.4 +/- 7.7, 71.7 +/- 8.4, 76.2 +/- 3.5 years [mean +/- SD], respectively) using Density-Based Spatial Clustering of Applications with Noise, which does not require a priori determination of the number of clusters. We found 5 distinct clusters (sizes: N = 38, 16, 24, 14, and 21) whose composition was independent of phenotypical groups. Two clusters showed biomarker profiles linked to neurodegenerative processes not associated with classical AD-related pathophysiology. One cluster was characterized by the neuroinflammation biomarker YKL-40. Combining nonlinear data aggregation with informative biomarkers can generate novel patient strata which are representative of cellular/molecular pathophysiology and may aid in predicting disease evolution and mechanistic drug response. (C) 2019 The Authors. Published by Elsevier Inc.

ASENT 2019 Abstracts # The American Society for Experimental NeuroTherapeutics, Inc. 2019 Active Immunotherapeutic Vaccine Platform Targeting Endogenous Proteins for the Treatment of Neurodegenerative Diseases Sharon Tamir; Hui Jing Yu, PhD; JC Dodart, PhD; and Chang Yi Wang, PhD; all of United Neuroscience United Neuroscience (UNS) is pioneering a new class of active immunotherapeutics to promote brain health, focusing on delivering safe, effective and accessible medicines to treat and ultimately prevent Alzheimer’s (AD), Parkinson’s disease (PD) and other neurological disorders. Leveraging a proven vaccine platform that has commercialized one of the only licensed active immunotherapies against an endogenous protein in the world and has scaled up manufacture to over 500 million doses annually for other animal indications. Success for a vaccine approach aimed at endogenous targets relies on the ability to break immune tolerance and generate a humoral antibody response against the desired epitopes, while also avoiding T-cell-mediated autoimmune reactions. UNS has evolved a novel epitope specific vaccine platform that can break immune tolerance towards endogenous proteins and selectively generate B-cell antibody responses while avoiding activation of T-cell inflammation. This UBITh vaccine platform utilizes a library of proprietary synthetic UBITh peptides linked to the desired target epitopes, to enlist the minimal T-Helper assistance required for triggering proliferation of B-cells recognizing the targeted epitope. This approach avoids generation of T-cell responses to the target epitope. UBITh peptides also do not elicit any antibody response against themselves, thus driving all the humoral response to the target. The first clinical therapeutic program utilizing this platform is the anti-beta amyloid (anti-Abeta) vaccine called UBITh AD Immunotherapeutic Vaccine (UB-311), which selectively targets epitopes exposed on oligomeric and fibrillar Abeta aggregates and is being developed for the treatment of Alzheimer’s disease (AD). In a completed Phase 1 and Phase 2a clinical studies, UB-311 elicited high anti-Abeta antibody levels with outstanding responder rate in patients with no evidence of diminished vaccine response with age, or immunosenescence. UB-311 has been well tolerated, has not shown evidence of meningoencephalitis or brain edema [ARIA-E]. The second UBITh vaccine platform called UB-312 is aimed at aggregated aSyn. UB-312 targets the Cterminus of aSyn and is chemically linked to a UBITh peptide. Phase 1 clinical study testing UB-312 in Parkinson’s patients is positioned to be initiated later this year. Neuroprotection by Endovascular Selective Cerebral Hypothermia; Experimental In Vivo Studies and Initial Clinical Experience Ronald Solar, PhD; Renaissance Biomedical, Inc. The neuroprotective effects of hypothermia following cardiac arrest and acute stroke have been demonstrated in experimental models and clinical trials. Experimental studies indicate that improved efficacy and broadened indications can be achieved with moderate to deep hypothermia. However, current techniques require systemic cooling, and are unable to cool rapidly and deeply without serious detrimental effects. A new catheter-based system and technique to rapidly, deeply and selectively cool the brain was investigated. Using a standard transfemoral technique in large swine (60-72 kg), the multilumen catheter was positioned to isolate the right or left common carotid artery. Blood was withdrawn from the aorta via one lumen, cooled extracorporeally, and reperfused through a second lumen into the carotid artery. Outflow blood was cooled to 5-20°C, and reperfused at rates of 80-250 ml/min for 30-180 minutes. Temperature was measured in bilateral frontal lobes, nasopharynx, ear, esophagus, jugular vein and descending aorta. In a porcine stroke reperfusionmodel, 25 pigs were randomly assigned to 3 hours of selective cerebral cooling to 26°C or normothermia following 3 hours of ischemia achieved by surgically clipping a middle cerebral artery. Brain MRI and histology were evaluated by experts who were blinded to the intervention. Cerebral cooling to as low as 15°C was achieved with no significant systemic cooling. Initial cooling rates of 1.8°C/min were attained, and were dependent on flow rate and temperature of the perfused blood. Passive rewarming did not result in rebound hyperthermia. No adverse events were observed. In the stroke reperfusion model, a significant reduction in stroke volume was observed by selective cerebral cooling to 26°C compared to the normothermic control group. In initial human experience, selective cerebral cooling to 26°C resulted in excellent outcomes with no neurological deficits in the settings of neurosurgery and out of hospital cardiac arrest. This new catheter-based system and technique shows promise in providing rapid, selective, deep cerebral hypothermia, and may offer an improved method for neuroprotection during neurosurgery, cardiac arrest, acute stroke and other ischemic insult. EEG biomarkers to improve the discovery of new neurotherapeutics Corinne Roucard, PhD; Céline Ruggiero, PhD; Alexis Evrard, Yann Roche, PhD; and Venceslas Duveaum; all of SynapCell SAS, SaintIsmier, France The purpose of this presentation is to describe how EEG biomarkers can help to identify new neurotherapeutics in epilepsy and Parkinson’s disease. Neurotherapeutics https://doi.org/10.1007/s13311-019-00751-2

Alzheimer’s disease (AD)—a complex disease showing multiple pathomechanistic alterations—is triggered by nonlinear dynamic interactions of genetic/epigenetic and environmental risk factors, which, ultimately, converge into a biologically heterogeneous disease. To tackle the burden of AD during early preclinical stages, accessible blood-based biomarkers are currently being developed. Specifically, next-generation clinical trials are expected to integrate positive and negative predictive blood-based biomarkers into study designs to evaluate, at the individual level, target druggability and potential drug resistance mechanisms. In this scenario, systems biology holds promise to accelerate validation and qualification for clinical trial contexts of use—including proof-of-mechanism, patient selection, assessment of treatment efficacy and safety rates, and prognostic evaluation. Albeit in their infancy, systems biology-based approaches are poised to identify relevant AD “signatures” through multifactorial and interindividual variability, allowing us to decipher disease pathophysiology and etiology. Hopefully, innovative biomarker-drug codevelopment strategies will be the road ahead towards effective disease-modifying drugs.

Systems biology studies have demonstrated that different (epi)genetic and pathophysiological alterations may be mapped onto a single tumor's clinical phenotype thereby revealing commonalities shared by cancers with divergent phenotypes. The success of this approach in cancer based on analyses of traditional and emerging body fluid-based biomarkers has given rise to the concept of liquid biopsy enabling a non-invasive and widely accessible precision medicine approach and a significant paradigm shift in the management of cancer. Serial liquid biopsies offer clues about the evolution of cancer in individual patients across disease stages enabling the application of individualized genetically and biologically guided therapies. Moreover, liquid biopsy is contributing to the transformation of drug research and development strategies as well as supporting clinical practice allowing identification of subsets of patients who may enter pathway-based targeted therapies not dictated by clinical phenotypes alone. A similar liquid biopsy concept is emerging for Alzheimer's disease, in which blood-based biomarkers adaptable to each patient and stage of disease, may be used for positive and negative patient selection to facilitate establishment of high-value drug targets and counter-measures for drug resistance. Going beyond the "one marker, one drug" model, integrated applications of genomics, transcriptomics, proteomics, receptor expression and receptor cell biology and conformational status assessments during biomarker-drug co-development may lead to a new successful era for Alzheimer's disease therapeutics. We argue that the time is now for implementing a liquid biopsy-guided strategy for the development of drugs that precisely target Alzheimer's disease pathophysiology in individual patients.

The early detection of cognitively normal individuals with multifactorial risk of developing Alzheimer's disease (AD), is an essential step to increase the rate of success of clinical trials investigating new and old drugs with a putative disease modifying effect. Indeed, all clinical trials targeting prodromal AD or even AD dementia have failed suggesting that pharmacological interventions should start in the preclinical phase. Breakthroughs in Artificial Intelligence technology may substantially help accomplish accurate disease prediction. In this study, we investigated 290 cognitively normal individuals with subjective memory complaints (SMC), a condition at risk for AD, of the French monocentric INSIGHT-preAD cohort, (Pitié-Salpêtrière University Hospital, Paris), over a 2-year period. DNA Neurox2 Illumina microarray chip was used to genotype the individuals, thus exploring 13,037,940 SNP variants, out of which 295,995 are not imputed and shared by at least two individuals of the cohort. In addition, the results of 15 cognitive neuropsychometric tests were collected for each individual and the evolution of these tests between baseline and up to 6 subsequent time points was monitored. The relationships between genomic biomarkers and cognitive outcomes were analyzed using the KEM®machine learning tool, an Artificial Intelligence technology based on association rules analysis. Focusing on cognitive tests results associated with a constant decline at all time points, fourteen genomic variants were identified as significantly linked to constant cognitive loss for 5 different tests (P< .05). By contrast, two genomic variants were identified as significantly linked to constant cognitive improvement or stability (P< .005). Our results demonstrate that the KEM®machine learning tool has the ability to identify putative preclinical disease biomarkers using Artificial Intelligence technology for the analysis of combined (gen)omic and cognitive tests results. The identified genomic variants may be explored as potential biological markers to identify prodromal individuals with high risk of progressing to mild cognitive impairment.

Precision medicine focuses on DNA abnormalities, but not all tumors have tractable genomic alterations. The WINTHER trial ( NCT01856296 ) navigated patients to therapy on the basis of fresh biopsy-derived DNA sequencing (arm A; 236 gene panel) or RNA expression (arm B; comparing tumor to normal). The clinical management committee (investigators from five countries) recommended therapies, prioritizing genomic matches; physicians determined the therapy given. Matching scores were calculated post-hoc for each patient, according to drugs received: for DNA, the number of alterations matched divided by the total alteration number; for RNA, expression-matched drug ranks. Overall, 303 patients consented; 107 (35%; 69 in arm A and 38 in arm B) were evaluable for therapy. The median number of previous therapies was three. The most common diagnoses were colon, head and neck, and lung cancers. Among the 107 patients, the rate of stable disease ≥6 months and partial or complete response was 26.2% (arm A: 23.2%; arm B: 31.6% (P = 0.37)). The patient proportion with WINTHER versus previous therapy progression-free survival ratio of >1.5 was 22.4%, which did not meet the pre-specified primary end point. Fewer previous therapies, better performance status and higher matching score correlated with longer progression-free survival (all P < 0.05, multivariate). Our study shows that genomic and transcriptomic profiling are both useful for improving therapy recommendations and patient outcome, and expands personalized cancer treatment. Prospective analysis of transcriptomic and genomic alterations increases the proportion of patients with solid cancer who are eligible for receiving matched therapies and shows promise in improving clinical outcomes.

The study of ‘gut-brain axis’ is providing a growing body of evidence identifying gut microbiota as a critical component of healthy brain function, elevating it to a clinically actionable organ. Nevertheless, very few clinical trials assess the relationship between Alzheimer's Disease (AD) and gut microbiota. Here we report the first microbiota search for biomarkers associated with drug response in AD. ANAVEX®2-73, a selective sigma-1 receptor (SIGMAR1) agonist, was investigated in a 57-week Phase 2a study with 32 mild-to-moderate AD dementia patients (NCT 02244541). The study met its primary endpoint, demonstrating a favorable safety profile, and was subsequently extended by an additional 208 weeks (NCT 02756858), during which patient's stool collection was performed to analyze gut microbiota. Stool samples of 16 available patients were collected. After 16s metagenomics sequencing (Illumina® MiSeq) a MOTHUR based, bioinformatics pipeline was used for taxonomic classification of sequences; abundances measurement of 32,875 operational taxonomic units (OTU) were mapped to 11 phylums, 81 families and 230 genera. The relationship between available microbiota biomarkers and efficacy outcome measures was investigated using a non-linear rules-based Formal Concept Analysis (FCA), as implemented in Ariana's KEM® software, generating all association rules. 8,143,928 hypotheses were explored and associations between gut microbiota abundances and cognitive outcome was identified. The abundances of two microbiota families, Ruminococcaceae and Porphyromonadaceae, were associated with improved response at week 148 (p<0.01 and p<0.04 respectively). Moreover, a non-significant trend was identified linking the abundance of Verrucomicrobia phylum with cognitive improvement (Lift: 1.29, Confidence: 60%). Broadly, the abundance was found to be different for cognitive improvement compared to decline.