Decoding depression: a comprehensive multi-cohort exploration of blood DNA methylation using machine learning and deep learning approaches.

The causes of depression are complex, and the current diagnosis methods rely solely on psychiatric evaluations with no incorporation of laboratory biomarkers in clinical practices. We investigated the stability of blood DNA methylation depression signatures in six different populations using six public and two domestic cohorts (n = 1942) conducting mega-analysis and meta-analysis of the individual studies. We evaluated 12 machine learning and deep learning strategies for depression classification both in cross-validation (CV) and in hold-out tests using merged data from 8 separate batches, constructing models with both biased and unbiased feature selection. We found 1987 CpG sites related to depression in both mega- and meta-analysis at the nominal level, and the associated genes were nominally related to axon guidance and immune pathways based on enrichment analysis and eQTM data. Random forest classifiers achieved the highest performance (AUC 0.73 and 0.76) in CV and hold-out tests respectively on the batch-level processed data. In contrast, the methylation showed low predictive power (all AUCs < 0.57) for all classifiers in CV and no predictive power in hold-out tests when used with harmonized data. All models achieved significantly better performance (>14% gain in AUCs) with pre-selected features (selection bias), with some of the models (joint autoencoder-classifier) reaching AUCs of up to 0.91 in the final testing regardless of data preparation. Different algorithmic feature selection approaches may outperform limma, however, random forest models perform well regardless of the strategy. The results provide an overview over potential future biomarkers for depression and highlight many important methodological aspects for DNA methylation-based depression profiling including the use of machine learning strategies.

Identifying proteomic risk factors for overall, aggressive, and early onset prostate cancer using Mendelian Randomisation and tumour spatial transcriptomics.

BACKGROUND: Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention. METHODS: We investigated the association of 2002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomisation (MR) and colocalisation. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalisation were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumour tissue to assess their role in tumour aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets. FINDINGS: We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which the majority replicated where data were available. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirmed an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also found an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that comparatively had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk also mapped to existing therapeutic interventions. INTERPRETATION: Our findings emphasise the importance of proteomics for improving our understanding of prostate cancer aetiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumours. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer. FUNDING: This work was supported by Cancer Research UK (grant no. C8221/A29017).

Depression proteomic profiling in adolescents with transcriptome analyses in independent cohorts.

Introduction: Depression is a major global burden with unclear pathophysiology and poor treatment outcomes. Diagnosis of depression continues to rely primarily on behavioral rather than biological methods. Investigating tools that might aid in diagnosing and treating early-onset depression is essential for improving the prognosis of the disease course. While there is increasing evidence of possible biomarkers in adult depression, studies investigating this subject in adolescents are lacking. Methods: In the current study, we analyzed protein levels in 461 adolescents assessed for depression using the Development and Well-Being Assessment (DAWBA) questionnaire as part of the domestic Psychiatric Health in Adolescent Study conducted in Uppsala, Sweden. We used the Proseek Multiplex Neuro Exploratory panel with Proximity Extension Assay technology provided by Olink Bioscience, followed by transcriptome analyses for the genes corresponding to the significant proteins, using four publicly available cohorts. Results: We identified a total of seven proteins showing different levels between DAWBA risk groups at nominal significance, including RBKS, CRADD, ASGR1, HMOX2, PPP3R1, CD63, and PMVK. Transcriptomic analyses for these genes showed nominally significant replication of PPP3R1 in two of four cohorts including whole blood and prefrontal cortex, while ASGR1 and CD63 were replicated in only one cohort. Discussion: Our study on adolescent depression revealed protein-level and transcriptomic differences, particularly in PPP3R1, pointing to the involvement of the calcineurin pathway in depression. Our findings regarding PPP3R1 also support the role of the prefrontal cortex in depression and reinforce the significance of investigating prefrontal cortex-related mechanisms in depression.

Exploring biomarkers in trigeminal neuralgia patients operated with microvascular decompression: A comparison with multiple sclerosis patients and non-neurological controls.

BACKGROUND: Trigeminal neuralgia (TN) is a severe facial pain condition often associated with a neurovascular conflict. However, neuroinflammation has also been implicated in TN, as it frequently co-occurs with multiple sclerosis (MS). METHODS: We analysed protein expression levels of TN patients compared to MS patients and controls. Proximity Extension Assay technology was used to analyse the levels of 92 proteins with the Multiplex Neuro-Exploratory panel provided by SciLifeLab, Uppsala, Sweden. Serum and CSF samples were collected from TN patients before (n = 33 and n = 27, respectively) and after (n = 28 and n = 8, respectively) microvascular decompression surgery. Additionally, we included samples from MS patients (n = 20) and controls (n = 20) for comparison. RESULTS: In both serum and CSF, several proteins were found increased in TN patients compared to either MS patients, controls, or both, including EIF4B, PTPN1, EREG, TBCB, PMVK, FKBP5, CD63, CRADD, BST2, CD302, CRIP2, CCL27, PPP3R1, WWP2, KLB, PLA2G10, TDGF1, SMOC1, RBKS, LTBP3, CLSTN1, NXPH1, SFRP1, HMOX2, and GGT5. The overall expression of the 92 proteins in postoperative TN samples seems to shift towards the levels of MS patients and controls in both serum and CSF, as compared to preoperative samples. Interestingly, there was no difference in protein levels between MS patients and controls. CONCLUSIONS: We conclude that TN patients showed increased serum and CSF levels of specific proteins and that successful surgery normalizes these protein levels, highlighting its potential as an effective treatment. However, the similarity between MS and controls challenges the idea of shared pathophysiology with TN, suggesting distinct underlying mechanisms in these conditions. SIGNIFICANCE: This study advances our understanding of trigeminal neuralgia (TN) and its association with multiple sclerosis (MS). By analysing 92 protein biomarkers, we identified distinctive molecular profiles in TN patients, shedding light on potential pathophysiological mechanisms. The observation that successful surgery normalizes many protein levels suggests a promising avenue for TN treatment. Furthermore, the contrasting protein patterns between TN and MS challenge prevailing assumptions of similarity between the two conditions and point to distinct pathophysiological mechanisms.

Evaluation of circulating plasma proteins in breast cancer using Mendelian randomisation.

Biomarkers for early detection of breast cancer may complement population screening approaches to enable earlier and more precise treatment. The blood proteome is an important source for biomarker discovery but so far, few proteins have been identified with breast cancer risk. Here, we measure 2929 unique proteins in plasma from 598 women selected from the Karolinska Mammography Project to explore the association between protein levels, clinical characteristics, and gene variants, and to identify proteins with a causal role in breast cancer. We present 812 cis-acting protein quantitative trait loci for 737 proteins which are used as instruments in Mendelian randomisation analyses of breast cancer risk. Of those, we present five proteins (CD160, DNPH1, LAYN, LRRC37A2 and TLR1) that show a potential causal role in breast cancer risk with confirmatory results in independent cohorts. Our study suggests that these proteins should be further explored as biomarkers and potential drug targets in breast cancer.

Identifying proteomic risk factors for overall, aggressive and early onset prostate cancer using Mendelian randomization and tumor spatial transcriptomics.

Background: Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention. Methods: We investigated the association of 2,002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomization (MR) and colocalization. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalization were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumor tissue to assess their role in tumor aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets. Results: We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which a majority were novel and replicated. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirm an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also find an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk mapped to existing therapeutic interventions. Conclusion: Our findings emphasize the importance of proteomics for improving our understanding of prostate cancer etiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumors. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer.

The international clinical trials registry platform (ICTRP): data integrity and the trends in clinical trials, diseases, and drugs.

Introduction: Clinical trials are the gold standard for testing new therapies. Databases like ClinicalTrials.gov provide access to trial information, mainly covering the US and Europe. In 2006, WHO introduced the global ICTRP, aggregating data from ClinicalTrials.gov and 17 other national registers, making it the largest clinical trial platform by June 2019. This study conducts a comprehensive global analysis of the ICTRP database and provides framework for large-scale data analysis, data preparation, curation, and filtering. Materials and methods: The trends in 689,793 records from the ICTRP database (covering trials registered from 1990 to 2020) were analyzed. Records were adjusted for duplicates and mapping of agents to drug classes was performed. Several databases, including DrugBank, MESH, and the NIH Drug Information Portal were used to investigate trends in agent classes. Results: Our novel approach unveiled that 0.5% of the trials we identified were hidden duplicates, primarily originating from the EUCTR database, which accounted for 82.9% of these duplicates. However, the overall number of hidden duplicates within the ICTRP seems to be decreasing. In total, 689 793 trials (478 345 interventional) were registered in the ICTRP between 1990 and 2020, surpassing the count of trials in ClinicalTrials.gov (362 500 trials by the end of 2020). We identified 4 865 unique agents in trials with DrugBank, whereas 2 633 agents were identified with NIH Drug Information Portal data. After the ClinicalTrials.gov, EUCTR had the most trials in the ICTRP, followed by CTRI, IRCT, CHiCTR, and ISRCTN. CHiCTR displayed a significant surge in trial registration around 2015, while CTRI experienced rapid growth starting in 2016. Conclusion: This study highlights both the strengths and weaknesses of using the ICTRP as a data source for analyzing trends in clinical trials, and emphasizes the value of utilizing multiple registries for a comprehensive analysis.

Nanoparticles in Clinical Trials: Analysis of Clinical Trials, FDA Approvals and Use for COVID-19 Vaccines.

Nanoparticles are heterologous small composites that are usually between 1 and 100 nanometers in size. They are applied in many areas of medicine with one of them being drug delivery. Nanoparticles have a number of advantages as drug carriers which include reduced toxic effects, increased bioavailability, and their ability to be modified for specific tissues or cells. Due to the exciting development of nanotechnology concomitant with advances in biotechnology and medicine, the number of clinical trials devoted to nanoparticles for drug delivery is growing rapidly. Some nanoparticles, lipid-based types, in particular, played a crucial role in the developing and manufacturing of the two COVID-19 vaccines-Pfizer and Moderna-that are now being widely used. In this analysis, we provide a quantitative survey of clinical trials using nanoparticles during the period from 2002 to 2021 as well as the recent FDA-approved drugs (since 2016). A total of 486 clinical trials were identified using the clinicaltrials.gov database. The prevailing types of nanoparticles were liposomes (44%) and protein-based formulations (26%) during this period. The most commonly investigated content of the nanoparticles were paclitaxel (23%), metals (11%), doxorubicin (9%), bupivacaine and various vaccines (both were 8%). Among the FDA-approved nanoparticle drugs, polymeric (29%), liposomal (22%) and lipid-based (21%) drugs were the most common. In this analysis, we also discuss the differential development of the diverse groups of nanoparticles and their content, as well as the underlying factors behind the trends.

Methylation in MAD1L1 is associated with the severity of suicide attempt and phenotypes of depression.

Depression is a multifactorial disorder representing a significant public health burden. Previous studies have linked multiple single nucleotide polymorphisms with depressive phenotypes and suicidal behavior. MAD1L1 is a mitosis metaphase checkpoint protein that has been linked to depression in GWAS. Using a longitudinal EWAS approach in an adolescent cohort at two time points (n = 216 and n = 154), we identified differentially methylated sites that were associated with depression-related genetic variants in MAD1L1. Three methylation loci (cg02825527, cg18302629, and cg19624444) were consistently hypomethylated in the minor allele carriers, being cross-dependent on several SNPs. We further investigated whether DNA methylation at these CpGs is associated with depressive psychiatric phenotypes in independent cohorts. The first site (cg02825527) was hypomethylated in blood (exp(ß) = 84.521, p value ~ 0.003) in participants with severe suicide attempts (n = 88). The same locus showed increased methylation in glial cells (exp(ß) = 0.041, p value ~ 0.004) in the validation cohort, involving 29 depressed patients and 29 controls, and showed a trend for association with suicide (n = 40, p value ~ 0.089) and trend for association with depression treatment (n = 377, p value ~ 0.075). The second CpG (cg18302629) was significantly hypomethylated in depressed participants (exp(ß) = 56.374, p value ~ 0.023) in glial cells, but did not show associations in the discovery cohorts. The last methylation site (cg19624444) was hypomethylated in the whole blood of severe suicide attempters; however, this association was at the borderline for statistical significance (p value ~ 0.061). This locus, however, showed a strong association with depression treatment in the validation cohort (exp(ß) = 2.237, p value ~ 0.003) with 377 participants. The direction of associations between psychiatric phenotypes appeared to be different in the whole blood in comparison with brain samples for cg02825527 and cg19624444. The association analysis between methylation at cg18302629 and cg19624444 and MAD1L1 transcript levels in CD14+ cells shows a potential link between methylation at these CpGs and MAD1L1 expression. This study suggests evidence that methylation at MAD1L1 is important for psychiatric health as supported by several independent cohorts.

Treatment of ADHD: Drugs, psychological therapies, devices, complementary and alternative methods as well as the trends in clinical trials.

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders having a high influence on social interactions. The number of approved treatments and clinical trials for ADHD have increased markedly during the recent decade. This analytical review provides a quantitative overview of the existing pharmacological and non-pharmacological methods of ADHD treatments investigated in clinical trials during 1999-2021. A total of 695 interventional trials were manually assessed from clinicaltrial.gov with the search term « ADHD¼, and trial data has been used for analysis. A clear majority of the studies investigated non-pharmacological therapies (∼80%), including many behavioral options, such as social skills training, sleep and physical activity interventions, meditation and hypnotherapy. Devices, complementary and other alternative methods of ADHD treatment are also gaining attention. The pharmacological group accounts for ∼20% of all the studies. The most common drug classes include central nervous system stimulants (e.g., methylphenidate hydrochloride, lisdexamfetamine dimesylate, amphetamine sulfate, mixed amphetamine salts, a combination of dexmethylphenidate hydrochloride and serdexmethylphenidate chloride), selective noradrenaline reuptake inhibitors (atomoxetine, viloxazine), and alpha2 adrenergic receptor agonists (guanfacine hydrochloride, clonidine hydrochloride). Several studies investigated antidepressants (e.g., bupropion hydrochloride, vortioxetine), and atypical antipsychotics (e.g., quetiapine, aripiprazole) but these are yet not approved by the FDA for ADHD treatment. We discuss the quantitative trends in clinical trials and provide an overview of the new drug agents and non-pharmacological therapies, drug targets, and novel treatment options.

Neoantigen Vaccines; Clinical Trials, Classes, Indications, Adjuvants and Combinatorial Treatments.

Personalized neoantigen vaccines are a highly specific cancer treatment designed to induce a robust cytotoxic T-cell attack against a patient's cancer antigens. In this study, we searched ClinicalTrials.gov for neoantigen vaccine clinical trials and systematically analyzed them, a total of 147 trials. Peptide vaccines are the largest neoantigen vaccine type, comprising up to 41% of the clinical trials. However, mRNA vaccines are a growing neoantigen vaccine group, especially in the most recent clinical trials. The most common cancer types in the clinical trials are glioma, lung cancer, and malignant melanoma, being seen in more than half of the clinical trials. Small-cell lung cancer and non-small-cell lung cancer are the largest individual cancer types. According to the results from the clinical trials, neoantigen vaccines work best when combined with other cancer treatments, and popular combination treatments include immune checkpoint inhibitors, chemotherapy, and radiation therapy. Additionally, half of the clinical trials combined neoantigen vaccines with an adjuvant to boost the immune effects, with poly-ICLC being the most recurrent adjuvant choice. This study clarifies the rapid clinical trial development of personalized neoantigen vaccines as an emerging class of cancer treatment with increasingly diversified opportunities in classes, indications, and combinatorial treatments.

Brain Cancer Drug Discovery: Clinical Trials, Drug Classes, Targets, and Combinatorial Therapies.

Brain cancer is a formidable challenge for drug development, and drugs derived from many cutting-edge technologies are being tested in clinical trials. We manually characterized 981 clinical trials on brain tumors that were registered in ClinicalTrials.gov from 2010 to 2020. We identified 582 unique therapeutic entities targeting 581 unique drug targets and 557 unique treatment combinations involving drugs. We performed the classification of both the drugs and drug targets based on pharmacological and structural classifications. Our analysis demonstrates a large diversity of agents and targets. Currently, we identified 32 different pharmacological directions for therapies that are based on 42 structural classes of agents. Our analysis shows that kinase inhibitors, chemotherapeutic agents, and cancer vaccines are the three most common classes of agents identified in trials. Agents in clinical trials demonstrated uneven distribution in combination approaches; chemotherapy agents, proteasome inhibitors, and immune modulators frequently appeared in combinations, whereas kinase inhibitors, modified immune effector cells did not as was shown by combination networks and descriptive statistics. This analysis provides an extensive overview of the drug discovery field in brain cancer, shifts that have been happening in recent years, and challenges that are likely to come. SIGNIFICANCE STATEMENT: This review provides comprehensive quantitative analysis and discussion of the brain cancer drug discovery field, including classification of drug, targets, and therapies.

Trends in kinase drug discovery: targets, indications and inhibitor design.

The FDA approval of imatinib in 2001 was a breakthrough in molecularly targeted cancer therapy and heralded the emergence of kinase inhibitors as a key drug class in the oncology area and beyond. Twenty years on, this article analyses the landscape of approved and investigational therapies that target kinases and trends within it, including the most popular targets of kinase inhibitors and their expanding range of indications. There are currently 71 small-molecule kinase inhibitors (SMKIs) approved by the FDA and an additional 16 SMKIs approved by other regulatory agencies. Although oncology is still the predominant area for their application, there have been important approvals for indications such as rheumatoid arthritis, and one-third of the SMKIs in clinical development address disorders beyond oncology. Information on clinical trials of SMKIs reveals that approximately 110 novel kinases are currently being explored as targets, which together with the approximately 45 targets of approved kinase inhibitors represent only about 30% of the human kinome, indicating that there are still substantial unexplored opportunities for this drug class. We also discuss trends in kinase inhibitor design, including the development of allosteric and covalent inhibitors, bifunctional inhibitors and chemical degraders.