Discovery of synthetic lethal interactions from large-scale pan-cancer perturbation screens

Despite the progress in precision oncology, development of cancer therapies is limited by the dearth of suitable drug targets1. Novel candidate drug targets can be identified based on the concept of synthetic lethality (SL), which refers to pairs of genes for which an aberration in either gene alone is non-lethal, but co-occurrence of the aberrations is lethal to the cell. We developed SLIdR (Synthetic Lethal Identification in R), a statistical framework for identifying SL pairs from large-scale perturbation screens. SLIdR successfully predicts SL pairs even with small sample sizes while minimizing the number of false positive targets. We applied SLIdR to Project DRIVE data2 and found both established and novel pan-cancer and cancer type-specific SL pairs. We identified and experimentally validated a novel SL interaction between AXIN1 and URI1 in hepatocellular carcinoma, thus corroborating the potential of SLIdR to identify new SL-based drug targets.

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Use of Antiplatelet Agents Decreases the Positive Predictive Value of Fecal Immunochemical Tests for Colorectal Cancer but Does Not Affect Their Sensitivity

Journal of Personalized Medicine ◽

10.3390/jpm11060497 ◽

2021 ◽

Vol 11 (6) ◽

pp. 497

Author(s):

Yoonsuk Jung ◽

Eui Im ◽

Jinhee Lee ◽

Hyeah Lee ◽

Changmo Moon

Keyword(s):

Colorectal Cancer ◽

Large Scale ◽

Screening Program ◽

Antiplatelet Agents ◽

Population Based ◽

Small Sample ◽

Antithrombotic Agents ◽

Predictive Values ◽

Prescription Rates ◽

Small Sample Sizes

Previous studies have evaluated the effects of antithrombotic agents on the performance of fecal immunochemical tests (FITs) for the detection of colorectal cancer (CRC), but the results were inconsistent and based on small sample sizes. We studied this topic using a large-scale population-based database. Using the Korean National Cancer Screening Program Database, we compared the performance of FITs for CRC detection between users and non-users of antiplatelet agents and warfarin. Non-users were matched according to age and sex. Among 5,426,469 eligible participants, 768,733 used antiplatelet agents (mono/dual/triple therapy, n = 701,683/63,211/3839), and 19,569 used warfarin, while 4,638,167 were non-users. Among antiplatelet agents, aspirin, clopidogrel, and cilostazol ranked first, second, and third, respectively, in terms of prescription rates. Users of antiplatelet agents (3.62% vs. 4.45%; relative risk (RR): 0.83; 95% confidence interval (CI): 0.78–0.88), aspirin (3.66% vs. 4.13%; RR: 0.90; 95% CI: 0.83–0.97), and clopidogrel (3.48% vs. 4.88%; RR: 0.72; 95% CI: 0.61–0.86) had lower positive predictive values (PPVs) for CRC detection than non-users. However, there were no significant differences in PPV between cilostazol vs. non-users and warfarin users vs. non-users. For PPV, the RR (users vs. non-users) for antiplatelet monotherapy was 0.86, while the RRs for dual and triple antiplatelet therapies (excluding cilostazol) were 0.67 and 0.22, respectively. For all antithrombotic agents, the sensitivity for CRC detection was not different between users and non-users. Use of antiplatelet agents, except cilostazol, may increase the false positives without improving the sensitivity of FITs for CRC detection.

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Collaboration and Open Science Initiatives in Primate Research

10.31219/osf.io/7c93a ◽

2021 ◽

Author(s):

ManyPrimates ◽

Alba Motes Rodrigo ◽

Charlotte Canteloup ◽

Sonja J. Ebel ◽

Christopher I Petkov ◽

...

Keyword(s):

Cognitive Abilities ◽

Large Scale ◽

Open Science ◽

Small Sample ◽

Primate Research ◽

Research Model ◽

Primate Cognition ◽

History Of ◽

Small Sample Sizes ◽

Successful Collaboration

Traditionally, primate cognition research has been conducted by independent teams on small populations of a few species. Such limited variation and small sample sizes pose problems that prevent us from reconstructing the evolutionary history of primate cognition. In this chapter, we discuss how large-scale collaboration, a research model successfully implemented in other fields, makes it possible to obtain the large and diverse datasets needed to conduct robust comparative analysis of primate cognitive abilities. We discuss the advantages and challenges of large-scale collaborations and argue for the need for more open science practices in the field. We describe these collaborative projects in psychology and primatology and introduce ManyPrimates as the first, successful collaboration that has established an infrastructure for large-scale, inclusive research in primate cognition. Considering examples of large-scale collaborations both in primatology and psychology, we conclude that this type of research model is feasible and has the potential to address otherwise unattainable questions in primate cognition.

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Age and sex dependent variability of type 2 dopamine receptors in the human brain: A large-scale PET cohort

10.1101/2021.08.10.455776 ◽

2021 ◽

Author(s):

Tuulia Malén ◽

Tomi Karjalainen ◽

Janne Isojärvi ◽

Aki Vehtari ◽

Paul-Christian Bürkner ◽

...

Keyword(s):

Human Brain ◽

Large Scale ◽

Movement Control ◽

Heterogeneous Data ◽

Small Sample ◽

Cross Sectional ◽

Psychiatric Conditions ◽

Small Sample Sizes ◽

Age And Sex

BACKGROUND: The dopamine system contributes to a multitude of functions ranging from reward and motivation to learning and movement control, making it a key component in goal-directed behavior. Altered dopaminergic function is observed in neurological and psychiatric conditions. Numerous factors have been proposed to influence dopamine function, but due to small sample sizes and heterogeneous data analysis methods in previous studies their specific and joint contributions remain unresolved. METHODS: In this cross-sectional register-based study we investigated how age, sex, body mass index (BMI), as well as cerebral hemisphere and regional volume influence striatal type 2 dopamine receptor (D2R) availability in the human brain. We analyzed a large historical dataset (n=156, 120 males and 36 females) of [11C]raclopride PET scans performed between 2004 and 2018. RESULTS: Striatal D2R availability decreased through age for both sexes and was higher in females versus males throughout age. BMI and striatal D2R availability were weakly associated. There was no consistent lateralization of striatal D2R. The observed effects were independent of regional volumes. These results were validated using two different spatial normalization methods, and the age and sex effects also replicated in an independent sample (n=135). CONCLUSIONS: D2R density is dependent on age and sex, which may contribute to the vulnerability of neurological and psychiatric conditions involving altering D2R expression.

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Epigenome-wide meta-analysis of blood DNA methylation and its association with subcortical volumes: findings from the ENIGMA Epigenetics Working Group

Molecular Psychiatry ◽

10.1038/s41380-019-0605-z ◽

2019 ◽

Cited By ~ 3

Author(s):

Tianye Jia ◽

Congying Chu ◽

Yun Liu ◽

Jenny van Dongen ◽

Evangelos Papastergios ◽

...

Keyword(s):

Dna Methylation ◽

Nucleus Accumbens ◽

Large Scale ◽

Biomarker Discovery ◽

Association Studies ◽

Meta Analysis ◽

Hippocampal Volume ◽

Small Sample ◽

Small Sample Sizes ◽

Meta Analyses

AbstractDNA methylation, which is modulated by both genetic factors and environmental exposures, may offer a unique opportunity to discover novel biomarkers of disease-related brain phenotypes, even when measured in other tissues than brain, such as blood. A few studies of small sample sizes have revealed associations between blood DNA methylation and neuropsychopathology, however, large-scale epigenome-wide association studies (EWAS) are needed to investigate the utility of DNA methylation profiling as a peripheral marker for the brain. Here, in an analysis of eleven international cohorts, totalling 3337 individuals, we report epigenome-wide meta-analyses of blood DNA methylation with volumes of the hippocampus, thalamus and nucleus accumbens (NAcc)—three subcortical regions selected for their associations with disease and heritability and volumetric variability. Analyses of individual CpGs revealed genome-wide significant associations with hippocampal volume at two loci. No significant associations were found for analyses of thalamus and nucleus accumbens volumes. Cluster-based analyses revealed additional differentially methylated regions (DMRs) associated with hippocampal volume. DNA methylation at these loci affected expression of proximal genes involved in learning and memory, stem cell maintenance and differentiation, fatty acid metabolism and type-2 diabetes. These DNA methylation marks, their interaction with genetic variants and their impact on gene expression offer new insights into the relationship between epigenetic variation and brain structure and may provide the basis for biomarker discovery in neurodegeneration and neuropsychiatric conditions.

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Predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization

BMC Bioinformatics ◽

10.1186/s12859-019-3197-3 ◽

2019 ◽

Vol 20 (S19) ◽

Author(s):

Jiang Huang ◽

Min Wu ◽

Fan Lu ◽

Le Ou-Yang ◽

Zexuan Zhu

Keyword(s):

Matrix Factorization ◽

Drug Targets ◽

Synthetic Lethality ◽

Cancer Therapeutics ◽

The Self ◽

Interaction Data ◽

Synthetic Lethal Interaction ◽

Synthetic Lethal ◽

Interaction Prediction ◽

Synthetic Lethal Interactions

Abstract Background Synthetic lethality has attracted a lot of attentions in cancer therapeutics due to its utility in identifying new anticancer drug targets. Identifying synthetic lethal (SL) interactions is the key step towards the exploration of synthetic lethality in cancer treatment. However, biological experiments are faced with many challenges when identifying synthetic lethal interactions. Thus, it is necessary to develop computational methods which could serve as useful complements to biological experiments. Results In this paper, we propose a novel graph regularized self-representative matrix factorization (GRSMF) algorithm for synthetic lethal interaction prediction. GRSMF first learns the self-representations from the known SL interactions and further integrates the functional similarities among genes derived from Gene Ontology (GO). It can then effectively predict potential SL interactions by leveraging the information provided by known SL interactions and functional annotations of genes. Extensive experiments on the synthetic lethal interaction data downloaded from SynLethDB database demonstrate the superiority of our GRSMF in predicting potential synthetic lethal interactions, compared with other competing methods. Moreover, case studies of novel interactions are conducted in this paper for further evaluating the effectiveness of GRSMF in synthetic lethal interaction prediction. Conclusions In this paper, we demonstrate that by adaptively exploiting the self-representation of original SL interaction data, and utilizing functional similarities among genes to enhance the learning of self-representation matrix, our GRSMF could predict potential SL interactions more accurately than other state-of-the-art SL interaction prediction methods.

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Defining the biological bases of individual differences in musicality

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2014.0092 ◽

2015 ◽

Vol 370 (1664) ◽

pp. 20140092 ◽

Cited By ~ 26

Author(s):

Bruno Gingras ◽

Henkjan Honing ◽

Isabelle Peretz ◽

Laurel J. Trainor ◽

Simon E. Fisher

Keyword(s):

Large Scale ◽

Twin Studies ◽

Small Sample ◽

Aptitude Tests ◽

Large Scale Assessment ◽

Genome Wide ◽

Entry Points ◽

High Heritability ◽

Small Sample Sizes ◽

Complex Human Traits

Advances in molecular technologies make it possible to pinpoint genomic factors associated with complex human traits. For cognition and behaviour, identification of underlying genes provides new entry points for deciphering the key neurobiological pathways. In the past decade, the search for genetic correlates of musicality has gained traction. Reports have documented familial clustering for different extremes of ability, including amusia and absolute pitch (AP), with twin studies demonstrating high heritability for some music-related skills, such as pitch perception. Certain chromosomal regions have been linked to AP and musical aptitude, while individual candidate genes have been investigated in relation to aptitude and creativity. Most recently, researchers in this field started performing genome-wide association scans. Thus far, studies have been hampered by relatively small sample sizes and limitations in defining components of musicality, including an emphasis on skills that can only be assessed in trained musicians. With opportunities to administer standardized aptitude tests online, systematic large-scale assessment of musical abilities is now feasible, an important step towards high-powered genome-wide screens. Here, we offer a synthesis of existing literatures and outline concrete suggestions for the development of comprehensive operational tools for the analysis of musical phenotypes.

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Massive Factorial Design Untangles Coding Sequences Determinants of Translation Efficacy

10.1101/208801 ◽

2017 ◽

Cited By ~ 2

Author(s):

Guillaume Cambray ◽

Joao C. Guimaraes ◽

Adam Paul Arkin

Keyword(s):

Large Scale ◽

Small Sample ◽

Translation Efficiency ◽

E Coli ◽

Synthetic Dna ◽

Critical Design ◽

Codon Composition ◽

Expression Activity ◽

Small Sample Sizes ◽

Epigenetic Disruption

AbstractComparative analyses of natural sequences or variant libraries are often used to infer mechanisms of expression, activity and evolution. Contingent selective histories and small sample sizes can profoundly bias such approaches. Both limitations can be lifted using precise design of large-scale DNA synthesis. Here, we precisely design 5E. coligenomes worth of synthetic DNA to untangle the relative contributions of 8 interlaced sequence properties described independently as major determinants of translation inEscherichia coli. To expose hierarchical effects, we engineer an inducible translational coupling device enabling epigenetic disruption of mRNA secondary structures. We find that properties commonly believed to modulate translation generally explain less than a third of the variation in protein production. We describe dominant effects of mRNA structures over codon composition on both initiation and elongation, and previously uncharacterized relationships among factors controlling translation. These results advance our understanding of translation efficiency and expose critical design challenges.

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Population Genetics Study of Isoniazid Resistance Mutations and Evolution of Multidrug-Resistant Mycobacterium tuberculosis

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.00112-06 ◽

2006 ◽

Vol 50 (8) ◽

pp. 2640-2649 ◽

Cited By ~ 225

Author(s):

Manzour Hernando Hazbón ◽

Michael Brimacombe ◽

Miriam Bobadilla del Valle ◽

Magali Cavatore ◽

Marta Inírida Guerrero ◽

...

Keyword(s):

Mycobacterium Tuberculosis ◽

Large Scale ◽

Sample Selection ◽

Multidrug Resistant ◽

Small Sample ◽

Inverse Association ◽

Resistance Mutations ◽

Isoniazid Resistance ◽

Large Scale Analysis ◽

Small Sample Sizes

ABSTRACT The molecular basis for isoniazid resistance in Mycobacterium tuberculosis is complex. Putative isoniazid resistance mutations have been identified in katG, ahpC, inhA, kasA, and ndh. However, small sample sizes and related potential biases in sample selection have precluded the development of statistically valid and significant population genetic analyses of clinical isoniazid resistance. We present the first large-scale analysis of 240 alleles previously associated with isoniazid resistance in a diverse set of 608 isoniazid-susceptible and 403 isoniazid-resistant clinical M. tuberculosis isolates. We detected 12 mutant alleles in isoniazid-susceptible isolates, suggesting that these alleles are not involved in isoniazid resistance. However, mutations in katG, ahpC, and inhA were strongly associated with isoniazid resistance, while kasA mutations were associated with isoniazid susceptibility. Remarkably, the distribution of isoniazid resistance-associated mutations was different in isoniazid-monoresistant isolates from that in multidrug-resistant isolates, with significantly fewer isoniazid resistance mutations in the isoniazid-monoresistant group. Mutations in katG315 were significantly more common in the multidrug-resistant isolates. Conversely, mutations in the inhA promoter were significantly more common in isoniazid-monoresistant isolates. We tested for interactions among mutations and resistance to different drugs. Mutations in katG, ahpC, and inhA were associated with rifampin resistance, but only katG315 mutations were associated with ethambutol resistance. There was also a significant inverse association between katG315 mutations and mutations in ahpC or inhA and between mutations in kasA and mutations in ahpC. Our results suggest that isoniazid resistance and the evolution of multidrug-resistant strains are complex dynamic processes that may be influenced by interactions between genes and drug-resistant phenotypes.

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Building theories of consistency and variability in children’s language development: A large-scale data approach

10.31234/osf.io/z7yrx ◽

2021 ◽

Author(s):

Angeline Tsui ◽

Virginia A. Marchman ◽

Michael C. Frank

Keyword(s):

Language Learning ◽

Data Aggregation ◽

Large Scale ◽

Meta Analysis ◽

Secondary Data ◽

Small Sample ◽

Early Language ◽

Large Scale Data ◽

Small Sample Sizes ◽

Scale Data

Young children typically begin learning words during their first two years of life. On the other hand, they also vary substantially in their language learning. Similarities and differences in language learning call for a quantitative theory that can predict and explain which aspects of early language are consistent and which are variable. However, current developmental research practices limit our ability to build such quantitative theories because of small sample sizes and challenges related to reproducibility and replicability. In this chapter, we suggest that three approaches – meta-analysis, multi-site collaborations, and secondary data aggregation – can together address some of the limitations of current research in the developmental area. We review the strengths and limitations of each approach and end by discussing the potential impacts of combining these three approaches.

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Using 26 thousand diary entries to show ovulatory changes in sexual desire and behaviour

10.31234/osf.io/jp2ym ◽

2017 ◽

Cited By ~ 2

Author(s):

Ruben C. Arslan ◽

Katharina Maya Schilling ◽

Tanja M. Gerlach ◽

Lars Penke

Keyword(s):

Sexual Desire ◽

Large Scale ◽

Hormonal Contraception ◽

Hormonal Contraceptive ◽

Mate Preferences ◽

Small Sample ◽

Control Group ◽

Ovulatory Cycle ◽

Small Sample Sizes ◽

Perceived Desirability

Previous research reported ovulatory changes in women’s appearance, mate preferences, extra- and in-pair sexual desire and behaviour, but has been criticised for small sample sizes, inappropriate designs, and undisclosed flexibility in analyses. In the present study, we sought to address these criticisms by preregistering our hypotheses and analysis plan and by collecting a large diary sample. We gathered over 26 thousand usable online self-reports in a diary format from 1043 women, of which 421 were naturally cycling. We inferred the fertile period from menstrual onset reports. We used hormonal contraceptive users as a quasi-control group, as they experience menstruation, but not ovulation. We probed our results for robustness to different approaches (including different fertility estimates, different exclusion criteria, adjusting for potential confounds, moderation by methodological factors). We found robust evidence supporting previously reported ovulatory increases in extra-pair desire and behaviour, in-pair desire, and self-perceived desirability, as well as no unexpected associations. Yet, we did not find predicted effects on partner mate retention behaviour, clothing choices, or narcissism. Contrary to some of the earlier literature, partners’ sexual attractiveness did not moderate the cycle shifts. Taken together, the replicability of the existing literature on ovulatory changes was mixed. We conclude with simulation-based recommendations for reading the past literature and for designing future large-scale preregistered within-subject studies to understand ovulatory cycle changes and the effects of hormonal contraception. Interindividual differences in the size of ovulatory changes emerge as an important area for further study.

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