Integrating Clinical Cancer and PTM Proteomics Data Identifies a Mechanism of ACK1 Kinase Activation.

Beyond the most common oncogenes activated by mutation (mut-drivers), there likely exists a variety of low-frequency mut-drivers, each of which is a possible frontier for targeted therapy. To identify new and understudied mut-drivers, we developed a machine learning (ML) model that integrates curated clinical cancer data and posttranslational modification (PTM) proteomics databases. We applied the approach to 62,746 patient cancers spanning 84 cancer types and predicted 3,964 oncogenic mutations across 1,148 genes, many of which disrupt PTMs of known and unknown function. The list of putative mut-drivers includes established drivers and others with poorly understood roles in cancer. This ML model is available as a web application. As a case study, we focused the approach on nonreceptor tyrosine kinases (NRTK) and found a recurrent mutation in activated CDC42 kinase-1 (ACK1) that disrupts the Mig6 homology region (MHR) and ubiquitin-association (UBA) domains on the ACK1 C-terminus. By studying these domains in cultured cells, we found that disruption of the MHR domain helps activate the kinase while disruption of the UBA increases kinase stability by blocking its lysosomal degradation. This ACK1 mutation is analogous to lymphoma-associated mutations in its sister kinase, TNK1, which also disrupt a C-terminal inhibitory motif and UBA domain. This study establishes a mut-driver discovery tool for the research community and identifies a mechanism of ACK1 hyperactivation shared among ACK family kinases. IMPLICATIONS: This research identifies a potentially targetable activating mutation in ACK1 and other possible oncogenic mutations, including PTM-disrupting mutations, for further study.

TidyGEO: preparing analysis-ready datasets from Gene Expression Omnibus.

TidyGEO is a Web-based tool for downloading, tidying, and reformatting data series from Gene Expression Omnibus (GEO). As a freely accessible repository with data from over 6 million biological samples across more than 4000 organisms, GEO provides diverse opportunities for secondary research. Although scientists may find assay data relevant to a given research question, most analyses require sample-level annotations. In GEO, such annotations are stored alongside assay data in delimited, text-based files. However, the structure and semantics of the annotations vary widely from one series to another, and many annotations are not useful for analysis purposes. Thus, every GEO series must be tidied before it is analyzed. Manual approaches may be used, but these are error prone and take time away from other research tasks. Custom computer scripts can be written, but many scientists lack the computational expertise to create such scripts. To address these challenges, we created TidyGEO, which supports essential data-cleaning tasks for sample-level annotations, such as selecting informative columns, renaming columns, splitting or merging columns, standardizing data values, and filtering samples. Additionally, users can integrate annotations with assay data, restructure assay data, and generate code that enables others to reproduce these steps.

Evaluating a large language model's ability to solve programming exercises from an introductory bioinformatics course.

Computer programming is a fundamental tool for life scientists, allowing them to carry out essential research tasks. However, despite various educational efforts, learning to write code can be a challenging endeavor for students and researchers in life-sciences disciplines. Recent advances in artificial intelligence have made it possible to translate human-language prompts to functional code, raising questions about whether these technologies can aid (or replace) life scientists' efforts to write code. Using 184 programming exercises from an introductory-bioinformatics course, we evaluated the extent to which one such tool-OpenAI's ChatGPT-could successfully complete programming tasks. ChatGPT solved 139 (75.5%) of the exercises on its first attempt. For the remaining exercises, we provided natural-language feedback to the model, prompting it to try different approaches. Within 7 or fewer attempts, ChatGPT solved 179 (97.3%) of the exercises. These findings have implications for life-sciences education and research. Instructors may need to adapt their pedagogical approaches and assessment techniques to account for these new capabilities that are available to the general public. For some programming tasks, researchers may be able to work in collaboration with machine-learning models to produce functional code.

Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition.

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user's timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user's timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.

The DNA methylation landscape of five pediatric-tumor types.

Fewer DNA mutations have been identified in pediatric tumors than in adult tumors, suggesting that alternative tumorigenic mechanisms, including aberrant DNA methylation, may play a prominent role. In one epigenetic process of regulating gene expression, methyl groups are attached at the 5-carbon of the cytosine ring, leading to 5-methylcytosine (5mC). In somatic cells, 5mC occurs mostly in CpG islands, which are often within promoter regions. In Wilms tumors and acute myeloid leukemias, increased levels of epigenetic silencing have been associated with worse patient outcomes. However, to date, researchers have studied methylation primarily in adult tumors and for specific genes-but not on a pan-pediatric cancer scale. We addressed these gaps first by aggregating methylation data from 309 noncancerous samples, establishing baseline expectations for each probe and gene. Even though these samples represent diverse, noncancerous tissue types and population ancestral groups, methylation levels were consistent for most genes. Second, we compared tumor methylation levels against the baseline values for 489 pediatric tumors representing five cancer types: Wilms tumors, clear cell sarcomas of the kidney, rhabdoid tumors, neuroblastomas, and osteosarcomas. Tumor hypomethylation was more common than hypermethylation, and as many as 41.7% of genes were hypomethylated in a given tumor, compared to a maximum of 34.2% for hypermethylated genes. However, in known oncogenes, hypermethylation was more than twice as common as in other genes. We identified 139 probes (31 genes) that were differentially methylated between at least one tumor type and baseline levels, and 32 genes that were differentially methylated across the pediatric tumor types. We evaluated whether genomic events and aberrant methylation were mutually exclusive but did not find evidence of this phenomenon.

The ability to classify patients based on gene-expression data varies by algorithm and performance metric.

By classifying patients into subgroups, clinicians can provide more effective care than using a uniform approach for all patients. Such subgroups might include patients with a particular disease subtype, patients with a good (or poor) prognosis, or patients most (or least) likely to respond to a particular therapy. Transcriptomic measurements reflect the downstream effects of genomic and epigenomic variations. However, high-throughput technologies generate thousands of measurements per patient, and complex dependencies exist among genes, so it may be infeasible to classify patients using traditional statistical models. Machine-learning classification algorithms can help with this problem. However, hundreds of classification algorithms exist-and most support diverse hyperparameters-so it is difficult for researchers to know which are optimal for gene-expression biomarkers. We performed a benchmark comparison, applying 52 classification algorithms to 50 gene-expression datasets (143 class variables). We evaluated algorithms that represent diverse machine-learning methodologies and have been implemented in general-purpose, open-source, machine-learning libraries. When available, we combined clinical predictors with gene-expression data. Additionally, we evaluated the effects of performing hyperparameter optimization and feature selection using nested cross validation. Kernel- and ensemble-based algorithms consistently outperformed other types of classification algorithms; however, even the top-performing algorithms performed poorly in some cases. Hyperparameter optimization and feature selection typically improved predictive performance, and univariate feature-selection algorithms typically outperformed more sophisticated methods. Together, our findings illustrate that algorithm performance varies considerably when other factors are held constant and thus that algorithm selection is a critical step in biomarker studies.

SGK2, 14-3-3, and HUWE1 Cooperate to Control the Localization, Stability, and Function of the Oncoprotein PTOV1.

PTOV1 is an oncogenic protein, initially identified in prostate cancer, that promotes proliferation, cell motility, and invasiveness. However, the mechanisms that regulate PTOV1 remain unclear. Here, we identify 14-3-3 as a PTOV1 interactor and show that high levels of 14-3-3 expression, like PTOV1, correlate with prostate cancer progression. We discover an SGK2-mediated phosphorylation of PTOV1 at S36, which is required for 14-3-3 binding. Disruption of the PTOV1-14-3-3 interaction results in an accumulation of PTOV1 in the nucleus and a proteasome-dependent reduction in PTOV1 protein levels. We find that loss of 14-3-3 binding leads to an increase in PTOV1 binding to the E3 ubiquitin ligase HUWE1, which promotes proteasomal degradation of PTOV1. Conversely, our data suggest that 14-3-3 stabilizes PTOV1 protein by sequestering PTOV1 in the cytosol and inhibiting its interaction with HUWE1. Finally, our data suggest that stabilization of the 14-3-3-bound form of PTOV1 promotes PTOV1-mediated expression of cJun, which drives cell-cycle progression in cancer. Together, these data provide a mechanism to understand the regulation of the oncoprotein PTOV1. IMPLICATIONS: These findings identify a potentially targetable mechanism that regulates the oncoprotein PTOV1.

Gender differences contribute to variability of serum lipid biomarkers for Alzheimer's disease.

Background: Alzheimer's disease (AD) cannot currently be diagnosed by a blood test. One reason may be gender differences. Another may be the statistical methods used. The authors evaluate these possibilities. Objective: The authors applied serum lipidomics to find AD biomarkers in men and women. They hypothesized that AD biomarkers would differ between genders and that machine-learning algorithms would improve diagnostic performance. Methods: Serum lipids were analyzed by mass spectrometry for a training set of AD cases and controls and in a blinded test set. Statistical analyses considered gender differences. Results: Lipids best classifying AD subjects differed significantly between men and women. Robust statistical algorithms did not improve diagnostic performance. Conclusion: Poor performance of AD biomarkers appears to be due primarily to inherent variability in AD patients.

Alzheimer's disease (AD) cannot be diagnosed by a blood test or radiologic study. Newer laboratory methods using mass spectrometers have successfully identified molecules in blood that mark the presence of other diseases, but such approaches have failed to find diagnostic biomarkers for AD. Often, initial studies of serum from AD cases and controls have provided promising diagnostic biomarkers, but follow-up studies have not confirmed their usefulness. This study attempts to clarify why this is so by carrying out a serum lipid (fatty molecule) analysis using mass spectrometry (MS) in both an initial serum set of AD cases and matched controls and applying those results to AD diagnosis in a second, independent set of specimens. Sources of variability that could prevent the discovery of useful markers using MS analyses of serum include specimen integrity, variable MS results, problems with statistical methods that analyze MS data and inherent AD patient variability reflected in their sera. Specifically, this study asked if biological gender contributes to nonreproducibility. This approach employed state-of-the-art methods for specimen preparation and MS analysis. The authors used MS approaches that guarded against instrument bias or irreproducibility. Statistical analyses tested several methods of defining useful diagnostic AD marker models. As with previous reports, the authors found promising AD diagnostic serum lipid biomarkers in the first study but failed to replicate the results in the blinded confirmatory study. Results were significantly different for men and women but analyzing men and women separately did not improve AD diagnosis. Overall, the largest source of variability was AD patient variability. AD is complicated, often affecting people who have other medical problems and are on medications. Differences in disease occurrence, disease progression, symptoms and areas of the brain affected are reflected in a highly variable serum lipid composition that may obscure disease-specific, and hence diagnostic, AD biomarkers.

trioPhaser: using Mendelian inheritance logic to improve genomic phasing of trios.

BACKGROUND: When analyzing DNA sequence data of an individual, knowing which nucleotide was inherited from each parent can be beneficial when trying to identify certain types of DNA variants. Mendelian inheritance logic can be used to accurately phase (haplotype) the majority (67-83%) of an individual's heterozygous nucleotide positions when genotypes are available for both parents (trio). However, when all members of a trio are heterozygous at a position, Mendelian inheritance logic cannot be used to phase. For such positions, a computational phasing algorithm can be used. Existing phasing algorithms use a haplotype reference panel, sequencing reads, and/or parental genotypes to phase an individual; however, they are limited in that they can only phase certain types of variants, require a specific genotype build, require large amounts of storage capacity, and/or require long run times. We created trioPhaser to address these challenges. RESULTS: trioPhaser uses gVCF files from an individual and their parents as initial input, and then outputs a phased VCF file. Input trio data are first phased using Mendelian inheritance logic. Then, the positions that cannot be phased using inheritance information alone are phased by the SHAPEIT4 phasing algorithm. Using whole-genome sequencing data of 52 trios, we show that trioPhaser, on average, increases the total number of phased positions by 21.0% and 10.5%, respectively, when compared to the number of positions that SHAPEIT4 or Mendelian inheritance logic can phase when either is used alone. In addition, we show that the accuracy of the phased calls output by trioPhaser are similar to linked-read and read-backed phasing. CONCLUSION: trioPhaser is a containerized software tool that uses both Mendelian inheritance logic and SHAPEIT4 to phase trios when gVCF files are available. By implementing both phasing methods, more variant positions are phased compared to what either method is able to phase alone.

Simplifying the development of portable, scalable, and reproducible workflows.

Command-line software plays a critical role in biology research. However, processes for installing and executing software differ widely. The Common Workflow Language (CWL) is a community standard that addresses this problem. Using CWL, tool developers can formally describe a tool's inputs, outputs, and other execution details. CWL documents can include instructions for executing tools inside software containers. Accordingly, CWL tools are portable-they can be executed on diverse computers-including personal workstations, high-performance clusters, or the cloud. CWL also supports workflows, which describe dependencies among tools and using outputs from one tool as inputs to others. To date, CWL has been used primarily for batch processing of large datasets, especially in genomics. But it can also be used for analytical steps of a study. This article explains key concepts about CWL and software containers and provides examples for using CWL in biology research. CWL documents are text-based, so they can be created manually, without computer programming. However, ensuring that these documents conform to the CWL specification may prevent some users from adopting it. To address this gap, we created ToolJig, a Web application that enables researchers to create CWL documents interactively. ToolJig validates information provided by the user to ensure it is complete and valid. After creating a CWL tool or workflow, the user can create 'input-object' files, which store values for a particular invocation of a tool or workflow. In addition, ToolJig provides examples of how to execute the tool or workflow via a workflow engine. ToolJig and our examples are available at https://github.com/srp33/ToolJig.

Toward a methodology for evaluating DNA variants in nuclear families.

The genetic underpinnings of most pediatric-cancer cases are unknown. Population-based studies use large sample sizes but have accounted for only a small proportion of the estimated heritability of pediatric cancers. Pedigree-based studies are infeasible for most human populations. One alternative is to collect genetic data from a single nuclear family and use inheritance patterns within the family to filter candidate variants. This approach can be applied to common and rare variants, including those that are private to a given family or to an affected individual. We evaluated this approach using genetic data from three nuclear families with 5, 4, and 7 children, respectively. Only one child in each nuclear family had been diagnosed with cancer, and neither parent had been affected. Diagnoses for the affected children were benign low-grade astrocytoma, Wilms tumor (stage 2), and Burkitt's lymphoma, respectively. We used whole-genome sequencing to profile normal cells from each family member and a linked-read technology for genomic phasing. For initial variant filtering, we used global minor allele frequencies, deleteriousness scores, and functional-impact annotations. Next, we used genetic variation in the unaffected siblings as a guide to filter the remaining variants. As a way to evaluate our ability to detect variant(s) that may be relevant to disease status, the corresponding author blinded the primary author to affected status; the primary author then assigned a risk score to each child. Based on this evidence, the primary author predicted which child had been affected in each family. The primary author's prediction was correct for the child who had been diagnosed with a Wilms tumor; the child with Burkitt's lymphoma had the second-highest risk score among the seven children in that family. This study demonstrates a methodology for filtering and evaluating candidate genomic variants and genes within nuclear families that may merit further exploration.

Predicting drug sensitivity of cancer cells based on DNA methylation levels.

Cancer cell lines, which are cell cultures derived from tumor samples, represent one of the least expensive and most studied preclinical models for drug development. Accurately predicting drug responses for a given cell line based on molecular features may help to optimize drug-development pipelines and explain mechanisms behind treatment responses. In this study, we focus on DNA methylation profiles as one type of molecular feature that is known to drive tumorigenesis and modulate treatment responses. Using genome-wide, DNA methylation profiles from 987 cell lines in the Genomics of Drug Sensitivity in Cancer database, we used machine-learning algorithms to evaluate the potential to predict cytotoxic responses for eight anti-cancer drugs. We compared the performance of five classification algorithms and four regression algorithms representing diverse methodologies, including tree-, probability-, kernel-, ensemble-, and distance-based approaches. We artificially subsampled the data to varying degrees, aiming to understand whether training based on relatively extreme outcomes would yield improved performance. When using classification or regression algorithms to predict discrete or continuous responses, respectively, we consistently observed excellent predictive performance when the training and test sets consisted of cell-line data. Classification algorithms performed best when we trained the models using cell lines with relatively extreme drug-response values, attaining area-under-the-receiver-operating-characteristic-curve values as high as 0.97. The regression algorithms performed best when we trained the models using the full range of drug-response values, although this depended on the performance metrics we used. Finally, we used patient data from The Cancer Genome Atlas to evaluate the feasibility of classifying clinical responses for human tumors based on models derived from cell lines. Generally, the algorithms were unable to identify patterns that predicted patient responses reliably; however, predictions by the Random Forests algorithm were significantly correlated with Temozolomide responses for low-grade gliomas.

A systematic review of datasets that can help elucidate relationships among gene expression, race, and immunohistochemistry-defined subtypes in breast cancer.

Scholarly requirements have led to a massive increase of transcriptomic data in the public domain, with millions of samples available for secondary research. We identified gene-expression datasets representing 10,214 breast-cancer patients in public databases. We focused on datasets that included patient metadata on race and/or immunohistochemistry (IHC) profiling of the ER, PR, and HER-2 proteins. This review provides a summary of these datasets and describes findings from 32 research articles associated with the datasets. These studies have helped to elucidate relationships between IHC, race, and/or treatment options, as well as relationships between IHC status and the breast-cancer intrinsic subtypes. We have also identified broad themes across the analysis methodologies used in these studies, including breast cancer subtyping, deriving predictive biomarkers, identifying differentially expressed genes, and optimizing data processing. Finally, we discuss limitations of prior work and recommend future directions for reusing these datasets in secondary analyses.

A Survey of Compound Heterozygous Variants in Pediatric Cancers and Structural Birth Defects.

Compound heterozygous (CH) variants occur when two recessive alleles are inherited and the variants are located at different loci within the same gene in a given individual. CH variants are important contributors to many different types of recessively inherited diseases. However, many studies overlook CH variants because identification of this type of variant requires knowing the parent of origin for each nucleotide. Using computational methods, haplotypes can be inferred using a process called "phasing," which estimates the chromosomal origin of most nucleotides. In this paper, we used germline, phased, whole-genome sequencing (WGS) data to identify CH variants across seven pediatric diseases (adolescent idiopathic scoliosis: n = 16, congenital heart defects: n = 709, disorders of sex development: n = 79, ewing sarcoma: n = 287, neuroblastoma: n = 259, orofacial cleft: n = 107, and syndromic cranial dysinnervation: n = 172), available as parent-child trios in the Gabriella Miller Kids First Data Resource Center. Relatively little is understood about the genetic underpinnings of these diseases. We classified CH variants as "potentially damaging" based on minor allele frequencies (MAF), Combined Annotation Dependent Depletion scores, variant impact on transcription or translation, and gene-level frequencies in the disease group compared to a healthy population. For comparison, we also identified homozygous alternate (HA) variants, which affect both gene copies at a single locus; HA variants represent an alternative mechanism of recessive disease development and do not require phasing. Across all diseases, 2.6% of the samples had a potentially damaging CH variant and 16.2% had a potentially damaging HA variant. Of these samples with potentially damaging variants, the average number of genes per sample was 1 with a CH variant and 1.25 with a HA variant. Across all samples, 5.1 genes per disease had a CH variant, while 35.6 genes per disease had a HA variant; on average, only 4.3% of these variants affected common genes. Therefore, when seeking to identify potentially damaging variants of a putatively recessive disease, CH variants should be considered as potential contributors to disease development. If CH variants are excluded from analysis, important candidate genes may be overlooked.

Topsentinol L Trisulfate, a Marine Natural Product That Targets Basal-like and Claudin-Low Breast Cancers.

Patients diagnosed with basal-like breast cancer suffer from poor prognosis and limited treatment options. There is an urgent need to identify new targets that can benefit patients with basal-like and claudin-low (BL-CL) breast cancers. We screened fractions from our Marine Invertebrate Compound Library (MICL) to identify compounds that specifically target BL-CL breast cancers. We identified a previously unreported trisulfated sterol, i.e., topsentinol L trisulfate (TLT), which exhibited increased efficacy against BL-CL breast cancers relative to luminal/HER2+ breast cancer. Biochemical investigation of the effects of TLT on BL-CL cell lines revealed its ability to inhibit activation of AMP-activated protein kinase (AMPK) and checkpoint kinase 1 (CHK1) and to promote activation of p38. The importance of targeting AMPK and CHK1 in BL-CL cell lines was validated by treating a panel of breast cancer cell lines with known small molecule inhibitors of AMPK (dorsomorphin) and CHK1 (Ly2603618) and recording the increased effectiveness against BL-CL breast cancers as compared with luminal/HER2+ breast cancer. Finally, we generated a drug response gene-expression signature and projected it against a human tumor panel of 12 different cancer types to identify other cancer types sensitive to the compound. The TLT sensitivity gene-expression signature identified breast and bladder cancer as the most sensitive to TLT, while glioblastoma multiforme was the least sensitive.

Effects of germline and somatic events in candidate BRCA-like genes on breast-tumor signatures.

Mutations in BRCA1 and BRCA2 cause deficiencies in homologous recombination repair (HR), resulting in repair of DNA double-strand breaks by the alternative non-homologous end-joining pathway, which is more error prone. HR deficiency of breast tumors is important because it is associated with better responses to platinum salt therapies and PARP inhibitors. Among other consequences of HR deficiency are characteristic somatic-mutation signatures and gene-expression patterns. The term "BRCA-like" (or "BRCAness") describes tumors that harbor an HR defect but have no detectable germline mutation in BRCA1 or BRCA2. A better understanding of the genes and molecular events associated with tumors being BRCA-like could provide mechanistic insights and guide development of targeted treatments. Using data from The Cancer Genome Atlas (TCGA) for 1101 breast-cancer patients, we identified individuals with a germline mutation, somatic mutation, homozygous deletion, and/or hypermethylation event in BRCA1, BRCA2, and 59 other cancer-predisposition genes. Based on the assumption that BRCA-like events would have similar downstream effects on tumor biology as BRCA1/BRCA2 germline mutations, we quantified these effects based on somatic-mutation signatures and gene-expression profiles. We reduced the dimensionality of the somatic-mutation signatures and expression data and used a statistical resampling approach to quantify similarities among patients who had a BRCA1/BRCA2 germline mutation, another type of aberration in BRCA1 or BRCA2, or any type of aberration in one of the other genes. Somatic-mutation signatures of tumors having a non-germline aberration in BRCA1/BRCA2 (n = 80) were generally similar to each other and to tumors from BRCA1/BRCA2 germline carriers (n = 44). Additionally, somatic-mutation signatures of tumors with germline or somatic events in ATR (n = 16) and BARD1 (n = 8) showed high similarity to tumors from BRCA1/BRCA2 carriers. Other genes (CDKN2A, CTNNA1, PALB2, PALLD, PRSS1, SDHC) also showed high similarity but only for a small number of events or for a single event type. Tumors with germline mutations or hypermethylation of BRCA1 had relatively similar gene-expression profiles and overlapped considerably with the Basal-like subtype; but the transcriptional effects of the other events lacked consistency. Our findings confirm previously known relationships between molecular signatures and germline or somatic events in BRCA1/BRCA2. Our methodology represents an objective way to identify genes that have similar downstream effects on molecular signatures when mutated, deleted, or hypermethylated.

Evaluation of the upregulation and surface expression of hypoxanthine guanine phosphoribosyltransferase in acute lymphoblastic leukemia and Burkitt's B cell lymphoma.

BACKGROUND: The aim of this study is to determine whether Hypoxanthine Guanine Phosphoribosyltransferase (HPRT) could be used as a biomarker for the diagnosis and treatment of B cell malignancies. With 4.3% of all new cancers diagnosed as Non-Hodgkin lymphoma, finding new biomarkers for the treatment of B cell cancers is an ongoing pursuit. HPRT is a nucleotide salvage pathway enzyme responsible for the synthesis of guanine and inosine throughout the cell cycle. METHODS: Raji cells were used for this analysis due to their high HPRT internal expression. Internal expression was evaluated utilizing western blotting and RNA sequencing. Surface localization was analyzed using flow cytometry, confocal microscopy, and membrane biotinylation. To determine the source of HPRT surface expression, a CRISPR knockdown of HPRT was generated and confirmed using western blotting. To determine clinical significance, patient blood samples were collected and analyzed for HPRT surface localization. RESULTS: We found surface localization of HPRT on both Raji cancer cells and in 77% of the malignant ALL samples analyzed and observed no significant expression in healthy cells. Surface expression was confirmed in Raji cells with confocal microscopy, where a direct overlap between HPRT specific antibodies and a membrane-specific dye was observed. HPRT was also detected in biotinylated membranes of Raji cells. Upon HPRT knockdown in Raji cells, we found a significant reduction in surface expression, which shows that the HPRT found on the surface originates from the cells themselves. Finally, we found that cells that had elevated levels of HPRT had a direct correlation to XRCC2, BRCA1, PIK3CA, MSH2, MSH6, WDYHV1, AK7, and BLMH expression and an inverse correlation to PRKD2, PTGS2, TCF7L2, CDH1, IL6R, MC1R, AMPD1, TLR6, and BAK1 expression. Of the 17 genes with significant correlation, 9 are involved in cellular proliferation and DNA synthesis, regulation, and repair. CONCLUSIONS: As a surface biomarker that is found on malignant cells and not on healthy cells, HPRT could be used as a surface antigen for targeted immunotherapy. In addition, the gene correlations show that HPRT may have an additional role in regulation of cancer proliferation that has not been previously discovered.

Compound Heterozygous Variants in Pediatric Cancers: A Systematic Review.

A compound heterozygous (CH) variant is a type of germline variant that occurs when each parent donates one alternate allele and these alleles are located at different loci within the same gene. Pathogenic germline variants have been identified for some pediatric cancer types but in most studies, CH variants are overlooked. Thus, the prevalence of pathogenic CH variants in most pediatric cancer types is unknown. We identified 26 studies (published between 1999 and 2019) that identified a CH variant in at least one pediatric cancer patient. These studies encompass 21 cancer types and have collectively identified 25 different genes in which a CH variant occurred. However, the sequencing methods used and the number of patients and genes evaluated in each study were highly variable across the studies. In addition, methods for assessing pathogenicity of CH variants varied widely and were often not reported. In this review, we discuss technologies and methods for identifying CH variants, provide an overview of studies that have identified CH variants in pediatric cancer patients, provide insights into future directions in the field, and give a summary of publicly available pediatric cancer sequencing data. Although considerable insights have been gained over the last 20 years, much has yet to be learned about the involvement of CH variants in pediatric cancers. In future studies, larger sample sizes, more pediatric cancer types, and better pathogenicity assessment and filtering methods will be needed to move this field forward.

Hypoxanthine Guanine Phosphoribosyltransferase expression is negatively correlated with immune activity through its regulation of purine synthesis.

INTRODUCTION: The purpose of this study was to examine the effects of elevated Hypoxanthine Guanine Phosphoribosyltransferase (HPRT) on the immune response in the tumor microenvironment. METHODOLOGY: HPRT expression was evaluated in cancer patients and correlated with cytokine expression, survival, and immune cell infiltration. An HPRT knockdown cell line was created to evaluate HPRT impact on purine expression and subsequent purine treatment was administered to immune cells to determine their influence on cell activation. RESULTS: HPRT expression was negatively correlated with the general expression of both pro-inflammatory and anti-inflammatory cytokines. Additionally, HPRT expression was also negatively correlated with the infiltration of immune cell subsets: B-cells, CD4 + T cells, macrophages, neutrophils, and dendritic cells (p < 0.001) and CD8 + T-cells (p < 0.01). When HPRT was knocked down in a Raji cell line, the levels of adenosine were reduced significantly compared to the wild type. When examining the level of Ca2+ influx of Raji compared to the HPRT Raji knockdown cell, there was a significant decrease in calcium influx in the knockdown cells when compared to the wild type cells. This demonstrates that HPRT had a significant impact on overall cell activation and the ability of the cells to properly influx calcium needed for their activation. CONCLUSIONS: We conclude that purine levels significantly reduce immune cell activation in cancer and the upregulation of HPRT in malignant tissue is a contributing factors to the immunosuppressive microenvironment.

ShinyLearner: A containerized benchmarking tool for machine-learning classification of tabular data.

BACKGROUND: Classification algorithms assign observations to groups based on patterns in data. The machine-learning community have developed myriad classification algorithms, which are used in diverse life science research domains. Algorithm choice can affect classification accuracy dramatically, so it is crucial that researchers optimize the choice of which algorithm(s) to apply in a given research domain on the basis of empirical evidence. In benchmark studies, multiple algorithms are applied to multiple datasets, and the researcher examines overall trends. In addition, the researcher may evaluate multiple hyperparameter combinations for each algorithm and use feature selection to reduce data dimensionality. Although software implementations of classification algorithms are widely available, robust benchmark comparisons are difficult to perform when researchers wish to compare algorithms that span multiple software packages. Programming interfaces, data formats, and evaluation procedures differ across software packages; and dependency conflicts may arise during installation. FINDINGS: To address these challenges, we created ShinyLearner, an open-source project for integrating machine-learning packages into software containers. ShinyLearner provides a uniform interface for performing classification, irrespective of the library that implements each algorithm, thus facilitating benchmark comparisons. In addition, ShinyLearner enables researchers to optimize hyperparameters and select features via nested cross-validation; it tracks all nested operations and generates output files that make these steps transparent. ShinyLearner includes a Web interface to help users more easily construct the commands necessary to perform benchmark comparisons. ShinyLearner is freely available at https://github.com/srp33/ShinyLearner. CONCLUSIONS: This software is a resource to researchers who wish to benchmark multiple classification or feature-selection algorithms on a given dataset. We hope it will serve as example of combining the benefits of software containerization with a user-friendly approach.