IKZF1plus is a frequent biomarker of adverse prognosis in Mexican pediatric patients with B-acute lymphoblastic leukemia.

Background: Recurrent genetic alterations contributing to leukemogenesis have been identified in pediatric B-cell Acute Lymphoblastic Leukemia (B-ALL), and some are useful for refining classification, prognosis, and treatment selection. IKZF1plus is a complex biomarker associated with a poor prognosis. It is characterized by IKZF1 deletion coexisting with PAX5, CDKN2A/2B, or PAR1 region deletions. The mutational spectrum and clinical impact of these alterations have scarcely been explored in Mexican pediatric patients with B-ALL. Here, we report the frequency of the IKZF1plus profile and the mutational spectrum of IKZF1, PAX5, CDKN2A/2B, and ERG genes and evaluate their impact on overall survival (OS) in a group of patients with B-ALL. Methods: A total of 206 pediatric patients with de novo B-ALL were included. DNA was obtained from bone marrow samples at diagnosis before treatment initiation. A custom-designed next-generation sequencing panel was used for mutational analysis. Kaplan-Meier analysis was used for OS estimation. Results: We identified the IKZF1plus profile in 21.8% of patients, which was higher than that previously reported in other studies. A significantly older age (p=0.04), a trend toward high-risk stratification (p=0.06), and a decrease in 5-year Overall Survival (OS) (p=0.009) were observed, although heterogeneous treatment protocols in our cohort would have impacted OS. A mutation frequency higher than that reported was found for IKZF1 (35.9%) and CDKN2A/2B (35.9%) but lower for PAX5 (26.6%). IKZF1MUT group was older at diagnosis (p=0.0002), and most of them were classified as high-risk (73.8%, p=0.02), while patients with CDKN2A/2BMUT had a higher leukocyte count (p=0.01) and a tendency toward a higher percentage of blasts (98.6%, >50% blasts, p=0.05) than the non-mutated patients. A decrease in OS was found in IKZF1MUT and CDKN2A/2BMUT patients, but the significance was lost after IKZF1plus was removed. Discussion: Our findings demonstrated that Mexican patients with B-ALL have a higher prevalence of genetic markers associated with poor outcomes. Incorporating genomic methodologies into the diagnostic process, a significant unmet need in low- and mid-income countries, will allow a comprehensive identification of relevant alterations, improving disease classification, treatment selection, and the general outcome.

Case report: A familial B-acute lymphoblastic leukemia associated with a new germline pathogenic variant in PAX5. The first report in Mexico.

B-cell acute lymphoblastic leukemia (B-ALL) is one of the most common childhood cancers worldwide. Although most cases are sporadic, some familial forms, inherited as autosomal dominant traits with incomplete penetrance, have been described over the last few years. Germline pathogenic variants in transcription factors such as PAX5, IKZF1, and ETV6 have been identified as causal in familial forms. The proband was a 7-year-old Mexican girl diagnosed with high-risk B-ALL at five years and 11 months of age. Family history showed that the proband's mother had high-risk B-ALL at 16 months of age. She received chemotherapy and was discharged at nine years of age without any evidence of recurrence of leukemia. The proband's father was outside the family nucleus, but no history of leukemia or cancer was present up to the last contact with the mother. We performed exome sequencing on the proband and the proband's mother and identified the PAX5 variant NM_016734.3:c.963del: p.(Ala322LeufsTer11), located in the transactivation domain of the PAX5 protein. The variant was classified as probably pathogenic according to the ACMG criteria. To the best of our knowledge, this is the first Mexican family with an inherited increased risk of childhood B-ALL caused by a novel germline pathogenic variant of PAX5. Identifying individuals with a hereditary predisposition to cancer is essential for modern oncological practice. Individuals at high risk of leukemia would benefit from hematopoietic stem cell transplantation, but family members carrying the pathogenic variant should be excluded as hematopoietic stem cell donors.

PIK3R1 fusion drives chemoresistance in ovarian cancer by activating ERK1/2 and inducing rod and ring-like structures.

Gene fusions are common in high-grade serous ovarian cancer (HGSC). Such genetic lesions may promote tumorigenesis, but the pathogenic mechanisms are currently poorly understood. Here, we investigated the role of a PIK3R1-CCDC178 fusion identified from a patient with advanced HGSC. We show that the fusion induces HGSC cell migration by regulating ERK1/2 and increases resistance to platinum treatment. Platinum resistance was associated with rod and ring-like cellular structure formation. These structures contained, in addition to the fusion protein, CIN85, a key regulator of PI3K-AKT-mTOR signaling. Our data suggest that the fusion-driven structure formation induces a previously unrecognized cell survival and resistance mechanism, which depends on ERK1/2-activation.

Machine Learning and COVID-19: Lessons from SARS-CoV-2.

Currently, methods in machine learning have opened a significant number of applications to construct classifiers with capacities to recognize, identify, and interpret patterns hidden in massive amounts of data. This technology has been used to solve a variety of social and health issues against coronavirus disease 2019 (COVID-19). In this chapter, we present some supervised and unsupervised machine learning techniques that have contributed in three aspects to supplying information to health authorities and diminishing the deadly effects of the current worldwide outbreak on the population. First is the identification and construction of powerful classifiers capable of predicting severe, moderate, or asymptomatic responses in COVID-19 patients starting from clinical or high-throughput technologies. Second is the identification of groups of patients with similar physiological responses to improve the triage classification and inform treatments. The final aspect is the combination of machine learning methods and schemes from systems biology to link associative studies with mechanistic frameworks. This chapter aims to discuss some practical applications in the use of machine learning techniques to handle data coming from social behavior and high-throughput technologies, associated with COVID-19 evolution.

Genomic Profile in a Non-Seminoma Testicular Germ-Cell Tumor Cohort Reveals a Potential Biomarker of Sensitivity to Platinum-Based Therapy.

Despite having a favorable response to platinum-based chemotherapies, ~15% of Testicular Germ-Cell Tumor (TGCT) patients are platinum-resistant. Mortality rates among Latin American countries have remained constant over time, which makes the study of this population of particular interest. To gain insight into this phenomenon, we conducted whole-exome sequencing, microarray-based comparative genomic hybridization, and copy number analysis of 32 tumors from a Mexican cohort, of which 18 were platinum-sensitive and 14 were platinum-resistant. We incorporated analyses of mutational burden, driver mutations, and SNV and CNV signatures. DNA breakpoints in genes were also investigated and might represent an interesting research opportunity. We observed that sensitivity to chemotherapy does not seem to be explained by any of the mutations detected. Instead, we uncovered CNVs, particularly amplifications on segment 2q11.1 as a novel variant with chemosensitivity biomarker potential. Our data shed light into understanding platinum resistance in a Latin-origin population.

Human cell transformation by combined lineage conversion and oncogene expression.

Cancer is the most complex genetic disease known, with mutations implicated in more than 250 genes. However, it is still elusive which specific mutations found in human patients lead to tumorigenesis. Here we show that a combination of oncogenes that is characteristic of liver cancer (CTNNB1, TERT, MYC) induces senescence in human fibroblasts and primary hepatocytes. However, reprogramming fibroblasts to a liver progenitor fate, induced hepatocytes (iHeps), makes them sensitive to transformation by the same oncogenes. The transformed iHeps are highly proliferative, tumorigenic in nude mice, and bear gene expression signatures of liver cancer. These results show that tumorigenesis is triggered by a combination of three elements: the set of driver mutations, the cellular lineage, and the state of differentiation of the cells along the lineage. Our results provide direct support for the role of cell identity as a key determinant in transformation and establish a paradigm for studying the dynamic role of oncogenic drivers in human tumorigenesis.

FUNGI: FUsioN Gene Integration toolset.

MOTIVATION: Fusion genes are both useful cancer biomarkers and important drug targets. Finding relevant fusion genes is challenging due to genomic instability resulting in a high number of passenger events. To reveal and prioritize relevant gene fusion events we have developed FUsionN Gene Identification toolset (FUNGI) that uses an ensemble of fusion detection algorithms with prioritization and visualization modules. RESULTS: We applied FUNGI to an ovarian cancer dataset of 107 tumor samples from 36 patients. Ten out of 11 detected and prioritized fusion genes were validated. Many of detected fusion genes affect the PI3K-AKT pathway with potential role in treatment resistance. AVAILABILITYAND IMPLEMENTATION: FUNGI and its documentation are available at https://bitbucket.org/alejandra_cervera/fungi as standalone or from Anduril at https://www.anduril.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Front-Line innovation: Rapid implementation of a nurse-driven protocol for care of outpatients with COVID-19.

AIMS AND OBJECTIVES: Our objective was to rapidly adapt and scale a registered nurse-driven Coordinated Transitional Care (C-TraC) programme to provide intensive home monitoring and optimise care for outpatient Veterans with COVID-19 in a large urban Unites States healthcare system. BACKGROUND: Our diffuse primary care network had no existing model of care by which to provide coordinated result tracking and monitoring of outpatients with COVID-19. DESIGN: Quality improvement implementation project. METHODS: We used the Replicating Effective Programs model to guide implementation, iterative Plan-Do-Study-Act cycles and SQUIRE reporting guidelines. Two transitional care registered nurses, and a geriatrician medical director developed a protocol that included detailed initial assessment, overnight delivery of monitoring equipment and phone-based follow-up tailored to risk level and symptom severity. We tripled programme capacity in time for the surge of cases by training Primary Care registered nurses. RESULTS: Between 23 March and 15 May 2020, 120 Veterans with COVID-19 were enrolled for outpatient monitoring; over one-third were aged 65 years or older, and 70% had medical conditions associated with poor COVID-19 outcomes. All Veterans received an initial call within a few hours of the laboratory reporting positive results. The mean length of follow-up was 8.1 days, with an average of 4.2 nurse and 1.3 physician or advanced practice clinician contacts per patient. The majority (85%) were managed entirely in the outpatient setting. After the surge, the model was disseminated to individual primary care teams through educational sessions. CONCLUSION: A model based on experienced registered nurses can provide comprehensive, effective and sustainable outpatient monitoring to high-risk populations with COVID-19.

Functional Profiling of FSH and Estradiol in Ovarian Granulosa Cell Tumors.

Adult-type granulosa cell tumors (AGCTs) are sex-cord derived neoplasms with a propensity for late relapse. Hormonal modulators have been used empirically in the treatment of recurrent AGCT, albeit with limited success. To provide a more rigorous foundation for hormonal therapy in AGCT, we used a multimodal approach to characterize the expressions of key hormone biomarkers in 175 tumor specimens and 51 serum samples using RNA sequencing, immunohistochemistry, RNA in situ hybridization, quantitative PCR, and circulating biomarker analysis, and correlated these results with clinical data. We show that FSH receptor and estrogen receptor beta (ERß) are highly expressed in the majority of AGCTs, whereas the expressions of estrogen receptor alpha (ERα) and G-protein coupled estrogen receptor 1 are less prominent. ERß protein expression is further increased in recurrent tumors. Aromatase expression levels show high variability between tumors. None of the markers examined served as prognostic biomarkers for progression-free or overall survival. In functional experiments, we assessed the effects of FSH, estradiol (E2), and the aromatase inhibitor letrozole on AGCT cell viability using 2 in vitro models: KGN cells and primary cultures of AGCT cells. FSH increased cell viability in a subset of primary AGCT cells, whereas E2 had no effect on cell viability at physiological concentrations. Letrozole suppressed E2 production in AGCTs; however, it did not impact cell viability. We did not find preclinical evidence to support the clinical use of aromatase inhibitors in AGCT treatment, and thus randomized, prospective clinical studies are needed to clarify the role of hormonal treatments in AGCTs.

Distinct subtypes of diffuse large B-cell lymphoma defined by hypermutated genes.

Diffuse large B-cell lymphoma (DLBCL) is a biologically and clinically heterogeneous disease whose personalized clinical management requires robust molecular stratification. Here, we show that somatic hypermutation (SHM) patterns constitute a marker for DLBCL molecular classification. The activity of SHM mutational processes delineated the cell of origin (COO) in DLBCL. Expression of the herein identified 36 SHM target genes stratified DLBCL into four novel SHM subtypes. In a meta-analysis of patients with DLBCL treated with immunochemotherapy, the SHM subtypes were significantly associated with overall survival (1642 patients) and progression-free survival (795 patients). Multivariate analysis of survival indicated that the prognostic impact of the SHM subtypes is independent from the COO classification and the International Prognostic Index. Furthermore, the SHM subtypes had a distinct clinical outcome within each of the COO subtypes, and strikingly, even within unclassified DLBCL. The genetic landscape of the four SHM subtypes indicated unique associations with driver alterations and oncogenic signaling in DLBCL, which suggests a possibility for therapeutic exploitation. These findings provide a biologically driven classification system in DLBCL with potential clinical applications.

Anduril 2: upgraded large-scale data integration framework.

SUMMARY: Anduril is an analysis and integration framework that facilitates the design, use, parallelization and reproducibility of bioinformatics workflows. Anduril has been upgraded to use Scala for pipeline construction, which simplifies software maintenance, and facilitates design of complex pipelines. Additionally, Anduril's bioinformatics repository has been expanded with multiple components, and tutorial pipelines, for next-generation sequencing data analysis. AVAILABILITYAND IMPLEMENTATION: Freely available at http://anduril.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Anagrelide for Gastrointestinal Stromal Tumor.

PURPOSE: Gastrointestinal stromal tumor (GIST) is a common type of soft-tissue sarcoma. Imatinib, an inhibitor of KIT, platelet-derived growth factor receptor alpha (PDGFRA), and a few other tyrosine kinases, is highly effective for GIST, but advanced GISTs frequently progress on imatinib and other approved tyrosine kinase inhibitors. We investigated phosphodiesterase 3 (PDE3) as a potential therapeutic target in GIST cell lines and xenograft models. EXPERIMENTAL DESIGN: The GIST gene expression profile was interrogated in the MediSapiens IST Online transcriptome database comprising human tissue and cancer samples, and PDE3A and PDE3B expression was studied using IHC on tissue microarrays (TMA) consisting of 630 formalin-fixed human tissue samples. GIST cell lines were screened for sensitivity to 217 anticancer compounds, and the efficacy of PDE inhibitors on GIST was further studied in GIST cell lines and patient-derived mouse xenograft models. RESULTS: GISTs expressed PDE3A and PDE3B frequently compared with other human normal or cancerous tissues both in the in silico database and the TMAs. Anagrelide was identified as the most potent of the PDE3 modulators evaluated. It reduced cell viability, promoted cell death, and influenced cell signaling in GIST cell lines. Anagrelide inhibited tumor growth in GIST xenograft mouse models. Anagrelide was also effective in a GIST xenograft mouse model with KIT exon 9 mutation that may pose a therapeutic challenge, as these GISTs require a high daily dose of imatinib. CONCLUSIONS: PDE3A and PDE3B are frequently expressed in GIST. Anagrelide had anticancer efficacy in GIST xenograft models and warrants further testing in clinical trials.

Modified penetrance of coding variants by cis-regulatory variation contributes to disease risk.

Coding variants represent many of the strongest associations between genotype and phenotype; however, they exhibit inter-individual differences in effect, termed 'variable penetrance'. Here, we study how cis-regulatory variation modifies the penetrance of coding variants. Using functional genomic and genetic data from the Genotype-Tissue Expression Project (GTEx), we observed that in the general population, purifying selection has depleted haplotype combinations predicted to increase pathogenic coding variant penetrance. Conversely, in cancer and autism patients, we observed an enrichment of penetrance increasing haplotype configurations for pathogenic variants in disease-implicated genes, providing evidence that regulatory haplotype configuration of coding variants affects disease risk. Finally, we experimentally validated this model by editing a Mendelian single-nucleotide polymorphism (SNP) using CRISPR/Cas9 on distinct expression haplotypes with the transcriptome as a phenotypic readout. Our results demonstrate that joint regulatory and coding variant effects are an important part of the genetic architecture of human traits and contribute to modified penetrance of disease-causing variants.

MicroRNAs regulate key cell survival pathways and mediate chemosensitivity during progression of diffuse large B-cell lymphoma.

Despite better therapeutic options and improved survival of diffuse large B-cell lymphoma (DLBCL), 30-40% of the patients experience relapse or have primary refractory disease with a dismal prognosis. To identify biological correlates for treatment resistance, we profiled microRNAs (miRNAs) of matched primary and relapsed DLBCL by next-generation sequencing. Altogether 492 miRNAs were expressed in the DLBCL samples. Thirteen miRNAs showed significant differential expression between primary and relapse specimen pairs. Integration of the differentially expressed miRNAs with matched mRNA expression profiles identified highly anti-correlated, putative targets, which were significantly enriched in cancer-associated pathways, including phosphatidylinositol (PI)), mitogen-activated protein kinase (MAPK), and B-cell receptor (BCR) signaling. Expression data suggested activation of these pathways during disease progression, and functional analyses validated that miR-370-3p, miR-381-3p, and miR-409-3p downregulate genes on the PI, MAPK, and BCR signaling pathways, and enhance chemosensitivity of DLBCL cells in vitro. High expression of selected target genes, that is, PIP5K1 and IMPA1, was found to be associated with poor survival in two independent cohorts of chemoimmunotherapy-treated patients (n = 92 and n = 233). Taken together, our results demonstrate that differentially expressed miRNAs contribute to disease progression by regulating key cell survival pathways and by mediating chemosensitivity, thus representing potential novel therapeutic targets.

SePIA: RNA and small RNA sequence processing, integration, and analysis.

BACKGROUND: Large-scale sequencing experiments are complex and require a wide spectrum of computational tools to extract and interpret relevant biological information. This is especially true in projects where individual processing and integrated analysis of both small RNA and complementary RNA data is needed. Such studies would benefit from a computational workflow that is easy to implement and standardizes the processing and analysis of both sequenced data types. RESULTS: We developed SePIA (Sequence Processing, Integration, and Analysis), a comprehensive small RNA and RNA workflow. It provides ready execution for over 20 commonly known RNA-seq tools on top of an established workflow engine and provides dynamic pipeline architecture to manage, individually analyze, and integrate both small RNA and RNA data. Implementation with Docker makes SePIA portable and easy to run. We demonstrate the workflow's extensive utility with two case studies involving three breast cancer datasets. SePIA is straightforward to configure and organizes results into a perusable HTML report. Furthermore, the underlying pipeline engine supports computational resource management for optimal performance. CONCLUSION: SePIA is an open-source workflow introducing standardized processing and analysis of RNA and small RNA data. SePIA's modular design enables robust customization to a given experiment while maintaining overall workflow structure. It is available at http://anduril.org/sepia.