Sequential tumor molecular profiling identifies likely germline variants.

PURPOSE: To identify likely germline DNA variants from sequential tumor profiling data from hematopoietic malignancies (HMs). METHODS: The coefficient of variance was calculated from variant allele frequency of next-generation sequencing assays. Variants' likelihood of being germline was ranked on a 1 to 5 scale. Outcomes were examined in patients with such variants. RESULTS: In a pilot set of 33 genes, 89% of grade 1, 77% of grade 2, 62% of grade 3, 52% of grade 4, and 21% of grade 5 variants were confirmed to be germline. Among those, 22% were pathogenic or likely pathogenic in genes recognized as conferring hereditary HM risk, including BRCA1/2, CHEK2, CSF3R, and DDX41. To determine if this approach identified genes with known autosomal dominant inheritance, we analyzed sequential data from 1336 genes in 1135 HM patients. Among unique variants, 16% occurred in hereditary HM genes, and 15% were deleterious. Patients with grade 1/2 alleles had decreased survival 2 years after initial molecular testing (78% versus 88%, P = .0037) and increased all-cause mortality compared with those without (hazard ratio 2.02, 95% CI 1.18-3.46, P = .019). CONCLUSION: Variant germline status may be predicted using sequential tumor profiling and patients with likely germline variants experience inferior outcomes compared with those without.

Truncating Variants in RFC1 in Cerebellar Ataxia, Neuropathy, and Vestibular Areflexia Syndrome.

BACKGROUND AND OBJECTIVE: Cerebellar ataxia, neuropathy, and vestibular areflexia syndrome (CANVAS) is an autosomal recessive neurodegenerative disease characterized by adult-onset and slowly progressive sensory neuropathy, cerebellar dysfunction, and vestibular impairment. In most cases, the disease is caused by biallelic (AAGGG)n repeat expansions in the second intron of the replication factor complex subunit 1 (RFC1). However, a small number of cases with typical CANVAS do not carry the common biallelic repeat expansion. The objective of this study was to expand the genotypic spectrum of CANVAS by identifying sequence variants in RFC1-coding region associated with this condition. METHODS: Fifteen individuals diagnosed with CANVAS and carrying only 1 heterozygous (AAGGG)n expansion in RFC1 underwent whole-genome sequencing or whole-exome sequencing to test for the presence of a second variant in RFC1 or other unrelated gene. To assess the effect of truncating variants on RFC1 expression, we tested the level of RFC1 transcript and protein on patients' derived cell lines. RESULTS: We identified 7 patients from 5 unrelated families with clinically defined CANVAS carrying a heterozygous (AAGGG)n expansion together with a second truncating variant in trans in RFC1, which included the following: c.1267C>T (p.Arg423Ter), c.1739_1740del (p.Lys580SerfsTer9), c.2191del (p.Gly731GlufsTer6), and c.2876del (p.Pro959GlnfsTer24). Patient fibroblasts containing the c.1267C>T (p.Arg423Ter) or c.2876del (p.Pro959GlnfsTer24) variants demonstrated nonsense-mediated mRNA decay and reduced RFC1 transcript and protein. DISCUSSION: Our report expands the genotype spectrum of RFC1 disease. Full RFC1 sequencing is recommended in cases affected by typical CANVAS and carrying monoallelic (AAGGG)n expansions. In addition, it sheds further light on the pathogenesis of RFC1 CANVAS because it supports the existence of a loss-of-function mechanism underlying this complex neurodegenerative condition.

Ibrutinib and venetoclax target distinct subpopulations of CLL cells: implication for residual disease eradication.

Ibrutinib inhibits Bruton tyrosine kinase while venetoclax is a specific inhibitor of the anti-apoptotic protein BCL2. Both drugs are highly effective as monotherapy against chronic lymphocytic leukemia (CLL), and clinical trials using the combination therapy have produced remarkable results in terms of rate of complete remission and frequency of undetectable minimal residual disease. However, the laboratory rationale behind the success of the drug combination is still lacking. A better understanding of how these two drugs synergize would eventually help develop other rational combination strategies. Using an ex vivo model that promotes CLL proliferation, we show that modeled ibrutinib proliferative responses, but not viability responses, correlate well with patients' actual clinical responses. Importantly, we demonstrate for the first time that ibrutinib and venetoclax act on distinct CLL subpopulations that have different proliferative capacities. While the dividing subpopulation of CLL responds to ibrutinib, the resting subpopulation preferentially responds to venetoclax. The combination of these targeted therapies effectively reduced both the resting and dividing subpopulations in most cases. Our laboratory findings help explain several clinical observations and contribute to the understanding of tumor dynamics. Additionally, our proliferation model may be used to identify novel drug combinations with the potential of eradicating residual disease.

Inflammatory Myofibroblastic Tumor of the Uterus: An Immunohistochemical Study of 23 Cases.

Inflammatory myofibroblastic tumors (IMT) of the uterus may be underrecognized as their morphology and immunophenotype may overlap with myxoid variants of uterine smooth muscle tumors and endometrial stromal tumors. Although ALK is a helpful biomarker, not all uterine IMTs are ALK-rearranged, and a small subset of myxoid leiomyosarcomas is ALK-positive. Herein, we evaluated a series of 23 IMTs for the novel endometrial stromal markers interferon-inducible transmembrane protein-1 (IFITM1) and BCOR, the novel myoid marker transgelin, and possible predictive markers p16 and p53 by immunohistochemistry to determine their expression profile and potential prognostic value. Patients' ages ranged from 8 to 59 (mean 39) years and tumors from 2 to 20 (mean 8.2) cm. Follow-up was available for 12/23 (52%) patients; 9/12 (75%) without evidence of disease, 2/12 (17%) alive with disease, and 1/12 (8%) dead from disease. Four IMTs were classified as malignant due to extrauterine disease at diagnosis and/or recurrence. IFITM1 was positive (combined score>2) in 19/23 (83%), BCOR in 8/20 (40%), and transgelin in 22/23 (96%) of tumors. IFITM1 and BCOR were more often expressed in the myxoid component, and transgelin in the compact areas. p16 expression was absent in 5/23 (22%) of IMTs, while p53 was wildtype in all tumors. p16-negative IMTs included all 4 classified as malignant and one where the patient was lost to follow-up. Molecular data were available in 2 malignant IMTs, both of which harbored CDKN2A deletions. We conclude that caution is advised when using IFITM1, BCOR, and transgelin as markers for endometrial and smooth muscle tumors, as these are commonly expressed in IMTs. However, we did identify an association among lack of p16 staining, CKDN2A deletions, and aggressive behavior that merits corroboration by other studies. As a result of this finding, we recommend the use of p16 in the diagnostic work-up of uterine IMTs due to its potential prognostic significance.

CDC25B partners with PP2A to induce AMPK activation and tumor suppression in triple negative breast cancer.

Cell division cycle 25 (CDC25) dual specificity phosphatases positively regulate the cell cycle by activating cyclin-dependent kinase/cyclin complexes. Here, we demonstrate that in addition to its role in cell cycle regulation, CDC25B functions as a regulator of protein phosphatase 2A (PP2A), a major cellular Ser/Thr phosphatase, through its direct interaction with PP2A catalytic subunit. Importantly, CDC25B alters the regulation of AMP-activated protein kinase signaling (AMPK) by PP2A, increasing AMPK activity by inhibiting PP2A to dephosphorylate AMPK. CDC25B depletion leads to metformin resistance by inhibiting metformin-induced AMPK activation. Furthermore, dual inhibition of CDC25B and PP2A further inhibits growth of 3D organoids isolated from patient derived xenograft model of breast cancer compared to CDC25B inhibition alone. Our study identifies CDC25B as a regulator of PP2A, and uncovers a mechanism of controlling the activity of a key energy metabolism marker, AMPK.

Alternating EM algorithm for a bilinear model in isoform quantification from RNA-seq data.

MOTIVATION: Estimation of isoform-level gene expression from RNA-seq data depends on simplifying assumptions, such as uniform read distribution, that are easily violated in real data. Such violations typically lead to biased estimates. Most existing methods provide bias correction step(s), which is based on biological considerations-such as GC content-and applied in single samples separately. The main problem is that not all biases are known. RESULTS: We have developed a novel method called XAEM based on a more flexible and robust statistical model. Existing methods are essentially based on a linear model Xß, where the design matrix X is known and is computed based on the simplifying assumptions. In contrast XAEM considers Xß as a bilinear model with both X and ß unknown. Joint estimation of X and ß is made possible by a simultaneous analysis of multi-sample RNA-seq data. Compared to existing methods, XAEM automatically performs empirical correction of potentially unknown biases. We use an alternating expectation-maximization (AEM) algorithm, alternating between estimation of X and ß. For speed XAEM utilizes quasi-mapping for read alignment, thus leading to a fast algorithm. Overall XAEM performs favorably compared to recent advanced methods. For simulated datasets, XAEM obtains higher accuracy for multiple-isoform genes. In a differential-expression analysis of a real single-cell RNA-seq dataset, XAEM achieves substantially better rediscovery rates in independent validation sets. AVAILABILITY AND IMPLEMENTATION: The method and pipeline are implemented as a tool and freely available for use at http://fafner.meb.ki.se/biostatwiki/xaem/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Next-generation sequencing identifies 2 genomically distinct groups among pyloric gland adenomas.

The molecular alterations identified among pyloric gland adenomas (PGAs) in the published literature are based on polymerase chain reaction of targeted genes, and next-generation sequencing (NGS) has not been performed. In this study, we performed NGS and correlated the molecular alterations with the histologic grade of dysplasia and immunohistochemical findings in a cohort of PGAs. Successful DNA extraction and sequencing were performed in 15 pyloric gland adenomas/adenocarcinoma from 12 patients. Additionally, 4 specimens of autoimmune gastritis were selected to serve as the control group. Ten PGAs with low-grade dysplasia were seen to have mutations in the triad of APC, KRAS, and GNAS genes. Five PGAs with high-grade dysplasia/adenocarcinoma exhibited mutations in several genes including APC, CTNNB1, KRAS, GNAS, TP53, CDKN2A, PIK3CA, and EPHA5 genes but did not exhibit mutations in the triad of APC, KRAS, and GNAS genes. The median tumor mutational burden was higher in PGAs with high-grade dysplasia/adenocarcinoma when compared with PGAs with low-grade dysplasia (5.25 and 4.38, respectively). PGAs with high-grade dysplasia/adenocarcinoma had more chromosomal gains and losses than PGAs with low-grade dysplasia. The molecular findings suggest that there are 2 separate mutator pathways of dysplasia development in PGAs.

Multi-Institutional Evaluation of Interrater Agreement of Variant Classification Based on the 2017 Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Cancer.

This multi-institutional study was undertaken to evaluate interrater reliability of the 2017 Association for Molecular Pathology/American Society of Clinical Oncology/College of American Pathologists guidelines for interpretation and reporting of oncology sequence variants and to assess current practices and perceptions surrounding these guidelines. Fifty-one variants were distributed to 20 participants from 10 institutions for classification using the new guidelines. Agreement was assessed using chance-corrected agreement (Cohen κ). κ was 0.35. To evaluate if data sharing could help resolve disagreements, a summary of variant classifications and additional information about each variant were distributed to all participants. κ improved to 0.7 after the original classifications were revised. Participants were invited to take a web-based survey regarding their perceptions of the guidelines. Only 20% (n = 3) of the survey respondents had prior experience with the guidelines in clinical practice. The main perceived barriers to guideline implementation included the complexity of the guidelines, discordance between clinical actionability and pathobiologic relevance, lack of familiarity with the new classifications, and uncertainty when applying criteria to potential germline variants. This study demonstrates noteworthy discordances between pathologists for variant classification in solid tumors when using the 2017 Association for Molecular Pathology/American Society of Clinical Oncology/College of American Pathologists guidelines. These findings highlight potential areas for clarification/refinement before mainstream clinical adoption.

Targeted mutational analysis of inflammatory bowel disease-associated colorectal cancers.

Inflammatory bowel disease-associated colorectal carcinomas (IBD-CRCs) develop in a background of chronic inflammation, and thus, the molecular landscape of these tumors likely differs from that of sporadic colorectal cancer. To add to emerging data on molecular alterations present in these tumors, we analyzed our institution's cohort of IBD-CRCs. CRCs resected from patients with IBD underwent molecular analysis via a 50-gene hot-spot solid tumor panel (OncoScreen ST2.0). In-house sporadic CRCs and The Cancer Genome Atlas project data were used for comparison. Fifty-five IBD-CRCs from 48 patients were successfully analyzed. Mutations in TP53 were most common and were present in 69% of IBD-CRCs; a similar percentage of TP53 mutations was detected in sporadic colorectal carcinomas (70%). APC and KRAS mutations were significantly less common in IBD-CRCs than in sporadic CRCs (15% versus 53%, P < .001 and 20% versus 38%, P = .02, respectively). Additionally, the potentially targetable IDH1 R132 mutation was present in 7% of IBD-CRCs but only 1% of sporadic CRCs and The Cancer Genome Atlas CRCs; alterations in other genes with potential targeted therapies were very rare. In conclusion, IBD-CRCs exhibit molecular differences when compared to sporadic CRCs, suggesting different pathways of carcinogenesis, although certain alterations are common to both types of tumors. IDH1 mutations are present in a subset of IBD-CRCs, which may expand therapeutic options in the future.

System for Informatics in the Molecular Pathology Laboratory: An Open-Source End-to-End Solution for Next-Generation Sequencing Clinical Data Management.

Next-generation sequencing (NGS) diagnostic assays increasingly are becoming the standard of care in oncology practice. As the scale of an NGS laboratory grows, management of these assays requires organizing large amounts of information, including patient data, laboratory processes, genomic data, as well as variant interpretation and reporting. Although several Laboratory Information Systems and/or Laboratory Information Management Systems are commercially available, they may not meet all of the needs of a given laboratory, in addition to being frequently cost-prohibitive. Herein, we present the System for Informatics in the Molecular Pathology Laboratory (SIMPL), a free and open-source Laboratory Information System/Laboratory Information Management System for academic and nonprofit molecular pathology NGS laboratories, developed at the Genomic and Molecular Pathology Division at the University of Chicago Medicine. SIMPL was designed as a modular end-to-end information system to handle all stages of the NGS laboratory workload from test order to reporting. We describe the features of SIMPL, its clinical validation at University of Chicago Medicine, and its installation and testing within a different academic center laboratory (University of Colorado), and we propose a platform for future community co-development and interlaboratory data sharing.

Isoform-level gene expression patterns in single-cell RNA-sequencing data.

Motivation: RNA sequencing of single cells enables characterization of transcriptional heterogeneity in seemingly homogeneous cell populations. Single-cell sequencing has been applied in a wide range of researches fields. However, few studies have focus on characterization of isoform-level expression patterns at the single-cell level. In this study, we propose and apply a novel method, ISOform-Patterns (ISOP), based on mixture modeling, to characterize the expression patterns of isoform pairs from the same gene in single-cell isoform-level expression data. Results: We define six principal patterns of isoform expression relationships and describe a method for differential-pattern analysis. We demonstrate ISOP through analysis of single-cell RNA-sequencing data from a breast cancer cell line, with replication in three independent datasets. We assigned the pattern types to each of 16 562 isoform-pairs from 4929 genes. Among those, 26% of the discovered patterns were significant (P<0.05), while remaining patterns are possibly effects of transcriptional bursting, drop-out and stochastic biological heterogeneity. Furthermore, 32% of genes discovered through differential-pattern analysis were not detected by differential-expression analysis. Finally, the effects of drop-out events and expression levels of isoforms on ISOP's performances were investigated through simulated datasets. To conclude, ISOP provides a novel approach for characterization of isoform-level preference, commitment and heterogeneity in single-cell RNA-sequencing data. Availability and implementation: The ISOP method has been implemented as a R package and is available at https://github.com/nghiavtr/ISOP under a GPL-3 license. Supplementary information: Supplementary data are available at Bioinformatics online.

Synchronous and Metastatic Papillary and Follicular Thyroid Carcinomas with Unique Molecular Signatures.

Despite the relatively high prevalence of thyroid cancer, the occurrence of multiple synchronous, distinct subtypes of primary thyroid carcinoma is uncommon. The incidental finding of papillary thyroid microcarcinoma in a gland with a biologically relevant follicular or medullary carcinoma is more frequent than the synchronous occurrence of multiple clinically significant carcinomas. We report a case of synchronous papillary and follicular thyroid carcinomas metastatic to lymph node and bone, respectively. Next generation sequencing showed BRAF V600E mutation in the primary papillary carcinoma and NRAS Q61R mutation in the primary follicular carcinoma and bony metastasis. To our knowledge, this is the first reported case of synchronous and metastatic primary papillary and follicular carcinomas, and the first report of synchronous BRAF V600E mutated papillary and NRAS mutated follicular carcinoma.

TSPYL Family Regulates CYP17A1 and CYP3A4 Expression: Potential Mechanism Contributing to Abiraterone Response in Metastatic Castration-Resistant Prostate Cancer.

The testis-specific Y-encoded-like protein (TSPYL) gene family includes TSPYL1 to TSPYL6. We previously reported that TSPYL5 regulates cytochrome P450 (CYP) 19A1 expression. Here we show that TSPYLs, especially TSPYL 1, 2, and 4, can regulate the expression of many CYP genes, including CYP17A1, a key enzyme in androgen biosynthesis, and CYP3A4, an enzyme that catalyzes the metabolism of abiraterone, a CYP17 inhibitor. Furthermore, a common TSPYL1 single nucleotide polymorphism (SNP), rs3828743 (G/A) (Pro62Ser), abolishes TSPYL1's ability to suppress CYP3A4 expression, resulting in reduced abiraterone concentrations and increased cell proliferation. Data from a prospective clinical trial of 87 metastatic castration-resistant prostate cancer patients treated with abiraterone acetate/prednisone showed that the variant SNP genotype (A) was significantly associated with worse response and progression-free survival. In summary, TSPYL genes are novel CYP gene transcription regulators, and genetic alteration within these genes significantly influences response to drug therapy through transcriptional regulation of CYP450 genes.

Model-based unsupervised learning informs metformin-induced cell-migration inhibition through an AMPK-independent mechanism in breast cancer.

We demonstrate that model-based unsupervised learning can uniquely discriminate single-cell subpopulations by their gene expression distributions, which in turn allow us to identify specific genes for focused functional studies. This method was applied to MDA-MB-231 breast cancer cells treated with the antidiabetic drug metformin, which is being repurposed for treatment of triple-negative breast cancer. Unsupervised learning identified a cluster of metformin-treated cells characterized by a significant suppression of 230 genes (p-value < 2E-16). This analysis corroborates known studies of metformin action: a) pathway analysis indicated known mechanisms related to metformin action, including the citric acid (TCA) cycle, oxidative phosphorylation, and mitochondrial dysfunction (p-value < 1E-9); b) 70% of these 230 genes were functionally implicated in metformin response; c) among remaining lesser functionally-studied genes for metformin-response was CDC42, down-regulated in breast cancer treated with metformin. However, CDC42's mechanisms in metformin response remained unclear. Our functional studies showed that CDC42 was involved in metformin-induced inhibition of cell proliferation and cell migration mediated through an AMPK-independent mechanism. Our results points to 230 genes that might serve as metformin response signatures, which needs to be tested in patients treated with metformin and, further investigation of CDC42 and AMPK-independence's role in metformin's anticancer mechanisms.

Clinical Validation of a Next-Generation Sequencing Genomic Oncology Panel via Cross-Platform Benchmarking against Established Amplicon Sequencing Assays.

Next-generation sequencing (NGS) genomic oncology profiling assays have emerged as key drivers of personalized cancer care and translational research. However, validation of these assays to meet strict clinical standards has been historically problematic because of both significant assay complexity and a scarcity of optimal validation samples. Herein, we present the clinical validation of 76 genes from a novel 1212-gene large-scale hybrid capture cancer sequencing assay (University of Chicago Medicine OncoPlus) using full-data comparisons against multiple clinical NGS amplicon-based assays to yield dramatic increases in per-sample data comparison efficiency compared with previously published validations. Using a sample set of 104 normal, solid tumor, and hematopoietic malignancy specimens, head-to-head NGS data analyses allowed for 6.8 million individual clinical base call comparisons, including 2729 previously confirmed variants, with 100% sensitivity and specificity. University of Chicago Medicine OncoPlus showed excellent performance for detection of single-nucleotide variants, insertions/deletions up to 52 bp, and FLT3 internal tandem duplications of up to 102 bp or larger. Highly concordant copy number variant and ALK/RET/ROS1 gene fusion detection were also observed. In addition to underlining the efficiency of NGS validation via full-data benchmarking against existing clinical NGS assays, this study also highlights the degree of performance similarity between hybrid capture and amplicon assays that is attainable with the application of strict quality control parameters and optimized computational analytics.

Clonal evolution underlying leukemia progression and Richter transformation in patients with ibrutinib-relapsed CLL.

Ibrutinib has generated remarkable responses in patients with chronic lymphocytic leukemia (CLL), including those with an unfavorable cytogenetic profile. However, patients develop resistance, with poor outcomes and no established treatment options. Mutations in BTK and PLCG2 have emerged as main mechanisms of drug resistance, but not all patients carry these mutations. Further understanding of mechanisms of resistance is urgently needed and will support rational development of new therapeutic strategies. To that end, we characterized the genomic profiles of serial samples from 9 patients with ibrutinib-relapsed disease, including 6 who had Richter transformation. Mutations, indels, copy-number aberrations, and loss of heterozygosity were assessed using next-generation sequencing and single-nucleotide polymorphism array. We found that 18p deletion (del(18p)), together with del(17p)/TP53 mutations, was present in 5 of 9 patients before ibrutinib therapy. In addition to BTKC481 , we identified BTKT316A , a structurally novel mutation located in the SH2 domain of BTK. Minor BTK clones with low allele frequencies were captured in addition to major BTK clones. Although TP53 loss predisposes patients for relapse, clone size of TP53 loss may diminish during disease progression while mutant BTK clone expands. In patients who had Richter transformation, we found that the transformed cells were clonal descendants of circulating leukemia cells but continued to undergo evolution and drifts. Surprisingly, transformed lymphoma cells in tissue may acquire a different BTK mutation from that in the CLL leukemia cells. Collectively, these results provide insights into clonal evolution underlying ibrutinib relapse and prompt further investigation on genomic abnormalities that have clinical application potential.

Circular RNAs and their associations with breast cancer subtypes.

Circular RNAs (circRNAs) are highly stable forms of non-coding RNAs with diverse biological functions. They are implicated in modulation of gene expression thus affecting various cellular and disease processes. Based on existing bioinformatics approaches, we developed a comprehensive workflow called Circ-Seq to identify and report expressed circRNAs. Circ-Seq also provides informative genomic annotation along circRNA fused junctions thus allowing prioritization of circRNA candidates. We applied Circ-Seq first to RNA-sequence data from breast cancer cell lines and validated one of the large circRNAs identified. Circ-Seq was then applied to a larger cohort of breast cancer samples (n = 885) provided by The Cancer Genome Atlas (TCGA), including tumors and normal-adjacent tissue samples. Notably, circRNA results reveal that normal-adjacent tissues in estrogen receptor positive (ER+) subtype have relatively higher numbers of circRNAs than tumor samples in TCGA. Similar phenomenon of high circRNA numbers were observed in normal breast-mammary tissues from the Genotype-Tissue Expression (GTEx) project. Finally, we observed that number of circRNAs in normal-adjacent samples of ER+ subtype is inversely correlated to the risk-of-relapse proliferation (ROR-P) score for proliferating genes, suggesting that circRNA frequency may be a marker for cell proliferation in breast cancer. The Circ-Seq workflow will function for both single and multi-threaded compute environments. We believe that Circ-Seq will be a valuable tool to identify circRNAs useful in the diagnosis and treatment of other cancers and complex diseases.

Beta-Poisson model for single-cell RNA-seq data analyses.

MOTIVATION: Single-cell RNA-sequencing technology allows detection of gene expression at the single-cell level. One typical feature of the data is a bimodality in the cellular distribution even for highly expressed genes, primarily caused by a proportion of non-expressing cells. The standard and the over-dispersed gamma-Poisson models that are commonly used in bulk-cell RNA-sequencing are not able to capture this property. RESULTS: We introduce a beta-Poisson mixture model that can capture the bimodality of the single-cell gene expression distribution. We further integrate the model into the generalized linear model framework in order to perform differential expression analyses. The whole analytical procedure is called BPSC. The results from several real single-cell RNA-seq datasets indicate that ∼90% of the transcripts are well characterized by the beta-Poisson model; the model-fit from BPSC is better than the fit of the standard gamma-Poisson model in > 80% of the transcripts. Moreover, in differential expression analyses of simulated and real datasets, BPSC performs well against edgeR, a conventional method widely used in bulk-cell RNA-sequencing data, and against scde and MAST, two recent methods specifically designed for single-cell RNA-seq data. AVAILABILITY AND IMPLEMENTATION: An R package BPSC for model fitting and differential expression analyses of single-cell RNA-seq data is available under GPL-3 license at https://github.com/nghiavtr/BPSC CONTACT: yudi.pawitan@ki.se or mattias.rantalainen@ki.se SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

HEATR1 Negatively Regulates Akt to Help Sensitize Pancreatic Cancer Cells to Chemotherapy.

Elucidating mechanisms of chemoresistance is critical to improve cancer therapy, especially for the treatment of pancreatic ductal adenocarcinoma (PDAC). Genome-wide association studies have suggested the less studied gene HEAT repeat-containing protein 1 (HEATR1) as a possible determinant of cellular sensitivity to different chemotherapeutic drugs. In this study, we assessed this hypothesized link in PDAC, where HEATR1 expression is downregulated significantly. HEATR1 silencing in PDAC cells increased resistance to gemcitabine and other chemotherapeutics, where this effect was associated with increased AKT kinase phosphorylation at the Thr308 regulatory site. Mechanistically, HEATR1 enhanced cell responsiveness to gemcitabine by acting as a scaffold to facilitate interactions between AKT and the protein phosphatase PP2A, thereby promoting Thr308 dephosphorylation. Consistent with these findings, treatment with the AKT inhibitor triciribine sensitized HEATR1-depleted PDAC cells to gemcitabine, suggesting that this therapeutic combination may overcome gemcitabine resistance in patients with low HEATR1 expression. Clinically, we found that HEATR1 downregulation in PDAC patients was associated with increased AKT phosphorylation, poor response to tumor resection plus gemcitabine standard-of-care treatment, and shorter overall survival. Collectively, our findings establish HEATR1 as a novel regulator of AKT and a candidate predictive and prognostic indicator of drug responsiveness and outcome in PDAC patients.

Metformin pharmacogenomics: a genome-wide association study to identify genetic and epigenetic biomarkers involved in metformin anticancer response using human lymphoblastoid cell lines.

Metformin is currently considered as a promising anticancer agent in addition to its anti-diabetic effect. To better individualize metformin therapy and explore novel molecular mechanisms in cancer treatment, we conducted a pharmacogenomic study using 266 lymphoblastoid cell lines (LCLs). Metformin cytotoxicity assay was performed using the MTS assay. Genome-wide association (GWA) analyses were performed in LCLs using 1.3 million SNPs, 485k DNA methylation probes, 54k mRNA expression probe sets, and metformin cytotoxicity (IC50s). Top candidate genes were functionally validated using siRNA screening, followed by MTS assay in breast cancer cell lines. Further study of one top candidate, STUB1, was performed to elucidate the mechanisms by which STUB1 might contribute to metformin action. GWA analyses in LCLs identified 198 mRNA expression probe sets, 12 SNP loci, and 5 DNA methylation loci associated with metformin IC50 with P-values <10−4 or <10−5. Integrated SNP/methylation loci-expression-IC50 analyses found 3 SNP loci or 5 DNA methylation loci associated with metformin IC50 through trans-regulation of expression of 11 or 26 genes with P-value <10−4. Functional validation of top 61 candidate genes in 4 IPA networks indicated down regulation of 14 genes significantly altered metformin sensitivity in two breast cancer cell lines. Mechanistic studies revealed that the E3 ubiquitin ligase, STUB1, could influence metformin response by facilitating proteasome-mediated degradation of cyclin A. GWAS using a genomic data-enriched LCL model system, together with functional and mechanistic studies using cancer cell lines, help us to identify novel genetic and epigenetic biomarkers involved in metformin anticancer response.