Wang, Liewei

Abstract 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 Xβ, where the design matrix X is known and is computed based on the simplifying assumptions. In contrast XAEM considers Xβ as a bilinear model with both X and β unknown. Joint estimation of X and β 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 β. 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.

Abstract INTRODUCTION: Breast cancer is the most common invasive cancer and the second leading cause of cancer death in women. Approximately 20% of breast cancer tumors overexpress human epidermal growth factor receptor 2 (HER2), which is associated with an aggressive clinical phenotype and with a poorer prognosis. The development of trastuzumab, an immunoglobulin G monoclonal antibody directed against HER2, has changed the treatment paradigm for this disease. However, approximately 15-20% still does not respond to anti-HER2 therapy. Furthermore, despite advances in the management of metastatic HER2-positive breast cancer, response rate varies greatly, with most patients eventually succumbing to their disease. It is crucial to develop additional therapeutic strategies to improve outcomes for HER2 positive patients, especially those with tumors that do not respond to anti-HER2 therapy. METHODS and RESULTS: Using HER2+ breast cancer cell lines, we found that an USP7 inhibitor, P5091, dramatically sensitized cancer cells to HER2 inhibitor, trastuzumab treatment. To confirm it in vivo, we tested the drug combination in BT474 xenografts and found P5091 and trastuzumab synergistically suppressed tumor growth. Furthermore, using trastuzumab resistant HER2+ breast cancer cell line, we found that USP7 inhibitor, P5091, can dramatically re-sensitize trastuzumab-resistant BT474 cell line to trastuzumab treatment both in vitro and in vivo. More importantly, using patient derived xenograft (PDX) HER2+ models, we confirmed the synergistic combination effect of P5091 and trastuzumab. Mechanistically, USP7 inhibitor, P5091 led to significantly increased degradation of all four HER family members (HER1-4). When we knocked down USP7 by RNA interference or small hairpin RNA (shRNA), we observed similar substantial decrease in HER family member proteins. CONCLUSIONS: We have demonstrated the feasibility of using USP7 inhibitor P5091 to sensitize HER2+ breast cancers to trastuzumab treatment. These results provide a rationale for the use of combination therapy of USP7 inhibitors and HER2 inhibitors to treat HER2+ breast cancer. This combination therapy might also be effective in other cancers with upregulated HER signaling, such as GI cancer, lung cancer or ovarian cancer. Citation Format: Jia Yu, Bo Qin, Judy Boughey, Matthew Goetz, Liewei Wang. The role of USP7 and USP7 inhibitor in HER2+ breast cancer treatment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2980.

Abstract The present project was undertaken to determine whether the activation of the EGFR family (EGFR/ErbB2/ErbB3/ErbB4) and its downstream signaling effector ERK1/2 plays a role in enzalutamide resistance and whether treatment with ErbB or ERK inhibitors overcome this resistance. C4-2B (enzalutamide sensitive) and 22Rv1 (partially enzalutamide resistant) cell lines cultured for prolonged periods in enzalutamide, developed resistance to this drug (C4-2B/MDVR and 22Rv1/MDVR, respectively). Whole genome comparison of enza-resistant to parental lines demonstrated that the ErbB signaling network was significantly upregulated in the enza-resistant lines. Comparison of various enza-sensitive and resistant lines demonstrated a strong correlation between phosphorylation at EGFR(Y1068) and enza resistance. Inhibition of EGFR, but not that of ErbB2 or ErbB3 prevented cell viability. The EGFR inhibitors erlotinib and dacomitinib, but not the ErbB2 inhibitor lapatinib, suppressed viability in combination with enzalutamide. We hypothesized that enza-resistant tumors expressing higher levels of EGFR would be more responsive to erlotinib. To test this hypothesis, we compared the effects of erlotinib and enzalutamide, individually or in combination, in organelles from PDX tumors expressing high or low EGFR levels. Significantly, tumors expressing high EGFR, but not those expressing low EGFR, were responsive to the combination. Finally, we investigated the cause for greater efficacy with erlotinib compared to lapatinib in PCa. Comparison of the effects of different ligands revealed that while both lapatinib and erlotinib inhibited EGFR phosphorylation, only erlotinib but not lapatinib was able to inhibit EGF-induced ERK phosphorylation. This indicated an important role for ERK in the mediation of EGFR ligand induced enzalutamide resistance. Continuous culture of enza-sensitive C4 cells in enzalutamide resulted in the development of ERK phosphorylation in these PTEN-null cells that normally do not express phosphorylated ERK, and the newly ERK phosphorylated cells also were more susceptible to erlotinib as well. In support of a role for ERK in mediating the effects of EGFR activation in enza-resistant cells, inhibition of EGFR but not ErbB2 or ErbB3 inhibited ERK phosphorylation, and the ERK inhibitor ulitertinib inhibited viability of enza-resistant lines. We conclude that enzalutamide treatment causes an increase in EGFR ligands that result in the phosphorylation of EGFR at Y1068, which further resulted in phosphorylation of ERK. EGFR inhibitors that prevent ERK phosphorylation were effective in suppressing viability of enza-resistant CRPC cells, whereas ErbB2 inhibitors that did not affect ERK phosphorylation were unable to affect cell viability. These results demonstrate why certain ErbB inhibitors failed to affect enza-resistant CRPC tumors but provide encouragement for others. Citation Format: Thomas M. Steele, Maitreyee K. Jathal, Salma Siddiqui, Sisi Qin, Clifford G. Tepper, Ralph W. deVere White, Manish Kohli, Liewei Wang, Allen C. Gao, Paramita M. Ghosh. Overcoming EGFR and ERK-mediated resistance to enzalutamide in castration-resistant prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4842.

Abstract Background: In early stage, ERα-positive breast cancer, the concurrent use of endocrine therapy and chemotherapy has not been shown to be superior to sequential use. We hypothesized that genetic biomarkers can aid in selecting patients who would benefit from chemo-endocrine therapy. Our previous genome-wide association study (GWAS) and functional studies revealed that a single nucleotide polymorphism (SNP) in ZNF423, rs9940645, determines tamoxifen response. Specifically, 4-hydroxytamoxifen (4-OH-TAM) increases the gene expression of ZNF423, a transcription factor for BRCA1, in cells that are carry the rs9940645 variant, but not the wildtype, genotype. In this study, we set out to identify additional genes regulated by ZNF423, which, upon identifying a relationship between ZNF423 and cell cycle genes, led us to investigate taxane response in a SNP- and tamoxifen-dependent fashion. Method: Gene correlation analysis using The Cancer Genome Atlas (TCGA) breast cancer dataset was used to identify genes with expression correlated with that of ZNF423. Quantitative reverse transcription PCR, chromatin immunoprecipitation, and luciferase reporter assays were used to validate the gene associations. To test ZNF423 rs9940645-specific phenotypes, we used ZR75-1 cells, which are homozygous for the variant SNP, and ZR75-1 cells that were generated to be homozygous wild type (WT) using CRISPR-Cas9 technology, in addition to Hs578T breast cells with ERα overexpression (WT) and HCC1500 cells (variant). Cell cycle was assessed by propidium iodide staining. Cytotoxicity was assessed using a colorimetric assay. Results: Mitosis-related genes VRK1 and PBK, which encode histone H3 kinases, were experimentally validated to be regulated by ZNF423. Specifically, ZNF423 knockdown resulted in decreased VRK1 and PBK expression and activity. Additionally, ZNF423 knockdown resulted in enhanced docetaxel-induced G2/M arrest and cytotoxicity which could be rescued by VRK1 or PBK overexpression. Lastly, breast cancer cells carrying the rs9940645 variant SNP genotype had increased G2-M arrest and decreased cell viability when treated with docetaxel in combination with estradiol and 4-OH-TAM. Conclusion: We identified ZNF423regulated genes involved in the G2/M phase of the cell cycle. We found that 4-OH-TAM sensitized ERα-positive breast cancer cells to docetaxel treatment in a ZNF423 SNP-dependent manner. Our findings suggest that patients with rs9940645 variant genotype may benefit from concurrent tamoxifen endocrine therapy and docetaxel chemotherapy. This would impact a substantial proportion of patients given the minor allele frequency of 0.47 for this SNP. Citation Format: Gen Wang, Sisi QIN, Jacqueline Zayas, James N. Ingle, Mohan Liu, Richard Weinshilboum, Kunwei Shen, Liewei Wang. 4-Hydroxytamoxifen enhances sensitivity of estrogen receptor α-positive breast cancer to docetaxel in an estrogen and ZNF423 SNP-dependent fashion [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2940.

e16559 Background: ARVs that develop early during treatment with abiraterone acetate/ prednisone (AA/P) may play a role in treatment resistance. We evaluated metastatic site and whole blood mRNA expression of full-length AR (AR-FL) and ARVs to characterize treatment-emergent ARVs (TE-ARVs) from men with mCRPC pre- and post- 12 weeks of AA/P collected in a prospective clinical trial (NCT#01953640). Methods: RNAseq performed on paired metastatic site biopsy (n = 40) and whole blood (n = 25) passed quality control. Reads were aligned to the GRCh38 reference genome with the spliced-alignment TopHat2 package. AR-FL or ARVs were detected if ≥ 2 splice reads aligned to unique splice junctions for AR-FL, AR-8, AR-45, AR-23, AR-V3, AR-V5V6, AR-V7, AR-V8, AR-V9, AR-V10, AR-V12, AR-V13, and AR-V14, and normalized to splice reads per million (SRPM). Cox proportional hazard regression analysis was performed on AR-FL and AR-Vs with ≥1 SRPM for association with time to treatment change (TTTC). Results: In metastatic site biopsies post-AA/P, the average number of splice reads was 27,376,541 (range 7,753,998 to 62,456,773). The median number of ARVs detected was 2 (range 0-8), with a total of total of 110 ARVs identified (Table). Dynamic shifts in ARV profiles were observed post-AAP, with 41 TE-ARVs identified in 17 unique patients. The most common TE-ARV was AR-8 (n = 8), followed by AR-45 (n = 5), AR-23 (n = 5), and AR-V7 (n = 4). The presence of AR-V7 post-AAP was adversely associated with TTTC (hazard ratio 2.46, p = 0.013). The identification of early TE- ARV was not associated with TTTC. In whole blood samples, post-AA/P detection of AR-FL was low (n = 3) and no ARVs were detected. Conclusions: No specific patterns were observed in ARV profiles obtained in the metastatic biopsies after 12 weeks of treatment with AA/P. Clinical trial information: 01953640. [Table: see text]

We set out to determine whether machine learning–based algorithms that included functionally validated pharmacogenomic biomarkers joined with clinical measures could predict selective serotonin reuptake inhibitor (SSRI) remission/response in patients with major depressive disorder (MDD). We studied 1,030 white outpatients with MDD treated with citalopram/escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN‐AMPS; n = 398), Sequenced Treatment Alternatives to Relieve Depression (STAR*D; n = 467), and International SSRI Pharmacogenomics Consortium (ISPC; n = 165) trials. A genomewide association study for PGRN‐AMPS plasma metabolites associated with SSRI response (serotonin) and baseline MDD severity (kynurenine) identified single nucleotide polymorphisms (SNPs) in DEFB1, ERICH3, AHR, and TSPAN5 that we tested as predictors. Supervised machine‐learning methods trained using SNPs and total baseline depression scores predicted remission and response at 8 weeks with area under the receiver operating curve (AUC) > 0.7 (P < 0.04) in PGRN‐AMPS patients, with comparable prediction accuracies > 69% (P ≤ 0.07) in STAR*D and ISPC. These results demonstrate that machine learning can achieve accurate and, importantly, replicable prediction of SSRI therapy response using total baseline depression severity combined with pharmacogenomic biomarkers.

Abstract Motivation Both single-cell RNA sequencing (scRNA-seq) and DNA sequencing (scDNA-seq) have been applied for cell-level genomic profiling. For mutation profiling, the latter seems more natural. However, the task is highly challenging due to the limited input materials from only two copies of DNA molecules, while whole-genome amplification generates biases and other technical noises. ScRNA-seq starts with a higher input amount, so generally has better data quality. There exists various methods for mutation detection from DNA sequencing, it is not clear whether these methods work for scRNA-seq data. Results Mutation detection methods developed for either bulk-cell sequencing data or scDNA-seq data do not work well for the scRNA-seq data, as they produce substantial numbers of false positives. We develop a novel and robust statistical method—called SCmut—to identify specific cells that harbor mutations discovered in bulk-cell data. Statistically SCmut controls the false positives using the 2D local false discovery rate method. We apply SCmut to several scRNA-seq datasets. In scRNA-seq breast cancer datasets SCmut identifies a number of highly confident cell-level mutations that are recurrent in many cells and consistent in different samples. In a scRNA-seq glioblastoma dataset, we discover a recurrent cell-level mutation in the PDGFRA gene that is highly correlated with a well-known in-frame deletion in the gene. To conclude, this study contributes a novel method to discover cell-level mutation information from scRNA-seq that can facilitate investigation of cell-to-cell heterogeneity. Availability and implementation The source codes and bioinformatics pipeline of SCmut are available at https://github.com/nghiavtr/SCmut. Supplementary information Supplementary data are available at Bioinformatics online.