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S1. Prostate cancer cells overexpressing FGFR3-S are resistance to the combination of dovitinib- and docetaxel-induced apoptotic activity.

Supplemental Table S4. IC50 values calculated for proliferation, migration, and invasion assays for PC-3 and LNCaP overexpressing cell lines from Fig 3.

Figure and Table legends for supplemental data.

PURPOSE The CLL14 study has established one-year fixed-duration treatment of venetoclax and obinutuzumab (Ven-Obi) for patients with previously untreated chronic lymphocytic leukemia. With all patients off treatment for at least three years, we report a detailed analysis of minimal residual disease (MRD) kinetics and long-term outcome of patients treated in the CLL14 study. PATIENTS AND METHODS Patients were randomly assigned to receive six cycles of obinutuzumab with 12 cycles of venetoclax or 12 cycles of chlorambucil (Clb-Obi). Progression-free survival (PFS) was the primary end point. Key secondary end points included rates of undetectable MRD and overall survival. To analyze MRD kinetics, a population-based growth model with nonlinear mixed effects approach was developed. RESULTS Of 432 patients, 216 were assigned to Ven-Obi and 216 to Clb-Obi. Three months after treatment completion, 40% of patients in the Ven-Obi arm (7% in the Clb-Obi arm) had undetectable MRD levels < 10−6 by next-generation sequencing in peripheral blood. Median MRD doubling time was longer after Ven-Obi than Clb-Obi therapy (median 80 v 69 days). At a median follow-up of 52.4 months, a sustained significant PFS improvement was observed in the Ven-Obi arm compared with Clb-Obi (median not reached v 36.4 months; hazard ratio 0.33; 95% CI, 0.25 to 0.45; P < .0001). The estimated 4-year PFS rate was 74.0% in the Ven-Obi and 35.4% in the Clb-Obi arm. No difference in overall survival was observed (hazard ratio 0.85; 95% CI, 0.54 to 1.35; P = .49). No new safety signals occurred. CONCLUSION Appearance of MRD after Ven-Obi is significantly slower than that after Clb-Obi with more effective MRD reduction. These findings translate into a superior long-term efficacy with the majority of Ven-Obi–treated patients remaining in remission.

Alternative splicing (AS) has been shown to participate in prostate cancer development and progression; however, a link between AS and prostate cancer health disparities has been largely unexplored. Here we report on the cloning of a novel splice variant of FGFR3 that is preferentially expressed in African American (AA) prostate cancer. This novel variant (FGFR3-S) omits exon 14, comprising 123 nucleotides that encode the activation loop in the intracellular split kinase domain. Ectopic overexpression of FGFR3-S in European American (EA) prostate cancer cell lines (PC-3 and LNCaP) led to enhanced receptor autophosphorylation and increased activation of the downstream signaling effectors AKT, STAT3, and ribosomal S6 compared with FGFR3-L (retains exon 14). The increased oncogenic signaling imparted by FGFR3-S was associated with a substantial gain in proliferative and antiapoptotic activities, as well as a modest but significant gain in cell motility. Moreover, the FGFR3-S–conferred proliferative and motility gains were highly resistant to the pan-FGFR small-molecule inhibitor dovitinib and the antiapoptotic gain was insensitive to the cytotoxic drug docetaxel, which stands in marked contrast with dovitinib- and docetaxel-sensitive FGFR3-L. In an in vivo xenograft model, mice injected with PC-3 cells overexpressing FGFR3-S exhibited significantly increased tumor growth and resistance to dovitinib treatment compared with cells overexpressing FGFR3-L. In agreement with our in vitro and in vivo findings, a high FGFR3-S/FGFR3-L expression ratio in prostate cancer specimens was associated with poor patient prognosis.Implications:This work identifies a novel FGFR3 splice variant and supports the hypothesis that differential AS participates in prostate cancer health disparities.

Although driver genes in hepatocellular carcinoma (HCC) have been investigated in various previous genetic studies, prevalence of key driver genes among heterogeneous populations is unknown. Moreover, the phenotypic associations of these driver genes are poorly understood. This report aims to reveal the phenotypic impacts of a group of consensus driver genes in HCC. We used MutSigCV and OncodriveFM modules implemented in the IntOGen pipeline to identify consensus driver genes across six HCC cohorts comprising 1,494 samples in total. To access their global impacts, we used The Cancer Genome Atlas (TCGA) mutations and copy-number variations to predict the transcriptomics data, under generalized linear models. We further investigated the associations of the consensus driver genes to patient survival, age, gender, race, and risk factors. We identify 10 consensus driver genes across six HCC cohorts in total. Integrative analysis of driver mutations, copy-number variations, and transcriptomic data reveals that these consensus driver mutations and their copy-number variations are associated with a majority (62.5%) of the mRNA transcriptome but only a small fraction (8.9%) of miRNAs. Genes associated with TP53, CTNNB1, and ARID1A mutations contribute to the tripod of most densely connected pathway clusters. These driver genes are significantly associated with patients' overall survival. Some driver genes are significantly linked to HCC gender (CTNNB1, ALB, TP53, and AXIN1), race (TP53 and CDKN2A), and age (RB1) disparities. This study prioritizes a group of consensus drivers in HCC, which collectively show vast impacts on the phenotypes. These driver genes may warrant as valuable therapeutic targets of HCC.

Glycerol is a biodiesel byproduct. In the present study, glycerol was used as a co-substrate during biodegradation of dibenzothiophene (DBT) by Paraburkholderia sp. C3. Polycyclic aromatic hydrocarbons (PAHs) are a group of persistent, ubiquitous and carcinogenic chemicals found in the environment. DBT is a major sulfur-containing PAH. The chemical properties of DBT make it an ideal model pollutant for examining the bioremediation of higher molecular weight PAHs. Bioremediation uses microbial catalysis for removal of environmental pollutants. Environmental microorganisms that encounter aromatic substrates such as heterocyclic PAHs develop unique characteristics that allow the uptake and assimilation of these cytotoxic substrates. Microbial adaptations include changes in membrane lipid composition, secretion of surface-active compounds and accumulation of lipid granules to withstand chemical toxicity. Biostimulation using more readily metabolized substrates can increase the biodegradation rate of PAHs, but the molecular mechanisms are not well understood. We analyzed the DBT biodegradation kinetics in C3, proteome changes and TEM micrographs in different culturing conditions. We utilized 2-bromoalkanoic lipid metabolic inhibitors to establish a correlation between polyhydroxyalkanoate (PHA) granule formation and the enhancement of DBT biodegradation induced by glycerol. This is the first description linking PHA biosynthesis, DBT biodegradation and 2-bromoalkanoic acids in a Paraburkholderia species.

Introduction The clinical course and outcomes are heterogenous among patients (pts) with chronic lymphocytic leukemia (CLL). Somatic hypermutation (SHM) status of the IGHV gene (IGHV-SHM) is an established prognostic marker for pts receiving certain therapeutic regimens. Pts with mutated IGHV (IGHV-M) have a better prognosis vs pts with unmutated IGHV (IGHV-UM). We demonstrate that IGHV-SHM could be accurately assessed utilizing primers located downstream of CDR1. Methods Adaptive's clonoSEQ ® NGS-based assay identifies tumor specific sequence(s) using a set of multiplexed, locus-specific primers that include the Ig heavy chain variable gene (IGHV; starting at the end of CDR1), and the kappa and lambda light chain. Sequences are tagged and used to assess measurable residual disease (MRD). SHM status is calculated as the percentage of somatic mutations deviating from the germline DNA sequence. IGHV-M was defined as ≥2% deviation. For patients with multiple IGHV sequences, the most mutated sequence determined IGHV-SHM status. Pt samples from 5 studies (Table 1) were analyzed by clonoSEQ to determine IGHV-SHM status and compared to IGHV-SHM status determined by institutional standard laboratory (Sanger sequencing or institutional NGS). Four datasets were used from MD Anderson Cancer Center (MDACC). Set1 (n=31) was the positive control as eligibility criteria mandated inclusion of pts with IGHV-M status. Results were correlated with outcome data available from set2 (n=62). Pts with B-cell acute lymphoblastic leukemia served as a negative control (set3; n=246). Validation was performed with CLL samples (set4; n=21 and set5; n=49). IGHV-SHM analysis performed by Sanger sequencing (and NGS in a subset of set1 and set4) were the comparator for Adaptive's IGHV-SHM assessment. Rearranged gDNA sequences from the IMGT database (est. 1989) were ascertained and IgBlast was used to annotate the domains and report the V-gene homology. To approximate a full-length (FL) rearrangement, 9080 sequences were identified where framework region 1 (FR1) had ≥ 75 nucleotides. We compared the mutation status obtained using this FL rearranged sequence to that same sequence beginning at the 5' end of FR2 (consistent with the 5' extent of the primers in the Adaptive Assay). Results In set1, 30/31 pts were IGHV-M by the MDACC sequencing IGHV-SHM analysis. We obtained concordant IGHV-SHM status in 29/30 IGHV-M pts, and the only IGHV-UM pt. From set2, 29 pts with IGHV-M and 24 IGHV-UM were identified; 8 pts had unknown IGHV-SHM status. The IGHV-SHM Assay classified IGHV-M in 24/29 pts and IGHV-UM in 23/24 pts. In the negative control (set3), no IGHV-M pts were identified. In set2, stratifying PFS by Adaptive's IGHV-SHM status a p=0.0067 was non-inferior to the p=0.012 by Sanger sequencing (Figure 1 A-D). Inclusion of 8 pts with unknown IGHV-SHM status by Sanger sequencing yields a p=0.0039 (n=61). In the validation dataset (set4), IGHV-M was concordant for 4/4 IGHV-M pts and 13/14 IGHV-UM pts. In a separate validation dataset (set5), the Adaptive IGHV-SHM Assay was concordant for 16/17 IGHV-M pts and 25/28 IGHV-UM pts. 3 and 4 pts had unknown IGHV-SHM status by MDACC analysis in set4 and set5 respectively. A concordance table compares mutation status with FL rearrangements from IMGT vs excluding FR1 & CDR1 (Table 2). 96.1% of the sequences had concordant SHM status. Of the 3.9% that are discordant, a majority are within ±1% of the 2% threshold, a recognized SHM "gray zone". Only 15 (0.17%) fall outside this borderline category and are categorized as IGHV-M converting to IGHV-UM based on the truncated analysis. Given improvements in sequencing since launching IMGT in 1989, it is likely that some sequencing quality issues may be present within the dataset such that the 0.17% discordance is an upper estimate of discordance. Conclusions 135/147 CLL pts were concordant to independent assessments of IGHV-SHM status; IGHV-SHM status was assessed in additional 15 pts who could not be evaluated by MDACC comparator assay(s). The assessment of IGHV-SHM status correlated with PFS and standard determination of SHM. This analysis highlights the utility of the Adaptive assay to identify trackable sequences and assess IGHV-SHM status from the same baseline sample. The ability of a single platform to determine both SHM and enable subsequent MRD assessment streamlines the clinical management of pts with CLL. Figure 1 Figure 1. Disclosures Lee: Adaptive Biotechnologies: Current Employment. Ching: Adaptive Biotechnologies: Current Employment, Current equity holder in publicly-traded company. Howie: Adaptive Biotechnologies: Current Employment, Current equity holder in publicly-traded company, Divested equity in a private or publicly-traded company in the past 24 months. Thompson: Janssen: Consultancy, Honoraria; AbbVie: Other: Institution: Advisory/Consultancy, Honoraria, Research Grant/Funding; Gilead: Other: Institution: Advisory/Consultancy, Honoraria; Pharmacyclics: Other: Institution: Advisory/Consultancy, Honoraria, Research Grant/Funding; Adaptive Biotechnologies: Other: Institution: Advisory/Consultancy, Honoraria, Research Grant/Funding, Expert Testimony; Genentech: Other: Institution: Advisory/Consultancy, Honoraria, Research Grant/Funding; Amgen: Other: Institution: Honoraria, Research Grant/Funding. Jain: Beigene: Honoraria; Janssen: Honoraria; Bristol Myers Squibb: Honoraria, Research Funding; TG Therapeutics: Honoraria; Servier: Honoraria, Research Funding; AstraZeneca: Honoraria, Research Funding; ADC Therapeutics: Honoraria, Research Funding; Fate Therapeutics: Research Funding; Cellectis: Honoraria, Research Funding; Adaptive Biotechnologies: Honoraria, Research Funding; Incyte: Research Funding; Genentech: Honoraria, Research Funding; Pfizer: Research Funding; Aprea Therapeutics: Research Funding; Precision Biosciences: Honoraria, Research Funding; AbbVie: Honoraria, Research Funding; Pharmacyclics: Research Funding. Ferrajoli: BeiGene: Other: Advisory Board, Research Funding; Janssen: Other: Advisory Board ; AstraZeneca: Other: Advisory Board, Research Funding. Burger: TG Therapeutics: Other: Travel/Accommodations/Expenses, Research Funding, Speakers Bureau; AstraZeneca: Consultancy; Beigene: Research Funding, Speakers Bureau; Gilead: Consultancy, Other: Travel/Accommodations/Expenses, Research Funding, Speakers Bureau; Pharmacyclics LLC: Consultancy, Other: Travel/Accommodations/Expenses, Research Funding, Speakers Bureau; Novartis: Other: Travel/Accommodations/Expenses, Speakers Bureau; Janssen: Consultancy, Other: Travel/Accommodations/Expenses, Speakers Bureau. Wierda: Sunesis: Research Funding; Cyclacel: Research Funding; Miragen: Research Funding; Janssen: Research Funding; KITE Pharma: Research Funding; Karyopharm: Research Funding; Pharmacyclics LLC, an AbbVie Company: Research Funding; Gilead Sciences: Research Funding; Oncternal Therapeutics, Inc.: Research Funding; AstraZeneca: Research Funding; GSK/Novartis: Research Funding; Acerta Pharma Inc.: Research Funding; Xencor: Research Funding; Loxo Oncology, Inc.: Research Funding; Juno Therapeutics: Research Funding; Genentech: Research Funding; Genzyme Corporation: Consultancy; AbbVie: Research Funding. Kirsch: Adaptive Biotechnologies: Current Employment, Current equity holder in publicly-traded company.

Two fungi from the genus Moniliella and Byssochlamys were isolated and identified from a water contaminated B20 biodiesel blend. The Moniliella and Byssochlamys isolates were able to metabolize both biodiesel and petroleum diesel individually. Further investigations with Moniliella wahieum Y12 T grown using blended fuels as the carbon substrate show that the degradation of ULSD was enhanced by 33.3% when blended with 5% biodiesel. This amount decreased to 11.1% when blended with 20% biodiesel. This is the first report describing an enhanced ULSD degradation rate that was increased through a cometabolic processes induced by biodiesel using Moniliella wahieum Y12 T .

Background Measurement of minimal residual disease (MRD) is an established and sensitive prognostic tool to assess the depth of response during and after treatment of chronic lymphocytic leukemia (CLL) and to understand disease dynamics after treatment. Retracing these kinetics is paramount to understand which group of patients is at risk of relapsing despite initial MRD response. To this end, we here provide an analysis of clonal growth patterns in patients treated within the CLL14 trial. Methods In total, 432 patients with previously untreated CLL and co-existing conditions were randomized to receive chlorambucil or venetoclax (216 patients per arm) until completion of cycle 12 (à 28 days), and in combination with obinutuzumab for the first 6 cycles. The primary endpoint was progression-free survival (PFS), MRD was a secondary endpoint. MRD was analyzed by ASO-PCR and, as reported here, by next-generation sequencing (Adaptive clonoSEQ Assay, cut-off: 10-4, 10-5 and 10-6). The limit-of-quantification of the clonoSEQ Assay is less than 10-6. Samples from peripheral blood (PB) are collected every 3 to 6 months until 9 years from last patient enrolled; for this analysis, the longest follow-up MRD sample was from month 24 after treatment completion. A patient-specific clonal growth rate model was formed: MRD level at time t is defined as MRD(t) = λ0 x 10μt, where λ0 is the baseline MRD (i.e. MRD at end of treatment (EoT)), and μ is the growth rate parameter. For the time from MRD status λ0 to new MRD state λ1 , tchange was assumed (1/μ) x log10 (λ1/λ0). An exponential regression model was fitted to each patient to estimate each respective λ0 and μ, by which the patients' MRD clone doubling time was calculated. Data are reported in relation to the intention-to-treat population; p-values are exploratory only. For modelling, only patients with at least two MRD assessments after EoT and who had not experienced PD before EoT were included. Results Two months after treatment completion (follow-up month 3), 40% (7%) of patients in the Ven-Obi arm (Clb-Obi arm) had uMRD levels Most patients (56%) who had undetectable MRD (uMRD) levels at EoT already had uMRD levels at cycle 7, i.e. after completing Ven-Obi therapy. In 25% of the Ven-Obi treated patients, MRD response deepened after continuing with 6 cycles of venetoclax monotherapy (Figure B). In a PFS landmark analysis after EoT, patients in the Ven-Obi arm with MRD levels ≤10-5 had a 2-year PFS after EoT of approximately (approx.) 93%, while patients with detectable MRD >10-2 had a 2-year PFS of approx. 37% (Figure D). Patient-specific clonal growth rates were estimated for 123 patients in the Ven-Obi arm and 143 patients in the Clb-Obi arm (Figure C). Of note, 38 patients in the Ven-Obi arm and 4 patients in the Clb-Obi arm had MRD results below the assays limit of quantification (LOQ), indicating the deepest response beyond the assay's sensitivity. These patients were not included as a growth rate could not be accurately assessed for those very-deep responders. The average clonal growth rate in the Ven-Obi arm (n=123) was µ = 0.0034 (95% CI: 0.0030-0.0038), translating into an MRD doubling time of approx. 89 days (95% CI: 79-102) and an increase of the MRD clone size by 1 with log10 scale within approx. 296 days (95% CI: 263-337) (Figure E). For patients in the Clb-Obi arm (n=143), µ was 0.0042 (95% CI: 0.0038-0.0047), corresponding to a doubling time of approx. 71 days (95% CI: 65-79), and an increase of the MRD clone size by 1 with log10 scale within approx. 237 days (95% CI: 215-263). The average growth rate was lower in the Ven-Obi arm compared to the Clb-Obi arm (p=0.0057). Analyses of growth rate patterns within various biological and clinical risk groups as well as genetic subgroups are currently being conducted and will be presented at the meeting. Conclusions This analysis demonstrates that individual clonal growth rates can be used to estimate the MRD doubling time after a fixed-duration treatment. Clonal growth was lower after Ven-Obi than after Clb-Obi, indicating more effective MRD eradication and clonal growth modulation with Ven-Obi. In a considerable subgroup of Ven-Obi treated patients, no clonal growth was measurable during observation, indicating deepest remissions. Figure Disclosures Al-Sawaf: AstraZeneca: Consultancy, Honoraria, Other: personal fees; BeiGene: Research Funding; AbbVie: Consultancy, Honoraria, Other: personal fees, Research Funding; Janssen: Consultancy, Honoraria, Other: personal fees, Research Funding; Gilead: Consultancy, Honoraria, Other: personal fees; Roche: Consultancy, Honoraria, Other: personal fees, Research Funding. Wilson:Roche Products Limited: Current Employment. Tandon:Roche Products Ltd.: Current equity holder in publicly-traded company. Ching:Adaptive Biotechnologies: Current Employment, Current equity holder in publicly-traded company. Fink:Janssen: Honoraria; Celgene: Research Funding; AbbVie: Other: travel grants. Ritgen:Gilead: Other: travel grants; F. Hoffman-La Roche: Consultancy, Honoraria, Other: travel grants, Research Funding; BMS: Consultancy, Honoraria, Other: travel grants; Pfizer: Consultancy, Honoraria. Tausch:Janssen-Cilag: Consultancy, Honoraria, Research Funding; AbbVie: Consultancy, Honoraria, Research Funding; Roche: Consultancy, Honoraria, Research Funding. Kreuzer:AbbVie: Consultancy, Honoraria, Research Funding; Hoffmann-La Roche: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Gilead: Consultancy, Honoraria, Research Funding; Janssen-Cilag: Consultancy, Honoraria, Research Funding; Mundipharma: Consultancy, Honoraria, Research Funding. Schary:AbbVie: Current Employment, Current equity holder in publicly-traded company. Wendtner:AbbVie: Consultancy; MorphoSys: Consultancy; Hoffmann-La Roche: Consultancy; Gilead: Consultancy; Janssen-Cilag: Consultancy. Eichhorst:AstraZeneca: Consultancy, Honoraria, Other: travel support, Research Funding; Celgene: Consultancy, Honoraria, Other: travel support, Research Funding; ArQule: Consultancy, Honoraria, Other: travel support, Research Funding; Novartis: Consultancy, Honoraria, Other: travel support, Research Funding; AbbVie: Consultancy, Honoraria, Other: travel support, Research Funding; Gilead: Consultancy, Honoraria, Other: travel support, Research Funding; Oxford Biomedica: Consultancy, Honoraria, Other: travel support, Research Funding; BeiGene: Consultancy, Honoraria, Other: travel support, Research Funding; Janssen-Cilag: Consultancy, Honoraria, Other: travel support, Research Funding; F. Hoffmann-LaRoche: Consultancy, Honoraria, Other: travel support, Research Funding. Stilgenbauer:Mundipharma: Consultancy, Honoraria, Other, Research Funding; Gilead: Consultancy, Honoraria, Other: travel support, Research Funding; Genzyme: Consultancy, Honoraria, Other: travel support, Research Funding; Celgene: Consultancy, Honoraria, Other: travel support, Research Funding; F. Hoffmann-LaRoche: Consultancy, Honoraria, Other: travel support, Research Funding; Genentech: Consultancy, Honoraria, Other: travel support, Research Funding; Boehringer-Ingelheim: Consultancy, Honoraria, Other: travel support, Research Funding; Amgen: Consultancy, Honoraria, Other: travel support, Research Funding; AbbVie: Consultancy, Honoraria, Other: travel support, Research Funding; GlaxoSmithKline: Consultancy, Honoraria, Other: travel support, Research Funding; Novartis: Consultancy, Honoraria, Other, Research Funding; Pharmacyclics: Consultancy, Honoraria, Other, Research Funding; Janssen-Cilag: Consultancy, Honoraria, Other: travel support, Research Funding. Jiang:F. Hoffmann-La Roche: Current equity holder in publicly-traded company; Genentech, Inc.: Current Employment. Hallek:Roche: Consultancy, Research Funding; Gilead: Consultancy, Research Funding; Mundipharma: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding. Fischer:F. Hoffmann-La Roche: Honoraria, Other: travel grants; AbbVie: Honoraria.

PurposePathological images are easily accessible data with the potential as prognostic biomarkers. Moreover, integration of heterogeneous data types from multi-modality, such as pathological image and gene expression data, is invaluable to help predicting cancer patient survival. However, the analytical challenges are significant.Experimental DesignHere we take the hepatocellular carcinoma (HCC) pathological image features extracted by CellProfiler, and apply them as the input for Cox-nnet, a neural network-based prognosis. We compare this model with conventional Cox-PH model, CoxBoost, Random Survival Forests and DeepSurv, using C-index and log ranked p-values on HCC testing samples. Further, to integrate pathological image and gene expression data of the same patients, we innovatively construct a two-stage Cox-nnet model, and compare it with another complex neural network model PAGE-Net.Resultspathological image based prognosis prediction using Cox-nnet is significantly more accurate than Cox-PH and random survival forests models, and comparable with CoxBoost and DeepSurv methods. Additionally, the two-stage Cox-nnet complex model combining histopathology image and transcriptomics RNA-Seq data achieves better prognosis prediction, with a median C-index of 0.75 and log-rank p-value of 6e-7 in the testing datasets. The results are much more accurate than PAGE-Net, a CNN based complex model (median C-index of 0.68 and log-rank p-value of 0.03). Imaging features present additional predictive information to gene expression features, as the combined model is much more accurate than the model with gene expression alone (median C-index 0.70). Pathological image features are modestly correlated with gene expression. Genes having correlations to top imaging features have known associations with HCC patient survival and morphogenesis of liver tissue.ConclusionThis work provides two-stage Cox-nnet, a new class of biologically relevant and relatively interpretable models, to integrate multi-modal and multiple types of data for survival prediction.

Additional file 11: Figure S5. Bias estimates in quantitative clonoSEQ Assay MRD measurements in ALL, CLL, and MM.

Additional file 4: Figure S3. Verification of LoD (top) and LoQ (bottom) across the tested range of total input DNA.

Additional file 6: Table S3. Precision of the clonoSEQ Assay in ALL samples.

Additional file 7: Table S4. Precision of the clonoSEQ Assay in CLL samples.

Additional file 9: Table S6. Linearity of the clonoSEQ Assay using clinical specimens from patients with ALL, CLL, and MM.

Additional file 8: Table S5. Linearity of the clonoSEQ Assay using cell lines.

Additional file 2: Figure S1. Precision study PCR run execution map.

Additional file 5: Table S2. Precision of the clonoSEQ Assay in MM samples.

Additional file 3: Figure S2. Probit model plot to calculate the LoD.

IntroductionPreeclampsia is a medical condition complicated with hypertension and proteinuria during pregnancy. While preeclampsia affects approximately 5% of pregnancies, it remains without a cure. In addition, women who had preeclampsia during pregnancy have been reported to have an increased risk for cardiovascular disease later in life. However, the disease etiology and molecular mechanisms remain poorly understood. The paucity in the literature on preeclampsia associated maternal cardiovascular risk in different ethnic populations also present a need for more research. Therefore, the objective of this study was to identify cardiovascular/metabolic single nucleotide polymorphisms (SNPs), genes, and regulatory pathways associated with early-onset preeclampsia.Materials and methodsWe compared maternal DNAs from 31 women with early-onset preeclampsia with those from a control group of 29 women without preeclampsia who delivered full-term normal birthweight infants. Women with multiple gestations and/or known medical disorders associated with preeclampsia (pregestational diabetes, chronic hypertension, renal disease, hyperthyroidism, and lupus) were excluded. The MetaboChip genotyping array with approximately 197,000 SNPs associated with metabolic and cardiovascular traits was used. Single nucleotide polymorphism analysis was performed using the SNPAssoc program in R. The Truncated Product Method was used to identify significantly associated genes. Ingenuity Pathway Analysis and Ingenuity Causal Network Analysis were used to identify significantly associated disease processes and regulatory gene networks respectively.ResultsThe early-onset preeclampsia group included 45% Filipino, 26% White, 16% other Asian, and 13% Native Hawaiian and other Pacific Islanders, which did not differ from the control group. There were no SNPs associated with early-onset preeclampsia after correction for multiple comparisons. However, through gene-based tests, 68 genes and 23 cardiovascular disease-related processes were found to be significantly associated. Associated gene regulatory networks involved cellular movement, cardiovascular disease, and inflammatory disease.ConclusionsMultiple cardiovascular genes and diseases demonstrate associations with early-onset preeclampsia. This unfolds new areas of research regarding the genetic determinants of early-onset preeclampsia and their relation to future cardiovascular disease.

Plant secretory (Class III) peroxidases are redox enzymes that rely on N-glycosylation for full enzyme activity and stability. Peroxidases from palm tree leaves comprise the most stable and active plant peroxidases characterized to date. Herein, site-specific glycosylation and microheterogeneity of windmill palm tree (Trachycarpus fortunei) peroxidase are reported. The workflow developed in this study includes novel tools, written in R, to aid plant glycan identification, pGlycoFilter, for annotation of glycopeptide fragmentation spectra, gPSMvalidator, and for relative quantitation of glycoforms, glycoRQ. Mass spectrometry analysis provided a detailed glycosylation profile at the 13 sites of N-linked glycosylation on windmill palm tree peroxidase. Glycan microheterogeneity was observed at each site. Site Asn211 was the most heterogeneous and contained 30 different glycans. Relative quantitation revealed 90% of each glycosylation site was occupied by three or fewer glycans, and two of the 13 sites were partially unoccupied. Although complex and hybrid glycans were identified, the majority of glycans were paucimannosidic, characteristic of plant vacuolar glycoproteins. Further studies pertaining to the glycan structure-activity relationships in plant peroxidases can benefit from the work outlined here.

A putatively novel basidiomycetous fungus termed Moniliella wahieum Y12T was isolated from a 20% biodiesel blend. The strain maximally degraded biodiesel at a rate of 3.56 × 10−2 mg/h during log phase growth. Induction of metal corrosion by the strain in a mass loss procedure using 1018 metal coupons showed total mass reduction exceeded that in controls by 70% through 30 days. Enhanced corrosion was observed at the pellicle and due to medium acidification. This is the first investigation of a Moniliella sp. and its impact on biodiesel stability and 1018 steel corrosion. (M. wahieum Y12T = ATCC MYA-4962).

Despite its popularity, characterization of subpopulations with transcript abundance is subject to a significant amount of noise. We propose to use effective and expressed nucleotide variations (eeSNVs) from scRNA-seq as alternative features for tumor subpopulation identification. We develop a linear modeling framework, SSrGE, to link eeSNVs associated with gene expression. In all the datasets tested, eeSNVs achieve better accuracies than gene expression for identifying subpopulations. Previously validated cancer-relevant genes are also highly ranked, confirming the significance of the method. Moreover, SSrGE is capable of analyzing coupled DNA-seq and RNA-seq data from the same single cells, demonstrating its value in integrating multi-omics single cell techniques. In summary, SNV features from scRNA-seq data have merits for both subpopulation identification and linkage of genotype-phenotype relationship., Identification of cell subpopulations using transcript abundance is noisy. Here, the authors developed a linear modeling framework, SSrGE, which utilizes effective and expressed nucleotide variations from single-cell RNA-seq to identify tumor subpopulations.

plantGlycoMS is a set of tools, implemented in R, which is used to assess and validate glycopeptide spectrum matches (gPSMs). Validity of gPSMs is based on characteristic fragmentation patterns of glycopeptides (gPSMvalidator), adherence of the glycan moiety to the known N-glycan biosynthesis pathway in plants (pGlycoFilter), and elution of the glycopeptide within the observed retention time window of other glycopeptides sharing the same peptide backbone (rt.Restrict). plantGlycoMS also contains a tool for relative quantitation of glycoforms based on selected ion chromatograms of glycopeptide ion precursors in the mass spectrometry level 1 data (glycoRQ). This protocol walks the user through this workflow with example mass spectrometry data obtained for a plant glycoprotein.

Long intergenic non-coding RNAs have been shown to play important roles in cancer. However, because lincRNAs are a relatively new class of RNAs compared to protein-coding mRNAs, the mutational landscape of lincRNAs has not been as extensively studied. Here we characterize expressed somatic nucleotide variants within lincRNAs using 12 cancer RNA-Seq datasets in TCGA. We build machine-learning models to discriminate somatic variants from germline variants within lincRNA regions (AUC 0.987). We build another model to differentiate lincRNA somatic mutations from background regions (AUC 0.72) and find several molecular features that are strongly associated with lincRNA mutations, including copy number variation, conservation, substitution type and histone marker features.

plantGlycoMS is a set of tools, implemented in R, which is used to assess and validate glycopeptide spectrum matches (gPSMs). Validity of gPSMs is based on characteristic fragmentation patterns of glycopeptides (gPSMvalidator), adherence of the glycan moiety to the known N-glycan biosynthesis pathway in plants (pGlycoFilter), and elution of the glycopeptide within the observed retention time window of other glycopeptides sharing the same peptide backbone (rt.Restrict). plantGlycoMS also contains a tool for relative quantitation of glycoforms based on selected ion chromatograms of glycopeptide ion precursors in the mass spectrometry level 1 data (glycoRQ). This protocol walks the user through this workflow with example mass spectrometry data obtained for a plant glycoprotein.

Clinical challenges exist in reducing prostate cancer (PCa) disparities. The RNA splicing landscape of PCa across racial populations has not been fully explored as a potential molecular mechanism contributing to race-related tumour aggressiveness. Here, we identify novel genome-wide, race-specific RNA splicing events as critical drivers of PCa aggressiveness and therapeutic resistance in African American (AA) men. AA-enriched splice variants of PIK3CD, FGFR3, TSC2 and RASGRP2 contribute to greater oncogenic potential compared with corresponding European American (EA)-expressing variants. Ectopic overexpression of the newly cloned AA-enriched variant, PIK3CD-S, in EA PCa cell lines enhances AKT/mTOR signalling and increases proliferative and invasive capacity in vitro and confers resistance to selective PI3Kδ inhibitor, CAL-101 (idelalisib), in mouse xenograft models. High PIK3CD-S expression in PCa specimens associates with poor survival. These results highlight the potential of RNA splice variants to serve as novel biomarkers and molecular targets for developmental therapeutics in aggressive PCa., In prostate cancer tumour aggressiveness can be related to race. Here, the authors identify an alternative RNA splice variant of PIK3CD as a potential mechanism to explain racial disparities in the incidence and aggressiveness of prostate cancer between African American and European American men.

Although driver genes in hepatocellular carcinoma (HCC) have been investigated in various previous genetic studies, prevalence of key driver genes among heterogeneous populations is unknown. Moreover, the phenotypic associations of these driver genes are poorly understood. This report aims to reveal the phenotypic impacts of a group of consensus driver genes in HCC. We used MutSigCV and OncodriveFM modules implemented in the IntOGen pipeline to identify consensus driver genes across six HCC cohorts comprising 1,494 samples in total. To access their global impacts, we used TCGA mutations and copy number variations to predict the transcriptomics data, under generalized linear models. We further investigated the associations of the consensus driver genes to patient survival, age, gender, race and risk factors. We identify 10 consensus driver genes across six HCC cohorts in total. Integrative analysis of driver mutations, copy number variations and transcriptomic data reveals that these consensus driver mutations and their copy number variations are associated with majority (62.5%) of the mRNA transcriptome, but only a small fraction (8.9%) of miRNAs. Genes associated withTP53, CTNNB1, andARID1Amutations contribute to the tripod of most densely connected pathway clusters. These driver genes are significantly associated with patients’ overall survival. Some driver genes are significantly linked to HCC gender (CTNNB1, ALB, TP53andAXIN1), race (TP53andCDKN2A), and age (RB1) disparities. This study prioritizes a group of consensus drivers in HCC, which collectively show vast impacts on the phenotypes. These driver genes may warrant as valuable therapeutic targets of HCC.

Clinical challenges exist in reducing prostate cancer (PCa) disparities. The RNA splicing landscape of PCa across racial populations has not been fully explored as a potential molecular mechanism contributing to race-related tumour aggressiveness. Here, we identify novel genome-wide, race-specific RNA splicing events as critical drivers of PCa aggressiveness and therapeutic resistance in African American (AA) men. AA-enriched splice variants of PIK3CD, FGFR3, TSC2 and RASGRP2 contribute to greater oncogenic potential compared with corresponding European American (EA)-expressing variants. Ectopic overexpression of the newly cloned AA-enriched variant, PIK3CD-S, in EA PCa cell lines enhances AKT/mTOR signalling and increases proliferative and invasive capacity in vitro and confers resistance to selective PI3Kδ inhibitor, CAL-101 (idelalisib), in mouse xenograft models. High PIK3CD-S expression in PCa specimens associates with poor survival. These results highlight the potential of RNA splice variants to serve as novel biomarkers and molecular targets for developmental therapeutics in aggressive PCa.

Pre-eclampsia is the leading cause of fetal and maternal morbidity and mortality. Early onset pre-eclampsia (EOPE) is a disorder that has severe maternal and fetal outcomes, whilst its etiology is poorly understood. We hypothesize that epigenetics plays an important role to mediate the development of EOPE and conducted a case–control study to compare the genome-wide methylome difference between chorioamniotic membranes from 30 EOPE and 17 full-term pregnancies using the Infinium Human Methylation 450 BeadChip arrays. Bioinformatics analysis tested differential methylation (DM) at CpG site level, gene level, and pathway and network level. A striking genome-wide hypermethylation pattern coupled with hypomethylation in promoters was observed. Out of 385 184 CpG sites, 9995 showed DM (2.6%). Of those DM sites, 91.9% showed hypermethylation (9186 of 9995). Over 900 genes had DM associated with promoters. Promoter-based DM analysis revealed that genes in canonical cancer-related pathways such as Rac, Ras, PI3K/Akt, NFκB and ErBB4 were enriched, and represented biological functional alterations that involve cell cycle, apoptosis, cancer signaling and inflammation. A group of genes previously found to be up-regulated in pre-eclampsia, including GRB2, ATF3, NFKB2, as well as genes in proteasome subunits (PSMA1, PMSE1, PSMD1 and PMSD8), harbored hypomethylated promoters. Contrarily, a cluster of microRNAs, including mir-519a1, mir-301a, mir-487a, mir-185, mir-329, mir-194, mir-376a1, mir-486 and mir-744 were all hypermethylated in their promoters in the EOPE samples. These findings collectively reveal new avenues of research regarding the vast epigenetic modifications in EOPE.

Artificial neural networks (ANN) are computing architectures with many interconnections of simple neural-inspired computing elements, and have been applied to biomedical fields such as imaging analysis and diagnosis. We have developed a new ANN framework called Cox-nnet to predict patient prognosis from high throughput transcriptomics data. In 10 TCGA RNA-Seq data sets, Cox-nnet achieves the same or better predictive accuracy compared to other methods, including Cox-proportional hazards regression (with LASSO, ridge, and mimimax concave penalty), Random Forests Survival and CoxBoost. Cox-nnet also reveals richer biological information, at both the pathway and gene levels. The outputs from the hidden layer node provide an alternative approach for survival-sensitive dimension reduction. In summary, we have developed a new method for accurate and efficient prognosis prediction on high throughput data, with functional biological insights. The source code is freely available at https://github.com/lanagarmire/cox-nnet., Author summary The increasing application of high-througput transcriptomics data to predict patient prognosis demands modern computational methods. With the re-gaining popularity of artificial neural networks, we asked if a refined neural network model could be used to predict patient survival, as an alternative to the conventional methods, such as Cox proportional hazards (Cox-PH) methods with LASSO or ridge penalization. To this end, we have developed a neural network extension of the Cox regression model, called Cox-nnet. It is optimized for survival prediction from high throughput gene expression data, with comparable or better performance than other conventional methods. More importantly, Cox-nnet reveals much richer biological information, at both the pathway and gene levels, by analyzing features represented in the hidden layer nodes in Cox-nnet. Additionally, we propose to use hidden node features as a new approach for dimension reduction during survival data analysis.

To identify what long non-coding RNAs (lncRNAs) are involved in non-small cell lung cancer (NSCLC), we analyzed microarray data on gene expression and methylation. Gene expression chip and HumanMethylation450BeadChip were used to interrogate genome-wide expression and methylation in tumor samples. Differential expression and methylation were analyzed through comparing tumors with adjacent non-tumor tissues. LncRNAs expressed differentially and correlated with coding genes and DNA methylation were validated in additional tumor samples using RT-qPCR and pyrosequencing. In vitro experiments were performed to evaluate lncRNA’s effects on tumor cells. We identified 8,500 lncRNAs expressed differentially between tumor and non-tumor tissues, of which 1,504 were correlated with mRNA expression. Two of the lncRNAs, LOC146880 and ENST00000439577, were positively correlated with expression of two cancer-related genes, KPNA2 and RCC2, respectively. High expression of LOC146880 and ENST00000439577 were also associated with poor survival. Analysis of lncRNA expression in relation to DNA methylation showed that LOC146880 expression was down-regulated by DNA methylation in its promoter. Lowering the expression of LOC146880 or ENST00000439577 in tumor cells could inhibit cell proliferation, invasion and migration. Analysis of microarray data on gene expression and methylation allows us to identify two lncRNAs, LOC146880 and ENST00000439577, which may promote the progression of NSCLC.

Single-cell RNA-Sequencing (scRNA-Seq) is a fast-evolving technology that enables the understanding of biological processes at an unprecedentedly high resolution. However, well-suited bioinformatics tools to analyze the data generated from this new technology are still lacking. Here we investigate the performance of non-negative matrix factorization (NMF) method to analyze a wide variety of scRNA-Seq datasets, ranging from mouse hematopoietic stem cells to human glioblastoma data. In comparison to other unsupervised clustering methods including K-means and hierarchical clustering, NMF has higher accuracy in separating similar groups in various datasets. We ranked genes by their importance scores (D-scores) in separating these groups, and discovered that NMF uniquely identifies genes expressed at intermediate levels as top-ranked genes. Finally, we show that in conjugation with the modularity detection method FEM, NMF reveals meaningful protein-protein interaction modules. In summary, we propose that NMF is a desirable method to analyze heterogeneous single-cell RNA-Seq data. The NMF based subpopulation detection package is available at:https://github.com/lanagarmire/NMFEM.

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems—patient classification, fundamental biological processes and treatment of patients—and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.