Approximating the Largest Root and Applications to Interlacing Families

We study the problem of approximating the largest root of a real-rooted polynomial of degree $n$ using its top $k$ coefficients and give nearly matching upper and lower bounds. We present algorithms with running time polynomial in $k$ that use the top $k$ coefficients to approximate the maximum root within a factor of $n^{1/k}$ and $1+O(\tfrac{\log n}{k})^2$ when $k\leq \log n$ and $k>\log n$ respectively. We also prove corresponding information-theoretic lower bounds of $n^{\Omega(1/k)}$ and $1+\Omega\left(\frac{\log \frac{2n}{k}}{k}\right)^2$, and show strong lower bounds for noisy version of the problem in which one is given access to approximate coefficients. This problem has applications in the context of the method of interlacing families of polynomials, which was used for proving the existence of Ramanujan graphs of all degrees, the solution of the Kadison-Singer problem, and bounding the integrality gap of the asymmetric traveling salesman problem. All of these involve computing the maximum root of certain real-rooted polynomials for which the top few coefficients are accessible in subexponential time. Our results yield an algorithm with the running time of $2^{\tilde O(\sqrt[3]n)}$ for all of them

Dupilumab Improves Nasal Polyp Burden and Asthma Control in Patients With CRSwNP and AERD

In the difficult-to-treat subgroup of patients with chronic rhinosinusitis with nasal polyps (CRSwNP) and comorbid aspirin-exacerbated respiratory disease, dupilumab significantly improved CRSwNP disease outcomes, along with asthma control and lung function. This is preliminary evidence of the effect of dupilumab in patients with CRSwNP and comorbid aspirin- exacerbated respiratory disease

The dChip survival analysis module for microarray data

International audienceBACKGROUND: Genome-wide expression signatures are emerging as potential marker for overall survival and disease recurrence risk as evidenced by recent commercialization of gene expression based biomarkers in breast cancer. Similar predictions have recently been carried out using genome-wide copy number alterations and microRNAs. Existing software packages for microarray data analysis provide functions to define expression-based survival gene signatures. However, there is no software that can perform survival analysis using SNP array data or draw survival curves interactively for expression-based sample clusters. RESULTS: We have developed the survival analysis module in the dChip software that performs survival analysis across the genome for gene expression and copy number microarray data. Built on the current dChip software's microarray analysis functions such as chromosome display and clustering, the new survival functions include interactive exploring of Kaplan-Meier (K-M) plots using expression or copy number data, computing survival p-values from the log-rank test and Cox models, and using permutation to identify significant chromosome regions associated with survival. CONCLUSIONS: The dChip survival module provides user-friendly way to perform survival analysis and visualize the results in the context of genes and cytobands. It requires no coding expertise and only minimal learning curve for thousands of existing dChip users. The implementation in Visual C++ also enables fast computation. The software and demonstration data are freely available at http://dchip-surv.chenglilab.org

Transcription factor-pathway co-expression analysis reveals cooperation between SP1 and ESR1 on dysregulating cell cycle arrest in non-hyperdiploid multiple myeloma

Multiple myeloma is a hematological cancer of plasma B-cells and remains incurable. Two major subtypes of myeloma, hyperdiploid (HMM) and non-hyperdiploid myeloma (NHMM), have distinct chromosomal alterations and different survival outcomes. Transcription factors (TrFs) have been implicated in myeloma oncogenesis but their dysregulation in myeloma subtypes are less studied. Here we develop a TrF-pathway co-expression analysis to identify altered co-expression between two sample types. We apply the method to the two myeloma subtypes and the cell cycle arrest pathway, which is significantly differentially expressed between the two subtypes. We find that TrFs MYC, NF-κB and HOXA9 have significantly lower co-expression with cell cycle arrest in HMM, co-occurring with their over-activation in HMM. In contrast, TrFs ESR1, SP1 and E2F1 have significantly lower co-expression with cell cycle arrest in NHMM. SP1 ChIP targets are enriched by cell cycle arrest genes. These results motivate a cooperation model of ESR1 and SP1 in regulating cell cycle arrest, and a hypothesis that their over-activation in NHMM disrupts proper regulation of cell cycle arrest. Co-targeting ESR1 and SP1 shows a synergistic effect on inhibiting myeloma proliferation in NHMM cell lines. Therefore, studying TrF-pathway co-expression dysregulation in human cancers facilitates forming novel hypotheses towards clinical utility

Potentials of airborne hyperspectral aviris-ng data in the exploration of base metal deposit—a study in the parts of Bhilwara, Rajasthan

In this study, we have processed the spectral bands of airborne hyperspectral data of Advanced Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) data for delineating the surface signatures associated with the base metal mineralization in the Pur-Banera area in the Bhilwara district, Rajasthan, India.The primaryhost rocks of the Cu, Pb, Zn mineralization in the area are Banded Magnetite Quartzite (BMQ), unclassified calcareous silicates, and quartzite. We used ratio images derived from the scale and root mean squares (RMS) error imagesusing the multi-range spectral feature fitting (MRSFF) methodto delineate host rocks from the AVIRIS-NG image. The False Color Composites (FCCs) of different relative band depth images, derived from AVIRIS-NG spectral bands, were also used for delineating few minerals. These minerals areeither associated with the surface alteration resulting from the ore-bearing fluid migration orassociated with the redox-controlled supergene enrichments of the ore deposit.The results show that the AVIRIS-NG image products derived in this study can delineate surface signatures of mineralization in 1:10000 to 1:15000 scales to narrow down the targets for detailed exploration.This study alsoidentified the possible structural control over the knownsurface distribution of alteration and lithocap minerals of base metal mineralizationusing the ground-based residual magnetic anomaly map. This observationstrengthens the importance of the identified surface proxiesas an indicator of mineralization. X-ray fluorescence analysis of samples collectedfromselected locations within the study area confirms the Cu-Pb-Zn enrichment. The sulfide minerals were also identified in the microphotographs of polished sections of rock samples collected from the places where surface proxies of mineralization were observed in the field. This study justified the investigation to uti-lize surface signatures of mineralization identified using AVIRIS-NG dataand validated using field observations, geophysical, geochemical, and petrographical data

Dupilumab improves symptoms, quality of life, and productivity in uncontrolled persistent asthma

BACKGROUND: In a pivotal, phase 2b study (NCT01854047) in patients with uncontrolled persistent asthma, despite using medium-to-high-dose inhaled corticosteroids plus long-acting β2 agonists, dupilumab improved lung function, reduced severe exacerbations, and showed an acceptable safety profile. OBJECTIVE: To assess the impact of dupilumab on asthma control, symptoms, quality of life (QoL), and productivity. METHODS: Data are shown for the intention-to-treat population receiving dupilumab 200/300 mg every 2 weeks (doses being assessed in phase 3; NCT02414854), or placebo. Predefined analyses of total scores were conducted at week 24 for the 5-item Asthma Control Questionnaire (ACQ-5), patient-reported morning/evening (AM/PM) asthma symptoms, Asthma Quality of Life Questionnaire (AQLQ), and asthma-related productivity loss. Responder rate analyses for these measures, subgroup analyses by baseline characteristics, and asthma-related productivity loss analyses were conducted post hoc. RESULTS: Data from 465 patients were analyzed (158 placebo; 307 dupilumab). Both dupilumab doses significantly improved scores through week 24 (all outcomes, overall population). The proportion of patients meeting or exceeding the minimal clinically important difference for the overall population were significantly greater vs placebo (P \u3c .05) for ACQ-5 (range, 72.6%-76.7% vs 61.4%), for AM/PM asthma symptoms score (48.7%-54.1% vs 34.2% and 52.7%-53.5% vs 34.2%, respectively) and for AQLQ (64.0%-65.0% vs 51.3%). The effect of dupilumab was consistent across most subgroups. Productivity loss was significantly higher in placebo- vs dupilumab-treated patients (P \u3c .0001). CONCLUSION: Dupilumab produced significant, clinically meaningful improvements in asthma control, symptoms, QoL, and productivity. REGISTRATION: ClinicalTrials.gov Identifier: NCT01854047

Dupilumab improves lung function in patients with uncontrolled, moderate-to-severe asthma

Background: Dupilumab, a fully human monoclonal antibody, blocks the shared receptor component for interleukin-4 and interleukin-13, key drivers of type 2 inflammation. In the phase 3 LIBERTY ASTHMA QUEST trial (NCT02414854) in patients with uncontrolled, moderate-to-severe asthma, add-on dupilumab 200 mg or 300 mg every 2 weeks reduced exacerbations and improved forced expiratory volume in 1 s (FEV1) and quality of life over 52 weeks. This analysis evaluates dupilimab's effect on lung function in the overall population, and subgroups with baseline elevated type 2 inflammatory biomarkers. Methods: Patients were randomised to 52 weeks of subcutaneous dupilumab 200 mg every 2 weeks, 300 mg every 2 weeks, or matched-volume placebos. Lung function outcomes were analysed in the overall population, in patients with ≥150 eosinophils·µL−1, ≥300 eosinophils·µL−1, ≥25 ppb fractional exhaled nitric oxide (FeNO), and both ≥150 eosinophils·µL−1 and ≥25 ppb FeNO, at baseline. Results: Dupilumab treatment (200 mg and 300 mg every 2 weeks) resulted in significant improvements versus placebo after 52 weeks in pre-bronchodilator FEV1 (0.20 and 0.13 L, respectively, versus placebo) and post-bronchodilator FEV1 (0.19 and 0.13 L, respectively), forced vital capacity (FVC) (0.20 and 0.14 L, respectively), forced expiratory flow (0.19 and 0.13 L·s−1, respectively) and pre-bronchodilator FEV1/FVC ratio (1.75% and 1.61%, respectively) in the overall population (p<0.001). Difference versus placebo in post-bronchodilator FEV1 slope of change (weeks 4–52) was significant (0.04 L·year−1; p<0.05). Greater improvements were achieved in patients with elevated baseline blood eosinophil and/or FeNO levels for most outcomes. Conclusions: Dupilumab improves lung function outcomes, including large and small airway measurements and fixed airway obstruction, in patients with uncontrolled, moderate-to-severe asthma; particularly in patients with elevated biomarkers of type 2 inflammation

Response and Acquired Resistance to Everolimus in Anaplastic Thyroid Cancer

Everolimus, an inhibitor of the mammalian target of rapamycin (mTOR), is effective in treating tumors harboring alterations in the mTOR pathway. Mechanisms of resistance to everolimus remain undefined. Resistance developed in a patient with metastatic anaplastic thyroid carcinoma after an extraordinary 18-month response. Whole-exome sequencing of pretreatment and drug-resistant tumors revealed a nonsense mutation in TSC2, a negative regulator of mTOR, suggesting a mechanism for exquisite sensitivity to everolimus. The resistant tumor also harbored a mutation in MTOR that confers resistance to allosteric mTOR inhibition. The mutation remains sensitive to mTOR kinase inhibitors

The impact of tumor profiling approaches and genomic data strategies for cancer precision medicine

Background: The diversity of clinical tumor profiling approaches (small panels to whole exomes with matched or unmatched germline analysis) may engender uncertainty about their benefits and liabilities, particularly in light of reported germline false positives in tumor-only profiling and use of global mutational and/or neoantigen data. The goal of this study was to determine the impact of genomic analysis strategies on error rates and data interpretation across contexts and ancestries. Methods: We modeled common tumor profiling modalities—large (n = 300 genes), medium (n = 48 genes), and small (n = 15 genes) panels—using clinical whole exomes (WES) from 157 patients with lung or colon adenocarcinoma. We created a tumor-only analysis algorithm to assess germline false positive rates, the impact of patient ancestry on tumor-only results, and neoantigen detection. Results: After optimizing a germline filtering strategy, the germline false positive rate with tumor-only large panel sequencing was 14 % (144/1012 variants). For patients whose tumor-only results underwent molecular pathologist review (n = 91), 50/54 (93 %) false positives were correctly interpreted as uncertain variants. Increased germline false positives were observed in tumor-only sequencing of non-European compared with European ancestry patients (p < 0.001; Fisher’s exact) when basic germline filtering approaches were used; however, the ExAC database (60,706 germline exomes) mitigated this disparity (p = 0.53). Matched and unmatched large panel mutational load correlated with WES mutational load (r2 = 0.99 and 0.93, respectively; p < 0.001). Neoantigen load also correlated (r2 = 0.80; p < 0.001), though WES identified a broader spectrum of neoantigens. Small panels did not predict mutational or neoantigen load. Conclusions: Large tumor-only targeted panels are sufficient for most somatic variant identification and mutational load prediction if paired with expanded germline analysis strategies and molecular pathologist review. Paired germline sequencing reduced overall false positive mutation calls and WES provided the most neoantigens. Without patient-matched germline data, large germline databases are needed to minimize false positive mutation calling and mitigate ethnic disparities. Electronic supplementary material The online version of this article (doi:10.1186/s13073-016-0333-9) contains supplementary material, which is available to authorized users

Assessing the clinical utility of cancer genomic and proteomic data across tumor types

Molecular profiling of tumors promises to advance the clinical management of cancer, but the benefits of integrating molecular data with traditional clinical variables have not been systematically studied. Here we retrospectively predict patient survival using diverse molecular data (somatic copy-number alteration, DNA methylation and mRNA, miRNA and protein expression) from 953 samples of four cancer types from The Cancer Genome Atlas project. We found that incorporating molecular data with clinical variables yielded statistically significantly improved predictions (FDR < 0.05) for three cancers but those quantitative gains were limited (2.2–23.9%). Additional analyses revealed little predictive power across tumor types except for one case. In clinically relevant genes, we identified 10,281 somatic alterations across 12 cancer types in 2,928 of 3,277 patients (89.4%), many of which would not be revealed in single-tumor analyses. Our study provides a starting point and resources, including an open-access model evaluation platform, for building reliable prognostic and therapeutic strategies that incorporate molecular data