The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies

The Open Quantum Materials Database (OQMD) is a high-throughput database currently consisting of nearly 300,000 density functional theory (DFT) total energy calculations of compounds from the Inorganic Crystal Structure Database (ICSD) and decorations of commonly occurring crystal structures. To maximise the impact of these data, the entire database is being made available, without restrictions, at www.oqmd.org/download. In this paper, we outline the structure and contents of the database, and then use it to evaluate the accuracy of the calculations therein by comparing DFT predictions with experimental measurements for the stability of all elemental ground-state structures and 1,670 experimental formation energies of compounds. This represents the largest comparison between DFT and experimental formation energies to date. The apparent mean absolute error between experimental measurements and our calculations is 0.096 eV/atom. In order to estimate how much error to attribute to the DFT calculations, we also examine deviation between different experimental measurements themselves where multiple sources are available, and find a surprisingly large mean absolute error of 0.082 eV/atom. Hence, we suggest that a significant fraction of the error between DFT and experimental formation energies may be attributed to experimental uncertainties. Finally, we evaluate the stability of compounds in the OQMD (including compounds obtained from the ICSD as well as hypothetical structures), which allows us to predict the existence of ~3,200 new compounds that have not been experimentally characterised and uncover trends in material discovery, based on historical data available within the ICSD

Reproducibility in high-throughput density functional theory: a comparison of AFLOW, Materials Project, and OQMD

A central challenge in high throughput density functional theory (HT-DFT) calculations is selecting a combination of input parameters and post-processing techniques that can be used across all materials classes, while also managing accuracy-cost tradeoffs. To investigate the effects of these parameter choices, we consolidate three large HT-DFT databases: Automatic-FLOW (AFLOW), the Materials Project (MP), and the Open Quantum Materials Database (OQMD), and compare reported properties across each pair of databases for materials calculated using the same initial crystal structure. We find that HT-DFT formation energies and volumes are generally more reproducible than band gaps and total magnetizations; for instance, a notable fraction of records disagree on whether a material is metallic (up to 7%) or magnetic (up to 15%). The variance between calculated properties is as high as 0.105 eV/atom (median relative absolute difference, or MRAD, of 6%) for formation energy, 0.65 {\AA}$^3$/atom (MRAD of 4%) for volume, 0.21 eV (MRAD of 9%) for band gap, and 0.15 $\mu_{\rm B}$/formula unit (MRAD of 8%) for total magnetization, comparable to the differences between DFT and experiment. We trace some of the larger discrepancies to choices involving pseudopotentials, the DFT+U formalism, and elemental reference states, and argue that further standardization of HT-DFT would be beneficial to reproducibility.Comment: Authors VIH and CKHB contributed equally to this wor

Triple-negative breast cancer and PTEN (phosphatase and tensin homologue)loss are predictors of BRCA1 germline mutations in women with early-onset and familial breast cancer, but not in women with isolated late-onset breast cancer

Introduction: Given that breast cancers in germline BRCA1 carriers are predominantly estrogen-negative and triple-negative, it has been suggested that women diagnosed with triple-negative breast cancer (TNBC) younger than 50 years should be offered BRCA1 testing, regardless of family cancer characteristics. However, the predictive value of triple-negative breast cancer, when taken in the context of personal and family cancer characteristics, is unknown. The aim of this study was to determine whether TNBC is a predictor of germline BRCA1 mutations, in the context of multiple predictive factors.Methods: Germline mutations in BRCA1 and BRCA2 were analyzed by Sanger sequencing and multiple ligation-dependent probe amplification (MLPA) analysis in 431 women from the Malaysian Breast Cancer Genetic Study, including 110 women with TNBC. Logistic regression was used to identify and to estimate the predictive strength of major determinants. Estrogen receptor (ER) and phosphatase and tensin homologue (PTEN) status were assessed and included in a modified Manchester scoring method.Results: Our study in an Asian series of TNBC patients demonstrated that 27 (24.5%) of 110 patients have germline mutations in BRCA1 (23 of 110) and BRCA2 (four of 110). We found that among women diagnosed with breast cancer aged 36 to 50 years but with no family history of breast or ovarian cancer, the prevalence of BRCA1 and BRCA2 mutations was similar in TNBC (8.5%) and non-TNBC patients (6.7%). By contrast, in women diagnosed with breast cancer, younger than 35 years, with no family history of these cancers, and in women with a family history of breast cancer, the prevalence of mutations was higher in TNBC compared with non-TNBC (28.0% and 9.9%; P = 0.045; and 42.1% and 14.2%; P < 0.0001, respectively]. Finally, we found that incorporation of estrogen-receptor and TNBC status improves the sensitivity of the Manchester Scoring method (42.9% to 64.3%), and furthermore, incorporation of PTEN status further improves sensitivity (42.9% to 85.7%).Conclusions: We found that TNBC is an important criterion for highlighting women who may benefit from genetic testing, but that this may be most useful for women with early-onset breast cancer (35 years or younger) or with a family history of cancers. Furthermore, addition of TNBC and PTEN status improves the sensitivity of the Manchester scoring method and may be particularly important in the Asian context, where risk-assessment models underestimate the number of mutation carriers. © 2012 Phuah et al.; licensee BioMed Central Ltd

Functional genomic analysis of drug sensitivity pathways to guide adjuvant strategies in breast cancer

The widespread introduction of high throughput RNA interference screening technology has revealed tumour drug sensitivity pathways to common cytotoxics such as paclitaxel, doxorubicin and 5-fluorouracil, targeted agents such as trastuzumab and inhibitors of AKT and Poly(ADP-ribose) polymerase (PARP) as well as endocrine therapies such as tamoxifen. Given the limited power of microarray signatures to predict therapeutic response in associative studies of small clinical trial cohorts, the use of functional genomic data combined with expression or sequence analysis of genes and microRNAs implicated in drug response in human tumours may provide a more robust method to guide adjuvant treatment strategies in breast cancer that are transferable across different expression platforms and patient cohorts

Genomic Predictors of Outcome in Prostate Cancer

CONTEXT: Given the highly variable behavior and clinical course of prostate cancer (PCa) and the multiple available treatment options, a personalized approach to oncologic risk stratification is important. Novel genetic approaches offer additional information to improve clinical decision making. OBJECTIVE: To review the use of genomic biomarkers in the prognostication of PCa outcome and prediction of therapeutic response. EVIDENCE ACQUISITION: Systematic literature review focused on human clinical studies reporting outcome measures with external validation. The literature search included all Medline, Embase, and Scopus articles from inception through July 2014. EVIDENCE SYNTHESIS: An improved understanding of the genetic basis of prostate carcinogenesis has produced an increasing number of potential prognostic and predictive tools, such as transmembrane protease, serine2:v-ets avian erythroblastosis virus E26 oncogene homolog (TMPRSS2:ERG) gene fusion status, loss of the phosphatase and tensin homolog (PTEN) gene, and gene expression signatures utilizing messenger RNA from tumor tissue. Several commercially available gene panels with external validation are now available, although most have yet to be widely used. The most studied commercially available gene panels, Prolaris, Oncotype DX Genomic Prostate Score, and Decipher, may be used to estimate disease outcome in addition to clinical parameters or clinical nomograms. ConfirmMDx is an epigenetic test used to predict the results of repeat prostate biopsy after an initial negative biopsy. Additional future strategies include using genetic information from circulating tumor cells in the peripheral blood to guide treatment decisions at the initial diagnosis and at subsequent decision points. CONCLUSIONS: Major advances have been made in our understanding of PCa biology in recent years. Our field is currently exploring the early stages of a personalized approach to augment traditional clinical decision making using commercially available genomic tools. A more comprehensive appreciation of value, limitations, and cost is important. PATIENT SUMMARY: We summarized current advances in genomic testing in prostate cancer with a special focus on the estimation of disease outcome. Several commercial tests are currently available, but further understanding is needed to appreciate the potential benefits and limitations of these novel tests