Predict Drug Resistance Research Articles

Tuberculosis

Tuberculosis is a bacterial infectious disease that is usually transmitted by inhalation of droplets containing the bacteria. The World Health Organization (WHO) estimates that approximately 10million patients were newly diagnosed with tuberculosis in 2017. Rapid diagnosis relies on acombination of imaging and microbiological, molecular, and, rarely, immunological tests. Genotypic methods enable early diagnosis and allow highly accurate prediction of drug resistance. Phenotypic (culture-based) methods are the diagnostic gold standard. Standard management of patients with pan drug-susceptible pulmonary tuberculosis includes acombination of rifampicin, isoniazid, ethambutol and pyrazinamide for 2months followed by rifampicin and isoniazid for additional 4months, which leads to cure rates of >80%. With individualized treatment schemes, similar cure rates can be achieved for patients with multidrug-resistant tuberculosis.

FGFR Aberrations Increase the Risk of Brain Metastases and Predict Drug Resistance in Metastatic Breast Cancer Patients

The survival status of patients with breast cancer with brain metastases (BCBM) receiving current treatments is poor. We designed a real-world study to investigate patient clinical and genetic aberrations to determine appropriate treatment regimens and forecast the prognoses of BCBM patients. 146 BCBM patients were recruited and their clinical features were analyzed for evaluating overall survival (OS). For genetic testing, 30 BCBM and 165 non-BM BC patients from Hunan Cancer Hospital, and 86 BCBM and 1416 non-BM BC patients from Geneplus database received ctDNA testing was compared and analyzed. Ki67 >14% and >3 metastatic brain tumors were significant risk factors associated with poor OS, while chemotherapy and brain radiotherapy were beneficial factors for better OS. Compared with non-BM metastatic breast cancer (MBC) patients, BCBM patients had significantly more fibroblast growth factor receptor (FGFR) aberrations. The combination of FGFR, TP53 and FLT1 aberrations plus immunohistochemisty (IHC) HER2-positive could effectively predict brain metastases (AUC=77.13%). FGFR aberration alone was not only a prediction factor (AUC=67.90%), but also a significant risk factor for poor PFS (Logrank p=0.029). Among FGFR family members, FGFR1 was the most frequent aberration and significantly higher in BMBC patients. Most FGFR1-amplified MBC patients progressed within 3 months. In conclusion, a group of clinical and genetic events, including FGFR, TP53 and FLT1 genetic aberrations, and HER2-positivity, could forecast the occurrence of BM in breast cancers. Moreover, FGFR genetic aberration alone could predict poor prognosis; FGFR aberrations, especially FGFR1 amplification, resulted in drug resistance and disease progression. Funding Statement: Hunan Provincial Health and Sanitation Committee Project No. 2019070 (Recipient: Zhe-Yu Hu); Hunan Provincial Natural Science Youth Foundation Project No.20190988 (Recipient: Zhe-Yu Hu). Declaration of Interests: The authors declare that they have no conflict of interests. Ethics Approval Statement: The study was approved by the Ethics Committee at Hunan Cancer Hospital, the Affiliated Cancer Hospital of Xiangya School of Medicine / Central South University. Patients consented to participate.

Whole genome sequencing of drug resistant Mycobacterium tuberculosis isolates from a high burden tuberculosis region of North West Pakistan

Tuberculosis (TB), caused by Mycobacterium tuberculosis bacteria, is a leading infectious cause of mortality worldwide, including in Pakistan. Drug resistant M. tuberculosis is an emerging threat for TB control, making it important to detect the underlying genetic mutations, and thereby inform treatment decision making and prevent transmission. Whole genome sequencing has emerged as the new diagnostic to reliably predict drug resistance within a clinically relevant time frame, and its deployment will have the greatest impact on TB control in highly endemic regions. To evaluate the mutations leading to drug resistance and to assess for evidence of the transmission of resistant strains, 81 M. tuberculosis samples from Khyber Pakhtunkhwa province (North West Pakistan) were subjected to whole genome sequencing and standard drug susceptibility testing for eleven anti-TB drugs. We found the majority of M. tuberculosis isolates were the CAS/Delhi strain-type (lineage 3; n = 57; 70.4%) and multi-drug resistant (MDR; n = 62; 76.5%). The most frequent resistance mutations were observed in the katG and rpoB genes, conferring resistance to isoniazid and rifampicin respectively. Mutations were also observed in genes conferring resistance to other first and second-line drugs, including in pncA (pyrazinamide), embB (ethambutol), gyrA (fluoroquinolones), rrs (aminoglycosides), rpsL, rrs and giB (streptomycin) loci. Whilst the majority of mutations have been reported in global datasets, we describe unreported putative resistance markers in katG, ethA (ethionamide), gyrA and gyrB (fluoroquinolones), and pncA. Analysis of the mutations revealed that acquisition of rifampicin resistance often preceded isoniazid in our isolates. We also observed a high proportion (17.6%) of pre-MDR isolates with fluoroquinolone resistance markers, potentially due to unregulated anti-TB drug use. Our isolates were compared to previously sequenced strains from Pakistan in a combined phylogenetic tree analysis. The presence of lineage 2 was only observed in our isolates. Using a cut-off of less than ten genome-wide mutation differences between isolates, a transmission analysis revealed 18 M. tuberculosis isolates clustering within eight networks, thereby providing evidence of drug-resistant TB transmission in the Khyber Pakhtunkhwa province. Overall, we have demonstrated that drug-resistant TB isolates are circulating and transmitted in North West Pakistan. Further, we have shown the usefulness of whole genome sequencing as a diagnostic tool for characterizing M. tuberculosis isolates, which will assist future epidemiological studies and disease control activities in Pakistan.

1035P - Hybrid capture-based genomic profiling of circulating tumour DNA (ctDNA) from patients with ovarian cancer

BackgroundGenomic profiling of blood-derived circulating tumour DNA (ctDNA) which is considered as a minimally invasive biopsy, has several potential applications in the management of ovarian cancer, including selection of targeted therapy, drug resistance prediction and the evaluation of treatment response. Therefore, we conducted this study to characterize genomic alterations (GA) in ctDNA from patients with ovarian cancer and compare GA detected in ctDNA with those detected in tissue databases. MethodsHybrid capture-based next generation sequencing (NGS) genomic profiling of 150 genes was performed on ctNDA from 76 patients with ovarian cancer in a CAP-accredited laboratory. The GA detected in ctDNA were compared with the 3D Med tissue database tested by NGS (N=260) and TCGA database tested by whole-exon sequencing (N=587). The fraction of ctDNA was estimated using the maximum somatic allele frequency (MSAF). GAs including single nucleotide variation (SNV), insertions/deletions, copy number variations, gene rearrangement and fusions were assessed. Germline alterations were excluded. Results89.5% (68/76) of patients (median age 57) detected ≥1 GA in ctDNA. Frequently altered genes detected in ctDNA for SNV were TP53 (52.6%), AR (11.8%), KRAS (9.2%), NOTCH2 (6.6%) and PIK3CA (5.3%) compared to 3D Med tissue database (median age 55) including 260 samples with TP53 (74.2%), AR (6.9%), KRAS (24.6%) NOTCH2 (11.5%) and PIK3CA (3.8%). In TCGA database including 587 patients with early stage diseases, the frequencies of the commonly altered genes detected in ctDNA were largely different with TP53 (65.9%), AR (1.0%), KRAS (1.0%), NOTCH2 (1.2%) and PIK3CA (2.6%). Alterations in five DNA damage repair associated-genes including BRCA1, BRCA2, ATM, ATR, RAD50 and CHEK2 were detected in 14.5% (11/76) of patients in ctDNA database compared to 19.2% (50/260) of patients in 3D Med tissue database and 12.6% (74/587) of patients in TCGA database. ConclusionsOur results suggest that genomic profiling of ctDNA could detected GAs in a significant subset of patients with ovarian cancer. Hybrid capture- NGS based ctDNA liquid biopsy has the potential capability to serve as an alternative to tissue biopsy and further studies are warranted. Legal entity responsible for the studyNanJing GaoChun People’s Hospital. FundingHas not received any funding. DisclosureY. Zhang: Full / Part-time employment: 3D Medicines Inc. S. Cai: Full / Part-time employment: 3D Medicines Inc. All other authors have declared no conflicts of interest.

Machine Learning Predicts Accurately Mycobacterium tuberculosis Drug Resistance From Whole Genome Sequencing Data.

Background: Tuberculosis disease, caused by Mycobacterium tuberculosis, is a major public health problem. The emergence of M. tuberculosis strains resistant to existing treatments threatens to derail control efforts. Resistance is mainly conferred by mutations in genes coding for drug targets or converting enzymes, but our knowledge of these mutations is incomplete. Whole genome sequencing (WGS) is an increasingly common approach to rapidly characterize isolates and identify mutations predicting antimicrobial resistance and thereby providing a diagnostic tool to assist clinical decision making.Methods: We applied machine learning approaches to 16,688 M. tuberculosis isolates that have undergone WGS and laboratory drug-susceptibility testing (DST) across 14 antituberculosis drugs, with 22.5% of samples being multidrug resistant and 2.1% being extensively drug resistant. We used non-parametric classification-tree and gradient-boosted-tree models to predict drug resistance and uncover any associated novel putative mutations. We fitted separate models for each drug, with and without “co-occurrent resistance” markers known to be causing resistance to drugs other than the one of interest. Predictive performance was measured using sensitivity, specificity, and the area under the receiver operating characteristic curve, assuming DST results as the gold standard.Results: The predictive performance was highest for resistance to first-line drugs, amikacin, kanamycin, ciprofloxacin, moxifloxacin, and multidrug-resistant tuberculosis (area under the receiver operating characteristic curve above 96%), and lowest for third-line drugs such as D-cycloserine and Para-aminosalisylic acid (area under the curve below 85%). The inclusion of co-occurrent resistance markers led to improved performance for some drugs and superior results when compared to similar models in other large-scale studies, which had smaller sample sizes. Overall, the gradient-boosted-tree models performed better than the classification-tree models. The mutation-rank analysis detected no new single nucleotide polymorphisms linked to drug resistance. Discordance between DST and genotypically inferred resistance may be explained by DST errors, novel rare mutations, hetero-resistance, and nongenomic drivers such as efflux-pump upregulation.Conclusion: Our work demonstrates the utility of machine learning as a flexible approach to drug resistance prediction that is able to accommodate a much larger number of predictors and to summarize their predictive ability, thus assisting clinical decision making and single nucleotide polymorphism detection in an era of increasing WGS data generation.

Cytogenetics and Cytogenomics Evaluation in Cancer.

The availability of cytogenetics and cytogenomics technologies improved the detection and identification of tumor molecular signatures as well as the understanding of cancer initiation and progression. The use of large-scale and high-throughput cytogenomics technologies has led to a fast identification of several cancer candidate biomarkers associated with diagnosis, prognosis, and therapeutics. The advent of array comparative genomic hybridization and next-generation sequencing technologies has significantly improved the knowledge about cancer biology, underlining driver genes to guide targeted therapy development, drug-resistance prediction, and pharmacogenetics. However, few of these candidate biomarkers have made the transition to the clinic with a clear benefit for the patients. Technological progress helped to demonstrate that cellular heterogeneity plays a significant role in tumor progression and resistance/sensitivity to cancer therapies, representing the major challenge of precision cancer therapy. A paradigm shift has been introduced in cancer genomics with the recent advent of single-cell sequencing, since it presents a lot of applications with a clear benefit to oncological patients, namely, detection of intra-tumoral heterogeneity, mapping clonal evolution, monitoring the development of therapy resistance, and detection of rare tumor cell populations. It seems now evident that no single biomarker could provide the whole information necessary to early detect and predict the behavior and prognosis of tumors. The promise of precision medicine is based on the molecular profiling of tumors being vital the continuous progress of high-throughput technologies and the multidisciplinary efforts to catalogue chromosomal rearrangements and genomic alterations of human cancers and to do a good interpretation of the relation genotype—phenotype.

Comprehensive Exploration to Identify Predictive DNA Markers of ΔNp63/SOX2 in Drug Resistance in Human Esophageal Squamous Cell Carcinoma.

OBP-801 is a novel histone deacetylase inhibitor being developed as an anticancer drug. In this study, we explored genes to predict drug resistance in human cancer. OBP-801 resistance was assessed in 37 strains of human cancer cell lines. Expression microarrays harboring 54,675 genes were used to focus on candidate genes, which were validated for both functional and clinical relevance in esophageal squamous cell carcinoma (ESCC). OBP-801 is sensitive to esophageal, gastric, and thyroid cancer, and resistant to some esophageal and colorectal cancers. We therefore used ESCC to explore genes. Comprehensive exploration focused on ΔNp63/SOX2, which were both genetically and epigenetically overexpressed in ESCC. Genomic amplifications of ΔNp63/SOX2 were tightly correlated each other (r = 0.81). Importantly, genomic amplification of ΔNp63/SOX2 in the resected tumors after neoadjuvant chemotherapy was significantly associated with histological grade of response (G1). Forced expression of either of these two genes did not induce each other, suggesting that their functional relevances were independent and showed robust drug resistance in OBP-801, as well as 5-fluorouracil. Furthermore, ΔNp63 could exert a potent oncogenic potential. RNA interference of ΔNp63 supported its oncological properties, as well as drug resistance. Comprehensive exploration of genes involved in anticancer drug residence could identify critical oncogenes of ΔNp63/SOX2 that would predict chemotherapy response in ESCC.

HIV drug resistance prediction with weighted categorical kernel functions

BackgroundAntiretroviral drugs are a very effective therapy against HIV infection. However, the high mutation rate of HIV permits the emergence of variants that can be resistant to the drug treatment. Predicting drug resistance to previously unobserved variants is therefore very important for an optimum medical treatment. In this paper, we propose the use of weighted categorical kernel functions to predict drug resistance from virus sequence data. These kernel functions are very simple to implement and are able to take into account HIV data particularities, such as allele mixtures, and to weigh the different importance of each protein residue, as it is known that not all positions contribute equally to the resistance.ResultsWe analyzed 21 drugs of four classes: protease inhibitors (PI), integrase inhibitors (INI), nucleoside reverse transcriptase inhibitors (NRTI) and non-nucleoside reverse transcriptase inhibitors (NNRTI). We compared two categorical kernel functions, Overlap and Jaccard, against two well-known noncategorical kernel functions (Linear and RBF) and Random Forest (RF). Weighted versions of these kernels were also considered, where the weights were obtained from the RF decrease in node impurity. The Jaccard kernel was the best method, either in its weighted or unweighted form, for 20 out of the 21 drugs.ConclusionsResults show that kernels that take into account both the categorical nature of the data and the presence of mixtures consistently result in the best prediction model. The advantage of including weights depended on the protein targeted by the drug. In the case of reverse transcriptase, weights based in the relative importance of each position clearly increased the prediction performance, while the improvement in the protease was much smaller. This seems to be related to the distribution of weights, as measured by the Gini index. All methods described, together with documentation and examples, are freely available at https://bitbucket.org/elies_ramon/catkern.

PREVALENCE AND STRUCTURE OF HIV-1 DRUG RESISTANCE AMONG TREATMENT NAÏVE PATIENTS SINCE THE INTRODUCTION OF ANTIRETROVIRAL THERAPY IN THE RUSSIAN FEDERATION

Aim:to analyze the prevalence, structure of drug resistance and drug resistance mutations in the protease and reverse transcriptase genes of HIV-1 among treatment naïve patients.Materials and methods.We analyzed protease and reverse transcriptase sequences from 1560 treatment naïve HIV-infected patients from all Federal Districts of the Russian Federation with the first positive immune blot during 1998–2017. Sequences were analyzed for the presence of drug resistance mutations and predicted drug resistance to antiretroviral drugs using two algorithms — Stanford HIVDR Database (HIVdb) and the 2009 SDRM list (CPR).Results.The prevalence of drug resistance mutations was 11,1%. More often the prevalence of drug resistance was found for non-nucleoside reverse transcriptase inhibitor drugs (rilpivirine, nevirapine, efavirenz). The prevalence of transmitted drug resistance associated with mutations from the SDRM list was 5,3%, which is classified by the WHO as a moderate level. However, it should be noted that since the large-scale use of antiretroviral drugs in the Russian Federation, there has been a trend towards a gradual increase in the level of the transmitted drug resistance, and in 2016 it has already reached 6,1%.Conclusion.The results demonstrate the need for regular surveillance of the prevalence of HIV drug resistance to antiretroviral drugs among treatment naïve patients in the Russian Federation.

Abstract 675: Network modeling of drug resistance mechanisms and drug combinations in breast cancer

Abstract Durable control of invasive solid tumors is thwarted by the lack of knowledge of effective drug combinations and of the acquired and intrinsic resistance mechanisms of drugs. In an effort to tackle this problem, the SU2C-NSF-TVF Drug Combination Convergence Team is using mechanistic models of cancer cell signaling based on therapeutic and cell line data in order to identify elements within cancer cells that might eventually be exploited through therapeutic combinations. Here we present a comprehensive mechanistic network model of signal transduction in ER+ PIK3CA-mutant breast cancer. Focusing on PI3K inhibitors, the model recapitulates known resistance mechanisms and predicts other possibilities for resistance: loss of RB1, FOXO3, P27, or PRAS40. To test these predictions, we analyzed genome-wide CRISPR screens of two breast cell lines in the presence of PI3K inhibitors (BYL719 and GDC0032) and found that the predicted genes (RB1, FOXO3, P27, and PRAS40) were significantly enriched in the screens. Some of these resistance genes (e.g. loss of RB1) were found to be cell-line specific and follow-up experiments in RB1-KO cells confirmed the cell-line-specific nature of PI3K-inhibitor resistance. The model also reveals known and novel combinatorial interventions that are more effective than PI3K inhibition alone. For example, the model predicts that the combination of PI3K inhibitors with inhibitors of anti-apoptotic protein MCL1 would be effective. Follow up experiments in cell lines using cell viability assays, cell death analyses, and dynamic BH3 profiling experiments to determine increases in apoptotic priming upon treatment confirmed that MCL1 inhibitors (S63845) enhance the effect of PI3K inhibitors (BYL719) and that this combinatorial effect is cell-line-specific, similarly to what was found in the resistance genes case. In conclusion, the model predicted drug resistance mechanisms and effective drug combinations, some of which were verified experimentally and found to be cell-line-specific. Next iterations of the model will incorporate the identified discrepancies and newly identified resistance mechanisms to drugs of clinical interest. Citation Format: Jorge Gómez Tejeda Zañudo, Pingping Mao, Joan Montero, Guotai Xu, Kailey J. Kowalski, Gabriela N. Johnson, José Baselga, Maurizio Scaltriti, Anthony G. Letai, Nikhil Wagle, Reka Albert. Network modeling of drug resistance mechanisms and drug combinations in breast 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 675.

Targeting HDAC/OAZ1 axis with a novel inhibitor effectively reverses cisplatin resistance in non-small cell lung cancer

Cisplatin yields significant efficacy and is generally used as a frontline therapy for non-small cell lung cancer (NSCLC). However, acquired resistance strongly limits its application. Here, we identified that a novel histone deacetylase (HDAC) inhibitor S11, with P-glycoprotein inhibitory activity, could obviously suppress cell growth in cisplatin-resistant NSCLC cell lines. In addition, S11 could increase the expression of Ac-H4 and p21, which confirmed its HDAC inhibitory action, suppress colony formation, and block cell migration of cisplatin-resistant NSCLC cells. Notably, co-treatment with S11 and cisplatin exhibited synergistically inhibitory efficacy in cisplatin-resistant NSCLC cells. Gene microarray data showed that OAZ1 was downregulated in resistant cells but upregulated after S11 treatment. Further study indicated that knockdown of OAZ1 by siRNA resulted in the decrease of sensitivity of resistant cells to cisplatin treatment and contributed to the increase of resistant cell migration. Additionally, ChIP assay data demonstrated that HDAC inhibitor S11 could increase the accumulation of Ac-H4 in OAZ1 promoter region, suggesting the direct regulation of OAZ1 by HDAC. Importantly, the combination of S11 and cisplatin overcome resistance through inhibiting HDAC activity and subsequently increasing the OAZ1 expression in preclinical model. Moreover, we observed that positive expression of HDAC1 was associated with the downregulation of OAZ1 in NSCLC patients with platinum-based treatment, and predicted drug resistance and poor prognosis. In summary, we demonstrated a role of HDAC/OAZ1 axis in cisplatin-resistant NSCLC and identified a promising compound to overcome cisplatin resistance.

Hippo signaling pathway and colorectal cancer

Hippo signaling pathway plays a significant role in the development of colorectal cancer, and acts as an important regulatory pathway which regulates cell proliferation and cell differentiation. Multiple proteins and genes in Hippo signaling pathway, especially the downstream YAP protein, play important roles in the occurrence, metastasis, drug resistance and recurrence of colorectal cancer. YAP and other related genes can be used as targeted markers for predicting drug resistance to colorectal cancer. Hippo signaling pathway gene network can be used as targeted therapy or combination therapy, thus providing new ideas for the treatment of colorectal cancer. Key words: Colorectal neoplasms; Molecular targeted therapy; Hippo signaling pathway; YAP protein

High Levels of HIV-1 Drug Resistance in Children Who Acquired HIV Infection Through Mother to Child Transmission in the Era of Option B+, Haiti, 2013 to 2014.

The main objective of this study was to determine the frequency and patterns of HIV drug resistance-associated mutations among children under 18 months of age born to HIV-1-positive mothers enrolled in the prevention of mother-to-child transmission services in Haiti. Between January 1, 2013 and December 31, 2014, HIV-positive remnant dried blood spots collected from children under 18 months of age for Early Infant Diagnosis at the National Public Health Laboratory were used for HIV-1 genotyping. HIV drug resistance mutations were analyzed using the Stanford Drug Resistance HIVdb program. Of the 3555 dried blood spots collected for Early Infant Diagnosis, 360 (10.1%) were HIV-positive and 355 were available for genotyping. Of these, 304 (85.6%) were successfully genotyped and 217 (71.4%) had ≥1 drug resistance mutation. Mutations conferring resistance to nucleoside reverse transcriptase inhibitor (NRTIs) and non-NRTIs were present in 40.5% (123) and 69.1% (210), respectively. The most frequent mutations were K103N/S (48.0%), M184V (37.5%), G190A/S (15.1%), and Y181C/G/V (14.1%). Predicted drug resistance analysis revealed that 68.8% of the children had high-level resistance to non-NRTIs and 11.5% had intermediate to high-level resistance to abacavir. This study showed high rates of resistance to NRTIs and non-NRTIs among newly HIV-diagnosed children in Haiti, suggesting that in the era of "Option B+" (initiation of lifelong combination antiretroviral therapy to pregnant women with HIV), the majority of children who acquire HIV infection through mother-to-child transmission of HIV have resistant HIV. These results have led the National HIV Program to revise the pediatric guidelines to include protease inhibitors in first-line regimens for all HIV-positive newborns.

Spectroscopic exploration of binding of new imidazolium-based palladium(II) saldach complexes with CT-DNA as anticancer agents against HER2/neu overexpression

The HER2/neu has shown a potential role in the choice of active chemotherapy for breast tumors because of its prognostic relevance and putative role in predicting drug resistance. Moreover, suppressing DNA replication has become an attractive strategy for treating cancer patients. In this attempt, the present study aimed to prepare new series of bis-imidazolium-based saldach {H2(Et)2saldach (nBu-Im+-X–)2} and their cis-Pd(II) complexes (saldach = N,N′-bis-(salicylidene)-R,R-1,2-diaminocyclohexane; X = Cl, PF6, BF4) as anticancer agents. The in vitro cytotoxicity activity of new cis-Pd(II) complexes against human breast adenocarcinoma cell lines (MCF-7) revealed higher growth-inhibitory effect than the native ligands. They induced a significant decrease for the protein HER2/neu expression with p < 0.05 and tumor necrosis factor (TNF-α) (p < 0.05), while the tumor suppressor protein P53 was expressed in a highly significant upregulation p < 0.01. Moreover, complex 5a was the most potent active compound (IC50 = 8.5 ± 0.2 μM) in inhibition of cell proliferation. Additionally, in vitro studies of Pd(II) complex (5a) using UV–Vis spectroscopy and binding affinity toward the calf thymus (CT) DNA) showed a combination of covalent, intercalation, hydrogen bonding interactions through formation of (CT-DNA).

Co-administration of 20(S)-protopanaxatriol (g-PPT) and EGFR-TKI overcomes EGFR-TKI resistance by decreasing SCD1 induced lipid accumulation in non-small cell lung cancer

BackgroundNon-small cell lung cancer (NSCLC) patients with sensitive epidermal growth factor receptor (EGFR) mutations are successfully treated with EGFR tyrosine kinase inhibitors (EGFR-TKIs); however, resistance to treatment inevitably occurs. Given lipid metabolic reprogramming is widely known as a hallmark of cancer and intimately linked with EGFR-stimulated cancer growth. Activation of EGFR signal pathway increased monounsaturated fatty acids (MUFA) and lipid metabolism key enzyme Stearoyl-CoA Desaturase 1 (SCD1) expression. However the correlation between EGFR-TKI resistance and lipid metabolism remains to be determined.MethodsIn this study the differences in lipid synthesis between paired TKI-sensitive and TKI-resistant patient tissues and NSCLC cell lines were explored. Oleic acid (OA, a kind of MUFA, the SCD1 enzymatic product) was used to simulate a high lipid metabolic environment and detected the affection on the cytotoxic effect of TKIs (Gefitinib and osimertinib) in cell lines with EGFR-activating mutations. (20S)-Protopanaxatriol (g-PPT), an aglycone of ginsenosides, has been reported to be an effective lipid metabolism inhibitor, was used to inhibit lipid metabolism. Additionally, synergism in cytotoxic effects and signal pathway activation were evaluated using CCK-8 assays, Western blotting, flow cytometry, Edu assays, plate clone formation assays and immunofluorescence. Furthermore, two xenograft mouse models were used to verify the in vitro results.ResultsGefitinib-resistant cells have higher lipid droplet content and SCD1 expression than Gefitinib-sensitive cells in both NSCLC cell lines and patient tissues. Additionally oleic acid (OA, a kind of MUFA, the SCD1 enzymatic product) abrogates the cytotoxic effect of both Gefitinib and osimertinib in cell lines with EGFR-activating mutations. As a reported effective lipid metabolism inhibitor, g-PPT significantly inhibited the expression of SCD1 in lung adenocarcinoma cells, and then down-regulated the content of intracellular lipid droplets. Combined treatment with Gefitinib and g-PPT reverses the resistance to Gefitinib and inhibits the activation of p-EGFR and the downstream signaling pathways.ConclusionsOur findings uncover a link between lipid metabolic reprogramming and EGFR-TKI resistance, confirmed that combination target both EGFR and abnormal lipid metabolism maybe a promising therapy for EGFR-TKI resistance and highlighting the possibility of monitoring lipid accumulation in tumors for predicting drug resistance.

Genomic prediction of tuberculosis drug-resistance: benchmarking existing databases and prediction algorithms

BackgroundIt is possible to predict whether a tuberculosis (TB) patient will fail to respond to specific antibiotics by sequencing the genome of the infecting Mycobacterium tuberculosis (Mtb) and observing whether the pathogen carries specific mutations at drug-resistance sites. This advancement has led to the collation of TB databases such as PATRIC and ReSeqTB that possess both whole genome sequences and drug resistance phenotypes of infecting Mtb isolates. Bioinformatics tools have also been developed to predict drug resistance from whole genome sequencing (WGS) data.Here, we evaluate the performance of four popular tools (TBProfiler, MyKrobe, KvarQ, PhyResSE) with 6746 isolates compiled from publicly available databases, and subsequently identify highly probable phenotyping errors in the databases by genetically predicting the drug phenotypes using all four software.ResultsOur results show that these bioinformatics tools generally perform well in predicting the resistance status for two key first-line agents (isoniazid, rifampicin), but the accuracy is lower for second-line injectables and fluoroquinolones. The error rates in the databases are also non-trivial, reaching as high as 31.1% for prothionamide, and that phenotypes from ReSeqTB are more susceptible to errors.ConclusionsThe good performance of the automated software for drug resistance prediction from TB WGS data shown in this study further substantiates the usefulness and promise of utilising genetic data to accurately profile TB drug resistance, thereby reducing misdiagnoses arising from error-prone culture-based drug susceptibility testing.

Sierra-local: A lightweight standalone application for drug resistance prediction

sierra-local: A lightweight standalone application for drug resistance prediction

Methylation and ovarian cancer: Can DNA methylation be of diagnostic use? (Review)

Ovarian cancer is a silent killer and, due to late diagnosis and frequent chemo resistance in patients, the primary cause of fatality amongst the various types of gynecological cancer. The discovery of a specific and sensitive biomarker for ovarian cancer could improve early diagnosis, thereby saving lives. Biomarkers could also improve treatment, by predicting which patients will benefit from specific treatment strategies. DNA methylation is an epigenetic mechanism, and 'methylation imbalance' is characteristic of cancer. Previous research suggests that changes in DNA methylation can be used diagnostically, and that they may predict resistance to treatment. This paper gives an up-to-date overview of research investigating the potential of DNA methylation-based markers for diagnostics, prognostics, screening and prediction of drug resistance for ovarian cancer patients. DNA methylation cancer-biomarkers may be useful for cancer treatment, particularly since they are chemically stable and since cancer-associated changes in methylation typically precedes tumor growth. DNA methylation markers could improve diagnosis and treatment and might even be used for screening in the future. Furthermore, DNA methylation biomarkers could facilitate the development of precision medicine. However, at this point no biomarkers for ovarian cancer have a sufficient combination of sensitivity and specificity in a clinical setting. A reason for this is that most studies have focused on a single or a few methylation sites. More large screenings and genome-wide studies must be performed to increase the chance of identifying a DNA methylation marker which can identify ovarian cancer.

Genetic Determinants and Prediction of Antibiotic Resistance Phenotypes in Helicobacter pylori.

Helicobacter pylori is a major human pathogen. Diagnosis of H. pylori infection and determination of its antibiotic susceptibility still mainly rely on culture and phenotypic drug susceptibility testing (DST) that is time-consuming and laborious. Whole genome sequencing (WGS) has recently emerged in medical microbiology as a diagnostic tool for reliable drug resistance prediction in bacterial pathogens. The aim of this study was to compare phenotypic DST results with the predictions based on the presence of genetic determinants identified in the H. pylori genome using WGS. Phenotypic resistance to clarithromycin, metronidazole, tetracycline, levofloxacin, and rifampicin was determined in 140 clinical H. pylori isolates by E-Test®, and the occurrence of certain single nucleotide polymorphisms (SNPs) in target genes was determined by WGS. Overall, there was a high congruence of >99% between phenotypic DST results for clarithromycin, levofloxacin, and rifampicin and SNPs identified in the 23S rRNA, gyrA, and rpoB gene. However, it was not possible to infer a resistance phenotype for metronidazole based on the occurrence of distinct SNPs in frxA and rdxA. All 140 H. pylori isolates analysed in this study were susceptible to tetracycline, which was in accordance with the absence of double or triple nucleotide substitutions in the 16S rRNA gene.

Advances in prediction for ovarian cancer treatment stratification.

Breakthrough findings reported in 2018 provide the foundations for paradigm shifts in the care of patients with ovarian cancer. These results have implications for treatment in the first-line setting (with improved and refined use of established treatment modalities), firmly introduce concepts of consolidation and maintenance therapy, and enable more accurate prediction of drug sensitivity and resistance.