Evaluation of a chemoresponse assay as a predictive marker in the treatment of recurrent ovarian cancer: Further analysis of a prospective study

BACKGROUND: Recently, a prospective study reported improved clinical outcomes for recurrent ovarian cancer patients treated with chemotherapies indicated to be sensitive by a chemoresponse assay, compared with those patients treated with non-sensitive therapies, thereby demonstrating the assay's prognostic properties. Due to cross-drug response over different treatments and possible association of in vitro chemosensitivity of a tumour with its inherent biology, further analysis is required to ascertain whether the assay performs as a predictive marker as well. METHODS: Women with persistent or recurrent epithelial ovarian cancer (n=262) were empirically treated with one of 15 therapies, blinded to assay results. Each patient's tumour was assayed for responsiveness to the 15 therapies. The assay's ability to predict progression-free survival (PFS) was assessed by comparing the association when the assayed therapy matches the administered therapy (match) with the association when the assayed therapy is randomly selected, not necessarily matching the administered therapy (mismatch). RESULTS: Patients treated with assay-sensitive therapies had improved PFS vs patients treated with non-sensitive therapies, with the assay result for match significantly associated with PFS (hazard ratio (HR)=0.67, 95% confidence interval (CI)=0.50–0.91, P=0.009). On the basis of 3000 simulations, the mean HR for mismatch was 0.81 (95% range=0.66–0.99), with 3.4% of HRs less than 0.67, indicating that HR for match is lower than for mismatch. While 47% of tumours were non-sensitive to all assayed therapies and 9% were sensitive to all, 44% displayed heterogeneity in assay results. Improved outcome was associated with the administration of an assay-sensitive therapy, regardless of homogeneous or heterogeneous assay responses across all of the assayed therapies. CONCLUSIONS: These analyses provide supportive evidence that this chemoresponse assay is a predictive marker, demonstrating its ability to discern specific therapies that are likely to be more effective among multiple alternatives

A comparison of ARMS and direct sequencing for EGFR mutation analysis and Tyrosine Kinase Inhibitors treatment prediction in body fluid samples of Non-Small-Cell Lung Cancer patients

<p>Abstract</p> <p>Background</p> <p>Epidermal growth factor receptor (<it>EGFR</it>) mutation is strongly associated with the therapeutic effect of tyrosine kinase inhibitors (TKIs) in patients with non-small-cell lung cancer (NSCLC). Nevertheless, tumor tissue that needed for mutation analysis is frequently unavailable. Body fluid was considered to be a feasible substitute for the analysis, but arising problems in clinical practice such as relatively lower mutation rate and poor clinical correlation are not yet fully resolved.</p> <p>Method</p> <p>In this study, 50 patients (32 pleural fluids and 18 plasmas) with TKIs therapy experience and with direct sequencing results were selected from 220 patients for further analysis. The <it>EGFR </it>mutation status was re-evaluated by Amplification Refractory Mutation System (ARMS), and the clinical outcomes of TKIs were analyzed retrospectively.</p> <p>Results</p> <p>As compared with direct sequencing, 16 positive and 23 negative patients were confirmed by ARMS, and the other 11 former negative patients (6 pleural fluids and 5 plasmas) were redefined as positive, with a fairly well clinical outcome (7 PR, 3 SD, and 1 PD). The objective response rate (ORR) of positive patients was significant, 81.3% (direct sequencing) and 72.7% (ARMS) for pleural fluids, and 80% (ARMS) for plasma. Notably, even reclassified by ARMS, the ORR for negative patients was still relatively high, 60% for pleural fluids and 46.2% for plasma.</p> <p>Conclusions</p> <p>When using body fluids for <it>EGFR </it>mutation analysis, positive result is consistently a good indicator for TKIs therapy, and the predictive effect was no less than that of tumor tissue, no matter what method was employed. However, even reclassified by ARMS, the correlation between negative results and clinical outcome of TKIs was still unsatisfied. The results indicated that false negative mutation still existed, which may be settled by using method with sensitivity to single DNA molecule or by optimizing the extraction procedure with RNA or CTC to ensure adequate amount of tumor-derived nucleic acid for the test.</p

Influence of Dopaminergically Mediated Reward on Somatosensory Decision-Making

This pharmacological fMRI study shows that during reward-based sensory decision-making, dopamine is crucially involved in reward-related modulation of human primary sensory cortex

Histone deacetylases in viral infections

Chromatin remodeling and gene expression are regulated by histone deacetylases (HDACs) that condense the chromatin structure by deacetylating histones. HDACs comprise a group of enzymes that are responsible for the regulation of both cellular and viral genes at the transcriptional level. In mammals, a total of 18 HDACs have been identified and grouped into four classes, i.e., class I (HDACs 1, 2, 3, 8), class II (HDACs 4, 5, 6, 7, 9, 10), class III (Sirt1–Sirt7), and class IV (HDAC11). We review here the role of HDACs on viral replication and how HDAC inhibitors could potentially be used as new therapeutic tools in several viral infections

HIV Promoter Integration Site Primarily Modulates Transcriptional Burst Size Rather Than Frequency

Mammalian gene expression patterns, and their variability across populations of cells, are regulated by factors specific to each gene in concert with its surrounding cellular and genomic environment. Lentiviruses such as HIV integrate their genomes into semi-random genomic locations in the cells they infect, and the resulting viral gene expression provides a natural system to dissect the contributions of genomic environment to transcriptional regulation. Previously, we showed that expression heterogeneity and its modulation by specific host factors at HIV integration sites are key determinants of infected-cell fate and a possible source of latent infections. Here, we assess the integration context dependence of expression heterogeneity from diverse single integrations of a HIV-promoter/GFP-reporter cassette in Jurkat T-cells. Systematically fitting a stochastic model of gene expression to our data reveals an underlying transcriptional dynamic, by which multiple transcripts are produced during short, infrequent bursts, that quantitatively accounts for the wide, highly skewed protein expression distributions observed in each of our clonal cell populations. Interestingly, we find that the size of transcriptional bursts is the primary systematic covariate over integration sites, varying from a few to tens of transcripts across integration sites, and correlating well with mean expression. In contrast, burst frequencies are scattered about a typical value of several per cell-division time and demonstrate little correlation with the clonal means. This pattern of modulation generates consistently noisy distributions over the sampled integration positions, with large expression variability relative to the mean maintained even for the most productive integrations, and could contribute to specifying heterogeneous, integration-site-dependent viral production patterns in HIV-infected cells. Genomic environment thus emerges as a significant control parameter for gene expression variation that may contribute to structuring mammalian genomes, as well as be exploited for survival by integrating viruses

Differential Pathogenesis of Lung Adenocarcinoma Subtypes Involving Sequence Mutations, Copy Number, Chromosomal Instability, and Methylation

Lung adenocarcinoma (LAD) has extreme genetic variation among patients, which is currently not well understood, limiting progress in therapy development and research. LAD intrinsic molecular subtypes are a validated stratification of naturally-occurring gene expression patterns and encompass different functional pathways and patient outcomes. Patients may have incurred different mutations and alterations that led to the different subtypes. We hypothesized that the LAD molecular subtypes co-occur with distinct mutations and alterations in patient tumors.The LAD molecular subtypes (Bronchioid, Magnoid, and Squamoid) were tested for association with gene mutations and DNA copy number alterations using statistical methods and published cohorts (n = 504). A novel validation (n = 116) cohort was assayed and interrogated to confirm subtype-alteration associations. Gene mutation rates (EGFR, KRAS, STK11, TP53), chromosomal instability, regional copy number, and genomewide DNA methylation were significantly different among tumors of the molecular subtypes. Secondary analyses compared subtypes by integrated alterations and patient outcomes. Tumors having integrated alterations in the same gene associated with the subtypes, e.g. mutation, deletion and underexpression of STK11 with Magnoid, and mutation, amplification, and overexpression of EGFR with Bronchioid. The subtypes also associated with tumors having concurrent mutant genes, such as KRAS-STK11 with Magnoid. Patient overall survival, cisplatin plus vinorelbine therapy response and predicted gefitinib sensitivity were significantly different among the subtypes.The lung adenocarcinoma intrinsic molecular subtypes co-occur with grossly distinct genomic alterations and with patient therapy response. These results advance the understanding of lung adenocarcinoma etiology and nominate patient subgroups for future evaluation of treatment response

Molecular control of HIV-1 postintegration latency: implications for the development of new therapeutic strategies

The persistence of HIV-1 latent reservoirs represents a major barrier to virus eradication in infected patients under HAART since interruption of the treatment inevitably leads to a rebound of plasma viremia. Latency establishes early after infection notably (but not only) in resting memory CD4+ T cells and involves numerous host and viral trans-acting proteins, as well as processes such as transcriptional interference, RNA silencing, epigenetic modifications and chromatin organization. In order to eliminate latent reservoirs, new strategies are envisaged and consist of reactivating HIV-1 transcription in latently-infected cells, while maintaining HAART in order to prevent de novo infection. The difficulty lies in the fact that a single residual latently-infected cell can in theory rekindle the infection. Here, we review our current understanding of the molecular mechanisms involved in the establishment and maintenance of HIV-1 latency and in the transcriptional reactivation from latency. We highlight the potential of new therapeutic strategies based on this understanding of latency. Combinations of various compounds used simultaneously allow for the targeting of transcriptional repression at multiple levels and can facilitate the escape from latency and the clearance of viral reservoirs. We describe the current advantages and limitations of immune T-cell activators, inducers of the NF-κB signaling pathway, and inhibitors of deacetylases and histone- and DNA- methyltransferases, used alone or in combinations. While a solution will not be achieved by tomorrow, the battle against HIV-1 latent reservoirs is well- underway

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery