Quality-adjusted time without symptoms of disease or toxicity and quality-adjusted progression-free survival with niraparib maintenance in first-line ovarian cancer in the PRIMA trial

Background: The PRIMA phase 3 trial showed niraparib significantly prolongs median progression-free survival (PFS) versus placebo in patients with advanced ovarian cancer (OC) responsive to first-line platinum-based chemotherapy, including those who had tumors with homologous recombination deficiency (HRd). This analysis of PRIMA examined the quality-adjusted PFS (QA-PFS) and quality-adjusted time without symptoms of disease or toxicity (Q-TWiST) of patients on maintenance niraparib versus placebo. Methods: Patients were randomized 2:1 to receive once-daily maintenance niraparib (n = 487) or placebo (n = 246). QA-PFS was defined as the PFS of patients adjusted for their health-related quality of life (HRQoL) prior to disease progression, measured using European Quality of Life Five-Dimension (EQ-5D) questionnaire index scores from the PRIMA trial. Q-TWiST was calculated by combining data on PFS, duration of symptomatic grade ⩾2 adverse events (fatigue or asthenia, nausea, vomiting, abdominal pain, and abdominal bloating) prior to disease progression, and EQ-5D index scores. Analyses used data collected up to the last date of PFS assessment (May 17, 2019). Results: The restricted mean QA-PFS was significantly longer with niraparib versus placebo in the HRd (n = 373) and overall intention-to-treat (ITT; n = 733) populations (mean gains of 6.5 [95% confidence interval; CI, 3.9–8.9] and 4.1 [95% CI, 2.2–5.8] months, respectively). There were also significant improvements in restricted mean Q-TWiST for niraparib versus placebo (mean gains of 5.9 [95% CI, 3.5–8.6] and 3.5 [95% CI, 1.7–5.6] months, respectively) in the HRd and ITT populations. Conclusions: In patients with advanced OC, first-line niraparib maintenance was associated with significant gains in QA-PFS and Q-TWiST versus placebo. These findings demonstrate that niraparib maintenance treatment is associated with a PFS improvement and that treatment benefit is maintained even when HRQoL and/or toxicity data are combined with PFS in a single measure. Trial registration: ClinicalTrials.gov: NCT02655016; trial registration date: January 13, 2016 Plain language summary: Background: In a large clinical trial called PRIMA, patients with advanced cancer of the ovary (ovarian cancer) were given either niraparib (a type of cancer medicine) or placebo (a pill containing no medicine/active substances) after having chemotherapy (another type of cancer medicine). Taking niraparib after chemotherapy is called maintenance therapy and aims to give patients more time before their cancer returns or gets worse than if they were not given any further treatment. In the PRIMA trial, patients who took niraparib did have more time before their cancer progressed than if they took placebo. However, it is important to consider patients’ quality of life, which can be made worse by cancer symptoms and/or side effects of treatment. Here, we assessed the overall benefit of niraparib for patients in PRIMA. Methods: Both the length of time before disease progression (or survival time) and quality of life were considered using two different analyses: ● The first analysis was called quality-adjusted PFS (QA-PFS) and looked at how long patients survived with good quality of life. ● The second analysis was called quality-adjusted time without symptoms of disease or toxicity (Q-TWiST) and looked at how long patients survived without cancer symptoms or treatment side effects. Results: The PRIMA trial included 733 patients; 487 took niraparib and 246 took placebo. Around half of the patients in both groups had a type of ovarian cancer that responds particularly well to drugs like niraparib – they are known as homologous recombination deficiency (HRd) patients. ● When information on quality of life (collected from patient questionnaires) and survival was combined in the QA-PFS analysis, HRd patients who took niraparib had approximately 6.5 months longer with a good quality of life before disease progression than those who took placebo. In the overall group of patients (including HRd patients and non-HRd patients), those who took niraparib had approximately 4 months longer than with placebo. ● Using the second analysis (Q-TWiST) to combine information on survival with cancer symptoms and treatment side effects, the HRd patients taking niraparib had approximately 6 months longer without cancer symptoms or treatment side effects (such as nausea or vomiting) than patients taking placebo. In the overall group of patients, those taking niraparib had approximately 3.5 months longer without these cancer symptoms/side effects than patients receiving placebo. Conclusions: These results show that the survival benefits of niraparib treatment remain when accounting for patients’ quality of life. These benefits were seen not only in HRd patients who are known to respond better to niraparib, but in the overall group of patients who took niraparib.publishedVersionPeer reviewe

A Parallel Decomposition Solver for SVM: Distributed Dual Ascend using Fenchel Duality

We introduce a distributed algorithm for solving large scale Support Vector Machines (SVM) problems. The algorithm divides the training set into a number of processing nodes each running independently an SVM sub-problem associated with its subset of training data. The algorithm is a parallel (Jacobi) block-update scheme derived from the convex conjugate (Fenchel Duality) form of the original SVM problem. Each update step consists of a modified SVM solver running in parallel over the sub-problems followed by a simple global update. We derive bounds on the number of updates showing that the number of iterations (independent SVM applications on sub-problems) required to obtain a solution of accuracy ɛ is O(log(1/ɛ)). We demonstrate the efficiency and applicability of our algorithms by running on large scale experiments on standardized datasets while comparing the results to the state-of-the-art SVM solvers

Intensity dependent estimation of noise in microarrays improves detection of differentially expressed genes

Abstract Background In many microarray experiments, analysis is severely hindered by a major difficulty: the small number of samples for which expression data has been measured. When one searches for differentially expressed genes, the small number of samples gives rise to an inaccurate estimation of the experimental noise. This, in turn, leads to loss of statistical power. Results We show that the measurement noise of genes with similar expression levels (intensity) is identically and independently distributed, and that this (intensity dependent) distribution is approximately normal. Our method can be easily adapted and used to test whether these statement hold for data from any particular microarray experiment. We propose a method that provides an accurate estimation of the intensity-dependent variance of the noise distribution, and demonstrate that using this estimation we can detect differential expression with much better statistical power than that of standard t-test, and can compare the noise levels of different experiments and platforms. Conclusions When the number of samples is small, the simple method we propose improves significantly the statistical power in identifying differentially expressed genes.</p

[F18]FDG PET/CT-Derived Metabolic and Volumetric Biomarkers Can Predict Response to Treatment in Locally Advanced Cervical Cancer Patients

(1) Purpose: Current study aimed at evaluating the relationship between quantitative metabolic and volumetric FDG PET/CT parameters and the response to definitive chemoradiation therapy in locally advanced cervical cancer patients; (2) Methods: Ninety newly diagnosed locally advanced cervical cancer patients (FIGO IB2-IVA) were investigated. All patients underwent PET/CT at staging and after treatment. Metabolic and volumetric parameters, including SUVmax, SUVmean, Total Lesion Glycolysis (TLG), and Metabolic Tumor Volume (MTV) of the primary tumor and metastatic lymph nodes were measured and compared between patients with and without complete metabolic response (CMR). A similar analysis was performed in a subgroup of FIGO IB2-IIB patients; (3) Results: SUVmax and SUVmean of the primary tumor as well as those of metastatic lymph nodes, MTV, and TLG were found to be significantly different between CMR and non-CMR patients. In a subgroup of patients with FIGO IB2-IIB disease, MTV and TLG identified women who will achieve CMR with a threshold of 31.1 cm3 for MTV and 217.8 for TLG; (4) Conclusions: PET/CT-derived quantitative metabolic and volumetric parameters are higher in locally advanced cervical cancer patients who will not respond to definitive chemoradiation therapy. Specifically, in patients who are not metastatic at staging, MTV and TLG values can serve as a predictor for treatment response and thus may alter treatment strategy

Can morphometric analysis of the fallopian tube fimbria predict the presence of uterine papillary serous carcinoma (UPSC)?

Uterine serous papillary carcinoma (UPSC) is an aggressive tumor, often diagnosed as a metastatic disease and characterized by a high recurrence rate and poor prognosis. UPSC represents a distinct subtype of endometrial cancer which is different in clinical and pathological behaviors from endometrioid endometrial carcinoma (EEC) and resembles more to serous ovarian carcinoma. Since tumors of serous papillary of the ovary are hypothesized to stem from cells of the fallopian tube's fimbria, we hypothesized that UPSC may also origin in the fallopian tubes. In our previous study, using a novel method of computerized morphometry of the fimbrial epithelium we have found significant differences between fimbriae of healthy women and serous ovarian cancer patients. In this study we showed the presence of morphologic differences between twenty-four fimbriae from healthy women, and twenty six fimbriae from uterus cancer (13 from UPSC patients and 13 from EEC patients). All fimbriae reported by the pathologist as "normal" were subjected to a computerized histomorphometric analysis. Two-step method of computerized histomorphometry, i.e. Fast Fourier transformation (FFT) followed by a co-occurrence matrix analysis and an additional analysis of the nuclear symmetry of the tubal fimbrial epithelium were applied. Using these novel methods, we were able to show differences in the morphometric characteristics of the fimbriae in UPSC patients compared to EEC and healthy patients. It is yet to be determined the clinical significance of this observation

Combining 16S rRNA gene variable regions enables high-resolution microbial community profiling.

BackgroundMost of our knowledge about the remarkable microbial diversity on Earth comes from sequencing the 16S rRNA gene. The use of next-generation sequencing methods has increased sample number and sequencing depth, but the read length of the most widely used sequencing platforms today is quite short, requiring the researcher to choose a subset of the gene to sequence (typically 16-33% of the total length). Thus, many bacteria may share the same amplified region, and the resolution of profiling is inherently limited. Platforms that offer ultra-long read lengths, whole genome shotgun sequencing approaches, and computational frameworks formerly suggested by us and by others all allow different ways to circumvent this problem yet suffer various shortcomings. There is a need for a simple and low-cost 16S rRNA gene-based profiling approach that harnesses the short read length to provide a much larger coverage of the gene to allow for high resolution, even in harsh conditions of low bacterial biomass and fragmented DNA.ResultsThis manuscript suggests Short MUltiple Regions Framework (SMURF), a method to combine sequencing results from different PCR-amplified regions to provide one coherent profiling. The de facto amplicon length is the total length of all amplified regions, thus providing much higher resolution compared to current techniques. Computationally, the method solves a convex optimization problem that allows extremely fast reconstruction and requires only moderate memory. We demonstrate the increase in resolution by in silico simulations and by profiling two mock mixtures and real-world biological samples. Reanalyzing a mock mixture from the Human Microbiome Project achieved about twofold improvement in resolution when combing two independent regions. Using a custom set of six primer pairs spanning about 1200&nbsp;bp (80%) of the 16S rRNA gene, we were able to achieve ~ 100-fold improvement in resolution compared to a single region, over a mock mixture of common human gut bacterial isolates. Finally, the profiling of a Drosophila melanogaster microbiome using the set of six primer pairs provided a ~ 100-fold increase in resolution and thus enabling efficient downstream analysis.ConclusionsSMURF enables the identification of near full-length 16S rRNA gene sequences in microbial communities, having resolution superior compared to current techniques. It may be applied to standard sample preparation protocols with very little modifications. SMURF also paves the way to high-resolution profiling of low-biomass and fragmented DNA, e.g., in the case of formalin-fixed and paraffin-embedded samples, fossil-derived DNA, or DNA exposed to other degrading conditions. The approach is not restricted to combining amplicons of the 16S rRNA gene and may be applied to any set of amplicons, e.g., in multilocus sequence typing (MLST)