A shared origin from a common ancestor: A case report of two histologically different tumors.

The origin of tumors has been under discussion over the years. Different theories have been suggested to explain this phenomenon. Among them, the Cancer-Stem Cells model, is one of the most outstanding. In this study, we reported a case of a 72-year-old man who presented two histologically different tumors with a 7-years gap, a Penile Squamous Cell Carcinoma and a Pleomorphic Undifferentiated Sarcoma, that share some molecular features. Phonotypical differences were showed and confirmed at histological and IHC levels. Molecular analysis showed an HPV infection in the carcinoma. Additionally, sequencing results revealed common (CDKN2A and TERT) and exclusive (FBXW7 and TP53) genetic alterations in both tumors (Table 1). The possible germline origin of common mutations was discarded after negative germline testing. Here we describe, for the first time a clinical case of a possible origin of two histologically different tumors from a common ancestor based on molecular data. Even if different hypothesis appear as possible, the Cancer Stem Cell-based model appears as the most suitable.

A Bayesian adaptive design for dual-agent phase I-II oncology trials integrating efficacy data across stages.

Combination of several anticancer treatments has typically been presumed to have enhanced drug activity. Motivated by a real clinical trial, this paper considers phase I-II dose finding designs for dual-agent combinations, where one main objective is to characterize both the toxicity and efficacy profiles. We propose a two-stage Bayesian adaptive design that accommodates a change of patient population in-between. In stage I, we estimate a maximum tolerated dose combination using the escalation with overdose control (EWOC) principle. This is followed by a stage II, conducted in a new yet relevant patient population, to find the most efficacious dose combination. We implement a robust Bayesian hierarchical random-effects model to allow sharing of information on the efficacy across stages, assuming that the related parameters are either exchangeable or nonexchangeable. Under the assumption of exchangeability, a random-effects distribution is specified for the main effects parameters to capture uncertainty about the between-stage differences. The inclusion of nonexchangeability assumption further enables that the stage-specific efficacy parameters have their own priors. The proposed methodology is assessed with an extensive simulation study. Our results suggest a general improvement of the operating characteristics for the efficacy assessment, under a conservative assumption about the exchangeability of the parameters a priori.

Stratified modestly weighted log-rank tests in settings with an anticipated delayed separation of survival curves.

Delayed separation of survival curves is a common occurrence in confirmatory studies in immuno-oncology. Many novel statistical methods that aim to efficiently capture potential long-term survival improvements have been proposed in recent years. However, the vast majority do not consider stratification, which is a major limitation considering that most large confirmatory studies currently employ a stratified primary analysis. In this article, we combine recently proposed weighted log-rank tests that have been designed to work well under a delayed separation of survival curves, with stratification by a baseline variable. The aim is to increase the efficiency of the test when the stratifying variable is highly prognostic for survival. As there are many potential ways to combine the two techniques, we compare several possibilities in an extensive simulation study. We also apply the techniques retrospectively to two recent randomized clinical trials.

Quantifying treatment differences in confirmatory trials under non-proportional hazards.

Proportional hazards are a common assumption when designing confirmatory clinical trials in oncology. With the emergence of immunotherapy and novel targeted therapies, departure from the proportional hazard assumption is not rare in nowadays clinical research. Under non-proportional hazards, the hazard ratio does not have a straightforward clinical interpretation, and the log-rank test is no longer the most powerful statistical test even though it is still valid. Nevertheless, the log-rank test and the hazard ratio are still the primary analysis tools, and traditional approaches such as sample size increase are still proposed to account for the impact of non-proportional hazards. The weighed log-rank test and the test based on the restricted mean survival time (RMST) are receiving a lot of attention as a potential alternative to the log-rank test. We conduct a simulation study comparing the performance and operating characteristics of the log-rank test, the weighted log-rank test and the test based on the RMST, including a treatment effect estimation, under different non-proportional hazards patterns. Results show that, under non-proportional hazards, the hazard ratio and weighted hazard ratio have no straightforward clinical interpretation whereas the RMST ratio can be interpreted regardless of the proportional hazards assumption. In terms of power, the RMST achieves a similar performance when compared to the log-rank test.

Design and analysis of group-sequential clinical trials based on a modestly weighted log-rank test in anticipation of a delayed separation of survival curves: A practical guidance.

BACKGROUND: A common feature of many recent trials evaluating the effects of immunotherapy on survival is that non-proportional hazards can be anticipated at the design stage. This raises the possibility to use a statistical method tailored towards testing the purported long-term benefit, rather than applying the more standard log-rank test and/or Cox model. Many such proposals have been made in recent years, but there remains a lack of practical guidance on implementation, particularly in the context of group-sequential designs. In this article, we aim to fill this gap. METHODS: We illustrate how the POPLAR trial, which compared immunotherapy versus chemotherapy in non-small-cell lung cancer, might have been re-designed to be more robust to the presence of a delayed effect using the modestly-weighted log-rank test in a group-sequential setting. CONCLUSION: We provide step-by-step instructions on how to analyse a hypothetical realization of the trial, based on this new design. Basic theory on weighted log-rank tests and group-sequential methods is covered, and an accompanying R package (including vignette) is provided.

Deep learning for fully automatic detection, segmentation, and Gleason grade estimation of prostate cancer in multiparametric magnetic resonance images.

Although the emergence of multi-parametric magnetic resonance imaging (mpMRI) has had a profound impact on the diagnosis of prostate cancers (PCa), analyzing these images remains still complex even for experts. This paper proposes a fully automatic system based on Deep Learning that performs localization, segmentation and Gleason grade group (GGG) estimation of PCa lesions from prostate mpMRIs. It uses 490 mpMRIs for training/validation and 75 for testing from two different datasets: ProstateX and Valencian Oncology Institute Foundation. In the test set, it achieves an excellent lesion-level AUC/sensitivity/specificity for the GGG[Formula: see text]2 significance criterion of 0.96/1.00/0.79 for the ProstateX dataset, and 0.95/1.00/0.80 for the IVO dataset. At a patient level, the results are 0.87/1.00/0.375 in ProstateX, and 0.91/1.00/0.762 in IVO. Furthermore, on the online ProstateX grand challenge, the model obtained an AUC of 0.85 (0.87 when trained only on the ProstateX data, tying up with the original winner of the challenge). For expert comparison, IVO radiologist's PI-RADS 4 sensitivity/specificity were 0.88/0.56 at a lesion level, and 0.85/0.58 at a patient level. The full code for the ProstateX-trained model is openly available at https://github.com/OscarPellicer/prostate_lesion_detection . We hope that this will represent a landmark for future research to use, compare and improve upon.

Combining cytotoxic agents with continuous dose levels in seamless phase I-II clinical trials.

Phase I-II cancer clinical trial designs are intended to accelerate drug development. In cases where efficacy cannot be ascertained in a short period of time, it is common to divide the study in two stages: i) a first stage in which dose is escalated based only on toxicity data and we look for the maximum tolerated dose (MTD) set and ii) a second stage in which we search for the most efficacious dose within the MTD set. Current available approaches in the area of continuous dose levels involve fixing the MTD after stage I and discarding all collected stage I efficacy data. However, this methodology is clearly inefficient when there is a unique patient population present across stages. In this article, we propose a two-stage design for the combination of two cytotoxic agents assuming a single patient population across the entire study. In stage I, conditional escalation with overdose control (EWOC) is used to allocate successive cohorts of patients. In stage II, we employ an adaptive randomization approach to allocate patients to drug combinations along the estimated MTD curve, which is constantly updated. The proposed methodology is assessed with extensive simulations in the context of a real case study.

Modeling synergism in early phase cancer trials with drug combination with continuous dose levels: is there an added value?

In parametric Bayesian designs of early phase cancer clinical trials with drug combinations exploring a discrete set of partially ordered doses, several authors claimed that there is no added value in including an interaction term to model synergism between the two drugs. In this paper, we investigate these claims in the setting of continuous dose levels of the two agents. Parametric models will be used to describe the relationship between the doses of the two agents and the probability of dose limiting toxicity and efficacy. Trial design proceeds by treating cohorts of two patients simultaneously receiving different dose combinations and response adaptive randomization. We compare trial safety and efficiency of the estimated maximum tolerated dose (MTD) curve between models that include an interaction term with models without the synergism parameter with extensive simulations. Under a selected class of dose-toxicity models and dose escalation algorithm, we found that not including an interaction term in the model can compromise the safety of the trial and reduce the pointwise reliability of the estimated MTD curve.

A modified weighted log-rank test for confirmatory trials with a high proportion of treatment switching.

In confirmatory cancer clinical trials, overall survival (OS) is normally a primary endpoint in the intention-to-treat (ITT) analysis under regulatory standards. After the tumor progresses, it is common that patients allocated to the control group switch to the experimental treatment, or another drug in the same class. Such treatment switching may dilute the relative efficacy of the new drug compared to the control group, leading to lower statistical power. It would be possible to decrease the estimation bias by shortening the follow-up period but this may lead to a loss of information and power. Instead we propose a modified weighted log-rank test (mWLR) that aims at balancing these factors by down-weighting events occurring when many patients have switched treatment. As the weighting should be pre-specified and the impact of treatment switching is unknown, we predict the hazard ratio function and use it to compute the weights of the mWLR. The method may incorporate information from previous trials regarding the potential hazard ratio function over time. We are motivated by the RECORD-1 trial of everolimus against placebo in patients with metastatic renal-cell carcinoma where almost 80% of the patients in the placebo group received everolimus after disease progression. Extensive simulations show that the new test gives considerably higher efficiency than the standard log-rank test in realistic scenarios.

Rebiopsy rate after transperineal or transrectal prostate biopsy.

BACKGROUND: In recent years, transperineal biopsies gained popularity for prostate cancer diagnosis; lower infective complications and improved sampling of the prostate are the main advantages of this technique. One question that remains unclear is whether an initial transperineal biopsy confers a lower risk for rebiopsy compared with the transrectal approach. METHODS: Six hundred seventy-one men were prospectively followed after an initial negative prostate biopsy for a median period of 49.50 (IQR: 37.62-61.17) months. Rebiopsy rate was analyzed attending to first biopsy approach (transrectal versus transperineal systematic) and clinical variables. RESULTS: Diagnostic rate was similar for transrectal and transperineal systematic biopsies. Targeted biopsies outperformed any systematic approach, and transperineal targeted in particular was superior to transrectal targeted. Rebiopsy rates were 15.4% and 5.26% for the transrectal and transperineal systematic groups, respectively. Prostate-specific antigen density and type of first biopsy were identified as rebiopsy predictors. CONCLUSION: Men undergoing transperineal systematic biopsies had a three times lower rate of rebiopsy over the study period compared with the traditional transrectal approach. This advantage could be added to the already described potential benefits of transperineal biopsies. Targeted biopsies had lower rebiopsy rate over the study period. Further innovations that decreased the cost of transperineal biopsies could favor this approach in the future.

A systematic review of nerve-sparing surgery for high-risk prostate cancer.

INTRODUCTION: We provide a systematic analysis of nerve-sparing surgery (NSS) to assess and summarize the risks and benefits of NSS in high-risk prostate cancer (PCa). EVIDENCE ACQUISITION: We have undertaken a systematic search of original articles using 3 databases: Medline/PubMed, Scopus, and Web of Science. Original articles in English containing outcomes of nerve-sparing radical prostatectomy (RP) for high-risk PCa were included. The primary outcomes were oncological results: the rate of positive surgical margins and biochemical relapse. The secondary outcomes were functional results: erectile function (EF) and urinary continence. EVIDENCE SYNTHESIS: The rate of positive surgical margins differed considerably, from zero to 47%. The majority of authors found no correlation between NSS and a positive surgical margin rate. The rate of biochemical relapse ranged from 9.3% to 61%. Most of the articles lacked data on odds ratio (OR) for positive margin and biochemical relapse. The presented results showed no effect of nerve sparing (NS) on positive margin (OR=0.81, 0.6-1.09) or biochemical relapse (hazard ratio [HR]=0.93, 0.52-1.64). A strong association between NSS and potency rate was observed. Without NSS, between 0% and 42% of patients were potent, with unilateral 79-80%, with bilateral - up to 90-100%. Urinary continence was not strongly associated with NSS and was relatively good in both patients with and without NSS. CONCLUSIONS: NSS may provide benefits for patients with urinary continence and significantly improves EF in high-risk patients. Moreover, it is not associated with an increased risk of relapse in short- and middle-term follow-up. However, the advantages of using such a surgical technique are unclear.

Always Lost but Never Forgotten: Gas-Phase Wall Losses Are Important in All Teflon Environmental Chambers.

Yields of secondary organic aerosol (SOA) formation from oxidation of volatile organic compounds are measured in laboratory chambers and then applied in regional and global models. Gas-phase losses to large Teflon-walled environmental chambers have been recently shown to reduce SOA yields. Historically, most chambers have operated in batch mode. Increasingly, however, continuous flow (CF) mode is being used, in which reactants and products are continuously introduced and exhausted from the chamber. Recent literature reports indicate a belief that SOA yields measured in CF chambers are not affected by gas-phase wall losses (GWL). Here, we use an experimentally-constrained box model to show that gas-phase wall losses impact both types of chambers when run under similar conditions. We find CF experiments do mitigate some effects of gas-phase wall losses after long (>2 days) experiment run times, but they have significant losses for typical literature experiment times of 1 day. However, this mitigation phenomenon is an experiment- and mechanism-dependent, and GWL still affects the absolute SOA yield. Finally, we show that at condensation sink values higher than the wall loss rate a lack of change in yield vs seed surface area does not necessarily indicate whether GWL affects the experiment and does not suggest the magnitude.

A Bayesian seamless phase I-II trial design with two stages for cancer clinical trials with drug combinations.

The use of drug combinations in clinical trials is increasingly common during the last years since a more favorable therapeutic response may be obtained by combining drugs. In phase I clinical trials, most of the existing methodology recommends a one unique dose combination as "optimal," which may result in a subsequent failed phase II clinical trial since other dose combinations may present higher treatment efficacy for the same level of toxicity. We are particularly interested in the setting where it is necessary to wait a few cycles of therapy to observe an efficacy outcome and the phase I and II population of patients are different with respect to treatment efficacy. Under these circumstances, it is common practice to implement two-stage designs where a set of maximum tolerated dose combinations is selected in a first stage, and then studied in a second stage for treatment efficacy. In this article we present a new two-stage design for early phase clinical trials with drug combinations. In the first stage, binary toxicity data is used to guide the dose escalation and set the maximum tolerated dose combinations. In the second stage, we take the set of maximum tolerated dose combinations recommended from the first stage, which remains fixed along the entire second stage, and through adaptive randomization, we allocate subsequent cohorts of patients in dose combinations that are likely to have high posterior median time to progression. The methodology is assessed with extensive simulations and exemplified with a real trial.

Cancer phase I trial design using drug combinations when a fraction of dose limiting toxicities is attributable to one or more agents.

Drug combination trials are increasingly common nowadays in clinical research. However, very few methods have been developed to consider toxicity attributions in the dose escalation process. We are motivated by a trial in which the clinician is able to identify certain toxicities that can be attributed to one of the agents. We present a Bayesian adaptive design in which toxicity attributions are modeled via copula regression and the maximum tolerated dose (MTD) curve is estimated as a function of model parameters. The dose escalation algorithm uses cohorts of two patients, following the continual reassessment method (CRM) scheme, where at each stage of the trial, we search for the dose of one agent given the current dose of the other agent. The performance of the design is studied by evaluating its operating characteristics when the underlying model is either correctly specified or misspecified. We show that this method can be extended to accommodate discrete dose combinations.

Properties of the weighted log-rank test in the design of confirmatory studies with delayed effects.

Proportional hazards are a common assumption when designing confirmatory clinical trials in oncology. This assumption not only affects the analysis part but also the sample size calculation. The presence of delayed effects causes a change in the hazard ratio while the trial is ongoing since at the beginning we do not observe any difference between treatment arms, and after some unknown time point, the differences between treatment arms will start to appear. Hence, the proportional hazards assumption no longer holds, and both sample size calculation and analysis methods to be used should be reconsidered. The weighted log-rank test allows a weighting for early, middle, and late differences through the Fleming and Harrington class of weights and is proven to be more efficient when the proportional hazards assumption does not hold. The Fleming and Harrington class of weights, along with the estimated delay, can be incorporated into the sample size calculation in order to maintain the desired power once the treatment arm differences start to appear. In this article, we explore the impact of delayed effects in group sequential and adaptive group sequential designs and make an empirical evaluation in terms of power and type-I error rate of the of the weighted log-rank test in a simulated scenario with fixed values of the Fleming and Harrington class of weights. We also give some practical recommendations regarding which methodology should be used in the presence of delayed effects depending on certain characteristics of the trial.

Direct Measurements of Gas/Particle Partitioning and Mass Accommodation Coefficients in Environmental Chambers.

Secondary organic aerosols (SOA) are a major contributor to fine particulate mass and wield substantial influences on the Earth's climate and human health. Despite extensive research in recent years, many of the fundamental processes of SOA formation and evolution remain poorly understood. Most atmospheric aerosol models use gas/particle equilibrium partitioning theory as a default treatment of gas-aerosol transfer, despite questions about potentially large kinetic effects. We have conducted fundamental SOA formation experiments in a Teflon environmental chamber using a novel method. A simple chemical system produces a very fast burst of low-volatility gas-phase products, which are competitively taken up by liquid organic seed particles and Teflon chamber walls. Clear changes in the species time evolution with differing amounts of seed allow us to quantify the particle uptake processes. We reproduce gas- and aerosol-phase observations using a kinetic box model, from which we quantify the aerosol mass accommodation coefficient (α) as 0.7 on average, with values near unity especially for low volatility species. α appears to decrease as volatility increases. α has historically been a very difficult parameter to measure with reported values varying over 3 orders of magnitude. We use the experimentally constrained model to evaluate the correction factor (Φ) needed for chamber SOA mass yields due to losses of vapors to walls as a function of species volatility and particle condensational sink. Φ ranges from 1-4.

Gas-Phase Carboxylic Acids in a University Classroom: Abundance, Variability, and Sources.

Gas-phase carboxylic acids are ubiquitous in ambient air, yet their indoor occurrence and abundance are poorly characterized. To fill this gap, we measured gas-phase carboxylic acids in real-time inside and outside of a university classroom using a high-resolution time-of-flight chemical ionization mass spectrometer (HRToF-CIMS) equipped with an acetate ion source. A wide variety of carboxylic acids were identified indoors and outdoors, including monoacids, diacids, hydroxy acids, carbonyl acids, and aromatic acids. An empirical parametrization was derived to estimate the sensitivity (ion counts per ppt of the analytes) of the HRToF-CIMS to the acids. The campaign-average concentration of carboxylic acids measured outdoors was 1.0 ppb, with the peak concentration occurring in daytime. The average indoor concentration of carboxylic acids was 6.8 ppb, of which 87% was contributed by formic and lactic acid. While carboxylic acids measured outdoors displayed a single daytime peak, those measured indoors displayed a daytime and a nighttime peak. Besides indoor sources such as off-gassing of building materials, evidence for acid production from indoor chemical reactions with ozone was found. In addition, some carboxylic acids measured indoors correlated to CO2 in daytime, suggesting that human occupants may contribute to their abundance either through direct emissions or surface reactions.

Gold nanoparticles stabilized by cationic carbosilane dendrons: synthesis and biological properties.

Gold nanoparticles (AuNPs) and polycationic macromolecules are used as gene carriers. Their behaviour is dependent on several factors, such as the size and type of the framework, charge, etc. We have combined both types of systems and prepared AuNPs covered with cationic carbosilane dendrons with the aim to evaluate their biocompatibility. Water soluble dendronized cationic AuNPs were prepared following a straightforward procedure from dendrons, a gold precursor and a reducing agent in water and were characterized by 1H NMR, transmission electron microscopy (TEM), dynamic light scattering (DLS), thermogravimetric analysis (TGA), ultraviolet spectroscopy (UV), and zeta potential (ZP). The biological properties of dendrons and AuNPs were determined by hemolysis, platelet aggregation and lymphocyte proliferation. These assays reflect modification of dendron properties when covering nanoparticles. For dendrons, hemolysis and platelet aggregation are generation dependent whilst, for AuNPs these properties are related to the bigger size of NPs. On the other hand, none of the systems induced lymphocyte proliferation. Selected cationic dendrons and AuNPs were chosen for gene delivery experiments employing a small interference RNA (siRNA Nef) against human immunodeficiency virus (HIV).

Ambient Gas-Particle Partitioning of Tracers for Biogenic Oxidation.

Exchange of atmospheric organic compounds between gas and particle phases is important in the production and chemistry of particle-phase mass but is poorly understood due to a lack of simultaneous measurements in both phases of individual compounds. Measurements of particle- and gas-phase organic compounds are reported here for the southeastern United States and central Amazonia. Polyols formed from isoprene oxidation contribute 8% and 15% on average to particle-phase organic mass at these sites but are also observed to have substantial gas-phase concentrations contrary to many models that treat these compounds as nonvolatile. The results of the present study show that the gas-particle partitioning of approximately 100 known and newly observed oxidation products is not well explained by environmental factors (e.g., temperature). Compounds having high vapor pressures have higher particle fractions than expected from absorptive equilibrium partitioning models. These observations support the conclusion that many commonly measured biogenic oxidation products may be bound in low-volatility mass (e.g., accretion products, inorganic-organic adducts) that decomposes to individual compounds on analysis. However, the nature and extent of any such bonding remains uncertain. Similar conclusions are reach for both study locations, and average particle fractions for a given compound are consistent within ∼25% across measurement sites.

Low positive rate of serum autoantibodies in colorectal cancer patients without systemic rheumatic diseases.

BACKGROUND: Antinuclear antibody (ANA) testing is useful for screening, diagnosis and follow-up of patients with systemic rheumatic diseases. Indirect immunofluorescence (IIF) on HEp-2 cells is the gold standard for ANA testing. However, ANA have also been detected in patients with different cancer types but without any autoimmune disease. To overcome these shortcomings, different automated solid-phase assays have been developed. AIM: To determine the positive rate of a new ANA detection method (EliA CTD Screen, Phadia, Germany), in CRC patients without systemic rheumatic diseases. Additionally, we compare this method with IIF. MATERIALS AND METHODS: Serum samples were obtained before a colonoscopy procedure in a patient cohort (n = 186) with a high clinical suspicion of CRC. Samples for ANA detection in CRC patients were processed in parallel by IIF on HEp-2 and the solid-phase fluoroenzymeimmunoassay EliA CTD Screen (Phadia, Germany) on the Phadia 250 instrument (Phadia GmbH, Freiburg, Germany). Positive samples by IIF and/or CTD were tested with EliA single ANA assays (Phadia, Germany) on the Phadia 250 instrument (Phadia GmbH, Freiburg, Germany). RESULTS: Forty-five patients diagnosed with CRC were included. Four cases were positive by CTD and 23 by IIF. Of the four positive patients by CTD, two were positive and one indeterminate for anti-dsDNA antibodies. Of the 23 positive by IIF, one patient was positive and another indeterminate for anti-dsDNA antibodies, and a third patient was positive for anti-U1RNP antibodies. CONCLUSIONS: The CTD assay shows a low false positive rate for detecting autoantibodies in a clinical context of CRC.