A Bayesian approach to Mendelian randomization using summary statistics in the univariable and multivariable settings with correlated pleiotropy.

Mendelian randomization uses genetic variants as instrumental variables to make causal inferences on the effect of an exposure on an outcome. Due to the recent abundance of high-powered genome-wide association studies, many putative causal exposures of interest have large numbers of independent genetic variants with which they associate, each representing a potential instrument for use in a Mendelian randomization analysis. Such polygenic analyses increase the power of the study design to detect causal effects; however, they also increase the potential for bias due to instrument invalidity. Recent attention has been given to dealing with bias caused by correlated pleiotropy, which results from violation of the "instrument strength independent of direct effect" assumption. Although methods have been proposed that can account for this bias, a number of restrictive conditions remain in many commonly used techniques. In this paper, we propose a Bayesian framework for Mendelian randomization that provides valid causal inference under very general settings. We propose the methods MR-Horse and MVMR-Horse, which can be performed without access to individual-level data, using only summary statistics of the type commonly published by genome-wide association studies, and can account for both correlated and uncorrelated pleiotropy. In simulation studies, we show that the approach retains type I error rates below nominal levels even in high-pleiotropy scenarios. We demonstrate the proposed approaches in applied examples in both univariable and multivariable settings, some with very weak instruments.

A Bayesian fine-mapping model using a continuous global-local shrinkage prior with applications in prostate cancer analysis.

The aim of fine mapping is to identify genetic variants causally contributing to complex traits or diseases. Existing fine-mapping methods employ Bayesian discrete mixture priors and depend on a pre-specified maximum number of causal variants, which may lead to sub-optimal solutions. In this work, we propose a Bayesian fine-mapping method called h2-D2, utilizing a continuous global-local shrinkage prior. We also present an approach to define credible sets of causal variants in continuous prior settings. Simulation studies demonstrate that h2-D2 outperforms current state-of-the-art fine-mapping methods such as SuSiE and FINEMAP in accurately identifying causal variants and estimating their effect sizes. We further applied h2-D2 to prostate cancer analysis and discovered some previously unknown causal variants. In addition, we inferred 369 target genes associated with the detected causal variants and several pathways that were significantly over-represented by these genes, shedding light on their potential roles in prostate cancer development and progression.

James-Stein for the leading eigenvector.

Recent research identifies and corrects bias, such as excess dispersion, in the leading sample eigenvector of a factor-based covariance matrix estimated from a high-dimension low sample size (HL) data set. We show that eigenvector bias can have a substantial impact on variance-minimizing optimization in the HL regime, while bias in estimated eigenvalues may have little effect. We describe a data-driven eigenvector shrinkage estimator in the HL regime called "James-Stein for eigenvectors" (JSE) and its close relationship with the James-Stein (JS) estimator for a collection of averages. We show, both theoretically and with numerical experiments, that, for certain variance-minimizing problems of practical importance, efforts to correct eigenvalues have little value in comparison to the JSE correction of the leading eigenvector. When certain extra information is present, JSE is a consistent estimator of the leading eigenvector.

Direct estimation and inference of higher-level correlations from lower-level measurements with applications in gene-pathway and proteomics studies.

This paper tackles the challenge of estimating correlations between higher-level biological variables (e.g. proteins and gene pathways) when only lower-level measurements are directly observed (e.g. peptides and individual genes). Existing methods typically aggregate lower-level data into higher-level variables and then estimate correlations based on the aggregated data. However, different data aggregation methods can yield varying correlation estimates as they target different higher-level quantities. Our solution is a latent factor model that directly estimates these higher-level correlations from lower-level data without the need for data aggregation. We further introduce a shrinkage estimator to ensure the positive definiteness and improve the accuracy of the estimated correlation matrix. Furthermore, we establish the asymptotic normality of our estimator, enabling efficient computation of P-values for the identification of significant correlations. The effectiveness of our approach is demonstrated through comprehensive simulations and the analysis of proteomics and gene expression datasets. We develop the R package highcor for implementing our method.

Identifying covariate-related subnetworks for whole-brain connectome analysis.

Whole-brain connectome data characterize the connections among distributed neural populations as a set of edges in a large network, and neuroscience research aims to systematically investigate associations between brain connectome and clinical or experimental conditions as covariates. A covariate is often related to a number of edges connecting multiple brain areas in an organized structure. However, in practice, neither the covariate-related edges nor the structure is known. Therefore, the understanding of underlying neural mechanisms relies on statistical methods that are capable of simultaneously identifying covariate-related connections and recognizing their network topological structures. The task can be challenging because of false-positive noise and almost infinite possibilities of edges combining into subnetworks. To address these challenges, we propose a new statistical approach to handle multivariate edge variables as outcomes and output covariate-related subnetworks. We first study the graph properties of covariate-related subnetworks from a graph and combinatorics perspective and accordingly bridge the inference for individual connectome edges and covariate-related subnetworks. Next, we develop efficient algorithms to exact covariate-related subnetworks from the whole-brain connectome data with an $\ell_0$ norm penalty. We validate the proposed methods based on an extensive simulation study, and we benchmark our performance against existing methods. Using our proposed method, we analyze two separate resting-state functional magnetic resonance imaging data sets for schizophrenia research and obtain highly replicable disease-related subnetworks.

Incorporating functional annotation with bilevel continuous shrinkage for polygenic risk prediction.

BACKGROUND: Genetic variants can contribute differently to trait heritability by their functional categories, and recent studies have shown that incorporating functional annotation can improve the predictive performance of polygenic risk scores (PRSs). In addition, when only a small proportion of variants are causal variants, PRS methods that employ a Bayesian framework with shrinkage can account for such sparsity. It is possible that the annotation group level effect is also sparse. However, the number of PRS methods that incorporate both annotation information and shrinkage on effect sizes is limited. We propose a PRS method, PRSbils, which utilizes the functional annotation information with a bilevel continuous shrinkage prior to accommodate the varying genetic architectures both on the variant-specific level and on the functional annotation level. RESULTS: We conducted simulation studies and investigated the predictive performance in settings with different genetic architectures. Results indicated that when there was a relatively large variability of group-wise heritability contribution, the gain in prediction performance from the proposed method was on average 8.0% higher AUC compared to the benchmark method PRS-CS. The proposed method also yielded higher predictive performance compared to PRS-CS in settings with different overlapping patterns of annotation groups and obtained on average 6.4% higher AUC. We applied PRSbils to binary and quantitative traits in three real world data sources (the UK Biobank, the Michigan Genomics Initiative (MGI), and the Korean Genome and Epidemiology Study (KoGES)), and two sources of annotations: ANNOVAR, and pathway information from the Kyoto Encyclopedia of Genes and Genomes (KEGG), and demonstrated that the proposed method holds the potential for improving predictive performance by incorporating functional annotations. CONCLUSIONS: By utilizing a bilevel shrinkage framework, PRSbils enables the incorporation of both overlapping and non-overlapping annotations into PRS construction to improve the performance of genetic risk prediction. The software is available at https://github.com/styvon/PRSbils .

MRI-based tumor shrinkage patterns after early neoadjuvant therapy in breast cancer: correlation with molecular subtypes and pathological response after therapy.

BACKGROUND: MRI-based tumor shrinkage patterns (TSP) after neoadjuvant therapy (NAT) have been associated with pathological response. However, the understanding of TSP after early NAT remains limited. We aimed to analyze the relationship between TSP after early NAT and pathological response after therapy in different molecular subtypes. METHODS: We prospectively enrolled participants with invasive ductal breast cancers who received NAT and performed pretreatment DCE-MRI from September 2020 to August 2022. Early-stage MRIs were performed after the first (1st-MRI) and/or second (2nd-MRI) cycle of NAT. Tumor shrinkage patterns were categorized into four groups: concentric shrinkage, diffuse decrease (DD), decrease of intensity only (DIO), and stable disease (SD). Logistic regression analysis was performed to identify independent variables associated with pathologic complete response (pCR), and stratified analysis according to tumor hormone receptor (HR)/human epidermal growth factor receptor 2 (HER2) disease subtype. RESULTS: 344 participants (mean age: 50 years, 113/345 [33%] pCR) with 345 tumors (1 bilateral) had evaluable 1st-MRI or 2nd-MRI to comprise the primary analysis cohort, of which 244 participants with 245 tumors had evaluable 1st-MRI (82/245 [33%] pCR) and 206 participants with 207 tumors had evaluable 2nd-MRI (69/207 [33%] pCR) to comprise the 1st- and 2nd-timepoint subgroup analysis cohorts, respectively. In the primary analysis, multivariate analysis showed that early DD pattern (OR = 12.08; 95% CI 3.34-43.75; p < 0.001) predicted pCR independently of the change in tumor size (OR = 1.37; 95% CI 0.94-2.01; p = 0.106) in HR+/HER2- subtype, and the change in tumor size was a strong pCR predictor in HER2+ (OR = 1.61; 95% CI 1.22-2.13; p = 0.001) and triple-negative breast cancer (TNBC, OR = 1.61; 95% CI 1.22-2.11; p = 0.001). Compared with the change in tumor size, the SD pattern achieved a higher negative predictive value in HER2+ and TNBC. The statistical significance of complete 1st-timepoint subgroup analysis was consistent with the primary analysis. CONCLUSION: The diffuse decrease pattern in HR+/HER2- subtype and stable disease in HER2+ and TNBC after early NAT could serve as additional straightforward and comprehensible indicators of treatment response. TRIAL REGISTRATION: Trial registration at https://www.chictr.org.cn/ . REGISTRATION NUMBER: ChiCTR2000038578, registered September 24, 2020.

Shrinkage-based Random Local Clocks with Scalable Inference.

Molecular clock models undergird modern methods of divergence-time estimation. Local clock models propose that the rate of molecular evolution is constant within phylogenetic subtrees. Current local clock inference procedures exhibit one or more weaknesses, namely they achieve limited scalability to trees with large numbers of taxa, impose model misspecification, or require a priori knowledge of the existence and location of clocks. To overcome these challenges, we present an autocorrelated, Bayesian model of heritable clock rate evolution that leverages heavy-tailed priors with mean zero to shrink increments of change between branch-specific clocks. We further develop an efficient Hamiltonian Monte Carlo sampler that exploits closed form gradient computations to scale our model to large trees. Inference under our shrinkage clock exhibits a speed-up compared to the popular random local clock when estimating branch-specific clock rates on a variety of simulated datasets. This speed-up increases with the size of the problem. We further show our shrinkage clock recovers known local clocks within a rodent and mammalian phylogeny. Finally, in a problem that once appeared computationally impractical, we investigate the heritable clock structure of various surface glycoproteins of influenza A virus in the absence of prior knowledge about clock placement. We implement our shrinkage clock and make it publicly available in the BEAST software package.

Detecting Nonadditive Biotic Interactions and Assessing Their Biological Relevance among Temperate Rainforest Trees.

AbstractInteractions between and within abiotic and biotic processes generate nonadditive density-dependent effects on species performance that can vary in strength or direction across environments. If ignored, nonadditivities can lead to inaccurate predictions of species responses to environmental and compositional changes. While there are increasing empirical efforts to test the constancy of pairwise biotic interactions along environmental and compositional gradients, few assess both simultaneously. Using a nationwide forest inventory that spans broad ambient temperature and moisture gradients throughout New Zealand, we address this gap by analyzing the diameter growth of six focal tree species as a function of neighbor densities and climate, as well as neighbor × climate and neighbor × neighbor statistical interactions. The most complex model featuring all interaction terms had the highest predictive accuracy. Compared with climate variables, biotic interactions typically had stronger effects on diameter growth, especially when subjected to nonadditivities from local climatic conditions and the density of intermediary species. Furthermore, statistically strong (or weak) nonadditivities could be biologically irrelevant (or significant) depending on whether a species pair typically interacted under average or more extreme conditions. Our study highlights the importance of considering both the statistical potential and the biological relevance of nonadditive biotic interactions when assessing species performance under global change.

Signal Amplification by Spatial Concentration for Immunoassay on Cellulose Media.

Immunoassay is one of the most common bioanalytical techniques from lab-based to point-of-care settings. Over time, various approaches have been developed to amplify signals for greater sensitivity. However, the need for effective, versatile, and simple signal amplification methods persists yet. This paper presents a novel signal amplification method for immunoassay that utilizes spatial concentration of a cellulose-based plate possessing sensor transducers, specifically gold nanoparticles. By modifying the dimensions of the plate, the density of nanoparticles increased, resulting in intensified color signals. The coating material, polydopamine, which is utilized to protect the gold nanoparticles. Chemical changes in nanocomposites are characterized using scanning electron microscopy, Raman spectroscopy, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, and scanning electron microscopy. The application of this method to colorimetric quantification demonstrated great consistency across various concentrations of nanoparticles, with better reliability at lower concentration ranges. A model immunoassay is designed to evaluate the analytical performance. As a result, this method successfully corrected a false-negative result with a lowered Kd of 0.509 pmol per zone. This method shows strong signal enhancement capability that can correct false-negative signals in the immunoassays, with potential benefits including versatility, simplicity, low cost, and the ability to operate multiple plates simultaneously.

The GR2D2 estimator for the precision matrices.

Biological networks are important for the analysis of human diseases, which summarize the regulatory interactions and other relationships between different molecules. Understanding and constructing networks for molecules, such as DNA, RNA and proteins, can help elucidate the mechanisms of complex biological systems. The Gaussian Graphical Models (GGMs) are popular tools for the estimation of biological networks. Nonetheless, reconstructing GGMs from high-dimensional datasets is still challenging. The current methods cannot handle the sparsity and high-dimensionality issues arising from datasets very well. Here, we developed a new GGM, called the GR2D2 (Graphical $R^2$-induced Dirichlet Decomposition) model, based on the R2D2 priors for linear models. Besides, we provided a data-augmented block Gibbs sampler algorithm. The R code is available at https://github.com/RavenGan/GR2D2. The GR2D2 estimator shows superior performance in estimating the precision matrices compared with the existing techniques in various simulation settings. When the true precision matrix is sparse and of high dimension, the GR2D2 provides the estimates with smallest information divergence from the underlying truth. We also compare the GR2D2 estimator with the graphical horseshoe estimator in five cancer RNA-seq gene expression datasets grouped by three cancer types. Our results show that GR2D2 successfully identifies common cancer pathways and cancer-specific pathways for each dataset.

Radiomic analysis reveals diverse prognostic and molecular insights into the response of breast cancer to neoadjuvant chemotherapy: a multicohort study.

BACKGROUND: Breast cancer patients exhibit various response patterns to neoadjuvant chemotherapy (NAC). However, it is uncertain whether diverse tumor response patterns to NAC in breast cancer patients can predict survival outcomes. We aimed to develop and validate radiomic signatures indicative of tumor shrinkage and therapeutic response for improved survival analysis. METHODS: This retrospective, multicohort study included three datasets. The development dataset, consisting of preoperative and early NAC DCE-MRI data from 255 patients, was used to create an imaging signature-based multitask model for predicting tumor shrinkage patterns and pathological complete response (pCR). Patients were categorized as pCR, nonpCR with concentric shrinkage (CS), or nonpCR with non-CS, with prediction performance measured by the area under the curve (AUC). The prognostic validation dataset (n = 174) was used to assess the prognostic value of the imaging signatures for overall survival (OS) and recurrence-free survival (RFS) using a multivariate Cox model. The gene expression data (genomic validation dataset, n = 112) were analyzed to determine the biological basis of the response patterns. RESULTS: The multitask learning model, utilizing 17 radiomic signatures, achieved AUCs of 0.886 for predicting tumor shrinkage and 0.760 for predicting pCR. Patients who achieved pCR had the best survival outcomes, while nonpCR patients with a CS pattern had better survival than non-CS patients did, with significant differences in OS and RFS (p = 0.00012 and p = 0.00063, respectively). Gene expression analysis highlighted the involvement of the IL-17 and estrogen signaling pathways in response variability. CONCLUSIONS: Radiomic signatures effectively predict NAC response patterns in breast cancer patients and are associated with specific survival outcomes. The CS pattern in nonpCR patients indicates better survival.

A nomogram and heat map based on LASSO-Cox regression for predicting the risk of early-stage severe fever with thrombocytopenia syndrome patients developing into critical illness at 7-day and 14-day.

Severe fever with thrombocytopenia syndrome (SFTS) represents an emerging infectious disease characterized by a substantial mortality risk. Early identification of patients is crucial for effective risk assessment and timely interventions. In the present study, least absolute shrinkage and selection operator (LASSO)-Cox regression analysis was conducted to identify key risk factors associated with progression to critical illness at 7-day and 14-day. A nomogram was constructed and subsequently assessed for its predictive accuracy through evaluation and validation processes. The risk stratification of patients was performed using X-tile software. The performance of this risk stratification system was assessed using the Kaplan-Meier method. Additionally, a heat map was generated to visualize the results of these analyses. A total of 262 SFTS patients were included in this study, and four predictive factors were included in the nomogram, namely viral copies, aspartate aminotransferase (AST) level, C-reactive protein (CRP), and neurological symptoms. The AUCs for 7-day and 14-day were 0.802 [95% confidence interval (CI): 0.707-0.897] and 0.859 (95% CI: 0.794-0.925), respectively. The nomogram demonstrated good discrimination among low, moderate, and high-risk groups. The heat map effectively illustrated the relationships between risk groups and predictive factors, providing valuable insights with high predictive and practical significance.

Gross Hematuria Does not Affect the Selection of Nephrectomy Types for Clinical Stage 1 Clear Cell Renal Cell Carcinoma: A Multicenter, Retrospective Cohort Study.

PURPOSE: This study aimed to discuss the correlation between gross hematuria and postoperative upstaging (from T1 to T3a) in patients with cT1 clear cell renal cell carcinoma (ccRCC) and to compare oncologic outcomes of partial nephrectomy (PN) and radical nephrectomy (RN) in patients with gross hematuria. METHODS: A total of 2145 patients who met the criteria were enrolled in the study (including 363 patients with gross hematuria). The least absolute selection and shrinkage operator logistic regression was used to evaluate the risk factor of postoperative pathological upstaging. The propensity score matching (PSM) and stable inverse probability of treatment weighting (IPTW) analysis were used to balance the confounding factors. The Kaplan-Meier analysis and multivariate Cox proportional risk regression model were used to assess the prognosis. RESULTS: Gross hematuria was a risk factor of postoperative pathological upstaging (odds ratio [OR] = 3.96; 95% confidence interval [CI] 2.44-6.42; P < 0.001). After PSM and stable IPTW adjustment, the characteristics were similar in corresponding patients in the PN and RN groups. In the PSM cohort, PN did not have a statistically significant impact on recurrence-free survival (hazard ratio [HR] = 1.48; 95% CI 0.25-8.88; P = 0.67), metastasis-free survival (HR = 1.24; 95% CI 0.33-4.66; P = 0.75), and overall survival (HR = 1.46; 95% CI 0.31-6.73; P = 0.63) compared with RN. The results were confirmed in sensitivity analyses. CONCLUSIONS: Although gross hematuria was associated with postoperative pathological upstaging in patients with cT1 ccRCC, PN should still be the preferred treatment for such patients.

Stem Growth and Dehydration Responses of Mediterranean Tree Species to Atmospheric and Soil Drought.

Stem growth responses to soil and atmospheric drought are critical to forecasting the tree carbon sink strength. Yet, responses of drought-prone forests remain uncertain despite global aridification trends. Stem diameter variations at an hourly resolution were monitored in five Mediterranean tree species from a mesic and a xeric site for 6 and 12 years. Stem growth and dehydration responses to soil (REW) and atmospheric (VPD) drought were explored at different timescales. Annually, growth was determined by the number of growing days and hours. Seasonally, growth was bimodal (autumn growth ≈ 8%-18% of annual growth), varying among species and sites across the hydrometeorological space, while dehydration consistently responded to REW. Sub-daily, substantial growth occurred during daytime, with nighttime-to-daytime ratios ranging between 1.2 and 3.5 (Arbutus unedo ≈ Quercus faginea < Quercus ilex < Pinus halepensis in the mesic site, and Juniperus thurifera < P. halepensis in the xeric site). Overall, time windows favourable for growth were limited by soil (rather than atmospheric) drought, modulating annual and seasonal growth in Mediterranean species, and stems maintained non-negligible growth during daytime. These patterns contrast with observations from wetter or cooler biomes, demonstrating the growth plasticity of drought-prone species to more arid climate conditions.

Depth of response and treatment outcomes of immune checkpoint inhibitor-based therapy in patients with advanced non-small cell lung cancer and high PD-L1 expression: An exploratory analysis of retrospective multicenter cohort.

The association between depth of response (DpR) and treatment outcomes has been documented across various types of cancer. Immune checkpoint inhibitor (ICI)-based treatment is globally used as first-line treatment for non-small cell lung cancer (NSCLC) with programmed death-ligand 1 (PD-L1) expression ≥ 50%. However, in this population, the significance of DpR is not elucidated. Patients with advanced NSCLC and PD-L1 expression ≥ 50% who received ICI-monotherapy or ICI plus chemotherapy were retrospectively enrolled into this study. Treatment responses were grouped into DpR 'quartiles' by percentage of maximal tumor reduction (Q1 = 1-25%, Q2 = 26-50%, Q3 = 51-75%, and Q4 = ≥ 76%), and no tumor reduction (NTR). The association between DpR and survival rates were determined using hazard ratios (HR) generated by the Cox proportional hazards model. The Kaplan-Meier method was used to determine survival outcomes. A total of 349 patients were included, of which 214 and 135 patients received pembrolizumab monotherapy and ICI plus chemotherapy, respectively, as first-line treatments. The majority of the patients were male. All DpR quartiles, especially Q4, showed an association with progression-free survival (PFS)/overall survival (OS). In the Q4 cohort, patients who received pembrolizumab had a longer PFS than those who received ICI plus chemotherapy. High DpR was associated with longer PFS and OS, with a more pronounced effect observed with pembrolizumab monotherapy than with ICI plus chemotherapy.

Type II endoleaks after fenestrated/branched endografting for juxtarenal and pararenal aortic aneurysms.

OBJECTIVE: Persistent type II endoleaks (pEL2s) are not uncommon after endovascular aneurysm repair and their impact on long-term outcomes is well-documented. However, their occurrence and natural history after fenestrated/branched endografting (F/B-EVAR) for juxtarenal and pararenal aneurysms (J/P-AAAs) have been scarcely investigated. Aim of this study was to report incidence, risk factors, and natural history of pEL2 after F/B-EVAR in J/P-AAAs. METHODS: Between 2016 and 2022, all J/P-AAAs undergoing F/B-EVAR were prospectively collected and retrospectively analyzed. EL2 were assessed at the completion angiography, at 30 days and after 6 months as primary outcomes. Preoperative risk factors for pEL2, follow-up survival, freedom from reinterventions (FFR) and aneurysm shrinkage (≥5 mm) were considered as secondary outcomes. RESULTS: Of 132 patients, there were 88 (67%) JAAAs and 44 (33%) PAAAs. Seventeen EL2 (13%) were detected at the completion angiography and 36 (27%) at 30-day computed tomography angiography. The mean follow-up was 28 ± 23 months. Eleven (31%) EL2 sealed spontaneously within 6 months and three new cases were detected, for an overall of 28 pEL2/107 patients (26%) with available radiological follow-up of ≥6 months. Preoperative antiplatelet therapy (odds ratio, 4.7; 95% confidence interval [CI[, 1-22.1; P = .05), aneurysm thrombus volume of ≤40% and six or more patent aneurysm afferent vessels (odds ratio, 7.2; 95% CI, 1.8-29.1; P = .005) were independent risk factors for pEL2. The estimated 3-year survival was 80%, with no difference between cases with and without pEL2 (78% vs 85%; P = .08). The estimated 3-year FFR was 86%, with no difference between cases with and without pEL2 (81% vs 87%; P = .41). Four cases (3%) of EL2-related reinterventions were performed. In 65 cases (49%), aneurysm shrinkage was detected. pEL2 was an independent risk factor for absence of aneurysm shrinkage during follow-up (hazard ratio, 3.2; 95% CI, 1.2-8.3; P = .014). Patients without shrinkage had lower follow-up survival (64% vs 86% at 3-year; P = .009) and FFR (74% vs 90% at 3 years; P = .014) than patients with shrinkage. CONCLUSIONS: PEL2 is not infrequent (26%) after F/B-EVAR for J/P-AAAs and is correlated with preoperative antiplatelet therapy, aneurysm thrombus volume of ≤40%, and six or more patent sac afferent vessels. Patients with pEL2 have a diminished aneurysm shrinkage, which is correlated with lower follow-up survival and FFR compared with patients with aneurysm shrinkage.

Utility of Assessing Early Tumor Shrinkage as an Efficacy Predictor in Patients with Non-Surgically Indicated or Recurrent Esophageal Cancer Treated with Nivolumab plus Ipilimumab.

INTRODUCTION: Nivolumab plus ipilimumab combination therapy has been administered as a first-line treatment in Japan since 2022 for patients with unresectable progressive or recurrent esophageal cancer. The efficacy and safety of this immune checkpoint inhibitor (ICI) doublet therapy are now being evaluated, and it is necessary to identify populations that benefit from this treatment at an early phase after initiation. For patients not showing early benefit, changing as soon as possible to other therapeutic strategies could improve their survival outcomes. Therefore, we attempted to identify decision-making factors such as early tumor shrinkage (ETS) based on treatment experience with ICI doublet therapy. METHODS: The study included 19 patients who received nivolumab plus ipilimumab for non-surgically indicated or recurrent esophageal cancer between July 2022 and November 2023. Tumors were assessed approximately every 2 months after treatment initiation. The effects of ETS, depth of response (DpR), and clinicopathologic features, including immune-related adverse events (irAEs), on progression-free and overall survival were evaluated using Kaplan-Meier plots and Cox proportional hazard models. RESULTS: The mean duration of ICI doublet administration was 5.89 months (range, 1-16 months). At first evaluation, patients who exhibited no tumor progression >20% indicated possible response to ICI doublet therapy, and patients whose tumors shrank even minimally exhibited favorable progression-free survival. Higher DpR at any cut-off line exhibited better progression-free survival than those with lower DpR. Fifteen patients experienced irAEs, with 13 of these patients experiencing irAEs within 3 months of treatment initiation. irAEs were associated with the efficacy of ICI doublet therapy, but efficacy could not be predicted based on early irAE experience. CONCLUSION: ETS-high, DpR-high, and irAEs might be associated with favorable responses to nivolumab plus ipilimumab. As a predictor of efficacy at an early phase, ETS >0% could be a deciding factor for continuing ICI doublet therapy.

Early tumor shrinkage as a prognostic predictor in chemotherapy-naïve patients with locally advanced pancreatic cancer treated with modified FOLFIRINOX or gemcitabine plus nab-paclitaxel combination therapy: An exploratory analysis of JCOG1407.

BACKGROUND: Early tumor shrinkage (ETS) is a prognostic predictor for patients treated with chemotherapy in colorectal cancer, although scarce studies evaluated its potential in locally advanced pancreatic cancer (LAPC). In this exploratory analysis of JCOG1407, a randomized phase II study comparing modified 5-fluorouracil, levofolinate, irinotecan, and oxaliplatin (mFOLFIRINOX) and gemcitabine plus nab-paclitaxel (GnP), we evaluated whether ETS can predict prognosis of patients with LAPC. METHODS: Of the 126 patients enrolled in JCOG1407, 112 with measurable lesions were included in this study. ETS was defined as a ≥20 % reduction in tumor diameter compared with baseline at the initial imaging assessment 6-10 weeks after initiating chemotherapy. Patients were divided into the ETS (achieved ETS) and non-ETS (failed to achieve ETS) groups based on their ETS status. The impact of ETS on overall survival (OS) was compared using multivariable Cox regression analysis. RESULTS: Fourteen of 55 (25.5 %) and 24 of 57 (42.1 %) patients in the mFOLFIRINOX and GnP arms, respectively, achieved ETS. In the overall population, mFOLFIRINOX arm, and GnP arm, the median OS in the ETS and non-ETS groups was 27.1 and 20.4, 29.8 and 20.6, and 24.1 and 20.4, months, respectively. The adjusted hazard ratios of OS for the ETS group in the overall population, mFOLFIRINOX arm, and GnP arm were 0.451 (95 % confidence interval [CI]: 0.270-0.754), 0.371 (95 % CI: 0.149-0.926), and 0.508 (95 % CI: 0.255-1.004), respectively. CONCLUSIONS: ETS may be a prognostic predictor in chemotherapy-naïve patients with LAPC treated with mFOLFIRINOX or GnP.

Artificial shrinkage before fresh blastocyst transfer and IVF outcomes: a pilot randomized controlled study.

RESEARCH QUESTION: Does artificial shrinkage before fresh blastocyst transfer improve clinical pregnancy rates in IVF? DESIGN: In this monocentric prospective, randomized, double-blind, controlled pilot study, 150 couples undergoing fresh single-blastocyst transfer were randomized between 20 May 2018 and 22 February 2022. In the artificial shrinkage group (AS group), a single laser pulse was directed to the cellular junction of the trophectoderm on the opposite side of the inner cell mass in each blastocyst. IVF outcomes were clinical pregnancy, multiple pregnancy and live birth rates. Cell-free DNA (cfDNA) concentration was also measured by quantitative real-time PCR in the blastocyst culture medium. RESULTS: In total, 142 couples underwent fresh single-blastocyst transfer: control group, no artificial shrinkage, n = 47; and AS group, artificial shrinkage, n = 95; An intention-to-treat (ITT) analysis was employed. After a reassessment and the exclusion of patients with major protocol deviations, 139 couples underwent fresh single-blastocyst transfer under optimal conditions: control group, n = 47; and AS group, n = 92; a per-protocol analysis was used here. The clinical and laboratory characteristics were not significantly different between the groups. The clinical pregnancy rate was similar in the control and AS groups (ITT: 48.9% versus 49.5%, P = 0.97; per protocol: 48.94% versus 51.1%, P = 0.89). The multiple pregnancy rate and the live birth rate were also similar between the groups. No significant differences in gestational age, birthweight or proportion of male/female newborns were observed. The concentration of cfDNA in the blastocyst culture medium was not associated with IVF outcome. CONCLUSIONS: Large-scale randomized controlled trials are required to confirm these preliminary results.