Identification of CT radiomic features robust to acquisition and segmentation variations for improved prediction of radiotherapy-treated lung cancer patient recurrence.

The primary objective of the present study was to identify a subset of radiomic features extracted from primary tumor imaged by computed tomography of early-stage non-small cell lung cancer patients, which remain unaffected by variations in segmentation quality and in computed tomography image acquisition protocol. The robustness of these features to segmentation variations was assessed by analyzing the correlation of feature values extracted from lesion volumes delineated by two annotators. The robustness to variations in acquisition protocol was evaluated by examining the correlation of features extracted from high-dose and low-dose computed tomography scans, both of which were acquired for each patient as part of the stereotactic body radiotherapy planning process. Among 106 radiomic features considered, 21 were identified as robust. An analysis including univariate and multivariate assessments was subsequently conducted to estimate the predictive performance of these robust features on the outcome of early-stage non-small cell lung cancer patients treated with stereotactic body radiation therapy. The univariate predictive analysis revealed that robust features demonstrated superior predictive potential compared to non-robust features. The multivariate analysis indicated that linear regression models built with robust features displayed greater generalization capabilities by outperforming other models in predicting the outcomes of an external validation dataset.

Faster clinical decisions in B-cell acute lymphoblastic leukaemia: A single flow cytometric 12-colour tube improves diagnosis and minimal residual disease follow-up.

Assessing minimal residual disease (MRD) in B-cell precursor acute lymphoblastic leukaemia (BCP-ALL) is essential for adjusting therapeutic strategies and predicting relapse. Quantitative polymerase chain reaction (qPCR) is the gold standard for MRD. Alternatively, flow cytometry is a quicker and cost-effective method that typically uses leukaemia-associated immunophenotype (LAIP) or different-from-normal (DFN) approaches for MRD assessment. This study describes an optimized 12-colour flow cytometry antibody panel designed for BCP-ALL diagnosis and MRD monitoring in a single tube. This method robustly differentiated hematogones and BCP-ALL cells using two specific markers: CD43 and CD81. These and other markers (e.g. CD73, CD66c and CD49f) enhanced the specificity of BCP-ALL cell detection. This innovative approach, based on a dual DFN/LAIP strategy with a principal component analysis method, can be used for all patients and enables MRD analysis even in the absence of a diagnostic sample. The robustness of our method for MRD monitoring was confirmed by the strong correlation (r = 0.87) with the qPCR results. Moreover, it simplifies and accelerates the preanalytical process through the use of a stain/lysis/wash method within a single tube (<2 h). Our flow cytometry-based methodology improves the BCP-ALL diagnosis efficiency and MRD management, offering a complementary method with considerable benefits for clinical laboratories.

Predictive clinical and dosimetric parameters for risk of relapse in early-stage non-small cell lung cancer treated by SBRT: A large single institution experience.

Purpose: To evaluate the impact of dosimetric parameters on efficacy of stereotactic body radiation therapy (SBRT) in early-stage non-small cell lung cancer (ES-NSCLC), using Hypofractionated Treatment Effects in the Clinic (HyTEC) reporting standards. Methods: From April 2010 to December 2020, 497 patients who received SBRT for ES-NSCLC at the University Hospital of Liège were retrospectively enrolled. A total dose of 40 to 60 Gy in 3-5 fractions (72-180 Gy biologically effective dose with an α/ß ratio of 10 (BED10)) was prescribed to the 80 % isodose line of the PTV. Potential clinical and dosimetric predictors of recurrence, overall survival (OS) and disease specific survival (DSS) were evaluated using univariate and multivariate analyses. Results: After a median follow-up of 32 months (range 3-143 months), the local control and disease-free survival (DFS) rates at 3 years were 91 % (95 % CI: 90 %-93 %) and 75 % (95 % CI: 73 %-77 %), respectively. The median OS was 41.6 months and the median DSS was not reached. On multivariate analysis, a higher gross tumor volume (GTV) Dmax (BED10) (cut-off 198 Gy) and a larger percent of the GTV receiving ≥110 % of the prescribed dose were predictive of a better local control, only GTV volume was correlated with DSS and no parameter was correlated with OS and regional or distant recurrences. Conclusion: Lung SBRT for ES-NSCLC in 3 to 5 fractions resulted in high local control rates. A higher percent of GTV receiving ≥110 % of the prescribed dose and a higher GTV Dmax (BED10) seem to allow a better local control.

Multicentric development and evaluation of [18F]FDG PET/CT and CT radiomic models to predict regional and/or distant recurrence in early-stage non-small cell lung cancer treated by stereotactic body radiation therapy.

PURPOSE: To develop machine learning models to predict regional and/or distant recurrence in patients with early-stage non-small cell lung cancer (ES-NSCLC) after stereotactic body radiation therapy (SBRT) using [18F]FDG PET/CT and CT radiomics combined with clinical and dosimetric parameters. METHODS: We retrospectively collected 464 patients (60% for training and 40% for testing) from University Hospital of Liège and 63 patients from University Hospital of Brest (external testing set) with ES-NSCLC treated with SBRT between 2010 and 2020 and who had undergone pretreatment [18F]FDG PET/CT and planning CT. Radiomic features were extracted using the PyRadiomics toolbox®. The ComBat harmonization method was applied to reduce the batch effect between centers. Clinical, radiomic, and combined models were trained and tested using a neural network approach to predict regional and/or distant recurrence. RESULTS: In the training (n = 273) and testing sets (n = 191 and n = 63), the clinical model achieved moderate performances to predict regional and/or distant recurrence with C-statistics from 0.53 to 0.59 (95% CI, 0.41, 0.67). The radiomic (original_firstorder_Entropy, original_gldm_LowGrayLevelEmphasis and original_glcm_DifferenceAverage) model achieved higher predictive ability in the training set and kept the same performance in the testing sets, with C-statistics from 0.70 to 0.78 (95% CI, 0.63, 0.88) while the combined model performs moderately well with C-statistics from 0.50 to 0.62 (95% CI, 0.37, 0.69). CONCLUSION: Radiomic features extracted from pre-SBRT analog and digital [18F]FDG PET/CT outperform clinical parameters in the prediction of regional and/or distant recurrence and to discuss an adjuvant systemic treatment in ES-NSCLC. Prospective validation of our models should now be carried out.

Novel bioinformatic methods and machine learning approaches reveal candidate biomarkers of the intensity and timing of past exposure to Plasmodium falciparum.

Accurately quantifying the burden of malaria over time is an important goal of malaria surveillance efforts and can enable effective targeting and evaluation of interventions. Malaria surveillance methods capture active or recent infections which poses several challenges to achieving malaria surveillance goals. In high transmission settings, asymptomatic infections are common and therefore accurate measurement of malaria burden demands active surveillance; in low transmission regions where infections are rare accurate surveillance requires sampling large subsets of the population; and in any context monitoring malaria burden over time necessitates serial sampling. Antibody responses to Plasmodium falciparum parasites persist after infection and therefore measuring antibodies has the potential to overcome several of the current obstacles to accurate malaria surveillance. Identifying which antibody responses are markers of the timing and intensity of past exposure to P. falciparum remains challenging, particularly among adults who tend to be re-exposed multiple times over the course of their lifetime and therefore have similarly high antibody responses to many Plasmodium antigens. A previous analysis of 479 serum samples from individuals in three regions in southern Africa with different historical levels of P. falciparum malaria transmission (high, intermediate, and low) revealed regional differences in antibody responses to P. falciparum antigens among children under 5 years of age. Using a novel bioinformatic pipeline optimized for protein microarrays that minimizes between-sample technical variation, we used antibody responses to Plasmodium antigens as predictors in random forest models to classify samples from adults into these three regions of differing historical malaria transmission with high accuracy (AUC = 0.99). Many of the most important antigens for classification in these models do not overlap with previously published results and are therefore novel candidate markers for the timing and intensity of past exposure to P. falciparum. Measuring antibody responses to these antigens could lead to improved malaria surveillance.

Radiomics and Delta-Radiomics Signatures to Predict Response and Survival in Patients with Non-Small-Cell Lung Cancer Treated with Immune Checkpoint Inhibitors.

The aim of our study was to determine the potential role of CT-based radiomics in predicting treatment response and survival in patients with advanced NSCLC treated with immune checkpoint inhibitors. We retrospectively included 188 patients with NSCLC treated with PD-1/PD-L1 inhibitors from two independent centers. Radiomics analysis was performed on pre-treatment contrast-enhanced CT. A delta-radiomics analysis was also conducted on a subset of 160 patients who underwent a follow-up contrast-enhanced CT after 2 to 4 treatment cycles. Linear and random forest (RF) models were tested to predict response at 6 months and overall survival. Models based on clinical parameters only and combined clinical and radiomics models were also tested and compared to the radiomics and delta-radiomics models. The RF delta-radiomics model showed the best performance for response prediction with an AUC of 0.8 (95% CI: 0.65-0.95) on the external test dataset. The Cox regression delta-radiomics model was the most accurate at predicting survival with a concordance index of 0.68 (95% CI: 0.56-0.80) (p = 0.02). The baseline CT radiomics signatures did not show any significant results for treatment response prediction or survival. In conclusion, our results demonstrated the ability of a CT-based delta-radiomics signature to identify early on patients with NSCLC who were more likely to benefit from immunotherapy.

A Bayesian hierarchical model for signal extraction from protein microarrays.

Protein microarrays are a promising technology that measure protein levels in serum or plasma samples. Due to their high technical variability and high variation in protein levels across serum samples in any population, directly answering biological questions of interest using protein microarray measurements is challenging. Analyzing preprocessed data and within-sample ranks of protein levels can mitigate the impact of between-sample variation. As for any analysis, ranks are sensitive to preprocessing, but loss function based ranks that accommodate major structural relations and components of uncertainty are very effective. Bayesian modeling with full posterior distributions for quantities of interest produce the most effective ranks. Such Bayesian models have been developed for other assays, for example, DNA microarrays, but modeling assumptions for these assays are not appropriate for protein microarrays. Consequently, we develop and evaluate a Bayesian model to extract the full posterior distribution of normalized protein levels and associated ranks for protein microarrays, and show that it fits well to data from two studies that use protein microarrays produced by different manufacturing processes. We validate the model via simulation and demonstrate the downstream impact of using estimates from this model to obtain optimal ranks.

Waldenstrom's macroglobulinemia highlighted by turbidity interference: a case report / Découverte d'une maladie de Waldenström via une interférence de turbidité : à propos d'un cas

A 65-year-old female was admitted to the Emergency Department for a fall. Upon admission, a blood sample was drawn for routine laboratory tests. Blood glucose measurement using the hexokinase method was made impossible due to a high lipemia index measured by the instrument despite a clear plasma specimen. It is well known that hemolysis, icterus, and lipemia interferences disrupt spectrophotometric quantifications. However, in this case, the absence of lipemia was confirmed by plasma triglycerides measurement in the normal reference range. Protein electrophoresis along with an immunofixation were carried out to characterize a monoclonal gammopathy since the presence of a monoclonal gammopathy in very high concentrations generates turbidity and interferes with spectrophotometric assays. A gamma peak evoking a monoclonal gammopathy was found on the serum electrophoresis and the immunofixation was positive for a monoclonal IgM kappa. Myelogram showed plasmocytes, lymphoplasmacytic cells and mast cells infiltration which led to the diagnosis of Waldenstrom macroglobulinemia. Based on this case, we propose a course of action to be taken in the case of a high lipemia index and clear plasma.

Une femme de 65 ans est admise aux urgences pour une chute. À l'admission, un échantillon de sang a été prélevé pour réaliser un bilan biologique de routine. Sur ce bilan, la mesure spectrophotométrique de la glycémie par la méthode de l'hexokinase était impossible en raison d'un indice de lactescence trop élevé et ce malgré un plasma d'aspect clair. Il est bien connu que l'hémolyse, l'ictère et la lactescence interfèrent dans le dosage spectrophotométrique. Cependant, dans ce cas, il n'y a pas de perturbation du bilan lipidique chez la patiente. Une prestation de conseil a donc été réalisée par le service de biochimie afin de recommander la réalisation d'une électrophorèse des protéines sériques car la présence d'une gammapathie monoclonale à des concentrations très élevées génère de la turbidité et interfère avec les dosages spectrophotométriques. Un pic dans la fraction des gammaglobulines évoquant une gammapathie monoclonale a été retrouvé sur l'électrophorèse sérique, l'immunofixation confirme la présence d'une IgM kappa monoclonale. Le myélogramme montre une infiltration de plasmocytes, de lymphoplasmocytes et de mastocytes ce qui a conduit au diagnostic de maladie de Waldenström. Sur la base de ce cas, nous proposons une conduite à tenir en cas d'indice de lactescence élevé avec un plasma d'aspect clair.

Periostin in lymph node pre-metastatic niches governs lymphatic endothelial cell functions and metastatic colonization.

Although lymph node (LN) metastasis is an important prognostic parameter in cervical cancer, the tissue remodeling at a pre-metastatic state is poorly documented in LNs. We here identified periostin (POSTN) as a component of non-metastatic LNs by applying proteomic analyses and computerized image quantifications on LNs of patients with cervical cancer. We provide evidence for remarkable modifications of POSTN and lymphatic vessel distributions and densities in non-metastatic sentinel and metastatic human LNs, when compared to distant non-metastatic LNs. POSTN deposition at a pre-metastatic stage was demonstrated in a pre-clinical murine model (the ear sponge assay). Its expression by fibroblastic LN cells was assessed by in situ hybridization and in vitro cultures. In vitro, POSTN promoted lymphatic endothelial cell functions and tumor cell proliferation. Accordingly, the in vivo injection of recombinant POSTN together with VEGF-C boosted the lymphangiogenic response, while the metastatic potential of tumor cells was drastically reduced using a POSTN blocking antibody. This translational study also supports the existence of an unprecedented dialog "in cascade", between the primary tumor and the first pelvic nodal relay in early cervical cancer, and subsequently from pelvic LN to para-aortic LNs in locally advanced cervical cancers. Collectively, this work highlights the association of POSTN deposition with lymphangiogenesis in LNs, and provides evidence for a key contribution of POSTN in promoting VEGF-C driven lymphangiogenesis and the seeding of metastatic cells.

Whole Blood Transfusion for Severe Malarial Anemia in a High Plasmodium falciparum Transmission Setting.

BACKGROUND: Severe malaria resulting from Plasmodium falciparum infection is the leading parasitic cause of death in children worldwide, and severe malarial anemia (SMA) is the most common clinical presentation. The evidence in support of current blood transfusion guidelines for patients with SMA is limited. METHODS: We conducted a retrospective cohort study of 911 hospitalized children with SMA in a holoendemic region of Zambia to examine the association of whole blood transfusion with in-hospital survival. Data were analyzed in adjusted logistic regression models using multiple imputation for missing data. RESULTS: The median age of patients was 24 months (interquartile range, 16-30) and overall case fatality was 16%. Blood transfusion was associated with 35% reduced odds of death in children with SMA (odds ratio, 0.65; 95% confidence interval, .52-.81; P = .0002) corresponding to a number-needed-to-treat (NNT) of 14 patients. Children with SMA complicated by thrombocytopenia were more likely to benefit from transfusion than those without thrombocytopenia (NNT = 5). Longer storage time of whole blood was negatively associated with survival and with the posttransfusion rise in the platelet count but was not associated with the posttransfusion change in hemoglobin concentration. CONCLUSIONS: Whole blood given to pediatric patients with SMA was associated with improved survival, mainly among those with thrombocytopenia who received whole blood stored for <4 weeks. These findings point to a potential use for incorporating thrombocytopenia into clinical decision making and management of severe malaria, which can be further assessed in prospective studies, and underline the importance of maintaining reliable blood donation networks in areas of high malaria transmission.

Applicability of the long-term uncertainty in measurement (LTUM) method for analytical performance assessment in clinical cytometry laboratories.

BACKGROUND: The estimation of uncertainty in measurement for quantitative analyses is an international obligation of the ISO15189 standard for laboratories. The most widespread method is the Internal Quality Control and External Quality Assessment (IQC + EQA). METHODS: We compared two methods to assess uncertainty in measurement for the quantification of the number of CD34+ stem cells and of the different lymphocyte subpopulations in blood samples: the IQC + EQA method and the Long-Term Uncertainty in Measurement (LTUM) method. RESULTS: We focused on the CD3+/CD4+ T lymphocyte subpopulation for a target value of 350 CD3+/CD4+/µl. The range in terms of uncertainty in the measurement of 350 CD3+/CD4+ cells/µl with the IQC + EQA method was [292.8; 407.2]. With the LTUM method, the uncertainty was 19.1% of the measured value. This represented a range of [283.2; 416.9]. CONCLUSIONS: The relative uncertainty calculated with the LTUM method can be adapted to any level of the measured parameter. IQC and EQA calculate the absolute uncertainty and need a clustering of values at different levels. This clustering can lead to some approximations in the uncertainty in measurement determination, particularly around the cut-off values. Unlike previous reports, uncertainty values were higher when calculated with the LTUM than with the IQC + EQA method. However, LTUM might be more representative of the daily routine practice with patient samples.

Accurate assignment of disease liability to genetic variants using only population data.

PURPOSE: The growing size of public variant repositories prompted us to test the accuracy of pathogenicity prediction of DNA variants using population data alone. METHODS: Under the a priori assumption that the ratio of the prevalence of variants in healthy population vs that in affected populations form 2 distinct distributions (pathogenic and benign), we used a Bayesian method to assign probability to a variant belonging to either distribution. RESULTS: The approach, termed Bayesian prevalence ratio (BayPR), accurately parsed 300 of 313 expertly curated CFTR variants: 284 of 296 pathogenic/likely pathogenic variants in 1 distribution and 16 of 17 benign/likely benign variants in another. BayPR produced an area under the receiver operating characteristic curve of 0.99 for 103 functionally confirmed missense CFTR variants, which is equal to or exceeds 10 commonly used algorithms (area under the receiver operating characteristic curve range = 0.54-0.99). Application of BayPR to expertly curated variants in 8 genes associated with 7 Mendelian conditions led to the assignment of a disease-causing probability of ≥80% to 1350 of 1374 (98.3%) pathogenic/likely pathogenic variants and of ≤20% to 22 of 23 (95.7%) benign/likely benign variants. CONCLUSION: Irrespective of the variant type or functional effect, the BayPR approach provides probabilities of pathogenicity for DNA variants responsible for Mendelian disorders using only the variant counts in affected and unaffected population samples.

A pre-processing pipeline to quantify, visualize, and reduce technical variation in protein microarray studies.

Technical variation, or variation from non-biological sources, is present in most laboratory assays. Correcting for this variation enables analysts to extract a biological signal that informs questions of interest. However, each assay has different sources and levels of technical variation, and the choice of correction methods can impact downstream analyses. Compared to similar assays such as DNA microarrays, relatively few methods have been developed and evaluated for protein microarrays, a versatile tool for measuring levels of various proteins in serum samples. Here, we propose a pre-processing pipeline to correct for some common sources of technical variation in protein microarrays. The pipeline builds upon an existing normalization method by using controls to reduce technical variation. We evaluate our method using data from two protein microarray studies and by simulation. We demonstrate that pre-processing choices impact the fluorescent-intensity based ranks of proteins, which in turn, impact downstream analysis.

Remodelling of the fibre-aggregate structure of collagen gels by cancer-associated fibroblasts: A time-resolved grey-tone image analysis based on stochastic modelling.

Introduction: Solid tumors consist of tumor cells associated with stromal and immune cells, secreted factors and extracellular matrix (ECM), which together constitute the tumor microenvironment. Among stromal cells, activated fibroblasts, known as cancer-associated fibroblasts (CAFs) are of particular interest. CAFs secrete a plethora of ECM components including collagen and modulate the architecture of the ECM, thereby influencing cancer cell migration. The characterization of the collagen fibre network and its space and time-dependent microstructural modifications is key to investigating the interactions between cells and the ECM. Developing image analysis tools for that purpose is still a challenge because the structural complexity of the collagen network calls for specific statistical descriptors. Moreover, the low signal-to-noise ratio of imaging techniques available for time-resolved studies rules out standard methods based on image segmentation. Methods: In this work, we develop a novel approach based on the stochastic modelling of the gel structure and on grey-tone image analysis. The method is then used to study the remodelling of a collagen matrix by migrating breast cancer-derived CAFs in a three-dimensional spheroid model of cellular invasion imaged by time-lapse confocal microscopy. Results: The structure of the collagen at the scale of a few microns consists in regions with high fibre density separated by depleted regions, which can be thought of as aggregates and pores. The approach developped captures this two-scale structure with a clipped Gaussian field model to describe the aggregates-and-pores large-scale structure, and a homogeneous Boolean model to describe the small-scale fibre network within the aggregates. The model parameters are identified by fitting the grey-tone histograms and correlation functions of the images. The method applies to unprocessed grey-tone images, and it can therefore be used with low magnification, noisy time-lapse reflectance images. When applied to the CAF spheroid time-resolved images, the method reveals different matrix densification mechanisms for the matrix in direct contact or far from the cells. Conclusion: We developed a novel and multidisciplinary image analysis approach to investigate the remodelling of fibrillar collagen in a 3D spheroid model of cellular invasion. The specificity of the method is that it applies to the unprocessed grey-tone images, and it can therefore be used with noisy time-lapse reflectance images of non-fluorescent collagen. When applied to the CAF spheroid time-resolved images, the method reveals different matrix densification mechanisms for the matrix in direct contact or far from the cells.

Intravitreal injection of anti-miRs against miR-142-3p reduces angiogenesis and microglia activation in a mouse model of laser-induced choroidal neovascularization.

Age-related macular degeneration (AMD) is a worldwide leading cause of blindness affecting individuals over 50 years old. The most aggressive form, wet AMD, is characterized by choroidal neovascularization (CNV) and inflammation involving microglia recruitment. By using a laser-induced CNV mouse model, we provide evidence for a key role played by miR-142-3p during CNV formation. MiR-142-3p was overexpressed in murine CNV lesions and its pharmacological inhibition decreased vascular and microglia densities by 46% and 30%, respectively. Consistently, miR-142-3p overexpression with mimics resulted in an increase of 136% and 126% of blood vessels and microglia recruitment. Interestingly, miR-142-3p expression was linked to the activation state of mouse microglia cells as determined by morphological analysis (cell solidity) through a computational method. In vitro, miR-142-3p overexpression in human microglia cells (HMC3) modulated microglia activation, as shown by CD68 levels. Interestingly, miR142-3p modulation also regulated the production of VEGF-A, the main pro-angiogenic factor. Together, these data strongly support the unprecedented importance of miR-142-3p-dependent vascular-inflammation axis during CNV progression, through microglia activation.

Efficiency of nilotinib to target chronic phase-chronic myeloid leukaemia primary mature CD34- and immature CD34+ cells.

Accumulation in target cells is an essential pharmacokinetic step of targeted therapies. Tyrosine Kinase Inhibitors (TKI) against the BCR-ABL fusion protein in Chronic Phase-Chronic Myeloid Leukaemia (CP-CML) cells constitute a unique model in terms of efficacy, specificity, and in vivo demonstration of response heterogeneity by target cells. The overall therapeutic response to nilotinib is heterogeneous with no satisfactory explanation. To better understand the patients' response heterogeneity, we quantified nilotinib uptake by primary CP-CML cells in standardized conditions using flow cytometry, which allowed also distinguishing mature (polymorphonuclear cells) from immature (CD34+) cells. Nilotinib was undetectable in 13.3% of PMN and 40% of CD34+ cells. Moreover, in CD34+ cells, intracellular nilotinib did not completely abolish BCR-ABL activity (monitored by CrkL phosphorylation inhibition), although nilotinib accumulated well in most CD34+ cell samples. Intracellular nilotinib concentration was inversely correlated with disease burden parameters, Sokal score, and early haematologic response at day 6 ± 1 only in PMN, suggesting an intrinsic ability to limit nilotinib entry in the forms with higher tumor cell burdenat diagnosis. These findings suggest that nilotinib accumulation in CP-CML cells is influenced by individual characteristics and intra-clonal heterogeneity, and might be used for pharmacokinetic studies and to assess the therapeutic response.

Assessing and communicating heterogeneity of treatment effects for patient subpopulations: Panel discussion on considerations in design and analysis.

Clinical trials are primarily conducted to understand the average effects treatments have on patients. However, patients are heterogeneous in the severity of the condition and in ways that affect what treatment effect they can expect. It is therefore important to understand and characterize how treatment effects vary. The design and analysis of clinical studies play critical roles in evaluating and characterizing heterogeneous treatment effects. This panel discussed considerations in design and analysis under the recognition that there are heterogeneous treatment effects across subgroups of patients. Panel members discussed many questions including: What is a good estimate of the treatment effect in me, a 65-year-old, bald, Caucasian-American, male patient? What magnitude of heterogeneity of treatment effects (HTE) is sufficiently large to merit attention? What role can prior evidence about HTE play in confirmatory trial design and analysis? Is there anything described in the 21st Century Cures Act that would benefit from greater attention to HTE? An example of a Bayesian approach addressing multiplicity when testing for treatment effects in subgroups will be provided. We can do more or better at understanding heterogeneous treatment effects and providing the best information on heterogeneous treatment effects.