Neat1, and experimentally confirm that the entry of NiCo NCs into cells is required for the observed inhibition of inflammasome activation. Furthermore, we find that NiCo NCs inhibit neutrophil recruitment in an acute peritonitis mouse model and relieve symptoms in a colitis mouse model, again by inhibiting inflammasome activation. Beyond demonstrating a highly surprising and apparently therapeutic impact for an inorganic nanomaterial on inflammatory responses, our work suggests that nickel- and cobalt-containing nanomaterials may offer an opportunity to design anti-inflammatory nanomedicines for the therapeutics of macrophage-mediated diseases.Activation of inflammasomes—immune system receptor sensor complexes that selectively activate inflammatory responses—has been associated with diverse human diseases, and many nanomedicine studies have reported that structurally and chemically diverse inorganic nanomaterials cause excessive inflammasome activation. Here, in stark contrast to reports of other inorganic nanomaterials, we find that nickel-cobalt alloy magnetic nanocrystals (NiCo NCs) actually inhibit activation of NLRP3, NLRC4 and AIM2 inflammasomes. We show that NiCo NCs disrupt the canonical inflammasome ASC speck formation process by downregulating the lncRNA  Nickel-Cobalt nanocrystals inhibited NLRP3, NLRC4, and AIM2 inflammasomes activation by downregulating the transcription of lncRNA Neat1, and significantly relieved symptoms of acute peritonitis and colitis mouse model. We. We36]. Numerous studies have reported that diverse nanomaterials induce NLRP3 inflammasome activation; such studies have typically used caspase-1 and IL-1β as activation markers . We foun2 NPs, ATP and monosodium urate (MSU) [2, by ATP and by MSU treatment  ,29. Suppent Fig. . These rWe also tested if NiCo NCs may exert inhibitory effects against the activation of other canonical inflammasomes, including the extensively studied NLRC4 and AIM2 inflammasomes. Indeed, similar to our observations for inhibition of NLRP3 inflammasome activation, we found that NiCo NCs blocked caspase-1 activation and IL-1β maturation in LPS-primed BMDMs stimulated with salmonella (an agonist of NLRC4 inflammasomes) or with poly (dA : dT) (an agonist of AIM2 inflammasomes) Fig. . As showTo reveal the mechanism by which NiCo NCs inhibit the activation of NLRP3, NLRC4 and AIM2 inflammasomes, we first assessed the expression of ASC, which is the common protein present in all three of these inflammasome types. As shown in Fig. To further explore the mechanism that NiCo NCs inhibited the formation of ASC speck, RNA sequencing, a method that is considered the most powerful and robust technique for measuring gene expression at genome-wide level, was carried out on different treated BMDM groups to find genes that may be involved in this process. The Pearson correlation coefficient of RNA sequencing signals suggested excellent reproducibility between the biological replicates . DespiteNeat1, which was upregulated by three inflammasome agonists treatment and downregulated by inflammasome agonists and NiCo NCs co-treatment, is a reported long noncoding RNA (lncRNA) that plays an important role in inflammasome activation. Neat1 was reported to associate with the NLRP3, NLRC4 and AIM2 inflammasomes to enhance their assembly and subsequent pro–caspase-1 processing. Neat1 deficiency significantly inhibits the activation of NLRP3, NLRC4 and AIM2 inflammasomes in BMDMs [Neat1 by real-time reverse transcription PCR  analysis , an unspecific transcription blocker. ActD or NiCo NCs treatment alone for 1.5 h significantly decreased the relative amount of Neat1 compared to the control group, but NiCo NCs did not further decrease the amount of Neat1 in the cells treated with ActD , a method for mapping genome-wide chromatin accessibility [Neat1 were significantly less accessible in the NiCo NCs treated group compare to the Blank group  analysis revealed that different macrophage cell lines internalized NiCo NCs in a dose-dependent manner . To confTo figure out the suppression related to geometry or elements, we synthesized nickel nanoparticles (Ni NPs) and cobalt nanoparticles (Co NPs) in identical conditions as the control. Both Ni NPs and Co NPs have a morphology that is quite different from that of NiCo NCs Fig. . Howeverin vivo, we examined whether NiCo NCs can suppress MSU-induced peritoneal inflammation in a mouse model. For context, MSU deposition in joints is associated with development of gout and pseudogout via NLRP3 inflammasome activation [in vivo that NiCo NCs can effectively attenuate activation of inflammasomes in a peritoneal inflammation mouse model.To verify the anti-inflammatory effects of NiCo NCs tivation , and MSUtivation . Upon MSin vivo experimental results to our in vitro mechanistic studies, we found that the extent of IL-1β maturation was significantly attenuated by NiCo NCs treatment of DSS model mice  model is thought to usefully mimic the pathogenesis of human inflammatory bowel disease and has been extensively used to explore immune mechanisms of colitis , and theice Fig. . Thus, tNeat1. The mechanism by which NiCo NCs repress Neat1 transcription also deserves further study. If so, then perhaps the commonly encountered, strong limitation of inflammasome activation that has to date faced engineered nanoparticle applications can be prevented or overcome by designing nickel- and/or cobalt-containing nanocrystals to achieve improved biocompatibility.Inflammasome activation is a major driver of many diseases, such as Alzheimer's disease, Parkinson's disease, pulmonary fibrosis, type II diabetes, gout and atherosclerosis. Macrophages serve as the primary defense mechanism of the body in the processing of nanoparticles, bringing about nanomaterials with a natural ability to target macrophages in comparison with small molecule drugs. This study provides an alternative approach to replacing small molecule drugs for the treatment of diseases mediated by hyperactivation of the inflammasome. In the present study, we found that NiCo NCs can effectively inhibit the activation of NLRP3, NLRC4 and AIM2 inflammasomes by blocking inflammasome assembly. We also show that NiCo NCs can inhibit neutrophil recruitment in an acute peritonitis mouse model, and can relieve symptoms in a colitis mouse model. Our work also establishes that these effects result from inhibition of IL-1β maturation. In sharp contrast to previous reports suggesting that inorganic nanoparticles typically induce inflammasome activation, we demonstrate that inorganic nanoparticles themselves can actually inhibit the activation of diverse types of inflammasomes. We have demonstrated that NiCo NCs exert broad-spectrum inhibitory effects on the activation of inflammasomes by downregulating Additionally, it is crucial to understand how the physicochemical attributes of nanostructured materials, such as size, charge, morphology, composition and surface chemistry, affect immune responses by specific cell types. There is value in applying related knowledge to designing customized immunomodulatory nanomaterials for specific diseases. There is also a great need for studies that focus on addressing the long-term clinical safety of immunomodulatory nanomaterials, and rigorously optimizing them for their expected therapeutic effects before reliable clinical translation .The details about the synthesis, characterizations, cell preparation, biological assay and analysis are in the nwad179_Supplemental_FileClick here for additional data file.


The hippocampal formation has been implicated in the pathophysiology of schizophrenia, with patients showing impairments in spatial and relational cognition, structural changes in entorhinal cortex and reduced theta coherence with medial prefrontal cortex. Both the entorhinal cortex and medial prefrontal cortex exhibit a 6-fold  modulation of neural activity during virtual navigation that is indicative of grid cell populations and associated with accurate spatial navigation.Here, we examined whether these grid-like patterns are disrupted in schizophrenia. We asked 17 participants with diagnoses of schizophrenia and 23 controls  to perform a virtual reality spatial navigation task during magnetoencephalography.The control group showed stronger 4–10 Hz theta power during movement onset, as well as hexadirectional modulation of theta band oscillatory activity in the right entorhinal cortex whose directional stability across trials correlated with navigational accuracy. This hexadirectional modulation was absent in schizophrenia patients, with a significant difference between groups.These results suggest that impairments in spatial and relational cognition associated with schizophrenia may arise from disrupted grid firing patterns in entorhinal cortex. et al. report that people with schizophrenia lack the hexadirectional modulation of theta power in right entorhinal cortex observed in a matched control group. Impairments in spatial and relational cognition associated with schizophrenia may thus arise from disrupted grid firing patterns in entorhinal cortex.Convertino  Several studies suggest a role for the hippocampal formation in the pathophysiology of schizophrenia.2–6 Specifically, patients exhibit structural changes in entorhinal cortex7–9 and reduced functional connectivity between the medial temporal lobe (MTL) and medial prefrontal cortex (mPFC).10–13 The hippocampal formation plays a fundamental role in episodic memory and spatial navigation.15 Consistent with this, patients with schizophrenia also exhibit impaired performance in a range of spatial navigation tasks.16–20Schizophrenia is characterized by distortion of thoughts and perception including delusions, hallucinations, disorganized or catatonic behaviour and diminished emotional expression or motivation  s windows around the onset of ≥2 s periods during which no translational movement occurred. This captured 51.4 ± 8.9% and 49.8 ± 7.2% of the task data for controls and patients, respectively  coordinates. This resulted in a set of linear weights for each participant that could generate 4–10 Hz band-pass filtered time series in source space from sensor-level data in each movement onset epoch.38 applied to alternate movement onset epochs from that block. Finally, we estimated the strength of hexadirectional modulation in each voxel for the remaining movement onset epochs by linearly regressing continuous theta power against the cosine of the angular deviation from that grid orientation, with 6-fold periodicity , we extracted continuous movement direction from the corresponding behavioural data and a measure of theta power by applying the Hilbert transform to band-pass filtered data in each voxel and Z-scoring the resultant time series . We then estimated grid orientation independently for each voxel using a quadrature filterhttps://osf.io/ght4r/.The data and custom written analysis code that support the findings of this study are freely available from 43 using desktop virtual reality (VR) inside a MEG scanner  = 2.10, P = 0.042, Hedge’s g = 0.66, confidence interval (CI) (0.028 1.32); We asked participants with a diagnosis of schizophrenia  and an age-, sex- and IQ-matched control group to perform an established spatial navigation task scanner . Consistt(22) = 5.58, P < 0.001] and patients , and greater in controls than patients  or preference to navigate close to the boundaries of the environment , and movement speed accelerated rapidly to a fixed top speed for all participants.Scalp plots  illustrate that 4–10 Hz theta power increases arise over bilateral frontal and temporal sensors in both groups, with controls showing greater movement-related theta power than patients over left frontal sensors . Importa38 (see P < 0.05 family-wise error (FWE) corrected across the whole brain.Next, we looked for hexadirectional modulation of movement-related theta power across the whole brain using established methods38 see . In contt(22) = 3.04, P = 0.0059] but not patients , and significantly stronger hexadirectional modulation for controls than patients , as was grid orientation across data partitions, each including half of the trials . However, grid orientation across blocks (within each data partition and voxel inside the region of interest) was no more consistent than expected by chance , suggesting that grid patterns randomly realigned with the visually distinct square environment encountered in each task block. Importantly, we found no evidence for a relationship between theta power during movement onset  and the strength of hexadirectional modulation inside the region of interest ; or between theta power during movement onset  and the strength of hexadirectional modulation in the same region . This suggests that differences in the magnitude of hexadirectional modulation across participants did not arise simply from differences in the power of the underlying theta oscillation.Reassuringly, grid orientation across voxels inside the right entorhinal region of interest (within each task block and data partition) was more consistent than expected by chance [5.33 ± 2.25°, chance = 15°; r = 0.15, P = 0.49), we did find a significant relationship between the consistency of the grid orientation across blocks and task performance .Finally, we looked for a relationship between the hexadirectional modulation of 4–10 Hz theta power inside the region of interest and our behavioural data. Although we found no evidence for a correlation between the strength of hexadirectional modulation and task performance across controls  is not clear, and merits further attention.Our results demonstrate that people with schizophrenia show worse spatial memory and less movement-related theta power during a virtual spatial navigation task than a matched control group. They also lack the hexadirectional modulation of theta power in right entorhinal cortex observed in the control group, which is consistent with the presence of stable grid cell firing patterns. Importantly, the stability of grid orientation across task blocks in the control population correlated positively with their performance in the spatial memory task, suggesting a functional relationship between grid firing patterns and spatial memory. This is the first demonstration of hexadirectional theta modulation in MEG, building on previous studies showing similar patterns in BOLD signal throughout the default mode network,16–20 In particular, people with schizophrenia are selectively impaired in spatial navigation strategies based on cognitive mapping, rather than single-landmark (response-based) strategies.20 Schizophrenia is also associated with impairments in associative inference and acquisition of relational knowledge,50–52 in which the hippocampal formation—and grid cells in particular—are thought to play a key role.28 Our findings therefore suggest that dysfunctional grid coding may underlie atypical inference and poor acquisition of relational knowledge in schizophrenia. Grid firing patterns may be supported by attractor network dynamics,53 and attractor states are thought to be more unstable in schizophrenia,55 potentially due to reduced α5-GABA-A receptor density in the MTL.56 We speculate that this may increase reliance on striatal learning mechanisms, making inferences more dependent on individual landmarks  than structured relational knowledge of the world.Previous studies have reported impaired spatial navigation associated with hippocampal anomalies in schizophrenia.22 and the hexadirectional modulation of theta power in bilateral entorhinal cortex.37We note that movement-related increases in theta power and the hexadirectional modulation of theta power by movement direction appear to be related but distinct phenomena. First, we found no correlation between movement-related theta power and the strength of hexadirectional modulation across our control group. Second, movement-related theta power increases at the sensor level are most prominent over left frontal regions, while the hexadirectional modulation of theta power by movement direction is restricted to right entorhinal cortex. In contrast, we found no evidence for hexadirectional theta modulation in an anatomically defined left entorhinal cortex region of interest, although we are reluctant to over-interpret this absence of evidence, given previous observations of grid cellsIn summary, in healthy volunteers performing a virtual spatial navigation task, we have shown grid-like modulation of MEG theta power localized to the right entorhinal cortex whose consistency of orientation across virtual environments correlates with spatial memory performance. Relative to this baseline, we have shown that people with a diagnosis of schizophrenia have impaired spatial memory performance, reduced movement-related theta oscillations and disrupted grid-like modulation of theta power. This extends previous work showing structural and functional impairment of the hippocampal formation in schizophrenia and selective deficits of hippocampus-dependent strategies in spatial navigation. Future studies could address a possible role of grid cell populations in impaired structural knowledge and inference in schizophrenia.awac416_Supplementary_DataClick here for additional data file.


Early changes in breast intratumor heterogeneity during neoadjuvant chemotherapy may reflect the tumor’s ability to adapt and evade treatment. We investigated the combination of precision medicine predictors of genomic and MRI data towards improved prediction of recurrence free survival (RFS).A total of 100 women from the ACRIN 6657/I-SPY 1 trial were retrospectively analyzed. We estimated MammaPrint, PAM50 ROR-S, and p53 mutation scores from publicly available gene expression data and generated four, voxel-wise 3-D radiomic kinetic maps from DCE-MR images at both pre- and early-treatment time points. Within the primary lesion from each kinetic map, features of change in radiomic heterogeneity were summarized into 6 principal components.p < 0.01) demonstrating significant Kaplan-Meier curve separation (p < 0.001). Adding phenotypes to established prognostic factors, functional tumor volume (FTV), MammaPrint, PAM50, and p53 scores in a Cox regression model improves the concordance statistic for predicting RFS from 0.73 to 0.79 (p = 0.002).We identify two imaging phenotypes of change in intratumor heterogeneity (These results demonstrate an important step in combining personalized molecular signatures and longitudinal imaging data towards improved prognosis. Early changes in tumor properties during treatment may tell us whether or not a patient’s tumor is responding to treatment. Such changes may be seen on imaging. Here, changes in breast cancer properties are identified on imaging and are used in combination with gene markers to investigate whether response to treatment can be predicted using mathematical models. We demonstrate that tumor properties seen on imaging early on in treatment can help to predict patient outcomes. Our approach may allow clinicians to better inform patients about their prognosis and choose appropriate and effective therapies. Chitalia, Miliotis et al. evaluate radiomic predictors of outcome in patients with breast cancer treated with neoadjuvant chemotherapy. The authors identify radiomic phenotypes related to changes in tumor heterogeneity that improve progression-free survival prediction when added to clinicopathological and molecular factors. Breast cancer heterogeneity specifically, is well-established, with intratumor heterogeneity arising due to genomic and transcriptomic variations leading to heterogeneous subpopulations driving prognosis and response to therapy4. As such, increased heterogeneity is thought to be associated with adverse clinical outcomes5.Cancer is a dynamic and heterogeneous disease, with heterogeneity manifesting both across and within tumors6. Additionally, women achieving pathologic complete response (pCR) after completing neoadjuvant chemotherapy may have improved survival outcomes8. Early prediction of response to neoadjuvant treatment can allow for personalized changes to treatment plans, including targeted therapies, and early discontinuation of inactive therapies10. Intratumor heterogeneity is thought to change in response to neoadjuvant chemotherapy leading to altered biomarker expressions11. Such changes may arise due to the acquired resistance by specific subclones during treatment12. Early, noninvasive characterization of such changes may indicate response versus resistance to treatment, enabling early treatment changes prior to treatment completion.Neoadjuvant chemotherapy (NACT) is an established course of treatment for locally advanced breast cancer (LABC) and can promote breast-conserving surgeries by reducing tumor size14. p53 mutation status is an established predictor for more aggressive tumor biology and therefore a worse prognosis in terms of recurrence free survival (RFS)15. Such precision-medicine predictors may improve clinical decision-making by deviating from the “one size fits all” approach to treating breast cancer. However, as such assays, mutation statuses, and established histopathologic biomarkers are determined largely from selective tissue sampling acquired by biopsy, they may fall short in fully capturing heterogeneous disease burden.Personalized gene expression-based molecular assays, such as the 70-gene MammaPrint microarray assay (Agendia BV) and the 50-gene PAM50 risk of recurrence score assay (ROR-S), provide risk stratification for future recurrence19. Hylton et al. demonstrated the prognostic and predictive value of measuring functional tumor volume (FTV) at various longitudinal time points during neoadjuvant chemotherapy20. Jahani et al. developed registration-based biomarkers for the early prediction of pCR and recurrence free survival (RFS) in tumors from baseline to early treatment time points21. While much progress has been made, these studies may be limited by not examining the associations between aggregate changes in intratumor heterogeneity that arise in response to therapy and the complementary information provided by genomics-based information22.Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can allow for longitudinal, non-invasive monitoring of heterogeneous tumors during the course of neoadjuvant chemotherapy. Previous studies have demonstrated the role of longitudinal patterns for tumor response during neoadjuvant chemotherapy and have examined their associations with treatment response and overall survivalThe purpose of this study was to identify imaging phenotypes of early changes in intratumor heterogeneity in DCE-MRI and evaluate their prognostic value in augmenting FTV measures and molecular profiling signatures scores for predicting RFS after breast NACT. We show that distinct heterogeneity imaging tumor profiles occur during neoadjuvant treatment for locally advanced breast cancer which can be utilized, in combination with personalized genomic biosignatures, to enhance current prognostic models and treatment management.24. Per the inclusion criteria of ACRIN 6657/I-SPY 1, women diagnosed with stage 2 or 3 breast cancer were selected for the study and underwent anthracycline-cyclophosphamide NACT. Longitudinal DCE-MRI was performed using a 1.5 T scanner at four time points: prior to the start of neoadjuvant therapy (T1), at least 2 weeks after the first cycle of chemotherapy (T2), between treatments (T3), and after the completion of chemotherapy, before surgery (T4). Data acquisition was as described in the ACRIN 6657/I-SPY 1 protocol19. The first and second post-contrast images were acquired 2.5 and 7.5 min after contrast injection.DCE-MR images of women enrolled in the ACRIN 6657/I-SPY1 trial, diagnosed with advanced invasive breast cancer from May 2002 through March 2006, were retrospectively analyzed24, we retained the 143 women for whom both complete clinical data and T1 and T2 DCE-MR imaging were available. For analyses involving gene expression, we used the subset of 100 women for whom gene expression information was available through the Gene Expression Omnibus26, under the accession number GSE2222627. Clinical and histopathologic data including age, hormone receptor (HR) status, human epidermal growth factor receptor 2 (HER2) status, and pCR status were available for each woman (Table 2), previously shown to have significant association with RFS20, was also calculated for each woman. RFS times were available, defined as time to recurrence (event), or time to death or last follow-up (censor).Of the 222 trial participants with publicly available datan = 143) for whom gene expression data was not publicly available, and a separate dataset of 49 women from the publicly available Breast MRI NACT Pilot study28. This study had similar inclusion criteria as the I-SPY 1 trial, and participants underwent a similar treatment and imaging protocol as the I-SPY 1 trial. Clinical information on age, HR status, and HER2 status and 3-year RFS information was available for each woman in the validation cohort . Details can be found here28. For this retrospective analysis, the requirement of informed consent was waived under institutional review board approval. Additional ethical approval for this retrospective study was not required as the data was publicly available and fully deidentified, hosted through the National Cancer Institute on the Cancer Imaging Archive24.All eligible patients selected for the I-SPY 1 TRIAL and Breast-MRI-NACT-Pilot study gave their written consent. In the I-SPY 1 TRIAL, the Health Insurance Portability and Accountability Act–compliant protocol and the written consent were approved by the American College of Radiology Institutional Review Board and local-site institutional review boards. More details regarding the trial’s study design and patients’ enrollment can be found here27. Specifically, we re-created three gene signatures in order to classify tumors regarding their metastatic potential, risk of recurrence, and p53 oncogene mutation status: the 70-gene signature (MammaPrint)31, PAM50 risk of recurrence (ROR-S)33, and p53 mutation signature34 respectively. Briefly, MammaPrint classification was achieved by calculating the cosine similarity of the expression of the 70-gene signature for each sample against a “good prognosis” sample set30, using thresholds as defined in the original study31. ROR-S sample categorization was determined by computing the weighted sum of the correlation coefficients33 of each sample against the intrinsic subtype sample sets of the PAM50 gene signature study32. Lastly, p53 mutation status was estimated by calculating the proximity of the I-SPY 1 samples and the p53 mutation signature centroids (wildtype vs. mutant) as Spearman’s correlation values, as described in the p53 gene signature study34. The integrity of our classification was examined by comparing our results with the original results of the Esserman et al. study27. We confirmed that our recreated results corresponded to the original results by comparing the numbers of individuals attributed to each class in the overall cohort.Molecular profiling of the I-SPY 1 enrolled women with gene expression information was built as previously described35. The largest contiguous volume of voxels included in the FTV was selected as the location for the primary lesion; this volume was then further refined using manual segmentation to remove isolated voxels and include voxels within the primary tumor lesion volume which were not initially selected by the FTV threshold35. Final tumor segmentations for T1 and T2 were visually confirmed by a board-certified and fellowship trained breast imaging radiologist (ESM). Images were preprocessed by N3 bias-field normalization to correct for bias field signal36.For each woman in the discovery cohort, the 3-D primary lesions at pre-treatment (T1) and early-treatment (T2) time points were selected by first identifying the functional tumor volume (FTV) within the publicly available bounding region, as previously reportedI0, I1, and I2, respectively).For each woman in the discovery cohort, at T1 and T2 time points, four voxel-wise kinetic image maps were calculated within the segmented tumor, the peak enhancement (PE) Eq. , and washttps://cbica.github.io/CaPTk/)37 (Supplementary Table T1 and fT2) were subsequently sign-adjusted such that increasing feature values corresponded to increasing lesion heterogeneity as per each feature definition. Subsequently, the change in each radiomic feature between the baseline and early treatment time points, or delta feature Δf, was calculated as:All kinetic image maps and tumor segmentations were resampled by linear interpolation to a spatial resolution of 256 × 256 voxels, the lowest resolution of the data cohort, to ensure consistent resolution across all scans. A total of 104 radiomic features characterizing lesion intensity, texture patterns, and morphology were extracted from the entire tumor region, from each kinetic map at each treatment time point, resulting in a total of 416 features at each time point for each woman. All features were extracted using the publicly available Cancer Imaging Phenomics Toolkit  were excluded from further analysis. Features characterizing tumor texture or morphology in only 2-D image dimensions were also excluded to allow for whole-tumor, 3-D analysis. This resulted in a total of 42 delta features included in our final analysis. To reduce dimensionality and identify correlated delta features, features were clustered in an agglomerative hierarchical manner using Pearson’s correlation as the distance metric, with highly correlated features being grouped together. Consensus clustering was used to determine the optimal number of stable delta feature groups, with each feature group consisting of highly correlated delta features. Within each feature group, principal component analysis (PCA) was performed and principal components (PCs) totaling greater than 85% explained variance were retained to represent each feature group. As higher values for each delta radiomic feature prior to PCA indicated increasing heterogeneity from T1 to T2, higher values of a PC incorporating primarily positive contributions of features were interpreted as increasing heterogeneity, and one with negative contributions were interpreted as decreasing heterogeneity39. To determine the optimal k number of clusters, consensus clustering40 was used to determine the number of stable phenotypes by repeatedly subsampling the data, performing unsupervised hierarchical clustering, and noting the proportion of subsamples in which, for every pair of tumors, they occupied the same cluster when they appeared in the same dataset. As such, a cumulative distribution function (CDF) was determined for each increase in k, and the stable number of clusters was determined to be the k at which the area under the CDF increased less than 10%. SigClust41 methods were used to determine the number of significant phenotypes by calculating the significance of the cluster index, a metric defined as the sum of within cluster sum of squares about the overall mean, tested against a null distribution at each cluster division. The significance of each phenotype split was tested at p < 0.05.To identify imaging phenotypes of early changes in tumor heterogeneity, tumors in the discovery cohort were classified via unsupervised hierarchical clustering, using the retained principal components to represent each tumor. The clusters identified through unsupervised clustering were interpreted as phenotypes of changes in heterogeneity seen in the study population. An overview schematic for how imaging phenotypes were generated can be found in Supplementary Fig. 42.Distributions of clinical and histopathologic covariate values and molecular profiling scores were assessed for differences across radiomic phenotypes using Chi-square and Kruskal-Wallis tests for categorical and continuous covariates, respectively. Statistical corrections for multiplicity were made using the Bonferroni correction2 values, with the log rank test used to determine statistical significance. RFS was also modeled via Cox proportional-hazards regression. Eight models were evaluated: univariable models for each molecular signature; the baseline model-using the covariates age, HR status, and HER2 status; baseline + FTV2; baseline + FTV2 + radiomic phenotype; and baseline + FTV2 + all molecular signatures, both with and without the addition of radiomic phenotype. All models were evaluated using 5-fold cross validation and averaged over 100 replicates.RFS times across phenotypes were evaluated using Kaplan-Meier survival curves, in both the whole cohort and within strata of HR status, HER2 status, TN status, and greater than and less than median FTV2.The prognostic value of radiomic phenotypes was further evaluated by generating a risk score for each woman, defined as the prediction score of covariates weighted by the corresponding Cox-proportional hazard’s coefficients. Kaplan-Meier survival was analyzed split on the median risk calculated by the Cox model using baseline factors and FTVLastly, confusion matrices for the categories of RFS event/censor were generated to assess the predictive performance of radiomic phenotypes compared to MammaPrint scores, ROR-s, and p53 mutation status.Tumor segmentations for cases in the validation cohort were generated similarly to those in the discovery cohort. Delta radiomic features were calculated using the same feature preprocessing methods used in the discovery cohort. The same delta features selected in the discovery cohort were also selected for the validation cohort. These resulting delta features were normalized using the mean and standard deviation values from the delta feature values in the discovery cohort, to standardize feature ranges.Features were subsequently grouped together based on the cluster assignment of correlated features determined from the discovery cohort. Within each validation feature cluster, features were projected into the discovery cohort feature groups’-principal component space to determine component values. The same numbers of PCs summarizing each feature group retained in the discovery cohort were selected from the validation cohort to form the validation cohort principal-component vectors.To determine phenotype assignment in the validation cohort, each tumor was assigned to the discovery cohort-identified phenotypes by minimizing the Euclidean distance between each validation cohort principal component vector and the discovery cohort phenotype centroid, defined as the average of principal component vector across all tumors in each phenotype.Further information on research design is available in the 35. Neoadjuvant and radiation therapy information was available for women in the discovery cohort  women included in the discovery cohort had future events of recurrence while 72 (72%) women did not have future events of recurrence Table . Median Of the women included in the validation cohort, 27 (29%) women had future events of recurrence while 65 71%) did not . Comparing the average of the six radiomic PC values observed for tumors in each phenotype allowed the two phenotypes to be interpreted as decreasing  and increasing  intratumor heterogeneity from T1 to T2 , with proportionally more recurrences in Phenotype 2 (increasing heterogeneity) than Phenotype 1 (decreasing heterogeneity), via the Chi-square test. Other clinical and histopathologic covariates, and molecular signatures, were not significantly different across phenotypes .Two radiomic phenotypes of early change in intratumor heterogeneity were identified using unsupervised hierarchical clustering and shown to be statistically significant using the SigClust method p < 0.0. Comparip = 0.3) and significant differences within the HER2 + and Triple Negative subtypes (both p = 0.02)  also demonstrated significant differences; curve separation on FTV2 itself was not significant . Within the low-risk tumors, further separation on phenotype demonstrated no significant curve differences. For high-risk tumors, separation by phenotype was significant (p < 0.01)  and FTVt p = 0.0. Within 2 to the baseline model (model 2) improved the c-statistic to 0.67 and adding,molecular signatures to the baseline and FTV2 model (model 3) resulted in a c-statistic of 0.61. A model of baseline, FTV2, and radiomic phenotype assignment (model 4) resulted in a c-statistic of 0.73 and a combined model of baseline, FTV2, molecular profile scores, and radiomic phenotype assignment (model 5) demonstrated improved discriminatory capacity with a c-statistic of 0.79. The improvement in the final combined model was significant compared to the baseline, FTV2, and molecular signature score model, as determined by the log-likelihood test (p < 0.01)  and negative predictive values (NPV) for MammaPrint, ROR-S, p53 mutation status and radiomic phenotype assignment demonstrated that radiomic phenotype status had the highest PPV and NPV out of the four models . Kaplan-Meier curves of tumors in the validation cohort split by phenotype also had a statistically significant difference (p < 0.01). A Bonferroni statistical correction calculated for the total number of comparisons being made resulted in a p-value < = 0.008 suggesting statistical significance in clinical covariate distribution across phenotypes. The proportional number of recurrences was significantly different across phenotypes (p = 0.004) using the Chi-square test, with Phenotype 2 (increasing heterogeneity) having proportionally more recurrence evens than Phenotype 1 (decreasing heterogeneity)  Fig. .Fig. 6ReTwo intrinsic radiomic phenotypes of early change in intratumor heterogeneity in response to neoadjuvant chemotherapy for locally advanced breast cancer were identified and validated. Interpretation of the two radiomic phenotypes as capturing an increase and decrease in intratumor heterogeneity from pre-treatment to early-treatment showed that tumors assigned to the phenotype with increasing intratumor heterogeneity had a greater number of future recurrences. This was further supported by significant separation in Kaplan Meier curves when stratifying women by phenotype assignment. Additionally, the stratification of women within FTV subgroups by phenotype demonstrates the added value of radiomic analysis in modeling prognosis Fig. . AugmentThere may be certain plausible explanations for our observations. By capturing changes in kinetic maps of the DCE-MRI data, the identified phenotypes could reflect changes in tumor composition and angiogenic properties in response to neoadjuvant chemotherapy. Increased heterogeneity may in turn reflect tumor plasticity, which can lead to acquired resistance. The imaging phenotype demonstrating increased heterogeneity from baseline to early-treatment exhibits an increased number of recurrence events, thus supporting the hypothesis that more heterogeneous tumors may result in more adverse clinical outcomes. In contrast, the radiomic phenotype demonstrating decreasing heterogeneity from pre-treatment to early-treatment included a higher number of tumors achieving pCR, which may suggest a relationship between decreased intratumor heterogeneity and an improved response to neoadjuvant chemotherapy Fig. .2 values, and genomic scores were assigned to separate imaging phenotypes based on their early change in heterogeneity. The tumor assigned to Phenotype 2, with an increase in intratumor heterogeneity after initiation of treatment, actually had a future event of recurrence while the tumor assigned to Phenotype 1, having a decrease in intratumor heterogeneity, did not have a future event of recurrence  techniques which, in contrast with microarrays, do not depend on specific probes for the quantification of the expression of pre-specified genes will allow for deeper and more rigorous analyses.Limitations to our study should be noted. First, our exploratory analysis included a relatively small sample size, as we restricted it to publicly available data from the ACRIN 6657/I-SPY 1 trial with both DCE-MRI and gene expression data available. In addition, the validation cohort utilized for this study did not include gene expression data which prevented us from validating the prognostic benefit of the molecular profiling scores. The publicly available microarray data used to generate the molecular profiling scores was also limited by older acquisition protocols and technology. Additionally, image analysis may have been limited by the older image acquisition protocol and technology used in the I-SPY 1 trial. However, the scan duration used for the dataset deriving from the I-SPY 1 trial was 4.5 min, which is similar to the current American College of Radiology (ACR) recommendation of < = 4 minIn conclusion, our exploratory results demonstrate that early changes in intratumor heterogeneity in response to neoadjuvant chemotherapy as captured by radiomic analysis of DCE-MRI may provide improved prediction of RFS for locally advanced breast cancer. Longitudinal non-invasive assessment of tumor phenotypes via imaging may allow for monitoring of heterogeneity and underlying tumor biology. Augmenting clinical, histopathologic, and molecular covariates with imaging phenotypes may allow for personalized risk stratification and early adaptation of treatment strategies.Peer Review FileSupplementary InformationReporting Summary


Idiopathic pulmonary fibrosis (IPF) is a heterogeneous disease that is pathologically characterized by areas of normal-appearing lung parenchyma, active fibrosis (transition zones including fibroblastic foci) and dense fibrosis. Defining transcriptional differences between these pathologically heterogeneous regions of the IPF lung is critical to understanding the distribution and extent of fibrotic lung disease and identifying potential therapeutic targets. Application of a spatial transcriptomics platform would provide more detailed spatial resolution of transcriptional signals compared to previous single cell or bulk RNA-Seq studies.We performed spatial transcriptomics using GeoMx Nanostring Digital Spatial Profiling on formalin-fixed paraffin-embedded (FFPE) tissue from 32 IPF and 12 control subjects and identified 231 regions of interest (ROIs). We compared normal-appearing lung parenchyma and airways between IPF and controls with histologically normal lung tissue, as well as histologically distinct regions within IPF .We identified 254 differentially expressed genes (DEGs) between IPF and controls in histologically normal-appearing regions of lung parenchyma; pathway analysis identified disease processes such as EIF2 signaling (important for cap-dependent mRNA translation), epithelial adherens junction signaling, HIF1α signaling, and integrin signaling. Within IPF, we identified 173 DEGs between transition and normal-appearing lung parenchyma and 198 DEGs between dense fibrosis and normal lung parenchyma; pathways dysregulated in both transition and dense fibrotic areas include EIF2 signaling pathway activation (upstream of endoplasmic reticulum (ER) stress proteins ATF4 and CHOP) and wound healing signaling pathway deactivation. Through cell deconvolution of transcriptome data and immunofluorescence staining, we confirmed loss of alveolar parenchymal signals , gain of secretory cell markers  as well as dysregulation of the upstream regulator ATF4, in histologically normal-appearing tissue in IPF.Our findings demonstrate that histologically normal-appearing regions from the IPF lung are transcriptionally distinct when compared to similar lung tissue from controls with histologically normal lung tissue, and that transition zones and areas of dense fibrosis within the IPF lung demonstrate activation of ER stress and deactivation of wound healing pathways.The online version contains supplementary material available at 10.1186/s12931-023-02572-6. Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal disease of the aging lung , 2 that KRT17-expressing “aberrant basaloid” cells [Previous investigations of transcriptional profiles of IPF lung have largely been performed on whole lung tissue and have identified several thousand genes that are differentially regulated in IPF –17; consd” cells  and “secd” cells  cells. Hd” cells , suggestIn the past several years, spatial transcriptomics technology has emerged as an approach to capture whole transcriptome information from histological or morphological regions of interest. These methods can be applied to frozen or FFPE tissue allowing for application to a broad range of studies involving patient samples. The general approach is to quantify RNA in FFPE tissue using probe-based mount specimens in which slides are stained directly with relevant morphology markers or serial staining with H&E. The slides are then imaged for selection of regions of interest (ROI) via a software platform Fig. . The plaA recent application of spatial transcriptomic technology in IPF focused on alveolar regions, fibroblastic foci, immune infiltrates, and blood vessel regions, however, this study was limited to only 10 cases of IPF . In addiWe used the Nanostring GeoMX Digital Spatial Profiler to obtain spatial transcriptomic data on histologically different regions of interest (ROIs) from FFPE tissue from IPF subjects and controls with histologically normal lung tissue. We compared whole transcriptome expression between IPF subjects and controls with histologically normal lung tissue within histologically normal tissue types . In addition, we compared increasingly diseased tissue regions to their comparative normal tissue in IPF subjects; dense fibrosis and transition zones which included fibroblastic foci were compared to normal appearing lung parenchyma, and honeycomb epithelial metaplasia was compared to normal appearing bronchiolar epithelium. To determine transcriptional differences independent of differences in cell composition, we estimated cell proportions of human lung cell types relevant to IPF by applying the cell deconvolution method, SpatialDecon . We perf2) for areas of classic UIP appearance or normal lung in controls with histologically normal lung tissue. The corresponding areas in the formalin-fixed paraffin-embedded (FFPE) tissue blocks were dissected and a new block consisting of up to 12 subjects was prepared. These macroarray blocks . Patients were consented and tissue was collected through the NHLBI-sponsored Lung Tissue Research Consortium (LTRC) and at the University of Colorado. IPF was diagnosed in the LTRC using American Thoracic Society/European respiratory Society (ATS/ERS) criteria [We used the Nanostring GeoMX Digital Spatial Profiler (DSP) platform to obtain human whole transcriptome data from lung tissue macroarrays . Region We followed the GeoMx RNA-Seq pipeline to convert raw DCC files to normalized expression data –30. We kPrincipal component analysis was performed on the log2 Q3 normalized counts. To perform differential expression, we modeled the log2 Q3 normalized counts using linear mixed effects model with a categorical variable representing disease and region as the main predictor (Eq. 2). Based on initial QC and investigation of PCA plots, all models were adjusted for batch based on day of macrorray processing. We included a random effect for subject to account for the correlation of ROIs within the same subject.ij is the Q3 normalized expression data for a given gene for sample i for subject j. βGroup represents the coefficient for the fixed effect term Groupij. Groupij is the disease region variable which included control normal lung parenchyma, control normal bronchiolar epithelium, IPF normal lung parenchyma, IPF normal bronchiolar epithelium, IPF transition, IPF dense fibrosis and IPF honeycomb epithelium metaplasia. We applied contrast statements to compute differential expression between groups of interest. For the disease-focused research question, we compared IPF to control within normal lung parenchyma and normal bronchiolar epithelium. For the region focused research question within IPF, we compared normal lung parenchyma tissue to transition and dense fibrosis tissue and compared normal bronchiolar epithelium to honeycomb epithelium. To adjust for multiple comparisons, we applied a Benjamini-Hochberg adjustment where genes with an FDR adjusted p-value < 0.05 were considered significant. We did not consider genes significant if the model was determined to be singular. Linear mixed models were computed using the lmerSeq R package [ package .To examine how the selected ROIs differ in the composition of different lung cell types, we applied a cell deconvolution method, SpatialDecon, developed specifically for spatial transcriptomic data . This meTo adjust for differences in cell composition, we applied the linear mixed model outlined above and included the estimated cell proportions as covariates. Our primary differential expression analysis adjusted for cell types estimated from the normal human lung reference and selected based on biological relevance in IPF pathology , alveolar epithelial cells Type 2 (AEC2), fibroblasts and ciliated cells).We applied the Ingenuity Pathways Analysis , 26 to tFor validation of GeoMx data, immunofluorescence microscopy was used to compare normal alveolar regions from control and IPF tissue using the same macroarrays included in the GeoMx assays above. Samples were deparaffinized using xylene and antigens retrieved by boiling in 10mM citrate, pH 6.0. Specimens were blocked for 1 h using 2.5% BSA in PBS, then labelled with specific antibodies overnight in PBS containing 0.25% BSA at 4̊C. Slides were washed 3x with 0.1% Triton-X100 in PBS, then labelled with secondary antibodies for one hour at room temperature. Samples were then washed 3x again in 0.1% Triton-X100 in PBS, washed again to remove detergent, and stained with DAPI, then mounted with Floromount G (Southern Biotech). For a complete list of antibodies and concentrations used in these studies, see Additional file We selected a total of 231 ROIs from 32 IPF subjects (ROI N = 196) and 12 controls with histologically normal lung tissue (ROI N = 35). The distribution of age, sex, smoking status and race were comparable between IPF subjects and controls with histologically normal lung tissue (Table −5) between IPF and controls with histologically normal lung tissue in the normal parenchymal regions  and B cell specific transcripts (IGKC). This was reflected in the canonical pathway analysis  between IPF and controls with histologically normal lung tissue in histologically normal appearing regions of lung parenchyma. The number of DEGs remained the same (N = 254) after adjusting for cell proportions of AEC1, AEC2, fibroblasts, and ciliated cells  or REDD1, a member of the same gene family as CHOP/DDIT3 which is known to be induced via HIF1α-mediated transcription [We observed a smaller number of transcriptional differences in normal bronchiolar regions between IPF and control samples. There were 61 DEGs between IPF and controls with histologically normal lung tissue in normal bronchiolar epithelium Fig. C. The nucription  and is iTo determine how histologically different regions of the IPF lung diverge transcriptionally, we compared transcriptional changes in non-fibrotic IPF lung to transition areas of lung fibrosis and to areas of dense fibrosis. To understand the progression of lung tissue from non-fibrotic lung parenchyma to dense fibrosis, we individually compared non-fibrotic regions of IPF lung parenchyma to either transition regions of lung fibrosis or regions of dense fibrosis, and then explored the overlap between these comparisons. We identified 347 DEGs between transition and non-fibrotic regions; however, the number of DEGs decreased to 173 after adjusting for cell composition of AEC1, AEC2, fibroblasts, and ciliated cells  and confirmed by histology . McDonouThe most highly activated canonical pathway that was identified in both the normal regions IPF lung and the areas of increased lung fibrosis was EIF2 signaling. EIF2α facilitates cap-dependent translation but can be phosphorylated by the unfolded protein response (UPR) activator protein PERK to promote translation of cap-independent ER stress proteins ATF4 and CHOP . We obseAmong the activated upstream regulators are HIF1α and ATF4. Activation of pathologic ER stress pathways, including ATF4 specifically, has been identified by others as a key molecular feature of IPF . In a luAmong upstream regulators that are activated in both transition zones and densely fibrotic areas of IPF lung are MYC and YAP transcriptional programs. Aberrant activation of YAP/TAZ has been associated with increased fibrotic remodeling in IPF in the alveolar epithelium and activated fibroblasts  as well The use of the GeoMX platform to perform spatial transcriptomics on lung specimens allowed for careful selection of normal appearing lung parenchyma, as well as disease relevant regions of interest. Current single cell approaches such as droplet-based technologies capture the transcriptional information for a single cell from dissociated tissue, where the loss of spatial information can be critical to understanding the disease process. This is especially relevant in the context of a heterogenous disease of the lung, as is the case in IPF. Though the GeoMx platform does not provide single cell level information, the ability to capture normal-appearing regions within IPF, as well as transition zones which include fibroblastic foci, makes this approach advantageous to current single cell studies on dissociated lung tissue. In this study, we used two existing single cell datasets to elucidate the cellular changes between histologically distinct regions, however, direct investigation of single cell data to characterize molecular changes could be useful. Future work to utilize emerging platforms that perform spatial transcriptomics with single cell resolution will greatly improve our understanding of the early disease processes in the normal parenchyma and transition zones.We recognize that the findings of this study could be limited to the patient demographic that was captured, predominantly males (69%), ever-smokers (66%) and NHW (94%); however, these demographics are representative of IPF . RegardiPatients with IPF are usually diagnosed only after the fibroproliferative process has caused permanent and extensive lung parenchymal damage. Considering the irreversible nature of this disease, even approved treatments for IPF . Figure S3. Mean cell proportions estimated with SpatialDecon based on normal human lung reference. Figure S4.A Lung-specific protein-protein interactome (PPI) network of IPF vs control normal alveolar DEGs. B Ingenuity upstream regulator analysis of IPF vs control normal alveolar DEGs adjusted for batch and estimated cell proportions of AEC1, AEC2, fibroblasts and ciliated cells. Figure S5. Ingenuity upstream regulator analysis of IPF transition vs normal alveolar (A) and IPF dense fibrosis vs normal alveolar DEGs adjusted for batch and estimated cell proportions of AEC1, AEC2, fibroblasts and ciliated cells. Figure S6. Immunofluorescence changes in YAP, CDKN1A, KRT8, KRT5, SCGB3A2 and SFTPB between IPF and control in normal parenchyma regions.  All pairwise comparisons are Mann-Whitney U test with significance set to (*) p<0.05, (**) p<0.01, (***) p<0.001, (****) p<0.0001. Table S1.  Estimates and corresponding p-values from comparing different cell type proportions from IPF reference between regions.  Proportions were log transformed and modeled by disease region using a linear mixed model adjusting for batch and subject as a random effect.  Estimates represent the difference in log transformed proportions and p-values have been adjusted for multiple comparisons for the number of cell types using a Benjamini-Hochberg adjustment. Table S2.  Antiobodies and concentrations used for this study.  Additional file 2: Datafile S1.  Differential expression results for main comparisons of interest (IPF normal appearing lung parenchyma vs control normal lung parenchyma, IPF normal bronchiolar vs control normal bronchiolar, IPF transition zones of fibrosis vs IPF normal appearing lung parenchyma, IPF dense fibrosis vs IPF normal appearing lung parenchyma and IPF honeycomb epithelium metaplasia vs IPF normal bronchiolar.  All gene lists are adjusted for batch and cell proportions of AECI, AECII, fibroblasts and ciliated.  ***Denotes McDonough core IPF gene.


Asthma and its associated exacerbation are heterogeneous. Although severe asthma attacks are systematically prescribed corticosteroids and often antibiotics, little is known about the variability of response to these therapies. Blood eosinophils and fractional exhaled nitric oxide (FeNO) are type 2 inflammation biomarkers that have established mechanistic, prognostic and theragnostic values in chronic asthma, but their utility in acute asthma is unclear. We speculate that the clinical and biological response to those treatments varies according to inflammometry and microbiological test results.9/L and/or FeNO ≥25 ppb) after treatment. Key secondary analyses will compare changes in symptom scores and the proportion of patients achieving a minimal clinically important difference. Exploratory analyses will assess the relationship between clinical, lung function, inflammatory and microbiome parameters; satisfaction plus reliability indices of POC tests; and sex–gender variability in treatment response. Ultimately, this pilot study will serve to plan a larger trial comparing the clinical and biological response to systemic corticosteroids according to inflammatory biomarkers, offering valuable guidance for more personalised therapeutic strategies in asthma attacks.An observational longitudinal pilot study with multimodal clinical and translational assessments will be performed on 50 physician-diagnosed ≥12-year-old asthmatics presenting with an asthma attack and 12 healthy controls, including blood eosinophil count (venous and point-of-care (POC) capillary blood), FeNO and testing for airway infection . People with asthma will be assessed on day 0 and after a 7-day corticosteroid course, with home monitoring performed in between. The primary analysis will be the change in the forced expiratory volume in 1 s according to type 2 inflammatory status . Results will be communicated in an international meeting and submitted to a peer-reviewed journal.NCT05870215).ClinicalTrials.gov Registry ( Asthma exacerbations pose a substantial burden on healthcare systems and individuals. Despite existing knowledge, there are still gaps in our understanding of the precise mechanisms underlying asthma exacerbations, the role of biomarkers and the potential for more personalised treatment approaches.Phenotyping the Responses to Systemic Corticosteroids in the Management of Asthma Attacks (PRISMA) is an observational longitudinal pilot study with multimodal clinical and translational assessments that aims to stratify the clinical and biological responses to systemic corticosteroids in asthma attacks according to type 2 inflammatory biomarkers  and will explore the relationship between other clinical, inflammatory biomarkers/proteins, and microbiological parameters and treatment response.By conducting an extensive array of clinical and translational assessments, this study aims to provide a holistic view of asthma attacks, shedding light on their multifaceted nature and paving the way for a comprehensive, larger-scale clinical trial.Asthma is a prevalent chronic respiratory disease affecting over 400 million people worldwide.7 8In stable severe asthma, the treatment paradigm is evolving towards targeting treatable traits6 14BEC and FeNO are effective biomarkers for several reasons. First, they provide complementary mechanistic information on different immune compartments involved in the pathogenesis of asthma. Blood eosinophilia is predominantly caused by circulating interleukin (IL)-5 activity and reflects the systemic component of type 2 inflammation,23 27 28The critical remaining questions are (a) how airway signalling (reflected by FeNO) and an increased systemic eosinophil pool (reflected by BEC) relate to the underlying biological pathways that contribute to asthma exacerbation and (b) how the type 2 inflammatory phenotype impacts the response to acute anti-inflammatory corticosteroid treatment. An often-cited confounder is airway infection, which is difficult to diagnose when relying on conventional sputum cultures but can indicate either greater susceptibility to macrolide antibiotics31In acute asthma, several studies have documented the heterogeneity of attacks,Concerning point-of-care (POC) measurements, it is noteworthy that capillary POC-BEC has been successfully studied in chronic airway diseases41et al previously showed that antibiotic use in acute airway disease dropped only once the physician had the results from the POC test, thus highlighting the importance of immediate notification of rapid multiplex testing.42Finally, following the COVID-19 pandemic, we have increasing evidence supporting the relevance of early knowledge of viral and/or bacterial infection. Brendish The treatable trait success story of chronic asthma certainly makes a case for the reappraisal of our approach to acute asthma.Based on previous knowledge from chronic stable asthma and reports that asthma attacks are heterogeneous events, we hypothesise that treatment responses to OCs in acute asthma vary according to the underlying inflammatory phenotype. Specifically, we speculate that events identified by raised BEC and/or FeNO benefit most from OCs and that events presenting with low type 2 biomarkers have a lesser objective clinical and biological anti-inflammatory response but perhaps higher rates of airway infection or dysfunction. If this proves to be the case, we will be in a solid position to move forward with a randomised clinical trial comparing biomarker-directed OC use with the current ‘one-size-fits-all’ approach. An important secondary hypothesis of our study is that POC inflammatory and microbiological assessments in acute asthma are possible, acceptable and reliable. The ramifications of such findings would be to plan a clinical trial assessing biomarker-guided asthma attack management in primary care.We planned an observational longitudinal pilot study with before/after multimodal assessments plus home monitoring . The stu1)/forced vital capacity <0.7 AND >10 pack-year smoking history or α-1-antitrypsin deficiency), a confounding immunological state, ongoing pregnancy or contraindication to corticosteroids.People ≥12 years old with physician-diagnosed asthma since >6 months experiencing an asthma attack as defined by GINA Although we will assess asthmatics with a history of physician-diagnosed asthma, patients included in the primary analysis and counting toward the target sample size will need to have objective proof of asthma as defined by international guidelinesNon-atopic, non-smoking healthy volunteers with normal spirometry and no history of lung disease will form the control cohort. All participants  must provide informed consent for the study protocol. Patients who cannot or refuse to participate in the diary card data collection for the home monitoring period will still be able to participate in all other study procedures.Visit 1 will be on day 0 of the asthma attack, with oral prednisone issued by the community pharmacy (40 mg×7 days) . ConcomiMedical history and examination, including vital signs, inhaler technique and adherence check (using prescription refills of community pharmacy), five-item Asthma Control Questionnaire (ACQ-5),FeNO measurement  and pre/post-bronchodilator oscillometry  and spirometry plus peak expiratory flow.1 > 60% predicted value or >1.5 L, induced sputum collection with hypersaline nebulisation would then be performed, as done in a previous asthma attack study.37Peripheral blood tests , POC-BEC , urine sample , nasosorption with nasal epithelial lining fluid (NELF), nasal swab for the ultrarapid ID NOW (Abbott Industries) SARS-CoV-2 molecular test, nasopharyngeal swab for rapid multiplex PCR using the BIOFIRE Respiratory 2.1 (RP2.1) Panel (bioMérieux). If no spontaneous sputum is available, following negative COVID-19 testing and post-bronchodilator FEVVisit 2 will additionally include a survey of adverse events relating to corticosteroids, patient satisfaction questionnaires and permission to contact the patient’s primary care provider for a physician survey about POC testing.Visit 3 is a web-based survey reviewing the number of subsequent exacerbations, medication changes and ACQ-5 score and allowing the patient to prompt a call from the study team (or vice-versa if the study team has a test to follow up on).All study visits will occur in the morning to avoid the confounding effect of circadian variation of type 2 inflammatory biomarkers.51–53Between visits 1 and 2, patients will be invited to complete daily symptom scores, FeNO and peak expiratory flow measurements via a paper diary card.Ninety days after visit 2, patients will be contacted by email to complete an internet form containing ACQ-5, peak flow measurement and questions about asthma management.Individual participant data will be collected prospectively and managed using REDCap electronic data capture tools hosted at the Université de Sherbrooke.www.biobanque.ca). Nasosorption strips will be spun with NELF eluted from a plastic mesh and then frozen (−80°C).Due to multimodal biobanking , the stuAt the end of the study, patient NELF, sputum DPBS-eluted supernatant, serum and urine will be thawed for inflammatory protein level measurements performed by multiplex electrochemiluminescent assays  and ELISA (sputum supernatant and urine cysteinyl leukotrienes corrected for creatinine). DNA and RNA will be extracted from nasopharyngeal brushes and sputum plugs. Quantitative digital droplet PCR, 16S ribosomal amplicon sequencing and shotgun metagenomics analysis will be used to detect and identify bacteria present in the samples. The 16S amplicon and shotgun metagenomics libraries will be sequenced using an Illumina Nextseq500 sequencer (Illumina).1 between visits, with particular emphasis on a comparison based on the presence or absence of type 2 inflammatory events within the patient cohort. Specifically, we will classify type 2 high events on the basis of visit 1 BEC ≥0.15×109 cells/L and/or FeNO ≥25 ppb.Change in the FEVChange in ACQ-5 between visits according to type 2 inflammatory status.1 and ≥0.5 points for ACQ-5, according to the presence of type 2 high or type 2 low events.The proportion of patients who achieve a minimal clinically important difference (MCID) ≥10% for FEVThe continuous association between baseline biomarkers (BEC and FeNO) and changes in symptom scores, sputum inflammometry, additional lung physiology parameters (impulse oscillometry), inflammatory mediators  and microbiome diversity across different immune compartments .Comparative agreement and reliability of gold-standard BEC  versus POC test (Sight OLO).An appraisal of POC biomarkers using patient satisfaction scores and primary care physician surveys.A sensitivity analysis on all of the above assessing how sex at birth, gender  and race (self-identified)2 (Fisher’s exact test) for categorical variables. Due to the small sample size, no imputation methods are planned a priori for missing data. Frequencies and percentages of missing will be presented, and listwise deletion will be used for concerned analyses.Descriptive statistics will be presented for the complete sample and separated by groups . The normality of variables will be assessed visually using Q–Q plots and histograms. Demographics will be compared between asthmatics and healthy controls using unpaired Student’s t-tests (Mann-Whitney) for continuous variables and Χ1 (primary outcome) and ACQ-5 (key secondary outcome) between visits 1 and 2 according to type 2 inflammatory status. Additionally, to assess whether the observed change in FEV1 within subgroups defined by type 2 high events is clinically meaningful, we will employ a Χ2 test to compare the proportion of patients achieving the MCID between the two groups, using an MCID of 10% relative change in FEV1 and 0.5 points in ACQ-5. Finally, the above results will allow for a power calculation to plan a randomised controlled trial on key outcomes.We will employ an unpaired two-sample t-test (Mann-Whitney U test if non-parametric) to assess the change in FEV1 will be included in the final multivariable model. A linear mixed-effects analysis of the longitudinal improvements in parameters measured at home  according to baseline FeNO and BEC.Exploratory analyses will include an association of the outcome measures with type 2 inflammatory mediators at visit 1 using further multivariable models. To consider the influence of potentially confounding variables, simple and multiple linear regression models will be performed for both independent variables (BEC and FeNO). Covariates considered are age, sex at birth, body mass index, atopic status, serum IgE, Asian race, nasal polyposis,Patient satisfaction scores, physician surveys and the percentage of tests completed during the home monitoring period will be analysed descriptively. An intraclass correlation coefficient  plus Bland-Altmann fixed bias estimate for gold standard (venous BEC performed in the laboratory) versus POC (capillary BEC via SightDx OLO) will be computed with 95% CIs.In both primary and secondary outcomes, we will explore how treatment response to OCs may be influenced by sex, gender and diversity by conducting univariable and multivariable sensitivity analyses on those factors while also controlling for the type 2 inflammatory status. We will also perform a sensitivity analysis, disaggregating all statistical outputs by sex. Results will be presented using interaction plots.All statistics will be analysed with a two-sided α of 0.05 and, when multiple inflammatory mediators are tested, by controlling for a false discovery rate <0.05.1 of 10±10%, n=46 will provide 90% of power to detect the difference in our primary outcome on an unpaired two-sample t-test. According to a recent Canadian study that assessed the proportion of randomly selected adults with physician-diagnosed asthma who fulfil objective criteria to confirm the diagnosis of asthma, 18.3% of participants reporting a severe asthma attack in the past year (11 of 60) did not have asthma confirmed.For our primary analysis, assuming a two-sided α error of 0.05, a T2:non-T2 event ratio of 1:1 and a mean±SD population difference in δ FEVIncluding a cohort of 12 healthy control participants, each assessed during a single visit, is intended to enhance our ability to relativise demographics, lung function and inflammatory measurements observed in the study. Recruitment will continue until the target number of confirmed asthmatics and healthy controls has completed the study.We acknowledge that the secondary and exploratory analyses will likely be underpowered and view these as exploratory but feasible within the proposed timeline of a pilot study serving to plan further clinical trials. Any significant finding for secondary and exploratory outcomes will require further confirmatory studies to support them.62The proposed observational and translational before/after pilot study of asthma attacks across the range of asthma severities will be the first to focus on the role of BEC and FeNO and explore the clinical and biological factors affecting OC treatment response in acute asthma. We view our pilot study of POC measurements for assessing type 2 inflammation and airway infection as a stepping stone to plan a clinical trial allowing for biomarker implementation in the primary care setting. Biomarker-guided management could avoid harm to individual patients and society stemming from inappropriate use of OCs and/or antibiotics.1 (primary outcome), (b) ACQ-5 scores (key secondary outcome), and (c) inflammatory mediators, symptoms, and other physiological aspects (exploratory outcomes). If the study hypothesis is confirmed—a phenotypical variability in treatment responses associated with type 2 airway inflammation—then the utility of BEC and FeNO could be studied to manage asthma attacks in a randomised controlled trial.We expect events identified by raised BEC and/or FeNO to benefit most from corticosteroids, and POC tests to be useful theragnostic and mechanistic tools. Specifically, we expect increased BEC and FeNO to predict greater improvements in (a) FEVThree distinct studies in asthma attacks are underway, each with its unique objectives. ExCluSie-F  is a prospective cohort that will explore the association between phenotypical characteristics and a composite outcome of treatment effectiveness. On the other hand, ‘APEX’  is a longitudinal cohort with participants who have experienced at least one asthma exacerbation in the previous year that aims to assess the proportion of patients displaying eosinophilic and non-eosinophilic phenotypes. Lastly, ‘BOOST’  is a prospective observational study involving adults on long-term biological treatment for asthma that will compare clinical recovery between individuals with high FeNO levels during their asthma attacks and those with low FeNO.63PRISMA (Phenotyping the Responses to Systemic Corticosteroids in the Management of Asthma Attacks) Study offers a distinctive and comprehensive perspective in asthma exacerbation research compared with the studies above, as it stands out by exploring the vital and often less-understood components of asthma exacerbation: their response to standard medications, the utility of BEC and FeNO in acute settings, and the potential role of microbial infections in exacerbation dynamics. PRISMA can also be viewed as instrumental in participating and/or launching further studies about asthma attacks—a new and exciting frontier for biological therapies , ABRA (NCT04098718135)).If the POC test results prove reliable and are also acceptable to patients and their primary care providers, the table will be set for a placebo-controlled trial, such as being done in chronic obstructive pulmonary disease.64–661 as the primary outcome variable: type 2 inflammation has not always been linked with different FEV1 values.12 38 Third, we anticipate many patients may reach out to us either after starting OCs or due to a lack of response to a recent course. To ensure the exclusion of volunteers who have recently taken OCs, we have established a requirement that participants should not have taken a dose within the last 48 hours. Although this exclusion criterion may lead to a bias towards recruiting individuals with less severe events, as those requiring immediate OCs will not be included, we expect that most participants will be recruited through our specialised asthma clinics, which will facilitate the inclusion of severe asthmatics and individuals with more severe airway events. A fourth important limitation is that this is an observational study with a relatively small target sample size; the former does not preclude interesting mechanistic signals from being observed, yet we acknowledge the latter.The main potential pitfall is the unknown proportion of type 2 high versus type 2 low events in our sample, which ranges from mild-to-severe asthma. In severe asthma, approximately 80% of exacerbations are type 2 high,We are mindful of the safety concerns associated with conducting sputum induction after the pandemic. As a mitigation strategy, we will use nasosorption as a surrogate measure for assessing airway compartment inflammation. Additionally, we plan to implement rapid COVID-19 molecular testing at the beginning of each study visit to ensure the safety of our staff and increase the likelihood of conducting sputum induction.To conclude, we have planned a study which is, to the best of our knowledge, the first to assess the variability in response to acute systemic therapies (corticosteroids and/or antibiotics) in asthma according to the inflammatory and microbial phenotypes. We hope that by providing this nuanced understanding, we can contribute to guiding the design of future biomarker-stratified randomised controlled trials that will be more finely tuned to the needs of individual patients with asthma.