Arabidopsis thaliana have extensive, experimentally derived annotations, whereas many non-model species do not. Here we employed a machine learning strategy, transfer learning, where knowledge from A. thaliana is transferred to predict gene functions in cultivated tomato with fewer experimentally annotated genes. The first tomato SM/GM prediction model using only tomato data performs well , but from manually curating 88 SM/GM genes, we found many mis-predicted entries were likely mis-annotated. When the SM/GM prediction models built with A. thaliana data were used to filter out genes where the A. thaliana-based model predictions disagreed with tomato annotations, the new tomato model trained with filtered data improved significantly (F-measure = 0.92). Our study demonstrates that SM/GM genes can be better predicted by leveraging cross-species information. Additionally, our findings provide an example for transfer learning in genomics where knowledge can be transferred from an information-rich species to an information-poor one.Plant specialized metabolites mediate interactions between plants and the environment and have significant agronomical/pharmaceutical value. Most genes involved in specialized metabolism (SM) are unknown because of the large number of metabolites and the challenge in differentiating SM genes from general metabolism (GM) genes. Plant models like  Solanum nigrum and S. lyratum produce glycosides that have anti-tumour activity in cancer cell lines  with known labels (SM or GM) to learn how the properties (i.e. features) of those instances can be best used to distinguish instances with different labels in the form of a predictive model . There aOnly enzyme genes were included in this study. A gene was considered to be an enzyme gene if it had an EC or RXN number annotation in TomatoCyc or assigned using E2P2 v3.0 , the evidence used for manual curation and publications supporting the evidence, see  The benchmark gene set was identified based on expert knowledge and literature mining. Tomato genes were defined as GM, SM or DF based on https://zenodo.org/record/3835883). These 7286 features are divided into several categories, each with different numbers of features: protein domains (4232 features), expression value (280), co-expression (2670), evolution (78) and gene duplication (26). Protein domain Hidden Markov Models from Pfam v.30 (pfam.xfam.org/) was used to identify protein domains in annotated tomato protein sequences with HMMER (https://www.ebi.ac.uk/Tools/hmmer/) using the trusted cut-off, then a binary matrix for each gene and domain was created where 1 indicates the protein sequence of a gene has a given domain and 0 indicates it does not.All gene feature values can be found in https://www.ncbi.nlm.nih.gov/) totalling 47 studies and 926 samples [see  ]. These data sets included development , hormone-related , mutant (14 studies which compared various mutants against wild type), stress treatment  and circadian (1 study with 60 samples). RNA-seq data were processed to determine both fold change and fragments per kilobase of transcript per million mapped reads (FPKM) (https://github.com/ShiuLab/RNAseq_pipeline). The SRA files were converted to fastq format and filtered with Trimmomatic (http://bowtie-bio.sourceforge.net/bowtie2/index.shtml) was used to create the genome index from the tomato NCBI Solanum lycopersicum genome 2.5, then RNA-seq reads were mapped to the tomato genome using TopHat  files for tomato were downloaded from National Center for Biotechnology Information  of genes was determined and used as feature values. Next, tomato genes were clustered into co-expression modules using six methods  across each individual expression data set, data set combination and all data sets combined (same as for expression correlation). This was done using both fold change and FPKM values. Using Random Forest (RF) from Python package Scikit-Learn , Spearman’s correlation and Partial Correlation (Corpcor). For each enzymatic gene (annotated and unknown), its expression correlation with each annotated SM/GM/DF gene was calculated (excluding self-correlation) using each method, each expression measure (fold change or FPKM) and each individual expression data set (with a distinct Gene Expression Omnibus GSE number), combination of data sets and all data sets combined https://phytozome.jgi.doe.gov/pz/portal.html), Sol Genomics Network , PlantGenIE (http://plantgenie.org/) or NCBI (www.ncbi.nlm.nih.gov/genome): Physcomitrella patens 318 v3.3 (Phytozome), Marchantia polymorpha 320 v3.1 (Phytozome), Selaginella moellendorffii 91 v1.0 (Phytozome), Picea abies V1.0 (PlantGenIE), Amborella trichopoda 291 v1.0 (Phytozome), Oryza sativa 323 v7.0 (Phytozome), Brassica rapa 277 V1.3 (Phytozome), Capsella rubella 183 V1.0 (Phytozome), Arabidopsis thaliana 167 TAIR10 (Phytozome), Arabidopsis lyrata v2.1 (Phytozome), Medicago truncatula 285 Mt4.0v1 (Phytozome), Vitis vinifera 145 Genoscope 12x (Phytozome), Aquilegia coerulea V3.1 (Phytozome), Populus trichocarpa 210 v3.0 (Phytozome), Theobroma cacao 233 v1.1 (Phytozome), Coffea canephora (SGN), Ipomoea trifida V1.0 (NCBI), Solanum tuberosum V3.4 (SGN), Solanum pennellii SPENNV200 (NCBI), S. lycopersicum V2.5 (NCBI), Capsicum annuum CM334 v.1.55 (SGN), Capsicum annuum var. glabriusculum V2.0 (SGN), Nicotiana tabacum TN90 AYMY-SS NGS (SGN), Nicotiana tomentosiformis V01 (NCBI), Solanum melongena r2.5.1 (SGN) and Petunia axillaris V1.6.2 (SGN).Orthologs and duplication nodes were determined using OrthoFinder . For inpE-value < 0.001), Markov clustering (inflation parameter = 0.1) to create initial orthogroups and dendroblast to create distance matrices between protein sequences of genes within each initial orthogroup. Initial gene trees were created using OrthoFinder. Three initial orthogroups were found to contain a single copy gene from each of the 26 species. Protein sequences of genes in each of these three orthogroups were aligned with MAFFT (dN/dS (non-synonymous to the synonymous substitution rate ratio) was calculated with the yn00 program using PAML version 4.4.5  or the Kruskal–Wallis test followed by Dunn Pairwise Comparisons (for >2 groups) was used for tests of significance. Statistical results are in Statistical calculations were performed using R and Python. For discrete features, their relationships with SM/GM designations were determined by the Fisher’s exact test. For continuous data, either the Mann–Whitney https://github.com/ShiuLab/ML-Pipeline. For each model, 10 % of the data was withheld from training as an independent, testing set. The remaining 90 % was used for training. Because the data set was unbalanced , 100 balanced data sets were created from random draws of GM genes to match the number of SM genes. Using the training data, grid searches over the parameter space of RF and SVM were performed. The optimal hyperparameters identified from the search were used to conduct a 10-fold cross-validation run  and recall (proportion of genes correctly predicted). In order to accurately compare across models that had different training sets, we compared using only one algorithm, RF, so that we would know that differences would be due to training sets and not the algorithm.Multiple prediction models were made using the Python Sci-kit learn package  used to F-measure was maximized. The models also have an importance score for each input feature, which takes into account the weight of the feature by assessing how well the feature (node) splits the data between SM and GM genes in a decision tree in the ‘forest’ and this is weighted by the proportion of samples reaching that node (impurity score). The decrease in impurity score from each decision tree is averaged across all decision trees in the forest so that the higher the number, the more important the feature  lists the shared features and their values for Arabidopsis and tomato. For binary data, the features that were shared by both species were kept. These included two types of binary features: (i) protein domains: ~4000 Pfam domains common between Arabidopsis and tomato; (ii) evolutionary features: presence of a homolog in one of the 26 species, pseudogene paralog and tandem paralog, and whether the most recent duplication events took place in the lineages leading to the nodes shared by both species (Nodes 0–7). The shared features also included the following continuous features: gene family size, genomic cluster gene count, median/maximum dN/dS values between genes and their homologs in each of the 26 species, median/maximum dN/dS values between genes and their paralogs and expression-based features. To generate shared expression features, expression data were placed into four categories—abiotic, biotic, hormone and development—in both species. For each category, the Arabidopsis expression breadth, breadth of differential expression and co-expression correlation values using PCC were obtained from an earlier study . For other measure of model performance, see  F-measure = 0.74 [see  ]. The Model 1 F-measure is significantly better than a random guess (0.5) but far from perfect (1). Using Model 1, 76.6 % of annotated SM genes and 71.0 % of annotated GM genes had predictions consistent with their TomatoCyc annotations  relative to GM genes in both cross-species  is also an important feature [see  ]. For example, many specialized metabolites important for fruit flavour and colour are produced during tomato fruit development  was significantly better than a random guess (0.5), 29 % of GM genes were mis-predicted as SM and 23 % of SM genes were mis-predicted as GM when using an SM score threshold determined based on the optimal F-measure , has a maximum dN/dS of 0.25 with its C. canephora homolog, which is much higher than that observed for GM→GM genes  genes were also GM→SM genes with high dN/dS values. These genes, GAME4, GAME12 and GAME17, are involved in steroidal glycoalkaloids production and should be considered SM genes. These examples demonstrate that a subset of mis-predictions is likely due to mis-annotation. In contrast to GM→SM genes, SM→GM genes have a maximum dN/dS score (median = 0.27) from comparisons to tomato paralogs that is significantly below that for SM→SM genes  and genes annotated as SM but predicted as GM (SM→GM) defied the general trend in the evolutionary feature, maximum [see  and  ] and protein domain compositions [see  ]. In general distributions of feature values for mis-predicted GM→SM genes mirrored those for annotated SM genes and feature distributions for SM→GM genes were similar to the overall distributions for annotated GM genes. These findings indicate that mis-predicted genes tend to possess feature values that are deviated from the norms, where some SM genes in TomatoCyc looked more like GM genes and some GM genes looked more like SM genes. An open question is whether these mis-predicted genes were mis-annotated in the first place or if they were correctly annotated but incorrectly predicted by a faulty model. This prompted us to look more closely at mis-predicted genes to see if their annotations were supported by compelling experimental evidence.Other evolutionary properties, duplication and expression features were skewed for mis-predicted genes. For example, while the general trend of SM genes is to be in larger gene families than GM genes, but GM→SM genes tended to belong to larger gene families (median = 5) than those with consistent GM annotations/predictions  and vice versa with SM genes . Additioin vitro enzyme activity and/or in planta characterization (see Methods). These 88 genes are collectively referred to as the benchmark set, and the curated evidence supporting their SM/GM/DF designations is shown in Based on comparison of feature value distributions, mis-predicted genes tend to possess properties more similar to the class (GM or SM) they were mis-predicted as. This is not a surprising outcome because our explicit goal was to learn about generalizable differences between annotated GM and SM genes. The unresolved question is why mis-predictions occur. Three factors may account for mis-predictions: (i) the genes were annotated correctly, and Model 1 was incorrect; (ii) Model 1 made correct predictions, but the annotations were incorrect; and (iii) both annotations and predictions were correct, because these genes have roles in both GM and SM, i.e. they have DFs. To assess these possibilities, we manually curated a set of 88 tomato genes (83 with annotations in TomatoCyc) encoding enzymes classified as SM, GM or DF based on published evidence of Methylketone synthase (XP_010323708), GAME4, GAME12 and GAME17. The three GAME genes contribute to glycoalkaloid biosynthesis in several Solanaceae species , which acquired a role in an SM pathway after the duplication of an ancestral IPMS gene involved in amino acid metabolism (GM pathway). IPMS3 is a tissue-specific SM gene involved in acylsugar production in glandular-trichome tip cells and is curated as an SM gene based on empirical evidence  are manually curated as DF genes, but annotated by TomatoCyc as GM and predicted by Model 1 as SM. The challenge in classifying these genes might arise from the fact that GGPS enzymes catalyse core reactions in isoprenoid biosynthesis, an ancient and diverse pathway that leads to the synthesis of both GMs and lineage-restricted SMs  were correctly predicted by Model 1 [see  ]. Taken together, our Model 1 predictions were mostly consistent with the SM benchmark classifications. However, the model clearly had trouble predicting known GM genes. With regard to TomatoCyc-annotated genes, the opposite was true—24 of 29 (82.8 %) benchmark GM genes were correctly annotated as GM, and 37 of 47 (78.7 %) benchmark SM genes were correctly annotated as SM. Therefore, for SM gene prediction, Model 1 has a lower error rate (11.8 %) compared with the TomatoCyc annotation (21.3 %), indicating that a higher proportion of benchmark SM genes were annotated in TomatoCyc than GM genes. However, for benchmark GM genes, Model 1 has a higher error rate (46 % of benchmark GM genes predicted as SM genes) than the TomatoCyc annotation (14.3 % of benchmark GM genes predicted as SM).Next, we focused on comparing the manually curated benchmark set to Model 1 predictions. We found that 17 out of 29 (58.6 %) total benchmark GM genes, and 13 of the 24 benchmark GM genes that were annotated as GM by TomatoCyc (54 %), were incorrectly predicted as SM by Model 1 F-measure = 0.74, same as Model 1; see  Based on analysis of the benchmark data, there are two major sources for mis-predictions. The first is that a subset of the TomatoCyc-annotated SM or GM genes were incorrectly annotated, and these mis-annotations were propagated into Model 1. The second is that Model 1 predicts these genes correctly. These two explanations are not mutually exclusive, and the extent to which each contributes to mis-predictions remains to be determined. To determine the most likely reason for the mis-predictions and to improve upon Model 1, we used both the benchmark gene set and the TomatoCyc annotations to build a new model (referred to as Model 2), but this did not improve the prediction accuracy  from the training set. Using this filtered training data set, a new tomato data-based model, Model 5, was generated using the same shared feature set between Arabidopsis and tomato for Models 3 and 4  will lead to suboptimal models making erroneous predictions. On the other hand, if the Arabidopsis Model 3-based predictions are completely uninformative, the removal of genes from the training set would not improve the prediction. Thus, to further test the above hypotheses, we removed TomatoCyc-annotated GM and SM genes that had contradictory predictions from Arabidopsis-based Model 3  using the same number of tomato SM and GM training genes as used for training Model 5, except that the genes were removed randomly. We found the median [see  ]. We found that in the case of filtered GM genes [see  ], the majority are most similar to the remaining, unfiltered SM genes [see  ], indicating they are mostly like average SM genes and, thus, are not simply hard-to-predict GM genes. In contrast, the SM scores of filtered SM genes [see  ] were much closer to the threshold compared to the remaining GM genes, indicating they may in fact be atypical, hard-to-predict SM genes [see  ]. However, consider separation of the filtered SM genes in the SM score space based on both Models 3 and 5, they are much more similar to GM genes than they are to SM. Thus, while there are edge cases, the improvement is likely mainly due to removal of mis-annotated genes.This improvement was likely achieved for two reasons. First, consistent with the manual annotation result reported earlier, this improvement is likely because the filtered tomato training data did not contain as many mis-annotated genes that would confuse the model. The second possibility is that filtered genes were hard-to-predict or edge cases—i.e. they simply have properties unlike the average SM or GM genes. If the filtered genes were edge cases, we would expect that their SM score would be close to the threshold SM score. To assess this, we plotted the filtered SM and GM gene SM scores between Models 3 and 5 [see  ], compared with 25 % for tomato Model 4 and 50 % for Arabidopsis Model 3 [see  and  ]. In contrast, there was no improvement in benchmark SM predictions when comparing Model 4  and are similar to Model 5 in predictions of the training, testing, benchmark genes and removed gene set [see  –]. Overall, using Arabidopsis Model 3 to remove potentially mis-annotated tomato genes, i.e. genes that were not good training examples, led to substantially improved models (Models 5 and 7).Additional models (Models 7 and 8) were trained using the same filtered gene set used in training Model 5 but with the full tomato feature data set , abscisic acid 8′-hydroxylase  and the cytochrome P450 SlKLUH (XP_004236064). In these cases, the mis-predictions were likely due to gene expression-related features. While LeACO1 exhibited a maximum log2 fold change of 7.0 based on the fruit ripening data set [see  ], which is consistent with the higher values observed for SM genes (median = 1.9) than for GM genes . Similarly, the variance of log2 fold change in expression during fruit ripening for SlKLUH is 2.5, which is consistent with significantly higher median variance for SM genes (1.5) compared with GM genes . CYP707A1 is upregulated under many developmental conditions (13), which is not typical for tomato GM genes . Additionally, the expression of LeACO1, CYP707A1 and SlKLUH correlates highly with that of other SM genes . The similarity of these expression feature values as those of SM genes likely contributed to their mis-prediction by Models 1 and 4.Models 5 and 7 substantially improved gene predictions in tomato compared with all other models because mis-annotated genes, mostly genes annotated as SM but predicted as GM by Arabidopsis Model 3, were removed from the training data. To better understand the reasons for the improvement in GM gene predictions, we looked into three examples where Models 5 and 7 predicted manually curated GM benchmark genes as GM genes, but where tomato-based Models 1 and 4 predicted the genes as SM genes: [see  ]. Similarly, when comparing Models 4 and 5, which both use the shared feature set but differ in whether filtered training data were used, the features expression breadth under development and expression correlation between SM genes were ranked higher for Model 4  than for Model 5  [see  ]. Model improvement is also due to higher ranking of evolutionary features, such as maximum dN/dS between tomato genes and C. canephora homologs, median dN/dS between tomato genes and homologs in A. lyrata, and maximum dN/dS between tomato genes and homologs in P. trichocarpa. In Model 5 these features were ranked 1, 2 and 3, respectively; in Model 4 they were ranked 2, 3 and 8, respectively; see  see  LeACO1 and CYP707A1 both have maximum dN/dS values from comparisons to C. canephora homologs (0.07) more similar to those of GM genes (median = 0.10) than to SM genes (0.17). Similarly, SlKLUH has a maximum dN/dS value from comparisons to A. lyrata of 0.11, which is closer to the GM median (0.09) than to the SM median (0.15). Because in Models 5 and 7 these dN/dS features were weighted more heavily and certain expression features were weighted less heavily, the dN/dS feature values contributed to their correct classification as GM genes.Importantly, Models 5 and 7 likely predict these three genes correctly as GM genes because of the reduced reliance of these models on features associated with gene expression. Models 1 and 7 both use the full feature set, but filtered training data were used to train Model 7. In Model 1, expression variance in fruit ripening was ranked 46 among important features, while in Model 7 it was ranked 120 PSY1 is a fruit ripening-related gene manually curated as an SM benchmark gene, but it was predicted as a GM gene by both Models 4 and 5. PSY1 represents an unusual case of duplication-associated subfunctionalization and is specifically expressed in chromoplast-containing tissues such as ripening fruits and petals , especially between tomato and C. canephora (maximum dN/dS = 0.06). Because this dN/dS feature was the most important feature for Model 5, this ultimately contributed to the mis-prediction of PSY1 as a GM gene.In addition to the features discussed thus far, we also found that gene family size was no longer the most important feature in Models 5 and 7, ranked 24 and 27, respectively, as it was Models 1, 3 and 4. Considering that some of the largest enzyme families—such as cytochrome P450 and terpene synthases—contain both SM and GM genes, this reduced importance likely contributed to improved predictions. Despite the improvement, Models 5 and 7 are by no means perfect and erroneous predictions still occur. For example, d petals . PSY1 hacopalyl diphosphate synthase  and kaurene synthase . Both CPS and KS are mis-predicted as SM genes in all models, presumably because of their high dN/dS values from comparisons to homologs in several species . These two enzymes were derived from an ancestral dual functional enzyme containing both copalyl diphosphate synthase and kaurene synthase activities . However, while our approach ensures the identification of typical SM/GM genes, SM/GM genes with atypical properties that defy the general trend still are likely mis-predicted.Other examples are two GM terpene synthases involved in the biosynthesis of gibberellin, a plant hormone : copalylMany SM genes are unknown due to the vast number of specialized metabolites are limited to specific species and SM and GM genes are difficult to distinguish because SM genes are often derived from GM genes. Additionally, many specialized metabolites of interest are found in medicinal plants or crops that are not well annotated. If data from a better annotated species such as Arabidopsis could be used, directly or indirectly, to make cross-species predictions in another species, such as tomato, this could greatly improve annotations in non-model species. Here we used machine learning to establish models for classifying genes with SM and GM functions in tomato, but consistent with the lower quality of the tomato annotation, these models established using tomato features had relatively poor performance compared with models built in Arabidopsis. We also found that a substantial number of important features and predictions differed between the models based on Arabidopsis (Model 3) and tomato (Model 4). We discovered that the differences in feature importance and model performance were likely the result of mis-annotation of some tomato genes, which contributed negatively to the performance of machine learning models. Therefore, we attempted to perform cross-species knowledge transfer by using a machine learning approach called transfer learning , where kBy filtering out TomatoCyc-annotated genes that had predictions opposite from those of the Arabidopsis-based Model 3 from the training data, we significantly improved the accuracy of tomato SM/GM gene predictions. We should emphasize that, for building the tomato Model 5, the testing genes were filtered similarly to the training set. This was intentional for testing whether removing genes with the Arabidopsis Model 3 would lead to improvement or not. If the Arabidopsis model was helpful in filtering out mislabelled tomato genes, we would expect the remaining tomato SM/GM genes to be better separated and lead to an improved tomato model. This separation would have been artificial if we used the Arabidopsis model to filter out genes and then applied the Arabidopsis model to classify tomato genes again. The improvement seen would be completely circular. Instead of the above, we established a new tomato model based on filtered SM/GM tomato annotations. Another important reason why the performance is not artificially inflated by removing genes based on Model 3 is because, if the Arabidopsis model was not effective in filtering out mislabelled data, we expected that the resulting tomato model would not improve. Compared to Model 6 that had genes randomly removed and did not see an improvement in the overall score, the model based on Arabidopsis filtered data fared much better. This improvement could also be due to the removal of hard-to-predict edge cases. We showed that filtered GM genes and, to a lesser extent, filtered SM genes have similar SM scores as remaining SM and GM genes, respectively. Thus, we demonstrated that this improvement is not simply due to the removal of hard-to-predict cases and would not have been possible without informed removal of potentially mis-annotated data. This approach can be applied more generally to any problem in a species that is relatively information-poor by transferring knowledge from an information-rich one. Using transfer learning we may also be able to better annotate less well-studied species.dN/dS) are incorporated. However, it is a challenge to generate high-quality instances, and expert knowledge dictates what kinds of features are incorporated. In addition, the representation of genes that are considered ‘atypical’ in the model can be limited by our ability to scour the literature for novel features to represent these genes. The inability to apply a general model like this one to atypical genes could include genes which encode enzymes but do not have direct roles in plant metabolism, such as regulatory genes like transcription factors or kinases, or ambiguous SM genes that may have roles in GM processes. For these types of genes, new training sets must be obtained in order to build a model that could correctly predict them.It is important to note that a limitation of the transfer learning approach is that it is only useful for transferring knowledge, mechanisms or phenomena that are similar across species. In our study, the transfer learning approach worked well by identifying GM genes conserved across species, and likely by default impacted the prediction of SM genes. This is likely because SM pathways are by definition specialized—what you learn in one species does not necessarily apply to another. A specific example of where transfer learning can suffer is in predicting genes with atypical properties. The machine learning approach excels at spotting patterns in data, and the performance of machine learning models improves as more high-quality instances  and more informative features  classification problem. Over the course of evolution, SM pathways may branch off from GM pathways or some SM pathways may ultimately become GM pathways because of increasingly wider taxonomic distribution. Thus, the extent to which a gene is considered to be SM is likely continuous, where genes at the end of an SM pathway may be more ‘SM-like’ than genes at the beginning of the pathway, which may be linked to GM pathways. The question is how to define the degree of involvement of a gene in SM pathways and determine whether continuous SM scores, where GM and SM genes have low and high scores, respectively, are good proxies for involvement in these pathways. This can be accomplished by mapping SM scores to pathways to see if they are predictive of where a gene lies in a pathway.The following additional information is available in the online version of this article—F-measure is shown on the y-axis and model is shown on the x-axis. Model type is denoted by colour. Gray indicates Models 1–8 variants  that are not described in the text. RF: Random Forest. SVM: Support Vector Machine. featsel25-1000: features selected, sets of 25–1000. For model names, see  y-axis and all scores are normalized to the score of the most important feature, which was set as 1. Red bars represent features that are enriched for specialized metabolism (SM) genes while the blue bars represent features enriched for general metabolism (GM) genes. Features are listed along the x-axis, with the colour denoting the feature category.U-test. (A–H) Distributions of the maximum or median dN/dS value for a given gene relative to their homolog in P. patens, S. moellendorffii, A. trichopoda, O. sativa, B. rapa, A. coerulea, P. trichocarpa and S. pennellii. (I and J) Distributions of log 10 of median FPKM values for the Inflorescence data set and Root data set. (K) Percent of genes with a given Pfam domain. Overrepresentation (+) and underrepresentation (−) was determined using those genes with a P-value less than 0.05 from a Fisher’s exact test between SM and GM genes with Benjamin–Hochberg multiple testing correction.post hoc comparisons were made using Dunn’s test. Different letters indicate statistically significant differences between groups (P < 0.05). For binary data (K), overrepresentation (+) and underrepresentation (−) were determined by the Fisher’s exact test where (+) is significant overrepresentation of a predicted class and (−) is significant underrepresentation. A P-value < 0.05 after Benjamin–Hochberg multiple testing correction was considered significant. (A–H) Distributions of the maximum or median dN/dS value for a given gene from comparisons to its homolog in P. patens, S. moellendorffii, A. trichopoda, O. sativa, B. rapa, A. coerulea, P. trichocarpa and S. pennellii. (I and J) Distributions of log10 (median FPKM) values for the Inflorescence (I) and Root (J) data sets. (K) Percentage of genes with a given Pfam domain.Solanum lycopersicum and A. thaliana model comparison and model performance. (A and B) Comparison of the specialized metabolism (SM) score distributions for tomato Model 4 (y-axis) and Arabidopsis Model 3 (x-axis). Support Vector Machine (SVM) and a shared feature set were used for both models. Density of data points ranges from high (yellow) to medium (blue-purple) to low (white). (A) SM scores for general metabolism (GM) genes; (B) SM scores for SM genes; (C–F) feature distributions for annotated SM and GM genes that are predicted as SM or GM genes by Arabidopsis Model 3 and tomato Model 4. The x-axis lists the annotations for each group of genes predicted using Arabidopsis Model 3 and tomato Model 4. P-values are from the Kruskal–Wallis test and post hoc comparisons were made using the Dunn’s test. Different letters indicate statistically significant differences between groups (P < 0.05). (C) Maximum Pearson’s Correlation Coefficient (PCC) between a given gene and all other SM genes under stress conditions; (D) maximum PCC between a given gene and all other SM genes during development; (E) maximum PCC between a given gene and all other GM genes under hormone treatment; (F) normalized median dN/dS values between tomato or Arabidopsis genes and their homologs in O. sativa.A. thaliana mis-predictions removed). Plots A and B show distributions of specialized metabolism (SM) likelihood scores. (A) Model 5 test set SM and general metabolism (GM) genes, which were held out from the model building process completely. (B) TomatoCyc SM and GM genes with annotations opposite to Arabidopsis Model 3 predictions removed from the filtered training set. For plots (A and B): SM likelihood score is shown on the x-axis, number of genes is shown on the y-axis. Prediction threshold, based on the score with the highest F-measure, is indicated by the dotted line, and predicted SM genes are shown to the right of the line in red while predicted GM genes are shown to the left of the line in blue. (C) Bar plots showing the percentage of manually annotated benchmark genes predicted as SM or GM by Model 5. For the first bar plot, the original annotation from TomatoCyc is shown first, followed by the benchmark annotation and then the prediction. The second bar plot shows overall benchmark predictions (not divided by TomatoCyc annotations).x-axis and Tomato Model 5 on the y-axis. Colour: data point density ranges from high (yellow) to medium (purple), to low (fading purple). (A) Filtered general metabolism (GM) genes—removed from Model 5 training; (B) remaining specialized metabolism (SM) genes—kept in Model 5 training; (C) filtered SM genes—removed from Model 5 training; (D) remaining GM genes—kept in Model 5 training.A. thaliana mis-predictions removed). (A) Schematic diagram showing the application of tomato Model 7 to tomato. The full tomato feature data set was used to build a binary model using TomatoCyc specialized metabolism (SM) and general metabolism (GM) annotations after removing genes mis-predicted by Arabidopsis Model 3. The model was then applied to tomato genes. (B) TomatoCyc filtered training set SM and GM genes from tomato Model 7. (C) Model 7 test set: SM and GM genes, which were held out completely from the tomato Model 7 building process. (D) Bar plot showing the percentage of manually annotated benchmark genes predicted as SM or GM by Model 7. The original annotation from TomatoCyc is shown first, followed by the benchmark annotation and then the prediction. (E) SM and GM genes removed from Model 7 training set. (F) Unannotated tomato enzymes. For plots : SM likelihood score is shown on the x-axis, number of genes is on the y-axis. Prediction threshold, based on the score with the highest F-measure, is indicated by the dotted line, and predicted SM genes are shown to the right of the line in red while predicted GM genes are shown to the left of the line in blue.diaa005_suppl_Supplementary_Figure_S1Click here for additional data file.diaa005_suppl_Supplementary_Figure_S2Click here for additional data file.diaa005_suppl_Supplementary_Figure_S3Click here for additional data file.diaa005_suppl_Supplementary_Figure_S4Click here for additional data file.diaa005_suppl_Supplementary_Figure_S5Click here for additional data file.diaa005_suppl_Supplementary_Figure_S6Click here for additional data file.diaa005_suppl_Supplementary_Figure_S7Click here for additional data file.diaa005_suppl_Supplementary_Figure_S8Click here for additional data file.diaa005_suppl_Supplementary_Figure_S9Click here for additional data file.diaa005_suppl_Supplementary_Table_S1Click here for additional data file.diaa005_suppl_Supplementary_Table_S2Click here for additional data file.diaa005_suppl_Supplementary_Table_S3Click here for additional data file.diaa005_suppl_Supplementary_Table_S4Click here for additional data file.diaa005_suppl_Supplementary_Table_S5Click here for additional data file.diaa005_suppl_Supplementary_Table_S6Click here for additional data file.diaa005_suppl_Supplementary_Dataset_S1Click here for additional data file.diaa005_suppl_Supplementary_Dataset_S2Click here for additional data file.Data and materials availability are as noted in the Methods section.


Luminal subtype is the most common subgroup of breast cancer (BC), accounting for more than 70% of this cancer. Long non-coding RNAs (lncRNAs) are a group of RNAs which play critical roles in diverse cellular processes. It is proved that dysregulation of them can contribute to the development of various cancers, including BC. LINC00961 was reported to be downregulated in several cancers, however, its expression level in BC remains largely unknown. The purpose of the present study was to investigate the possible role of LINC00961 in luminal A and B subtypes of BC.To obtain novel lncRNAs associated with different cancers and differentially expressed lncRNAs (DElncRNAs) between BC tumor and normal tissues, Lnc2Cancer and GDC databases were used, respectively. After performing literature review, the expression level of the selected lncRNA (LINC00961) was evaluated in 79 luminal A and B BC specimens and adjacent non-cancerous tissues by Quantitative Reverse Transcription PCR (qRT-PCR). LINC00961 expression was also evaluated in two luminal A BC cell lines, compared to a normal breast cell line. The comparison of the differences between tumor and adjacent non-tumor samples was performed by paired sample t-test. Moreover, correlation analysis between LINC00961 expression and clinicopathological features was performed using the chi-square, fisher exact, and independent t-test. In order to investigate the possible roles of LINC00961 in luminal A and B BC, different bioinformatics analyses such as functional annotation of the LINC00961 co-expressed genes and protein–protein interaction (PPI) networks construction were also performed.LINC00961 was selected as a significant DElncRNA which had not been studied in BC. According to q-RT PCR assay, LINC00961 was downregulated in luminal BC tissues and cell lines. Its expression was correlated with smoking status and the age of menarche in luminal BC patients. Also, the results of the bioinformatics analysis were consistent with the data obtained from q-RT PCR assay. The final results indicated that LINC00961 might be involved in multiple cancer-associated pathways such as chemokine, Ras and PI3K–Akt signaling pathways, GPCR ligand binding, and signal transduction in luminal subtypes of BC. CDH5, GNG11, GNG8, SELL, S1PR1, CCL19, FYN, ACAN, CD3E, ACVRL1, CAV1, and PPARGC1A were identified as the top hub genes of the PPI networks across luminal subgroup.Our findings suggested that LINC00961 was significantly downregulated in luminal A and B subtypes of BC. Moreover, bioinformatics analysis provided a basis for better identification of the potential role of LINC00961 in luminal subtype of BC. The luminal A subtype has the highest frequency (50%) of invasive breast cancer, showing the best prognosis among all of BC subtypes. However, luminal B with positive expression of ER/PR and variable expression of HER2/neu constitutes 20% of invasive breast cancers. This subgroup showed worse prognosis and higher levels of ki-67 than the luminal A subtype [As breast cancer (BC) is one of the most common human neoplasms, it poses major financial challenges to health systems and families of patients globally , 2. It i subtype . It has requency 0% of invLong non-coding RNAs (lncRNAs) include a class of non-coding RNAs with a length of more than 200 nucleotides –8. The lLINC00961 (SPAAR), located in chromosome 9 (9p13.3), was found to act as a tumor suppressor in various cancers, such as oral squamous cell carcinoma (OSCC) , skin meIn this research, the expression level of lncRNA LINC00961 was examined in luminal A and B BC tissues and luminal A cell lines compared with non-tumoral ones by qRT-PCR. Then, the correlation between expression level of LINC00961 and the clinicopathological features were explored. Moreover, bioinformatics and systems biology studies were accomplished.Lnc2Cancer database was used in order to identify novel lncRNAs associated with various cancers. This database provides 4989 lncRNA-cancer correlations between 1614 lncRNAs and 165 cancer subgroups . Also, aIn the current case–control study, 79 paired tissues  were obtained from luminal BC patients who had not received any chemotherapy or hormone therapy prior to surgery. The samples were collected from Breast Cancer Research Center Biobank (BCRC-BB) . BCRC-BB+, HER2−) BC cell lines named MCF-7 and T47D were cultured in DMEM  added with 10% fetal bovine serum , 100 U/ml penicillin, and 100 μg/ml streptomycin . MCF-7 is a human epithelial breast adenocarcinoma cell line, while T47D is a human epithelial breast ductal carcinoma cell line. MCF10-A, a normal breast cell line, was maintained in DMEM supplemented with 5% fetal bovine serum, epidermal growth factor (EGF) (20 ng/ml), insulin (10 µg/ml), hydrocortisone (0.5 mg/ml), cholera toxin (100 ng/ml), penicillin (100 U/ml), and streptomycin (100 μg/ml). The cells were incubated in an incubator containing 5% CO2 at 37 °C.Two luminal A  and luminal A cell lines were extracted using RiboEx™ (GeneAll), following the manufacturer's instructions. Then, the obtained RNA was dissolved in diethyl pyrocarbonate-treated (DEPC) water. Next, RNA quality and quantity were checked using gel electrophoresis and spectrophotometry, respectively. The samples were treated with RNase inhibitor and afterwards, 1 µg of RNA samples was converted into cDNA using the Biobasic cDNA Synthesis Kit (5× All-In-One RT MasterMix) in accordance with the manufacturer’s protocol. Finally, qRT-PCR analysis was performed in duplicate on LightCycler96 Roche by utilizing AMPLIQON Real Q Plus 2× Master Mix Green low ROX. The condition of qRT-PCR was set at 95 °C for 15 min, and 40 cycles of 95 °C for 10 s and 60 °C for 25 s. The annealing temperature was determined by obtaining the temperature at which each pair of primers had the best efficiency. β2M was used as a reference gene. Data was analyzed and calculated using the 2The discovery of lncRNA expression patterns provides fundamental information for realizing the biological functions of lncRNAs in cells. To this end, the differential expression analysis of lncRNA LINC00961 was explored using different databases.First, the Expression Atlas was used to check the expression of LINC00961 in normal breast tissues. The Expression Atlas is an open science resource that provides a comprehensive representation of gene expression pattern in various tissues or cell types of different species by using microarray and RNA‐seq datasets. TPM (transcripts per million) and FPKM (fragments per kilobase of exon model per million reads mapped) are used for estimating gene expression in this software . Then, GGEPIA , an application based on the Genotype-Tissue Expression (GTEx) and TCGA datasets, was utilized for obtaining gene expression differential analysis in luminal A and B BC . Then, LFPKM file of 420 luminal BC samples, including 230 luminal A and 190 luminal B samples, were downloaded from the GDC database and a list of co-expressed genes with LINC00961 across the mentioned samples was obtained. Pearson correlation analysis of the obtained LINC00961 co-expressed genes has been computed using a standard method. Then, analyzing the expression of LINC00961 and its co-expressed genes across luminal A and B Breast invasive carcinoma (BRCA) dataset was done by the GEPIA web server .The Cancer Genome Atlas (TCGA) copy number portal was used to search for the somatic copy number alterations (SCNA) of LINC00961. The TCGA is a broad portal that helps increasing our knowledge of the molecular basis of different cancers by investigating the SCNA of different genes which drive cancer progression. It includes information on 33 different cancers from 11,328 patients .Expression of LINC00961 in different subtypes of BC was evaluated by TANRIC database .DAVID  is an integrated bioinformatics resource which assists in deep understanding of biological mechanisms by providing an expansive set of functional annotation tools , 32. GenThe obtained data were analyzed using IBM SPSS Statistics . Paired sample t-test was used for comparison of the differences between the means of tumoral and adjacent non-tumoral samples. The data is represented as mean ± standard deviation (SD). In order to perform clinicopathological correlation analysis, median expression of LINC00961 was used as a cut-off value for dividing 79 BC patients into two categories: relatively high expression of LINC00961 (n = 39) and relatively low expression of LINC00961 (n = 40). The chi-square and Fisher exact test for qualitative and independent t-test for quantitative variables were used to determine the difference of clinicopathological characteristics between low and high expression groups of LINC00961. Moreover, the receiver operating characteristic (ROC) curve was illustrated by IBM SPSS Statistics. This curve was used to evaluate the diagnostic effectiveness and clinical value of LINC00961 in luminal A and B subtypes of BC. p-value < 0.05 was considered as significant.luminal A = − 2.08, padj. = 8.45E−14) .According to the lnc2Cancer database, a list containing more than 4000 important lncRNAs associated with different cancers was obtained. Also, lists of 1178 and 1266 different DElncRNAs in luminal A and B BC paired tissues  were obtained, respectively. According to these two lists of lncRNAs and literature review, LINC00961 was selected as a target lncRNA of the present study     analysis consists of different terms for characterizing the activity and function of various genes. A list of GO terms for biological process (BP), cellular component (CC), and molecular function (MF) were obtained as a result of GO term enrichment analysis performed by DAVID. The significant GO terms  of these three groups were listed in Additional file Finally, using STRING database, it was found that of the 358 co-expressed genes with LINC00961 across luminal subtype of BC, 100 genes have strong interactions (Interaction score > 0.4) with each other Fig. . MoreoveThe luminal subtype of BC accounts for more than 70% of this cancer , 8. LncRIn the present study, LINC00961 was identified as a novel dysregulated lncRNA in luminal BC. LINC00961 was significantly downregulated in luminal A and B BC tissues and luminal A cell lines , compared with their adjacent non-tumoral tissues and non-malignant breast cell line (MCF10A), respectively. Downregulation of LINC00961 was detected in 86% of cases, which is a considerable fraction. The upregulation of this lncRNA in the remaining 14% of the samples may be due to lncRNA's inter-individual expression diversity . MoreoveLINC00961 expression was negatively correlated with smoking in luminal BC patients. Also, LINC00961 expression correlated with the age of menarche in luminal BC patients. However, due to the influence of other genetic and environmental factors in the age of menarche, more research is needed on this factor.Further analysis of the expression and potential role of LINC00961 in luminal BC was performed by obtaining a combination of various databases. According to subtype PAM50 classification from TANRIC database, LINC00961 expression level was lower in luminal A, luminal B and HER2 subtypes compared to other subtypes of BC. Also, results obtained from different datasets in GENEVESTIGATOR and GEPIA web server were consistent with the data of our study which indicated downregulation of LINC00961 in luminal BC tissues and cell lines.+ HER2− patients, including luminal A and B BC patients. It is proved that different substitution mutations could have various effects on the secondary structure of RNAs. On the other hand, RNA secondary structure plays vital roles in RNA regulation, particularly in the miRNA’s interactions and RNA-binding proteins [According to the data obtained from the TCGA copy number portal, LINC00961 has not been significantly amplified or deleted in invasive breast carcinoma dataset. Also, it was found that all LINC00961 mutations in BC were substitutions occurring more frequently in ERproteins . Also, sproteins . TherefoAccording to the “guilt-by-association” principle, it is probable that a set of genes which is involved in a same biological pathway are controlled by a common regulatory system and therefore, are co-expressed with each other . In otheData obtained from the STRING database illustrated that 100 of the co-expressed genes with LINC00961 had significant interactions with each other. These data confirmed the hypothesis that LINC00961 and its co-expressed genes are involved in the same biological pathways. These genes were shown to be mostly involved in multiple cancer-associated pathways, such as chemokine, Ras and PI3K–Akt signaling pathways, GPCR ligand binding, and signal transduction. Chemokine signaling has been shown to enhance cell migration and metastasis in BC. For example, CCL2/CCR2 chemokine signaling has been overactive in several cancers, including breast cancer, and has been associated with poor prognosis . Also, CTaken together, to the best of our knowledge, the present study is the first project that proves the dysregulation of LINC00961 in luminal A and B subtype of BC so far. Also, the bioinformatics analysis performed in this study could provide basic information about the potential role of lncRNA LINC00961 in luminal BC. Nonetheless, the current study had some limitations related to functional analysis which should be considered to provide a detailed insight into the mechanism of downregulation of LINC00961 in breast carcinogenesis. Another limitation was the lack of complete overall survival data of BC patients. Investigating whether LINC00961 can be used as a prognostic biomarker for luminal subtypes of BC require data on the overall survival of BC patients in the future follow-up studies.In conclusion, according to the q-RT PCR assay and the bioinformatics analyses, LINC00961 was considerably downregulated in luminal A and B BC tissues and cell lines. Also, according to different datasets, LINC00961 might be involved in various cancer-related processes, such as chemokine, Ras and PI3K–Akt signaling pathways, and GPCR ligand binding which still requires more detailed research. Collectively, the findings of the present study could provide a new insight into the possible role of LINC00961 in luminal subtype of breast cancer.Additional file 1: Figure S1. The baseline expression of LINC00961 across normal breast tissues, obtained using Gene Expression Atlas software.Additional file 2: Figure S2. LINC00961 expression level across 12 breast cancer categories, including 1910 luminal A and B samples, compared to normal breast tissue, obtained by GENEVESTIGATOR software.Additional file 3: Figure S3. LINC00961 expression level across 13 luminal A and B breast cancer cell lines, compared to 3 normal breast cell lines, obtained by GENEVESTIGATOR software.Additional file 4: File S1. The list of co-expressed genes with LINC00961, across luminal A and B samples.Additional file 5: File S2. The expression level of the co-expressed genes with LINC00961.Additional file 6: File S3. The list of LINC00961 mutations across breast cancer datasets, obtained using the ICGC portal.Additional file 7: Figure S4. Differential LINC00961 expression analysis across different subtypes of breast cancer, obtained using TANRIC database.Additional file 8: File S4. Some of the significant GO terms associated to biological process, molecular function and cellular component of the LINC00961 co-expressed genes, obtained using DAVID database.Additional file 9: Figure S5. The summarization of all GO terms related to biological process. Each node is a GO term and node color indicates the statistical significance of p‐value. Node size suggests how frequent the GO term is among the complete set. The summarization and visualization were done by the REViGO and Cytoscape, respectively.Additional file 10: Figure S6. The summarization of all GO terms related to cellular component. Each node is a GO term and node color indicates the statistical significance of p‐value. Node size suggests how frequent the GO term is among the complete set. The summarization and visualization were done by the REViGO and Cytoscape, respectively.Additional file 11: Figure S7. The summarization of all GO terms related to molecular function. Each node is a GO term and node color indicates the statistical significance of p‐value. Node size suggests how frequent the GO term is among the complete set. The summarization and visualization were done by the REViGO and Cytoscape, respectively.Additional file 12: Figure S8. The expression level of 7 out of 12 hub genes, retrieved by GEPIA web server.


The COVID-19 pandemic has placed significant pressure on health and social care. Survivors of COVID-19 may be left with substantial functional deficits requiring ongoing care. We aimed to determine whether pre-admission frailty was associated with increased care needs at discharge for patients admitted to hospital with COVID-19.Patients were included if aged over 18 years old and admitted to hospital with COVID-19 between 27 February and 10 June 2020. The Clinical Frailty Scale (CFS) was used to assess pre-admission frailty status. Admission and discharge care levels were recorded. Data were analysed using a mixed-effects logistic regression adjusted for age, sex, smoking status, comorbidities, and admission CRP as a marker of severity of disease.Thirteen hospitals included patients: 1671 patients were screened, and 840 were excluded including, 521 patients who died before discharge (31.1%). Of the 831 patients who were discharged, the median age was 71 years  and 369 (44.4%) were women. The median length of hospital stay was 12 days (IQR 6–24). Using the CFS, 438 (47.0%) were living with frailty (≥ CFS 5), and 193 (23.2%) required an increase in the level of care provided. Multivariable analysis showed that frailty was associated with an increase in care needs compared to patients without frailty (CFS 1–3). The adjusted odds ratios (aOR) were as follows: CFS 4, 1.99 (0.97–4.11); CFS 5, 3.77 (1.94–7.32); CFS 6, 4.04 (2.09–7.82); CFS 7, 2.16 (1.12–4.20); and CFS 8, 3.19 (1.06–9.56). Around a quarter of patients admitted with COVID-19 had increased care needs at discharge. Pre-admission frailty was strongly associated with the need for an increased level of care at discharge. Our results have implications for service planning and public health policy as well as a person's functional outcome, suggesting that frailty screening should be utilised for predictive modelling and early individualised discharge planning. COVID-19 has resulted in large numbers of people being admitted to hospital worldwide . The UK People at higher risk of requiring hospital admission due to COVID-19 are older with greater levels of multimorbidity and frailty , 9. FraiTo date, most studies in older people with COVID-19 have focussed on mortality with little attention to functional outcomes. However, in older adults, mortality is not always the most important outcome. Previous studies assessing the impact of frailty on quality of life in other conditions have demonstrated that for older adults, independent living is a more important outcome than death. A higher value is placed on continuing day-to-day societal roles, reducing the risk of isolation and loneliness and avoiding poor future health outcomes –16. CurrData were obtained as part of a multicentre observational study: COPE (COVID-19 in Older People study). The study was authorised by the Health Research Authority (20/HRA/1898) in the UK and the Ethics Committee of Policlinico Hospital Modena (Reference 369/2020/OSS/AOUMO) in Italy. The full study details can be found within the COPE protocol , and theThe COPE-Discharge study used an established network of twelve UK sites and one Italian site. The UK centres included Ysbyty Ystrad Fawr in Caerphilly, Royal Gwent Hospital in Newport, Nevill Hall Hospital in Abergavenny, University Hospital of Wales in Cardiff, Southmead Hospital in Bristol, Aberdeen Royal Infirmary, Royal Alexandra Hospital in Paisley, Inverclyde Royal Hospital, Salford Royal Hospital, Glasgow Royal Infirmary, Maidstone Hospital, and Ysbyty Gwynedd in Bangor. The Italian centre was the University Hospital of Modena Policlinico.Each site research team screened hospital admission lists daily. The ethical approval was such that formal written consent from participants was deemed as not being required as all data were routinely collected in hospital records..The study included consecutive hospitalised patients aged 18 years or older with a confirmed diagnosis of COVID-19 admitted between 27 February and 10 June 2020; diagnostic criteria included laboratory-confirmed SARS-CoV-2-positive swab or a clinical diagnosis of COVID-19 based on signs, symptoms, and supporting radiology. Patients were excluded due to missing care level at admission and discharge, were not discharged from hospital, and that died prior to dischargeThe primary outcome was increased care needs at discharge. Care was recorded as an ordinal variable, with seven levels of increasing dependence care needs: at home without formal carers, own home with carers , intermediate care, increased number of daily carer visits, sheltered care, residential home, and nursing home.. Sheltered care was the accommodation of private independent units with shared facilities such as gardens and lounges and a warden on site. Residential care was defined as 24-h supported care managed by non-nursing trained care staff. Nursing care comprised service users receiving 24-h support from nursing staff, requiring assistance with most personal daily activities, or support with complex physical and/or psychological needs. Intermediate care varied depending on local provision and was defined as short-term care  designed to facilitate the transition from hospital to home [The number of daily carer visits required by patients was measured at admission and discharge to home , 19. IntDemographic and clinical characteristics recorded at admission were age, sex, smoking status , C-reactive protein (CRP) as a marker of disease severity, estimated glomerular filtration rate (eGFR), previous history of coronary artery disease (CAD), diabetes mellitus, chronic obstructive pulmonary disease (COPD), and hypertension .Frailty was scored based on a functional status history from 2 weeks prior to admission and was measured using the Clinical Frailty Scale (CFS). The CFS is a 9-point score, from 1 being very fit, 2 well, 3 managing well, 4 living with very mild frailty, 5 living with mild frailty, 6 living with moderate frailty, 7 living with severe frailty, 8 living with very severe frailty, and 9 terminally ill but otherwise living with severe frailty . For the2). Both crude odds ratios (OR) and adjusted odds ratios (aOR) were calculated with 95% confidence intervals (95% CI). The analysis was carried out using Stata version 15.We analysed the change in care level using a mixed-effects logistic regression, fitted with a random effect model to account for variation occurring at each hospital site. Care level was associated with baseline frailty and adjusted for patient age group , sex, smoking status , CRP , diabetes (no/yes), hypertension (no/yes/yes and on treatment), coronary artery disease (no/yes), and reduced renal function  lived independently at home without a carer and a further 74 (8.9%) lived at home with carer support. The remaining 19.1% were living in either a sheltered, residential, or nursing care setting Table . Across As shown in Table p = 0.004), CFS 5 OR = 5.63 , CFS 6 OR = 5.90 , CFS 7 OR = 3.50 , and CFS 8 OR = 4.30 . The covariates associated with an increased level of care at discharge were age ; ≥ 80 OR = 5.79 ) and elevated CRP (≥ 40 mg/L) OR = 0.65 .In the crude logistic regression analysis, CFS was associated with an increase in care at discharge Table . Comparep = 0.062), CFS 5 aOR = 3.77 , CFS 6 aOR = 4.04 , CFS 7 aOR = 2.16 , and CFS 8 aOR = 3.19 . Other covariates associated with an increased level of care were age ; > 80 aOR = 3.87 ) and CAD .Of the 831 included participants, 810 (97.5%) exhibited complete data and were included in the mixed-effects multivariable logistic regression analysis. After adjustment for the other comorbidities and CRP on admission, an increase in the level of care at discharge was associated with frailty. Compared to CFS 1–3, CFS 4 aOR = 1.99  at discharge found frailty was associated with an increased care needs, compared to CFS 1–3.Our study has demonstrated that pre-admission frailty status is associated with a person’s level of independence after discharge from hospital following admission with COVID-19. Although many patients had no formal care prior to admission, almost a quarter acquired COVID-19-related functional dependence exhibited by additional care required on discharge. These findings are in keeping with other studies demonstrating worse functional outcomes associated with greater pre-hospital levels of frailty .Although not synonymous with advancing age, frailty is more prevalent in the older population, and as a consequence, older people are more likely to require care. These data have shown that risk of increased care needs following hospitalisation with COVID-19 is also associated with increasing age, with those over 80 years or older significantly more likely to require increased care at discharge than younger people.The association of frailty and age with increased functional dependence on discharge can be used to assist with early identification, at the point of hospital admission, of people at risk of needing more care. These data can aid clinicians in providing early tailored assessments and management plans to patients. One paradigm of this tailored assessment is a comprehensive geriatric assessment (CGA) effected through geriatric medicine clinicians in conjunction with the multidisciplinary team. CGA produces a personalised and integrated management plan using a multimodal, multimorbidity, and multidisciplinary approach over time. It has been shown to reduce post-acute long-term functional dependence and mortality –23. EnsuThese data may be unique to hospital admissions during the pandemic and not pertain to non-COVID times. Hospital-based teams are being faced with unique challenges: streamlined discharge processes aimed at maintaining capacity within the acute setting, reduced access to specialist inpatient therapy resources due to high service demand, sickness-related staff absences, and the breakdown of pre-pandemic informal care arrangements due to social distancing restrictions . It is dTo minimise the personal and societal burden of COVID-19-related functional decline and increased care needs, urgent strategies are required: firstly, avoiding transmission of the virus through public health techniques such as contact tracing, infection control measures, and social distancing including shielding; secondly, modification of frailty pre-admission may ameliorate the risk of post-acute deterioration –28; thirThis is a multicentre study with a large sample size receiving real-world care. Data were collected and curated by clinicians with an interest in older adults and processes aimed at maximising data completion and decreasing bias. Baseline patient characteristics and clinical outcomes demonstrated are in line with other COVID-19 publications suggesting external validity, for example, almost three quarters of patients in this cohort were 65 years of age or older consistent with published evidence .This study had several limitations: data to be cautious include the inter-hospital consistency of the services delivered within the term intermediate care; the difference between crude and adjusted effect of CAD; and the use of the CSF in people of all ages as this tool has been primarily validated in populations over 65 years. This study describes an increased care level at the time of hospital discharge but not beyond that time.Frailty is associated with an increased level of care post hospital admission with COVID-19. In this study, 23.2% of discharged patients required an increased care level on discharge. This study suggests that future public health approaches must take into account a large number of patients with increased care needs and position adequate resources to ensure robust supported discharge schemes for those admitted to hospital. Frailty screening should become a standard practice at admission in order to identify patients early who are most likely to benefit from multidisciplinary health and social care attention.


Although ST-elevation MI (STEMI) and non-ST-elevation MI (NSTEMI) differ mechanistically, few studies have investigated the incidence of cardiovascular diseases according to infarction type; these studies have yielded inconsistent findings. This study aimed to examine whether PM size .2.5; for PM10, the largest significant effect was observed at lag 4  for single and lags 0–7 . RRs were significantly higher in < 65-year-olds than in ≥65-year-olds. Additionally, RRs between the BMI < 25 and BMI ≥ 25 groups were not different; statistically significant effects were observed for concentration at lags 0–5  and lags 0–6  in the BMI < 25 group. A negative exposure-response association was observed between daily average visibility-adjusted PM and STEMI and daily average visibility-adjusted PM in < 65-year-olds.No significant associations were observed between Asian dust and AMI. By adjusting for different lag structures, a significant effect was exclusively observed in STEMI. For moving average lags, the largest value at lag 5  for single and lags 0–7  was observed for PM2.5 and PM10 emissions, particularly during the days of Asian dust, may be crucial and reduce STEMI and AMI incidence among < 65-year-olds. These results indicate that the Asian dust alarm system needs revision to protect vulnerable populations.Reducing PM Dust storms affect human health by impairing visibility and promoting interactions with microscopic organisms, such as bacteria and fungi . They ha2.5) increased the risk of STEMI, unlike NSTEMI [2.5 on STEMI risk [There is an increasing evidence that increased exposure to particulate matter (PM) leads to cardiovascular disease, particularly among patients with a history of myocardial infarction (MI) –11. Althe NSTEMI . Moreovee NSTEMI , 11. ConEMI risk , 14.< 10 μm (PM10) [2+, Mg2+, Na+, K+), anthropogenic pollutants , microorganisms, fungal spores, chemical components, and gaseous pollutants can also be mixed and carried in the dust [The European project “MED-PARTICLES” underscored that the adverse health effect of the desert component of PM can be affected by concentrations of anthropogenic-derived PM size m (PM10) . Howeverthe dust . It is pthe dust ; therefoTherefore, this study aimed to evaluate the association between AMI and Asian dust with PM, and assess the variation of the association according to infarction type (STEMI and NSTEMI), age group, body mass index (BMI), and smoking status.2.5 data were available. Of these, patients with inadequate information were excluded. The first medical contact time was defined as the time of the primary manifestation of AMI symptoms. In addition, stratified analyses were performed by age, smoking status, and MI status (STEMI and NSTEMI).The KAMIR is a prospective study of approximately 37,880 patients in 57 general emergency rooms of Korean hospitals that collects data using a standardized case report form. The rationale and design of this nationwide mandatory registration have been previously explained . This st10 data were obtained from the National Institute of Environmental Research in Korea, whereas PM2.5 data were collected from 27 monitoring sites managed by the Seoul Research Institute of Public Health and Environment.Climate data were obtained from the Korean Meteorological Administration, including those on the daily mean temperature, humidity, sea level pressure, and visibility from 2005 to 2017. Based on previous studies, we defined Asian dust events as days when visibility was < 10 km , 4. PM102.5 and PM10 would enhance MI risk on Asian dust days, single- and moving average lags of PM2.5 and PM10 were included in the basic model. The largest lag days of Asian dust in the basic model were used in the PM-adjusted model. Similarly, stratified analyses were conducted by age, smoking status (never smoking/past smoking/current smoking), and obesity to evaluate the potential effect of the association between Asian dust and AMI. BMI was categorized into two categories based on the standard definition as follows [2) and overweight/obese (BMI ≥25 kg/m2).The time-series data for this ecological study design were analyzed using generalized linear models to accommodate the nonlinear relationships of emergency visits for AMI. Natural cubic spline functions were used to consider non-linearity for time and weather variables. In the basic model, single lag day effects of Asian dust were examined from the current day (lag 0) to the sixth day, adjusting for meteorological effects that could affect AMI, including two-day moving average of temperature, current day relative humidity, and sea level pressure (lag 0), as covariates. In the evaluation of the hypothesis that PM follows : normal/Finally, sensitivity analyses were performed to assess the validity of the findings by modified different degrees of freedom for time trends and various temperature lag structures. Additionally, because higher relative humidity could plausibly affect visibility, models were repeated to include interaction terms between daily mean visibility and an indicator variable of high level of humidity days, wherein relative humidity was higher than 70% or 80%, respectively. All statistical analyses were performed using R software 3.4.0 .Table 3, were higher during Asian dust days than during days without Asian dust events. We found that Pearson’s correlation coefficients of PM concentrations was more strongly related with visibility than were other air pollutants such as NO2, SO2, O3, and CO . On considering PM2.5 in the model, the most significant effect was observed at lag 5  for single lags and lags 0–7  for moving average lags. In the course of controlling for PM10 in the model, the largest significant effect was observed at lag 4  for single lags and lags 0–7  for moving average lags. On the contrary, Asian dust exposure had no significant risk for the onset of NSTEMI for both single- and moving average lags.Figure 2.5 demonstrated a larger impact of Asian dust on the incidence of AMI, using an age group- and BMI-based stratification analysis, the effect of PM2.5 was further examined  in the BMI < 25 kg/m2 group. When PM10 was considered in the model, a similar pattern was observed with PM2.5 for both age group and BMI; however, a significant effect was exclusively observed in < 65-year-old age group , which indicates relatively small particles in the PMze range . Therefoze range . Studiesze range  and Taiwze range  observedze range . Similarze range . Further2.5 concentrations. Similar results were reported by Huang et al. in Shanghai, China [2.5 and visibility, with higher PM2.5 concentrations corresponding to lower visibility [2.5 and PM10 concentrations and thus clarify the relationship between visibility and cardiovascular events.To the best of our knowledge, this study is the first to thoroughly investigate the dose-response relationship between visibility and STEMI incidence. The results suggest an approximately negative linear exposure-response curve and indicate that visibility degradation varies according to PMi, China , who wersibility . These sThis study exhibits a number of strengths. First, a long study period and a relatively large sample size from the KAMIR database were used; the KAMIR is a prospective, open, observational, multi-center on-line registry on AMI. In contrast, other studies have been limited to a short temporal scope and a small number of patients. Second, arrival time at the hospital due to AMI symptoms was used rather than hospital admission day. Therefore, the data provide novel insights into the mechanisms by which PM exposure may increase risk of AMI associated with Asian dust. Third, estimates from the models were quite robust in relation to inclusion of the weather variables, the use of interaction terms between the daily means of visibility and humidity, and varying lagged structures of PM.2.5 exposure seemed particularly problematic, as personal exposure can widely vary depending on lifestyle choices, such as time-activity patterns of individuals, occupational characteristics, and residential environment [However, this study equally has a number of limitations. First, the ambient air pollution concentration was used as proxy exposure from an air pollution monitoring site rather than personal exposure levels. However, a previous study reported that outdoor air pollution exposure and personal PMironment . Althoug2.5 and PM10 emissions, particularly during Asian dust days, and this would reduce the incidence of STEMI and AMI among young patients. Identifying modifier effects of PM could provide knowledge for future risk assessment studies.This study provides new insights regarding the effects of PM on AMI during Asian dust days. These results suggest that it is crucial to reduce PMAdditional file 1. Correlations between exposure variables during 2003–2013 in Seoul.Additional file 2. Relative risks of AMI per 10 μg/m3 increase in current-day PM2.5 and PM10 concentrations by subgroup with single lags.Additional file 3. Relative risk (RR) of Asian dust associated with acute myocardial infarction, stratified by age and body mass index (BMI) after adjusting for PM10.Additional file 4. Association between Asian dust and acute myocardial infarction by smoking status, after adjustment for PM2.5 by lag days.Additional file 5. Relative risk (RR) of Asian dust associated with ST elevation myocardial infarction after adjustment for PM2.5 with single lag days (from current day to the previous 6 days): results of the main analyses models and sensitivity analyses models.


Venetoclax is efficacious in relapsed/refractory t multiple myeloma, thus warranting investigation in light-chain amyloidosis (AL). This retrospective cohort includes 43 patients with previously treated AL, from 14 centers in the US and Europe. Thirty-one patients harbored t, 11 did not, and one t status was unknown. Patients received a venetoclax-containing regimen for at least one 21- or 28-day cycle; the median prior treatments was three. The hematologic response rate for all patients was 68%; 63% achieved VGPR/CR. t patients had higher hematologic response (81% vs. 40%) and higher VGPR/CR rate  than non-t patients. For the unsegregated cohort, median progression-free survival (PFS) was 31.0 months and median OS was not reached (NR). For t, median PFS was NR and for non-t median PFS was 6.7 months . Multivariate analysis incorporating age, sex, prior lines of therapy, and disease stage suggested a risk reduction for progression or death in t patients. Median OS was NR for either subgroup. The organ response rate was 38%; most responders harbored t. Grade 3 or higher adverse events occurred in 19% with 7% due to infections. These promising results require confirmation in a randomized clinical trial. Studies are limited regarding the annual incidence of AL, but estimates range from 3 to 13 cases per million person-years9.Systemic monoclonal immunoglobulin light-chain amyloidosis (AL) is an acquired form of amyloidosis, in which unstable light chains are produced by a neoplastic monoclonal plasma cell population. These light chains form oligomers and bind to cellular and extracellular matrix proteins causing direct toxicity and amyloid deposition in organs such as the heart and kidneys10, the backbone of treatment in patients with AL remains suppression of the neoplastic plasma cell clone. Treatment paradigms are borrowed from multiple myeloma (MM) and include combination chemotherapy regimens16 though to date no FDA-approved regimen for the treatment of AL exists. Unfortunately <20% of patients are transplant-eligible17 and patients receiving high-dose melphalan with autologous stem cell transplant (ASCT) face a hematologic relapse rate of over 30% with a time to relapse of 2–4 years18. Additionally, recent data suggest high hospitalization rates, progression to dialysis, and early mortality from ASCT in AL patients with renal dysfunction19. Daratumumab, a monoclonal antibody targeting CD38 on plasma cells, has shown promising efficacy in the relapsed/refractory (RR) setting as monotherapy and in combination therapy23. Daratumumab is also currently being studied in a randomized phase 3 trial in combination with CyBorD  versus CyBorD alone in the upfront setting (NCT03201965). Unfortunately, patients with AL develop significant toxicities when treated with immunomodulatory medications , limiting their use25. As such, options remain bleak for patients who have progressed on chemotherapy, ASCT, and antibody therapy-based approaches.While there has been researching into targeting different steps of amyloid formation28 with biological rationale for evaluation in plasma cell disorders. Preclinical data demonstrated that all human myeloma cell lines sensitive to venetoclax were restricted to the Cyclin D1 (CCND1) subgroup ) and expressed elevated BCL-2:myeloid cell leukemia-1 (MCL-1) ratios29. High expression of MCL-1 leads to venetoclax resistance, however, this can be overcome with the addition of bortezomib, which is known to inhibit MCL-132. Dexamethasone favorably alters sensitivity to venetoclax by increasing expression of both BCL-2 and BCL-2-like protein 11 (Bim) and shifting binding of Bim to BCL-233. Together, this provided a clinical rationale to study venetoclax monotherapy and in combination with bortezomib and steroids in MM. In a phase I study, venetoclax monotherapy was found to have a favorable safety profile and the greatest efficacy was noted in patients with heavily treated t MM where 40% of patients had a hematologic response with a 27% VGPR/CR rate34. The results from a phase 1b study examining the use of venetoclax and bortezomib in patients with RR MM showed a hematologic response rate of 67% in all patients and 94% in patients with high BCL-2 expression (with a VGPR/CR rate of 66%)35.Venetoclax (ABT-199) is an oral selective B-cell lymphoma 2 (BCL-2) inhibitor FDA-approved for the treatment of chronic lymphocytic leukemia (CLL) and acute myelogenous leukemia (AML)36, despite a significantly higher progression-free survival (PFS) in patients receiving venetoclax. The alarming impact on OS led to an FDA-hold on the study of venetoclax–bortezomib combination therapy in MM. Interim results presented at the American Society of Hematology 2019 annual meeting demonstrated an improved HR for PFS of 0.095 (95% CI: 0.020–0.458) and a trend toward OS improvement with a HR of 0.649 (95% CI: 0.129–3.253) in t patients37, renewing interest of venetoclax in t RR MM.While this led to excitement over a potential targeted therapy in MM, preliminary results of the BELLINI trial (NCT02755597), which examined the use of bortezomib and dexamethasone + /− venetoclax in RR MM, showed an increased hazard ratio (HR) for all patients of 2.207 (95% CI: 1.042–3.945) for overall survival (OS) in the venetoclax arm42. Given the biological rationale, many clinicians have been using venetoclax-containing regimens in patients with RR AL, with anecdotal evidence of efficacy demonstrated in case reports and small case series46. Unlike MM, t AL patients are traditionally less sensitive to bortezomib47; however, a recent case series showed a very good partial response or complete response (VGPR/CR) rate of 88% (seven out of eight patients) in t AL patients treated with ventoclax-containing regimens, with all three patients who were treated with a venetoclax/bortezomib backbone achieving VGPR/CR48. While promising, larger datasets are needed to validate response and provide a strong foundation of evidence for a prospective clinical trial. Here, we report a multicenter, international, retrospective cohort study regarding treatment with venetoclax monotherapy and combination therapies for RR AL.Although present in only 17% of patients with MM, up to 60% of AL patients harbor tWe conducted a retrospective cohort study involving 43 patients with RR AL that was approved by the Columbia University Institutional Review Board . Data Use Agreements were carried out with participating institutions as applicable. Data were collected from 14 centers in the United States and Europe (see Supplementary Information).Inclusion criteria consisted of patients with an established diagnosis of AL who had progressed on at least one prior line of therapy and were treated with a venetoclax-containing regimen for at least one 21- or 28-day cycle  with pre-planned stratification by t status. Other outcomes included hematologic response rate, organ response rate, and reason for discontinuation of therapy.49, venetoclax-containing regimen along with information regarding the depth of hematologic response, organ response, toxicity, PFS, and OS following initiation of a venetoclax-based regimen. Patients were considered to harbor t if it was detected on karyotyping or fluorescent in situ hybridization irrespective of the proportion of plasma cells harboring t.De-identified data extracted by local investigators and collected included patient demographics and clinical characteristics such as age, gender, light-chain subtype, t status, the presence of high-risk cytogenetic features (del(17p) or monosomy 17, t, t, and gain (1q)), prior regimens, stage according to the Revised Prognostic Staging System for Light Chain AmyloidosisPatient demographic and clinical characteristics information were then stratified based on t status.50. A pre-specified comparison of response and outcome between patients with and without t was planned prior to data analysis. Organ responses were evaluated by criteria set forth by the Roundtable of Clinical Research in Immunoglobulin Light Chain Amyloidosis51.Treatment response was reported as determined by local investigators using the “New Criteria for Response to Treatment in Immunoglobulin Light Chain Amyloidosis Based on Free Light Chain Measurement”P value of less than 0.05 was considered to indicate statistical significance. Data analyses were performed using R version 3.6.3 (R Foundation) and SAS 9.4 (SAS Institute).We created a 2 by 2 contingency table for VGPR/CR hematologic response outcomes by t11;14)-positive and -negative subgroups. The odds ratio was reported, and then a Fisher’s Exact test was used to analyze the subgroups. For survival outcomes, PFS and OS curves were estimated using the Kaplan–Meier product limit estimator with medians and 95% confidence interval bounds. A data cutoff date was set for January 15, 2020, and patients were censored at their last follow-up date. PFS is defined as the time from the first day of treatment until hematologic progression or death. OS is defined as the time from the commencement of the venetoclax-containing regimen to death or last follow-up. A two-tailed log-rank test was used to perform survival comparisons between the patient groups. Hazard ratios (HRs) and 95% confidence intervals (CIs) between the patient groups were reported for survival outcomes using Cox regression methods. A multivariate analysis using Cox regression modeling was used to assess the effect of hematologic response and t status on OS and PFS after adjusting for the effects of age, sex, stage, and prior lines of therapy. Chi-square tests and logistic regression were used to assess if t status is significantly different between patient demographics and characteristics  stratification are shown in Table Patients were heavily pretreated, having received a median of three (range 1–10) prior lines of therapy. Nearly, all patients received prior treatment with a proteasome inhibitor (98%) and the majority had prior treatment with cyclophosphamide (79%) and daratumumab (58%). In addition, 21% of patients had prior ASCT.Overall patient demographics and disease characteristics were comparable between both t and non-t patients, and there were no statistically significant differences. Both subgroups were lambda light-chain subtype predominant- 68% in t and 64% in non-t. Although t patients had a median age younger than non-t patients (66 vs. 72-year old), more t patients were aged 75 or younger: 81% vs 73%. t patients had a greater degree of male predominance (71% vs. 45%) and had fewer median prior lines of treatment (three vs. five). The median stage was two for both groups.Venetoclax-containing regimens and dosage varied, however, the majority of patients (81%) were treated with either venetoclax + /− glucocorticoid (58%) or venetoclax + proteasome inhibitor (PI) + /− glucocorticoid (23%) therapy. Venetoclax dosing ranged from 100 mg daily to 800 mg daily.n = 43), 42 patients were evaluable for PFS and OS , 11 without t, one patient whose t status is unknown and one with t who was deemed non-evaluable for PFS and OS by local provider). The median follow-up was 14.5 months with a median PFS of 31.0 months (95% CI: 18.2 months—NR) and a median OS that was not reached (NR)  Fig. . For theNR) Fig.  and 12-mP = 0.017). Of the 25 daratumumab-refractory patients, 12-month PFS and OS were 80%  disease) and 92%  disease), respectively. Twenty-two daratumumab-refractory patients were evaluable for hematologic response  patients and six non-t patients) and 73% of patients had a hematologic response  patients and 67% in non-t patients) with a VGPR/CR rate of 64%  patients and 50% in non-t patients).Hematologic response rate  was evaluable in 38 patients  patients, 10 patients without t, and one patient whose t status was unknown) and was 68%, with a VGPR/CR rate of 63% and a median time to best response of 8.5 weeks. When stratifying by t status, 81% (22 out of 27) of patients with t achieved at least a PR and 78% (21 out of 27) a VGPR/CR. In ten evaluable non-t patients, 40% (four patients) responded with at least a PR and 30% (three patients) with a VGPR/CR Fig. . PatientP = 0.192) compared to non-t patients.Thirty-five patients had full demographic and disease characteristics available and were evaluable for PFS, OS, and hematologic response. When stratified by t status and adjusted for age, sex, prior lines of therapy, and disease stage, the data are highly suggestive that patients harboring t have a reduction of risk for progression or death  for the follow-up period, and patients with a PR or VGPR/CR showed a reduction in risk of progression or death in comparison to non-responders . Hematologic response at 12 months was also significant for predicting the risk of progression and death (P = 0.045); patients with a PR or VGPR/CR showed a reduction in risk of progression or death in comparison to non-responders . Patients achieving VGPR/CR also had an improvement in risk of progression and death at 12 months compared to non-responders . There was no statistically significant difference in OS by t status or hematologic response for either the follow-up period or at 12 months.Multivariate analysis revealed that hematologic response was significant for predicting for risk of progression or death  patients had responses in at least one listed organ system. Of the 12 patients with organ response, 10 harbored t (83%) and two (17%) were non-t. Of 20 evaluable patients with cardiac involvement, six patients (30%) had organ response. Of 20 evaluable patients with renal involvement, eight patients (40%) had organ response. Of four evaluable patients with neurologic involvement, none had a response. Two patients had hepatic involvement however neither met the criteria for hepatic response. Other organs involved pulmonary (two), skin (two), lip (one), salivary glands (one), and soft tissue (one). One patient had acquired AL-related Factor X deficiency, which reversed following treatment with venetoclax and achievement of VGPR.n = 27), 15 (56%) received venetoclax + /− glucocorticoid therapy and experienced a VGPR/CR rate of 73% . In this same subgroup, five patients (19%) were treated with venetoclax + PI + /− glucocorticoid and experienced a VGPR/CR rate of 100% . In non-t patients evaluable for hematologic response (n = 10), four (40%) were treated with venetoclax + PI + /− glucocorticoid with one patient having a hematologic response which was a VGPR (VGPR/CR rate of 25%). In this same population, five (50%) were treated with venetoclax + /− glucocorticoid with the hematologic response in three and two achieving VGPR/CR (40%).In the t patients evaluable for hematologic response  experienced a grade 3 or higher non-hematologic adverse event attributed to therapy Table  and eighThis multicenter, international, retrospective cohort study is the largest study, to our knowledge, that reports on outcomes of patients with RR AL treated with venetoclax-containing regimens. Given the retrospective nature and the heterogeneity of regimens and dosage used, the data reported have certain limitations; however, important insights can be gleaned. First, it is important to note that this is the largest cohort to report on targeted therapy for patients with AL based on cytogenetics. Stratification by t demonstrates a large and statistically significant difference in high quality responses (VGPR/CR 78% vs. 30%) favoring t patients with comparable hematologic responses in daratumumab-refractory patients. While OS was not reached for either subgroup due to a short follow-up period, the Kaplan–Meier survival curves trended toward a better response in t patients. PFS analysis revealed a significant 86% reduction in risk of progression or death in t patients (71% after multivariate adjustment).While patient demographics and characteristics were generally comparable between t patients and non-t patients, it is important to highlight some potentially important differences. First, while t patients had a younger median age than non-t patients, a greater proportion of t patients were actually ≤75 years old. Additionally, although both subgroups were heavily pretreated, t patients had fewer median prior lines of treatment than non-t patients (3 vs. 5), though this is not statistically significant. The disease stage was the same for both subgroups and thus did not confound subgroup analysis.P = 0.036).Our analysis showed there was no statistical difference in PFS or OS between older and younger patients, sex, or prior lines of therapy in our cohort. However, disease stage did show a statistically significant increase in the risk of progression or death with each increase in stage  is associated with a lower bone marrow plasma cell count, with the involved clone generally being the main clone in AL compared to a subclone in non-AL plasma cell dyscrasias53. In MM, t patients are characterized by lower levels of monoclonal protein as well as more mature plasma cell morphology54. In addition, MGUS is associated with a higher incidence of t, suggesting that this translocation alone is not sufficient for plasma cells to acquire full malignant potential and secondary cytogenetic aberrations are required for myeloma progression56. It seems that overexpression of BCL-1 and BCL-2 associated with t inhibits apoptosis and BCL-2 sequestration by venetoclax allows plasma cells to undergo apoptosis and more effectively induces deep and long-lasting remissions in “low malignancy” plasma cell dyscrasias like AL. Given the retrospective nature of this study, the results require confirmation by a prospective clinical trial, though given the high proportion of AL patients harboring t, venetoclax may have larger therapeutic implications than in MM.While there is conflicting data in the literature about the prognostic relevance of t in AL21 and a 10-month OS between 80% and 94%57. More than half of our patients were daratumumab-refractory and had a median of three prior lines of treatment. With a 12-month PFS and OS of 78%  disease) and 93%  disease) in the overall cohort respectively and a 12-month PFS and OS of 80%  disease) and 92%  disease) in daratumumab-refractory patients, we believe our results compare favorably.Other important findings in this study are OS and PFS for patients with RR AL, regardless of t status. Given the rarity of AL, and even more infrequent encounters with RR disease, data are lacking regarding PFS and OS in this patient population. Published data for patients with RR AL treated with daratumumab-based therapy show a 10-month PFS of 89%While overall grade 3 or higher toxicity was noted to be 19% with a large portion being infection-related (7%), other grade 3 or higher toxicities were rare. The impact of administering prophylactic intravenous immunoglobulin and antibiotic regimens for patients in this cohort is not known, and this approach may be a potential way to abrogate infection risk, which could be examined in a randomized clinical trial. Toxicity did not appear to differ based on t status. One patient died as a consequence of therapy (venetoclax + bortezomib + dexamethasone) due to sepsis and was not neutropenic at the time. The second death in the cohort was due to heart failure but was not deemed to be due to therapy per the primary investigator. Though early death cannot be fully assessed as patients in this study must have completed at least one cycle of venetoclax therapy, one death occurred within four weeks in this cohort.37, though the high rates of serious infection and increased HR for OS in the venetoclax arm was not seen in the phase 1b trial35. In the 31 patient Phase II NCT01794520, six patients (19%) died  and three patients developed sepsis (10%). In addition, the most common grade 3 or 4 treatment-emergent adverse events were lymphopenia (32%), thrombocytopenia (11%) and hypertension (10%)58. While both patients in this data set and the discussed clinical trials were heavily pretreated, the favorable toxicity profile seen here may be due to better bone marrow reserve in AL patients who tend to have a more indolent neoplastic plasma cell clone than patients with MM. In addition, the toxicity profile of venetoclax-based combinations in this study compares favorably to well-established safety data in newly diagnosed CLL in combination with obinutuzumab59 and in combination with a hypomethylating agent in newly diagnosed elderly patients with AML60.We believe the findings of our study compare favorably to the toxicity profile seen in the BELLINI and NCT01794520  trials for patients with RR MM. In BELLINI, patients treated with venetoclax + bortezomib + dexamethasone were found to have a rate of serious adverse events of 51% and a rate of serious infection of 30%, with 18% of patients developing neutropenian = 15), venetoclax + PI + /− glucocorticoid had a VGPR/CR rate of 100% (n = 5), suggesting potential synergy between venetoclax and PIs in t patients. The numbers, however, are small and thus a definitive conclusion cannot be drawn.The most commonly used regimens were venetoclax + /− glucocorticoid and venetoclax + PI + /− glucocorticoid (see Supplementary Information). Both regimens were highly efficacious, especially in t patients, and provided high rates of VGPR/CR. While venetoclax + /− glucocorticoid had a VGPR/CR rate of 73%  achieved a response including 30% of patients with cardiac involvement and 40% of patients with renal involvement. It is important to note that some of the evaluable patients had improvement in organ function biomarkers but did not quite meet the criteria for partial organ response. In addition, some of these patients were still receiving treatment with venetoclax and continuing to have hematologic response and reduction in involved-organ biomarkers, so it is unclear at this time if they will eventually meet the criteria for organ response, which is typically delayed in AL. These data suggest that venetoclax can prevent and even improve organ dysfunction in patients with AL.This study has several limitations aside from its retrospective nature. Given the heterogeneity of venetoclax-containing regimens, a wide range of dosages used, and irregular follow-up, identifying the optimal venetoclax combination with drug dosing was not possible. Additionally, the lack BCL-2 expression data make it unclear whether all t AL patients overexpressed BCL-2 and if any of the non-t patients who had good responses concurrently overexpressed BCL-2, perhaps by another mechanism. The lack of minimal residual disease data also limits the depth of response analysis and prognostic implications. Finally, the toxicities related to venetoclax-based regimens may have been underreported given the lack of a formal process to capture the adverse effects in a cohort that was approached retrospectively.Taken together, however, the results of our multi-institutional study indicate a strong rationale toward a biomarker-driven approach for AL patients. Though our findings demonstrate the efficacy of venetoclax in the RR setting, they additionally serve as a platform for targeted treatment in newly diagnosed t AL. Larger prospective studies are required to validate our findings that promote a new and convenient therapeutic approach, with a manageable toxicity profile and the potential to favorably alter the clinical course of AL.For original data please contact Vikram Premkumar (vikram.premkumar@gmail.com). Data including all individual participant data following de-identification will be available to researchers who provide a methodologically sound proposal, to achieve the aims of the approved proposal. The study protocol is available upon request. Data will be available beginning 9 months and ending 36 months following publication of the article.SupplementReproducibility Checklist