dataesr/scientific-paragraphs-categorization huggingface dataset:
Usage Notes Myctobase will enhance research capacity by facilitating international effort toward observing and modelling mesopelagic fish taxa. The data held in Myctobase are suitable for a number of applications, for example, investigating the biophysical determinants shaping patterns of occurrence and biodiversity. Further examples of data use can be found in the citations listed in Online-only Table  1. Data can be analysed using a variety of statistical software such as R 49 or Matlab  60. Myctobase contains data from 72 different research cruises across the Southern Ocean. Each cruise employed a specific sampling strategy and provided varying levels of detail on methodology and samples collected. The different sampling methodologies used between datasets necessitates caution when comparing abundance estimates, as well as length and weight measurements across research cruises.
The biases associated with net type and mesh size of the net have previously been described  8, 21  , and are likely to influence the final output of analyses that use data from different projects. For example, the IYGPT has a larger mesh size at the front of the net limiting the size of fish that may be caught due to smaller fish escaping through the mesh. The minimum size limit has been suggested to be approximately 25-35 mm  61. This suggests that nets with larger mesh sizes may not be appropriate for calculating densities of species that can escape the net. However this is a complex issue which requires further study as the net will also exert a herding effect on fish  32. Biases should be considered and acknowledged. Myctobase provides detailed information for each sampling event to ensure that researchers can account for these differences in their analyses or to enable comparison of data using similar methods.
Further, abundance values should be treated as relative rather than absolute values due to the patchy distribution of species and the limited spatial and temporal coverage of sampling creating uncertainty around estimates  4, 8. Data are best used to demonstrate the community composition, distribution, and occurrence of fish species, life history and relative abundance within the Southern Ocean. For example, there are a multitude of available modelling techniques which may be used to predict a species geographic distribution in relation to environmental variables  62, 63. Furthermore, this information provides the data necessary for ground-truthing acoustic data  64. We anticipate that Myctobase will continue to grow into a fully circumpolar database with continued collaboration from across the scientific marine community.
Acknowledgements The authors would like to thank and acknowledge the contributions of all researchers and their institutions. We also thank  Dave Connell  and  Petra Ten Hoopen  for their assistance and support with data acquisition,  Sophie Bestley  for providing custom code for calculating day/night periods. The relevant project funders and acknowledgements for each dataset provided to Myctobase can be accessed through associated citations listed in Online-only Table 1. For those without citations, the authors acknowledge  Institut Polaire Français Paul-Emile Victor (IPEV)  for the support of the programme "IPEKER". The Japanese survey was supported by  JSPS KAKENHI  Grant No. JP17H06319  to  M. Moteki  and  Japanese Antarctic Research Expedition  (No. AP-0939  to  M. Moteki ). This Publication was funded by the  Belgian Science Policy Office (BELSPO , contract  n°FR/36/AN1/ AntaBIS ) in the Framework of  EU-Lifewatch.
The Predicting Response to Immunotherapy for Melanona with Gut Microbiome and Metabolomics (PRIMM) studies are two separate prospective observational cohort studies recruiting subjects in parallel in the United Kingdom (PRIMM-UK) and the Netherlands (PRIMM-NL) since 2018. These cohorts of previously ICI-naive patients with advanced melanoma provide extensive biosamples, including stool, serum and peripheral blood mononuclear cells, before and during ICI treatment, with detailed clinical and dietary data collected at regular intervals longitudinally. To study the role of the gut microbiome in ICI response, we performed shotgun metagenomic sequencing of baseline stool samples from the PRIMM cohorts, as well as three additional cohorts of ICI-naive patients with advanced cutaneous melanoma (originating from Barcelona, Leeds and Manchester).
Results In total, we recruited 175 patients from five distinct cohorts across the United Kingdom, the Netherlands and Spain with unresectable stage III and stage IV cutaneous melanoma who received ICI treatment after collecting stool samples (Table  1 ). We initially focused on the two single prospectively recruited PRIMM cohorts with more than 50 samples each, both of which exceed the sample size of all previously published datasets. In these cohorts, comprehensive clinical data, standardized radiological response assessments (RECIST v1. 1) and biological specimens were collected before treatment and longitudinally at each cycle of treatment. There were statistically relevant clinical differences between these two cohorts, including the proportion of subjects who had received previous systemic targeted therapy (40% within PRIMM-NL and 20% within PRIMM-UK, P = 0.
1c ). When exploring the relative abundance of each detected microbial species in this framework, we found substantial microbiome prediction capability in PRIMM-NL when using PFS12 as the endpoint (area under the receiver operating characteristic curve (AUC-ROC) 0. 64) and in PRIMM-UK when using ORR (AUC-ROC 0. 78), but ORR in PRIMM-NL and PFS12 in PRIMM-UK achieved much lower prediction levels (0. 53 and 0. 57, respectively). We then looked at the predicted functional potential of the microbiome by estimating the presence and relative abundance of microbial gene families (specifically Kyoto Encyclopedia of Genes and Genomes (KEGG) ortholog families; Fig. 1c ). Here, the AUC-ROC exceeded 0. 59 for both datasets and endpoints (ORR and PFS12). When we only considered patients who did not progress between 6 and 12 months (Fig.
Limited reproducibility of response predictions across cohorts. We next performed an integrated and cross-cohort analysis of the PRIMM cohorts together with three additional smaller cohorts from Barcelona, Leeds and Manchester, as well as five publicly available cohorts with consistent metagenomic and immunotherapy response data. The three cohorts we provide here in addition to PRIMM-UK and PRIMM-NL consist of a total of 55 samples (Table  1 ) and include patients who also collected stool samples and have metadata available as for the PRIMM cohorts. In this analysis, we also included four previously published cohorts with available metagenomic sequencing data and with ORR endpoints available  5, 6, 12, 16  and two with PFS12 endpoints available  16, 18. As expected, the different datasets exhibited strong cohort-dependent effects in the microbial population, with 'cohort' explaining nearly ten times more variance than any other variable (Fig.
2a ). Restricting the analysis to the datasets we sequenced, the batch effects were less strong, but even when batch-correction adjustment using an empirical Bayes framework  19  on centered log-ratio-transformed species-level relative abundances was applied, ORR and PFS12 still only explained a limited fraction of the total variation in the microbiome (Fig. 2a ) and were not associated with increased or decreased alpha diversity (Extended Data Fig. 1b, c ). Next, we assessed whether and how a microbiome-based machine learning model can predict ICI response in samples or whole cohorts not considered in the training of the classification model  13, 14, 20  (Fig. 2b ). We confirmed some single datasets had ORR-prediction levels higher than PRIMM-UK (e. g. , Barcelona AUC-ROC 0. 76) and PFS12-prediction levels higher than PRIMM-NL (WindTT_2020 AUC-ROC 0.
90), but the higher AUC-ROC values usually corresponded to small cohorts on which the estimations are more variable when assessing cross-validation, and overall, the AUC-ROC values for the additional cohorts only occasionally exceeded 0. 6 (Fig. 2b ). The same sample-size limitations may also explain the AUC-ROC values obtained when a response-specific microbiome model was fitted on one cohort and then tested on a different one; such values reached 0. 7 in only two cases and exceeded 0. is associated with the overall microbiome structure for PRIMM-UK (P = 0. 05), but not for PRIMM-NL or PFS12, as represented visually using principal-component analysis (PCA) of species-level centered log-ratio-transformed relative abundances. P values were calculated using adonis and 999 permutations (Extended Data Figs.
Nature MediciNe around one-third of the cases (31. 4%), with large variability among predictions. Interestingly, more consistent results, albeit still limited in their absolute values, were obtained when the model was fit on all but one cohort and applied on the left-out one (Fig. 2b ). In this setting, which tried to alleviate cohort-specific effects by considering all but one cohort in the same model, we produced prediction values averaging 0. 59 and 0. 60 across the ORR and PFS12 endpoints, respectively, encompassing largely overlapping datasets. Functional characteristics of the microbiome profiled via classifications such as the enzyme category system  21  or KEGG orthology (KO)  22  achieved higher predictions of response compared to species relative abundances in several cases (Fig.
Few reproducible biomarkers of response across cohorts. We then looked for microbial taxa or functions consistently associated with response to ICIs across the available cohorts in relation to ORR and PFS12 (Fig. 3 , Extended Data Fig. 6  and Supplementary Tables  3  and 4 ). This analysis revealed two uncultivated Roseburia species associated with response; Roseburia sp. CAG:182 (also associated with response when using PFS12) and Roseburia sp. CAG:471 increased on average in responders consistently across all datasets with ORR endpoints available. Of note, Roseburia sp. CAG:182 was found to be in the panel of the five bacterial species most associated with favorable nutritional and cardiometabolic health markers in a recent large metagenomic population-based study  20  , with a particularly strong inverse correlation with the inflammatory surrogate glycoprotein acetyl  23.
With respect to those patients for whom PFS12 data were available, Phascolarctobacterium succinatutens and Lactobacillus vaginalis were both enriched in responders across all seven datasets, and each was detected as significant by three of the eight meta-analysis approaches using the same significance thresholds. A. muciniphila and Dorea formicigenerans were two species with high overall prevalence (65. 8% and 85. 9%, respectively) associated with ORR and PFS12 (by three and six meta-analysis methods, respectively). Bacteroides clarus instead exhibited higher relative abundances in nonresponders, both for ORR and PFS12, for all the seven datasets in which it can be detected and was significantly associated with nonresponders in PRIMM-NL after covariate adjustment (Fig. 3d ). No single bacterium was a fully consistent biomarker of response across all datasets; however, applying the same meta-analysis methods in the context of colorectal cancer as a methodological control confirmed strong and consistent biomarkers across cohorts, reinforcing previous meta-analyses  13, 14  (Extended Data Fig.
A consistent panel of clinical information within our cohorts, ranging from PPI usage before treatment to Fig. 2  | integrated analysis of newly sequenced and publicly available datasets for cross-cohort response-microbiome association. a, Contribution of variables to the overall microbial community composition highlights the heterogeneity of the microbiome structure across cohorts that has a substantially higher effect than both anthropometric and clinical parameters. We either used all available cohorts or newly sequenced cohorts for which additional metadata were available. Batch-correction methods were applied to species-level abundances prior to distance calculations. The plot on the left uses ORR as the outcome variable, whereas the plot on the right adopts PFS12. b, Prediction matrix for microbiome-based prediction of response assessed via ORR (left matrix) and PFS12 (right matrix) within each cohort (values on the diagonal), across pairs of cohorts (one cohort used to train the model and the other for testing) and in the leave-one-cohort-out setting (training the model on all but one cohort and testing on the left-out cohort).
We report the AUC-ROC values obtained from Lasso models on species-level relative abundances. Values on the diagonal refer to the median AUC-ROC values of 100-repeated fivefold-stratified cross-validations. Off-diagonal values refer to AUC-ROC values obtained by training the classifier on the cohort of the corresponding row and applying it to the cohort of the corresponding column. The leave-one-out row refers to the performances obtained by training the model using all but the cohort of the corresponding column and applying it to the cohort of the corresponding column. The same prediction matrix using functional microbiome profiles are available in Extended Data Fig. 4. c, ORR (n = 284) cross-validation AUC-ROC values obtained from Lasso models trained using 100-repeated fivefold-stratified cross-validations (boxplots) and leave-one-dataset-out AUC-ROC values from Lasso models obtained by training the model using species-level relative abundances and all but the corresponding (circles).
Moreover, ORR in PRIMM-UK reached the same level of prediction based on the taxonomic-based microbiome model, which confirmed that the response-microbiome signature is independent of that of PPI use given the limited overlap in nonresponding patients who reported PPI use before ICI treatment (P = 1, Fisher's exact test). When looking at the functional potential, we also found that PPI use was the covariate best predicted by the gut microbiome in a leave-one-dataset-out setting for both PRIMM cohorts (Extended Data Fig. 9 ). Performance status was also quite well inferred from the microbiome in PRIMM-UK and also in PRIMM-NL when   considering the leave-one-dataset-out strategy (Fig. 4a  and Extended Data Fig. 9 ). ICI-induced toxicity and colitis, for which preliminary immunological, genomic and microbiology biomarkers are available  33  , were less associated with the microbiome in our study, in which 43% and 36% of individuals experienced grade 3 or higher immune-related adverse events in Values inside the cells refer to unadjusted P values < 0.
PFS12 Fig. 4  | Covariate associations with the gut microbiome from the PRiMM cohorts. a, Cross-validation AUC-ROC values obtained from Lasso models trained using 100-repeated fivefold-stratified cross-validations (boxplots) and leave-one-dataset-out AUC-ROC values from Lasso models obtained by training the model using species-level relative abundances and all but the corresponding PRIMM cohort (circles). The lower and upper hinges of boxplots correspond to the 25th and 75th percentiles, respectively. The midline is the median. The upper and lower whiskers extend from the hinges to the largest (or smallest) value no further than 1. 5× interquartile range from the hinge, defined as the distance between the 25th and 75th percentiles (PRIMM-NL, n = 55; PRIMM-UK, n = 55). b-d, Species associated with PPI use (<3 months after the start of ICI), toxicity and colitis identified by ANCOM-BC with and without covariate adjustment (PRIMM-NL, n = 47; PRIMM-UK, n = 53).
Discussion In this study, we present the largest metagenomic study to date to identify gut microbiome associations with ICI response by integrating published melanoma cohorts (n = 147) with five new cohorts (n = 165). This large set of real-world cohorts was meta-analyzed extending previous attempts  34  to verify whether the partially conflicting biomarkers of response to ICI found in single small datasets  5, 11, 12, 16, 18  could be reconciled. Using ORR and PFS12 by RECIST v1. 1 at 6 and 12 months, respectively, as endpoints, we confirmed both the presence of cohort-specific biomarkers and the absence of taxonomic or functional microbiome biomarkers that are consistent across all datasets. The lack of solid cross-study reproducible microbial biomarkers of ICI response is not attributable to analytical choices, as applying the same meta-analysis methods in the context of colorectal cancer confirmed the strong and consistent biomarkers across cohorts described elsewhere  13, 14.
It is unclear why PFS12 was seen to have substantially higher microbiome prediction capability in PRIMM-NL, whereas ORR performed substantailly better within the PRIMM-UK cohort. Nevertheless, aside from the large sample size of the combined cohorts, our study has multiple strengths; samples for this study came from three European countries and a number of publicly available datasets, representing a diverse and heterogeneous cohort of real-world patients, and we account for a variety of important confounders and prognostic factors that are often overlooked. The gut microbiome has an important role to play in ICI response  7, 8  , as we also confirmed here, but this role appears likely to be more complex than previously reported in initial studies, extending beyond differential microbial abundances and encompassing complex interactions of the gut microbiome with clinical predictors and biological factors that may be specific to geographies in patients who benefit from treatment with ICIs.
Methods The prospective PRIMM cohorts and the other enrolled cohorts. We prospectively collected fecal samples from patients receiving ICIs between August 2015 and January 2020 for patients with advanced cutaneous melanoma treated in the United Kingdom (PRIMM-UK study) and the Netherlands (PRIMM-NL study, made up of eligible patients from the COLIPI, POINTING and OncoLifeS studies). For the present study, we analyzed stool samples collected prior to treatment with ICIs (at baseline). PRIMM-UK (NCT03643289) is sponsored by East and North Hertfordshire NHS Trust with ethical approval from King's College London. OncoLifeS (Medisch Ethische Toetsingsingscommissie (METc) 2010/109), COLIPI (METc 2012/085, NCT02600143) and POINTING (METc 2018/350, NCT04193956) have all been approved by the METc of the University Medical Center Groningen in the Netherlands.
Informed written consent was obtained for collection of samples and data, sharing anonymized data and working with collaborators whether academic or commercial. Samples collected within the three external cohorts of Barcelona, Manchester and Leeds were subject to similar ethical approvals. Patients who fulfilled the following criteria were eligible for these analyses: (i) histologically or cytologically confirmed nonresectable advanced (stage III or IV) cutaneous melanoma, (ii) treatment with ICIs (nivolumab, pembrolizumab or ipilimumab or a combination of ipilimumab and nivolumab) at the recommended dose as a first-line ICI and (iii) 18 years of age or older. Written informed consent was obtained from all patients. High-quality fecal samples were collected from these patients before initiation of ICI treatment (n = 55 for the UK cohort and n = 55 for the Dutch cohort).
Additional patients were enrolled from cohorts outside the setting a prospective clinical trial and performed in Leeds (n = 19), Barcelona (n = 12) and Manchester (n = 30) between March 2015 and November 2019 but from whom fecal samples were collected at time points similar to those used in our included prospective studies. Written informed consent was obtained from all patients. Sample and data collection. Baseline demographics, including sex, age, BMI, Eastern Cooperative Oncology Group performance status and medication use, were collected, along with tumor staging and previous anticancer therapy data. Demographic and dietary data were collected as part of a screening visit up to 14 days before ICI treatment began. All baseline antibiotic or PPI use within 3 months of commencing ICI treatment was documented.
Dietary data collection. In 93 of the 110 (84. 5%) prospectively recruited patients, dietary data were collected before treatment and within 14 days of commencing ICIs. Dietary intake was assessed through two different FFQs: the Dutch Healthy Diet-FFQ  39  and the EPIC-Norfolk FFQ  40, 41. Food items were mapped to create one dataset. Four food-based scores were calculated to address relative dietary quality as confounder in the microbiome-response analysis: the modified Mediterranean diet score  42  , original plant-based diet index  43  , healthy plant-based diet index  43  and unhealthy plant-based diet index  43. The composition of scores by food groups in the UK and Dutch cohorts is given in Supplementary Table  2. DNA extraction and sequencing. DNA was isolated at King's College London using the Max Core protocol.
Samples with a high-quality DNA profile were further processed. Sequencing libraries were prepared using the Illumina Nextera DNA Flex Library Prep Kit according to the manufacturer's protocols. Libraries were multiplexed using dual indexing and sequenced for 300-bp paired-end reads using the Illumina NovaSeq6000 platform according to the manufacturer's protocols. We obtained a total of 1,283 Gb with an average of 53,919,210 reads per sample before quality control and preprocessing. The publicly available datasets considered. We download metagenomic data from four publicly available datasets (PetersBA_2020, GopalakrishnanV_2018, MatsonV_2018 and FrankelAE_2017) through the Sequence Read Archive using the accession numbers SRP197281 (ref. 18 ), ERP104610 (ref. 6 ), SRP116709 (ref. 5 ) and SRP115355 (ref.
12 ). Metagenomic data and metadata from WindTT_2020 (ref. 16 ) were provided by the authors of the study. These publicly available cohorts are shown in Supplementary Table  11. We excluded any samples taken after the start of ICI therapy, nonmetagenomic samples, nonfecal samples and samples with low sequencing depth (less than one million reads). We classified patients into responder and nonresponder groups according to RECIST 1. 1 criteria; patients with complete or partial response, as well as stable disease at first evaluation, were classified as responders, whereas patients with PD were classified as nonresponders. Metagenome quality control and preprocessing. Shotgun metagenomic sequencing was performed at the NGS Core Facility at University of Trento. The quality of all sequenced metagenomes was controlled using the preprocessing pipeline implemented in https://github.
To calculate the depth of coverage of each viral group, we first mapped the raw reads of each sample against a nonredundant collection of representative sequences for each viral group. Then, a second mapping was performed on the best-matching sequence for each group and each sample. Depth of coverage was calculated with CMSeq (https://github. com/SegataLab/cmseq). Statistical analysis. Multivariate survival analyses were performed using Cox regression models to determine hazard ratios and 95% confidence intervals for PFS and adjusting for other clinicopathologic features using the coxph function from the R survival package (v3. 2-7). We conducted PERMANOVA using the function adonis from the vegan R package (v2. 5-7) with the Atchinson distance on centered log ratios using both species and KO relative abundances.