Introduction: Peripheral nerve sheath tumours comprise benign tumours; namely schwannomas and neurofibromas, and only rarely comprise hybrid benign tumours and their malignant counterpart, malignant peripheral nerve sheath tumours (MPNST). There may be diagnostic difficulties in histopathology analysis, especially in core needle biopsies where there is a limited amount of tissue. Immunohistochemistry (IHC) can play a beneficial role, especially in atypical and cellular histological variants and rarely hybrid tumours.Methods: A total of 45 cases of benign peripheral nerve sheath tumours were included in the study; there were 27 cases of neurofibroma including variants like plexiform and cellular neurofibromas and 18 cases of schwannomas including variants like ancient schwannoma and cellular schwannoma. Immunohistochemical staining (IHC) on these tumour tissues using S-100, CD56 and calretinin was done and scoring was done based on extent and intensity.Results and discussion: No significant differences were observed between neurofibromas and schwannomas on patient age and anatomical locations of these tumours. IHC results did not show statistically significant patterns of expression of S-100 protein between the schwannoma and neurofibromas groups (p=0.75). CD56 protein was expressed strongly (3+) in 90% of cases of schwannoma and negative in 86% of neurofibromas, the differential expression between the two groups was found to be statistically significant (p <0.0001). Calretinin was positive in 39% of schwannomas including one case of cellular schwannoma and negative in all (100%) cases of neurofibroma while the differential expression of calretinin between schwannoma and neurofibroma groups was found to be statistically significant (p < 0.005).Conclusion: Our study shows that S-100 does not show differential expression between schwannomas and neurofibromas. CD56 could be a potentially useful IHC marker to aid in the diagnosis of peripheral nerve sheath tumours with significantly higher expression in schwannomas compared to neurofibromas. Calretinin was also found to be preferentially expressed in schwannomas, though the difference is statistically significantly lower compared to CD56. A panel of all these markers could be used for accurate diagnosis. Peripheral nerve sheath tumours comprise benign tumours, namely schwannomas and neurofibromas and their malignant counterpart, malignant peripheral nerve sheath tumours (MPNST). Schwannomas are composed of immature Schwann cells derived from the peripheral nerve sheath, whereas neurofibromas are composed of Schwann cells and axons, perineurial cells, blood vessels and fibroblasts and mast cells -2.Recent approaches in the diagnosis and management of soft tissue tumours include core needle biopsy analysis for initial diagnosis followed by neoadjuvant chemotherapy in case of malignant lesions (sarcomas) and reducing the size of the tumour, followed by chemotherapy. The type of surgery planned may also depend on the cell of origin and type of tumour, e.g. nerve-sparing surgery, nerve resection and /or nerve repair requirement etc.An inherent limitation of core needle biopsies is that there will be a limited amount of tissue which may not be representative of the entire lesion which may be heterogenous nature, e.g., alternating hypercellular and hypocellular lesions, varying grades of nuclear atypia in different parts of the tumour and can even affect the grade, especially between intermediate grade and malignant tumours .Some schwannomas with predominantly Antoni B areas, sparse cellularity and/ or large myxomatous areas may resemble a neurofibroma. Some schwannomas may not have palisading nuclei and verocay bodies and may resemble a cellular neurofibroma. Cellular schwannomas may resemble low-grade MPNST and leiomyosarcoma. Plexiform schwannomas are usually devoid of a capsule and composed predominantly of cellular Antoni A areas; when they exhibit increased cellularity and mitotic figures, they may resemble MPNST. Most epithelioid MPNST arise from pre-existing schwannoma -5.The distinction between low-grade MPNST or hypocellular areas of the tumour from atypical neurofibroma and cellular neurofibroma may also be difficult. Neurofibromas exhibiting degenerative atypia and increased cellularity can be mistaken for MPNST . NeurofiIn the present study, we have evaluated the histomorphological features and immunohistochemical expression profiles of S-100, CD 56 and calretinin in peripheral nerve sheath tumours and correlated any difference in morphological features with variations in IHC expression profiles.The study was a retrospective cross-sectional study carried out at the Department of Pathology. Institutional Scientific and Ethical Committee clearance (1998/EIC/2020) was obtained. Cases representing peripheral nerve sheath tumours received in the department during the period from August 2019 to August 2021 were included in the study. A total of 45 cases collected in the above study period were included in the study.The inclusion criteria were (i) cases representing peripheral nerve sheath tumours received in the histopathology section; (ii) both biopsy and resected specimens of clinically diagnosed cases; and (iii) available basic clinical details including age, gender, and location of tumours.Exclusion criteria were (i) cases for which overall tissue material is inadequate for immunohistochemical analysis; and (ii) biopsy cases where representative areas were not available.Clinical data including age, gender, location of the tumour, clinical presentation (whether there was the presence of neurofibromatosis), family history of similar tumours etc., were obtained from the case records.Hematoxylin and eosin (H & E)-stained microscopic slides of both resection specimens and biopsies were analysed and morphological features were noted and a diagnosis was rendered along with histological variants and special morphological features.Immunohistochemistry (IHC) involved the peroxidase-antiperoxidase system. Tissue sections (4-5 µ) were made on a rotary microtome . Positively charged slides  were used for taking the sections for the IHC procedure. Antigen retrieval on tissue sections was done by microwave method  .Immunohistochemical staining was done on sections using the following primary and secondary antibody detection system:The primary antibody detection system for IHC from PathnSitu are as follows (a) S-100 beta ; (b) CD56 ; (c) Calretinin .The secondary antibody and detection system from PathnSitu are as follows: poly-excel HRP (horseradish peroxidase) /DAB  detection system .Statistical analysisStatistical analysis was done using SPSS software v 24.0 . The two-tailed Student t-test method was used to determine the statistical significance between the two groups and the corresponding p-value. A p-value of <0.05 was found to be significant.The study included a total of 45 benign peripheral nerve sheath tumours: 18 cases were schwannoma including histological variants, and 27 cases were neurofibroma including histological variants.The age distribution of the participants and the anatomical location of schwannomas are summarized in Table The age of patients ranges from 13-72 years with a median age of 42.5 years. The anatomical locations for schwannomas were as follows: head and neck (n= 5), trunk (n=3), back (n=1), upper limb (n =7), and lower limb (n=2).The age distribution and anatomical location of neurofibromas are summarized in Table The age distribution and anatomical location of neurofibromas are summarized in Table The histomorphological features of cases diagnosed with schwannoma and neurofibroma with histological variants and distributions of schwannoma cases with the proportion of Antonia A and Antoni B areas are summarized in Table Out of 18 cases diagnosed as schwannoma by histopathology, 11 cases had both Antoni A and B areas whereas six cases had only Antoni B areas with hypocellular, reticular, microcystic areas and lacking palisading nuclei. Two cases of cellular schwannomas Figures -2B and tOut of 27 cases diagnosed as neurofibroma by histopathology, 22 cases were of the diffuse type, two of plexiform neurofibroma, one showing extensive myxoid change and two of cellular neurofibroma Figures -3B.Immunohistochemistry (IHC) resultsThe IHC results  were recorded as per the scoring criteria in Table IHC expression profiles of all three markers for neurofibromas and schwannomas are summarized in Tables IHC results showed diffuse strong positivity (3+) of S-100 in both schwannomas Figure  and neurCD56 was expressed strongly (3+) in 90% (n=16) of cases of schwannoma Figure  and two Calretinin was positive in seven cases 39%) of schwannomas Figure  includin% of schwClinical parametersNo significant differences were observed between schwannomas on patient age (median age group being 42.5 and 46 yrs) and anatomical distribution Table  that conS-100 S-100 is a protein seen in neural or neural crest-derived tissues and is also expressed in chondrocytes, melanocytes and Langerhans cells. It is a common marker for neurogenic tumours. S-100 has been reported to be positive in both Schwannomas and neurofibromas, many previous studies including Park et al. , Awf Sh The present study, however, did not show any statistically significant difference p=0.75) in staining (extent and intensity) between schwannomas and neurofibromas. Diffuse strong positivity (3+) of S100 in both schwannomas and neurofibromas was observed except for four cases (14.2%) of neurofibromas that showed moderate staining intensity (2+). On S100 staining, more than 75% of cells were positive in cases of cellular neurofibroma, plexiform neurofibroma and diffuse neurofibroma. With the above observations, it is found that S100 expression is maximum in all cases of schwannoma attributing to its origin from Schwann cells while the expression is decreased in neurofibroma as it is composed of a mixed population of cells such as Schwann cells, perineurial-like cells and endoneurial fibroblasts. Staining of tumour cells for S100 was in concordance with the study done by Fine et al [ in stainCD56 Park et al. , and GueIn the present study, there is a consistency with previous studies on preferential expression in schwannomas but there is a statistically significant difference (p <0.0001) with 100 % negative expression in neurofibromas.CalretininCalretinin is a calcium-binding protein expressed in certain neurons in the central nervous system and peripheral nerves and also in mesothelial cells as reported by Dabbs . Fine etIn the present study, calretinin was positive in only seven cases (39%) of schwannomas including one case of cellular schwannoma and negative in all cases (100%) of neurofibromas. The differential expression of calretinin between schwannoma and neurofibroma groups was found to be statistically significant (p < 0.005).In the present study, CD56 was found to be more significantly and selectively expressed in Schwannomas compared to Calretinin.Using IHC Results for Resolving Differential Diagnoses on HistopathologyMost of the cases were distinguished based on the histological pattern and cellular components but three cases out of 45 benign peripheral nerve sheath tumours were difficult to differentiate on H&E stained sections due to a lack of nuclear palisading and the presence of only hypercellular areas. Hence, differential diagnoses of cellular neurofibroma and cellular schwannoma were thought of. For these three cases, differentiation was done based on the IHC marker CD56 and calretinin. One out of three cases was positive for CD56 and calretinin and hence, was reported as cellular schwannoma and two of three cases were non-reactive for both CD56 and calretinin. Hence was reported as cellular neurofibroma. All these three cases were positive for S100 diffusely with strong intensity (3+). The study has a few limitations and can be followed up with an increased representation of histological variants with morphological overlap and also expand this IHC panel to include newer neural markers including SOX-10 ,14. FollAccurate diagnosis of peripheral nerve sheath tumours is important, especially on core-needle biopsies with limited tissue which may not be completely representative of the tumour. Diagnosis of histological variants including atypical/ cellular neurofibroma, cellular schwannoma, schwannomas lacking palisading nuclei, more hypocellular areas from low-grade malignant peripheral nerve sheath tumour (MPNST), the possibility of hybrid tumours with both components could be challenging on histopathology alone. In such settings, immunohistochemistry has been shown to play a role in arriving at the right diagnosis which may be important to plan the management including the type of surgery (including nerve-sparing techniques) after the histopathology report on the core needle biopsy. Many of the schwannomas may be treated without resection of the nerve while neurofibromas may require partial resection and nerve repair.Our study shows that S-100 does not show differential expression between schwannomas and neurofibromas. However, CD56 has been found to be a more sensitive marker aid in the diagnosis of peripheral nerve sheath tumours with statistically significantly higher expression in schwannomas compared to neurofibromas. Calretinin was also found to be statistically significantly highly expressed in schwannomas compared to neurofibromas, though the difference is statistically significantly lower compared to CD56. Thus we conclude from our findings that immunohistochemical analysis of CD56 and Calretinin will be a valuable ancillary aid to the histopathologist in rendering the right diagnosis of peripheral nerve sheath tumours, especially in the era of core needle/ small biopsies for soft tissue neoplasms and also when there are histological overlapping features including variants.


Embryonic development requires tight control of gene expression levels, activity, and localisation. This control is coordinated by multiple levels of regulation on DNA, RNA and protein. RNA-binding proteins (RBPs) are recognised as key regulators of post-transcriptional gene regulation, where their binding controls splicing, polyadenylation, nuclear export, mRNA stability, translation rate and decay. In brain development, the ELAVL family of RNA binding proteins undertake essential functions across spatiotemporal windows to help regulate and specify transcriptomic programmes for cell specialisation. Despite their recognised importance in neural tissues, their molecular roles and connections to pathology are less explored. Here we provide an overview of the neuronal ELAVL family, noting commonalities and differences amongst different species, their molecular characteristics, and roles in the cell. We bring together the available molecular genetics evidence to link different ELAVL proteins to phenotypes and disease, in both the brain and beyond, including ELAVL2, which is the least studied ELAVL family member. We find that ELAVL-related pathology shares a common neurological theme, but different ELAVL proteins are more strongly connected to different phenotypes, reflecting their specialised expression across time and space. Embryonic development requires tight control of gene expression levels, localisation, and timing. Levels of gene expression in cells are finely balanced and are influenced and regulated by a multitude of factors. Although transcription is largely responsible for gene expression regulation, it has become more appreciated that RNA processing at the post-transcriptional level also plays an influential role in determining the functional outcome of many RNA molecules.cis consensus elements primarily localised to the 3ʹ untranslated region (3ʹUTR) of mRNA transcripts , 14ELAV-ELAVL1 is ubiquitously expressed, displaying a wide range of functions [ELAVL2, ELAVL3, ELAVL4 have historically been more specifically associated with neurodevelopment with expression enriched in the central and peripheral nervous system and are therefore referred to as the neural ELAVLs (nELAVL).unctions . Predomiunctions , 17. ConRBPs typically harbour multiple (two or more) conserved RNA-binding domains that drive the RNA-protein interactions . All fouBinding to RNA targets is facilitated by the RRM1 and RRM2 domains preferentially recognising AU-rich elements in the 3ʹUTR of transcripts . ImmunopAll three of the RRM domains function to achieve the stability of RNA transcripts. RRM1 and RRM2 directly associate with the target RNA , while RNuclear export and localisation signals located in the hinge region of the ELAVL proteins are responsible for the shuttling of RNAs between the nucleus and cytoplasm , 27. ShuA common mechanism observed among RBPs is the formation of multimeric protein complexes. While multimer formation can increase binding affinity of the protein to RNA targets, for some RBPs, the presence of RNA is required for efficient interaction . nELAVL Drosophila melanogaster differs from that of mammalian ELAVL and consists of three paralogous genes, Elav, Rbp9 (RNA-binding protein 9) and Fne (Found in neurons). Elav was the first protein identified, through the characterisation of Drosophila mutants that were embryonic lethal and had abnormal vision [Elav occurs throughout all stages of development and is required for the differentiation and maintenance of post-mitotic neurons [Elav gene family, Rbp9, which encodes a nuclear RBP expressed in neuronal cells of the adult nervous system after morphogenesis [Rbp9 is also expressed in the gonads particularly during oogenesis in the cytoplasm of cystocytes and oocytes [Elav mutants, Rbp9 mutants are viable, displaying no gross neurodevelopmental defects. However, mutants displayed a shorter lifespan and reduced locomotor activity, and female Rbp9 mutants are sterile [Fne shares similarities with Elav as it displays neuronal specific expression with strikingly similar transcript and protein expression patterns. Localised to the neuronal cytoplasm (as opposed to Elav in the nucleus), Fne is important for dendritic growth, cytoskeletal regulation and extracellular matrix adhesion [fne mutants are viable, but have brain defects leading to impaired behaviour such as in male courtship [The Elav family in l vision . Primari neurons . The secogenesis . Rbp9 is oocytes . Unlike  sterile . Fne shaadhesion . Fne hasadhesion . Null fnourtship .Drosophila has been pivotal to unravelling the roles of human ELAVL proteins. One of the main functions of Elav is to promote the alternative splicing and polyadenylation of mRNA transcripts. The specific transcript binding and activity of each ELAV family member creates unique neuronal transcriptomic signatures. Such specificity between paralogues is also observed in other species, where studying the ELAV paralogues has helped gain knowledge into the cellular and developmental activities of these proteins [Understanding the function of Elav in proteins , 40, 41.Drosophila Elav, nELAVL are expressed in the nervous system, however they display a more specific expression pattern, with no expression in astrocytes and oligodendrocytes [Elavl2 expression is enriched in the outer layer cells within the ventricular zone and the neurons of the intermediate zone. Elavl4 is predominately expressed in intermediate zone neurons, whereas Elavl3 is abundantly expressed in the cortical plate, but absent from the intermediate neurons and ventricular zone cells  [A. melifera genome only encodes a single member of the Elavl family, Elavl2, which is more similar to fne. Lack of paralogues has been compensated for somewhat by complex alternative splicing producing more than 40 protein isoforms [The relevance of Elavl2 to brain function has been studied through RNAi in a non-model organism, neybees)  - studieisoforms . BehavioElavl2−/− knockout mice were generated to investigate the role of Elavl2 in the formation of primordial follicles [Ddx6 in oocytes, which in turn directs the assembly of P-body like granules prior to the formation of primordial follicles. RNA immunoprecipitation and sequencing of Elavl2 binding targets in mouse ovaries confirmed a significant role for Elavl2 in RNA processing and cytoplasmic ribonucleoprotein granule formation [Although primarily localised to the brain, Elavl2 also appears necessary for gonadal development, particularly oocytes. In 2014, Chalupnikova et al. reported the first functional analysis of an oocyte specific Elavl2 isoform . Unlike ollicles . Morpholollicles . In femaollicles . It was ormation .ELAVL2 is enriched in spermatogonial cells but is reduced in non-obstructive azoospermia patients [A more recent study using both human and mouse testicular cell lines has investigated the function of ELAVL2 in spermatogenesis . ELAVL2 patients . This stELAVL2 is clearly a fundamental gene required for organism development, particularly in the brain and gonads. The exact cellular roles of ELAVL2 and RNA targets under ELAVL2 regulation are yet to be fully elucidated across multiple systems.Investigations into ELAVL2 are still in their infancy, with many areas remaining unexplored. ELAVL2 as a candidate gene for ASD and intellectual disability, and several loss-of-function variants have been identified in large cohort sequencing studies [Given the established link between ELAVL proteins and brain development, ELAVL2 regulation was studied as a pathway of interest in neurodevelopmental disorders. To investigate the potential targets of ELAVL2, primary human neurons were depleted of ELAVL2 via RNAi-mediated knockdown . Using d studies –56.ELAVL2 and disease, but the significance was not maintained during replication studies or correcting for multiple testing. The specific SNP of interest, rs10491817, lies within the first intron of ELAVL2, but any functional consequence of the SNP has not yet been confirmed. Independent replication of this finding is required before a firm association between ELAVL2 and schizophrenia can be concluded.In 2011, a genome-wide association study (GWAS) approach was used to determine schizophrenia susceptibility genes using three independent cohorts from Chinese and Japanese populations . AnalysiELAVL2 has been identified as a candidate causative gene in primary ovarian insufficiency (POI) [ELAVL2. This is an intriguing link given the requirement for Elavl2 in oogenesis [Recently cy (POI) . POI comogenesis , 48, butogenesis .ELAVL2 is the least studied of the nELAVL paralogues, and while there is clear evidence supporting a neurological role, ELAVL2 is obviously required in other tissues, particularly within the reproductive system Fig. . A homozElavl3−/− null mice have no gross anatomical brain defects and were viable and fertile, although some strain-dependent lethality was observed [Elavl3−/− and Elavl3+/− mice displayed seizure activity upon EEG. As Elavl3 is the only nElavl expressed in Purkinje neurons, a rotarod assay was performed to assess cerebellar function. In young adult mice, Elavl3−/− mice had poorer balance and coordination compared to Elavl3+/− controls, indicative of cerebellar dysfunction [Elavl3+/+ littermates, so whether there is a more subtle reduction in rotarod performance in Elavl3+/− mice compared to wildtype mice is not clear. Elavl3 expression is maintained during adulthood in the cerebellum. As Elavl3−/− mice aged, progressive severe cerebellar ataxia was observed [observed . Howeverfunction . There wobserved . This atobserved .Elavl3−/− mice models have been instrumental in gaining insight into the pathognomonic mechanisms in the cerebellum. High-throughput sequencing analysis of alternative splicing in the cerebellum of Elavl3−/− mice highlighted the convergence of targeted transcripts on glutamate levels and neuronal excitability—linking RNA regulation to the seizure activity observed in Elavl3−/− mice [AnkyrinG (AnkG), has also been explored [ANK3, has been linked to multiple neurodevelopmental phenotypes [AnkG transcript. This exon is excluded from the canonical transcript and appears to regulate localisation of AnkyrinG at the axon initial segment, and through this, neuronal activity [Elavl3−/− mice at both 2 and 9 months of age, exon 34 was erroneously included in AnkG transcripts in neurons [−/− mice . The phyexplored , 61. Ankexplored . The humenotypes . Elavl3  neurons .Elavl3 expression is upregulated during the differentiation of neural stem cells into inhibitory GABAergic neurons, in parallel with 3’UTR lengthening. Depletion of Elavl3 during this differentiation process caused a shift towards using proximal polyA sites compared to control cells, with a concomitant delay in neural stem cell differentiation [ntiation .Elavl3 as one of the most downregulated genes [ELAVL3 mRNA as well as nuclear depletion of protein, which was more commonly observed than cytoplasmic accumulation [SOD1 or C9orf72, and in fact, in this study, ELAVL3 abnormalities were more common than TDP-43 abnormalities [Transcriptomic profiles of motor neurons from sporadic amyotrophic lateral sclerosis nervous systems identified ed genes . Neuropamulation . Similarmalities . Using amalities , suggestELAVL2, the curated SFARI web resource also lists ELAVL3 as a candidate disease gene underlying ASD [Similar to ying ASD , 68–70. ying ASD . Of noteELAVL3 could generate significant clues into the exact role of ELAVL3 in brain development and function. Alternative splicing is one process regulated by ELAVL3 but given the diverse roles nELAVLs can undertake in the cell, it will be interesting to investigate further potential functions of ELAVL3.Studies focused on ELAVL3 has become particularly centred on putative roles in ALS, supported by mouse models showing ataxia at a later age. Further characterising the germline variants in Elavl4−/− mice were born at expected ratios, with no significant differences in survival, growth, or gross brain morphology [Elavl4−/− mice developed abnormal hindlimb reflexes, which is often associated with basal ganglia and cortical defects and in turn motor deficits. Motor coordination in adult mice was tested using the rotarod assay, with Elavl4−/− mice performing significantly worse than WT littermates. Elavl4+/− mice were not tested in these assays. The authors noted poor reproductive performance of Elavl4−/− mice but did not provide any further detail on this [Elavl3−/− or Elavl3+/− mice, Elavl4−/− mice do not have spontaneous seizures, but are predisposed to auditory-induced seizure activity, compared to controls [rphology . Within  on this . Unlike controls . Behaviocontrols .Elavl4−/−derived neural stem cells, the proportion of cell spheres composed of neurons significantly decreased compared to glia or astrocytes [Elavl4−/− embryos, there were fewer post-mitotic neurons, especially in the intermediate zone, whereas there were more proliferating cells in the ventricular zone. Together, this suggests that the number of cells exiting the cell cycle was decreased in Elavl4−/− embryos. The overall brain size was maintained despite this reduced differentiation by an increase in apoptosis in the ventricular zone [Proliferation potential and differentiation capacity were tested using ex-vivo neurosphere production, where tissue from brain regions is separated into single cells and then cultivated. The number of spheres reflects proliferation capacity and potential. In cultures of trocytes . In Elavlar zone . SimilarElavl4−/− mice have also been studied to explore the role of Elavl4 in neuron specification and dendritogenesis [in utero Elavl4 shRNA (E13). Following several days of culture, dendritic length was decreased in the neurons, despite no difference in overall dendrite number, supporting a role for Elavl4 in neurite outgrowth.ogenesis . StaininElavl3−/− and Elavl4−/− single knockout mice have been crucial in understanding the importance of the individual Elavl proteins, an Elavl3/4 double knockout mouse was generated to investigate the cumulative role of nElavl proteins in RNA regulation [Elavl3−/−;Elavl4−/− mice survived only several hours after birth, but were indistinguishable from WT littermates. The double knockout removed approximately 65% of nElavl protein in the cortex. This study has proved fundamental in understanding the specific motifs and targets of nElav proteins and their role in splicing and RNA processing and splicing. Both exclusion and inclusion of exons occurred as a result of nElavl depletion; sequence analysis suggested that U-rich sequences flanked nElavl dependent exons, whereas independent exons lacked this U-rich region. Glutamine amino acid biosynthesis was highlighted as a core pathway under nElavl regulation, and measurement of glutamate levels in cortical tissue of double knockout and WT mice, showed that knockout mice had a 50% reduction in glutamate levels [As gulation . Elavl3−e levels .Elavl4−/− mice has been used to identify transcriptome-wide changes to alternative splicing and alternative polyadenylation, two mechanisms known to be regulated by the nElavl proteins [Elavl4 depletion, including many transcripts known to be bound by Elavl4. The largest proportion of events were exon skipping, with approximately equal numbers of exon inclusion versus exon exclusion in different transcripts. Pathway analysis revealed that the genes affected by alternative splicing were involved in processes such as cell death and survival, neurological disease, and nervous system development and function. When further subcategorised, pathways related to loss and viability of neurons and synaptic transmission of nervous system tissue were enriched. An analysis of alternative polyadenylation identified 53 genes which changed 3’UTR length, mostly switching to a shorter 3’UTR. A meta-analysis found a mutually exclusive grouping of transcripts that underwent alternative splicing versus alternate polyadenylation.RNA-seq data obtained from the neocortex of 3 month old proteins . SeveralELAVL4 has been associated with various neurological disorders such as ALS, Alzheimer’s disease, and Parkinson’s disease through either genetic or cellular mechanisms, and this evidence is nicely reviewed in Silvestri et al. 2022 . The genELAVL4 as a candidate gene.ELAVL4 has been the most well-studied family member, using both mouse and molecular models. Disruption to protein levels causes progressive neurological phenotypes, supported by in vitro studies finding effects on neural differentiation and outgrowth. For human disease, ELAVL4 has mainly be considered as a candidate risk gene for neurological later-onset disorders, which is appropriate given the phenotypes observed in the mouse model. These genetic studies deserve to be repeated with the larger cohorts and more sophisticated sequencing technology now available, to definitively test ELAVL2, other developmental systems also. Molecular studies in these models or in vitro human studies are revealing the binding targets and cellular consequences when nELAVL genes are disrupted. There is accumulating evidence supporting the relevance of this gene family to later onset neurodegenerative diseases, but data is emerging also implicating these genes in neurodevelopmental disorders, and for ELAVL2, a potential link to primary ovarian insufficiency. The continuing development of techniques to more sensitively interrogate RBP-RNA interactions, such as STAMP, which efficiently detects RBP-RNA interactions in single cells [The nELAVL family are well established factors in brain development, but the specific roles for each gene are still being uncovered. Animal models have provided significant insight into the functions of the nELAVL family during neurogenesis, and for le cells , will su


Because ICB targets cell–cell interactions2, we investigated the impact of multicellular spatial organization on response, and explored how ICB remodels the tumour microenvironment. We show that cell phenotype, activation state and spatial location are intimately linked, influence ICB effect and differ in sensitive versus resistant tumours early on-treatment. We used imaging mass cytometry3 to profile the in situ expression of 43 proteins in tumours from patients in a randomized trial of neoadjuvant ICB, sampled at three timepoints . Multivariate modelling showed that the fractions of proliferating CD8+TCF1+T cells and MHCII+ cancer cells were dominant predictors of response, followed by cancer–immune interactions with B cells and granzyme B+ T cells. On-treatment, responsive tumours contained abundant granzyme B+ T cells, whereas resistant tumours were characterized by CD15+ cancer cells. Response was best predicted by combining tissue features before and on-treatment, pointing to a role for early biopsies in guiding adaptive therapy. Our findings show that multicellular spatial organization is a major determinant of ICB effect and suggest that its systematic enumeration in situ could help realize precision immuno-oncology.Immune checkpoint blockade (ICB) benefits some patients with triple-negative breast cancer, but what distinguishes responders from non-responders is unclear Imaging mass cytometry is used to map the multicellular dynamics of immune checkpoint blockade-treated triple-negative breast cancer, finding that key proliferative fractions and cell–cell interactions drive response, and immunotherapy distinctively remodels tumour structure. Triple-negative breast cancer (TNBC), which lacks hormone receptor and human epidermal growth factor 2 (HER2) expression, is an aggressive subtype for which new therapies are needed6. In TNBC, trials of immune checkpoint blockade (ICB) targeting the interaction between programmed death protein 1 (PD-1) and programmed cell death ligand 1 (PD-L1) have shown that some patients benefit7, but we lack a reliable biomarker to identify responders1Immunotherapy has transformed the treatment of solid tumours but its best use in breast cancer remains unclear2. In cancer, chronic T cell stimulation leads to dysfunction, owing to interactions between cells expressing immune checkpoint receptors and ligands. ICB prevents this to invigorate dysfunctional T cells2 and these invigorated T cells must then interact with target cancer cells to induce cell death8. The efficacy of ICB therefore depends on both the cellular composition and multicellular organization of tumours because they orchestrate these interactions. Breast tumours are heterocellular ecosystems of cancer and tumour microenvironment (TME) cells12 that self-organize as distinct, recurring multicellular structures13. Despite this, the relationship between phenotypic spatial organization of tumours and ICB response has been little explored.Interactions between proximate specialized cells in distinct activation states underpin the effect of ICBAlthough multicellular organization pre-treatment may indicate whether the immune response can be augmented by ICB, how ICB remodels tissue structure to achieve this remains obscure. Serial tumour sampling before, during and after treatment could uncover treatment-induced remodelling but is challenging in routine clinical practice. This may explain why the relationship between tissue dynamics during treatment and response is unknown.3 (IMC) to precisely quantify the phenotype, activation state and spatial location of cells in tumours sampled at three timepoints from patients enrolled in a randomized trial of neoadjuvant immunotherapy. We found that both the cellular composition and spatial organization of tumours pre-treatment were predictive of immunotherapy response, that sensitive tumours were distinct from resistant tumours early on-treatment and that response was best predicted by combining features from both timepoints.To characterize the relationship between tissue structure, its dynamics on therapy and immunotherapy response in TNBC, we used imaging mass cytometry14 : pre-treatment , on the first day of the second treatment cycle  and at surgical excision of the tumour bed following treatment  tissue collected from patients with TNBC enrolled in the NeoTRIP randomized controlled trial14 Fig. . NeoTRIP15 and derived proteomic profiles. For cell phenotyping, we separated epithelial (cancer cells) and TME cells using several methods  cells defined by FOXP3 expression and activated cytotoxic T cells with high granzyme B expression (CD8+GZMB+T). There were two cell phenotypes positive for PD-L1; these were both CD11c+ antigen presenting cells (APCs), of which one was IDO+. We also identified B cells, plasma cells, macrophages, dendritic cells, neutrophils, endothelial cells and three fibroblast phenotypes. We next correlated the cellular compositions of different images from the same tumour to estimate the extent of spatial heterogeneity , and the mesenchymal tumour subtype contained the greatest proportion of all three stromal cell phenotypes. Together, these findings corroborated the quality of our multiplexed image data and cell phenotypes.To determine the relevance of cell phenotypes to established tumour pathology, we investigated the relationship between cell phenotype proportions and relevant clinical features. In comparison with clinical PD-L1 status assessed by centralized pathology review, we confirmed that PD-L1 expression by IMC was greater in PD-L1-positive tumours  to test whether the association between a given feature and response significantly differed by treatment. To estimate the impact of multiple testing, we also computed the false discovery rate (FDR). We first asked whether the densities of different epithelial and TME cell phenotypes differed in their capacity to predict response  response, although this was associated with an elevated FDR  is that the surrogate end point of pathological complete response (pCR), defined as the absence of invasive cancer cells after treatment, can be used to identify responders before time-to-event follow-up has maturednse Fig. . Only PD+B  and epithelial–CD8+GZMB+T  cell interactions showing the greatest differential effect  and tested for associations with pCR , but when patients were treated with immunotherapy, 12 epithelial and 16 TME cell phenotypes predicted response  cells , whereas the proliferative fraction of CD8+TCF1+T cells was the strongest immunotherapy response predictor overall . These features (proliferative fractions of MHCI&IIhi cancer cells and CD8+TCF1+T cells) were, however, only moderately correlated ; the density of CD79a+Plasma cells was not, however, associated with response  and, consistent with their strong correlation, this also held for homotypic CD8+GZMB+T interactions   cell phenotype was distinct because it was associated with resistance to immunotherapy (but not chemotherapy) when quantified as a density  or cell–cell interaction metric , we investigated the link between on-treatment cell densities, cell–cell interactions and immunotherapy response Fig. –11. The nse Fig. . Among o.3) Fig. , but witons Fig. . We alsoion Fig. .Fig. 4CeGiven the predictive value of proliferation in treatment-naive tumours, we asked whether it would perform similarly on-treatment. We found that although the functional significance of proliferation was largely preserved on-treatment, proliferation itself was reduced and proliferating cell fractions were not predictive of response , and this revealed conserved dynamics across subgroups. Immune cells increased dramatically on-treatment and decreased after treatment, accompanied by a reduction in epithelial (cancer) cells and increase in stromal cells  and found more were predictive of immunotherapy than chemotherapy response (112 versus 26), and more of these were found at baseline than on-treatment (70 versus 42). We therefore asked whether their combined predictive performance would also differ by treatment and timepoint. For each treatment arm, we fit three regularized multivariate logistic regression models: using baseline data, on-treatment data and data from both timepoints Fig. . Predict20 to identify the dominant drivers of response , whereas the next two most important predictors corresponded to CD15+ cancer cell abundance (homotypic interactions and density). In summary, feature importance analysis revealed distinct immunotherapy response drivers at baseline versus on-treatment.It remained unclear whether response to immunotherapy was driven by the combined effect of many features or just a few. We used an established methodnse Fig. . This annse Fig. . By far ied Fig. . The twoBy mapping the multicellular tumour ecosystem in situ in TNBC, we uncovered key ICB response predictors and showed that ICB distinctively remodels tumour structure.hi cancer cells and CD8+TCF1+T cells. Past work has linked expression of MHCII by cancer cells to neoadjuvant ICB response in TNBC21. The triggers of aberrant cancer cell MHCII expression remain obscure but include local inflammatory signals such as interferon-γ (IFN-γ) and nuclear factor κB (NF-κB)22. Quiescent cancer cells residing in immunosuppressive niches in TNBC have been shown to resist ICB23 and could explain why lower MHCI&IIhi proliferative fractions are associated with ICB resistance. Proliferating CD8+TCF1+T cells, however, proved the dominant response driver. CD8+TCF1+T cells are thought to be a progenitor stem-like population that underlies the proliferative burst of CD8+ T cells after ICB16, predicts ICB response in melanoma24 and resides in intratumoural niches characterized by dense APCs19. Our finding that proliferating CD8+TCF1+T cells are more often in contact with MHCII+ cells supports the idea of an intratumoural niche, and links this population to MHCII expression. Proliferative CD8+ T cells positive for exhaustion markers have previously been characterized as a differentiating population that drives ICB response in melanoma25 and co-locates with Treg cells in multicellular structures in breast tumours13. In addition to their heightened activation state and distinct spatial location, therefore, recent studies also corroborate proliferative CD8+TCF1+T cells as drivers of ICB response.The top two predictors of ICB response at baseline were the proliferative fractions of MHCI&II27. Correlations with gene expression and markers of immune activity revealed that these diverse TMEs are also functionally distinct26. The intimate relationship between structure and function is supported by our findings that T cells contacting cancer cells are more often proliferative and express higher levels of activation markers. T cells reactive to neoepitopes are also characterized by distinct activation programmes that include expression of checkpoints such as PD-1 (refs. 29), raising the possibility that spatial cell–cell interactions enrich for these tumour-reactive T cells. The predictive power of cell–cell interactions may therefore be explained by their close alignment with the underlying function of both the participant cells and the wider immune response.Proliferative fractions and cell–cell interactions were closely linked in our analysis and enriched for cells in distinct activation states. Past analyses of TNBC showed that the interface between cancer cells and infiltrating leukocytes defines diverse, spatially distinct TMEs+ T cells characterized by high PRF1 and GZMB expression30. This suggests that on-treatment CD8+GZMB+T cell expansion is driven by ICB and explains why it proved the top on-treatment response predictor in our study. We also found that CD15+ cancer cells on-treatment predicted resistance to ICB. CD15+ breast cancer cells have been previously described31. CD15 is a carbohydrate blood group antigen, expressed by neutrophils and monocytes, that plays a role in cell adhesion31. In some instances (in keeping with previous findings31) we saw a striking mosaic expression pattern of intimately admixed CD15+ and CD15− cancer cells, and heterotypic aggregations comprising CD15+ cancer cells surrounded by CD15+ leukocytes including neutrophils . NeoTRIP was a neoadjuvant immunotherapy trial of early high-risk TNBC in which 280 patients were randomized to receive neoadjuvant carboplatin and nab-paclitaxel on days 1 and 8, with or without atezolizumab (anti-PD-L1) on day 1 . Following the full course of therapy, tumours were surgically removed (post-treatment). Tissue microarrays (TMAs) were constructed for surgical excisions only. Regions of tumour, tumour–TME interface and adjacent stroma were annotated by a breast pathologist (G.V.) on corresponding H&E slides to guide TMA construction. Cores of 1 mm in diameter in the identified regions were removed and processed as TMAs.Homo sapiens reference using STAR v.2.5.2 (ref. 34). Transcript assembly and expression analysis were performed on each sample with cufflinks v.2.2.1 (ref. 35), resulting in fragments per kilobase million (FPKM) values for each transcript in the genes of interest. The TNBC subtypes were determined using the minimal 101-gene TNBCtype36 . Pools of 48 libraries were sequenced on a NextSeq 500 or NextSeq 2000 sequencing system (Illumina) with a high-output reagent kit for 75 base pair paired-end reads, with a mean of 10 million paired-end reads per sample per run. Pools were sequenced across replicate runs to achieve over 40 million paired-end reads per sample. Base call files from each sequencing run were converted to fastq format using bcl2fastq conversion software v.2.20, replicate fastq files for each sample were merged and files were aligned to the Ensembl GRCh37 Candidate commercial antibodies intended for use in IMC were first validated by immunofluorescence using tonsil and breast cancer tissue to confirm optimal staining intensity, specificity and signal-to-noise ratio. Antibodies that passed validation by immunofluorescence were conjugated to metal isotopes and validated using IMC to ensure preservation of staining specificity and intensity. Sensitivity and specificity were further validated in multiplexed IMC experiments to ensure appropriate patterns of marker co-localization. Finally, optimal concentrations of all metal-conjugated antibodies were determined by visual inspection of IMC images in both tonsil and breast cancer tissue.37. Conjugation of bismuth to the antibody required substitution of the L buffer from the Maxpar X8 labelling kit with 5% nitric acid (HNO3) during loading of the metal onto the polymer and deionized water (MilliQ) during the washes39. Metal-tagged antibodies were stored in a Candor Antibody Stabilizer (Candor Biosciences) at 4 °C. For a full list of antibodies and the metal conjugates, see Supplementary Table Indium, yttrium and lanthanide metals were conjugated to antibodies according to the manufacturer’s instructions . Platinum isotopes were conjugated directly to the reduced antibody without the polymer40. Tissue underwent antigen retrieval  before blocking with 3% BSA in TBS for 1 h. Slides were incubated with unconjugated primary antibodies  overnight at 4 °C, then with metal-conjugated secondary anti-mouse and anti-rabbit antibodies for 3 h at room temperature. Next, slides were incubated with the remainder of the metal-tagged antibodies overnight at 4 °C, then with 0.5 μM iridium for DNA detection  for 30 min. Slides were washed with TBS 0.1% Tween between each labelling step, and air-dried following the final incubation.FFPE slides were dewaxed in xylene and rehydrated in an alcohol gradient2 were identified by a breast pathologist (H.R.A.) for acquisition by IMC using the Aperio eSlideManager web application (Leica Biosystems). Three ROIs were selected for each sample unless the biopsy was too small or, for baseline samples, contained no tumour cells. In on-treatment biopsies, if no invasive cancer was identified, regions of tumour bed were instead targeted for IMC.Two sequential sections of FFPE tissue were prepared from core biopsy (baseline and on-treatment) and TMA blocks (post-treatment). One was stained with H&E using an autostainer (Leica ST5020 Stainer/TS5025 Transfer Station/CV5030 Coverslipper Workstation). H&E slides were scanned using the Leica Aperio AT2 Automated Digital Whole Slide Scanner. For core biopsies, ROIs measuring 500 × 500 µm3.The adjacent section was labelled with antibodies for IMC as described above. ROIs were mapped by manual inspection of annotated H&E images and subjected to IMC (Fluidigm): tissue was raster laser-ablated at 1 µm resolution, then ablated tissue aerosol was ionized using inductively coupled plasma, and resulting isotopic ion reporters quantified using time-of-flight mass spectrometry to infer protein abundance41 package and subsequently used to correct single-cell measurements.In mass cytometry applications such as IMC, signal from one channel may spill over to another channel due to trace amounts of contaminating isotopes in metal stock solutions. To account for this, all metal-conjugated antibodies in our panel were spotted separately onto glass slides and dried. Quantification by IMC of all metal isotopes in the panel was then conducted for each dried antibody spot on the slide. A ‘spillover matrix’ quantifying crosstalk was generated using the Bioconductor CATALYST42. To identify regions of contiguous epithelium, we labelled pixels as epithelial based on their expression of cytokeratins and used a random-forest pixel classifier (Ilastik43) to assign all remaining pixels a probability of belonging to an epithelial region. Probability maps were saved as a red, green and blue (RGB) tiff file, and epithelial regions segmented to generate image masks using standard segmentation tools.This process is described by Extended Data Fig. 15: a nuclear channel (sum of Histone H3 and Ir191) and a cytoplasmic channel (sum of panCK and CK5). Whole-cell image masks were used for downstream measurements (single-cell proteomic profiles and size) using CellProfiler44. Nuclei were mapped to whole-cell regions, and whole cells mapped to epithelial masks. To be considered ‘related’ to an epithelial mask, at least 30% of the pixels from a whole cell had to overlap with the epithelial region. Before taking measurements, multistack tiff files were filtered for single hot pixels. Single-cell proteomic measurements were taken by computing the mean ion count for each segmented whole cell; these data were spillover corrected using the spillover matrix described above with a non-negative least squares linear model implemented in CATALYST41. Small objects (cells with area less than 31 µm2) were excluded from analyses. We left one platinum isotopic channel empty for detection of carboplatin. Carboplatin signal detected in other platinum isotopes, for which conjugated antibodies were included , was corrected by fitting a linear model to all cells: log-transformed cell expression was predicted using the carboplatin isotope, and the resulting model residuals were taken as corrected values.For segmentation of single cells, ‘salt and pepper’ noise was removed using a median filter and relevant channels rescaled per image to lie between zero and one. A two-channel image was passed to the Mesmer deep learning single-cell segmentation modelCell phenotypes were assigned by semi-supervised clustering. Cells were first classified as epithelial or TME using multiple classification methods, in which the best performing method for each image was manually selected by visual inspection of tissue morphology and cytokeratin expression  was used for clustering, and values were rescaled to lie between zero and one per image. Clusters were categorized as either epithelial or TME by manual inspection of annotated images.A two-component Gaussian mixture model was fit to the log-transformed sum of all cytokeratins  to distinguish cells as positive or negative for cytokeratin. Cells related to epithelial masks based on a 30% area overlap were deemed ‘mask-positive’. All images were annotated with mask-positive, cytokeratin-positive or double-positive cells (those that were positive by both criteria). All images were inspected, using tissue morphology as the standard, to determine which method best captured epithelial cells. Most epithelial cells were accurately classified by this approach, but some infiltrating leukocytes were misclassified. To capture infiltrating leukocytes, cells that were mask-positive and cells that were cytokeratin-positive were subclustered using a combination of key epithelial  and immune markers . Inspection of average expression profiles per cluster was used to identify infiltrating leukocytes. This process was repeated until satisfactory results (determined by inspection of images annotated with cell phenotypes) were obtained. Tumours poorly classified by these approaches (often owing to low cytokeratin expression) were subjected to unsupervised clustering by Phenograph46, then median expression values per SOM node were passed to Phenograph45 and resulting clusters mapped back to single cells. Heatmaps of scaled median expression values were inspected, and clusters lacking meaningful differences merged. Resulting clusters were labelled based on their expression profiles. Cluster validity was further investigated by inspecting images annotated with cluster labels and expression profiles to ensure cell morphology and expression values were concordant with the cluster label.Cell phenotypes were derived separately for epithelial and TME cells. Only proteins known to be expressed by epithelial or TME cells based on previous knowledge or manual inspection were included. The proliferation marker Ki67 was excluded from cell clustering. Expression values were clipped at the 99th centile, mean centred and scaled before clustering. Clustering was performed in two steps. First, a self-organizing map (SOM) was created using GigaSOMExtensive image curation was conducted under the supervision of a pathologist (H.R.A.) to identify invasive cancer cells and exclude in situ or normal epithelial cells from clinical correlative analyses. All TME cells were retained for downstream analysis. For a full breakdown of cell numbers and image numbers acquired for this study, refer to Supplementary Tables We identified thresholds for assigning a cell as ‘positive’ for a given marker by inspecting a random selection of at least 50 images for which cells passing a quantile threshold  were highlighted. This procedure was repeated at differing quantile thresholds until the value that most closely aligned with marker positivity was identified.47 . Cell phenotype proportion per tumour was taken as the response variable and predicted by the binary predictor. The precision of proportion estimates varied substantially between tumours because the total number of cells sampled was also highly variable. When more cells were sampled, proportion estimates were more precise. To account for this variable precision, generalized linear models were weighted by the total number of cells. Proportion values were computed separately by epithelial or TME compartments. The same approach was taken to investigate the relationship between stromal infiltrating lymphocytes and cell phenotype proportions, fitting stromal infiltrating lymphocytes as a continuous predictor. Model coefficients were exponentiated, log2-transformed and reported as log2 odds ratios. P values were adjusted for multiple testing by the Benjamini–Hochberg method.We used generalized linear models under a binomial distribution with logit link function to determine whether abundance of a given cell phenotype differed between categorical groups2; Fig. P values and point estimates for associations between cell densities and response can be found in Supplementary Tables Cell phenotype densities were calculated by dividing the number of total cells obtained per biopsy by the total area of the tissue acquired  are depicted: these were derived from trivariate logistic regression models that included the feature of interest, treatment (C or C&I) and a term for statistical interaction between the feature of interest and treatment. Pinteraction values corresponded to the statistical term for interaction computed in these models. All predictors  were square-root-transformed and modelled as continuous. Model coefficients and 95% confidence intervals were exponentiated and reported as odds ratios. When necessary, predictor values were multiplied  so that more interpretable odds ratios could be derived. All clinical correlative analyses were limited to the per-protocol patient population . To account for multiple testing and to evaluate the likelihood of false positives among significant associations, we computed the FDR using the Benjamini–Hochberg method.We used pCR as a response end point and fitted univariate logistic regression models to test for associations between response and tissue features. Plots illustrating estimates of association between tissue features and pCR depict two odds ratios  per predictor: one for each treatment arm, resulting from a univariate logistic regression model restricted to the relevant (C or C&I) study population. Adjacent to these two odds ratios, + cells. The same analysis was conducted when comparing proliferating versus non-proliferating CD8+TCF1+T cells.To compare the differential activation state between T cells in contact with tumour cells versus those not in contact, we computed the mean expression level  per tumour per timepoint for each group of T cells (in contact and not) and compared the resulting distributions using two-sided Wilcoxon tests. The same method, taking per-tumour averages, was deployed for comparison of the proportion of Ki67Z-scored per phenotype and illustrated as trend plots. Significant differences between treatments were illustrated as boxplots and tested using a two-sided Wilcoxon test.We compared the cellular composition of tumours through treatment to identify changes that characterized sensitivity and resistance to immunotherapy. We plotted the mean proportion of each cell phenotype  across timepoints, treatments and response. Means were We fitted regularized logistic regression models (using the R package glmnet) to determine the discriminatory performance of tissue features taken in aggregate to predict pCR. We derived three distinct sets of features for each tumour, separating epithelial and TME compartments: cell phenotype densities; cell interaction metrics as described above; proliferative fractions of cell phenotypes.A total of 148 variables were derived for single timepoints and 296 when baseline and on-treatment timepoints were combined. Only variables with more than six unique values across samples were retained. We further reduced the feature space for multivariate models by identifying groups of highly correlated variables (Spearman rank correlation > 0.95) and selecting one representative variable at random. To identify highly correlated groups, we first built a graph of variables with at least one correlation of greater than 0.95 (edges were weighted by the correlation coefficient) and used Louvain clustering to discretise subgraphs representing groups of highly correlated variables. Next, data were randomly split into training (75%) and test (25%) sets, for which the proportion of responders was approximately balanced between the two. Regularized logistic regression models were fitted to the training set  and predictions made using the test data. An AUC statistic was computed using the prediction probabilities in the test data. To estimate the precision of AUC values, and to derive 95% confidence intervals, the random split procedure, model fitting and testing were repeated 100 times. This whole process was conducted separately by treatment arm, by timepoint (baseline and on-treatment) and for a combined model (data from both baseline and on-treatment timepoints).20. The principle of this method is repeated comparison of true values with randomly shuffled features to identify which outperform random data more often than would occur by chance. Briefly, all predictors are randomly shuffled, doubling the original predictor set , and a random-forest classifier fitted to determine the importance of all predictors in the doubled dataset . The maximum feature importance achieved among all the randomly shuffled predictors is the threshold for a true feature to then be deemed important . This process was repeated 1,000 times to generate a binomial distribution of the number of times a given feature was regarded as important, and a final set of important variables identified based on a threshold of P < 0.01. Taking only those features that outperformed randomly shuffled data more often than expected by chance (at Bonferroni-corrected P < 0.01), we plotted the distribution of their importance values  across all 1,000 runs to rank their importance.To determine which predictors were most important in driving predictions we used an established feature selection algorithm (implemented in the R package Boruta)Further information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-023-06498-3.Supplementary InformationSupplementary Figs. 1 and 2 and legends for Supplementary Tables.Reporting SummarySupplementary TablesSupplementary Tables 1–13.