Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin (H&E)-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an approach for predicting response to multiple targeted and immunotherapies from H&E-slides. In difference from existing approaches that aim to predict treatment response directly from the slides, ENLIGHT-DeepPT is an indirect two-step approach consisting of (1) DeepPT, a new deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response based on the DeepPT inferred expression values. DeepPT successfully predicts transcriptomics in all 16 TCGA cohorts tested and generalizes well to two independent datasets. Our key contribution is showing that ENLIGHT-DeepPT successfully predicts true responders in five independent patients’ cohorts involving four different treatments spanning six cancer types with an overall odds ratio of 2.44, increasing the baseline response rate by 43.47% among predicted responders, without the need for any treatment data for training. Furthermore, its prediction accuracy on these datasets is comparable to a supervised approach predicting the response directly from the images, which needs to be trained and tested on the same cohort. ENLIGHT-DeepPT future application could provide clinicians with rapid treatment recommendations to an array of different therapies and importantly, may contribute to advancing precision oncology in developing countries. Histopathology has long been considered the gold standard of clinical diagnosis and prognosis in cancer. In recent years, the use of tumour molecular profiling within the clinic has allowed for more accurate cancer diagnostics, as well as the delivery of precision oncology –3. Rapiddirectly from images [Previous work has already impressively unravelled the potential of harnessing next-generation digital pathology to predict response to therapies m images –37. In twhich does not require matched WSI and response datasets for training. To accomplish this, we have taken an indirect two-step approach: First, we developed DeepPT (Deep Pathology for Transcriptomics), a novel deep-learning framework for imputing (predicting) gene expression from H&E slides, which extends upon previous valuable work on this topic [measured tumor transcriptomics, to predict response from the DeepPT imputed transcriptomics.To overcome this challenge, we turned to develop and study a generic methodology for generating WSI-based predictors of patients’ response for a broad range of cancer types and therapies, is topic –43. The is topic , originaWe proceed to provide an overview of DeepPT’s architecture and a brief recap of ENLIGHT’s workings, the study design and the cohorts analysed. We then describe the results obtained, in each of the two steps of ENLIGHT-DeepPT. First, we study the ability to predict tumor expression, showing the performance of the trained DeepPT models in predicting the gene bulk expression in 16 TCGA cohorts and in two independent, unseen cohorts. Second, we analyze five independent clinical trial datasets of patients with different cancer types that were treated with various targeted and immune therapies. Critically, those are test cohorts, on which DeepPT was never trained. We show that ENLIGHT, adhering to the parameters used in its original publication  without ENLIGHT-DeepPT may make precision oncology more accessible to patients in low- and middle-income countries (LMICs) and other under-served regions. As have been noted recently, LMICs now have increasing access to cancer medicines, primarily due to the WHO’s expansion of the list of essential medicines (EMLs), and different partnerships [Finally, and importantly, we believe that nerships . Howevernerships , there aFor each cancer type, a specific DeepPT model is constructed by training on formalin-fixed, paraffin-embedded (FFPE) whole slide images and their corresponding bulk gene expression profiles from TCGA patient samples. The model obtained can then be used to predict gene expression from new WSI both for internal held-out and external datasets.Supplementary Figure S1): a CNN model for feature extraction, an auto-encoder for feature compression, and a multi-layer perceptron (MLP) for the final regression. Rather than training a model for each gene separately or for all genes together as was done in previous studies [In difference from previous studies aimed at predicting gene expression from WSI, which have focused on fine tuning the last layer of pre-trained convolutional neural networks (CNN), DeepPT is composed of three main components . Each cancer type was processed, trained and evaluated separately. We performed a five-fold cross-validation to evaluate the model performance: In each loop, the patients were randomly split into five disjoint sets. Each of these sets was selected in turn as the held-out test set (20%), while the rest were used for training (80%). Note that the test set remained completely unseen during the model training, and the splits were performed at the patient level so that slides from the same patients are assigned to the same set to avoid information leakage between test and training sets.The study design is depicted in Then, we performed external testing of DeepPT’s gene expression predictions. We applied the trained models of the respective cancer types to predict gene expression on two external datasets: the TransNEO breast cancer cohort (TransNEO-Breast) consisting of 160 fresh frozen (FF) slides , and an Table S2 for full details). We show that combining ENLIGHT with DeepPT enables robust prediction of response to treatment in these independent test sets. Remarkably, this is done without adapting either DeepPT, which was trained once on TCGA samples, or ENLIGHT, which is not trained on any treatment response data in general, and specifically, on none of the clinical cohorts used here for evaluation purposes.Our final goal is to use the inferred gene expression as input to ENLIGHT based predictions of patients treatment response. To this end, we further applied the DeepPT models to predict gene expression in five clinical trial datasets . These results outperform the recently published state-of-the-art expression prediction approach, HE2RNA by Schmauch et al [Supplementary Figure S3). For breast cancer, for instance, DeepPT predicted 1,812 genes with mean correlation greater than 0.4, more than doubling the number of genes predicted at that correlation level reported by HE2RNA, which was 786 genes [As illustrated in ch et al , in most86 genes , furtherTo assess the dependence of the number of significantly predicted genes on sample size, for each cohort, we randomly selected 30 subsets composed of 200 samples each  and measured the number of genes that were significantly predicted in each subset. We observed that most cohorts had at least 3,000 genes that were significantly predicted .Supplementary Figure S4). Despite these differences, without any further training or tuning, we found 2,248 genes that were significantly predicted . Notably, the two datasets were generated independently at different facilities, with two different preparation methods (TCGA slides are FFPE while TransNEO slides are FF), so the histological features extracted from these two datasets are quite distinct  focused on cancer hallmarks. Specifically, we looked for enrichment among 10 cancer hallmarks described by Hanahan and Weinberg  and for The observation that genes that promote proliferation and metastasis, which are well known prognostic markers, are specifically well predicted by DeepPT, led us to explore how well do such prognostic markers predict patient survival, when calculated over the gene expression predicted by DeepPT. To this end, we calculated three proliferation signatures known to be linked to cancer progression and poor prognosis using TCGA patients tumor data. These signatures include: (a) the expression of the MK67 gene, a well-known marker for cell proliferation, (b) the proliferation index derived in Whitfield et al.  and (c) As described earlier, the goal of ENLIGHT-DeepPT is to predict patients’ response from WSI without any training on the evaluation cohort. Given a previously unseen tumor slide image, we first apply the pre-trained, cancer-type specific DeepPT model to predict the tumor transcriptomics. Second, based on this predicted gene expression, we apply our published precision oncology algorithm, ENLIGHT , to predTable S2 for more details). As ENLIGHT does not predict response to chemotherapies, only the targeted agents were considered for response prediction.We tested the ability of ENLIGHT-DeepPT to accurately predict patient response in five clinical cohorts, treated with various targeted and immunotherapies, for which patient slides and response data were available. Those include two HER2+ breast cancer patient cohorts treated with chemotherapy plus Trastuzumab ,52, a BRwithout any changes and with no further training, to predict the gene expression values from the H&E slide of each patient’s pre-treated tumor. We then applied ENLIGHT to these predicted gene expression values to produce ENLIGHT Matching Scores (EMS) based on the genetic interaction network of the given drug, as was originally published in [Supplementary Figure S9).For each cohort, we used the DeepPT model previously trained on the appropriate TCGA cohort, ished in . ImportaThe prediction accuracy of ENLIGHT-DeepPT in each of these five datasets individually and in aggregate is shown in Complementing the OR measure, which is of major translational interest as it quantifies the performance at a specific decision threshold, p = 1.28 × 10−6, one sided proportion test). The AP is 0.48 , the OR of ENLIGHT-DeepPT is 2.44  to evaluate the performance of the direct supervised models. This in-cohort training gives an inherent advantage to the Direct Supervised approach over ENLIGHT-DeepPT, which was not exposed to these datasets at all. Since no tuning data was available to calibrate a single threshold for the Direct Supervised methods as was done in [One of the advantages of ENLIGHT’s unsupervised approach is that it does not rely on data labelled with response to treatment data which is usually scarce. Such data is required for training supervised models, which in theory, given sufficiently large datasets, are expected to yield higher performance on unseen datasets than unsupervised methods like ENLIGHT. The question remains whether such supervised methods are advantageous for realistically small datasets as studied in this work. To compare the performance of our two-step indirect model with a direct supervised model, we trained the same computational deep learning pipeline as the one used in DeepPT on H&E slides and their corresponding response data on each of the five evaluation datasets described above, except that we replaced the regression component with a classification component. We used the same training strategy that has been widely applied previously in the literature for direct H&E slide-based classification ,20,23,42 done in , we calc done in  based on1 to 0.27 when this model was tested on Trastuzumab2, and from 0.5 to 0.43 in the other direction. This suggests that the results obtained for the supervised models may be overfitted. Clearly, for cases where large data exists, a supervised method can outperform ENLIGHT-DeepPT. In contrast, ENLIGHT-DeepPT is unsupervised, and the results presented here testify to its potential generalizability. In addition, any supervised model can only be obtained for drugs with coupled H&E and response data (4 drugs in this study), while ENLIGHT-DeepPT can produce predictions to virtually any targeted treatment.Ideally, an external dataset is required to study the generalizability of the model. Among the datasets of this study, this was possible only for Trastuzumab which appeared in more than one dataset. When we tested the model trained on one Trastuzumab dataset on the other set and vice versa we saw low generalizability: the AP went down from 0.52 in LOOCV for Trastuzumab1), RNA sequencing of the tumor gene expression was also available and was previously analyzed by ENLIGHT in [measured expression values . Using the previously established threshold of 0.54, the OR of ENLIGHT-DeepPT is 3.2, which is lower than the OR of 6.95 obtained by ENLIGHT-actual, but still significantly higher than expected by chance . The positive predictive value (PPV)  of ENLIGHT-DeepPT was 53.3%, slightly higher than but not significantly different from the PPV of 52% when using ENLIGHT-actual, and 80% higher than the basic ORR of 29.7% observed in this study. However, the sensitivity (the fraction of responders correctly identified) of ENLIGHT-DeepPT is markedly lower than that of ENLIGHT-actual, 42.1% vs. 68.%.For one of the datasets .Finally, we sought to compare ENLIGHT-DeepPT to other predictive models for drug response. For the drugs analyzed in this study, the only available mRNA-based model for response is the multi-omic machine learning predictor that uses DNA, RNA and clinical data, published by Sammut et al.  denoted ENLIGHT-DeepPT approach for H&E-based prediction of clinical response to a host of targeted and immune therapies. We began by showing that DeepPT significantly outperforms the current state-of-the-art method in predicting mRNA expression profiles from H&E slides. Then, we showed that the aggregate signal from multiple genes can overcome weak correlation at the individual gene level. Finally, and most importantly, ENLIGHT-DeepPT successfully predicts the true responders in several clinical datasets from different indications, treated with a variety of targeted drugs directly from the H&E images, demonstrating its potential clinical utility throughout. Notably, its prediction accuracy on these datasets is on par with that of direct predictors from the images, as is the current practice, even though the latter have been trained and tested in a cross validation in-cohort manner.Our study demonstrates that combining DeepPT, a novel deep learning framework for predicting gene expression from H&E slides, with ENLIGHT, a published unsupervised computational approach for predicting patient response from pre-treated tumor transcriptomics, could be used to form a new Combining DeepPT with ENLIGHT is a promising approach for predicting response directly from H&E slides because it does not require response data on which to train. This is a crucial advantage compared to the more common practice of using response data to train classifiers in a supervised manner. Indeed, while sources like TCGA lack response data that would enable building a supervised predictor of response to targeted and immune treatments, applying ENLIGHT to predicted expression has successfully enabled the prediction of response to four different treatments in five datasets spanning six cancer types with considerable accuracy, without the need for any treatment data for training. While supervised models can only be obtained for drugs with available H&E and response data, ENLIGHT-DeepPT can produce predictions to virtually any targeted treatment, and importantly, including ones in early stages of development where such training data is still absent.A notable finding of this study is the robustness of response predictions based on H&E slides when combining DeepPT and ENLIGHT. First, despite the inevitable noise introduced by the prediction of gene expression, the original ENLIGHT GI networks, designed to predict response from measured RNA expression, worked well as-is in predicting response based on the DeepPT-predicted expression. In fact, when restricting the GI networks to include only significantly predicted genes, the results are not improved. Second, though DeepPT was trained using FFPE slides, it generalized well and could be used as-is to predict expression values from FF slides. This demonstrates the applicability of DeepPT for predicting RNA expression either from FF or from FFPE slides. Nevertheless, as promising as the results presented here are, they should of course be further tested and expanded upon by applying the generic pipeline presented here to many more cancer types and treatments.There are several limitations of our study that should be noted: (i) as explained above, while DeepPT can predict expression reliably for many genes based on a relatively small number of samples, it is still reliant on sufficient training data and future studies should aim to enlarge the number of genes whose expression is accurately predicted. (ii) ENLIGHT-DeepPT should be more extensively validated on additional indications and treatments, as data is accumulated. (iii) Here we used ENLIGHT’s clinical decision threshold as is from . FurtherENLIGHT-DeepPT may have considerable impact, making precision oncology more accessible to patients in Low- and middle-income countries (LMICs), in under-served regions and in other situations where sequencing is less feasible. Specifically, affordable cancer diagnostics is critical in LMICs since their limited access to cancer diagnostics is a bottleneck for effectively leveraging the increasing access to cancer medicine [prospective studies before they may be applied in the clinic. We are hopeful that the results presented here will expedite such efforts by others going forward.Developing a response prediction pipeline from H&E slides, if reasonably accurate and further carefully tested and validated in clinical settings, could obviously be of utmost benefit, as NGS results often take 4–6 weeks after initiation to return a result. Many patients who have advanced cancers require treatment immediately, and this method can potentially offer treatment options within a shorter time frame. Moreover, obtaining H&E images can be done at relatively low cost, compared to the expenses incurred by NGS. Increasing efforts to harness the rapid advances in deep learning are likely to improve precision oncology approaches, including by leveraging histopathology images. Given its general and unsupervised nature, we are hopeful that medicine . While phttps://portal.gdc.cancer.gov). Only diagnostic slides from primary tumors were selected, making a total of 6,269 formalin-fixed paraffin-embedded (FFPE) slides from 5,528 patients with breast cancer , lung cancer , brain cancer , kidney cancer , colorectal , prostate , gastric , head & neck , cervical , pancreatic cancer .TCGA histological images and their corresponding gene expression profiles were downloaded from GDC , cervical cancer (16 patients), and head and neck cancer (33 patients). FFPE slides were made available from the NCI.1 cohort is a subset of TransNEO-Breast dataset mentioned above, consisting of 64 patients who had received a combination of chemotherapy and Trastuzumab.The Trastuzumab2, a HER2+ breast cancer cohort treated with a combination of Trastuzumab and chemotherapy, consisted of 85 patients and their FFPE slides [https://www.cancerimagingarchive.net).TrastuzumabE slides , 50. FFPThe ALKi dataset consisted of 14 NSCLC ALK mutated patients treated with Crizotinib or Alectinib. Corresponding FFPE slides were made available from Colorado University.The PARPi dataset consisted of 13 germline BRCA mutated pancreatic cancer patients treated with PARP inhibitors. FFPE slides were made available from Sheba Medical Center.1 and Trastuzumab2, response was defined as pCR while non-responders were defined as RD. For Bintrafusp alfa, responders were defined as patients with partial response or complete response. For ALKi, response was defined as more than 18 months progression free survival. For PARPi, response was defined as more than 36 months overall survival. Full details can be found in Table S2.For each dataset, the classification of patients to responders and non-responders was based on the criterion used by the clinicians running the respective trial: for TrastuzumabSupplementary Figure S7). In contrast, TransNEO slides for example are much smaller, resulting in 100 to 1,000 tiles per slide . To minimize staining variation (heterogeneity and batch effects), color normalization was applied to the selected tiles.We first used Sobel edge detection  to identSupplementary Figure S8). To reduce the range of gene expression values, and to minimize discrepancies in library size between experiments and batches, a normalization was performed as described in our previous work [Gene expression profiles were obtained as read counts for approximately 60,483 gene identifiers. Genes considered expressed were identified using edgeR, resulting in approximately 18,000 genes for each cancer type. The median expression across samples of each gene varied from 10 to 10,000 reads for each dataset .Our model architecture was composed of three main units ((1)The pre-trained ResNet50 CNN model, trained with 14 million natural images from the ImageNet database ,56 was u(2)Supplementary Figure S5, a large number of ResNet features are constantly zero . This data sparsity is considerably reduced in the autoencoder features .We applied an autoencoder, which consists of a bottleneck of 512 neurons, to reduce the number of features from 2,048 to 512. This helps to exclude noise, to avoid overfitting, and finally to reduce the computational demands. As shown in (3)The purpose of this component is to build a predictive model linking the aforementioned auto-encoded features to whole-genome gene expression. The model consists of three layers: (1) an input layer with 512 nodes, reflecting the size of the auto-encoded vector; (2) a hidden layer whose size depends on the number of genes under shared consideration; and (3) an output layer with one node per gene. The rationale behind this architecture is to leverage similarity among the genes under shared consideration, as captured by the weights connecting the input layer to the hidden layer. The weights connecting the hidden layer to the output layer model the subsequent relationship between the hidden layer and each individual gene. This follows the philosophy of multi-task learning. If the prediction of each gene’s expression level represents a single task, then our strategy is to first group these tasks for shared learning, followed by optimization of each individual task. In our default whole-genome approach, we bin genes into groups of 4,096 whose median expression levels are similar, and we use 512 hidden nodes. Because the training data consists of gene expression at the slide level , we average our per-tile predictions to obtain a mean value at the slide level.Supplementary Figure S6). Because each patient can have more than one slide, we average the slide-level predictions to obtain patient-level predictions.We trained and evaluated each cancer type independently. To evaluate our model performance, we applied 5×5 nested cross-validation. For each outer loop, we split the entire patients (of each cohort) into training (80%) and held-out test (20%) set. We further split the training set into internal training and evaluation set, according to five-fold cross validation. The models were trained and evaluated independently with each pair of training/validation sets. Averaging the predictions from the five different models represents our final prediction for each single gene on each held-out test set. We repeated this procedure five times across the five held-out test sets, making a total of 25 trained models. These models trained with TCGA cohorts were used to predict the expression of each gene in a given external cohort by computing the mean over the predicted values of all models  supervised model was designed to classify responders and non-responders directly from their slides, without the intermediate step of gene expression prediction. To this end, we applied the same computational deep learning framework that was used for prediction of gene expression, except that the MLP regression component was replaced by an MLP classification component. Each of the five evaluation datasets were processed independently. Following previous approaches ,20,23,42All analysis in this study was performed in Python 3.9.7 and R 4.1.0 with the libraries including Numpy 1.20.3, Pandas 1.3.4, Scikit-learn 1.1.1, Matplotlib 3.4.3, and edgeR 3.28.0. Image processing including tile partitioning and color normalization was conducted with OpenSlide 1.1.2, OpenCV 4.5.4, PIL 8.4.0. The histopathology feature extraction was carried out using TensorFlow 2.8.0. The feature compression (autoencoder unit) and MLP regression parts were implemented using PyTorch 1.12.0. Pearson correlation was calculated using Scipy 1.5.0.GI engine identifies the clinically relevant genetic interaction (GI) partners of the drug’s target gene(s). The GI engine first identifies a list of initial candidate Synthetic lethal/Synthetic rescue (SL/SR) interactions by analyzing cancer cell line dependencies based on the principle that SL/SR interactions should decrease/increase tumor cell viability, respectively, when ‘activated’ . It then selects those pairs that are more likely to be clinically relevant by analysing a database of tumor samples with associated transcriptomics and survival data, requiring a significant association between the joint inactivation of target and partner genes and better patient survival for SL interactions, and analogously for SR interactions. (ii) The drug-specific GI partners are then used to predict a patient’s response to each drug based on the gene expression profile of the patient’s tumor. The ENLIGHT Matching Score (EMS), which evaluates the match between patient and treatment, is based on the overall activation state of the set of GI partner genes of the drug targets, deduced from the gene expression, reflecting the notion that a tumor would be more susceptible to a drug that induces more active SL interactions and fewer active SR interactions.ENLIGHT’s drug response prediction comprises two steps: (i) Given a drug, the Supplementary Figure S11), we excluded it as it highly weighs a single gene and is hence much more susceptible to perturbation resulting from noisy prediction of that one gene”.We applied ENLIGHT in its original version as described in . The onl


The causes of pediatric cancers’ distinctiveness compared to adult-onset tumors of the same type are not completely clear and not fully explained by their genomes. In this study, we used an optimized multilevel RNA clustering approach to derive molecular definitions for most childhood cancers. Applying this method to 13,313 transcriptomes, we constructed a pediatric cancer atlas to explore age-associated changes. Tumor entities were sometimes unexpectedly grouped due to common lineages, drivers or stemness profiles. Some established entities were divided into subgroups that predicted outcome better than current diagnostic approaches. These definitions account for inter-tumoral and intra-tumoral heterogeneity and have the potential of enabling reproducible, quantifiable diagnostics. As a whole, childhood tumors had more transcriptional diversity than adult tumors, maintaining greater expression flexibility. To apply these insights, we designed an ensemble convolutional neural network classifier. We show that this tool was able to match or clarify the diagnosis for 85% of childhood tumors in a prospective cohort. If further validated, this framework could be extended to derive molecular definitions for all cancer types. A new multilevel clustering approach applied retrospectively to 13,000 transcriptomes of different tumors reveals a new diagnostic classification of childhood cancers, in some cases allowing a better prediction of disease outcomes. Compared to adult cancers, childhood tumors are more likely to emerge from embryonic tissue and impact different cell types5. Most adult extracranial solid tumors are carcinomas, whereas mesodermal and embryonal tumors are more frequent in children6. One-third of childhood cancers are leukemias, which are proportionally not as common in adults. The same is true of neuroblastoma, a heterogeneous cancer ranging from a spontaneously regressing form in infants to a malignant progressing entity in older children and adolescents and rarely found in adults3.Over 400,000 childhood cancers are diagnosed per year worldwide7, and can be disconnected from the tumor’s current phenotype. On the other hand, RNA sequencing (RNA-seq) is reflective of the tumor’s ongoing expression program and can differentiate tumors independent of genomic origin8. Because a critical number of childhood tumor transcriptomes are or will soon be available9, RNA-seq has the potential to become a standalone ‘universal diagnostic assay’.Currently, no comprehensive molecular assay can aid in the diagnosis of all pediatric cancers. Genome sequencing can reveal the tumor’s history, including mutations preceding its malignant transformation11. Stromal or immune infiltration also adds to this diversity13. In this Article, we identify features that define the unique gene expression profiles of childhood cancers compared to adult neoplasms. By incorporating measures of transcriptional entropy, we calculate their heterogeneity at multiple levels, both between subtly different tumor types as well as across major classes of cancer. Far from being ‘quiet’14, as suggested by DNA analyses15, childhood tumors have more transcriptional diversity, both between and within tumor types, than most adult cancers. Accounting for this variability can be leveraged to improve the tools used to diagnose childhood cancer. To this end, we built RACCOON (Resolution-Adaptive Coarse-to-fine Clusters OptimizatiON), a scale-adaptive clustering approach for the unsupervised classification of tumor subtypes using RNA-seq. It yielded an atlas of 455 tumor and normal classes when applied to a cohort of 13,313 samples, which were organized into a hierarchical tree based on their expression similarities. We also designed a classifier for childhood cancer, called OTTER (Oncologic TranscripTome Expression Recognition), an ensemble of convolutional neural networks (CNNs) targeting this extensive hierarchy. It is unique in scope and performs robustly even when using a fraction of the RNA-seq data . When applied to a held-out cohort, OTTER was concordant with clinical pathology diagnoses in 82% of patients, helping to clarify the diagnosis for an additional 7% of the cases. Collectively, this work both defines the transcriptional distinctiveness of childhood cancer and uses this to validate a novel, pan-cancer diagnostic assay.Most transcriptome-based classifiers are fully supervised tools, reliant on the tumors’ pre-existing labels without allowing for much phenotypic variability. However, intra-tumoral transcriptional differences can be so pronounced that they result in both favorable and poor prognostic signatures within the same tumorTo develop molecular definitions of childhood cancers, we designed a method that reduces the complexity of RNA-sequenced tumors and then groups them into hierarchically organized clusters Fig. . This wa19 , in line with previous observations21. This suggests that a large number of patterns need to be evaluated to accurately diagnose cancer but that there is limited complexity of interactions between the genes involved.We then built a set of mono-dimensional CNNsThe top three performing CNNs were integrated into an ensemble, OTTER, which achieved higher scores across all metrics than any single model , many of which represented novel cancer subtypes.Tumors from a similar tissue typically co-clustered at the top level, as expectedSimilarly, non-neoplastic samples were first grouped by tissue of origin, yet, occasionally, the transcriptional stratification transcended the organ of origin . These fluctuations were quantified using Shannon entropy . This suggests that a tumor’s transcriptional variability may be predetermined by its tissue of origin.Non-neoplastic tissue was less disordered than cancers. Normal cells appear to allow for a narrow range of expression, whereas tumor types can tolerate more variation in gene expression while still maintaining a characteristic expression profile  of the per-gene entropy distributions: small values mean that most genes are similarly entropic, and high values mean that their disorder level can vary widely.In childhood cancer types, the transcriptional disorder is broader, impacting different genes to different degrees, with a higher MAD score than adult tumors (Supplementary Table 24). In most types, the top 10% cumulative importance genes are also those with the highest entropy opy Fig. . These c25 (T070), Wilms tumor27 (T092), choroid plexus carcinoma28 (T102) and testicular non-seminoma germ cell29 (T101 and T105), also clustered with sarcomas, whereas Ewing sarcoma (ES) (T005) did not. Overall, the transcriptional contribution from the tissue of origin appears to be greater in sarcoma than carcinoma , leiomyosarcoma (T067), fusion-positive and fusion-negative rhabdomyosarcomas T094 and T093), synovial sarcomas (T100) and others. Importantly, other cancers that are thought to derive from the mesoderm, such as mesotheliomama Figs.  and 4.Fi and T093CIC-driven or BCOR-driven sarcomas  of ES tumors by standard pathology may be misdiagnosed All non-ES sarcomas were constrained to two mesodermal superclasses. The first (T002) comprised osteosarcomas, leiomyosarcomas, mesotheliomas and a diverse collection of less differentiated soft-tissue sarcomas found predominantly in adults. The second (T003) assembled rhabdomyosarcomas, synovial sarcomas and other predominantly pediatric sarcomas and mesodermal tumors. T002 and T003 were then subtyped into 24 and 29 mesodermal subclasses, respectively , along with high overall stromal content  and undifferentiated pleomorphic sarcomas (9/30). Despite the variety of tumor subtypes, this class has a homogeneity of expression, possibly due to the immune transcriptional signal and the lack of idiosyncratic profiles because of their undifferentiation.For instance, T080 SARC IMMhigh is a small subclass of mixed solid tumors characterized by high leukocyte fraction, M2 macrophages and CD8P < 0.001; Extended Data Fig. 34, confirmed using three independent methods36  involved high markers of ‘stemness’. Stemness markers were among the most significantly enriched gene sets  is a class of malignancies with more committed differentiation, characterized by high stromal content and an active immune profile. In contrast, T003 (STEMhigh) includes sarcomas with a more immature phenotype, possibly reflecting their embryonic origin. It is likely that their common mesodermal lineage brings these solid tumors together while keeping them apart from the rest of cancer.IDH1 mutations (T030 and T029), and ependymomas (T032), among others. Within the leukemias, one can differentiate BCR–ABL1-positive acute lymphocytic leukemia (ALL), as well as Ph-like variants  and distinct subclusters driven by fusions in CBFB–MYH11 (T145), PML–RARA (T147), TCF3–PBX1 (T135) and RBM15–MKL1 (T515), as well as acute myeloid leukemia (AML) with KMT2A internal tandem duplications (T153), KMT2A rearrangements (T159) and additional leukemia subtypes , 1p/19q codel gliomas (T044), as well as those with/without n = 12) but highly specific cluster of young childhood tumors . This cluster (T031) was composed of both central nervous system (CNS) and extra-CNS cancers with BCOR-associated gene expression programs37 . All had expression patterns reflective of CIC–DUX4 fusions , NTRK1 (T063), MYCN (T064) and TERT (T065), these subtypes may be rooted in the tumor’s lineage45  were not previously identified as MYCN amplified by standard testing but still maintained significant enrichment of downstream MYCN amplification pathways. That is, these patients with neuroblastoma had the transcriptional fingerprint of activated MYCN  to prospectively classify new patients’ tumors. OTTER outperformed current alternatives BCOR-rearranged sarcomas, a kidney clear cell sarcoma with a BCOR internal tandem duplication (ITD)51, two infant lymphoblastic leukemias with MLL partial tandem duplications and two megakaryoblastic leukemias with sarcomatous components. Altogether, OTTER’s classification was correct, in that it either matched or refined the pathologists’ diagnosis, for 88.9% of cases.OTTER’s tumor classification was concordant with the pathologists’ diagnosis for 65.6% of the cases Fig. . In an aBecause OTTER’s prediction probabilities are multiclass (samples can be assigned to more than one class), we could identify samples with high contamination by non-tumor tissue. Normal tissue expression was the dominant profile in 4% (6/163) of the samples and present to a lesser degree in an additional 4% (7/163) of tumors. Overall, there was no correlation between tumor cellularity and the confidence to which the tool assigned each specimen. Indeed, we confirmed the diagnosis of 89% (16/18) of the samples with <50% tumor purity. Nine percent (15/163) of the entire cohort showed signs of necrosis or other quality-related issues. Six samples (4%) remained inconclusive due to the current lack of a comparable match in the reference hierarchy.SMARCB1-associated tumor, a subtype currently absent from our reference. Taken together, OTTER’s tumor type predictions are highly concordant with those of pathology, can help to clarify ambiguous diagnoses and stay consistent across time even as the tumors evolve.To evaluate the robustness of OTTER’s predictions over time, we sequenced multi-timepoint samples . Twenty-one patients had more than one tumor sequenced . The heterogeneous assignment of neuroblastoma subtypes seems to be unique among well-characterized tumors. In contrast, all but one of the sequenced osteosarcomas were assigned to a unique subtype  was mostly made up of multiple childhood sarcomas, all segregating because of strong features of stemness and stem-like expression programs.55. This low mutation burden is usually attributed to a limited number of cell divisions after fertilization and limited exposure to mutagens. Another possibility is that transcription itself facilitates or directs DNA repair. We observed that most DNA repair pathways are overexpressed in childhood tumors  Program is a prospective study of a demographically diverse population of children and adolescents and young adults (AYAs) with refractory, metastatic, relapsed or rare cancers, as well as children with unresolved suspicion of cancer predisposition. It was launched in April 2016 and is an ongoing study. Guardians or capable patients are guided through an informed consent discussion with a trained genetic counsellor or pediatric oncologist. KiCS has been approved by The Hospital for Sick Children’s Research Ethics Board. The first trimester human fetal tissue was collected from an elective termination of pregnancy procedure at Addenbrooke’s Hospital through the ethically approved Wellcome-MRC Cambridge Stem Cell Institute and Department of Clinical Neurosciences tissue bank (REC-96/085). Written informed consent was given for tissue collection by the patient in accordance with the Declaration of Helsinki 2000.9 as a reference dataset to build the hierarchy of subtypes and train the ensemble CNN classifier. This cohort includes 11,750 tumor samples from TCGA16, TARGET17 and other contributing institutions, prepared with either poly(A) selection or ribosomal depletion. Gene expression counts from the STAR + RSEM Toil RNA-seq pipeline59 of samples in the compendium are publicly available and cover more than 58,000 genes, raw or normalized by log2 transcripts per million (TPM). The same pipeline was applied to any other data in this publication. Alternatively, counts obtained with Kallisto were used for performance benchmarks. To expand the pediatric reference, we added 313 further samples from St. Jude Children’s Hospital Pediatric Cancer Genome Project (PCGP)18, run through the same pipeline after filtering them by alignment quality. While building the subtypes hierarchy, we removed samples with particularly low purity but kept them to boost the ensemble CNN training. Ribodepleted samples showed consistent batch effects across tumor types during the clusters search. We, thus, chose to exclude them from the rest of the analysis and the CNN training; ribodepleted-only classes were removed from the hierarchy. Finally, we added 1,735 normal tissue samples with the best coverage and quality scores from 51 different organ sites from the GTEx project19 to the dataset. To avoid degradation in the output from tumor samples with normal contamination within the Treehouse cohort, the tumor and normal datasets were kept separate at the first stage of the clustering and merged later as separate branches of the classification hierarchy.We used the UCSC Treehouse Childhood Cancer Initiative Compendium In input to the clustering algorithm, genes mapping to non-coding sections of the RNA were removed. Among these remaining, only genes with high variability, accounting for 99% of the cumulative variance on the full cohort, were kept. This reduced the feature space to 18,010 functional genes and pseudogenes, allowing us to speed up the rest of the analysis with a negligible loss of information.Diagnoses and genomic markers reported by the sharing institutions were used, when available, as a reference for tumor type comparisons and the annotation of clusters.19. We included GTEx samples with a high number of sequenced reads (as a proxy for coverage). St. Jude samples from the PCGP cohort were filtered based on TPM distribution. Samples were ranked based on the number of protein-coding genes found to have zero expression (TPM = 0) and excluded if more than 25% of their protein-coding genes were not expressed. This resulted in an improvement in RACCOON’s clustering and clarity of each cluster  and single-sample GSEA (ssGSEA) were carried out with gseapy61 version 0.9.5 in Python version 3.6.9 and GSVA62 version 1.36.3 in R version 4.0.2. ssGSEA-based scores were also calculated with gseapy version 0.9.5 on TPM-normalized counts and scaled between 0 and 1 to assure consistency in the comparisons. They were used as part of stemness, immune activity and neuroblastoma identity scores . The two-sided Mann–Whitney U-test was used when evaluating significance in comparing these scores and any other distribution between paired groups of samples throughout the text.log63.Plots and diagrams were produced with Matplotlib64). Where available, outcomes were defined based on overall survival times provided by the sharing institution. A Cox survival regression of neuroblastoma subtypes was performed with the same library on 161 neuroblastoma observations, of which 81 were censored.Survival analyses and log-rank tests were carried out with lifelines version 0.21  are optimized by maximizing a clustering quality score. For this project, the tunable parameters were optimized with a grid search, and the total silhouette coefficient of the dataset66 was set as the objective function. This score quantifies the quality of clustering by calculating the ratio between the clusters cohesion and their separation. Ranging between −1, when all points are incorrectly assigned, and 1, when all points are well assigned, we set here 0 as the minimum threshold for accepting a set. In a scenario where the best combinations of parameters found still leads to a negative score, the cluster under scrutiny is not split.Given a set of data points, RACCOON removes low-information features, reduces their complexity with a non-linear dimensionality reduction algorithm and finally identifies clusters with a density-based approach. The search is continued depth-first for each of the clusters identified iteratively. The search is terminated only when further splits would lead to a particularly suboptimal value of the objective function or the class population is lower than a pre-set bound . The features removal cutoff, the number of neighbors employed by uniform manifold approximation and projection (UMAP)We applied RACCOON to our extended dataset to build a hierarchical tree of tumor and normal subtype clusters. The number of final (reduced) dimensions was empirically set to 12, a choice that proved to be a good compromise between accuracy and computational cost. A population cutoff of 25 was applied to stop the search, and nodes with fewer than ten samples were pruned, because their annotation and training for the classifier would be too unreliable. This method initially yielded more than 700 individual clusters. A subset of low-population leaf nodes was removed after manual annotation, for the lack of sufficient biological and gene expression information to support any finding, together with classes populated exclusively by ribodepleted samples. This process left us with a total of 455 clusters , of which 303 are non-overlapping independent terminal (leaf) nodes.Multilevel clustering was applied independently to the normal tissue samples and malignant subsets. Normal and tumor samples from the same organ would have been grouped in common classes at the highest level if they had been mixed. Clustering quality decreases, as less aggressive or low-purity tumors can be difficult to separate from healthy normal samples.During training, this choice forced OTTER to learn high-level features that distinguish normal tissue from neoplasms, independently from their anatomical location. This boosted OTTERʼs ability to recognize tumor populations in low-quality samples, as the tumor–normal separation is prioritized in the hierarchy. Similarly, we expect generalization to unseen normal tissues to also be improved.https://rna-atlas.github.io/.Clusters obtained with RACCOON were annotated based on their most characteristic transcriptional features compared to the closest members of their hierarchical family. Differential gene expression and GSEA were carried out to identify each clusterʼs defining gene and pathway expression. Limited clinical information, including age, sex and a diagnostic label, were available for each sample. All 455 separate classes were first annotated to assign a unique label (a code and a name). We then extended the annotation for the five major families of pediatric tumors: CNS, leukemia, neuroblastoma and the two mesodermal classes, as well as the branch stemming from the healthy normal samples. More details on the annotation of these groups can be found at S is a much more appropriate and robust measure. It is a generalization to Boltzmannʼs thermodynamics entropy; it quantifies the information content or the randomness of a given distribution23. It is defined as follows:Pi is the probability of an event i, in our case the probability that a certain gene will lead to a specific expression count.Expression variance and its derived quantities  could be used as a proxy of variability; however, they fail when dealing with multimodal or discontinuous distributions. Shannon entropy 67).Starting from TMM-normalized data, which already account for the skew introduced by extreme values across the population, the expression was standardized along genes to limit heteroscedasticity. Being additive, Shannon entropy cannot be naively measured on groups with different populations, and it requires enough samples to approximate a continuous distribution. In our case, this holds true for a good number of classes but not for the smallest leaf nodes. We, thus, first approximate the expression distribution of every single gene independently with a fixed-bandwidth Gaussian kernel density estimation and then extract the probabilities for Shannon entropy from the estimated distribution on a 100,000-points grid. Higher-resolution grids approach the limit of differential entropy and approximate the integral better, yet they lead only to marginal changes and increase the computational cost considerably. A mesh to 250,000 points led to a change in entropy of less than 2%, confirming that our choice was close to convergence. The entropy calculation was limited to a subset of more than 14,000 highly variant genes, by filtering those with both consistent low expression across samples and low entropy across all classes. The final values obtained for each gene and each class were divided by the median entropy of the normal tissues cohort class N000. All calculations were carried out with scikit-learn 0.22.2.post1. We, thus, fit a linear model on the entropy and adjusted the score to account for its dependency on the median expression  was used throughout the manuscript.We observed a fair correlation between entropy and mean expression across all groups  of Ln and the cardinality of L – that is, the number of leaf nodes that are offspring of n, over the total number of leaf nodes, weighted by the inverse of the log population ratio of n. This last term was added to account for the fact that smaller clusters will have less probability to be split by the algorithm.Although entropy entails the overall variability of gene expression within a population, part of this can be translated into a different pattern of activation of relevant biological pathways, thus defining different tumor types. This inter-tumoral heterogeneity is explicitly accounted for by the cluster hierarchy itself. We can define a score based on the number of offspring nodes that a specific group generates and measure it on the classes that we identified. We call this PaWS and define it as follows:P = 1.120 × 10−5) between median entropy and PaWS score, after removing all the leaf nodes, to avoid including clusters with possible subtypes but insufficient population to be split by the algorithm. Entropy is thus a good proxy for intra-cluster expression disorder, as it accounts for that part of expression differences within a population that are not coherent enough to be translated into clear subtypes and yet not able to disrupt the overarching patterns that define the parent class.The relationship between these quantities is not trivial; we observed a weak correlation 33 in TCGA samples where the information was available. The score was then normalized for each inter-tumor type comparison. Immune deconvolution scores and immune cell type ratios were obtained with CIBERSORT68.The immune activity score was calculated as the average between Reactome immune system45. For each sample, the final score was obtained as the difference between the mesenchymal/NCC-like unified score and the adrenergic unified score, and it was scaled to range between 0 (more adrenergic) and 1 .A unified neuroblastoma cell lineage score was calculated by first averaging separately neural crest-like and mesenchymal identity ssGSEA scores and adrenergic identity scores from three different publicationsFetal age (post-conception weeks (PCWs)) was estimated using the independent measurement of the crown rump length (CRL), using the formula PCW (days) = 0.9022 × CRL (mm) + 27.372.−1 Liberase TH working solution prepared from Liberase TH powder  and 1× PBS on a shaking platform (750 r.p.m.) at 37 °C for 30 minutes. The tissue was gently agitated using a P1000 pipette after 15 minutes. Then, 5 ml of 2% FBS in PBS was added to stop the dissociation, before second-stage digestion with 0.25% trypsin solution for a further 30 minutes at 37 °C, with pipette agitation every 5 minutes. Cells were then spun down at 750g at 4 °C for 5 minutes and resuspended in 50–200 µl of 2% FBS in PBS. Fetal cells were loaded for single-cell RNA-seq directly after sample processing.Paired femora, tibiae and fibulae were dissected from the fetal hind limbs by a specialist bone and soft tissue pathologist (P.B.) under a microscope using sterile microsurgical instruments. The femora were further dissected into proximal and distal halves, to give eight samples in total . Each sample was then processed into single-cell suspensions. In brief, tissue was digested in a 5 µg mlSingle-cell suspensions from the eight samples were loaded onto a separate channel of a Chromium 10x Genomics Single Cell 3′ v2 library chip as per the manufacturerʼs protocol (PN-120233), aiming for a cell capture recovery of 3,000–5,000 cells. cDNA sequencing libraries were prepared according to the manufacturerʼs protocol and sequenced on an Illumina HiSeq 4000 (2 × 50-bp paired-end reads).69 (10x Genomics) with default parameters.Raw sequence reads in FASTQ format from fetal samples were processed and aligned to the GRCh38-1.2.0 human reference transcriptome using the Cell Ranger version 2.1.1 pipeline70) to estimate and remove cell-free mRNA contamination before analysis. Cells with fewer than 300 genes and more than 7,500 genes were filtered, as well as those in which mitochondrial genes represented 10% or more of total gene expression. A quantitative estimation of cell cycle stage was performed on the remaining cells with Seurat version 3.0 (ref. 71). Log-normalization was then performed before data scaling, which used cell cycle score, mitochondrial gene expression level and the total unique molecular identifiers (UMIs) per cell as regression variables.The resulting expression matrices were processed with SoupX version 1.3.0 (ref. 2 (TPM + 1) and randomly selected 25,000 samples. The resulting dataset was merged with the bulk RNA-seq sarcoma data (T002 MESODM IMMhigh and T003 MESODM STEMhigh). A low-information filtering step was applied, to boost the signal-to-noise ratio and partially remove batch effects, before projecting the data to a lower-dimensionality space with UMAP65. The nearest neighbors cutoff was set as the square root of the total population. The centroid distance between T002 and single-cell data was constantly higher than that between T003 and the single-cell cluster, independently of how the dimensionality reduction was parametrized over a grid of combinations  as input and returns the membership probability to any or multiple of the 455 hierarchical classes.We built a set of mono-dimensional CNNs, called OTTER, which takes the RSEM gene expression reduced output  score was chosen as the objective function to guide the search. We enriched this group of models with a number of manually tuned architectures.To identify optimal architectures, we emplyed Hyperopt, a Bayesian hyperparameter optimization library based on a Tree-structured Parzen Estimator (TPE)All models included one-dimensional convolutional (CV) layers followed by fully connected (FC) layers. The number of filters of CV layers was tuneable and shared across layers, and so was the kernel size. Each CV layer was followed by batch normalization and max pooling with fixed size 4 and stride 2. The size of hidden dense layers was also tuneable and halved at every successive layer, and dropout was activated at parametrizable percentage. The loss function was binary cross-entropy. Adadelta was set as optimizer with a starting learning rate of 0.001 and early stopping.The top-scoring models among the pool of all candidates were subject to five randomized rounds of five-fold cross. The splitting into train and test sets was stratified to assure a proportional coverage for every class, and early stopping after three epochs was activated to avoid overfitting. Five candidate models were then selected according to their different performances on a set of scores including macro-F1 (MF1) and macro precision recall area under the curve (MAUCPR).The final classifier was built as an ensemble average of a subset of these models, an unweighted arithmetic mean of three. The ensemble classifier led to an improvement in most scores while limiting the shortcomings of each single model and adding robustness to the final predictions. Finally, the models were trained on all available samples, with an adequate number of epochs to avoid overfitting for each separate case. A comparison with alternative tumor type classification RNA-seq models available in literature can be found in Supplementary Table The models were built as multilabel and multiclass; both the input labels and the output membership assignment are not exclusive to a single class. A post-processing step ensures consistency among the probabilities of classes within a family: if the classifier assigns higher probability to an offspring node than to its parent, the average of the two is assigned to both classes, and sibling nodes are adjusted accordingly. This correction is then propagated upward along the hierarchy.The scores in output from the final ensemble are not binarized to allow the user a full picture of confidence scores. To make up for the strong class imbalance in the training dataset, we recalibrated these output probabilities.J statistic  and then transformed the output scores linearly so that this cutoff value falls at 0.5 of the final probability. Although this change does not affect markedly the resulting output , it helps relieve some of the overfitting on minority classes. The median cutoff was .585 , suggesting that this is an overall balanced classifier.We identified a binary classification cutoff that maximizes an adapted Youden 2-normalized expressions. The input gene arrays are scaled to a 0–1 range, and the labels were transformed to a one-hot Boolean encoding.The input data features were ordered by correlation following a quick agglomerative clustering on their log73 version 1.10.1 backend. All code was run with Python version 3.6.9, and model training was run on our local high-performance computing machine with eight Xeon E5-2670 v2 @ 2.50-GHz or Xeon Gold 6140 CPU @ 2.30-GHz cores and 64 GB of RAM.All models were built with Keras version 2.2.2 and TensorFlow52 for a set of CNS tumors. In a previous work57, we profiled 252 high-risk pediatric cancers through multiple sequencing technologies. Sixty-three of these are CNS tumors with data from both DNA methylation and RNA-seq and can be directly compared.We compared the results of our transcriptional classifier to a DNA methylation-based classifierAfter a manual curation, the methylation classifier matched these tumors to their presenting clinical diagnosis in 86% of the cases. The remaining 14% are either matched to a wrong subtype but within the correct parent family or do not match the expected subtype. The two classifiers agree in almost all of these cases, within the limits of tumor types available in the respective reference datasets, and complement each other in the few exceptions.The dataset includes a number of tumor subtypes that are rare or absent in our reference cohort  and meningiomas); for the purpose of this comparison, consistency in their assignment to a transcriptionally similar subtype  was considered a match.Among the 8% of samples matched only to the parent family according to DNA methylation, OTTER, our transcription-based classifier, could correctly identify the subtype of three samples: a medulloblastoma, called as retinoblastoma by DNA methylation; a low-grade glioma, instead of a high-grade glioma; and an ependymoma, whose methylation profile was reflecting immune infiltration. In two cases, the classifiers were in agreement, in spite of a mismatch with respect to the pathologist diagnosis: an IDH wild-type glioma and a medulloblastoma of the G3 subtype. Finally, there are three cases in which the transcriptional classifier fell short, where the DNA methylation matched the correct subtype: an ependymoma, which was not recognized by OTTER due to low purity and high immune infiltration, and two atypical teratoid rhabdoid tumors, a subtype that is absent in our RNA-seq reference.Both classifiers can provide highly accurate predictions and complement each other in the most complex cases.H), a union/intersection score based on the graph information content similarity (SimGIC) that measures the proximity of two points along the class tree while accounting for its structure and populations:v1, v2 are the membership assignment probability vectors of two samples; nodes(vx) is the list of nodes or classes activated in such vectors; w(n) are the nodes’ weights. These are calculated as information content—that is, the probability of a sample falling into the lower node connected to the edge, which can be approximated to the class frequency of observations in the training dataset:To evaluate the accuracy of predictions within the hierarchical framework, we employ the hierarchical similarity score (η), which looks only at the branches active in the ground truth while disregarding false positives:We also define the partial hierarchical similarity score (8 reads (on paired FASTQ files), and its OTTER output was set as ground truth. Accurate classification  can be obtained with OTTER with 1 million reads. Although less accurate, Kallisto is considerably faster  treated with ribosomal depletion , were included, and so were samples with myofibromatosis. The classifier can still identify the correct tumor type, albeit with lower confidence. Although ribdopleted libraries are somewhat compatible with our classifier, the results should be treated cautiously, and the user should be aware that different tumor types will have an unequal impact on the classifierʼs performance.OTTER was trained on a dataset of poly(A) sequencing samples from fresh-frozen (FF) tissue. To evaluate its generalizability to alternative library preparation techniques, we tested its performance on a set of 247 samples from the Treehouse Childhood Cancer Initiative compendium version 9 . This set includes a slice of the KiCS cohort and samples from two different publications76. We also stratified the FFPE tumors by library preparation to demonstrate that the impact of library preparation and storage are additive  is commonly used for long-term storage of samples, yet degradation of the DNA and RNA in FFPE samples has been described in literature, and most molecular-based analyses seem incompatible with FFPE dataSMARCB1-inactivating alterations as confirmed with FISH, MLPA, targeted Sanger sequencing or high-throughput sequencing analyses. RNA-seq libraries were prepared using the Illumina TruSeq RNA Sample Preparation Kit for poly-adenylated mRNA selection and sequenced at the Centre for Applied Genomics77. To these, we added a single AT/RT sample from the KiCS cohort.We investigated the behavior of OTTER in inference on data from tumor types missing from our transcriptional phenotypesʼ hierarchy, and we measured the effect of adding such data to the RACCOON clustering. To this end, we selected 19 atypical teratoid/rhabdoid tumors (AT/RTs) from an unseen dataset, which was not used for clustering or training, and ran them through the current version of OTTER. Eighteen AT/RT snap-frozen tumor materials and clinical information were collected at The Hospital for Sick Children or through an international collaborative network with consent as per protocols approved by the hospital research ethics boards at participating institutions. All AT/RTs had negative BAF47 immunohistochemistry stain and biallelic Our reference cohort currently contains three samples that have been labeled as AT/RT. Two of these are found in T040 GLI HG/GBM MES, a group of high-grade gliomas and glioblastomas of the mesenchymal subtypes. The remaining sample was grouped by RACCOON with lung adenocarcinoma, likely due to contamination or low tumor purity. A true AT/RT target class is not available to OTTER. Fifteen of 19 samples are assigned to classes along the T040 CNS branch with at least 5% of confidence. AT/RTs also possessed some signal of high stemness, yielding a partial match to the mesodermal stem high class (T004) in 15 of 19 samples.We then clustered the group of 21 AT/RTs  using RACCOON together with samples from the CNS class . This demonstrates that a critical threshold of AT/RTs was reached to create a new subtype. To study what the exact threshold is, we performed subsetting experiments. Clustering was repeated using different numbers of AT/RT samples along with 100 other CNS tumors, both of which had been randomly selected five separate times. TPEs were used to speed up the search for repeated runs. We computed the adjusted mutual information (AMI) score on the clustering result by assuming a perfect separation of AT/RTs from all other CNS samples as ground truth, to assess how close the resulting partition was to having an AT/RT-only class  or exclusive to one sex , and genes on sex chromosomes may play a substantial role in their pathophysiologies  that matched to a tumor subtype of the expected cell type or tissue of origin to that of the expected diagnosis; (2) where multiple samples with the same diagnosis match the same subtype with the same probability profile; or (3) that match a tumor subtype in which we found reference samples of the same diagnosis but for which there are currently too few samples to create their own class. Finally, each putative match was reviewed by a pediatric oncologist to determine whether the data were sufficient to consider the diagnosis as being confirmed. In total, 16.5% of the tumors were in this category.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/s41591-023-02221-x.Supplementary InformationSupplementary figs. 1–17.Reporting SummarySupplementary TableSupplementary tables 1–8.


Neoepitopes have attracted much attention as targets for immunotherapy against cancer. Therefore, efficient neoepitope screening technology is an essential step in the development of personalized vaccines. Circular RNAs (circRNAs) are generated by back-splicing and have a single-stranded continuous circular structure. So far, various circRNAs have been poorly characterized, though new evidence suggests that a few translated circRNAs may play a role in cancer. In the present study, circRNA was used as a source of neoepitope, a novel strategy as circRNA-derived neoepitopes have never been previously explored. The present study reports CIRC_neo (circRNA-derived neoepitope prediction pipeline), which is a comprehensive and automated bioinformatic pipeline for the prediction of circRNA-derived neoepitopes from RNA sequencing data. The computational prediction from sequencing data requires complex computational workflows to identify circRNAs, derive the resulting peptides, infer the types of human leukocyte antigens (HLA I and HLA II) in patients, and predict the neoepitopes binding to these antigens. The present study proposes a novel source of neoepitopes. The study focused on cancer-specific circRNAs, which have greatly expanded the source pool for neoepitope discovery. The statistical analysis of different features of circRNA-derived neoepitopes revealed that circRNAs could produce long proteins or truncated proteins. Because the peptides were completely foreign to the human body, they could be highly immunogenic. Importantly, circRNA-derived neoepitopes capable of binding to HLA were discovered. In the current study, circRNAs were systematically analyzed, revealing potential targets and novel research clues for cancer diagnosis, treatment, and prospective personalized vaccine research. The clinical objective of immunotherapy is to utilize the natural immune system of the human body for targeting and destroying cancer cells, thereby improving the overall survival rate of cancer patients . TherefoRecent studies have reported that transcripts harbor frameshift mutations, while aberrant splicing patterns may produce antigenic peptides –8. Therevia different mechanisms, including interactions with microRNAs and RNA-binding proteins (RBP), consequently having important biological functions in eukaryotes   , 15. Sim (IRESs) , 20. Bec (IRESs) . Certain (IRESs) , 21, 22.In the present study, the first automated high-throughput data analysis pipeline to detect circRNAs, predict its protein-coding potential and identify circRNA-derived peptides binding to HLA was developed and designated as CIRC_neo. RNA-seq data were used to identify circRNAs, which were then translated into protein sequences. Subsequently, the binding of the generated peptides to HLA was predicted. The identified binding peptides could serve as sources of neoepitopes. A strict screening process was adopted in the identification process to ensure that only those peptides that presented HLA and exhibited adequate expression levels were selected, as these were most likely to produce the required immune response when these were used in the follow-up studies.To construct a catalog of cancer-specific circRNAs, CIRC_neo was applied to identify thousands of distinct circRNAs from the ribosomal RNA-depleted total RNA-seq or circRNA-seq data of human cancer tissues and normal tissues. Following that, cancer-specific neoepitopes were systematically identified and characterized using this catalog. Furthermore, the relationship between these neoepitopes and immunity and clinical practice was investigated. The current study suggested that circRNAs are a new source of cancer neoepitopes.The published datasets of glioblastoma (GBM), bladder cancer (BC), and chronic lymphocytic leukemia (CLL) were used for collecting the data. The GBM circ-RNA-seq FASTQ files associated with Y. Liu et al. were downloaded from NCBI SRA: PRJNA525736 . The GBMRaw FASTQ reads aligned to the reference rRNA, and tRNA genome using bowtie2 were discarded. Next, STAR  was emplCircRNAs require two basic elements for translation: IRES and ORF. IRESfinder  is a comThe input data used to detect the human leukocyte antigen (HLA) allele in the present study comprised the cleaned FASTQ files. Normal tissue RNA sequencing and cancer tissue RNA sequencing were performed for the 4-digit HLA class I and II determination. The OptiType version 1.3.1  and HLA-NetMHCpan4.0 is commonly used to predict patient-specific HLA class I binding peptides  as it cohttp://www.iedb.org/) on June 22, 2022. Queries were performed broadly for Homo sapiens , Epitope Structure (Linear Sequence), Host: Homo sapiens (human), Disease Data: cancer (ID:162), and selecting positive assays in the contexts of T-cell and MHC ligands. The characteristics of each unique epitope  were tabulated. The IEDB had 264668 curated epitopes (HLA I) and 94341 epitopes (HLA II). These epitopes had been derived from human tumor correlation.T-cell epitopes for cancer were identified by searching the IEDB  pathway analysis, which was performed in R using the function enrich KEGG from the package clusterProfiler. When the FDR  of each pathway did not exceed 20%, the pathway was designated as significantly enriched.https://github.com/summerjiaqi/CIRC_neo.git. All statistical analyses were performed in python and R. The p-values less than 0.05 were considered statistically significant.The neoepitope pipeline for identifying circRNAs, generating peptides, and predicting HLA class I/II binding affinity (CIRC_neo) is available for download at To investigate the landscape of neoepitopes in human cancer, a simple, performant analytical pipeline for predicting neoepitopes was created in the present study. The pipeline integrates cancer transcriptomics data and was named the CIRC-neo pipeline  . The FBXROBO2 encodes a protein that belongs to the ROBO family. Members of the ROBO family are a small subfamily of the immunoglobulin superfamily. The developed CIRC-neo predicted that the second exon of the ROBO2 gene was cyclized and produced a circRNA. The track height on exon 2 is higher in the cancer sample than in the normal sample, as shown in E-cadherin (circ-E-Cad) RNA junction reads in ribosome profiling were detected in the GBM samples, while no junction reads were detected in normal brain samples . The stuCircGprc5a was translated into a protein in a cap-independent manner and is, therefore, an example of a protein-coding circRNA in cancer. The circGprc5a encoding “FDTKPMNLCGR” played a biological role in BC . SimilarFurthermore, an internal ribosomal entry site was required for the 5’-cap-independent translation. A few circRNAs comprised the initiation codon and putative ORFs of a favorable length, which suggested an unexpected protein-coding potential of these circRNAs. This finding also indicated that a circRNA encoding a protein was closely associated with the occurrence and inhibition of tumors. This observation suggested that circRNAs could be a powerful source of neoepitopes, and the protein sequences encoded by circRNAs could further enrich the antigen library.The CIRC-neo pipeline was created to find cancer-specific peptides and evaluate their potential as neoepitopes. The study yielded a comprehensive list of putative circRNA-derived neoepitopes for each sample. CIRC-neo was applied to the RNA-seq data from BC, CLL, and GBM, and the number of circRNAs and circRNA-derived neoepitopes from each sample were counted.Multi-method consensus approaches were adopted to ensure robust results in the case of suboptimal data. The predicted results obtained using the find_circ and CIRCexplorer2 approaches are presented in Overall, using the CIRC-neo pipeline developed in the present study, the number of circRNAs in the cancer samples could be determined  and the normal tissues corresponding to them had to be removed , as depicted in Approximately 25%~50% of HLA class-I neoepitopes and 35%~50% of HLA class-II neoepitopes were shared by more than patient  was evaluated to further investigate the correlation between circRNA-derived neoepitope load and the immune environment. The results revealed that in GBM, the number of neoepitopes was negatively correlated with 4 immune checkpoint-related genes , and perforin-1 (PRF1), the canonical markers of immune cytolytic activity. Subsequently, the predicted neoepitope load and the immune gene expression were plotted, as depicted in The splicing form of circRNAs contributes to increasing the transcriptomic complexity, which could be exploited in cancer therapy. In this regard, the role of circRNA parent genes and circRNA-derived neoepitope genes in cancer was studied. Next, the circRNA parent genes were subjected to pathway enrichment analysis. The 20 most enriched terms are presented in The next analysis involved studying the circRNA-derived neoepitope genes in the three cancer datasets, including 3881 genes in the GBM, 1138 genes in the CLL, and 733 genes in the BC. As depicted in Back-splicing (biogenesis of circRNAs) may alter the ORF of RNA, which would alter the terminal sequences of its protein product. Accumulating evidence suggests that circRNA-encoded proteins play key roles in regulating various cellular events. In addition, the distribution of circRNA is reported to be specific, with the main circRNA distribution observed in the cytoplasm . In compThe CIRC-neo approach was used for identifying the neoepitopes derived from circRNAs. After the identification of circRNAs, their reasonable translation, prediction of HLA and peptide binding, and the integrated automatic analysis screening process led to the identification of the candidate neoepitopes. The CIRC-neo pipeline exhibited certain evident advantages over the other methods available for neoepitope recognition: 1) The translation product prediction based on the RNA-seq data provided a further representative state of the protein repertoire expressed. 2) New sources of neoepitopes were proposed, and the identified novel sources of circRNAs were validated through calculation and confirmed to be reliable. This compensated for the difficulty in distinguishing neoepitopes from non-synonymous mutation sources in cancers with low mutation loads. 3) The interactions between the peptide and HLA II were also studied. 4) Multiple filtering steps were used to eliminate false positives. 5) The parameters could be adjusted based on tumor type or sequencing depth, making CIRC-neo even more suitable for specific research needs.In order to demonstrate the reliability and effectiveness of CIRC-neo, the circRNAs that have been previously demonstrated to encode peptides/proteins in human cancers were explored and summarized. It was observed that a few of these previously reported peptides/proteins could also be identified using CIRC-neo. In the present study, circRNAs as a source of neoepitopes were identified computationally in cancer samples, and the relationship between the number of neoepitopes and other factors was also determined. In order to investigate the correlation between the predicted neoepitope load and the immune microenvironment, the expression levels of the genes CD8A, GZMA, PRF1, CD274, CTLA4, PDCD1, and PDCD1LG2 were determined. Furthermore, to demonstrate the high sensitivity of the developed CIRC-neo pipeline, the functions of the genes associated with the neoantigens that were closely correlated to protein processing, transportation, degradation, and immunity in cells were determined. The result data supported the hypothesis that circRNAs could be translated into immunogenic peptides, loaded on the HLA, and presented to the immune system. However, detailed integrated molecular characterization of a large cohort of patients is required to identify the features of circRNA-derived neoepitopes.The identification of truly immunogenic neoepitopes is a difficult process fraught with difficulties. For example, in the current study, the results were analyzed based on an extremely small amount of total RNA or poly (A)-depleted RNA sequencing data, limiting the possibility of drawing a strong conclusion. In-depth analyses on larger patient cohorts, involving both T cell recognition profiling and high-throughput sequencing data, would allow for the impact on immunogenicity and other features to be determined. The identification of circRNA-encoded proteins has been considered a difficult task for a long time, mainly due to the large sequence overlap between circRNAs and the linear mRNAs homologous to host genes. Owing to the unpredictability of intracellular protein degradation and the immune system’s complexity, a major challenge is the rapid and efficient identification of the neoepitopes that are most likely to induce an immune response from high-throughput sequencing data. In addition, personalized RNA or peptide vaccines have been shown to prime host immunity against tumor cells. However, most patients do not experience clinical benefit from these therapies. Improved identification of tumor neoantigens that elicit T cell responses will be needed to increase the scope of benefit from cancer immunotherapy . HoweverThe potential circRNA-derived neoepitopes appear promising and are expected to become novel vaccines for cancer treatment in the future. The accurate and comprehensive determination of neoepitope sequences is a critical first step in personalized immunotherapy. This step broadens the researchers’ understanding and provides a convenient and practical tool. The CIRC-neo pipeline proposed here is a computational pipeline for identifying circRNA-derived neoepitopes from RNA-seq data. The prediction of cancer-specific circRNA-derived neoepitopes has the potential to contribute to the development of cancer vaccines.https://www.ncbi.nlm.nih.gov/).Publicly available datasets were analyzed in this study, these can be found in National Center for Biotechnology Information (The study conception and design were performed by JX and PZ. Material preparation, data collection, and, analysis were performed by JX, SL and BR. JX was responsible for data analysis and writing of the draft of the manuscript. PZ reviewed/edited the manuscript. All authors contributed to the article and approved the submitted version.