Gene Selection (GS) is a strategy method targeted at reducing redundancy, limited expressiveness, and low informativeness in gene expression datasets obtained by DNA Microarray technology. These datasets contain a plethora of diverse and high-dimensional samples and genes, with a significant discrepancy in the number of samples and genes present. The complexities of GS are especially noticeable in the context of microarray expression data analysis, owing to the inherent data imbalance. The main goal of this study is to offer a simplified and computationally effective approach to dealing with the conundrum of attribute selection in microarray gene expression data. We use the Black Widow Optimization algorithm (BWO) in the context of GS to achieve this, using two unique methodologies: the unaltered BWO variation and the hybridized BWO variant combined with the Iterated Greedy algorithm (BWO-IG). By improving the local search capabilities of BWO, this hybridization attempts to promote more efficient gene selection. A series of tests was carried out using nine benchmark datasets that were obtained from the gene expression data repository in the pursuit of empirical validation. The results of these tests conclusively show that the BWO-IG technique performs better than the traditional BWO algorithm. Notably, the hybridized BWO-IG technique excels in the efficiency of local searches, making it easier to identify relevant genes and producing findings with higher levels of reliability in terms of accuracy and the degree of gene pruning. Additionally, a comparison analysis is done against five modern wrapper Feature Selection (FS) methodologies, namely BIMFOHHO, BMFO, BHHO, BCS, and BBA, in order to put the suggested BWO-IG method's effectiveness into context. The comparison that follows highlights BWO-IG's obvious superiority in reducing the number of selected genes while also obtaining remarkably high classification accuracy. The key findings were an average classification accuracy of 94.426, average fitness values of 0.061, and an average number of selected genes of 2933.767. Gene expression results are often repetitive and noisy, with just a small percentage of them presenting different profiles for various types of samples. As a result, in the field of bioinformatics, It's becoming more and more interesting to select highly discriminative genes from gene expression data . A microGene expression data are a highly dimensional, large-scale type of data. Therefore, the gene selection (GS) process is applied to gene expression data as an important preprocessing stage for correct disease classification ,4. The mFeature selection involves choosing a subset of variables from the input that can effectively explain the input data ,12. The Feature selection has been recognized as a difficult problem for several years, and researchers have been trying to find the best way to improve the precision and productivity of FS . It is nNumerous metaheuristic strategies have been suggested for the FS problem, such as the genetic algorithm (GA), GRASP, tabu search (TS), artificial bee colony (ABC), and harmony search (HS), among others ,26. AlthAmong the techniques that have been used in this area are metaheuristic algorithms. Due to the increasing number of metaheuristics that have been proposed and their efficiency in achieving exact outcomes, metaheuristic algorithms are frequently used for optimization ,28. NowaIn this paper, nine microarray GS datasets are used to test the effectiveness of the proposed approach. These benchmark datasets are from the GS data warehouse and have been used in a lot of the studies that are reviewed. These datasets are used to fully analyze and achieve further generalization of the findings. The nine datasets are MLL, Colon, Leukemia, leukemia_c3, Leukemia_c4, CNS, Ovarian, SRBCT, and lymphoma.•Addressing the complexities and data imbalances inherent in gene selection in the context of microarrays•presenting a simplified, computationally efficient approach to dealing with the conundrum of trait selection in microarray gene expression data.•Use the Black Widow Optimization algorithm in the context of gene selection.•Using two unique methodologies: variation of the Widow Optimization Algorithm and the hybrid BWO variant combined with the Iterated Greedy (BWO-IG) algorithm to process gene selection•Improving the local search capabilities of the Widow optimization algorithm to promote more efficient gene selection.•The proposed methods were applied to nine benchmark datasets obtained from the Gene Expression Data Repository, seeking empirical validation.The key contributions of this study are as follows.The rest of the paper includes: Section 22.1Metaheuristic algorithms are a type of optimization approach that is frequently utilized in engineering, economics, science, and artificial intelligence to address complicated optimization issues. These approaches are intended to solve optimization problems with a wide search space and non-differentiable, noisy, or complex objective functions.Metaheuristics work by directing the search process using a set of rules, heuristics, or probabilistic tactics, in contrast to classical optimization approaches that rely on gradient-based techniques for smooth and well-defined functions. They frequently give up the assurance that the global optimal will be discovered in favor of their prowess at navigating complex and varied problem environments.In order to improve their exploration and exploitation techniques over time, metaheuristic algorithms can adapt and learn from the progress made throughout the search. They are ideal for optimization issues with potential for dynamic change or those without a straightforward mathematical description because of their versatility.Because of their versatility, adaptability, and capacity to solve optimization issues satisfactorily in challenging and dynamic situations, metaheuristic approaches have been widely used to address a variety of optimization problems. The optimization problem's characteristics and the application's particular needs, however, will determine which algorithm is best.single-objective area, there are several studies that are proposed in the literature. For instance, Kootanaee et al. [In general, metaheuristic algorithms are classified as single-objective and multi-objective. In the e et al.  proposedTwo studies were put forward by Kassaymeh et al. ,35 to adShirvani et al. , provideThe software effort prediction (SEP) and software test prediction (STP) issues were addressed by Kassaymeh et al.  by combimulti-objective area, there are various studies that are proposed in the literature. For instance, Farzai et al. [In the i et al.  create aGiven that schedulers must take into account task interdependencies, underlying heterogeneity, cost variance, and different virtual machine (VM) configurations during the scheduling process, scheduling scientific processes on a hybrid cloud architecture is a difficult issue. So, with the intention of minimizing both financial cost and makepan, the problem of scientific workflow scheduling running on a hybrid cloud architecture is presented as a two-objective optimization problem. Shirvani et al.  proposedcontinuous optimization and discrete optimization methods that have been proposed to address combinatorial problems. In the context of continuous optimization, Asghari et al. [On the other hand, there are i et al.  describeWhere the consolidation of virtual machines is described as an integer linear programming issue and abstracted to a two-dimensional bin-packing problem. In order to lower the total power consumption (TPC) of the datacenter, Saeedi et al.'s  main focThe management of energy and lifetime maximization of such resource-limited networks remain among the most significant concerns because business continuity in vital industries heavily relies on the network's lifetime. Where The target coverage problem, which calls for each target to be watched by k-different sensors, boosts dependability and promotes fault-tolerant observation in these types of enterprises. If there is significant energy consumption compared to a single coverage issue. In order to escape a local trap, Ekhlas et al.  develope2.2Microarray datasets are compilations of genetic data generated using microarray technology and are used in the domains of pharmacy, medicine, clinical research, and physician studies. These databases give important information about the levels of gene expression or genetic variants in biological samples like patient tissues or cells. The significance of each of these datasets in each domain is described below:Microarray datasets can be used in pharmacy research to examine how different medications or pharmaceutical substances affect cells or tissues. Microarrays are used by researchers to analyze how gene expression profiles change before and after pharmacological treatments. Pharmacologists can design more effective and targeted drugs by studying these databases to find the genes and biological pathways linked to drug reactions. For instance, using microarray data, researchers may examine how cancer cells react to various chemotherapy treatments, resulting in the creation of individualized treatment regimens.Microarray datasets are used in the medical field to investigate the genetic causes of diseases and comprehend their molecular mechanisms. Comparing the gene expression profiles of healthy and sick tissues is a common task for these datasets. For instance, microarrays in cancer research assist in the identification and categorization of different types of cancer by identifying genes that are elevated or downregulated in tumor cells. The creation of novel treatments and biomarkers for the diagnosis and prognosis of diseases can also be guided by this information.In clinical research, particularly personalized treatment, microarray datasets are essential. Clinicians can choose the best course of treatment by examining the genetic data of their patients. For instance, using microarray data, oncologists might choose targeted therapy depending on the genetic makeup of a patient's tumor. This strategy can lessen negative effects while improving therapeutic results.Microarray datasets can be used in medical research to examine the genetic components of particular medical problems or the reactions of patients to therapies. This data can be used by doctors to make data-driven decisions about patient care. Microarrays, for instance, can be used in cardiology to find genetic risk factors for cardiovascular disease and help doctors determine the best preventative measures or treatments for a patient.The study of microarray datasets in each of these fields calls for sophisticated computational and bioinformatics methods. In order to pinpoint relevant gene expression patterns, pathways, and prospective therapeutic targets, researchers employ a variety of data mining and statistical techniques. A crucial step in translational research, which uses discoveries from the lab to enhance patient care and outcomes, is the integration of clinical and genetic data from microarrays.Overall, the availability of a wealth of genetic data from microarray datasets has transformed our understanding of diseases, medication responses, and customized medicine. This data can be used to promote research, diagnosis, and treatment in the fields of pharmacy, medicine, clinical practice, and physician studies.2.3The theory behind metaheuristic algorithms is to find a particular mathematical strategy that optimizes a specific problem. Improvement is achieved by multiple development efforts appropriate to the problem . SeveralZhang et al.  proposedUsing a hybrid approach, Gao et al.  proposedIn more recent studies, Zhang et al.  Binary  proposedIn another piece of research, Marie-Sainte and Alalyani  proposedFurthermore, Han et al.  suggesteUsing a genetic algorithm (GA), Rostami et al.  suggesteIn a recent study, Alweshah et al.  proposedFor Gene Selection Problems, Khurma et al.  proposedIn another study on gene selection, Almugren and Alshamlan  applied As mentioned, FS is a difficult problem that necessitates the use of intelligent dataset-handling methods in order to select the appropriate attributes for the research problem. Following the submission of literature, a variety of algorithms are used to solve the problem. The majority of these methods have been used multiple times, either alone or in combination with other algorithms, or by refining the same algorithm to provide good methods in this area. Furthermore, it is self-evident that no one solution can be used to solve any type of problem effectively because no approach is best in all cases and each method has advantages in different fields of interest, with outcomes varying depending on the type and complexity of the problem. Also, gene expression results are often repetitive and noisy, with just a small percentage of them presenting different profiles for various types of samples. As a result, in the field of bioinformatics, It's becoming more and more interesting to select highly discriminative genes from gene expression data.Thus, in this study, a new approach is applied to reduce the features of gene expression that are often repetitive and noisy and to produce resolutions with more reliable results in a sensible period of time. The proposed approach is based on the use of the BWO, which is one of the latest metaheuristic algorithms. It has been used in various applications and demonstrates its ability to deal with a variety of problems in various fields of engineering optimization, for example, document clustering, cloud computing optimization, and information retrieval. As well, it has the capability to deal with the challenges of real-world optimization, the ability to have a high degree of convergence, and the ability to offer a high-quality performance . For tha3Hayyolalam and Kazem  introducThere are four main stages in BWO: initialization, procreation, cannibalism, and mutation. The procreate stage emphasizes the discovery of the quest domain by producing many offspring. Then, by omitting incorrect solutions in the cannibalism stage, the BWO algorithm can progress quickly toward the right solution. The mutation process ensures that the exploitation and discovery phases are performed in the correct order .The first step is to customize the BWO algorithm's parameters. Then, the next step is to create the initial population, which defines the GS problem's candidate solutions (widows). To increase the diversity among the widows, the created population is then updated using a crossover and mutation relational algorithm. The fitness values of the widows are calculated next, and the best widow is determined. Then the BWO upgrades the widow based on the fitness function's consistency (measured by its probability). The BWO is used if the probability of the fitness function for the new widow is greater than 0.5 . The fitThen, the best widow is chosen from the updated population. Following that. If the stopping condition is satisfied, the best gene subset is evaluated on the gene datasets . The steps from calculating the chance of the new widow to the end are replicated if the stopping condition is not satisfied. After a certain number of loops, the algorithm comes to a halt and outputs the best gene subset that has been discovered. The finest gene subset is then produced as the preferred set of genes for use in the classification system's architecture .Spiders (order Araneae) are air-breathing arthropods with eight legs and venomous fangs on their chelicerae (in the jaw area). Spiders constitute the largest order of arachnids, and they rank seventh among all animals in terms of species diversity . The basThe female black widow is mostly nocturnal, remaining hidden throughout the day and spinning her web at night. A female widow, in most cases, spends the majority of her adult life in the same place. A female black widow attracts a male by spraying pheromone on certain parts of her net. By minimizing the size of their web, the first male to enter the web decreases the attraction of females to rivals. The female eats the male during or after mating, then moves the eggs to her egg sock. The offspring engage in sibling cannibalism after hatching from their eggs. They also remain on their mother's web for a limited time, and they can even eat the mother. Fit and strong individuals survive as a result of this cycle. Modanu et al. .The male black widow spider is one of two known creatures that deliberately assists the female in sexual cannibalism. During the mating procedure, the female consumes the male fully in only two out of every three instances. Males that are not eaten succumb to their injuries immediately after mating .The BWO algorithm starts with a population of spiders, each representing a potential solution. In pairs, the first spiders try to reproduce the new generation. The male black widow is consumed by the female black widow during or after mating. She then transports and releases the sperm she has stored in her sperm thecae to the egg sacs. Spiderlings can emerge from egg sacs up to 11 days after they have been laid. They cohabitate on the maternal web for a few days to a week, during which time sibling cannibalism occurs. They are then swept away by the storm .3.13.1.1In order to solve an optimization problem, the values of the problem variables must form an appropriate basis for solving the current problem. This arrangement is referred to as a “chromosome” and a “particle position” in GA and PSO terminology, respectively, and as a “widow” in the BWO context. The potential solution to each problem is modeled after a black widow spider. The values of the problem variables can be seen on these spiders. Hence, when. When solving benchmark functions, the structure can be seen as an array.A widow is exemplified as an array of 1 × Nvar, signifying the problem solution as a Nvar-dimensional optimization issue, and is defined as per Eq.  indicates the number of floating points. A widow's fitness is achieved by testing its fitness model f  by Eq. Each variable value  is generated with an initial population of spiders. The male black widow is eaten by the female black widow before or during mating, and pairs of parents are selected at random to carry out the procreation stage of mating.To begin the optimization algorithm, a candidate widow matrix of size 3.1.2As the pairs are self-contained, they begin mating in order to reproduce the new generation in tandem, as they do in nature. Regardless of the others, each pair has its own network. In the real world, each mating releases about 1000 eggs, but some spider babies live and grow up to be larger. Now, in order for this algorithm to reproduce, an array called alpha should be created that is as long as the widow array and includes random numbers, and offspring should be created by using Eq. This procedure is replicated Nvar/2 times, with the exception that the numbers chosen at random cannot be duplicated. The children and their mother are then added to an array and sorted by fitness value; some of the best ones are then added to the newly formed population based on their cannibalism ratings.3.1.3There are three different types of cannibalism in this scenario. The first is sexual cannibalism, in which a female black widow consumes the male during or after mating. Note that it is possible to distinguish between female and male fitness values using the BWO algorithm. Another type of cannibalism is sibling cannibalism, in which stronger spiderlings kill their weaker siblings. In this algorithm, a cannibalism rate (CR) is set, which determines the number of survivors. The third type of cannibalism is when baby spiders eat their mothers. The fitness value is used to determine how strong or frail the spiderlings are.3.1.4In the mutation stage, a mutepop number of individuals are chosen from the population. As seen in 3.1.5In BWO, as in most evolutionary algorithms, there are three stopping criteria that are considered for convergence: (a) the number of iterations, which is predetermined; (b) the discovery of no difference in the strongest widow's fitness value over many iterations; and (c) attainment of the appropriate level of precision.3.1.6•The PP specifies how many individuals can take part in the procreation stage. This parameter, by regulating the development of different offspring, contributes to diversification and allows for more detailed exploration of the search space.•The CR excludes unacceptable individuals from the community. By switching search investigators from local to global search and vice versa, the right value for this parameter enables great efficiency in the exploitation stage.•The PM indicates the percentage of the population that is undergoing mutation. This parameter's right value ensures that such exploitation and exploration stages are balanced. This parameter can be used to monitor the progress of the search agents as they move from the global to the local level and can also be used to guide them to the best solution.To boost the algorithm's performance in finding superior solutions, the parameters should be modified accordingly. The higher the risk of leaping out of any local optimum and the greater the potential to explore the quest space internationally, the more parameters can be fine-tuned. As a result, the correct number of parameters will ensure a good balance between exploitation and exploration. The BWO algorithm has three important control parameters: the procreation percentage (PP), cannibalism rate (CR), and mutation rate, which are defined as follows.The BWO algorithm's general pseudocode is shown below.4A stochastic search approach, the iterated greedy algorithm (IG), is used in this study. IG reconstructs a full candidate solution by iterating across greedy, constructive heuristics that build a succession of solutions. An acceptance criterion determines whether the newly developed solution will replace the original solution when it has been completed. IG repeats these actions until a stop requirement is reached. Iterative Local Search (ILS) is comparable to IG, except that instead of iterating more than a local search like ILS does, IG iterates over construction heuristics .During the destruction process, a simple random sample of solution genes d is chosen at random and removed from the solution without repetition, yielding two partial solutions. It is first delivered as SR, with only a size d of genes, as well as the genes that were deleted in the same order that they were removed. The original final solution, with size n-d of genes, has been donated as SD and does not contain any of the deleted genes .The reintroduction of genes that have already been eliminated from the solution occurs during the building process. The NEH insertion heuristic has been used as a constructive strategy to complete the answer. The first gene, SR1, gets put into all accessible n-d+1 sites in the destroyed solution SD, culminating in n-d+1 obtaining a solution. Only the n-d+1 partial solution that are better is picked and saved for the next iteration. The technique is repeated till the SR is exhausted or the ultimate solution is found, at which time the second gene is investigated. Once again, SD has a size of n ,80.Following the demolition and construction stages, we then decide if the new sequence is acceptable as the incumbent solution for the following iteration. Accepting new sequences if only they produce a higher value is one of the basic success criteria. However, because of insufficient diversification, an IG algorithm utilizing this acceptance criterion may result in search stagnation. Then, a basic simulated annealing-like acceptance criterion with a constant temperature (similar to that criterion in Ref. ) was use5In the FS generation step, a collection of attributes is chosen from the entire dataset and subjected to an evaluation process to determine whether it matches the solution or not. Forward selection or backward exclusion may be used in this process. Both methods perform an exhaustive search of the solution space. A full search means that no optimum subset is skipped, but it takes a long time to compute such a search because the GS problem is massive.However, there is another way to extrapolate subsets that is based on random sampling. This type of method can be useful for the FS process in that it reduces the need to wait before the search is done because an iteration value is used to complete the search. And despite the fact that the random process reduces calculation time, it is not always the best option. In the FS process, a metaheuristic algorithm can be used to randomly categorize subsets. It gathers data during the hunt to help guide the process and then creates new solutions by integrating one or more good solutions.Feature selection is regarded as a binary optimization problem, where solutions are restricted to binary numbers . When using BWO for FS problems, this means that any optimization strategy that is used to solve FS problems must be converted into a binary version, so that the solutions are expressed as 0 and 1 in a one-dimensional vector, where the value of 1 reflects that the attribute is chosen; otherwise, the value would be (0).In this study, the K-nearest neighbor KNN classifier is used as a wrapper FS method . The KNNIn the current study, two models are proposed. To choose the most important features from a dataset, both models employ the wrapper FS technique. The first is based on the basic BWO, while the second combines the BWO with the IG to establish a balance in the BWO between exploration and exploitation.More specifically, the present research posits a gradual hybridization of the BWO and IG. During the hybridization procedure, the IG is incorporated into the BWO improvement process. The hybridization process starts with a certain number of BWO repeats. The IG obtains the best solution and greatest fitness found by the BWO after the required number of iterations and begins its improvement process. The IG then submits the obvious solution and fit that it identifies to the BWO to continue the development process. This reciprocal operation is repeated once all of the BWO iterations are complete and the halting condition is met.The proposed BWO-IG technique employs BWO to generate the initial population of viable solutions. In the second step, the IG analyzes the fitness value of all candidates to discover better solutions, guaranteeing efficient convergence and high-quality solutions and eventually reaching the optimal parameter values, enhancing classification accuracy. The recommended BWO-IG with KNN solution to the FS issue is shown in As is known, the method of choosing attributes is determined by two factors: the degree of precision and the number of features chosen. The fitness feature was created to assess the efficiency of the process in achieving a balance between these two factors. The fitness function criterion used is described by Eq.  = (1–α) .6After the explanation of the problem, the initial step before building the methodology is to define the problem domain, and this is done via a mixture of means to accumulate the data that is related to the problem.6.1In this section, we present the datasets that will be used in experiments. Nine microarray datasets are applied to test the efficiency of the intended method. The used datasets are MLL, Colon, Leukemia, Leukemia_c3, Leukemia_c4, CNS, Ovarian, SRBCT, and lymphoma. These data sets, which include small, medium, and high-dimensional data sets, are widely used in many experiments. Experiments were carried out on nine benchmark datasets from the gene expression data warehouse  to asses6.26.2.1A class of human acute lymphoblastic leukemias known as mixed-lineage leukemias (MLL) have a chromosomal translocation affecting the mashed leukemia gene. MLL transposable elements are most frequent in babies and chemotherapy-induced leukemias, and they have an especially bad diagnosis. 24 examples of acute lymphoblastic leukemia (ALL) diagnosis groups (33.3%) MLL (mixed-lineage leukemia): 20 case reports (27.8%) 28 cases of acute myeloid leukemia (AML) (38.9%).6.2.2This dataset is identical to the yeast gene expression dataset in that it includes expression amounts for 2000 genes from 62 samples. It is noted whether each sample came from a tumor surgery or not. It can be used in two ways: the 62 samples can be treated as records in a high-dimensional vacuum, or the genes can be treated as records with 62 attributes.6.2.3The representation levels of 7129 genes were measured in 72 samples for the leukemia data collection. The labels show which of two types of leukemia were found in the study: AML with 25 samples or ALL with 47 samples. Since this dataset is similar to the colon cancer dataset, it should be included in the same studies.6.2.4Central Nervous System There are 60 clinical samples in the data set. There are 21 winners (labeled “Class1″) and 39 defeats (labeled “Class2″) (labeled “Class0″). There are 7129 genes in the dataset.6.2.5This dataset incorporates information from four randomized double-blind clinical trials in advanced ovarian cancer . The aim of these studies was to compare the efficacy of cyclophosphamide plus cisplatin (CP) versus cyclophosphamide plus adriamycin plus cisplatin (CAP) for advanced ovarian cancer treatment.6.2.6The small round blue cell tumors (SRBCTs) are a group of four childhood tumors that have a common existence on regular histopathologic examinations, making accurate doctor's manifestations difficult. Even so, correct diagnosis is important because medical therapy, medication reactions, and prognoses differ considerably on the basis of the diagnosis Several among these are Ewing's tumors (EWS), neuroblastoma (NB), non-Hodgkin lymphoma , and rhabdomyosarcoma (RMS).6.2.7The lymphoma dataset includes 42 samples of diffuse large B-cell lymphoma (DLBCL), 9 samples of follicular lymphoma (FL), and 11 samples of chronic lymphocytic leukemia (CLL). The DBLCL, FL, and CLL classes are coded as 0, 1, and 2 in the y vector, respectively. Data on gene expression was normalized, imputed, log converted, and standardized through genes to a zero mean and unit variance.7In this section, the proposed approaches, BWO-IG and BWO, were evaluated using the nine gene expression datasets to determine their efficiency for GS problems, and the findings were then compared with the results of other methods in the literature. All Experiments on all datasets were executed using Matlab R2015b on an Intel ® CoreTM i5 8th generation CPU with 12 GB RAM and, on a Windows 10 operating system.7.1A thorough examination covering all aspects of our study considerably enhanced the evaluation process. In order to begin this study, we compared two fundamental versions, BWO and BWO-IG, primarily in terms of their classification precision and sample size. In Examining the statistical elements, wherein we compared BWO with BWO-IG, gave our opinion more depth. This required a thorough analysis of the statistics and related p-values obtained from the T-test for accuracy, which is beautifully displayed in We scrutinized the convergence speed of BWO and BWO-IG to acquire a comprehensive grasp of their behavior, which is skillfully depicted in We next compared the classification accuracy of BWO-IG to the most advanced optimization techniques in an effort to put our results into context. We combined all of the techniques used in our investigation in terms of classification accuracy to provide a more comprehensive view of the landscape of our study, which is beautifully represented in We also investigated the performance of BWO-IG in comparison to cutting-edge optimizers, concentrating on their average fitness values, which are neatly arranged in Additionally, we compared the average number of genes chosen, a key component of our study, between our suggested procedures and cutting-edge techniques. The interaction between the number of genes chosen by each method and their corresponding average fitness values was our final focus. In conclusion, our review process covered a wide range of analyses, offering light on different aspects of our study's contributions and the relative effectiveness of the methodologies under discussion.7.2The wrapper FS model is used in the proposed method for the evaluation process, while the BWO and IG algorithms are used to find the (near) optimal function subset. A KNN classifier is used to classify the data, and K = 5 is used as an evaluator to rate the quality of the function subsets chosen. Peterson , K-fold 7.3This section presents details of the classification accuracy that was achieved by applying the BWO-IG and BWO methods to each dataset. The results are presented in As shown in Also, a T-test was performed to evaluate the efficacy of the BWO and BWO-IG methods when applied to the nine datasets. The findings statistics are calculated using the stated approaches, which are dependent on the correctness of the results particular to each dataset. It is widely accepted that superior solutions result from a steady and rapid rate of convergence. Therefore, one of the goals of this study was to speed up convergence by reducing randomization and the duration of the discovery process, thereby finding the best solution in a shorter amount of time. The BWO and BWO-IG can efficiently find optimal solutions for the nine datasets within a quick convergence time. 7.4In this section, a comparison is made between BWO-IG and five methods in the literature, which are Binary Improved Moth Flame Optimization and Harris Hawks Optimization (BIMFOHHO) KHURMA et al. , Binary As seen, the BWO-IG outperformed all five methods in terms of accuracy in all nine datasets. Furthermore, As shown, the mean fitness value of BWO-IG achieved the minimum fitness value (shown in bold) as compared with most of the other approaches except for BIMFOHHO. The BWO algorithm performed better than BHHO in eight datasets. On the other hand, in comparison with BSC, it is clear that BWO-IG performed better in all datasets except for the CNS dataset. However, the results of BSC and BWO-IG in the case of the CNS dataset were very close..In respect of the number of selected genes, BWO-IG achieved the best reduction rate across the nine datasets, as shown in Fig. 6In the experiments, the effectiveness of the proposed approach in achieving a satisfactory balance between exploration and exploitation was demonstrated in a variety of ways. First, the BWO algorithm outperformed the other compared algorithms in terms of classification accuracy and selection size over most of the datasets. Furthermore, the proposed method was able to reduce the gap between maximum and minimum accuracy in an efficient manner, which allowed for quicker convergence. It is widely known that a consistent and fast rate of convergence leads to improved results, so this result is particularly promising. The convergence speed behavior curves were obtained by applying them to each dataset over 100 iterations in order to further measure the efficiency of the proposed process. The results for convergence speed showed that the BWO had the ability to change the exploitation search, which is comprised of two criteria: the number of iterations needed to achieve the optimal solution and the solution's initial starting point.The proposed method was able to accelerate the rate of convergence during its search for the right solutions due to the improved balance. The most suitable features were chosen to ensure that the solutions found converged as far as possible. Thus, randomization-assisted exploration enabled the search of the solution space from a local perspective while also increasing the range of solutions.8The key goal of this study was to propose a new approach for finding effective solutions and optimizing the GS process. In order to achieve this goal, the black widow optimization algorithm was applied as a potential solution to the GS problem. To propose a more effective approach for addressing GS problems. BWO is used as an optimizer in two ways: it was applied in its original form as well as hybridized with the iterated greedy algorithm (BWO-IG) to enhance its local search capabilities. Nine benchmark datasets from the gene expression data warehouse were used in the experiments. The results showed that the BWO-IG enhances the BWO's search capabilities; it can pick suitable genes and produce more reliable results than the BWO algorithm, whether in the accuracy rate or in the number of reduced genes. The performance of the proposed BWO-IG method was compared with that of five recent wrapper FS methods, namely, BIMFOHHO, BMFO, BHHO, BCS, and BBA. The comparison revealed that BWO-IG was significantly superior in terms of reducing the number of genes selected and getting highly accurate classification accuracy.A thorough examination of dataset properties, such as dimensionality and dataset size, can be a future research path. Although the majority of feature selection (FS) research so far has focused on problems with dimensionality limited to a few thousand features, it is unclear how FS techniques will perform when faced with datasets comprising millions of features. Future research in the area will need to have a key focus on how to solve this scaling problem.Additionally, expanding the application domain of suggested FS approaches beyond their original domain can produce insightful results. It is important to assess these techniques' effectiveness and applicability in various applications, including electroencephalogram (EEG) analysis, face recognition, and hyperspectral image processing. This broadening of the algorithm's scope can reveal fresh possibilities for enhancing various AI-driven applications.Furthermore, improving FS algorithms to function in a multi-objective framework represents an interesting direction for future research. It may be possible to develop more flexible and adaptable feature selection strategies that are suited to the complexities of real-world AI challenges by researching algorithms that may simultaneously optimize many objectives, such as accuracy, interpretability, and robustness.Mohammed Alweshah: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper. Yasmeen Aldabbas: Bilal Abu-Salih: Saleh Oqeil: Hazem Hasan: Saleh Alkhalaileh: Sofian Kassaymeh: Contributed reagents, materials, analysis tools or data; Wrote the paper.Data will be made available on request.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Radiation therapy (RT) is an important treatment for cancer. Advances in technology over the last 100 years have helped to deliver radiation to tumours  with increasing accuracy. Comparatively little progress has been made in adjusting radiation treatment based on genetic differences from one person to another. Techniques proposed for adjusting dose have been assessed in clinical datasets, but studies have not considered how technical aspects affect predictions or gene signature specificity to radiation. This work shows that preprocessing has a large influence on model predictions and demonstrates a lack of evidence for radiation specificity in existing gene expression-based models.Transcriptomic personalisation of radiation therapy has gained considerable interest in recent years. However, independent model testing on in vitro data has shown poor performance. In this work, we assess the reproducibility in clinical applications of radiosensitivity signatures. Agreement between radiosensitivity predictions from published signatures using different microarray normalization methods was assessed. Control signatures developed from resampled in vitro data were benchmarked in clinical cohorts. Survival analysis was performed using each gene in the clinical transcriptomic data, and gene set enrichment analysis was used to determine pathways related to model performance in predicting survival and recurrence. The normalisation approach impacted calculated radiosensitivity index (RSI) values. Indeed, the limits of agreement exceeded 20% with different normalisation approaches. No published signature significantly improved on the resampled controls for prediction of clinical outcomes. Functional annotation of gene models suggested that many overlapping biological processes are associated with cancer outcomes in RT treated and non-RT treated patients, including proliferation and immune responses. In summary, different normalisation methods should not be used interchangeably. The utility of published signatures remains unclear given the large proportion of genes relating to cancer outcome. Biological processes influencing outcome overlapped for patients treated with or without radiation suggest that existing signatures may lack specificity. Radiation therapy (RT) is a key tool in modern cancer treatment ,2. Over The development of pan-cancer radiosensitivity signatures has utilized microarray transcriptomics in cancer cell lines alongside radiosensitivity measurements from in vitro assays  to identify genes that correlate with cell response to radiation treatment. Clinical data has also been used directly to build radiosensitivity signatures for breast and prostate cancer ,5,6. AltThe radiosensitivity index (RSI) is a 10-gene signature that was developed using a linear model of ProbeSets from the Affymetrix HU6800 microarray to predict SF2 with the covariates of tissue-of-origin, ras status and p53 status . The 500Several factors may contribute to differences between in vitro and clinical results. An important, often overlooked, element is the confounding of the relationship between genomics and clinical outcome by general biological processes  as opposed to those of specific interest . Previous work has shown that associations between gene expression signatures and outcome in breast cancer are largely abrogated after adjusting for proliferation . The samWe investigate the application of in vitro radiosensitivity signatures to publicly available clinical datasets. Signatures were tested for association with time to first recurrence and overall survival, comparing results with resampled control signatures. Single gene models were examined in order to assess the correlation between individual gene expression and outcome as well as investigating the biological processes related to RT. The effects of different normalisation methods upon radiosensitivity estimation were also evaluated.Clinical and microarray data were obtained from four cohorts: the glioblastoma (GBM) dataset from The Cancer Genome Atlas (TCGA) , lung adhttp://gene.moffitt.org/libaffy/ (accessed on 21 July 2022)) for IRON [Where microarray data is used, there is a need to remove non-biological sources of noise from the data. This is often achieved using an algorithm for identifying and eliminating biases. Three algorithms that have been used for this purpose are: RMA (Robust multiarray average), MAS5  and IRON . The use of rankings of genes after normalisation  in the RSI model may help to negate the differences arising from the application of different normalisation approaches, but this assumption has not been tested despite all three algorithms being used for training and application in publications . The RSIfor IRON .Previous clinical studies have shown that a large portion of the transcriptome is related to cancer outcomes, leading to a high probability that a randomly sampled signature will correlate with outcome . This bap-values from univariate Cox regression analysis was tested against a uniform distribution  than expected by chance. The single gene model hazard ratios were also used to assess the biological process involved in outcomes using functional annotation.Along with radiosensitivity predictions from combinations of genes in signatures, univariate Cox regression models were investigated for all genes on the clinical microarrays. Cell processes, such as proliferation, involve changes in a substantial proportion of the transcriptome and can influence the progression and effectiveness of treatment in cancer. Univariate gene models allow calculation of the hazard ratio  associated with increased expression of a given gene for different treatments . The distribution of pothesis ) using tpothesis  in orderp-values from enrichment tests were adjusted by the Benjamini–Hochberg method [Overrepresentation analysis  was usedg method . We alsorrvgo (v1.8.0) with a similarity threshold of 0.7 for both overrepresentation and GSEA analysis [Gene set enrichment analysis (GSEA) is often used to analyse ranked lists of fold changes for genes between two phenotypes; GSEA allows for the identification of biological processes that are differently expressed between the conditions studied. Instead of examining relative expression between groups, here we use the ranked list of hazard ratios  for all genes on the microarrays . This apanalysis . The semanalysis . GSEA anRSI values were calculated using three different normalisation algorithms on identical clinical data. Although mean differences between the methods are small (range: 0.02 to 0.06), the 95% limits of agreement were −0.22–0.16 for MAS5/RMA, −0.26–0.14 for MAS5/IRON and −0.16–0.21 for IRON/RMA . These rp-value of less than 0.05 increased with signature size; 15.0% of the 500 resampled 10-gene signatures were significant compared to 19.5% for the 100-gene signatures  increase per SD and in the OS_RT group with a 24% (95% CI: 7–44%) increase per SD . The RSIgnatures .Overrepresentation analysis identified actin filament organization, cell substrate adhesion, and membrane raft organisation among 20 clusters from 74 biological processes overrepresented in the Kim signature . A totalp-values from fitting Cox regression models to every gene from the microarray data were tested for differences from the null  distribution. Distributions for all groups were significantly (p < 0.001) different from uniform, with the peak occurring in the <0.05 range, which suggests that many more genes were related to cancer outcome than expected by chance  and divided based on treatment . Significant pathways were clustered by semantic similarity and the top 10 clusters were visualised. Genes that were overrepresented at the top (or bottom) of the ranked list of univariate hazard ratios for FR_noRT were involved in nuclear chromosome segregation, cell cycle DNA replication, chromosome organization, and cell division, which suggests that genes involved in the proliferation processes were indicative of first recurrence risk . SeveralGenes involved in proliferation and immune response were also enriched in the OS_noRT group; the top clusters included chromosome organization, regulation of DNA templated DNA replication, mitotic cell cycle checkpoint signalling, and adaptive immune repsonse . AnalysiRadiation has been a mainstay in cancer treatment for over a century but still lacks biological personalisation. Attempts have been made to predict an individual’s intrinsic sensitivity to radiation based on gene expression data and laboratory tests of radiosensitivity. These models have proven inaccurate when independently tested on held out in vitro data, but clinical studies have shown association with outcome . Here, wThe normalisation algorithm choice influenced radiosensitivity predictions. The limits of agreement for different normalisation algorithms were wide for the RSI signature, suggesting that calculations using different methods are not equivalent and should not be used interchangeably. The RSI estimates survival fraction, which theoretically ranges from 0 to 1, and the limits of agreement exceeded 0.2 in all comparisons, indicating that a difference of >20% of the range can result from data normalisation choice alone. When applied in clinical data, this translated into a difference in the estimated hazard ratio of up to 0.25, significantly impacting on the apparent performance of signatures . Of courThe published models were tested using a subset of publicly available data from the latest validation of the RSI model (in GARD form)  and an ap-value of less than 0.05 in a Cox regression for recurrence, while around 12% are significant in overall survival models. Given that multiple genes are typically combined in radiosensitivity signatures, there is a tangible risk of developing a signature that significantly correlates with outcome without necessarily being specific to the process of interest, such as radiation response. This is evidenced by 15% of resampled 10-gene signatures being significant, with the proportion increasing to 19.5% for 100-gene signatures  and immune regulation. A recent study has shown that the RSI is associated with immune response in a large sample of tumours, which is not unexpected given the inclusion of STAT1 and IRF1 . HoweverEnrichment analysis with hazard ratios from single gene models suggested that immune response is important across multiple groups and in cohorts that did not receive radiation. Immune processes are increasingly being recognised as crucial to cancer progression and treatment response . ProlifeFuture development of radiation response signatures may include multiple genomic data types that are already publicly available, such as, for example, NCI60  and CCLEOur work has shown that future studies of radiosensitivity prediction should implement more intensive validation in the model development stage to help ensure wide applicability and progression to clinical use. In particular, benchmarking of signatures against collections of resampled genes allows for contextualisation of prediction errors. Other sources of variation between training and application of models, such as normalisation, should also be quantified in the model development phase. Robust error estimation, as we outline here, may be incorporated into radiosensitivity signature training, improving specificity, and helping to drive advances in biological personalisation of radiation therapy.


Checkpoint immunotherapy (CPI) has increased survival for some patients with advanced-stage bladder cancer (BCa). However, most patients do not respond. Here, we characterized the tumor and immune microenvironment in pre- and post-treatment tumors from the PURE01 neoadjuvant pembrolizumab immunotherapy trial, using a consolidative approach that combined transcriptional and genetic profiling with digital spatial profiling. We identify five distinctive genetic and transcriptomic programs and validate these in an independent neoadjuvant CPI trial to identify the features of response or resistance to CPI. By modeling the regulatory network, we identify the histone demethylase KDM5B as a repressor of tumor immune signaling pathways in one resistant subtype  and demonstrate that inhibition of KDM5B enhances immunogenicity in FGFR3-mutated BCa cells. Our study identifies signatures associated with response to CPI that can be used to molecularly stratify patients and suggests therapeutic alternatives for subtypes with poor response to neoadjuvant immunotherapy. The response to checkpoint immunotherapy within bladder cancer patients is highly variable. Here, the authors use RNA-seq, ATAC-seq and digital spatial profiling of pre- and post-treatment samples from the PURE01 trial to identify subtypes associated with treatment response. Despite improvements in smoking cessation, surgery, and systemic therapy, there has been no improvement in survival for BCa for over twenty years. The current standard for locally advanced BCa  is three to four cycles of cisplatin-based chemotherapy before radical cystectomy. In 2016, the FDA approved therapeutic antibodies targeting the program cell death protein-1/program cell death death-ligand-1 (PD-1/PD-L1) checkpoint 2. Despite improved survival in second-line and even first-line metastatic BCas treated with CPI, only 25% of tumors are responsive to CPI; however, responders are likely to experience a durable cure3. Currently, the most effective therapy for locally advanced and metastatic BCa is unknown, with some treated with chemotherapy and others with immunotherapy. Therefore, accurate biomarkers that distinguish CPI responders from non-responders are critically needed. A “neoadjuvant paradigm” in which pre-treatment tumors are biopsied before administration of systemic therapy and cystectomy is essential to gain insights into mechanisms of therapeutic response6.Over 80,000 people in the US are diagnosed with bladder cancer (BCa) each year, with almost 18,000 deaths9. PURE01 reported a complete pathologic response (ypT0N0) of 36%. Increased CD8+ immune infiltration, PD-L1 status, immune-infiltrated basal subtype, and tumor mutation burden (TMB) were all associated with favorable pathologic response to pembrolizumab9. ABACUS (NCT02662309)10, a Phase II trial of neoadjuvant atezolizumab in patients with muscle invasive urothelial cancer, reported similar efficacy (31% complete pathologic response), but the response was not associated with TMB or PD-L1. While identifying biological features associated with response or resistance to CPI could help clinicians stratify patients who should receive neoadjuvant chemotherapy instead of immunotherapy or identify potential combination treatments to improve response and, ultimately, survival, the features associated with response to CPI remains an area of investigation. Here, we dissected the heterogenous cancer cell program associated with response and resistance to CPI using multi-omics analysis of pre-treatment and matched post-treatment tumors from PURE01 (82 pre- and 31 post-treatment). We identified five MIBC expression subtypes with distinct genomic, transcriptomic, and pathologic profiles. We developed a single-patient classifier, which we applied to identify the least-responsive subtype  in an independent neoadjuvant CPI trial, ABACUS. Further investigation of S1/LumE tumors, which were enriched for FGFR3 mutations, identified KDM5B as a negative regulator of an immune-related gene network. Enhanced KDM5B activity was unique to S1 tumors, in contrast to the other CPI-resistant subtype, S4. Targeted inhibition of either FGFR3 or KDM5B enhanced immunogenicity in S1-like urothelial cells, suggesting a potential combination treatment for tumors classified as S1/LumE. These results suggest that the biologic classification of MIBC tumors may aid clinicians and patients in precision approaches to CPI prior to radical cystectomy.Due to the limited but favorable response of metastatic BCa to CPI, multiple small Phase II trials have evaluated the pathologic response to CPI instead of chemotherapy before radical cystectomy. The largest trial of neoadjuvant immunotherapy for MIBC is PURE01 (NCT02736266), in which 143 patients were treated with three cycles of pembrolizumab before cystectomy9. We have now performed a comprehensive multi-omics analysis of 82 pre-treatment tumors. In addition, we have expanded our cohort to include 31 post-treatment tumors (27 matched pre- and post-pembrolizumab specimens), which may identify features associated with treatment response.Patients were enrolled in the PURE01 clinical trial, a Phase II prospective study of neoadjuvant pembrolizumab before radical cystectomy (NCT02736266). We used tumor samples from 82 patients as a biomarker-evaluable subset representative of the overall study cohort of 114 patients. The median follow-up for 68 patients without progression was 22 months, and 14 patients had recurrence after a median of 11 months. A detailed summary of clinical and histopathological information is provided in Supplementary Data n = 30) vs. non-responders  for the overall cohort  had higher proportions of PD-L1(+) tumors. In contrast, subtypes with the lowest pathological response rates  were enriched in PD-L1(−) tumors  companion biomarker detects PD-L1 expression in tumor and immune cells and has been weakly associated with response to CPIors Fig. . We obse13, TCGA14, consensusMIBC (cMIBC)15, and MD Anderson (MDA)18 18 Fig. . We foun)18 Fig. . Most S119 and Mariathasan20 gene sets  using MSigDB Hallmarkets Fig. . Tumors 21 data, seeking to identify perturbations that resulted in signatures similar or opposed to the expression signature in each subtype ype Fig. . Each suype Fig. , CMap idFinally, we evaluated the histomorphology of tumors from the five subtypes, comparing the epithelial, immune, and stromal components  Spearman correlations between PURE01 and corresponding ABACUS subtypes . In this Phase III MIBC trial, participants were randomized to atezolizumab or observation after radical cystectomy. When we applied the classifier to cystectomy specimens of the cohort, we found that the “responsive” subtypes , treated with 12 months of atezolizumab, had an improvement in outcomes compared to “unresponsive” subtypes (S1 and S4) (p = 0.046)  Fig. . In the 46) Fig. . CollectTo identify the genetic features of each subtype, we ompared somatic mutations and copy number variations for tumors in the PURE01 and ABACUS cohorts compared to mutations from ABACUS. We started by evaluating the association of Hallmark gene sets to mutations found in at least 10% of tumors. We found mutation patterns associated with the repression or activation of the cell cycle, DNA repair, and inflammatory pathways Fig. . Tumors 25, were more frequent in S1 tumors (31%) . While the distribution of tumor mutational burden (TMB) was not statistically associated with subtype    in each expression subtype Fig. . Overall26. We sought to evaluate the unique stromal and immune features of the TME in each subtype by both bulk RNA-Seq and spatial profiling . Using ESTIMATE27 and MCP-counter28 immune deconvolution algorithms, we observed differences between subtypes in immune cell populations  have been associated with the immune response to CPI. For example, an ‘inflamed’ immune phenotype, with increased immune cells and low numbers of fibroblasts, is associated with improved response to CPI compared to immune-desert and immune-excluded TMEons Fig. –c, 8. Ovons Fig. , S5 tumoons Fig. , 9a. In ons Fig. .Fig. 4Pr29. Applying the ImmunoMind platform’s R package immunarch to TRUST4-processed bulk RNA-Seq data, we tested PURE01 pre-treatment B-cell repertoires for specific immunoglobulin signatures associated with response to neoadjuvant pembrolizumab. Gene usage analysis of the IGH, IGK, and IGL segments showed that some V(D)J genes were used more frequently. Fitting generalized linear models for overall response rate (OR: CR + PR) vs. NR, we found that the level of IGHJ130 varied across subtypes and was lower in OR samples than in NR samples   and three non-responders (NR)  Fig. . This meNR) Fig. . While TTo further investigate features associated with resistance to pembrolizumab within each subtype, we then compared the expression of immune cell markers and immune regulatory proteins in TME AOIs from CR and NR tumors Fig. . TMEs frp = 0.15, Fisher’s Exact test)   Fig. , with nost) Fig. .Fig. 5Cop = 4.1 × 10−9, Kruskal test)  Fig. . Hallmar32. Interestingly, for S7, CMap analysis also identified MAPK and MEK inhibitors among the top negative correlations, suggesting upregulated MAPK/ERK pathway activity in this subtype. In addition to identifying possible adjuvant therapies, the CMap analysis confirmed that S6 and S7 subtypes have different biologies and suggested that subtype-specific combination therapies may have a role after cystectomy in CPI-resistant tumors.Tumors resistant to neoadjuvant therapy are associated with recurrence and progression, and analysis of post-treatment tumors from PURE01 may identify new therapeutic options for patients with CPI-resistant bladder cancers. We applied the CMap perturbagen gene signatures to identify potential therapeutic targets for S6 and S7 Fig. . This id33 network analysis and Reactome pathways to infer and functionally interpret the interactome of KDM5B’s negative target genes and found that the network was enriched in genes involved in immune modulation   of the 21 CPI-resistant tumors in S6 and S7 were originally from S1 Fig. , we next35 in S1-like RT4 bladder cancer cells, which harbor an oncogenic FGFR3-TACC3 fusion36. The enzymatic inhibition of KDM5 with C70 in these cells caused upregulation of genes that were negative KDM5B regulon targets , 454 were also significantly differentially expressed after treatment with this FGFR inhibitor (FGFRi)   C70ets Fig. . Since 3ets Fig. , and theets Fig. , we comptes Fig.  and enrites Fig. . Taken ttes Fig. , which hThe neoadjuvant paradigm, in which a tumor is sampled before systemic therapy, and then again post-treatment at radical cystectomy, may offer opportunities to identify biological features that can guide higher-efficacy treatment strategies for BCa patients. Currently, no reliable biomarkers are available to guide the selection of individualized neoadjuvant or adjuvant chemo- or immunotherapy in MIBC patients. Unfortunately, the companion PD-L1 biomarker for CPI therapy has not shown a strong association with the response across trials with anti-PD1/PDL1 antibodies, challenging investigators to find other biologic predictors. Our study describes comprehensive multi-omic profiling of MIBC tumors from PURE01, the largest study available of neoadjuvant CPI before radical cystectomy. The study’s overall goal was to dissect the molecular heterogeneity of MIBCs to further define features associated with resistance.40. S5 tumors had a stroma-rich, immune-exclusion phenotype with a high frequency of T cells in the TME with increased IFN signatures; however, S5 had few to no immune cells infiltrating the tumors and had elevated expression of immune checkpoint signatures, potentially explaining this subtype’s relatively poor pathologic response rate. Yet, the mechanistic differences between clinical and pathologic responses require further investigation. Collectively, our data identified expression signatures that were associated with intrinsic resistance to CPI, providing further granularity into features associated with response to checkpoint immunotherapy.By unsupervised consensus clustering, we identified five transcriptomic MIBC subtypes associated with clinical and pathological responses to pembrolizumab Fig. . We asse41. IDO1 inhibition in combination with anti-PD-1 therapy (nivolumab) and chemotherapy is being tested in a Phase III clinical trial for MIBC (NCT03661320)43. While further investigation of the spatial heterogeneity is necessary for a larger cohort, our results may help explain the lack of CPI activity in the small fraction of non-responders within the S2 subtype.We integrated bulk transcriptome and spatial protein data profiles to identify features of non-responsive tumors Fig. . Spatial44. Upper tract urothelial carcinoma (UTUC), which harbors mutations in FGFR3 in 74% of patients44, exhibited a particularly poor response to adjuvant nivolumab in the CheckMate274 trial45  for ureteral tumors). Herein, we identified a higher frequency of FGFR3 mutations in S1 tumors and KDM5B as a regulator of the immune suppression program in this subtype. While only 35% of tumors had mutations in FGFR3, FGFR3 expression was enhanced in tumors from S1. In vitro, we demonstrated an increase in immune pathway gene sets with KDM5B or FGFR3 inhibition in a luminal cell-line model representative of the S1 subtype. While erdafitinib has been associated with immune activation in lung cancer46, little is known about mutant FGFR3-dependent immune regulation in BCa. We demonstrated that KDM5B inhibition alters the chromatin accessibility of FGFR3-mutated RT4 cells, resulting in increased expression of proinflammatory genes. These results suggest that genetic and epigenetic drivers of tumor subtypes could be targeted to transform a resistant tumor into a more CPI-responsive state. Transcriptomic analysis identified an increase in immune infiltration and stromal expansion in post-treatment, pembrolizumab-resistant tumors. Furthermore, in S6-subtype post-treatment tumors, CMap analysis identified FGFR inhibitors as a potential therapeutic target, likely reflecting a continued dependence of these tumors on S1 gene expression patterns post-therapy, e.g., FGFR3-related pathways. If an S1 tumor were identified before treatment, it might be possible to change its susceptibility to CPI by using an inhibitor targeting FGFR3 or KDM5B. In contrast, S7 tumors were associated with greater immune activation, and CMap analysis identified the MAPK pathway and WEE1 as potential targets in these tumors. A combination of WEE1 inhibitors with CPI is currently being evaluated in a Phase Ib study in patients with muscle invasive bladder cancer (NCT0254666147). These examples further support using an RNA-based platform to identify potential new therapeutics.Prior work has suggested activating mutations in FGFR3 may be associated with an immune-evasive phenotype and poor response to CPIWe acknowledge the limitations of this study. We developed tumor subtypes from a unique cohort with limited sample numbers and attempted to validate the subtypes in two cohorts of tumors from different clinical trials. ABACUS and PURE01 had different rates of PD-L1 positive tumors, clinical stage, and duration/doses of therapy. In addition, the two cohorts were treated with different immune checkpoint blockade agents  and assessed PD-L1 with different IHC assays. At a technical level, RNA and DNA from the tumors were processed by different extraction and sequencing methods, and we did not attempt to correct for potential batch effects. We hypothesize that differences in outcomes within subtypes between trials may be secondary to differences in immunotherapy drugs; however, we cannot validate this hypothesis with mechanistic studies. In addition, our spatial proteomic evaluation involved areas of interest from a limited number of tumors; further investigation of spatial heterogeneity should be performed in MIBC. Finally, as almost all post-treatment tumors from ABACUS were from non-responsive tumors, we could not compare the effect of immunoediting or how TCR/BCR diversity changes in responsive tumors. Similarly, tumors from IMvigor010 were biased towards more aggressive tumors.Our work identifies the heterogeneity associated with responses to CPI in muscle invasive bladder cancer. We describe five subtypes with unique genomic and transcriptomic cancer cell programs and distinct tumor microenvironments with differential responses to neoadjuvant pembrolizumab in bladder cancer. In addition, we have uncovered FGFR3 mutations and KDM5B overexpression as resistance biomarkers to CPI and describe therapeutic alternatives that can help guide patient stratification for combinatorial therapies.For ABACUS, clinical/metadata were previously reported, and we accessed FASTQ files from the European Genome-Phenome Archive (EGAS00001004445). For ABACUS, we describe how pathological response was assigned as CR, PR, NR, and Unknown in the ‘Pathological response’ section below. We defined PD-L1(+) as IC2+. For the IMvigor010 cohort, we downloaded metadata from EGAD00001007575.Unstained formalin-fixed and paraffin-embedded (FFPE) bladder tissue/tumor sections were received from the PURE-01 study. A representative slide from each patient was stained with hematoxylin and eosin. Images were taken with a Zeiss Axioskop/Nuance microscope using a 10x objective.2, including necrotic tumor) for each slide.To quantify the amount of tumor in each bladder specimen, the H&E-stained slides were scanned at low magnification and digitized using the TissueFAXS system . The acquired images were analyzed using TissueGnostics’ HistoQuest cell analysis software. Prior to morphometry, the tumor was marked by a GU pathologist (BC) with a black marker. We then used the automated region of interest (auto-ROI) function to identify the borders of the specimen and manually excluded any non-urinary bladder components that auto-ROI had included. Finally, in the HistoQuest software, we traced the area indicated as tumor by our pathologist and recorded the total tumor area , following the manufacturer’s instructions. Stranded total RNA-Seq was conducted in the Northwestern University NUSeq Core Facility. Briefly, RNA quantity was determined with a Qubit fluorometer. Total RNA examples were also checked for fragment sizing using Agilent Bioanalyzer 2100. The Illumina TruSeq Stranded Total RNA Library Preparation Kit was used to prepare sequencing libraries, following the manufacturer’s recommendations, including rRNA depletion with RiboZero Gold, cDNA synthesis, 3′ end adenylation, adapter ligation, library PCR amplification, and validation. Libraries were pooled and sequenced on an Illumina HiSeq 4000, generating 50 bp single-end (SE) reads at a sequencing depth of 20–25 M reads/sample. For ABACUS, RNA sequence data from FFPE samples, and clinical/metadata, were made available by T. Powles and Genentech from the European Genome-Phenome Archive (EGAS00001004445).48 and converted SAM files to sorted BAMs with Samtools v1.6. We generated read counts with ‘htseq-count’ from ‘htslib’ v0.11.2, using Ensembl v95 (GRCh38) gene annotations. To generate gene-level FPKM values, we used ‘cuffquant’ and ‘cuffnorm’ from Cufflinks v2.2.149, with these gene annotations. For the IMvigor010 cohort, we downloaded RNA-Seq FASTQ files and metadata from EGAD00001007575. We removed adapters from reads with Trimmomatic v0.39, then aligned trimmed reads to the GRCh38 reference genome with STAR v2.5.2, and generated read counts for Ensembl v95 gene annotations with HTseq count v0.11.1.For RNA sequence data for both PURE01 and ABACUS, we trimmed Illumina adapters with TrimGalore v0.6.5. We aligned the trimmed reads to the GRCh38.p12 reference human genome with STAR v2.7.5an = 82, we input FPKM profiles for the 1950 coding genes with the largest variance into ConsensusClusterPlus v1.52.050, and evaluated results for hierarchical, PAM, and k-means clustering, and for Pearson, Spearman, and Euclidean distances. Specifically, for runs using Pearson and Spearman distances, we input a distance matrix via as.dist), where T was the log10-transformed, median-centered FPKMs. We chose to work with a five-cluster solution with Spearman distances, PAM clustering, random subsets of samples set by pItem = 0.85, and 10,000 iterations. For PURE01 n = 113, we chose to report on a seven-cluster solution using Spearman distances, PAM clustering, pItem = 0.875, and 50,000 iterations.For PURE01 51 v3.28.1’s ‘filterByExpr’ function, we retained the 15,918 of the 19,951 coding genes that had sufficiently large RNA-Seq counts in the PURE01 n = 82 cohort . We then used DESeq252 v1.26.0 to identify genes that were differentially expressed (DEGs) between subtypes, using default parameters for pairwise contrasts. The heatmap shows FPKM gene expression levels for genes with |log2FC| > 2 and adjusted P < 10−4.We generated a heatmap for the consensus clusters Fig.  as follo12 with FPKM gene expression profiles. To transform ten Lund subtypes into five simpler Lund subtypes, we combined ‘Ba/Sq’ and ‘Ba/Sq-Inf’ into ‘Ba/Sq’; ‘GU’ and ‘GU-Inf’ into ‘GU’; and ‘UroA-Prog,’ ‘UroB,’ and ‘UroC’ into ‘Uro.’ We assessed the association of each set of (predicted) subtypes with CR/PR/NR response, using a Chi-square tests, then Bonferroni-correcting the p-values for multiple hypothesis testing.To predict Lund, TCGA, and consensusMIBC subtypes across the consensus clusters, we used the R-based BLCAsubtyping and consensusMIBC subtype classifiersn = 82 pre-treatment samples, clinical data reported CR, PR, and NR pathological responses.For PURE01 n = 84 pre-treatment samples, clinical data reported ‘pathological complete response’ (PCR) and ‘major pathological response’ (MPR) for each sample. We assigned response = CR to 20 samples that had PCR == “Yes” and MPR == “No”, response = PR to 5 samples with PCR = = “No” and MPR == “Yes”, response = NR to 57 samples with PCR == “No” and MPR == “No”, and response = Unknown to 2 samples with PCR == “Yes” & MPR == “Yes”. For the IMvigor010 cohort, for MIBC samples in the atezolizumab trial arm, n = 127 samples with relapseID “relapse” were assigned as non-responders (NR), while n = 144 samples with relapseID = “nonrelapse” were assigned as complete responders (CR).For ABACUS n = 30 CR (complete responder) vs. n = 33 NR (non-responder) tumor samples from the PURE01 pre-treatment cohort (n = 82), we used FPKMs for 15,488 expressed coding genes. We sorted the gene symbols by the signal-to-noise ratio (S2N). Given two groups of samples , we calculated S2N for each gene as the difference in mean FPKM in the two groups, divided by the sum of FPKM standard deviations53. We then used tmod v0.46.2, in R v4.1.2, to run CERNO tests on the ranked gene-symbol vector and on its reverse54. We tested 50 MSigDB v7.2 Hallmark19 gene sets and, separately, 20 gene sets which were available as Imvigor210CoreBiologies_1.0.0.tar.gz from research-pub.gene.com/Imvigor210CoreBiologies. We reported tmod evidencePlots and tabular results, typically showing only gene sets with AUC > 0.60 and FDR < 0.01.For GSEA with 20, we used CERNO tests54, typically with qval = 0.05, to identify enriched and repressed gene sets for each subtype or predicted subtype. We summarized gene set results with ‘dot’ diagrams that we generated with ComplexHeatmap v2.6.255, setting dot radii to be proportional to the CERNO areas under the curve (AUCs).For each PURE01 expression subtype, we ranked ~15.5 thousand expressed protein-coding genes by the signal-to-noise ratio (S2N). For a similar analysis with predicted subtypes in ABACUS, we used ~15.8 thousand coding genes. Then, working with 50 MSigDB Hallmark gene sets and the twenty-gene set from Mariathasan et al.To summarize these results, we thresholded CERNO results at pAdj  = 0.1, then, for Fig. 55. For comparisons of mutation frequencies between the different subtypes, we used R’s Fisher’s Exact test unless otherwise noted. In Fig. p-values that we Bonferroni-corrected for multiple hypothesis testing; in Fig. p-values to FDRs.For PURE01, DNA was extracted from FFPE tissue from pretherapy transurethral resection of the bladder (TURB) samples, and a hybridization-capture panel of approximately 400 cancer-related genes was sequenced to a median coverage of 743x, then analyzed for mutations and copy number alterations by Foundation Medicine , as described previously. For ABACUS, we downloaded mutation and copy number data from EGA as dataset EGAD00001006201. We generated oncoprints of mutations and copy number variations using ComplexHeatmap v2.6.252 to rank genes by differential expression in samples in which a selected gene was mutated vs. samples with the wild-type gene. We processed each list of DEGs with tmod v0.46.2, using MSigDB Hallmark v7.5.1 gene sets as an input, to identify gene sets that were enriched or repressed in the mutated samples relative to the wild type. We used ComplexHeatmap v2.10.0 to generate dot graphics representing the area-under-the-curve (AUC) values from a tmod GSEA analysis for each gene mutation.To understand biological pathways impacted by mutations in a set of selected genes, we used DESeq2 v1.30.0p-values. For PURE01, we calculated times-to-events as follows: if RELAPSE == 1, time = Time to recurrence; otherwise, time = Time to last follow-up. Times were from the date of the first pembrolizumab to the date of relapse . Prior to KM calculations, we censored data for status and times-to-event to 24 months. For the ABACUS pre-treatment cohort (n = 84), and the Atezolizumab arm of the MIBC samples in the IMvigor010 cohort, we used clinical DFS data.For time-to-event analysis, we used the R survival package v3.2–7 to generate Kaplan–Meier (KM) plots and calculate log-rank 25 and with ESTIMATE v1.0.1327 (bioinformatics.mdanderson.org/estimate/rpackage.html). To generate heatmaps for MCP-counter, we log2-transformed the FPKM inputs to MCP-counter result for a cell type (adding one); median-centered each record; removed results for NK cells, Endothelial cells, and Neutrophils because they had very low scores; then generated a heatmap with ComplexHeatmap v2.6.2, using the original CCP heatmap column order, and splitting columns by consensus clusters. We re-ordered subtypes in the PDF file by hand, using Affinity Publisher v1.9.3. For ESTIMATE results, we median-centered the output scores, then generated a heatmap with ComplexHeatmap v2.6.2. We compared ESTIMATE and MCP-counter results to distributions of digital spatial profiling (DSP) data for 71 proteins, SNR-normalized, for areas of interest (AOIs) corresponding to each slide’s tumor microenvironment (TME) . To test differences in ESTIMATE ImmuneScore and StromalScore for n = 113 subtypes S6 vs. S7  for inflammatory CAFs (iCAFs) and five  for mCAFsn = 82 PURE01 pre-treatment samples with TRUST457 v0.2.054, then processed the TRUST4 output with the immunarch v0.6.5 R package . In TRUST4’s output, we separated the mixed TCR and BCR receptor sequences and removed out-of-frame, partial, and erroneous CDR3 amino acid sequences. While the data contained too few TCR sequences to allow estimating TCR diversity, it contained a large enough number of BCR sequences to support robustly estimating BCR repertoire diversity. We assessed gene usage for IGH, IGK, and IGL subrepertoires, retaining the n = 74, 75, and 66 of 82 samples that had at least 100 clonotypes for each subrepertoire, and fitting generalized linear models against overall response rate  vs. NR (n = 33).We processed RNA-Seq data for p-value for PD-L1(+) vs. subtypes with a Fisher exact test. For ABACUS, using PDL1_IC (by immune cells) data from a Ventana SP142 IHC assay , we scored samples as PD-L1(+) when PDL1_IC was 2+.For PURE01, using data from a Dako 22C3 immunohistochemical (IHC) assay, we scored samples as PD-L1(+) if the combined positive score (CPS) was >10%. We set “Neg” CPS values to 0.0 and deleted the “%” and “<” symbols. We calculated a covariate independence 59. The TFs are available from RTN’s ‘tfsData’ object as ‘Lambert2018’. Briefly, RNA-Seq expression profiles were used to estimate the associations between a TF and all of its potential targets. We used two metrics to identify potential TF-target associations: Mutual Information (MI) and Spearman’s correlation. MI-based inference indicates whether a TF’s expression is informative of the expression of a potential target gene, while Spearman’s correlation indicates whether the ‘direction’ of an inferred TF-target association is positive or negative. Associations whose MI were below a threshold were eliminated by permutation analysis , and unstable interactions were then removed by bootstrapping. RTN regulons were additionally processed by the ARACNe algorithm, which uses the data processing inequality (DPI) theorem to enrich the regulons with direct TF-target interactions58. The regulatory network construction and analysis were performed in R v3.x .We calculated a transcriptional regulatory network for 1612 transcription factors (TFs) using RTN v2.13.2, as described elsewheren = 404), we used two approaches. In the first approach, we compared TF-target associations inferred for the PURE01 n = 82 KDM5B regulon targets  with those for the TCGA-BLCA RNA-Seq data14. We downloaded the batch-corrected RNA-Seq data for the TCGA-BLCA cohort (n = 404) from gdc.cancer.gov/node/977, and used RTN to compute Mutual Information (MI) between KDM5B and each of its inferred PURE01 regulon target genes, in the TCGA-BLCA RNA-Seq data, using RTN’s tni.permutation function to assign a BH-adjusted p-value to each MI value. We then stringently filtered by the TCGA-BLCA adjusted p-values (padj < 0.001) and required that each PURE01 gene symbol be present in the TCGA-BLCA RNA-Seq data. This retained 59 (82%) of KDM5B’s 72 PURE01 target genes  into 20501 unique gene symbols, then ran ESTIMATE on the log2-transformed RNA-Seq data.To calculate ESTIMATE ImmuneScores for the PURE01 Finally, we generated scatterplots of KDM5B regulon activity vs. ESTIMATE ImmuneScore, for PURE01 and TCGA-BLCA results, coloring scatterplot dots (each dot represented a cohort sample) by the activated/undefined/repressed activity status of the KDM5B regulon in each respective cohort. To complement each scatterplot, we generated rank-sorted KDM5B activity profiles for each cohort, using red/gray/blue to represent activated/undefined/repressed KDM5B regulon activity status. We note that these rank-sorted activity/status barplots are a specific representation of the KDM5B regulon activity across a cohort, and the ranges of repressed/undefined/activated samples in a barplot are not expected to match the corresponding ranges of ImmuneScore values in the corresponding scatterplot.33, and generated a ‘full’ PPI network, using the highest confidence (0.90), and allowing up to 10 first and second shell interacting proteins. We exported a table of Reactome60 pathways that were enriched in this network, then displayed the statistical significance values of the top six Reactome pathways as a horizontal barplot, using a custom R script.Given KDM5B’s regulon, we input gene symbols for the 56 negative targets into STRING v11.0 (string-db.org)We chose representative CR and NR slides for each luminal subtype, and a representative CR slide for each basal/mesenchymal subtype: S1 , S2 , S4 , S3 (CR = sample 50), and S5 (CR = sample 34). 61, slides were deparaffinized and rehydrated by incubating in CitriSolv (3 × 5 min), 100% ethanol (2 × 5 min), 95% ethanol (2 × 5 min), and deionized water (2 × 5 min). For antigen retrieval, slides in 1X Citrate Buffer (pH 6) were placed into a pressure cooker at high temperature and pressure for 15 min. After releasing the pressure, slides were equilibrated to room temperature for 25 min. Slides were washed , blocked with Buffer W  in a humidified chamber for 1 h at room temperature, and stained overnight with a panel of 77 oligo-conjugated detection antibodies at a final concentration of ~0.25 µg/ml for each antibody. The next day, slides were washed , postfixed in 4% PFA for 30 min at room temperature, washed , and nuclei were stained with 500 nM SYTO 83 (Thermo Fisher S11364) for 15 min at room temperature in a humidity chamber, and rinsed with 1X TBS-T.Using methods previously describedPrior to slides being loaded into a GeoMx Digital Spatial Profiler , they were stained with immunofluorescent antibodies to facilitate identification of the tumor and tumor microenvironment components: pan-cytokeratin  for epithelial/tumor cells; CD3  for T cells; and smooth muscle actin  for muscle. Once scanned, the digital image for each tissue section was assessed by a pathologist, and regions of interest (ROI) were selected that were representative of the tumor morphology and tumor microenvironment for each sample.Supplementary Data For each slide (see above), DSP data were generated for regions-of-interest (ROIs), and each ROI could yield one or more areas-of-interest (AOIs) after color filtering  IgG. For the 21 CR AOIs for S1, S2, and S4 Fig. , and 42 25 were combined with total RNA to isolate poly(A) + transcripts following the NEBNext Poly(A) mRNA Magnetic Isolation Module manual . All library construction followed the manufacturer’s instructions for the NEBNext Ultra II Non-Directional RNA Library Prep Kit for Illumina . Final library quantity was assessed by a Qubit 2.0 , and quality was assessed by TapeStation D1000 ScreenTape . The final average library constructs and insert sizes were, respectively, approximately 380 bp and 260 bp. Illumina 8-nt dual indices were used for demultiplexing. Libraries were equimolar pooled based on QC values. The pools were sequenced on an Illumina NovaSeq S4  as 150-bp PE reads, generating 20 M read pairs per sample. BCL2fastq was used to convert BCL files generated by the sequencer into fastq format, and fastq files were processed using the Ceto pipeline created by Elizabeth Bartom (https://github.com/ebartom/NGSbartom). Briefly, RNA sequencing reads were aligned to the GRCh38 reference genome using STAR v2.7.5. Gene expression was quantified using HTSeq v0.11.1, and differential gene expression profiles were generated using edgeR v3.16.551. HTSeq FPKM values were used for downstream analysis in R v3.6.3. Volcano plots and dotplots were generated using ggplot2 v3.3.2, and gene set enrichment analysis was performed using tmod v0.46.2 CERNO tests and Hallmark gene sets from MSigDB v7.219. A Venn diagram showing the overlap of significantly expressed genes between KDM5i C70 and FGFRi treated cells were manually generated using numbers calculated from the HT-seq/edgeR output files. Coverage profiles were generated using deepTools and were displayed in the UCSC genome browser.RT4 cells were purchased from ATCC and cultured in McCoy’s medium (Thermo Fisher Scientific) with 10% FBS. RT4 cells were treated with DMSO, 5 µM KDM5i KDM5-C70  for 72 h with or 5 µM FGFRi Erdafitinib  for 48 h. All experiments were done with biological triplicates. RNA was isolated using a Qiagen RNeasy Mini kit, and on-column DNAse digestion was performed to avoid DNA contamination. RNA quality was assessed by a High Sensitivity RNA Tapestation  and quantified by a Qubit 2.0 RNA HS assay . Paramagnetic beads coupled with oligo d(T)63. We also tested a random forest (RF) model and a simpler nearest-centroid (NSC) model, all in 3-fold repeated cross-validation runs. Consistently, the best-tuned elastic net models outperformed both alternative models  to fit four different kinds of models: nearest shrunken centroids , elastic net (glmnet v4.1-1), multi-class random forest, and the combination of five one-vs-all random forest models (ranger v0.12.1). We evaluated each model using repeated cross-validation  and, using levels suggested by the R package dials (v0.0.9), tuned parameters to maximize cross-validation accuracy. For NSC, we tuned thresholds (10 levels); for the elastic net, we tuned α (10 levels) and λ (20 levels); and for the random forest, using ‘gini’ as the splitting rule, we tuned the number of trees (6 levels between 2 and 18) and minimum node size (4 levels between 2 and 15). From this assessment, we chose a 100-gene GLMnet model with parameters α = 0.1 and λ = 0.05, which had an estimated cross-validation accuracy of 88%. The R-based classifier, with installation and usage instructions, is available at https://github.com/csgroen/mibcCPIclass.We used log2-transformed FPKM RNA-Seq data from the n = 84 samples corresponded to one of the five PURE01 expression subtypes.We applied the 100-gene classifier to log2-transformed FPKM RNA-Seq data from the ABACUS pre-treatment cohort, predicting probabilities that each of the 52 PURE01 RNA-Seq data in the same manner as described previously and used these classifiers to predict in VST-transformed ABACUS RNA-Seq data. We found no benefit in using VST normalization for building the single-sample classifier . Given this, we recommend using log2(FPKMs) with the GLMnet classifier, rather than VST-transformed RNA-Seq data.For comparison, we trained 100-gene and 500-gene GLMnet and random forest classifiers with VST-transformed64 with the following modifications. To reduce false positive readouts from cells with low gene counts, we removed from the analysis cells with fewer than 200 unique features and features that were identified in fewer than 200 cells, as well as cells for which greater than 20% of UMIs mapped to mitochondrial genes and greater than 5% of UMIs mapped to ribosomal genes. Post-filtering, the remaining features  across 15922 unique cells were scaled and centered to define the relative expression of features across cells. Next, we used the SCSA65 package to annotate cell types within each cluster. After identifying the main cell lineages , we used unsupervised clustering to refine the cell states identified for the epithelial cell lineage, identifying nine sub-clusters. We generated a luminal gene expression score using scanpy.tl.score genes and 16 luminal markers: CYP2J2, ERBB2, ERBB3, FGFR3, FOXA1, GATA3, GPX2, KRT18, KRT19, KRT20, KRT7, KRT8, PPARG, XBP1, UPK1A, and UPK2, which are associated with the luminal subtype in muscle invasive bladder cancer14. Next, we used the top ten principal components as input to Louvain graph-based clustering, with a resolution parameter of 0.4. All gene expression and clustering results were visualized using the UMAP function in Scanpy. Regulon analysis was conducted using pyScenic v0.11.266. For the epithelial cell sub-cluster, an AnnData object was created and used as an input to the pyScenic pipeline. The count matrix was used as an input to pyScenic and processed with default parameters. Next, regulon prediction was performed using cisTarget with default parameters and three hg38 .feather ranking databases. pyScenic runs using default settings detect only activating targets and not repressive targets. We modified this behavior by using the –all-modules option in the -ctx command to generate a list of all enriched motifs for activating and repressive targets, then used aucell to quantify the activity of each gene signature across single cells in the epithelial cell sub-cluster. Finally, results from pyScenic were integrated into a Scanpy AnnData object. For marker genes in each epithelial cell sub-cluster, we show the top five genes, ranked by adjusted p-value, requiring a minimum absolute value log2-fold change of 1.5.Three fresh bladder tumors from patients undergoing TURBT  were collected under IRB STU00204352. Tissue was cut into small pieces and enzymatically digested to achieve a single-cell suspension. Next, cells were re-suspended in 0.04% BSA in PBS solution and loaded into a 10X Genomics Chromium platform for Gel Bead-In Emulsion (GEM) generation and barcoding. Samples were processed using the Chromium Single GEM Single Cell 3’ reagent kit v3.1, following the manufacturer’s instructions. Libraries from three 10X channels were pooled and sequenced on one lane of an Illumina HiSeq X as 150-bp paired-end reads. Read sequences were demultiplexed to generate FASTQ files, which were further processed using 10X Genomics’ Cell Ranger v4.0.0 pipeline. Reads were aligned to the GRCh38 genome, and GENCODE v32/Ensembl 98 gene counts were quantified using the counts command in Cell Ranger to generate feature-barcode matrices. Samples were further analyzed in Python3.8 using Scanpy v1.8.267. Briefly, 50,000 cells were washed in cold PBS and lysed. Transposition was performed at 37 °C for 30 min using Illumina Tagment DNA Enzyme and Buffer kit . DNA was isolated using the Qiagen MinElute Reaction Cleanup kit and was amplified for five cycles. The final number of additional PCR cycles was evaluated by real-time PCR. The final library was purified using double-sided AMPure XP beads purification to remove primer dimers and >1000 bp fragments. Library quality was assessed by running 1 µl of library on a High Sensitivity DNA Bioanalysis chip . The DNA concentration was quantified by QuBit. Samples were sequenced on an Illumina HiSeq as 150-bp paired-end reads, with an average of 92.5 M paired-end reads generated per sample.Replicate samples from RT4 cells treated with DMSO or 5 µM KDM5i-C70 for 3 days were processed for chromatin profiling by ATAC-sequencing using a previously described protocol68, all positive-strand reads were shifted 4 bp downstream, and all negative-strand reads were shifted 5 bp upstream using the alignmentSieve tool in deepTools v3.1.1. Shifted reads were pooled by condition, and peaks were identified using MACS2 v2.1.0. Profile plots and heatmaps for the ATAC-seq signal profiles around transcription start sites (TSS) were generated using plotHeatmap and plotProfile tools in deepTools v3.1.1 by pooling DESeq2 v1.30.0 normalized read counts for each condition. ATAC-seq coverage tracks were generated using deepTools and were visualized using the UCSC genome browser.For the raw sequencing data, quality metrics were generated using FastQC v0.11.5. Cutadapt v3.3 was used to trim adapter sequences. Paired-end-reads were aligned to the GRCh38 reference human genome using Bowtie2 v2.4.1. Non-uniquely mapped reads were removed. Using a previously described method that accounts for Tn5 transposase binding during analysis69 to identify perturbed gene expression signatures that were similar (vs dissimilar) to the gene expression signatures of each of these subtypes. For each subtype, we ranked coding genes by the signal-to-noise ratio (SNR) and submitted gene symbols for the top 150 high-SNR and 150 low-SNR genes to the expression Query tool (https://clue.io) using the Touchstone v1 L1000 dataset70. Results from each query assigned a connectivity score between −100 to 100 to each CMap perturbagen. A positive score indicated that the query signature was similar to a perturbagen signature , while a negative score indicated the opposite . We ranked perturbagens based on the connectivity scores, and generated score-rank plots with Graphpad Prism, then manually generated per-subtype heatmaps of selected perturbagens.To identify potential drug targets for S6 and S7 subtypes, and to compare PURE01 subtypes S1-5 and ABACUS predicted subtypes S1-5, we utilized the Connectivity Map (CMap) LINCS gene expression resourceP-values reported were corrected for multiple hypothesis testing using Benjamini–Hochberg (FDR) or Bonferroni approaches, where noted. Boxplots were generated in R using default settings. Each box spans the 25th to 75th percentile range in the data  and shows a horizontal line at the median value. Whiskers extend 1.5 times the IQR from the box, and dots, where shown, show all data values, including minima  and maxima . Violin plots (Fig. We did calculations in R, typically with v4.x. Statistical tests were typically done with R’s two-sided fisher.test, kruskal.test, and chisq.test functions. ots Fig.  were genFurther information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15Supplementary Data 16Supplementary Data 17Supplementary Data 18Supplementary Data 19Supplementary Data 20:Supplementary Data 21:Supplementary Data 22Supplementary Data 23Supplementary Data 24Supplementary Data 25