While animal and human decision strategies are typically explained by model-free and model-based reinforcement learning (RL), their choice sequences often follow simple procedures based on working memory (WM) of past actions and rewards. Here, we address how working memory-based choice strategies, such as win-stay-lose-switch (WSLS), are represented in the prefrontal and motor cortico-basal ganglia loops by simultaneous recording of neuronal activities in the dorsomedial striatum (DMS), the dorsolateral striatum (DLS), the medial prefrontal cortex (mPFC), and the primary motor cortex (M1). In order to compare neuronal representations when rats employ working memory-based strategies, we developed a new task paradigm, a continuous/intermittent choice task, consisting of choice and no-choice trials. While the continuous condition (CC) consisted of only choice trials, in the intermittent condition (IC), a no-choice trial was inserted after each choice trial to disrupt working memory of the previous choice and reward. Behaviors in CC showed high proportions of win-stay and lose-switch choices, which could be regarded as “a noisy WSLS strategy.” Poisson regression of neural spikes revealed encoding specifically in CC of the previous action and reward before action choice and prospective coding of WSLS action during action execution. A striking finding was that the DLS and M1 in the motor cortico-basal ganglia loop carry substantial WM information about previous choices, rewards, and their interactions, in addition to current action coding. Working memory (WM)-based decision strategies, such as win-stay-lose-switch (WSLS), are widely observed in humans and animals. To address neuronal bases of these strategies, we recorded neuronal activities of rat prefrontal and motor cortico-basal ganglia loops during continuous/intermittent choice tasks. The rat choice strategy was a noisy WSLS in the continuous choice condition, whereas non-WSLS was selected in the intermittent choice condition. In the continuous choice condition, the primary motor cortex (M1) and the dorsolateral striatum (DLS) in the motor loop more strongly conveyed information about previous choices, rewards, and their interactions than the medial prefrontal cortex (mPFC) and the dorsomedial striatum (DMS) in the prefrontal loop. These results demonstrate that the motor cortico-basal ganglia loop contributes to working memory-based decision strategies.Human and animal decision-making processes can be modeled by reinforcement learning (RL) theory, in which agents update the expected reward for each choice . HoweverPrevious studies tested how availability of WM affected choice strategies by increasing the number of visual stimuli to remember , requiriWhile the basal ganglia play a major role in model-free reinforcement learning , the neun = 6; 260–310 g body weight; 16–37 weeks old at the first recording session) were housed individually under a light/dark cycle . Experiments were performed during the light phase. Food was provided after training and recording sessions so that body weights decreased no lower than 90% of initial levels. Water was supplied ad libitum. The Okinawa Institute of Science and Technology Graduate University Animal Research Committee approved the study.Male Long–Evans rats . The chamber was equipped with three nose-poke holes on one wall and a pellet dish on the opposite wall A. Each nAnimals were trained to perform a choice trial and a no-choice trial using nose-poke responses. In either task, each trial began with a tone presentation . When the rat performed a nose-poke in the center hole for 500–1000 ms, one of two cue tones  was presented B.After onset of the choice tone , the rat was required to perform a nose-poke in either the left or right hole within 2 s after exiting the center hole. If the rat exited the center hole before the offset of the choice tone, the choice tone was stopped. When the rat nose-poked either the left or right hole, either a reward tone  or a no-reward tone  was presented probabilistically, depending on the selected action. The reward tone was followed by delivery of a sucrose pellet (25 mg) in the food dish. If the rat did not perform a nose-poke in either the left or right hole within 2 s, the trial was ended as an error trial after presentation of an error tone .For the no-choice tone , the rat was required not to perform left nor right nose pokes during 2 s after the exit from the center hole. Then, the trial was correctly finished by presentation of the no-reward tone. In this no-choice trial, the rat could not obtain any pellets, but if the rat could not perform this trial correctly, that is, if the rat incorrectly performed left or right nose-poke despite the no-choice tone, the trial was ended as an error trial after the error tone presentation, and the no-choice trial was repeated again in the next trial.We designed the continuous condition (CC) that consisted only of choice trials, and the intermittent condition (IC) that had a no-choice trial inserted after every choice trial C. A blocThe first (CC) and the third (CC) blocks were terminated when the choice frequency of the 75% reward side in the last 10 choice trials reached 80% A. The seAfter rats trained to perform the CC and the IC tasks, they were anesthetized with pentobarbital sodium  and placed in a stereotaxic frame. The skull was exposed and holes were drilled in the skull over the recording site. Four drivable electrode bundles were implanted and fixed in the DMS in the left hemisphere , the DLS in the right hemisphere , the mPFC in the left hemisphere , and the M1 in the right hemisphere  using pink dental cement with confirmed effects on the brain .An electrode bundle was composed of eight Formvar-insulated, 25-μm bare diameter nichrome wires (A-M Systems) and was inserted into a stainless-steel guide cannula . Tips of the microwires were cut with sharp surgical scissors so that ∼1.5 mm of each tip protruded from the cannula. Each tip was electroplated with gold to obtain an impedance of 100–200 kΩ at 1 kHz. Electrode bundles were advanced by 125 μm per recording day to acquire activity from new neurons.The amplitude of waveforms was <7× the SD of background noise.The firing rate calculated by perievent time histograms  was <1.0 Hz for all time bins of all PSTHs.The estimated recording site was considered outside the target.Recordings were made while rats performed choice tasks. Neuronal signals were passed through a head amplifier at the head stage and then fed into the main amplifier through a shielded cable. Signals passed through a band pass filter (50–3000 Hz) to a data acquisition system , by which all waveforms that exceeded an amplitude threshold were time-stamped and saved at a sampling rate of 20 kHz. The threshold amplitude for each channel was adjusted so that action potential-like waveforms were not missed while minimizing noise. After a recording session, the following off-line spike sorting was performed using a template-matching algorithm and principal component analysis with Spike2 : recorded waveforms were classified into several groups based on their shapes, and a template waveform for each group was computed by averaging. Groups of waveforms that generated templates that appeared to be action potentials were accepted, and others were discarded. Then, to test whether accepted waveforms were recorded from multiple neurons, principal component analysis was applied to the waveforms. Clusters in principal component space were detected by fitting a mixture Gaussian model, and each cluster was identified as signals from a single neuron. This procedure was applied to each 50-min data segment. If stable results were not obtained, data were discarded. Then, gathered spike data were refined by omitting data from neurons that satisfied at least one of the three following conditions:Furthermore, considering the possibility that the same neuron was recorded from different electrodes in the same bundle, we calculated cross-correlation histograms with 1-ms time bins for all pairs of neurons that were recorded from different electrodes in the same bundle. If the frequency at 0 ms was 10× larger than the mean frequency  and their PETHs had similar shapes, either one of the pair was removed from the database.After all experiments were completed, rats were anesthetized as described in the surgery section, and a 10-μA positive current was passed for 30 s through one or two recording electrodes of each bundle to mark their final recording positions. Rats were perfused with 10% formalin containing 3% potassium hexacyanoferrate (II), and brains were carefully removed so that the microwires did not cause tissue damage. Sections were cut at 60 μm on an electrofreeze microtome and stained with cresyl violet. Final positions of electrode bundles were confirmed using dots of Prussian blue. The position of each recorded neuron was estimated from the final position and the distance that the bundle of electrodes moved. If the position was outside the DMS, DLS, PL, or M1, recorded data were discarded.βi is the regression coefficient for each variable (regressor), a(t)r(t)c (0 ≤ c ≤ 1) specifies the decay rate of past actions and rewards. For each setting of c, regression coefficients βi are derived by the “fitglm” function of MATLAB and the optimal c was selected between 0 and 1 with a line search. The optimal m was determined by comparing adjusted R2 with c set at the optimal value. The adjusted R2 became maximal with m = 9.We performed logistic regression analysis to examine the influence of past actions and outcomes on the next choice using the regression model:t, μ(t):We performed Poisson regression analyses to examine what kinds of variables were encoded in neuronal spikes. The first Poisson regression analysis considered a Poisson model in which the number of spikes at a certain phase is sampled from a Poisson distribution with the expected number of spikes at trial μ(t) is represented byβi is the regression coefficient for each explanatory variable (regressor). b(t) is the monotonically increasing factor, namely, b(t) =t, which is inserted to capture task-event-independent monotonic increases or decreases in firing pattern. a(t) r(t) c(t)d(t) is the time duration of a phase. We also analyzed the differential reward responses of neurons following ipsilateral or contralateral action choice using a(t)×r(t) a(t) and r(t) separately.Optimal regression coefficients are determined so that the objective function, the log likelihood for all trials, is maximized.β represents a set of coefficients. For this calculation, a function in MATLAB Statistics and Machine Learning Toolbox “fitglm” was used.Here, μ(t). Then, we used the Bayesian information criterion (BIC):β* is the optimized β that maximizes the log likelihood L. k is the number of parameters, and n is the trial number in a session. BIC can be regarded as a fitting measure taking into account the penalty for the number of parameters (the number of β) in the model.Next, we found the minimal necessary regressors in (A) to predict 5 models for all combinations. We calculated the BIC for all possible models, and then we selected a set of regressors that showed the smallest BIC. Then, we tested the statistical significance of each regression coefficient in the selected model using the regular Poisson regression analysis. If p < 0.01, the corresponding variable was regarded as being coded in the firing rate. This variable selection was conducted independently for each neuron and for each time bin.Better models have smaller BICs. Because the full regression model includes five regressors (including the constant variable for 0), we can consider 2t) is the variable taking 1 if the rat selected left and rewarded in the trial t. Otherwise, it takes 0. L0(t), R1(t), and R0(t) are the variables taking 1 only for L0, R1, or R0, respectively.Then, we used the following full model to find neuron coding actions, rewards, interactions between actions and rewards, or prospective action using a WSLS strategy:Strictly speaking, this final variable R0(t) is unnecessary, because R0(t) can be represented by 1 – L1(t) – L0(t) – R(t). Therefore, we were unable to find a unique solution of the full model that minimized likelihood. So, we considered 26–1 combinations of coefficients without the full model, and found the model with the smallest BIC.According to the variables included in the best model and the signs of these coefficients, we classified neurons into five types: neurons coding action in the previous choice trial, neurons coding reward in the previous choice trial, neurons coding action-reward-interaction (A×R) in the previous choice trial, prospective-action-coding, and the noncoding neurons .If the activity codes action and not reward, coefficients of the model for L1 and L0, or for R1 and R0 should have the same signs. If the activity codes reward, but not action, coefficients of L1 and R1, or L0 and R0, should be necessary and should have the same signs. If the activity codes an interaction between action and reward (A×R), one coefficient among L1, L0, R1, and R0, should be necessary.If activity codes a prospective action by a WSLS strategy (Pros Action), the coefficient of L1 and R0 , or L0 and R1  should be necessary. Because multiple tests changed the ratio of false positive errors, thresholds indicating a significant proportion of these information-coding neurons were calculated so that the ratio of false positives becomes 0.05 . To compare proportions of these information-coding neurons between CC and IC, data in CC and IC were analyzed separately with this Poisson regression analysis.T = 100 ms time window in each trial, the neuronal activity was defined as a random variable F, taking the number of spikes from 0 to maxf= T/2. X is a random variable taking x1, x2, x3, or x4, corresponding to previous A×R: left rewarded, left nonrewarded, right rewarded or right nonrewarded, respectively. Mutual information between F and X is defined by the following:To elucidate when and how much information from previous A×R was coded in neuronal activity, the mutual information between neural firing and previous A×R was calculated using the method described previously , 2015a. For each neuron, mutual information (bits) was estimated  recorded over a total of 78 sessions in six rats. The minimum and maximum number of trials per session were 436 and 1105, respectively.t test, Mann–Whitney U test, Wilcoxon signed-rank test, binominal test, and χ2 tests with or without Bonferroni’s multiple comparison tests. Differences were considered statistically significant when p < 0.05. See Results for details.We used appropriate statistical tests when applicable, i.e., paired or unpaired To investigate whether and how the availability of working memory (WM) influences the choice strategy of rats, we designed a choice task composed of choice trials and no-choice trials. In the continuous condition (CC), choice trials were repeated and in the intermittent condition (IC), a no-choice trial was inserted between each pair of choice trials . This inWe trained six Long–Evans rats to perform the choice task B. When tWe compared choice sequences in CC and IC in response to the change of reward probabilities C. In CC,p = 1.6e−14 in CC and p = 1.1e−7 in IC, Wilcoxon signed-rank test), confirming that choice behavior in both CC and IC adapted to reward-probability changes.The choice probability of 75% reward side in each session was distributed between 0.5 and 0.75 with a median value of 0.632 in CC, and between 0.4 and 0.65 with a median of 0.538 in IC D. In bot2: CC; 1.87, IC; 0.42, p = 3.3e-12, Wilcoxon signed-rank test; c: CC; 0.23, IC; 0.62, p = 0.048 with no significant difference in the coefficient β3: CC; 0.13, IC: 0.19, p = 0.49). These results indicate that rats recognized the side with a larger reward probability and changed their action selection in both CC and IC, while insertion of no-choice trials in IC made learning slower.To investigate choice strategies in more detail, using logistic regression, we examined the influence of past actions and outcomes on the subsequent choice . The interaction of the action and the outcome of the last trial affected the subsequent action more strongly in CC than in IC (median of coefficient for a(t-1)*r(t-1) βp = 1.2e-16, Mann–Whitney U test; lose-switch: 0.82 vs 0.51, p = 2.6e-23). Since behaviors in CC showed high P(stay| win) and P(switch| lose), these could be regarded as “a noisy WSLS strategy” (cf. the regular WSLS is deterministic).Next, to test the hypothesis that the choice strategy is closer to WSLS in CC than in IC, we calculated WSLS indices, composed of the win-stay ratio P(stay| reward), the frequency that the rats chose the same action after the rewarded choice trial, and the lose-switch ratio P(switch| no-reward), the frequency that the rats switch the action after no-reward trials. WSLS indices calculated for each session are plotted two-dimensionally, P(stay| reward) versus P(switch| no-reward) F. WSLS iWe recorded neuronal activity in DMS, DLS, mPFC, and M1 of rats performing the choice task. Each rat was implanted with four bundles of eight microwires. After all experiments were completed, locations of bundles were confirmed by Nissl staining A,B. StabRepresentative examples of spike perievent time histograms (PETHs) with intertrial time alignment  are showThe DLS neuron in The mPFC neuron in PETHs of the M1 neuron F showed To see the distribution of peak timings of all DMS neurons, PETHs for all DMS neurons were normalized and represented by color, with neuron indices sorted on the basis of peak activity timing G. ActiviWe next applied Poisson regression analyses of spike counts in each phase to quantify neuronal coding of actions, rewards, and task conditions. Note that action coding in phase 1 and 2 represents an action command or plan before an action is performed, and that the outcome regressor means reward prediction in phases 1–3 A. Averagp = 0.037, mPFC: 1.3%, M1: 13%, p = 9.5e-05; phase 4: DMS: 27%, DLS: 37%, p = 0.039, mPFC: 22%, M1: 38%, p = 0.0032, χ2 test with Bonferroni correction). The proportion was significantly larger in M1 than in the mPFC in phase 3 . A contralateral bias was clearly observed during action (phase 3) in M1, but also in DMS neurons . These results indicate that the motor loop is more strongly involved in action preparation, execution, and memory than the prefrontal loop.The proportion of neurons correlated with action was significantly larger in the motor loop than in the prefrontal loop in phases 2 and 4 (p = 0.044). Neurons in the DLS and M1 were significantly more activated by reward than no-reward experience . These results indicate that DLS neurons can more strongly distinguish reward-associated sensory stimulus than those in the DMS.The proportion of neurons correlated with the outcome variable was significantly larger in the DLS than in the DMS in phase 4 . DLS had significantly stronger sensitivity to contralateral reward than ipsilateral reward in both CC and IC conditions  was <10% (data not shown), which suggests that neuronal activities did not directly encode task condition, but does not exclude the possibility that information coding of action or outcome in the previous choice trial was modulated by task conditions.Next, to examine how information from choice trials transferred to the subsequent choice trial with and without WM interference, we applied a Poisson regression analysis to spike data in CC and IC separately . Coding of the interaction of the previous action and reward was seen in all four areas, with more DMS neurons in CC than in IC .In phase 1 A, on entp = 0.044, M1: CC; 5.3%, IC; 0.40%, p = 0.044). More neurons in DLS, mPFC, and M1 neurons coded the interaction of the previous action and reward in CC than in IC .In phase 2 B, while p = 2.2e-05, DLS: CC; 6.7%, IC; 0%, p = 0.00057, M1: CC; 6.9%, IC; 0%, p = 0.00011). Coding of the current action was seen in all four areas, with higher proportions in CC than in IC in the DMS and M1 .In phase 3 C, when rp = 3.0e-07) and the current actions  remained higher in CC than in IC, the proportion of neurons coding the previous reward was larger in IC than in CC in the DLS and M1 .In phase 4 D, when rp = 0.027; phase 2, previous action: DMS vs DLS; 0.63% vs 5.2%, p = 0.0063, mPFC vs M1; 0% vs 5.3%, p = 0.027; phase 2, previous A×R: DMS vs DLS; 11% vs 22%, p = 0.0019; phase 3, previous action: DMS vs DLS; 1.9% vs 14%, p = 2.2e-07; phase 3, previous A×R: mPFC vs M1; 8.9% vs 20%, p = 0.023; phase 4, previous action: DMS vs DLS; 0.63% vs 7.1%, p = 0.00025; phase 4, previous A×R: DMS vs DLS; 13% vs 27%, p = 0.00041, χ2 test with Bonferroni correction), although working memory is regarded as a major function of the prefrontal cortex; 35–48% neurons coding previous A×R in each area encoded it across phases (data not shown).Interestingly, throughout the four phases in CC, neurons coding working memory about the previous trial were more prevalent in the motor loop than in the prefrontal loop , similar the result for all trials in p = 0.042, previous A×R: DMS vs DLS; 12% vs 26%, p = 0.00029), which were not observed with all trials combined. The result confirms the role of DLS in working memory both in early and late stages of learning, with the coding in the latter related more to the next action.We further focused on the neural activities just before and after the reward probabilities were changed. We performed an analysis using the WSLS-related variables as in We developed a choice task that manipulated WM availability for rats and showed that disturbance of WM disrupted the WSLS choice strategy .Poisson regression of neural spikes showed that the proportions of neurons coding the current action before and after action choice were larger in the DLS than DMS, and in M1 than the mPFC, and that neurons in DLS showed stronger reward response following contralateral action than ipsilateral action .Before action choice, the proportion of neurons coding the previous reward was larger in CC in DLS, mPFC, and M1, and the proportion coding the previous action was larger in CC in DLS and M1. During action execution in CC, neuronal activities of DMS, DLS, and M1 represented prospective action by the WSLS strategy .Throughout the trial, working memories of previous actions, rewards, and their interactions were more prevalent in the motor loop than in the prefrontal loop . Another is to compute the next action using a WSLS rule soon after reward feedback (prospective action), that is L for L1 or R0, and R for L0 or R1 and retain that until the next choice (keeping future plan).Indeed, before action execution in CC, coding of the previous action was seen in the DLS and M1 and coding of the previous reward was seen in the DLS, mPFC, and M1 A,B. CombOur results showed for the first time that the motor loop (DLS and M1) retains that information more strongly than the prefrontal loop (DMS and mPFC) when a WM-based strategy was observed.Previous studies suggested that different types of uncertainty, such as the difference of the reward probabilities and the volatility of reward contingency, affects choice strategies and learning rates . While sAnother possible factor that might affect the choice strategy and the involvement of the motor loop is the simple repeated choice sequence acquired in the CC compared with a more complex choice/no-choice sequence acquired in the IC condition. However, the analysis of neuronal representation soon after reward contingency change still shows higher previous action coding in of the motor loop . This suIn the present study, rats were overtrained with the task before neuronal recording. It was reported that overtraining induced a shift from goal-directed to habitual behavior , which iFrom the viewpoint of information processing, keeping a future plan is more efficient in that it requires less memory capacity. Memory of future action has been termed “prospective action” in previous studies . HoweverIn conclusion, this experiment showed that the availability of WM affects choice strategies in rats and revealed WM-related neuronal activities in DMS, DLS, mPFC, and M1. A striking finding was that DLS and M1 in the motor cortico-basal ganglia loop carry substantial WM information about previous choices, rewards, and their interactions, in addition to action coding during action execution.


Fusobacterium nucleatum, and non-responders showed enrichment of fungi, as well as several bacteria. These microbes correlated with immune-related gene expression signatures. Finally, we found that models for predicting prolonged survival with immunotherapy using both microbe abundances and gene expression outperformed models using either dataset alone. Our findings warrant further investigation and potentially support therapeutic strategies to modify the tumor microbiome in order to improve treatment outcomes with ICIs.Emerging evidence supports the important role of the tumor microbiome in oncogenesis, cancer immune phenotype, cancer progression, and treatment outcomes in many malignancies. In this study, we investigated the metastatic melanoma tumor microbiome and potential roles in association with clinical outcomes, such as survival, in patients with metastatic disease treated with immune checkpoint inhibitors (ICIs). Baseline tumor samples were collected from 71 patients with metastatic melanoma before treatment with ICIs. Bulk RNA-seq was conducted on the formalin-fixed paraffin-embedded (FFPE) tumor samples. Durable clinical benefit  following ICIs was defined as overall survival ≥24 months and no change to the primary drug regimen (responders). We processed RNA-seq reads to carefully identify exogenous sequences using the {exotic} tool. The 71 patients with metastatic melanoma ranged in age from 24 to 83 years, 59% were male, and 55% survived >24 months following the initiation of ICI treatment. Exogenous taxa were identified in the tumor RNA-seq, including bacteria, fungi, and viruses. We found differences in gene expression and microbe abundances in immunotherapy responsive versus non-responsive tumors. Responders showed significant enrichment of several microbes including  Advances in immunotherapy, including immune checkpoint inhibitors (ICIs), have transformed the standard of care for many types of cancer, including melanoma. While ICIs have improved outcomes for melanoma patients, many patients suffer from primary or secondary tumor resistance. For example, at 6.5 years, the overall survival rates with ipilimumab plus nivolumab, nivolumab, and ipilimumab were 49%, 42%, and 23%, respectively, as reported in the pivotal CheckMate 067 trial . FurtherNumerous biomarkers that predict the response of melanoma to ICIs are under investigation, including those based on clinical characteristics, genomics, transcriptomics, and epigenomics. For genomics data, these predictive biomarkers include tumor mutational burden (TMB) , neoantiRecently, high-throughput transcriptome, genome, or amplicon-based sequencing data demonstrated an abundance and variety of microbes’ nucleic acids inside tumors . In someHere, we describe the use of tumor RNA sequencing (RNA-seq) to predict response to ICIs in patients with melanoma . We demo® (TCC). As part of the TCC, participants agree to have their clinical data followed over time, to undergo germline and tumor sequencing, and to be contacted in the future by their provider if an appropriate clinical trial or other study becomes available  is an alliance of 18 US cancer centers. All ORIEN alliance members utilize a standard IRB-approved protocol: Total Cancer Carevailable . TCC is In this study, we assembled RNA-seq data from the tumor samples of 71 patients with metastatic melanoma treated with ICIs. We defined durable clinical benefit  following ICIs as overall survival ≥24 months and no change to the primary drug regimen (hereafter referred to as responders).ORIEN Avatar specimens undergo nucleic acid extraction and sequencing at HudsonAlpha  or Fulgent Genetics . For frozen and OCT tissue DNA extraction, Qiagen QIASymphony DNA purification is performed, generating a 213 bp average insert size. For frozen and OCT tissue RNA extraction, Qiagen RNAeasy plus mini kit is performed, generating 216 bp average insert size. For formalin-fixed paraffin-embedded (FFPE) tissue, a Covaris Ultrasonication FFPE DNA/RNA kit is utilized to extract DNA and RNA, generating a 165 bp average insert size. RNA-seq is performed using the Illumina TruSeq RNA Exome with single library hybridization, cDNA synthesis, library preparation, and sequencing  to a coverage of 100M total reads/50M paired reads.limma (v. 3.54.0) and edgeR (v. 3.40.0) packages where genes that have log2 fold change (log2FC) greater or less than 1 and adjusted p-value ≤0.1 were considered as significant DEG. For gene set enrichment analysis (GSEA) of responders vs. non-responders, we used the Java version of gsea (v. 4.3.2) using the gene set permutation of 1000 using Hallmark gene sets or TIMEx cell types. Gene sets or cell types that have adjusted p-value <0.1 were considered significant. Normalized enrichment score (NES) and adjusted p-value were provided in the plot.RNA-seq Tumor Pipeline Analysis is processed according to the workflow outlined below using GRCh38/hg38 human genome reference sequencing and GenCode build version 32. Adapter sequences are trimmed from the raw tumor sequencing FASTQ file. Adapter trimming via k-mer matching is performed along with quality trimming and filtering, contaminant filtering, sequence masking, GC filtering, length filtering, and entropy filtering. The trimmed FASTQ file is used as input to the read alignment process. The tumor adapter-trimmed FASTQ file is aligned to the human genome reference (GRCh38/hg38) and the Gencode genome annotation v32 using the STAR aligner. The STAR aligner generates multiple output files for Gene Fusion Prediction and Gene Expression Analysis. RNA expression values are calculated and reported using estimated mapped reads, fragments per kilobase of transcript per million (FPKM) mapped reads, and transcripts per million (TPM) mapped reads at both the transcript and gene levels based on transcriptome alignment generated by STAR. RSEM pipeline and gene expressions were quantified as TPM. Gene expressions (GE) were log2(TPM+1) transformed, and downstream analyses were performed using the GE matrix. To determine differentially expressed genes (DEG) of responders vs. non-responders, we used the vegan.RNA-seq reads are used to calculate microbe abundances using the {exotic} pipeline, as described previously . BrieflyGene signature scores were calculated using the IOSig and tmesig R packages. In brief, for each published gene signature, we collected and harmonized gene names using the NCBI Entrez gene number. To quantify the published gene expression score, we first transformed the gene expressions across samples within a cohort into a Z-score. Next, we averaged the standardized Z-score across the number of genes in the signature as previously described ,23,24. TFor pathway analysis, single-sample GSEA (ssGSEA), via the ssGSEA method in the GSVA R package, was utilized to investigate the enriched gene sets in each sample. GSVA was run using the log2(TPM+1) gene expression values with Gaussian kernel. The Hallmark gene sets, TIMEx cell types, and the collected previously published gene expression signatures were used as the gene sets. The Hallmark gene sets are a curated list of gene sets that signify well-understood pathways that display reliable gene expression . The TIMrandomForest R package. Models were based on 5 sets of input data: . The age of the patients in this cohort ranges from 24 to 83; 59% were male; and 55% survived >24 months following the initiation of ICI treatment . ICI treCLEC12A, GBP1P1, CD96, CCL4, IDO1) that were over-expressed in the responders as compared to the non-responders with log2FC >1 and adjusted p-value <0.1  higher abundances of CD8+ T-cells, activated CD4+ memory T-cells, activated NK cells, and M1 macrophages relative to non-responders, who were shown to have a significantly higher amount of resting mast cells . SimilarTo further delineate the immune phenotypes of responders vs. non-responders of ICIs, we used previously published gene signatures. We collected 30 gene expression signatures from the literature that have been implicated to be predictive of ICIs . By perfWe next used our recently developed IOSig portal  to evaluFirmicutes being the most abundant phylum, followed by Uroviricota , DNA damage repairs, intrinsic cell signaling pathways , and metabolisms   . These rcolysis) .Fusobacterium nucleatum and Porphyromonas asaccharolytica, were highly correlated with the enrichment of tumor-infiltrated immune cell types, including CD8+ T cells, which are known predictors of immunotherapy response  and performed 5 rounds of 5-fold cross-validation on the melanoma cohort . The aveWe utilized tumor RNA-seq from melanoma patients to explore the tumor microbiome’s influence on clinical outcomes, specifically in response to ICIs. We observed microbes in all samples, and showed that tumors that responded to ICIs had significantly different taxa present from those that didn’t respond to treatment. Consistent with previous findings, gene expression seems to be predictive of response to ICIs. In addition, we showed that microbes are also predictive of response to ICIs, particularly when combined with gene expression, suggesting that the inclusion of microbes in these models enhances predictive ability.A correlation between the gut microbiome and response to ICIs has been consistently indicated in previous research –38. AlteWe observed the presence of microbiota in all 71 tumor samples, as is consistent with previous findings regarding the tumor microbiome ,42. Our Bacteroides fragilis biofilms on colon polyps have been found to secrete a toxin that directly damages DNA -bound peptides in melanoma presented by tumor cells could elicit immune reactivity. This intratumoral bacteria peptide repertoire could be further explored to understand the mechanism by which bacteria modulate the immune system and responses to therapy  to treatment with ICIs from those who didn’t respond. Furthermore, the microbial communities had the ability to predict response when incorporated into machine learning models. The tumor microbiome further enhanced models to predict response when combined with gene expression data. Future research has the potential to support therapeutic strategies to modify the tumor microbiome to improve ICI treatment outcomes.Supplement 1Supplement 2


RNA molecules play crucial roles in various biological processes. They mediate their function mainly by interacting with other RNAs or proteins. At present, information about these interactions is distributed over different resources, often providing the data in simple tab-delimited formats that differ between the databases. There is no standardized data format that can capture the nature of all these different interactions in detail.RNA interaction format (RIF) for the detailed representation of RNA–RNA and RNA–Protein interactions and provide reference implementations in C/C++, Python, and JavaScript. RIF is released under licence GNU General Public License version 3 (GNU GPLv3) and is available on https://github.com/RNABioInfo/rna-interaction-format.Here, we propose the  Application Programmable Interface (API) to conveniently integrate the data into bioinformatics analyses. As a consequence, the data need to be downloaded in a non-standardized form, such as in tab-delimited text format, which differs for each database. A detailed description and comparison of existing databases and formats are given in the Systems Biology Markup Language , initially developed for Cytoscape (Nested Network Format (NNF) that additionally allows to nest subnetworks for a node. In contrast, the Graph Modelling Language (GML) consists of hierarchically organized key-value pairs and allows to represent arbitrary data structures in which additional information can be attached to every object. In terms of biological networks, graphs are represented using the node and edge attributes. The JavaScript Object Notation (JSON) is a universal data interchange format that has been widely used in different applications and can also be used to describe graphs. It is human-readable and uses key/value pairs and arrays in JavaScript syntax to describe the data. It is language-independent and can be easily parsed in multiple programming languages. In recent years, there have been efforts to create a JSON graph specification to standardize the description of graphs using the JSON Schema. For that, the JSON Graph Format (JGF) (https://jsongraphformat.info/) has been introduced. At its core is a graph object that represents single or multiple conceptual graphs and contains arbitrary objects at the nodes and edges. It has certain pre-defined key/value pairs, but also allows to specify user-defined values, thereby allowing the specification of metadata objects. This makes it the most comprehensive data format that is able to capture graphs in full. However, JGF describes a specific graph structure that is not able to represent all properties of RNA interactions. Here, we present RNA interaction format (RIF), which is a general JSON-based RNA-RNA/Protein interaction format.RNAs have important functions in numerous biological processes, and they typically exert them through direct interaction with other RNAs or proteins. Although there are several databases to collect RNA-based interactions, e.g. sRNATarBase  and lists of ordered values . A RIF file is composed of a list of interaction objects that, in turn, consist of six top-level elements: ID, version, class, type, evidence, and partners. Details about the elements and their mandatory properties are summarized in The design of RIF follows some simple rules that should ascertain unambiguous parsability, portability, flexible use with different types of biological molecules, a well-defined minimum amount of mandatory information, handling of user-defined auxiliary information, simple extensibility, and the possibility to validate format compliance. Therefore, we chose ID is the identifier of the interaction within the data file. This enables direct access to a specific interaction and has been implemented as a string to ensure the most flexibility. The version attribute of type string describes the used version of the data format. In future versions, this allows for combining different versions of RIF in a single interaction file. The class attribute describes the interaction complex that can either be RNA–RNA, RNA–Protein, or Protein–RNA–RNA. The type of interaction, i.e. the molecular mechanism, is given in the type property. For example, this includes ‘base-pairing’, ‘hydrophobic’, ‘electrostatic’, or any other user-defined type.These key-value pairs are mandatory and contain general information about the interaction. evidence is an ordered list of evidence objects, which consist of mandatory key-value pairs type, method, and data that describe the supporting evidence for the interaction. The type key-value pair describes the type of evidence and can be an arbitrary term, but should be descriptive, e.g. target prediction, pull-down assay, overexpression. The method attribute declares the technique by which the supporting evidence for the interaction has been generated. This can either be a computational tools such as RNAcofold  and then visualized in standard genome browsers.We created reference implementations for C/C++, Python and JavaScript and provide a JSON schema based on draft 2020–12. In all, we implemented the functionality to read and write RIF files, to validate the data against the introduced schema, and to retrieve or modify interactions. In addition, the interactions stored in RIF can be exported as Browser Extensible Data (BED) format (RNAnue (GraphProt (PEPPI (Here, we present a general-purpose interaction format for describing RNA–RNA/Protein interactions, termed RIF, and provide reference implementations for common programming languages. RIF allows to define RNA interaction networks, and simplifies the integration from multiple sources of interaction data. For example, computational tools such as RNAnue , GraphPrFurthermore, by design the format should be easy to adapt or to extend to new types of interactions and new classes of biological molecules.btad665_Supplementary_DataClick here for additional data file.


Thylacinus cynocephalus) preserved in desiccation at room temperature in a museum collection. The transcriptional profiles closely resemble those of extant species, revealing specific anatomical features such as slow muscle fibers or blood infiltration. Metatranscriptomic analysis, RNA damage, tissue-specific RNA profiles, and expression hotspots genome-wide further confirm the thylacine origin of the sequences. RNA sequences are used to improve protein-coding and noncoding annotations, evidencing missing exonic loci and the location of ribosomal RNA genes while increasing the number of annotated thylacine microRNAs from 62 to 325. We discover a thylacine-specific microRNA isoform that could not have been confirmed without RNA evidence. Finally, we detect traces of RNA viruses, suggesting the possibility of profiling viral evolution. Our results represent the first successful attempt to obtain transcriptional profiles from an extinct animal species, providing thought-to-be-lost information on gene expression dynamics. These findings hold promising implications for the study of RNA molecules across the vast collections of natural history museums and from well-preserved permafrost remains.Paleogenomics continues to yield valuable insights into the evolution, population dynamics, and ecology of our ancestors and other extinct species. However, DNA sequencing cannot reveal tissue-specific gene expression, cellular identity, or gene regulation, which are only attainable at the transcriptional level. Pioneering studies have shown that useful RNA can be extracted from ancient specimens preserved in permafrost and historical skins from extant canids, but no attempts have been made so far on extinct species. We extract, sequence, and analyze historical RNA from muscle and skin tissue of a ∼130-year-old Tasmanian tiger ( Over the past decade, high-throughput sequencing techniques have propelled the analysis of ancient DNA (aDNA) molecules, enabling the study of genomes from extinct or extant species that lived up to around 2 million years ago  or in moAside from the well-established field of paleogenomics, and the emerging field of paleoproteomics , the anaThylacinus cynocephalus), also referred to as the thylacine. Thylacines were the largest carnivorous marsupials across the Holocene  .Considering the age and preservation status of the specimen, we anticipated a significant fragmentation in the historical RNA sequences possibly present within the tissue matrix. Therefore, we used an RNA extraction protocol targeting small RNA molecules, specifically designed for miRNA sequencing, on each of the six tissue samples obtained (see Methods). The samples were ground in liquid nitrogen and incubated in a digestion buffer to homogenize keratinous hard fibrous tissues  while miThe read length distribution of mapped trimmed reads showed a predominance of short sequences <30 nt, indicative of time-dependent fragmentation of RNA transcripts from thylacine origin. This pattern was more pronounced in skeletal muscle compared with skin samples . A smallThe observed damage profiles of RNA reads mapping to the thylacine genome were indicative of the historical nature of the tissues, displaying increased deamination and other nucleotide substitutions . This paWe aimed to determine the origins of the RNA sequences derived from thylacine tissues and distinguish endogenous reads from additional sources of contamination like other metazoans, microbes, or any other microscopic life. To accomplish this, we used a metatranscriptomic analysis based on the classification of long RNA reads (>30 nt) using KrakenUniq software  and the Monodelphis domestica), and 7% to Tasmanian devil (Sarcophilus harrisii), accounting for a total of 68% of reads assigned to these three marsupial species  that could not be classified with KrakenUniq. Instead, we mapped them directly against a custom reference NT database using a dedicated short-read mapper (see Methods). This additional analysis is crucial because we anticipate that true historical RNAs would be fragmented and therefore shorter in length. Consistent with our findings from the longer reads (>30 nt), the thylacine was the species with the highest proportion of uniquely traceable short reads, surpassing by about threefold the percentage of short reads unambiguously assigned to the most prevalent contaminant source detected, humans, in both skeletal muscle and skin tissues B,C. ThisEscherichia, Acinetobacter, Myroides, Streptomyces, Rheinheimera, and Corynebacterium. These bacteria are typically associated with environmental contamination . We discovered that most of the sequences successfully aligned to the zebrafish genome (∼80%) mapped to specific regions of its Chromosome 4, which contains numerous repetitive transfer RNAs  and ribosomal RNAs   . BecauseOne potential explanation for some of the reads assigned to nonthylacine genomes might be a technical bias in our NCBI NT database. Because humans, mice, and zebrafish are model organisms, their reference contigs may be overrepresented. Consequently, a significant fraction of reads, not necessarily of thylacine origin, might be falsely assigned to these species by default. Supporting this hypothesis, when we realigned the reads classified as zebrafish, human, and mouse contamination to the thylacine, Tasmanian devil, and opossum assemblies, a considerable fraction of them were successfully mapped . Therefore, it would be premature to dismiss the possibility of an endogenous thylacine origin for at least some of the reads assigned to other nonmarsupial species.In summary, we consistently observe a predominance of sequences originating from the thylacine genome using different complementary computational approaches, thus supporting the authenticity of the historical RNA sequences.To understand the molecular nature of the sequenced RNAs, we traced them back to the nuclear and mitochondrial genomes of the thylacine (see Methods). Most reads mapped to noncoding RNA genes, whereas a smaller proportion mapped to protein-coding genes , in agreThe incorporation of reads >30 nt is widely accepted as the lower threshold for aDNA data to avoid spurious alignments . PreviouWe further investigated an unexpected enrichment in long trimmed reads of 42 nt mapped to the thylacine assembly   by crossIn summary, we find that numerous RNA fragments can be reliably traced to thylacine protein-coding and noncoding genes.We investigated whether RNA reads mapped to protein-coding genes were concentrated on exonic regions compared with intronic regions. This is important because potential DNA contamination traces could perturb our analyses when working with sparse amounts of highly fragmented historical RNA material. Sequencing mature mRNA molecules after splicing should show an enrichment of reads mapped to exonic regions and few reads mapped to intronic regions, as opposed to the even distribution expected for DNA sequencing data. The analysis of RNA reads mapped to protein-coding loci with reliable breadth of coverage (>10%) revealed 93% and 77% of reads mapping to exonic regions in skeletal muscle and skin tissue, respectively. This finding agrees well with previous paleotranscriptomic analyses in a Pleistocene canid  and indiWe also aimed to investigate the presence of RNA reads spanning exon–exon junctions, indicative of mature intron-less mRNAs rather than nascent transcripts or DNA contamination. Approximately 1% of the RNA reads mapped to exonic regions across all annotated protein-coding loci spanned exon–exon junctions . In contIn summary, we provide evidence of the sequencing of RNA fragments from mature cytoplasmic mRNAs that are enriched in exonic sequences and span exon–exon junctions.Contrary to aDNA sequences, we expect that ancient/historical RNA fragments would map unevenly across the thylacine genome, representing variations in gene expression from distinct loci. We found that the breadth of coverage for the thylacine genome is 0.17% in skeletal muscle and 0.32% in skin. Moreover, the average depth genome-wide was ∼0.008× for skeletal muscle and ∼0.015× for skin . When anTo investigate the variation in transcriptional depth throughout the genome, we aggregated reads mapping to consecutive nonoverlapping genomic windows of 250 kb. This analysis revealed several genome-wide expression hotspots , includiWe further investigated the causes and origin of RNA reads mapping to intergenic regions, which are not expected to produce transcriptional products. Approximately 29% of mapped reads in skeletal muscle and 38% in skin were assigned to intergenic regions . To asseTTN), which also showed low coverage (28.70%), and this was reproduced in both trimmed and untrimmed reads , several were characteristic of skeletal muscle tissue metabolism and structure . The mosed reads . These tarcomere . To discarcomere , indicatLOC100913894, which corresponds to a subunit of actin alpha (ACTA); LOC100925998, corresponding to myosin heavy chain 7 (MYH7); myosin heavy chain 2 (MYH2); tropomyosin beta (TPM2); and troponin I1, slow skeletal type and troponin I2, fast skeletal type (TNNI1 and TNNI2), all of which form integral part of the sarcomere unit of striated muscle cells  and myoglobin (MB), important for muscle cell metabolism , which contribute to the structure of tissues as type I collagen constituents  and keratin 2 and 5 (KRT2 and KRT5) consistent with the epithelial nature of the skin tissue (ACTG1), a cytoplasmic actin isoform expressed ubiquitously except in muscle tissues, also showed high coverage  loci included keratin-derived transcripts such as keratin 14  were shared between the tissues  and Tasmanian devil (S. harrisii) genome assemblies. In this way, we expanded the thylacine miRNA repertoire from 62 to 325 annotated miRNA genes  . In skelS. harrisii), opossum (M. domestica), and dog (Canis familiaris) species available at MirGeneDB 2.1  and Tasmanian devil (S. harrisii) tissue atlases, including reliably profiled protein-coding mRNA genes (breadth of coverage >10%), and with shared homologous loci among sheep, Tasmanian devil, and the thylacine (N = 261)  . In thisN = 261) , as prevIn summary, the miRNA and mRNA profiles in historical samples from the extinct thylacine resemble extant modern animal counterparts, supporting the authenticity of the sequences and showing that tissue-specific expression profiles can be preserved in dried museum specimens.Species-specific miRNAs can only be confirmed through direct sequencing of their RNA molecules. Therefore, RNA sequencing data offer a unique opportunity to identify species-specific miRNA genes from extinct organisms like the thylacine. We used the miRNA discovery algorithm miRDeep2  to prediRNF144A) gene. This putative novel miRNA did not match any known noncoding RNA loci in the commonly used RNA databases (see Methods). However, mature miRNA transcripts from both stems of the precursor molecule were detected as expressed in Tasmanian devil  showed differences in the number of exonic regions between the thylacine and Tasmanian devil. Although the current thylacine annotation had three exons, the corresponding homologous locus in the Tasmanian devil assembly had four , also known as the thylacine. The recovery of RNA expression profiles no longer existing in living cells expands the possibility of delving into the biology of extinct animals. Previous studies have reported the sequencing of RNA molecules from an extremely well preserved permafrozen canid that lived in the late Pleistocene, with an estimated age of ∼14,300 yr, as well as from historical wolf skins . However, this value was more reduced for longer untrimmed reads, possibly owing to contamination from highly conserved genomic regions across species. The unexpected abundance of long (42-nt) trimmed reads mapped to the human assembly, and their reduced damage profile supports this interpretation. Moreover, the reduced number of long untrimmed reads successfully mapped to the thylacine assembly, along with their damage profiles resembling those of shorter reads, indicates time-dependent nucleotide modifications in our data, further reinforcing the antiquity and the probable thylacine origin of most sequences, whether short or long.Second, we observed distinctive abundance profiles in the skeletal muscle and skin samples, with rRNAs and tRNAs being the most abundant noncoding transcripts, as expected for eukaryote transcriptomes , and proThird, the sequenced RNA molecules showed characteristic patterns of nucleotide substitutions, similar to damage patterns observed in previous ancient RNA studies .Despite these compelling results, we observed sample-dependent variability in the recovered RNA profiles. One of the three samples per tissue generally provided most of the tissue-specific resolution in coding loci, whereas the other two had more limited profiles. Nevertheless, the overall relative tissue-specific RNA abundance was preserved, as evidenced by the tissue clustering at the miRNA and protein-coding mRNA level using both merged samples and individual samples from skeletal muscle and skin thylacine tissues. The observed differences among samples may have originated from inherent differences in RNA preservation owing to the sampling strategy, as well as technical biases introduced during RNA extraction, library preparation, or sequencing. However, for the majority of the results presented in this study, a merged sample composite for each tissue was considered in order to maximize the resolution and statistical power of our analyses. Because no specific methodologies were applied to remove highly abundant and repetitive elements of the transcriptome, the depth of coverage for RNA transcripts other than highly expressed loci, such as rRNAs or tRNAs, is expected to be low. Future developments could benefit from applying rRNA and/or tRNA fragment depletion protocols to increase the breadth and depth of coverage of low-abundant target transcripts with a similar sequencing effort.Aside from unraveling patterns of gene activity, another application of RNA sequencing is to detect molecules not present as DNA copies, such as RNA viruses. We found a limited number of RNA molecules that could be assigned to viral genomes. Their presence in such old remains suggests the potential to profile the RNA virome from specimens of extant and extinct species stored in museum dry collections. Evidence of the reconstruction of historical viral genomes has been previously reported , and traAncient/historical RNA sequencing opens an unprecedented opportunity to obtain expression evidence of still unknown loci that are virtually impossible to annotate from DNA information alone. We used several complementary approaches to annotate thylacine miRNA loci from our RNA sequence data, increasing the total number of annotated thylacine miRNAs from 62 to 325, thus bringing it on par with other extant mammalian species . In addiThe unique characteristics of ancient/historical RNA profiles provide new opportunities to gain deeper knowledge of the genomic architecture and gene expression regulation of extinct species such as the thylacine, a still unexplored area that might benefit recent efforts in the field of de-extinction . MoreoveSkeletal muscle and skin tissue samples were collected from an ∼130-yr-old embalmed desiccated thylacine specimen (NRM-MA590213), preserved at room temperature and available at the Stockholm Natural History Museum (Naturhistoriska Riksmuseet [NRM]). This adult specimen was captured on the island of Tasmania and arrived at the NRM collection in 1891 as a donation from the Australian Museum of Sydney. The exact date of death and sex are unknown. Tissue samples were obtained in triplicate and stored at −20°C until use. Skeletal muscle tissue was collected from the inner surface of the left scapula bone, whereas skin tissue was collected from three different sections of the ventrolateral skin flaps and the inguinal region, as shown in All laboratory work, including tissue subsampling and homogenization, RNA extraction, and library preparation, were performed in dedicated aDNA facilities within the Centre for Palaeogenetics (CPG) at Stockholm University, following strict standard guidelines for working with ancient/historical biomolecules .2O and repeating the elution flow through the filter cartridge twice. The total RNA concentration from each eluted extract was determined in triplicate using both Qubit miRNA and Qubit RNA HS (high sensitivity) assay kits in a dedicated Qubit 2.0 fluorometer equipment (Thermo Fisher Scientific). Sequencing libraries were prepared using the NEXTflex small RNA-seq kit v3 protocol (Bioo Scientific) and allowing 23× PCR amplification cycles with no size selection. A positive control sample was included by using a 21-nt miRNA-like sequence not matching any known miRNA in miRBase database  from the Seqtk tool (https://github.com/lh3/seqtk) and retained as sequence ID tags for further deduplication procedures.Raw sequenced reads were processed to remove sequencing adapters using cutadapt 3.2 software , with a function  to removk-mer mapping, commonly used with the standard BLASTN algorithm (k-mers and more than 200 species-specific reads (taxReads) were considered.We performed a taxonomic analysis of the skeletal muscle and skin sequences to estimate the amount of endogenous RNA and identify potential additional RNA contamination. The analysis was conducted before PCR deduplication collapsing and after adapter trimming and UMI removal. Additionally, the same analyses were performed on RNA reads for which sequencing adapters were not identified and were kept untrimmed. The taxonomic classification of the reads was performed using the KrakenUniq software  with thelgorithm . The resk-mer length of 31), we performed additional alignment using Bowtie 2 v.2.4.2 (‐‐end-to-end and ‐‐very-sensitive and selected ultrashort reads (18–30 nt) that mapped uniquely to one of the references in the merged database (MAPQ ≥ 1). The sequence IDs were matched with their corresponding taxIDs using the seqid2taxid.map mapping file constructed using Kraken2 software . Exonic, intronic, and intron–intron maps were deduced from genome-wide and transcriptome-wide annotations. Only the longest transcript isoforms per gene were considered for further analyses. PCR deduplicated and adapter-trimmed/untrimmed sequences without UMIs from skeletal muscle and skin tissues were mapped against a composite of the thylacine nuclear and mitochondrial genomes (NC_011944.1) (-n 1 -l 18 -k 1 -y ‐‐best). To account for sequence replication, repetitive and degenerated UMIs were removed using the dedup function from UMI-Tools v1.1.2 software (Transcriptome sequence models and exon–exon maps for annotated genes in the thylacine chromosome-based nuclear genome assembly  were gen11944.1) , as well11944.1) . The alisoftware  with theRNA transcript abundance on a per gene basis was quantified based on UMI-deduplicated alignments, and the breadth of coverage for each gene was determined using the coverage function of BEDTools v2.30.0 . Genes w-v 0 -k 1 ‐‐best -y) (-n 1 -l 18 -k 1 -y ‐‐best) to the thylacine  using the Bowtie aligner v1.3.0 (best -y) . Additiohylacine , TasmaniAdditional analyses describing the identification of expression hotspots genome-wide, rRNA annotation, DNA contamination, and exonic/intronic RNA enrichment are detailed in the platypus function from PMDtools software (Because no UDG treatment  was implsoftware . Damage We used the thylacine genome assembly  together-n 1 -l 18 -k 1 -y ‐‐best)  . A simil ‐‐best) . Reads mS. harrisii), opossum (M. domestica), and domestic dog (C. familiaris)   availabliliaris)  were selO. aries)  algorithm (https://github.com/tkonopka/umap) with the following specifications: n_neighbors = 5, metric =“pearson,” spread = 10, random_state = 30. Thylacine tissues were excluded from the initial embedding estimation. Finally, the thylacine expression profiles of protein-coding mRNAs and miRNAs were projected onto their corresponding previously learned UMAP embeddings.For protein-coding mRNAs, we used a gene expression atlas in sheep (. aries)  availabl. aries) , along w. aries) . The sellgorithm  was implmapper.pl function (-d -c -m) was used to jointly align trimmed PCR- and UMI-deduplicated thylacine skeletal muscle and skin RNA sequences against the thylacine assembly , numbat (Myrmecobius fasciatus), Eastern quoll , fat-tailed dunnart (Sminthopsis crassicaudata), brush-tailed phascogale , wombat (Vombatus ursinus), opossum , swamp wallaby , and koala . The brush-tailed phascogale, numbat, and swamp wallaby genome assemblies were obtained from the publicly available DNA Zoo Consortium (https://www.dnazoo.org/) database generated through 3D de novo assembly (-v 2 -k 1 ‐‐best -y). Small RNA-seq data sets are available at the NCBI Gene Expression Omnibus  under accession numbers GSE18352  under accession number PRJNA900297.The sequence data generated in this study have been submitted to the NCBI BioProject database (Supplement 1Supplement 2Supplement 3Supplement 4Supplement 5Supplement 6Supplement 7Supplement 8Supplement 9


Background: Bladder cancer (BCa) is the leading reason for death among genitourinary malignancies. RNA modifications in tumors closely link to the immune microenvironment. Our study aimed to propose a promising model associated with the “writer” enzymes of five primary RNA adenosine modifications , thus characterizing the clinical outcome, immune landscape and therapeutic efficacy of BCa.Methods: Unsupervised clustering was employed to categorize BCa into different RNA modification patterns based on gene expression profiles of 34 RNA modification “writers”. The RNA modification “writers” score (RMS) signature composed of RNA phenotype-associated differentially expressed genes (DEGs) was established using the least absolute shrinkage and selection operator (LASSO), which was evaluated in meta-GEO (including eight independent GEO datasets) training cohort and the TCGA-BLCA validation cohort. The hub genes in the RMS model were determined via weighted gene co-expression network analysis (WGCNA) and were further validated using human specimen. The potential applicability of the RMS model in predicting the therapeutic responsiveness was assessed through the Genomics of Drug Sensitivity in Cancer database and multiple immunotherapy datasets.Results: Two distinct RNA modification patterns were determined among 1,410 BCa samples from a meta-GEO cohort, showing radically varying clinical outcomes and biological characteristics. The RMS model comprising 14 RNA modification phenotype-associated prognostic DEGs positively correlated with the unsatisfactory outcome of BCa patients in meta-GEO training cohort  and TCGA-BLCA validation cohort . The infiltration of immunosuppressive cells and the activation of EMT, angiogenesis, IL-6/JAK/STAT3 signaling were markedly enriched in RMS-high group. A nomogram exhibited high prognostic prediction accuracy, with a concordance index of 0.785. The therapeutic effect of chemotherapeutic agents and antibody-drug conjugates was significantly different between RMS-low and -high groups. The combination of the RMS model and conventional characteristics  achieved an optimal AUC value of 0.828 in differentiating responders from non-responders to immunotherapy.Conclusion: We conferred the first landscape of five forms of RNA modifications in BCa and emphasized the excellent power of an RNA modifications-related model in evaluating BCa prognosis and immune landscape. BCa is a common malignancy in women and is the fourth most diagnosed malignancy in men globally, with an estimated 500,000 new cases and 200,000 deaths annually . Non-musCurrently, epigenetic mechanisms implicated in cancer-associated genes and inflammatory genes have gradually been becoming the center of BCa etiology research . Epigene6-methyladenosine (m6A), N6,2′-O-dimethyladenosine (m6Am), N1-methyladenosine (m1A), N7-methylguanosine (m7G), and alternative polyadenylation (APA)  editing alterations potentially via a disturbance of RNA secondary structure essential for the deamination . Above modifications are commonly generated through the activity of enzymes referred to as “writers”  . As repomination . Moreoveblastoma . Concernwriters” .6A is defined as the methylation occurring at the sixth nitrogen atom of adenine base, which is the most plentiful and better characterized internal RNA modification form in eukaryotic cells  is subsequently methylated at the N6 position to convert into m6Am RNA modification, which is generally mediated by methyltransferase (including PCIF1 and METTL4) , probably participates in tumor development through modulating RNA splicing, mRNA stability and cap-dependent translation  (as the first nucleotide adjacent to m METTL4) . m6Am, knslation . Specififeration .1A can be defined as a reversible modification in tRNA, rRNA, mRNA, lncRNA and mitochondrial transcripts, affecting the first nitrogen atom of adenine base  tail, and further generate multifarious transcript isoforms with diverse lengths of 3′-untranslated region (3′UTR) . The APAA-to-I editing is one of the most abundant RNA modification events affecting adenosine in humans, where adenosine deaminase acting on RNA (ADAR) enzymes  shift adenosine nucleotides towards inosines through the deamination and eventually lead to specific nucleotide alterations at RNA level and changes in the sequence of amino acids in protein without influencing DNA sequence . A previ6A modification also facilitates the translation of CD40, CD80 and cytokine IL-12 transcripts to accelerate dendritic cell (DC) activation  is currently on the cutting edge and profiled as the most promising immunotherapeutic strategy in tumor. High tumor mutation burden (TMB) of BCa renders it susceptible to ICB therapy, specifically for monoclonal antibodies targeted programmed cell death-1 (PD-1) and its ligand, PD-L1. Nevertheless, merely lower than 30% of BCa patients yield an objective response from ICB . Thus, ttivation . Thus, RPrecise prognostic model is extremely crucial in tumor immunotherapy. Nevertheless, current studies have principally concentrated on single RNA modification “writer” on account of methodological limitations, while a highly coordinated interaction of various tumor-inhibiting factors is responsible for the antitumor effect of these RNA modification regulators . Additiovia integrating RMS mdoel and clinicopathological characteristics to predict long-term survival probabilities for BCa patients. The concordance index (C-index), ROC curve, calibration curve analysis, and decision curve analysis (DCA) were applied for assessing the predictive power and accuracy of the nomogram. Ultimately, weighted correlation network analysis (WGCNA) was performed to identify the hub genes associated with RNA modifications. We also validated the hub gene expressions in human BLCA samples compared to tumor-adjacent control samples. In summary, our results emphasize the predictive efficiency of RNA modifications-related model in evaluating the prognosis and therapeutic efficiency of BCa and confer novel insights to elucidate the mechanisms of immune regulation linked to RNA modifications in BCa patients.In our study, the transcriptomics data combined with clinicopathological parameters of 1,410 BCa cases were extracted from eight independent Gene Expression Omnibus (GEO) datasets. Firstly, two RNA modification clusters were determined through conducting an unsupervised clustering of gene-expression profiles of 34 RNA modification “writers”. We further correlated RNA modification pattern with the prognosis of BCa and the infiltrating characteristics of multiple immune cells in the TME. Secondly, on the basis of DEGs between two distinct RNA modification patterns, we established RMS model by least absolute shrinkage and selection operator (LASSO) regression, thus individually predicting the prognosis and patients’ responsiveness to chemotherapy drugs, ADCs and ICBs. Then, because the exploration of model-associated molecular mechanism is potentially conducive to its future clinical practice, we investigated the association between the risk score and biological functions, immune characteristics through gene set enrichment analysis (GSEA) and correlation analysis. Additionally, survival analysis, Cox proportional hazards model and receiver operating characteristic (ROC) analysis were performed to evaluate the prognostic performance of RMS model. A nomogram was formulated https://portal.gdc.cancer.gov/repository). The somatic copy number variation (CNV) status for 409 BCa cases, and the RNA-sequencing (RNA-seq) data and corresponding clinical datasheets for 411 BCa tissues were extracted from the University of California Santa Cruz (UCSC) Xena browser (https://xenabrowser.net). The clinicopathologic parameters included age, gender, T, N, M classification, tumor stage, histological grade, overall survival (OS) and survival status. RNA-seq data  were further converted into TPM values to make samples more comparable. The waterfall plot that depicted the mutant landscape of TCGA-BLCA cohort was established through “maftools” R package  cohorts were extracted from the supplementary files of original manuscript or downloaded from  package , includi package , GSE3289 package , GSE3254 package , GSE1289 package , GSE3168 package , GSE4807 package , GSE1049 package , GSE8358 package . The Com package . Eventuap values was applied for assessing the correlation between MEs and clinical traits, such as tumor stage, histological grade, and survival status. By convention, one module with the greatest absolute of module significance (MS) was chosen for subsequent analysis. For each module, module membership (MM) was characterized with the correlation coefficient between ME and gene expression profile. Gene Significance (GS) value was applied to quantify the correlation between individual gene and clinical factors  for 3 min. The TMA was incubated overnight with primary antibodies  at 4°C overnight. After incubating with secondary antibody at room temperature for 30 min, the TMA was counter-stained with hematoxylin, dehydrated and covered. The degree of immunostaining of the TMA was evaluated by two independent pathologists blinded to the histopathological characteristics of the samples. The proportion of positively stained cells was scored on a scale of 0–4 . The staining intensity was scored with four scoring levels: 0 (negative), 1 (weak), 2 (medium) and 3 (strong). The staining score was staining by multiplying the proportion of positively stained cells with the intensity score. The total scores were relatively stratified into three grades, <3 scores, 3 to 6 scores, and >6 scores, which corresponded to negative, weak positive and strong positive staining, respectively.Based on the manufacturer’s instructions, TRIzol reagent  was utilized to isolate total RNA from tissues. Then, cDNA was generated by reverse transcription using the HiScript II Q RT SuperMix Kit . RT-qPCR was performed using the SYBR Green approach  in a Roche LightCycler 480 II PCR system . GAPDH was applied for normalizing target gene expression. The RT-qPCR primer sequences were: KIAA1429, forward 5′- TCGATAGGTTGGGAAGCCTGG-3′ and reverse 5′- TACCAGCCTCTTAGCACCAGA-3′.via an SDS-polyacrylamide gel and was further moved to a polyvinylidene fluoride membrane. The membrane was incubated overnight at 4°C with primary antibody rabbit monoclonal anti-KIAA1429  and mouse monoclonal anti-beta-tubulin .RIPA Lysis Buffer  was used to extract total protein from fresh tissue samples and the BCA Protein Assay Kit  was applied for measuring protein concentrations. Subsequently, total protein was separated Three immunotherapeutic cohorts with accessible genomic/transcriptomic data and sufficient clinical annotations were enrolled into our report to investigate the association between the RMS and efficacy of immunotherapy . IMvigorhttp://research-pub.gene.com/IMvigor210CoreBiologies. The raw data were normalized through “edgeR” R package and were subsequently converted to TPM values. Similarly, data of Snyder UC cohort were extracted from http://doi.org/10.5281/zenodo.546110. Furthermore, RNA-seq and clinical information from Montoya melanoma cohort were deposited in GSE35640 (N = 55).For IMvigor210 cohort, according to the Creative Commons 3.0 License, the gene expression data and detailed clinical annotation were downloaded from via the consensus clustering algorithm with 1,000 times repetitions.To identify the optimal number of clusters, we utilized unsupervised consensus hierarchical clustering algorithm through “ConsensuClusterPlus” R package, to perform clustering analysis of 34 RNA modification “writers” of 1,410 BCa samples in the meta-GEO cohort . The robet al. established gene set (download from http://research-pub.gene.com/IMvigor210CoreBiologies)  . GO funcologies) .2fold change (FC)│> 1 and an adjusted p-value <0.001 was considered as the significance criteria.A total of 1,410 BCa patients were stratified into two different RNA modification patterns in line with the preceding consensus clustering algorithm. RNA phenotype-related DEGs between Cluster 1 and Cluster 2 were determined using the empirical Bayesian method of “limma” R package . DEG witp < 0.05) based on univariate Cox regression analysis, pivotal prognostic DEGs were further identified by the LASSO with L1-penalty using “glmnet” R package  = 1 and average value = 0 in the TCGA-BLCA and meta-GEO cohort. To further validate the RMS model, the risk score calculation for each patient and the stratification of patients in the TCGA-BLCA was determined according to the same formula and the identical cutoff value derived from the meta-GEO cohort, respectively.We further established a scoring model to assess RNA modification pattern of each BCa patient—the signature of RNA modification “writers”, and we termed as the RMS. Initially, univariate Cox regression analysis was carried out to estimate the HR of RNA phenotype-related DEGs using “survminer” R package. Among the resulting DEGs with significantly prognostic power  was applied for exploring the mRNA expression, CNV, and pathway activity of RMS-related DEGs in pan-cancer  (https://github.com/ZhengXia/DaPars) has been used to estimate the relative polyA site usage in 3′UTR caused by APA through the Percentage of Distal polyA site Usage Index (PDUI) that is a quantitative index to determine 3′UTR lengthening (positive index) or shortening (negative index)  . The DaPe index) . The WilUnivariate Cox regression analysis was performed to determine the prognostic significance of each differential APA event between RMS-high and -low group using “survival” R package. All BCa patients in the TCGA were stratified into two groups in accordance with PDUI value, and Kaplan-Meier curve with log-rank test was established to assess their survival difference.https://www.cancerrxgene.org/)  . Spearmahttp://cis.hku.hk/TISIDB/; up to 15 March 2021) was utilized to unravel the correlation between the abundance of various tumor-infiltrating immune cells and the expression of RNA modification “writers” genes , 2 m6Am modification “writers” (PCIF1 and METTL4), 5 m1A modification “writers” , 15 APA modification enzymes , and 3 A-to-I modification enzymes   .To delineate genetic landscape of RNA modification “writers” in BLCA, we evaluated the frequency of non-silent somatic mutations in 34 “writers” based on mutational information of the TCGA-BLCA database. Specifically, 127 of 412 BLCA cases (30.83%) experienced mutations of RNA modification “writers”. METTL3 displayed the greatest mutation frequency (4%), followed by PCF11 (4%), KIAA1429 (3%), and WDR33 (3%). While the mutation frequency of ADARB1, METTL16 and CPSF7 was extremely low in BCa samples. Missense mutation constitutes the predominant type of mutations for each writer .p < 0.001) (MUT) group (N = 126) and “writers” wild‐type (“Writers” WT) group (N = 285) of patients in the TCGA-BLCA. “Writers” WT group was markedly enriched in carcinogenic activation pathways, such as angiogenesis, PI3K signaling, MAPK activity, P53 signaling, Jun kinase activity, and canonical Wnt signaling pathway  , indicat pathway . Hence, versus normal bladder tissues. Notably, certain “writers” (such as ADARB2 and PCF11) with widespread frequency of CNV gain harbored decreased mRNA level in BCa compared to adjacent non-tumor tissues   .To further comprehensively expound the expression pattern of 34 “writers” in BCa, 1,410 BCa patients from eight independent GSE sets were combined into a meta-GEO group in our study . As reve6A “writer” KIAA1429 on epigenetic modifications, we further emphatically discussed the prognostic significance of KIAA1429 in BCa. Initially, a total of 5,657 prognosis-associated genes were extracted in 411 BCa patients, among which 3,497 genes were associated with favorable prognosis  and 2,160 genes were related to unfavorable prognosis . To select pivotal hub genes associated with BCa progression, above 5,657 prognosis-associated genes were applied to cluster analysis by the “WGCNA” R package. On the basis of the standard scale‐free network distribution, we carefully set the soft threshold power value as 7 to formulate a hierarchical clustering tree (dendrogram) of 5,657 genes (p < 0.0001), which was considered as the most significant module to the prognosis of BCa (p < 0.0001) (Considering that the broad impacts of m57 genes . Accordi57 genes . We ultis of BCa . There w 0.0001) . Thus, KTo clarify the role of KIAA1429 in BCa, we used qRT-PCR to examine the expression of KIAA1429 in 20 pairs of human primary BCa tissues and paracancerous normal samples. Upregulation of KIAA1429 mRNA was revealed in BCa tumor samples compared with the corresponding non-cancerous samples . Consistp = 0.013)  . We furt= 0.013) .p < 0.0001) and Th17 cells (p < 0.0001). Conversely, cases in Cluster 2 exhibited prominent infiltration of activated CD8+ T cells with pronounced antitumor activity (p < 0.001), effector memory CD4+ T cells (p < 0.0001), and central memory CD8+ T cells (p = 0.032). Consistently, compared with cases in Cluster 2, those in Cluster 1 had significantly increased levels of MDSC marker genes  while significant downregulation of activated CD8+ T cell marker genes   . In summIFNLR1 + 0.1822 *ExprPCDHB11 + (−0.1428) * Expr TIMM21 +……+ 0.0057 * Expr CRELD1+0.0028* Expr FOXG1. Ultimately, we calculated the RMS for each BCa case in the meta-GEO and stratified all cases into RMS-high and -low cohorts based on the median value (3.344)  .p = 1.06e−11) (p = 0.006) (p < 0.001) (p = 0.033) (p = 0.025). We found that 579 out of 742 (78.03%) samples with high RMS were overlapped with the samples in Cluster 1, and 184 out of 668 (27.54%) cases in RMS-low group overlapped with the individuals in Cluster 2 (As showed in .06e−11)  and TCGA= 0.006) . Additio< 0.001) . In GSE3= 0.033) . We alsoluster 2 . Therefop = 0.00032), advanced N classification , and high RMS  remained adverse and independent prognostic factor in BCa   . PreviouInitially, we explored the correlation between CNV and mRNA expression in 14 RMS model-associated genes in 33 kinds of tumors and revealed that CHMP7 expression was significantly modulated by CNV in almost all cancers, followed by SEPHS1 and AASDHPPT . Specifip < 0.05)  and PAOX  both underwent statistically significant 3′UTR shortening in patients with high RMS, which was associated with worse survival in BLCA   and RACK1/miR-302b/c/d-3p-mediated CCNO inhibition can dampen the progression of CSCC  and 96 transcripts with markedly shortening 3′UTR (ΔPDUI <0.1) in RMS-high group, respectively ( < 0.05) , and sho < 0.05) , thus inor PAOX) . A repor of CSCC . Suppres cancers . Thus, wp = 0.005), CSF1R inhibitor GW-2580 , and AR inhibitor Bicalutamide . Conversely, 26 pairs displayed drug resistance significantly related to the RMS, including JNK1 inhibitor ZG-10  and CDK9 inhibitor THZ-2–49   . AdditioCurrently, certain ADCs have been approved by the US Food and Drug Administration (FDA) for the cancer therapy  Bross e. Two tarp = 0.040) (p = 0.037)  . For IMv= 0.037) . Likewis= 0.037) . Signifi= 0.037) . Additio= 0.037) . Collectp = 0.003 for Low RMS with high TMB; HR = 0.48, 95% CI: 0.31–0.76, p = 0.002 for Low RMS with high neoantigen burden)  . We furt burden) . As illu burden) . BrieflyThe building blocks of RNA are canonically confined to four bases, nevertheless, RNA modifications can tremendously expand the chemical diversity of RNA. It is therefore not surprising that RNA modifications have attracted much attention recently owing to their sophisticated and widespread impacts on inflammation, innate immunity, antitumor activity, and the response to immunotherapy through the cross-talk among multifarious “writers”. With the exception of certain studies centralized a single type of RNA modification “writer”, there has been no literature so far of a comprehensive elucidation in the multifaceted association and effects of diverse types of “writers” on malignancy . Therefo6A “writers” METTL3 and KIAA1429 and APA enzymes PCF11 were more predisposed to mutation than additional “writers” in BCa, while mutations of the “writers” CPSF1, ADARB2 and KIAA1429 were proved to be more frequent in hepatocellular carcinoma (HCC) and the mutation frequency of the “writers” ZC3H13, PCF11 and KIAA1429 was the highest in colorectal cancer (CRC)  . We alsoer (CRC) . The carer (CRC) . Intriguer (CRC) . Thus, t6A, m6Am, m1A, APA, and A-to-I RNA editing enzymes at transcriptional and genetic levels and their mutual correlation in BCa. Specifically, m6A “writer” KIAA1429 was the third most common mutant gene and had relatively prevalent CNV gains, with a negative association with the prognosis of BCa patients, indicating the potential function of KIAA1429 in promoting carcinogenesis and metastasis. Prior studies have confirmed significant overexpression of KIAA1429 in multifarious human cancers, including hepatocellular carcinoma (HCC)  , breast ma (HCC) , non-smama (HCC) , gastricma (HCC) , and ostma (HCC) , which wma (HCC) . Furthergression . KIAA142in NSCLC .In addition to elucidating the specific role of individual RNA modification “writer” in the prognosis and immunity of BCa, we also investigated the clustering result of 34 RNA modification “writers”. Two distinct RNA modification patterns (Cluster 1 and Cluster 2) were identified based on 34 RNA modification enzymes. We confirmed that MDSCs and Th17 cells were accumulated in Cluster 1 cases that was characterized with poor survival and low response rate to immunotherapy. MDSCs are a cluster of pivotal immunosuppressive cells in the TME, which are endowed with the capacity to impede T cell, NK cell and B cell functions partly through stimulating the expression of ARG1, indoleamine 2, 3-dioxygenase and inducible nitric oxide synthase . MDSCs aWe then formulated a scoring model, RMS, to evaluate the efficacy of RNA modification “writers” in each case. RMS-high group was related to worse prognosis, which was partly attributed to significant activation of EMT, Notch, IL-2/STAT5, IL-6/JAK/STAT3, angiogenesis signaling pathways, which was instrumental in tumor invasion. The biological process of EMT involves epithelial cells assuming a mesenchymal phenotype, with reinforced capability for invasion and metastasis to accelerate malignant progression of BCa . In studvia the topoisomerase inhibitor SN-38. Trop-2, a transmembrane calcium signal transducing glycoprotein, plays an integral part in cell growth and migration and is upregulated in various epithelial tumors including UC  by a linker to a monoclonal antibody (mAb) which can specifically reach target antigens expressed on cancer cellular surface and deliver a potent cytotoxic payload to the tumor location, ultimately strengthening the chemotherapeutic efficacy and minimizing toxicity to normal tissue. The target antigen should be abundantly expressed on tumor cells while is not expressed or at a low level in normal tissues in an ideal setting, thus lowering off-target toxicity . Recent uding UC . In our Eventually, considering the remarkable effect of RNA modification patterns on immune infiltration in the TME, we showed enormous interest in the capacity of the RMS to predict the potential therapeutic effects of ICB therapy. Our findings highlight that the RMS was a potent predictor to assess the clinical outcome of distinct immunotherapy regimens (anti-PD1/L1 or anti-MAGE-3), which was validated in two UC immunotherapy cohort and two melanoma immunotherapy cohorts. The RMS combined with TMB could differentiate non-responders who underwent immunotherapy from responders with a more robust capability and a remarkably increased accuracy. Thus, our results allow the development of personalized cancer immunotherapy and advance our capacity to exploit an additional approach through which the response rate of immunotherapy can be enhanced.6A) in biological processes and tumor pathogenesis. Considering that epitranscriptome embraces various RNA modifications and a direct interaction exists between the most abundant RNA modifications such as m6A and A-to-I  and the prognostic characterization and immunologic landscape in BCa for the first time. Additionally, numerous studies have documented the modulators in RNA modification pathways, including “writers”, “erasers” and “readers”. Among these modulators, “writers” exert a major catalytic role and install the methylation in RNA modification process. We comprehensively summarized and identified 34 RNA modification “writers” from all relevant published literature. The consensus clustering results for 34 “writers” are satisfactory, thanks to the potential synergistic effect of “writers”. Herein, for the first time, we demonstrated that the mutant landscape, expression level, immune modulation, prognostic significance, and tumor-related pathways of single “writer” (such as KIAA1429) in BCa, which sets the stage and heightens interest in comprehending the biological function and underlying mechanisms of RNA modifications “writer” in BCa. Secondly, a total of 1801 BCa patients were incorporated into our report. Eight independent GEO datasets consisting of 1,410 BCa cases were merged into one meta‐GEO as training cohort. The TCGA-BLCA dataset was considered as independent validation cohort to externally validate the robustness and application of our RMS model. Thus, our model was developed and validated in varying platforms and large populations, which can be served as a promising prognostic signature to optimize BCa patient management. Also, WGCNA-based analysis determined the most weighted prognostic marker (KIAA1429) and its expression levels in BCa were analyzed via human tissue samples, thus guaranteeing the dependability of the results in this report. Thirdly, based on multiple ICB therapy cohorts, we validated that RNA modifications-related model could efficiently predict the efficacy of immunotherapy and might achieve optimal predictive performance when combined with traditional indicators .Specific innovativeness and advantages should be emphasized in our report. Firstly, thus far, an impressive number of studies primarily focus on the importance of only one type of RNA modification (especially md A-to-I , we elucDespite its promising results, several limitations should be mentioned in our study. Firstly, we merely utilized a median cutoff of the RMS based on the meta-GEO cohort to stratify BCa patients. The results need to be validated in a prospective cohort of patients treated with immunotherapy, thus more comprehensively defining the cutoff value to be used. Furthermore, considering the primary clinical significance of distinct tumor regions, it is necessary to systematically assess immune characteristics in the core of the tumor and at the invasive margin. Because not all cases with low RMS exhibit enduring and effective response to immunotherapy, other clinicopathological parameters should be included into the model to improve predictive performance. Thirdly, the special role and underlying mechanism of novel predictive indicators in the RMS model require further experiment research.In summary, our profound and comprehensive analysis of five forms of RNA modification “writers” highlighted an extensive modulatory mechanism by which they exert effects on TME and their correlation with BCa prognosis. We determined two distinct RNA modification-associated subtypes in BCa and constructed an individual RNA modification “writers” profile scoring system that unraveled the interplay and regulatory roles of the “writers” in BCa prognosis, molecular subtypes and post-transcriptional events and depicted their predictive performance in chemotherapy, ADC therapy and immunotherapy. Our study emphasizes the pivotal clinical significance of the interaction among RNA modifications and advances our capacity to guide more effective and personalized immunotherapy for BCa.