TP53 is a master tumor suppressor gene, mutated in approximately half of all human cancers. Given the many regulatory roles of the corresponding p53 protein, it is possible to infer loss of p53 activity – which may occur due to alterations in trans – from gene expression patterns. Several such alterations that phenocopy p53 loss are known, however additional ones may exist, but their identity and prevalence among human tumors are not well characterized.TP53 loss: they are likely deficient in the activity of the p53 pathway, while not bearing obvious TP53 inactivating mutations. While some of these cases are explained by amplifications in the known phenocopying genes MDM2, MDM4 and PPM1D, many are not. An association analysis of cancer genomic scores jointly with CRISPR/RNAi genetic screening data identified an additional common TP53-loss phenocopying gene, USP28. Deletions in USP28 are associated with a TP53 functional impairment in 2.9–7.6% of breast, bladder, lung, liver and stomach tumors, and have comparable effect size to MDM4 amplifications. Additionally, in the known copy number alteration (CNA) segment harboring MDM2, we identify an additional co-amplified gene (CNOT2) that may cooperatively boost the TP53 functional inactivation effect of MDM2. An analysis of cancer cell line drug screens using phenocopy scores suggests that TP53 (in)activity commonly modulates associations between anticancer drug effects and various genetic markers, such as PIK3CA and PTEN mutations, and should thus be considered as a drug activity modifying factor in precision medicine. As a resource, we provide the drug-genetic marker associations that differ depending on TP53 functional status.We perform a large-scale statistical analysis on transcriptomes of ~ 7,000 tumors and ~ 1,000 cell lines, estimating that 12% and 8% of tumors and cancer cell lines, respectively, phenocopy TP53 genetic alterations but that phenocopy p53 activity loss are common, and the USP28 gene deletions are one likely cause.Human tumors that do not bear obvious The online version contains supplementary material available at 10.1186/s12915-023-01595-1. TP53 tumor suppressor gene are a very common feature across almost all types of human cancer. These mutations abrogate or reduce TP53 activity via various mechanisms: dominant-negative acting missense mutations, loss-of-function missense, nonsense, frameshift indel, splice site, or synonymous mutations, or copy number losses that frequently delete one TP53 allele while the other allele is inactivated by a mutation. That such TP53 genetic alterations occur at high frequency in many cancer types implies that they have very strong selective advantages for the expanding cancer cell clones [Mutations in the l clones , 2; indel clones .TP53 losses are consistent with its roles in arresting the cell cycle or triggering apoptosis upon threats to genome integrity. TP53-null cells better tolerate genomic instability, which can result from endogenous causes, most prominently oncogene-overexpressing and thus replication-stressed cancerous genetic backgrounds. Consistently, TP53-mutant tumors have higher frequencies of segmental copy number alterations (CNA), whole-genome duplication, and overall mutation rates [TP53-null cells better tolerate DNA damaging conditions that would normally trigger cell cycle checkpoints, such as those resulting from DNA-acting drugs or radiation [TP53 mutation-bearing tumors tend to be more resistant to various cancer chemotherapies [TP53 R273 and R248 mutants are associated with accelerated cancer progression in colorectal tumors [The large selective advantage of on rates . Moreoveadiation , 8. Consherapies , 9–11 anherapies –12, and l tumors .TP53 mutations – highest of all cancer genes, standing at 37% in The Cancer Genome Atlas (TCGA) cohort – indicates that most cancers benefit from the loss of TP53. However, there are nonetheless many tumors which do not bear a mutation in TP53. A part of those is explained by genetic events that phenocopy TP53 loss i.e. that have similar downstream phenotypic consequences as TP53 gene loss itself. There are three established examples of TP53 loss phenocopying events occurring in tumors. Most prominently, this is the amplification of the MDM2 and MDM4 oncogenes and overexpression of the corresponding proteins. These negatively regulate TP53 protein levels by promoting its proteasomal degradation, and otherwise inhibit TP53 activity by binding to its transactivation domain [PPM1D, whose amplification overexpresses a serine/threonine phosphatase acting upon various targets including TP53, reducing its activity. .TP53 activity loss in cancer evolution, we hypothesized that TP53 loss phenocopying in human cancers extends beyond these known examples of MDM2, MDM4 and PPM1D alterations. If indeed other common mechanisms of TP53 phenocopying exist, this would be relevant to predicting tumor cell response to various drugs, and to predicting tumor aggressiveness, thus having implications to personalized medicine. Because TP53 loss has clear consequences on the mRNA expression levels of various downstream targets [TP53-null-like phenotype can be inferred from large scale transcriptomic data [TP53 phenocopying genetic events and impact on drug sensitivity. We find that TP53 loss phenocopies are remarkably common across tumors and cancer cell lines, and we identify USP28 gene deletions as one cause of TP53 loss phenocopying, and reveal many links between drugs and their targets that are modulated by TP53 activity.Given the strong selective advantages of the  targets , the TP5mic data –23. HereTP53 loss phenocopies in tumors and cell lines, integrating RNA-seq data with TP53 mutation data in a logistic regression, regularized with an Elastic Net penalty . Regression models were trained using cross-validation on mRNA levels of 7131 tumor samples from the TCGA project, across 20 different cancer types, controlling for cancer type. In addition to using this global analysis of mRNA expression levels to infer the functional TP53 status state of each tumor, we also identified the genes whose expression patterns are associated with TP53 status. Tumors with TP53 putatively causal mutations were included as positive examples (TP53 status was categorized according to Genomics of Drug Sensitivity in Cancer Project (GDSC) methodology; see Methods). Previously known phenocopying events , as well as samples with TP53 deletions were excluded from the training set . Our classifier learned a combination of relevant gene weights that differentiate samples with an aberrant TP53 activity. Tumor samples that are not TP53 mutated (by GDSC criteria), but are classified as mutated by the machine learning model are considered to be TP53 loss phenocopies.We developed a machine learning method to detect TP53 status in tumor samples the classifier was not exposed to, with an area under precision-recall curve = 0.9654. The TP53 loss phenocopy scores for each TCGA tumor sample are provided in Additional file Our classifier showed a high performance with an area under the receiver operating characteristic (AUROC) curve of 96% in cross-validation on TCGA tumors (out-of-sample accuracy), and 95% on the testing set  cross-validation showed that classifier performance was robust to removal of individual cancer types, with LOCTO AUCs ranging from 0.951  to 0.964  compared to an are under the curve (AUC) of 0.961 using the full dataset  was the gene with the highest absolute importance score. This exonuclease is a direct TP53 target whose expression is regulated by the phosphorylation of TP53 and its tumor suppressor role has been reported [AEN are expected to be p53 functional, and indeed highly expressed AEN was associated with TP53 WT status in our classifier’s coefficients. On the other extreme, COP1, a ubiquitin ligase that acts as an important p53 negative regulator, was the highest negative importance score with TP53 mutated status in the classifier [: intrinsic apoptotic signalling pathway in response to DNA damage by p53 class mediator  = 4.2%), pyrimidine deoxyribonucleoside monophosphate biosynthetic process  and response to UV-B   and TP53 deep deletions, which were held out from the training set, were largely scored as TP53 mutated. Tumors harbouring a known phenocopying amplification were assigned higher scores than the rest of TP53 wild-type tumors . Cells harbouring a TP53 deep deletion also had higher scores . We tested multiple threshold values for defining TP53 loss phenocopies, which expectedly yielded varying percentages of phenocopies across cancer types  of this classifier at 63% , based on the known TP53 phenocopy score. There were significant positive correlations between three known phenocopying genes copy-number, and the TP53 phenocopy score in TP53 wild-type tumors , and the ors Fig. c.TP53-altered samples compared to TP53 wild-type samples, both in a pan-cancer analysis  and in individual cancer types . Together, these data independently validate our TP53 phenocopy classifier and findings. The TP53 function loss phenocopy scores for each PCAWG tumor sample are provided in Additional file To independently validate our TP53 phenocopy classifier, we applied it to RNA-seq data from 555 tumors across 7 cancer types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) study. Using the same procedure as with the TCGA dataset , the FDR on this validation cohort was 8.8%  are not associated with a known phenocopying event , indicating that our classifier also can capture differential functional impact of TP53 missense variants. Nonsense mutations and frameshifting indels often have higher TP53 phenocopy scores than missense variants  mutations can result in either complete or partial LoF. Our TP53 phenocopy score distinguished between TP53 alterations that likely disrupt p53 protein activity to varying extents  and also gene expression (“GE” tests) pattern. Each of these two genomic data types is considered in two tests, one comparing ll lines , 27. In me panel , 29. As TP53 phenocopying genes.In summary, we tested differences of tumor genomics CNV and GE patterns (two tests each as above), additionally considering “CRISPR” and “RNAi” test scores from genetic screens, for each gene, performing tests stratified by cancer type. Our final score combines the 6 tests together, providing a ranking of potential MDM2, MDM4 and PPM1D genes  in terms of overall statistical significance , and in particular scored highly on CRISPR codependency  by transcriptome score that did not bear TP53 mutations nor deep deletion and that were not associated with known phenocopy mechanisms of MDM2/MDM4/PPM1D amplification . Similar results to CRISPR analyses were observed using RNAi screening codependency scores, further supporting the association of the USP28 losses with TP53 phenocopying, as well as the MSI2 gains  and thus with less clear relevance to diverse tumor types. Top prioritized genes and significance calls from the individual association tests are provided at Additional file Additional hits from this association study might provide promising genes for follow-up. For instance,  cancers –38; we n and ATM , 40 wereMDM2, MDM4 and PPM1D commonly underlie TP53 phenocopies. Such CNA genetic events however often affect multiple adjacent genes, where an open question in cancer genomics is which gene or genes in the affected segment are causal [TP53 phenocopying gene CNA segments might in some cases harbor more than one causal gene. Our genomic combination testing approach can prioritize genes with enriched gene expression and/or CNA in the TP53 loss phenocopying group. Considered together with the prioritization by CRISPR and RNAi codependency, this method provided a plausible ranking of possible TP53 loss phenocopying genes. Applied globally, this identified USP28 as a novel phenocopying gene (see above). To more formally study if the USP28-adjacent genes could contribute to this, we considered that the same method could be applied on a local scale: by examining profiles of CNVs along chromosomes, our genomic prioritization scores would be able to single out the causal gene(s) in the segment of recurrent CNA.Amplifications of certain chromosomal segments (or whole arms) in case of e causal . We hypoMDM4 amplification was found to be enriched in the TP53-phenocopying group of tumor samples, but not in the rest of tumor groups – the TP53 mutant and the non-phenocopying TP53 wild-type . USP28 enrichment was comparable to MDM4 region enrichments of 2.5–3.7-fold   and TP53 mutant samples. The local profile of enrichments presents a plateau-like pattern rather than a sharp peak, and USP28 is within the top-ranked genes in the plateau, however some neighbouring genes do appear similarly so. Therefore, we further used the CRISPR and RNAi codependency scores to prioritize the causal genes in this deletion segment; these scores clearly distinguishes USP28 from its immediate neighbours  for ATM were less strong than for USP28 . Consistently, comparing the RNAi and CRISPR TP53-codependency scores of ATM versus USP28 shows a more robust effect of the USP28 knockout  than the USP28 deleted but ATM non-mutated tumors  as the USP28 deleted but ATM non-mutated cases. This analysis of ATM mutations supports that USP28 deletion, rather than ATM disruption, is the causal change in the deleted segment at chr11 q22-q23.In the broader neighborhood of USP28 finding, we analyzed an independent CRISPR data set, consisting of 3 genome-wide screens performed on TP53 wild-type and TP53 -/- isogenic pairs of cell lines: one on the A549 cell line pair and two on the RPE1 cell line pairs (see details in Methods). In the TP53 wild-type background, the TP53 k.o. increases cell fitness, as expected for a high-effect tumor suppressor gene  for 10 out of 10 neighboring genes in 2 out of 3 screens, and for 8 out of 10 neighboring genes in the remaining screen . To further support this finding, we asked if the fitness gain resulting from USP28 loss is because of downstream effects on TP53 activity. We thus considered the isogenic cells where TP53-/- was ablated, in which indeed the fitness gain from USP28 k.o. was attenuated or disappeared . In particular, we determined in which tumor types USP28 deletions had a higher TP53 phenocopy score than USP28 copy number-amplified samples. As expected, statistical significance (when comparing the TP53 phenocopy score) of USP28 copy number-neutral tumor samples versus those bearing deletions was stronger than when comparing copy number-neutral to amplifications. This supports the impact of USP28 deletions on TP53 loss phenocopy score. The strongest effect was found in BLCA, STAD, BRCA, LIHC and LUAD  and BLCA also had the most significant value when comparing deletions to neutral copy number TP53 phenocopy score , and is also highly ranked in comparison of phenocopy score between deletion versus neutral USP28 CNV tumors .To elucidate the cancer type spectrum of the reported , which mUSP28 deletion is a novel TP53 phenocopying alteration that commonly affects major cancer types such as breast cancer  and also bladder, lung, liver and stomach cancer .Taken together, these results suggest that TP53 loss phenocopying genes such as MDM2. We thus hypothesized that there exist cases of ‘collaboration’ of two or more neighboring genes, affected by a single copy-number alteration event, which jointly bear upon the TP53 loss phenotype. This would represent a special case of epistasis between two genes, caused by a single alteration that spans and affects both genes. Our data suggests that the CNOT2 gene, residing in the MDM2 segment in the chromosome 12q15, is a likely example of this.Some of the top hits found in our combined testing approach were located near to known MDM2 was frequently co-amplified with CNOT2, in 72% of the cases of MDM2 amplification  do not significantly associate with TP53 phenocopy score  than the CNOT2-gain but MDM2-neutral genetic backgrounds  in the TP53 wild-type backgrounds than in TP53-mutant background cell lines . In other words, fitness effects of CNOT2 disruption by CRISPR are conditional upon MDM2 alterations and TP53 alterations, implicating CNOT2 in a three-way genetic interaction with the two other genes.This genomic evidence is supported by recent experimental studies, indicating a role for CNOT2 in boosting the TP53-phenocopying effect of MDM2 amplification may be variable across tissues. Our data suggests that in some cancer types TP53 functional loss seems to rely on amplifications of both genes together, rather than solely MDM2, but not in all  implies a high mean TP53 phenocopy score .We hypothesized that this role of MDM2-CNOT2 pair. For instance, the MSI2 gene, another highly prioritized hit in our combined test  shifts in gene expression patterns, compared to their tumor tissue of origin, we applied an adjustment methodology as in our recent work [TP53 classifier and obtained ranked scores. As a positive control, the classifier assigned a significantly higher TP53 phenocopy score to TP53 mutated cell lines , therefore cell line data served as an independent validation set for the classifier. Of the 610 cell lines labeled as TP53 mutant based on genomic sequence (see Methods), 87% were classified as having a TP53-loss phenotype . The high-confidence set was composed of 76 cell lines . Cell lines harboring one of these CNA in known phenocopying genes had higher scores than the rest of TP53 wild-type cell lines  also had higher phenocopy scores than samples without deletion . 28% of the cell lines in the high-confidence harbor a TP53 deep deletion . These data support that the apparent false positives are often bona fide TP53 phenocopying events in cancer cell lines. All TP53 phenocopy scores and cell line functional TP53 status information are provided in Additional file Some of the apparent false positive cell lines had a TP53 functional status as determined by the TP53 mutations, and additionally as determined by our TP53 phenocopy scores. To identify drugs to which response is affected by TP53 functional status, we predicted drug response (log IC50) values of 449 GDSC drugs individually, using TP53 status as an independent variable (see Methods).Next, we considered the GDSC drug response distributions for various drugs, in light of the TP53 had a lower FDR when testing using TP53 phenocopy score, over when using the TP53 CFE labels   or bleomycin , exhibited a stronger association with the TP53 score than with TP53 CFE mutation labels , the associations with TP53 phenocopying cell lines is more similar to the TP53 mutated cell-lines than to the TP53 wild-type cell lines. In drugs known to be affected by TP53 status, such as bleomycin  for each drug, from cancer type and each cancer gene mutation status , and additionally introducing TP53 status (either via TP53 mutation (“CFE”), or via TP53 phenocopy score) as an interaction term in the regression. The TP53 phenocopy score was binarized (> 0.6 used as threshold) to be able to compare fairly with the TP53 mutation status. Comparing TP53 phenocopy association FDRs against TP53 mutation association FDRs suggested that the application of phenocopy score allowed to more confidently identify the drug-gene associations where TP53 status modulates the effect size , 34% were identified only by using the TP53 phenocopy score, but not by the TP53 mutation status . Stratifying by TP53 functional status, we identified a number of drug-gene CFE associations that would not be significant if ignoring the TP53 status during testing ; all associations from the “two-way” replication test are listed in Additional file Next, we aimed to select the more robust associations. To this end, we applied the “two-way” testing approach to identify replicated drug-marker links  seen  acion test  was furtPIK3CA oncogene are associated with sensitivity to HDAC1 inhibition in a manner conditional upon TP53 mutation. In other words, when TP53 is functional, the resistance to HDAC1 inhibitor AR-42 due to PIK3CA mutation is higher than when TP53 is mutant or otherwise inactivated as indicated by phenocopy score . In particular, in LUAD the difference in AR-42 sensitivity  between PIK3CA mutant and PIK3CAwild-type is hardly evident: 0.26 versus 0.24 respectively, while in TP53-functional LUAD this difference is -0.43 (PIK3CA wild-type) versus 0.35 (PIK3CA wild-mutant). This response is observed across three different HDAC inhibitors and in three different cancer types. For instance, AR-42 and belinostat were found significantly associated with PIK3CA mutation in HNSC + LUSC (here considered jointly because of known molecular similarities of the cancer types), in BRCA, and in LUAD cancer types  in prolonging p53 life and triggering apoptosis , 57. We TP53 functional status) was only recovered when controlling for TP53 phenocopy score, but not when using simply the TP53 mutation status  as an interaction term  was low, nonetheless the association of ATRi IC50 values was found to be more robustly significant in a TP53 wild-type context than in a TP53 deficient context. This means there was a more prominent difference in cell fitness upon ATRi treatment comparing PTEN-mutated to PTEN wild-type cells in a TP53-proficient background (TP53 wild-type IC50-PTEN Cohen’s d = -0.41 vs TP53 deficient AZD6738 IC50-PTEN Cohen's d = -0.05).Recent reports have pointed out the potential therapeutic benefit of ATR inhibitors such as VE-821 or VE-822 in PTEN-deficient breast, glioma and melanoma cells , 59. ATRTP53 functional status modulates the association between HDAC1 inhibitors and PIK3CA driver mutations, and ATR inhibitors and PTEN driver mutations. There were however many other significant associations involving TP53 status, cancer driver gene mutations (or CNA) and activity of drugs . We also noted this effect depended on the particular gene: drug responses in genes such as KRAS or TP53BP1 are well explained by gene mutational status alone, not benefitting from TP53 stratification . Overall, the above data suggests that TP53 functionality should be considered when matching drugs to cancer patients based on the various driver gene mutations in their tumor, and that this TP53 functional status should preferentially be estimated via a phenocopying score rather than TP53 gene mutations.To estimate the importance in considering TP53 status in discovering drug associations more generally, we considered overlap in associations recovered when TP53 provides cancer cells with important advantages such as avoiding cell cycle arrest or apoptosis upon replication stress or DNA damage. Because TP53 acts as a transcription factor controlling expression of hundreds of genes, a functional read-out of TP53 activity can be obtained using gene expression data, both at the level of RNA or at the protein level [TP53 deficiencies, and applied it to ~ 7,000 tumors and ~ 1,000 cancer cell lines, to answer three questions.Disabling the master tumor suppressor gene in level –23. Thesin level , 61. In TP53-mutation phenocopying events across human cancers. We estimated a 12% frequency of TP53 loss phenocopies, compared to a 58% prevalence of TP53 mutant tumors. In some cancer types such as BRCA and BLCA, the TP53 phenocopies may constitute a high fraction of 19% and 16% tumor samples, respectively, suggesting that the TP53 status of tumors should preferentially be measured via a functional readout  rather than considering only the TP53 gene mutations. Supporting this notion, a recent study using a four-gene expression signature of TP53 activity demonstrated that this significantly predicts patient survival across 11 cancer types, and that in the majority of those it performs better than considering TP53 mutations [Firstly, we asked how common are the utations .TP53 phenocopies, we asked if there exist additional genetic events that are associated with these phenocopies. We developed a method that considers CNA profiles jointly with gene expression in tumors, further integrating experimental data from CRISPR and RNAi screens, which confidently identified the USP28 gene deletion as a common TP53-loss phenocopying event. This is relevant for at least five cancer types: BLCA, STAD, BRCA, LIHC and LUAD, and affects 2.9%-7.6% tumor samples therein. The same statistical methodology also highlighted additional genes neighbouring the known phenocopies MDM2 and PPM1D – the CNOT2 and MSI2 genes, respectively – which are often co-amplified with the ‘primary’ gene in the CNA gain segment and may boost the resulting TP53-loss phenotype. Our analysis provides an example illustrating a more general principle of how molecular phenotypes  can be used to identify causal genes in a CNA segment. Our consideration of CRISPR k.o. screening data allowed us to circumvent the confounding effects of genetic linkage within the recurrent, phenocopying CNA segments, because the CRISPR reagent effects are not strongly coupled to the localization of the genes. Related genomics methodologies could be applied in future work to interrogate various recurrent CNA events observed in tumors, for which the causal gene(s) are often not known with confidence.Secondly, given the high prevalence of TP53 functional inactivation status may be helpful for predicting cancer cell response to antitumor drugs based on genetic markers. Given that TP53 deficiencies have myriad downstream consequences on the cell, including e.g. suppression of cell cycle checkpoints, or inactivation of various DNA repair pathways [TP53 background may affect the activity of various drugs to kill cancer cells, including drugs targeted towards a particular driver mutation outside of TP53 itself. We searched large data sets for three-way statistical interactions involving TP53 status, each drug activity, and each mutated cancer driver gene. This suggested for instance that TP53 status modulates the selective resistance towards HDAC1-inhibitor drugs in PIK3CA-mutant cells. The associations we identified were filtered to retain those supported in multiple compounds targeting the same protein or pathway; enforcing agreement across multiple measurements may allay concerns of reproducibility in cell line screening databases [TP53 status , the errors it makes may be of interest. While the apparent false-positives are often TP53 loss phenocopies using still-unknown mechanisms, extending the USP28 example addressed in this study, it would also be interesting to look into the apparent false negatives in future work. These TP53-mutant tumors classified as TP53wild-type-like by our transcriptome score were not considered here, because of their relatively modest number, making statistical analyses difficult. Going forward, analyses of genomes from larger cohorts of cancer patients may provide enough examples to reveal mechanisms of re-establishing activity of mutated TP53 in certain cancers. Conceivably, this may happen by normalizing expression of the TP53-downstream genes which have been dysregulated by the TP53 mutation; understanding these events may inspire new avenues for therapy of TP53 mutant tumors.The statistical model that we employed to identify TP53 also to other driver gene phenocopying events which may occur in tumors. For instance, RAS pathway activation transcriptomic scores were proposed [TP53 phenocopies.The general statistical approach presented here could be applied beyond proposed , and simproposed , 67. ConUSP28 gene deletions as a common TP53-loss phenocopying event in tumors, and in addition we identified auxiliary genes neighbouring MDM2 and PPM1D that may enhance their phenocopying effect. Furthermore, our study suggests that a measurement of TP53 functional status can improve the prediction of cancer response to antitumor drugs, such as HDAC and ATR inhibitors.Our study provides insights into the prevalence and implications of TP53 deficiency in human cancers, which often happens by phenocopying, thus highlighting the need to consult a functional readout of TP53 activity in precision medicine efforts. Integration of a transcriptomic signature of TP53 inactivity with experimental data from CRISPR and RNAi screens identified We downloaded gene expression data  from GDC Data Portal  for humaWe filtered out genes with missing values in more than 100 samples and selected the overlapping genes between cell lines and tumors. Cancer types with less than 10 samples were filtered out. Low expressed genes were removed (TPM < 1 in 90% of the samples) and applied a square-root transformation to TPM. Cancer types. Tumors with less than 10 samples were filtered out. In total, we have 12,419 features for 966 CL samples and 9149 TCGA samples.Tumor purity estimates were obtained from TCGA ABSOLUTE . Samples with top quality (ABSOLUTE score = 86) were removed for correlation testing.We collected CNA from GDC Data Portal  for TCGAFor external validation, the RNA-Seq data and CNV data for PCAWG  was downIn order to later generalize the model to cell lines we proceed to align TCGA and CL data. For this, we applied ComBat, a batch adjustment method, to account for intrinsic differences between tumor signal and cell lines signal . For theTP53 mutations . We identify recurrent variants that are likely to contribute to carcinogenesis. We considered mutated variants: non-synonymous missense mutations, indels (in frame insertions and deletions and out of frame insertions and deletions), nonsense mutations and specific splice-site mutations (such as “p.X125_splice”). Samples without any of these mutations annotated were considered TP53 wild type. In only 5% of the cases (179 out of 3416) our labels differed from the ones provided by Iorio et al. In total, we obtained TP53 labels for 7788 TCGA tumors. TP53 variant pathogenic scores were obtained from the EVE data portal [simple somatic mutation (SNVs and indels)—coding driver mutations, [TCGA Pan-Cancer Atlas somatic mutation data were extracted from the MC3 Public MAF (v0.2.8) data set . We folla portal  and froma portal . For thetations, ).TP53 status (Y matrix). As a reference (Y) during training we used TP53 mutation status labels.We used the aligned human tumor data to train a supervised elastic –23 net pTP53 phenocopying genes  or a deep deletion of TP53 (n = 2065), to prevent the model from relying too much on dosage effects of these genes, instead of the downstream response. Known phenocopies and TP53 gene expression were removed from the training dataset with the same purpose.For the training set, we excluded the tumor samples that have an amplification  in previously known In addition, to control for cancer type specific signals we included cancer type as a dummy variable. To control for class imbalance, we included weights in the classifier.TP53 status. The score from the models determines the probability of a given tumor of being TP53 deficient. Optimization of the penalized regression formula and further details of the classifier can be consulted at [The model learns a vector of gene-specific weights that better classifies ulted at .We used PCAWG gene expression data aligned with TCGA data using ComBat to train an Elastic Net classifier with the same features and structure as above. The model is trained using RNAseq data (X matrix) to infer TP53 status (Y matrix). In this case the models were fit excluding from the training data all examples from one cancer type, iterating over cancer types. Metrics and curves from Additional file TP53 mutational state) we performed cross-validation. We measured the performance of the training set (folds used for training) and the testing set (10% held out). Areas under the Receiving Operating Curve (AUROC) and the Precision Recall curve (AUPRC) were calculated for both training  and testing sets.Using 90% of the training set and 5 balanced folds  or TP53 deletions ) were considered as true positives when calculating FDR.FDR was calculated by sample using each sample probability score from the classifier as threshold for determining positive and negative samples FDR = false positive / . Samples harboring an amplification  of known phenocopying genes  and TP53 (deletions) CNA over/under the 95/0.05 th quantile. All TP53 Phenocopy scores  are provided at Data In Fig. For the validation data (PCAWG), the same procedure was followed. In this case, we used the downloaded consensus CNV data to determine samples that were amplified for MDM4/MDM2/PPM1D or deep-deleted for TP53.Precision and recall in the TCGA and PCAWG dataset were obtained similarly to FDR. MDM2/PPM1D/MDM4 amplification and TP53 deep deletion were considered to be positive cases.The classifier coefficients were analyzed using the GO enrichment tool ShinyGO . The 12,TP53 model should be interpreted with care, for several reasons: some of these genes may change in expression levels via indirect association meaning they may not be directly regulated by TP53; the gene set may omit genes that are bona fide TP53 targets if the information contained in them is redundant with other genes; and finally these genes may individually be only weakly associated with TP53 status, since the method optimizes the expression markers’ collective power. Visualization was performed using Revigo [The coefficients of the g Revigo .TP53 COSMIC labels [MDM2, MDM4, PPM1D) or TP53 deletions as true positives.Using the downloaded RNAseq from GDSC cell lines data we applied our trained tumor classifier to cell lines. As stated above, RNAseq data was square rooted, normalized and ComBat batch corrected. Cell line prediction performance was measured using as reference C labels  combinedC labels  as we diTP53 deep deletions (-2) (threshold = 0.93) . Therefore, we determine 3 sets derived from our Phenocopy score: high-confidence set , TP53 mutant (classified and labeled as mutant) and TP53 wild type (classified and labeled as wild type). All cell line predictions are provided at Data Using the classifier scores we separate the cell lines high-confidence set (FDR <  = 18%) using as threshold reference GISTIC tresholded TP53 mutant were not considered further. However, in the future, analyses with a higher number of cancer genomes may reveal mechanisms of re-establishing TP53 activity in some TP53 mutant cancers .Due to a lack of positive controls, samples that were classified as wild type being originally labeled as Following data of the 2021 Q4 release downloaded from the DepMap project website: CRISPR data from PROJECT Score   effect using Pearson's correlation test. We tested 700 cell lines for our 12,419 genes. This score was calculated both for pan-cancer and by cancer type.Similarly as with CRISPR codependency data we correlated gene knockdown effect with TP53 loss phenocopying genes testing different data: copy number variant data, gene expression data (RNAseq), RNAi codependency score and CRISPR codependency score. We used the downloaded tumor data (previously described) and our TP53 Phenocopy score to test for differences across our 3 main TP53 groups: TP53 wild type (labeled and classified as wild type), TP53 mutated (labeled and classified as mutated) and predicted TP53 phenocopied (labeled as wild type but classified as mutated). We guessed that phenocopying genes should have a differential expression in the phenocopies group when comparing to wild type and mutated TP53 groups individually. We tested 12,419 genes (by cancer type) in the following manner (via Student's t-test):TP53_wt versus CNV_gene_TP53_phenocopies (CNV0 test),CNV_gene_TP53_mut versus CNV_gene_TP53_phenocopies (CNV1 test)CNV_gene_TP53_wt versus GE_gene_TP53_phenocopies (GE0 test)GE_gene_TP53_mut versus GE_gene_TP53_phenocopies (GE1 test)GE_gene_versus RNAi_score_TP53 RNAi_score_gene versus CRISPR_score_TP53 .CRISPR_score_gene We sought to rank possible p-values values from all available tests by cancer type using Fisher's method for combining p-values. For each category (CNV and GE) we only use in the combination the worst p-value (i.e. maximum) between CNV0 and CNV1 test, and separately between GE1 and GE0 test, as a way of aggregating. Genes in which the test direction is not coherent in CNV, GE and codependency score were dropped. A gene with a negative codependency score, such as a negative regulator such as MDM2, is expected to cause a phenocopy of TP53 by amplification and overexpression (therefore a higher expression in the phenocopies group that TP53 wt or mut). P-values were FDR adjusted using Benjamini–Hochberg method (p.adjust function of the stats R package). We further merged each cancer type combined p-values into one single p- value using Fisher's approach, and FDR adjusted. That way we obtained the final Prioritization score for each gene in a cancer-combined way. Pearson R score of CRISPR/RNAi codependency was merged across cancer types by taking its mean value.Three thousand ten genes did not have gene expression data so GE1 and GE0 tests were omitted from the combination test for those genes. We combined the MDM2, MDM4, and PPM1D) FDRs and CRISPR codependency scores. To establish a stringent threshold for new candidate phenocopying genes, we required that a gene's prioritization score should have an FDR as significant as the best-ranked known phenocopying gene (considered by cancer type). Same requirement was applied for the CRISPR codependency score. For example, the known phenocopying genes with the best score by cancer type was MDM4 in the LUAD cancer type, with an combined FDR of 4e-05 and a CRISPR codependency score of -0.21. Finally, we additionally applied a pan-cancer CRISPR and RNAi codependency score threshold, requiring genes to have at least a modest correlation  between the TP53 knockout/knockdown fitness a candidate gene knockout fitness in both CRISPR and RNAi screens (codependency score); this filter removes many of the passenger neighboring genes that are co-amplified with known phenocopying genes.We set as reference points the known phenocopying genes’  of either Brunello  and GeckBrunello  librarieWe collected GDSC  drug dathttps://www.cancerrxgene.org/). We filtered out NA values and transformed IC50 scores to log scale. We downloaded GDSC mutational Cancer Functional Events (CFEs) [TP53 Phenocopy score and GDSC TP53 CFEs and to test other gene status-drug response associations, while controlling for TP53 status. Mutational CFEs consist of a GDSC curated set of cancer genes (CGs) for which the mutation pattern in whole-exome sequencing (WES) data is consistent with positive selection.We also collected each drug putative target and target pathway information from the GDSC website (s (CFEs)  in orderTP53 status using cancer type as control variable. We fit three different regressions per drug response: against TP53 CFEs, against predicted TP53 Phenocopy thresholded scores and against TP53 random labels.We first used drug response (IC50) values of 449 GDSC drugs to fit a pan-cancer regressions against TP53 status we used the groups obtained from our phenocopy score being the TP53 high-confidence phenocopying set  and TP53 mutant set  the TP53 deficient set (TP53.status = 1) and TP53 wild type  as wild type set (TP53.status = 0). Due to uncertainty, we filtered out samples with a TP53 mutation classified as wild-type. Cancer types with less than 3 cases for any category were filtered out. We used the esc R package to calculate effect size (cohens_d function). P-values of associations were FDR corrected using the Benjamini-Hochberg (“fdr”) correction of the p.adjust function (stats package).For the TP53 CFEs. For the visualization we plotted raw IC50 values of different drugs and all drugs together across the different cell line defined sets. For further analysis, we merged the cancer types that are thought to have some similarity: HNSC with LUSC (jointly known as HNSC_LUSC), GBM with LGG (LGG_GBM) and OV with UCEC (OV_UCEC).We separate the drugs into groups according to the pathway the gene they target belong to. By pathway, we calculated the slope resulting from the comparison of the FDR phenocopy score regression versus the FDR We collected drug screening data from the PRISM project  and GDSCTP53 loss Phenocopy score and the interaction term as it follows:First, we fit a regression for each drug and gene CFE including TP53 random and TP53 CFEs instead of TP53 Phenocopy.status.For comparison, we performed the same analysis using TP53 status and gen CFEs. Regressions were fitted by cancer type using glm package (glment 4.0–2 R package). We selected genCFEs p.value and FDR correct using the Benjamini-Hochberg (“fdr”) correction of the p.adjust function (stats R package). The coefficient of the genCFEs variable informs us about the fold change of the different variable states (mutant:1-wildtype:0) when TP53Phenocopy.status is set to its reference levels (wildtype:0). We compared these scores when using TP53 Phenocopy to TP53 CFEs by plotting FDR values and calculating slope  for TP53 interaction in a more stringent way we implemented a version of the “two-way association test” approach recently developed by us and reported in Levatic et al. [TP53 status.To further analyze c et al. . In thisTP53 status (as stated above) by cancer type. We tested the different labels in the regression: TP53 CFEs, TP53 random labels and TP53 phenocopy labels. We considered associations by cancer type. We calculated the two-way association score by averaging the estimates (effect size) obtained between drug 1 and drug 2. To calculate the p-value for each drug-drug combination, we shuffled the TP53 labels and compared the obtained random estimates with the actual estimate as described in our previous work [For this, we curated 996 sets of two drugs with the same target . For the two drugs separately, we fitted a regression comparing the GDSC drug response against gene mutational status (CFE) controlling for ous work .TP53 interaction term variable in regression is significant (FDR < 25%). This informs us of deviation from the behavior of the regression variables gen_status = 1 and gen_status = 0 when TP53 is controlled as interaction. We filtered out cases with less than 3 samples in any category (mutated:1 or wildtype:0) for TP53 status and gene CFEs in a cancer type-specific manner. Supported hits by this methodology are reported at Fig. 6 b,c, Additional file For an association to be selected, we require that it is observed in more than one cancer type (merged cancer types excluded), FDR < 25% across all cancer types where the hit is observed and that the direction  is maintained across drugs. When selecting relevant hits we also required that each hit In addition, as a validation for some hits we performed a “two-way” using PRISM data. In this case we enforced that, for a given drug, an association between a gene feature  and GDSC drug response is reproduced in the same drug using the PRISM dataset. The rest of the methodology was applied in the same manner (see GDSC “two-way test” above).p-values of gen CFEs coefficient in regressions with and without interaction term were compared. We made different types of comparisons: by gene associations . FDR corrected Additional file 1: Supplementary Fig. 1. Mechanisms related to TP53 loss phenocopying in tumors. Supplementary Fig. 2. Validation of TP53 phenocopy scores. Supplementary Fig. 3. Prioritization score methodology. Supplementary Fig. 4. Further evidence supporting the role of individual TP53-loss phenocopying genes. Supplementary Fig. 5. Evidence supporting USP28 as a chromosome 11 phenocopying gene. Supplementary Fig. 6. Supporting evidence for effects of CNOT2 and MSI2 in the amplified segments bearing the MDM2 and PPM1D genes, respectively. Supplementary Fig. 7. Additional data on effects of TP53 loss and phenocopying thereof on cancer cell line drug sensitivity. Supplementary Fig. 8. Pathway-wise effects of TP53 mutations or phenocopies on drug sensitivity of cancer cell lines. Supplementary Fig. 9. Cancer type specific effect of TP53 phenocopies or mutations in drug testing. Supplementary Fig. 10. Associations between mutation status of various genes and the sensitivity to various drugs can be modulated by the TP53 functional status. Supplementary Fig. 11. Additional analyses of TP53 status modulating associations between driver mutations and drugs.Additional file 2.Additional file 3: Supplementary Data 1. TCGA TP53 Phenocopy scores. Supplementary Data 2. Gene coefficients. Supplementary Data 3. PCAWG TP53 Phenocopy scores. Supplementary Data 4. Prioritization scores. Supplementary Data 5. USP28/ATM fitness effect. Supplementary Data 6. Cell lines TP53 Phenocopy score.Additional file 4: Supplementary Data S7. Two-way association tests for drug activity.


In recent years, a huge amount of biomedical data has been generated by high throughput sequencing facilities. These data include, but not limited to, genomic sequences, transcriptome profiles, non-coding RNA profiles, epigenetics profiles, and single cell RNA-Seq profiles. These data sets are normally large and noisy. Therefore, careful analysis with machine learning methods is often needed to extract meaningful patterns from these data. Machine learning problems were normally grouped into classification and regression problems, whose variables under consideration were categorical  and numerical (quantitative), respectively .This Research Topic of Frontiers in Genetics features a Research Topic of three articles for identifying meaningful molecular patterns from clinical samples and an article that introduces a web-based tool for visualization of multi-omics microbial data sets.Qi et al. collected 75 whole-blood transcriptome profiles from 45 patients with various non-blood cancers  and 30 normal controls. They first identified 900 differentially expressed genes (DEGs) from a training set with 53 samples . Then, the support vector machine (SVM) algorithm was used to build models based on these 900 DEGs, 120 very long intergenic non-coding RNAs (vlincRNAs), and 780 non-vlincRNA genes, respectively. Qi et al. showed that these SVM-based models were accurate for pan-cancer detection on the independent testing data with 22 samples. They also demonstrated that vlincRNAs had superior performance when compared to protein-coding mRNAs .Early detection of cancer is critical for better outcome and lower mortality . The tasZhong et al. constructed a hypoxia-related prognosis model, namely, HPM based on gene signatures and machine leaning methods to predict the survival of acute myeloid leukemia patients. The proposed HPM and the derivative models with clinical risk factors were validated with various experimental studies including Kaplan-Meier survival analysis, time-dependent ROC analysis, clinical characteristics analysis, and hypoxia-related immune and metabolic alterations . The HPM and its derivative models were able to effectively predict the survival of AML patients, which might improve risk classification .Cai et al. identified 956 cuproptosis-related lncRNAs (CRLs). Then, univariate Cox regression was utilized to identify 69 CRLs in the training group and multivariate Cox regression was used to identify 3 CRLs as independent prognostic factors . And the regression model based on these three CRLs could accurately predict the prognosis of bladder cancer (BC) patients . They also showed that high-risk BC patients benefited more from immunotherapy and had stronger immune responses, and their overall survival was better .Based on cuproptosis-related genes reported in , Cai et Li et al. developed MicrobioSee, a web-based toolkit for visualizing multi-omics data sets of microorganisms. MicrobioSee provided non-expert users friendly interfaces for 17 different analysis tasks of major omics data of microorganisms .In summary, the work in this Research Topic provided some practical examples of employing machine learning methods in multi-omics profiles of diseases. Interesting patterns were identified in these studies in the contexts of classification and regression problems.


Canis familiaris, is quickly gaining traction as an advantageous model for use in the study of cancer, one of the leading causes of death worldwide. Naturally occurring canine cancers share clinical, histological, and molecular characteristics with the corresponding human diseases.The domestic dog, In this study, we take a deep-learning approach to test how similar the gene expression profile of canine glioma and bladder cancer (BLCA) tumors are to the corresponding human tumors. We likewise develop a tool for identifying misclassified or outlier samples in large canine oncological datasets, analogous to that which was developed for human datasets.We test a number of machine learning algorithms and found that a convolutional neural network outperformed logistic regression and random forest approaches. We use a recently developed RNA-seq-based convolutional neural network, TULIP, to test the robustness of a human-data-trained primary tumor classification tool on cross-species primary tumor prediction. Our study ultimately highlights the molecular similarities between canine and human BLCA and glioma tumors, showing that protein-coding one-to-one homologs shared between humans and canines, are sufficient to distinguish between BLCA and gliomas.The results of this study indicate that using protein-coding one-to-one homologs as the features in the input layer of TULIP performs good primary tumor prediction in both humans and canines. Furthermore, our analysis shows that our selected features also contain the majority of features with known clinical relevance in BLCA and gliomas. Our success in using a human-data-trained model for cross-species primary tumor prediction also sheds light on the conservation of oncological pathways in humans and canines, further underscoring the importance of the canine model system in the study of human disease. SimilarComparative oncology is a research field that investigates and compares tumor development and progression across species and –4 previoThe rapid generation of omics datasets from studies in oncology coupled with increasingly sophisticated machine learning and deep learning methodologies are ushering in a new era of precision-oncology research. One recently developed tool; the TUmor CLassIfication Predictor (TULIP) is a classification tool that has been developed for the prediction of primary tumor types based on human RNA-seq data . TULIP wSuch a tool would also be useful in identifying any sample-based issues in canine oncological datasets that is analogous to the human sample implementation. Unfortunately, the sample size of canine studies is small in comparison to the large human datasets held in the GDC. As a result, the training of a deep learning model on canine data for the purpose of classifying canine primary tumor types is not practical. Intriguingly, further evaluation of TULIP-derived models on non-TCGA kidney cancer RNA-seq data suggests that the models that were derived from TULIP generalize well to accurately predict the primary tumor types of other non-TCGA data .Several publications have shown that canine and human cancers exhibit clinical, molecular, and histological similarities , 9, 10. Invasive urothelial carcinoma (InvUC) is a highly invasive type of bladder cancer in which tumors grow into the muscle of the bladder , 13. CanGliomas are a common type of brain tumor originating in the glial cells that surround and support neurons . Human gTo test how similar the gene expression profile of canine glioma and bladder cancer tumors are to the corresponding human tumors and to identify misclassified or outlier samples in large canine oncological datasets, we sought to determine whether a neural network trained to identify tumor types from human RNA-seq data  can accu22.1https://caninecommons.cancer.gov/#/study/UBC02) (http://www.barkbase.org) (An overview of the methods used in this study can be found in y/UBC02) . Fastq fy/UBC02) . Fastq fase.org) , and datase.org) . All rawase.org) , 22, 23.All RNA-seq data analyses follow the mRNA analysis pipeline established by the National Cancer Institute’s (NCI’s) Genomic Data Commons (GDC) . Reads w2.2Human (Grch38.p13) to Canine (CanFam3.1) orthologs were downloaded from the biomaRt query page for Ensembl Genes 104 , 28. The2.3https://github.com/CBIIT/CTULIP.The canine-adapted version of TULIP (cTULIP) is a deep learning Python-based classification tool that utilizes a 1-dimensional (1D) convolutional neural network (CNN) framework . It take2.41D-CNN) models. All parameters were kept at default values. We evaluated the performance of the cTULIP (1D-CNN) models along with the random forest (RF) and logistic regression (LR) models using the test dataset by computing the weighted average of precision, recall and F1-score for imbalanced data. The formulas for calculating precision, recall, and F1-score are below.We built random forest (RF) and logistic regression (LR) models using the scikit-learn package for comparing with the cTULIP  (https://ggplot2.tidyverse.org). A principal component analysis (PCA) was performed and visualized with the DEseq2 v1.24.0 package in Bioconductor (A t-distributed stochastic neighbor embedding (t-SNE) was performed by using the Rtsne package v0.15 with perplexity is 4 and 5000 iterations (e/Rtsne) . The t-Sonductor . Both t-2.6https://ggplot2.tidyverse.org)  .2.7Glioma and bladder cancer-associated genes were downloaded from the Online Mendelian Inheritance in Man (OMIM) database , 36. Gen2.8The enrichment of hallmark signatures (H) and oncogenic signature genes C6) from the Molecular Signatures Database (MSigDB) on individual samples for all canine bladder cancer data, canine glioma tumor data, human bladder cancer data, and human glioma data was assessed by using a single-sample gene set enrichment analysis (ssGSEA) , 39. For from the33.1The selection of features is a critical first step to enable the use of a cross-species classifier on canine data. Initially, one-to-one homologs between canine and human genes were selected as the features for the input layer of the models . A totalTo visually inspect whether protein-coding one-to-one homologs are sufficient to distinguish between primary tumor types in canine, we employed t-SNE and a PCA on canine glioma and bladder cancer RNA-seq data (cCNN-17-PC) achieves an accuracy of 96.2% whereas the model with all one-to-one homologs (cCNN-17) achieves an accuracy of 95.7%. Ultimately, the model trained with protein-coding one-to-one homologs achieves a higher accuracy, precision, recall, and F1 score than the model trained on all one-to-one homologs  (cCNN-18-PC model) that includes the primary tumor types in the 17 primary tumor type model with the addition of glioblastoma . Both the cCNN-17-PC and the cCNN-18-PC models outperformed the cCNN-32-PC model with an accuracy of greater than 96%  models with other standard machine learning algorithms, we constructed random forest (RF) and logistic regression (LR) models with the 17 and 18 primary tumor types. The comparison indicates that the accuracy of the 1D-CNN models surpassed both the RF and LR models in all metrics Table 2.3.3cCNN-17-PC and the cCNN-18-PC models classify human primary tumor types robustly  were identified in canine bladder cancer tumors and 8,215 DEGs were identified in canine glioma tumors 5B, C. F3.5The evaluation of these human-trained models suggests that TULIP can perform cross-species primary tumor classification, however, it cannot accurately distinguish between grades of canine glioma tumors Table 3.First, we performed a single-sample gene set enrichment analysis (ssGSEA) to examine whether the one-to-one protein-coding homologs that are used in the input layer of the model recapitulate the human glioma and bladder cancer molecular signatures in the canine data. As the heat maps show, glioma and bladder cancer exhibit distinct gene expression patterns  is negatively impacting our ability to identify DEGs. However, our study only highlights the similarity of the transcriptomic profiles and the cancer relevance of selected features in both canine and humans. Previous studies on canine methylation patterns reveal that different grades of canine glioma exhibit distinct DNA methylation profiles  and PRJNA396033 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA396033) or at the National Cancer Institute’s Integrated Canine Data Commons with the accession ID: 000005 (https://caninecommons.cancer.gov/#/study/UBC02).Publicly available datasets were analyzed in this study. This data can be found here: The datasets analyzed in this study can be found in the NCBI SRA database with the BioProject accession IDs: PRJNA579792 (MB, NL, SG, and JL contributed to the conception and design of the study. SG, JL, HK, and SJ performed deep learning, bioinformatics, and statistical analyses. NL, DD, JO, and DK identified datasets and comparative measures as well as provided biological context. JL and NL wrote the first draft of the manuscript. MB, SJ, SG, and HK wrote sections of the manuscript. MB, NL, SG, SJ, HK, and JL interpreted the data. All authors contributed to the article and approved the submitted version.


Multi-omic analyses are necessary to understand the complex biological processes taking place at the tissue and cell level, but also to make reliable predictions about, for example, disease outcome. Several linear methods exist that create a joint embedding using paired information per sample, but recently there has been a rise in the popularity of neural architectures that embed paired -omics into the same non-linear manifold. This work describes a head-to-head comparison of linear and non-linear joint embedding methods using both bulk and single-cell multi-modal datasets. We found that non-linear methods have a clear advantage with respect to linear ones for missing modality imputation. Performance comparisons in the downstream tasks of survival analysis for bulk tumor data and cell type classification for single-cell data lead to the following insights: First, concatenating the principal components of each modality is a competitive baseline and hard to beat if all modalities are available at test time. However, if we only have one modality available at test time, training a predictive model on the joint space of that modality can lead to performance improvements with respect to just using the unimodal principal components. Second, -omic profiles imputed by neural joint embedding methods are realistic enough to be used by a classifier trained on real data with limited performance drops. Taken together, our comparisons give hints to which joint embedding to use for which downstream task. Overall, product-of-experts performed well in most tasks and was reasonably fast, while early integration (concatenation) of modalities did quite poorly. In the past years, there has been a tendency to produce more and more multi-modal -omics data , i.e. toA prime example of such multi-modal datasets is that of The Cancer Genome Atlas (TCGA), which contains mutation, gene expression, DNA methylation and copy number profiles from the same tumor sample for thousands of primary tumors from different cancer types . More reThis rapid progress in our ability to generate data has naturally increased the need for computational tools to deal with the challenges of analyzing increasing amounts of multi-modal data. One popular class of such tools is called joint dimensionality reduction or joint embedding and involves projecting all modalities into the same lower-dimensional space . The ’joSeveral joint embedding methods have been proposed and many of them have been especially designed for -omics data. The majority of such methods are extensions of single modality dimensionality reduction such as factor analysis or principal components analysis. For example, Multi-Omics Factor Analysis (MOFA+)  is a genRecently, the use of neural networks and deep learning for dimensionality reduction has gained popularity in several domains, including computational biology and mainly single-cell transcriptomics . This iset al. [Advances in multi-modal deep learning from other fields, such as computer vision, can also be applied in computational biology. One such model which also uses VAEs is called Product of Experts (PoE) autoencoder . It useset al.  used MoEet al. compared nine linear joint embedding methods at various tasks using both bulk and single-cell data [et al. compared the performance of several deep-learning-based joint representation learners as a function of the number of cells in the datasets [The large number of available methods calls for comparisons between them. Cantini ell data . The maiell data  tended tell data , 25, andell data . Therefodatasets . This stet al. [In this paper, we compare several neural network architectures for joint representation learning to each other as well as to two popular linear methods that also showed promise according to Cantini et al. . An overWe first compared two linear joint embedding methods, MCIA , Figure A and MOFUsing 6752 samples from 33 tumor types from the TCGA with gene expression (GE), methylation (ME) and Copy Number Variation (CNV) data available, we learned joint embeddings for GE + ME and GE + CNV, and evaluated the ability to impute a missing modality using a held-out test set of 844 samples A. We alsDespite the fact that we also tested hyperparameter configurations without any hidden layers , we found that, for all five models, the optimal configuration for this task, based on the validation set, was non-linear with either one  or two  hidden layers. The selection of the remaining hyperparameters was highly dependent on the choice of model and dataset .CGVAE, PoE, MoE and UniPort performed better than the other three methods, but only PoE was significantly better than the GLM baseline in all four cases . CGVAE and MoE offered significant improvements with respect to the baseline in general, but they both failed at predicting CNVs from GE B. UniPorOverall, we observed large improvements in imputation performance with respect to the baseline for the prediction of methylation from gene expression , while in most of the remaining cases the differences were marginal . Taken together, we can conclude that PoE is the best performing method in the imputation task.We additionally compared the coherence of the latent space of the different joint embedding methods by testing whether decodings of the same point in the latent space are classified as the same cancer type by a neural network trained to predict the cancer type using the measured training data B. The prFrom these results, we conclude that there is a discrepancy between imputation and coherence as no architecture is very good at both at the same time. On the other hand, we found that in all tasks the top-performing method was a non-linear one.We then tested whether the pretraining of joint dimensionality reduction would lead to performance gains in the supervized downstream task of survival analysis C and D. As baseline, we used a model with only the covariates mentioned above. Additionally, we compared to three more simple models: two single-omic models based on the 32 principal components of each data modality . The AIC measures the quality of a model’s fit to the data by taking the number of model parameters into account, as it is in principle easier for models with more parameters to overfit to the training data. The results of this experiment (the lower the AIC the better) are shown in Our baseline model using only the patients’ sex, age and cancer type (and no -omics) information already provided a statistically significant fit (FWER < 0.05) compared to a null model with only an intercept (result not shown). The inclusion of the 32 principal components of gene expression or methylation (PCA) lead to large improvements C. In facCox models trained on the latent space of joint embedding methods using both gene expression and methylation (GE+ME) did improve upon the baseline, but were all outperformed by PCA, with ccVAE, CGVAE and PoE ranking second, third and fourth respectively when both modalities are included C. MCIA hAs for models trained on the latent space of gene expression and copy number GE+CNV, D, joint When we restrict our comparison to methods that only use one modality, we hypothesized that a joint embedding will be beneficial, as the joint pre-training can provide additional information from other modalities. For gene expression , this was in general not the case, as only three of the seven joint embedding methods  trained on the joint space of gene expression and methylation (GE+ME) outperformed training only on GE data. UniPort’s gene expression embeddings gave the best predictions, even outperforming all methods that use both GE and ME. Cox models that use the gene expression data embedded in the GE+CNV joint space performed worse than the Cox model trained on the PCA of the GE data, with the exception of UniPort, which was again the top-performing method. This implies that the common information between gene expression and copy number is at large not related to disease progression, possibly because CNV data have relatively small prognostic power D. NevertOn the other hand, training a model on the joint methylation space (GE+ME) does improve the AIC performance when comparing to training on only ME data for all joint models C. MoreovFinally, we devised a method to test whether each latent factor encodes for joint signal shared by both modalities or is modality-specific . This method works by estimating the mutual information between a latent factor and each input data modality and is applicable only to VAE-like methods that can encode each modality separately. Applying it on the models we trained on the TCGA data, we found that in many of the cases, the latent space is a mixture of joint and modality-specific factors, although we did observe models with mostly joint factors, with factors of PoE being slightly more associated with both modalities than other methods  and B. OTo evaluate the methods on single-cell data, we first used a dataset of 10 412 peripheral blood mononuclear cells (PBMCs) containing both gene expression and chromatin accessibility profiles for each cell. The dataset was split into training (80%), validation (10%) and test sets (10%) stratified per cell type.We found that PoE, MoE and UniPort did the best at imputation, outperforming the baseline , while cWe also compared the pre-trained models on the supervized task of cell type classification C, using  of 0.93 . None ofIn summary, for this single-cell dataset, Uniport and PoE performed well at imputation, while ccVAE did quite poorly and PCA was a very strong baseline for downstream supervized classification. These results in all three tasks  are very much in line with our findings on the bulk TCGA data. What was different, is that most of the latent factors learned by the models in this dataset were not found to share significant information with both modalities according to our mutual information-based test. Instead, they were mostly modality-specific .We then compared the same seven joint embedding methods plus totalVI on a CITE-Seq dataset profiling PBMCs from eight different individuals . We usedThe best configurations for the non-linear models were as follows : the PoEWe first compared the methods on missing modality imputation, using a similar GLM-based baseline method as before A. All meImputation of ADT from RNA followed the pattern of the TCGA experiments, with MOFA+, ccVAE and totalVI performing worse than the GLM baseline, as did PoE. CGVAE was the top method . In thisP-value < 1e-6, chi-squared test, It is possible that the observed association between RNA imputation performance and cell type abundance is due to bias towards abundant cell types introduced by the pre-selection of genes, as this association was less prominent for imputing protein expression. To test this hypothesis, we replaced the 5000 most variable genes with 819 (level-2) cell type marker genes, derived using COSG  Supplem. We founTo evaluate generation coherence B, we buiFor cell type classification C, we useWe again found that joint unsupervized pre-training on the same dataset does not yield any considerable advantage to downstream performance if both modalities are available at test time, as concatenating the PCs of gene and protein expression (RNA+ADT) gave competitive performance using both classifiers . PoE embNext, we evaluated all methods when only one modality is available at test time and compared the models that are pre-trained in the joint space against a classifier trained on the PCs of the single modality. In this setting, there was a drop in the performances with respect to the case when both modalities are always available. However, we observed considerable improvements caused by the joint pre-trainining using MOFA+, PoE, totalVI and MoE, as demonstrated in As for the RNA+ATAC-Seq data, most latent factors of CGVAE and ccVAE had significantly large mutual information with only RNA , while CFinally, to test the potential bias towards the set of highly variable genes, we investigated the effect of using the COSG-derived marker genes (Supplementary Methods) on cell type classification. Next, we considered whether it is possible to use the joint embedding methods to impute the missing modality and then classify the test cells using the MLP on the concatenated principal components of the two modalities .We found that non-linear methods developed in other fields (PoE and MoE) generally outperformed the linear and simple non-linear ones at imputing missing modalities. On the other hand, these methods underperformed with respect to baseline non-linear methods in terms of generation coherence. Regarding downstream supervized tasks, we observed that joint embedding can lead to improved performance when only a single modality is available in the test data, which verifies previous results for linear methods . If dataInterestingly, we additionally showed that joint embedding methods can be used to impute missing modalities which can be fed to a multi-modal classifier trained only on real data. This means that the tested models are able to generate realistic enough -omic profiles. On the other hand, these profiles were projected to the principal components space before being fed to the classifier, which provides an additional denoising step. In most cases, this approach of imputing and classifying with two modalities was worse than training a model in the joint space using one modality. This is not surprising, as imputation errors are bound to be propagated into the classifier despite the denoising. On the other hand this approach did show improvement over using a supervized model trained on a single modality.To make sure we fairly compare all methods, we performed an extensive hyperparameter search to find the best settings for each one. We used a held-out validation set to calculate the validation loss for each hyperparameter combination and select the optimal combination. The performances of the methods were estimated using another held-out test set comprising previously unseen data points. If, for some reason, the validation loss is not predictive of the performance at a specific downstream task – as we have previously shown can be the case for VAEs trained on RNA-Seq data  – then tExamining the results on generation coherence, we found that CGVAE and ccVAE did better than UniPort, PoE and MoE on both bulk and single-cell data, while they typically underperformed in the other tasks. ccVAE uses a single encoder for the concatenation of both modalities, which might be beneficial for generation coherence, as the latent space is directly and concurrently influenced by matched samples from all modalities. However, the architecture of CGVAE is identical to that of MoE and PoE with separate encoders per modality. What makes CGVAE different from these models is an additional loss term that penalizes the Wasserstein distance between the posterior distribution of the latent variables given a pair of input modalities. This encourages the latent embeddings from both modalities to be the same for the same input sample. Coherence of MoE and PoE could be potentially be improved by adding such a loss term, but that also introduces an additional hyperparameter to weigh the contribution of that loss component with respect to the total loss. Here, we used the CGVAE model as a reference, thus we did not tune this hyperparameter value.Our results showed that our adaptation of MOFA+  with outet al. concluded that many of the linear joint embedding methods they tested performed well on single-cell data [et al., MCIA, was not even possible to train despite using a considerable amount of computational power. This points to an advantage of VAEs: they have been designed to work with stochastic or batch gradient descent to accommodate large training datasets [et al. [Cantini ell data . Here, wdatasets . Of the  [et al. , only MO [et al.  and scik [et al.  offer ba [et al. ) might aAn important consequence of our experimental set-up is, that the number of data points used to train the joint embedding methods is equal to the number of points used to train the downstream supervised models. In practice, additional unlabeled multi-modal data might be available, which can be used during the learning of the joint space. In such cases, especially as the amount of unlabeled data increases, we expect an additional benefit of joint embedding, even if both modalities are available at test time.Moreover, we followed the common approach of selecting the most variable features of high-dimensional modalities to include for joint embedding. In experiments on the CITE-seq dataset, we noted an association between RNA imputation performance and cell type abundance, which was partly alleviated when replacing variable genes with COSG-derived marker genes as RNA features. In addition, for some models the level-3 cell type classification also improved when using the COSG-derived marker genes, but for this classification task the best overall performances were still achieved using variable genes.if available) for feature pre-selection is a promising research direction relevant for both multi-omic and single-omic embedding methods.There are several possible explanations why cell type-specific marker genes show improved performance. One possible reason is that the most variable genes are indeed biased towards abundant cell types. But, likewise, another reason might be that the marker genes introduce a bias towards rare cell types, although this might be less likely because the imputation of abundant cell types also improved when using the COSG-derived marker genes. Alternatively, it might be that cell-type specific marker genes are easier to predict than the highly variable genes, because they have a more binary-like expression pattern . Further exploration on the utility of exploiting prior knowledge on the cell types present in a dataset  matrices that map a sample The simplest method to combine different modalities is to concatenate the features into one vector entclass1pt{minimaocument}  Another simple non-linear joint representation learning architecture consists of one VAE for each data source. To force the encoders to embed the data in a common space we want the decoders to be able to reconstruct any modality from the latent representation of any encoder. To do so, we minimize the sum of the reconstruction losses for all possible combinations of input and output (reconstructed) modalities. We further add a loss term penalizing the second-order Wasserstein distance between the posterior distribution of TotalVI  is an exUniPort was mainly designed for integrating unpaired datasets from different modalities, but it also enables joint embedding of paired data . It doesThe Product of Experts (PoE) approach  uses a slication .Mixture of Experts (MoE)  uses thelication , when trstimator  to reducThe TCGA dataset  consiststed from .https://support.10xgenomics.com/single-cell-multiome-atac-gex/datasets/1.0.0/pbmc_granulocyte_sorted_10k. We filtered out cells with fewer than 1000 or more than 25 000 measured genes and similarly cells with fewer than 5000 or more than 70 000 detected ATAC peaks. We additionally removed cells with more than 20% of their RNA-Seq reads mapping to mitochondrial genes, leaving us with 10 412 cells. We used Seurat [We downloaded a dataset of 11 910 PBMCs from Raw RNA-Seq counts are modelled with a negative binomial log-likelihood with mean  totalVI . The decWe used a CITE-Seq dataset  containiWe selected the 5,000 most variable genes with the same approach as for the RNA+ATAC dataset above and again modelled the raw RNA counts with a negative binomial distribution with gene-specific dispersion. We retained all 217 protein features and modeled the raw counts with a two-component mixture distribution as in totalVI [We split each dataset into a training, a validation and a test set. We evaluated the ability of joint embedding methods on predicting (imputing) one data modality from the other, by holding out one modality at a time from the test data A.We used the log-likelihood of the held-out data given the model predictions as evaluation measure. For example, when imputing, say ME from GE, we give each GE profile  were stratified per tumor type to account for the vast imbalances between them. We trained then models using two modalities at a time: GE + ME and GE + CNV. Similarly, on the RNA+ATAC-Seq data, we performed a 80–10–10% split stratified per cell type. As the inter-individual variations are very large in the CITE-Seq dataset, to avoid information leaks stemming from using cells from the same individual during training and testing, we split the cells based on the eight donors. Specifically, we used all cells from six individuals for training, one individual for validation and one for testing. Although this split is not stratified per cell type, it provides a more realistic and less biased evaluation.Given a point in the latent space, we expect all decoders to generate instances of each modality with similar properties . We asseSpecifically, we trained five two-layer perceptrons, one for each modality , where the input is a modality profile and the target output is the cancer type or the (level-2) cell type, respectively. Again using ME and GE as an example, for each model, we randomly sampled 2000 points from the prior distribution of Here, we are not interested in whether the predicted labels of the random points are correct or not, as long as the two perceptrons make the same prediction for profiles generated from the same value of We compared the latent features obtained from the different joint embedding methods on their ability to predict disease progression. To this end, we performed survival analysis using Cox regression C with Prlidation . TherefoTo distil effects of each individual modality, this evaluation took place using the joint embeddings of either both modalities (We included the patients’ sex (2 one-hot encoded features), age, and cancer type (33 one-hot encoded binary features) as covariates in all models to account for the effects of these variables. A model with only the covariates (i.e. without any -omics features) was used as a baseline. We also compared to three stronger baselines per dataset, namely the 32 principal components of each modality and the concatenation of these pairs of 32 latent features D. The nuWe followed the same experimental set-up for both the RNA+ATAC-Seq and the CITE-Seq dataset. We compared the performance of the different embeddings when both modalities are available for all samples, and when both modalities are available for the training and validation data, but only one modality is measured in the test data.For each joint embedding method, we thus obtained three latent vectors: (1) using only RNA, (2) using only proteins (or chromatin accessibility) and 3) using both RNA and proteins (or RNA and chromatic accessibility) (Supplementary Methods).We trained two different classifiers for each feature vector: a linear Support Vector Machine (SVM) and a two-layer perceptron. For the SVM, we chose the best value for the weight of the L2 regularization from the values [We additionally built baseline classifiers that did not use any joint embedding, but were trained on (a) the 32 principal components of the RNA data, (b) the 32 principal components of the ADT or ATAC data and (c) the concatenation of a and b. We used the same classifiers with the same settings as above and again did not tune the number of principal components for the task.Finally, We then compare the predictions of the multi-modal classifier when using two measured modalities versus one measured and one imputed modality as well as to a classifier trained on the joint space of the measured modality C. We onlBecause we observed that predicting the level-2 assignments was a relatively easy task, in the CITE-Seq dataset , we usedIn conclusion, both linear and non-linear joint embedding methods are applicable in many different biological problems, but it is necessary to compare them to appropriate baseline methods to identify the added effect of employing them.Imputation of missing modalities requires non-linear modeling.The performance of joint embedding methods should always be compared to the performance of the concatenated principal components of each modality.Training a classifier in the joint space increases downstream supervized performance if only one modality is measured at test time.Artificial -omics profiles generated by non-linear joint embedding methods can be used for supervized tasks with limited performance drops.supplement_bbad416Click here for additional data file.