Explainable Machine Learning Identifies Factors for Dosage Compensation in Aneuploid Human Cancer Cells

Explainable Machine Learning Identifies Factors for Dosage Compensation in Aneuploid Human Cancer Cells | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Explainable Machine Learning Identifies Factors for Dosage Compensation in Aneuploid Human Cancer Cells View ORCID Profile Erik Marcel Heller , View ORCID Profile Karen Barthel , View ORCID Profile Markus Räschle , View ORCID Profile Klaske M. Schukken , View ORCID Profile Jason M. Sheltzer , View ORCID Profile Zuzana Storchová doi: https://doi.org/10.1101/2025.05.12.653427 Erik Marcel Heller 1 Department of Molecular Genetics, Faculty of Biology, Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau , Kaiserslautern, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Erik Marcel Heller Karen Barthel 1 Department of Molecular Genetics, Faculty of Biology, Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau , Kaiserslautern, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Karen Barthel Markus Räschle 1 Department of Molecular Genetics, Faculty of Biology, Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau , Kaiserslautern, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Markus Räschle Klaske M. Schukken 2 Department of Surgery, Yale School of Medicine, Yale University , New Haven, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Klaske M. Schukken Jason M. Sheltzer 2 Department of Surgery, Yale School of Medicine, Yale University , New Haven, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jason M. Sheltzer For correspondence: zuzana.storchova{at}rptu.de jason.sheltzer{at}yale.edu Zuzana Storchová 1 Department of Molecular Genetics, Faculty of Biology, Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau , Kaiserslautern, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Zuzana Storchová For correspondence: zuzana.storchova{at}rptu.de jason.sheltzer{at}yale.edu Abstract Full Text Info/History Metrics Preview PDF Abstract Aneuploidy, a hallmark of cancer, leads to widespread changes in chromosome copy number, altering the abundance of hundreds or thousands of proteins. How-ever, evidence suggests that levels of proteins encoded on affected chromosomes are often buffered toward their abundances observed in diploid cells. Despite its preval-ence, the molecular mechanisms driving this protein dosage compensation remain largely unknown. It is unclear whether all proteins are buffered to a similar degree, what factors determine buffering, and whether dosage compensation varies across different cell lines or tumor types. Moreover, its potential adaptive advantage and therapeutic relevance remain unexplored. Here, we established a novel approach to quantify protein dosage buffering in a gene copy number-dependent manner, show-ing that dosage compensation is widespread but variable in cancer cell lines and in vivo tumor samples. By developing multifactorial machine learning models, we identify mean gene dependency, protein complex participation, haploinsufficiency, and mRNA decay as key predictors of buffering. We also show that dosage com-pensation can affect oncogenic potential and that higher buffering correlates with reduced proteotoxic stress and increased drug resistance. These findings highlight protein dosage compensation as a crucial regulatory mechanism and a potential therapeutic target in aneuploid cancers. Introduction Chromosome and chromosome arm copy number changes, commonly referred to as chro-mosomal aneuploidy, are a widespread hallmark of cancer ( Santaguida & Amon, 2015 ; Sdeor et al., 2024 ; Taylor et al., 2018 ). Aneuploidy alters the expression of many genes located on the affected chromosomes, including tumor suppressors and oncogenes. The altered gene expression disrupts cellular regulation, driving tumorigenesis and promoting cancer progression. This likely explains why certain chromosomes or chromosome arms are recurrently gained or lost across various cancers and why specific aneuploidies are fre-quently associated with distinct cancer types ( Davoli et al., 2013 ; Shih et al., 2023 ). For example, trisomy 12 is common in chronic lymphocytic leukemia, while loss of chromo-some 13q is prevalent in retinoblastoma. Aneuploidy is often associated with chromosomal instability and together they facilitate tumor evolution by providing selective advantage to cancer cells, increasing drug resistance, and metastatic dissemination ( Andrade et al., 2023 ; Lukow & Sheltzer, 2022 ; Replogle et al., 2020 ; Shukla et al., 2020 ). However, aneuploidy affects the copy number of hundreds of genes, and this deregulated gene expression may have negative effects on cellular proliferation. Evidence from in vitro models of aneuploidy in budding yeast, murine and human cells revealed that aneuploidy often impairs proliferation, induces cellular senescence, increases genomic instability, and disrupts metabolic homeostasis. Remarkably, aneuploid cells exhibit increased sensitivity to compounds that interfere with protein folding and turnover ( Donnelly & Storchová, 2015 ; Donnelly et al., 2014 ; Tang et al., 2011 ; Torres et al., 2010 ). This suggests that the aberrant gene expression in aneuploid cells overwhelms cellular quality control sys-tems involved in the maintenance of protein homeostasis. Indeed, aneuploid cells often experience an imbalance in the synthesis and degradation of proteins, leading to the ac-cumulation of misfolded proteins, which must be resolved by the proteasome and other protein degradation pathways ( Donnelly & Storchová, 2015 ; Donnelly et al., 2014 ; Oro-mendia & Amon, 2014; Oromendia et al., 2012 ). Moreover, secondary global (i.e., in trans ) gene expression changes are often observed in aneuploid cells, as gene expression of multiple genes across the entire genome is altered in response to the stresses induced by aneuploidy ( Sheltzer et al., 2012 ; Stingele et al., 2012 ). Transcriptome and proteome analysis of engineered aneuploid cells have provided useful insights into the gene expression changes triggered by aneuploidy. For instance, in bud-ding yeast strains with an extra chromosome, most genes on the aneuploid chromosome show an increased abundance of both mRNA and proteins, reflecting the proportional increase in gene dosage ( Dephoure et al., 2014 ; Torres et al., 2007 ), but the abundance of approximately ∼20% of proteins encoded on extra chromosomes shows dosage compensa-tion, meaning the protein levels are adjusted to levels similar to euploid levels ( Figure 1A ; Dephoure et al., 2014 ). This dosage compensation, also referred to as protein abundance attenuation or buffering, is particularly apparent for subunits of multi-protein complexes and largely depends on proteasomal and lysosomal degradation of the excess proteins, and their sequestration into aggregates ( Brennan et al., 2019 ; Dephoure et al., 2014 ; Senger & Schaefer, 2021 ; Taggart et al., 2020 ). In naturally occurring aneuploid yeast strains, the dosage compensation affects an even larger fraction of proteins and is ac-companied by enhanced protein turnover via proteasomal degradation ( Muenzner et al., 2024 ). Analysis of engineered cancerous and non-cancerous human cell lines harboring chromosome gains demonstrated that most genes on gained chromosomes are transcribed and translated proportionally to the chromosome copy number gain. However, protein complex subunits, protein kinases, and several other proteins showed an apparent dosage compensation at the protein level ( Stingele et al., 2012 ; Viganó et al., 2018 ). Similarly, dosage compensation modulates the abundance of proteins in cells from individuals with trisomy syndromes ( Hwang et al., 2021 ; Liu et al., 2017 ). Loss of a single chromosome copy is also compensated in engineered monosomic human cells ( Chunduri et al., 2021 ). How dosage changes of chromosomes and single genes shape the proteome in human can-cers is poorly understood. Gene amplification often results in increased protein abund-ance, but in up to ∼30% of cases the abundance changes do not scale linearly ( Gonçalves et al., 2017 ; Mertins et al., 2016 ; Sousa et al., 2019 ; B. Zhang et al., 2014 ; H. Zhang et al., 2016 ). Recently, the Cancer Cell Line Encyclopedia (DepMap CCLE) provided data for hundreds of cell lines with matched DNA copy number, RNA expression, and pro-tein expression measurement levels ( DepMap, Broad Institute, 2023 ; Ghandi et al., 2019 ; Nusinow et al., 2020 ). Analysis of this dataset revealed that the mRNA abundances of the majority of genes scale with gene dosage changes, while protein abundance frequently changes less than expected based on the chromosome copy number ( Schukken & Sheltzer, 2022 ). Thus, aneuploidy is subject to dosage compensation at the protein level even in cancer cell lines. The existence of dosage compensation has been demonstrated in an-euploid ovarian tumors ( Schukken & Sheltzer, 2022 ). However, a comprehensive overview of dosage compensation across multiple in vivo tumor types remains to be established. Previous studies by Schukken & Sheltzer aimed to identify the mechanisms involved in dosage compensation, but the predictive quality of individual factors analyzed in the study was rather low ( Schukken & Sheltzer, 2022 ). Download figure Open in new tab Figure 1: Quantification of protein dosage compensation in aneuploid cancer cells (A) Illustration of protein dosage compensation upon gene copy number gain (left) and loss (right). (B) Illustration of the buffering classes “Scaling”, “Buffered”, and “Anti-Scaling”. A threshold on the buffering ratio (BR) was used to distinguish “Buffered” (green) from “Scaling” (indigo) observations, while protein log2 fold-change thresholds were used to distinguish “Buffered” and “Anti-Scaling” (grey) observations. (C) Mean change in protein abundance relative to protein abundance baseline for proteins in each buffering class in the DepMap CCLE pan-cancer cell line dataset. (D) Difference in pro-tein abundance between the different buffering classes (shown dataset: DepMap). The mean disomic protein abundance baseline is represented as an orange line. (E) Categorical distribution of buffering classes across pan-cancer datasets (DepMap, ProCan, CPTAC) upon gene copy number gain and loss. (F) Categorical distribution of buffering classes across DepMap CCLE and lab-engineered cell lines upon chromosome arm gain and loss. (G) Difference in buffering ratio distribution of all proteins with either gene copy number gain or loss between pan-cancer datasets (DepMap, ProCan, CPTAC). (H) Difference in buffering ratio distribution upon gene copy number gain and loss across pan-cancer datasets (DepMap, CPTAC). (I) Difference in buffering ratio distribution upon chromo-some arm gain and loss across DepMap CCLE and lab-engineered cell lines. (J) Mean and standard deviation of buffering ratio of proteins across samples from the pan-cancer tumor sample dataset CPTAC. Highlighted are 50 genes with lowest buffering ratio vari-ance among frequently buffered genes across all pan-cancer datasets (DepMap, ProCan, CPTAC). (K) Top 5 enriched terms per selected data source in over-representation ana-lysis (ORA) of highlighted low-variance genes. Here, we present a comprehensive analysis of protein dosage compensation in human samples by developing a novel approach that quantifies compensation at single-gene resol-ution across copy number changes of somatic chromosomes. Exploiting pan-cancer cell line and tumor sample datasets from DepMap CCLE, ProCan, CPTAC, and lab-engineered aneuploid cells lines, our method quantifies dosage compensation for each gene within single samples using focal gene and chromosome-wide copy number alterations, provid-ing increased granularity. By integrating diverse biochemical and genetic variables from multiple resources into explainable machine learning models, we achieved high predictive accuracy, uncovering critical mRNA- and protein-level features that drive compensation of aneuploid somatic chromosomes. Notably, our findings reveal that cancers with low dosage compensation exhibit more vulnerabilities than those with high compensation — a disparity that can be exploited for targeted therapies. Overall, our results position dosage compensation as a factor contributing to tumorigenesis and cancer resilience. Results Calculating protein-level attenuation using absolute gene copy numbers in human cell lines and tumor samples Precise quantification of dosage compensation relies on the analysis of copy number changes across the entire genome matched to the changes observed on the proteome level. In previous studies, copy number changes were commonly evaluated as gains or losses of entire chromosomes or chromosome arms. However, we frequently observed sub-arm copy number changes in tumor tissues and cancer cells ( Figure S1A ). To increase the granu-larity of such analyses, we derived a novel indicator we term Buffering Ratio (BR) which compares both focal gene and chromosome-wide copy number changes against changes in protein abundance. The BR is defined as the difference between protein abundance log2 fold-change (Log2FC) and (gene or chromosome) copy number Log2FC (see Methods). This definition allows the use of numeric gene and chromosome arm copy number differ-ences for buffering quantification, thus increasing the precision of the buffering estimate, compared to a priori categorization of chromosome arm copy number alterations (CNAs) into gain, neutral, and loss as previously applied ( Schukken & Sheltzer, 2022 ). The BR also allows protein buffering quantification for each gene within each sample (cell line or tumor sample), thus improving analysis granularity compared to methods that average protein buffering per gene across a set of samples. Based on the BR and the co-directionality of the protein abundance changes with the gene copy number, we categorized genes into the buffering classes Scaling , if the change in protein abundance was proportional to the change in gene copy number, Buffered if the protein abundance change was less than proportional, and Anti-Scaling if these val-ues were anti-proportional ( Figure 1B ). Scaling and Buffered proteins were distinguished using a threshold on the copy number-dependent BR, whereas Buffered and Anti-Scaling proteins were differentiated using a copy number-independent protein abundance Log2FC threshold. We calculated the BR and buffering classes for each gene and sample using chromosome arm CNAs ( Cohen-Sharir et al., 2021 ) and absolute gene copy numbers ( Carter et al., 2012 ; DepMap, Broad Institute, 2023 ) for the pan-cancer cell line datasets DepMap CCLE containing proteomics data for 375 cell lines, Sanger ProCan with 949 cell lines, and to the pan-cancer tumor sample dataset CPTAC with 1026 tumor samples and 523 normal tissue samples. We removed tumor samples with a purity below 40% to control for tumor purity. Additionally, we analyzed near-diploid p53-deficient human retinal pigment epithelium cell lines (RPE-1) engineered to carry either monosomies or trisomies on single chromosomes ( Chunduri et al., 2021 ; and data from this study). As expected, proteins classified as Scaling using the BR showed a change in protein abundance proportional to the change in copy number, whereas Buffered proteins re-tained near-disomic levels ( Figure 1C & D ). Across datasets, we found that the majority of proteins were either Buffered (38%-53%) or Anti-Scaling (26%-41%) upon gene copy number gain and loss across pan-cancer cell line (DepMap, ProCan) and tumor sample datasets (CPTAC; Figure 1E ). CPTAC had the highest fraction of Buffered proteins upon gene copy number gain (42%), and DepMap had the highest fraction of Buffered proteins upon gene copy number loss (53%). Additionally, the fractions of Buffered observations were higher upon gene copy number loss than upon gain (gain: 38%-42%, loss: 45%-53%), whereas Anti-Scaling was reduced upon copy number loss (gain: 37%-41%, loss: 26%-33%). This pattern was preserved in the pan-cancer cell line dataset DepMap CCLE when using chromosome arm CNA data ( Figure 1F ), and was also preserved when con-trolling for whole genome doubling (WGD; Figure S1C ). Interestingly, lab-engineered cell lines showed less Anti-Scaling proteins compared to pan-cancer datasets and almost all proteins affected by chromosome arm loss were classified as Buffered ( Figure 1F , Supple-mentary Table 1 ). Thus, gene dosage buffering is a widespread phenomenon in engineered aneuploid cells. View this table: View inline View popup Table 1: Factors used for the dosage compensation factor analysis grouped by data source. Preprocessing describes how datasets have been transformed to be used as factors Comparing BR distributions between datasets showed that tumor samples have a signific-antly increased BR upon gain and loss compared to cell lines, in line with the observed in-crease in Buffered and Anti-Scaling proteins ( Figure 1G ). We did not observe a significant correlation between tumor purity and BR that could confound these results ( Figure S1D ). Instead, we observed slightly increased immune and stromal cell invasion scores in tumor samples with high average BRs ( Figure S1D ). To understand the changes in buffering in different conditions, we compared the BR distributions between (gene copy number and chromosome arm) gains and losses. We observed that the BR was significantly higher upon loss than upon gain events in lab-engineered cell lines and pan-cancer cell lines, while in tumor samples the BR was higher upon gene copy number gain ( Figure 1H & I ). This might be caused by the fact that tumor samples harbored increased fractions of genes with strong copy number amplifications ( Figure S1A ). Therefore, tumor samples may also show increased buffering to restore the protein abundance of these genes to near-disomic levels. Interestingly, the BR was lower upon gene copy number loss than upon gain in cell lines that underwent WGD, but was higher upon loss in Non-WGD cell lines (Fig-ure S1). This suggests that buffering might be especially important if only one gene copy is available. We conclude that more protein buffering occurs upon copy number loss than upon gain events in aneuploid cell lines independent of whether these are chromosomal aberrations or focal gene copy number variations. Having calculated the buffering ratios per gene and per sample, we asked which genes are frequently and consistently buffered independent of cell states. To do this, we filtered for genes which were classified as Buffered in more than 33% of samples in all pan-cancer datasets (DepMap, ProCan, CPTAC) and whose BR had a standard deviation smal-ler than 2. We then aggregated their BR standard deviation ranks across datasets. In the top 10 frequently buffered genes with low BR variation were NUP88, NUP214, and NUP58, three subunits of the nuclear pore complex; ribonucleoproteins LARP7, SF3A1, and LSM6; DNA repair proteins XRCC6, and DDB1; proteasomal subunit PSMB1; and VCP, a protein that segregates protein molecules from large protein assemblies to facilitate their degradation in the proteasome ( Figure 1J ). All these proteins are subunits of macro-molecular complexes, which were previously suggested to be subject to extensive dosage compensation ( Stingele et al., 2012 ). The only exception is the segregase VCP/p97, which interacts with many proteins but is not a stable member of a macromolecular complex. Importantly, VCP has a key role in protein quality control, which is often challenged in aneuploid cells ( Gwon et al., 2021 ; Kadowaki et al., 2015 ; Oromendia & Amon, 2014 ; Oromendia et al., 2012 ; Turakhiya et al., 2018 ). Over-representation analysis (ORA) further confirmed that RNA binding and processing, spliceosome, and proteasome genes were significantly enriched among the top 50 frequently buffered genes ( Figure 1K ). This confirms the previous notion that subunits of macromolecular complexes need to be main-tained at stoichiometric levels. These results were reproducible, as we observed a 50% mean correlation of gene copy number-derived BRs between the cell line datasets DepMap and ProCan and a 55% mean correlation for the top 50 frequently buffered genes (Spearman; Figure S1E & F ). Average buffering per sample relates to cellular and clinical fea-tures of cancer Next, we asked whether protein dosage compensation differs among different cell lines and tumor types. Using the same datasets as previously, we calculated sample buffering ratios (sample BRs) by calculating the mean BR per sample (cell line or tumor sample). Calculating the z-scores of sample BRs showed significant differences in average buffering between cell lines ( Figure 2A ). These results were consistent across cell line datasets, as the calculated sample BRs ratios were highly correlated between DepMap and ProCan. The high correlation of the BR allowed us to aggregate the sample BR ranks of cell lines across the datasets using the mean normalized rank (MNR, Upadhya & Ryan, 2022 ) to provide a resource of dataset-independent average buffering estimates for each cell line ( Figure 2B , Supplementary Table 3). Plotting the mean sample BR per cancer type and dataset using the OncoTree classification ( Kundra et al., 2021 ) showed differences in average buffering between cancer types ( Figure 2C ). Among cancer types with low average buffer-ing we observed fibrosarcomas, hepatoblastomas, Erwing sarcomas, neuroblastomas, and myeloid/lymphoid neoplasms, whereas pancreatic neuroendocrine tumors, pleural meso-theliomas, hepatocellular carcinomas, anaplastic thyroid cancers, and invasisve breast carcinomas were among the high-buffering cancer types. In particular, pediatric cancers possessed lower sample BRs on average than adult cancers and that high-buffering tumor samples showed slightly lower patient survival probabilities ( Figure S2C & D ). Interest-ingly, TP53 mutations had a non-uniform effect, but we observed that the sample BR is positively correlated with the aneuploidy score (AS) and average ploidy of cell lines ( Figure 2C & D , Figure S2A,B & E ). Furthermore, separating cell lines into cohorts with or without whole genome doubling (WGD) showed significant differences in sample BR between these cohorts suggesting that WGD-positive cells exhibit more protein buffering on average (Wilcoxon rank-sum test, ProCan: p = 8.0 ∗ 10 − 44 ). We conclude that WGD-positive cells, highly aneuploid cells, and cells from adult cancers exhibit more protein buffering on average and that the degree of aneuploidy and the WGD status are potential confounding factors that need to be controlled for when analyzing average cell sample buffering. Download figure Open in new tab Figure 2: Average buffering per sample relates to cellular and clinical features of cancer (A) Z-score of average buffering ratio per sample derived from pan-cancer cell line data-sets (DepMap, ProCan). The sample buffering ratio z-scores between samples in DepMap and ProCan are highly correlated (Spearman’s ρ = 0.954 , p = 3.6 ∗ 10 − 145 ). (B) Mean normalized ranks (MNR) of sample buffering ratios of cell lines in ProCan and DepMap. (C) Mean sample buffering ratio and aneuploidy score of cancer types (OncoTree code) in pan-cancer datasets. Diamond glyphs indicate the fraction of cell lines grown in suspen-sion cultures. (D) Scatter plot showing a positive correlation between sample buffering ratio and aneuploidy score in the ProCan dataset (Spearman’s ρ = 0.674 , p = 4.2 ∗ 10 − 79 ). Marginal distributions are separated and compared by whole genome doubling (WGD) status (Wilcoxon rank-sum test). (E) Difference in sample buffering ratio between cell lines grown in suspension and adherent cell culture types (ProCan). Controlled for an-euploidy by removing cell lines from the adherent cohort with aneuploidy scores above the maximum of the suspension cohort (middle), and by using stratified sampling to ensure an equal distribution of aneuploidy scores (right). We noticed that non-solid cancers like myeloid and lymphoid neoplasms (MNM, LNM) and other cell lines grown in suspension cultures show significantly lower average protein buffering than those with adherent growth patterns ( Figure 2E ). Since aneuploidy is a confounding factor of the sample BR, and suspension-grown cell lines frequently show low AS, we controlled for differences in the degree of aneuploidy between these two growth pattern cohorts by removing cell lines from the adherent cohort with a higher aneuploidy score than cell lines grown in suspension. We additionally applied a stratified sampling scheme to the adherent growth cohort to ensure equal distributions of the aneuploidy score between the two growth pattern cohorts. Even in this setting, we observed de-creased average buffering in cell lines grown in suspension cultures compared to those with adherent growth patterns ( Figure 2E , Figure S2F & G ). Thus, the average degree of protein buffering differs between cells and cancer types and may be an intrinsic feature affected by cellular physiology and tissue type. Gene dependency, protein complex participation, and random allelic expression are key predictive factors of protein buffering To investigate which factors influence protein buffering, we compiled and integrated multiple datasets of 35 factors describing biochemical and genetic properties of each pro-tein. We then assessed the predictive power of each factor individually to determine its effectiveness in predicting whether a protein is buffered or not by calculating the re-ceiver operating characteristic area under the curve (ROC AUC). We calculate the ROC AUCs on BR-based (per-gene, per-sample) buffering classes derived from absolute gene copy numbers and chromosome arm copy numbers (GeneCN, ChrArm) for all pan-cancer datasets (DepMap, ProCan, CPTAC). Additionally, we determined buffering classes per gene across all samples within a dataset (ChrArm (avg.)) by a previously described method that applies average Log2FC thresholds for grouping proteins into Scaling , Buf-fered , and Anti-Scaling using chromosome arm CNAs categorized into gained, neutral, and lost chromosome arms ( Schukken & Sheltzer, 2022 ). This resulted in three separate analysis variants (Gene CN, ChrArm, ChrArm (avg.)) providing multiple perspectives of differing granularity when analyzing factors contributing to protein buffering. We then aggregated the ROC AUC ranks of all analysis variants (Gene CN, ChrArm, ChrArm (avg.)) and all pan-cancer datasets (DepMap, ProCan, CPTAC) using mean normalized ranks (MNR), to summarize which factors are most relevant in predicting protein buffer-ing upon gain and loss events in all samples, additionally separated by their WGD status (Supplementary Table 4). The most important factors for predicting protein buffering upon gain were the mean dependency score (i.e., essentiality) of a gene across cell lines derived from CRISPR knock-out screens ( DepMap, Broad Institute, 2023 ), the number of protein complexes a protein participates in ( Giurgiu et al., 2019 ), the frequency of random allelic expression (RAE) of a gene compared to bi-allelic expression ( Kravitz et al., 2023 ), the number of ubiquitination and methylation sites on a protein ( Hornbeck et al., 2015 ), the probability of triplosensitivity (i.e., duplication intolerance; Collins et al., 2022 ) of a gene, and the number of transcription factors (TFs) that target the gene ( Badia-i-Mompel et al., 2022 ; Garcia-Alonso et al., 2019 ) ( Figure 3A ). Upon loss, the most important factors were the mean gene dependency score, protein complex participation, the degree of non-exponential decay (NED) of a protein (Δ-score, McShane et al., 2016 ), RAE, and triplosensitivity (Fig-ure 3B). This shows that there is only a partial overlap of the predictive power of factors between gains and losses. While the NED Δ-score and the probability of haploinsuffi-ciency (i.e., deletion intolerance, Collins et al., 2022 ) were more predictive upon loss than upon gain events, the mean mRNA decay rate ( Yang et al., 2003 ) was more important upon gain than loss. Download figure Open in new tab Figure 3: Gene dependency, protein complex participation, and random allelic expression are key predictive factors of protein buffering (A-B) Mean normalized ranks (MNRs) of ROC AUCs of each factor used for classifying whether a gene is Buffered or Scaling on protein level ( Anti-Scaling excluded). Heatmaps show a selection of factors with an MNR of at least 0.5 in one of the shown conditions. (A) MNRs calculated separately for whole genome doubled (WGD) and Non-WGD cell lines of the DepMap dataset across ROC AUCs of all analysis variants for gain events. (B) MNRs calculated separately for whole genome doubled (WGD) and Non-WGD cell lines of the DepMap dataset across ROC AUCs of all analysis variants for loss events. (C) Median ROC AUCs and rank correlation of factor ROC AUCs (Kendall’s τ ) between analysis variants by bootstrapping the ProCan dataset (n = 10000). Only factors with a median ROC AUC of 0.51 across analysis variants are shown. Significance levels: (∗) p < 0.01, (∗∗) p < 0.001, (∗ ∗ ∗) p < 0.0001. We next asked whether we could identify factors whose importance in predicting protein buffering is affected by genome doubling. The number of ubiquitination and methylation sites and the number of protein-protein-interactions of a protein ( Alanis-Lobato et al., 2017 ) were more important in Non-WGD samples upon gain, while the mean mRNA decay rate ( Yang et al., 2003 ), haploinsufficiency probability, and number of TFs with activating modes of action were more important in WGD-positive samples ( Figure 3A ). Comparing factors between WGD and Non-WGD samples upon loss events revealed that the number of TFs (activating + repressing), protein length, and number of phosphorylation sites were more important in Non-WGD samples. In contrast, the number of ubiquitination and acetylation sites, mRNA abundance and transcription rate ( Hausser et al., 2019 ), and the number of protein-protein interactions were more important in WGD samples ( Figure 3B ). Next, we determined whether the predictiveness of factors significantly differs between gain and loss events by bootstrapping buffering classes and calculating ROC AUC rank correlations of factors (see Methods). To control for differences caused by the choice of the analysis variant, we also evaluated the factor rank differences between buffering classes determined either by using gene copy number data or chromosome arm CNA data. The factor rank correlation was high when comparing gene copy number with chromosome arm gain (or loss) analyses, while there was no significant correlation between gain and loss analyses when using the same copy number source ( Figure 3C ). We therefore con-clude that protein buffering is at least partially influenced by different factors upon gain than upon loss, independent of whether copy number aberration occurs on gene level or chromosome arm level. We observed that the maximum achieved ROC AUC using the unifactorial prediction was 0.672, providing a moderate discrimination performance between Buffered and Scal-ing proteins (Supplementary Table 4). Furthermore, the raw values and bootstrapped ROC AUCs of factors such as protein abundance, transcription rate, and mRNA abund-ance were frequently and strongly pairwise correlated, indicating that these factors carry equivalent information in predicting protein buffering ( Figure S3B-D ). Calculating the correlation between factor value and (gene-level) Buffering Ratio confirmed mean gene dependency score, protein complex participation, triplosensitivity, mRNA decay rate, and ubiquitination sites to be important factors, with higher values being predictive for protein buffering ( Figure S3E ). Higher values of random allelic expression frequency and TF count were more predictive for scaling proteins. However, only a weak correlation was achieved (max(| ρ |) = 0.27). We conclude that a unifactorial analysis is able to identify factors with higher predictive power relative to each other, but does not enable a strong prediction of protein buffering in total. Explainable machine learning uncovers directional relationships between predictive factors and protein buffering We considered that using all factors simultaneously in a combined model will improve the prediction. To approach this, we trained a series of machine learning models that use a subset of all collected factors simultaneously to predict whether a gene is Buffered or Scaling , based on the classification scheme established for the unifactorial prediction (Fig-ure S4A). Highly correlated factors that contained redundant information were removed to prevent the models from overfitting on an arbitrary subset of factors. We derived training and test datasets from different analysis variants (Gene CN, ChrArm, ChrArm (avg.)), copy number events (gain, loss), and pan-cancer datasets (DepMap, ProCan, CPTAC), and trained the models on each training set (see Methods). Evaluating the performance of each model on its associated test set showed that multi-factorial models perform better than using individual factors for predicting protein buffer-ing, achieving a strong discrimination performance ( Figure 4A-C ; Figure S4B ). Moreover, the models trained on gene copy number-derived buffering classes consistently outper-formed models using chromosome arm CNA derived buffering classes. Especially upon gene copy number gain, the models showed a higher performance across datasets compared to copy number loss. This is likely because gene copy numbers provide more fine-grained copy number information, which is lost when using chromosome arm copy numbers. This information can then be used in conjunction with the BR to generate more accurate buf-fering classes that can be predicted more confidently. Additionally, the performance of tumor sample derived models (CPTAC, Gene CN gain: ROC AUC = 0.846, loss: ROC AUC = 0.811) surpassed the predictive performance of models trained on cell line data (DepMap, Gene CN gain: ROC AUC = 0.773, loss: ROC AUC = 0.684). Next, we evaluated the prediction performance of the models on unseen datasets (out-of-sample prediction). We observed that models trained on cell line datasets perform well on other cell line datasets upon gene copy number gain, but show a weaker performance on tumor sample data and vice versa ( Figure S4C ). Based on these observations we assume that factors affect protein buffering slightly differently in tumor samples and cell lines. Download figure Open in new tab Figure 4: Explainable machine learning uncovers directional relationships between pre-dictive factors and protein buffering (A-C) ROC curves and AUCs of multifactorial xgbLinear models trained on predicting Buffered and Scaling classes for different copy number events, analysis variants and data-sets. The ROC curves depict the model’s performance on its respective test set (80/20 training/test-split). (D, E) SHAP values generated by evaluating the trained CPTAC gene copy number models on a random subset of the respective test set (n = 300). Negative SHAP values depict a higher contribution of a factor within the model towards predicting an observation as Buffered . Each dot represents a gene in a sample. Color represents the min-max-scaled value of the underlying factor used for prediction in the model for this observation. (F) Heatmap showing the correlation (Spearman’s ρ ) between SHAP values estimated on a random subset of the respective test set (n = 300) and their underlying factor values used for prediction. Negative correlation indicates stronger contribution of a factor towards predicting observations as Buffered if the factor value is high. Correlation is shown for pan-cancer datasets (DepMap, ProCan, CPTAC) upon gene copy number gain and loss with significance levels indicated (Benjamini-Hochberg adjusted p-values, (∗) p adj < 0.01, (∗∗) p adj < 0.001, (∗ ∗ ∗) p adj < 0.0001) To determine how each factor contributed to the prediction of each model, we calcu-lated SHapley Additive exPlanation values (SHAP values, Lundberg & Lee, 2017 ), which quantify the contribution of each feature to a model’s prediction by attributing the differ-ence between the actual and baseline predictions to individual features. As SHAP values can be obtained for each factor and prediction, we calculated SHAP values on a subset of the model’s test set using only observations where the model’s prediction was correct. Negative SHAP values indicated that for a given protein in a sample the model is more confident towards classifying the observation as Buffered using the corresponding factor value. For example, higher values of mean gene dependency were associated with lower SHAP values, meaning that the model was more confident in predicting a protein in a sample as Buffered if the gene is essential in many samples ( Figure 4D & E ). To quantify the direction of influence of a factor in predicting protein buffering, we cal-culated the Spearman correlation between SHAP values and corresponding factor values for each factor. This approach achieved a stronger correlation than by directly correl-ating BRs and factor values as done in the unifactorial analysis (max(| ρ |) = 0.95). We observed that frequent participation in macromolecular complexes and a high mean gene dependency are consistently predictive for Buffered proteins upon gene copy number gain and loss ( Figure 4F ). In contrast, a high number of TFs binding to a gene, high fraction of TFs with activating regulatory modes, high RAE frequency, and high number of sites known for regulating a protein’s function are consistently predictive for Scaling proteins upon gene copy number gain and loss. Interestingly, an increased number of ubiquitina-tion sites is more predictive for Buffered proteins upon gain than upon loss in DepMap and ProCan, whereas in CPTAC an increased number of ubiquitination sites is more pre-dictive for Buffered proteins upon gene copy number loss. We also observed this pattern in the unifactorial correlation analysis ( Figure S3E ). In conclusion, multifactorial machine learning models outperform unifactorial analyses in predicting protein buffering. We showed that increased mean gene dependency and pro-tein complex participation contribute to a higher probability of protein buffering, whereas a higher number of activating TF interactions, protein regulatory sites, and higher RAE frequency contribute to a lower probability of protein buffering. Cancer samples with high average buffering show altered differ-ential expression patterns related to protein folding Next, we investigated if high average protein buffering in cell lines and tumor samples is associated with specific gene expression patterns. Using the previously calculated sample BRs, we split the samples into low- and high-buffering groups, and inspected which pro-teins were differentially expressed in high-buffering samples compared to low buffering samples ( Figure 5A ). We identified the cancer-drivers LMNA (structural protein of the nuclear lamina lamin A), EGFR (transmembrane tyrosine kinase receptor of the epi-dermal growth factor), RRAS (member of the RAS family of small GTPases), CTNNB1 ( β -catenin, a key component of the Wnt signaling pathway), and many others, as sig-nificantly upregulated in high-buffering cell lines in both pan-cancer cell line datasets (DepMap, ProCan). Among proteins downregulated in high-buffering cell lines in ProCan we observed LCP1 (an actin binding protein linked to hereditary cancer syndrome) and DNA methyltransferase DNMT1. ORA of the downregulated genes in cell lines with high average buffering revealed that terms related to protein folding, such as the chaperonin containing complex CCT, DNA replication, and nucleotide binding were enriched (Fig-ure 5B). The CCT/TRiC complex is involved in folding 10% of the proteome including actins, tubulins, and cell cycle regulators ( Gestaut et al., 2019 ). Among the set of up-regulated proteins, integrin complex partners, cell adhesion proteins, cadherin binding proteins, and proteins involved in the organization of the extracellular matrix were en-riched ( Figure 5C ). Download figure Open in new tab Figure 5: Cancer samples with high average buffering show altered differential expression patterns related to protein folding (A) Volcano plot showing a log2 fold-change between high (>80% quantile of sample buffering ratios) and low (<20% quantile) buffering cell lines in the ProCan dataset plotted against the Benjamini-Hochberg adjusted student’s t-test p-values. Labeled are genes with significant differential expression (| Log 2 F C | > 0.5 , p adj < 0.05). Genes with bold labels are significant in all cell line and control datasets. (B, C) Over-representation analysis (ORA) of genes that are significantly down- (B) and up-regulated (C) in highly buffering cell lines. (D) Selected network clusters within the set of significantly down-regulated genes in highly buffering cell lines. Highlighted gene sets: unfolded protein binding (red, GO:MF), nucleotide biosynthesis (blue, Reactome), MCM complex (green, GO:CC), and protein sumoylation (yellow, GO:BP). (E) Selected network clusters within the set of significantly up-regulated genes in highly buffering cell lines. Highlighted gene sets: ECM receptor interaction (red, KEGG), Cadherin binding (blue, GO:MF), unfolded protein binding (green, GO:MF), and lysosome (yellow, KEGG). The rings around the dots indicate the log2-fold change (blue: negative, red: positive). (F) 2D enrichment plot comparing the normalized enrichment scores of MSigDB HALLMARK gene sets between CPTAC and ProCan. Significance cutoff of Benjamini-Hochberg adjusted p-values: p adj < 0.05. (G) Scatter plot showing a negative correlation between sample buffering ratios and single-sample enrichment scores for the Unfolded Protein Response HALLMARK gene set using CPTAC tumor sample proteome data (Spearman’s ρ = −0.362 , p = 8.6 ∗ 10 − 08 ). Color indicates the estimated aneuploidy score of a tumor sample. (H) Scatter plot showing a weak negative correlation between mean normalized buffering ratio ranks and the proliferation (day1-to-day4 growth ratio) of corresponding cell lines (Spearman’s ρ = −0.094 , p = 0.022). Color represents a cell line’s aneuploidy score. We noticed that blood cancers, which are frequently grown in suspension, have particularly low sample BRs. To avoid confounding effects of the cell growth method, we created an adherent control by calculating the differential expression between high and low buffering cell lines that were only grown adherently. Using this adherent control, cell adhesion, cell motility, and extracellular matrix organizing proteins were still upregulated, while nuc-leotide metabolism and biosynthesis proteins remained downregulated in high-buffering cells ( Figure S5A-C ). We furthermore found 27 genes that were commonly deregulated upon high buffering in all cell line and adherent control datasets (Supplementary Table 8). STRING was used to visualize how differentially expressed genes in ProCan are involved in known and predicted protein-protein interactions ( Szklarczyk et al., 2023 ; Figure 5D & E ). In addition to the CCT/TRiC complex, we observed that genes involved in nucleotide biosynthesis, protein sumoylation-related genes, and DNA replication factors associated with the MCM complex were downregulated ( Figure 5D ). The deregulation of the MCM complex has been linked to increased genomic instability and to aneuploidy ( Bochman & Schwacha, 2009 ; Passerini et al., 2016 ; Shima et al., 2007 ). Furthermore, both PCNA and UBE2N were down-regulated; these genes play a role in error-free post-replication repair ( Hoege et al., 2002 ; S. Kim et al., 2024 ; Moldovan et al., 2007 ; Motegi et al., 2008 ; Unk et al., 2008 ). In agreement with the ORA results, clusters containing integrins and proteins related to extra-cellular matrix (ECM) receptor interaction were upregulated ( Figure 5E ). In particular, the integrins ITGA3, ITGA5, and ITGB1 were upregulated in highly buf-fering cell lines; their deregulation has been shown to alter cell adhesion and promote cell invasion ( Fukushi et al., 2004 ; Mueller et al., 1999 ). Cadherin-binding proteins were upregulated as well, including the oncogene EGFR which positively regulates cell migra-tion ( Ghosh et al., 2010 ), and Catenin beta-1 (CTNNB1) involved in the regulation of cell adhesion ( Brembeck et al., 2006 ; W. K. Kim et al., 2019 ). We observed that EGFR, among other oncogenes, had a higher BR upon chromosome arm loss, implying that the upregulation of EGFR in highly buffering cells is due to EGFR itself being buffered upon loss ( Figure S5E-G ). EGFR has been shown to frequently scale with chromosome arm gain ( Schukken & Sheltzer, 2022 ), which has been confirmed in our analysis (Fisher’s Exact test, OR = 0.27 , p = 7.9 ∗ 10 − 4 ). This suggests that cancer cell lines retain elevated EGFR levels independent of chromosome arm gain or loss. Furthermore, the lysosomal proteins Lysosomal-membrane-associated glycoprotein 1 (LAMP1), Cathepsin D (CTSD), and Cathepsin B (CTSB) were upregulated. CTSD and CTSB overexpression as well as increased LAMP1 cell-surface expression is associated with increased metastatic potential and tumor invasion ( Jensen et al., 2013 ; Lee et al., 2024 ; Rochefort et al., 1990 ; Ruan et al., 2016 ; Sarafian et al., 1998 ). Similar to the downregulated genes, we also found clusters of proteins involved in unfolded protein binding in upregulated genes (UGGT1, CANX, TAPBP), playing a role in the retention and quality control of unfolded and misfolded proteins in the endoplasmic reticulum ( Adams et al., 2020 ; Swanton et al., 2003 ). This in combination with the downregulation of the CCT/TRiC complex suggests an increase in protein folding quality control in cell lines with high average protein buffering while demand for overall protein folding may be decreased. To further investigate which cancer hallmarks are deregulated in cells with high average buffering, we applied gene set enrichment analysis (GSEA) using the MSigDB hallmark gene sets and compared enrichment scores between tumor samples and cell lines. MYC target, G2M checkpoint, MTORC1 signaling and E2F target gene sets were significantly downregulated in both high-buffering tumor samples and cell lines, while DNA repair gene sets were significantly downregulated in cell lines ( Figure 5F ). As we identified aneuploidy as a potential confounding variable for BR-based analyses, we also performed GSEA on differential expression data between high and low aneuploid samples and compared the results to the GSEA analysis results between high and low buffering samples. DNA re-pair, E2F target, and MTORC1 signaling pathways were upregulated in highly aneuploid tumor samples, showing that the downregulation of these pathways were unique to tu-mor samples with high average buffering and independent of the influence of aneuploidy ( Figure S5H ). Highly aneuploid cell lines and primary tumors suffer from increased proteotoxic stress and have an increased Unfolded Protein Response (UPR) ( Ippolito et al., 2024 ; Oromen-dia & Amon, 2014). We observed that the UPR gene set was significantly downregulated in tumor samples with high average buffering, but not in cell lines, suggesting that tumor samples could use protein buffering to alleviate proteotoxic stress and do so more effect-ively than cell lines ( Figure 5F ). To test this hypothesis, we applied single-sample GSEA (ssGSEA, Barbie et al., 2009 ) to CPTAC using the UPR hallmark gene set and correlated the single-sample enrichment scores with the sample BR. Indeed, tumor samples that exhibit high average buffering showed a decreased UPR, especially in highly aneuploid WGD-negative samples, implying that protein buffering reduces proteotoxic stress (Fig-ure 5G). Sub-tumor heterogeneity and low tumor purity could potentially confound these results, but the UPR enrichment score and tumor purity were not significantly correl-ated (Spearman’s ρ = −0.025 , p = 0.72; Figure S5 ) and tumors with a purity above 70% exhibited an increased UPR (UPR > 0.2) less frequently than low-purity tumors ( OR = 0.41 , p = 0.021). Futhermore, the correlation between sample BR and UPR en-richment score increased when removing tumor samples with a purity below 50% (Spear-man’s ρ = −0.43 , p = 6.4 ∗ 10 − 3 ), suggesting that the results are not confounded by tumor samples with low purity. Therefore, we asked whether cell lines exhibiting high levels of protein buffering could have a proliferative advantage, as they might be able to better mitigate the proteotoxic stress introduced by aneuploidy. Using growth rates from DMSO-treated cell lines in the GDSC drug screen dataset ( Iorio et al., 2016 ) showed that some cell lines proliferated slightly worse the more aneuploid they were (particularly WGD-positive cell lines: Spear-man’s ρ = −0.270 , p = 8.2 ∗ 10 − 6 ). However, we did not observe a strongly significant correlation between growth rates and average buffering estimates in cell lines ( Figure 5H ). We conclude that high buffering alters protein homeostasis in cell lines, affecting DNA repair, protein folding, and cell adhesion pathways. Interestingly, increased buffering did not provide a general proliferative advantage. CRISPR knock-out screens reveal vulnerabilities of weakly buf-fering cells As high average protein buffering does not appear to provide a general proliferative ad-vantage in aneuploid cell lines, we investigated whether these cell lines show differential dependency on certain genes or if increased buffering mitigates aneuploidy-induced vul-nerabilities. We divided the cell lines into groups with high and low average buffering (>80%, <20% sample BR MNR), and compared the dependency scores of genes between these groups. The dependency scores were derived from CRISPR-KO screens and quantify how dependent a cell line is on a particular gene. Cell lines with high average buffering were more dependent on the oncogenes CRKL (a protein kinase possibly activating RAS and JUN) and Hippo signaling pathway transcription coactivator WWTR1. On the other hand, they are less dependent on the oncogenes MDM4 (a p53 regulator), TAF1 (TATA binding factor); tumor suppressor genes POT1 (telomere-protecting protein), SDHD (sub-unit of the mitochondrial respiratory chain), and a sensor of oxidative stress KEAP1 ( Figure 6A ). Furthermore, ITGAV, FERMT2, PTK2, GPR61 and RELA were exclus-ively essential in high-buffering cells, while CENPI, DROSHA, RNF40, TUBD1, TMX2, RAD1, DPY30, PTBP1, TBCE, MED10, WDR48, TUBE1, and NAMPT were exclus-ively essential in low buffering cell lines. Download figure Open in new tab Figure 6: CRISPR knock-out screens reveal vulnerabilities of weakly buffering cells (A) Volcano plot showing the difference of the CRISPR-KO dependency score between high (>80% quantile of sample BR MNRs) and low (<20% quantile) buffering cell lines plotted against Benjamini-Hochberg adjusted Wilcoxon rank-sum test p-values. (B, C) Over-representation analysis (ORA) of genes with significantly reduced (B) and increased (C) CRISPR-KO dependency scores in highly buffering cell lines. (D) Median drug effect on cell viability across drugs from the PRISM Repurposing dataset for high (>80% quantile of sample BR MNRs) and low (80% quantile of median drug effect) and responsive (<20% quantile) to drug treatment are shown as controls. (E) Drug mechanisms with significant log2 fold-change of mean drug effect between high and low buffering cell lines (Student’s t-test, | Log 2 F C | > 0.2 , p adj < 0.05) and significant correlation between sample BR MNRs and drug effect (Spearman’s ρ ) with significance levels shown (Benjamini-Hochberg adjusted p-values, (∗) p adj < 0.05, (∗∗) p adj < 0.01, (∗ ∗ ∗) p adj < 0.001). ORA of the genes with significant dependency score differences showed that cell lines with high average buffering were less dependent on DNA damage response, DNA repair, and cellular stress response genes than cell lines with low average buffering ( Figure 6B ). Interestingly, high-buffering cells were more dependent on cell adhesion regulation, sub-strate adhesion-dependent cell spreading, kinase binding, and integrin binding genes (Fig-ure 6C). As before we created a control subset of cell lines that were adherently grown, and compared the differential dependency between high and low buffering cell lines within this subset ( Figure S6A , Supplementary Table 9). ORA showed that general transcrip-tion initiation factor binding genes were less essential in high-buffering cell lines (Fig-ure S6B). Furthermore, genes that remained exclusively essential in low buffering cell lines after controlling for growth method were CENPI (Centromere Protein I), RAD1 (RAD1 Checkpoint DNA Exonuclease), TUBD1 (Tubulin Delta 1), and TBCE (tubulin folding protein). This analysis further highlights the physiological differences between high and low buffering cell lines. Next, we analyzed whether samples with different degrees of average protein buffering ex-hibit an altered sensitivity to drug treatment. We collected drug effect scores quantifying the changes in cell viability between drug and DMSO treatment from the PRISM Repur-posing dataset ( Corsello et al., 2020 ; DepMap, Broad Institute, 2023 ), where lower values indicate a decreased growth of the cells after drug treatment compared to the control using DMSO. We categorized cell lines as drug-resistant if their median drug effect was higher than 80% of the drug effect scores, while cell lines with a median drug effect score below the 20% percentile were categorized as drug responsive. As before, we also categorized cell lines as high and low buffering based on the 80% and 20% percentile of the sample BR MNR. Remarkably, the median drug effect on cell viability of high-buffering cells is closer to the drug-resistant cell cohort, and the drugs have a significantly lower median effect on the viability of high-buffering cells compared to cell lines with low average protein buffering, although this difference is small ( Figure 6D ). To test whether high buffering and drug resistance are frequently co-occurring, we separated all available cell lines by median sample BR MNR and median drug effect. This showed that high-buffering cell lines were more frequently drug-resistant than cell lines with a sample BR MNR below median (Fisher’s exact test, OR = 1.525 , p = 2.1 ∗ 10 − 2 ). The increased drug resistance was largely explained by increased aneuploidy, however, high buffering samples were still slightly more frequently drug resistant independent of aneuploidy status ( Figure S6C-E ; High Aneuploidy: OR = 1.088 , p = 0.021; Low Aneuploidy: OR = 1.182 , p = 0.57). While highly buffering cell lines showed no sensitivity to any group of drugs, the low buffering cell lines were significantly more sensitive to HSP, CHK, MDM, IKK, microtu-bule, and tubulin polymerization inhibitors ( Figure 6E ). Thus, cells that cannot efficiently buffer protein imbalance arising from aneuploidy are particularly sensitive to drugs that target protein folding, mitotic spindle function, innate immune response, and DNA dam-age response. As a control, we analyzed the drug effect difference between high and low aneuploid cells. Drug mechanisms that were not significant in highly aneuploid cells and were unique to low buffering cells were CHK and HSP inhibitors (Supplementary Table 10). In conclusion, efficiently buffering aneuploid cells showed increased resistance to drugs in general and did not respond to any of the tested drug mechanisms, indicating that these cells are particularly difficult to treat. Discussion Abnormal chromosomal content, or aneuploidy, strongly alters protein homeostasis due to aberrant expression of genes located on the affected chromosomes. Yet, a significant fraction of the aberrant expression caused by altered gene dosage is mitigated by adjust-ing the protein abundance ( Chunduri et al., 2021 ; Dephoure et al., 2014 ; Stingele et al., 2012 ). This means protein abundance in aneuploid cells often resembles diploid levels rather than the abundance expected based on the gene dosage. Previous research indicated that gene dosage buffering is widespread in aneuploid cell lines and that its degree is affected by certain gene and protein features, such as the number of ubiquitylation sites, or participation in macromolecular complexes ( Schukken & Sheltzer, 2022 ). To obtain additional insight into the factors affecting dosage compensation, we established a novel indicator we termed “Buffering Ratio” (BR), where we quantified the protein-level attenuation for each gene within single samples. This approach was designed to deliver a gene copy number-sensitive quantification of protein abundance changes in an-euploid samples that may be “averaged out” when using protein-average Log2FC cutoffs as in Schukken & Sheltzer ( Figure 1B ). While this approach allows us to evaluate the effect of whole-genome doubling, or identify variability of buffering, it also has its limitations. In particular, the BR is sensitive to noise from both proteomics and copy number data. To circumvent these issues, we calculated confidence scores for the BRs, and removed BR measurements with low confidence. Using this approach, we demonstrate that gene dosage compensation occurs not only in aneuploid in vitro cancer cell lines but also extensively and efficiently in aneuploid in vivo tumor samples ( Figure 1 ). The widespread phenomenon of gene dosage buffering high-lights the importance of maintaining protein homeostasis despite aberrant chromosome and gene copy numbers. While buffering in pan-cancer cell lines and tumor samples is comparable, chromosome-engineered aneuploid human cell lines are buffered to a greater extent. The underlying causes of these differences remain unclear. We hypothesize that the distinct genomic alterations in each sample type may play a role: engineered cell lines exhibit gains and losses of individual chromosomes, whereas cancer samples typic-ally harbor copy number variations affecting multiple chromosomes, as well as plethora of mutations. To identify factors that contribute to gene dosage buffering, we applied both unifactorial and multifactorial models to analyze their effects and predictive capacity. While uni-factorial models predict factors contributing to buffering with a low predictive power (∼0.6 ROC AUC, Figure 3 ; Schukken & Sheltzer, 2022 ), our novel multifactorial ML models per-formed significantly better, with up to 0.85 ROC AUC ( Figure 4 ). Using these models, we established gene dependency as the key factor in gene dosage buffering after chromosome or gene gains and losses in all analyzed datasets. This demonstrates that the abundance of essential genes must be maintained at optimal levels independently of the correspond-ing gene copy number. Additionally, being a member of macromolecular complexes, as well as having a high number of ubiquitylated sites is strongly predictive of buffering, as previously observed in other works ( Figure 3 & 4 ; Ishikawa et al., 2017 ; Schukken & Sheltzer, 2022 ; Stingele et al., 2012 ). This supports the hypothesis that protein buffer-ing is in parts conveyed by degradation of non-stochiometric subunits of macromolecular complexes. In contrast, scaling was affected mainly by transcriptional features (tran-scription factors, bi-allelic expression, etc.) and mRNA turnover. Remarkably, we found a different predictive power of individual factors for buffering protein abundance upon gene/chromosome gain and loss ( Figure 3 ). This suggests that chromosome gains and losses may affect cellular physiology differently and mitigation of the protein imbalance occurs partly by different mechanisms. Out-of-sample prediction showed that our models performed well across datasets, highlighting the reproducibility of our results. However, the predictiveness differed between ML models trained on cell line datasets compared to tumor sample-trained models. This suggests, that tumor samples buffer changes in pro-tein abundance to a different extent than cell lines do. Our approach of buffering ratio quantification also allowed us to differentiate the con-sequences of gene copy number changes among cell lines and tumor types. Some cell lines were extremely effective in buffering, while others did not compensate for the changes in copy number as much ( Figure 2 ). Tumor samples also showed this variability. Cell lines grown in suspension showed lower levels of average buffering than cell lines with adherent growth patterns. Interestingly, our analysis suggests that aneuploidy is a strong confound-ing factor of gene dosage buffering, as we observed a strong correlation of the BR with the respective aneuploidy score (AS) of each cell line. Separating aneuploidy and buffering is difficult, as only aneuploid cells present a protein imbalance that can be buffered. The fact that highly aneuploid cells buffer the gene dosage more efficiently than cells with low AS may also suggest that aneuploid cells adaptively rewire their gene expression network to minimize the impact of gene dosage changes. Other possible confounding factors (e.g., p53 status) showed negligible effects. We hypothesize that relatively low levels of buffering in pediatric cancers suggest that these cancers developed quickly and have not adapted to altered protein homeostasis. In contrast, the cancer types with increased buffering sug-gest an etiology related to environmental and age-related factors. These cells might have gained different aneuploidies in multiple steps and adapted to aneuploid-induced stresses through buffering, leading to a more balanced proteostasis. We found that cell lines with highly efficient gene dosage buffering downregulated the chaperonin complex CCT/TRiC, replication factors, and factors required for nucleotide biosynthesis. In contrast, pathways related to integrin signaling, extracellular matrix, and cell adhesion were upregulated in highly buffering aneuploid cells, even when controlling for confounding factors. Our analyses point out that protein folding plays a critical role in buffering, and that buffering correlates with DNA and RNA metabolism, as well as with extracellular matrix regulation ( Figure 5 ). But which of these consequences are due to the efficient buffering, and what is a cellular response to aneuploidy? To disentangle these interlinked phenomena, we performed gene set enrichment analysis (GSEA) for high vs. low buffering samples and compared the results with analysis in high vs. low aneuploid samples. This approach clearly shows that DNA repair, E2F targets, and mTORC1 tar-gets are differentially regulated in samples with high buffering ratio, independently of aneuploidy. Tumor samples with higher average buffering also showed a reduced unfolded protein response, particularly in WGD-negative, but highly aneuploid cells. Controlling for confounding effects of tumor heterogeneity confirmed these results, indicating that buffering alleviates proteotoxic stress introduced by aneuploidy. Furthermore, the upreg-ulation of oncogenes in high-buffering cells suggests that buffering affects the oncogenic potential of cells, in particular by increasing the abundance EGFR upon loss of chromo-some arm 7p ( Figure 5A , S5E-G ). The differences in buffering capacity between tumor samples and cell lines, as well as between cancer types, raised the question of whether efficient buffering influences their growth, survival, and response to treatment. This was suggested by recent observa-tions that naturally occurring adapted aneuploid strains of Saccharomyces cerevisiae effi-ciently buffer the gene dosage by increasing their ubiquitin-proteasome-dependent protein turnover ( Muenzner et al., 2024 ). Intriguingly, we observed no direct improvement of pro-liferation in efficiently buffering cell lines compared to low-buffering cells. In fact, highly aneuploid cancer cell lines showed a decreased proliferation compared to low aneuploid cells in-vitro . Furthermore, we observed that increased buffering is associated with high aneuploidy. We, therefore, propose that buffering mitigates the fitness costs of aneuploidy and thus enables increased tolerance to aneuploidy. It should be noted that this analysis has been performed only for cell lines in a pan-cancer analysis that does not control for genetic and tissue-specific differences. The conditions may be different from in vivo tumor growth. However, There are no data currently available which would allow to directly test the hypothesis that increased buffering is an adaptive feature and facilitates the pro-liferation of aneuploid tumors. Finally, we considered the possibility that the buffering capacity may affect gene depend-ency or drug sensitivity. Strikingly, we found that high-buffering cells exhibit increased drug resistance and are less dependent on genes related to DNA damage repair and re-sponse to cellular stresses, but more dependent on genes involved in cell adhesion. This pattern aligns with their differential expression profiles ( Figure 5 & 6 ), but the surpris-ing link between cell adhesion and gene dosage buffering remains unclear. We note that increased aneuploidy is likely the dominating cause of increased drug resistance in high-buffering cells ( Andrade et al., 2023 ). In contrast, low-buffering cells are generally more sensitive, in particular to HSP, MDM, CHK, IKK, microtubule, and tubulin polymeriza-tion inhibitors. Intriguingly, low-buffering cells remained sensitive to HSP and CHK in-hibitors even when controlling for aneuploidy. These findings support the hypothesis that low-buffering cells experience greater proteotoxic stress and that inhibiting chaperones in these cells further exacerbate these stresses, thereby reducing growth. Furthermore, TUBD1 (Tubulin Delta 1), TBCE (tubulin folding protein), and CENPI (Centromere Protein I) participate in the correct formation of the mitotic spindle and are essential in low buffering cells, suggesting that cells with weakly buffered protein abundance rely on the proper progression of mitosis, as they cannot tolerate increased aneuploidy. Based on our results we hypothesize that cells that efficiently buffer changes in protein abundance have an adaptive advantage by altering the adhesion to the extracellular mat-rix, and alleviating aneuploidy-induced proteotoxic stress. Future research should uncover the links between gene dosage buffering and further factors identified in our analysis. Methods Cell Line Generation p53 deficient hTert immortalized human retinal pigment epithelium (RPE1p53-/-; Mardin et al., 2015 ) cell lines were cultured in DMEM supplemented with GlutaMAX™, 10% fetal bovine serum (FBS; Gibco), and 1% Penicillin-Streptomycin (Gibco). All cell lines were cultured at 37 ◦ C in a humidified incubator with 5% CO 2 . To generate aneuploid cell lines we used an adapted protocol from Soto et al. based on a combination of CENP-E and MPS1 inhibition ( Soto et al., 2017 ). Stable cell lines were grown and sequenced to determ-ine the karyotype. The obtained aneuploid cell lines were labeled RM13 (monosomy 13) and Rtr13 (trisomy 13). Cell lines were routinely checked for mycoplasma contamination (MycoStrip detection kit by InvivoGen) and authenticated through sequencing. MS Sample Preparation and Labeling of Peptides by Tandem Mass Tags (TMT) Peptide preparation and TMT labeling were carried out following the manufacturer’s protocol. Briefly, cells were harvested by trypsinization from plates. 10 6 cells were washed twice with PBS, pelleted, and stored at −80 ◦ C. Cells were lysed by adding 100 μL lysis buffer (10% SDS in 100 mM Triethylammonium bicarbonate (TEAB)) us-ing strong ultrasonication. The lysates were clarified via centrifugation at 16, 000 × g for 10 minutes at 4 ◦ C, and protein concentrations were determined using the BCA protein assay kit (Thermo Scientific). 50 μg of protein were reduced using 5 mM Tris 2-carboxyethylphosphine (TCEP) for 1 hour at 55 ◦ C and alkylated with 10 mM iodo-acetamide in the dark for 30 minutes at 25 ◦ C. The proteins were then precipitated overnight at −20 ◦ C by adding six volumes of acetone. After precipitation, the pro-teins were resuspended in 100 mM TEAB (pH 8.5) and digested overnight at 37 ◦ C with sequencing-grade modified trypsin. For TMT labeling, the trypsin-digested pep-tide samples were labeled with isobaric tags (TMT 10-plex, Thermo Fisher Scientific), with individual tags assigned as follows: RM13_01 (TMT126), RM13_02 (TMT127N), RM13_03 (TMT127C), RPE1_p53-/-_01 (TMT128N), RPE1_p53-/-_01 (TMT1228C), RPE1_p53-/-_03 (TMT129N), Rtr13_01 (TMT129C), Rtr13_02 (TMT130N), Rtr13_03 (TMT130C), and equal mixture of all cell samples (TMT131). After labeling the reaction was quenched by adding 5% hydroxylamine and incubation for 15 min at room temperat-ure. Then all samples were combined in equal amounts and dried under vacuum. Pooled peptides were fractionated into eight fractions (5, 10, 12.5, 15, 17.5, 20, 22.5, 25, 50% ACN) using the Pierce ™ High pH Reversed Phase Peptide Fractionation Kit (Thermo Scientific). Each fraction was dried under vacuum, resuspended in buffer A (0.1% formic acid) containing 0.01% trifluoro acetic acid and analyzed using nanoflow liquid chro-matography coupled to a Q-Exactive HF mass spectrometer (Thermo Fisher Scientific). Peptide separation was carried out with an EASY-nLC 1200 ultra-high-pressure liquid chromatography system using chromatography columns (50 cm, 75 mm inner diameter) packed in house with ReproSil-Pur C18-AQ 1.9 μm resin (Dr. Maisch GmbH). Peptides were loaded in buffer A (0.1% formic acid) and eluted with a non-linear gradient of 5-60% buffer B (0.1% formic acid, 80% acetonitrile) at a rate of 250 nL / min over 180 min. Column temperature was maintained at 60 ◦ C. Data acquisition alternated between a full scan (120 K resolution, maximum injection time of 8 ms, AGC target of 3e6) and 15 data-dependent MS/MS scans (60 K resolution, maximum injection time of 100 ms, AGC target of 1e5). The isolation window was 0.7 m/z, and normalized collision energy was 32. Dynamic exclusion was set to 30 seconds and fixed first mass to 100 m/z. Mass spectrometry data were processed using MaxQuant software (version 2.0.1.0). The data were searched against the human reference proteome database (UniProt: UP000005640) with a false discovery rate (FDR) of less than 1% for peptides and proteins. All raw files and MaxQuant output tables have been deposited in the PRIDE repository. Data Acquisition and Preprocessing We note that different datasets use different types of identifiers to refer to a gene or protein. For all datasets we mapped different ID types to HGNC gene symbols and UniProt IDs using the biomaRt and AnnotationDbi R-packages, and updated gene symbols with the HGNChelper package ( Durinck et al., 2005 ; Durinck et al., 2017 ; Hervé Pagès, 2017 ; Oh et al., 2022 ). Dosage Compensation Factor Datasets We use a series of data sources to identify the factors affecting dosage compensation. In Table 1 we list the datasets, how the data in the dataset has been processed, and their usage for each factor. Missing values of counted factors (e.g., known PTM sites, protein interactions, protein complex participation) were imputed with 0. The factors were combined by an inner join on gene symbol and UniProt ID. Genes with more than 25% of features missing were removed. DNA Copy Number Datasets For the cell line datasets DepMap and ProCan we obtained absolute gene copy numbers generated with the ABSOLUTE algorithm and chromosome arm copy number alteration (CNA) calls from the DepMap CCLE 23Q2 release ( Carter et al., 2012 ; Cohen-Sharir et al., 2021 ; DepMap, Broad Institute, 2023 ). We downloaded the absolute gene copy numbers for the in vivo tumor samples from the genomic data commons (GDC) data portal of the National Cancer Institute (NCI) at the National Institutes of Health (NIH), a part of the U.S. Department of Health and Human Services, using the TCGAbiolinks R package ( Antonio Colaprico, 2017 ; Colaprico et al., 2016 ; Mounir et al., 2019 ; Silva et al., 2016 ). The copy numbers were generated using the AscatNGS algorithm which infers the absolute copy number by comparing whole genome sequencing (WGS) data between tumor samples and matched normal samples ( Raine et al., 2016 ). The parameters used for the GDC query were: project = “CPTAC-3”, workflow.type = “AscatNGS”, data.category = “Copy Number Variation”, data.type = “Gene Level Copy Number” . Proteomics Datasets We downloaded pan-cancer cell line proteome and model annotation data from the DepMap CCLE project of the Broad Institute ( Dephoure et al., 2014 ; DepMap, Broad Institute, 2023 ; DepMap portal; 375 cell lines) and the Cell Model Passports ProCan project of the Wellcome Sanger Institute ( Gonçalves et al., 2022 ; Cell Model Passports portal; 949 cell lines). We downloaded pan-cancer proteome and clinical data for in vivo tumor samples from the CPTAC project of the NCI ( Li et al., 2023 ; Proteomic Data Commons; 1026 tumor samples, 523 normal tissue samples; BCM harmonized dataset). We measured the proteome of engineered cell lines in three biological replicates for each RM13, Rtr13, and RPE1p53-/-; this dataset was named P0211 ( Figure S7 ). The dataset of engineered cell lines was created by combining P0211 with previously published data from cell lines: RPE1 p53 KO, RM 10;18, RM 13, RM 19p ( Chunduri et al., 2021 ; PRIDE Accession: PXD018440). We only used cell line and tumor samples with proteomics data with matched chromo-some arm or gene copy number data. From all pan-cancer proteomics data, we removed measurements from proteins encoded on non-autosomal chromosomes. To avoid a dis-proportional influence of measurement noise, we removed values below 0.1% of protein abundance measurements within each dataset. To ensure the reproducibility of our results across datasets we used the protein-protein reproducibility ranks from Upadhya & Ryan and removed proteins with reproducibility ranks below the 10% percentile ( Upadhya & Ryan, 2022 ). We normalized the measurements between samples within a dataset with cyclic LOESS using the normalizeBetweenArrays() function of the limma R-package ( Ritchie et al., 2015 ). When analyzing pan-cancer cell lines we only included genes in our buffering analysis that had protein expression data in at least 10 cell lines in which the chromosome arm that encoded this gene was gained, 10 cell lines where this chro-mosome arm was lost, and 10 cell lines in which this chromosome arm had a neutral ploidy (see Schukken & Sheltzer, 2022 ). We decreased this threshold to three cell lines per chromosome arm CNA group for the dataset of lab-engineered cell lines. Aneuploidy Score, WGD, CRISPR-KO, Drug Screen, and Mutation Datasets For cell lines, we obtained aneuploidy scores, whole genome doubling, and ploidy calls from the DepMap portal ( Cohen-Sharir et al., 2021 ; DepMap, Broad Institute, 2023 ). We estimated the aneuploidy score of tumor samples similar to previous approaches (Böken-kamp et al., 2025; Taylor et al., 2018 ). Using absolute gene copy numbers obtained for CPTAC, we called gene copy number gain and loss by determining if the copy number was above/below the rounded average ploidy of the tumor sample. We called chromosome arm gain and loss by determining if over 75% of genes encoded on the chromosome arm had copy number gains/losses. We then estimated the aneuploidy score by counting the number of chromosome arm copy number gain and loss events in a tumor sample. To obtain a list of cancer driver genes we downloaded the cancer gene list from OncoKB ™ and included genes that were present in at least two sources ( https://www.oncokb.org/ cancer-genes; Chakravarty et al., 2017 ; Suehnholz et al., 2024 ). Drug sensitivity data and compound metadata was obtained from the PRISM Repurposing screens through the DepMap 23Q2 release (DepMap portal; Corsello et al., 2020 ; DepMap, Broad Institute, 2023 ). Gene dependency scores were obtained from gene dependency probability estim-ates, generated from CRISPR Knock-Out screens using the Chronos algorithm, through the DepMap 23Q2 release (DepMap portal; Dempster et al., 2019 , 2021 ; DepMap, Broad Institute, 2023 ; Meyers et al., 2017 ; Pacini et al., 2021 ). The matrix of damaging muta-tions obtained through the DepMap 23Q2 release was used to derive TP53 mutation status (DepMap portal; DepMap, Broad Institute, 2023 ). Buffering Ratio We define the buffering ratio (BR) for quantifying the degree of dosage compensation as The variables c o and p o represent the raw copy number and protein abundance of a gene observed in a sample, while c b > 0 and p b > 0 are estimated baselines for the copy number and protein abundance of the gene across multiple samples. The sign function obtains the sign (+1 or −1) of the difference c o − c b . Confidence Score The BR is influenced by variability and noise in the proteomics measurement and has by definition an increased sensitivity to changes in the protein abundance, when the change in copy number is small. We defined a confidence score for the BR to estimate the uncertainty of the buffering estimate based on these factors: p n is a vector that contains the protein abundances of a gene in all samples within a dataset where the copy number of that gene was neutral, meaning there was neither a gain or a loss of copy number relative to the copy number baseline. is the sample coefficient of variation (CV) applied to p n . Buffering Classes BR-based We defined the scaling direction of protein abundance and copy number as positive, if the observed protein abundance and copy number change in the same direction relative to the baseline (formally: ( c o − c b ) ∗ ( p o − p b ) > 0), and negative otherwise. A negative scaling direction implies that the protein abundance increased while the copy number decreased relative to the baseline or vice versa. Using protein abundance log2 fold-change (Log2FC) based thresholds, BR based thresholds, and the scaling direction we classified genes as Scaling , Buffered , or Anti-Scaling ( Table 2 ). View this table: View inline View popup Download powerpoint Table 2: Thresholds used for classifying genes as Scaling , Buffered , and Anti-Scaling using the Buffering Ratio Log2FC-based As a control we classified genes as Scaling , Buffered , or Anti-Scaling using Log2FC-based thresholds only as previously published ( Table 3 ; Schukken & Sheltzer, 2022 ). View this table: View inline View popup Download powerpoint Table 3: Log2FC thresholds used for classifying genes as Scaling , Buffered , and Anti-Scaling Protein Dosage Compensation Analysis We analyzed protein dosage compensation using both absolute gene copy numbers (CNs, ABSOLUTE algorithm) and chromosome arm copy number alterations (CNAs). We defined four different analysis variants ( Table 4 ) that use different copy number data sources, different classification methods, and produce buffering classes either for each gene in each sample (per-gene, per-sample) or for each gene across all samples (per-gene). View this table: View inline View popup Download powerpoint Table 4: Variants of the protein dosage compensation analysis For the analysis variant GeneCN, we calculated the copy number and protein abund-ance baselines c b and p b for each gene as the median absolute gene CN and median protein abundance across all samples where the respective chromosome was disomic. We then cal-culated BR, the confidence score of the BR, and determined BR-based buffering classes using the protein abundances and absolute gene CNs of each gene within each sample. For ChrArm we applied a similar method but used the rounded ploidy of the sample as the copy number baseline c b and added the chromosome arm CNA (either +1, 0, or −1) to c b to obtain the observed copy number c o . ChrArm (Log2FC) uses the same baseline estimation as ChrArm but uses thresholds based on the Log2FC between observed and baseline protein abundance to determine buffering classes. For ChrArm (avg.) we used the method described by Schukken & Sheltzer: For each gene we separated the samples into gain, neutral, and loss cohorts based on the chromosome arm CNA for the chromo-some that encodes this specific gene. We then calculated the mean protein abundance Log2FC between gain and neutral, and between loss and neutral cohorts, and applied Log2FC-based thresholds to obtain the buffering classes ( Schukken & Sheltzer, 2022 ). For the tumor sample dataset CPTAC we applied the GeneCN analysis variant differently by assuming a copy number baseline of c b = 2 for all genes, using the median abundance of each protein within normal (non-tumor) samples as the protein baseline p b , using ab-solute copy numbers generated using the AscatNGS algorithm as observed copy numbers c o , and using the protein abundance of the corresponding tumor samples as the observed protein abundance p o . Tumor samples with no corresponding normal samples or tumor samples with a purity below 40% were removed. Rank Aggregation We implemented the mean normalized rank (MNR) to aggregate and unify estimates (e.g. sample BR and ROC AUCs) from different datasets or analysis variants ( Upadhya & Ryan, 2022 ). Let S 1 , . . ., S n be n ∈ ℕ sets, each containing m features, meaning | S 1 | = · · · = | S n | = m . These features can either have present or missing values. For each set S i , with i ∈ {1 , . . ., n }, we calculated the rank of each feature with value in S i and divided the rank by the number of features m . We then removed features that have more than ⌊ n/ 2⌋ missing ranks across all n sets. Finally, we obtained the MNR for each feature f j , j ∈ {1 , . . ., m } by calculating the arithmetic mean of the ranks of the feature f i across all sets S 1 , . . ., S n . Buffering Ratio Variance Analysis To determine which genes were consistently buffered, we analyzed the variance of the BR for each gene. For each gene within DepMap, ProCan, and CPTAC, we calculated the mean and standard deviation of the BR (GeneCN analysis variant), the number of samples with an observable BR, the fraction of samples where this gene was classified buffered, and performed a one-sided one-sample Wilcoxon signed-rank test to determine if the BR was significantly above the BR threshold for the Buffered class. Within each dataset, we retained genes with more than 20 BR observations, more than 33% of Buffered observations, a Buffered -fraction higher than the median Buffered -fraction of all genes, a mean BR above the Buffered threshold ( BR > 0.2), a significant Benjamini-Hochberg adjusted p-value ( p adj < 0.05), and a BR standard deviation below 2. We calculated the mean normalized rank (MNR) of the BR standard deviation for each gene across all datasets, and selected 50 genes with the lowest MNR as consistently buffered. Sample Buffering Ratio We defined the sample buffering ratio (sample BR) to quantify the average degree of buffering a sample (aneuploid cell line or tumor sample) exhibits. We removed all non-aneuploid samples, i.e., samples with an aneuploidy score of 0 and a rounded average ploidy of 2. We then removed BR values of genes within a sample with low confidence scores ( BR c < 0.3) and with a low absolute copy number Log2FC (|log 2 | < 0.3). Furthermore, we only kept genes that were common across DepMap, ProCan, and CPTAC to ensure comparability of the sample BRs between different proteomics approaches and datasets. Next, we removed all samples that had less than 50 non-missing gene copy number derived BR values left after filtering. Finally, the sample BRs were calculated as the mean gene copy number derived BR (GeneCN analysis variant) across all genes within a sample after filtering. We calculated the z-scores by standardizing the sample BRs within each proteomics dataset they were derived from. Analysis of Sample Properties and Cancer Types Cancer type annotations from DepMap, ProCan, and CPTAC were mapped to OncoTree codes with the same hierarchy (level 2) using the mskcc.oncotree R-package ( Kundra et al., 2021 ). When analyzing sample BR differences between growth patterns (adher-ent/suspension) we controlled for low aneuploidy by removing cell lines from the adher-ent cohort with aneuploidy scores above the maximum of the suspension cohort. We controlled for equal aneuploidy by resampling the adherent (target) cohort based on the aneuploidy score distribution in the suspension (reference) cohort. We divided the range of aneuploidy scores into k = 6 equal strata by applying the quantile function to the reference cohort aneuploidy scores. We then calculated the fraction of values that fell into each aneuploidy score stratum in the reference and target cohort. For each stratum, we divided the reference sample fraction by the target sample fraction and used this value as the probability for randomly sampling cell lines from the target cohort in the current stratum (without replacement). We then verified that the aneuploidy score difference between the two cohorts is not significantly different after resampling using a Wilcoxon rank-sum (Mann-Whitney U) test. We chose the number of strata to be k = 6 using this test condition. Unifactorial Analysis We performed this analysis to understand how well each factor individually predicts whether a gene is buffered or not. We filtered for genes in samples with copy num-ber or chromosome arm gain or loss, by comparing observed copy number against the baseline copy number (for ChrArm, GeneCN) or by filtering for non-zero chromosome arm CNAs (for ChrArm (avg.)). We established a binary classification scheme by remov-ing Anti-Scaling observations, setting Buffered observations as the case (true) and Scaling as the control (false) class. The receiver operating characteristic area under the curve (ROC AUC) was calculated for each factor, within each analysis variant, dataset, and copy number condition (gain, loss) using the pROC package ( Robin et al., 2011 ). WGD-controlled results for DepMap and ProCan were generated by separating WGD-positive and WGD-negative samples, determining buffering classes for all analysis vari-ants, and calculating unifactorial ROC AUCs as described above. We then calculated mean normalized ranks of the ROC AUCs for each factor and copy number condition (gain, loss) combining all analysis variants across all datasets, all WGD-positive datasets, and all WGD-negative datasets. Bootstrapped Analysis To determine significant differences of factor ROC AUCs between copy number conditions and analysis variants, we applied filtering and binary classification schemes for each data-set, analysis variant, and copy number condition as described above, bootstrapped the resulting datasets ( n = 10000 random samples each, with replacement), and calculated ROC AUCs for each factor and bootstrapped dataset. Bootstrapping the ROC AUCs was parallelized using the parallel R-package. We calculated the rank correlation of factors between gain and loss conditions within BR-based analysis variants (ChrArm, GeneCN), and between analysis variants within gain and loss conditions using Kendall’s rank correl-ation coefficient. We removed noisy factors with low predictiveness (median ROC AUC < 0.51) from the rank correlation and further excluded factors that were highly correlated with included factors and contained redundant information (protein/mRNA abundance, activating/repressing transcription factors). BR-Factor Correlation Analysis Correlations between Buffering Ratio (GeneCN) and factor values for each factor, copy number event, and pan-cancer dataset (DepMap, ProCan, CPTAC) were obtained using Spearman’s rank correlation coefficient and p-values were adjusted by the Benjamini-Yekutieli procedure. Multifactorial Analysis We trained machine learning models to use all available factors for predicting protein buffering. Before training we imputed missing values of each factor with its median value and applied min-max normalization to improve convergence during training. For each dataset, analysis variant, and copy number condition we then filtered for gain or loss, and established a binary classification scheme as described in the unifactorial analysis. The observations in the resulting datasets were then shuffled and split into training and test subsets using a 80/20-split. Models were trained on each of the training datasets using the caret R-package with 3-fold cross validation for hyperparameter tuning ( Kuhn, 2008 ). Model performance was evaluated by predicting Buffered / Scaling labels in the associated test dataset of the model and calculating the ROC AUC on the Buffered class probab-ilities returned by the model. We trained xgbLinear (extreme gradient boosting using linear classifiers), rf (random forest), and pcaNNet (artificial neural network with prior PCA feature transformation) model architectures available in caret , however xgbLinear produced the highest ROC AUCs in pan-cancer datasets (DepMap, ProCan, CPTAC) among these architectures. Before training we removed factors that were highly correl-ated, provided redundant information, and confounded the interpretability of the model (e.g. due to overfitting): “Homology Score”, “Protein Abundance”, “mRNA Abundance”, “Transcription Factors (Repressor)”, “Transcription Factors (Activator)”, “Triplosensitiv-ity”. Feature Explanation We removed observations from a model’s test set where the model’s prediction was in-correct, and calculated SHapley Additive exPlanation values (SHAP values, Lundberg & Lee, 2017 ) for a random subset of the remaining observations for each feature of the model (300 Buffered , 300 Scaling observations). To calculate SHAP values, we used a variant of the KernelSHAP algorithm that takes the statistical dependence between features into account and allows for categorically distributed features by using the ctree method of the shapr R-package ( Aas et al., 2021 ; Redelmeier et al., 2020 ). We specified ϕ 0 , i.e., the expected prediction without any features, as the fraction of Buffered observations in the training set of the model, and set the number of sampled feature combinations to 300. Of the trained model architectures, shapr only supported calculating SHAP values for xgbLinear . To understand what feature values lead to a higher chance of predicting an observation as Buffered or Scaling when including this feature in the model, we cal-culated for each feature the Spearman’s correlation coefficient between SHAP values and the associated values of the feature at every observation. Out-of-sample Evaluation To evaluate the performance of a model across datasets, analysis variants, and copy num-ber conditions, we evaluated the performance of each model on the test set of every other model using the ROC AUC on the Buffered class prediction probabilities as described above. Differential Expression Analysis Samples were considered low-buffering if their sample BR was below the 20% percentile of sample BRs and high-buffering if their sample BR was above the 80% percentile. For each gene, we calculated mean protein abundance log 2 fold-changes (log 2 p high − log 2 p low ), per-formed unpaired two-tailed Welch’s unequal variances t-tests, and adjusted the p-values using the Benjamini-Hochberg procedure to determine significant protein abundance dif-ferences between high- and low-buffering samples. Genes were considered significantly deregulated if | Log 2 F C | > 0.5 and if the adjusted p-value was below 0.05. We created an additional adherent control dataset by removing all cell lines with non-adherent growth patterns from the ProCan dataset. We then performed splitting and differential expres-sion analysis on this dataset as described above. Genes were called to be commonly deregulated in high/low buffering cell lines if they were significantly up-/downregulated in DepMap, ProCan, and the adherent control. Enrichment Analysis Over-representation analysis was performed using the gprofiler2 R-package. Plots show the five most enriched terms within each selected term dataset (e.g. KEGG pathway, Gene Ontology, CORUM; Kolberg et al., 2023 ). We performed gene set enrichment analysis (GSEA) for DepMap, ProCan, and CPTAC using the human hallmark gene sets of the molecular signatures database (MSigDB; Liberzon et al., 2015 ; Subramanian et al., 2005 ). Normalized enrichment scores have been generated with the fGSEA R-package using the log2 fold-change ranked lists of genes from the differential expression analysis ( Korotkevich et al., 2016 ). We generated aneuploid controls for DepMap, ProCan, and CPTAC by per-forming differential expression analysis on cell lines separated by aneuploidy score (>80%, <20%) and applying GSEA on the Log2FC ranks. Network analysis of significantly dereg-ulated genes has been performed with STRING using the STRINGdb R package ( Szklarczyk et al., 2023 ). We retained edges in the network with a 90% confidence or higher, removed disconnected nodes, and performed clustering using the MCL algorithm with an inflation parameter of 2. Proteotoxic Stress Analysis To estimate the degree of proteotoxic stress per tumor sample, we performed single-sample GSEA (ssGSEA) using the GSVA R-package, the MSigDB human hallmark gene sets, and protein abundance measurements from the CPTAC dataset ( Hänzelmann et al., 2013 ). We then calculated Spearman’s rank correlation coefficient between enrichment scores for the UNFOLDED_PROTEIN_RESPONSE gene set and the sample BRs of the tumor samples. Proliferation Analysis To estimate the proliferation we obtained the day4-day1 growth ratios from DMSO-treated cell lines in the GDSC drug screen dataset, removed ratios with less than three replicates, and used the ratio with the most replicates for each cell line (Cell Model Passports portal; Iorio et al., 2016 ). Spearman’s rank correlation coefficient was calculated between day4-to-day1 growth ratios of cell lines, sample BRs calculated for the ProCan and DepMap datasets, and sample BR mean normalized ranks. Gene Essentiality Analysis Differential dependency of buffered cell lines on gene functions was determined using dependency scores generated from CRISPR-KO screens (see Data Acquisition and Pre-processing). We separated cell lines into high- and low-buffering cohorts using 80% and 20% percentile cutoffs on the sample BR mean normalized ranks for cell lines, calculated the mean dependency score difference per gene ( d high − d low ), performed two-tailed Wil-coxon rank-sum (Mann-Whitney U) tests, and adjusted the p-values using the Benjamini-Hochberg procedure. High-/low-buffering cell lines were considered differentially depend-ent on a gene if the adjusted p-value of the gene was below 0.05 and if | Log 2 F C | > 0.1. Genes were called exclusively essential in high-buffering cell lines, if the mean dependency score of the gene was significantly higher in high buffering cell lines, its mean dependency score was below 0.5 in low-buffering cell lines, and above 0.5 in high-buffering cell lines. Vice versa, genes were considered exclusively essential in low buffering cell lines for sig-nificantly higher dependency scores in low buffering cell lines, a mean dependency score above 0.5 in low buffering cell lines, and below 0.5 in high buffering cell lines. Similar to the differential expression analysis, we generated an adherent control dataset by re-using the sample BR MNR datset, removing cell lines with non-adherent growth pat-terns, splitting by sample BR MNR, and performing the differential dependency analysis as described above. Drug Sensitivity Analysis We calculated the sensitivity of high-buffering cells on individual drugs, drug targets and drug mechanisms in two ways. First, we calculated the Spearman correlation coefficient of cell line sample BR mean normalized ranks and median drug effect scores from the PRISM Repurposing screens (see Data Acquisition and Preprocessing) per drug, drug target, and drug mechanism of action. Second, we separated cell lines based on their sample BR MNR (>80%, <20%), calculated the mean drug effect score Log2FC between high- and low-buffering cell lines (log 2 e high − log 2 e low ), and performed unpaired two-tailed Welch’s unequal variances t-tests for each drug, drug target, and drug mechanism. Both correlation and differential drug effect p-values were adjusted using the Benjamini-Hochberg procedure. Mean drug effect score differences were considered significant if the adjusted p-value was below 0.05 and if | Log 2 F C | > 0.2. We controlled for aneuploidy by performing differential drug effect analysis on cell lines split by aneuploidy score instead of sample BR MNR percentiles (>80%, <20%). We called drug sensitivities unique for high/low-buffering cell lines, if both correlation and differential drug effect differences were significant and coincided, and if the drug effect differences between high and low aneuploid cell lines were insignificant or significant in the opposite direction. Statistical Analysis Within all boxplots the box depicts the interquartile range with the central band repres-enting the median value of the data. The whiskers represent the furthest datapoint within 1.5 times the interquartile range. Two-tailed Wilcoxon rank-sum (Mann-Whitney U) tests were performed to determine significant differences in BR-derived quantities (e.g. BR, sample BR) when comparing two conditions. Correlations involving BR-derived quantit-ies were calculated using Spearman’s rank correlation coefficient. Unless stated otherwise, tests with p < 0.05 were considered significant. Author contributions Z.S., J.M.S., K.M.S., and E.M.H. conceived the idea, E.M.H. performed the study, K.B. and M.R. performed the proteomics experiment, E.M.H. and Z.S. wrote the manuscript, all authors read and commented on the manuscript. Disclosure and competing interest statement J.M.S. is an inventor on a patent related to chromosome engineering and the construction of aneuploid genomes. J.M.S. is a co-founder of and equity holder in Meliora Therapeutics and KaryoVerse Therapeutics. Data Availability The datasets and computer code produced in this study are available in the following databases: Mass spectrometry proteomics data of the chromosome engineered RPE-1 cell lines (P0211): PRIDE PXD060017 ( https://www.ebi.ac.uk/pride/archive/projects/PXD060017 , username: reviewer_pxd060017{at}ebi.ac.uk , password: NypjXh6UGyYq) Computational study code: GitHub ( https://github.com/sheltzer-lab/DosageCompensationFactors ) Supplemental Figures Download figure Open in new tab Figure S1: Controlling for whole genome doubling status and tumor purity confirms observed protein buffering patterns. (A) Distribution of gene copy numbers among cell lines (ProCan) and tumor samples (CPTAC) separated by their whole genome doubling (WGD) status. (B) Difference in buffering ratio distribution of all proteins between gene copy number gain and loss sep-arated by the WGD status. (C) Categorical distribution of buffering classes across pan-cancer cell line datasets upon gene copy number gain and loss separated by WGD status. (D) Correlation between mean buffering ratio per tumor sample (CPTAC) and purity scores obtained from metadata provided by CPTAC (Spearman’s ρ ). (E) Correlation of buffering ratios per cell line between DepMap and ProCan (Spearman’s ρ ), separated by buffering ratios created using either gene (GeneCN) or chromosome arm copy num-bers (ChrArm). (F) Gene-wise buffering ratio correlation between DepMap and ProCan (GeneCN, Spearman’s ρ ), separated by their presence in the list of frequently buffered genes with low BR variance. Download figure Open in new tab Figure S2: Average buffering per sample shows relationship with cellular and clinical features of cancer. (A) Scatter plot showing a positive correlation between sample buffering ratio and an-euploidy score in all pan-cancer datasets (Spearman’s ρ ). (B) Scatter plot showing a LOESS-smoothed non-linear relationship between sample buffering ratio and cell line ploidy (ProCan). (C) Kaplan-Meyer plots showing a decreased progression-free and over-all survival probability for tumor samples with high buffering (>80% quantile of sample buffering ratio) compared to low buffering (<20% quantile, CPTAC). (D) Difference in sample buffering ratio mean normalized ranks between age categories shows decreased sample-wide buffering in pediatric cancers (Wilcoxon rank-sum test). (E) Difference in sample buffering ratio between samples with and without damaging TP53 mutations (Wilcoxon rank-sum test). (F) Difference in aneuploidy between cell lines grown in suspension and adherent cell culture types when applying aneuploidy control methods (DepMap). Controlled for aneuploidy by removing cell lines from the adherent cohort with aneuploidy scores above the maximum of the suspension cohort (middle), and by using stratified sampling to ensure an equal distribution of aneuploidy scores (right). (G) Difference in sample buffering ratio between cell lines grown in suspension and adherent cell culture types, controlled for aneuploidy (DepMap). Download figure Open in new tab Figure S3: Correlation patterns of potential dosage compensation factors. (A) Mean normalized ranks (MNRs) of ROC AUCs of each factor used for classifying whether a gene is Buffered on protein level or Scaling , while excluding the Anti-Scaling class. MNRs were calculated across all analysis variants (GeneCN, ChrArm, ChrArmAvg) and pan-cancer datasets (DepMap, ProCan, CPTAC), grouped by gene or chromosome arm copy number gain and loss. WGD-controlled subsets were excluded. (B-D) Clustered heatmaps showing the correlation (Spearman’s ρ ) between factors. Correlations with in-significant Benjamini-Hochberg adjusted p-values were crossed out ( p adj ≥ 0.05). Correl-ations were calculated using factor values (B) , bootstrapped ROC AUCs for predicting protein buffering upon gene copy number gain (C) , and bootstrapped ROC AUCs upon gene copy number loss (D) . (D) Spearman correlation between factors values and buffer-ing ratios upon gene copy number loss and gain across all pan-cancer datasets (DepMap, ProCan, CPTAC). Download figure Open in new tab Figure S4: Multifactorial models improve prediction performance of protein buffering. (A) Illustration of training and evaluation procedure of multifactorial models. (B) Model performance (ROC AUC) of trained models, separated by model architecture (xgbLin-ear, rf, pcaNNet), dataset (DepMap, ProCan, CPTAC, Engineered), subset (All, WGD, Non-WGD), analysis variant (GeneCN, ChrArm, ChrArmAvg), and copy number event (Gain, Loss). (C) Out-of-sample performance of multifactorial models trained on buf-fering classes derived from gene and chromosome arm copy number gain and loss data from different pan-cancer datasets (DepMap, ProCan, CPTAC). Model performance was evaluated using the ROC AUC of model predictions on merged training and test datasets. Download figure Open in new tab Figure S5: Enrichment patterns suggest oncogenic effect of protein buffering. (A) Volcano plots showing log2 fold-change between high (>80% quantile of sample buffering ratios) and low ( 0.5, p adj < 0.05). (B, C) Over-representation analysis (ORA) of genes that are significantly down- (B) and up-regulated (C) in highly buffering cell lines with adherent growth patterns (ProCan, adherent con-trol). (D) Correlation between tumor purity and single-sample enrichment scores for the Unfolded Protein Response HALLMARK gene set using CPTAC tumor sample pro-teome data. (E) Buffering ratio distribution of oncogenes (OG) significantly upregulated in high buffering cell lines and of downregulated tumor supressor genes (TSG) upon gene copy number gain and loss (ProCan, all cell lines). (F) Buffering ratio distribu-tion of selected oncogenes upon gene copy number gain and loss (ProCan). (G) Cat-egorical distribution of buffering classes for EGFR upon gain and loss of chromosome arm 7p (ProCan). (H) Normalized enrichment scores of MSigDB HALLMARK gene sets for pan-cancer datasets (DepMap, ProCan, CPTAC). Enrichment scores have been generated by comparing high against low buffering samples and comparing high against low aneuploid samples (>80% and <20% quantiles of sample buffering ratio and an-euploidy score estimates). Significance levels of Benjamini-Hochberg adjusted p-values: (∗) p adj < 0.01, (∗∗) p adj < 0.001, (∗ ∗ ∗) p adj < 0.0001. Removed gene sets with no signi-ficant enrichment scores. Download figure Open in new tab Figure S6: Adherent and aneuploid controls for CRISPR knock-out and drug sensitivity analyses. (A) Volcano plot showing the difference of the CRISPR-KO dependency score between high (>80% quantile of sample BR MNRs) and low (<20% quantile) buffering cell lines plotted against Benjamini-Hochberg adjusted Wilcoxon rank-sum test p-values. Cell lines without adherent growth patterns were removed prior to the analysis. Genes were highlighted if they were significant in the adherent control or among the top10 significant genes in Figure 6A . (B) Over-representation analysis (ORA) of genes with significantly reduced CRISPR-KO dependency scores in highly buffering cell lines with adherent growth patterns. (C) Median drug effect on cell viability across drugs from the PRISM Repurposing dataset for cell lines high (>80% quantile of aneuploidy score), mod-erate (20%-80%), and low aneuploidy (50% sample BR MNR) and low buffering cell lines, separated by median aneuploidy score. (E) Categorical distribution of drug resistant (>50% median drug effect) and responsive cells between high (>50% sample BR MNR) and low buffering cell lines, separated by median aneuploidy score. Download figure Open in new tab Figure S7: Proteome and protein buffering analysis of RPE-1 cell lines with engineered aneuploidies on chromosome 13 (P0211). (A) Mean log2 fold-change of protein abundance per chromosome between cell line rep-licates and disomic baseline (median protein abundance of unaltered RPE-1 replicates per protein). (B) Principal components analysis (PCA) of P0211 replicates after normaliz-ation (first two principal components are displayed). (C) Protein abundance log2 fold-change of proteins encoded on chromosome 13 between aneuploid replicates and disomic baseline. (D) Chromosome arm copy number-derived buffering ratios (BR) of proteins encoded on chromosome 13 in aneuploid replicates. Proteins are classified as Buffered (=) and Anti-Scaling (#) using BR-based thresholds (see Methods). (E) Mean protein abundance log2 fold-changes of proteins encoded on chromosome 13 between aneuploid and non-aneuploid cell lines. Proteins were classified as Buffered (=) and Anti-Scaling (#) using Log2FC-based thresholds (see Methods). Acknowledgments The proteomics mass spectra of the engineered RPE-1 cell lines (P0211) were meas-ured by the Center for MS Analytics at RPTU Kaiserslautern ( https://bio.rptu.de/center-ms-analytics ). This project was supported by grant projects BioComp 4.0 and MLKL (funded by Research Initiative of Rhineland-Palatine to Z.S.). 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