The scale of the undertaking is considerable. The researchers assembled a panel of cell lines representing the major organs, tissues and cancers of the body: epithelial, mesenchymal, endothelial and fibroblast morphologies; cancerous and noncancerous phenotypes; primary and immortalized cells; suspension and adherent cultures; and even embryonal cells and induced pluripotent stem cells to capture early developmental signaling. Each line was profiled under two conditions, serum-starved and serum-stimulated, to sweep in both basal and dynamically regulated phosphorylation events. To boost coverage of the notoriously elusive phosphotyrosine sites, the team also treated colorectal cancer DLD-1 cells with pervanadate, a broad-spectrum inhibitor of tyrosine phosphatases that traps tyrosine phosphorylation at detectable levels. The result is a deliberately broad sampling of the signaling states a human cell can occupy.

Technically, the atlas was built on state-of-the-art mass spectrometry. Both unenriched peptide mixtures, representing each cell’s proteome, and phosphopeptide-enriched samples were analyzed on an Evosep One liquid chromatography system coupled to an Orbitrap Astral mass spectrometer operated in narrow-window data-independent acquisition, or nDIA, mode. Using a 30-samples-per-day gradient with a 200-hertz acquisition method, the team quantified an average of 9,681 protein groups per file. Collectively, the proteome library comprises 543,301 peptide precursors mapping to 16,454 protein groups, roughly 80 percent of the canonical human proteome, with a median protein sequence coverage of 50 percent. Crucially, more than 90 percent of protein kinases, phosphatases, transcription factors, oncogenes and tumor suppressors were identified, and nearly the entire human kinome, around 500 kinases, was covered without detectable bias across kinase families.

The phosphoproteome side of the atlas is even more striking. From the enriched samples, the researchers identified a mean of 30,446 class I phosphosites per file, and the combined phosphopeptide spectral library contains over 500,000 phosphopeptide precursors spanning 225,832 distinct phosphorylation sites. Almost two-thirds of these sites were assigned a localization probability of 0.99 or higher, meaning the exact position of the phosphate on the protein sequence could be pinned down with high confidence. The detected sites comprised 66 percent phosphoserine, 25 percent phosphothreonine and 9 percent phosphotyrosine, the latter proportion inflated by the pervanadate-treated cells; within individual cell lines, tyrosine phosphorylation settled back to the expected few percent. Benchmarking against public databases showed that over 60 percent of the detected sites overlapped with mass-spectrometry-derived resources such as PhosphoSitePlus, and that many sites previously catalogued without direct MS evidence could now be reproduced in a single unified experiment.

One of the most consequential outputs is the empirical spectral library itself, and the team put it through a rigorous stress test. They profiled a heterogeneous pool of differentiating stem cells, deliberately absent from the atlas, across four chromatographic gradients, and searched the data both with the empirical library and with library-free approaches in two widely used software packages, Spectronaut and DIA-NN. The library-based searches increased phosphoproteome depth under every condition tested, delivering a 60 percent gain in quantified phosphosites under high-flow conditions and 40 percent under low-flow conditions in Spectronaut when completeness filtering was applied. The gains came primarily through better-localized sites: library-based searches at a stringent localization cutoff of 0.98 matched the site counts of library-free searches at a much looser 0.75, indicating that high-quality reference spectra resolve the single biggest weakness of library-free phosphoproteomics, confident site localization.

The computational savings were equally dramatic. In DIA-NN, replacing a theoretical in silico phosphopeptide library of nearly 47 million precursors with the empirical library of roughly 540,000 slashed processing time for six files from 9 hours and 55 minutes to just 22 minutes, an average 30-fold speedup across gradients. For a field increasingly drowning in data from automated sample preparation and ultrafast instruments, that difference could reshape how large phosphoproteomics studies are designed. The library also proved its worth in low-input experiments: when the team analyzed EGF-stimulated HeLa cells with peptide inputs ranging from 1 to 20 micrograms, library-based searches yielded an average 18 percent increase in quantified phosphosites and, tellingly, more consistent detection of canonical EGFR activation sites such as the autophosphorylation site Y1110, which appeared in all stimulated replicates and none of the controls when the library was used.

Beyond cataloguing sites, the atlas ventures into one of the hardest quantitative questions in signaling biology: what fraction of a given protein is actually phosphorylated at a given moment, the so-called site stoichiometry. By pairing phosphoproteome measurements with matched proteome data under serum-stimulated and control conditions, the team calculated absolute occupancy for 19,378 phosphosites across 31 cell lines. Occupancies were generally low, below 20 percent, and rose upon serum stimulation. Motif analysis of the sites that gained phosphate revealed basophilic sequences characteristic of AGC-family kinases such as PKA, PKB and S6K, while dephosphorylated sites were enriched for proline-directed motifs targeted by MAPKs and CDKs, indicating phosphatase-mediated shutdown of those pathways. Three independent kinase inference algorithms agreed on the most serum-activated kinases, including ribosomal protein kinases, AKT and MAPK family members, while MARK kinases and cell-cycle CDKs emerged as the most strongly deactivated.

The stoichiometry data also delivered a sobering insight about tyrosine signaling: regulatory phosphotyrosine sites showed the lowest baseline occupancy despite displaying the largest fold changes upon stimulation. Only a small fraction of a tyrosine site needs to be phosphorylated to drive crucial downstream signaling, which explains why global profiling of tyrosine phosphorylation has historically required antibody-based enrichment. The atlas now provides baseline occupancy values for 182 known activation sites and 1,202 regulatory sites, a resource the authors say can help define the occupancy thresholds at which signaling events actually fire.

The culmination of the study is a new metric called the combined kinase activity score, or Cscore, designed to fix a persistent problem in the field: closely related kinases, especially tyrosine kinases, share so many substrates that existing inference tools frequently produce false positives. The team showed that three popular tools assigned EGFR activity to blood cancer cell lines that express no EGFR at all, and failed to flag the constitutively active FLT3 mutant in specific leukemia lines. The Cscore instead integrates five independent lines of evidence: kinase abundance at the proteome and phosphoproteome levels, substrate phosphorylation based on in vitro kinase-reaction relationships, sequence motif enrichment, and quantification of activation-loop sites on the kinase itself, with weights correcting for redundancy among components. Applied across the panel, it produced activity scores for 385 kinases in 31 cell lines and correctly scored directionality, assigning zero activity to EGFR and ERBB2 in suspension cells and high activity to ERBB2 in the BT-474 breast cancer line.

The translational payoff came when the researchers cross-referenced Cscore-predicted vulnerabilities with drug-sensitivity data from the Genomics of Drug Sensitivity in Cancer project. Of 210 kinase-cell line associations identified, several matched known dependencies: the neuroblastoma line NB1 with high ALK activity was sensitive to ALK inhibitors, FLT3-active MOLM-13 and MV-4-11 cells responded to FLT3 inhibitors, and BT-474 cells were sensitive to the ERBB-family inhibitors afatinib and CI-1033. Some predictions, such as NTRK1 activity in OCI-M1 cells, did not validate, a reminder that drug off-target effects and incomplete inhibitor specificity profiles complicate such comparisons. Yet DepMap genetic dependency data independently confirmed vulnerabilities including ALK in NB1, FLT3 in the leukemia lines, ERBB2 in BT-474 and SYK in SU-DHL-4. The authors also flag unvalidated but tantalizing targets, such as the ephrin kinases EPHA3 and EPHA4 in pancreatic cancer cells and ERN1 in HepG2 liver cancer cells. With the full dataset, spectral libraries, stoichiometry tables and Cscore scores deposited in public repositories and browsable through a web interface, the atlas is positioned to become a community standard, one that could turn the noisy landscape of cellular phosphorylation into a navigable map for drug discovery.

Subject of Research: A mass spectrometry-based phosphoproteome atlas of human cell lines mapping kinase activity and signaling vulnerabilities

Article Title: A phosphoproteome atlas of human cell lines reveals the landscape of kinase activity

Article References: Koenig, C., Cho, H., Emdal, K. B., Piga, I., Sabatier, P., Lozano-Juárez, S., Martinez-Val, A., & Olsen, J. V. (2026). A phosphoproteome atlas of human cell lines reveals the landscape of kinase activity. Nature Structural & Molecular Biology. https://doi.org/10.1038/s41594-026-01877-6

Image Credits: AI Generated

DOI: 10.1038/s41594-026-01877-6

Keywords: phosphoproteomics, kinase activity, mass spectrometry, cell signaling, cancer, spectral library, DIA, phosphorylation, precision oncology, proteomics, drug sensitivity, cell lines

The study, published in the open-access journal Stress Biology, combined state-of-the-art quantitative proteomics with phosphoproteomics to track what actually happens inside latex cells after ethephon treatment. Led by Linling Yang, Tian Sang, and Xuchu Wang, the researchers worked with ten-year-old rubber trees of the clone RY 7-33-97 at an experimental farm of the Chinese Academy of Tropical Agricultural Sciences in Hainan Province. They sprayed the tapping surfaces of sixty trees with either 3 percent ethephon or ultrapure water, then harvested latex at one, three, and five days after treatment. Each sample was flash-frozen in liquid nitrogen and processed for mass spectrometry, allowing the team to quantify thousands of proteins and their attached phosphate groups in a single, systematic sweep.

The technical heart of the study lies in its use of data-independent acquisition, or DIA, mass spectrometry. Unlike older data-dependent approaches, which stochastically select which ions to fragment and therefore miss low-abundance regulatory proteins, DIA systematically cycles through fixed mass windows, fragmenting every precursor ion in each cycle. This produces highly reproducible, deep coverage across samples. The payoff was dramatic: the team quantified more than 3,700 proteins in the latex proteome and, after enriching phosphopeptides with immobilized metal affinity chromatography, identified 7,369 unique phosphopeptides carrying 5,550 phosphorylation sites on 2,013 proteins. Of those sites, 84.9 percent were on serine residues, 11.2 percent on threonine, and 3.9 percent on tyrosine, a distribution typical of plant phosphoproteomes.

Principal component analysis cleanly separated ethephon-treated samples from water-treated controls at every time point, confirming that the hormone treatment reshapes the latex molecular landscape rather than nudging it. In the proteome alone, the researchers catalogued 1,452 differentially expressed proteins. Upregulated proteins clustered in pathways for protein processing in the endoplasmic reticulum, ribosome biogenesis, amino acid biosynthesis, and signal transduction, while downregulated proteins were enriched in fundamental metabolism, including fatty acid metabolism and photosynthesis-related processes. The pattern suggests that ethephon does not simply pour more precursor into the rubber pipeline; it reorganizes the entire cellular factory, shifting resources toward protein synthesis, vesicle trafficking, and stress management.

But the real story emerged from the phosphorylation data. Because phosphorylation often changes a protein’s activity without changing its abundance, static protein measurements can miss regulatory events entirely. By integrating the two datasets, the researchers could distinguish proteins whose levels changed from proteins whose activity was being switched on or off by kinases. Motif enrichment analysis on the differentially phosphorylated sites revealed a striking signature: sequences containing the serine/threonine-proline motif, the canonical recognition sequence for MAPKs and other proline-directed kinases such as cyclin-dependent kinases and glycogen synthase kinase 3, accounted for roughly half of all enriched motifs. Hierarchical clustering of the phosphoproteome into six temporal clusters showed MAPK pathway proteins enriched in every single one, with phosphorylation rising early and, in some clusters, peaking at day five, hinting at both immediate and sustained signaling roles.

To confirm that these phosphoproteomic patterns reflected genuine kinase activation, the team turned to immunoblotting with antibodies that recognize the phosphorylated T-x-Y activation loop motif shared by active MAPKs. The results were unambiguous: MPK3 and MPK6, two of the best-characterized plant MAPKs, became strongly phosphorylated after ethephon treatment. Interestingly, the water-treated controls also showed baseline MAPK activation at day three, consistent with the well-known fact that mechanical wounding from tapping itself triggers MAPK signaling. The critical difference was that ethephon made the response markedly stronger and more sustained, stretching through day five. The researchers also observed a transient dip in MAPK kinase phosphorylation at day three, which they interpret as possible feedback inhibition, a common feature of MAPK cascades in which phosphatases dampen signaling amplitude and duration.

The downstream targets of this activated cascade are exactly the proteins rubber biologists care about most. Rubber elongation factor (REF) and small rubber particle protein (SRPP), the two core structural components of rubber particles, both showed dynamic phosphorylation changes. REF Ser104 phosphorylation rose on day one, peaked on day three, and returned to baseline by day five, a trajectory the authors note coincides with unpublished preliminary data on latex yield dynamics. SRPP Ser156 and REF Ser43 were strongly upregulated at days one and five but downregulated at day three. Several sites on 14-3-3 adaptor proteins, including Ser2, Ser167, and Ser246, were upregulated early. Crucially, the flanking sequences of many of these sites contain the [S/T-P] motif, meaning MAPKs could directly phosphorylate the very proteins that stabilize rubber particles and regulate the polymerization of isoprene chains.

The study also caught the mevalonate pathway, the metabolic route that generates isopentenyl pyrophosphate, the monomer of rubber, in the act of being phospho-regulated. Key enzymes including acetyl-CoA acetyltransferase, HMG-CoA synthase, HMG-CoA reductase (HMGR), and phosphomevalonate kinase all showed coordinated phosphorylation oscillations: up on day one, down on day three, and rebounding by day five. HMGR is the rate-limiting enzyme of the pathway, and the laticifer-specific isoform HbHMGR1 is known to be ethylene-induced and positively correlated with latex regeneration. The authors propose that this synchronized phosphorylation acts as a global metabolic reprogramming strategy, dynamically tuning enzyme activity to redirect carbon flux toward IPP production. Meanwhile, phosphorylation of UDENN domain-containing proteins, which function as guanine nucleotide exchange factors for Rab GTPases, implicates membrane trafficking in the formation of the small rubber particles that ethephon is known to increase.

What makes this work more than a catalog is the mechanistic thread it weaves. Ethylene signaling activates the MAPK cascade; the cascade’s preferred substrate motif appears on REF, SRPP, 14-3-3, HMGR, and UDENN proteins; and those proteins collectively govern precursor supply, particle assembly, particle stability, and vesicle trafficking. Phosphorylation of 14-3-3 is particularly intriguing because these adaptor proteins bind phosphorylated serines and threonines on their targets, potentially acting as molecular bridges that coordinate the assembly of the rubber biosynthetic complex on particle membranes. By analogy with phosphorylation-regulated starch synthase complexes in maize, REF phosphorylation could alter surface charge or conformation, enhancing its interactions with the rubber particle membrane. The authors are careful to frame these as hypotheses, but the convergence of motif evidence, temporal dynamics, and biochemical validation makes the MAPK-to-rubber-particle axis the most compelling explanation yet for how ethylene boosts yield.

The practical implications are considerable. Natural rubber biosynthesis cannot yet be replicated industrially at scale, so improving the tree remains the only route to meeting global demand. The comprehensive phosphoproteomic dataset, deposited publicly on the jPOST repository, gives breeders and synthetic biologists a molecular target list: specific phosphorylation sites on REF, HMGR, and MAPK components that could be edited or selected for. The authors outline plans to use CRISPR/Cas9 gene editing and transgenic overexpression to functionally validate the key phosphoproteins and kinase components, aiming to develop high-yielding varieties through molecular breeding. If the MAPK-phosphorylation axis holds up under functional testing, the humble rubber tree may finally reveal the full recipe for its most valuable trick, and scientists may learn to tune it deliberately rather than dousing bark with hormone and hoping for the best.

Subject of Research: MAPK cascade regulation of ethylene-induced natural rubber biosynthesis in Hevea brasiliensis

Article Title: Integrated proteomic and phosphoproteomic analysis reveals the MAPK cascade as a key regulator of ethylene-induced latex production in Hevea brasiliensis

Article References: Yang, L., Yuan, B., Fang, F., He, M., Li, W., Hui, S., Du, X., He, L., Lui, H., Sang, T., & Wang, X. (2026). Integrated proteomic and phosphoproteomic analysis reveals the MAPK cascade as a key regulator of ethylene-induced latex production in Hevea brasiliensis. Stress Biology, 6(1), Article 18. https://doi.org/10.1007/s44154-026-00290-9

Image Credits: AI Generated

DOI: 10.1007/s44154-026-00290-9

Keywords: Hevea brasiliensis, natural rubber, ethylene, ethephon, MAPK cascade, phosphoproteomics, proteomics, latex, rubber particles, REF, SRPP, mevalonate pathway

A new study published in Molecular Systems Biology by Charlie George Barker, Sumana Sharma, Evangelia Petsalaki and colleagues at the European Molecular Biology Laboratory and collaborating institutions across Europe offers one of the most detailed maps yet of how this resistance is built. The team focused on ARID1A, a component of the SWI/SNF chromatin remodeling complex that is mutated in approximately 11.5 percent of melanomas. Unlike the hotspot mutations that activate BRAF, ARID1A mutations are scattered across the gene, a pattern typical of tumor suppressors whose loss confers a survival advantage in many different ways. Previous genome-wide CRISPR screens had already flagged ARID1A loss as a driver of resistance to MAPK pathway inhibitors, but the precise circuitry behind that resistance remained obscure.

To resolve it, the researchers engineered a drug-resistant version of the A375 melanoma cell line, a BRAF V600E model that is normally exquisitely sensitive to BRAF and MEK inhibitors, by knocking out ARID1A. They then treated both the parental and knockout lines with vemurafenib, trametinib, or the combination, harvesting cells after six hours, a time point chosen to capture the earliest adaptive signaling responses before cell death begins. From these samples they generated an unusually rich dataset: mass spectrometry quantified 8,139 proteins and 3,207 phosphosites, while RNA sequencing measured the expression of 14,376 genes. They supplemented this with functional kinomics using PamChip peptide microarrays, which measure the actual enzymatic activity of dozens of kinases in cell lysates, and with Luminex-based phosphoprotein assays to confirm that the drugs were genuinely suppressing their targets.

The analytical challenge was to make sense of four orthogonal data layers simultaneously. The team’s solution combined two computational tools. First, they applied multi-omics factor analysis, or MOFA, an unsupervised matrix factorization method that identifies latent factors explaining variation across all data modalities at once. This revealed three dominant sources of variation: two factors capturing adaptive responses to drug treatment, and a third reflecting the sustained reprogramming caused by ARID1A loss. Second, they fed the factor loadings into phuEGO, a network propagation method that places the most extreme genes and phosphosites into their functional interaction context, producing minimal signaling networks for each factor. By merging positive and negative weights, the approach generated upregulated and downregulated network modules that capture the opposing molecular programs at play in drug response and resistance.

The first factor revealed changes that occurred regardless of which drug was used, pointing to mechanisms common to all MAPK inhibition. The most striking finding was the coordinated decrease of negative feedback regulators of the pathway, including the DUSP phosphatases DUSP1, DUSP2 and DUSP4, along with SPRY1/2/4 and SPRED1/2. These molecules normally act as brakes on receptor tyrosine kinase and MAPK signaling, terminating signals after they fire. When their abundance drops, the brakes come off, growth factor signaling intensifies, and the effect of the inhibitors is blunted. At the same time, the functional kinomics data showed activation of several receptor-associated kinases, including PRKD1, FYN and IGF1R, even though the abundance of their mRNAs and proteins barely changed. This dissociation between expression and activity underscores why measuring phosphorylation and kinase function, not just gene expression, is essential for understanding drug response.

The second factor isolated changes specific to combination therapy, and here the phosphoproteomics told an unexpected story. The team detected altered phosphorylation on a series of DNA repair proteins. TP53BP1, which promotes non-homologous end joining of double-strand DNA breaks, was heavily phosphorylated on serine 1101, a site associated with ionizing radiation damage, while losing phosphorylation at several other residues. RIF1, a key TP53BP1 regulator, was modulated at a site close to a known inhibitory phosphorylation mark, and ULK1 serine 556, an ATM-dependent autophagy trigger, was strongly upregulated. Enrichment analysis of the downregulated network highlighted terms related to ATM-mediated repair protein phosphorylation and recruitment of repair proteins to double-strand breaks, alongside suppressed immune signaling through TNF-alpha, interferons and interleukins. Intriguingly, in this particular cell line the combination therapy did not kill more cells than vemurafenib alone, suggesting these DNA damage signatures may mark cellular stress responses rather than enhanced therapeutic efficacy.

The third factor, tied to ARID1A loss, produced the study’s most consequential insights. Although the knockout cells mounted transcriptional and proteomic drug responses that correlated strongly with those of parental cells, with transcriptomic correlations of 0.91 to 0.93, their signaling behavior diverged dramatically. ARID1A knockout cells sustained MAPK1/3 and JNK activity after treatment, indicating the drugs no longer fully silenced these pathways. The researchers traced this to a fundamentally altered baseline state. The knockout cells showed elevated abundance of several receptor tyrosine kinases, including EGFR and ROS1, along with integrins and CD44. Flow cytometry confirmed a drastic increase in EGFR on the cell surface, consistent with impaired receptor endocytosis in resistant melanoma cells. Elevated membrane receptors can sustain chronic signaling rather than transient, ligand-dependent pulses, providing a persistent growth signal that survives MAPK blockade.

Using random walk network propagation and maximum-flow analysis, the team showed that signals from EGFR and ROS1 converge on the transcription factor JUN through the adaptor proteins FYN, PRKD1, PTPN6 and NCK1. In parental cells, drug treatment activates PRKD1, which suppresses the JNK/c-Jun axis linked to apoptosis in BRAF-mutant melanoma. In ARID1A knockout cells, this regulatory logic is inverted: PRKD1 activation is suppressed, JNK inhibition is relieved, and JUN activity rises, a known driver of BRAF inhibitor resistance. The predictive power of the network approach was validated experimentally. When the researchers deleted EGFR using two independent guide RNAs, the drug-resistant phenotype of the knockout cells was abolished, and cell death exceeded even that of parental cells under combination therapy. Ephrin receptor signaling, also elevated in the knockout cells, emerged as an additional resistance route through NCK1 and related adaptors.

The study also connected ARID1A loss to immune evasion, with implications for immunotherapy. The knockout cells showed reduced activity of the RFX5, RFXAP, RFXANK and NFYC transcription factors, which drive MHC class II gene expression, and correspondingly reduced surface levels of HLA-DQ and HLA-DR. Analysis of 472 melanoma patients from The Cancer Genome Atlas confirmed the pattern: the 80 patients with ARID1A mutations or deletions had significantly lower predicted activity of these MHC regulators and elevated collagen and laminin expression, suggesting extracellular matrix remodeling that could physically impede T-cell infiltration. Together, the findings position PRKD1, JUN and NCK1 as key resistance nodes and highlight a sobering lesson about redundancy: with multiple receptors, including EGFR, ROS1 and FGFR1, feeding into rewired signaling, targeting any single protein is unlikely to succeed. Instead, the study’s integrative framework, which disentangles drug responses from baseline reprogramming and prioritizes experimentally testable vulnerabilities, points toward combination strategies aimed at JUN or immune pathway restoration, offering a systems-level blueprint for outmaneuvering one of cancer’s most adaptable escape artists.

Subject of Research: Multi-omics analysis of ARID1A-dependent resistance to BRAF and MEK inhibitor therapy in melanoma

Article Title: Integrative multi-omics defines melanoma drug response networks and ARID1A-dependent resistance mechanisms

Article References: Barker, C. G., Sharma, S., Santos, A. M., Nikolakopoulos, K.-S., Velentzas, A. D., Tormo-Garcia, C., Sharma, A., Völlmy, F. I., Minia, A., Pliaka, V., Clarke, J., Altelaar, M., Wright, G. J., Alexopoulos, L. G., Stravopodis, D. J., & Petsalaki, E. (2026). Integrative multi-omics defines melanoma drug response networks and ARID1A-dependent resistance mechanisms. Molecular Systems Biology, 22(5), 685-711. https://doi.org/10.1038/s44320-025-00183-5

Image Credits: AI Generated

DOI: 10.1038/s44320-025-00183-5

Keywords: melanoma, ARID1A, drug resistance, BRAF inhibitors, MEK inhibitors, MAPK signaling, multi-omics, phosphoproteomics, EGFR, JUN, immune evasion, network biology

The study, led by Dennis Friedel, Rhaissa Ribeiro da Silva, Felix Sahm and Philipp Sievers of University Hospital Heidelberg together with collaborators across the German Cancer Research Center, set out to answer a deceptively simple question: can proteomic and phosphoproteomic profiling of ordinary archived tissue recover biologically meaningful signaling information that complements established molecular diagnostics? The researchers focused on ten cases of IDH-wildtype glioblastoma, the most aggressive adult brain tumor, split evenly between five tumors carrying EGFR amplification — one of the most frequent and well-characterized genomic alterations in this disease — and five without it. Every tumor had already undergone complete diagnostic workup, including DNA methylation profiling on the Infinium EPIC array with classification by the Heidelberg Brain Tumor Classifier and targeted sequencing of 220 CNS tumor genes, providing a rigorous genomic and epigenomic reference against which the new protein-level data could be judged.

The technical achievement underlying the study lies in how the tissue was prepared. Formalin fixation and paraffin embedding have long been the nemesis of proteomics, because cross-linking and wax infiltration make proteins difficult to extract. The Heidelberg team applied an optimized plate-based workflow that dispensed with conventional xylene deparaffinization altogether. Tiny tissue punches were placed in a 96-well plate with sodium dodecyl sulfate and magnetic beads, then processed in a BeatBox tissue homogenizer, where an alternating magnetic field drives the beads to mechanically shatter tissue and surrounding paraffin. Four cycles of ten minutes of bead-based disruption alternated with twenty minutes of heating at 95 degrees Celsius liberated the proteins, which were then cleaned up and digested using an automated single-pot solid-phase-enhanced sample preparation, or SP3, workflow on a liquid-handling robot, with 55 micrograms of protein input per sample.

Phosphopeptide enrichment was performed with iron-NTA cartridges on an automated platform before analysis on an Orbitrap Exploris 480 mass spectrometer. Global proteomes were measured using a 90-minute data-independent acquisition method, while phosphoproteomes used data-dependent acquisition, with rigorous quality controls including an MCF7 reference sample processed alongside the tumors and regular injections of a HeLa peptide standard to monitor instrument stability. The results were striking: global profiling identified a median of 7,647 proteins per tumor, with 5,417 proteins detected in all ten samples. The phosphoproteomic analysis reached a total of 13,467 phosphosites, with a cohort median of 5,457 confidently localized sites per sample, and 1,233 phosphosites reproducibly found in every single tumor. Comparable intensity distributions across samples confirmed that the quantification was robust and consistent.

To interpret phosphorylation correctly, the researchers made a crucial methodological choice: phosphosite intensities were adjusted by subtracting the measured abundance of the corresponding protein from the matched global proteome. This protein-abundance adjustment helps ensure that differences in phosphosite levels reflect genuine changes in phosphorylation state rather than simply more or less of the underlying protein being present. With this adjustment in place, the team asked whether EGFR amplification left a detectable signature on the phosphoproteome — effectively using a known genomic alteration as a biological benchmark for the new technology.

The answer was yes, but with an important nuance. Principal component analysis of the adjusted phosphoproteomic data showed only partial separation between the two groups, with EGFR amplification status significantly associated with the first principal component, which accounted for 29.9 percent of total variance. Differential analysis identified 443 phosphosites meeting predefined exploratory criteria, and among the sites enriched in EGFR-amplified tumors were two phosphorylation sites on the EGFR receptor itself, Y1110 and Y1197, which remained enriched even after adjustment for protein abundance — a biologically coherent finding given the known signaling role of the amplified receptor. Kinase-substrate enrichment analysis, which infers kinase activity from the coordinated behavior of annotated substrate sites, revealed higher activity of EGFR together with SRC-family kinases including YES1, FYN, SRC, LCK and LYN in the amplified tumors, while kinases such as PRKACA, PRKG1, MAPKAPK2 and PDPK1 showed lower inferred activity in that group.

That nuance — substantial overlap between the groups despite the clear genomic difference — may be the study’s most conceptually significant message. Two tumors can carry the same driver alteration yet differ markedly in how that alteration is expressed at the level of downstream signaling. Genomics establishes that a driver is present; phosphorylation patterns reveal whether and how its pathway is actually being used. The authors argue that this makes phosphoproteomics an orthogonal functional layer that complements, rather than duplicates, genomic and epigenomic classification. The global proteome told a parallel story: EGFR protein itself was more abundant in amplified tumors, E2F target programs were enriched in that group, while non-amplified tumors showed enrichment of stress-, immune- and microenvironment-associated programs including hypoxia, interferon-gamma response, complement and apoptosis signaling.

To show how this might work in practice, the team built a prototype research-use-only report for one representative EGFR-amplified tumor, generated entirely from that single sample without reference to the rest of the cohort. The report combined analytical quality-control metrics with pathway-level enrichment and representative phosphosites, grouped into therapeutically relevant signaling categories such as EGFR/ERBB, RAS–RAF–MEK–ERK/MAPK, PI3K–AKT–mTOR and VEGF-associated signaling, with sites including EGFR Y869 and Y1197 supporting the pathway-level findings. The authors stress that this report is a demonstration of a potential reporting framework, not a validated diagnostic or therapeutic readout, and implies nothing about drug sensitivity.

The broader context makes the approach timely. Large-scale proteogenomic studies of glioblastoma have already shown that signaling states undergo substantial post-translational remodeling during tumor evolution, meaning functional state cannot be inferred from genomic alterations alone. Elsewhere in oncology, phosphoproteomic profiling has identified patient-specific drug targets in cholangiocarcinoma and defined clinically distinct subtypes with actionable vulnerabilities in hepatocellular carcinoma. In neuro-oncology, the N2M2/NOA-20 umbrella trial has already demonstrated the clinical value of functional pathway assessment, selecting glioblastoma patients with activated mTOR signaling — judged by phospho-mTOR staining — for treatment with temsirolimus. Multiplexed phosphoproteomics could in principle extend such strategies by assessing many signaling pathways simultaneously, though the authors caution that prospective validation and clinically applicable thresholds will be required.

Significant hurdles remain before this technology reaches the clinic. The study cohort comprised only ten tumors, so individual phosphosites and inferred kinase activities must be treated as hypothesis-generating rather than candidate biomarkers. Bulk tissue analysis cannot resolve the spatial and subclonal heterogeneity that defines glioblastoma, and the protein-abundance adjustment, while informative, does not constitute a direct measurement of phosphorylation stoichiometry. Kinase-substrate enrichment infers activity from annotated substrate behavior rather than measuring enzymatic activity directly. Analytical complexity, infrastructure requirements and the cost of mass spectrometry will likely confine the approach to selected clinical and translational settings for now, and standardization of tissue processing, pipelines, thresholds and reporting will be essential for broader implementation. Nevertheless, the core demonstration stands: routine FFPE archive tissue, long valued primarily for morphology and DNA, also preserves a readable record of the tumor’s active signaling life. Integrating that functional layer with genomic and epigenomic diagnostics could eventually give neuro-oncologists a more complete picture — connecting who a tumor is with what it is actually doing.

Subject of Research: Proteomic and phosphoproteomic profiling of archived FFPE glioblastoma tissue to complement genomic and epigenomic CNS tumor diagnostics

Article Title: Functional proteomic and phosphoproteomic profiling of routine FFPE CNS tumor tissue complements genomic and epigenomic characterization

Article References: Friedel, D., da Silva, R. R., Ahmed, I. A., Wolff, B. M., Neuerburg, A., Irsevic, R., Ahmadi, S., Jayavelu, A. K., Kulozik, A. E., Witt, O., Pfister, S. M., Krieg, S. M., Wick, W., von Deimling, A., Reuss, D. E., Sahm, F., & Sievers, P. (2026). Functional proteomic and phosphoproteomic profiling of routine FFPE CNS tumor tissue complements genomic and epigenomic characterization. Acta Neuropathologica, 152(1), Article 42. https://doi.org/10.1007/s00401-026-03091-6

Image Credits: AI Generated

DOI: 10.1007/s00401-026-03091-6

Keywords: phosphoproteomics, proteomics, FFPE tissue, glioblastoma, EGFR amplification, mass spectrometry, molecular diagnostics, CNS tumors, DNA methylation profiling, kinase signaling, precision oncology, neuropathology

The team began with a strikingly simple question: if you look at the complete inventory of proteins in a failing left ventricle, do hearts that failed for different reasons actually look different from one another? To answer it, they used high-resolution mass spectrometry to quantify 3,788 proteins in cardiac tissue samples collected from patients undergoing advanced care, alongside non-failing control hearts. The patients spanned ischemic cardiomyopathy, dilated and hypertrophic cardiomyopathy, arrhythmogenic cardiomyopathy, myocarditis, adult congenital heart disease and non-ischemic dilated cardiomyopathy, among other diagnoses. Statistical models adjusted for age, sex and batch effects compared protein abundance in each etiology group against controls.

The first major finding was one of convergence. When patients were grouped by clinical diagnosis, the vast majority of significantly altered proteins were shared across etiologies rather than unique to any single one. Upset plots and enrichment analyses showed that common changes dwarfed diagnosis-specific ones, and functional enrichment mapped all the heart failure groups onto a shared landscape: metabolic reprogramming, altered contractile machinery, extracellular matrix remodeling and inflammatory signaling appeared everywhere. In other words, by the time the heart reaches end-stage failure, the terminal proteomic state looks remarkably similar regardless of what started the decline. This convergence echoes earlier single-nucleus RNA sequencing work, which likewise found common transcriptional signatures across cardiomyopathies, and it helps explain why a one-size-fits-all therapeutic approach has historically achieved only incremental gains.

But convergence at the disease endpoint was only half the story. When the researchers abandoned clinical labels altogether and let an unbiased computational method, non-negative matrix factorization, sort patients purely by proteomic similarity, the cohort split cleanly into two groups. Group 1 and Group 2, comprising 59 and 63 patients with reduced ejection fraction respectively, cut across every clinical etiology: a patient with ischemic disease and a patient with myocarditis could sit in the same molecular camp, while two patients with the same diagnosis could fall into opposing ones. The clustering was robust, reproducible in principal component analyses, and independent of demographic confounders.

What distinguished the two groups was not the cause of disease but the character of the heart’s remodeling response. Group 2 hearts were enriched for proteins derived from activated fibroblasts, smooth muscle cells and pro-inflammatory myeloid cells, signatures consistent with aggressive scar formation and immune infiltration. Overlaying the protein signatures onto a published atlas of 15 cardiac cell types from single-cell transcriptomics revealed that Group 2 mirrored the cellular states observed in dilated cardiomyopathy, including expansions of disease-associated cardiomyocyte populations, activated fibroblast states FB5 through FB9, and pro-inflammatory macrophages, monocytes and dendritic cells. Group 1, by contrast, retained a profile closer to non-failing tissue, relatively enriched for cardiomyocyte contractile and metabolic proteins. The subgrouping was independently validated in bulk RNA sequencing data from 198 heart failure samples in a public cohort, where the same protein signature reproduced the same molecular split at the transcript level.

The researchers then descended one level deeper into biology by profiling the phosphoproteome, the layer of phosphate tags that flip proteins on and off and transmit signals through kinases. From the same tissues they quantified 3,014 phosphosites, and the phosphorylation patterns reproduced the proteomic stratification, confirming that the two subgroups differ not only in which proteins they contain but in which signaling circuits are actually running. Critically, phosphoproteomics is where drug discovery lives: kinases, phosphatases and their downstream effectors are among the most pharmacologically tractable targets in medicine.

By integrating protein abundance, phosphosite regulation and prior knowledge networks of kinases, transcription factors and upstream regulators, the team identified selectively activated, druggable signaling networks confined to one molecular subgroup. Among the circuits implicated were RhoA and ROCK2 signaling, components of the actin cytoskeleton such as the Arp2/3 complex, and mitogen-activated protein kinase pathways including ERK1/2 and p38, alongside regulatory nodes such as glycogen synthase kinase 3 and RAF1. The researchers cross-referenced these targets against the Drug Repurposing Hub, ChEMBL, Open Targets and Mendelian randomization evidence for causal heart failure associations, filtering for known cardiac toxicity, and assembled a shortlist of candidate therapeutic entry points that would apply only to the molecularly defined patients whose networks show that activation. This is precision cardiology at the level of the cell’s control systems rather than its genome.

Perhaps the most clinically consequential result came from the UK Biobank. The remodeling-associated protein signature, originally discovered in end-stage explanted hearts, was tested against plasma proteomic data from an entirely independent cohort of heart failure patients who are, on average, at much earlier stages of disease and treated in the community rather than the transplant center. The signature stratified these patients into subgroups and predicted survival trajectories, with accelerated failure time modeling showing that the molecular group assignments carried prognostic information. This extends the relevance of the tissue-based findings far beyond the operating theater: a blood test grounded in the same biology may eventually help identify which ambulatory patients carry a fibrotic, inflammatory molecular phenotype and which do not.

The implications for drug development are considerable. Heart failure trials have long been plagued by modest average effect sizes, and a plausible explanation is that molecularly heterogeneous populations dilute real benefits among patients whose disease is not driven by the targeted pathway. If a candidate anti-fibrotic or kinase inhibitor benefits only Group 2 biology, enrolling an unstratified population would bury the signal. Stratifying trials by these protein-defined subgroups could sharpen effect estimates, reduce required sample sizes and rescue otherwise promising mechanisms. The prognostic signal in plasma also raises the prospect of patient stratification with routine blood draws rather than myocardial biopsy, something no clinician would contemplate for diagnosis in most heart failure settings.

The study also leaves open questions that will shape the next phase of work. The cohorts are dominated by end-stage disease and reduced ejection fraction, so the behavior of the subgrouping in heart failure with preserved ejection fraction remains untested. The two groups describe biology at a moment in time, and longitudinal studies will need to establish whether patients transition between molecular states as disease evolves, or whether subgroup membership is fixed from the outset. And while the plasma findings demonstrate prognostic value, translating them into a clinically deployable test will require targeted assays and prospective validation. Still, the message of the work is unambiguous: the failing heart’s molecular interior tells a story that diagnosis codes cannot, and listening to that story, in both tissue and blood, may finally make it possible to match heart failure therapies to the biology of the individual patient rather than the label on the chart.

Subject of Research: Molecular subgrouping of human heart failure by proteomic and phosphoproteomic profiling of cardiac tissue

Article Title: Cardiac proteomic and phosphoproteomic profiling defines clinically relevant molecular subgroups in human heart failure

Article References: Hamed, O., Reitz, C. J., Kuzmanov, U., Gramolini, S., Hu, M., Halpern, J., Billia, F., & Gramolini, A. O. (2026). Cardiac proteomic and phosphoproteomic profiling defines clinically relevant molecular subgroups in human heart failure. Nature Cardiovascular Research. https://doi.org/10.1038/s44161-026-00880-w

Image Credits: AI Generated

DOI: 10.1038/s44161-026-00880-w

Keywords: heart failure, proteomics, phosphoproteomics, cardiac remodeling, precision medicine, biomarkers, UK Biobank, fibrosis, kinase signaling, cardiomyopathy, left ventricle, mass spectrometry

The research emerged from the Copenhagen Prospective Personalized Oncology trial, known as CoPPO, an ongoing precision oncology study in which patients with advanced cancer undergo molecular profiling to guide therapy selection. In this setting, metastatic biopsies are routinely processed using the Qiagen AllPrep system, a commercial kit that simultaneously extracts DNA and RNA from a single sample. During that extraction, proteins are separated from the nucleic acids and end up in a flowthrough fraction that is normally discarded. The team, led by Filip Mundt and including renowned proteomics pioneer Matthias Mann, systematically asked whether this residual protein fraction could serve as a reliable starting material for mass spectrometry-based proteomics, and the answer turned out to be a resounding yes.

The technical challenge was considerable. Protein recovered from extraction flowthroughs is diluted in chemical buffers designed for nucleic acid work, and it must be recovered, concentrated, and cleaned up before it can be digested into peptides and analyzed by liquid chromatography coupled to mass spectrometry. The researchers developed and optimized procedures for each of these steps, including protein precipitation, enzymatic digestion, and peptide cleanup, and they evaluated multiple mass spectrometry acquisition strategies to determine which would deliver the best performance from this unconventional source material. Both data-dependent acquisition, which favors depth of identification, and data-independent acquisition, which favors quantitative consistency across samples, were tested in the context of real clinical biopsy specimens rather than idealized reference material.

The results were striking. Using optimized single-shot workflows, the team consistently generated deep proteomes from the AllPrep-derived material, quantifying more than 10,000 protein groups in data-independent acquisition analyses. That depth of coverage, from material that would otherwise have been discarded, places the quality of these measurements on par with dedicated proteomics workflows that consume precious additional tissue. Perhaps even more importantly for clinical applications, the proteomes retained tissue-associated biological signatures, meaning the protein profiles faithfully reflected the biology of the tumors from which the biopsies were taken rather than artifacts introduced by the extraction process.

One of the most clinically significant findings concerns compatibility with existing genomic diagnostics. The researchers found that the proteomes derived from the flowthrough material quantified protein products corresponding to approximately 71 percent of the genes represented on the FoundationOne CDx panel, a widely used clinical genomic profiling assay. This overlap means that the protein data can be directly interpreted alongside the genomic results that oncologists already rely on, providing a functional layer of information on top of the genetic alterations detected in the same specimen. Where genomics reveals which genes are mutated or amplified, proteomics reveals whether those changes are actually reflected in the abundance of the proteins they encode, and whether downstream signaling pathways are active.

Beyond total protein abundance, the team extended the workflow to phosphoproteomics, the analysis of phosphorylation marks that act as molecular switches on proteins and report on the activity of signaling pathways. Phosphoproteomic analyses of the recovered material identified more than 10,000 phosphorylation sites, including many associated with key oncogenic signaling pathways driven by AKT1, BRAF, and EGFR, proteins that sit at the heart of some of the most commonly targeted pathways in modern cancer therapy. This is particularly valuable in precision oncology, where a tumor may harbor a mutation in a druggable gene, yet the downstream pathway may or may not be activated. Phosphoproteomics offers a direct readout of that functional state, information that genomic sequencing alone cannot provide.

Practical considerations for clinical deployment were also addressed head-on. The researchers assessed sample stability during long-term storage and found that protein integrity was preserved in samples kept at minus 80 degrees Celsius for up to five years. This finding has immediate operational implications, because it means that flowthrough fractions recovered during routine DNA and RNA extraction can simply be archived in existing freezers, creating a retrospective proteomic resource from biopsies processed years earlier. Hospitals would not need to change their current extraction protocols or acquire new tissue; they would only need to stop discarding the protein fraction and store it instead.

Scalability, often the Achilles heel of clinical proteomics, was another focus of the study. Mass spectrometry throughput has historically been a bottleneck for large-scale clinical studies, but the team implemented short liquid chromatography gradients that enabled high-throughput analysis of up to 60 proteomes per day without compromising data quality. At that rate, a single instrument could theoretically process thousands of patient samples per year, bringing proteomic profiling within reach of the kind of scale that genomic sequencing pipelines already achieve routinely. The analytical reproducibility of the workflow was also evaluated, confirming that measurements were consistent enough to support the comparisons that clinical decision-making would require.

The implications for precision oncology are substantial. Current molecular tumor boards typically work from genomic and transcriptomic data, which describe the blueprint and the transcriptional activity of a tumor but say little about the actual effector molecules, the proteins, that carry out cellular processes and respond to drugs. Proteins are, as the authors note, the active effectors of cellular signaling, and integrating proteomic and phosphoproteomic measurements with genomic data has become an important objective in translational oncology. By demonstrating that this integration can be achieved from a single biopsy already being processed in routine clinical workflows, the study removes one of the biggest practical obstacles: the need for extra tissue that patients often cannot spare and that clinics often cannot obtain.

The Copenhagen team frames the work as a framework for integrated proteogenomic studies in precision oncology, and the phrase is apt. What they have built is not merely a new laboratory technique but a bridge between the molecular diagnostics that hospitals already perform and the deeper functional characterization that proteomics can add. As mass spectrometry instruments become faster and more sensitive, and as workflows like this one demonstrate that clinical material streams can feed them without added burden on patients, the vision of truly multi-layered molecular profiling, spanning genome, transcriptome, proteome, and phosphoproteome from a single needle biopsy, moves closer to routine reality. For patients with advanced cancer, whose treatment options often hinge on the quality and completeness of the molecular information available, that could mean better-matched therapies chosen from a far richer picture of what their tumor is actually doing.

Subject of Research: Recovery of protein from DNA/RNA extraction flowthroughs for scalable clinical proteomics and integrated proteogenomic profiling of cancer biopsies

Article Title: Scalable clinical proteomics from DNA/RNA extraction flowthroughs enables integrated proteogenomic profiling from a single cancer biopsy

Article References: Mundt, F., Bach Nielsen, A., Wang, J., Kerzel Duel, J., Westmose Yde, C., Amnitzbøll Eriksen, M., Lassen, U., Cilius Nielsen, F., Rohrberg, K., & Mann, M. (2026). Scalable clinical proteomics from DNA/RNA extraction flowthroughs enables integrated proteogenomic profiling from a single cancer biopsy. Clinical Proteomics. https://doi.org/10.1186/s12014-026-09632-1

Image Credits: AI Generated

DOI: 10.1186/s12014-026-09632-1

Keywords: proteomics, precision oncology, mass spectrometry, phosphoproteomics, cancer biopsy, proteogenomics, DNA/RNA extraction, clinical proteomics, CoPPO trial, FoundationOne CDx, data-independent acquisition, signaling pathways