The immunomodulatory drugs (IMiDs) lenalidomide and pomalidomide are highly effective treatments for multiple myeloma. However, virtually all patients eventually relapse due to acquired drug resistance with resistance-causing genetic alterations being found only in a small subset of cases. To identify non-genetic mechanisms of drug resistance, we here perform integrated global quantitative tandem mass tag (TMT)-based proteomic and phosphoproteomic analyses and RNA sequencing in five paired pre-treatment and relapse samples from multiple myeloma patients. These analyses reveal a CDK6-governed protein resistance signature that includes myeloma high-risk factors such as TRIP13 and RRM1. Overexpression of CDK6 in multiple myeloma cell lines reduces sensitivity to IMiDs while CDK6 inhibition by palbociclib or CDK6 degradation by proteolysis targeting chimeras (PROTACs) is highly synergistic with IMiDs in vitro and in vivo. This work identifies CDK6 upregulation as a druggable target in IMiD-resistant multiple myeloma and highlights the use of proteomic studies to uncover non-genetic resistance mechanisms in cancer.

Multiple myeloma (MM) is a plasma cell malignancy of the bone marrow. Despite therapeutic advances, MM remains incurable, and better risk stratification as well as new therapies are therefore highly needed. The proteome of MM has not been systematically assessed before and holds the potential to uncover insight into disease biology and improved prognostication in addition to genetic and transcriptomic studies. Here we provide a comprehensive multiomics analysis including deep tandem mass tag-based quantitative global (phospho)proteomics, RNA sequencing, and nanopore DNA sequencing of 138 primary patient-derived plasma cell malignancies encompassing treatment-naive MM, plasma cell leukemia and the premalignancy monoclonal gammopathy of undetermined significance, as well as healthy controls. We found that the (phospho)proteome of malignant plasma cells are highly deregulated as compared with healthy plasma cells and is both defined by chromosomal alterations as well as posttranscriptional regulation. A prognostic protein signature was identified that is associated with aggressive disease independent of established risk factors in MM. Integration with functional genetics and single-cell RNA sequencing revealed general and genetic subtype-specific deregulated proteins and pathways in plasma cell malignancies that include potential targets for (immuno)therapies. Our study demonstrates the potential of proteogenomics in cancer and provides an easily accessible resource for investigating protein regulation and new therapeutic approaches in MM.

Proteasome inhibition is a highly effective treatment for multiple myeloma (MM). However, virtually all patients develop proteasome inhibitor resistance, which is associated with a poor prognosis. Hyperactive small ubiquitin-like modifier (SUMO) signaling is involved in both cancer pathogenesis and cancer progression. A state of increased SUMOylation has been associated with aggressive cancer biology. We found that relapsed/refractory MM is characterized by a SUMO-high state, and high expression of the SUMO E1-activating enzyme (SAE1/UBA2) is associated with poor overall survival. Consistently, continuous treatment of MM cell lines with carfilzomib (CFZ) enhanced SUMO pathway activity. Treatment of MM cell lines with the SUMO E1-activating enzyme inhibitor subasumstat (TAK-981) showed synergy with CFZ in both CFZ-sensitive and CFZ-resistant MM cell lines, irrespective of the TP53 state. Combination therapy was effective in primary MM cells and in 2 murine MM xenograft models. Mechanistically, combination treatment with subasumstat and CFZ enhanced genotoxic and proteotoxic stress, and induced apoptosis was associated with activity of the prolyl isomerase PIN1. In summary, our findings reveal activated SUMOylation as a therapeutic target in MM and point to combined SUMO/proteasome inhibition as a novel and potent strategy for the treatment of proteasome inhibitor–resistant MM.

Protein-protein interactions mediated by intrinsically disordered regions are often based on short linear motifs (SLiMs). SLiMs are implicated in signal transduction and gene regulation yet remain technically laborious and notoriously challenging to study. Here, we present an optimized method for a protein interaction screen on a peptide matrix (PRISMA) in combination with quantitative MS. The protocol was benchmarked with previously described SLiM-based protein-protein interactions using peptides derived from EGFR, SOS1, GLUT1, and CEBPB and extended to map binding partners of kinase activation loops. The detailed protocol provides practical considerations for setting up a PRISMA screen and subsequently implementing PRISMA on a liquid-handling robotic platform as a cost-effective high-throughput method. Optimized PRISMA can be universally applied to systematically study SLiM-based interactions and associated post-translational modifications or mutations to advance our understanding of the largely uncharacterized interactomes of intrinsically disordered protein regions.

Mass spectrometry-based proteomics is a powerful tool for identifying and quantifying proteins in biological samples. While it is routinely used for the characterization of simple cell line systems, the analysis of the cell specific proteome in multicellular organisms and tissues poses a significant challenge. Isolating a subset of cells from tissues requires mechanical and biochemical separation or sorting, a process which can alter cellular signaling, and thus, the composition of the proteome. Recently, several approaches for cell selective labeling of proteins, that include bioorthogonal amino acids, biotinylating enzymes, and genetic tools, have been developed. These tools facilitate the selective labeling of proteins, their interactome, or of specific cell types within a tissue or an organism, while avoiding the difficult and contamination-prone biochemical separation of cells from the tissue. In this review, we give an overview of existing techniques and their application in cell culture models and whole animals.

Purpose: Antibody–drug conjugates (ADC) targeting trophoblast cell surface antigen 2 (TROP-2) and cMET are entering clinical trials in non–small cell lung cancer (NSCLC). Their translation depends on reliable biomarker assessment, a task still dominated by subjective visual scoring and inconsistent reproducibility. Experimental Design: We built a modular Artificial Intelligence (AI) pipeline that detects cells, classifies carcinoma cells, and quantifies membranous and cytoplasmic expression. A membranous scorer trained on TROP-2 was applied zero-shot to cMET, human epidermal growth factor receptor 2 (HER2), and PD-L1. For TROP-2, cMET, and HER2, expression was quantified using the H-score, defined as (1× % weakly positive cells) + (2× % moderately positive cells) + (3× % strongly positive cells), with negative cells excluded (range, 0–300), and categorized as negative (0–50), weakly positive (50–100), moderately positive (100–200), or strongly positive (200–300). PD-L1 expression was assessed using both the H-score and the tumor proportion score (TPS), defined as the percentage of viable tumor cells showing membranous PD-L1 staining relative to all viable tumor cells, multiplied by 100. The analysis covered 1,142 resected NSCLCs, integrating expression maps with clinicopathologic, molecular, and tumor microenvironment (TME) features. Results: The AI scorer recapitulated pathologist annotations with near-perfect correlation [Pearsons's correlation (r) = 0.98–0.99, Spearman's rho (ρ) = 97–98, Kendall's tau (τ) = 0.88–0.89, Lin's concordance correlation coefficient (CCC) = 0.97–0.98] for TROP-2 and generalized to other markers [cMET r = 0.99, ρ = 0.96, τ = 0.91, CCC = 0.96; HER2 r = 0.93, ρ = 0.72, τ = 0.60, CCC = 0.92; and PD-L1 (TPS) r = 0.85, ρ = 0.84, τ = 0.68, CCC = 0.82/(H-score) r = 0.87, ρ = 0.86, τ = 0.70, CCC = 0.85]. Its agreement with six pathologists matched interobserver variability (0.86–0.96). Expression maps revealed contrasting spatial and cellular patterns: TROP-2 dominated lung squamous cell carcinoma [LUSC; mean H-Scores 141.3/103.2 vs. 74.5/45.7 in lung adenocarcinoma (LUAD) for membrane/cytoplasm] and marked immune-deserted tumors. cMET prevailed in LUAD (mean 50.4 vs. 20.6 in LUSC), colocalized with fibroblast-rich, immune-active TME, and KRAS mutations. Conclusions: Foundation model–based scoring produces expert-level, scalable biomarker quantification. The resulting TME phenotypes—TROP-2–high immune-deserted versus cMET-high, immune-active—reveal therapeutic implications for combining ADCs with immunotherapies or kinase inhibitors.