Quantitative Proteomics of the Cancer Cell Line Encyclopedia

Multiomic profiling of breast cancer cells uncovers stress MAPK-associated sensitivity to AKT degradation

More than 50% of human tumors display hyperactivation of the serine/threonine kinase AKT. Despite evidence of clinical efficacy, the therapeutic window of the current generation of AKT inhibitors could be improved. Here, we report the development of a second-generation AKT degrader, INY-05-040, which outperformed catalytic AKT inhibition with respect to cellular suppression of AKT-dependent phenotypes in breast cancer cell lines. A growth inhibition screen with 288 cancer cell lines confirmed that INY-05-040 had a substantially higher potency than our first-generation AKT degrader (INY-03-041), with both compounds outperforming catalytic AKT inhibition by GDC-0068. Using multiomic profiling and causal network integration in breast cancer cells, we demonstrated that the enhanced efficacy of INY-05-040 was associated with sustained suppression of AKT signaling, which was followed by induction of the stress mitogen-activated protein kinase (MAPK) c-Jun N-terminal kinase (JNK). Further integration of growth inhibition assays with publicly available transcriptomic, proteomic, and reverse phase protein array (RPPA) measurements established low basal JNK signaling as a biomarker for breast cancer sensitivity to AKT degradation. Together, our study presents a framework for mapping the network-wide signaling effects of therapeutically relevant compounds and identifies INY-05-040 as a potent pharmacological suppressor of AKT signaling.

A phase 1b study of zilovertamab in combination with paclitaxel for locally advanced/unresectable or metastatic Her2-negative breast cancer

BACKGROUND

Zilovertamab is a humanized monoclonal antibody targeting ROR1, an onco-embryonic antigen expressed by malignant cells of a variety of solid tumors, including breast cancer. A prior phase 1 study showed that zilovertamab was well tolerated and effective in inhibiting ROR1-signaling, which leads to activation of ERK1/2, NF-κB, and NRF2 target genes. This phase 1b study evaluated the safety and tolerability of zilovertamab with paclitaxel in patients with advanced breast cancer.

PATIENTS AND METHODS

Eligible patients had locally advanced, unresectable, or metastatic HER2- breast cancer with Eastern Cooperative Group performance status of 0-2 and without prior taxane therapy in the advanced setting. Study treatment included 600 mg of zilovertamab administered intravenously (IV) on Days 1 and 15 of Cycle 1 and then Day 1 of each 28-day cycle along with paclitaxel weekly at 80 mg/m2 IV.

RESULTS

Study patients had received a median of 4 prior therapies (endocrine therapy + chemotherapy) for locally advanced, unresectable, or metastatic disease. No patient discontinued therapy due to toxicity ascribed to zilovertamab. Adverse events were consistent with the known safety profile of paclitaxel. Of 16 patients, 6 (38%) had a partial response, and 6/16 (38%) patients had stable disease as best tumor response.

CONCLUSION

The combination of zilovertamab and paclitaxel was safe and well tolerated in heavily pre-treated advanced breast cancer patients. Further evaluation of ROR1 targeting in breast cancer patients with zilovertamab is warranted.

TRIAL REGISTRATION

NCT02776917. Registered on ClinicalTrials.gov on 05/17/2016.

Current Advances in the Use of Tissue Engineering for Cancer Metastasis Therapeutics

Cancer is a leading cause of death worldwide and results in nearly 10 million deaths each year. The global economic burden of cancer from 2020 to 2050 is estimated to be USD 25.2 trillion. The spread of cancer to distant organs through metastasis is the leading cause of death due to cancer. However, as of today, there is no cure for metastasis. Tissue engineering is a promising field for regenerative medicine that is likely to be able to provide rehabilitation procedures to patients who have undergone surgeries, such as mastectomy and other reconstructive procedures. Another important use of tissue engineering has emerged recently that involves the development of realistic and robust in vitro models of cancer metastasis, to aid in drug discovery and new metastasis therapeutics, as well as evaluate cancer biology at metastasis. This review covers the current studies in developing tissue-engineered metastasis structures. This article reports recent developments in in vitro models for breast, prostate, colon, and pancreatic cancer. The review also identifies challenges and opportunities in the use of tissue engineering toward new, clinically relevant therapies that aim to reduce the cancer burden.

8-Br-cGMP activates HSPB6 and increases the antineoplastic activity of quinidine in prostate cancer

Heat shock protein family B [small] member 6 (HSPB6), widely found in various muscles, has been recently identified as a tumor suppressor gene. However, its role in prostate cancer remains unexplored. Herein, we investigated the expression of HSPB6 in prostate cancer and its association with prognosis. Our findings revealed that HSPB6 downregulation in prostate cancer correlated with a poor prognosis. Moreover, we discovered that HSPB6 can be phosphorylated and activated by 8-Br-cGMP, leading to apoptosis in prostate cancer cells by activating Cofilin. Additionally, we demonstrated that knocking down E2F1 by quinidine administration enhances the transcriptional level of HSPB6. Furthermore, we evaluated the combination of quinidine and 8-Br-cGMP as a potential therapeutic strategy for prostate cancer. Our results revealed that the combined treatment was more effective than either treatment alone in inhibiting the growth of prostate cancer through the HSPB6 pathway, both in vitro and in vivo. Overall, our study provides compelling evidence that HSPB6 suppresses malignant behavior in prostate cancer by inducing apoptosis. The combination of quinidine and 8-Br-cGMP emerges as a promising approach for the treatment of prostate cancer.

The protein phosphatase EYA4 promotes homologous recombination (HR) through dephosphorylation of tyrosine 315 on RAD51

Efficient DNA repair and limitation of genome rearrangements rely on crosstalk between different DNA double-strand break (DSB) repair pathways, and their synchronization with the cell cycle. The selection, timing and efficacy of DSB repair pathways are influenced by post-translational modifications of histones and DNA damage repair (DDR) proteins, such as phosphorylation. While the importance of kinases and serine/threonine phosphatases in DDR have been extensively studied, the role of tyrosine phosphatases in DNA repair remains poorly understood. In this study, we have identified EYA4 as the protein phosphatase that dephosphorylates RAD51 on residue Tyr315. Through its Tyr phosphatase activity, EYA4 regulates RAD51 localization, presynaptic filament formation, foci formation, and activity. Thus, it is essential for homologous recombination (HR) at DSBs. DNA binding stimulates EYA4 phosphatase activity. Depletion of EYA4 decreases single-stranded DNA accumulation following DNA damage and impairs HR, while overexpression of EYA4 in cells promotes dephosphorylation and stabilization of RAD51, and thereby nucleoprotein filament formation. Our data have implications for a pathological version of RAD51 in EYA4-overexpressing cancers.

UPP1 promotes lung adenocarcinoma progression through the induction of an immunosuppressive microenvironment

The complexity of the tumor microenvironment (TME) is a crucial factor in lung adenocarcinoma (LUAD) progression. To gain deeper insights into molecular mechanisms of LUAD, we perform an integrative single-cell RNA sequencing (scRNA-seq) data analysis of 377,574 cells from 117 LUAD patient samples. By linking scRNA-seq data with bulk gene expression data, we identify a cluster of prognostic-related UPP1high tumor cells. These cells, primarily situated at the invasive front of tumors, display a stronger association with the immunosuppressive components in the TME. Our cytokine array analysis reveals that the upregulation of UPP1 in tumor cells leads to the increased release of various immunosuppressive cytokines, with TGF-β1 being particularly prominent. Furthermore, this UPP1 upregulation also elevates the expression of PD-L1 through the PI3K/AKT/mTOR pathway, which contributes to the suppression of CD8 + T cells. Cytometry by time-of-flight (CyTOF) analysis provides additional evidence of the role of UPP1 in shaping the immunosuppressive nature of the TME. Using patient-derived organoids (PDOs), we discover that UPP1high tumors exhibit relatively increased sensitivity to Bosutinib and Dasatinib. Collectively, our study highlights the immunosuppressive role of UPP1 in LUAD, and these findings may provide insights into the molecular features of LUAD and facilitate the development of personalized treatment strategies.

A data analysis framework for combining multiple batches increases the power of isobaric proteomics experiments

We present a framework for the analysis of multiplexed mass spectrometry proteomics data that reduces estimation error when combining multiple isobaric batches. Variations in the number and quality of observations have long complicated the analysis of isobaric proteomics data. Here we show that the power to detect statistical associations is substantially improved by utilizing models that directly account for known sources of variation in the number and quality of observations that occur across batches.In a multibatch benchmarking experiment, our open-source software (msTrawler) increases the power to detect changes, especially in the range of less than twofold changes, while simultaneously increasing quantitative proteome coverage by utilizing more low-signal observations. Further analyses of previously published multiplexed datasets of 4 and 23 batches highlight both increased power and the ability to navigate complex missing data patterns without relying on unverifiable imputations or discarding reliable measurements.

Uncovering the Hidden World of Aqueous Humor Proteins for Discovery of Biomarkers for Marfan Syndrome

Ectopia lentis is a hallmark of Marfan syndrome (MFS), a genetic connective tissue disorder affecting 1/5000 to 1/10 000 individuals worldwide. Early detection in ophthalmology clinics and timely intervention of cardiovascular complications can be lifesaving. In this study, a modified proteomics workflow with liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based data-independent acquisition (DIA) and field asymmetric ion mobility spectrometry (FAIMS) to profile the proteomes of aqueous humor (AH) and lens tissue from MFS children with ectopia lentis is utilized. Over 2300 and 2938 comparable proteins are identified in AH and the lens capsule, respectively. Functional enrichment analyses uncovered dysregulation of complement and coagulation-related pathways, collagen binding, and cell adhesion in MFS. Through weighted correlation network analysis (WGCNA) and machine learning, distinct modules associated with clinical traits are constructed and a unique biomarker panel (Q14376, Q99972, P02760, Q07507; gene names: GALE, MYOC, AMBP, DPT) is defined. These biomarkers are further validated using advanced parallel reaction monitoring (PRM) in an independent patient cohort. The results provide novel insights into the proteome characterization of ectopia lentis and offer a promising approach for developing a valuable biomarker panel to aid in the early diagnosis of Marfan syndrome via AH proteome.

102-Plex Approach for Accurate and Multiplexed Proteome Quantification

Hyperplexing approaches have been aimed to meet the demand for large-scale proteomic analyses. Currently, the analysis capacity has expanded to up to 54 samples within a single experiment by utilizing different isotopic and isobaric reagent combinations. In this report, we propose a super multiplexed approach to enable the analysis of up to 102 samples in a single experiment, by the combination of our recently developed TAG-TMTpro and TAG-IBT16 labeling. We systematically investigated the identification and quantification performance of the 102-plex approach using the mixtures of E. coli and HeLa peptides. Our results revealed that all labeling series demonstrated accurate and reliable quantification performance. The combination of TAG-IBT16 and TAG-TMTpro approaches expands the multiplexing capacity to 102 plexes, providing a more multiplexed quantification method for even larger-scale proteomic analysis. Data are available via ProteomeXchange with the identifier PXD042398.

Computational inference of eIF4F complex function and structure in human cancers

The canonical eukaryotic initiation factor 4F (eIF4F) complex, composed of eIF4G1, eIF4A1, and the cap-binding protein eIF4E, plays a crucial role in cap-dependent translation initiation in eukaryotic cells. An alternative cap-independent initiation can occur, involving only eIF4G1 and eIF4A1 through internal ribosome entry sites (IRESs). This mechanism is considered complementary to cap-dependent initiation, particularly in tumors under stress conditions. However, the selection and molecular mechanism of specific translation initiation remains poorly understood in human cancers. Thus, we analyzed gene copy number variations (CNVs) in TCGA tumor samples and found frequent amplification of genes involved in translation initiation. Copy number gains in EIF4G1 and EIF3E frequently co-occur across human cancers. Additionally, EIF4G1 expression strongly correlates with genes from cancer cell survival pathways including cell cycle and lipogenesis, in tumors with EIF4G1 amplification or duplication. Furthermore, we revealed that eIF4G1 and eIF4A1 protein levels strongly co-regulate with ribosomal subunits, eIF2, and eIF3 complexes, while eIF4E co-regulates with 4E-BP1, ubiquitination, and ESCRT proteins. Utilizing Alphafold predictions, we modeled the eIF4F structure with and without eIF4E binding. For cap-dependent initiation, our modeling reveals extensive interactions between the N-terminal eIF4E-binding domain of eIF4G1 and eIF4E. Furthermore, the eIF4G1 HEAT-2 domain positions eIF4E near the eIF4A1 N-terminal domain (NTD), resulting in the collaborative enclosure of the RNA binding cavity within eIF4A1. In contrast, during cap-independent initiation, the HEAT-2 domain directly binds the eIF4A1-NTD, leading to a stronger interaction between eIF4G1 and eIF4A1, thus closing the mRNA binding cavity without the involvement of eIF4E.

Ribosomal protein S6 kinase 2 (RPS6KB2) is a potential immunotherapeutic target for cancer that upregulates proinflammatory cytokines

BACKGROUND

Cancer is still a leading cause of mortality. Over the years, cancer therapy has undergone significant advances driven by advancements in science and technology. A promising area of drug discovery in this field involves the development of therapeutic targets for cancer treatment. The urgent need to identify new pharmacological targets arises from the impact of tumor resistance on the effectiveness of current medications. Specifically, the RPS6KB2 gene on chromosome 11 has been implicated in cell cycle regulation and exhibits higher expression levels in tumor tissue. Given this association, there is a potential for this gene to serve as a target for cancer treatment.

METHODS

We conducted an analysis using the GTEx, TCGA, and CCLE databases to explore the relationship between RPS6KB2 and immune infiltration, the tumor microenvironment (TME), microsatellite instability (MSI), and more. Cell proliferation was assessed using EDU detection, while cell invasion and migration were evaluated via wound healing and Transwell assays. Additionally, western blot analysis was employed to measure expression of Bax, Bcl-2, MMP2, MMP9, PCNA, and proinflammatory factors.

RESULTS

Through data analysis and molecular biology methods, our study carefully examined the potential role of RPS6KB2 in cancer therapy. The data revealed that RPS6KB2 is aberrantly expressed in most cancers and is associated with poor prognosis. Further analysis indicated its involvement in cancer cell apoptosis and migration, as well as its role in cancer immune processes. We validated the significance of RPS6KB2 in hepatocellular carcinoma (HCC), highlighting its capacity to upregulate proinflammatory cytokines.

CONCLUSION

Our research indicates that RPS6KB2 is a prognostic biomarker associated with immune infiltration in cancer that can affect antitumor immunity by increasing secretion of proinflammatory factors, providing a potential drug target for cancer treatment.

A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening

It is well-known that cancers of the same histology type can respond differently to a treatment. Thus, computational drug response prediction is of paramount importance for both preclinical drug screening studies and clinical treatment design. To build drug response prediction models, treatment response data need to be generated through screening experiments and used as input to train the prediction models. In this study, we investigate various active learning strategies of selecting experiments to generate response data for the purposes of (1) improving the performance of drug response prediction models built on the data and (2) identifying effective treatments. Here, we focus on constructing drug-specific response prediction models for cancer cell lines. Various approaches have been designed and applied to select cell lines for screening, including a random, greedy, uncertainty, diversity, combination of greedy and uncertainty, sampling-based hybrid, and iteration-based hybrid approach. All of these approaches are evaluated and compared using two criteria: (1) the number of identified hits that are selected experiments validated to be responsive, and (2) the performance of the response prediction model trained on the data of selected experiments. The analysis was conducted for 57 drugs and the results show a significant improvement on identifying hits using active learning approaches compared with the random and greedy sampling method. Active learning approaches also show an improvement on response prediction performance for some of the drugs and analysis runs compared with the greedy sampling method.

Chromatin accessibility and pioneer factor FOXA1 restrict glucocorticoid receptor action in prostate cancer

Treatment of prostate cancer relies predominantly on the inhibition of androgen receptor (AR) signaling. Despite the initial effectiveness of the antiandrogen therapies, the cancer often develops resistance to the AR blockade. One mechanism of the resistance is glucocorticoid receptor (GR)-mediated replacement of AR function. Nevertheless, the mechanistic ways and means how the GR-mediated antiandrogen resistance occurs have remained elusive. Here, we have discovered several crucial features of GR action in prostate cancer cells through genome-wide techniques. We detected that the replacement of AR by GR in enzalutamide-exposed prostate cancer cells occurs almost exclusively at pre-accessible chromatin sites displaying FOXA1 occupancy. Counterintuitively to the classical pioneer factor model, silencing of FOXA1 potentiated the chromatin binding and transcriptional activity of GR. This was attributed to FOXA1-mediated repression of the NR3C1 (gene encoding GR) expression via the corepressor TLE3. Moreover, the small-molecule inhibition of coactivator p300's enzymatic activity efficiently restricted GR-mediated gene regulation and cell proliferation. Overall, we identified chromatin pre-accessibility and FOXA1-mediated repression as important regulators of GR action in prostate cancer, pointing out new avenues to oppose steroid receptor-mediated antiandrogen resistance.

Artificial Intelligence for Drug Discovery: Are We There Yet?

Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning, and multiproperty optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.

Unraveling Hematotoxicity of α-Amanitin in Cultured Hematopoietic Cells

Amanita phalloides poisonings account for the majority of fatal mushroom poisonings. Recently, we identified hematotoxicity as a relevant aspect of Amanita poisonings. In this study, we investigated the effects of the main toxins of Amanita phalloides, α- and β-amanitin, on hematopoietic cell viability in vitro. Hematopoietic cell lines were exposed to α-amanitin or β-amanitin for up to 72 h with or without the pan-caspase inhibitor Z-VAD(OH)-FMK, antidotes N-acetylcysteine, silibinin, and benzylpenicillin, and organic anion-transporting polypeptide 1B3 (OATP1B3) inhibitors rifampicin and cyclosporin. Cell viability was established by trypan blue exclusion, annexin V staining, and a MTS assay. Caspase-3/7 activity was determined with Caspase-Glo assay, and cleaved caspase-3 was quantified by Western analysis. Cell number and colony-forming units were quantified after exposure to α-amanitin in primary CD34+ hematopoietic stem cells. In all cell lines, α-amanitin concentration-dependently decreased viability and mitochondrial activity. β-Amanitin was less toxic, but still significantly reduced viability. α-Amanitin increased caspase-3/7 activity by 2.8-fold and cleaved caspase-3 by 2.3-fold. Z-VAD(OH)-FMK significantly reduced α-amanitin-induced toxicity. In CD34+ stem cells, α-amanitin decreased the number of colonies and cells. The antidotes and OATP1B3 inhibitors did not reverse α-amanitin-induced toxicity. In conclusion, α-amanitin induces apoptosis in hematopoietic cells via a caspase-dependent mechanism.

Increasing the Throughput and Reproducibility of Activity-Based Proteome Profiling Studies with Hyperplexing and Intelligent Data Acquisition

Intelligent data acquisition (IDA) strategies, such as a real-time database search (RTS), have improved the depth of proteome coverage for experiments that utilize isobaric labels and gas phase purification techniques (i.e., SPS-MS3). In this work, we introduce inSeqAPI, an instrument application programing interface (iAPI) program that enables construction of novel data acquisition algorithms. First, we analyze biotinylated cysteine peptides from ABPP experiments to demonstrate that a real-time search method within inSeqAPI performs similarly to an equivalent vendor method. Then, we describe PairQuant, a method within inSeqAPI designed for the hyperplexing approach that utilizes protein-level isotopic labeling and peptide-level TMT labeling. PairQuant allows for TMT analysis of 36 conditions in a single sample and achieves ∼98% coverage of both peptide pair partners in a hyperplexed experiment as well as a 40% improvement in the number of quantified cysteine sites compared with non-RTS acquisition. We applied this method in the ABPP study of ligandable cysteine sites in the nucleus leading to an identification of additional druggable sites on protein- and DNA-interaction domains of transcription regulators and on nuclear ubiquitin ligases.

labelSeg: segment annotation for tumor copy number alteration profiles

Somatic copy number alterations (SCNAs) are a predominant type of oncogenomic alterations that affect a large proportion of the genome in the majority of cancer samples. Current technologies allow high-throughput measurement of such copy number aberrations, generating results consisting of frequently large sets of SCNA segments. However, the automated annotation and integration of such data are particularly challenging because the measured signals reflect biased, relative copy number ratios. In this study, we introduce labelSeg, an algorithm designed for rapid and accurate annotation of CNA segments, with the aim of enhancing the interpretation of tumor SCNA profiles. Leveraging density-based clustering and exploiting the length-amplitude relationships of SCNA, our algorithm proficiently identifies distinct relative copy number states from individual segment profiles. Its compatibility with most CNA measurement platforms makes it suitable for large-scale integrative data analysis. We confirmed its performance on both simulated and sample-derived data from The Cancer Genome Atlas reference dataset, and we demonstrated its utility in integrating heterogeneous segment profiles from different data sources and measurement platforms. Our comparative and integrative analysis revealed common SCNA patterns in cancer and protein-coding genes with a strong correlation between SCNA and messenger RNA expression, promoting the investigation into the role of SCNA in cancer development.

CAP-RNAseq: an integrated pipeline for functional annotation and prioritization of co-expression clusters

Cluster analysis is one of the most widely used exploratory methods for visualization and grouping of gene expression patterns across multiple samples or treatment groups. Although several existing online tools can annotate clusters with functional terms, there is no all-in-one webserver to effectively prioritize genes/clusters using gene essentiality as well as congruency of mRNA-protein expression. Hence, we developed CAP-RNAseq that makes possible (1) upload and clustering of bulk RNA-seq data followed by identification, annotation and network visualization of all or selected clusters; and (2) prioritization using DepMap gene essentiality and/or dependency scores as well as the degree of correlation between mRNA and protein levels of genes within an expression cluster. In addition, CAP-RNAseq has an integrated primer design tool for the prioritized genes. Herein, we showed using comparisons with the existing tools and multiple case studies that CAP-RNAseq can uniquely aid in the discovery of co-expression clusters enriched with essential genes and prioritization of novel biomarker genes that exhibit high correlations between their mRNA and protein expression levels. CAP-RNAseq is applicable to RNA-seq data from different contexts including cancer and available at http://konulabapps.bilkent.edu.tr:3838/CAPRNAseq/ and the docker image is downloadable from https://hub.docker.com/r/konulab/caprnaseq.

Immunogenic profiling of metastatic uveal melanoma discerns a potential signature related to prognosis

BACKGROUND

Uveal melanoma (UM) is an aggressive intraocular malignant tumor. The present study aimed to identify the key genes associated with UM metastasis and established a gene signature to analyze the relationship between the signature and prognosis and immune cell infiltration. Later, a predictive model combined with clinical variables was developed and validated.

METHODS

Two UM gene expression profile chip datasets were downloaded from TCGA and GEO databases. Immune-related genes (IRGs) were obtained from IMPORT database. First, these mRNAs were intersected with IRGs, and weighted gene co-expression network analysis (WGCNA) was used to identify the co-expression of genes primarily associated with metastasis of UM. Univariate Cox regression analysis screened the genes related to prognosis. LASSO-Cox established a risk score to distinguish high-risk group and low-risk group. Then the GSEA enrichment pathway and immune cell infiltration of the two groups were compared. And combined with clinical variables, a predictive model was constructed. The time-dependent receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) curve were used to verify the stability and accuracy of the final predictive model, and a nomogram was then drawn.

RESULTS

The MEblack, MEpurple, and MEblue modules were significantly associated with the metastasis of UM patients (P value < 0.001, = 0.001, = 0.022, respectively). Four genes (UBXN2B, OTUD3, KAT8, LAMTOR2) were obtained by Pearson correlation analysis, weighted gene correlation network analysis (WGCNA), univariate Cox, and LASSO-Cox. And a novel prognostic risk score was established. Immune-related prognostic signature can well classify UM patients into high-risk and low-risk groups. Kaplan-Meier curve showed that the OS of high-risk patients was worse than that of low-risk patients. In addition, the risk score played an important role in evaluating the signaling pathway and immune cell infiltration of UM patients in high-risk and low-risk groups. Both the training set and validation set of the model showed good predictive accuracy in the degree of differentiation and calibration (e.g., 1-year overall survival: AUC = 0.930 (0.857-1.003)). Finally, a nomogram was established to serve in clinical practice.

SIGNIFICANCE

UM key gene signature and prognosis predictive model might provide insights for further investigation of the pathogenesis and development of UM at the molecular level, and provide theoretical basis for determining new prognostic markers of UM and immunotherapy.

Translation of non-canonical open reading frames as a cancer cell survival mechanism in childhood medulloblastoma

A hallmark of high-risk childhood medulloblastoma is the dysregulation of RNA translation. Currently, it is unknown whether medulloblastoma dysregulates the translation of putatively oncogenic non-canonical open reading frames (ORFs). To address this question, we performed ribosome profiling of 32 medulloblastoma tissues and cell lines and observed widespread non-canonical ORF translation. We then developed a stepwise approach using multiple CRISPR-Cas9 screens to elucidate non-canonical ORFs and putative microproteins implicated in medulloblastoma cell survival. We determined that multiple lncRNA-ORFs and upstream ORFs (uORFs) exhibited selective functionality independent of main coding sequences. A microprotein encoded by one of these ORFs, ASNSD1-uORF or ASDURF, was upregulated, associated with MYC-family oncogenes, and promoted medulloblastoma cell survival through engagement with the prefoldin-like chaperone complex. Our findings underscore the fundamental importance of non-canonical ORF translation in medulloblastoma and provide a rationale to include these ORFs in future studies seeking to define new cancer targets.

Kinase inhibitor pulldown assay (KiP) for clinical proteomics

Protein kinases are frequently dysregulated and/or mutated in cancer and represent essential targets for therapy. Accurate quantification is essential. For breast cancer treatment, the identification and quantification of the protein kinase ERBB2 is critical for therapeutic decisions. While immunohistochemistry (IHC) is the current clinical diagnostic approach, it is only semiquantitative. Mass spectrometry-based proteomics offers quantitative assays that, unlike IHC, can be used to accurately evaluate hundreds of kinases simultaneously. The enrichment of less abundant kinase targets for quantification, along with depletion of interfering proteins, improves sensitivity and thus promotes more effective downstream analyses. Multiple kinase inhibitors were therefore deployed as a capture matrix for kinase inhibitor pulldown (KiP) assays designed to profile the human protein kinome as broadly as possible. Optimized assays were initially evaluated in 16 patient derived xenograft models (PDX) where KiP identified multiple differentially expressed and biologically relevant kinases. From these analyses, an optimized single-shot parallel reaction monitoring (PRM) method was developed to improve quantitative fidelity. The PRM KiP approach was then reapplied to low quantities of proteins typical of yields from core needle biopsies of human cancers. The initial prototype targeting 100 kinases recapitulated intrinsic subtyping of PDX models obtained from comprehensive proteomic and transcriptomic profiling. Luminal and HER2 enriched OCT-frozen patient biopsies subsequently analyzed through KiP-PRM also clustered by subtype. Finally, stable isotope labeled peptide standards were developed to define a prototype clinical method. Data are available via ProteomeXchange with identifiers PXD044655 and PXD046169.

Combined transcriptomics and proteomics studies on the effect of electrical stimulation on spinal cord injury in rats

Electrical stimulation (ES) of the spinal cord is a promising therapy for functional rehabilitation after spinal cord injury (SCI). However, the specific mechanism of action is poorly understood. We designed and applied an implanted ES device in the SCI area in rats and determined the effect of ES on the treatment of motor dysfunction after SCI using behavioral scores. Additionally, we examined the molecular characteristics of the samples using proteomic and transcriptomic sequencing. The differential molecules between groups were identified using statistical analyses. Molecular, network, and pathway-based analyses were used to identify group-specific biological features. ES (0.5 mA, 0.1 ms, 50 Hz) had a positive effect on motor dysfunction and neuronal regeneration in rats after SCI. Six samples (three independent replicates in each group) were used for transcriptome sequencing; we obtained 1026 differential genes, comprising 274 upregulated genes and 752 downregulated genes. A total of 10 samples were obtained: four samples in the ES group and six samples in the SCI group; for the proteome sequencing, 48 differential proteins were identified, including 45 up-regulated and three down-regulated proteins. Combined transcriptomic and proteomic studies have shown that the main enrichment pathway is the hedgehog signaling pathway. Western blot results showed that the expression levels of Sonic hedgehog (SHH) (P < 0.001), Smoothened (SMO) (P = 0.0338), and GLI-1 (P < 0.01) proteins in the ES treatment group were significantly higher than those in the SCI group. The immunofluorescence results showed significantly increased expression of SHH (P = 0.0181), SMO (P = 0.021), and GLI-1 (P = 0.0126) in the ES group compared with that in the SCI group. In conclusion, ES after SCI had a positive effect on motor dysfunction and anti-inflammatory effects in rats. Moreover, transcriptomic and proteomic sequencing also provided unique perspectives on the complex relationships between ES on SCI, where the SHH signaling pathway plays a critical role. Our study provides a significant theoretical foundation for the clinical implementation of ES therapy in patients with SCI.

Novel cellular systems unveil mucosal melanoma initiating cells and a role for PI3K/Akt/mTOR pathway in mucosal melanoma fitness

BACKGROUND

Mucosal Melanomas (MM) are highly aggressive neoplasms arising from mucosal melanocytes. Current treatments offer a limited survival benefit for patients with advanced MM; moreover, the lack of pre-clinical cellular systems has significantly limited the understanding of their immunobiology.

METHODS

Five novel cell lines were obtained from patient-derived biopsies of MM arising in the sino-nasal mucosa and designated as SN-MM1-5. The morphology, ultrastructure and melanocytic identity of SN-MM cell lines were validated by transmission electron microscopy and immunohistochemistry. Moreover, in vivo tumorigenicity of SN-MM1-5 was tested by subcutaneous injection in NOD/SCID mice. Molecular characterization of SN-MM cell lines was performed by a mass-spectrometry proteomic approach, and their sensitivity to PI3K chemical inhibitor LY294002 was validated by Akt activation, measured by pAkt(Ser473) and pAkt(Thr308) in immunoblots, and MTS assay.

RESULTS

This study reports the validation and functional characterization of five newly generated SN-MM cell lines. Compared to the normal counterpart, the proteomic profile of SN-MM is consistent with transformed melanocytes showing a heterogeneous degree of melanocytic differentiation and activation of cancer-related pathways. All SN-MM cell lines resulted tumorigenic in vivo and display recurrent structural variants according to aCGH analysis. Of relevance, the microscopic analysis of the corresponding xenotransplants allowed the identification of clusters of MITF-/CDH1-/CDH2 + /ZEB1 + /CD271 + cells, supporting the existence of melanoma-initiating cells also in MM, as confirmed in clinical samples. In vitro, SN-MM cell lines were sensitive to cisplatin, but not to temozolomide. Moreover, the proteomic analysis of SN-MM cell lines revealed that RICTOR, a subunit of mTORC2 complex, is the most significantly activated upstream regulator, suggesting a relevant role for the PI3K-Akt-mTOR pathway in these neoplasms. Consistently, phosphorylation of NDRG1 and Akt activation was observed in SN-MM, the latter being constitutive and sustained by PTEN loss in SN-MM2 and SN-MM3. The cell viability impairment induced by LY294002 confirmed a functional role for the PI3K-Akt-mTOR pathway in SN-MM cell lines.

CONCLUSIONS

Overall, these novel and unique cellular systems represent relevant experimental tools for a better understanding of the biology of these neoplasms and, as an extension, to MM from other sites.

CancerProteome: a resource to functionally decipher the proteome landscape in cancer

Advancements in mass spectrometry (MS)-based proteomics have greatly facilitated the large-scale quantification of proteins and microproteins, thereby revealing altered signalling pathways across many different cancer types. However, specialized and comprehensive resources are lacking for cancer proteomics. Here, we describe CancerProteome (http://bio-bigdata.hrbmu.edu.cn/CancerProteome), which functionally deciphers and visualizes the proteome landscape in cancer. We manually curated and re-analyzed publicly available MS-based quantification and post-translational modification (PTM) proteomes, including 7406 samples from 21 different cancer types, and also examined protein abundances and PTM levels in 31 120 proteins and 4111 microproteins. Six major analytical modules were developed with a view to describe protein contributions to carcinogenesis using proteome analysis, including conventional analyses of quantitative and the PTM proteome, functional enrichment, protein-protein associations by integrating known interactions with co-expression signatures, drug sensitivity and clinical relevance analyses. Moreover, protein abundances, which correlated with corresponding transcript or PTM levels, were evaluated. CancerProteome is convenient as it allows users to access specific proteins/microproteins of interest using quick searches or query options to generate multiple visualization results. In summary, CancerProteome is an important resource, which functionally deciphers the cancer proteome landscape and provides a novel insight for the identification of tumor protein markers in cancer.

Pan-Cancer Analysis of P3H1 and Experimental Validation in Renal Clear Cell Carcinoma

Prolyl 3-hydroxylase 1 (P3H1) has been implicated in cancer development, but no pan-cancer analysis has been conducted on P3H1. In this study, for the first time, aspects associated with P3H1, such as the mRNA expression, any mutation, promoter methylation, and prognostic significance, the relationship between P3H1 and clinicopathological parameters, drug sensitivity, and immune cell infiltration were investigated by searching several databases including The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), cBioPortal, and The Tumor Immune Evaluation Resource (TIMER2.0) using bioinformatics tools. The findings indicate significant differential expression of P3H1 in most tumors when compared to normal tissues, with a strong association with clinical prognosis. A pan-cancer Cox regression analysis revealed that high P3H1 expression is significantly associated with low overall survival in patients with brain lower grade glioma, kidney clear cell carcinoma, adrenocortical cancer, liver hepatocellular carcinoma, mesothelioma, sarcoma, uveal melanoma, bladder urothelial carcinoma, kidney papillary cell carcinoma, kidney chromophobe, thymoma, and thyroid carcinoma. A negative correlation was observed between P3H1 DNA methylation and its expression. P3H1 is significantly associated with infiltrating cells, immune-related genes, tumor mutation burden, microsatellite instability, and mismatch repair. Finally, A significant correlation was found between P3H1 expression and sensitivity to nine drugs. Thus, enhanced P3H1 expression is associated with poor prognosis in a variety of tumors, which may be due to its role in tumor immune regulation and tumor microenvironment. This pan-cancer analysis provides insight into the function of P3H1 in tumorigenesis of different cancers and provides a theoretical basis for further in-depth studies to follow.

Proteomics: A new dimension to decode small cell lung cancer

Small cell lung cancer (SCLC) is a recalcitrant malignancy. Conquering it will require deep insight into its biology. In this issue of Cell, Liu and colleagues describe proteomic and phosphoproteomic landscapes of resected SCLC tumors and illustrate the potential of this knowledge to identify new SCLC vulnerabilities.

Illuminating phenotypic drug responses of sarcoma cells to kinase inhibitors by phosphoproteomics

Kinase inhibitors (KIs) are important cancer drugs but often feature polypharmacology that is molecularly not understood. This disconnect is particularly apparent in cancer entities such as sarcomas for which the oncogenic drivers are often not clear. To investigate more systematically how the cellular proteotypes of sarcoma cells shape their response to molecularly targeted drugs, we profiled the proteomes and phosphoproteomes of 17 sarcoma cell lines and screened the same against 150 cancer drugs. The resulting 2550 phenotypic profiles revealed distinct drug responses and the cellular activity landscapes derived from deep (phospho)proteomes (9-10,000 proteins and 10-27,000 phosphorylation sites per cell line) enabled several lines of analysis. For instance, connecting the (phospho)proteomic data with drug responses revealed known and novel mechanisms of action (MoAs) of KIs and identified markers of drug sensitivity or resistance. All data is publicly accessible via an interactive web application that enables exploration of this rich molecular resource for a better understanding of active signalling pathways in sarcoma cells, identifying treatment response predictors and revealing novel MoA of clinical KIs.

Advanced progress of spatial metabolomics in head and neck cancer research

Head and neck cancer ranks as the sixth most prevalent malignancy, constituting 5 % of all cancer cases. Its inconspicuous onset often leads to advanced stage diagnoses, prompting the need for early detection to enhance patient prognosis. Currently, research into early diagnostic markers relies predominantly on genomics, proteomics, transcriptomics, and other methods, which, unfortunately, necessitate tumor tissue homogenization, resulting in the loss of temporal and spatial information. Emerging as a recent addition to the omics toolkit, spatial metabolomics stands out. This method conducts in situ mass spectrometry analyses on fresh tissue specimens while effectively preserving their spatiotemporal information. The utilization of spatial metabolomics in life science research offers distinct advantages. This article comprehensively reviews the progress of spatial metabolomics in head and neck cancer research, encompassing insights into cancer cell metabolic reprogramming. Various mass spectrometry imaging techniques, such as secondary ion mass spectrometry, stroma-assisted laser desorption/ionization, and desorption electrospray ionization, enable in situ metabolite analysis for head and neck cancer. Finally, significant emphasis is placed on the application of presently available techniques for early diagnosis, margin assessment, and prognosis of head and neck cancer.

ATP1A1/BCL2L1 predicts the response of myelomonocytic and monocytic acute myeloid leukemia to cardiac glycosides

Myelomonocytic and monocytic acute myeloid leukemia (AML) subtypes are intrinsically resistant to venetoclax-based regimens. Identifying targetable vulnerabilities would limit resistance and relapse. We previously documented the synergism of venetoclax and cardiac glycoside (CG) combination in AML. Despite preclinical evidence, the repurposing of cardiac glycosides (CGs) in cancer therapy remained unsuccessful due to a lack of predictive biomarkers. We report that the ex vivo response of AML patient blasts and the in vitro sensitivity of established cell lines to the hemi-synthetic CG UNBS1450 correlates with the ATPase Na+/K+ transporting subunit alpha 1 (ATP1A1)/BCL2 like 1 (BCL2L1) expression ratio. Publicly available AML datasets identify myelomonocytic/monocytic differentiation as the most robust prognostic feature, along with core-binding factor subunit beta (CBFB), lysine methyltransferase 2A (KMT2A) rearrangements, and missense Fms-related receptor tyrosine kinase 3 (FLT3) mutations. Mechanistically, BCL2L1 protects from cell death commitment induced by the CG-mediated stepwise triggering of ionic perturbation, protein synthesis inhibition, and MCL1 downregulation. In vivo, CGs showed an overall tolerable profile while impacting tumor growth with an effect ranging from tumor growth inhibition to regression. These findings suggest a predictive marker for CG repurposing in specific AML subtypes.

Optimizing the PROTREC network-based missing protein prediction algorithm

This article summarizes the PROTREC method and investigates the impact that the different hyper-parameters have on the task of missing protein prediction using PROTREC. We evaluate missing protein recovery rates using different PROTREC score selection approaches (MAX, MIN, MEDIAN, and MEAN), different PROTREC score thresholds, as well as different complex size thresholds. In addition, we included two additional cancer datasets in our analysis and introduced a new validation method to check both the robustness of the PROTREC method as well as the correctness of our analysis. Our analysis showed that the missing protein recovery rate can be improved by adopting PROTREC score selection operations of MIN, MEDIAN, and MEAN instead of the default MAX. However, this may come at a cost of reduced numbers of proteins predicted and validated. The users should therefore choose their hyper-parameters carefully to find a balance in the accuracy-quantity trade-off. We also explored the possibility of combining PROTREC with a p-value-based method (FCS) and demonstrated that PROTREC is able to perform well independently without any help from a p-value-based method. Furthermore, we conducted a downstream enrichment analysis to understand the biological pathways and protein networks within the cancerous tissues using the recovered proteins. Missing protein recovery rate using PROTREC can be improved by selecting a different PROTREC score selection method. Different PROTREC score selection methods and other hyper-parameters such as PROTREC score threshold and complex size threshold introduce accuracy-quantity trade-off. PROTREC is able to perform well independently of any filtering using a p-value-based method. Verification of the PROTREC method on additional cancer datasets. Downstream Enrichment Analysis to understand the biological pathways and protein networks in cancerous tissues.

Synergistic anticancer effect by targeting CDK2 and EGFR-ERK signaling

The EGFR-RAS-ERK pathway is one of the most important signaling cascades in cell survival, growth, and proliferation. Aberrant activation of this pathway is a common mechanism in various cancers. Here, we report that CDK2 is a novel regulator of the ERK pathway via USP37 deubiquitinase (DUB). Mechanistically, CDK2 phosphorylates USP37, which is required for USP37 DUB activity. Further, USP37 deubiquitinates and stabilizes ERK1/2, thereby enhancing cancer cell proliferation. Thus, CDK2 is able to promote cell proliferation by activating USP37 and, in turn, stabilizing ERK1/2. Importantly, combined CDK1/2 and EGFR inhibitors have a synergetic anticancer effect through the downregulation of ERK1/2 stability and activity. Indeed, our patient-derived xenograft (PDX) results suggest that targeting both ERK1/2 stability and activity kills cancer cells more efficiently even at lower doses of these two inhibitors, which may reduce their associated side effects and indicate a potential new combination strategy for cancer therapy.

Lipid metabolism-associated genes serve as potential predictive biomarkers in neoadjuvant chemoradiotherapy combined with immunotherapy in rectal cancer

BACKGROUND

The aim of this study was to investigate the potential role of lipid metabolism-associated genes (LMAGs) in neoadjuvant chemoradiotherapy (nCRT) and immunotherapy for rectal cancer.

METHODS

Differential LMAGs were characterized and functional enrichment analysis was performed. Multiple machine learning algorithms were combined to explore candidate LMAGs. ROC analysis was performed to evaluate the predicting accuracy of candidate LMAGs. The expression patterns, prognostic value, genetic alterations, and immune cell infiltration of the top-ranked LMAGs were investigated.

RESULTS

We identified 45 LMAGs that were differentially expressed in tumor samples of nCRT responders and non-responders. These LMAGs were closely associated with lipid metabolism-related biological processes and pathways. ROC analysis revealed that the SREBF2 gene, an important transcription factor in regulating lipid metabolism, was the highest predictor of nCRT in rectal cancer. SREBF2 was highly expressed in rectal cancer tissues and high expression of SREBF2 was associated with favorable prognosis. Multivariate analysis showed that SREBF2 was an independent prognostic factor, and we integrated it with other clinical factors to establish an effective prognostic nomogram. SREBF2 also played a synergistic role with its co-expressed genes in the prognostic process of rectal cancer. Furthermore, SREBF2 was demonstrated to be closely associated with multiple immune infiltrating cells, and immunotherapy-related genes and may be used to predict the response to immunotherapy.

CONCLUSION

Our study suggests that LMAGs may serve as promising biomarkers in nCRT combined with immunotherapy for rectal cancer. However, large-scale clinical trials and biological experiments are necessary to demonstrate the efficacy and underlying mechanisms.

CDK6 protein expression is associated with disease progression and treatment resistance in multiple myeloma

Multiple myeloma (MM) is a heterogeneous malignancy of plasma cells. Despite improvement in the prognosis of MM patients after the introduction of many new drugs in the past decades, MM remains incurable since most patients become treatment-resistant. Cyclin-dependent kinase 6 (CDK6) is activated in many types of cancer and has been associated with drug resistance in MM. However, its association with disease stage, genetic alterations, and outcome has not been systematically investigated in large cohorts. Here, we analyzed CDK6 expression using immunohistochemistry in 203 formalin-fixed paraffin-embedded samples of 146 patients and four healthy individuals. We found that 61.5% of all MM specimens express CDK6 at various levels. CDK6 expression increased with the progression of disease with a median of 0% of CDK6-positive plasma cells in monoclonal gammopathy of undetermined significance (MGUS) (n = 10) to 30% in newly diagnosed MM (n = 78) and up to 70% in relapsed cases (n = 55). The highest median CDK6 was observed in extramedullary myeloma (n = 12), a highly aggressive manifestation of MM. Longitudinal analyses revealed that CDK6 is significantly increased in lenalidomide-treated patients but not in those who did not receive lenalidomide. Furthermore, we observed that patients who underwent lenalidomide-comprising induction therapy had significantly shorter progression-free survival when their samples were CDK6 positive. These data support that CDK6 protein expression is a marker for aggressive and drug-resistant disease and describes a potential drug target in MM.

A proteomic landscape of pharmacologic perturbations for functional relevance

Pharmacological perturbation studies based on protein-level signatures are fundamental for drug discovery. In the present study, we used a mass spectrometry (MS)-based proteomic platform to profile the whole proteome of the breast cancer MCF7 cell line under stress induced by 78 bioactive compounds. The integrated analysis of perturbed signal abundance revealed the connectivity between phenotypic behaviors and molecular features in cancer cells. Our data showed functional relevance in exploring the novel pharmacological activity of phenolic xanthohumol, as well as the noncanonical targets of clinically approved tamoxifen, lovastatin, and their derivatives. Furthermore, the rational design of synergistic inhibition using a combination of histone methyltransferase and topoisomerase was identified based on their complementary drug fingerprints. This study provides rich resources for the proteomic landscape of drug responses for precision therapeutic medicine.

Up-regulation of SLC7A11/xCT creates a vulnerability to selenocystine-induced cytotoxicity

The SLC7A11/xCT cystine and glutamate antiporter has emerged as an attractive target for cancer therapy due to its selective overexpression in multiple cancers and its role in preventing ferroptosis. Utilizing pharmacological and genetic approaches in hepatocellular carcinoma cell lines, we demonstrate that overexpression of SLC7A11 engenders hypersensitivity towards l-selenocystine, a naturally occurring diselenide that bears close structural similarity to l-cystine. We find that the abundance of SLC7A11 positively correlates with sensitivity to l-selenocystine, but surprisingly, not to Erastin, an inhibitor of SLC7A11 activity. Our data indicate that SLC7A11 acts as a transport channel for l-selenocystine, which preferentially incites acute oxidative stress and damage eventuating to cell death in cells that highly express SLC7A11. Hence, our findings raise the prospect of l-selenocystine administration as a novel strategy for targeting cancers that up-regulate SLC7A11 expression.

Pan-cancer Analysis Identifies AIMP2 as a Potential Biomarker for Breast Cancer

Introduction

Aminoacyl tRNA synthetase complex interacting with multifunctional protein 2 (AIMP2) is a significant regulator of cell proliferation and apoptosis. Despite its abnormal expression in various tumor types, the specific functions and effects of AIMP2 on tumor immune cell infiltration, proliferation, and migration remain unclear.

Materials and Methods

To assess AIMP2's role in tumor immunity, we conducted a pan-cancer multi-database analysis using the Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Lines Encyclopedia (CCLE) datasets, examining expression levels, prognosis, tumor progression, and immune microenvironment. Additionally, we investigated AIMP2's impact on breast cancer (BRCA) proliferation and migration using cell counting kit 8 (CCK-8) assay, transwell assays, and western blot analysis.

Results

Our findings revealed that AIMP2 was overexpressed in 24 tumor tissue types compared to normal tissue and was associated with four tumor stages. Survival analysis indicated that AIMP2 expression was strongly correlated with overall survival (OS) in certain cancer patients, with high AIMP2 expression linked to poorer prognosis in five cancer types.

Conclusion

Finally, siRNA-mediated AIMP2 knockdown inhibited BRCA cell proliferation and migration in vitro. In conclusion, our pan-cancer analysis suggests that AIMP2 may play a crucial role in tumor immunity and could serve as a potential prognostic marker, particularly in BRCA.

Functional and spatial proteomics profiling reveals intra- and intercellular signaling crosstalk in colorectal cancer

Precision oncology approaches for patients with colorectal cancer (CRC) continue to lag behind other solid cancers. Functional precision oncology-a strategy that is based on perturbing primary tumor cells from cancer patients-could provide a road forward to personalize treatment. We extend this paradigm to measuring proteome activity landscapes by acquiring quantitative phosphoproteomic data from patient-derived organoids (PDOs). We show that kinase inhibitors induce inhibitor- and patient-specific off-target effects and pathway crosstalk. Reconstruction of the kinase networks revealed that the signaling rewiring is modestly affected by mutations. We show non-genetic heterogeneity of the PDOs and upregulation of stemness and differentiation genes by kinase inhibitors. Using imaging mass-cytometry-based profiling of the primary tumors, we characterize the tumor microenvironment (TME) and determine spatial heterocellular crosstalk and tumor-immune cell interactions. Collectively, we provide a framework for inferring tumor cell intrinsic signaling and external signaling from the TME to inform precision (immuno-) oncology in CRC.

Identification of ACBD3 as a new molecular biomarker in pan-cancers through bioinformatic analysis: a preclinical study

BACKGROUND

Acyl-CoA-binding domain-containing 3 (ACBD3) is a multifunctional protein, that plays essential roles in cellular signaling and membrane domain organization. Although the precise roles of ACBD3 in various cancers remain unclear. Thus, we aimed to determine the diverse roles of ACBD3 in pan-cancers.

METHODS

Relevant clinical and RNA-sequencing data for normal tissues and 33 tumors from The Cancer Genome Atlas (TCGA) database, the Human Protein Atlas, and other databases were applied to investigate ACBD3 expression in various cancers. ACBD3-binding and ACBD3-related target genes were obtained from the STRING and GEPIA2 databases. The possible functions of ACBD3-binding genes were explored using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. We also applied the diagnostic value and survival prognosis analysis of ACBD3 in pan-cancers using R language. The mutational features of ACBD3 in various TCGA cancers were obtained from the cBioPortal database.

RESULTS

When compared with normal tissues, ACBD3 expression was statistically upregulated in eleven cancers and downregulated in three cancers. ACBD3 expression was remarkably different among various pathological stages of tumors, immune and molecular subtypes of cancers, cancer phosphorylation levels, and immune cell infiltration. The survival of four tumors was correlated with the expression level of ACBD3, including pancreatic adenocarcinoma, adrenocortical carcinoma, sarcoma, and glioma. The high accuracy in diagnosing multiple tumors and its correlation with prognosis indicated that ACBD3 may be a potential biomarker of pan-cancers.

CONCLUSION

According to our pan-cancer analysis, ACBD3 may serve as a remarkable prognostic and diagnostic biomarker of pan-cancers as well as contribute to tumor development. ACBD3 may also provide new directions for cancer treatment targets in the future.

Systematic identification and characterization of genes in the regulation and biogenesis of photosynthetic machinery

Photosynthesis is central to food production and the Earth's biogeochemistry, yet the molecular basis for its regulation remains poorly understood. Here, using high-throughput genetics in the model eukaryotic alga Chlamydomonas reinhardtii, we identify with high confidence (false discovery rate [FDR] < 0.11) 70 poorly characterized genes required for photosynthesis. We then enable the functional characterization of these genes by providing a resource of proteomes of mutant strains, each lacking one of these genes. The data allow assignment of 34 genes to the biogenesis or regulation of one or more specific photosynthetic complexes. Further analysis uncovers biogenesis/regulatory roles for at least seven proteins, including five photosystem I mRNA maturation factors, the chloroplast translation factor MTF1, and the master regulator PMR1, which regulates chloroplast genes via nuclear-expressed factors. Our work provides a rich resource identifying regulatory and functional genes and placing them into pathways, thereby opening the door to a system-level understanding of photosynthesis.

RL2 Enhances the Elimination of Breast Cancer Cells by Doxorubicin

RL2 (recombinant lactaptin 2), a recombinant analogon of the human milk protein Κ-Casein, induces mitophagy and cell death in breast carcinoma cells. Furthermore, RL2 was shown to enhance extrinsic apoptosis upon long-term treatment while inhibiting it upon short-term stimulation. However, the effects of RL2 on the action of chemotherapeutic drugs that induce the intrinsic apoptotic pathway have not been investigated to date. Here, we examined the effects of RL2 on the doxorubicin (DXR)-induced cell death in breast cancer cells with three different backgrounds. In particular, we used BT549 and MDA-MB-231 triple-negative breast cancer (TNBC) cells, T47D estrogen receptor alpha (ERα) positive cells, and SKBR3 human epidermal growth factor receptor 2 (HER2) positive cells. BT549, MDA-MB-231, and T47D cells showed a severe loss of cell viability upon RL2 treatment, accompanied by the induction of mitophagy. Furthermore, BT549, MDA-MB-231, and T47D cells could be sensitized towards DXR treatment with RL2, as evidenced by loss of cell viability. In contrast, SKBR3 cells showed almost no RL2-induced loss of cell viability when treated with RL2 alone, and RL2 did not sensitize SKBR3 cells towards DXR-mediated loss of cell viability. Bioinformatic analysis of gene expression showed an enrichment of genes controlling metabolism in SKBR3 cells compared to the other cell lines. This suggests that the metabolic status of the cells is important for their sensitivity to RL2. Taken together, we have shown that RL2 can enhance the intrinsic apoptotic pathway in TNBC and ERα-positive breast cancer cells, paving the way for the development of novel therapeutic strategies.

D2H2: diabetes data and hypothesis hub

Motivation

There is a rapid growth in the production of omics datasets collected by the diabetes research community. However, such published data are underutilized for knowledge discovery. To make bioinformatics tools and published omics datasets from the diabetes field more accessible to biomedical researchers, we developed the Diabetes Data and Hypothesis Hub (D2H2).

Results

D2H2 contains hundreds of high-quality curated transcriptomics datasets relevant to diabetes, accessible via a user-friendly web-based portal. The collected and processed datasets are curated from the Gene Expression Omnibus (GEO). Each curated study has a dedicated page that provides data visualization, differential gene expression analysis, and single-gene queries. To enable the investigation of these curated datasets and to provide easy access to bioinformatics tools that serve gene and gene set-related knowledge, we developed the D2H2 chatbot. Utilizing GPT, we prompt users to enter free text about their data analysis needs. Parsing the user prompt, together with specifying information about all D2H2 available tools and workflows, we answer user queries by invoking the most relevant tools via the tools' API. D2H2 also has a hypotheses generation module where gene sets are randomly selected from the bulk RNA-seq precomputed signatures. We then find highly overlapping gene sets extracted from publications listed in PubMed Central with abstract dissimilarity. With the help of GPT, we speculate about a possible explanation of the high overlap between the gene sets. Overall, D2H2 is a platform that provides a suite of bioinformatics tools and curated transcriptomics datasets for hypothesis generation.

Availability and implementation

D2H2 is available at: https://d2h2.maayanlab.cloud/ and the source code is available from GitHub at https://github.com/MaayanLab/D2H2-site under the CC BY-NC 4.0 license.

The deubiquitinase USP9X regulates RIT1 protein abundance and oncogenic phenotypes

RIT1 is a rare and understudied oncogene in lung cancer. Despite structural similarity to other RAS GTPase proteins such as KRAS, oncogenic RIT1 activity does not appear to be tightly regulated by nucleotide exchange or hydrolysis. Instead, there is a growing understanding that the protein abundance of RIT1 is important for its regulation and function. We previously identified the deubiquitinase USP9X as a RIT1 dependency in RIT1-mutant cells. Here, we demonstrate that both wild-type and mutant forms of RIT1 are substrates of USP9X. Depletion of USP9X leads to decreased RIT1 protein stability and abundance and resensitizes cells to EGFR tyrosine kinase inhibitors. Our work expands upon the current understanding of RIT1 protein regulation and presents USP9X as a key regulator of RIT1-driven oncogenic phenotypes.

Opportunities for High-plex Spatial Transcriptomics in Solid Organ Transplantation

The last 5 y have seen the development and widespread adoption of high-plex spatial transcriptomic technology. This technique detects and quantifies mRNA transcripts in situ, meaning that transcriptomic signatures can be sampled from specific cells, structures, lesions, or anatomical regions while conserving the physical relationships that exist within complex tissues. These methods now frequently implement next-generation sequencing, enabling the simultaneous measurement of many targets, up to and including the whole mRNA transcriptome. To date, spatial transcriptomics has been foremost used in the fields of neuroscience and oncology, but there is potential for its use in transplantation sciences. Transplantation has a clear dependence on biopsies for diagnosis, monitoring, and research. Transplant patients represent a unique cohort with multiple organs of interest, clinical courses, demographics, and immunosuppressive regimens. Obtaining high complexity data on the disease processes underlying rejection, tolerance, infection, malignancy, and injury could identify new opportunities for therapeutic intervention and biomarker identification. In this review, we discuss currently available spatial transcriptomic technologies and how they can be applied to transplantation.

Betacoronaviruses SARS-CoV-2 and HCoV-OC43 infections in IGROV-1 cell line require aryl hydrocarbon receptor

The emergence of novel betacoronaviruses has posed significant financial and human health burdens, necessitating the development of appropriate tools to combat future outbreaks. In this study, we have characterized a human cell line, IGROV-1, as a robust tool to detect, propagate, and titrate betacoronaviruses SARS-CoV-2 and HCoV-OC43. IGROV-1 cells can be used for serological assays, antiviral drug testing, and isolating SARS-CoV-2 variants from patient samples. Using time-course transcriptomics, we confirmed that IGROV-1 cells exhibit a robust innate immune response upon SARS-CoV-2 infection, recapitulating the response previously observed in primary human nasal epithelial cells. We performed genome-wide CRISPR knockout genetic screens in IGROV-1 cells and identified Aryl hydrocarbon receptor (AHR) as a critical host dependency factor for both SARS-CoV-2 and HCoV-OC43. Using DiMNF, a small molecule inhibitor of AHR, we observed that the drug selectively inhibits HCoV-OC43 infection but not SARS-CoV-2. Transcriptomic analysis in primary normal human bronchial epithelial cells revealed that DiMNF blocks HCoV-OC43 infection via basal activation of innate immune responses. Our findings highlight the potential of IGROV-1 cells as a valuable diagnostic and research tool to combat betacoronavirus diseases.

Identification of topoisomerase 2A as a novel bone metastasis-related gene in liver hepatocellular carcinoma

BACKGROUND

Bone is the second most frequent site of metastasis for Liver hepatocellular carcinoma (LIHC), which leads to an extremely poor prognosis. Identifying novel biomarkers and therapeutic targets for LIHC patients with bone metastasis is urgently needed.

METHODS

In this study, we used multiple databases for comprehensive bioinformatics analysis, including TCGA, GEO, ICGC, GTEx, TISIDB, and TIMER, to identify key genes related to bone metastasis of LIHC. Clinical tissues and tissue microarray were adopted to assess the expression of TOP2A through qRT-PCR and immunohistochemistry analyses in LIHC. Gene enrichment analysis, DNA methylation, gene mutation, prognosis, and tumor immunity associated with TOP2A in LIHC were investigated. In vitro and in vivo experiments were performed to explore the functional role of TOP2A in LIHC bone metastasis.

RESULTS

We identified that TOP2A was involved in LIHC bone metastasis. Clinically, TOP2A was highly expressed in LIHC tumoral specimens, with the highest level in the bone metastasis lesions. TOP2A was an independent prognostic factor that higher expression of TOP2A was markedly associated with poorer prognosis in LIHC. Moreover, the abnormal expression of TOP2A might be related to DNA hypomethylation, often accompanied by TP53 mutation, immune escape and immunotherapy failure. Enrichment analysis and validation experiments unveiled that TOP2A stimulated the Hippo-YAP signaling pathway in LIHC. Functional assays confirmed that TOP2A could promote bone-specific metastatic potential and tumor-induced osteolysis in LIHC.

CONCLUSIONS

These findings unveil that TOP2A might be a novel prognostic biomarker and therapeutic target for LIHC bone metastasis.

A Cell Surface-Binding Antibody Atlas Nominates a MUC18-Directed Antibody-Drug Conjugate for Targeting Melanoma

Recent advances in targeted therapy and immunotherapy have substantially improved the treatment of melanoma. However, therapeutic strategies are still needed for unresponsive or treatment-relapsed patients with melanoma. To discover antibody-drug conjugate (ADC)-tractable cell surface targets for melanoma, we developed an atlas of melanoma cell surface-binding antibodies (pAb) using a proteome-scale antibody array platform. Target identification of pAbs led to development of melanoma cell killing ADCs against LGR6, TRPM1, ASAP1, and MUC18, among others. MUC18 was overexpressed in both tumor cells and tumor-infiltrating blood vessels across major melanoma subtypes, making it a potential dual-compartment and universal melanoma therapeutic target. AMT-253, an MUC18-directed ADC based on topoisomerase I inhibitor exatecan and a self-immolative T moiety, had a higher therapeutic index compared with its microtubule inhibitor-based counterpart and favorable pharmacokinetics and tolerability in monkeys. AMT-253 exhibited MUC18-specific cytotoxicity through DNA damage and apoptosis and a strong bystander killing effect, leading to potent antitumor activities against melanoma cell line and patient-derived xenograft models. Tumor vasculature targeting by a mouse MUC18-specific antibody-T1000-exatecan conjugate inhibited tumor growth in human melanoma xenografts. Combination therapy of AMT-253 with an antiangiogenic agent generated higher efficacy than single agent in a mucosal melanoma model. Beyond melanoma, AMT-253 was also efficacious in a wide range of MUC18-expressing solid tumors. Efficient target/antibody discovery in combination with the T moiety-exatecan linker-payload exemplified here may facilitate discovery of new ADC to improve cancer treatment.

SIGNIFICANCE

Discovery of melanoma-targeting antibodies using a proteome-scale array and use of a cutting-edge linker-payload system led to development of a MUC18-targeting antibody-exatecan conjugate with clinical potential for treating major melanoma subtypes.

Development of mesothelioma-specific oncolytic immunotherapy enabled by immunopeptidomics of murine and human mesothelioma tumors

Malignant pleural mesothelioma (MPM) is an aggressive tumor with a poor prognosis. As the available therapeutic options show a lack of efficacy, novel therapeutic strategies are urgently needed. Given its T-cell infiltration, we hypothesized that MPM is a suitable target for therapeutic cancer vaccination. To date, research on mesothelioma has focused on the identification of molecular signatures to better classify and characterize the disease, and little is known about therapeutic targets that engage cytotoxic (CD8+) T cells. In this study we investigate the immunopeptidomic antigen-presented landscape of MPM in both murine (AB12 cell line) and human cell lines (H28, MSTO-211H, H2452, and JL1), as well as in patients' primary tumors. Applying state-of-the-art immuno-affinity purification methodologies, we identify MHC I-restricted peptides presented on the surface of malignant cells. We characterize in vitro the immunogenicity profile of the eluted peptides using T cells from human healthy donors and cancer patients. Furthermore, we use the most promising peptides to formulate an oncolytic virus-based precision immunotherapy (PeptiCRAd) and test its efficacy in a mouse model of mesothelioma in female mice. Overall, we demonstrate that the use of immunopeptidomic analysis in combination with oncolytic immunotherapy represents a feasible and effective strategy to tackle untreatable tumors.

Optimized infrared photoactivatable ribonucleoside-enhanced crosslinking and immunoprecipitation (IR-PAR-CLIP) protocol identifies novel IGF2BP3-interacting RNAs in colon cancer cells

The conserved family of RNA-binding proteins (RBPs), IGF2BPs, plays an essential role in posttranscriptional regulation controlling mRNA stability, localization, and translation. Mammalian cells express three isoforms of IGF2BPs: IGF2BP1-3. IGF2BP3 is highly overexpressed in cancer cells, and its expression correlates with a poor prognosis in various tumors. Therefore, revealing its target RNAs with high specificity in healthy tissues and in cancer cells is of crucial importance. Photoactivatable-ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP) identifies the binding sites of RBPs on their target RNAs at nucleotide resolution in a transcriptome-wide manner. Here, we optimized the PAR-CLIP protocol to study RNA targets of endogenous IGF2BP3 in a human colorectal carcinoma cell line. To this end, we first established an immunoprecipitation protocol to obtain highly pure endogenous IGF2BP3-RNA complexes. Second, we modified the protocol to use highly sensitive infrared (IR) fluorescent dyes instead of radioactive probes to visualize IGF2BP3-crosslinked RNAs. We named the modified method "IR-PAR-CLIP." Third, we compared RNase cleavage conditions and found that sequence preferences of the RNases impact the number of the identified IGF2BP3 targets and introduce a systematic bias in the identified RNA motifs. Fourth, we adapted the single adapter circular ligation approach to increase the efficiency in library preparation. The optimized IR-PAR-CLIP protocol revealed novel RNA targets of IGF2BP3 in a human colorectal carcinoma cell line. We anticipate that our IR-PAR-CLIP approach provides a framework for studies of other RBPs.

Berberine: An inspiring resource for the treatment of colorectal diseases

Colorectal cancer is a prevalent malignant tumor with a complex and diverse pathogenesis. In recent years, natural products have shown promising application prospects as sources of anticancer drugs. BBR, a class of benzoquinoline alkaloids extracted from various plants, is widely used in disease treatments owing to its pharmacological activities, including antibacterial, anti-inflammatory, antioxidant, anticancer, and anti-angiogenesis properties. Research has demonstrated that BBR exerts an anti-Salmonella and -Escherichia coli infection effect, attenuating inflammatory reactions by inhibiting harmful bacteria. During the stage of colorectal precancerous lesions, BBR inhibits the activity of cell cyclin by regulating the PI3K/AKT, MAPK, and Wnt signaling pathways, thereby decelerating the cell cycle progression of polyp or adenoma cells. Moreover, the inhibitory effect of BBR on colorectal cancer primarily occurs through the regulation of the cancer cell cycle, anti-angiogenesis, gut microbiota, and antioxidant pathways. The specific involved pathways include the MPK/ERK, NF-kB, and EGFR signaling pathways, encompassing the regulation of Bcl-2 family proteins, vascular endothelial growth factor, and superoxide dismutase. This study reviews and summarizes, for the first time, the specific mechanisms of action of BBR in the carcinogenesis process of colorectal cancer, providing novel insights for its clinical application in intestinal diseases.

Targeted Proteomic Quantitation of NRF2 Signaling and Predictive Biomarkers in HNSCC

The NFE2L2 (NRF2) oncogene and transcription factor drives a gene expression program that promotes cancer progression, metabolic reprogramming, immune evasion, and chemoradiation resistance. Patient stratification by NRF2 activity may guide treatment decisions to improve outcome. Here, we developed a mass spectrometry-based targeted proteomics assay based on internal standard-triggered parallel reaction monitoring to quantify 69 NRF2 pathway components and targets, as well as 21 proteins of broad clinical significance in head and neck squamous cell carcinoma (HNSCC). We improved an existing internal standard-triggered parallel reaction monitoring acquisition algorithm, called SureQuant, to increase throughput, sensitivity, and precision. Testing the optimized platform on 27 lung and upper aerodigestive cancer cell models revealed 35 NRF2 responsive proteins. In formalin-fixed paraffin-embedded HNSCCs, NRF2 signaling intensity positively correlated with NRF2-activating mutations and with SOX2 protein expression. Protein markers of T-cell infiltration correlated positively with one another and with human papilloma virus infection status. CDKN2A (p16) protein expression positively correlated with the human papilloma virus oncogenic E7 protein and confirmed the presence of translationally active virus. This work establishes a clinically actionable HNSCC protein biomarker assay capable of quantifying over 600 peptides from frozen or formalin-fixed paraffin-embedded archived tissues in under 90 min.