Proteomic patterns associated with response to breast cancer neoadjuvant treatment

A micropeptide TREMP encoded by lincR-PPP2R5C promotes Th2 cell differentiation by interacting with PYCR1 in allergic airway inflammation

BACKGROUND

Allergic asthma is largely dominated by Th2 lymphocytes. Micropeptides in Th2 cells and asthma remain unmasked. Here, we aimed to demonstrate a micropeptide, T-cell regulatory micropeptide (TREMP), in Th2 cell differentiation in asthma.

METHODS

TREMP translated from lincR-PPP2R5C was validated using Western blotting and mass spectrometry. TREMP knockout mice were generated using CRISPR/Cas9. Coimmunoprecipitation revealed that TREMP targeted pyrroline-5-carboxylate reductase 1 (PYCR1), which was further explored in vitro and in vivo. The levels of TREMP and PYCR1 in Th2 cells from clinical samples were determined by flow cytometry.

RESULTS

TREMP, encoded by lincR-PPP2R5C, was in the mitochondrion. The lentivirus encoding TREMP promoted Th2 cell differentiation. In contrast, Th2 differentiation was suppressed in TREMP-/- CD4+ T cells. In the HDM-induced model of allergic airway inflammation, TREMP was increased in pulmonary tissues. Allergic airway inflammation was relieved in TREMP-/- mice treated with HDM. Mechanistically, TREMP interacted with PYCR1, which regulated Th2 differentiation via glycolysis. Glycolysis was decreased in Th2 cells from TREMP-/- mice and PYCR1-/- mice. Similar to TREMP-/- mice, allergic airway inflammation was mitigated in HDM-challenged PYCR1-/- mice. Moreover, we measured TREMP and PYCR1 in asthma patients. And we found that, compared with those in healthy controls, the levels of TREMP and PYCR1 in Th2 cells were significantly increased in asthmatic patients.

CONCLUSIONS

The micropeptide TREMP encoded by lincR-PPP2R5C promoted Th2 differentiation in allergic airway inflammation by interacting with PYCR1 and enhancing glycolysis. Our findings highlight the importance of neglected micropeptides from noncoding RNAs in allergic diseases.

Eimeria tenella pyrroline -5-carboxylate reductase is a secreted protein and involved in host cell invasion

Chicken coccidiosis, which caused by Eimeria spp, is a parasitic protozoal disease. At present, control measures of this disease depend mainly on anticoccidial drugs and live vaccines. But these control strategies have drawbacks such as drug resistance and limitations in live vaccines production. Therefore, novel control approaches are urgently need to study to control this disease effectively. In this study, the function and characteristics of the pyrroline-5-carboxylate reductase of Eimeria tenella (EtPYCR) protein were preliminary analyzed. The transcription and translation level were analyzed by using qPCR and Western blot. The results showed that the mRNA transcription and translation levels of EtPYCR were higher in unsporulated oocysts (UO) and second generation merozoites (Mrz) than that in sporulated oocysts (SO) and sporozoites. Enzyme activity showed that the enzyme activity of EtPYCR was also higher in the UO and Mrz than that in the SO and sporozoites. Immunofluorescence localization showed EtPYCR was mainly located on the top of sporozoites and the whole cytoplasm and surface of Mrz. The secretion assay indicated that EtPYCR was secretion protein, but not from micronemes. Invasion inhibition assay showed that rabbit anti-rEtPYCR polyclonal antibodies can effectively inhibit sporozoite invasion of DF-1 cells. These results showed that EtPYCR possess several important roles that separate and distinct from its conversion 1-pyrroline-5-carboxylate (P5C) into proline and maybe involved in the host cell invasion and development of parasites in host cells.

Novel Fragment Inhibitors of PYCR1 from Docking-Guided X-ray Crystallography

The proline biosynthetic enzyme Δ1-pyrroline-5-carboxylate (P5C) reductase 1 (PYCR1) is one of the most consistently upregulated enzymes across multiple cancer types and central to the metabolic rewiring of cancer cells. Herein, we describe a fragment-based, structure-first approach to the discovery of PYCR1 inhibitors. Thirty-seven fragment-like carboxylic acids in the molecular weight range of 143-289 Da were selected from docking and then screened using X-ray crystallography as the primary assay. Strong electron density was observed for eight compounds, corresponding to a crystallographic hit rate of 22%. The fragments are novel compared to existing proline analog inhibitors in that they block both the P5C substrate pocket and the NAD(P)H binding site. Four hits showed inhibition of PYCR1 in kinetic assays, and one has lower apparent IC50 than the current best proline analog inhibitor. These results show proof-of-concept for our inhibitor discovery approach and provide a basis for fragment-to-lead optimization.

Proteomic analysis of breast cancer based on immune subtypes

BACKGROUND

Immunotherapy is applied to breast cancer to resolve the limitations of survival gain in existing treatment modalities. With immunotherapy, a tumor can be classified into immune-inflamed, excluded and desert based on the distribution of immune cells. We assessed the clinicopathological features, each subtype's prognostic value and differentially expressed proteins between immune subtypes.

METHODS

Immune subtyping and proteomic analysis were performed on 56 breast cancer cases with neoadjuvant chemotherapy. The immune subtyping was based on the level of tumor-infiltrating lymphocytes (TILs) and Klintrup criteria. If the level of TILs was ≥ 10%, it was classified as immune-inflamed type without consideration of the Klintrup criteria. In cases of 1-9% TIL, Klintrup criteria 1-3 were classified as the immune-excluded subtype and Klintrup criteria not available (NA) was classified as NA. Cases of 1% TILs and Klintrup 0 were classified as the immune-desert subtype. Mass spectrometry was used to identify differentially expressed proteins in formalin-fixed paraffin-embedded biopsy tissues.

RESULTS

Of the 56 cases, 31 (55%) were immune-inflamed, 21 (38%) were immune-excluded, 2 (4%) were immune-desert and 2 (4%) were NA. Welch's t-test revealed two differentially expressed proteins between immune-inflamed and immune-excluded/desert subtypes. Coronin-1A was upregulated in immune-inflamed tumors (adjusted p = 0.008) and α-1-antitrypsin was upregulated in immune-excluded/desert tumors (adjusted p = 0.008). Titin was upregulated in pathologic complete response (pCR) than non-pCR among immune-inflamed tumors (adjusted p = 0.036).

CONCLUSIONS

Coronin-1A and α-1-antitrypsin were upregulated in immune-inflamed and immune-excluded/desert subtypes, respectively. Titin's elevated expression in pCR within the immune-inflamed subtype may indicate a favorable prognosis. Further studies involving large representative cohorts are necessary to validate these findings.

Recent Contributions of Mass Spectrometry-Based "Omics" in the Studies of Breast Cancer

Breast cancer (BC) is one of the most heterogeneous groups of cancer. As every biotype of BC is unique and presents a particular "omic" signature, they are increasingly characterized nowadays with novel mass spectrometry (MS) strategies. BC therapeutic approaches are primarily based on the two features of human epidermal growth factor receptor 2 (HER2) and estrogen receptor (ER) positivity. Various strategic MS implementations are reported in studies of BC also involving data independent acquisitions (DIAs) of MS which report novel differential proteomic, lipidomic, proteogenomic, phosphoproteomic, and metabolomic characterizations associated with the disease and its therapeutics. Recently many "omic" studies have aimed to identify distinct subsidiary biotypes for diagnosis, prognosis, and targets of treatment. Along with these, drug-induced-resistance phenotypes are characterized by "omic" changes. These identifying aspects of the disease may influence treatment outcomes in the near future. Drug quantifications and characterizations are also done regularly and have implications in therapeutic monitoring and in drug efficacy assessments. We report these studies, mentioning their implications toward the understanding of BC. We briefly provide the MS instrumentation principles that are adopted in such studies as an overview with a brief outlook on DIA-MS strategies. In all of these, we have chosen a model cancer for its revelations through MS-based "omics".

Identification of VWA5A as a novel biomarker for inhibiting metastasis in breast cancer by machine-learning based protein prioritization

Distant metastasis is the leading cause of death in breast cancer (BC). The timing of distant metastasis differs according to subtypes of BCs and there is a need for identification of biomarkers for the prediction of early and late metastasis. To identify biomarker candidates whose abundance level can discriminate metastasis types, we performed a high-throughput proteomics assay using tissue samples from BCs with no metastasis, late metastasis, and early metastasis, processed data with machine learning-based feature selection, and found that low VWA5A could be responsible for shorter duration of metastasis-free interval. Low expression of VWA5A gene in METABRIC cohort was associated with poor survival in BCs, especially in hormone receptor (HR)-positive BCs. In-vitro experiments confirmed tumor suppressive effect of VWA5A on BCs in HR+ and triple-negative BC cell lines. We found that expression of VWA5A can be assessed by immunohistochemistry (IHC) on archival tissue samples. Decreasing nuclear expression of VWA5A was significantly associated with advanced T stage and lymphatic invasion in consecutive BCs of all subtypes. We discovered lower expression of VWA5A as the potential biomarker for metastasis-prone BCs, and our results support the clinical utility of VWA5A IHC, as an adjunctive tools for prognostication of BCs.

Protein signaling and drug target activation signatures to guide therapy prioritization: Therapeutic resistance and sensitivity in the I-SPY 2 Trial

Molecular subtyping of breast cancer is based mostly on HR/HER2 and gene expression-based immune, DNA repair deficiency, and luminal signatures. We extend this description via functional protein pathway activation mapping using pre-treatment, quantitative expression data from 139 proteins/phosphoproteins from 736 patients across 8 treatment arms of the I-SPY 2 Trial (ClinicalTrials.gov: NCT01042379). We identify predictive fit-for-purpose, mechanism-of-action-based signatures and individual predictive protein biomarker candidates by evaluating associations with pathologic complete response. Elevated levels of cyclin D1, estrogen receptor alpha, and androgen receptor S650 associate with non-response and are biomarkers for global resistance. We uncover protein/phosphoprotein-based signatures that can be utilized both for molecularly rationalized therapeutic selection and for response prediction. We introduce a dichotomous HER2 activation response predictive signature for stratifying triple-negative breast cancer patients to either HER2 or immune checkpoint therapy response as a model for how protein activation signatures provide a different lens to view the molecular landscape of breast cancer and synergize with transcriptomic-defined signatures.

Realm of proteomics in breast cancer management and drug repurposing to alleviate intricacies of treatment

Breast cancer, a multi-networking heterogeneous disease, has emerged as a serious impediment to progress in clinical oncology. Although technological advancements and emerging cancer research studies have mitigated breast cancer lethality, a precision cancer-oriented solution has not been achieved. Thus, this review will persuade the acquiescence of proteomics-based diagnostic and therapeutic options in breast cancer management. Recently, the evidence of breast cancer health surveillance through imaging proteomics, single-cell proteomics, interactomics, and post-translational modification (PTM) tracking, to construct proteome maps and proteotyping for stage-specific and sample-specific cancer subtyping have outperformed conventional ways of dealing with breast cancer by increasing diagnostic efficiency, prognostic value, and predictive response. Additionally, the paradigm shift in applied proteomics for designing a chemotherapy regimen to identify novel drug targets with minor adverse effects has been elaborated. Finally, the potential of proteomics in alleviating the occurrence of chemoresistance and enhancing reprofiled drugs' effectiveness to combat therapeutic obstacles has been discussed. Owing to the enormous potential of proteomics techniques, the clinical recognition of proteomics in breast cancer management can be achievable and therapeutic intricacies can be surmountable.

Omics Technologies Improving Breast Cancer Research and Diagnostics

Breast cancer (BC) has yielded approximately 2.26 million new cases and has caused nearly 685,000 deaths worldwide in the last two years, making it the most common diagnosed cancer type in the world. BC is an intricate ecosystem formed by both the tumor microenvironment and malignant cells, and its heterogeneity impacts the response to treatment. Biomedical research has entered the era of massive omics data thanks to the high-throughput sequencing revolution, quick progress and widespread adoption. These technologies-liquid biopsy, transcriptomics, epigenomics, proteomics, metabolomics, pharmaco-omics and artificial intelligence imaging-could help researchers and clinicians to better understand the formation and evolution of BC. This review focuses on the findings of recent multi-omics-based research that has been applied to BC research, with an introduction to every omics technique and their applications for the different BC phenotypes, biomarkers, target therapies, diagnosis, treatment and prognosis, to provide a comprehensive overview of the possibilities of BC research.

Spatial immunophenotypes orchestrate prognosis in triple-negative breast cancer with Miller-Payne grade 4 following neoadjuvant chemotherapy

Some triple-negative breast cancer (TNBC) patients evaluated as Miller-Payne 4 with ypN0 after neoadjuvant chemotherapy (NACT) who have better prognoses should avoid escalation of therapy. We aim to identify these patients by evaluating pretherapeutic spatial distributions of immunophenotypes. Our retrospective study in patients with TNBC assessed as Miller-Payne grade 4/5 with ypN0 showed that Miller-Payne 4 with ypN0 group had poorer 5-year disease-free survival (DFS, 63.8% vs. 83.0%, p = 0.003) and the 5-year overall survival (OS, 71.0% vs. 85.5%, p = 0.007) than Miller-Payne 5 with ypN0 group. High TILs were significantly associated with better DFS and OS in patients with Miller-Payne 4 and ypN0 (both p = 0.016). Spatially, detected by multiplexed ion beam imaging by the time of flight combined with proteomics, tumors assessed as Miller-Payne 4 and ypN0 with good prognosis exhibited an inflamed phenotype, with dominant CD8+ T cells on tumor center, few scattered CD68+ myeloid-derived cells far away from T cells, and deposit of increased activated molecules of lymphocyte. While those with poor prognoses presented excluded phenotypes, with few CD8+ T cells restricted to invasive margins and a high density of CD14⁺CD68⁺CD11c⁺ myeloid cells. A good classifier model based on 29 spatial immunophenotypes was established by the random forest algorithm (AUC = 0.975), for identifying patients with Miller-Payne 4 and ypN0 who had favorable prognoses. We also observed similar signatures in patients with Miller-Payne 5 and ypN0. Taken together, spatial immunophenotypes may assess the prognosis in TNBC patients with Miller-Payne 4 and ypN0 after NACT.

Functional impact of protein-RNA variation in clinical cancer analyses

Comprehensive molecular characterization of tumors aims to uncover cancer vulnerabilities, drug resistance mechanisms and biomarkers. Identification of cancer drivers was suggested as the basis for patient-tailored therapy, and transcriptomic analyses were proposed to reveal the phenotypic outcome of cancer mutations. With the maturation of the proteomic field, studies of protein-RNA discrepancies suggested that RNA analyses are insufficient to predict cellular functions. In this manuscript we discuss the importance of direct mRNA-protein comparisons in clinical cancer studies. We make use of the large amount of data generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), which includes protein and mRNA expression analyses from the exact same samples. Analysis of protein-RNA correlations showed marked differences among cancer types, and highlighted the protein-RNA similarities and discrepancies among functional pathways and drug targets. Additionally, unsupervised clustering of the data based on protein or RNA showed substantial differences in tumor classification and the cellular processes that differentiate between clusters. These analyses show the difficulty to predict protein levels from mRNAs, and the critical role of protein analyses for phenotypic tumor characterization.

Integration of multiomics data shows down regulation of mismatch repair and tubulin pathways in triple-negative chemotherapy-resistant breast tumors

BACKGROUND

Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype. Patients with TNBC are primarily treated with neoadjuvant chemotherapy (NAC). The response to NAC is prognostic, with reductions in overall survival and disease-free survival rates in those patients who do not achieve a pathological complete response (pCR). Based on this premise, we hypothesized that paired analysis of primary and residual TNBC tumors following NAC could identify unique biomarkers associated with post-NAC recurrence.

METHODS AND RESULTS

We investigated 24 samples from 12 non-LAR TNBC patients with paired pre- and post-NAC data, including four patients with recurrence shortly after surgery (< 24 months) and eight who remained recurrence-free (> 48 months). These tumors were collected from a prospective NAC breast cancer study (BEAUTY) conducted at the Mayo Clinic. Differential expression analysis of pre-NAC biopsies showed minimal gene expression differences between early recurrent and nonrecurrent TNBC tumors; however, post-NAC samples demonstrated significant alterations in expression patterns in response to intervention. Topological-level differences associated with early recurrence were implicated in 251 gene sets, and an independent assessment of microarray gene expression data from the 9 paired non-LAR samples available in the NAC I-SPY1 trial confirmed 56 gene sets. Within these 56 gene sets, 113 genes were observed to be differentially expressed in the I-SPY1 and BEAUTY post-NAC studies. An independent (n = 392) breast cancer dataset with relapse-free survival (RFS) data was used to refine our gene list to a 17-gene signature. A threefold cross-validation analysis of the gene signature with the combined BEAUTY and I-SPY1 data yielded an average AUC of 0.88 for six machine-learning models. Due to the limited number of studies with pre- and post-NAC TNBC tumor data, further validation of the signature is needed.

CONCLUSION

Analysis of multiomics data from post-NAC TNBC chemoresistant tumors showed down regulation of mismatch repair and tubulin pathways. Additionally, we identified a 17-gene signature in TNBC associated with post-NAC recurrence enriched with down-regulated immune genes.

Targeting Mitochondria with ClpP Agonists as a Novel Therapeutic Opportunity in Breast Cancer

Breast cancer is the most frequently diagnosed malignancy worldwide and the leading cause of cancer mortality in women. Despite the recent development of new therapeutics including targeted therapies and immunotherapy, triple-negative breast cancer remains an aggressive form of breast cancer, and thus improved treatments are needed. In recent decades, it has become increasingly clear that breast cancers harbor metabolic plasticity that is controlled by mitochondria. A myriad of studies provide evidence that mitochondria are essential to breast cancer progression. Mitochondria in breast cancers are widely reprogrammed to enhance energy production and biosynthesis of macromolecules required for tumor growth. In this review, we will discuss the current understanding of mitochondrial roles in breast cancers and elucidate why mitochondria are a rational therapeutic target. We will then outline the status of the use of mitochondria-targeting drugs in breast cancers, and highlight ClpP agonists as emerging mitochondria-targeting drugs with a unique mechanism of action. We also illustrate possible drug combination strategies and challenges in the future breast cancer clinic.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.

Serum metabolic traits reveal therapeutic toxicities and responses of neoadjuvant chemoradiotherapy in patients with rectal cancer

Neoadjuvant chemoradiotherapy (nCRT) has become the standard treatment for patients with locally advanced rectal cancer (LARC). Therapeutic efficacy of nCRT is significantly affected by treatment-induced diarrhea and hematologic toxicities. Metabolic alternations in cancer therapy are key determinants to therapeutic toxicities and responses, but exploration in large-scale clinical studies remains limited. Here, we analyze 743 serum samples from 165 LARC patients recruited in a phase III clinical study using untargeted metabolomics and identify responsive metabolic traits over the course of nCRT. Pre-therapeutic serum metabolites successfully predict the chances of diarrhea and hematologic toxicities during nCRT. Particularly, levels of acyl carnitines are linked to sex disparity in nCRT-induced diarrhea. Finally, we show that differences in phenylalanine metabolism and essential amino acid metabolism may underlie distinct therapeutic responses of nCRT. This study illustrates the metabolic dynamics over the course of nCRT and provides potential to guide personalized nCRT treatment using responsive metabolic traits.

Experimental reproducibility limits the correlation between mRNA and protein abundances in tumor proteomic profiles

Large-scale studies of human proteomes have revealed only a moderate correlation between mRNA and protein abundances. It is unclear to what extent this moderate correlation reflects post-transcriptional regulation and to what extent it reflects measurement error. Here, by analyzing replicate profiles of tumors and cell lines, we show that there is considerable variation in the reproducibility of measurements of transcripts and proteins from individual genes. Proteins with more reproducible measurements tend to have a higher mRNA-protein correlation, suggesting that measurement reproducibility accounts for a substantial fraction of the unexplained variation between mRNA and protein abundances. The reproducibility of individual proteins is somewhat consistent across studies, and we exploit this to develop an aggregate reproducibility score that explains a substantial amount of the variation in mRNA-protein correlations across multiple studies. Finally, we show that pathways previously reported to have a higher-than-average mRNA-protein correlation may simply contain members that can be more reproducibly quantified.

Survival and clinicopathological significance of PYCR1 expression in cancer: A meta-analysis

Background

Proline metabolism is closely related to the occurrence and development of cancer. Δ1-Pyrroline-5-carboxylate reductase (PYCR) is the last enzyme in proline biosynthesis. As one of the enzyme types, PYCR1 takes part in the whole process of the growth, invasion, and drug resistance of cancer cells. This study investigated PYCR1 expressions in cancers together with their relationship to clinical prognosis.

Methods

A thorough database search was performed in PubMed, EMBASE, and Cochrane Library. RevMan5.3 software was used for the statistical analysis.

Results

Eight articles were selected, and 728 cancer patients were enrolled. The cancer types include lung, stomach, pancreatic ductal adenocarcinoma, hepatocellular carcinoma, and renal cell carcinoma. The meta-analysis results showed that the expression of PYCR1 was higher in the clinical stage III–IV group than that in the clinical stage I–II group (OR = 1.67, 95%CI: 1.03–2.71), higher in the lymph node metastasis group than in the non-lymph node metastasis group (OR = 1.57, 95%CI: 1.06–2.33), and higher in the distant metastasis group than in the non-distant metastasis group (OR = 3.46, 95%CI: 1.64–7.29). However, there was no statistical difference in PYCR1 expression between different tumor sizes (OR = 1.50, 95%CI: 0.89–2.53) and degrees of differentiation (OR = 0.82, 95%CI: 0.54–1.24).

Conclusion

PYCR1 had a high expression in various cancers and was associated with cancer volume and metastasis. The higher the PYCR1 expression was, the poorer the cancer prognosis was. The molecular events and biological processes mediated by PYCR1 might be the underlying mechanisms of metastasis.

SHetA2 Attack on Mortalin and Colleagues in Cancer Therapy and Prevention

Heat Shock Proteins of the 70-kDa family (HSP70s) do not cause cancer by themselves, but instead protect cells as they transform into cancer. These molecular chaperones bind numerous client proteins and utilize ATP hydrolysis to facilitate proper protein folding, formation of functional complexes and cellular localizations, or degradation of irreparably damaged proteins. Their transient upregulation by stressful situations avoids induction of programmed cell death. Continued upregulation of the mortalin, heat shock cognate (hsc70) and glucose regulated protein 78 (Grp78) support cancer development and progression by supporting pro-proliferative and metabolic functions and repressing pro-death functions of oncoproteins and tumor suppressor proteins. This review describes the discovery and development of a lead anti-cancer compound, sulfur heteroarotinoid A2 (SHetA2, NSC726189), which was originally developed to bind retinoic acid receptors, but was subsequently found to work independently of these receptors. The discovery and validation of mortalin, hsc70 and Grp78 as SHetA2 target proteins is summarized. The documented and hypothesized roles of these HSP70 proteins and their clients in the mechanism of SHetA2 inhibition of cancer without toxicity are discussed. Use of this mechanistic data to evaluate drug action in a cancer clinical trial and develop synergistic drug combinations is explained. Knowledge needed to optimize SHetA2 analogs for use in cancer therapy and prevention is proposed as future directions.

Plasma Proteome Signature to Predict the Outcome of Breast Cancer Patients Receiving Neoadjuvant Chemotherapy

The plasma proteome of 51 non-metastatic breast cancer patients receiving neoadjuvant chemotherapy (NCT) was prospectively analyzed by high-resolution mass spectrometry coupled with nano-flow liquid chromatography using blood drawn at the time of diagnosis. Plasma proteins were identified as potential biomarkers, and their correlation with clinicopathological variables and survival outcomes was analyzed. Of 51 patients, 20 (39.2%) were HR+/HER2-, five (9.8%) were HR+/HER2+, five (9.8%) were HER2+, and 21 (41.2%) were triple-negative subtype. During a median follow-up of 52.0 months, there were 15 relapses (29.4%) and eight deaths (15.7%). Four potential biomarkers were identified among differentially expressed proteins: APOC3 had higher plasma concentrations in the pathological complete response (pCR) group, whereas MBL2, ENG, and P4HB were higher in the non-pCR group. Proteins statistically significantly associated with survival and capable of differentiating low- and high-risk groups were MBL2 and P4HB for disease-free survival, P4HB for overall survival, and MBL2 for distant metastasis-free survival (DMFS). In the multivariate analysis, only MBL2 was a consistent risk factor for DMFS (HR: 9.65, 95% CI 2.10-44.31). The results demonstrate that the proteomes from non-invasive sampling correlate with pCR and survival in breast cancer patients receiving NCT. Further investigation may clarify the role of these proteins in predicting prognosis and thus their therapeutic potential for the prevention of recurrence.

Structure, biochemistry, and gene expression patterns of the proline biosynthetic enzyme pyrroline-5-carboxylate reductase (PYCR), an emerging cancer therapy target

Proline metabolism features prominently in the unique metabolism of cancer cells. Proline biosynthetic genes are consistently upregulated in multiple cancers, while the proline catabolic enzyme proline dehydrogenase has dual, context-dependent pro-cancer and pro-apoptotic functions. Furthermore, the cycling of proline and Δ1-pyrroline-5-carboxylate through the proline cycle impacts cellular growth and death pathways by maintaining redox homeostasis between the cytosol and mitochondria. Here we focus on the last enzyme of proline biosynthesis, Δ1-pyrroline-5-carboxylate reductase, known as PYCR in humans. PYCR catalyzes the NAD(P)H-dependent reduction of Δ1-pyrroline-5-carboxylate to proline and forms the reductive half of the proline metabolic cycle. We review the research on the three-dimensional structure, biochemistry, inhibition, and cancer biology of PYCR. To provide a global view of PYCR gene upregulation in cancer, we mined RNA transcript databases to analyze differential gene expression in 28 cancer types. This analysis revealed strong, widespread upregulation of PYCR genes, especially PYCR1. Altogether, the research over the past 20 years makes a compelling case for PYCR as a cancer therapy target. We conclude with a discussion of some of the major challenges for the field, including developing isoform-specific inhibitors, elucidating the function of the long C-terminus of PYCR1/2, and characterizing the interactome of PYCR.

Application of Proteomics in Cancer: Recent Trends and Approaches for Biomarkers Discovery

Proteomics has become an important field in molecular sciences, as it provides valuable information on the identity, expression levels, and modification of proteins. For example, cancer proteomics unraveled key information in mechanistic studies on tumor growth and metastasis, which has contributed to the identification of clinically applicable biomarkers as well as therapeutic targets. Several cancer proteome databases have been established and are being shared worldwide. Importantly, the integration of proteomics studies with other omics is providing extensive data related to molecular mechanisms and target modulators. These data may be analyzed and processed through bioinformatic pipelines to obtain useful information. The purpose of this review is to provide an overview of cancer proteomics and recent advances in proteomic techniques. In particular, we aim to offer insights into current proteomics studies of brain cancer, in which proteomic applications are in a relatively early stage. This review covers applications of proteomics from the discovery of biomarkers to the characterization of molecular mechanisms through advances in technology. Moreover, it addresses global trends in proteomics approaches for translational research. As a core method in translational research, the continued development of this field is expected to provide valuable information at a scale beyond that previously seen.

Multi-Omic Approaches to Breast Cancer Metabolic Phenotyping: Applications in Diagnosis, Prognosis, and the Development of Novel Treatments

Breast cancer (BC) is characterized by high disease heterogeneity and represents the most frequently diagnosed cancer among women worldwide. Complex and subtype-specific gene expression alterations participate in disease development and progression, with BC cells known to rewire their cellular metabolism to survive, proliferate, and invade. Hence, as an emerging cancer hallmark, metabolic reprogramming holds great promise for cancer diagnosis, prognosis, and treatment. Multi-omics approaches (the combined analysis of various types of omics data) offer opportunities to advance our understanding of the molecular changes underlying metabolic rewiring in complex diseases such as BC. Recent studies focusing on the combined analysis of genomics, epigenomics, transcriptomics, proteomics, and/or metabolomics in different BC subtypes have provided novel insights into the specificities of metabolic rewiring and the vulnerabilities that may guide therapeutic development and improve patient outcomes. This review summarizes the findings of multi-omics studies focused on the characterization of the specific metabolic phenotypes of BC and discusses how they may improve clinical BC diagnosis, subtyping, and treatment.

PYCR, a key enzyme in proline metabolism, functions in tumorigenesis

Pyrroline-5-carboxylate reductase (PYCR), the last enzyme in proline synthesis that converts P5C into proline, was found promoting cancer growth and inhibiting apoptosis through multiple approaches, including regulating cell cycle and redox homeostasis, and promoting growth signaling pathways. Proline is abnormally up-regulated in multiple cancers and becomes one of the critical players in the reprogramming of cancer metabolism. As the last key enzymes in proline generation, PYCRs have been the subject of many investigations, and have been demonstrated to play an indispensable role in promoting tumorigenesis and cancer progression. In this article, we will thoroughly review the recent investigations on PYCRs in cancer development.

Extracellular Vesicle Proteome of Breast Cancer Patients with and Without Cognitive Impairment Following Anthracycline-based Chemotherapy: An Exploratory Study

Cognitive impairment due to cancer and its therapy is a major concern among cancer patients and survivors. Extracellular vesicle (EVs) composition altered by cancer and chemotherapy may affect neurological processes such as neuroplasticity, potentially impacting the cognitive abilities of cancer patients and survivors. We investigated the EV proteome of breast cancer patients with and without cognitive impairment following anthracycline-based chemotherapy from longitudinally collected plasma. EVs were cup-shaped and positive for Flotillin-1 and TSG-101. We identified 517 differentially expressed EV proteins between the cognitive impaired and non-impaired groups during and post-chemotherapy. The observed decreased expression of p2X purinoceptor, cofilin-1, ADAM 10, and dynamin-1 in the plasma EVs of the cognitive impaired group may suggest alterations in the mechanisms underlying synaptic plasticity. The reduced expression of tight junction proteins among cognitive-impaired patients may imply weakening of the blood-brain barrier. These EV protein signatures may serve as a fingerprint that underscores the mechanisms underlying cognitive impairment in cancer patients and survivors.

PRG2 and AQPEP are misexpressed in fetal membranes in placenta previa and percreta†

The obstetrical conditions placenta accreta spectrum (PAS) and placenta previa are a significant source of pregnancy-associated morbidity and mortality, yet the specific molecular and cellular underpinnings of these conditions are not known. In this study, we identified misregulated gene expression patterns in tissues from placenta previa and percreta (the most extreme form of PAS) compared with control cases. By comparing this gene set with existing placental single-cell and bulk RNA-Seq datasets, we show that the upregulated genes predominantly mark extravillous trophoblasts. We performed immunofluorescence on several candidate molecules and found that PRG2 and AQPEP protein levels are upregulated in both the fetal membranes and the placental disk in both conditions. While this increased AQPEP expression remains restricted to trophoblasts, PRG2 is mislocalized and is found throughout the fetal membranes. Using a larger patient cohort with a diverse set of gestationally aged-matched controls, we validated PRG2 as a marker for both previa and PAS and AQPEP as a marker for only previa in the fetal membranes. Our findings suggest that the extraembryonic tissues surrounding the conceptus, including both the fetal membranes and the placental disk, harbor a signature of previa and PAS that is characteristic of EVTs and that may reflect increased trophoblast invasiveness.

Heterogeneity of Lipid and Protein Cartilage Profiles Associated with Human Osteoarthritis with or without Type 2 Diabetes Mellitus

Osteoarthritis (OA) is a multifactorial pathology and comprises a wide range of distinct phenotypes. In this context, the characterization of the different molecular profiles associated with each phenotype can improve the classification of OA. In particular, OA can coexist with type 2 diabetes mellitus (T2DM). This study investigates lipidomic and proteomic differences between human OA/T2DM- and OA/T2DM+ cartilage through a multimodal mass spectrometry approach. Human cartilage samples were obtained after total knee replacement from OA/T2DM- and OA/T2DM+ patients. Label-free proteomics was employed to study differences in protein abundance and matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) for spatially resolved-lipid analysis. Label-free proteomic analysis showed differences between OA/T2DM- and OA/T2DM+ phenotypes in several metabolic pathways such as lipid regulation. Interestingly, phospholipase A2 protein was found increased within the OA/T2DM+ cohort. In addition, MALDI-MSI experiments revealed that phosphatidylcholine and sphingomyelin species were characteristic of the OA/T2DM- group, whereas lysolipids were more characteristic of the OA/T2DM+ phenotype. The data also pointed out differences in phospholipid content between superficial and deep layers of the cartilage. Our study shows distinctively different lipid and protein profiles between OA/T2DM- and OA/T2DM+ human cartilage, demonstrating the importance of subclassification of the OA disease for better personalized treatments.

Serine Biosynthesis Is a Metabolic Vulnerability in IDH2-Driven Breast Cancer Progression

Cancer-specific metabolic phenotypes and their vulnerabilities represent a viable area of cancer research. In this study, we explored the association of breast cancer subtypes with different metabolic phenotypes and identified isocitrate dehydrogenase 2 (IDH2) as a key player in triple-negative breast cancer (TNBC) and HER2. Functional assays combined with mass spectrometry-based analyses revealed the oncogenic role of IDH2 in cell proliferation, anchorage-independent growth, glycolysis, mitochondrial respiration, and antioxidant defense. Genome-scale metabolic modeling identified phosphoglycerate dehydrogenase (PHGDH) and phosphoserine aminotransferase (PSAT1) as the synthetic dosage lethal (SDL) partners of IDH2. In agreement, CRISPR-Cas9 knockout of PHGDH and PSAT1 showed the essentiality of serine biosynthesis proteins in IDH2-high cells. The clinical significance of the SDL interaction was supported by patients with IDH2-high/PHGDH-low tumors, who exhibited longer survival than patients with IDH2-high/PHGDH-high tumors. Furthermore, PHGDH inhibitors were effective in treating IDH2-high cells in vitro and in vivo. Altogether, our study creates a new link between two known cancer regulators and emphasizes PHGDH as a promising target for TNBC with IDH2 overexpression. SIGNIFICANCE: These findings highlight the metabolic dependence of IDH2 on the serine biosynthesis pathway, adding an important layer to the connection between TCA cycle and glycolysis, which can be translated into novel targeted therapies.