Breast cancer biomarkers: proteomic discovery and translation to clinically relevant assays

Phosphoproteome of signaling by ErbB2 in ovarian cancer cells

The gene for receptor tyrosine kinase ErbB2 is amplified in breast and ovarian tumours. The linear pathway by which signals are transduced through ErbB2 are well known. However, second generation questions that address spatial aspects of signaling remain. To address this, we have undertaken a mass spectrometry approach to identify phosphoproteins specific for ErbB2 using the inhibitors Lapatinib and CP724714 in ovarian cancer cells. The ErbB2 specific proteins identified in SKOV-3 cells were Myristoylated alanine-rich C-kinase substrate, Protein capicua homolog, Protein peptidyl isomerase G, Protein PRRC2C, Chromobox homolog1 and PRP4 homolog. We have evaluated three phosphoproteins PKM2, Aldose reductase and MARCKS in SKOV-3 cells. We observed that PKM2 was phosphorylated by EGF but was not inhibited by Lapatinib and CP724714. The activity of aldose reductase in reducing NADPH as a substrate was significantly higher in EGF stimulated cells which was inhibited by Lapatinib and CP724714 but not by Geftinib (EGFR inhibitor). MARCKS was phosphorylated on stimulation of SKOV-3 cells with EGF that was inhibited by Lapatinib and CP724714 which was dependent on the kinase activity of ErbB2. These results have identified phosphoproteins that are specific to ErbB2 which have not been previously reported and sets the basis for future experiments.

Circulating proteins reveal prior use of menopausal hormonal therapy and increased risk of breast cancer

Accessible risk predictors are crucial for improving the early detection and prognosis of breast cancer. Blood samples are widely available and contain proteins that provide important information about human health and disease, however, little is still known about the contribution of circulating proteins to breast cancer risk prediction. We profiled EDTA plasma samples collected before diagnosis from the Swedish KARMA breast cancer cohort to evaluate circulating proteins as molecular predictors. A data-driven analysis strategy was applied to the molecular phenotypes built on 700 circulating proteins to identify and annotate clusters of women. The unsupervised analysis of 183 future breast cancer cases and 366 age-matched controls revealed five stable clusters with distinct proteomic plasma profiles. Among these women, those in the most stable cluster (N = 19; mean Jaccard index: 0.70 ± 0.29) were significantly more likely to have used menopausal hormonal therapy (MHT), get a breast cancer diagnosis, and were older compared to the remaining clusters. The circulating proteins associated with this cluster (FDR < 0.001) represented physiological processes related to cell junctions (F11R, CLDN15, ITGAL), DNA repair (RBBP8), cell replication (TJP3), and included proteins found in female reproductive tissue (PTCH1, ZP4). Using a data-driven approach on plasma proteomics data revealed the potential long-lasting molecular effects of menopausal hormonal therapy (MHT) on the circulating proteome, even after women had ended their treatment. This provides valuable insights concerning proteomics efforts to identify molecular markers for breast cancer risk prediction.

Affinity proteomic profiling of plasma for proteins associated to area-based mammographic breast density

Background

Mammographic breast density is one of the strongest risk factors for breast cancer, but molecular understanding of how breast density relates to cancer risk is less complete. Studies of proteins in blood plasma, possibly associated with mammographic density, are well-suited as these allow large-scale analyses and might shed light on the association between breast cancer and breast density.

Methods

Plasma samples from 1329 women in the Swedish KARMA project, without prior history of breast cancer, were profiled with antibody suspension bead array (SBA) assays. Two sample sets comprising 729 and 600 women were screened by two different SBAs targeting a total number of 357 proteins. Protein targets were selected through searching the literature, for either being related to breast cancer or for being linked to the extracellular matrix. Association between proteins and absolute area-based breast density (AD) was assessed by quantile regression, adjusting for age and body mass index (BMI).

Results

Plasma profiling revealed linear association between 20 proteins and AD, concordant in the two sets of samples (p < 0.05). Plasma levels of seven proteins were positively associated and 13 proteins negatively associated with AD. For eleven of these proteins evidence for gene expression in breast tissue existed. Among these, ABCC11, TNFRSF10D, F11R and ERRF were positively associated with AD, and SHC1, CFLAR, ACOX2, ITGB6, RASSF1, FANCD2 and IRX5 were negatively associated with AD.

Conclusions

Screening proteins in plasma indicates associations between breast density and processes of tissue homeostasis, DNA repair, cancer development and/or progression in breast cancer. Further validation and follow-up studies of the shortlisted protein candidates in independent cohorts will be needed to infer their role in breast density and its progression in premenopausal and postmenopausal women.

Electronic supplementary material

The online version of this article (10.1186/s13058-018-0940-z) contains supplementary material, which is available to authorized users.

Isobaric Labeling-Based LC-MS/MS Strategy for Comprehensive Profiling of Human Pancreatic Tissue Proteome

The pancreas is an organ with both endocrine and exocrine functions, and various pathologies, such as pancreatic cancer and diabetes are associated with this organ. Owing to the limited pancreatic biopsy samples available for research, it is critical to make the best use of cadaveric pancreatic tissue for biomarker studies and mechanistic understanding of pancreas-related pathologies. Discovery-phase quantitative proteomics has attracted a lot of attention for its capabilities in large-scale protein identification and accurate protein quantification. Here, we describe a workflow using isobaric labeling (tandem mass tag or TMT) based quantitative proteomics to confidently identify and quantify human pancreatic tissue proteome, including sample preparation, isobaric tag labeling, peptide level fractionation, LC-MS/MS, database search, and statistical analysis.

Diagnostic and prognostic significance of serum apolipoprotein C-I in triple-negative breast cancer based on mass spectrometry

Women with triple-negative breast cancer (TNBC) have poor prognosis because of the aggressive nature of the tumor, delayed diagnosis and non-specific symptoms in the early stages. Identification of novel specific TNBC serum biomarkers for screening and therapeutic purposes therefore remains an urgent clinical requirement.We obtained serum samples from a total of 380 recruited individuals split into mining and testing sets, with the aim of screening for reliable protein biomarkers from TNBC and non-TNBC (NTNBC) sera. Samples were assessed using mass spectrometry, followed by receiver operating characteristic (ROC), survival and hazard function curve as well as multivariate Cox regression analyses to ascertain the potential of the protein constituents as diagnostic and prognostic biomarkers for TNBC.We identified upregulated apolipoprotein C-I (apoC-I) with a validated positive effect on TNBC tumorigenesis, with confirmation in an independent test set and minimization of systematic bias by pre-analytical parameters. The apoC-I protein had superior diagnostic ability in distinguishing between TNBC and NTNBC cases. Moreover, the protein presented a more robust potential prognostic factor for TNBC than NTNBC. The apoC-I protein identified in this study presents an effective novel diagnostic and prognostic biomarker for TNBC, indicating that measurement of the peak intensity at 7785 Da in serum samples could facilitate improved early detection and estimation of postoperative survival prognosis for TNBC.

An integrated cell line-based discovery strategy identified follistatin and kallikrein 6 as serum biomarker candidates of breast carcinoma

Secreted proteins constitute a relevant source of putative cancer biomarkers. Here, we compared the secretome of a series of four genetically-related breast cancer cell lines as a model of aggressiveness using quantitative mass spectrometry. 537 proteins (59.5% of the total identified proteins) predicted to be released or shed from cells were identified. Using a scoring system based on i) iTRAQ value, ii) breast cancer tissue mRNA expression levels, and iii) immunohistochemical staining (public database), a short list of 10 candidate proteins was selected. Using specific ELISA assays, the expression level of the top five proteins was measured in a verification set of 56 patients. The four significantly differentially expressed proteins were then validated in a second independent set of 353 patients. Finally, follistatin (FST) and kallikrein 6 (KLK6) in serum were significantly higher (p-value < 0.0001) in invasive breast cancer patients compared with non-cancerous controls. Using specific cut-off values, FST distinguished breast cancer samples from healthy controls with a sensitivity of 65% and an accuracy of 68%, whereas KLK6 achieved a sensitivity of 55% and an accuracy of 61%. Therefore, we concluded that FST and KLK6 may have significance in breast cancer detection.

BIOLOGICAL SIGNIFICANCE

Discovery of new serum biomarkers that exhibit increased sensitivity and specificity compared to current biomarkers appears to be an essential field of research in cancer. Most biological markers show insufficient diagnostic sensitivity for early breast cancer detection and, for the majority of them, their concentrations are elevated only in metastatic forms of the disease. It is therefore essential to identify clinically reliable biomarkers and develop effective approaches for cancer diagnosis. One promising approach in this field is the study of secreted proteins through proteomic analysis of in vitro progression breast cancer models. Here we have shown that FST and KLK6 are elevated in breast cancer patient serum compared to healthy controls. We expect that our discovery strategy will help to identify cancer-specific and body-fluid-accessible biomarkers.

Type 1 diabetes cadaveric human pancreata exhibit a unique exocrine tissue proteomic profile

Type 1 diabetes (T1D) is an autoimmune disorder resulting from a self-destruction of pancreatic islet beta cells. The complete proteome of the human pancreas, where both the dysfunctional beta cells and their proximal environment co-exist, remains unknown. Here, we used TMT10-based isobaric labeling and multidimensional LC-MS/MS to quantitatively profile the differences between pancreatic head region tissues from T1D (N = 5) and healthy subjects (N = 5). Among the 5357 (1% false discovery rate) confidently identified proteins, 145 showed statistically significant dysregulation between T1D and healthy subjects. The differentially expressed pancreatic proteome supports the growing notion of a potential role for exocrine pancreas involvement in T1D. This study also demonstrates the utility for this approach to analyze dysregulated proteins as a means to investigate islet biology, pancreatic pathology and T1D pathogenesis.

4-protein signature predicting tamoxifen treatment outcome in recurrent breast cancer

Estrogen receptor (ER) positive tumors represent the majority of breast malignancies, and are effectively treated with hormonal therapies, such as tamoxifen. However, in the recurrent disease resistance to tamoxifen therapy is common and a major cause of death. In recent years, in-depth proteome analyses have enabled identification of clinically useful biomarkers, particularly, when heterogeneity in complex tumor tissue was reduced using laser capture microdissection (LCM). In the current study, we performed high resolution proteomic analysis on two cohorts of ER positive breast tumors derived from patients who either manifested good or poor outcome to tamoxifen treatment upon recurrence. A total of 112 fresh frozen tumors were collected from multiple medical centers and divided into two sets: an in-house training and a multi-center test set. Epithelial tumor cells were enriched with LCM and analyzed by nano-LC Orbitrap mass spectrometry (MS), which yielded >3000 and >4000 quantified proteins in the training and test sets, respectively. Raw data are available via ProteomeXchange with identifiers PXD000484 and PXD000485. Statistical analysis showed differential abundance of 99 proteins, of which a subset of 4 proteins was selected through a multivariate step-down to develop a predictor for tamoxifen treatment outcome. The 4-protein signature significantly predicted poor outcome patients in the test set, independent of predictive histopathological characteristics (hazard ratio [HR] = 2.17; 95% confidence interval [CI] = 1.15 to 4.17; multivariate Cox regression p value = 0.017). Immunohistochemical (IHC) staining of PDCD4, one of the signature proteins, on an independent set of formalin-fixed paraffin-embedded tumor tissues provided and independent technical validation (HR = 0.72; 95% CI = 0.57 to 0.92; multivariate Cox regression p value = 0.009). We hereby report the first validated protein predictor for tamoxifen treatment outcome in recurrent ER-positive breast cancer. IHC further showed that PDCD4 is an independent marker.

A novel truncated form of S100P predicts disease-free survival in patients with lymph node positive breast cancer

The calcium-binding protein S100P is overexpressed in various cancers and may contribute to the oncogenic phenotype. This study used mass spectrometry to characterize a novel 9.2-kDa C-terminally truncated form of S100P (t-S100P), and to investigate its potential prognostic value in breast cancer. Univariate analysis demonstrated the association between breast tissue t-S100P levels (n = 148) and conventional pathological markers. Across all tumor samples, high t-S100P was strongly prognostic for poor disease-free survival (P = 0.005), its efficacy confined to lymph node-positive tumors (n = 74, P = 0.007). Matrix-assisted laser desorption/ionization imaging mass spectrometry confirmed differential t-S100P abundance between breast cancer and unaffected adjacent tissue. t-S100P was exclusively located in the cell nucleus of breast cancer tissue, and full-length S100P was essentially undetectable by mass spectrometry. We conclude that t-S100P is the predominant form of S100P in breast cancer tissue and is strongly prognostic for disease-free survival in women with lymph node-positive disease.

Bayesian proteoform modeling improves protein quantification of global proteomic measurements

As the capability of mass spectrometry-based proteomics has matured, tens of thousands of peptides can be measured simultaneously, which has the benefit of offering a systems view of protein expression. However, a major challenge is that, with an increase in throughput, protein quantification estimation from the native measured peptides has become a computational task. A limitation to existing computationally driven protein quantification methods is that most ignore protein variation, such as alternate splicing of the RNA transcript and post-translational modifications or other possible proteoforms, which will affect a significant fraction of the proteome. The consequence of this assumption is that statistical inference at the protein level, and consequently downstream analyses, such as network and pathway modeling, have only limited power for biomarker discovery. Here, we describe a Bayesian Proteoform Quantification model (BP-Quant)(1) that uses statistically derived peptides signatures to identify peptides that are outside the dominant pattern or the existence of multiple overexpressed patterns to improve relative protein abundance estimates. It is a research-driven approach that utilizes the objectives of the experiment, defined in the context of a standard statistical hypothesis, to identify a set of peptides exhibiting similar statistical behavior relating to a protein. This approach infers that changes in relative protein abundance can be used as a surrogate for changes in function, without necessarily taking into account the effect of differential post-translational modifications, processing, or splicing in altering protein function. We verify the approach using a dilution study from mouse plasma samples and demonstrate that BP-Quant achieves similar accuracy as the current state-of-the-art methods at proteoform identification with significantly better specificity. BP-Quant is available as a MatLab® and R packages.

Plasma Proteomic Profiling in Hereditary Breast Cancer Reveals a BRCA1-Specific Signature: Diagnostic and Functional Implications

Background

Breast cancer (BC) is a leading cause of death among women. Among the major risk factors, an important role is played by familial history of BC. Germ-line mutations in BRCA1/2 genes account for most of the hereditary breast and/or ovarian cancers. Gene expression profiling studies have disclosed specific molecular signatures for BRCA1/2-related breast tumors as compared to sporadic cases, which might help diagnosis and clinical follow-up. Even though, a clear hallmark of BRCA1/2-positive BC is still lacking. Many diseases are correlated with quantitative changes of proteins in body fluids. Plasma potentially carries important information whose knowledge could help to improve early disease detection, prognosis, and response to therapeutic treatments. The aim of this study was to develop a comprehensive approach finalized to improve the recovery of specific biomarkers from plasma samples of subjects affected by hereditary BC.

Methods

To perform this analysis, we used samples from patients belonging to highly homogeneous population previously reported. Depletion of high abundant plasma proteins, 2D gel analysis, liquid chromatography-tandem mass spectrometry (LC-MS/MS) and bioinformatics analysis were used into an integrated approach to investigate tumor-specific changes in the plasma proteome of BC patients and healthy family members sharing the same BRCA1 gene founder mutation (5083del19), previously reported by our group, with the aim to identify specific signatures.

Results

The comparative analysis of the experimental results led to the identification of gelsolin as the most promising biomarker.

Conclusions

Further analyses, performed using a panel of breast cancer cell lines, allowed us to further elucidate the signaling network that might modulate the expression of gelsolin in breast cancer.

Contemporary proteomic strategies for clinical epigenetic research and potential impact for the clinic

Novel proteomic methods are revealing the intricacy of the epigenetic landscape affecting gene regulation and improving our knowledge of the pathogenesis of complex diseases. Despite the enormous amount of data regarding epigenetic modifications present in DNA and histones, deciphering their biological relevance in the context of the disease and health is currently still an ongoing process. Here, we consider the relationship between epigenetic research in tumorigenesis and the prospect of knowledge transfer to clinical use, focusing primarily on the epigenetic histone post-translational modifications, which could be used as biomarkers. We additionally focus on the use of proteomic techniques in research and evaluate their usefulness in clinical setting.

Strategies for modern biomarker and drug development in oncology

Technological advancements in the molecular characterization of cancers have enabled researchers to identify an increasing number of key molecular drivers of cancer progression. These discoveries have led to multiple novel anticancer therapeutics, and clinical benefit in selected patient populations. Despite this, the identification of clinically relevant predictive biomarkers of response continues to lag behind. In this review, we discuss strategies for the molecular characterization of cancers and the importance of biomarkers for the development of novel antitumor therapeutics. We also review critical successes and failures in oncology, and detail the lessons learnt, which may aid in the acceleration of anticancer drug development and biomarker discovery.

Novel serum protein biomarker panel revealed by mass spectrometry and its prognostic value in breast cancer

Introduction

Serum profiling using proteomic techniques has great potential to detect biomarkers that might improve diagnosis and predict outcome for breast cancer patients (BC). This study used surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry (MS) to identify differentially expressed proteins in sera from BC and healthy volunteers (HV), with the goal of developing a new prognostic biomarker panel.

Methods

Training set serum samples from 99 BC and 51 HV subjects were applied to four adsorptive chip surfaces (anion-exchange, cation-exchange, hydrophobic, and metal affinity) and analyzed by time-of-flight MS. For validation, 100 independent BC serum samples and 70 HV samples were analyzed similarly. Cluster analysis of protein spectra was performed to identify protein patterns related to BC and HV groups. Univariate and multivariate statistical analyses were used to develop a protein panel to distinguish breast cancer sera from healthy sera, and its prognostic potential was evaluated.

Results

From 51 protein peaks that were significantly up- or downregulated in BC patients by univariate analysis, binary logistic regression yielded five protein peaks that together classified BC and HV with a receiver operating characteristic (ROC) area-under-the-curve value of 0.961. Validation on an independent patient cohort confirmed the five-protein parameter (ROC value 0.939). The five-protein parameter showed positive association with large tumor size (P = 0.018) and lymph node involvement (P = 0.016). By matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS, immunoprecipitation and western blotting the proteins were identified as a fragment of apolipoprotein H (ApoH), ApoCI, complement C3a, transthyretin, and ApoAI. Kaplan-Meier analysis on 181 subjects after median follow-up of >5 years demonstrated that the panel significantly predicted disease-free survival (P = 0.005), its efficacy apparently greater in women with estrogen receptor (ER)-negative tumors (n = 50, P = 0.003) compared to ER-positive (n = 131, P = 0.161), although the influence of ER status needs to be confirmed after longer follow-up.

Conclusions

Protein mass profiling by MS has revealed five serum proteins which, in combination, can distinguish between serum from women with breast cancer and healthy control subjects with high sensitivity and specificity. The five-protein panel significantly predicts recurrence-free survival in women with ER-negative tumors and may have value in the management of these patients.

Hands-on workshops as an effective means of learning advanced technologies including genomics, proteomics and bioinformatics

Genomics and proteomics have emerged as key technologies in biomedical research, resulting in a surge of interest in training by investigators keen to incorporate these technologies into their research. At least two types of training can be envisioned in order to produce meaningful results, quality publications and successful grant applications: (1) immediate short-term training workshops and (2) long-term graduate education or visiting scientist programs. We aimed to fill the former need by providing a comprehensive hands-on training course in genomics, proteomics and informatics in a coherent, experimentally-based framework. This was accomplished through a National Heart, Lung, and Blood Institute (NHLBI)-sponsored 10-day Genomics and Proteomics Hands-on Workshop held at National Jewish Health (NJH) and the University of Colorado School of Medicine (UCD). The course content included comprehensive lectures and laboratories in mass spectrometry and genomics technologies, extensive hands-on experience with instrumentation and software, video demonstrations, optional workshops, online sessions, invited keynote speakers, and local and national guest faculty. Here we describe the detailed curriculum and present the results of short- and long-term evaluations from course attendees. Our educational program consistently received positive reviews from participants and had a substantial impact on grant writing and review, manuscript submissions and publications.

Human induced pluripotent stem cells from basic research to potential clinical applications in cancer

The human induced pluripotent stem cells (hiPSCs) are derived from a direct reprogramming of human somatic cells to a pluripotent stage through ectopic expression of specific transcription factors. These cells have two important properties, which are the self-renewal capacity and the ability to differentiate into any cell type of the human body. So, the discovery of hiPSCs opens new opportunities in biomedical sciences, since these cells may be useful for understanding the mechanisms of diseases in the production of new diseases models, in drug development/drug toxicity tests, gene therapies, and cell replacement therapies. However, the hiPSCs technology has limitations including the potential for the development of genetic and epigenetic abnormalities leading to tumorigenicity. Nowadays, basic research in the hiPSCs field has made progress in the application of new strategies with the aim to enable an efficient production of high-quality of hiPSCs for safety and efficacy, necessary to the future application for clinical practice. In this review, we show the recent advances in hiPSCs' basic research and some potential clinical applications focusing on cancer. We also present the importance of the use of statistical methods to evaluate the possible validation for the hiPSCs for future therapeutic use toward personalized cell therapies.

Omic-profiling in breast cancer metastasis to bone: implications for mechanisms, biomarkers and treatment

Despite well-recognised advances in breast cancer treatment, there remain substantial numbers of patients who develop metastatic disease, of which up to 70% involves spread to bone, resulting in skeletal complications which have a major negative impact on mortality and quality of life. Bisphosphonates and newer bone-targeted agents have reduced the prevalence of skeletal complications, yet there remains significant unmet clinical need, particularly for the development of more specific therapies for the prevention and treatment of metastatic bone disease, for the prediction of risk of its development in individual patients and for the prediction of response to treatments. Modern 'omic' strategies can potentially make a major contribution to meeting this need. Technological advances in the field of nucleic acid sequencing, mass spectrometry and metabolic profiling have driven progress in genomics, transcriptomics (functional genomics), proteomics and metabolomics. This review appraises the recent application of these approaches to studies of breast cancer metastasis (particularly to bone), with a focus on understanding how omic approaches may lead to new therapeutic options and to novel biomarker molecules or molecular signatures with potential value in clinical practise. The increasingly recognised need for rigorous sample quality control and both pre-clinical and clinical validation to meet the ultimate goals of clinical utility and patient benefit is discussed. Future directions of omic driven research in breast cancer metastasis are considered, in particular micro-RNAs and their role in the post-transcriptional regulation of gene function and the possible role of cancer-stem cells and epigenetic modifications in the development of distant metastases.

Tropomyosin-4 correlates with higher SBR grades and tubular differentiation in infiltrating ductal breast carcinomas: an immunohistochemical and proteomics-based study

The aim of this study is to evaluate tropomyosin-4 (TM4) expression in infiltrating ductal breast carcinomas (IDCAs), as well as its prognostic significance. Using a 2-DE/MALDI-TOF mass spectrometry investigation coupled with an immunohistochemical approach, we have assessed the expression of TM4 in IDCAs, as well as in other types of breast tumors. Proteomic analyses revealed an increased expression of tropomyosin-4 in IDCA tumors. Using immunohistochemistry, overexpression of tropomyosin-4 was confirmed in 51 additional tumor specimens. Statistical analyses revealed, however, no significant correlations between tropomyosin-4 expression and clinicopathological parameters of the disease including tumor stage, patient age, estrogen and progesterone receptor status, and lymph node metastasis occurrence. A significant association was found, however, with a high Scarf-Bloom-Richardson (SBR) grade, a known marker of tumor severity. Additionally, the SBR component showing a correlation with TM4 expression was the tubular differentiation status. This study demonstrates the upregulation of tropomyosin-4 in IDCA tissues, which may highlight its involvement in breast cancer development. Our findings also support a link between tropomyosin-4 expression and aggressiveness of IDCA tumors.

Mass spectrometry for the detection of potential psychiatric biomarkers

The search for molecules that can act as potential biomarkers is increasing in the scientific community, including in the field of psychiatry. The field of proteomics is evolving and its indispensability for identifying biomarkers is clear. Among proteomic tools, mass spectrometry is the core technique for qualitative and quantitative identification of protein markers. While significant progress has been made in the understanding of biomarkers for neurodegenerative diseases such as Alzheimer's disease, multiple sclerosis and Parkinson's disease, psychiatric disorders have not been as extensively investigated. Recent and successful applications of mass spectrometry-based proteomics in fields such as cardiovascular disease, cancer, infectious diseases and neurodegenerative disorders suggest a similar path for psychiatric disorders. In this brief review, we describe mass spectrometry and its use in psychiatric biomarker research and highlight some of the possible challenges of undertaking this type of work. Further, specific examples of candidate biomarkers are highlighted. A short comparison of proteomic with genomic methods for biomarker discovery research is presented. In summary, mass spectrometry-based techniques may greatly facilitate ongoing efforts to understand molecular mechanisms of psychiatric disorders.