A fine-needle aspiration-based protein signature discriminates benign from malignant breast lesions

Evaluation of a Proteomics-Guided Protein Signature for Breast Cancer Detection in Breast Tissue

The distinction between noncancerous and cancerous breast tissues is challenging in clinical settings, and discovering new proteomics-based biomarkers remains underexplored. Through a pilot proteomic study (discovery cohort), we aimed to identify a protein signature indicative of breast cancer for subsequent validation using six published proteomics/transcriptomics data sets (validation cohorts). Sequential window acquisition of all theoretical (SWATH)-based mass spectrometry revealed 370 differentially abundant proteins between noncancerous tissue and breast cancer. Protein-protein interaction-based networks and enrichment analyses revealed dysregulation in pathways associated with extracellular matrix organization, platelet degranulation, the innate immune system, and RNA metabolism in breast cancer. Through multivariate unsupervised analysis, we identified a four-protein signature (OGN, LUM, DCN, and COL14A1) capable of distinguishing breast cancer. This dysregulation pattern was consistently verified across diverse proteomics and transcriptomics data sets. Dysregulation magnitude was notably higher in poor-prognosis breast cancer subtypes like Basal-Like and HER2 compared to Luminal A. Diagnostic evaluation (receiver operating characteristic (ROC) curves) of the signature in distinguishing breast cancer from noncancerous tissue revealed area under the curve (AUC) ranging from 0.87 to 0.9 with predictive accuracy of 80% to 82%. Upon stratifying, to solely include the Basal-Like/Triple-Negative subtype, the ROC AUC increased to 0.922-0.959 with predictive accuracy of 84.2%-89%. These findings suggest a potential role for the identified signature in distinguishing cancerous from noncancerous breast tissue, offering insights into enhancing diagnostic accuracy.

Fine needle aspiration of human lymph nodes reveals cell populations and soluble interactors pivotal to immunological priming

Lymph node (LN) fine needle aspiration (LN FNA) represents a powerful technique for minimally invasive sampling of human LNs in vivo and has been used effectively to directly study aspects of the human germinal center response. However, systematic deep phenotyping of the cellular populations and cell-free proteins recovered by LN FNA has not been performed. Thus, we studied human cervical LN FNAs as a proof-of-concept and used single-cell RNA-sequencing and proteomic analysis to benchmark this compartment, define the purity of LN FNA material, and facilitate future studies in this immunologically pivotal environment. Our data provide evidence that LN FNAs contain bone-fide LN-resident innate immune populations, with minimal contamination of blood material. Examination of these populations reveals unique biology not predictable from equivalent blood-derived populations. LN FNA supernatants represent a specific source of lymph- and lymph node-derived proteins, and can, aided by transcriptomics, identify likely receptor-ligand interactions. This represents the first description of the types and abundance of immune cell populations and cell-free proteins that can be efficiently studied by LN FNA. These findings are of broad utility for understanding LN physiology in health and disease, including infectious or autoimmune perturbations, and in the case of cervical nodes, neuroscience.

Minimally invasive biopsy-based diagnostics in support of precision cancer medicine

Precision cancer medicine (PCM) to support the treatment of solid tumors requires minimally invasive diagnostics. Here, we describe the development of fine-needle aspiration biopsy-based (FNA) molecular cytology which will be increasingly important in diagnostics and adaptive treatment. We provide support for FNA-based molecular cytology having a significant potential to replace core needle biopsy (CNB) as a patient-friendly potent technique for tumor sampling for various tumor types. This is not only because CNB is a more traumatic procedure and may be associated with more complications compared to FNA-based sampling, but also due to the recently developed molecular methods used with FNA. Recent studies show that image-guided FNA in combination with ultrasensitive molecular methods also offers opportunities for characterization of the tumor microenvironment which can aid therapeutic decisions. Here we provide arguments for an increased implementation of molecular FNA-based sampling as a patient-friendly diagnostic method, which may, due to its repeatability, facilitate regular sampling that is needed during different treatment lines, to provide tumor information, supporting treatment decisions, shortening lead times in healthcare, and benefit healthcare economics.

Multiplex protein analysis and ensemble machine learning methods of fine needle aspirates from prostate cancer patients reveal potential diagnostic signatures associated with tumour grade

BACKGROUND

Improved molecular diagnosis is needed in prostate cancer (PC). Fine needle aspiration (FNA) is a minimally invasive biopsy technique, less traumatic compared to core needle biopsy, and could be useful for diagnosis of PC. Molecular biomarkers (BMs) in FNA-samples can be assessed for prediction, eg of immunotherapy efficacy before treatment as well as at treatment decision time points during disease progression.

METHODS

In the present pilot study, the expression levels of 151 BM proteins were analysed by proximity extension assay in FNA-samples from 16 patients, including benign prostate lesions (n = 3) and cancers (n = 13). An ensemble data analysis strategy was applied using several machine learning models.

RESULTS

Twelve potentially predictive BM proteins correlating with International Society of Urological Pathology grade groups were identified, among them vimentin, tissue factor pathway inhibitor 2, and integrin beta-5. The validity of the results was supported by network analysis that showed functional associations between most of the identified putative BMs. We also showed that multiple immune checkpoint targets can be assessed (eg PD-L1, CD137, and Galectin-9), which may support the selection of immunotherapy in advanced PC. Results are promising but need further validation in a larger cohort.

CONCLUSIONS

Our pilot study represents a "proof of concept" and shows that multiplex profiling of potential diagnostic and predictive BM proteins is feasible on tumour material obtained by FNA sampling of prostate cancer. Moreover, our results demonstrate that an ensemble data analysis strategy may facilitate the identification of BM signatures in pilot studies when the patient cohort is limited.

Proximity Extension Assay in Combination with Next-Generation Sequencing for High-throughput Proteome-wide Analysis

Understanding the dynamics of the human proteome is crucial for developing biomarkers to be used as measurable indicators for disease severity and progression, patient stratification, and drug development. The Proximity Extension Assay (PEA) is a technology that translates protein information into actionable knowledge by linking protein-specific antibodies to DNA-encoded tags. In this report we demonstrate how we have combined the unique PEA technology with an innovative and automated sample preparation and high-throughput sequencing readout enabling parallel measurement of nearly 1500 proteins in 96 samples generating close to 150,000 data points per run. This advancement will have a major impact on the discovery of new biomarkers for disease prediction and prognosis and contribute to the development of the rapidly evolving fields of wellness monitoring and precision medicine.

Sensitive Measurement of Drug-Target Engagement by a Cellular Thermal Shift Assay with Multiplex Proximity Extension Readout

The ability to monitor target engagement in cellular contexts is a key for successful drug discovery and also valuable in clinical routine. A cellular thermal shift assay (CETSA) provides realistic information about drug binding in cells and tissues, revealing drug-target engagement in clinically relevant samples. The CETSA combined with mass spectrometry (MS) detection can be applied in the early hit identification phase to generate target engagement data for large sets of proteins. However, the analysis is slow, requires substantial amounts of the sample material, and often misses proteins of specific interest. Here, we combined the CETSA and the multiplex proximity extension assay (PEA) for analysis of target engagement of a set of 67 proteins from small amounts of the sample material treated with kinase inhibitors. The results were concordant with the corresponding analyses read out via MS. Our approach allows analyses of large numbers of specific target proteins at high sensitivity in limited sample aliquots. Highly sensitive multiplex CETSA-PEA assays are therefore promising for monitoring drug-target engagement in small sample aliquots in the course of drug development and potentially in clinical settings.

Multiplex immune protein profiling of fine-needle aspirates from patients with non-small-cell lung cancer reveals signatures associated with PD-L1 expression and tumor stage

Biomarker signatures identified through minimally invasive procedures already at diagnosis of non‐small‐cell lung cancer (NSCLC) could help to guide treatment with immune checkpoint inhibitors (ICI). Here, we performed multiplex profiling of immune‐related proteins in fine‐needle aspirate (FNA) samples of thoracic lesions from patients with NSCLC to assess PD‐L1 expression and identify related protein signatures. Transthoracic FNA samples from 14 patients were subjected to multiplex antibody‐based profiling by proximity extension assay (PEA). PEA profiling employed protein panels relevant to immune and tumor signaling and was followed by Qlucore^® Omics Explorer analysis. All lesions analyzed were NSCLC adenocarcinomas, and PEA profiles could be used to monitor 163 proteins in all but one sample. Multiple key immune signaling components (including CD73, granzyme A, and chemokines CCL3 and CCL23) were identified and expression of several of these proteins (e.g., CCL3 and CCL23) correlated to PD‐L1 expression. We also found EphA2, a marker previously linked to inferior NSCLC prognosis, to correlate to PD‐L1 expression. Our identified protein signatures related to stage included, among others, CXCL10 and IL12RB1. We conclude that transthoracic FNA allows for extensive immune and tumor protein profiling with assessment of putative biomarkers of important for ICI treatment selection in NSCLC.

Exploration of Circulating Tumour Cell (CTC) Biology: A Paradigm Shift in Liquid Biopsy

Circulating tumour cells (CTCs), are disseminated tumour cells found in the blood in solid tumour malignancies. Identification of CTCs act as emerging tools in the field of the Liquid Biopsy. Majority of the studies focused on detection and enumeration of CTCs due to technological challenges those results from the rarity of CTCs in the blood. Enumeration of CTCs has already proven their value as prognostic as well as predictive biomarkers for disease prognosis. However, recent advances in technology permitted to study the molecular and functional features of CTCs and these features have the potential to change the diagnostic, prognostic and predictive landscape in oncology. In this review, we summarize the paradigm shift in the field of liquid biopsy-based cancer diagnostics using CTC isolation and detection. We have discussed recent advances in the technologies for molecular characterization of CTCs which have aided a shift from CTC enumeration to an in-depth analysis of the CTC genome, transcriptomes, proteins, epigenomes along with various functional features. Finally, as a prognosticating strategy, the potentials of CTCs as a tool of liquid biopsy to predict micrometastasis, monitor prognosis and how to use them as an additional tool for cancer staging has been discussed.

Immune related proteins and tumor infiltrating CD8+ lymphocytes in hypopharyngeal cancer in relation to human papillomavirus (HPV) and clinical outcome

BACKGROUND

Hypopharyngeal cancer (HPSCC) shows a poor clinical outcome, while HPSCC, caused by human papillomavirus (HPV), presents a better outcome. Here, HPCC, immune proteins, and tumor infiltrating CD8+ lymphocytes (CD8+ TILs) were evaluated in relation to HPV and outcome.

METHODS

Fresh frozen tissue from four HPV-positive HPSCC, 39 HPV-negative HPSCC, and normal samples were analyzed for protein expression by the Proseek immuno-oncology immunoassay. CD8+ TIL numbers evaluated by immunohistochemistry on 144 formalin-fixed biopsies were analyzed in relation to clinical outcome.

RESULTS

Proteins differing between HPV-positive and negative HPSCC included CD8A, PD-L1, Fas ligand, and chemokines. High CD8+ TIL numbers were correlated to improve clinical outcome in HPV-negative HPSCC.

CONCLUSIONS

High expression of immune proteins in HPV-positive HPSCC may explain the better clinical outcome. CD8+ TILs are of relevance for outcome of HPV-negative HPSCC, while tumors with high immune activity but poor patient survival suggest a role for immune therapy.

Protein profiling of fine-needle aspirates reveals subtype-associated immune signatures and involvement of chemokines in breast cancer

There are increasing demands for informative cancer biomarkers, accessible via minimally invasive procedures, both for initial diagnostics and for follow-up of personalized cancer therapy, including immunotherapy. Fine-needle aspiration (FNA) biopsy provides ready access to relevant tissue samples; however, the minute amounts of sample require sensitive multiplex molecular analysis to be of clinical biomarker utility. We have applied proximity extension assays (PEA) to analyze 167 proteins in FNA samples from patients with breast cancer (BC; n = 25) and benign lesions (n = 32). We demonstrate that the FNA BC samples could be divided into two main clusters, characterized by differences in expression levels of the estrogen receptor (ER) and the proliferation marker Ki67. This clustering corresponded to some extent to established BC subtypes. Our analysis also revealed several proteins whose expression levels differed between BC and benign lesions (e.g., CA9, GZMB, IL-6, VEGFA, CXCL11, PDL1, and PCD1), as well as several chemokines correlating with ER and Ki67 status (e.g., CCL4, CCL8, CCL20, CXCL8, CXCL9, and CXCL17). Finally, we also identified three signatures that could predict Ki67 status, ER status, and tumor grade, respectively, based on a small subset of proteins, which was dominated by chemokines. To our knowledge, expression profiles of CCL13 in benign lesions and BC have not previously been described but were shown herein to correlate with proliferation (P = 0.00095), suggesting a role in advanced BC. Given the broad functional range of the proteins analyzed, immune-related proteins were overrepresented among the observed alterations. Our pilot study supports the emerging role of chemokines in BC progression. Due to the minimally traumatic sampling and clinically important molecular information for therapeutic decisions, this methodology is promising for future immunoscoring and monitoring of treatment efficacy in BC.