Multiparameter imaging reveals clinically relevant cancer cell-stroma interaction dynamics in head and neck cancer.

Epithelial tumors are characterized by abundant inter- and intra-tumor heterogeneity, which complicates diagnostics and treatment. The contribution of cancer-stroma interactions to this heterogeneity is poorly understood. Here, we report a paradigm to quantify phenotypic diversity in head and neck squamous cell carcinoma (HNSCC) with single-cell resolution. By combining cell-state markers with morphological features, we identify phenotypic signatures that correlate with clinical features, including metastasis and recurrence. Integration of tumor and stromal signatures reveals that partial epithelial-mesenchymal transition (pEMT) renders disease outcome highly sensitive to stromal composition, generating a strong prognostic and predictive signature. Spatial transcriptomics and subsequent analyses of cancer spheroid dynamics identify the cancer-associated fibroblast-pEMT axis as a nexus for intercompartmental signaling that reprograms pEMT cells into an invasive phenotype. Taken together, we establish a paradigm to identify clinically relevant tumor phenotypes and discover a cell-state-dependent interplay between stromal and epithelial compartments that drives cancer aggression.

Performance assessment of computational tools to detect microsatellite instability.

Microsatellite instability (MSI) is a phenomenon seen in several cancer types, which can be used as a biomarker to help guide immune checkpoint inhibitor treatment. To facilitate this, researchers have developed computational tools to categorize samples as having high microsatellite instability, or as being microsatellite stable using next-generation sequencing data. Most of these tools were published with unclear scope and usage, and they have yet to be independently benchmarked. To address these issues, we assessed the performance of eight leading MSI tools across several unique datasets that encompass a wide variety of sequencing methods. While we were able to replicate the original findings of each tool on whole exome sequencing data, most tools had worse receiver operating characteristic and precision-recall area under the curve values on whole genome sequencing data. We also found that they lacked agreement with one another and with commercial MSI software on gene panel data, and that optimal threshold cut-offs vary by sequencing type. Lastly, we tested tools made specifically for RNA sequencing data and found they were outperformed by tools designed for use with DNA sequencing data. Out of all, two tools (MSIsensor2, MANTIS) performed well across nearly all datasets, but when all datasets were combined, their precision decreased. Our results caution that MSI tools can have much lower performance on datasets other than those on which they were originally evaluated, and in the case of RNA sequencing tools, can even perform poorly on the type of data for which they were created.

Somatic mutations of MLL4/COMPASS induce cytoplasmic localization providing molecular insight into cancer prognosis and treatment.

Cancer genome sequencing consortiums have recently catalogued an abundance of somatic mutations, across a wide range of human cancers, in the chromatin-modifying enzymes that regulate gene expression. Defining the molecular mechanisms underlying the potentially oncogenic functions of these epigenetic mutations could serve as the basis for precision medicine approaches to cancer therapy. MLL4 encoded by the KMT2D gene highly mutated in a large number of human cancers, is a key histone lysine monomethyltransferase within the Complex of Proteins Associated with Set1 (COMPASS) family that regulates gene expression through enhancer function, potentially functioning as a tumor suppressor. We report that the KMT2D mutations which cause MLL4 protein truncation also alter MLL4's subcellular localization, resulting in loss-of-function in the nucleus and gain-of-function in the cytoplasm. We demonstrate that isogenic correction of KMT2D truncation mutation rescues the aberrant localization phenotype and restores multiple regulatory functions of MLL4, including COMPASS integrity/stabilization, histone H3K4 mono-methylation, enhancer activation, and therefore transcriptional regulation. Moreover, isogenic correction diminishes the sensitivity of KMT2D-mutated cancer cells to targeted metabolic inhibition. Using immunohistochemistry, we identified that cytoplasmic MLL4 is unique to the tissue of bladder cancer patients with KMT2D truncation mutations. Using a preclinical carcinogen model of bladder cancer in mouse, we demonstrate that truncated cytoplasmic MLL4 predicts response to targeted metabolic inhibition therapy for bladder cancer and could be developed as a biomarker for KMT2D-mutated cancers. We also highlight the broader potential for prognosis, patient stratification and treatment decision-making based on KMT2D mutation status in MLL4 truncation-relevant diseases, including human cancers and Kabuki Syndrome.

CoraL: interpretable contrastive meta-learning for the prediction of cancer-associated ncRNA-encoded small peptides.

NcRNA-encoded small peptides (ncPEPs) have recently emerged as promising targets and biomarkers for cancer immunotherapy. Therefore, identifying cancer-associated ncPEPs is crucial for cancer research. In this work, we propose CoraL, a novel supervised contrastive meta-learning framework for predicting cancer-associated ncPEPs. Specifically, the proposed meta-learning strategy enables our model to learn meta-knowledge from different types of peptides and train a promising predictive model even with few labeled samples. The results show that our model is capable of making high-confidence predictions on unseen cancer biomarkers with only five samples, potentially accelerating the discovery of novel cancer biomarkers for immunotherapy. Moreover, our approach remarkably outperforms existing deep learning models on 15 cancer-associated ncPEPs datasets, demonstrating its effectiveness and robustness. Interestingly, our model exhibits outstanding performance when extended for the identification of short open reading frames derived from ncPEPs, demonstrating the strong prediction ability of CoraL at the transcriptome level. Importantly, our feature interpretation analysis discovers unique sequential patterns as the fingerprint for each cancer-associated ncPEPs, revealing the relationship among certain cancer biomarkers that are validated by relevant literature and motif comparison. Overall, we expect CoraL to be a useful tool to decipher the pathogenesis of cancer and provide valuable information for cancer research. The dataset and source code of our proposed method can be found at https://github.com/Johnsunnn/CoraL.

Inhibition of the Alternative Complement Pathway May Cause Secretion of Factor B, Enabling an Early Detection of Pancreatic Cancer.

This study aims to elucidate the cellular mechanisms behind the secretion of complement factor B (CFB), known for its dual roles as an early biomarker for pancreatic ductal adenocarcinoma (PDAC) and as the initial substrate for the alternative complement pathway (ACP). Using parallel reaction monitoring analysis, we confirmed a consistent ∼2-fold increase in CFB expression in PDAC patients compared with that in both healthy donors (HD) and chronic pancreatitis (CP) patients. Elevated ACP activity was observed in CP and other benign conditions compared with that in HD and PDAC patients, suggesting a functional link between ACP and PDAC. Protein-protein interaction analyses involving key complement proteins and their regulatory factors were conducted using blood samples from PDAC patients and cultured cell lines. Our findings revealed a complex control system governing the ACP and its regulatory factors, including Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation, adrenomedullin (AM), and complement factor H (CFH). Particularly, AM emerged as a crucial player in CFB secretion, activating CFH and promoting its predominant binding to C3b over CFB. Mechanistically, our data suggest that the KRAS mutation stimulates AM expression, enhancing CFH activity in the fluid phase through binding. This heightened AM-CFH interaction conferred greater affinity for C3b over CFB, potentially suppressing the ACP cascade. This sequence of events likely culminated in the preferential release of ductal CFB into plasma during the early stages of PDAC. (Data set ID PXD047043.).

Extracellular vesicles in cancer: challenges and opportunities for clinical laboratories.

Extracellular vesicles (EVs) are nano-sized particles secreted by most cells. They transport different types of biomolecules (nucleic acids, proteins, and lipids) characteristic of their tissue or cellular origin that can mediate long-distance intercellular communication. In the case of cancer, EVs participate in tumor progression by modifying the tumor microenvironment, favoring immune tolerance and metastasis development. Consequently, EVs have great potential in liquid biopsy for cancer diagnosis, prognosis and follow-up. In addition, EVs could have a role in cancer treatment as a targeted drug delivery system. The intense research in the EV field has resulted in hundreds of patents and the creation of biomedical companies. However, methodological issues and heterogeneity in EV composition have hampered the advancement of EV validation trials and the development of EV-based diagnostic and therapeutic products. Consequently, only a few EV biomarkers have moved from research to clinical laboratories, such as the ExoDx Prostate IntelliScore (EPI) test, a CLIA/FDA-approved EV prostate cancer diagnostic test. In addition, the number of large-scale multicenter studies that would clearly define biomarker performance is limited. In this review, we will critically describe the different types of EVs, the methods for their enrichment and characterization, and their biological role in cancer. Then, we will specially focus on the parameters to be considered for the translation of EV biology to the clinic laboratory, the advances already made in the field of EVs related to cancer diagnosis and treatment, and the issues still pending to be solved before EVs could be used as a routine tool in oncology.

Multiomics Analysis of the PHLDA Gene Family in Different Cancers and Their Clinical Prognostic Value.

The PHLDA (pleckstrin homology-like domain family) gene family is popularly known as a potential biomarker for cancer identification, and members of the PHLDA family have become considered potentially viable targets for cancer treatments. The PHLDA gene family consists of PHLDA1, PHLDA2, and PHLDA3. The predictive significance of PHLDA genes in cancer remains unclear. To determine the role of pleckstrin as a prognostic biomarker in human cancers, we conducted a systematic multiomics investigation. Through various survival analyses, pleckstrin expression was evaluated, and their predictive significance in human tumors was discovered using a variety of online platforms. By analyzing the protein-protein interactions, we also chose a collection of well-known functional protein partners for pleckstrin. Investigations were also carried out on the relationship between pleckstrins and other cancers regarding mutations and copy number alterations. The cumulative impact of pleckstrin and their associated genes on various cancers, Gene Ontology (GO), and pathway analyses were used for their evaluation. Thus, the expression profiles of PHLDA family members and their prognosis in various cancers may be revealed by this study. During this multiomics analysis, we found that among the PHLDA family, PHLDA1 may be a therapeutic target for several cancers, including kidney, colon, and brain cancer, while PHLDA2 can be a therapeutic target for cancers of the colon, esophagus, and pancreas. Additionally, PHLDA3 may be a useful therapeutic target for ovarian, renal, and gastric cancer.

New CEACAM-targeting 2A3 single-domain antibody-based chimeric antigen receptor T-cells produce anticancer effects in vitro and in vivo.

Recently, a breakthrough immunotherapeutic strategy of chimeric antigen receptor (CAR) T-cells has been introduced to hematooncology. However, to apply this novel treatment in solid cancers, one must identify suitable molecular targets in the tumors of choice. CEACAM family proteins are involved in the progression of a range of malignancies, including pancreatic and breast cancers, and pose attractive targets for anticancer therapies. In this work, we used a new CEACAM-targeted 2A3 single-domain antibody-based chimeric antigen receptor T-cells to evaluate their antitumor properties in vitro and in animal models. Originally, 2A3 antibody was reported to target CEACAM6 molecule; however, our in vitro co-incubation experiments showed activation and high cytotoxicity of 2A3-CAR T-cells against CEACAM5 and/or CEACAM6 high human cell lines, suggesting cross-reactivity of this antibody. Moreover, 2A3-CAR T-cells tested in vivo in the BxPC-3 xenograft model demonstrated high efficacy against pancreatic cancer xenografts in both early and late intervention treatment regimens. Our results for the first time show an enhanced targeting toward CEACAM5 and CEACAM6 molecules by the new 2A3 sdAb-based CAR T-cells. The results strongly support the further development of 2A3-CAR T-cells as a potential treatment strategy against CEACAM5/6-overexpressing cancers.

Keratin 17 modulates the immune topography of pancreatic cancer.

BACKGROUND: The immune microenvironment impacts tumor growth, invasion, metastasis, and patient survival and may provide opportunities for therapeutic intervention in pancreatic ductal adenocarcinoma (PDAC). Although never studied as a potential modulator of the immune response in most cancers, Keratin 17 (K17), a biomarker of the most aggressive (basal) molecular subtype of PDAC, is intimately involved in the histogenesis of the immune response in psoriasis, basal cell carcinoma, and cervical squamous cell carcinoma. Thus, we hypothesized that K17 expression could also impact the immune cell response in PDAC, and that uncovering this relationship could provide insight to guide the development of immunotherapeutic opportunities to extend patient survival. METHODS: Multiplex immunohistochemistry (mIHC) and automated image analysis based on novel computational imaging technology were used to decipher the abundance and spatial distribution of T cells, macrophages, and tumor cells, relative to K17 expression in 235 PDACs. RESULTS: K17 expression had profound effects on the exclusion of intratumoral CD8+ T cells and was also associated with decreased numbers of peritumoral CD8+ T cells, CD16+ macrophages, and CD163+ macrophages (p < 0.0001). The differences in the intratumor and peritumoral CD8+ T cell abundance were not impacted by neoadjuvant therapy, tumor stage, grade, lymph node status, histologic subtype, nor KRAS, p53, SMAD4, or CDKN2A mutations. CONCLUSIONS: Thus, K17 expression correlates with major differences in the immune microenvironment that are independent of any tested clinicopathologic or tumor intrinsic variables, suggesting that targeting K17-mediated immune effects on the immune system could restore the innate immunologic response to PDAC and might provide novel opportunities to restore immunotherapeutic approaches for this most deadly form of cancer.

Recent developments in mass-spectrometry-based targeted proteomics of clinical cancer biomarkers.

Routine measurement of cancer biomarkers is performed for early detection, risk classification, and treatment monitoring, among other applications, and has substantially contributed to better clinical outcomes for patients. However, there remains an unmet need for clinically validated assays of cancer protein biomarkers. Protein tumor markers are of particular interest since proteins carry out the majority of biological processes and thus dynamically reflect changes in cancer pathophysiology. Mass spectrometry-based targeted proteomics is a powerful tool for absolute peptide and protein quantification in biological matrices with numerous advantages that make it attractive for clinical applications in oncology. The use of liquid chromatography-tandem mass spectrometry (LC-MS/MS) based methodologies has allowed laboratories to overcome challenges associated with immunoassays that are more widely used for tumor marker measurements. Yet, clinical implementation of targeted proteomics methodologies has so far been limited to a few cancer markers. This is due to numerous challenges associated with paucity of robust validation studies of new biomarkers and the labor-intensive and operationally complex nature of LC-MS/MS workflows. The purpose of this review is to provide an overview of targeted proteomics applications in cancer, workflows used in targeted proteomics, and requirements for clinical validation and implementation of targeted proteomics assays. We will also discuss advantages and challenges of targeted MS-based proteomics assays for clinical cancer biomarker analysis and highlight some recent developments that will positively contribute to the implementation of this technique into clinical laboratories.

Biological roles of SLC16A1-AS1 lncRNA and its clinical impacts in tumors.

Recent studies have increasingly highlighted the aberrant expression of SLC16A1-AS1 in a variety of tumor types, where it functions as either an oncogene or a tumor suppressor in the pathogenesis of different cancers. The expression levels of SLC16A1-AS1 have been found to significantly correlate with clinical features and the prognosis of cancer patients. Furthermore, SLC16A1-AS1 modulates a range of cellular functions, including proliferation, migration, and invasion, through its interactions with diverse molecules and signaling pathways. This review examines the latest evidence regarding the role of SLC16A1-AS1 in the progression of various tumors and explores its potential clinical applications as a novel prognostic and diagnostic biomarker. Our comprehensive review aims to deepen the understanding of SLC16A1-AS1's multifaceted role in oncology, underscoring its potential as a significant biomarker and therapeutic target.

ARAP1-AS1: a novel long non-coding RNA with a vital regulatory role in human cancer development.

Long non-coding RNAs (lncRNAs) have garnered significant attention in biomedical research due to their pivotal roles in gene expression regulation and their association with various human diseases. Among these lncRNAs, ArfGAP With RhoGAP Domain, Ankyrin Repeat, And PH Domain 1 - Antisense RNA 1 (ARAP1-AS1) has recently emerged as an novel oncogenic player. ARAP1-AS1 is prominently overexpressed in numerous solid tumors and wields influence by modulating gene expression and signaling pathways. This regulatory impact is realized through dual mechanisms, involving both competitive interactions with microRNAs and direct protein binding. ARAP1-AS1 assumes an important role in driving tumorigenesis and malignant tumor progression, affecting biological characteristics such as tumor expansion and metastasis. This paper provides a concise review of the regulatory role of ARAP1-AS1 in malignant tumors and discuss its potential clinical applications as a biomarker and therapeutic target. We also address existing knowledge gaps and suggest avenues for future research. ARAP1-AS1 serves as a prototypical example within the burgeoning field of lncRNA studies, offering insights into the broader landscape of non-coding RNA molecules. This investigation enhances our comprehension of the complex mechanisms that govern the progression of cancer.

The role of pregnancy associated plasma protein-A in triple negative breast cancer: a promising target for achieving clinical benefits.

Pregnancy associated plasma protein-A (PAPP-A) plays an integral role in breast cancer (BC), especially triple negative breast cancer (TNBC). This subtype accounts for the most aggressive BC, possesses high tumor heterogeneity, is least responsive to standard treatments and has the poorest clinical outcomes. There is a critical need to address the lack of effective targeted therapeutic options available. PAPP-A is a protein that is highly elevated during pregnancy. Frequently, higher PAPP-A expression is detected in tumors than in healthy tissues. The increase in expression coincides with increased rates of aggressive cancers. In BC, PAPP-A has been demonstrated to play a role in tumor initiation, progression, metastasis including epithelial-mesenchymal transition (EMT), as well as acting as a biomarker for predicting patient outcomes. In this review, we present the role of PAPP-A, with specific focus on TNBC. The structure and function of PAPP-A, belonging to the pappalysin subfamily, and its proteolytic activity are assessed. We highlight the link of BC and PAPP-A with respect to the IGFBP/IGF axis, EMT, the window of susceptibility and the impact of pregnancy. Importantly, the relevance of PAPP-A as a TNBC clinical marker is reviewed and its influence on immune-related pathways are explored. The relationship and mechanisms involving PAPP-A reveal the potential for more treatment options that can lead to successful immunotherapeutic targets and the ability to assist with better predicting clinical outcomes in TNBC.

Long-term follow-up of efficacy and safety of selinexor maintenance treatment in patients with TP53wt advanced or recurrent endometrial cancer: A subgroup analysis of the ENGOT-EN5/GOG-3055/SIENDO study.

OBJECTIVE: To report long-term efficacy and safety of selinexor maintenance therapy in adults with TP53 wild-type (TP53wt) stage IV or recurrent endometrial cancer (EC) who achieved partial remission (PR) or complete remission (CR) following chemotherapy. METHODS: Analysis of the prespecified, exploratory subgroup of patients with TP53wt EC from the phase 3 SIENDO study was performed. Progression-free survival (PFS) benefit in patients with TP53wt EC and across other patient subgroups were exploratory endpoints. Safety and tolerability were also assessed. RESULTS: Of the 263 patients enrolled in the SIENDO trial, 113 patients had TP53wt EC; 70/113 (61.9%) had TP53wt/proficient mismatch repair (pMMR) EC, and 29/113 (25.7%) had TP53wt/deficient mismatch repair (dMMR) EC. As of April 1, 2024, the median PFS (mPFS) for TP53wt patients who received selinexor compared with placebo was 28.4 versus 5.2 months (36.8-month follow-up, HR 0.44; 95% CI 0.27-0.73). A benefit in mPFS was seen with selinexor versus placebo regardless of MMR status (patients with TP53wt/pMMR EC: 39.5 vs 4.9 months, HR 0.36; 95% CI 0.19-0.71; patients with TP53wt/dMMR EC: 13.1 vs 3.7 months, HR 0.49; 95% CI 0.18-1.34). Selinexor treatment was generally manageable, with no new safety signals identified. CONCLUSION: In the phase 3 SIENDO study, selinexor maintenance therapy showed a promising efficacy signal and a manageable safety profile in the prespecified subgroup of patients with TP53wt EC who achieved a PR or CR following chemotherapy. These results are being further evaluated in an ongoing randomized phase 3 trial (NCT05611931).

Aggregation enhanced fluorescence and Raman signals for highly sensitive cancer detection.

The implementation of early cancer detection benefits the treatment outcomes with remarkably improved survival rate through the detection of rare circulating biomarkers in body fluids. Spectroscopic technologies play a crucial role in sensitive biomarker measurements by outputting extremely strong signals. In particular, the aggregation enhanced fluorescence and Raman technologies feature the detection of targets down to single-molecule level, thereby demonstrating the great promise of early cancer detection. In this review, we focus on the aggregation-induced emission (AIE) and aggregation-related surface-enhanced Raman scattering (SERS) spectroscopic strategies for detecting cancer biomarkers. We discuss the AIE and SERS based biomarker detection using target-driven aggregation as well as the aggregated nanoprobes. Furthermore, we deliberate on the progress of developing AIE and SERS integrated platforms. Ultimately, we put forth the potential challenges and perspectives on the way to use these two spectroscopic technologies in clinical settings. It is expected this review can inspire the design of AIE and SERS integrated platform for highly sensitive and accurate cancer detection.

Dynamic Measurement of a Cancer Biomarker: Towards In Situ Application of a Fiber-Optic Ball Resonator Biosensor in CD44 Protein Detection.

The accuracy and efficacy of medical treatment would be greatly improved by the continuous and real-time monitoring of protein biomarkers. Identification of cancer biomarkers in patients with solid malignant tumors is receiving increasing attention. Existing techniques for detecting cancer proteins, such as the enzyme-linked immunosorbent assay, require a lot of work, are not multiplexed, and only allow for single-time point observations. In order to get one step closer to clinical usage, a dynamic platform for biosensing the cancer biomarker CD44 using a single-mode optical fiber-based ball resonator biosensor was designed, constructed and evaluated in this work. The main novelty of the work is an in-depth study of the capability of an in-house fabricated optical fiber biosensor for in situ detection of a cancer biomarker (CD44 protein) by conducting several types of experiments. The main results of the work are as follows: (1) Calibration of the fabricated fiber-optic ball resonator sensors in both static and dynamic conditions showed similar sensitivity to the refractive index change demonstrating its usefulness as a biosensing platform for dynamic measurements; (2) The fabricated sensors were shown to be insensitive to pressure changes further confirming their utility as an in situ sensor; (3) The sensor's packaging and placement were optimized to create a better environment for the fabricated ball resonator's performance in blood-mimicking environment; (4) Incubating increasing protein concentrations with antibody-functionalized sensor resulted in nearly instantaneous signal change indicating a femtomolar detection limit in a dynamic range from 7.1 aM to 16.7 nM; (5) The consistency of the obtained signal change was confirmed by repeatability studies; (6) Specificity experiments conducted under dynamic conditions demonstrated that the biosensors are highly selective to the targeted protein; (7) Surface morphology studies by AFM measurements further confirm the biosensor's exceptional sensitivity by revealing a considerable shift in height but no change in surface roughness after detection. The biosensor's ability to analyze clinically relevant proteins in real time with high sensitivity offers an advancement in the detection and monitoring of malignant tumors, hence improving patient diagnosis and health status surveillance.

Circular RNAs: Novel Players in Cancer Mechanisms and Therapeutic Strategies.

Circular RNAs (circRNAs) are a novel class of noncoding RNAs that have emerged as pivotal players in gene regulation. Our understanding of circRNAs has greatly expanded over the last decade, with studies elucidating their biology and exploring their therapeutic applications. In this review, we provide an overview of the current understanding of circRNA biogenesis, outline their mechanisms of action in cancer, and assess their clinical potential as biomarkers. Furthermore, we discuss circRNAs as a potential therapeutic strategy, including recent advances in circRNA production and translation, along with proof-of-concept preclinical studies of cancer vaccines.

Exploratory Analyses of Circulating Neoplastic-Immune Hybrid Cells as Prognostic Biomarkers in Advanced Intrahepatic Cholangiocarcinoma.

Existing clinical biomarkers do not reliably predict treatment response or disease progression in patients with advanced intrahepatic cholangiocarcinoma (ICC). Circulating neoplastic-immune hybrid cells (CHCs) have great promise as a blood-based biomarker for patients with advanced ICC. Peripheral blood specimens were longitudinally collected from patients with advanced ICC enrolled in the HELIX-1 phase II clinical trial (NCT04251715). CHCs were identified by co-expression of pan-cytokeratin (CK) and CD45, and levels were correlated to patient clinical disease course. Unsupervised machine learning was then performed to extract their morphological features to compare them across disease courses. Five patients were included in this study, with a median of nine specimens collected per patient. A median of 13.5 CHCs per 50,000 peripheral blood mononuclear cells were identified at baseline, and levels decreased to zero following the initiation of treatment in all patients. Counts remained undetectable in three patients who demonstrated end-of-trial clinical treatment response and conversely increased in two patients with evidence of therapeutic resistance. In the post-trial surveillance period, interval counts increased prior to or at the time of clinical progression in three patients and remain undetectable in one patient with continued long-term disease stability. Using our machine learning platform, treatment-resistant CHCs exhibited upregulation of CK and downregulation of CD45 relative to treatment-responsive CHCs. CHCs represent a promising blood-based biomarker to supplement traditional radiographic and biochemical measures.

Soluble CD44 in oral rinses for the early detection of cancer: a prospective cohort study in high-risk individuals.

BACKGROUND: There are 54,000 new cases of oral cavity and oropharyngeal cancer in the United States and more than 476,000 worldwide each year. Oral cavity and oropharyngeal squamous cell carcinoma make up most tumors with five-year survival rates of 50% due to prevalence of late-stage diagnoses. Improved methods of early detection in high-risk individuals are urgently needed. We aimed to assess the tumorigenic biomarkers soluble CD44 (solCD44) and total protein (TP) measured using oral rinses as affordable convenient screening tools for cancer detection. METHODS: In this prospective cohort study, we recruited 150 healthy current or former smokers through a community screening program. Baseline and four annual visits were conducted from March 2011-January 2016 with records followed until August 2020. Participants provided oral rinses, received head and neck exams, and completed questionnaires. SolCD44 and TP levels were measured and compared across groups and time. Participants were placed in the cancer group if malignancy developed in the study period, the suspicious group if physical exams were concerning for premalignant disease or cancer in the head and neck, and the healthy group if there were no suspicious findings. This analysis used two-sample t-test for comparison of means and two-sample Wilcoxon Test for comparison of medians. For subjects with follow-ups, estimated means of biomarkers were obtained from a fitted Repeated Measures Analysis of Variance (RANOVA) model including group, visit, and their interaction. Pairwise comparisons of mean solCD44 were made, including intergroup and intragroup comparison of values at different years. RESULTS: Most participants were males (58.7%), < 60 years of age. (90.7%), and Black (100%). Baseline mean solCD44 was elevated (2.781 ng/ml) in the cancer group compared to the suspicious group (1.849 ng/ml) and healthy group (1.779 ng/ml). CONCLUSION: This study supports the feasibility of a CD44-based oral rinse test as an affordable and convenient adjunctive tool for early detection of aerodigestive tract and other cancers in high-risk populations.

Quantile Index Biomarkers Based on Single-Cell Expression Data.

Current histocytometry methods enable single-cell quantification of biomolecules in tumor tissue sections by multiple detection technologies, including multiplex fluorescence-based immunohistochemistry or in situ hybridization. Quantitative pathology platforms can provide distributions of cellular signal intensity (CSI) levels of biomolecules across the entire cell populations of interest within the sampled tumor tissue. However, the heterogeneity of CSI levels is usually ignored, and the simple mean signal intensity value is considered a cancer biomarker. Here we consider the entire distribution of CSI expression levels of a given biomolecule in the cancer cell population as a predictor of clinical outcome. The proposed quantile index (QI) biomarker is defined as the weighted average of CSI distribution quantiles in individual tumors. The weight for each quantile is determined by fitting a functional regression model for a clinical outcome. That is, the weights are optimized so that the resulting QI has the highest power to predict a relevant clinical outcome. The proposed QI biomarkers were derived for proteins expressed in cancer cells of malignant breast tumors and demonstrated improved prognostic value compared with the standard mean signal intensity predictors. The R package Qindex implementing QI biomarkers has been developed. The proposed approach is not limited to immunohistochemistry data and can be based on any cell-level expressions of proteins or nucleic acids.