Towards personalized tumor markers

Application of machine learning for high-throughput tumor marker screening

High-throughput sequencing and multiomics technologies have allowed increasing numbers of biomarkers to be mined and used for disease diagnosis, risk stratification, efficacy assessment, and prognosis prediction. However, the large number and complexity of tumor markers make screening them a substantial challenge. Machine learning (ML) offers new and effective ways to solve the screening problem. ML goes beyond mere data processing and is instrumental in recognizing intricate patterns within data. ML also has a crucial role in modeling dynamic changes associated with diseases. Used together, ML techniques have been included in automatic pipelines for tumor marker screening, thereby enhancing the efficiency and accuracy of the screening process. In this review, we discuss the general processes and common ML algorithms, and highlight recent applications of ML in tumor marker screening of genomic, transcriptomic, proteomic, and metabolomic data of patients with various types of cancers. Finally, the challenges and future prospects of the application of ML in tumor therapy are discussed.

Beyond the amyloid hypothesis: how current research implicates autoimmunity in Alzheimer's disease pathogenesis

The amyloid hypothesis has so far been at the forefront of explaining the pathogenesis of Alzheimer's Disease (AD), a progressive neurodegenerative disorder that leads to cognitive decline and eventual death. Recent evidence, however, points to additional factors that contribute to the pathogenesis of this disease. These include the neurovascular hypothesis, the mitochondrial cascade hypothesis, the inflammatory hypothesis, the prion hypothesis, the mutational accumulation hypothesis, and the autoimmunity hypothesis. The purpose of this review was to briefly discuss the factors that are associated with autoimmunity in humans, including sex, the gut and lung microbiomes, age, genetics, and environmental factors. Subsequently, it was to examine the rise of autoimmune phenomena in AD, which can be instigated by a blood-brain barrier breakdown, pathogen infections, and dysfunction of the glymphatic system. Lastly, it was to discuss the various ways by which immune system dysregulation leads to AD, immunomodulating therapies, and future directions in the field of autoimmunity and neurodegeneration. A comprehensive account of the recent research done in the field was extracted from PubMed on 31 January 2022, with the keywords "Alzheimer's disease" and "autoantibodies" for the first search input, and "Alzheimer's disease" with "IgG" for the second. From the first search, 19 papers were selected, because they contained recent research on the autoantibodies found in the biofluids of patients with AD. From the second search, four papers were selected. The analysis of the literature has led to support the autoimmune hypothesis in AD. Autoantibodies were found in biofluids (serum/plasma, cerebrospinal fluid) of patients with AD with multiple methods, including ELISA, Mass Spectrometry, and microarray analysis. Through continuous research, the understanding of the synergistic effects of the various components that lead to AD will pave the way for better therapeutic methods and a deeper understanding of the disease.

Small-Molecule Fluorescent Probes for Detecting Several Abnormally Expressed Substances in Tumors

Malignant tumors have always been the biggest problem facing human survival, and a huge number of people die from cancer every year. Therefore, the identification and detection of malignant tumors have far-reaching significance for human survival and development. Some substances are abnormally expressed in tumors, such as cyclooxygenase-2 (COX-2), nitroreductase (NTR), pH, biothiols (GSH, Cys, Hcy), hydrogen sulfide (H2S), hydrogen sulfide (H2O2), hypochlorous acid (HOCl) and NADH. Consequently, it is of great value to diagnose and treat malignant tumors due to the identification and detection of these substances. Compared with traditional tumor detection methods, fluorescence imaging technology has the advantages of an inexpensive cost, fast detection and high sensitivity. Herein, we mainly introduce the research progress of fluorescent probes for identifying and detecting abnormally expressed substances in several tumors.

Comparison of two multiplexed technologies for profiling >1,000 serum proteins that may associate with tumor burden

Background: In this pilot study, we perform a preliminary comparison of two targeted multiplex proteomics technologies for discerning serum protein concentration changes that may correlate to tumor burden in ovarian cancer (OC) patients. Methods: Using the proximity extension assay (PEA) and Quantibody® Kiloplex Array (QKA), we measured >1,000 proteins in the pre-surgical and post-surgical serum from nine OC patients (N=18 samples). We expect that proteins that have decreased significantly in the post-surgical serum concentration may correlate to tumor burden in each patient. Duplicate sera from two healthy individuals were used as controls (N=4 samples). We employed in-house ELISAs to measure five proteins with large serum concentration changes in pre- and post-surgical sera, from four of the original nine patients and the two original controls. Results: Both platforms showed a weak correlation with clinical cancer antigen 125 (CA125) data. The two multiplexed platforms showed a significant correlation with each other for >400 overlapping proteins. PEA uncovered 15 proteins, while QKA revealed 11 proteins, with more than a two-fold post-surgical decrease in at least six of the nine patients. Validation using single enzyme-linked immunosorbent assays (ELISAs) showed at least a two-fold post-surgical decrease in serum concentration of the same patients, as indicated by the two multiplex assays. Conclusion: Both methods identified proteins that had significantly decreased in post-surgical serum concentration, as well as recognizing proteins that had been implicated in OC patients. Our findings from a limited sample size suggest that novel targeted proteomics platforms are promising tools for identifying candidate serological tumor-related proteins.  However further studies are essential for the improvement of accuracy and avoidance of false results.

Investigation of olfactory receptor family 51 subfamily j member 1 (OR51J1) gene susceptibility as a potential breast cancer-associated biomarker

Among cancer treatment methods, targeted therapy using cancer-associated biomarkers has minimum side effects. Recently olfactory receptor (OR) family attracts the researcher’s attention as a favorable biomarker of cancer. Here, a statistical approach using complete data from the human protein atlas database was used to evaluate the potential of OR51J1 gene as a cancer-associated biomarker. To confirm the findings of statistical analysis, the OR51J1 mRNA and protein expression levels in breast tumor and normal tissue were measured using quantitative Real Time PCR (qRT-PCR) and immunohistochemistry (IHC) techniques. The association with clinicopathological factors was analyzed. Statistical analysis revealed that OR51J1 has a high expression level in more than 20 types of cancer tissues without any expression in 44 normal tissues. In 15 cancer types, including breast cancer, expression score was more than 90%. The qRT-PCR analysis in breast cancer showed OR51J1 have significantly higher expression level in tumors than normal tissues (2.91 fold). The IHC results showed OR51J1 expression on other cellular subtypes than tumor and normal cells, including myoepithelium, fibroblast, and lymphocytes. OR51J1 protein expression in invasive cells, as well as its overall score, showed a significant correlation with ER and PR expression and breast cancer (BC) subtypes. Results revealed the potential of OR51J1 as a cancer-associated biomarker for the diagnosis of breast cancer at the mRNA level.

Investigating a novel multiplex proteomics technology for detection of changes in serum protein concentrations that may correlate to tumor burden

Background: To account for cancer heterogeneity, we previously introduced the concept of "personalized" tumor markers, which are biomarkers that are informative in subsets of patients or even a single patient. Recent developments in various multiplex protein technologies create excitement for the discovery of markers of tumor burden in individual patients, but the reliability of the technologies remains to be tested for this purpose. Here, we sought to explore the potential of a novel proteomics platform, which utilizes a multiplexed antibody microarray, to detect changes in serum protein concentration that may correlate to tumor burden in pancreatic cancer. Methods: We applied the Quantibody® Human Kiloplex Array to simultaneously measure 1,000 proteins in sera obtained pre- and post-surgically from five pancreatic cancer patients. We expected that proteins which decreased post-surgery may correlate to tumor burden. Sera from two healthy individuals, split into two aliquots each, were used as controls. To validate the multiplexed results, we used single-target ELISA assays to measure the proteins with the largest serum concentration changes after surgery in sera collected pre- and post-surgically from the previous five patients and 10 additional patients. Results: The multiplexed array revealed nine proteins with more than two-fold post-surgical decrease in at least two of five patients. However, validation using single ELISAs showed that only two proteins tested displayed more than two-fold post-surgical decrease in one of the five original patients. In the independent cohort, six of the proteins tested showed at least a two-fold decrease post-surgery in at least one patient. Conclusions: Our study found that the Quantibody® Human Kiloplex Array results could not be reliably replicated with individual ELISA assays and most hits would likely represent false positives if applied to biomarker discovery. These findings suggest that data from novel, high-throughput proteomic platforms need stringent validation to avoid false discoveries.

Investigating a novel multiplex proteomics technology for detection of changes in serum protein concentrations that may correlate to tumor burden

Background: To account for cancer heterogeneity, we previously introduced the concept of "personalized" tumor markers, which are biomarkers that are informative in subsets of patients or even a single patient. Recent developments in various multiplex protein technologies create excitement for the discovery of markers of tumor burden in individual patients, but the reliability of the technologies remains to be tested for this purpose. Here, we sought to explore the potential of a novel proteomics platform, which utilizes a multiplexed antibody microarray, to detect changes in serum protein concentration that may correlate to tumor burden in pancreatic cancer. Methods: We applied the Quantibody® Human Kiloplex Array to simultaneously measure 1,000 proteins in sera obtained pre- and post-surgically from five pancreatic cancer patients. We expected that proteins which decreased post-surgery may correlate to tumor burden. Sera from two healthy individuals, split into two aliquots each, were used as controls. To validate the multiplexed results, we used single-target ELISA assays to measure the proteins with the largest serum concentration changes after surgery in sera collected pre- and post-surgically from the previous five patients and 10 additional patients. Results: The multiplexed array revealed nine proteins with more than two-fold post-surgical decrease in at least two of five patients. However, validation using single ELISAs showed that only two proteins tested displayed more than two-fold post-surgical decrease in one of the five original patients. In the independent cohort, six of the proteins tested showed at least a two-fold decrease post-surgery in at least one patient. Conclusions: Our study found that the Quantibody® Human Kiloplex Array results could not be reliably replicated with individual ELISA assays and most hits would likely represent false positives if applied to biomarker discovery. These findings suggest that data from novel, high-throughput proteomic platforms need stringent validation to avoid false discoveries.

Monitoring HSP70 exosomes in cancer patients' follow up: a clinical prospective pilot study

Exosomes are nanovesicles released by all cells that can be found in the blood. A key point for their use as potential biomarkers in cancer is to differentiate tumour-derived exosomes from other circulating nanovesicles. Heat shock protein-70 (HSP70) has been shown to be abundantly expressed by cancer cells and to be associated with bad prognosis. We previously showed that exosomes derived from cancer cells carried HSP70 in the membrane while those from non-cancerous cells did not. In this work, we opened a prospective clinical pilot study including breast and lung cancer patients to determine whether it was possible to detect and quantify HSP70 exosomes in the blood of patients with solid cancers. We found that circulating exosomal HSP70 levels, but not soluble HSP70, reflected HSP70 content within the tumour biopsies. Circulating HSP70 exosomes increased in metastatic patients compared to non-metastatic patients or healthy volunteers. Further, we demonstrated that HSP70-exosome levels correlated with the disease status and, when compared with circulating tumour cells, were more sensitive tumour dissemination predictors. Finally, our case studies indicated that HSP70-exosome levels inversely correlated with response to the therapy and that, therefore, monitoring changes in circulating exosomal HSP70 might be useful to predict tumour response and clinical outcome.

GRP78 expression in peripheral blood mononuclear cells is a new predictive marker for the benefit of taxanes in breast cancer neoadjuvant treatment

BACKGROUND

Breast cancer treatment is tailored to the specific cancer subtype. Often, systemic treatment is given prior to surgery. Chemotherapy induces significant endoplasmic reticulum (ER) stress-mediated cell death and upregulation of 78-kDa glucose-regulated protein (GRP78). We hypothesized that chemotherapy induces ER stress not only in the tumor tissue but also in immune cells, which may affect the response to anti-cancer treatment.

METHODS

We determined the surface expression of GRP78 on 15 different peripheral blood mononuclear cell (PBMC) subpopulations in 20 breast cancer patients at three time points of the neoadjuvant treatment, i.e., at baseline, after anthracycline treatment, and after taxanes treatment. For this purpose, we performed flow cytometric analyses and analyzed the data using ANOVA and the Tukey test. Serum cytokine levels were also evaluated, and their levels were correlated with response to treatment using the t-test after log transformation and Mann-Whitney U Wilcoxon W test.

RESULTS

A significant increase in GRP78 expression in PBMCs was documented during the taxane phase, only in patients who achieved pathological complete response (pCR). GRP78-positive clones correlated with increased serum levels of interferon gamma (IFNγ).

CONCLUSIONS

The presence of GRP78-positive clones in certain PBMC subpopulations in pCR patients suggests a dynamic interaction between ER stress and immune responsiveness. The correlation of GRP78-positive clones with increased levels of IFNγ supports the idea that GRP78 expression in PBMCs might serve as a new predictive marker to identify the possible benefits of taxanes in the neoadjuvant setting.

Pitfalls in Cancer Biomarker Discovery and Validation with Emphasis on Circulating Tumor DNA

Despite significant investment of funds and resources, few new cancer biomarkers have been introduced to the clinic in the last few decades. Although many candidates produce promising results in the laboratory, deficiencies in sensitivity, specificity, and predictive value make them less than desirable in a patient setting. This review will analyze these challenges in detail as well as discuss false discovery, problems with reproducibility, and tumor heterogeneity. Circulating tumor DNA (ctDNA), an emerging cancer biomarker, is also analyzed, particularly in the contexts of assay specificity, sensitivity, fragmentation, lead time, mutant allele fraction, and clinical relevance. Emerging artificial intelligence technologies will likely be valuable tools in maximizing the clinical utility of ctDNA which is often found in very small quantities in patients with early-stage tumors. Finally, the implications of challenging false discoveries are examined and some insights about improving cancer biomarker discovery are provided.See all articles in this CEBP Focus section, "NCI Early Detection Research Network: Making Cancer Detection Possible."

Cluster miRNAs and cancer: Diagnostic, prognostic and therapeutic opportunities

MiRNAs are class of noncoding RNA important for gene expression regulation in many plants, animals and viruses. MiRNA clusters contain a set of two or more miRNA encoding genes, transcribed together as polycistronic miRNAs. Currently, there are approximately 159 miRNA clusters reported in the human genome consisting of miRNAs ranging from two or more miRNA genes. A large proportion of clustered miRNAs resides in and around the fragile sites or cancer associated genomic hotspots and plays an important role in carcinogenesis. Altered expression of miRNA cluster can be pro-tumorigenic or anti-tumorigenic and can be targeted for clinical management of cancer. Over the past few years, manipulation of miRNA clusters expression is attempted for experimental purpose as well as for diagnostic, prognostic and therapeutic applications in cancer. Re-expression of miRNAs by epigenetic therapy, genome editing such as clustered regulatory interspaced short palindromic repeats (CRISPR) and miRNA mowers showed promising results in cancer therapy. In this review, we focused on the potential of miRNA clusters as a biomarker for diagnosis, prognosis, targeted therapy as well as strategies for modulating their expression in a therapeutic context. This article is categorized under: Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs RNA Processing > Processing of Small RNAs RNA in Disease and Development > RNA in Disease Regulatory RNAs/RNAi/Riboswitches > Biogenesis of Effector Small RNAs.

High Tiam1 expression predicts positive lymphatic metastasis and worse survival in patients with malignant solid tumors: a systematic review and meta-analysis

Background

Many studies have explored the prognostic value of T-cell lymphoma invasion and metastasis inducing factor 1 (Tiam1) and its association with lymphatic metastasis in malignant solid tumors, but the conclusions remain controversial. Therefore, we performed a meta-analysis to systematically assess the prognostic value of Tiam1 expression and its association with lymphatic metastasis in malignant solid tumors.

Methods

We searched eligible studies in PubMed, Web of Science and EMBASE databases (from inception up to October 2018). The combined HR with 95% CI was used to estimate the prognostic value of Tiam1 expression. The correlation between Tiam1 expression and lymphatic metastasis was assessed using the combined odds ratio (OR) with 95% CI.

Results

A total of 17 studies with 2,228 patients with solid tumors were included in this meta-analysis. The overall estimated results showed that high Tiam1 expression was significantly associated with shorter overall survival (HR= 2.08, 95% CI: 1.62-2.68, P<0.01), and disease-free survival (HR = 1.86, 95% CI: 1.49-2.32, P<0.01). Besides, we also found that there was a close relationship between high Tiam1 expression and positive lymphatic metastasis (OR=2.63; 95% CI: 1.79-3.84, P<0.01).

Conclusion

High Tiam1 expression was significantly associated with shorter survival and positive lymphatic metastasis in patients with malignant solid tumors. Therefore, Tiam1 may be a promising prognostic biomarker and an effective therapeutic target for malignant solid tumors.

The sensitivity and specificity of serum glycan-based biomarkers for cancer detection

Most of clinically used serum biomarkers for cancer detection were established in early 1980s when the Nobel Prize in physiology or medicine was awarded for the "discovery of the principle for the production of monoclonal antibodies." Using this "Nobel" technology, various monoclonal antibodies were obtained when different types of cancer cells were injected into mice and the ligands on the cancer cell surface were characterized. Both aberrant glycan structures and aberrant glycan-associated glycoproteins were revealed as a common feature of cancer cell surfaces through the specific interactions with the monoclonal antibodies. These results indicate that the biosynthesis of the environment-sensitive glycan structures goes awry in cancer cells, which is beyond genetic mutations. Later on, the glycan-related biomarkers were detected in the sera of cancer patients and then developed into serum biomarkers, such as CA125, CA153, CA195, CA199, CA242, CA27.29, CA50, and CA724, which are still in clinical use as of today. During the past 30 years, even with the advancement of different OMICS technologies not limited to genomics, epigenomics, proteomics, glycomics, lipidomics, and metabolomics, very few serum biomarkers have been introduced into clinical practice. The reason is that most of the newly discovered cancer biomarkers are inferior in terms of sensitivity and specificity to these biomarkers. We will summarize the reported sensitivity and specificity of currently used cancer biomarkers, especially the glycan-related biomarkers, in the forms of tables and radar plots and discuss the pros and cons of currently used cancer biomarkers.

Proteomics biomarkers for solid tumors: Current status and future prospects

Cancer is a heterogeneous multifactorial disease, which continues to be one of the main causes of death worldwide. Despite the extensive efforts for establishing accurate diagnostic assays and efficient therapeutic schemes, disease prevalence is on the rise, in part, however, also due to improved early detection. For years, studies were focused on genomics and transcriptomics, aiming at the discovery of new tests with diagnostic or prognostic potential. However, cancer phenotypic characteristics seem most likely to be a direct reflection of changes in protein metabolism and function, which are also the targets of most drugs. Investigations at the protein level are therefore advantageous particularly in the case of in-depth characterization of tumor progression and invasiveness. Innovative high-throughput proteomic technologies are available to accurately evaluate cancer formation and progression and to investigate the functional role of key proteins in cancer. Employing these new highly sensitive proteomic technologies, cancer biomarkers may be detectable that contribute to diagnosis and guide curative treatment when still possible. In this review, the recent advances in proteomic biomarker research in cancer are outlined, with special emphasis placed on the identification of diagnostic and prognostic biomarkers for solid tumors. In view of the increasing number of screening programs and clinical trials investigating new treatment options, we discuss the molecular connections of the biomarkers as well as their potential as clinically useful tools for diagnosis, risk stratification and therapy monitoring of solid tumors.

The 9th Santorini Conference: Systems Medicine, Personalised Health and Therapy. "The Odyssey from Hope to Practice", Santorini, Greece, 30 September⁻3 October 2018

The 9th traditional biannual conference on Systems Medicine, Personalised Health & Therapy—“The Odyssey from Hope to Practice”, inspired by the Greek mythology, was a call to search for practical solutions in cardio-metabolic diseases and cancer, to resolve and overcome the obstacles in modern medicine by creating more interactions among disciplines, as well as between academic and industrial research, directed towards an effective ‘roadmap’ for personalised health and therapy. The 9th Santorini Conference, under the Presidency of Sofia Siest, the director of the INSERM U1122; IGE-PCV (www.u1122.inserm.fr), University of Lorraine, France, offered a rich and innovative scientific program. It gathered 34 worldwide distinguished speakers, who shared their passion for personalised medicine with 160 attendees in nine specific sessions on the following topics: First day: The Odyssey from hope to practice: Personalised medicine—landmarks and challenges Second day: Diseases to therapeutics—genotype to phenotype an “-OMICS” approach: focus on personalised therapy and precision medicine Third day: Gene-environment interactions and pharmacovigilance: a pharmacogenetics approach for deciphering disease “bench to clinic to reality” Fourth day: Pharmacogenomics to drug discovery: a big data approach and focus on clinical data and clinical practice. In this article we present the topics shared among the participants of the conference and we highlight the key messages.

Optimizing cancer immunotherapy: Is it time for personalized predictive biomarkers?

Cancer immunotherapy, a treatment that selectively augments a patient's anti-tumor immune response, is a breakthrough advancement in personalized medicine. A subset of cancer patients undergoing immunotherapy have displayed robust and long-lasting therapeutic responses. Currently, the spotlight is on the use of blocking antibodies against the T-cell checkpoint molecules, cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) and programed cell death-1 (PD-1)/programed death-ligand 1 (PD-L1), which have been effectively used to combat many cancers types. Despite the overall enthusiasm, immune checkpoint blockade inhibitors suffer from significant limitations such as high cost, serious toxicity in a substantial proportion of patients, and a response rate as low as 10%-40% in some clinical trials. Consequently, there is an urgent and unmet medical need for companion biomarkers that could both predict the response of individual patients to these therapies, and provide the means for precise monitoring of their therapeutic outcome. In this era of precision medicine, predictive biomarkers are a hot commodity because they can effectively separate responders from non-responders, and spare non-responders from serious therapy-related toxicity. Emerging predictive biomarkers for immune checkpoint blockade are: PD-L1 expression, increased amounts of tumor-infiltrating lymphocytes, increased mutational load and mismatch repair deficiency. Other well-studied biomarkers include inflammatory infiltrate, absolute lymphocyte count and lactate dehydrogenase levels. We review recent progress on predictive cancer biomarkers in immunotherapy, with a special emphasis on serum autoantibodies that have the potential to be personalized for optimal clinical outcomes.

Electrophoretic cytopathology resolves ERBB2 forms with single-cell resolution

In addition to canonical oncoproteins, truncated isoforms and proteolysis products are implicated in both drug resistance and disease progression. In HER2-positive breast tumors, expression of truncated HER2 isoforms resulting from alternative translation and/or carboxy-terminal fragments (CTFs) resulting from proteolysis (collectively, t-erbB2) have been associated with shortened progression-free survival of patients. Thus, to advance clinical pathology and inform treatment decisions, we developed a high-selectivity cytopathology assay capable of distinguishing t-erbB2 from full-length HER2 expression without the need for isoform-specific antibodies. Our microfluidic, single-cell western blot, employs electrophoretic separations to resolve full-length HER2 from the smaller t-erbB2 in each ~28 pL single-cell lysate. Subsequently, a pan-HER2 antibody detects all resolved HER2 protein forms via immunoprobing. In analysis of eight breast tumor biopsies, we identified two tumors comprised of 15% and 40% t-erbB2-expressing cells. By single-cell western blotting of the t-erbB2-expressing cells, we observed statistically different ratios of t-erbB2 proteins to full-length HER2 expression. Further, target multiplexing and clustering analyses scrutinized signaling, including ribosomal S6, within the t-erbB2-expressing cell subpopulation. Taken together, cytometric assays that report both protein isoform profiles and signaling state offer cancer classification taxonomies with unique relevance to precisely describing drug resistance mechanisms in which oncoprotein isoforms/fragments are implicated.