Prognostic Protein Biomarker Screening for Thyroid Carcinoma Based on Cancer Proteomics Profiles

Thyroid carcinoma (THCA) ranks among the most prevalent cancers globally. Integrating advanced genomic and proteomic analyses to construct a protein-based prognostic model promises to identify effective biomarkers and explore new therapeutic avenues. In this study, proteomic data from The Cancer Proteomics Atlas (TCPA) and clinical data from The Cancer Genome Atlas (TCGA) were utilized. Using Kaplan-Meier, Cox regression, and LASSO penalized Cox analyses, we developed a prognostic risk model comprising 13 proteins (S100A4, PAI1, IGFBP2, RICTOR, B7-H3, COLLAGENVI, PAR, SNAIL, FAK, Connexin-43, Rheb, EVI1, and P90RSK_pT359S363). The protein prognostic model was validated as an independent predictor of survival time in THCA patients, based on risk curves, survival analysis, receiver operating characteristic curves and independent prognostic analysis. Additionally, we explored the immune cell infiltration and tumor mutational burden (TMB) related to these features. Notably, our study proved a novel approach for predicting treatment responses in THCA patients, including those undergoing chemotherapy and targeted therapy.

Design and Evaluation of a Robust CRISPR Kinetic Assay for Hot-Spot Genotyping

Next-generation sequencing offers highly multiplexed and accurate detection of nucleic acid sequences but at the expense of complex workflows and high input requirements. The ease of use of CRISPR-Cas12 assays is attractive and may enable highly accurate detection of sequences implicated in, for example, cancer pathogenic variants. CRISPR assays often employ end-point measurements of Cas12 trans-cleavage activity after Cas12 activation by the target; however, end point-based methods can be limited in accuracy and robustness by arbitrary experimental choices. To overcome such limitations, we develop and demonstrate here an accurate assay targeting a mutation of the epidermal growth factor gene implicated in lung cancer (exon 19 deletion). The assay is based on characterizing the kinetics of Cas12 trans-cleavage to discriminate the mutant from wild-type targets. We performed extensive experiments (780 reactions) to calibrate key assay design parameters, including the guide RNA sequence, reporter sequence, reporter concentration, enzyme concentration, and DNA target type. Interestingly, we observed a competitive reaction between the target and reporter molecules that has important consequences for the design of CRISPR assays, which use preamplification to improve sensitivity. Finally, we demonstrate the assay on 18 tumor-extracted amplicons and 100 training iterations with 99% accuracy and discuss discrimination parameters and models to improve wild type versus mutant classification.

Cancer treatment with biosimilar drugs: A review

Biosimilars are biological drugs created from living organisms or that contain living components. They share an identical amino-acid sequence and immunogenicity. These drugs are considered to be cost-effective and are utilized in the treatment of cancer and other endocrine disorders. The primary aim of biosimilars is to predict biosimilarity, efficacy, and treatment costs; they are approved by the Food and Drug Administration (FDA) and have no clinical implications. They involve analytical studies to understand the similarities and dissimilarities. A biosimilar manufacturer sets up FDA-approved reference products to evaluate biosimilarity. The contribution of next-generation sequencing is evolving to study the organ tumor and its progression with its impactful therapeutic approach on cancer patients to showcase and target rare mutations. The study shall help to understand the future perspectives of biosimilars for use in gastro-entero-logic diseases, colorectal cancer, and thyroid cancer. They also help target specific organs with essential mutational categories and drug prototypes in clinical practices with blood and liquid biopsy, cell treatment, gene therapy, recombinant therapeutic proteins, and personalized medications. Biosimilar derivatives such as monoclonal antibodies like trastuzumab and rituximab are common drugs used in cancer therapy. Escherichia coli produces more than six antibodies or antibody-derived proteins to treat cancer such as filgrastim, epoetin alfa, and so on.

Enhancing Lung Cancer Classification through Integration of Liquid Biopsy Multi-Omics Data with Machine Learning Techniques

Early detection of lung cancer is crucial for patient survival and treatment. Recent advancements in next-generation sequencing (NGS) analysis enable cell-free DNA (cfDNA) liquid biopsy to detect changes, like chromosomal rearrangements, somatic mutations, and copy number variations (CNVs), in cancer. Machine learning (ML) analysis using cancer markers is a highly promising tool for identifying patterns and anomalies in cancers, making the development of ML-based analysis methods essential. We collected blood samples from 92 lung cancer patients and 80 healthy individuals to analyze the distinction between them. The detection of lung cancer markers Cyfra21 and carcinoembryonic antigen (CEA) in blood revealed significant differences between patients and controls. We performed machine learning analysis to obtain AUC values via Adaptive Boosting (AdaBoost), Multi-Layer Perceptron (MLP), and Logistic Regression (LR) using cancer markers, cfDNA concentrations, and CNV screening. Furthermore, combining the analysis of all multi-omics data for ML showed higher AUC values compared with analyzing each element separately, suggesting the potential for a highly accurate diagnosis of cancer. Overall, our results from ML analysis using multi-omics data obtained from blood demonstrate a remarkable ability of the model to distinguish between lung cancer and healthy individuals, highlighting the potential for a diagnostic model against lung cancer.

Development and evaluation of an adenosine-to-inosine RNA editing-based prognostic model for survival prediction of bladder cancer patients

Adenosine-to-inosine RNA editing (ATIRE) is a common form of ribonucleic acid (RNA) editing, which has highlighted the importance of ATIRE in tumors. However, its role in bladder cancer (BLCA) remains poorly understood. To study ATIRE impact on BLCA patient prognosis, we obtained ATIRE, gene expression, and clinical data from the Cancer Genome Atlas (TCGA) database for 251 patients, randomly dividing them into training and testing groups. Univariate proportional hazards model (COX) regression identified prognosis-associated ATIRE loci, while the least absolute shrinkage and selection operator (LASSO) selected final loci to construct prognostic models and generate ATIRE scores. We developed a nomogram to predict BLCA patients' overall survival (OS) and analyzed the effect of ATIRE editing levels on host gene expression. We also compared immune cell infiltration and drug treatment between patients with high and low ATIRE scores. The ATIRE prognostic prediction model was constructed using ten ATIRE loci that are closely associated with BLCA survival. Patients with high ATIRE scores showed significantly worse OS than those with low ATIRE scores. Furthermore, the nomogram, which incorporates the ATIRE score, can better predict the prognosis of patients. Multiple functional and pathway changes associated with immune responses, as well as significant differences in immune cell infiltration levels and response to drug therapy were observed between patients with high and low ATIRE scores. This study represented the first comprehensive analysis of the role of ATIRE events in BLCA patient prognosis and provided new insights into potential prognostic markers for BLCA research.

A patient-specific functional module and path identification technique from RNA-seq data

With the advancement of new technologies, a huge amount of high dimensional data is being generated which is opening new opportunities and challenges to the study of cancer and diseases. In particular, distinguishing the patient-specific key components and modules which drive tumorigenesis is necessary to analyze. A complex disease generally does not initiate from the dysregulation of a single component but it is the result of the dysfunction of a group of components and networks which differs from patient to patient. However, a patient-specific network is required to understand the disease and its molecular mechanism. We address this requirement by constructing a patient-specific network by sample-specific network theory with integrating cancer-specific differentially expressed genes and elite genes. By elucidating patient-specific networks, it can identify the regulatory modules, driver genes as well as personalized disease networks which can lead to personalized drug design. This method can provide insight into how genes are associating with each other and characterized the patient-specific disease subtypes. The results show that this method can be beneficial for the detection of patient-specific differential modules and interaction between genes. Extensive analysis using existing literature, gene enrichment and survival analysis for three cancer types STAD, PAAD and LUAD shows the effectiveness of this method over other existing methods. In addition, this method can be useful for personalized therapeutics and drug design. This methodology is implemented in the R language and is available at https://github.com/riasatazim/PatientSpecificRNANetwork.

Bioinformatics and cheminformatics approaches to identify pathways, molecular mechanisms and drug substances related to genetic basis of cervical cancer

Cervical cancer (CC) is a global threat to women and our knowledge is frighteningly little about its underlying genomic contributors. Our research aimed to understand the underlying molecular and genetic mechanisms of CC by integrating bioinformatics and network-based study. Transcriptomic analyses of three microarray datasets identified 218 common differentially expressed genes (DEGs) within control samples and CC specimens. KEGG pathway analysis revealed pathways in cell cycle, drug metabolism, DNA replication and the significant GO terms were cornification, proteolysis, cell division and DNA replication. Protein-protein interaction (PPI) network analysis identified 20 hub genes and survival analyses validated CDC45, MCM2, PCNA and TOP2A as CC biomarkers. Subsequently, 10 transcriptional factors (TFs) and 10 post-transcriptional regulators were detected through TFs-DEGs and miRNAs-DEGs regulatory network assessment. Finally, the CC biomarkers were subjected to a drug-gene relationship analysis to find the best target inhibitors. Standard cheminformatics method including in silico ADMET and molecular docking study substantiated PD0325901 and Selumetinib as the most potent candidate-drug for CC treatment. Overall, this meticulous study holds promises for further in vitro and in vivo research on CC diagnosis, prognosis and therapies. Communicated by Ramaswamy H. Sarma.

Identification of the most common BRCA alterations through analysis of germline mutation databases: Is droplet digital PCR an additional strategy for the assessment of such alterations in breast and ovarian cancer families?

Breast and ovarian cancer represent two of the most common tumor types in females worldwide. Over the years, several non‑modifiable and modifiable risk factors have been associated with the onset and progression of these tumors, including age, reproductive factors, ethnicity, socioeconomic status and lifestyle factors, as well as family history and genetic factors. Of note, BRCA1 and BRCA2 are two tumor suppressor genes with a key role in DNA repair processes, whose mutations may induce genomic instability and increase the risk of cancer development. Specifically, females with a family history of breast or ovarian cancer harboring BRCA1/2 germline mutations have a 60‑70% increased risk of developing breast cancer and a 15‑40% increased risk for ovarian cancer. Different databases have collected the most frequent germline mutations affecting BRCA1/2. Through the analysis of such databases, it is possible to identify frequent hotspot mutations that may be analyzed with next‑generation sequencing (NGS) and novel innovative strategies. In this context, NGS remains the gold standard method for the assessment of BRCA1/2 mutations, while novel techniques, including droplet digital PCR (ddPCR), may improve the sensitivity to identify such mutations in the hereditary forms of breast and ovarian cancer. On these bases, the present study aimed to provide an update of the current knowledge on the frequency of BRCA1/2 mutations and cancer susceptibility, focusing on the diagnostic potential of the most recent methods, such as ddPCR.

Protein-based prognostic signature for predicting the survival and immunotherapeutic efficiency of endometrial carcinoma

Background

Endometrial cancer (EC) is the most frequent malignancy of the female genital tract worldwide. Our study aimed to construct an effective protein prognostic signature to predict prognosis and immunotherapy responsiveness in patients with endometrial carcinoma.

Methods

Protein expression data, RNA expression profile data and mutation data were obtained from The Cancer Proteome Atlas (TCPA) and The Cancer Genome Atlas (TCGA). Prognosis-related proteins in EC patients were screened by univariate Cox regression analysis. Least absolute shrinkage and selection operator (LASSO) analysis and multivariate Cox regression analysis were performed to establish the protein-based prognostic signature. The CIBERSORT algorithm was used to quantify the proportions of immune cells in a mixed cell population. The Immune Cell Abundance Identifier (ImmuCellAI) and The Cancer Immunome Atlas (TCIA) web tools were used to predict the response to immunochemotherapy. The pRRophetic algorithm was used to estimate the sensitivity of chemotherapeutic and targeted agents.

Results

We constructed a prognostic signature based on 9 prognostic proteins, which could divide patients into high-risk and low-risk groups with distinct prognoses. A novel prognostic nomogram was established based on the prognostic signature and clinicopathological parameters to predict 1, 3 and 5-year overall survival for EC patients. The results obtained with Clinical Proteomic Tumor Analysis Consortium (CPTAC), Human Protein Atlas (HPA) and immunohistochemical (IHC) staining data from EC samples in our hospital supported the predictive ability of these proteins in EC tumors. Next, the CIBERSORT algorithm was used to estimate the proportions of 22 immune cell types. The proportions of CD8 T cells, T follicular helper cells and regulatory T cells were higher in the low-risk group. Moreover, we found that the prognostic signature was positively associated with high tumor mutation burden (TMB) and high microsatellite instability (MSI-H) status in EC patients. Finally, ImmuCellAI and TCIA analyses showed that patients in the low-risk group were more inclined to respond to immunotherapy than patients in the high-risk group. In addition, drug sensitivity analysis indicated that our signature had potential predictive value for chemotherapeutics and targeted therapy.

Conclusion

Our study constructed a novel prognostic protein signature with robust predictive ability for survival and efficiency in predicting the response to immunotherapy, chemotherapy and targeted therapy. This protein signature represents a promising predictor of prognosis and response to cancer treatment in EC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09402-w.

Prognostic gene expression signatures of breast cancer are lacking a sensible biological meaning

The identification of prognostic biomarkers for predicting cancer progression is an important problem for two reasons. First, such biomarkers find practical application in a clinical context for the treatment of patients. Second, interrogation of the biomarkers themselves is assumed to lead to novel insights of disease mechanisms and the underlying molecular processes that cause the pathological behavior. For breast cancer, many signatures based on gene expression values have been reported to be associated with overall survival. Consequently, such signatures have been used for suggesting biological explanations of breast cancer and drug mechanisms. In this paper, we demonstrate for a large number of breast cancer signatures that such an implication is not justified. Our approach eliminates systematically all traces of biological meaning of signature genes and shows that among the remaining genes, surrogate gene sets can be formed with indistinguishable prognostic prediction capabilities and opposite biological meaning. Hence, our results demonstrate that none of the studied signatures has a sensible biological interpretation or meaning with respect to disease etiology. Overall, this shows that prognostic signatures are black-box models with sensible predictions of breast cancer outcome but no value for revealing causal connections. Furthermore, we show that the number of such surrogate gene sets is not small but very large.

An alternative splicing signature model for predicting hepatocellular carcinoma-specific survival

BACKGROUND

Alternative splicing (AS) is a transcriptional regulation mechanism, which can expand the coding ability of genome and contribute to the occurrence and development of cancer. A systematic analysis of AS in hepatocellular carcinoma (HCC) is lacking and urgently needed.

METHODS

Univariate and multivariate Cox regression analyses were used to distinguish survival-related AS events and to calculate the risk score. Kaplan-Meier analysis and receiver operating characteristic (ROC) curves were used to evaluate the AS events' clinical significance to build a risk model in HCC.

RESULTS

Data of AS events was obtained from the Splice-Seq database. The corresponding clinical information of HCC was downloaded from The Cancer Genome Atlas (TCGA) data portal. We analyzed 78,878 AS events from 13,045 genes in HCC patients. A total of 2,440 and 2,888 AS events were significantly related to HCC patients' disease-free survival (DFS) and overall survival (OS). The two prognostic models (DFS and OS) were constructed based on a total of seven AS types from survival-related AS events above. The area under the curve (AUC) of the ROC curves was 0.769 in the DFS cohort and 0.886 in the OS cohort.

CONCLUSIONS

The prognostic model constructed by AS events can be used to predict the prognosis of HCC patients and provide potential therapeutic targets for further validation.

Systematic evaluation of PAXgene® tissue fixation for the histopathological and molecular study of lung cancer

Whilst adequate for most existing pathological tests, formalin is generally considered a poor DNA preservative and use of alternative fixatives may prove advantageous for molecular testing of tumour material; an increasingly common approach to identify targetable driver mutations in lung cancer patients. We collected paired PAXgene® tissue-fixed and formalin-fixed samples of block-sized tumour and lung parenchyma, Temno-needle core tumour biopsies and fine needle tumour aspirates (FNAs) from non-small cell lung cancer resection specimens. Traditionally processed formalin fixed paraffin wax embedded (FFPE) samples were compared to paired PAXgene® tissue fixed paraffin-embedded (PFPE) samples. We evaluated suitability for common laboratory tests (H&E staining and immunohistochemistry) and performance for downstream molecular investigations relevant to lung cancer, including RT-PCR and next generation DNA sequencing (NGS). Adequate and comparable H&E staining was seen in all sample types and nuclear staining was preferable in PAXgene® fixed Temno tumour biopsies and tumour FNA samples. Immunohistochemical staining was broadly comparable. PFPE samples enabled greater yields of less-fragmented DNA than FFPE comparators. PFPE samples were also superior for PCR and NGS performance, both in terms of quality control metrics and for variant calling. Critically we identified a greater number of genetic variants in the epidermal growth factor receptor gene when using PFPE samples and the Ingenuity® Variant Analysis pipeline. In summary, PFPE samples are adequate for histopathological diagnosis and suitable for the majority of existing laboratory tests. PAXgene® fixation is superior for DNA and RNA integrity, particularly in low-yield samples and facilitates improved NGS performance, including the detection of actionable lung cancer mutations for precision medicine in lung cancer samples.

Identification of key genes involved in the metastasis of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common and lethal renal malignant tumor in adults. The aim of the present study was to identify the key genes involved in ccRCC metastasis. Expression profiling data for ccRCC patients with metastasis and without metastasis were obtained from The Cancer Genome Atlas database. The datasets were used to identify differentially expressed genes (DEGs) between the metastasis group and the non-metastasis group using the DESeq2 package. Function enrichment analyses of DEGs were performed. The protein-protein interaction (PPI) network was constructed and analyzed using the Search Tool for the Retrieval of Interacting Genes and Cytoscape for further analysis of the identified hub genes. A total of 472 DEGs were identified, including 247 that were upregulated and 225 that were downregulated in the metastasis group. Gene Ontology enrichment analysis revealed that DEGs were mainly enriched in cell transmembrane movement and mitotic cell cycle process. Kyoto Encyclopedia of Genes Genomes pathway analysis revealed that the DEGs were mainly involved in the ‘cell cycle’ (hsa04110), ‘collecting duct acid secretion’ (hsa04966), ‘complement and coagulation cascades’ (hsa04610) and ‘aldosterone-regulated sodium reabsorption’ (hsa04960) pathways. Using the PPI network, 35 hub genes were identified, and the majority of them were upregulated in ccRCC tissue compared with normal kidney tissue. The expression levels of certain hub genes (CDKN3, TPX2, BUB1B, CDCA8, UBE2C, NDC80, RRM2, NCAPG, NCAPH, PTTG1, FAM64A, ANLN, KIF4A, CEP55, CENPF, KIF20A, ASPM and HJURP) were significantly associated with overall survival and recurrence-free survival in ccRCC. The present study has identified key genes associated with the metastasis of ccRCC.

ACGH detects distinct genomic alterations of primary intrahepatic cholangiocarcinomas and matched lymph node metastases and identifies a poor prognosis subclass

Lymph node metastases (LNM) are an important prognostic factor for patients with intrahepatic cholangiocarcinoma, but underlying genetic alterations are poorly understood. Whole genome array comparative genomic hybridization (aCGH) was performed in 37 tumors and 14 matched LNM. Genomic analyses of tumors confirmed known and identified new (gains in 19q) copy number alterations (CNA). Tumors with LNM (N1) had more alterations and exclusive gains (3p, 4q, 5p, 13q) and losses (17p and 20p). LNM shared most alterations with their matched tumors (86%), but 79% acquired new isolated gains [12q14 (36%); 1p13, 2p23, 7p22, 7q11, 11q12, 13q13 and 14q12 (>20%)]. Unsupervised clustering revealed a poor prognosis subclass with increased alterations significantly associated to tumor differentiation and survival. TP53 and KRAS mutations occurred in 19% of tumors and 6% of metastases. Pathway analyses revealed association to cancer-associated pathways. Advanced tumor stage, microvascular/perineural invasion, and microscopic positive resection margin (R1) were significantly correlated to metastases, while N1-status, R1-resection, and poor tumor differentiation were significantly correlated to survival. ACGH identified clear differences between N0 (no LNM) and N1 tumors, while N1 tumors and matched LNM displayed high clonality with exclusive gains in the metastases. A novel subclass with increased CNAs and poor tumor differentiation was significantly correlated to survival.

A comparative study of germline BRCA1 and BRCA2 mutation screening methods in use in 20 European clinical diagnostic laboratories

BACKGROUND

Thousands of clinically relevant variations in BRCA1 and BRCA2 have been discovered and this poses a significant challenge with respect to the accurate detection, analysis turn-around time, characterisation and interpretation of these sequence variants.

METHODS

We evaluated the performance of different BRCA1/2 gene testing practices in routine diagnostic use in 20 European laboratories, with a focus on next-generation sequencing-based strategies as this is the technical approach implemented by or under adoption by most European clinical laboratories. Participant laboratories, selected on expertise and diagnostic service quality, tested 10 identical DNA samples containing a range of challenging pathogenic variants.

RESULTS

A small number of errors in the detection of pathogenic and significant variants were identified (2.6% diagnostic error rate). There was a high degree of concordance (>97%) across all laboratories for all variants detected. No systematic technical flaw was identified in the strategies employed across the participating laboratories.

CONCLUSIONS

The discrepancies identified are most likely due to human error or the way the methodology has been implemented locally, for example, next-generation sequencing bioinformatics pipelines, rather than technical limitations of the methods. The choice of BRCA1/2 testing method will therefore depend on multiple factors including required throughput and turn-around times, access to equipment, expertise and budget.

New challenges for BRCA testing: a view from the diagnostic laboratory

Increased demand for BRCA testing is placing pressures on diagnostic laboratories to raise their mutation screening capacity and handle the challenges associated with classifying BRCA sequence variants for clinical significance, for example interpretation of pathogenic mutations or variants of unknown significance, accurate determination of large genomic rearrangements and detection of somatic mutations in DNA extracted from formalin-fixed, paraffin-embedded tumour samples. Many diagnostic laboratories are adopting next-generation sequencing (NGS) technology to increase their screening capacity and reduce processing time and unit costs. However, migration to NGS introduces complexities arising from choice of components of the BRCA testing workflow, such as NGS platform, enrichment method and bioinformatics analysis process. An efficient, cost-effective accurate mutation detection strategy and a standardised, systematic approach to the reporting of BRCA test results is imperative for diagnostic laboratories. This review covers the challenges of BRCA testing from the perspective of a diagnostics laboratory.

Role of innate immunity-triggered pathways in the pathogenesis of Sickle Cell Disease: a meta-analysis of gene expression studies

Despite the detailed characterization of the inflammatory and endothelial changes observed in Sickle Cell Disease (SCD), the hierarchical relationship between elements involved in the pathogenesis of this complex disease is yet to be described. Meta-analyses of gene expression studies from public repositories represent a novel strategy, capable to identify key mediators in complex diseases. We performed several meta-analyses of gene expression studies involving SCD, including studies with patient samples, as well as in-vitro models of the disease. Meta-analyses were performed with the Inmex bioinformatics tool, based on the RankProd package, using raw gene expression data. Functional gene set analysis was performed using more than 60 gene-set libraries. Our results demonstrate that the well-characterized association between innate immunity, hemostasis, angiogenesis and heme metabolism with SCD is also consistently observed at the transcriptomic level, across independent studies. The enrichment of genes and pathways associated with innate immunity and damage repair-associated pathways supports the model of erythroid danger-associated molecular patterns (DAMPs) as key mediators of the pathogenesis of SCD. Our study also generated a novel database of candidate genes, pathways and transcription factors not previously associated with the pathogenesis of SCD that warrant further investigation in models and patients of SCD.

Cancer susceptibility syndromes in children in the area of broad clinical use of massive parallel sequencing

Children diagnosed with cancer are considered for inherited cancer susceptibility testing according to well-established clinical criteria. With increasing efforts to personalize cancer medicine, comprehensive genome analyses will find its way into daily clinical routine in pediatric oncology. Whole genome and exome sequencing unavoidably generates incidental findings. The somatic "molecular make-up" of a tumor genome may suggest a germline mutation in a cancer susceptibility syndrome. At least two mechanisms are well-known, (a) chromothripsis (Li-Fraumeni syndrome) and (b) a high total number of mutational events which exceeds that of other samples of the same tumor type (defective DNA mismatch repair). Hence, pediatricians are faced with the fact that genetic events within the tumor genome itself can point toward underlying germline cancer susceptibility. Whenever genetic testing including next-generation sequencing (NGS) is initiated, the pediatrician has to inform about the benefits, risks, and alternatives, discuss the possibility of incidental findings and its disclosure, and to obtain informed consent prior to testing.

CONCLUSIONS

Genetic testing and translational research in pediatric oncology can incidentally uncover an underlying cancer susceptibility syndrome with implications for the entire family. Pediatricians should therefore increase their awareness of chances and risks that accompany the increasingly wide clinical implementation of NGS platforms.

Management of familial cancer: sequencing, surveillance and society

The clinical management of familial cancer begins with recognition of patterns of cancer occurrence suggestive of genetic susceptibility in a proband or pedigree, to enable subsequent investigation of the underlying DNA mutations. In this regard, next-generation sequencing of DNA continues to transform cancer diagnostics, by enabling screening for cancer-susceptibility genes in the context of known and emerging familial cancer syndromes. Increasingly, not only are candidate cancer genes sequenced, but also entire 'healthy' genomes are mapped in children with cancer and their family members. Although large-scale genomic analysis is considered intrinsic to the success of cancer research and discovery, a number of accompanying ethical and technical issues must be addressed before this approach can be adopted widely in personalized therapy. In this Perspectives article, we describe our views on how the emergence of new sequencing technologies and cancer surveillance strategies is altering the framework for the clinical management of hereditary cancer. Genetic counselling and disclosure issues are discussed, and strategies for approaching ethical dilemmas are proposed.

Cancer biomarker discovery: current status and future perspectives

PURPOSE

Cancer is a multigene disease which arises as a result of mutational and epigenetic changes coupled with activation of complex signaling networks. The use of biomarkers for early cancer detection, staging and individualization of therapy might improve patient care. A few fundamental issues such as tumor heterogeneity, a highly dynamic nature of the intrinsic and extrinsic determinants of radio- and chemoresistance, along with the plasticity and diversity of cancer stem cells (CSC) make biomarker development a challenging task. In this review we outline the preclinical strategies of cancer biomarker discovery including genomic, proteomic, metabolomic and microRNomic profiling, comparative genome hybridization (CGH), single nucleotide polymorphism (SNP) analysis, high throughput screening (HTS) and next generation sequencing (NGS). Other promising approaches such as assessment of circulating tumor cells (CTC), analysis of CSC-specific markers and cell-free circulating tumor DNA (ctDNA) are also discussed.

CONCLUSIONS

The emergence of powerful proteomic and genomic technologies in conjunction with advanced bioinformatic tools allows the simultaneous analysis of thousands of biological molecules. These techniques yield the discovery of new tumor signatures, which are sensitive and specific enough for early cancer detection, for monitoring disease progression and for proper treatment selection, paving the way to individualized cancer treatment.

Evidence for the importance of personalized molecular profiling in pancreatic cancer

OBJECTIVES

There is a growing body of evidence that targeted gene therapy holds great promise for the future treatment of cancer. A crucial step in this therapy is the accurate identification of appropriate candidate genes/pathways for targeted treatment. One approach is to identify variant genes/pathways that are significantly enriched in groups of afflicted individuals relative to control subjects. However, if there are multiple molecular pathways to the same cancer, the molecular determinants of the disease may be heterogeneous among individuals and possibly go undetected by group analyses.

METHODS

In an effort to explore this question in pancreatic cancer, we compared the most significantly differentially expressed genes/pathways between cancer and control patient samples as determined by group versus personalized analyses.

RESULTS

We found little to no overlap between genes/pathways identified by gene expression profiling using group analyses relative to those identified by personalized analyses.

CONCLUSIONS

Our results indicate that personalized and not group molecular profiling is the most appropriate approach for the identification of putative candidates for targeted gene therapy of pancreatic and perhaps other cancers with heterogeneous molecular etiology.

Establishing benchmarks and metrics for disruptive technologies, inappropriate and obsolete tests in the clinical laboratory

Benchmarks and metrics related to laboratory test utilization are based on evidence-based medical literature that may suffer from a positive publication bias. Guidelines are only as good as the data reviewed to create them. Disruptive technologies require time for appropriate use to be established before utilization review will be meaningful. Metrics include monitoring the use of obsolete tests and the inappropriate use of lab tests. Test utilization by clients in a hospital outreach program can be used to monitor the impact of new clients on lab workload. A multi-disciplinary laboratory utilization committee is the most effective tool for modifying bad habits, and reviewing and approving new tests for the lab formulary or by sending them out to a reference lab.

Autoantibody repertoires, natural biomarkers, and system controllers

The immune system is composed of networks of interacting cells and molecules; therefore, to understand and control immune behavior we need to adopt the thinking and tools of systems immunology. This review describes the use of an antigen microarray device and informatics to profile the repertoires of autoantibodies in health and disease. Autoantibody profiling provides an insight into the biomarkers used by the immune system in its dialog with the body. Heat shock protein 60 (HSP60) and HSP70 are cited as examples of key hubs in physiological regulatory networks; HSP molecules and peptides can be viewed as natural regulators because the immune system itself deploys them to modulate inflammatory reactions. The discovery of such natural biomarkers paves the way towards natural control.