Cytogenetics and Cytogenomics Evaluation in Cancer

Molecular methods in cancer diagnostics: a short review

BACKGROUND

One of the ailments with the greatest fatality rates in the 21st century is cancer. Globally, molecular methods are widely employed to treat cancer-related disorders, and the body of research on this subject is growing yearly. A thorough and critical summary of the data supporting molecular methods for illnesses linked to cancer is required.

OBJECTIVE

In order to guide clinical practice and future research, it is important to examine and summarize the systematic reviews (SRs) that evaluate the efficacy and safety of molecular methods for disorders associated to cancer.

METHODS

We developed a comprehensive search strategy to find relevant articles from electronic databases like PubMed, Google Scholar, Web of Science (WoS), or Scopus. We looked through the literature and determined which diagnostic methods in cancer genetics were particularly reliable. We used phrases like 'cancer genetics', genetic susceptibility, Hereditary cancer, cancer risk assessment, 'cancer diagnostic tools', cancer screening', biomarkers, and molecular diagnostics, reviews and meta-analyses evaluating the efficacy and safety of molecular therapies for cancer-related disorders. Research that only consider treatment modalities that don't necessitate genetic or molecular diagnostics fall under the exclusion criteria.

RESULTS

The results of this comprehensive review clearly demonstrate the transformative impact of molecular methods in the realm of cancer genetics.This review underscores how these technologies have empowered researchers and clinicians to identify and understand key genetic alterations that drive malignancy, ranging from point mutations to structural variations. Such insights are instrumental in pinpointing critical oncogenic drivers and potential therapeutic targets, thus opening the door for methods in precision medicine that can significantly improve patient outcomes.

LIMITATION

The search does not specify a timeframe for publication inclusion, it may have missed recent advancements or changes in the field's landscape of molecular methods for cancer. As a result, it may not have included the most recent developments in the field.

CONCLUSION

After conducting an in-depth study on the molecular methods in cancer genetics, it is evident that these cutting-edge technologies have revolutionized the field of oncology, providing researchers and clinicians with powerful tools to unravel the complexities of cancer at the genetic level. The integration of molecular methods techniques has not only enhanced our understanding of cancer etiology, progression, and treatment response but has also opened new avenues for personalized medicine and targeted therapies, leading to improved patient outcomes.

Cancer cytogenetics in the era of artificial intelligence: shaping the future of chromosome analysis

Artificial intelligence (AI) has rapidly advanced in the past years, particularly in medicine for improved diagnostics. In clinical cytogenetics, AI is becoming crucial for analyzing chromosomal abnormalities and improving precision. However, existing software lack learning capabilities from experienced users. AI integration extends to genomic data analysis, personalized medicine and research, but ethical concerns arise. In this article, we discuss the challenges of the full automation in cytogenetic test interpretation and focus on its importance and benefits.

Section E6.7-6.12 of the American College of Medical Genetics and Genomics (ACMG) Technical Laboratory Standards: Cytogenomic studies of acquired chromosomal abnormalities in solid tumors

Clinical cytogenomic studies of solid tumor samples are critical to the diagnosis, prognostication, and treatment selection for cancer patients. An overview of current cytogenomic techniques for solid tumor analysis is provided, including standards for sample preparation, clinical and technical considerations, and documentation of results. With the evolving technologies and their application in solid tumor analysis, these standards now include sequencing technology and optical genome mapping, in addition to the conventional cytogenomic methods, such as G-banded chromosome analysis, fluorescence in situ hybridization, and chromosomal microarray analysis. This updated Section E6.7-6.12 supersedes the previous Section E6.5-6.8 in Section E: Clinical Cytogenetics of the American College of Medical Genetics and Genomics Standards for Clinical Genetics Laboratories.

A Review on Anaplastic Lymphoma Kinase (ALK) Rearrangements and Mutations: Implications for Gastric Carcinogenesis and Target Therapy

Gastric adenocarcinoma is a complex disease with diverse genetic modifications, including Anaplastic Lymphoma Kinase (ALK) gene changes. The ALK gene is located on chromosome 2p23 and encodes a receptor tyrosine kinase that plays a crucial role in embryonic development and cellular differentiation. ALK alterations can result from gene fusion, mutation, amplification, or overexpression in gastric adenocarcinoma. Fusion occurs when the ALK gene fuses with another gene, resulting in a chimeric protein with constitutive kinase activity and promoting oncogenesis. ALK mutations are less common but can also result in the activation of ALK signaling pathways. Targeted therapies for ALK variations in gastric adenocarcinoma have been developed, including ALK inhibitors that have shown promising results in pre-clinical studies. Future studies are needed to elucidate the ALK role in gastric cancer and to identify predictive biomarkers to improve patient selection for targeted therapy. Overall, ALK alterations are a relevant biomarker for gastric adenocarcinoma treatment and targeted therapies for ALK may improve patients' overall survival.

Prediction of immune subtypes and overall survival in lung squamous cell carcinoma

OBJECTIVE

Lung squamous cell carcinoma (LUSC), one of the most common subtypes of lung cancer, is a leading cause of cancer-caused deaths in the world. It has been well demonstrated that a deep understanding of the tumor environment in cancer would be helpful to predict the prognosis of patients. This study aimed to evaluate the tumor environment in LUSC, and to construct an efficient prognosis model involved in specific subtypes.

METHODS

Four expression files were downloaded from the Gene Expression Omnibus (GEO) database. Three datasets (GSE19188, GSE2088, GSE6044) were considered as the testing group and the other dataset (GSE11969) was used as the validation group. By performing LUSC immune subtype consensus clustering (CC), LUSC patients were separated into two immune subtypes comprising subtype 1 (S1) and subtype 2 (S2). Weighted gene co-expression network (WGCNA) and least absolute shrinkage and selection operator (LASSO) were performed to identify and narrow down the key genes among subtype 1 related genes that were closely related to the overall survival (OS) of LUSC patients. Using immune subtype related genes, a prognostic model was also constructed to predict the OS of LUSC patients.

RESULTS

It showed that LUSC patients in the S1 immune subtype exhibited a better OS than in the S2 immune subtype. WGCNA and LASSO analyses screened out important immune subtype related genes in specific modules that were closely associated with LUSC prognosis, followed by construction of the prognostic model. Both the testing datasets and validation dataset confirmed that the prognostic model could be efficiently used to predict the OS of LUSC patients in subtype 1.

CONCLUSION

We explored the tumor environment in LUSC and established a risk prognostic model that might have the potential to be applied in clinical practice.

Clinically relevant fusion oncogenes: detection and practical implications

Mechanistically, chimeric genes result from DNA rearrangements and include parts of preexisting normal genes combined at the genomic junction site. Some rearranged genes encode pathological proteins with altered molecular functions. Those which can aberrantly promote carcinogenesis are called fusion oncogenes. Their formation is not a rare event in human cancers, and many of them were documented in numerous study reports and in specific databases. They may have various molecular peculiarities like increased stability of an oncogenic part, self-activation of tyrosine kinase receptor moiety, and altered transcriptional regulation activities. Currently, tens of low molecular mass inhibitors are approved in cancers as the drugs targeting receptor tyrosine kinase (RTK) oncogenic fusion proteins, that is, including ALK, ABL, EGFR, FGFR1-3, NTRK1-3, MET, RET, ROS1 moieties. Therein, the presence of the respective RTK fusion in the cancer genome is the diagnostic biomarker for drug prescription. However, identification of such fusion oncogenes is challenging as the breakpoint may arise in multiple sites within the gene, and the exact fusion partner is generally unknown. There is no gold standard method for RTK fusion detection, and many alternative experimental techniques are employed nowadays to solve this issue. Among them, RNA-seq-based methods offer an advantage of unbiased high-throughput analysis of only transcribed RTK fusion genes, and of simultaneous finding both fusion partners in a single RNA-seq read. Here we focus on current knowledge of biology and clinical aspects of RTK fusion genes, related databases, and laboratory detection methods.

Cytogenomic Analysis of Long-Term Epilepsy-Associated Tumors Using an Array-Based CGH Strategy

A palette of copy number changes in long-term epilepsy-associated tumors (LEATs) have been reported, but the data are heterogeneous. To better understand the molecular basis underlying the development of LEATs, we performed array-comparative genomic hybridization analysis to investigate chromosomal imbalances across the entire genome in 8 cases of LEATs. A high number of aberrations were found in 4 patients, among which deletions predominated. Both whole-chromosome and regional abnormalities were observed, including monosomy 19, deletion of 1p, deletions of 4p, 12p, and 22q, and gain of 20p. The common altered regions are located mainly on chromosomes 19 and 4p, identifying genes potentially involved in biological processes and cellular mechanisms related to tumorigenesis. Our study highlights new genomic alterations and reinforces others previously reported, offering new molecular insights that may help in diagnosis and therapeutic decision-making.

Guiding the global evolution of cytogenetic testing for hematologic malignancies

Cytogenetics has long represented a critical component in the clinical evaluation of hematologic malignancies. Chromosome banding studies provide a simultaneous snapshot of genome-wide copy number and structural variation, which have been shown to drive tumorigenesis, define diseases, and guide treatment. Technological innovations in sequencing have ushered in our present-day clinical genomics era. With recent publications highlighting novel sequencing technologies as alternatives to conventional cytogenetic approaches, we, an international consortium of laboratory geneticists, pathologists, and oncologists, describe herein the advantages and limitations of both conventional chromosome banding and novel sequencing technologies and share our considerations on crucial next steps to implement these novel technologies in the global clinical setting for a more accurate cytogenetic evaluation, which may provide improved diagnosis and treatment management. Considering the clinical, logistic, technical, and financial implications, we provide points to consider for the global evolution of cytogenetic testing.

Comprehensive bioinformatics analyses of APOBECs family and identification of APOBEC3D as the unfavorable prognostic biomarker in clear cell renal cell carcinoma

Purpose: At present, how early screening for ccRCC is still a thorny issue for urologists. Probing the mechanisms underlying the development of ccRCC and finding relevant prognostic biomarkers remains crucial. Therefore, we systematically analyzed the APOBEC family in this study and identified APOBEC3D as a prognostic biomarker. Methods: In this study, based on the TCGA database, we systematically assessed the expression and prognosis of the APOBEC family and analyzed potential bioinformatic pathways. We then constructed nomograms to predict the prognosis of ccRCC patients better. Afterward, we further focused on APOBEC3D in our data on ccRCC specimens. The APOBEC3D should be extensively studied in ccRCC in the future. Results: The results showed that the APOBEC family showed the most significant changes in expression in ccRCC. The pathway enrichment analysis showed that APOBEC3 family members mainly regulated cytidine and cytosine-related processes. Subsequently, the Cox regression was used to construct prognostic signature, and validated in ICGC and GEO databases. Next, a nomogram was created integrating clinical parameters showing good predictive performance. Finally, we screened for APOBEC3D and found in our clinical sample that patients with high expression of APOBEC3D had a worse prognosis. Conclusion: Based on these results, APOBEC family members play important roles in the development of ccRCC, and APOBEC3D could serve as the biomarker for predicting patient prognosis.

Cytogenetic and Biochemical Genetic Techniques for Personalized Drug Therapy in Europe

For many authorized drugs, accumulating scientific evidence supports testing for predictive biomarkers to apply personalized therapy and support preventive measures regarding adverse drug reactions and treatment failure. Here, we review cytogenetic and biochemical genetic testing methods that are available to guide therapy with drugs centrally approved in the European Union (EU). We identified several methods and combinations of techniques registered in the Genetic Testing Registry (GTR), which can be used to guide therapy with drugs for which pharmacogenomic-related information is provided in the European public assessment reports. Although this registry provides information on genetic tests offered worldwide, we identified limitations regarding standard techniques applied in clinical practice and the information on test validity rarely provided in the according sections.

Clonal Architectures Predict Clinical Outcome in Gastric Adenocarcinoma Based on Genomic Variation, Tumor Evolution, and Heterogeneity

Stomach adenocarcinoma (STAD) is a highly heterogeneous disease. Due to the lack of effective molecular markers and personalized treatment, the prognosis of gastric cancer patients is still very poor. The ABSOLUTE algorithm and cancer cell fraction were used to evaluate the clonal and subclonal status of 349 TCGA (The Cancer Genome Cancer Atlas)-STAD patients. Non-negative matrix factorization was used to identify the mutation characteristics of the samples. Univariate Cox regression analysis was used to determine the relationship between clonal/subclonal events and prognosis, and the Spearman correlation was used to evaluate the relationship of clonal/subclonal events to tumor mutation burden (TMB) and neoantigens. The evolution pattern of STAD demonstrated great tumor heterogeneity. TP53, USH2A, and GLI3 appeared earliest in STAD and may drive STAD. CTNNB1, LRP1B, and ERBB4 appeared the latest in STAD, and may be related to STAD’s progress. Univariate Cox regression analysis identified four early genes, eight intermediate genes, and seven late genes significantly associated with overall survival. The number of subclonal events in the T stage was significantly different. The N stage, gender, and histological type were significantly different for clonal events, and there was a significant correlation between clonal/subclonal events and TMB/neoantigens. Our results highlight the importance of systematic evaluation of evolutionary models in the clinical management of STAD and personalized gastric cancer treatment.

A genome-wide study of the relationship between chromosomal abnormalities and gene expression in colorectal tumors

The role of somatic copy number alterations (SCNAs) that occur in colorectal tumors is poorly understood. SCNAs are correlated with corresponding gene expression changes that may contribute to neoplastic progression. Thus, we examined SCNAs and the expression of messenger RNAs (mRNAs) located at corresponding loci in colorectal neoplasia, a progression model of human neoplasm. We used 42 colorectal neoplastic samples, including adenomas, intramucosal cancers (IMC) and invasive colorectal cancers (CRC) that were microsatellite stable (MSS) using a genome-wide SNP array and gene expression array (first cohort). In addition, validation analyses were examined (37 colorectal neoplasias). None of the mRNAs with a corresponding SCNA was found in the adenomas. However, three mRNAs, including ARFGEF2 at 20q13.13, N4BP2L2 at 13q13.1 and OLFM4 at 13q14.3 with a copy number (CN) gain at the corresponding locus were upregulated in IMCs of the first cohort. Moreover, upregulated expression of ARFGEF2 and OLFM4 was upregulated in the validation analysis. Finally, 28 mRNAs with gains of corresponding loci were pooled in invasive CRC of the first cohort. The mRNAs, including ACSS2 (20q11.22), DDX27 (20q13.13), MAPRE1 (20q11.21), OSBPL2 (20q11.22) and PHF20 (20q11.22-q11.23) with CN gains of the corresponding loci were identified in 28 mRNAs. Four of these mRNAs (DDX27, MAPRE1, OSBPL2 and PHF20) were upregulated in the invasive CRC in the validation analysis. We conclude that specific 13q and 22q CN gains with gene expression changes in the corresponding loci may play an important role in IMC cells' progression into invasive CRC.

Development and clinical validation of a novel 9-gene prognostic model based on multi-omics in pancreatic adenocarcinoma

The prognoses of patients with pancreatic adenocarcinoma (PAAD) remain poor due to the lack of biomarkers for early diagnosis and effective prognosis prediction. RNA sequencing, single nucleotide polymorphism, and copy number variation data were downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression was used to identify prognosis-related genes. GISTIC 2.0 was used to identify significantly amplified or deleted genes, and Mutsig 2.0 was used to analyze the mutation data. The Lasso method was used to construct a risk prediction model. The Rms package was used to evaluate the overall predictive performance of the signature. Finally, Western blot and polymerase chain reaction were performed to evaluate gene expression. A total of 54 candidate genes were obtained after integrating the genomic mutated genes and prognosis-related genes. The Lasso method was used to ascertain 9 characteristic genes, including UNC13B, TSPYL4, MICAL1, KLHDC7B, KLHL32, AIM1, ARHGAP18, DCBLD1, and CACNA2D4. The 9-gene signature model was able to help stratify samples at risk in the training and external validation cohorts. In addition, the overall predictive performance of our model was found to be superior to that of other models. KLHDC7B, AIM1, DCBLD1, TSPYL4, and MICAL1 were significantly highly expressed in tumor tissues compared to normal tissues. ARHGAP18 and CACNA2D4 had no difference in expression between tumor and normal tissues. UNC13B and KLHL32 expression in the normal group was higher than in the tumor group. The 9-gene signature constructed in this study can be used as a novel prognostic marker to predict the survival of patients with pancreatic adenocarcinoma.