Next Generation Sequencing (NGS): A Revolutionary Technology in Pharmacogenomics and Personalized Medicine in Cancer

Genetic variant classification by predicted protein structure: A case study on IRF6

Next-generation genome sequencing has revolutionized genetic testing, identifying numerous rare disease-associated gene variants. However, to impute pathogenicity, computational approaches remain inadequate and functional testing of gene variant is required to provide the highest level of evidence. The emergence of AlphaFold2 has transformed the field of protein structure determination, and here we outline a strategy that leverages predicted protein structure to enhance genetic variant classification. We used the gene IRF6 as a case study due to its clinical relevance, its critical role in cleft lip/palate malformation, and the availability of experimental data on the pathogenicity of IRF6 gene variants through phenotype rescue experiments in irf6-/- zebrafish. We compared results from over 30 pathogenicity prediction tools on 37 IRF6 missense variants. IRF6 lacks an experimentally derived structure, so we used predicted structures to explore associations between mutational clustering and pathogenicity. We found that among these variants, 19 of 37 were unanimously predicted as deleterious by computational tools. Comparing in silico predictions with experimental findings, 12 variants predicted as pathogenic were experimentally determined as benign. Even with the recently published AlphaMissense model, 15/18 (83%) of the predicted pathogenic variants were experimentally determined as benign. In comparison, mapping variants to the protein revealed deleterious mutation clusters around the protein binding domain, whereas N-terminal variants tend to be benign, suggesting the importance of structural information in determining pathogenicity of mutations in this gene. In conclusion, incorporating gene-specific structural features of known pathogenic/benign mutations may provide meaningful insights into pathogenicity predictions in a gene-specific manner and facilitate the interpretation of variant pathogenicity.

Optimizing the Use of Next-Generation Sequencing Assays in Patients With Urothelial Carcinoma: Recommendations by the 2023 San Raffaele Retreat Panel

BACKGROUND

The application of precision medicine in clinical practice implies a thorough evaluation of actionable genomic alterations to streamline therapeutic decision making. Comprehensive genomic profiling of tumor via next-generation sequencing (NGS) represents a great opportunity but also several challenges. During the 2023 San Raffaele Retreat, we aimed to provide expert recommendations for the optimal use of NGS in urothelial carcinoma (UC).

MATERIALS AND METHODS

A modified Delphi method was utilized, involving a panel of 12 experts in UC from European and United States centers, including oncologists, urologists, pathologists, and translational scientists. An initial survey, conducted before the meeting, delivered 15 statements to the panel. A consensus was defined when ≥70% agreement was reached for each statement. Statements not meeting the consensus threshold were discussed during the meeting.

RESULTS

Nine of the 15 statements covering patient selection, cancer characteristics, and type of NGS assay, achieved a consensus during the survey. The remaining six statements addressing the optimal timing of NGS use, the ideal source of tumor biospecimen for NGS testing, and the subsequent need to evaluate the germline nature of certain genomic findings were discussed during the meeting, leading to unanimous agreement at the end of the conference.

CONCLUSION

This consensus-building effort addressed multiple unanswered questions regarding the use of NGS in UC. The opinion of experts was in favor of broader use of NGS. In a setting where recommendations/guidelines may be limited, these insights may aid clinicians to provide informed counselling and raise the bar of precision and personalized therapy.

Identification of key differentially expressed immune related genes in patients with persistent atrial fibrillation: an integrated bioinformation analysis

OBJECTIVE

We aimed to investigate key differentially expressed immune related genes in persistent atrial fibrillation.

METHODS

Gene expression profiles were downloaded from Gene Expression Omnibus (GEO) using "GEO query" package. "limma" package and "sva" package were used to conduct normalization and eliminate batch effects, respectively. We screened out differentially expressed genes (DEGs) based on "limma" package with the standard of |log fold change (FC)| ≥ 1.5 and false discovery rate (FDR) < 0.05. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were performed by "clusterProfler" package. We further applied LASSO to select key DEGs, and intersected key DEGs with immune related genes from ImmPort database. The ROC curve of each DEIRG was constructed to evaluate its diagnostic efficiency for AF.

RESULTS

A total of 103 DEGs we were screened out, of them, 48 genes were down-regulated and 55 genes were up-regulated. Result of functional enrichment analysis show that, most of DEGs were related to immune response, inflammation, and oxidative stress. Ultimately, CYBB, RORB, S100A12, and CHGB were determined as key DEIRGs, each of which displayed a favor efficiency for diagnosing persistent AF.

CONCLUSION

CYBB, RORB, S100A12, and CHGB were identified as key DEIRGs in persistent AF, and future studies are needed to further explore the underlying roles of CYBB, RORB, S100A12, and CHGB in persistent AF.

Exploring the Role of Clustered Mutations in Carcinogenesis and Their Potential Clinical Implications in Cancer

Abnormal cell proliferation and growth leading to cancer primarily result from cumulative genome mutations. Single gene mutations alone do not fully explain cancer onset and progression; instead, clustered mutations-simultaneous occurrences of multiple mutations-are considered to be pivotal in cancer development and advancement. These mutations can affect different genes and pathways, resulting in cells undergoing malignant transformation with multiple functional abnormalities. Clustered mutations influence cancer growth rates, metastatic potential, and drug treatment sensitivity. This summary highlights the various types and characteristics of clustered mutations to understand their associations with carcinogenesis and discusses their potential clinical significance in cancer. As a unique mutation type, clustered mutations may involve genomic instability, DNA repair mechanism defects, and environmental exposures, potentially correlating with responsiveness to immunotherapy. Understanding the characteristics and underlying processes of clustered mutations enhances our comprehension of carcinogenesis and cancer progression, providing new diagnostic and therapeutic approaches for cancer.

It Is in Our DNA: Bringing Electronic Health Records and Genomic Data Together for Precision Medicine

Health care is at a turning point. We are shifting from protocolized medicine to precision medicine, and digital health systems are facilitating this shift. By providing clinicians with detailed information for each patient and analytic support for decision-making at the point of care, digital health technologies are enabling a new era of precision medicine. Genomic data also provide clinicians with information that can improve the accuracy and timeliness of diagnosis, optimize prescribing, and target risk reduction strategies, all of which are key elements for precision medicine. However, genomic data are predominantly seen as diagnostic information and are not routinely integrated into the clinical workflows of electronic medical records. The use of genomic data holds significant potential for precision medicine; however, as genomic data are fundamentally different from the information collected during routine practice, special considerations are needed to use this information in a digital health setting. This paper outlines the potential of genomic data integration with electronic records, and how these data can enable precision medicine.

Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer

Gastric cancer (GC) is one of the most common cancers worldwide. Most patients are diagnosed at the progressive stage of the disease, and current anticancer drug advancements are still lacking. Therefore, it is crucial to find relevant biomarkers with the accurate prediction of prognoses and good predictive accuracy to select appropriate patients with GC. Recent advances in molecular profiling technologies, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have enabled the approach of GC biology at multiple levels of omics interaction networks. Systemic biological analyses, such as computational inference of "big data" and advanced bioinformatic approaches, are emerging to identify the key molecular biomarkers of GC, which would benefit targeted therapies. This review summarizes the current status of how bioinformatics analysis contributes to biomarker discovery for prognosis and prediction of therapeutic efficacy in GC based on a search of the medical literature. We highlight emerging individual multi-omics datasets, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics, for validating putative markers. Finally, we discuss the current challenges and future perspectives to integrate multi-omics analysis for improving biomarker implementation. The practical integration of bioinformatics analysis and multi-omics datasets under complementary computational analysis is having a great impact on the search for predictive and prognostic biomarkers and may lead to an important revolution in treatment.

A first-in-class inhibitor of HSP110 to potentiate XPO1-targeted therapy in primary mediastinal B-cell lymphoma and classical Hodgkin lymphoma

BACKGROUND

Primary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) are distinct hematological malignancies of B-cell origin that share many biological, molecular, and clinical characteristics. In particular, the JAK/STAT signaling pathway is a driver of tumor development due to multiple recurrent mutations, particularly in STAT6. Furthermore, the XPO1 gene that encodes exportin 1 (XPO1) shows a frequent point mutation (E571K) resulting in an altered export of hundreds of cargo proteins, which may impact the success of future therapies in PMBL and cHL. Therefore, targeted therapies have been envisioned for these signaling pathways and mutations.

METHODS

To identify novel molecular targets that could overcome the treatment resistance that occurs in PMBL and cHL patients, we have explored the efficacy of a first-in-class HSP110 inhibitor (iHSP110-33) alone and in combination with selinexor, a XPO1 specific inhibitor, both in vitro and in vivo.

RESULTS

We show that iHSP110-33 decreased the survival of several PMBL and cHL cell lines and the size of tumor xenografts. We demonstrate that HSP110 is a cargo of XPO1wt as well as of XPO1E571K. Using immunoprecipitation, proximity ligation, thermophoresis and kinase assays, we showed that HSP110 directly interacts with STAT6 and favors its phosphorylation. The combination of iHSP110-33 and selinexor induces a synergistic reduction of STAT6 phosphorylation and of lymphoma cell growth in vitro and in vivo. In biopsies from PMBL patients, we show a correlation between HSP110 and STAT6 phosphorylation levels.

CONCLUSIONS

These findings suggest that HSP110 could be proposed as a novel target in PMBL and cHL therapy.

Drug repurposing for personalized medicine

Personalized medicine has emerged as a revolutionary approach to healthcare in the 21st century. By understanding a patient's unique genetic and biological characteristics, it aims to tailor treatments specifically to the individual. This approach takes into account factors such as an individual's lifestyle, genetic makeup, and environmental factors to provide targeted therapies that have the potential to be more effective and lower the risk of side reactions or ineffective treatments. It is a paradigm shift from the traditional "one size fits all" approach in medicine, where patients with similar symptoms or diagnoses receive the same standard treatments regardless of their differences. It leads to improved clinical outcomes and more efficient use of healthcare resources. Drug repurposing is a strategy that uses existing drugs for new indications and aims to take advantage of the known safety profiles, pharmacokinetics, and mechanisms of action of these drugs to accelerate the development process. Precision medicine may undergo a revolutionary change as a result, enabling the rapid development of novel treatment plans utilizing drugs that traditional methods would not otherwise link to. In this chapter, we have focused on a few strategies wherein drug repurposing has shown great success for precision medicine. The approach is particularly useful in oncology as there are many variations induced in the genetic material of cancer patients, so tailored treatment approaches go a long way. We have discussed the cases of breast cancer, glioblastoma and hepatocellular carcinoma. Other than that, we have also looked at drug repurposing approaches in anxiety disorders and COVID-19.

Investigation toward the economic feasibility of personalized medicine for healthcare service providers: the case of bladder cancer

In today's complex healthcare landscape, the pursuit of delivering optimal patient care while navigating intricate economic dynamics poses a significant challenge for healthcare service providers (HSPs). In this already complex dynamic, the emergence of clinically promising personalized medicine-based treatment aims to revolutionize medicine. While personalized medicine holds tremendous potential for enhancing therapeutic outcomes, its integration within resource-constrained HSPs presents formidable challenges. In this study, we investigate the economic feasibility of implementing personalized medicine. The central objective is to strike a balance between catering to individual patient needs and making economically viable decisions. Unlike conventional binary approaches to personalized treatment, we propose a more nuanced perspective by treating personalization as a spectrum. This approach allows for greater flexibility in decision-making and resource allocation. To this end, we propose a mathematical framework to investigate our proposal, focusing on Bladder Cancer (BC) as a case study. Our results show that while it is feasible to introduce personalized medicine, a highly efficient but highly expensive one would be short-lived relative to its less effective but cheaper alternative as the latter can be provided to a larger cohort of patients, optimizing the HSP's objective better.

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.

Efficacy of Pembrolizumab vs. Nivolumab Plus Ipilimumab in Metastatic NSCLC in Relation to PD-L1 and TMB Status

The efficacy of immune checkpoint inhibitor (ICI) therapy concerning programmed death ligand 1 (PD-L1) status is well established in patients diagnosed with non-small cell lung cancer (NSCLC). However, there remains a paucity of evidence regarding the efficacy concerning tumor mutational burden (TMB) in both clinical trials and real-world data (RWD). In the current article, clinicopathological and molecular epidemiological data were meticulously collected, and treatment modalities were meticulously recorded. The final analysis included a study population of 194 patients. Median age was 67 years (range 37-86), with the majority being male (71.13%), and 85.71% of patients were either current or former smokers at diagnosis. Adenocarcinoma accounted for most diagnoses (71.65%), followed by squamous cell carcinoma (24.23%). In terms of PD-L1 status, 42.78% had an expression level below 1%, 28.35% had an expression between 1-49%, and 28.87% had an expression above 50%. The TMB ranged from 0 to 75, with a median of 10.31 (range 0-75) for PD-L1 expression below 1%, with a median of 9.73 (range 0.95-39.63) for PD-L1 expression between 1-49%, and a median of 9.72 (range 0.95-48) for PD-L1 expression above 50%. Corresponding to patients with low PDL-1 less than 1% and low TMB (0-5), the median overall survival (mOS) was 16 (p = 0.18), and 15 months (p = 0.22), patients with medium PDL-1 (1-49%) and medium TMB (5-10), the mOS was 15 (p = 0.18) and 16 months (p = 0.22), patients with high PDL-1 (>50) and high TMB (>10), the mOS was 24 (p = 0.18) and 21 (p = 0.22) months. This study represents the largest academic RWD dataset concerning PD-L1 and TMB status in patients with locally advanced and metastatic NSCLC.

A perspective on a precision approach to pain in cancer; moving beyond opioid therapy

PURPOSE

Cancer-related pain is primarily treated with opioids which while effective can add significant patient burden due to side effects, associated stigma, and timely access. The purpose of this perspective discussion is to argue for a precision approach to pain in cancer based on a biopsychosocial and spiritual model which we argue can offer a higher quality of life while limiting opioid use.

CONCLUSIONS

Pain in cancer represents a heterogenous process with multiple contributing and modulating factors. Specific characterization of pain as either nociceptive, neuropathic, nociplastic, or mixed can allow for targeted treatments. Additional assessment of biopsychosocial and spiritual issues can elucidate further points of targeted intervention which can lead to overall greater pain control.

De Novo Generation of Chemical Structures of Inhibitor and Activator Candidates for Therapeutic Target Proteins by a Transformer-Based Variational Autoencoder and Bayesian Optimization

Deep generative models for molecular generation have been gaining much attention as structure generators to accelerate drug discovery. However, most previously developed methods are chemistry-centric approaches, and comprehensive biological responses in the cell have not been taken into account. In this study, we propose a novel computational method, TRIOMPHE-BOA (transcriptome-based inference and generation of molecules with desired phenotypes using the Bayesian optimization algorithm), to generate new chemical structures of inhibitor or activator candidates for therapeutic target proteins by integrating chemically and genetically perturbed transcriptome profiles. In the algorithm, the substructures of multiple molecules that were selected based on the transcriptome analysis are fused in the design of new chemical structures by exploring the latent space of a Transformer-based variational autoencoder using Bayesian optimization. Our results demonstrate the usefulness of the proposed method in terms of having high reproducibility of existing ligands for 10 therapeutic target proteins when compared with previous methods. Moreover, this method can be applied to proteins without detailed 3D structures or known ligands and is expected to become a powerful tool for more efficient hit identification.

Neoadjuvant BRAF-targeted therapy for ameloblastoma of the mandible: an organ preservation approach

BACKGROUND

Ameloblastoma is a rare odontogenic neoplasm frequently located in the mandible. Standard treatment involves radical bone resection and immediate reconstruction, causing functional, aesthetic, and psychological impairments. The BRAF V600E mutation is present in approximately 80% of mandible ameloblastomas, and BRAF inhibitors have demonstrated sustained responses in unresectable cases.

METHODS

We identified ameloblastoma patients planned for ablative surgery and screened them for BRAF V600E mutation. Neoadjuvant BRAF inhibitors were offered to facilitate jaw preservation surgery. Retrospective data collection encompassed treatment regimens, tolerability, tumor response, and conversion to mandible preservation surgery.

RESULTS

Between 2017 and 2022, a total of 11 patients received dabrafenib (n = 6) or dabrafenib with trametinib (n = 5). The median age was 19 (range = 10-83) years. Median treatment duration was 10 (range = 3-20) months. All (100%) patients achieved a radiological response. Ten (91%) patients successfully converted to mandible preservation surgery with residual tumor enucleation. One patient attained complete radiological response, and surgery was not performed. Among the 10 surgically treated patients, all exhibited a pathological response, with 4 achieving near complete response and 6 partial response. At a median follow-up of 14 (range = 7-37) months after surgery, 1 case of recurrence was observed. Grade 1-2 adverse effects were reported in 8 (73%) patients, with a single case of grade 3 (hepatitis). Dose modification was necessary for 3 patients, and 4 experienced treatment interruptions, while 1 patient permanently discontinued therapy.

CONCLUSIONS

Neoadjuvant BRAF inhibition may offer a safe and effective strategy for organ preservation in mandible ameloblastoma treatment.

Current treatment and novel insights regarding ROS1-targeted therapy in malignant tumors

BACKGROUND

The proto-oncogene ROS1 encodes an intrinsic type I membrane protein of the tyrosine kinase/insulin receptor family. ROS1 facilitates the progression of various malignancies via self-mutations or rearrangements. Studies on ROS1-directed tyrosine kinase inhibitors have been conducted, and some have been approved by the FDA for clinical use. However, the adverse effects and mechanisms of resistance associated with ROS1 inhibitors remain unknown. In addition, next-generation ROS1 inhibitors, which have the advantage of treating central nervous system metastases and alleviating endogenous drug resistance, are still in the clinical trial stage.

METHOD

In this study, we searched relevant articles reporting the mechanism and clinical application of ROS1 in recent years; systematically reviewed the biological mechanisms, diagnostic methods, and research progress on ROS1 inhibitors; and provided perspectives for the future of ROS1-targeted therapy.

RESULTS

ROS1 is most expressed in malignant tumours. Only a few ROS1 kinase inhibitors are currently approved for use in NSCLC, the efficacy of other TKIs for NSCLC and other malignancies has not been ascertained. There is no effective standard treatment for adverse events or resistance to ROS1-targeted therapy. Next-generation TKIs appear capable of overcoming resistance and delaying central nervous system metastasis, but with a greater incidence of adverse effects.

CONCLUSIONS

Further research on next-generation TKIs regarding the localization of ROS1 and its fusion partners, binding sites for targeted drugs, and coadministration with other drugs is required. The correlation between TKIs and chemotherapy or immunotherapy in clinical practice requires further study.

The rise of big data: deep sequencing-driven computational methods are transforming the landscape of synthetic antibody design

Synthetic antibodies (Abs) represent a category of artificial proteins capable of closely emulating the functions of natural Abs. Their in vitro production eliminates the need for an immunological response, streamlining the process of Ab discovery, engineering, and development. These artificially engineered Abs offer novel approaches to antigen recognition, paratope site manipulation, and biochemical/biophysical enhancements. As a result, synthetic Abs are fundamentally reshaping conventional methods of Ab production. This mirrors the revolution observed in molecular biology and genomics as a result of deep sequencing, which allows for the swift and cost-effective sequencing of DNA and RNA molecules at scale. Within this framework, deep sequencing has enabled the exploration of whole genomes and transcriptomes, including particular gene segments of interest. Notably, the fusion of synthetic Ab discovery with advanced deep sequencing technologies is redefining the current approaches to Ab design and development. Such combination offers opportunity to exhaustively explore Ab repertoires, fast-tracking the Ab discovery process, and enhancing synthetic Ab engineering. Moreover, advanced computational algorithms have the capacity to effectively mine big data, helping to identify Ab sequence patterns/features hidden within deep sequencing Ab datasets. In this context, these methods can be utilized to predict novel sequence features thereby enabling the successful generation of de novo Ab molecules. Hence, the merging of synthetic Ab design, deep sequencing technologies, and advanced computational models heralds a new chapter in Ab discovery, broadening our comprehension of immunology and streamlining the advancement of biological therapeutics.

Pediatric CNS tumors and 2021 WHO classification: what do oncologists need from pathologists?

The fifth edition of the WHO Classification of Tumors of the Central Nervous System (CNS), published in 2021, established new approaches to both CNS tumor nomenclature and grading, emphasizing the importance of integrated diagnoses and layered reports. This edition increased the role of molecular diagnostics in CNS tumor classification while still relying on other established approaches such as histology and immunohistochemistry. Moreover, it introduced new tumor types and subtypes based on novel diagnostic technologies such as DNA methylome profiling. Over the past decade, molecular techniques identified numerous key genetic alterations in CSN tumors, with important implications regarding the understanding of pathogenesis but also for prognosis and the development and application of effective molecularly targeted therapies. This review summarizes the major changes in the 2021 fifth edition classification of pediatric CNS tumors, highlighting for each entity the molecular alterations and other information that are relevant for diagnostic, prognostic, or therapeutic purposes and that patients' and oncologists' need from a pathology report.

N-of-1 medicine

ABSTRACT

The fields of precision and personalised medicine have led to promising advances in tailoring treatment to individual patients. Examples include genome/molecular alteration-guided drug selection, single-patient gene therapy design and synergy-based drug combination development, and these approaches can yield substantially diverse recommendations. Therefore, it is important to define each domain and delineate their commonalities and differences in an effort to develop novel clinical trial designs, streamline workflow development, rethink regulatory considerations, create value in healthcare and economics assessments, and other factors. These and other segments are essential to recognise the diversity within these domains to accelerate their respective workflows towards practice-changing healthcare. To emphasise these points, this article elaborates on the concept of digital health and digital medicine-enabled N-of-1 medicine, which individualises combination regimen and dosing using a patient's own data. We will conclude with recommendations for consideration when developing novel workflows based on emerging digital-based platforms.

Liquid biopsy in T-cell lymphoma: biomarker detection techniques and clinical application

T-cell lymphoma is a highly invasive tumor with significant heterogeneity. Invasive tissue biopsy is the gold standard for acquiring molecular data and categorizing lymphoma patients into genetic subtypes. However, surgical intervention is unfeasible for patients who are critically ill, have unresectable tumors, or demonstrate low compliance, making tissue biopsies inaccessible to these patients. A critical need for a minimally invasive approach in T-cell lymphoma is evident, particularly in the areas of early diagnosis, prognostic monitoring, treatment response, and drug resistance. Therefore, the clinical application of liquid biopsy techniques has gained significant attention in T-cell lymphoma. Moreover, liquid biopsy requires fewer samples, exhibits good reproducibility, and enables real-time monitoring at molecular levels, thereby facilitating personalized health care. In this review, we provide a comprehensive overview of the current liquid biopsy biomarkers used for T-cell lymphoma, focusing on circulating cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), Epstein-Barr virus (EBV) DNA, antibodies, and cytokines. Additionally, we discuss their clinical application, detection methodologies, ongoing clinical trials, and the challenges faced in the field of liquid biopsy.

Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances

Synthetic antibodies (Abs) represent a category of engineered proteins meticulously crafted to replicate the functions of their natural counterparts. Such Abs are generated in vitro, enabling advanced molecular alterations associated with antigen recognition, paratope site engineering, and biochemical refinements. In a parallel realm, deep sequencing has brought about a paradigm shift in molecular biology. It facilitates the prompt and cost-effective high-throughput sequencing of DNA and RNA molecules, enabling the comprehensive big data analysis of Ab transcriptomes, including specific regions of interest. Significantly, the integration of artificial intelligence (AI), based on machine- and deep- learning approaches, has fundamentally transformed our capacity to discern patterns hidden within deep sequencing big data, including distinctive Ab features and protein folding free energy landscapes. Ultimately, current AI advances can generate approximations of the most stable Ab structural configurations, enabling the prediction of de novo synthetic Abs. As a result, this manuscript comprehensively examines the latest and relevant literature concerning the intersection of deep sequencing big data and AI methodologies for the design and development of synthetic Abs. Together, these advancements have accelerated the exploration of antibody repertoires, contributing to the refinement of synthetic Ab engineering and optimizations, and facilitating advancements in the lead identification process.

TIROSEC: Molecular, Clinical and Histopathological Profile of Papillary Thyroid Carcinoma in a Colombian Cohort

INTRODUCTION

In Colombia, thyroid cancer ranks among the highest incidences, yet our population lacks studies on its molecular profile. This study aims to characterize clinical, histopathologic and molecular data in a Colombian cohort with papillary thyroid carcinoma (PTC).

METHODS

A retrospective review of clinical history, clinicopathologic characteristics, treatment and 5-10-year follow-up for all patients was done. DNA and RNA were extracted from formalin-fixed paraffin-embedded (FFPE) tissue using the Quick-DNA & RNA FFPE Min iPrep kit (Zymo Research). Next-generation sequencing (NGS) analysis was performed with SOPHiA Solid Tumor Solutions kit (SOPHiA GENETICS). Tumor mutation genomic analysis used SOPHiA DDM™ platform, with descriptive analysis reporting frequencies, means and associations via chi-square analysis.

RESULTS

Among 231 sequenced patients, mean age at diagnosis was 46 (± 12.35) years, with higher frequency in women (81.82%). Two cases were reclassified as non-invasive follicular thyroid neoplasm (NIFT-P); an NRAS mutation was found in one of them. Predominant histologic subtype was classic PTC (57.64%) followed by tall cell (28.82%). Of the 229 sequenced carcinomas, mutations were identified in 186 cases, including BRAF, IDH1, RAS and PIK3CA. Notable copy number variations (CNVs) were PDGFRA, CDK4 and KIT, with RET being the most frequent gene fusion, including CCDC6-RET in two classic subtype cases.

CONCLUSION

This is the first study in Colombia (TIROSEC) to our knowledge that integrates molecular and histopathologic profiles enriching our local comprehension and knowledge of PTC. The identification of target mutations such as BRAF, RET and NTRK fusions holds the potential to guide targeted therapies for tumor recurrence and predict aggressive behavior.

Real-World Experiences of Next-Generation Sequencing in Oncology: From an Indian Multicenter Registry and Collaborative Centers

Background The integration of next-generation sequencing (NGS) in guiding personalized therapy for oncology faces the challenges, primarily, of cost and drug accessibility. Limited data from Indian academic centers accentuate the need for comprehensive insights into the real-world applications of NGS in oncology.

Methods The Network of Oncology Clinical Trials in India (NOCI), accessible at www.noci-india.com, compiled data on patients who underwent NGS for solid organ cancers from January 1, 2018, to December 31, 2021. This study aimed to elucidate the testing indications, sample types analyzed, and the resultant impact on patient care.

Results Analysis of data from six centers included 278 subjects, with 24 specimens (9%) excluded due to quality test failure. Tissue constituted 59.7% of specimens, blood 38.5%, and both 1.8%. Predominantly, NGS was employed for identifying BRCA1/2 mutations (56%) and for targeted therapy in non-small-cell lung cancer (NSCLC; 28%). Only 41 (16%) patients with other cancers underwent multigene NGS panels in pursuit of targetable mutations. Among them, 13 exhibited targetable mutations, and 3 received treatment based on NGS findings.

Conclusion This study underscores that the majority of NGS applications focused on screening for BRCA1/2 mutations and identifying targetable mutations in NSCLC. However, among those undergoing NGS for advanced cancers, only a limited number received personalized therapy. The findings underscore the challenges of utilizing NGS in off-label indications within resource-constrained settings.

Assessment of Genetic Stability in Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes by Using Droplet Digital PCR

Unintended genetic modifications that occur during the differentiation and proliferation of human induced pluripotent stem cells (hiPSCs) can lead to tumorigenicity. This is a crucial concern in the development of stem cell-based therapies to ensure the safety and efficacy of the final product. Moreover, conventional genetic stability testing methods are limited by low sensitivity, which is an issue that remains unsolved. In this study, we assessed the genetic stability of hiPSCs and hiPSC-derived cardiomyocytes using various testing methods, including karyotyping, CytoScanHD chip analysis, whole-exome sequencing, and targeted sequencing. Two specific genetic mutations in KMT2C and BCOR were selected from the 17 gene variants identified by whole-exome and targeted sequencing methods, which were validated using droplet digital PCR. The applicability of this approach to stem cell-based therapeutic products was further demonstrated with associated validation according to the International Council for Harmonisation (ICH) guidelines, including specificity, precision, robustness, and limit of detection. Our droplet digital PCR results showed high sensitivity and accuracy for quantitatively detecting gene mutations, whereas conventional qPCR could not avoid false positives. In conclusion, droplet digital PCR is a highly sensitive and precise method for assessing the expression of mutations with tumorigenic potential for the development of stem cell-based therapeutics.

Possible prognostic impact of PKCι genetic variants in prostate cancer

BACKGROUND

Single nucleotide polymorphisms (SNPs) have been linked with prostate cancer (PCa) and have shown potential as prognostic markers for advanced stages. Loss of function mutations in PKCι have been linked with increased risk of malignancy by enhancing tumor cell motility and invasion. We have evaluated the impact of two coding region SNPs on the PKCι gene (PRKCI) and their prognostic potential.

METHODS

Genotypic association of non-synonymous PKCι SNPs rs1197750201 and rs1199520604 with PCa was determined through tetra-ARMS PCR. PKCι was docked with interacting partner Par-6 to determine the effect of these variants on PKCι binding capabilities. Molecular dynamic simulations of PKCι docked with Par-6 were performed to determine variant effects on PKCι protein interactions. The possible impact of changes in PKCι protein interactions on epithelial cell polarity was hypothesized.

RESULTS

PKCι rs1199520604 mutant genotype TT showed association with PCa (p = 0.0055), while rs1197750201 mutant genotype AA also showed significant association with PCa (P = 0.0006). The binding interaction of PKCι with Par-6 was altered for both variants, with changes in Van der Waals energy and electrostatic energy of docked structures.

CONCLUSION

Genotypic analysis of two non-synonymous PKCι variants in association with PCa prognosis was performed. Both variants in the PB1 domain showed potential as a prognostic marker for PCa. In silico analysis of the effect of the variants on PKCι protein interactions indicated they may be involved in PCa progression through aberration of epithelial cell polarity pathways.

Single-Cell Transcriptome Analysis of Acute Myeloid Leukemia Cells Using Methanol Fixation and Cryopreservation

INTRODUCTION

The application of single-cell RNA sequencing has greatly improved our understanding of various cellular and molecular mechanisms involved in physiological and pathophysiological processes. However, obtaining living cells for this technique can be difficult under certain conditions. To solve this problem, the methanol fixation method appeared as a promising alternative for routine clinical use.

MATERIALS AND METHODS

In this study, we selected two AML samples that had been fixed in methanol for 12-18 months. Once the cells were rehydrated, these samples were subjected to single-cell RNA sequencing. We then compared the results obtained from these samples with those obtained from the same samples cryopreserved in DMSO.

RESULTS

We used a previously validated methanol fixation protocol to perform scRNA-seq on DMSO cryopreserved cells and cells fixed in methanol for more than one year. Preliminary results show that methanol fixation induces some genetic and transcriptional modification compared with DMSO cryopreservation but remains a valuable method for single-cell analysis of primary human leukemia cells.

CONCLUSIONS

The initial findings from this study highlight certain resemblances in methanol fixation over a 12-month period and cryopreservation with DMSO, along with associated transcriptional level modifications. However, we observed genetic degradation in the fixation condition when extending beyond one year. Despite certain study limitations, it is evident that short-term methanol fixation can be effectively used for leukemia blast samples. Its ease of implementation holds the potential to simplify the integration of this technique into routine clinical practice.

Exploring Thiazolopyridine AV25R: Unraveling of Biological Activities, Selective Anti-Cancer Properties and In Silico Target and Binding Prediction in Hematological Neoplasms

Thiazolopyridines are a highly relevant class of small molecules, which have previously shown a wide range of biological activities. Besides their anti-tubercular, anti-microbial and anti-viral activities, they also show anti-cancerogenic properties, and play a role as inhibitors of cancer-related proteins. Herein, the biological effects of the thiazolopyridine AV25R, a novel small molecule with unknown biological effects, were characterized. Screening of a set of lymphoma (SUP-T1, SU-DHL-4) and B- acute leukemia cell lines (RS4;11, SEM) revealed highly selective effects of AV25R. The selective anti-proliferative and metabolism-modulating effects were observed in vitro for the B-ALL cell line RS4;11. Further, we were able to detect severe morphological changes and the induction of apoptosis. Gene expression analysis identified a large number of differentially expressed genes after AV25R exposure and significant differentially regulated cancer-related signaling pathways, such as VEGFA-VEGFR2 signaling and the EGF/EGFR pathway. Structure-based pharmacophore screening approaches using in silico modeling identified potential biological AV25R targets. Our results indicate that AV25R binds with several proteins known to regulate cell proliferation and tumor progression, such as FECH, MAP11, EGFR, TGFBR1 and MDM2. The molecular docking analyses indicates that AV25R has a higher binding affinity compared to many of the experimentally validated small molecule inhibitors of these targets. Thus, here we present in vitro and in silico analyses which characterize, for the first time, the molecular acting mechanism of AV25R, including cellular and molecular biologic effects. Additionally, this predicted the target binding of the molecule, revealing a high affinity to cancer-related proteins and, thus, classified AVR25 for targeted intervention approaches.

Integration of liquid biopsy and immunotherapy: opening a new era in colorectal cancer treatment

Immunotherapy has revolutionized the conventional treatment approaches for colorectal cancer (CRC), offering new therapeutic prospects for patients. Liquid biopsy has shown significant potential in early screening, diagnosis, and postoperative monitoring by analyzing circulating tumor cells (CTC) and circulating tumor DNA (ctDNA). In the era of immunotherapy, liquid biopsy provides additional possibilities for guiding immune-based treatments. Emerging technologies such as mass spectrometry-based detection of neoantigens and flow cytometry-based T cell sorting offer new tools for liquid biopsy, aiming to optimize immune therapy strategies. The integration of liquid biopsy with immunotherapy holds promise for improving treatment outcomes in colorectal cancer patients, enabling breakthroughs in early diagnosis and treatment, and providing patients with more personalized, precise, and effective treatment strategies.

[Treatment of Ampullary Adenocarcinoma]

The ampulla of Vater is a small projection formed by the confluence of the main pancreatic duct and common bile duct in the second part of the duodenum. Primary ampullary adenocarcinoma is a rare malignancy, accounting for only 0.2% of gastrointestinal cancers and approximately 7% of all periampullary cancers. Jaundice from a biliary obstruction is the most common symptom of ampullary adenocarcinoma. In the early stages, radical pancreatoduodenectomy is the standard surgical approach. On the other hand, no randomized controlled trial has provided evidence to guide physicians on the choice of adjuvant/palliative chemotherapy because of the rarity of the disease and the paucity of related research. This paper reports the biology, histology, current therapeutic strategies, and potential future therapies of ampullary adenocarcinoma.

Bayesian sample size determination in basket trials borrowing information between subsets

Basket trials are increasingly used for the simultaneous evaluation of a new treatment in various patient subgroups under one overarching protocol. We propose a Bayesian approach to sample size determination in basket trials that permit borrowing of information between commensurate subsets. Specifically, we consider a randomized basket trial design where patients are randomly assigned to the new treatment or control within each trial subset ("subtrial" for short). Closed-form sample size formulae are derived to ensure that each subtrial has a specified chance of correctly deciding whether the new treatment is superior to or not better than the control by some clinically relevant difference. Given prespecified levels of pairwise (in)commensurability, the subtrial sample sizes are solved simultaneously. The proposed Bayesian approach resembles the frequentist formulation of the problem in yielding comparable sample sizes for circumstances of no borrowing. When borrowing is enabled between commensurate subtrials, a considerably smaller trial sample size is required compared to the widely implemented approach of no borrowing. We illustrate the use of our sample size formulae with two examples based on real basket trials. A comprehensive simulation study further shows that the proposed methodology can maintain the true positive and false positive rates at desired levels.

Hyperprogression after anti-programmed death-1 therapy in a patient with urothelial bladder carcinoma: A case report

BACKGROUND

Immune checkpoint inhibitors, including programmed death-ligand 1 (PD-L1) and programmed death-1 (PD-1) have recently been approved to treat locally advanced and metastatic urothelial carcinoma (UC). However, some patients experience rapid tumor progression rather than any clinical benefit from anti-PD-L1/PD-1 therapy.

CASE SUMMARY

A 73-year-old woman with bladder UC showed the progression of multiple metastases after surgery and chemotherapy for over 12 mo. The patient could not tolerate further chemotherapy. Next-generation sequencing was performed, and the results indicated that the tumor mutational burden was 6.4 mutations/Mb. The patient received the anti-PD-L1 agent toripalimab combined with albumin-bound paclitaxel. Compared with the baseline staging before immunotherapy, the patient had a treatment failure time of < 2 mo, an increase in tumor burden of > 50%, and a > 2-fold increase in progression, indicating hyperprogression.

CONCLUSION

Selecting patients most likely to respond to treatment with immunotherapeutic agents remains challenging. For older patients with advanced UC who have already exhausted multi-line chemotherapy options, immunotherapy should be used prudently if no effective biomarker is available. Further studies are required to clarify the causes and mechanisms of hyperprogression.

De novo drug design based on patient gene expression profiles via deep learning

Computational de novo drug design is a challenging issue in medicine, and it is desirable to consider all of the relevant information of the biological systems in a disease state. Here, we propose a novel computational method to generate drug candidate molecular structures from patient gene expression profiles via deep learning, which we call DRAGONET. Our model can generate new molecules that are likely to counteract disease-specific gene expression patterns in patients, which is made possible by exploring the latent space constructed by a transformer-based variational autoencoder and integrating the substructures of disease-correlated molecules. We applied DRAGONET to generate drug candidate molecules for gastric cancer, atopic dermatitis, and Alzheimer's disease, and demonstrated that the newly generated molecules were chemically similar to registered drugs for each disease. This approach is applicable to diseases with unknown therapeutic target proteins and will make a significant contribution to the field of precision medicine.

A Systematic Review of Mesenchymal Epithelial Transition Factor (MET) and Its Impact in the Development and Treatment of Non-Small-Cell Lung Cancer

Lung cancer represents the leading cause of annual cancer-related deaths worldwide, accounting for 12.9%. The available treatment options for patients who experience disease progression remain limited. Targeted therapeutic approaches are promising but further understanding of the role of genetic alterations in tumorigenesis is imperative. The MET gene has garnered great interest in this regard. The aim of this systematic review was to analyze the findings from multiple studies to provide a comprehensive and unbiased summary of the evidence. A systematic search was conducted in the reputable scientific databases Embase and PubMed, leading to the inclusion of twenty-two articles, following the PRISMA guidelines, elucidating the biological role of MET in lung cancer and targeted therapies. The systematic review was registered in PROSPERO with registration ID: CRD42023437714. MET mutations were detected in 7.6-11.0% of cases while MET gene amplification was observed in 3.9-22.0%. Six studies showed favorable treatment outcomes utilizing MET inhibitors compared to standard treatment or placebo, with increases in PFS and OS ranging from 0.9 to 12.4 and 7.2 to 24.2 months, respectively, and one study reporting an increase in ORR by 17.3%. Furthermore, patients with a higher mutational burden may derive greater benefit from treatment with MET tyrosine kinase inhibitors (TKIs) than those with a lower mutational burden. Conversely, two studies reported no beneficial effect from adjunctive treatment with a MET targeted therapy. Given these findings, there is an urgent need to identify effective therapeutic strategies specifically targeting the MET gene in lung cancer patients.

Pharmacogenomics: Driving Personalized Medicine

Personalized medicine tailors therapies, disease prevention, and health maintenance to the individual, with pharmacogenomics serving as a key tool to improve outcomes and prevent adverse effects. Advances in genomics have transformed pharmacogenetics, traditionally focused on single gene-drug pairs, into pharmacogenomics, encompassing all "-omics" fields (e.g., proteomics, transcriptomics, metabolomics, and metagenomics). This review summarizes basic genomics principles relevant to translation into therapies, assessing pharmacogenomics' central role in converging diverse elements of personalized medicine. We discuss genetic variations in pharmacogenes (drug-metabolizing enzymes, drug transporters, and receptors), their clinical relevance as biomarkers, and the legacy of decades of research in pharmacogenetics. All types of therapies, including proteins, nucleic acids, viruses, cells, genes, and irradiation, can benefit from genomics, expanding the role of pharmacogenomics across medicine. Food and Drug Administration approvals of personalized therapeutics involving biomarkers increase rapidly, demonstrating the growing impact of pharmacogenomics. A beacon for all therapeutic approaches, molecularly targeted cancer therapies highlight trends in drug discovery and clinical applications. To account for human complexity, multicomponent biomarker panels encompassing genetic, personal, and environmental factors can guide diagnosis and therapies, increasingly involving artificial intelligence to cope with extreme data complexities. However, clinical application encounters substantial hurdles, such as unknown validity across ethnic groups, underlying bias in health care, and real-world validation. This review address the underlying science and technologies germane to pharmacogenomics and personalized medicine, integrated with economic, ethical, and regulatory issues, providing insights into the current status and future direction of health care. SIGNIFICANCE STATEMENT: Personalized medicine aims to optimize health care for the individual patients with use of predictive biomarkers to improve outcomes and prevent adverse effects. Pharmacogenomics drives biomarker discovery and guides the development of targeted therapeutics. This review addresses basic principles and current trends in pharmacogenomics, with large-scale data repositories accelerating medical advances. The impact of pharmacogenomics is discussed, along with hurdles impeding broad clinical implementation, in the context of clinical care, ethics, economics, and regulatory affairs.

An Overview of the Use of Precision Population Medicine in Cancer Care: First of a Series

Advances in science and technology in the past century and a half have helped improve disease management, prevention, and early diagnosis and better health maintenance. These have led to a longer life expectancy in most developed and middle-income countries. However, resource- and infrastructure-scarce countries and populations have not enjoyed these benefits. Furthermore, in every society, including in developed nations, the lag time from new advances, either in the laboratory or from clinical trials, to using those findings in day-to-day medical practice often takes many years and sometimes close to or longer than a decade. A similar trend is seen in the application of "precision medicine" (PM) in terms of improving population health (PH). One of the reasons for such lack of application of precision medicine in population health is the misunderstanding of equating precision medicine with genomic medicine (GM) as if they are the same. Precision medicine needs to be recognized as encompassing genomic medicine in addition to other new developments such as big data analytics, electronic health records (EHR), telemedicine, and information communication technology. By leveraging these new developments together and applying well-tested epidemiological concepts, it can be posited that population/public health can be improved. In this paper, we take cancer as an example of the benefits of recognizing the potential of precision medicine in applying it to population/public health. Breast cancer and cervical cancer are taken as examples to demonstrate these hypotheses. There exists significant evidence already to show the importance of recognizing "precision population medicine" (PPM) in improving cancer outcomes not only in individual patients but also for its applications in early detection and cancer screening (especially in high-risk populations) and achieving those goals in a more cost-efficient manner that can reach resource- and infrastructure-scarce societies and populations. This is the first report of a series that will focus on individual cancer sites in the future.

European Society of Toxicologic Pathology (Pathology 2.0 Molecular Pathology Special Interest Group): Review of In Situ Hybridization Techniques for Drug Research and Development

In situ hybridization (ISH) is used for the localization of specific nucleic acid sequences in cells or tissues by complementary binding of a nucleotide probe to a specific target nucleic acid sequence. In the last years, the specificity and sensitivity of ISH assays were improved by innovative techniques like synthetic nucleic acids and tandem oligonucleotide probes combined with signal amplification methods like branched DNA, hybridization chain reaction and tyramide signal amplification. These improvements increased the application spectrum for ISH on formalin-fixed paraffin-embedded tissues. ISH is a powerful tool to investigate DNA, mRNA transcripts, regulatory noncoding RNA, and therapeutic oligonucleotides. ISH can be used to obtain spatial information of a cell type, subcellular localization, or expression levels of targets. Since immunohistochemistry and ISH share similar workflows, their combination can address simultaneous transcriptomics and proteomics questions. The goal of this review paper is to revisit the current state of the scientific approaches in ISH and its application in drug research and development.

Łabaj P, Mangul S. RNA-seq data science: From raw data to effective interpretation

RNA sequencing (RNA-seq) has become an exemplary technology in modern biology and clinical science. Its immense popularity is due in large part to the continuous efforts of the bioinformatics community to develop accurate and scalable computational tools to analyze the enormous amounts of transcriptomic data that it produces. RNA-seq analysis enables genes and their corresponding transcripts to be probed for a variety of purposes, such as detecting novel exons or whole transcripts, assessing expression of genes and alternative transcripts, and studying alternative splicing structure. It can be a challenge, however, to obtain meaningful biological signals from raw RNA-seq data because of the enormous scale of the data as well as the inherent limitations of different sequencing technologies, such as amplification bias or biases of library preparation. The need to overcome these technical challenges has pushed the rapid development of novel computational tools, which have evolved and diversified in accordance with technological advancements, leading to the current myriad of RNA-seq tools. These tools, combined with the diverse computational skill sets of biomedical researchers, help to unlock the full potential of RNA-seq. The purpose of this review is to explain basic concepts in the computational analysis of RNA-seq data and define discipline-specific jargon.

Next-Generation Diagnostics for Pathogens

Next-generation sequencing (NGS) was initially developed to aid sequencing of the human genome. This molecular method is cost effective for sequencing and characterizing genomes, not only those of humans or animals but also those of bacteria and other pathogens. However, rather than sequencing a single organism, a targeted NGS method can be used to specifically amplify pathogens of interest in a clinical sample for detection and characterization by sequencing. Targeted NGS is an ideal method for ruminant syndromic testing due to its ability to detect a variety of pathogens in a sample with a single test.

Getting personal in metastatic melanoma: neoantigen-based vaccines as a new therapeutic strategy

PURPOSE OF REVIEW

Cancer vaccines are facing renewed interest, thanks to the progress recently achieved in the immunotherapy field, including the success of immune checkpoint inhibitors (CPIs). The advances in understanding the CPI mode of action revealed a central role of neoantigens for the outcome of such treatments. Neoantigens became the preferred antigens for cancer vaccines and have been evaluated in several clinical trials. Here, we review the recent results from neoantigen-based vaccines in melanoma patients and discuss avenues for improvement.

RECENT FINDINGS

The importance of neoantigens for tumor control comes from the positive correlation between tumor mutational burden (TMB) and response to CPI. Preclinical studies have proved the effectiveness of neoantigen vaccines in models, expediting their clinical testing. Tumor mutations are not shared in most tumor types including melanoma, mandating the need of a personalized approach. Several clinical studies have shown the safety, feasibility, immunogenicity and preliminary evidence of antitumor activity of personalized vaccination. Currently, new trials have been started aiming to both confirm clinical activity and combining vaccines with other immunotherapies for improved efficacy.

SUMMARY

Personalized vaccines hold the promise for highly mutated and immunogenic cancers, including melanoma. Continuous efforts are underway to increase their likelihood of success.

Spatial RNA sequencing methods show high resolution of single cell in cancer metastasis and the formation of tumor microenvironment

Cancer metastasis often leads to death and therapeutic resistance. This process involves the participation of a variety of cell components, especially cellular and intercellular communications in the tumor microenvironment (TME). Using genetic sequencing technology to comprehensively characterize the tumor and TME is therefore key to understanding metastasis and therapeutic resistance. The use of spatial transcriptome sequencing enables the localization of gene expressions and cell activities in tissue sections. By examining the localization change as well as gene expression of these cells, it is possible to characterize the progress of tumor metastasis and TME formation. With improvements of this technology, spatial transcriptome sequencing technology has been extended from local regions to whole tissues, and from single sequencing technology to multimodal analysis combined with a variety of datasets. This has enabled the detection of every single cell in tissue slides, with high resolution, to provide more accurate predictive information for tumor treatments. In this review, we summarize the results of recent studies dealing with new multimodal methods and spatial transcriptome sequencing methods in tumors to illustrate recent developments in the imaging resolution of micro-tissues.

From alchemy to personalised medicine: the journey of laboratory medicine

This review summarises the long period in which man has approached nature to understand its powers, and has tried to control it through physical and chemical, and also magical, practices. From the attempt to manage nature to the development of primordial drugs and medical practices and later to achieve modern biomedical science, laboratory practices always played a pivotal role. Over the years and centuries, the laboratory has acquired more and more importance in the improvement of health.In addition to the well-known importance of laboratory medicine in the early diagnosis and appropriateness, the discoveries of the last 50 years have also given the Laboratory a decisive role in regenerative and personalised medicine.This paper examines the evolution of the laboratory and is not meant to be a treatise on the history of medicine. The goal is to highlight the moments of the transition from magic and alchemy to laboratory science.-------------------------------Roberto Verna is President of the World Association of Societies of Pathology and Laboratory Medicine and President of the Academy for Health and Clinical Research.

Clinical application and detection techniques of liquid biopsy in gastric cancer

Gastric cancer (GC) is one of the most common tumors worldwide and the leading cause of tumor-related mortality. Endoscopy and serological tumor marker testing are currently the main methods of GC screening, and treatment relies on surgical resection or chemotherapy. However, traditional examination and treatment methods are more harmful to patients and less sensitive and accurate. A minimally invasive method to respond to GC early screening, prognosis monitoring, treatment efficacy, and drug resistance situations is urgently needed. As a result, liquid biopsy techniques have received much attention in the clinical application of GC. The non-invasive liquid biopsy technique requires fewer samples, is reproducible, and can guide individualized patient treatment by monitoring patients' molecular-level changes in real-time. In this review, we introduced the clinical applications of circulating tumor cells, circulating free DNA, circulating tumor DNA, non-coding RNAs, exosomes, and proteins, which are the primary markers in liquid biopsy technology in GC. We also discuss the current limitations and future trends of liquid biopsy technology as applied to early clinical biopsy technology.

Identification and validation of signature for prognosis and immune microenvironment in gastric cancer based on m6A demethylase ALKBH5

Background

N6-methyladenosine (m6A) RNA regulators play important roles in cancers, but their functions and mechanism have not been demonstrated clearly in gastric cancer (GC).

Methods

In this study, the GC samples with clinical information and RNA transcriptome were downloaded from The Cancer Genome Atlas database. The different expression genes were compared by the absolute value and median ± standard deviation. Samples with complete information were randomly divided into a training dataset and a test dataset. The differential expression genes (DEGs) between ALKBH5-low and ALKBH5-high subgroups were identified in the training dataset and constructed a risk model by Cox and least absolute shrinkage and selection operator regression. The model was testified in test datasets, overall survival (OS) was compared with the Kaplan-Meier method, and immune cell infiltration was calculated by the CIBERSORT algorithm in the low-risk and high-risk subgroups based on the model. The protein levels of ALKBH5 were detected with immunohistochemistry. The relative expression of messenger-ribonucleic acid (mRNA) was detected with quantitative polymerase chain reaction.

Results

ALKBH5 was the only regulator whose expression was lower in tumor samples than that in normal samples. The low expression of ALKBH5 led to the poor OS of GC patients and seemed to be an independent protective factor. The model based on ALKBH5-regulated genes was validated in both datasets (training/test) and displayed a potential capacity to predict a clinical prognosis. Gene Ontology analysis implied that the DEGs were involved in the immune response; CIBERSORT results indicated that ALKBH5 and its related genes could alter the immune microenvironment of GC. The protein levels of ALKBH5 were verified as lowly expressed in GC tissues. SLC7A2 and CGB3 were downregulated with ALKBH5 knockdown.

Conclusions

In this study, we found that ALKBH5 might be a suppressor of GC; ALKBH5 and its related genes were latent biomarkers and immunotherapy targets.

Application value of whole exome sequencing in screening and identifying novel mutations of hypopharyngeal cancer

The research on targeted therapy of hypopharyngeal cancer is very scarce. The discovery of new targeted driver genes will promote the progress of hypopharyngeal cancer therapy to a great extent. In our research, whole-exome sequencing in 10 patients with hypopharyngeal cancer was performed to identify single nucleotide variations (SNVs) and insertions and deletions (INDELs). American College of Medical Genetics and Genomics (ACMG) guidelines were used to evaluate the pathogenicity of the selected variants. 8113 mutation sites in 5326 genes were identified after strict screening. We identified 72 pathogenic mutations in 53 genes according to the ACMG guidelines. Gene Ontology (GO) annotation and KEGG enrichment analysis show the effect of these genes on cancer. Protein-protein interaction (PPI) was analyzed by string online software. The validation results of the ualcan database showed that 22 of the 53 genes may be related to the poor prognosis of patients with hypopharyngeal cancer. RBM20 has the most significant correlation with hypopharyngeal cancer, and it is likely to be the driver gene of hypopharyngeal cancer. In conclusion, we found possible therapeutic targets for hypopharyngeal cancer, especially RBM20 and KMT2C. Our study provides a basis for the pathogenesis and targeted therapy of hypopharyngeal cancer.

Case report: A case study on the treatment using icaritin soft capsules in combination with lenvatinib achieving impressive PR and stage reduction in unresectable locally progressive pancreatic cancer and a literature review

Background: Pancreatic cancer is one of the most deadly malignancies in the world. It is characterized by rapid progression and a very poor prognosis. The five-year survival rate of pancreatic cancer in China is only 7.2%, which is the lowest among all cancers and the use of combined paclitaxel albumin, capecitabine, and digital has been the clinical standard treatment for advanced pancreatic cancer since 1997. Also, the application of multidrug combinations is often limited by the toxicity of chemotherapy. Therefore, there is an urgent need for a more appropriate and less toxic treatment modality for pancreatic cancer. Case presentation: The patient was a 79-year-old woman, admitted to the hospital with a diagnosis of unresectable locally advanced pancreatic cancer (T3N0M0, stage IIA), with its imaging showing overgrowth of SMV involvement and unresectable reconstruction of the posterior vein after evaluation. As the patient refused chemotherapy, lenvatinib (8 mg/time, qd) and icaritin soft capsules (three tablets/time, bid) were recommended according to our past experience and a few clinical research cases. The tumor lesion was greatly reduced by 57.5% after the treatment, and the extent of vascular involvement also decreased. The aforementioned medication resulted in a significant downstaging of the patient's tumor. Conclusion: Better results were achieved in the treatment with icaritin soft capsules and lenvatinib in this case. Because of its less toxic effect on the liver and kidney and bone marrow suppression, it was suitable to combine icaritin soft capsules with targeted drugs for treating intermediate and advanced malignancies, which brings hope to patients who cannot or refuse to take chemotherapy.

Expert consensus of the Spanish Society of Pathology and the Spanish Society of Medical Oncology on the determination of biomarkers in pancreatic and biliary tract cancer

Pancreatic cancer and biliary tract cancer have a poor prognosis. In recent years, the development of new diagnostic techniques has enabled the identification of the main genetic alterations involved in the development of these tumours. Multiple studies have assessed the ability to predict response to treatment of certain biomarkers, such as BRCA in pancreatic cancer, IDH1 or FGFR2 in biliary tract cancer and microsatellite instability or NTRK fusions in an agnostic tumour fashion. In this consensus, a group of experts selected by the Spanish Society of Medical Oncology (SEOM) and the Spanish Society of Pathology (SEAP) reviewed the role played by these mutations in the process of carcinogenesis and their clinical implications. Based on their results, a series of recommendations are made to optimize the determination of these biomarkers and thus help standardize the diagnosis and treatment of these tumours.

Expert consensus of the Spanish Society of Pathology and the Spanish Society of Medical Oncology on the determination of biomarkers in pancreatic and biliary tract cancer

Pancreatic cancer and biliary tract cancer have a poor prognosis. In recent years, the development of new diagnostic techniques has enabled the identification of the main genetic alterations involved in the development of these tumours. Multiple studies have assessed the ability of certain biomarkers, such as BRCA in pancreatic cancer, IDH1 or FGFR2 in biliary tract cancer and microsatellite instability or NTRK fusions in an agnostic tumour fashion, to predict response to treatment.In this consensus, a group of experts selected by the Spanish Society of Medical Oncology (SEOM) and the Spanish Society of Pathology (SEAP) reviewed the role played by these mutations in the process of carcinogenesis and their clinical implications. As a result, this article proposes a series of recommendations to optimize the determination of these biomarkers to help standardize the diagnosis and treatment of these tumours.

Advances in liquid biopsy in neuroblastoma

Even with intensive treatment of high-risk neuroblastoma (NB) patients, half of high-risk NB patients still relapse. New therapies targeting the biological characteristics of NB have important clinical value for the personalized treatment of NB. However, the current biological markers for NB are mainly analyzed by tissue biopsy. In recent years, circulating biomarkers of NB based on liquid biopsy have attracted more and more attention. This review summarizes the analytes and methods for liquid biopsy of NB. We focus on the application of liquid biopsy in the diagnosis, prognosis assessment, and monitoring of NB. Finally, we discuss the prospects and challenges of liquid biopsy in NB.

Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information

Since U.S. President Barack Obama announced the Precision Medicine Initiative in his New Year's State of the Union address in 2015, the establishment of a precision medicine system has been emphasized worldwide, particularly in the field of oncology. With the advent of next-generation sequencers specifically, genome analysis technology has made remarkable progress, and there are active efforts to apply genome information to diagnosis and treatment. Generally, in the process of feeding back the results of next-generation sequencing analysis to patients, a molecular tumor board (MTB), consisting of experts in clinical oncology, genetic medicine, etc., is established to discuss the results. On the other hand, an MTB currently involves a large amount of work, with humans searching through vast databases and literature, selecting the best drug candidates, and manually confirming the status of available clinical trials. In addition, as personalized medicine advances, the burden on MTB members is expected to increase in the future. Under these circumstances, introducing cutting-edge artificial intelligence (AI) technology and information and communication technology to MTBs while reducing the burden on MTB members and building a platform that enables more accurate and personalized medical care would be of great benefit to patients. In this review, we introduced the latest status of elemental technologies that have potential for AI utilization in MTB, and discussed issues that may arise in the future as we progress with AI implementation.

Identification and validation of a novel tumor driver gene signature for diagnosis and prognosis of head and neck squamous cell carcinoma

Objective: Head and neck squamous cell carcinoma (HNSCC) is a common heterogeneous cancer with complex carcinogenic factors. However, the current TNM staging criteria to judge its severity to formulate treatment plans and evaluate the prognosis are particularly weak. Therefore, a robust diagnostic model capable of accurately diagnosing and predicting HNSCC should be established. Methods: Gene expression and clinical data were retrieved from The Cancer Genome Atlas and Gene Expression Omnibus databases. Key prognostic genes associated with HNSCC were screened with the weighted gene co-expression network analysis and least absolute shrinkage and selection operator (LASSO) Cox regression model analysis. We used the timeROC and survival R packages to conduct time-dependent receiver operating characteristic curve analyses and calculated the area under the curve at different time points of model prediction. Patients in the training and validation groups were divided into high- and low-risk subgroups, and Kaplan-Meier (K-M) survival curves were plotted for all subgroups. Subsequently, LASSO and support vector machine algorithms were used to screen genes to construct diagnostic model. Furthermore, we used the Wilcoxon signed-rank test to compare the half-maximal inhibitory concentrations of common chemotherapy drugs among patients in different risk groups. Finally, the expression levels of eight genes were measured using quantitative real-time polymerase chain reaction and immunohistochemistry. Results: Ten genes (SSB, PFKP, NAT10, PCDH9, SHANK2, PAX8, CELSR3, DCLRE1C, MAP2K7, and ODF4) with prognostic potential were identified, and a risk score was derived accordingly. Patients were divided into high- and low-risk groups based on the median risk score. The K-M survival curves confirmed that patients with high scores had significantly worse overall survival. Receiver operating characteristic curves proved that the prognostic signature had good sensitivity and specificity for predicting the prognosis of patients with HNSCC. Univariate and multivariate Cox regression analyses confirmed that the gene signature was an independent prognostic risk factor for HNSCC. Diagnostic model was built by identifying eight genes (SSB, PFKP, NAT10, PCDH9, CELSR3, DCLRE1C, MAP2K7, and ODF4). The high-risk group showed higher sensitivity to various common chemotherapeutic drugs. DCLRE1C expression was higher in normal tissues than in HNSCC tissues. Conclusion: Our study identified the important role of tumor-driver genes in HNSCC and their potential clinical diagnostic and prognostic values to facilitate individualized management of patients with HNSCC.