Research and application of single-cell sequencing in tumor heterogeneity and drug resistance of circulating tumor cells

From Tyrosine Kinases to Tyrosine Phosphatases: New Therapeutic Targets in Cancers and Beyond

Protein tyrosine kinases (PTKs) and protein tyrosine phosphatases (PTPs) regulate the level of tyrosine phosphorylation in proteins. PTKs are key enzymes that catalyze the transfer of an ATP phosphoric acid to a tyrosine residue on target protein substrates. Protein tyrosine phosphatases (PTPs) are responsible for the dephosphorylation of tyrosine residues and play a role in countering PTK overactivity. As widespread oncogenes, PTKs were once considered to be promising targets for therapy. However, tyrosine kinase inhibitors (TKIs) now face a number of challenges, including drug resistance and toxic side effects. Treatment strategies now need to be developed from a new perspective. In this review, we assess the current state of TKIs and highlight the role of PTPs in cancer and other diseases. With the advances of allosteric inhibition and the development of multiple alternative proprietary drug strategies, the reputation of PTPs as "undruggable" targets has been overturned, and they are now considered viable therapeutic targets. We also discuss the strategies and prospects of PTP-targeted therapy, as well as its future development.

Isolation and characterizations of multidrug-resistant human cancer cells by a biodegradable nano-sensor

Multidrug resistance (MDR) remains a significant challenge in cancer therapy, with inherent and acquired resistance distinct. While conventional drug selection processes enable the isolation of cancer cells with acquired multidrug resistance, identifying cancer cells with inherent drug resistance remains challenging. Herein, we proposed a molecular beacon (MB)-based strategy to identify and isolate the inherent MDR cancer cells. A lipid/PLGA core-shell nanoparticulate system (DNCP) was designed to deliver MB for intracellular MDR1 mRNA imaging. DNCP-MB - possess a surface potential of -8 mV and a size of 150 nm - demonstrated effective delivery of MB, remarkable selectivity towards the selected intracellular mRNA targets, and low cytotoxicity. Following DNCP transfection, fluorescence-activated cell sorting (FACS) was employed to differentiate MCF-7 cells into two distinct sub-populations: the Top 10 cells with a high level of MDR gene expression and the Bottom 10 cells with a low level of MDR gene expression, which represent inherent drug-resistant and non-drug-resistant cells, respectively. Intriguingly, we observed a positive correlation between elevated MDR1 mRNA expression and increased migration, enhanced proliferation rate, and tighter spheroid formation. Moreover, we conducted RNA sequencing analysis on the Top 10, Bottom 10, and MCF-7/ADR cells. The findings revealed a notable disparity in the gene ontology enrichment analysis of differentially expressed genes between the Top 10 and Bottom 10 cells when compared to the Bottom 10 and MCF-7/ADR cells. This novel approach provides a promising avenue for isolating inherent drug-resistant cells and holds significant potential in unraveling the mechanisms underlying inherent drug resistance.

Bibliometric and visual analysis of single-cell sequencing from 2010 to 2022

Background: Single-cell sequencing (SCS) is a technique used to analyze the genome, transcriptome, epigenome, and other genetic data at the level of a single cell. The procedure is commonly utilized in multiple fields, including neurobiology, immunology, and microbiology, and has emerged as a key focus of life science research. However, a thorough and impartial analysis of the existing state and trends of SCS-related research is lacking. The current study aimed to map the development trends of studies on SCS during the years 2010-2022 through bibliometric software. Methods: Pertinent papers on SCS from 2010 to 2022 were obtained using the Web of Science Core Collection. Research categories, nations/institutions, authors/co-cited authors, journals/co-cited journals, co-cited references, and keywords were analyzed using VOSviewer, the R package "bibliometric", and CiteSpace. Results: The bibliometric analysis included 9,929 papers published between 2010 and 2022, and showed a consistent increase in the quantity of papers each year. The United States was the source of the highest quantity of articles and citations in this field. The majority of articles were published in the periodical Nature Communications. Butler A was the most frequently quoted author on this topic, and his article "Integrating single-cell transcriptome data across diverse conditions, technologies, and species" has received numerous citations to date. The literature and keyword analysis showed that studies involving single-cell RNA sequencing (scRNA-seq) were prominent in this discipline during the study period. Conclusion: This study utilized bibliometric techniques to visualize research in SCS-related domains, which facilitated the identification of emerging patterns and future directions in the field. Current hot topics in SCS research include COVID-19, tumor microenvironment, scRNA-seq, and neuroscience. Our results are significant for scholars seeking to identify key issues and generate new research ideas.

An exploratory study of cell stiffness as a mechanical label-free biomarker across multiple musculoskeletal sarcoma cells

BACKGROUND

Cancer cells are characterized by changes in cell cytoskeletal architecture and stiffness. Despite advances in understanding the molecular mechanisms of musculoskeletal cancers, the corresponding cellular mechanical properties remain largely unexplored. The aim of this study was to investigate the changes in cellular stiffness and the associated cytoskeleton configuration alterations in various musculoskeletal cancer cells.

METHODS

Cell lines from five main sarcoma types of the musculoskeletal system (chondrosarcoma, osteosarcoma, Ewing sarcoma, fibrosarcoma and rhabdomyosarcoma) as well as their healthy cell counterparts (chondrocytes, osteoblasts, mesenchymal stem cells, fibroblasts, skeletal muscle cells) were subjected to cell stiffness measurements via atomic force microscopy (AFM). Biochemical and structural changes of the cytoskeleton (F-actin, β-tubulin and actin-related protein 2/3) were assessed by means of fluorescence labelling, ELISA and qPCR.

RESULTS

While AFM stiffness measurements showed that the majority of cancer cells (osteosarcoma, Ewing sarcoma, fibrosarcoma and rhabdomyosarcoma) were significantly less stiff than their corresponding non-malignant counterparts (p < 0.001), the chondrosarcoma cells were significant stiffer than the chondrocytes (p < 0.001). Microscopically, the distribution of F-actin differed between malignant entities and healthy counterparts: the organisation in well aligned stress fibers was disrupted in cancer cell lines and the proteins was mainly concentrated at the periphery of the cell, whereas β-tubulin had a predominantly perinuclear localization. While the F-actin content was lower in cancer cells, particularly Ewing sarcoma (p = 0.018) and Fibrosarcoma (p = 0.023), this effect was even more pronounced in the case of β-tubulin for all cancer-healthy cell duos. Interestingly, chondrosarcoma cells were characterized by a significant upregulation of β-tubulin gene expression (p = 0.005) and protein amount (p = 0.032).

CONCLUSION

Modifications in cellular stiffness, along with structural and compositional cytoskeleton rearrangement, constitute typical features of sarcomas cells, when compared to their healthy counterpart. Notably, whereas a decrease in stiffness is typically a feature of malignant entities, chondrosarcoma cells were stiffer than chondrocytes, with chondrosarcoma cells exhibiting a significantly upregulated β-tubulin expression. Each Sarcoma entity may have his own cellular-stiffness and cytoskeleton organisation/composition fingerprint, which in turn may be exploited for diagnostic or therapeutic purposes.

Advances in single-cell RNA sequencing and its applications in cancer research

Cancers are a group of heterogeneous diseases characterized by the acquisition of functional capabilities during the transition from a normal to a neoplastic state. Powerful experimental and computational tools can be applied to elucidate the mechanisms of occurrence, progression, metastasis, and drug resistance; however, challenges remain. Bulk RNA sequencing techniques only reflect the average gene expression in a sample, making it difficult to understand tumor heterogeneity and the tumor microenvironment. The emergence and development of single-cell RNA sequencing (scRNA-seq) technologies have provided opportunities to understand subtle changes in tumor biology by identifying distinct cell subpopulations, dissecting the tumor microenvironment, and characterizing cellular genomic mutations. Recently, scRNA-seq technology has been increasingly used in cancer studies to explore tumor heterogeneity and the tumor microenvironment, which has increased the understanding of tumorigenesis and evolution. This review summarizes the basic processes and development of scRNA-seq technologies and their increasing applications in cancer research and clinical practice.

Cancer-Associated Fibroblasts Affect Tumor Metabolism and Immune Microenvironment in Gastric Cancer and Identification of Its Characteristic Genes

Background

Cancer-associated fibroblasts (CAFs) have reported widely involved in cancer progression. However, its underlying mechanism in gastric cancer is still not clarified.

Methods

The data used in this study were all downloaded from the Cancer Genome Atlas database. R software and the R packages were used for all the analyses.

Results

In our study, we first quantified the CAFs infiltration using the ssGSEA algorithm. The clinical correlation result showed that CAFs were associated with a worse prognosis and clinical features. Pathway enrichment also indicated several oncogenic pathways in GC patients with high CAFs infiltration, including epithelial-mesenchymal transition (EMT), myogenesis, allograft rejection, the inflammatory response, and IL2/STAT5 signaling. Furthermore, FNDC1 and RSPO3 were identified as the characteristic genes of CAFs through two machine learning algorithms, LASSO logistic regression and SVM-RFE. The following analysis showed that FNDC1 and RSPO3 were associated with more progressive clinical features and had a good prediction efficiency of the CAFs infiltration status in GC patients. Pathway enrichment and genomic instability were performed to explore the underlying mechanisms of FNDC1 and RSPO3. Immune infiltration analysis showed that CAFs were positively correlated with M2 macrophages. Moreover, we found that the GC patients with low CAFs infiltration were more sensitive to immunotherapy. Also, the CAFs, FNDC1, and RSPO3 could generate a certain effect on the sensitivity of doxorubicin, mitomycin, and paclitaxel.

Conclusions

In summary, our study comprehensively investigated the role of CAFs in GC, which might be associated with immunotherapy sensitivity. Meanwhile, FNDC1 and RSPO3 were identified as the underlying targets of GC.

Current Status of Circulating Tumor Cells in Head and Neck Squamous Cell Carcinoma: A Review

OBJECTIVE

Circulating tumor cells (CTCs) are found in the blood of patients with cancer, including head and neck squamous cell carcinomas (HNSCCs). The aim is to review the most up-to-date status of CTCs for applications in patients with HNSCC.

DATA SOURCES

English articles in PubMed.

REVIEW METHODS

All the studies on CTCs in HNSCCs in the literature were reviewed.

CONCLUSIONS

There is emerging information on the diagnostic and prognostic value of CTCs in HNSCCs. Evidence also highlights the advantages of various downstream analysis approaches over circulating tumor DNA (ctDNA), such as single-CTC analysis, ex vivo, and in vivo expansion of CTCs. Multiple phenotypic surface markers (cytokeratins, EpCAM, vimentin, etc.), used for CTCs characterization using different immunoassays, could predict disease progression as well as patients' response to treatment efficacy. Immune checkpoint inhibitors' status in CTCs could also provide better insight into treatment. Clonal expansion of CTCs and single-cell analysis of CTCs are the most emerging fields nowadays which may offer an understanding of the mechanism of tumor evolution as well as therapeutic efficacy. Although several clinical trials are ongoing, limitations still exist in the detection and characterization of CTCs. Due to the lack of a gold standard protocol, the sensitivity and specificity of CTC enumeration methods vary.

IMPLICATIONS FOR PRACTICE

Prospective clinical trials are still needed before CTCs can be employed as diagnostic and prognostic markers in the clinical management of patients with HNSCC.

Exploration of the underlying biological differences and targets in ovarian cancer patients with diverse immunotherapy response

Background

Preclinical trials of immunotherapy in ovarian cancer (OC) have shown promising results. This makes it meaningful to prospectively examine the biological mechanisms explaining the differences in response performances to immunotherapy among OC patients.

Methods

Open-accessed data was obtained from the Cancer Genome Atlas and Gene Expression Omnibus database. All the analysis was conducted using the R software.

Results

We firstly performed the TIDE analysis to evaluate the immunotherapy response rate of OC patients. The machine learning algorithm LASSO logistic regression and SVM-RFE were used to identify the characteristic genes. The genes DPT, RUNX1T1, PTPRN, LSAMP, FDCSP and COL6A6 were selected for molecular typing. Our result showed that the patients in Cluster1 might have a better prognosis and might be more sensitive to immunotherapy, including PD-1 and CTLA4 therapy options. Pathway enrichment analysis showed that in Cluster2, the pathway of EMT, TNFα/NF-kB signaling, IL2/STAT5 signaling, inflammatory response, KRAS signaling, apical junction, complement, interferon-gamma response and allograft rejection were significantly activated. Also, genomic instability analysis was performed to identify the underlying genomic difference between the different Cluster patients. Single-cell analysis showed that the DPT, COL6A6, LSAMP and RUNX1T1 were mainly expressed in the fibroblasts. We then quantified the CAFs infiltration in the OC samples. The result showed that patients with low CAFs infiltration might have a lower TIDE score and a higher proportion of immunotherapy responders. Also, we found all the characteristic genes DPT, RUNX1T1, PTPRN, LSAMP, FDCSP and COL6A6 were upregulated in the patients with high CAFs infiltration. Immune infiltration analysis showed that the patients in Cluster2 might have a higher infiltration of naive B cells, activated NK cells and resting Dendritic cells.

Conclusions

In summary, our study provides new insights into ovarian cancer immunotherapy. Meanwhile, specific targets DPT, RUNX1T1, PTPRN, LSAMP, FDCSP, COL6A6 and CAFs were identified for OC immunotherapy.

Metabolic reprogramming and crosstalk of cancer-related fibroblasts and immune cells in the tumor microenvironment

It is notorious that cancer cells alter their metabolism to adjust to harsh environments of hypoxia and nutritional starvation. Metabolic reprogramming most often occurs in the tumor microenvironment (TME). TME is defined as the cellular environment in which the tumor resides. This includes surrounding blood vessels, fibroblasts, immune cells, signaling molecules and the extracellular matrix (ECM). It is increasingly recognized that cancer cells, fibroblasts and immune cells within TME can regulate tumor progression through metabolic reprogramming. As the most significant proportion of cells among all the stromal cells that constitute TME, cancer-associated fibroblasts (CAFs) are closely associated with tumorigenesis and progression. Multitudinous studies have shown that CAFs participate in and promote tumor metabolic reprogramming and exert regulatory effects via the dysregulation of metabolic pathways. Previous studies have demonstrated that curbing the substance exchange between CAFs and tumor cells can dramatically restrain tumor growth. Emerging studies suggest that CAFs within the TME have emerged as important determinants of metabolic reprogramming. Metabolic reprogramming also occurs in the metabolic pattern of immune cells. In the meanwhile, immune cell phenotype and functions are metabolically regulated. Notably, immune cell functions influenced by metabolic programs may ultimately lead to alterations in tumor immunity. Despite the fact that multiple previous researches have been devoted to studying the interplays between different cells in the tumor microenvironment, the complicated relationship between CAFs and immune cells and implications of metabolic reprogramming remains unknown and requires further investigation. In this review, we discuss our current comprehension of metabolic reprogramming of CAFs and immune cells (mainly glucose, amino acid, and lipid metabolism) and crosstalk between them that induces immune responses, and we also highlight their contributions to tumorigenesis and progression. Furthermore, we underscore potential therapeutic opportunities arising from metabolism dysregulation and metabolic crosstalk, focusing on strategies targeting CAFs and immune cell metabolic crosstalk in cancer immunotherapy.

The combination of computed tomography features and circulating tumor cells increases the surgical prediction of visceral pleural invasion in clinical T1N0M0 lung adenocarcinoma

Background

Visceral pleural invasion (VPI) is a clinical manifestation associated with a poor prognosis, and diagnosing it preoperatively is highly imperative for successful sublobar resection of these peripheral tumors. We evaluated the roles of computed tomography (CT) features and circulating tumor cells (CTCs) for improving VPI detection in patients with clinical T1N0M0 invasive lung adenocarcinoma.

Methods

Three hundred and ninety-one patients were reviewed retrospectively in this study, of which 234 presented with a pleural tag or pleural contact on CT images. CTCs positive for the foliate receptors were enriched and analyzed prior to surgery. Logistic regression analyses were performed to assess the association of CT features and CTCs with VPI, and the receiver operating characteristic (ROC) curve was generated to compare the predictive power of these variables.

Results

Patients mostly underwent either segmentectomies (18.9%) or lobectomies (79.0%). Only 49 of the 234 patients with pleural involvement on CT showed pathologically confirmed VPI. Multivariate logistic regression analysis revealed that CTC level ≥10.42 FU/3 mL was a significant VPI risk factor for invasive adenocarcinoma cases ≤30 mm [adjusted odds ratio (OR) =4.62, 95% confidence interval (CI): 2.05–10.44, P<0.001]. Based on CT features, subgroup analyses showed that the solid portion size was a statistically significant independent predictor of VPI for these peripheral nodules with pleural tag, while the solid portion length of the interface was an independent predictor of pleural contact. The receiver operating curve analyses showed that the combination of CTC and CT features were highly predictive of VPI [area under the curve (AUC) =0.921 for pleural contact and 0.862 for the pleural tag, respectively].

Conclusions

CTC, combined with CT features of pleural tag or pleural contact, could significantly improve VPI detection in invasive lung adenocarcinomas at clinical T1N0M0 stage prior to the patient’s surgery.

A transcriptional biosensor to monitor single cancer cell therapeutic responses by bioluminescence microscopy

When several life-prolonging drugs are indicated for cancer treatment, predictive drug-response tumor biomarkers are essential to guide management. Most conventional biomarkers are based on bulk tissue analysis, which cannot address the complexity of single-cell heterogeneity responsible for drug resistance. Therefore, there is a need to develop alternative drug response predictive biomarker approaches that could directly interrogate single-cell and whole population cancer cell drug sensitivity. In this study, we report a novel method exploiting bioluminescence microscopy to detect single prostate cancer (PCa) cell response to androgen receptor (AR)-axis-targeted therapies (ARAT) and predict cell population sensitivity.

Methods: We have generated a new adenovirus-delivered biosensor, PCA3-Cre-PSEBC-ITSTA, which combines an integrated two-step transcriptional amplification system (ITSTA) and the activities of the prostate cancer antigen 3 (PCA3) and modified prostate-specific antigen (PSEBC) gene promoters as a single output driving the firefly luciferase reporter gene. This system was tested on PCa cell lines and on primary PCa cells. Single cells, exposed or not to ARAT, were dynamically imaged by bioluminescence microscopy. A linear discriminant analysis (LDA)-based method was used to determine cell population sensitivities to ARAT.

Results: We show that the PCA3-Cre-PSEBC-ITSTA biosensor is PCa-specific and can dynamically monitor single-cell AR transcriptional activity before and after ARAT by bioluminescence microscopy. After biosensor transduction and bioluminescence microscopy single-cell luminescence dynamic quantification, LDA analysis could discriminate the cell populations overall ARAT sensitivity despite heterogeneous single-cell responses. Indeed, the biosensor could detect a significant decrease in AR activity following exposure to conventional ARAT in hormone-naive primary PCa cells, while in castration-resistant PCa patients, treatment response correlated with the observed clinical ARAT resistance.

Conclusion: The exploitation of bioluminescence microscopy and multi-promoter transcriptionally-regulated biosensors can aptly define the overall treatment response of patients by monitoring live single cell drug response from primary cancer tissue. This approach can be used to develop predictive biomarkers for drug response in order to help clinicians select the best drug combinations or sequences for each patient.

Exploration of the immune microenvironment of breast cancer in large population cohorts

Tumor immune microenvironment is associated with tumor progression. However, previous studies have not fully explored the breast cancer (BC) immune microenvironment. All the data analyzed in this study were obtained from the open-access database, including The Cancer Genome Atlas, Gene Expression Omnibus (TCGA), and cBioPortal databases. R software v4.0 and SPSS 13.0 were used to perform all the statistical analysis. Firstly, the clinical and expression profile information of TCGA, GSE20685, GSE20711, GSE48390, GSE58812, and METABRIC cohorts was collected. Then, 53 immune terms were quantified using the single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm. A prognosis model based on HER2_Immune_PCA, IL12_score, IL13_score, IL4_score, and IR7_score was established, which showed great prognosis prediction efficiency in both training group and validation group. A nomogram was then established for a better clinical application. Clinical correlation showed that elderly BC patients might have a higher riskscore. Pathway enrichment analysis showed that the pathway of oxidative phosphorylation, E2F targets, hedgehog signaling, adipogenesis, DNA repair, glycolysis, heme metabolism, and mTORC1 signaling was activated in the high-risk group. Moreover, Tumor Immune Dysfunction and Exclusion and Genomics of Drug Sensitivity in Cancer analysis showed that low-risk patients might be more sensitive to PD-1 therapy, cisplatin, gemcitabine, paclitaxel, and sunitinib. Finally, four genes, XCL1, XCL2, TNFRSF17, and IRF4, were identified for risk group classification. In summary, our signature is a useful tool for the prognosis and prediction of the drug sensitivity of BC.

Identification of the alpha linolenic acid metabolism-related signature associated with prognosis and the immune microenvironment in nasopharyngeal carcinoma

BACKGROUND

Tumor metabolism is important for cancer progression. Nevertheless, the role of the metabolism pathway and related molecules in nasopharyngeal carcinoma (NPC) is limited.

METHODS

Open-accessed data was downloaded from The Cancer Genome Atlas database. All the analysis was performed using the R software and the package in R environments.

RESULTS

In our study, we firstly explored the role of 21 metabolism-related pathways in NPC patients. We found that the steroid biosynthesis and biosynthesis of unsaturated fatty acids were risk factors, while the alpha linolenic acid metabolism was a protective factor. Then, the alpha linolenic acid metabolism aroused our interest. A total of 128 differentially expressed genes (DEGs) were identified, including 71 downregulated and 57 upregulated genes identified between high and low alpha linolenic acid metabolism level. Based on these DEGs, we constructed a prognosis model including DEFB4B, FOXL2NB, MDGA2, RTL1, SLURP2, TMEM151B and TSPAN19, which showed great prediction efficiency in both training and validation cohorts. Clinical correlation analysis showed that high-risk patients might have worse clinical pathology parameters. Pathway enrichment analysis showed that riskscore was positively correlated with angiogenesis, DNA repair, G2/M checkpoints, IL6/JAK/STAT3 signaling, KRAS signaling up, WNT beta-catenin signaling, PI3K/AKT/mTOR signaling, yet positively correlated with inflammatory response, xenobiotic metabolism, TNF-α signaling via NFKB and interferon-gamma response. Immune infiltration analysis showed that the riskscore was positively correlated with the M2 and M0 macrophages, but negatively correlated with neutrophils, plasma cells, follicular helper T cells and resting dendritic cells Moreover, we found that the low-risk patients might be more sensitive to immunotherapy and lapatinib.

CONCLUSIONS

In all, our study identified the genes associated with alpha linolenic acid metabolism and constructed an effective prognosis model which could robustly predict NPC patients prognosis.

Application and prospects of single cell sequencing in tumors

Cancer is an intricate disease with inherent intra-tumor heterogeneity at the cellular level because of genetic changes and environmental differences. Cellular heterogeneity exists even within the same tumor type. Small deviations in a genome or transcriptome can lead to significant differences in function. Conventional bulk population sequencing, which produces admixed populations of cells, can only provide an average expression signal for one cell population, ignoring differences between individual cells. Important advances in sequencing have been made in recent years. Single cell sequencing starts in a single cell, thereby increasing our capability to characterize intratumor heterogeneity. This technology has been used to analyze genetic variation, specific metabolic activity, and evolutionary processes in tumors, which may help us understand tumor occurrence and development and improve our understanding of the tumor microenvironment. In addition, it provides a theoretical basis for the development of clinical treatments, especially for personalized medicine. In this article, we briefly introduce Single cell sequencing technology, summarize the application of Single cell sequencing to study the tumor microenvironment, as well as its therapeutic application in different clinical procedures.

Assessment of 5-Aminolevulinic Acid-Mediated Photodynamic Therapy on Bone Metastases: An in Vitro Study

Simple Summary

Bone metastases are typically associated with a short-term prognosis. Photodynamic therapy (PDT) emerges as a promising alternative treatment for targeting metastatic lesions. In this study we investigated the effect of 5-aminolevulinic acid-mediated PDT treatment on both primary and human bone metastatic cancer cell lines. We found that human cell lines have different sensitivity to the same doses and exposure of 5-ALA PDT resulting in two different cell fates, apoptosis or senescence, depending on the extent of the cellular damage. As such, PDT has potential applicability in bone metastases of invasive ductal carcinoma.

Abstract

Bone is a frequent site of metastases, being typically associated with a short-term prognosis in affected patients. Photodynamic therapy (PDT) emerges as a promising alternative treatment for controlling malignant disease that can directly target interstitial metastatic lesions. The aim of this study was to assess the effect induced by PDT treatment on both primary (giant cell bone tumor) and human bone metastatic cancer cell lines (derived from a primary invasive ductal breast carcinoma and renal carcinoma). After 24 h post light delivery (blue light-wavelength 436 nm) with 5-aminolevulinic acid, the effect on cellular migration, viability, apoptosis, and senescence were assessed. Our results showed that bone metastasis derived from breast cancer reacted with an inhibition of cell migration coupled with reduced viability and signs of apoptosis such as nuclei fragmentation following PDT exposure. A limited effect in terms of cellular viability inhibition was observed for the cells of giant cell bone tumors. In contrast, bone metastasis derived from renal carcinoma followed a different fate—cells were characterized by senescent features, without a notable effect on cell migration or viability. Collectively, our study illustrates that PDT could act as a successful therapy concept for local tumor control in some entities of bone metastases.

Implications of prognosis-associated genes in pancreatic tumor metastasis: lessons from global studies in bioinformatics

Pancreatic cancer (PC) is a highly lethal malignancy with a 5-year survival rate of 10%. The occurrence of metastasis, among other hallmarks, is the main contributor to its poor prognosis. Consequently, the elucidation of metastatic genes involved in the aggressive nature of the disease and its poor prognosis will result in the development of new treatment modalities for improved management of PC. There is a deep interest in understanding underlying disease pathology, identifying key prognostic genes, and genes associated with metastasis. Computational approaches, which have become increasingly relevant over the last decade, are commonly used to explore such interests. This review aims to address global studies that have employed global approaches to identify prognostic and metastatic genes, while highlighting their methods and limitations. A panel of 48 prognostic genes were identified across these studies, but only five, including ANLN, ARNTL2, PLAU, TOP2A, and VCAN, were validated in multiple studies and associated with metastasis. Their association with metastasis has been further explored here, and the implications of these genes in the metastatic cascade have been interpreted.

Analyzing Modern Biomolecules: The Revolution of Nucleic-Acid Sequencing - Review

Recent developments have revolutionized the study of biomolecules. Among them are molecular markers, amplification and sequencing of nucleic acids. The latter is classified into three generations. The first allows to sequence small DNA fragments. The second one increases throughput, reducing turnaround and pricing, and is therefore more convenient to sequence full genomes and transcriptomes. The third generation is currently pushing technology to its limits, being able to sequence single molecules, without previous amplification, which was previously impossible. Besides, this represents a new revolution, allowing researchers to directly sequence RNA without previous retrotranscription. These technologies are having a significant impact on different areas, such as medicine, agronomy, ecology and biotechnology. Additionally, the study of biomolecules is revealing interesting evolutionary information. That includes deciphering what makes us human, including phenomena like non-coding RNA expansion. All this is redefining the concept of gene and transcript. Basic analyses and applications are now facilitated with new genome editing tools, such as CRISPR. All these developments, in general, and nucleic-acid sequencing, in particular, are opening a new exciting era of biomolecule analyses and applications, including personalized medicine, and diagnosis and prevention of diseases for humans and other animals.

Antibody-drug conjugates for the treatment of lymphoma: clinical advances and latest progress

Antibody-drug conjugates (ADCs) are a promising class of immunotherapies with the potential to specifically target tumor cells and ameliorate the therapeutic index of cytotoxic drugs. ADCs comprise monoclonal antibodies, cytotoxic payloads with inherent antitumor activity, and specialized linkers connecting the two. In recent years, three ADCs, brentuximab vedotin, polatuzumab vedotin, and loncastuximab tesirine, have been approved and are already establishing their place in lymphoma treatment. As the efficacy and safety of ADCs have moved in synchrony with advances in their design, a plethora of novel ADCs have garnered growing interest as treatments. In this review, we provide an overview of the essential elements of ADC strategies in lymphoma and elucidate the up-to-date progress, current challenges, and novel targets of ADCs in this rapidly evolving field.

Recent Advances in Integrative Multi-Omics Research in Breast and Ovarian Cancer

The underlying molecular heterogeneity of cancer is responsible for the dynamic clinical landscape of this disease. The combination of genomic and proteomic alterations, including both inherited and acquired mutations, promotes tumor diversity and accounts for variable disease progression, therapeutic response, and clinical outcome. Recent advances in high-throughput proteogenomic profiling of tumor samples have resulted in the identification of novel oncogenic drivers, tumor suppressors, and signaling networks; biomarkers for the prediction of drug sensitivity and disease progression; and have contributed to the development of novel and more effective treatment strategies. In this review, we will focus on the impact of historical and recent advances in single platform and integrative proteogenomic studies in breast and ovarian cancer, which constitute two of the most lethal forms of cancer for women, and discuss the molecular similarities of these diseases, the impact of these findings on our understanding of tumor biology as well as the clinical applicability of these discoveries.