Clonal Heterogeneity and Tumor Evolution: Past, Present, and the Future

A Bayesian framework to study tumor subclone-specific expression by combining bulk DNA and single-cell RNA sequencing data

Genetic and gene expression heterogeneity is an essential hallmark of many tumors, allowing the cancer to evolve and to develop resistance to treatment. Currently, the most commonly used data types for studying such heterogeneity are bulk tumor/normal whole-genome or whole-exome sequencing (WGS, WES); and single-cell RNA sequencing (scRNA-seq), respectively. However, tools are currently lacking to link genomic tumor subclonality with transcriptomic heterogeneity by integrating genomic and single-cell transcriptomic data collected from the same tumor. To address this gap, we developed scBayes, a Bayesian probabilistic framework that uses tumor subclonal structure inferred from bulk DNA sequencing data to determine the subclonal identity of cells from single-cell gene expression (scRNA-seq) measurements. Grouping together cells representing the same genetically defined tumor subclones allows comparison of gene expression across different subclones, or investigation of gene expression changes within the same subclone across time (i.e., progression, treatment response, or relapse) or space (i.e., at multiple metastatic sites and organs). We used simulated data sets, in silico synthetic data sets, as well as biological data sets generated from cancer samples to extensively characterize and validate the performance of our method, as well as to show improvements over existing methods. We show the validity and utility of our approach by applying it to published data sets and recapitulating the findings, as well as arriving at novel insights into cancer subclonal expression behavior in our own data sets. We further show that our method is applicable to a wide range of single-cell sequencing technologies including single-cell DNA sequencing as well as Smart-seq and 10x Genomics scRNA-seq protocols.

Advancements in Preclinical Models of Pancreatic Cancer

ABSTRACT

Pancreatic cancer remains one of the deadliest of all cancer types with a 5-year overall survival rate of just 12%. Preclinical models available for understanding the disease pathophysiology have evolved significantly in recent years. Traditionally, commercially available 2-dimensional cell lines were developed to investigate mechanisms underlying tumorigenesis, metastasis, and drug resistance. However, these cells grow as monolayer cultures that lack heterogeneity and do not effectively represent tumor biology. Developing patient-derived xenografts and genetically engineered mouse models led to increased cellular heterogeneity, molecular diversity, and tissues that histologically represent the original patient tumors. However, these models are relatively expensive and very timing consuming. More recently, the advancement of fast and inexpensive in vitro models that better mimic disease conditions in vivo are on the rise. Three-dimensional cultures like organoids and spheroids have gained popularity and are considered to recapitulate complex disease characteristics. In addition, computational genomics, transcriptomics, and metabolomic models are being developed to simulate pancreatic cancer progression and predict better treatment strategies. Herein, we review the challenges associated with pancreatic cancer research and available analytical models. We suggest that an integrated approach toward using these models may allow for developing new strategies for pancreatic cancer precision medicine.

Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer

BACKGROUND

Chemotherapy resistance is the main cause of ovarian cancer progression and even death. However, there are no clear indicators for predicting the risk of drug resistance in patients. Intra-tumor heterogeneity (ITH) is one of the characteristics of malignant tumors, which is associated with the treatment and prognosis of tumors. Accordingly, our study aims to investigate the correlation between the image features of intra-tumor heterogeneity and drug resistance of ovarian cancer based on artificial intelligence.

METHODS

We obtained hematoxylin and eosin staining frozen histopathological images of ovarian cancer and paracarcinoma tissues from the Cancer Genome Atlas. We extracted quantitative image features of whole-slide images based on the automatic image nuclear segmentation processing technology. After that, we used bioinformatics analysis to find the relationship between image features of intra-tumor heterogeneity and drug resistance.

RESULTS

Our results show that our automatic image processing process based on computer artificial intelligence can extract image features effectively, and the key image features extracted are closely related to ITH. Among them, the Perimeter.sd image feature with the most prominent ITH feature can accurately predict the risk of platinum-based chemotherapy drug resistance in ovarian cancer patients.

CONCLUSION

Automatic image processing and feature extraction based on artificial intelligence have excellent results. Perimeter.sd can be used as a useful image feature indicator for evaluating ITH. ITH is associated with drug resistance of ovarian cancer, so ITH characteristics can be used as an effective indicator to evaluate drug resistance in patients with ovarian cancer.

Unravelling the heterogeneity of oral squamous cell carcinoma by integrative analysis of single-cell and bulk transcriptome data

Oral squamous cell carcinoma (OSCC) is a prevalent malignancy of the head and neck with rising global incidence. Despite advances in treatment modalities, OSCC prognosis remains diverse due to the complex molecular and cellular heterogeneity within tumours, as well as the heterogeneity in tumour microenvironment (TME). In this study, we utilized single-cell RNA sequencing (scRNA-seq) analysis to explore distinct subpopulations of tumour cells in OSCC tissues and their interaction with components in TME. We identified four major tumour cell subpopulations (C0, C1, C2 and C3) with unique molecular characteristics and functional features. Pathway enrichment analysis revealed that C0 primarily expressed genes involved in extracellular matrix interactions and C1 showed higher proliferation levels, suggesting that the two cell subpopulations exhibited tumour aggressiveness. Conversely, C2 and C3 displayed features associated with keratinization and cornified envelope formation. Accordingly, C0 and C1 subpopulations were associated with shorter overall and disease-free survival times, while C2 and C3 were weakly correlated with longer survival. Genomic analysis showed that C1 demonstrated a positive correlation with tumour mutation burden. Furthermore, C0 exhibited resistant to cisplatin treatment, while C1 showed more sensitive to cisplatin treatment, indicating that C0 might exhibit more aggressive compared to C1. Additionally, C0 had a higher level of communication with fibroblasts and endothelial cells in TME via integrin-MAPK signalling, suggesting that the function of C0 was maintained by that pathway. In summary, this study provided critical insights into the molecular and cellular heterogeneity of OSCC, with potential implications for prognosis prediction and personalized therapeutic approaches.

Dose-dependent bidirectional pharmacological effects of vinorelbine-based metronomic combination chemotherapy on tumor growth and metastasis and mechanisms in melanoma mouse model

BACKGROUND

There is evidence that the empirical setting of doses and schedules of antineoplastic agents in metronomic chemotherapy (MC) might lead to undesirable outcomes, such as promoting tumor growth or metastasis at certain low doses. However, details about the dose effect of antineoplastic agents in MC have not been fully known yet.

OBJECTIVES

Vinorelbine combined with cisplatin or fluorouracil (VNR/CDDP or VNR/FU) was selected to investigate its effects on tumor growth or metastasis as well as mechanisms.

METHODS

Experimental techniques, including immunohistochemistry, western blot, immunofluorescence, and flow cytometry, were used to explore the mechanisms, along with cell proliferation, apoptosis, migration, and invasion.

RESULTS

The results showed that VNR/CDDP or VNR/FU promoted tumor growth and metastasis at low doses and inhibited them at high ones. Except that expressions of apoptotic proteins were elevated at both low and high doses, low-dose treatments enhanced angiogenesis and promoted the mobilization and recruitment of myeloid-derived suppressor cells (MDSCs), while high-dose treatments reversed these effects. Additionally, low concentrations of VNR/CDDP or VNR/FU stimulated tumor cell functions such as anti-apoptosis, migration, and invasion, but high concentrations only suppressed cell proliferation and increased apoptosis.

CONCLUSION

This study elucidated a bidirectional action mode regulated by multiple mechanisms at different doses in MC and also highlighted the risks of low-dose metronomic administration of antineoplastic agents in the clinic. More preclinical and clinical studies focusing on the dose-effect of metronomic regimens are urgently needed because an effective therapeutic regimen should be an optimal setting of drugs, doses, schedules, or combinations.

NQO1 Mediates Lenvatinib Resistance by Regulating ROS-induced Apoptosis in Hepatocellular Carcinoma

OBJECTIVE

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-associated death worldwide. As a first-line drug for advanced HCC treatment, lenvatinib faces a significant hurdle due to the development of both intrinsic and acquired resistance among patients, and the underlying mechanism remains largely unknown. The present study aims to identify the pivotal gene responsible for lenvatinib resistance in HCC, explore the potential molecular mechanism, and propose combinatorial therapeutic targets for HCC management.

METHODS

Cell viability and colony formation assays were conducted to evaluate the sensitivity of cells to lenvatinib and dicoumarol. RNA-Seq was used to determine the differences in transcriptome between parental cells and lenvatinib-resistant (LR) cells. The upregulated genes were analyzed by GO and KEGG analyses. Then, qPCR and Western blotting were employed to determine the relative gene expression levels. Afterwards, the intracellular reactive oxygen species (ROS) and apoptosis were detected by flow cytometry.

RESULTS

PLC-LR and Hep3B-LR were established. There was a total of 116 significantly upregulated genes common to both LR cell lines. The GO and KEGG analyses indicated that these genes were involved in oxidoreductase and dehydrogenase activities, and reactive oxygen species pathways. Notably, NAD(P)H:quinone oxidoreductase 1 (NQO1) was highly expressed in LR cells, and was involved in the lenvatinib resistance. The high expression of NQO1 decreased the production of ROS induced by lenvatinib, and subsequently suppressed the apoptosis. The combination of lenvatinib and NQO1 inhibitor, dicoumarol, reversed the resistance of LR cells.

CONCLUSION

The high NQO1 expression in HCC cells impedes the lenvatinib-induced apoptosis by regulating the ROS levels, thereby promoting lenvatinib resistance in HCC cells.

Aneuploidy and complex genomic rearrangements in cancer evolution

Mutational processes that alter large genomic regions occur frequently in developing tumors. They range from simple copy number gains and losses to the shattering and reassembly of entire chromosomes. These catastrophic events, such as chromothripsis, chromoplexy and the formation of extrachromosomal DNA, affect the expression of many genes and therefore have a substantial effect on the fitness of the cells in which they arise. In this review, we cover large genomic alterations, the mechanisms that cause them and their effect on tumor development and evolution.

Intratumor genetic heterogeneity and head and neck cancer relapse

BACKGROUND

Head and neck squamous cell carcinomas are treated by surgery, radiotherapy (RT), chemoradiotherapy (CRT) or combinations thereof, but locoregional recurrences (LRs) occur in 30-40% of treated patients. We have previously shown that in approximately half of the LRs after CRT, cancer driver mutations are not shared with the index tumor.

AIM

To investigate two possible explanations for these genetically unrelated relapses, treatment-induced genetic changes and intratumor genetic heterogeneity.

METHODS

To investigate treatment-induced clonal DNA changes, we compared copy number alterations (CNAs) and mutations between primary and recurrent xenografted tumors after treatment with (C)RT. Intratumor genetic heterogeneity was studied by multi-region sequencing on DNA from 31 biopsies of 11 surgically treated tumors.

RESULTS

Induction of clonal DNA changes by (C)RT was not observed in the xenograft models. Multi-region sequencing demonstrated variations in CNA profiles between paired biopsies of individual tumors, with copy number heterogeneity scores varying from 0.027 to 0.333. In total, 32 cancer driver mutations could be identified and were shared in all biopsies of each tumor. Remarkably, multi-clonal mutations in these same cancer driver genes were observed in 6 of 11 tumors. Genetically distinct heterogeneous cell cultures could also be established from single tumors, with different biomarker profiles and drug sensitivities.

CONCLUSION

Intratumor genetic heterogeneity at the level of the cancer driver mutations might explain the discordant mutational profiles in LRs after CRT, while there are no indications in xenograft models that these changes are induced by CRT.

Multidimensional progressive single-cell sequencing reveals cell microenvironment composition and cancer heterogeneity in lung cancer

Despite substantial advances in cancer biology and treatment, the clinical outcomes of patients with lung cancer remain unsatisfactory. The tumor microenvironment (TME) is a potential target. Using single-cell RNA sequencing, we could distinguish eight distinct cell types in the lung cancer microenvironment, demonstrating substantial intratumoral heterogeneity in 19 different lung cancer tumor samples. Through the re-dimensional grouping of cancer-associated fibroblasts (CAFs), myeloid cells, epithelial cells, natural killer (NK) cells, and T cells, the difference in the TME of lung cancer was revealed. We discovered SFTPB, SFN, and KRT8 as possible predictive biomarkers for lung cancer by assessing the gene expression patterns in epithelial cells. Examining cell-to-cell communications showed a robust association between the quantity of matrix CAFs, epithelial cells, and macrophages in the thrombospondin signaling pathway. Additionally, we found that the amyloid precursor protein signaling pathway primarily originated from the matrix, and inflammatory cancer-associated endothelial and fibroblast cells showed a co-expression relationship with myeloid cells and B cells. Through cell-to-cell correlation analysis, we found positive regulation between NK cells, regulatory T cells, GZMB-CD8 T cells, and GZMK-CD8 T cells, which could play a role in developing immune TMEs. These findings support studies on cancer heterogeneity and add to our understanding of lung cancer's cellular microenvironment.

Tunable Multivalent Aptamer-Based DNA Nanostructures To Regulate Multiheteroreceptor-Mediated Tumor Recognition

Precise mapping and regulation of cell surface receptors hold immense significance in disease treatment, such as cancer, infection, and neurodisorders, but also face enormous challenges. In this study, we designed a series of adjustable multivalent aptamer-based DNA nanostructures to precisely control their interaction with receptors in tumor cells. By profiling surface receptors on 12 cell lines using 10 different aptamers, we generated a heatmap that accurately distinguished between various tumor types based on multiple markers. We then incorporated these aptamers onto DNA origami structures to regulate receptor recognition, with patch-like structures demonstrating a tendency to be trapped on the cell surface and with tube-like structures showing a preference for internalization. Through precise control of aptamer species, valence, and geometric patterns, we found that multiheteroreceptor-mediated recognition not only favored the specific binding of nanostructures to tumor cells but also greatly enhanced intracellular uptake by promoting clathrin-dependent endocytosis. Specifically, we achieved over 5-fold uptake in different tumor cells versus normal cells using tube-like structures modified with different diheteroaptamer pairs, facilitating targeted drug delivery. Moreover, patch-like structures with triheteroaptamers guided specific interactions between macrophages and tumor cells, leading to effective immune clearance. This programmable multivalent system allows for the precise regulation of cell recognition using multiple parameters, demonstrating great potential for personalized tumor treatment.

Computational validation of clonal and subclonal copy number alterations from bulk tumor sequencing using CNAqc

Copy number alterations (CNAs) are among the most important genetic events in cancer, but their detection from sequencing data is challenging because of unknown sample purity, tumor ploidy, and general intra-tumor heterogeneity. Here, we present CNAqc, an evolution-inspired method to perform the computational validation of clonal and subclonal CNAs detected from bulk DNA sequencing. CNAqc is validated using single-cell data and simulations, is applied to over 4000 TCGA and PCAWG samples, and is incorporated into the validation process for the clinically accredited bioinformatics pipeline at Genomics England. CNAqc is designed to support automated quality control procedures for tumor somatic data validation.

Dual-energy CT-based radiomics in predicting EGFR mutation status non-invasively in lung adenocarcinoma

Objectives

Patients with epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma (LUAD) can benefit from individualized targeted therapy. This study aims to develop, compare, analyse prediction models based on dual-energy spectral computed tomography (DESCT) and CT-based radiomic features to non-invasively predict EGFR mutation status in LUAD.

Materials and methods

Patients with LUAD (n = 175), including 111 patients with and 64 patients without EGFR mutations, were enrolled in the current study. All patients were randomly divided into a training dataset (122 cases) and validation dataset (53 cases) at a ratio of 7:3. After extracting CT-based radiomic, DESCT and clinical features, we built seven prediction models and a nomogram of the best prediction. Receiver operating characteristic (ROC) curves and the mean area under the curve (AUC) values were used for comparisons among the models to obtain the best prediction model for predicting EGFR mutations.

Results

The best distinguishing ability is the combined model incorporating radiomic, DESCT and clinical features for predicting the EGFR mutation status with an AUC of 0.86 (95 % CI: 0.79-0.92) in the training group and an AUC value of 0.83 (95 % CI: 0.73, 0.96) in the validation group.

Conclusions

Our study provides a predictive nomogram non-invasively with a combination of CT-based radiomic, DESCT and clinical features, which can provide image-based biological information for targeted therapy of LUAD with EGFR mutations.

Deep reinforcement learning identifies personalized intermittent androgen deprivation therapy for prostate cancer

The evolution of drug resistance leads to treatment failure and tumor progression. Intermittent androgen deprivation therapy (IADT) helps responsive cancer cells compete with resistant cancer cells in intratumoral competition. However, conventional IADT is population-based, ignoring the heterogeneity of patients and cancer. Additionally, existing IADT relies on pre-determined thresholds of prostate-specific antigen to pause and resume treatment, which is not optimized for individual patients. To address these challenges, we framed a data-driven method in two steps. First, we developed a time-varied, mixed-effect and generative Lotka-Volterra (tM-GLV) model to account for the heterogeneity of the evolution mechanism and the pharmacokinetics of two ADT drugs Cyproterone acetate and Leuprolide acetate for individual patients. Then, we proposed a reinforcement-learning-enabled individualized IADT framework, namely, I$^{2}$ADT, to learn the patient-specific tumor dynamics and derive the optimal drug administration policy. Experiments with clinical trial data demonstrated that the proposed I$^{2}$ADT can significantly prolong the time to progression of prostate cancer patients with reduced cumulative drug dosage. We further validated the efficacy of the proposed methods with a recent pilot clinical trial data. Moreover, the adaptability of I$^{2}$ADT makes it a promising tool for other cancers with the availability of clinical data, where treatment regimens might need to be individualized based on patient characteristics and disease dynamics. Our research elucidates the application of deep reinforcement learning to identify personalized adaptive cancer therapy.

Immunogram defines four cancer-immunity cycle phenotypes with distinct clonal selection patterns across solid tumors

BACKGROUND

The cancer-immunity cycle (CI cycle) provides a theoretical framework to illustrate the process of the anticancer immune response. Recently, the update of the CI cycle theory emphasizes the importance of tumor's immunological phenotype. However, there is lack of immunological phenotype of pan-cancer based on CI cycle theory.

METHODS

Here, we applied a visualizing method termed 'cancer immunogram' to visualize the state of CI cycle of 8460 solid tumors from TCGA cohort. Unsupervised clustering of the cancer immunogram was performed using the nonnegative matrix factorization (NMF) analysis. We applied an evolutionary genomics approach (dN/dS ratio) to evaluate the clonal selection patterns of tumors with distinct immunogram subtypes.

RESULTS

We defined four major CI cycle patterns across 32 cancer types using a cancer immunogram approach. Immunogram-I was characterized by 'hot' and 'exhausted' features, indicating a favorable prognosis. Strikingly, immunogram-II, immunogram-III, and immunogram-IV represented distinct immunosuppressive patterns of 'cold' tumor. Immunogram-II was characterized by 'cold' and 'radical' features, which represented increased expression of immune inhibitor molecules and high levels of positive selection, indicating the worst prognosis. Immunogram-III was characterized by 'cold' and 'recognizable' features and upregulated expression of MHC I molecules. Immunogram-IV was characterized by 'cold' and 'inert' features, which represented overall immunosuppression, lower levels of immunoediting and positive selection, and accumulation of more tumor neoantigens. In particular, favorable overall survival was observed in metastatic urothelial cancer patients with immunogram-I and immunogram-IV after immune checkpoint inhibitor (ICI) therapy. Meanwhile, a higher response rate to ICI therapy was observed in metastatic gastric cancer patients with immunogram-I phenotype.

CONCLUSIONS

Our findings provide new insight into the interaction between immunity and cancer evolution, which may contribute to optimizing immunotherapy strategies.

Clinical application of tumour-in-normal contamination assessment from whole genome sequencing

The unexpected contamination of normal samples with tumour cells reduces variant detection sensitivity, compromising downstream analyses in canonical tumour-normal analyses. Leveraging whole-genome sequencing data available at Genomics England, we develop a tool for normal sample contamination assessment, which we validate in silico and against minimal residual disease testing. From a systematic review of [Formula: see text] patients with haematological malignancies and sarcomas, we find contamination across a range of cancer clinical indications and DNA sources, with highest prevalence in saliva samples from acute myeloid leukaemia patients, and sorted CD3+ T-cells from myeloproliferative neoplasms. Further exploration reveals 108 hotspot mutations in genes associated with haematological cancers at risk of being subtracted by standard variant calling pipelines. Our work highlights the importance of contamination assessment for accurate somatic variants detection in research and clinical settings, especially with large-scale sequencing projects being utilised to deliver accurate data from which to make clinical decisions for patient care.

Modulating ferroptosis sensitivity: environmental and cellular targets within the tumor microenvironment

Ferroptosis, a novel form of cell death triggered by iron-dependent phospholipid peroxidation, presents significant therapeutic potential across diverse cancer types. Central to cellular metabolism, the metabolic pathways associated with ferroptosis are discernible in both cancerous and immune cells. This review begins by delving into the intricate reciprocal regulation of ferroptosis between cancer and immune cells. It subsequently details how factors within the tumor microenvironment (TME) such as nutrient scarcity, hypoxia, and cellular density modulate ferroptosis sensitivity. We conclude by offering a comprehensive examination of distinct immunophenotypes and environmental and metabolic targets geared towards enhancing ferroptosis responsiveness within the TME. In sum, tailoring precise ferroptosis interventions and combination strategies to suit the unique TME of specific cancers may herald improved patient outcomes.

Epigenetic reprogramming shapes the cellular landscape of schwannoma

Mechanisms specifying cancer cell states and response to therapy are incompletely understood. Here we show epigenetic reprogramming shapes the cellular landscape of schwannomas, the most common tumors of the peripheral nervous system. We find schwannomas are comprised of 2 molecular groups that are distinguished by activation of neural crest or nerve injury pathways that specify tumor cell states and the architecture of the tumor immune microenvironment. Moreover, we find radiotherapy is sufficient for interconversion of neural crest schwannomas to immune-enriched schwannomas through epigenetic and metabolic reprogramming. To define mechanisms underlying schwannoma groups, we develop a technique for simultaneous interrogation of chromatin accessibility and gene expression coupled with genetic and therapeutic perturbations in single-nuclei. Our results elucidate a framework for understanding epigenetic drivers of tumor evolution and establish a paradigm of epigenetic and metabolic reprograming of cancer cells that shapes the immune microenvironment in response to radiotherapy.

Exploring the relationship between metabolism and immune microenvironment in osteosarcoma based on metabolic pathways

BACKGROUND

Metabolic remodeling and changes in tumor immune microenvironment (TIME) in osteosarcoma are important factors affecting prognosis and treatment. However, the relationship between metabolism and TIME needs to be further explored.

METHODS

RNA-Seq data and clinical information of 84 patients with osteosarcoma from the TARGET database and an independent cohort from the GEO database were included in this study. The activity of seven metabolic super-pathways and immune infiltration levels were inferred in osteosarcoma patients. Metabolism-related genes (MRGs) were identified and different metabolic clusters and MRG-related gene clusters were identified using unsupervised clustering. Then the TIME differences between the different clusters were compared. In addition, an MRGs-based risk model was constructed and the role of a key risk gene, ST3GAL4, in osteosarcoma cells was explored using molecular biological experiments.

RESULTS

This study revealed four key metabolic pathways in osteosarcoma, with vitamin and cofactor metabolism being the most relevant to prognosis and to TIME. Two metabolic pathway-related clusters (C1 and C2) were identified, with some differences in immune activating cell infiltration between the two clusters, and C2 was more likely to respond to two chemotherapeutic agents than C1. Three MRG-related gene clusters (GC1-3) were also identified, with significant differences in prognosis among the three clusters. GC2 and GC3 had higher immune cell infiltration than GC1. GC3 is most likely to respond to immune checkpoint blockade and to three commonly used clinical drugs. A metabolism-related risk model was developed and validated. The risk model has strong prognostic predictive power and the low-risk group has a higher level of immune infiltration than the high-risk group. Knockdown of ST3GAL4 significantly inhibited proliferation, migration, invasion and glycolysis of osteosarcoma cells and inhibited the M2 polarization of macrophages.

CONCLUSION

The metabolism of vitamins and cofactors is an important prognostic regulator of TIME in osteosarcoma, MRG-related gene clusters can well reflect changes in osteosarcoma TIME and predict chemotherapy and immunotherapy response. The metabolism-related risk model may serve as a useful prognostic predictor. ST3GAL4 plays a critical role in the progression, glycolysis, and TIME of osteosarcoma cells.

Perspective: rethinking therapeutic strategies in oncology

Immuno-oncology has revolutionized cancer care, drug development, the design of clinical trials, standard treatment paradigms, and the evaluation of response to therapy. These are all areas, however, that have not fully incorporated principles of tumor immunology. Insufficient emphasis is put on the effect drugs have on the immune system, and specifically, the impact that multiple lines of therapy can have on the functioning of the immune system, hindering a robust anti-tumor immune response. A paradigm shift in how we approach the development of novel immunotherapeutic agents is necessary to facilitate the effective improvements in patient outcomes.

Coefficient of Variation in Metastatic Lymph Nodes Determined by 18F-FDG PET/CT in Patients with Advanced NSCLC: Combination with Coefficient of Variation in Primary Tumors

Purpose The aim of the present study was to test whether the coefficient of variation (CoV) of 18F-FDG PET/CT images of metastatic lymph nodes and primary tumors may predict clinical outcome in patients with advanced non-small cell lung cancer (NSCLC). Materials and Methods Fifty-eight NSCLC patients who had undergone 18F-FDG PET/CT at diagnosis were evaluated. SUVmax, SUVmean, CoV, MTV and TLG were determined in targeted lymph nodes and corresponding primary tumors along with Total MTV (MTVTOT) and Whole-Body TLG (TLGWB) of all malignant lesions. Univariate analysis was performed using Cox proportional hazards regression whereas the Kaplan-Meier method and log-rank tests were used for survival analysis. Results Fifty-eight metastatic lymph nodes were analyzed and average values of SUVmax, SUVmean, CoV, MTV and TLG were 11.89 ± 8.54, 4.85 ± 1.90, 0.37 ± 0.16, 46.16 ± 99.59 mL and 256.84 ± 548.27 g, respectively, whereas in primary tumors they were 11.92 ± 6.21, 5.47 ± 2.34, 0.36 ± 0.14, 48.03 ± 64.45 mL and 285.21 ± 397.95 g, respectively. At univariate analysis, overall survival (OS) was predicted by SUVmax (p = 0.0363), SUVmean (p = 0.0200) and CoV (p = 0.0139) of targeted lymph nodes as well as by CoV of primary tumors (p = 0.0173), MTVTOT (p = 0.0007), TLGWB (p = 0.0129) and stage (p = 0.0122). Using Kaplan-Meier analysis, OS was significantly better in patients with CoV of targeted lymph nodes ≤ 0.29 than those with CoV > 0.29 (p = 0.0147), meanwhile patients with CoV of primary tumors > 0.38 had a better prognosis compared to those with CoV ≤ 0.38 (p = 0.0137). Finally, we combined the CoV values of targeted lymph nodes and primary tumors in all possible arrangements and a statistically significant difference was found among the four survival curves (p = 0.0133). In particular, patients with CoV of targeted lymph nodes ≤ 0.29 and CoV of primary tumors > 0.38 had the best prognosis. Conclusions The CoV of targeted lymph nodes combined with the CoV of primary tumors can predict prognosis of NSCLC patients.

Deciphering the role of neddylation in tumor microenvironment modulation: common outcome of multiple signaling pathways

Neddylation is a post-translational modification process, similar to ubiquitination, that controls several biological processes. Notably, it is often aberrantly activated in neoplasms and plays a critical role in the intricate dynamics of the tumor microenvironment (TME). This regulatory influence of neddylation permeates extensively and profoundly within the TME, affecting the behavior of tumor cells, immune cells, angiogenesis, and the extracellular matrix. Usually, neddylation promotes tumor progression towards increased malignancy. In this review, we highlight the latest understanding of the intricate molecular mechanisms that target neddylation to modulate the TME by affecting various signaling pathways. There is emerging evidence that the targeted disruption of the neddylation modification process, specifically the inhibition of cullin-RING ligases (CRLs) functionality, presents a promising avenue for targeted therapy. MLN4924, a small-molecule inhibitor of the neddylation pathway, precisely targets the neural precursor cell-expressed developmentally downregulated protein 8 activating enzyme (NAE). In recent years, significant advancements have been made in the field of neddylation modification therapy, particularly the integration of MLN4924 with chemotherapy or targeted therapy. This combined approach has demonstrated notable success in the treatment of a variety of hematological and solid tumors. Here, we investigated the inhibitory effects of MLN4924 on neddylation and summarized the current therapeutic outcomes of MLN4924 against various tumors. In conclusion, this review provides a comprehensive, up-to-date, and thorough overview of neddylation modifications, and offers insight into the critical importance of this cellular process in tumorigenesis.

Shift in Tissue-Specific Immune Niches and CD137 Expression in Tuberculoma of Pembrolizumab-Treated Nasopharyngeal Carcinoma Patients

The use of immune checkpoint inhibitors (ICIs) in cancer treatment has shown promise but can also have unintended consequences, such as reactivating latent tuberculosis (TB). To develop treatments that address ICIs-related adverse events, it is essential to understand cellular heterogeneity across healthy and pathological tissues. We performed cross-tissue multiplexed staining analysis on samples from two patients with TB reactivation during pembrolizumab treatment for metastatic nasopharyngeal carcinoma. CD8+ T cells, rather than CD4+ T cells, accumulated preferentially in the tuberculoma and were associated with increased production of IFNγ and expression of CD137. Additionally, CD137 enrichment played a role in the spatial organization of the tuberculoma, with specific interaction limited to spatial proximal cells between IFNγ+ CD137+ CD8+ T cells and IL12+ CD137+ type-1 macrophages. This unique feature was not observed in non-tumoral or tumoral tissues. Our analysis of public transcriptomic datasets supported the notion that this cellular interaction was more prominent in patients with durable ICI responses compared to those with non-ICI-related TB. We suggest that shifts towards CD137-rich immune niches are correlated with both off-target immune-related adverse events and anti-tumor efficacy. Targeting the tumor microenvironment through conditional activation of anti-CD137 signaling in combination with ICIs can modulate the reactivity of T cells and macrophages for localized tumor killing without the potential off-target immune-related risks associated with ICIs alone.

Pathogenesis, clinical features, and treatment of plurihormonal pituitary adenoma

Plurihormonal pituitary adenoma (PPA) is a type of pituitary tumor capable of producing two or more hormones and usually presents as an aggressive, large adenoma. As yet, its pathogenesis remains unclear. This is the first study to systematically summarize the underlying pathogenesis of PPA. The pathogenesis is related to plurihormonal primordial stem cells, co-transcription factors, hormone co-expression, differential gene expression, and cell transdifferentiation. We conducted a literature review of PPA and analyzed its clinical characteristics. We found that the average age of patients with PPA was approximately 40 years, and most showed only one clinical symptom. The most common manifestation was acromegaly. Currently, PPA is treated with surgical resection. However, recent studies suggest that immunotherapy may be a potentially effective treatment.

Phenotype-structured model of intra-clonal heterogeneity and drug resistance in multiple myeloma

Multiple myeloma (MM) is a genetically complex hematological cancer characterized by the abnormal proliferation of malignant plasma cells in the bone marrow. This disease progresses from a premalignant condition known as monoclonal gammopathy of unknown significance (MGUS) through sequential genetic alterations involving various genes. These genetic changes contribute to the uncontrolled growth of multiple clones of plasma cells. In this study, we present a phenotype-structured model that captures the intra-clonal heterogeneity and drug resistance in multiple myeloma (MM). The model accurately reproduces the branching evolutionary pattern observed in MM progression, aligning with a previously developed multiscale model. Numerical simulations reveal that higher mutation rates enhance tumor phenotype diversity, while access to growth factors accelerates tumor evolution and increases its final size. Interestingly, the model suggests that further increasing growth factor access primarily amplifies tumor size rather than altering clonal dynamics. Additionally, the model emphasizes that higher mutation frequencies and growth factor availability elevate the chances of drug resistance and relapse. It indicates that the timing of the treatment could trajectory of tumor evolution and clonal emergence in the case of branching evolutionary pattern. Given its low computational cost, our model is well-suited for quantitative studies on MM clonal heterogeneity and its interaction with chemotherapeutic treatments.

Characterization of dabrafenib-induced drug insensitivity via cellular barcoding and collateral sensitivity to second-line therapeutics

Drug insensitivity is arguably one of the biggest challenges in cancer therapeutics. Although effective therapeutic solutions in cancer are limited due to the emergence of drug insensitivity, exploiting evolutionary understanding in this context can provide potential second-line therapeutics sensitizing the drug insensitive populations. Targeted therapeutic agent dabrafenib is used to treat CRC patients with BRAF V600E genotype and insensitivity to dabrafenib is often observed. Understanding underlying clonal architecture of dabrafenib-induced drug insensitivity and identification of potential second-line therapeutics that could sensitize dabrafenib insensitive populations remain to be elucidated. For this purpose, we utilized cellular barcoding technology to decipher dabrafenib-induced clonal evolution in BRAF V600E mutant HT-29 cells. This approach revealed the detection of both pre-existing and de novo barcodes with increased frequencies as a result of dabrafenib insensitivity. Furthermore, our longitudinal monitoring of drug insensitivity based on barcode detection from floating DNA within used medium enabled to identify temporal dynamics of pre-existing and de novo barcodes in relation to dabrafenib insensitivity in HT-29 cells. Moreover, whole-exome sequencing analysis exhibited possible somatic CNVs and SNVs contributing to dabrafenib insensitivity in HT-29 cells. Last, collateral drug sensitivity testing demonstrated oxaliplatin and capecitabine, alone or in combination, as successful second-like therapeutics in inducing collateral sensitivity in dabrafenib-insensitive HT-29 cells. Overall, our findings demonstrate clonal dynamics of dabrafenib-insensitivity in HT-29 cells. In addition, oxaliplatin and capecitabine, alone or in combination, were successful second-line therapeutics in inducing collateral sensitivity in dabrafenib-insensitive HT-29 cells.

Development of a molecular barcode detection system for pancreaticobiliary malignancies and comparison with next-generation sequencing

BACKGROUND

Obtaining sufficient tumor tissue for genomic profiling is challenging in pancreaticobiliary cancer (PBCA). We determined the utility of molecular barcoding (MB) of liquid biopsies (bile, duodenal fluid, and plasma) for highly sensitive genomic diagnosis and detection of druggable mutations for PBCA.

METHODS

Two in-house panels of 60 genes (non-MB panel) and 21 genes using MB (MB panel) were used for the genomic analysis of 112 DNA samples from 20 PBCA patients. We measured the yield of DNA and compared the genomic profiles of liquid samples obtained using the non-MB panel and the MB panel. The utility of the panels in detecting druggable mutations was investigated.

RESULTS

A significantly greater amount of DNA was obtained from bile supernatants and precipitates compared to tumor samples (P < 0.001 and P = 0.001, respectively). The number of mutations per patient was significantly higher using the MB panel than using the non-MB panel (2.8 vs. 1.3, P = 0.002). Tumor-derived mutations were detected more frequently using the MB panel than the non-MB panel (P = 0.023). Five drug-matched mutations were detected in liquid samples.

CONCLUSIONS

Liquid biopsy with MB may have utility in providing genomic information for the prognosis of patients with PBCA.

ZNF689 deficiency promotes intratumor heterogeneity and immunotherapy resistance in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is an aggressive disease characterized by remarkable intratumor heterogeneity (ITH), which poses therapeutic challenges. However, the clinical relevance and key determinant of ITH in TNBC are poorly understood. Here, we comprehensively characterized ITH levels using multi-omics data across our center's cohort (n = 260), The Cancer Genome Atlas cohort (n = 134), and four immunotherapy-treated cohorts (n = 109). Our results revealed that high ITH was associated with poor patient survival and immunotherapy resistance. Importantly, we identified zinc finger protein 689 (ZNF689) deficiency as a crucial determinant of ITH formation. Mechanistically, the ZNF689-TRIM28 complex was found to directly bind to the promoter of long interspersed element-1 (LINE-1), inducing H3K9me3-mediated transcriptional silencing. ZNF689 deficiency reactivated LINE-1 retrotransposition to exacerbate genomic instability, which fostered ITH. Single-cell RNA sequencing, spatially resolved transcriptomics and flow cytometry analysis confirmed that ZNF689 deficiency-induced ITH inhibited antigen presentation and T-cell activation, conferring immunotherapy resistance. Pharmacological inhibition of LINE-1 significantly reduced ITH, enhanced antitumor immunity, and eventually sensitized ZNF689-deficient tumors to immunotherapy in vivo. Consistently, ZNF689 expression positively correlated with favorable prognosis and immunotherapy response in clinical samples. Altogether, our study uncovers a previously unrecognized mechanism underlying ZNF689 deficiency-induced ITH and suggests LINE-1 inhibition combined with immunotherapy as a novel treatment strategy for TNBC.

Immunoblot Analysis from Single Cells Using Milo™ Single-Cell Western Platform

In this new era of precision medicine, characterization of single-cell subpopulations to better understand disease etiology is paramount. It is thus an opportune time to explore techniques that allow molecular analysis of single cells and to better understand the basis of pathogenesis of diseases like cancer. Single-cell western blotting is one such method that allows analysis of single cells at the protein level. In contrast to traditional western blotting, which relies heavily on bulk analysis of lysates generated from tissues and is often indicative of the population average, this technique allows analysis of lysates from single-cell subpopulations thereby providing a glimpse into cell heterogeneity. The method entails the use of a chip containing 30 μm thick photoactivated polyacrylamide gel spotted with nearly 6400 microwells. Single cells loaded on the chip are captured in the microwells by passive gravity and are then lysed and electrophoresed using the MILO™ single-cell western platform. This method forgoes the use of transfer of proteins on a PVDF and a nitrocellulose membrane, as performed in traditional western blotting, and all other steps including probing of primary and fluorescent secondary antibodies against the protein of interest are performed directly on the chip. The proteins of interest can then be visualized by scanning a chip with the use of a microarray scanner. The entire procedure can be performed in as less as 4-6 h, and thus this method provides several advantages over traditional western blotting.

Oral tumor heterogeneity, its implications for patient monitoring and designing anti-cancer strategies

Oral cancer tumors occur in the mouth and are mainly derived from oral mucosa linings. It is one of the most common and fatal malignant diseases worldwide. The intratumor heterogeneity (ITH) of oral cancerous tumor is vast, so it is challenging to study and interpret. Due to environmental selection pressures, ITH arises through diverse genetic, epigenetic, and metabolic alterations. The ITH also talks about peri-tumoral vascular/ lymphatic growth, perineural permeation, tumor necrosis, invasion, and clonal expansion/ the coexistence of multiple subclones in a single tumor. The heterogeneity offers tumors the adaptability to survive, induce growth/ metastasis, and, most importantly, escape antitumor therapy. Unfortunately, the ITH is prioritized less in determining disease pathology than the traditional TNM classifications or tumor grade. Understanding ITH is challenging, but with the advancement of technology, this ITH can be decoded. Tumor genomics, proteomics, metabolomics, and other modern analyses can provide vast information. This information in clinics can assist in understanding a tumor's severity and be used for diagnostic, prognostic, and therapeutic decision-making. Lastly, the oral tumor ITH can lead to individualized, targeted therapy strategies fighting against OC.

Ovarian Cancer Diagnosis and Prognosis Based on Cell-Free DNA Methylation

Background: Ovarian cancer stands as the deadliest malignant tumor within the female reproductive tract. As a result of the absence of effective diagnostic and monitoring markers, 75% of ovarian cancer cases are diagnosed at a late stage, leading to a mere 50% survival rate within five years. The advancement of molecular biology is essential for accurate diagnosis and treatment of ovarian cancer. Methods: A review of several randomized clinical trials, focusing on the ovarian cancer, was undertaken. The advancement of molecular biology and diagnostic methods related to accurate diagnosis and treatment of ovarian cancer were examined. Results: Liquid biopsy is an innovative method of detecting malignant tumors that has gained increasing attention over the past few years. Cell-free DNA assay-based liquid biopsies show potential in delineating tumor status heterogeneity and tracking tumor recurrence. DNA methylation influences a multitude of biological functions and diseases, especially during the initial phases of cancer. The cell-free DNA methylation profiling system has emerged as a sensitive and non-invasive technique for identifying and detecting the biological origins of cancer. It holds promise as a biomarker, enabling early screening, recurrence monitoring, and prognostic evaluation of cancer. Conclusions: This review evaluates recent advancements and challenges associated with cell-free DNA methylation analysis for the diagnosis, prognosis monitoring, and assessment of therapeutic responses in the management of ovarian cancers, aiming to offer guidance for precise diagnosis and treatment of this disease.

Epigenetic modifications: Key players in cancer heterogeneity and drug resistance

Cancer heterogeneity and drug resistance remain pivotal obstacles in effective cancer treatment and management. One major contributor to these challenges is epigenetic modifications - gene regulation that does not involve changes to the DNA sequence itself but significantly impacts gene expression. As we elucidate these phenomena, we underscore the pivotal role of epigenetic modifications in regulating gene expression, contributing to cellular diversity, and driving adaptive changes that can instigate therapeutic resistance. This review dissects essential epigenetic modifications - DNA methylation, histone modifications, and chromatin remodeling - illustrating their significant yet complex contributions to cancer biology. While these changes offer potential avenues for therapeutic intervention due to their reversible nature, the interplay of epigenetic and genetic changes in cancer cells presents unique challenges that must be addressed to harness their full potential. By critically analyzing the current research landscape, we identify knowledge gaps and propose future research directions, exploring the potential of epigenetic therapies and discussing the obstacles in translating these concepts into effective treatments. This comprehensive review aims to stimulate further research and aid in developing innovative, patient-centered cancer therapies. Understanding the role of epigenetic modifications in cancer heterogeneity and drug resistance is critical for scientific advancement and paves the way towards improving patient outcomes in the fight against this formidable disease.

Tissue or liquid rebiopsy? A prospective study for simultaneous tissue and liquid NGS after first-line EGFR inhibitor resistance in lung cancer

INTRODUCTION

According to current International Association for the Study of Lung Cancer guideline, physicians may first use plasma cell-free DNA (cfDNA) methods to identify epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI)-resistant mechanisms (liquid rebiopsy) for lung cancer. Tissue rebiopsy is recommended if the plasma result is negative. However, this approach has not been evaluated prospectively using next-generation sequencing (NGS).

METHODS

We prospectively enrolled patients with lung cancer with first-line EGFR-TKI resistance who underwent tissue rebiopsy. The rebiopsied tissues and cfDNA were sequenced using targeted NGS, ACTDrug®+, and ACTMonitor®Lung simultaneously. The clinicopathological characteristics and treatment outcomes were analyzed.

RESULTS

Totally, 86 patients were enrolled. Twenty-six (30%) underwent tissue biopsy but the specimens were inadequate for NGS. Among the 60 patients with paired tissue and liquid rebiopsies, two-thirds (40/60) may still be targetable. T790M mutations were found in 29, including 14 (48%) only from tissue and 5 (17%) only from cfDNA. Twenty-four of them were treated with osimertinib, and progression-free survival was longer in patients without detectable T790M in cfDNA than in patients with detectable T790M in cfDNA (p = 0.02). For the 31 T790M-negative patients, there were six with mesenchymal-epithelial transition factor (MET) amplifications, four with ERBB2 amplifications, and one with CCDC6-RET fusion. One with MET amplification and one with ERBB2 amplification responded to subsequent MET and ERBB2 targeting agents respectively.

CONCLUSIONS

NGS after EGFR-TKI resistance may detect targetable drivers besides T790M. To do either liquid or tissue NGS only could miss patients with T790M. To do tissue and liquid NGS in parallel after EGFR-TKI resistance may find more patients with targetable cancers.

In Vitro Evaluation of Cancer Cell Immunogenicity and Antigen-Specific T-Cell Cytotoxicity by Flow Cytometry

A cardinal principle of oncoimmunology is that cancer cells can be eliminated by tumor-infiltrating cytotoxic CD8 T lymphocytes. This has been widely demonstrated during the last 20 years and also recently harnessed for therapy. However, emerging evidence indicates that even neoplasms showing striking initial responses to conventional and targeted (immuno)therapies often acquire resistance, resulting in tumor relapse, increased aggressiveness, and metastatization. Indeed, tumors are complex ecosystems whose malignant and nonmalignant cells, constituting the tumor microenvironment, constantly interact and evolve in space and time. Together with patient's own genetic factors, such environmental interplays may curtail antitumor immune responses leading to cancer immune evasion and natural/acquired (immuno)therapy resistance. In this context, cancer stem cells (CSCs) are thought to be the roots of therapy failure. Flow cytometry is a powerful technology that finds extensive applications in cancer biology. It offers several unique advantages as it allows the rapid, quantitative, and multiparametric analysis of cell populations or functions at the single-cell level. In this chapter, we discuss a two-color flow cytometric protocol to evaluate cancer cell immunogenicity by analyzing the proliferative and tumor-killing potential of ovalbumin (OVA)-specific CD8 OT-1 T cells exposed to OVA-expressing MCA205 sarcoma cells and their CSC counterparts.

Nrf2, A Target for Precision Oncology in Cancer Prognosis and Treatment

Activating nuclear factor-erythroid 2-related factor (Nrf2), a master regulator of redox homeostasis, has been shown to suppress initiation of carcinogenesis in normal cells. However, this transcription factor has recently been reported to promote proliferation of some transformed or cancerous cells. In tumor cells, Nrf2 is prone to mutations that result in stabilization and concurrent accumulation of its protein product. A hyperactivated mutant form of Nrf2 could support the cancer cells for enhanced proliferation, invasiveness, and resistance to chemotherapeutic agents and radiotherapy, which are associated with a poor clinical outcome. Hence understanding mutations in Nrf2 would have a significant impact on the prognosis and treatment of cancer in the era of precision medicine. This perspective would provide an insight into the genetic alterations in Nrf2 and suggest the application of small molecules, RNAi, and genome editing technologies, particularly CRISR-Cas9, in therapeutic intervention of cancer in the context of the involvement of Nrf2 mutations.

A Novel Tissue-Free Method to Estimate Tumor-Derived Cell-Free DNA Quantity Using Tumor Methylation Patterns

Estimating the abundance of cell-free DNA (cfDNA) fragments shed from a tumor (i.e., circulating tumor DNA (ctDNA)) can approximate tumor burden, which has numerous clinical applications. We derived a novel, broadly applicable statistical method to quantify cancer-indicative methylation patterns within cfDNA to estimate ctDNA abundance, even at low levels. Our algorithm identified differentially methylated regions (DMRs) between a reference database of cancer tissue biopsy samples and cfDNA from individuals without cancer. Then, without utilizing matched tissue biopsy, counts of fragments matching the cancer-indicative hyper/hypo-methylated patterns within DMRs were used to determine a tumor methylated fraction (TMeF; a methylation-based quantification of the circulating tumor allele fraction and estimate of ctDNA abundance) for plasma samples. TMeF and small variant allele fraction (SVAF) estimates of the same cancer plasma samples were correlated (Spearman's correlation coefficient: 0.73), and synthetic dilutions to expected TMeF of 10-3 and 10-4 had estimated TMeF within two-fold for 95% and 77% of samples, respectively. TMeF increased with cancer stage and tumor size and inversely correlated with survival probability. Therefore, tumor-derived fragments in the cfDNA of patients with cancer can be leveraged to estimate ctDNA abundance without the need for a tumor biopsy, which may provide non-invasive clinical approximations of tumor burden.

Optimized DOX Drug Deliveries via Chitosan-Mediated Nanoparticles and Stimuli Responses in Cancer Chemotherapy: A Review

Chitosan nanoparticles (NPs) serve as useful multidrug delivery carriers in cancer chemotherapy. Chitosan has considerable potential in drug delivery systems (DDSs) for targeting tumor cells. Doxorubicin (DOX) has limited application due to its resistance and lack of specificity. Chitosan NPs have been used for DOX delivery because of their biocompatibility, biodegradability, drug encapsulation efficiency, and target specificity. In this review, various types of chitosan derivatives are discussed in DDSs to enhance the effectiveness of cancer treatments. Modified chitosan-DOX NP drug deliveries with other compounds also increase the penetration and efficiency of DOX against tumor cells. We also highlight the endogenous stimuli (pH, redox, enzyme) and exogenous stimuli (light, magnetic, ultrasound), and their positive effect on DOX drug delivery via chitosan NPs. Our study sheds light on the importance of chitosan NPs for DOX drug delivery in cancer treatment and may inspire the development of more effective approaches for cancer chemotherapy.

Spatial Proteomics toward Subcellular Resolution by Coupling Deep Ultraviolet Laser Ablation with Nanodroplet Sample Preparation

Multiplexed molecular profiling of tissue microenvironments, or spatial omics, can provide critical insights into cellular functions and disease pathology. The coupling of laser microdissection with mass spectrometry-based proteomics has enabled deep and unbiased mapping of >1000 proteins. However, the throughput of laser microdissection is often limited due to tedious two-step procedures, sequential laser cutting, and sample collection. The two-step procedure also hinders the further improvement of spatial resolution to <10 μm as needed for subcellular proteomics. Herein, we developed a high-throughput and high-resolution spatial proteomics platform by seamlessly coupling deep ultraviolet (DUV) laser ablation (LA) with nanoPOTS (Nanodroplet Processing in One pot for Trace Samples)-based sample preparation. We demonstrated the DUV-LA system can quickly isolate and collect tissue samples at a throughput of ∼30 spots/min and a spatial resolution down to 2 μm from a 10 μm thick human pancreas tissue section. To improve sample recovery, we developed a proximity aerosol collection approach by placing DMSO droplets close to LA spots. We demonstrated the DUV-LA-nanoPOTS platform can detect an average of 1312, 1533, and 1966 proteins from ablation spots with diameters of 7, 13, and 19 μm, respectively. In a proof-of-concept study, we isolated and profiled two distinct subcellular regions of the pancreas tissue revealed by hematoxylin and eosin (H&E) staining. Quantitative proteomics revealed proteins specifically enriched to subcellular compartments.

Circulating Tumor DNA and Management of Colorectal Cancer

Although the incidence of colorectal cancer (CRC) has decreased as a result of increased screening and awareness, it still remains a major cause of cancer-related death. Additionally, early detection of CRC recurrence by conventional means such as CT, endoscopy, and CEA has not translated into an improvement in survival. Liquid biopsies, such as the detection circulating tumor DNA (ctDNA), have been investigated as a biomarker for patients with CRC in terms of prognosis and recurrence, as well as their use to guide therapy. In this manuscript, we provide an overview of ctDNA as well as its utility in providing prognostic information, using it to guide therapy, and monitoring for recurrence in patients with CRC. In addition, we discuss the influence the site of disease may have on the ability to detect ctDNA in patients with metastatic CRC.

Cancer-Associated Fibroblast-Like Tumor Cells Remodel the Ewing Sarcoma Tumor Microenvironment

PURPOSE

Despite limited genetic and histologic heterogeneity, Ewing sarcoma (EwS) tumor cells are transcriptionally heterogeneous and display varying degrees of mesenchymal lineage specification in vitro. In this study, we investigated if and how transcriptional heterogeneity of EwS cells contributes to heterogeneity of tumor phenotypes in vivo.

EXPERIMENTAL DESIGN

Single-cell proteogenomic-sequencing of EwS cell lines was performed and integrated with patient tumor transcriptomic data. Cell subpopulations were isolated by FACS for assessment of gene expression and phenotype. Digital spatial profiling and human whole transcriptome analysis interrogated transcriptomic heterogeneity in EwS xenografts. Tumor cell subpopulations and matrix protein deposition were evaluated in xenografts and patient tumors using multiplex immunofluorescence staining.

RESULTS

We identified CD73 as a biomarker of highly mesenchymal EwS cell subpopulations in tumor models and patient biopsies. CD73+ tumor cells displayed distinct transcriptional and phenotypic properties, including selective upregulation of genes that are repressed by EWS::FLI1, and increased migratory potential. CD73+ cells were distinguished in vitro and in vivo by increased expression of matrisomal genes and abundant deposition of extracellular matrix (ECM) proteins. In epithelial-derived malignancies, ECM is largely deposited by cancer-associated fibroblasts (CAF), and we thus labeled CD73+ EwS cells, CAF-like tumor cells. Marked heterogeneity of CD73+ EwS cell frequency and distribution was detected in tumors in situ, and CAF-like tumor cells and associated ECM were observed in peri-necrotic regions and invasive foci.

CONCLUSIONS

EwS tumor cells can adopt CAF-like properties, and these distinct cell subpopulations contribute to tumor heterogeneity by remodeling the tumor microenvironment. See related commentary by Kuo and Amatruda, p. 5002.

Single cell multi-omics reveal intra-cell-line heterogeneity across human cancer cell lines

Human cancer cell lines have long served as tools for cancer research and drug discovery, but the presence and the source of intra-cell-line heterogeneity remain elusive. Here, we perform single-cell RNA-sequencing and ATAC-sequencing on 42 and 39 human cell lines, respectively, to illustrate both transcriptomic and epigenetic heterogeneity within individual cell lines. Our data reveal that transcriptomic heterogeneity is frequently observed in cancer cell lines of different tissue origins, often driven by multiple common transcriptional programs. Copy number variation, as well as epigenetic variation and extrachromosomal DNA distribution all contribute to the detected intra-cell-line heterogeneity. Using hypoxia treatment as an example, we demonstrate that transcriptomic heterogeneity could be reshaped by environmental stress. Overall, our study performs single-cell multi-omics of commonly used human cancer cell lines and offers mechanistic insights into the intra-cell-line heterogeneity and its dynamics, which would serve as an important resource for future cancer cell line-based studies.

Emerging Therapeutic Targets and Drug Resistance Mechanisms in Immunotherapy of Hematological Malignancies

CAR-T cell therapy has revolutionized the treatment of hematological malignancies with high remission rates in the case of ALL and NHL. This therapy has some limitations such as long manufacturing periods, persistent restricted cell sources and high costs. Moreover, combination regimens increase the risk of immune-related adverse events, so the identification new therapeutic targets is important to minimize the risk of toxicities and to guide more effective approaches. Cancer cells employ several mechanisms to evade immunosurveillance, which causes resistance to immunotherapy; therefore, a very important therapeutic approach is to focus on the development of rational combinations of targeted therapies with non-overlapping toxicities. Recent progress in the development of new inhibitory clusters of differentiation (CDs), signaling pathway molecules, checkpoint inhibitors, and immunosuppressive cell subsets and factors in the tumor microenvironment (TME) has significantly improved anticancer responses. Novel strategies regarding combination immunotherapies with CAR-T cells are the most promising approach to cure cancer.

Adaptive DNA amplification of synthetic gene circuit opens a way to overcome cancer chemoresistance

Drug resistance continues to impede the success of cancer treatments, creating a need for experimental model systems that are broad, yet simple, to allow the identification of mechanisms and novel countermeasures applicable to many cancer types. To address these needs, we investigated a set of engineered mammalian cell lines with synthetic gene circuits integrated into their genome that evolved resistance to Puromycin. We identified DNA amplification as the mechanism underlying drug resistance in 4 out of 6 replicate populations. Triplex-forming oligonucleotide (TFO) treatment combined with Puromycin could efficiently suppress the growth of cell populations with DNA amplification. Similar observations in human cancer cell lines suggest that TFOs could be broadly applicable to mitigate drug resistance, one of the major difficulties in treating cancer.

Localization, tissue biology and T cell state - implications for cancer immunotherapy

Tissue localization is a critical determinant of T cell immunity. CD8+ T cells are contact-dependent killers, which requires them to physically be within the tissue of interest to kill peptide-MHC class I-bearing target cells. Following their migration and extravasation into tissues, T cells receive many extrinsic cues from the local microenvironment, and these signals shape T cell differentiation, fate and function. Because major organ systems are variable in their functions and compositions, they apply disparate pressures on T cells to adapt to the local microenvironment. Additional complexity arises in the context of malignant lesions (either primary or metastatic), and this has made understanding the factors that dictate T cell function and longevity in tumours challenging. Moreover, T cell differentiation state influences how cues from the microenvironment are interpreted by tissue-infiltrating T cells, highlighting the importance of T cell state in the context of tissue biology. Here, we review the intertwined nature of T cell differentiation state, location, survival and function, and explain how dysfunctional T cell populations can adopt features of tissue-resident memory T cells to persist in tumours. Finally, we discuss how these factors have shaped responses to cancer immunotherapy.

Immune checkpoint targeting antibodies hold promise for combinatorial cancer therapeutics

Through improving the immune system's ability to recognize and combat tumor cells as well as its receptivity to changes in the tumor microenvironment, immunotherapy has emerged as a highly successful addition to the treatment of cancer. However, tumor heterogeneity poses a significant challenge in cancer therapy as it can undermine the anti-tumor immune response through the manipulation of the extracellular matrix. To address these challenges and improve targeted therapies and combination treatments, the food and drug administration has approved several immunomodulatory antibodies to suppress immunological checkpoints. Combinatorial therapies necessitate the identification of multiple targets that regulate the intricate communication between immune cells, cytokines, chemokines, and cellular responses within the tumor microenvironment. The purpose of this study is to provide a comprehensive overview of the ongoing clinical trials involving immunomodulatory antibodies in various cancer types. It explores the potential of these antibodies to modulate the immune system and enhance anti-tumor responses. Additionally, it discusses the perspectives and prospects of immunomodulatory therapeutics in cancer treatment. Although immunotherapy shows great promise in cancer treatment, it is not exempt from side effects that can arise due to hyperactivity of the immune system. Therefore, understanding the intricate balance between immune activation and regulation is crucial for minimizing these adverse effects and optimizing treatment outcomes. This study aims to contribute to the growing body of knowledge surrounding immunomodulatory antibodies and their potential as effective therapeutic options in cancer treatment, ultimately paving the way for improved patient outcomes and deepening our perception of the intricate interactivity between the immune system and tumors.

Delta-radiomics in cancer immunotherapy response prediction: A systematic review

Background

The new immunotherapies have not only changed the oncological therapeutic approach but have also made it necessary to develop new imaging methods for assessing the response to treatment. Delta radiomics consists of the analysis of radiomic features variation between different medical images, usually before and after therapy.

Purpose

This review aims to evaluate the role of delta radiomics in the immunotherapy response assessment.

Methods

A systematic search was performed in PubMed, Scopus, and Web Of Science using "delta radiomics AND immunotherapy" as search terms. The included articles' methodological quality was measured using the Radiomics Quality Score (RQS) tool.

Results

Thirteen articles were finally included in the systematic review. Overall, the RQS of the included studies ranged from 4 to 17, with a mean RQS total of 11,15 ± 4,18 with a corresponding percentage of 30.98 ± 11.61 %. Eleven articles out of 13 performed imaging at multiple time points. All the included articles performed feature reduction. No study carried out prospective validation, decision curve analysis, or cost-effectiveness analysis.

Conclusions

Delta radiomics has been demonstrated useful in evaluating the response in oncologic patients undergoing immunotherapy. The overall quality was found law, due to the lack of prospective design and external validation. Thus, further efforts are needed to bring delta radiomics a step closer to clinical implementation.

Integrating SNVs and CNAs on a phylogenetic tree from single-cell DNA sequencing data

Single-cell DNA sequencing enables the construction of evolutionary trees that can reveal how tumors gain mutations and grow. Different whole-genome amplification procedures render genomic materials of different characteristics, often suitable for the detection of either single-nucleotide variation or copy number aberration, but not ideally for both. Consequently, this hinders the inference of a comprehensive phylogenetic tree and limits opportunities to investigate the interplay of SNVs and CNAs. Existing methods such as SCARLET and COMPASS require that the SNVs and CNAs are detected from the same sets of cells, which is technically challenging. Here we present a novel computational tool, SCsnvcna, that places SNVs on a tree inferred from CNA signals, whereas the sets of cells rendering the SNVs and CNAs are independent, offering a more practical solution in terms of the technical challenges. SCsnvcna is a Bayesian probabilistic model using both the genotype constraints on the tree and the cellular prevalence to search the optimal solution. Comprehensive simulations and comparison with seven state-of-the-art methods show that SCsnvcna is robust and accurate in a variety of circumstances. Particularly, SCsnvcna most frequently produces the lowest error rates, with ability to scale to a wide range of numerical values for leaf nodes in the tree, SNVs, and SNV cells. The application of SCsnvcna to two published colorectal cancer data sets shows highly consistent placement of SNV cells and SNVs with the original study while also supporting a refined placement of ATP7B, illustrating SCsnvcna's value in analyzing complex multitumor samples.

Role of Tumor-informed Personalized Circulating Tumor DNA Assay in Informing Recurrence in Patients With Peritoneal Metastases From Colorectal and High-grade Appendix Cancer Undergoing Curative-intent Surgery

OBJECTIVE

To investigate the role of a personalized, tumor-informed circulating tumor DNA (ctDNA) assay in informing recurrence in patients with peritoneal metastases (PM) from colorectal (CRC) and high-grade appendix (HGA) cancer after curative cytoreductive surgery with hyperthermic intraperitoneal chemotherapy (CRS-HIPEC).

BACKGROUND

Over 50% of patients with CRC/HGA-PM recur after optimal CRS-HIPEC. The limited sensitivity of axial imaging and diagnostic biomarkers is a significant cause of delay in the detection of recurrence and initiation of further therapies. Plasma ctDNA has a promising role in monitoring response to treatment and/or recurrence after primary cancer resection.

METHODS

Patients with CRC/HGA-PM who underwent curative CRS-HIPEC and serial postresection ctDNA assessments were included. Patients with rising postoperative ctDNA levels were compared with those with stable, undetectable ctDNA levels. Primary outcomes were the percentage of patients with recurrence and disease-free survival (DFS). Secondary outcomes were overall survival, ctDNA sensitivity, lead time, and performance of ctDNA compared with carcinoembryonic antigen.

RESULTS

One hundred thirty serial postresection ctDNA assessments [median 4, interquartile range (IQR), 3 to 5] were performed in 33 patients (n = 13 CRC, n = 20 HGA) who underwent completeness of cytoreduction-0/1 CRS with a median follow-up of 13 months. Of the 19 patients with rising ctDNA levels, 90% recurred versus 21% in the stable ctDNA group (n = 14, < 0.001). Median DFS in the rising ctDNA cohort was 11 months (IQR, 6 to 12) and not reached in the stable ( P = 0.01). A rising ctDNA level was the most significant factor associated with DFS (hazard ratio: 3.67, 95% CI: 1.06-12.66, P = 0.03). The sensitivity and specificity of rising ctDNA levels in predicting recurrence were 85% and 84.6%, respectively. The median ctDNA lead time was 3 months (IQR, 1 to 4). Carcinoembryonic antigen was less sensitive (50%) than ctDNA.

CONCLUSIONS

This study supports the clinical validity of serial ctDNA assessment as a strong prognostic biomarker in informing recurrence in patients with CRC/HGA-PM undergoing curative resection. It also holds promises for informing future clinical trial designs and further research.

Global lineage evolution pattern of sars-cov-2 in Africa, America, Europe, and Asia: A comparative analysis of variant clusters and their relevance across continents

Objective

The objective of this study is to provide a comparative analysis of variant clusters and their relevance across Africa, America, Europe, and Asia, in order to understand the evolutionary patterns of the virus across different regions and to inform the development of targeted interventions and genomic surveillance eforts.

Methods

The study analyzed the global lineage evolution pattern of 74, 075 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from 32 countries across four continents, focusing on variant clusters and their relevance across regions. Variants were weighted according to their hierarchical level. The correlation between variants was visualized through Dimensionality reduction analysis and Pairwise Pearson's correlation. We presented a reconstructed phylogenetic tree based on correlation analysis and variant weights.

Results

The analysis revealed that each continent had distinct variant clusters and different evolutionary patterns. The Americas had two clustered variants before lineage divergence and a downstream confluence lineage, Europe had bifurcation into two global lineages with an early occurrence of certain cluster while Asia had a downstream confluence of two large lineages diverging by two distinct clusters. Based on the cluster patterns of shared variants of the SARS-CoV-2 virus, Africa demonstrated a relatively clear distinction among three distinct regions.

Conclusions

The study provides insights into the evolutionary patterns of SARS-CoV-2 and highlights the importance of international collaboration in tracking and responding to emerging variants. The study found that the global pandemic was driven by Omicron variants that evolved with significant differences between countries and regions, and with different patterns across continents.

Assessment of aggressive bladder cancer mutations in plasma cell-free DNA

Background and aims

The spatial and temporal genetic heterogeneity of bladder cancer (BC) makes challenging to find specific drivers of metastatic disease, thus preventing to determine those BC patients at high risk of tumor progression. Our aim was to identify DNA mutations providing aggressive behavior to bladder tumors and analyze them in patients' cell-free DNA (cfDNA) during their follow-up after radical cystectomy (RC) in order to monitor tumor evolution.

Methods

Six BC patients who underwent RC and presented disease progression during their follow-up were included. Next-generation sequencing was used to determine somatic mutations in several primary tumor and metastatic specimens from each patient. Shared DNA mutations between primary bladder tumor and metastatic sites were identified in cfDNA samples through droplet digital PCR.

Results

Besides BC genetic heterogeneity, specific mutations in at least one of these genes -TERT, ATM, RB1, and FGFR3- were found in primary tumors and their metastases in all patients. These mutations were also identified in the patients' cfDNA at different follow-up time points. Additionally, the dynamic changes of these mutations in cfDNA allowed us to determine tumor evolution in response to treatment.

Conclusion

The analysis of BC mutations associated with poor prognosis in plasma cfDNA could be a valuable tool to monitor tumor evolution, thus improving the clinical management of BC patients.