Bayesian graphical modeling for heterogeneous causal effects

There is a growing interest in current medical research to develop personalized treatments using a molecular-based approach. The broad goal is to implement a more precise and targeted decision-making process, relative to traditional treatments based primarily on clinical diagnoses. Specifically, we consider patients affected by Acute Myeloid Leukemia (AML), an hematological cancer characterized by uncontrolled proliferation of hematopoietic stem cells in the bone marrow. Because AML responds poorly to chemotherapeutic treatments, the development of targeted therapies is essential to improve patients' prospects. In particular, the dataset we analyze contains the levels of proteins involved in cell cycle regulation and linked to the progression of the disease. We evaluate treatment effects within a causal framework represented by a Directed Acyclic Graph (DAG) model, whose vertices are the protein levels in the network. A major obstacle in implementing the above program is represented by individual heterogeneity. We address this issue through a Dirichlet Process (DP) mixture of Gaussian DAG-models where both the graphical structure as well as the allied model parameters are regarded as uncertain. Our procedure determines a clustering structure of the units reflecting the underlying heterogeneity, and produces subject-specific estimates of causal effects based on Bayesian Model Averaging (BMA). With reference to the AML dataset, we identify different effects of protein regulation among individuals; moreover, our method clusters patients into groups that exhibit only mild similarities with traditional categories based on morphological features.

Histopathologic and proteogenomic heterogeneity reveals features of clear cell renal cell carcinoma aggressiveness

Clear cell renal cell carcinomas (ccRCCs) represent ∼75% of RCC cases and account for most RCC-associated deaths. Inter- and intratumoral heterogeneity (ITH) results in varying prognosis and treatment outcomes. To obtain the most comprehensive profile of ccRCC, we perform integrative histopathologic, proteogenomic, and metabolomic analyses on 305 ccRCC tumor segments and 166 paired adjacent normal tissues from 213 cases. Combining histologic and molecular profiles reveals ITH in 90% of ccRCCs, with 50% demonstrating immune signature heterogeneity. High tumor grade, along with BAP1 mutation, genome instability, increased hypermethylation, and a specific protein glycosylation signature define a high-risk disease subset, where UCHL1 expression displays prognostic value. Single-nuclei RNA sequencing of the adverse sarcomatoid and rhabdoid phenotypes uncover gene signatures and potential insights into tumor evolution. In vitro cell line studies confirm the potential of inhibiting identified phosphoproteome targets. This study molecularly stratifies aggressive histopathologic subtypes that may inform more effective treatment strategies.

Patient-derived glioblastoma organoids reflect tumor heterogeneity and treatment sensitivity

Background

Treatment resistance and tumor relapse are the primary causes of mortality in glioblastoma (GBM), with intratumoral heterogeneity playing a significant role. Patient-derived cancer organoids have emerged as a promising model capable of recapitulating tumor heterogeneity. Our objective was to develop patient-derived GBM organoids (PGO) to investigate treatment response and resistance.

Methods

GBM samples were used to generate PGOs and analyzed using whole-exome sequencing (WES) and single-cell karyotype sequencing. PGOs were subjected to temozolomide (TMZ) to assess viability. Bulk RNA sequencing was performed before and after TMZ.

Results

WES analysis on individual PGOs cultured for 3 time points (1-3 months) showed a high inter-organoid correlation and retention of genetic variants (range 92.3%-97.7%). Most variants were retained in the PGO compared to the tumor (range 58%-90%) and exhibited similar copy number variations. Single-cell karyotype sequencing demonstrated preservation of genetic heterogeneity. Single-cell multiplex immunofluorescence showed maintenance of cellular states. TMZ treatment of PGOs showed a differential response, which largely corresponded with MGMT promoter methylation. Differentially expressed genes before and after TMZ revealed an upregulation of the JNK kinase pathway. Notably, the combination treatment of a JNK kinase inhibitor and TMZ demonstrated a synergistic effect.

Conclusions

Overall, these findings demonstrate the robustness of PGOs in retaining the genetic and phenotypic heterogeneity in culture and the application of measuring clinically relevant drug responses. These data show that PGOs have the potential to be further developed into avatars for personalized adaptive treatment selection and actionable drug target discovery and as a platform to study GBM biology.

Tumor heterogeneity: preclinical models, emerging technologies, and future applications

Heterogeneity describes the differences among cancer cells within and between tumors. It refers to cancer cells describing variations in morphology, transcriptional profiles, metabolism, and metastatic potential. More recently, the field has included the characterization of the tumor immune microenvironment and the depiction of the dynamics underlying the cellular interactions promoting the tumor ecosystem evolution. Heterogeneity has been found in most tumors representing one of the most challenging behaviors in cancer ecosystems. As one of the critical factors impairing the long-term efficacy of solid tumor therapy, heterogeneity leads to tumor resistance, more aggressive metastasizing, and recurrence. We review the role of the main models and the emerging single-cell and spatial genomic technologies in our understanding of tumor heterogeneity, its contribution to lethal cancer outcomes, and the physiological challenges to consider in designing cancer therapies. We highlight how tumor cells dynamically evolve because of the interactions within the tumor immune microenvironment and how to leverage this to unleash immune recognition through immunotherapy. A multidisciplinary approach grounded in novel bioinformatic and computational tools will allow reaching the integrated, multilayered knowledge of tumor heterogeneity required to implement personalized, more efficient therapies urgently required for cancer patients.

Differential regional importance mapping for thyroid nodule malignancy prediction with potential to improve needle aspiration biopsy sampling reliability

Objective

Existing guidelines for ultrasound-guided fine-needle aspiration biopsy lack specifications on sampling sites, but the number of biopsies improves diagnostic reliability. We propose the use of class activation maps (CAMs) and our modified malignancy-specific heat maps that locate important deep representations of thyroid nodules for class predictions.

Methods

We applied adversarial noise perturbations to the segmented concentric "hot" nodular regions of equal sizes to differentiate regional importance for the malignancy diagnostic performances of an accurate ultrasound-based artificial intelligence computer-aided diagnosis (AI-CADx) system using 2,602 retrospectively collected thyroid nodules with known histopathological diagnosis.

Results

The AI system demonstrated high diagnostic performance with an area under the curve (AUC) value of 0.9302 and good nodule identification capability with a median dice coefficient >0.9 when compared to radiologists' segmentations. Experiments confirmed that the CAM-based heat maps reflect the differentiable importance of different nodular regions for an AI-CADx system to make its predictions. No less importantly, the hot regions in malignancy heat maps of ultrasound images in comparison with the inactivated regions of the same 100 malignant nodules randomly selected from the dataset had higher summed frequency-weighted feature scores of 6.04 versus 4.96 rated by radiologists with more than 15 years of ultrasound examination experience according to widely used ultrasound-based risk stratification American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) in terms of nodule composition, echogenicity, and echogenic foci, excluding shape and margin attributes, which could only be evaluated on the whole rather than on the sub-nodular component levels. In addition, we show examples demonstrating good spatial correspondence of highlighted regions of malignancy heat map to malignant tumor cell-rich regions in hematoxylin and eosin-stained histopathological images.

Conclusion

Our proposed CAM-based ultrasonographic malignancy heat map provides quantitative visualization of malignancy heterogeneity within a tumor, and it is of clinical interest to investigate in the future its usefulness to improve fine-needle aspiration biopsy (FNAB) sampling reliability by targeting potentially more suspicious sub-nodular regions.

Deciphering functional tumor states at single-cell resolution

Within a tumor, cancer cells exist in different states that are associated with distinct tumor functions, including proliferation, differentiation, invasion, metastasis, and resistance to anti-cancer therapy. The identification of the gene regulatory networks underpinning each state is essential for better understanding functional tumor heterogeneity and revealing tumor vulnerabilities. Here, we review the different studies identifying tumor states by single-cell sequencing approaches and the mechanisms that promote and sustain these functional states and regulate their transitions. We also describe how different tumor states are spatially distributed and interact with the specific stromal cells that compose the tumor microenvironment. Finally, we discuss how the understanding of tumor plasticity and transition states can be used to develop new strategies to improve cancer therapy.

Extrachromosomal DNA (ecDNA): an origin of tumor heterogeneity, genomic remodeling, and drug resistance

The genome of cancer cells contains circular extrachromosomal DNA (ecDNA) elements not found in normal cells. Analysis of clinical samples reveal they are common in most cancers and their presence indicates poor prognosis. They often contain enhancers and driver oncogenes that are highly expressed. The circular ecDNA topology leads to an open chromatin conformation and generates new gene regulatory interactions, including with distal enhancers. The absence of centromeres leads to random distribution of ecDNAs during cell division and genes encoded on them are transmitted in a non-mendelian manner. ecDNA can integrate into and exit from chromosomal DNA. The numbers of specific ecDNAs can change in response to treatment. This dynamic ability to remodel the cancer genome challenges long-standing fundamentals, providing new insights into tumor heterogeneity, cancer genome remodeling, and drug resistance.

Clonal transcriptomics identifies mechanisms of chemoresistance and empowers rational design of combination therapies

Tumour heterogeneity is thought to be a major barrier to successful cancer treatment due to the presence of drug resistant clonal lineages. However, identifying the characteristics of such lineages that underpin resistance to therapy has remained challenging. Here, we utilise clonal transcriptomics with WILD-seq; Wholistic Interrogation of Lineage Dynamics by sequencing, in mouse models of triple-negative breast cancer (TNBC) to understand response and resistance to therapy, including BET bromodomain inhibition and taxane-based chemotherapy. These analyses revealed oxidative stress protection by NRF2 as a major mechanism of taxane resistance and led to the discovery that our tumour models are collaterally sensitive to asparagine deprivation therapy using the clinical stage drug L-asparaginase after frontline treatment with docetaxel. In summary, clonal transcriptomics with WILD-seq identifies mechanisms of resistance to chemotherapy that are also operative in patients and pin points asparagine bioavailability as a druggable vulnerability of taxane-resistant lineages.

Vascular Niche Facilitates Acquired Drug Resistance to c-Met Inhibitor in Originally Sensitive Osteosarcoma Cells

Osteosarcoma (OS) is the most common primary bone tumor in children and adolescents characterized by drug resistance and poor prognosis. As one of the key oncogenes, c-Met is recognized as a promising therapeutic target for OS. In this report, we show that c-Met inhibitor PF02341066 specifically killed OS cells with highly phosphorylated c-Met in vitro. However, the inhibitory effect of PF02341066 was abrogated in vivo due to interference from the vascular niche. OS cells adjacent to microvessels or forming vascular mimicry suppressed c-Met expression and phosphorylation. Moreover, VEGFR2 was activated in OS cells and associated with acquired drug resistance. Dual targeting of c-Met and VEGFR2 could effectively shrink the tumor size in a xenograft model. c-Met-targeted therapy combined with VEGFR2 inhibition might be beneficial to achieve an ideal therapeutic effect in OS patients. Together, our results confirm the pivotal role of tumor heterogeneity and the microenvironment in drug response and reveal the molecular mechanism underlying acquired drug resistance to c-Met-targeted therapy.

Integrated analysis of racial disparities in genomic architecture identifies a trans-ancestry prognostic subtype in bladder cancer

The incidence of bladder cancer and patient survival vary greatly among different populations, but the influence of the associated molecular features and evolutionary processes on its clinical treatment and prognostication remains unknown. Here, we analyze the genomic architectures of 505 bladder cancer patients from Asian/Black/White populations. We identify a previously unknown association between AHNAK mutations and activity of the APOBEC-a mutational signature, the activity of which varied substantially across populations. All significantly mutated genes but only half of arm-level somatic copy number alterations (SCNAs) are enriched with clonal events, indicating large-scale SCNAs as rich sources of bladder cancer clonal diversities. The prevalence of TP53 and ATM clonal mutations as well as the associated burden of SCNAs is significantly higher in Whites/Blacks than in Asians. We identify a trans-ancestry prognostic subtype of bladder cancer characterized by enrichment of non-muscle-invasive patients and muscle-invasive patients with good prognosis, increased CREBBP/FGFR3/HRAS/NFE2L2 mutations, decreased intra-tumor heterogeneity and genome instability, and an activated tumor microenvironment.

Integrated single-cell transcriptome analysis of the tumor ecosystems underlying cervical cancer metastasis

Cervical cancer (CC) is one of the most frequent female malignancies worldwide. However, the molecular mechanism of lymph node metastasis in CC remains unclear. In this study, we investigated the transcriptome profile of 51,507 single cells from primary tumors, positive lymph nodes (P-LN), and negative lymph nodes (N-LN) using single-cell sequencing. Validation experiments were performed using bulk transcriptomic datasets and immunohistochemical assays. Our results indicated that epithelial cells in metastatic LN were associated with cell- cycle-related signaling pathways, such as E2F targets, and mitotic spindle, and immune response-related signaling pathways, such as allograft rejection, IL2_STAT5_signaling, and inflammatory response. However, epithelial cells in primary tumors exhibited high enrichment of epithelial-mesenchymal translation (EMT), oxidative phosphorylation, and interferon alpha response. Our analysis then indicated that metastasis LN exhibited an early activated tumor microenvironment (TME) characterized by the decrease of naive T cells and an increase of cytotoxicity CD8 T cells, NK cells, FOXP3+ Treg cells compared with normal LN. By comparing the differently expressed gene of macrophages between tumor and metastatic LN, we discovered that C1QA+ MRC1^low macrophages were enriched in a tumor, whereas C1QA+ MRC1^high macrophages were enriched in metastatic LN. Finally, we demonstrated that cancer-associated fibroblasts (CAFs) in P-LN were associated with immune regulation, while CAFs in tumor underwent EMT. Our findings offered novel insights into the mechanisms of research, diagnosis, and therapy of CC metastasis.

Small cell lung cancer transformation and tumor heterogeneity after sequential targeted therapy and immunotherapy in EGFR-mutant non-small cell lung cancer: A case report

Background

Histological transformation from non-small cell lung cancer (NSCLC) to small cell lung cancer (SCLC) is one of mechanisms of the acquired resistance to epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKI). However, SCLC transformation and tumor heterogeneity have never been reported in sequential targeted therapy and immunotherapy.

Case presentation

Here, we described a patient with advanced EGFR-mutant NSCLC, who received erlotinib and underwent the resistance with EGFR T790M (-). The patient then received chemotherapy plus immunotherapy of programmed cell death 1 (PD-1) inhibitor, encountered progression with pathological transformation from NSCLC to SCLC that was overcome by chemotherapy of etoposide plus carboplatin (EC) with the main lesion significantly shrinking while metastatic nodules increasing. The pathology of the metastatic nodule showed NSCLC with EGFR T790M (+). Based on the tumor heterogeneity, EC chemotherapy combined with osimertinib was used, and patients responded well. The patient experienced four lung biopsies in all, which helped to provide the patient with precise treatment.

Conclusions

This case suggested that SCLC transformation and tumor heterogeneity should be paid attention to when disease progression occurred in advanced NSCLC whether receiving targeted therapy or immunotherapy.

scAnalyzeR: A Comprehensive Software Package With Graphical User Interface for Single-Cell RNA Sequencing Analysis and its Application on Liver Cancer

Introduction: The application of single-cell RNA sequencing to delineate tissue heterogeneity and complexity has become increasingly popular. Given its tremendous resolution and high-dimensional capacity for in-depth investigation, single-cell RNA sequencing offers an unprecedented research power. Although some popular software packages are available for single-cell RNA sequencing data analysis and visualization, it is still a big challenge for their usage, as they provide only a command-line interface and require significant level of bioinformatics skills. Methods: We have developed scAnalyzeR, which is a single-cell RNA sequencing analysis pipeline with an interactive and user-friendly graphical interface for analyzing and visualizing single-cell RNA sequencing data. It accepts single-cell RNA sequencing data from various technology platforms and different model organisms (human and mouse) and allows flexibility in input file format. It provides functionalities for data preprocessing, quality control, basic summary statistics, dimension reduction, unsupervised clustering, differential gene expression, gene set enrichment analysis, correlation analysis, pseudotime cell trajectory inference, and various visualization plots. It also provides default parameters for easy usage and allows a wide range of flexibility and optimization by accepting user-defined options. It has been developed as a docker image that can be run in any docker-supported environment including Linux, Mac, and Windows, without installing any dependencies. Results: We compared the performance of scAnalyzeR with 2 other graphical tools that are popular for analyzing single-cell RNA sequencing data. The comparison was based on the comprehensiveness of functionalities, ease of usage and flexibility, and execution time. In general, scAnalyzeR outperformed the other tested counterparts in various aspects, demonstrating its superior overall performance. To illustrate the usefulness of scAnalyzeR in cancer research, we have analyzed the in-house liver cancer single-cell RNA sequencing dataset. Liver cancer tumor cells were revealed to have multiple subpopulations with distinctive gene expression signatures. Conclusion: scAnalyzeR has comprehensive functionalities and demonstrated usability. We anticipate more functionalities to be adopted in the future development.

Systematic multi-omics cell line profiling uncovers principles of Ewing sarcoma fusion oncogene-mediated gene regulation

Ewing sarcoma (EwS) is characterized by EWSR1-ETS fusion transcription factors converting polymorphic GGAA microsatellites (mSats) into potent neo-enhancers. Although the paucity of additional mutations makes EwS a genuine model to study principles of cooperation between dominant fusion oncogenes and neo-enhancers, this is impeded by the limited number of well-characterized models. Here we present the Ewing Sarcoma Cell Line Atlas (ESCLA), comprising whole-genome, DNA methylation, transcriptome, proteome, and chromatin immunoprecipitation sequencing (ChIP-seq) data of 18 cell lines with inducible EWSR1-ETS knockdown. The ESCLA shows hundreds of EWSR1-ETS-targets, the nature of EWSR1-ETS-preferred GGAA mSats, and putative indirect modes of EWSR1-ETS-mediated gene regulation, converging in the duality of a specific but plastic EwS signature. We identify heterogeneously regulated EWSR1-ETS-targets as potential prognostic EwS biomarkers. Our freely available ESCLA (http://r2platform.com/escla/) is a rich resource for EwS research and highlights the power of comprehensive datasets to unravel principles of heterogeneous gene regulation by chimeric transcription factors.

Identification of characteristic markers correlated with Th2 cell infiltration and metabolism molecular subtype in pancreatic adenocarcinoma

Background

Pancreatic adenocarcinoma, the deadliest malignant cancer, has gradually become the third leading cause of cancer-related death. Multidisciplinary therapy has been difficult to implement because of the particularity of pancreatic adenocarcinoma. Research has increasingly indicated the significance of metabolic adaption in pancreatic adenocarcinoma. The difference in metabolism may influence immune cell infiltration in pancreatic adenocarcinoma. Novel immune-related metabolism biomarkers are needed to improve the therapeutic outcomes of existing targeted therapies.

Methods

We enrolled whole-genome sequencing data and clinical information about 168 pancreatic adenocarcinoma samples from The Cancer Genome Atlas (TCGA) database, other pancreatic adenocarcinoma samples, and clinical information from other cohorts. We used the gene set variation analysis (GSVA) package to calculate feature score, the weighted gene co-expression network analysis (WGCNA) and randomSurvivalForest package to screen hub genes, the ConsenClusterPlus package to classify subtypes, the pRRopthetic package to evaluate drug sensibility, the maftools package to analyze mutation information and the Seurat package to analyze single cell sequencing data.

Results

We revealed the prognosis significance of Th2 cell infiltration, classified two subtypes based on hub genes, compared immune cell infiltration, substance metabolism, cellular processes, gene mutation, and copy number variation (CNV) between subtypes and explored the clinical and biological features of Th2 cell infiltration.

Conclusions

We displayed the poor prognosis significance of Th2 cell infiltration and the significant difference of simple nucleotide polymorphism, CNV, natural killer (NK) CD56 bright cell infiltration, substance metabolism, autophagy and necroptosis between subtypes. Additionally, we discovered the sensitivity difference of chemotherapy drug and the Th2 cell infiltration changes after chimeric antigen receptor T cells (CAR-T) cell therapy and radiotherapy and explored the differences between normal liver and metastatic liver tissues of pancreatic adenocarcinoma patients.

New Cluster Analysis Method for Quantitative Dynamic Contrast-Enhanced MRI Assessing Tumor Heterogeneity Induced by a Tumor-Microenvironmental Ameliorator (E7130) Treatment to a Breast Cancer Mouse Model

BACKGROUND

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can provide insight into tumor perfusion. However, a method that can quantitatively measure the intra-tumor distribution of tumor voxel clusters with a distinct range of K^trans and v_e values remains insufficiently explored.

HYPOTHESIS

Two-dimensional cluster analysis may quantify the distribution of a tumor voxel subregion with a distinct range of K^trans and v_e values in human breast cancer xenografts.

STUDY TYPE

Prospective longitudinal study.

ANIMAL MODEL

Twenty-two female athymic nude mice with MCF-7 xenograft, treated with E7130, a tumor-microenvironmental ameliorator, or saline.

FIELD STRENGTH/SEQUENCE

9.4 Tesla, turbo rapid acquisition with relaxation enhancement, and spoiled gradient-echo sequences.

ASSESSMENT

We performed two-dimensional k-means clustering to identify tumor voxel clusters with a distinct range of K^trans and v_e values on Days 0, 2, and 5 after treatment, calculated the ratio of the number of tumor voxels in each cluster to the total number of tumor voxels, and measured the normalized distances defined as the ratio of the distance between each tumor voxel and the nearest tumor margin to a tumor radius.

STATISTICAL TESTS

Unpaired t-tests, Dunnett's multiple comparison tests, and Chi-squared test were used.

RESULTS

The largest and second largest clusters constituted 44.4% and 27.5% of all tumor voxels with cluster centroid values of K^trans at 0.040 min^-1 and 0.116 min^-1 , and v_e at 0.131 and 0.201, respectively. At baseline (Day 0), the average normalized distances for the largest and second largest clusters were 0.33 and 0.24, respectively. E7130-treated group showed the normalized distance of the initial largest cluster decreasing to 0.25, while that of the second largest cluster increasing to 0.31. Saline-treated group showed no change.

DATA CONCLUSION

A two-dimensional cluster analysis might quantify the spatial distribution of a tumor subregion with a distinct range of K^trans and v_e values.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY: Stage 1.

Single-cell RNA-sequencing dissects cellular heterogeneity and identifies two tumor-suppressing immune cell subclusters in HPV-related cervical adenosquamous carcinoma

The intratumor heterogeneity of human papillomavirus (HPV)-related cervical cancer remains poorly defined. We performed single-cell RNA sequencing on 18 046 individual cells derived from two HPV-related cervical adenosquamous carcinoma samples to analyze the transcriptional heterogeneity of both epithelial and immune constituents, identifying seven epithelial (Epi1-7) and 11 immune subclusters. Based on expression of known cervical cancer markers, Epi1-2 primarily displayed features of adenocarcinoma, whereas Epi3-6 were instead characterized by features of squamous carcinoma. Our analyses also revealed that hypoxia and Kirsten rat sarcoma viral oncogene signaling were highly represented within Epi1; metabolic pathways mediating glycolysis and oxidative phosphorylation were enriched in Epi2-4; while Epi5 was enriched in p53 pathway components and features of epithelial-mesenchymal transition. Moreover, CD8⁺ FGFBP2⁺ T cells and FGFBP2⁺ natural killer cells were found to display high levels of cytotoxic effectors (GZMA, GZMB, GNLY, and PRF1) and low levels of inhibitory markers (PDCD1, TIGIT, and CTLA4), such that tumor infiltration by these populations was positively associated with survival in a cohort of n = 165 patients with HPV-related cervical cancer from The Cancer Genome Atlas database (p = 0.017 and 0.014, respectively). These results shed new light on the intratumor heterogeneity of HPV-related cervical adenosquamous carcinoma, which will help to refine diagnostic and treatment approaches.

Assessing Spatial Distribution of Multicellular Self-Assembly Enables the Prediction of Phenotypic Heterogeneity in Glioblastoma

Phenotypic heterogeneity of glioblastomas is a leading determinant of therapeutic resistance and treatment failure. However, functional assessment of the heterogeneity of glioblastomas is lacking. We developed a self-assembly-based assessment system that predicts inter/intracellular heterogeneity and phenotype associations, such as cell proliferation, invasiveness, drug responses, and gene expression profiles. Under physical constraints for cellular interactions, mixed populations of glioblastoma cells are sorted to form a segregated architecture, depending on their preference for binding to cells of the same phenotype. Cells distributed at the periphery exhibit a reduced temozolomide (TMZ) response and are associated with poor patient survival, whereas cells in the core of the aggregates exhibit a significant response to TMZ. Our results suggest that the multicellular self-assembly pattern is indicative of the intertumoral and intra-patient heterogeneity of glioblastomas, and is predictive of the therapeutic response.

Tumor microenvironment penetrating chitosan nanoparticles for elimination of cancer relapse and minimal residual disease

Chitosan and its derivatives are among biomaterials with numerous medical applications, especially in cancer. Chitosan is amenable to forming innumerable shapes such as micelles, niosomes, hydrogels, nanoparticles, and scaffolds, among others. Chitosan derivatives can also bring unprecedented potential to cross numerous biological barriers. Combined with other biomaterials, hybrid and multitasking chitosan-based systems can be realized for many applications. These include controlled drug release, targeted drug delivery, post-surgery implants (immunovaccines), theranostics, biosensing of tumor-derived circulating materials, multimodal systems, and combination therapy platforms with the potential to eliminate bulk tumors as well as lingering tumor cells to treat minimal residual disease (MRD) and recurrent cancer. We first introduce different formats, derivatives, and properties of chitosan. Next, given the barriers to therapeutic efficacy in solid tumors, we review advanced formulations of chitosan modules as efficient drug delivery systems to overcome tumor heterogeneity, multi-drug resistance, MRD, and metastasis. Finally, we discuss chitosan NPs for clinical translation and treatment of recurrent cancer and their future perspective.

The dynamic dysregulated network identifies stage-specific markers during lung adenocarcinoma malignant progression and metastasis

Brain metastasis occurs in approximately 30% of patients with lung adenocarcinoma (LUAD) and is closely associated with poor prognosis, recurrence, and death. However, dynamic gene regulation and molecular mechanism driving LUAD progression remain poorly understood. In this study, we performed a comprehensive single-cell transcriptome analysis using data from normal, early stage, advanced stage, and brain metastasis LUAD. Our single-cell-level analysis reveals the cellular composition heterogeneity at different stages during LUAD progression. We identified stage-specific risk genes that could contribute to LUAD progression and metastasis by reprogramming immune-related and metabolic-related functions. We constructed an early advanced metastatic dysregulated network and revealed the dynamic changes in gene regulations during LUAD progression. We identified 6 early advanced (HLA-DRB1, HLA-DQB1, SFTPB, SFTPC, PLA2G1B, and FOLR1), 8 advanced metastasis (RPS15, RPS11, RPL13A, RPS24, HLA-DRB5, LYPLA1, KCNJ15, and PSMA3), and 2 common risk genes in different stages (SFTPD and HLA-DRA) as prognostic markers in LUAD. Particularly, decreased expression of HLA-DRA, HLA-DRB1, HLA-DQB1, and HLA-DRB5 refer poor prognosis in LUAD by controlling antigen processing and presentation and T cell activation. Increased expression of PSMA3 and LYPLA1 refer poor prognosis by reprogramming fatty acid metabolism and RNA catabolic process. Our findings will help further understanding the pathobiology of brain metastases in LUAD.

Cervical cancer heterogeneity: a constant battle against viruses and drugs

Cervical cancer is the first identified human papillomavirus (HPV) associated cancer and the most promising malignancy to be eliminated. However, the ever-changing virus subtypes and acquired multiple drug resistance continue to induce failure of tumor prevention and treatment. The exploration of cervical cancer heterogeneity is the crucial way to achieve effective prevention and precise treatment. Tumor heterogeneity exists in various aspects including the immune clearance of viruses, tumorigenesis, neoplasm recurrence, metastasis and drug resistance. Tumor development and drug resistance are often driven by potential gene amplification and deletion, not only somatic genomic alterations, but also copy number amplifications, histone modification and DNA methylation. Genomic rearrangements may occur by selection effects from chemotherapy or radiotherapy which exhibits genetic intra-tumor heterogeneity in advanced cervical cancers. The combined application of cervical cancer therapeutic vaccine and immune checkpoint inhibitors has become an effective strategy to address the heterogeneity of treatment. In this review, we will integrate classic and recently updated epidemiological data on vaccination rates, screening rates, incidence and mortality of cervical cancer patients worldwide aiming to understand the current situation of disease prevention and control and identify the direction of urgent efforts. Additionally, we will focus on the tumor environment to summarize the conditions of immune clearance and gene integration after different HPV infections and to explore the genomic factors of tumor heterogeneity. Finally, we will make a thorough inquiry into completed and ongoing phase III clinical trials in cervical cancer and summarize molecular mechanisms of drug resistance among chemotherapy, radiotherapy, biotherapy, and immunotherapy.

Spatial epitope barcoding reveals clonal tumor patch behaviors

Intratumoral heterogeneity is a seminal feature of human tumors contributing to tumor progression and response to treatment. Current technologies are still largely unsuitable to accurately track phenotypes and clonal evolution within tumors, especially in response to genetic manipulations. Here, we developed epitopes for imaging using combinatorial tagging (EpicTags), which we coupled to multiplexed ion beam imaging (EpicMIBI) for in situ tracking of barcodes within tissue microenvironments. Using EpicMIBI, we dissected the spatial component of cell lineages and phenotypes in xenograft models of small cell lung cancer. We observed emergent properties from mixed clones leading to the preferential expansion of clonal patches for both neuroendocrine and non-neuroendocrine cancer cell states in these models. In a tumor model harboring a fraction of PTEN-deficient cancer cells, we observed a non-autonomous increase of clonal patch size in PTEN wild-type cancer cells. EpicMIBI facilitates in situ interrogation of cell-intrinsic and cell-extrinsic processes involved in intratumoral heterogeneity.

The Role of the Immune Phenotype in Tumor Progression and Prognosis of Patients with Mycosis Fungoides: A Quantitative Immunohistology Whole Slide Approach

BACKGROUND AND OBJECTIVES

Mycosis fungoides (MF) is the most common type of cutaneous T-cell lymphomas, characterized by mature, skin-tropic CD4+ T-helper cells. In order to study the immune tumor microenvironment in MF patients, we performed immunohistochemical stains on MF biopsies, digitized whole-slide tissue sections, and performed quantitative analysis of the different immune cell subsets to correlate tissue parameters with the clinical data of patients, such as progression-free survival or overall survival.

PATIENTS AND METHODS

Overall, 35 patients who were treated between 2009 and 2019 and for whom one or more paraffin tissue blocks were available have been included in the present study (58 tissue specimens in total). Conventional immunohistochemistry stains for CD3, CD4, CD8, CD20 and CD30 were used for the analysis of the immune phenotype, and quantitative analysis was performed using QuPath as a quantitative digital pathology tool for bioimage analysis of whole slides.

RESULTS

Analysis of tissue parameters for prognostic significance revealed that patients with a stronger infiltration by CD8+ lymphocytes within the tumor cell compartment had a higher risk of disease progression (p = 0.031) and showed a shorter progress-free survival (p = 0.038). Furthermore, a significant association of the percentage of CD30+ cells (median: 7.8%) with the risk of disease progression (p = 0.023) and progression-free survival (p = 0.023) was found. In relation to the clinical features of our patient cohort, a higher risk of disease progression (p = 0.015) and a shorter progression-free survival (p = 0.032) for older patients (>61 years) were observed.

CONCLUSIONS

Our results demonstrated the prognostic relevance of large-cell transformation in mycosis fungoides and its strong association with the presence of CD30+ lymphocytes. Unlike previous reports, our study suggests an adverse prognostic role for CD8+ T cells in patients with mycosis fungoides. Moreover, our data indicate that the immune phenotype within the tumor microenvironment shows strong temporal heterogeneity and is altered in the course of tumor progression.

Unsupervised Analysis Based on DCE-MRI Radiomics Features Revealed Three Novel Breast Cancer Subtypes with Distinct Clinical Outcomes and Biological Characteristics

Background: This study aimed to reveal the heterogeneity of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of breast cancer (BC) and identify its prognosis values and molecular characteristics. Methods: Two radiogenomics cohorts (n = 246) were collected and tumor regions were segmented semi-automatically. A total of 174 radiomics features were extracted, and the imaging subtypes were identified and validated by unsupervised analysis. A gene-profile-based classifier was developed to predict the imaging subtypes. The prognostic differences and the biological and microenvironment characteristics of subtypes were uncovered by bioinformatics analysis. Results: Three imaging subtypes were identified and showed high reproducibility. The subtypes differed remarkably in tumor sizes and enhancement patterns, exhibiting significantly different disease-free survival (DFS) or overall survival (OS) in the discovery cohort (p = 0.024) and prognosis datasets (p ranged from <0.0001 to 0.0071). Large sizes and rapidly enhanced tumors usually had the worst outcomes. Associations were found between imaging subtypes and the established subtypes or clinical stages (p ranged from <0.001 to 0.011). Imaging subtypes were distinct in cell cycle and extracellular matrix (ECM)-receptor interaction pathways (false discovery rate, FDR < 0.25) and different in cellular fractions, such as cancer-associated fibroblasts (p < 0.05). Conclusions: The imaging subtypes had different clinical outcomes and biological characteristics, which may serve as potential biomarkers.

Patient-Derived Organoids from Colorectal Cancer with Paired Liver Metastasis Reveal Tumor Heterogeneity and Predict Response to Chemotherapy

There is no effective method to predict chemotherapy response and postoperative prognosis of colorectal cancer liver metastasis (CRLM) patients. Patient-derived organoid (PDO) has become an important preclinical model. Herein, a living biobank with 50 CRLM organoids derived from primary tumors and paired liver metastatic lesions is successfully constructed. CRLM PDOs from the multiomics levels (histopathology, genome, transcriptome and single-cell sequencing) are comprehensively analyzed and confirmed that this organoid platform for CRLM could capture intra- and interpatient heterogeneity. The chemosensitivity data in vitro reveal the potential value of clinical application for PDOs to predict chemotherapy response (FOLFOX or FOLFIRI) and clinical prognosis of CRLM patients. Taken together, CRLM PDOs can be utilized to deliver a potential application for personalized medicine.

Drug independence and the curability of cancer by combination chemotherapy

Combination chemotherapy can cure certain leukemias and lymphomas, but most solid cancers are only curable at early stages. We review quantitative principles that explain the benefits of combining independently active cancer therapies in both settings. Understanding the mechanistic principles underlying curative treatments, including those developed many decades ago, is valuable for improving future combination therapies. We discuss contemporary evidence for long-established but currently neglected ideas of how combination therapy overcomes tumor heterogeneity. We show that a unified model of interpatient and intratumor heterogeneity describes historical progress in the treatment of pediatric acute lymphocytic leukemia (ALL), in which increasingly intensive combination regimens ultimately achieved high cure rates. We also describe three distinct aspects of drug independence that apply at different biological scales. The ability of these principles to quantitatively explain curative regimens suggests that supra-additive (synergistic) drug interactions are not required for successful combination therapy.

A review of spatial profiling technologies for characterizing the tumor microenvironment in immuno-oncology

Interpreting the mechanisms and principles that govern gene activity and how these genes work according to -their cellular distribution in organisms has profound implications for cancer research. The latest technological advancements, such as imaging-based approaches and next-generation single-cell sequencing technologies, have established a platform for spatial transcriptomics to systematically quantify the expression of all or most genes in the entire tumor microenvironment and explore an array of disease milieus, particularly in tumors. Spatial profiling technologies permit the study of transcriptional activity at the spatial or single-cell level. This multidimensional classification of the transcriptomic and proteomic signatures of tumors, especially the associated immune and stromal cells, facilitates evaluation of tumor heterogeneity, details of the evolutionary trajectory of each tumor, and multifaceted interactions between each tumor cell and its microenvironment. Therefore, spatial profiling technologies may provide abundant and high-resolution information required for the description of clinical-related features in immuno-oncology. From this perspective, the present review will highlight the importance of spatial transcriptomic and spatial proteomics analysis along with the joint use of other sequencing technologies and their implications in cancers and immune-oncology. In the near future, advances in spatial profiling technologies will undoubtedly expand our understanding of tumor biology and highlight possible precision therapeutic targets for cancer patients.

[Analysis of progress characteristics of retinoblastoma based on single cell transcriptome sequencing]

Retinoblastoma (RB) is the most common intraocular malignant tumor in infants and young children. The key causative factors in the progression of RB remain unclear. Therefore, identifying genes closely associated with RB progression may provide important clues for disease diagnosis and gene therapy. However, tumor tissues have strong cellular heterogeneity. There may be significant differences in cell function and gene expression among cells in different pathological states. In this study, we downloaded single-cell transcriptome sequencing data of RB tumors and adjacent tissues from the GEO public database. Subsequently, we analyzed RB tumor transcriptional profiles with different disease duration at the single-cell level and identified cell groups and gene sets potentially associated with RB progression. The results showed that the tumor tissue and the adjacent tissues had overall consistency in the single-cell transcriptional map, but there were obvious differences in the distribution proportions of G1 phase cells, G2 phase cells, and microglia cells of cone precursors in RB tumor and the adjacent tissues. Furthermore, the role of three cell populations in the progression of RB tumors was emphatically analyzed. We found that in the early stage of RB tumors, cone precursor cells proliferated abnormally in G1 phase. With the progression of RB tumors, the proportion of cone precursor cells in G2 phase increased significantly. Meanwhile, the results of differential analysis of microglial populations during RB progression showed that the key genes mainly involved in immune response include RPL23, B2M, and HLA superfamily genes. This study provides new perspectives and data resources for the research of RB pathogenesis and progress.

Loss of HER2 in breast cancer: biological mechanisms and technical pitfalls

Loss of HER2 in previously HER2-positive breast tumors is not rare, occurring in up to 50% of breast cancers; however, clinical research and practice underestimate this issue. Many studies have reported the loss of HER2 after neoadjuvant therapy and at metastatic relapse and identified clinicopathological variables more frequently associated with this event. Nevertheless, the biological mechanisms underlying HER2 loss are still poorly understood. HER2 downregulation, intratumoral heterogeneity, clonal selection, and true subtype switch have been suggested as potential causes of HER2 loss, but translational studies specifically investigating the biology behind HER2 loss are virtually absent. On the other side, technical pitfalls may justify HER2 loss in some of these samples. The best treatment strategy for patients with HER2 loss is currently unknown. Considering the prevalence of this phenomenon and its apparent correlation with worse outcomes, we believe that correlative studies specifically addressing HER2 loss are warranted.

Investigating Two Modes of Cancer-Associated Antigen Heterogeneity in an Agent-Based Model of Chimeric Antigen Receptor T-Cell Therapy

Simple Summary

Chimeric antigen receptor (CAR) T-cell therapy has shown much promise in liquid tumors but often fails in solid tumors. This work uses a computational model to examine under what conditions this therapy might fail or be successful. The model includes interactions between cancer cells, CAR T-cells (treatment), and vascular cells (that feed and support tumor growth). From our results, we determined specific tumor conditions in which CAR T-cell therapy is predicted to fail and suggest a combination treatment that might improve the efficacy of the treatment.

Abstract

Chimeric antigen receptor (CAR) T-cell therapy has been successful in treating liquid tumors but has had limited success in solid tumors. This work examines unanswered questions regarding CAR T-cell therapy using computational modeling, such as, what percentage of the tumor must express cancer-associated antigens for treatment to be successful? The model includes cancer cell and vascular and CAR T-cell modules that interact with each other. We compare two different models of antigen expression on tumor cells, binary (in which cancer cells are either susceptible or are immune to CAR T-cell therapy) and gradated (where each cancer cell has a probability of being killed by a CAR T-cell). We vary the antigen expression levels within the tumor and determine how effective each treatment is for the two models. The simulations show that the gradated antigen model eliminates the tumor under more parameter values than the binary model. Under both models, shielding, in which the low/non-antigen-expressing cells protect high antigen-expressing cells, reduced the efficacy of CAR T-cell therapy. One prediction is that a combination of CAR T-cell therapies that targets the general population of cells as well as one that specifically targets cancer stem cells should increase its efficacy.

GBM tumors are heterogeneous in their fatty acid metabolism and modulating fatty acid metabolism sensitizes cancer cells derived from recurring GBM tumors to temozolomide

Glioblastoma is a highly lethal grade of astrocytoma with very low median survival. Despite extensive efforts, there is still a lack of alternatives that might improve these prospects. We uncovered that the chemotherapeutic agent temozolomide impinges on fatty acid synthesis and desaturation in newly diagnosed glioblastoma. This response is, however, blunted in recurring glioblastoma from the same patient. Further, we describe that disrupting cellular fatty acid homeostasis in favor of accumulation of saturated fatty acids such as palmitate synergizes with temozolomide treatment. Pharmacological inhibition of SCD and/or FADS2 allows palmitate accumulation and thus greatly augments temozolomide efficacy. This effect was independent of common GBM prognostic factors and was effective against cancer cells from recurring glioblastoma. In summary, we provide evidence that intracellular accumulation of saturated fatty acids in conjunction with temozolomide based chemotherapy induces death in glioblastoma cells derived from patients.

An update on the molecular biology of glioblastoma, with clinical implications and progress in its treatment

Glioblastoma multiforme (GBM) is the most aggressive and common malignant primary brain tumor. Patients with GBM often have poor prognoses, with a median survival of ∼15 months. Enhanced understanding of the molecular biology of central nervous system tumors has led to modifications in their classifications, the most recent of which classified these tumors into new categories and made some changes in their nomenclature and grading system. This review aims to give a panoramic view of the last 3 years' findings in glioblastoma characterization, its heterogeneity, and current advances in its treatment. Several molecular parameters have been used to achieve an accurate and personalized characterization of glioblastoma in patients, including epigenetic, genetic, transcriptomic and metabolic features, as well as age- and sex-related patterns and the involvement of several noncoding RNAs in glioblastoma progression. Astrocyte-like neural stem cells and outer radial glial-like cells from the subventricular zone have been proposed as agents involved in GBM of IDH-wildtype origin, but this remains controversial. Glioblastoma metabolism is characterized by upregulation of the PI3K/Akt/mTOR signaling pathway, promotion of the glycolytic flux, maintenance of lipid storage, and other features. This metabolism also contributes to glioblastoma's resistance to conventional therapies. Tumor heterogeneity, a hallmark of GBM, has been shown to affect the genetic expression, modulation of metabolic pathways, and immune system evasion. GBM's aggressive invasion potential is modulated by cell-to-cell crosstalk within the tumor microenvironment and altered expressions of specific genes, such as ANXA2, GBP2, FN1, PHIP, and GLUT3. Nevertheless, the rising number of active clinical trials illustrates the efforts to identify new targets and drugs to treat this malignancy. Immunotherapy is still relevant for research purposes, given the amount of ongoing clinical trials based on this strategy to treat GBM, and neoantigen and nucleic acid-based vaccines are gaining importance due to their antitumoral activity by inducing the immune response. Furthermore, there are clinical trials focused on the PI3K/Akt/mTOR axis, angiogenesis, and tumor heterogeneity for developing molecular-targeted therapies against GBM. Other strategies, such as nanodelivery and computational models, may improve the drug pharmacokinetics and the prognosis of patients with GBM.

Signatures of Co-Deregulated Genes and Their Transcriptional Regulators in Lung Cancer

Despite the significant progress made towards comprehending the deregulated signatures in lung cancer, these vary from study to study. We reanalyzed 25 studies from the Gene Expression Omnibus (GEO) to detect and annotate co-deregulated signatures in lung cancer and in single-gene or single-drug perturbation experiments. We aimed to decipher the networks that these co-deregulated genes (co-DEGs) form along with their upstream regulators. Differential expression and upstream regulators were computed using Characteristic Direction and Systems Biology tools, including GEO2Enrichr and X2K. Co-deregulated gene expression profiles were further validated across different molecular and immune subtypes in lung adenocarcinoma (TCGA-LUAD) and lung adenocarcinoma (TCGA-LUSC) datasets, as well as using immunohistochemistry data from the Human Protein Atlas, before being subjected to subsequent GO and KEGG enrichment analysis. The functional alterations of the co-upregulated genes in lung cancer were mostly related to immune response regulating the cell surface signaling pathway, in contrast to the co-downregulated genes, which were related to S-nitrosylation. Networks of hub proteins across the co-DEGs consisted of overlapping TFs (SOX2, MYC, KAT2A) and kinases (MAPK14, CSNK2A1 and CDKs). Furthermore, using Connectivity Map we highlighted putative repurposing drugs, including valproic acid, betonicine and astemizole. Similarly, we analyzed the co-DEG signatures in single-gene and single-drug perturbation experiments in lung cancer cell lines. In summary, we identified critical co-DEGs in lung cancer providing an innovative framework for their potential use in developing personalized therapeutic strategies.

DAP10 integration in CAR-T cells enhances the killing of heterogeneous tumors by harnessing endogenous NKG2D

Although chimeric antigen receptor T (CAR-T) cells have achieved remarkable successes in hematological malignancies, the efficacies of CAR-T cells against solid tumors remains unsatisfactory. Heterogeneous antigen expression is one of the obstacles on its effective elimination of solid cancer cells. DNAX-activating protein 10 (DAP10) interacts with natural killer group 2D (NKG2D), acting as an adaptor that targets various malignant cells for surveillance. Here, we designed a DAP10 chimeric receptor that utilized native NKG2D on T cells to target NKG2D ligand-expressing cancer cells. We then tandemly incorporated it with anti-glypican 3 (GPC3) single-chain variable fragment (scFv) to construct a dual-antigen-targeting system. T cells expressing DAP10 chimeric receptor (DAP10-T cells) displayed with an enhancement on both cytotoxicity and cytokine secretion against solid cancer cell lines, and its tandem connection with anti-GPC3 scFv (CAR GPC3-DAP10-T cells) exhibited a dual-antigen-targeting capacity on eliminating heterogeneous cancer cells in vitro and suppressing the growth of heterogeneous cancer in vivo. Thus, this novel dual-targeting system enabled a high efficacy on killing cancer cells and extended the recognition profile of CAR-T cells toward tumors, which providing a potential strategy on treatment of solid cancer clinically.

Comparing Apparent Diffusion Coefficient and FNCLCC Grading to Improve Pretreatment Grading of Soft Tissue Sarcoma-A Translational Feasibility Study on Fusion Imaging

Simple Summary

Histological subtype and grading are essential for the planning of soft tissue sarcoma. Pretherapeutic grading based on core needle biopsies is frequently not reliable due to intratumoral heterogeneity. This pilot study assessed the ability of functional radiological imaging to improve histopathological grading. Multiple biopsies were taken from the sarcoma specimens during tumor resection and radiopaque markers were placed. Subsequently, fusion of preoperative magnetic resonance imaging and postoperative computed tomography of the specimen allowed for comparison of histopathological grading and diffusion-weighted imaging. The apparent diffusion coefficient appears to correlate with FNCLCC criteria and may supplement pretreatment assessment and multimodal treatment allocation in soft tissue sarcoma.

Abstract

Histological subtype and grading are cornerstones of treatment decisions in soft tissue sarcoma (STS). Due to intratumoral heterogeneity, pretreatment grading assessment is frequently unreliable and may be improved through functional imaging. In this pilot study, 12 patients with histologically confirmed STS were included. Preoperative functional magnetic resonance imaging was fused with a computed tomography scan of the resected specimen after collecting core needle biopsies and placing radiopaque markers at distinct tumor sites. The Fédération Nationale des Centres de Lutte Contre le Cancer (FNCLCC) grading criteria of the biopsies and apparent diffusion coefficients (ADCs) of the biopsy sites were correlated. Concordance in grading between the specimen and at least one biopsy was achieved in 9 of 11 cases (81.8%). In 7 of 12 cases, fusion imaging was feasible without relevant contour deviation. Functional analysis revealed a tendency for high-grade regions (Grade 2/3 (G2/G3)) (median (range) ± standard deviation: 1.13 (0.78–1.70) ± 0.23 × 10⁻³ mm²/s) to have lower ADC values than low-grade regions (G1; 1.43 (0.64–2.03) ± 0.46 × 10⁻³ mm²/s). In addition, FNCLCC scoring of multiple tumor biopsies proved intratumoral heterogeneity as expected. The ADC appears to correlate with the FNCLCC grading criteria. Further studies are needed to determine whether functional imaging may supplement histopathological grading.

Monitoring of Current Cancer Therapy by Positron Emission Tomography and Possible Role of Radiomics Assessment

Evaluation of cancer therapy with imaging is crucial as a surrogate marker of effectiveness and survival. The unique response patterns to therapy with immune-checkpoint inhibitors have facilitated the revision of response evaluation criteria using FDG-PET, because the immune response recalls reactive cells such as activated T-cells and macrophages, which show increased glucose metabolism and apparent progression on morphological imaging. Cellular metabolism and function are critical determinants of the viability of active cells in the tumor microenvironment, which would be novel targets of therapies, such as tumor immunity, metabolism, and genetic mutation. Considering tumor heterogeneity and variation in therapy response specific to the mechanisms of therapy, appropriate response evaluation is required. Radiomics approaches, which combine objective image features with a machine learning algorithm as well as pathologic and genetic data, have remarkably progressed over the past decade, and PET radiomics has increased quality and reliability based on the prosperous publications and standardization initiatives. PET and multimodal imaging will play a definitive role in personalized therapeutic strategies by the precise monitoring in future cancer therapy.

Multiregional single-cell proteogenomic analysis of ccRCC reveals cytokine drivers of intratumor spatial heterogeneity

Intratumor heterogeneity (ITH) represents a major challenge for anticancer therapies. An integrated, multidimensional, multiregional approach dissecting ITH of the clear cell renal cell carcinoma (ccRCC) tumor microenvironment (TME) is employed at the single-cell level with mass cytometry (CyTOF), multiplex immunofluorescence (MxIF), and single-nucleus RNA sequencing (snRNA-seq) and at the bulk level with whole-exome sequencing (WES), RNA-seq, and methylation profiling. Multiregional analyses reveal unexpected conservation of immune composition within each individual patient, with profound differences among patients, presenting patient-specific tumor immune microenvironment signatures despite underlying genetic heterogeneity from clonal evolution. Spatial proteogenomic TME analysis using MxIF identifies 14 distinct cellular neighborhoods and, conversely, demonstrated architectural heterogeneity among different tumor regions. Tumor-expressed cytokines are identified as key determinants of the TME and correlate with clinical outcome. Overall, this work signifies that spatial ITH occurs in ccRCC, which may drive clinical heterogeneity and warrants further interrogation to improve patient outcomes.

PRC2 Heterogeneity Drives Tumor Growth in Medulloblastoma

Intratumor epigenetic heterogeneity is emerging as a key mechanism underlying tumor evolution and drug resistance. Epigenetic abnormalities frequently occur in medulloblastoma, the most common childhood malignant brain tumor. Medulloblastoma is classified into four subtypes including SHH medulloblastoma, which is characterized by elevated sonic hedgehog (SHH) signaling and a cerebellum granule neuron precursor (CGNP) cell-of-origin. Here, we report that the histone H3K27 methyltransferase polycomb repressor complex 2 (PRC2) is often heterogeneous within individual SHH medulloblastoma tumors. In mouse models, complete deletion of the PRC2 core subunit EED inhibited medulloblastoma growth, while a mosaic deletion of EED significantly enhanced tumor growth. EED is intrinsically required for CGNP maintenance by inhibiting both neural differentiation and cell death. Complete deletion of EED led to CGNP depletion and reduced occurrence of medulloblastoma. Surprisingly, medulloblastomas with mosaic EED levels grew faster than control wild-type tumors and expressed increased levels of oncogenes such as Igf2, which is directly repressed by PRC2 and has been demonstrated to be both necessary and sufficient for SHH medulloblastoma progression. Insulin-like growth factor 2 (IGF2) mediated the oncogenic effects of PRC2 heterogeneity in tumor growth. Assessing clones of a human medulloblastoma cell line with different EED levels confirmed that EEDlow cells can stimulate the growth of EEDhigh cells through paracrine IGF2 signaling. Thus, PRC2 heterogeneity plays an oncogenic role in medulloblastoma through both intrinsic growth competence and non-cell autonomous mechanisms in distinct tumor subclones.

SIGNIFICANCE

The identification of an oncogenic function of PRC2 heterogeneity in medulloblastoma provides insights into subclone competition and cooperation during heterogeneous tumor evolution.

Colorectal Cancer Patient-Derived 2D and 3D Models Efficiently Recapitulate Inter- and Intratumoral Heterogeneity

Pre-existing drug resistance and tumorigenicity of cancer cells are highly correlated with therapeutic failure and tumor growth. However, current cancer models are limited in their application to the study of intratumor functional heterogeneity in personalized oncology. Here, an innovative two-dimensional (2D) and three-dimensional (3D) model for patient-derived cancer cells (PDCCs) and air-liquid interface (ALI) organotypic culture is established from colorectal cancer (CRC). The PDCCs recapitulate the genomic landscape of their parental tumors with high efficiency, high proliferation rate, and long-term stability, while corresponding ALI organotypic cultures retain histological architecture of their original tumors. Interestingly, both 2D and 3D models maintain the transcriptomic profile of the corresponding primary tumors and display the same trend in response to 5-Fluoruracil, regardless of their difference in gene expression profiles. Furthermore, single-cell-derived clones() are efficiently established and pre-existing drug-resistant clones and highly tumorigenic clones within individual CRC tumors are identified. It is found that tumorigenic cancer cells do not necessarily possess the stem cells characteristics in gene expression. This study provides valuable platform and resource for exploring the molecular mechanisms underlying the pre-existing drug resistance and tumorigenicity in cancer cells, as well as for developing therapeutic targets specifically for pre-existing drug-resistant or highly tumorigenic clones.

Spatial organization of heterogeneous immunotherapy target antigen expression in high-grade glioma

High-grade (WHO grades III-IV) glioma remains one of the most lethal human cancers. Adoptive transfer of tumor-targeting chimeric antigen receptor (CAR)-redirected T cells for high-grade glioma has revealed promising indications of anti-tumor activity, but objective clinical responses remain elusive for most patients. A significant challenge to effective immunotherapy is the highly heterogeneous structure of these tumors, including large variations in the magnitudes and distributions of target antigen expression, observed both within individual tumors and between patients. To obtain a more detailed understanding of immunotherapy target antigens within patient tumors, we immunochemically mapped at single cell resolution three clinically-relevant targets, IL13Rα2, HER2 and EGFR, on tumor samples drawn from a 43-patient cohort. We observed that within individual tumor samples, expression of these antigens was neither random nor uniform, but rather that they mapped into local neighborhoods - phenotypically similar cells within regions of cellular tumor - reflecting not well understood properties of tumor cells and their milieu. Notably, tumor cell neighborhoods of high antigen expression were not arranged independently within regions. For example, in cellular tumor regions, neighborhoods of high IL13Rα2 and HER2 expression appeared to be reciprocal to those of EGFR, while in areas of pseudopalisading necrosis, expression of IL13Rα2 and HER2, but not EGFR, appeared to reflect the radial organization of tumor cells around hypoxic cores. Other structural features affecting expression of immunotherapy target antigens remain to be elucidated. This structured but heterogeneous organization of antigen expression in high grade glioma is highly permissive for antigen escape, and combinatorial antigen targeting is a commonly suggested potential mitigating strategy. Deeper understanding of antigen expression within and between patient tumors will enhance optimization of combination immunotherapies, the most immediate clinical application of the observations presented here being the importance of including (wild-type) EGFR as a target antigen.

Multi-modal optimization to identify personalized biomarkers for disease prediction of individual patients with cancer

Finding personalized biomarkers for disease prediction of patients with cancer remains a massive challenge in precision medicine. Most methods focus on one subnetwork or module as a network biomarker; however, this ignores the early warning capabilities of other modules with different configurations of biomarkers (i.e. multi-modal personalized biomarkers). Identifying such modules would not only predict disease but also provide effective therapeutic drug target information for individual patients. To solve this problem, we developed a novel model (denoted multi-modal personalized dynamic network biomarkers (MMPDNB)) based on a multi-modal optimization mechanism and personalized dynamic network biomarker (PDNB) theory, which can provide multiple modules of personalized biomarkers and unveil their multi-modal properties. Using the genomics data of patients with breast or lung cancer from The Cancer Genome Atlas database, we validated the effectiveness of the MMPDNB model. The experimental results showed that compared with other advanced methods, MMPDNB can more effectively predict the critical state with the highest early warning signal score during cancer development. Furthermore, MMPDNB more significantly identified PDNBs containing driver and biomarker genes specific to cancer tissues. More importantly, we validated the biological significance of multi-modal PDNBs, which could provide effective drug targets of individual patients as well as markers for predicting early warning signals of the critical disease state. In conclusion, multi-modal optimization is an effective method to identify PDNBs and offers a new perspective for understanding tumor heterogeneity in cancer precision medicine.

Classifying Germinal Center Derived Lymphomas-Navigate a Complex Transcriptional Landscape

Classification of lymphoid neoplasms is based mainly on histologic, immunologic, and (rarer) genetic features. It has been supplemented by gene expression profiling (GEP) in the last decade. Despite the considerable success, particularly in associating lymphoma subtypes with specific transcriptional programs and classifier signatures of up- or downregulated genes, competing molecular classifiers were often proposed in the literature by different groups for the same classification tasks to distinguish, e.g., BL versus DLBCL or different DLBCL subtypes. Moreover, rarer sub-entities such as MYC and BCL2 "double hit lymphomas" (DHL), IRF4-rearranged large cell lymphoma (IRF4-LCL), and Burkitt-like lymphomas with 11q aberration pattern (mnBLL-11q) attracted interest while their relatedness regarding the major classes is still unclear in many respects. We explored the transcriptional landscape of 873 lymphomas referring to a wide spectrum of subtypes by applying self-organizing maps (SOM) machine learning. The landscape reveals a continuum of transcriptional states activated in the different subtypes without clear-cut borderlines between them and preventing their unambiguous classification. These states show striking parallels with single cell gene expression of the active germinal center (GC), which is characterized by the cyclic progression of B-cells. The expression patterns along the GC trajectory are discriminative for distinguishing different lymphoma subtypes. We show that the rare subtypes take intermediate positions between BL, DLBCL, and FL as considered by the 5th edition of the WHO classification of haemato-lymphoid tumors in 2022. Classifier gene signatures extracted from these states as modules of coregulated genes are competitive with literature classifiers. They provide functional-defined classifiers with the option of consenting redundant classifiers from the literature. We discuss alternative classification schemes of different granularity and functional impact as possible avenues toward personalization and improved diagnostics of GC-derived lymphomas.

Cell-by-Cell: Unlocking Lung Cancer Pathogenesis

For lung cancers, cellular trajectories and fates are strongly pruned by cell intrinsic and extrinsic factors. Over the past couple of decades, the combination of comprehensive molecular and genomic approaches, as well as the use of relevant pre-clinical models, enhanced micro-dissection techniques, profiling of rare preneoplastic lesions and surrounding tissues, as well as multi-region tumor sequencing, have all provided in-depth insights into the early biology and evolution of lung cancers. The advent of single-cell sequencing technologies has revolutionized our ability to interrogate these same models, tissues, and cohorts at an unprecedented resolution. Single-cell tracking of lung cancer pathogenesis is now transforming our understanding of the roles and consequences of epithelial-microenvironmental cues and crosstalk during disease evolution. By focusing on non-small lung cancers, specifically lung adenocarcinoma subtype, this review aims to summarize our knowledge base of tumor cells-of-origin and tumor-immune dynamics that have been primarily fueled by single-cell analysis of lung adenocarcinoma specimens at various stages of disease pathogenesis and of relevant animal models. The review will provide an overview of how recent reports are rewriting the mechanistic details of lineage plasticity and intra-tumor heterogeneity at a magnified scale thanks to single-cell studies of early- to late-stage lung adenocarcinomas. Future advances in single-cell technologies, coupled with analysis of minute amounts of rare clinical tissues and novel animal models, are anticipated to help transform our understanding of how diverse micro-events elicit macro-scale consequences, and thus to significantly advance how basic genomic and molecular knowledge of lung cancer evolution can be translated into successful targets for early detection and prevention of this lethal disease.

Microscopy-based single-cell proteomic profiling reveals heterogeneity in DNA damage response dynamics

Single-cell proteomics has the potential to decipher tumor heterogeneity, and a method like single-cell proteomics by mass spectrometry (SCoPE-MS) allows profiling several tens of single cells for >1,000 proteins per cell. This method, however, cannot link the proteome of individual cells with phenotypes of interest. Here, we developed a microscopy-based functional single-cell proteomic-profiling technology, called FUNpro, to address this. FUNpro enables screening, identification, and isolation of single cells of interest in a real-time fashion, even if the phenotypes are dynamic or the cells of interest are rare. We applied FUNpro to proteomically profile a newly identified small subpopulation of U2OS osteosarcoma cells displaying an abnormal, prolonged DNA damage response (DDR) after ionizing radiation (IR). With this, we identified the PDS5A protein contributing to the abnormal DDR dynamics and helping the cells survive after IR.

Heterogeneity of triple-negative breast cancer: understanding the Daedalian labyrinth and how it could reveal new drug targets

INTRODUCTION

Triple-negative breast cancer (TNBC) is considered the most aggressive breast cancer subtype with the least favorable outcomes. However, recent research efforts have generated an enhanced knowledge of the biology of the disease and have provided a new, more comprehensive understanding of the multifaceted ecosystem that underpins TNBC.

AREAS COVERED

In this review, the authors illustrate the principal biological characteristics of TNBC, the molecular driver alterations, targetable genes, and the biomarkers of immune engagement that have been identified across the subgroups of TNBC. Accordingly, the authors summarize the landscape of the innovative and investigative biomarker-driven therapeutic options in TNBC that emerge from the unique biological basis of the disease.

EXPERT OPINION

The therapeutic setting of TNBC is rapidly evolving. An enriched understanding of the tumor spatial and temporal heterogeneity and the surrounding microenvironment of this complex disease can effectively support the development of novel and tailored opportunities of treatment.

Somatic Mutations in Exon 7 of the TP53 Gene in Index Colorectal Lesions Are Associated with the Early Occurrence of Metachronous Adenoma

Simple Summary

Identifying patients with an increased risk of early recurrence of colorectal lesions is still a problem. In our study, we focused on improving this identification by determining the mutation profile of index lesions. We found a statistically significant association between the mutation in exon 7 of the TP53 gene in the index lesion and the risk of early metachronous adenoma.

Abstract

(1) Background: this prospective study was focused on detailed analysis of the mutation heterogeneity in colorectal lesions removed during baseline (index) colonoscopy to identify patients at high risk of early occurrence of metachronous adenomas. (2) Methods: a total of 120 patients after endoscopic therapy of advanced colorectal neoplasia size ≥10 mm (index lesion) with subsequent surveillance colonoscopy after 10–18 months were included. In total, 143 index lesions and 84 synchronous lesions in paraffin blocks were divided into up to 30 samples. In each of them, the detection of somatic mutations in 11 hot spot gene loci was performed. Statistical analysis to correlate the mutation profiles and the degree of heterogeneity of the lesions with the risk of metachronous adenoma occurrence was undertaken. (3) Results: mutation in exon 7 of the TP53 gene found in the index lesion significantly correlated with the early occurrence of metachronous adenoma (log-rank test p = 0.003, hazard ratio 2.73, 95% confidence interval 1.14–6.56). We did not find an association between the risk of metachronous adenomas and other markers monitored. (4) Conclusions: the findings of this study could lead to an adjustment of existing recommendations for surveillance colonoscopies in a specific group of patients with mutations in exon 7 of the TP53 gene in an index lesion, where a shortening of surveillance interval may be warranted.

Patient-Derived Xenograft: A More Standard "Avatar" Model in Preclinical Studies of Gastric Cancer

Despite advances in diagnosis and treatment, gastric cancer remains the third most common cause of cancer-related death in humans. The establishment of relevant animal models of gastric cancer is critical for further research. Due to the complexity of the tumor microenvironment and the genetic heterogeneity of gastric cancer, the commonly used preclinical animal models fail to adequately represent clinically relevant models of gastric cancer. However, patient-derived models are able to replicate as much of the original inter-tumoral and intra-tumoral heterogeneity of gastric cancer as possible, reflecting the cellular interactions of the tumor microenvironment. In addition to implanting patient tissues or primary cells into immunodeficient mouse hosts for culture, the advent of alternative hosts such as humanized mouse hosts, zebrafish hosts, and in vitro culture modalities has also facilitated the advancement of gastric cancer research. This review highlights the current status, characteristics, interfering factors, and applications of patient-derived models that have emerged as more valuable preclinical tools for studying the progression and metastasis of gastric cancer.