The role of tumour heterogeneity and clonal cooperativity in metastasis, immune evasion and clinical outcome

Computed tomography-based radiomic markers are independent prognosticators of survival in advanced laryngeal cancer: a pilot study

OBJECTIVE

Advanced laryngeal cancers are clinically complex; there is a paucity of modern decision-making models to guide tumour-specific management. This pilot study aims to identify computed tomography-based radiomic features that may predict survival and enhance prognostication.

METHODS

Pre-biopsy, contrast-enhanced computed tomography scans were assembled from a retrospective cohort (n = 72) with advanced laryngeal cancers (T3 and T4). The LIFEx software was used for radiomic feature extraction. Two features: shape compacity (irregularity of tumour volume) and grey-level zone length matrix - grey-level non-uniformity (tumour heterogeneity) were selected via least absolute shrinkage and selection operator-based Cox regression and explored for prognostic potential.

RESULTS

A greater shape compacity (hazard ratio 2.89) and grey-level zone length matrix - grey-level non-uniformity (hazard ratio 1.64) were significantly associated with worse 5-year disease-specific survival (p < 0.05). Cox regression models yielded a superior C-index when incorporating radiomic features (0.759) versus clinicopathological variables alone (0.655).

CONCLUSIONS

Two radiomic features were identified as independent prognostic biomarkers. A multi-centre prospective study is necessary for further exploration. Integrated radiomic models may refine the treatment of advanced laryngeal cancers.

Nanomedicine as a multimodal therapeutic paradigm against cancer: on the way forward in advancing precision therapy

Recent years have witnessed dramatic improvements in nanotechnology-based cancer therapeutics, and it continues to evolve from the use of conventional therapies (chemotherapy, surgery, and radiotherapy) to increasingly multi-complex approaches incorporating thermal energy-based tumor ablation (e.g. magnetic hyperthermia and photothermal therapy), dynamic therapy (e.g. photodynamic therapy), gene therapy, sonodynamic therapy (e.g. ultrasound), immunotherapy, and more recently real-time treatment efficacy monitoring (e.g. theranostic MRI-sensitive nanoparticles). Unlike monotherapy, these multimodal therapies (bimodal, i.e., a combination of two therapies, and trimodal, i.e., a combination of more than two therapies) incorporating nanoplatforms have tremendous potential to improve the tumor tissue penetration and retention of therapeutic agents through selective active/passive targeting effects. These combinatorial therapies can correspondingly alleviate drug response against hypoxic/acidic and immunosuppressive tumor microenvironments and promote/induce tumor cell death through various multi-mechanisms such as apoptosis, autophagy, and reactive oxygen-based cytotoxicity, e.g., ferroptosis, etc. These multi-faced approaches such as targeting the tumor vasculature, neoangiogenic vessels, drug-resistant cancer stem cells (CSCs), preventing intra/extravasation to reduce metastatic growth, and modulation of antitumor immune responses work complementary to each other, enhancing treatment efficacy. In this review, we discuss recent advances in different nanotechnology-mediated synergistic/additive combination therapies, emphasizing their underlying mechanisms for improving cancer prognosis and survival outcomes. Additionally, significant challenges such as CSCs, hypoxia, immunosuppression, and distant/local metastasis associated with therapy resistance and tumor recurrences are reviewed. Furthermore, to improve the clinical precision of these multimodal nanoplatforms in cancer treatment, their successful bench-to-clinic translation with controlled and localized drug-release kinetics, maximizing the therapeutic window while addressing safety and regulatory concerns are discussed. As we advance further, exploiting these strategies in clinically more relevant models such as patient-derived xenografts and 3D organoids will pave the way for the application of precision therapy.

Chronic arsenic exposure induces malignant transformation of human HaCaT cells through both deterministic and stochastic changes in transcriptome expression

Biological processes are inherently stochastic, i.e., are partially driven by hard to predict random probabilistic processes. Carcinogenesis is driven both by stochastic and deterministic (predictable non-random) changes. However, very few studies systematically examine the contribution of stochastic events leading to cancer development. In differential gene expression studies, the established data analysis paradigms incentivize expression changes that are uniformly different across the experimental versus control groups, introducing preferential inclusion of deterministic changes at the expense of stochastic processes that might also play a crucial role in the process of carcinogenesis. In this study, we applied simple computational techniques to quantify: (i) The impact of chronic arsenic (iAs) exposure as well as passaging time on stochastic gene expression and (ii) Which genes were expressed deterministically and which were expressed stochastically at each of the three stages of cancer development. Using biological coefficient of variation as an empirical measure of stochasticity we demonstrate that chronic iAs exposure consistently suppressed passaging related stochastic gene expression at multiple time points tested, selecting for a homogenous cell population that undergo transformation. Employing multiple balanced removal of outlier data, we show that chronic iAs exposure induced deterministic and stochastic changes in the expression of unique set of genes, that populate largely unique biological pathways. Together, our data unequivocally demonstrate that both deterministic and stochastic changes in transcriptome-wide expression are critical in driving biological processes, pathways and networks towards clonal selection, carcinogenesis, and tumor heterogeneity.

ES-SCLC Patients with PD-L1+ CTCs and High Percentages of CD8+PD-1+T Cells in Circulation Benefit from Front-Line Immunotherapy Treatment

SCLC is an aggressive cancer type with high metastatic potential and bad prognosis. CTCs are a valuable source of tumor cells in blood circulation and are among the major contributors to metastasis. In this study we evaluated the number of CTCs that express PD-L1 in treatment-naïve ES-SCLC patients receiving ICI in a front-line setting. Moreover, we explored the percentages of different immune T-cell subsets in circulation to assess their potential role in predicting responses. A total of 43 patients were enrolled-6 of them with LS-SCLC, and 37 with ES-SCLC disease. In addition, PBMCs from 10 healthy donors were used as a control group. Different T-cell subtypes were examined through multicolor FACS analysis and patients' CTCs were detected using immunofluorescence staining. SCLC patients had higher percentages of PD-1-expressing CD3+CD4+ and CD3+CD8+ T-cells, as well as elevated PD-1 protein expression compared to healthy individuals. Additionally, in ES-SCLC patients, a positive correlation between CD3+CD8+PD-1+ T-cells and PD-L1+ CTCs was detected. Importantly, patients harboring higher numbers of CD3+CD8+PD-1+ T-cells together with PD-L1+CTCs had a survival advantage when receiving front-line immunotherapy. Thus, this study proposes, for first time possible, immune cell-CTCs interaction, as well as a potential novel clinical biomarker for ICI responses in ES-SCLC patients.

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.

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.

Metabolic Patterns of High-Invasive and Low-Invasive Oral Squamous Cell Carcinoma Cells Using Quantitative Metabolomics and 13C-Glucose Tracing

Most current metabolomics studies of oral squamous cell carcinoma (OSCC) are mainly focused on identifying potential biomarkers for early screening and diagnosis, while few studies have investigated the metabolic profiles promoting metastasis. In this study, we aimed to explore the altered metabolic pathways associated with metastasis of OSCC. Here, we identified four OSCC cell models (CAL27, HN6, HSC-3, SAS) that possess different invasive heterogeneity via the transwell invasion assay and divided them into high-invasive (HN6, SAS) and low-invasive (CAL27, HSC-3) cells. Quantitative analysis and stable isotope tracing using [U-13C6] glucose were performed to detect the altered metabolites in high-invasive OSCC cells, low-invasive OSCC cells and normal human oral keratinocytes (HOK). The metabolic changes in the high-invasive and low-invasive cells included elevated glycolysis, increased fatty acid metabolism and an impaired TCA cycle compared with HOK. Moreover, pathway analysis demonstrated significant differences in fatty acid biosynthesis; arachidonic acid (AA) metabolism; and glycine, serine and threonine metabolism between the high-invasive and low-invasive cells. Furthermore, the high-invasive cells displayed a significant increase in the percentages of 13C-glycine, 13C-palmitate, 13C-stearic acid, 13C-oleic acid, 13C-AA and estimated FADS1/2 activities compared with the low-invasive cells. Overall, this exploratory study suggested that the metabolic differences related to the metastatic phenotypes of OSCC cells were concentrated in glycine metabolism, de novo fatty acid synthesis and polyunsaturated fatty acid (PUFA) metabolism, providing a comprehensive understanding of the metabolic alterations and a basis for studying related molecular mechanisms in metastatic OSCC cells.

Clonal heterogeneity in ER+ breast cancer reveals the proteasome and PKC as potential therapeutic targets

Intratumoral heterogeneity impacts the success or failure of anti-cancer therapies. Here, we investigated the evolution and mechanistic heterogeneity in clonal populations of cell models for estrogen receptor positive breast cancer. To this end, we established barcoded models of luminal breast cancer and rendered them resistant to commonly applied first line endocrine therapies. By isolating single clones from the resistant cell pools and characterizing replicates of individual clones we observed inter- (between cell lines) and intra-tumor (between different clones from the same cell line) heterogeneity. Molecular characterization at RNA and phospho-proteomic levels revealed private clonal activation of the unfolded protein response and respective sensitivity to inhibition of the proteasome, and potentially shared sensitivities for repression of protein kinase C. Our in vitro findings are consistent with tumor-heterogeneity that is observed in breast cancer patients thus highlighting the need to uncover heterogeneity at an individual patient level and to adjust therapies accordingly.

A comparison of 18 F-FDG PET-based radiomics and deep learning in predicting regional lymph node metastasis in patients with resectable lung adenocarcinoma: a cross-scanner and temporal validation study

OBJECTIVE

The performance of 18 F-FDG PET-based radiomics and deep learning in detecting pathological regional nodal metastasis (pN+) in resectable lung adenocarcinoma varies, and their use across different generations of PET machines has not been thoroughly investigated. We compared handcrafted radiomics and deep learning using different PET scanners to predict pN+ in resectable lung adenocarcinoma.

METHODS

We retrospectively analyzed pretreatment 18 F-FDG PET from 148 lung adenocarcinoma patients who underwent curative surgery. Patients were separated into analog (n = 131) and digital (n = 17) PET cohorts. Handcrafted radiomics and a ResNet-50 deep-learning model of the primary tumor were used to predict pN+ status. Models were trained in the analog PET cohort, and the digital PET cohort was used for cross-scanner validation.

RESULTS

In the analog PET cohort, entropy, a handcrafted radiomics, independently predicted pN+. However, the areas under the receiver-operating-characteristic curves (AUCs) and accuracy for entropy were only 0.676 and 62.6%, respectively. The ResNet-50 model demonstrated a better AUC and accuracy of 0.929 and 94.7%, respectively. In the digital PET validation cohort, the ResNet-50 model also demonstrated better AUC (0.871 versus 0.697) and accuracy (88.2% versus 64.7%) than entropy. The ResNet-50 model achieved comparable specificity to visual interpretation but with superior sensitivity (83.3% versus 66.7%) in the digital PET cohort.

CONCLUSION

Applying deep learning across different generations of PET scanners may be feasible and better predict pN+ than handcrafted radiomics. Deep learning may complement visual interpretation and facilitate tailored therapeutic strategies for resectable lung adenocarcinoma.

Biophysical phenotype mixtures reveal advantages for tumor muscle invasion in vivo

Bladder, colon, gastric, prostate, and uterine cancers originate in organs surrounded by laminin-coated smooth muscle. In human prostate cancer, tumors that are organ confined, without extracapsular extension through muscle, have an overall cancer survival rate of up to 97% compared with 32% for metastatic disease. Our previous work modeling extracapsular extension reported the blocking of tumor invasion by mutation of a laminin-binding integrin called α6β1. Expression of the α6AA mutant resulted in a biophysical switch from cell-ECM (extracellular matrix) to cell-cell adhesion with drug sensitivity properties and an inability to invade muscle. Here we used different admixtures of α6AA and α6WT cells to test the cell heterogeneity requirements for muscle invasion. Time-lapse video microscopy revealed that tumor mixtures self-assembled into invasive networks in vitro, whereas α6AA cells assembled only as cohesive clusters. Invasion of α6AA cells into and through live muscle occurred using a 1:1 mixture of α6AA and α6WT cells. Electric cell-substrate impedance sensing measurements revealed that compared with α6AA cells, invasion-competent α6WT cells were 2.5-fold faster at closing a cell-ECM or cell-cell wound, respectively. Cell-ECM rebuilding kinetics show that an increased response occurred in mixtures since the response was eightfold greater compared with populations containing only one cell type. A synthetic cell adhesion cyclic peptide called MTI-101 completely blocked electric cell-substrate impedance sensing cell-ECM wound recovery that persisted in vitro up to 20 h after the wound. Treatment of tumor-bearing animals with 10 mg/kg MTI-101 weekly resulted in a fourfold decrease of muscle invasion by tumor and a decrease of the depth of invasion into muscle comparable to the α6AA cells. Taken together, these data suggest that mixed biophysical phenotypes of tumor cells within a population can provide functional advantages for tumor invasion into and through muscle that can be potentially inhibited by a synthetic cell adhesion molecule.

Capture of circulating metastatic cancer cell clusters from a lung cancer patient can reveal a unique genomic profile and potential anti-metastatic molecular targets: A proof of concept study

Metastasis remains the leading cause of cancer deaths worldwide and lung cancer, known for its highly metastatic progression, remains among the most lethal of malignancies. The heterogeneous genomic profile of lung cancer metastases is often unknown. Since different metastatic events can selectively spread to multiple organs, strongly suggests more studies are needed to understand and target these different pathways. Unfortunately, access to the primary driver of metastases, the metastatic cancer cell clusters (MCCCs), remains difficult and limited. These metastatic clusters have been shown to be 100-fold more tumorigenic than individual cancer cells. Capturing and characterizing MCCCs is a key limiting factor in efforts to help treat and ultimately prevent cancer metastasis. Elucidating differentially regulated biological pathways in MCCCs will help uncover new therapeutic drug targets to help combat cancer metastases. We demonstrate a novel, proof of principle technology, to capture MCCCs directly from patients' whole blood. Our platform can be readily tuned for different solid tumor types by combining a biomimicry-based margination effect coupled with immunoaffinity to isolate MCCCs. Adopting a selective capture approach based on overexpressed CD44 in MCCCs provides a methodology that preferentially isolates them from whole blood. Furthermore, we demonstrate a high capture efficiency of more than 90% when spiking MCCC-like model cell clusters into whole blood. Characterization of the captured MCCCs from lung cancer patients by immunofluorescence staining and genomic analyses, suggests highly differential morphologies and genomic profiles., This study lays the foundation to identify potential drug targets thus unlocking a new area of anti-metastatic therapeutics.

Targeted treatments after chemoradiotherapy failure in a patient with relapsed, advanced non‑small cell lung cancer with on‑therapy circulating tumor biomarker monitoring: A case report

Ongoing investigations of targeted therapeutic agents and their increased clinical applications, together with research in genomics and proteomics, have explored a variety of novel approaches for treatment of lung cancer, and 'molecular subtypes' have been defined based on specific actionable genetic aberrations. Liquid biopsies, including circulating tumor DNA (ctDNA) testing, are of value for cancer diagnosis and comprehensive genomic profiling, such as the identification of cancer subtypes and major genetic alterations in cancer cells. The case of a 66-year-old male patient with newly-diagnosed driver mutation-negative advanced non-small cell lung cancer (NSCLC) who underwent conventional therapy is described in the present report. The patient underwent regular monitoring, including continuous ctDNA analysis, imaging and assessment of tumor marker levels such as carcinoembryonic antigen (CEA). The patient initially presented with deep vein thrombosis which affected both lower extremities and without any symptoms in the lung, with a positron emission tomography scan identifying irregular pulmonary nodules in the right lower lobe and enlarged right supraclavicular lymph nodes. Subsequent ultrasound-guided fine-needle aspiration with nodule biopsy indicated advanced unresectable disease at stage IIIB based on the Tumor-Node-Metastasis staging system by the American Joint Committee on Cancer. Next-generation sequencing of tumor tissue and peripheral blood confirmed driver mutation-negative genes, including epidermal growth factor receptor, rat sarcoma, ALK receptor tyrosine kinase, ROS1 proto-oncogene receptor tyrosine kinase and rearrangement during transfection (RET). After 5 years of chemoradiotherapy and surveillance of ctDNA and CEA levels, detectable kinesin family member 5B (KIF5B)-RET fusion in ctDNA and rising CEA levels prompted early scans, which identified disease progression. The patient subsequently received the oral RET inhibitor pralsetinib, with treatment being currently ongoing for ≥17 months without detectable KIF5B-RET ctDNA or elevated CEA levels, with an ongoing minor response and stable disease based on Response Evaluation Criteria in Solid Tumors v1.1 on imaging. The present case illustrates the potential role of on-therapy circulating tumor biomarker monitoring as a non-traumatic method to evaluate therapy response and detect early disease progression in patients with advanced NSCLC. Integration of circulating tumor biomarker testing into the management of patients with advanced NSCLC requires additional prospective studies to actively assess and elucidate optimal treatment strategies.

Nanotechnology-Aided Advancement in Combating the Cancer Metastasis

Modern medicine has been working to find a cure for cancer for almost a century, but thus far, they have not been very successful. Although cancer treatment has come a long way, more work has to be carried out to boost specificity and reduce systemic toxicity. The diagnostic industry is on the cusp of a technological revolution, and early diagnosis is essential for improving prognostic outlook and patient quality of life. In recent years, nanotechnology's use has expanded, demonstrating its efficacy in enhancing fields such as cancer treatment, radiation therapy, diagnostics, and imaging. Applications for nanomaterials are diverse, ranging from enhanced radiation adjuvants to more sensitive early detection instruments. Cancer, particularly when it has spread beyond the original site of cancer, is notoriously tough to combat. Many people die from metastatic cancer, which is why it remains a huge issue. Cancer cells go through a sequence of events known as the "metastatic cascade" throughout metastasis, which may be used to build anti-metastatic therapeutic techniques. Conventional treatments and diagnostics for metastasis have their drawbacks and hurdles that must be overcome. In this contribution, we explore in-depth the potential benefits that nanotechnology-aided methods might offer to the detection and treatment of metastatic illness, either alone or in conjunction with currently available conventional procedures. Anti-metastatic drugs, which can prevent or slow the spread of cancer throughout the body, can be more precisely targeted and developed with the help of nanotechnology. Furthermore, we talk about how nanotechnology is being applied to the treatment of patients with cancer metastases.

A convolutional neural network STIFMap reveals associations between stromal stiffness and EMT in breast cancer

Intratumor heterogeneity associates with poor patient outcome. Stromal stiffening also accompanies cancer. Whether cancers demonstrate stiffness heterogeneity, and if this is linked to tumor cell heterogeneity remains unclear. We developed a method to measure the stiffness heterogeneity in human breast tumors that quantifies the stromal stiffness each cell experiences and permits visual registration with biomarkers of tumor progression. We present Spatially Transformed Inferential Force Map (STIFMap) which exploits computer vision to precisely automate atomic force microscopy (AFM) indentation combined with a trained convolutional neural network to predict stromal elasticity with micron-resolution using collagen morphological features and ground truth AFM data. We registered high-elasticity regions within human breast tumors colocalizing with markers of mechanical activation and an epithelial-to-mesenchymal transition (EMT). The findings highlight the utility of STIFMap to assess mechanical heterogeneity of human tumors across length scales from single cells to whole tissues and implicates stromal stiffness in tumor cell heterogeneity.

Comprehensive characterization of tumor microenvironment in colorectal cancer via molecular analysis

Colorectal cancer (CRC) remains a challenging and deadly disease with high tumor microenvironment (TME) heterogeneity. Using an integrative multi-omics analysis and artificial intelligence-enabled spatial analysis of whole-slide images, we performed a comprehensive characterization of TME in colorectal cancer (CCCRC). CRC samples were classified into four CCCRC subtypes with distinct TME features, namely, C1 as the proliferative subtype with low immunogenicity; C2 as the immunosuppressed subtype with the terminally exhausted immune characteristics; C3 as the immune-excluded subtype with the distinct upregulation of stromal components and a lack of T cell infiltration in the tumor core; and C4 as the immunomodulatory subtype with the remarkable upregulation of anti-tumor immune components. The four CCCRC subtypes had distinct histopathologic and molecular characteristics, therapeutic efficacy, and prognosis. We found that the C1 subtype may be suitable for chemotherapy and cetuximab, the C2 subtype may benefit from a combination of chemotherapy and bevacizumab, the C3 subtype has increased sensitivity to the WNT pathway inhibitor WIKI4, and the C4 subtype is a potential candidate for immune checkpoint blockade treatment. Importantly, we established a simple gene classifier for accurate identification of each CCCRC subtype. Collectively our integrative analysis ultimately established a holistic framework to thoroughly dissect the TME of CRC, and the CCCRC classification system with high biological interpretability may contribute to biomarker discovery and future clinical trial design.

Defining multiple layers of intratumor heterogeneity based on variations of perturbations in multi-omics profiling

BACKGROUND

Intratumor heterogeneity (ITH) plays a crucial role in tumor progression, relapse, immune evasion, and drug resistance. Existing ITH quantification methods based on a single molecular level are inadequate to capture ITH evolving from genotype to phenotype.

METHODS

We designed a set of information entropy (IE)-based algorithms for quantifying ITH at the genome (somatic copy number alterations and mutations), mRNA, microRNA (miRNA), long non-coding RNA (lncRNA), protein, and epigenome level, respectively. We evaluated the performance of these algorithms by analyzing the correlations between their ITH scores and ITH-associated molecular and clinical features in 33 TCGA cancer types. Moreover, we evaluated the correlations between the ITH measures at different molecular levels by Spearman correlation and clustering analysis.

RESULTS

The IE-based ITH measures had significant correlations with unfavorable prognosis, tumor progression, genomic instability, antitumor immunosuppression, and drug resistance. The mRNA ITH showed stronger correlations with the miRNA, lncRNA, and epigenome ITH than with the genome ITH, supporting the regulatory relationships of miRNA, lncRNA, and DNA methylation towards mRNA. The protein-level ITH displayed stronger correlations with the transcriptome-level ITH than with the genome-level ITH, supporting the central dogma of molecular biology. Clustering analysis based on the ITH scores identified four subtypes of pan-cancer showing significantly different prognosis. Finally, the ITH integrating the seven ITH measures displayed more prominent properties of ITH than that at a single level.

CONCLUSIONS

This analysis provides landscapes of ITH at various molecular levels. Combining the ITH observation from different molecule levels will improve personalized management for cancer patients.

Clonal interactions in cancer: integrating quantitative models with experimental and clinical data

Tumors consist of different genotypically distinct subpopulations-or subclones-of cells. These subclones can influence neighboring clones in a process called "clonal interaction." Conventionally, research on driver mutations in cancer has focused on their cell-autonomous effects that lead to an increase in fitness of the cells containing the driver. Recently, with the advent of improved experimental and computational technologies for investigating tumor heterogeneity and clonal dynamics, new studies have shown the importance of clonal interactions in cancer initiation, progression, and metastasis. In this review we provide an overview of clonal interactions in cancer, discussing key discoveries from a diverse range of approaches to cancer biology research. We discuss common types of clonal interactions, such as cooperation and competition, its mechanisms, and the overall effect on tumorigenesis, with important implications for tumor heterogeneity, resistance to treatment, and tumor suppression. Quantitative models-in coordination with cell culture and animal model experiments-have played a vital role in investigating the nature of clonal interactions and the complex clonal dynamics they generate. We present mathematical and computational models that can be used to represent clonal interactions and provide examples of the roles they have played in identifying and quantifying the strength of clonal interactions in experimental systems. Clonal interactions have proved difficult to observe in clinical data; however, several very recent quantitative approaches enable their detection. We conclude by discussing ways in which researchers can further integrate quantitative methods with experimental and clinical data to elucidate the critical-and often surprising-roles of clonal interactions in human cancers.

Genomic-transcriptomic evolution in lung cancer and metastasis

Intratumour heterogeneity (ITH) fuels lung cancer evolution, which leads to immune evasion and resistance to therapy1. Here, using paired whole-exome and RNA sequencing data, we investigate intratumour transcriptomic diversity in 354 non-small cell lung cancer tumours from 347 out of the first 421 patients prospectively recruited into the TRACERx study2,3. Analyses of 947 tumour regions, representing both primary and metastatic disease, alongside 96 tumour-adjacent normal tissue samples implicate the transcriptome as a major source of phenotypic variation. Gene expression levels and ITH relate to patterns of positive and negative selection during tumour evolution. We observe frequent copy number-independent allele-specific expression that is linked to epigenomic dysfunction. Allele-specific expression can also result in genomic-transcriptomic parallel evolution, which converges on cancer gene disruption. We extract signatures of RNA single-base substitutions and link their aetiology to the activity of the RNA-editing enzymes ADAR and APOBEC3A, thereby revealing otherwise undetected ongoing APOBEC activity in tumours. Characterizing the transcriptomes of primary-metastatic tumour pairs, we combine multiple machine-learning approaches that leverage genomic and transcriptomic variables to link metastasis-seeding potential to the evolutionary context of mutations and increased proliferation within primary tumour regions. These results highlight the interplay between the genome and transcriptome in influencing ITH, lung cancer evolution and metastasis.

Designing experimental conditions to use the Lotka-Volterra model to infer tumor cell line interaction types

The Lotka-Volterra model is widely used to model interactions between two species. Here, we generate synthetic data mimicking competitive, mutualistic and antagonistic interactions between two tumor cell lines, and then use the Lotka-Volterra model to infer the interaction type. Structural identifiability of the Lotka-Volterra model is confirmed, and practical identifiability is assessed for three experimental designs: (a) use of a single data set, with a mixture of both cell lines observed over time, (b) a sequential design where growth rates and carrying capacities are estimated using data from experiments in which each cell line is grown in isolation, and then interaction parameters are estimated from an experiment involving a mixture of both cell lines, and (c) a parallel experimental design where all model parameters are fitted to data from two mixtures (containing both cell lines but with different initial ratios) simultaneously. Each design is tested on data generated from the Lotka-Volterra model with noise added, to determine efficacy in an ideal sense. In addition to assessing each design for practical identifiability, we investigate how the predictive power of the model - i.e., its ability to fit data for initial ratios other than those to which it was calibrated - is affected by the choice of experimental design. The parallel calibration procedure is found to be optimal and is further tested on in silico data generated from a spatially-resolved cellular automaton model, which accounts for oxygen consumption and allows for variation in the intensity level of the interaction between the two cell lines. We use this study to highlight the care that must be taken when interpreting parameter estimates for the spatially-averaged Lotka-Volterra model when it is calibrated against data produced by the spatially-resolved cellular automaton model, since baseline competition for space and resources in the CA model may contribute to a discrepancy between the type of interaction used to generate the CA data and the type of interaction inferred by the LV model.

Genomic and Glycolytic Entropy Are Reliable Radiogenomic Heterogeneity Biomarkers for Non-Small Cell Lung Cancer

Radiogenomic heterogeneity features in ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET) have become popular in non-small cell lung cancer (NSCLC) research. However, the reliabilities of genomic heterogeneity features and of PET-based glycolytic features in different image matrix sizes have yet to be thoroughly tested. We conducted a prospective study with 46 NSCLC patients to assess the intra-class correlation coefficient (ICC) of different genomic heterogeneity features. We also tested the ICC of PET-based heterogeneity features from different image matrix sizes. The association of radiogenomic features with clinical data was also examined. The entropy-based genomic heterogeneity feature (ICC = 0.736) is more reliable than the median-based feature (ICC = -0.416). The PET-based glycolytic entropy was insensitive to image matrix size change (ICC = 0.958) and remained reliable in tumors with a metabolic volume of <10 mL (ICC = 0.894). The glycolytic entropy is also significantly associated with advanced cancer stages (p = 0.011). We conclude that the entropy-based radiogenomic features are reliable and may serve as ideal biomarkers for research and further clinical use for NSCLC.

Spatial genomic diversity associated with APOBEC mutagenesis in squamous cell carcinoma arising from ovarian teratoma

Although the gross and microscopic features of squamous cell carcinoma arising from ovarian mature cystic teratoma (MCT-SCC) vary from case to case, the spatial spreading of genomic alterations within the tumor remains unclear. To clarify the spatial genomic diversity in MCT-SCCs, we performed whole-exome sequencing by collecting 16 samples from histologically different parts of two MCT-SCCs. Both cases showed histological diversity within the tumors (case 1: nonkeratinizing and keratinizing SCC and case 2: nonkeratinizing SCC and anaplastic carcinoma) and had different somatic mutation profiles by histological findings. Mutation signature analysis revealed a significantly enriched apolipoprotein B mRNA editing enzyme catalytic subunit (APOBEC) signature at all sites. Intriguingly, the spread of genomic alterations within the tumor and the clonal evolution patterns from nonmalignant epithelium to cancer sites differed between cases. TP53 mutation and copy number alterations were widespread at all sites, including the nonmalignant epithelium, in case 1. Keratinizing and nonkeratinizing SCCs were differentiated by the occurrence of unique somatic mutations from a common ancestral clone. In contrast, the nonmalignant epithelium showed almost no somatic mutations in case 2. TP53 mutation and the copy number alteration similarities were observed only in nonkeratinizing SCC samples. Nonkeratinizing SCC and anaplastic carcinoma shared almost no somatic mutations, suggesting that each locally and independently arose in the MCT. We demonstrated that two MCT-SCCs with different histologic findings were highly heterogeneous tumors with clearly different clones associated with APOBEC-mediated mutagenesis, suggesting the importance of evaluating intratumor histological and genetic heterogeneity among multiple sites of MCT-SCC.

Glucose metabolic heterogeneity correlates with pathological features and improves survival stratification of resectable lung adenocarcinoma

OBJECTIVE

We investigated whether glycolytic heterogeneity correlated with histopathology, and further stratified the survival outcomes pertaining to resectable lung adenocarcinoma.

METHODS

We retrospectively analyzed the ¹⁸F-fluorodeoxyglucose positron emission tomography-derived entropy and histopathology from 128 patients who had undergone curative surgery for lung adenocarcinoma. Disease-free survival (DFS) and overall survival (OS) were analyzed using univariate and multivariate Cox regression models. Independent predictors were used to construct survival prediction models.

RESULTS

Entropy significantly correlated with histopathology, including tumor grades, lympho-vascular invasion, and visceral pleural invasion. Furthermore, entropy was an independent predictor of unfavorable DFS (p = 0.031) and OS (p = 0.004), while pathological nodal metastasis independently predicted DFS (p = 0.009). Our entropy-based models outperformed the traditional staging system (c-index = 0.694 versus 0.636, p = 0.010 for DFS; c-index = 0.704 versus 0.630, p = 0.233 for OS). The models provided further survival stratification in subgroups comprising different tumor grades (DFS: HR = 2.065, 1.315, and 1.408 for grade 1-3, p = 0.004, 0.001, and 0.039, respectively; OS: HR = 25.557, 6.484, and 2.570, for grade 1-3, p = 0.006, < 0.001, and = 0.224, respectively).

CONCLUSION

The glycolytic heterogeneity portrayed by entropy is associated with aggressive histopathological characteristics. The proposed entropy-based models may provide more sophisticated survival stratification in addition to histopathology and may enable personalized treatment strategies for resectable lung cancer.

The combined tumor-nodal glycolytic entropy improves survival stratification in nonsmall cell lung cancer with locoregional disease

OBJECTIVE

To investigate whether combining primary tumor and metastatic nodal glycolytic heterogeneity on 18 F-fluorodeoxyglucose PET ( 18 F-FDG PET) improves prognostic prediction in nonsmall cell lung cancer (NSCLC) with locoregional disease.

METHODS

We retrospectively analyzed 18 F-FDG PET-derived features from 94 patients who had undergone curative treatments for regional nodal metastatic NSCLC. Overall survival (OS) and progression-free survival (PFS) were analyzed using univariate and multivariate Cox regression models. We used the independent prognosticators to construct models to predict survival.

RESULTS

Combined entropy (entropy derived from the combination of the primary tumor and metastatic nodes) and age independently predicted OS (both P = 0.008) and PFS ( P = 0.007 and 0.050, respectively). At the same time, the Eastern Cooperative Oncology Group status was another independent risk factor for unfavorable OS ( P = 0.026). Our combined entropy-based models outperformed the traditional staging system (c-index = 0.725 vs. 0.540, P < 0.001 for OS; c-index = 0.638 vs. 0.511, P = 0.003 for PFS) and still showed prognostic value in subgroups according to sex, histopathology, and different initial curative treatment strategies.

CONCLUSION

Combined primary tumor-nodal glycolytic heterogeneity independently predicted survival outcomes. In combination with clinical risk factors, our models provide better survival predictions and may enable tailored treatment strategies for NSCLC with locoregional disease.

Differential Expression in the Tumor Microenvironment of mRNAs Closely Associated with Colorectal Cancer Metastasis

BACKGROUND

Metastasis of colorectal cancer (CRC) is a major cause of CRC-related mortality. However, the detailed molecular mechanism of CRC metastasis remains unknown. A recent study showed that the tumor microenvironment, which includes cancer cells and the surrounding stromal cells, plays a major role in tumor invasion and metastasis. Identification of altered messenger RNA (mRNA) expression in the tumor microenvironment is essential to elucidation of the mechanisms responsible for tumor progression. This study investigated the mRNA expression of genes closely associated with metastatic CRC compared with non-metastatic CRC.

METHODS

The samples examined were divided into cancer tissue and isolated cancer stromal tissue. The study examined altered mRNA expression in the cancer tissues using The Cancer Genome Atlas (TCGA) (377cases) and in 17 stromal tissues obtained from our laboratory via stromal isolation using an array-based analysis. In addition, 259 patients with CRC were enrolled to identify the association of the candidate markers identified with the prognosis of patients with stage 2 or 3 CRC. The study examined the enriched pathways identified by gene set enrichment analysis (GSEA) based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) module in both the TCGA dataset and isolated stromal tissue.

RESULTS

As a result, whereas tenascin-C, secreted phosphoprotein 1 and laminin were expressed in metastatic CRC cells, olfactory receptors (ORs) 11H1 and OR11H4 were expressed in stromal tissue cells isolated from metastatic CRC cases. Finally, upregulated expression of tenascin-C and OR11H4 was correlated with the outcome for CRC patients.

CONCLUSION

The authors suggest that upregulated expression levels of tenascin-C and OR11H1 play an important role in CRC progression.

Diabetogenic liver metastasis from pancreatic cancer: a case report

BACKGROUND

Although new-onset diabetes has been described in up to 20% of patients with newly diagnosed pancreatic cancer, reports regarding new-onset diabetes associated with newly developed liver metastasis from pancreatic cancer are limited.

CASE PRESENTATION

A 60-year-old man was diagnosed with pancreatic tail cancer without impaired glycemic control. A curative-intent distal pancreatectomy with adjuvant S-1 chemotherapy was performed. Two years after surgery, a high HbA1c concentration and solitary liver metastasis were identified on follow-up examination. Two major chemotherapy regimens, gemcitabine/nab-paclitaxel and modified FOLFIRINOX, were sequentially administered to the patient; however, his carbohydrate 19-9 concentration continued to increase. Because the patient's glycemic control rapidly worsened in synchrony with the tumor growth, insulin therapy was initiated. Although the liver metastasis was refractory to chemotherapy, curative-intent left hepatectomy was performed because only one tumor remained. His impaired glycemic control improved immediately after surgery, and insulin therapy was terminated. When writing this report (2 years after hepatectomy), the patient was alive and recurrence-free.

CONCLUSIONS

New-onset diabetes appeared with the progression of metachronous liver metastasis from pancreatic cancer, without recurrence at any other site. The patient's diabetic state was improved by resection of the liver tumor, and liver metastasis itself was proven to have caused the glucometabolic disorder by increasing insulin resistance.

The actin cytoskeleton: Morphological changes in pre- and fully developed lung cancer

Actin, a primary component of the cell cytoskeleton can have multiple isoforms, each of which can have specific properties uniquely suited for their purpose. These monomers are then bound together to form polymeric filaments utilizing adenosine triphosphate hydrolysis as a source of energy. Proteins, such as Arp2/3, VASP, formin, profilin, and cofilin, serve important roles in the polymerization process. These filaments can further be linked to form stress fibers by proteins called actin-binding proteins, such as α-actinin, myosin, fascin, filamin, zyxin, and epsin. These stress fibers are responsible for mechanotransduction, maintaining cell shape, cell motility, and intracellular cargo transport. Cancer metastasis, specifically epithelial mesenchymal transition (EMT), which is one of the key steps of the process, is accompanied by the formation of thick stress fibers through the Rho-associated protein kinase, MAPK/ERK, and Wnt pathways. Recently, with the advent of "field cancerization," pre-malignant cells have also been demonstrated to possess stress fibers and related cytoskeletal features. Analytical methods ranging from western blot and RNA-sequencing to cryo-EM and fluorescent imaging have been employed to understand the structure and dynamics of actin and related proteins including polymerization/depolymerization. More recent methods involve quantifying properties of the actin cytoskeleton from fluorescent images and utilizing them to study biological processes, such as EMT. These image analysis approaches exploit the fact that filaments have a unique structure (curvilinear) compared to the noise or other artifacts to separate them. Line segments are extracted from these filament images that have assigned lengths and orientations. Coupling such methods with statistical analysis has resulted in development of a new reporter for EMT in lung cancer cells as well as their drug responses.

CT perfusion as a potential biomarker for pancreatic ductal adenocarcinoma during routine staging and restaging

PURPOSE

To evaluate the significance of CT perfusion parameters predicting response to neoadjuvant therapy in patients with pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

Seventy patients with PDAC prospectively had CT perfusion acquisition incorporated into baseline multiphase staging CT. Twenty-eight who were naïve to therapy were retained for further investigation. Perfusion was performed 5-42.5 s after contrast, followed by parenchymal and portal venous phases. Blood flow (BF), blood volume (BV), and permeability surface area product (PS) were calculated using deconvolution algorithms. Patients were categorized as responders or non-responders per RECIST 1.1. Perfusion variables with AUC ≥ 0.70 in differentiating responders from non-responders were retained. Logistic regression was used to assess associations between baseline perfusion variables and response.

RESULTS

18 of 28 patients showed favorable response to therapy. Baseline heterogeneity variables in tumor max ROI were higher in non-responders than responders [median BF coefficient of variation (CV) 0.91 vs. 0.51 respectively, odds ratio (OR) 6.8 per one standard deviation (1-SD) increase, P = 0.047; median PS CV 1.6 vs. 0.68, OR 3.9 per 1-SD increase, P = 0.047; and median BV CV 0.75 vs. 0.54, OR = 4.0 per 1-SD increase, P = 0.047]. Baseline BV mean in tumor center was lower in non-responders than responders (median BV mean: 0.74 vs. 2.9 ml/100 g respectively, OR 0.28 per 1-SD increase, P = 0.047).

CONCLUSION

For patients with PDAC receiving neoadjuvant therapy, lower and more heterogeneous perfusion parameters correlated with an unfavorable response to therapy. Such quantitative information can be acquired utilizing a comprehensive protocol interleaving perfusion CT acquisition with standard of care multiphase CT scans using a single contrast injection, which could be used to identify surgical candidates and predict outcome.

Adding recombinant AAVs to the cancer therapeutics mix

Gene therapy is a powerful biological tool that is reshaping therapeutic landscapes for several diseases. Researchers are using both non-viral and viral-based gene therapy methods with success in the lab and the clinic. In the cancer biology field, gene therapies are expanding treatment options and the possibility of favorable outcomes for patients. While cellular immunotherapies and oncolytic virotherapies have paved the way in cancer treatments based on genetic engineering, recombinant adeno-associated virus (rAAV), a viral-based module, is also emerging as a potential cancer therapeutic through its malleability, specificity, and broad application to common as well as rare tumor types, tumor microenvironments, and metastatic disease. A wide range of AAV serotypes, promoters, and transgenes have been successful at reducing tumor growth and burden in preclinical studies, suggesting more groundbreaking advances using rAAVs in cancer are on the horizon.

ATAXIC: An Algorithm to Quantify Transcriptomic Perturbation Heterogeneity in Single Cancer Cells

The single-cell RNA sequencing (scRNA-seq) has recently been widely utilized to quantify transcriptomic profiles in single cells of bulk tumors. The transcriptomic profiles in single cells facilitate the investigation of intratumor heterogeneity that is unlikely confounded by the nontumor components. We proposed an algorithm (ATAXIC) to quantify the heterogeneity of transcriptomic perturbations (TPs) in single cancer cells. ATAXIC calculated the TP heterogeneity level of a single cell based on the standard deviations of the absolute z-scored gene expression values for tens of thousands of genes, reflecting the asynchronous degree of transcriptomic alterations relative to the central (mean) tendency. By analyzing scRNA-seq datasets for eight cancer types, we revealed that ATAXIC scores were likely to correlate positively with the enrichment scores of various proliferation and oncogenic signatures, DNA damage repair, treatment resistance, and unfavorable phenotypes and outcomes in cancer. The ATAXIC scores varied among different cancer types, with lung cancer and melanoma having the lowest average scores and clear cell renal cell carcinoma having the highest average scores. The low TP heterogeneity in lung cancer and melanoma could bestow relatively higher response rates to immune checkpoint inhibitors on both cancer types. In conclusion, ATAXIC is a useful algorithm to quantify the TP heterogeneity in single cancer cells, as well as providing new insights into tumor biology.

A case of malignant phyllodes tumor that responded to pazopanib and developed pneumothorax

Here, we present a 59-year-old female with recurrent malignant phyllodes tumor with multiple lung and lymph node metastases who developed a pneumothorax after the administration of pazopanib. The patient received pazopanib as the second-line chemotherapy. After 2.5 months of the therapy, computed tomography (CT) showed a decrease in the sizes and cavitation of lung lesions; however, a left pneumothorax was newly observed. It was difficult to distinguish the pneumothorax by upright chest X-ray. Typical symptom or physical finding of pneumothorax, such as dyspnea, chest pain or decreased breath sound was not observed. As the pneumothorax was small and asymptomatic, the administration of pazopanib was discontinued and follow-up chest X-ray and CT were performed. After 1 week, CT showed an improvement in the pneumothorax. Chemotherapy was switched to eribulin; however, a rapid increase in sizes of lung lesions was observed after the first administration of eribulin, pazopanib was reintroduced. Careful follow-up by chest X-ray and CT was performed and the pneumothorax has not recurred.

Untangling the web of intratumour heterogeneity

Intratumour heterogeneity (ITH) is a hallmark of cancer that drives tumour evolution and disease progression. Technological and computational advances have enabled us to assess ITH at unprecedented depths, yet this accumulating knowledge has not had a substantial clinical impact. This is in part due to a limited understanding of the functional relevance of ITH and the inadequacy of preclinical experimental models to reproduce it. Here, we discuss progress made in these areas and illuminate future directions.

Clinical Utility of Next-generation Sequencing in Real-world Cases: A Single-institution Study of Nine Cases

BACKGROUND/AIM

Targeted next-generation sequencing (NGS) is a well-established technique to detect pathogenic alterations in tumors. Indeed, it is the cornerstone of targeted therapy in precision medicine. We investigated the clinical utility of next-generation sequencing in real-world cases.

PATIENTS AND METHODS

We retrospectively selected six representative cancer cases, wherein targeted NGS played a pivotal role in the diagnosis and treatment of patients. Additionally, we analyzed three cases with rare, unusual pathogenic alterations.

RESULTS

Our NGS analysis revealed that four patients had TPR-ROS1, EGFR-RAD51, and NCOA4-RET fusions and MET exon 14 skipping mutation, respectively, which can be treated with targeted therapy. Furthermore, we used NGS as a diagnostic tool to confirm the origin of unknown primary malignant tumors in two cases. Interestingly, NGS also helped us identify the following cases: patients exhibiting BRCA1 and TP53 mutations that exhibited histological and immunohistochemical characteristics consistent with endometrioid carcinoma, patients with high-grade serous carcinoma not possessing a TP53 mutation, and patients with small cell lung cancer with a ERBB2 mutation and displaying no loss of RB1.

CONCLUSION

We recommend targeted NGS for the diagnoses and targeted therapy of cancer patients.

Therapeutic applications of engineered chimeric antigen receptors-T cell for cancer therapy

Background

Findings of new targeted treatments with adequate safety evaluations are essential for better cancer cures and mortality rates. Immunotherapy holds promise for patients with relapsed disease, with the ability to elicit long-term remissions. Emerging promising clinical results in B-cell malignancy using gene-altered T-lymphocytes uttering chimeric antigen receptors have sparked a lot of interest. This treatment could open the path for a major difference in the way we treat tumors that are resistant or recurring.

Main body

Genetically altered T cells used to produce tumor-specific chimeric antigen receptors are resurrected fields of adoptive cell therapy by demonstrating remarkable success in the treatment of malignant tumors. Because of the molecular complexity of chimeric antigen receptors-T cells, a variety of engineering approaches to improve safety and effectiveness are necessary to realize larger therapeutic uses. In this study, we investigate new strategies for enhancing chimeric antigen receptors-T cell therapy by altering chimeric antigen receptors proteins, T lymphocytes, and their relations with another solid tumor microenvironment (TME) aspects. Furthermore, examine the potential region of chimeric antigen receptors-T cells therapy to become a most effective treatment modality, taking into account the basic and clinical and practical aspect.

Short conclusions

Chimeric antigen receptors-T cells have shown promise in the therapy of hematological cancers. Recent advancements in protein and cell editing, as well as genome-editing technologies, have paved the way for multilayered T cell therapy techniques that can address numerous important demands. At around the same time, there is crosstalk between various intended aspects within the chimeric antigen receptors-T cell diverse biological complexity and possibilities. These breakthroughs substantially improve the ability to comprehend these complex interactions in future solid tumor chimeric antigen receptor-T cell treatment and open up new treatment options for patients that are currently incurable.

Nanotechnology-aided advancement in the combating of cancer metastasis

Cancer, especially when it has metastasized to different locations in the body, is notoriously difficult to treat. Metastatic cancer accounts for most cancer deaths and thus remains an enormous challenge. During the metastasis process, cancer cells negotiate a series of steps termed the “metastatic cascadeˮ that offer potential for developing anti-metastatic therapy strategies. Currently available conventional treatment and diagnostic methods addressing metastasis come with their own pitfalls and roadblocks. In this contribution, we comprehensively discuss the potential improvements that nanotechnology-aided approaches are able to bring, either alone or in combination with the existing conventional techniques, to the identification and treatment of metastatic disease. We tie specific nanotechnology-aided strategies to the complex biology of the different steps of the metastatic cascade in order to open up new avenues for fine-tuned targeting and development of anti-metastatic agents designed specifically to prevent or mitigate the metastatic outgrowth of cancer. We also present a viewpoint on the progress of translation of nanotechnology into cancer metastasis patient care.

Genetic Clonality as the Hallmark Driving Evolution of Non-Small Cell Lung Cancer

Simple Summary

Limited knowledge about NSCLC evolution has affected therapeutic strategies for many decades. The application of NGS-based techniques to studies on ITH has provided genetic insight into the contribution of clonality primary seeding, as well as to distant dissemination. To date, multiregional ITH affects accurate diagnosis and treatment decisions and is considered the main hallmark of anticancer therapy failure. Understanding the evolutionary trajectories that drive the metastatic process is critical for improving treatment strategies for this deadly condition. In this review, we discuss how the clonality of genetic alterations influence the seeding of primary and metastatic lesions of NSCLC, highlighting that wide genetic analyses may reveal the phylogenetic lineages of NSCLC evolution.

Abstract

Data indicate that many driver alterations from the primary tumor of non-small cell lung cancer (NSCLC) are predominantly shared across all metastases; however, disseminating cells may also acquire a new genetic landscape across their journey. By comparing the constituent subclonal mutations between pairs of primary and metastatic samples, it is possible to derive the ancestral relationships between tumor clones, rather than between tumor samples. Current treatment strategies mostly rely on the theory that metastases are genetically similar to the primary lesions from which they arise. However, intratumor heterogeneity (ITH) affects accurate diagnosis and treatment decisions and it is considered the main hallmark of anticancer therapy failure. Understanding the genetic changes that drive the metastatic process is critical for improving the treatment strategies of this deadly condition. Application of next generation sequencing (NGS) techniques has already created knowledge about tumorigenesis and cancer evolution; however, further NGS implementation may also allow to reconstruct phylogenetic clonal lineages and clonal expansion. In this review, we discuss how the clonality of genetic alterations influence the seeding of primary and metastatic lesions of NSCLC. We highlight that wide genetic analyses may reveal the phylogenetic trajectories of NSCLC evolution, and may pave the way to better management of follow-up and treatment.

DEPTH2: an mRNA-based algorithm to evaluate intratumor heterogeneity without reference to normal controls

Background

Intratumor heterogeneity (ITH) is associated with tumor progression, unfavorable prognosis, immunosuppression, genomic instability, and therapeutic resistance. Thus, evaluation of ITH levels is valuable in cancer diagnosis and treatment.

Methods

We proposed a new mRNA-based ITH evaluation algorithm (DEPTH2) without reference to normal controls. DEPTH2 evaluates ITH levels based on the standard deviations of absolute z-scored transcriptome levels in tumors, reflecting the asynchronous level of transcriptome alterations relative to the central tendency in a tumor.

Results

By analyzing 33 TCGA cancer types, we demonstrated that DEPTH2 ITH was effective in measuring ITH for its significant associations with tumor progression, unfavorable prognosis, genomic instability, reduced antitumor immunity and immunotherapy response, and altered drug response in diverse cancers. Compared to other five ITH evaluation algorithms (MATH, PhyloWGS, ABSOLUTE, DEPTH, and tITH), DEPTH2 ITH showed a stronger association with unfavorable clinical outcomes, and in characterizing other properties of ITH, such as its associations with genomic instability and antitumor immunosuppression, DEPTH2 also displayed competitive performance.

Conclusions

DEPTH2 is expected to have a wider spectrum of applications in evaluating ITH in comparison to other algorithms.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03355-1.

A Systematic Review and Meta-analysis of Patient Survival and Disease Recurrence Following Percutaneous Ablation of Pulmonary Metastasis

BACKGROUND

Pulmonary metastasectomy has been widely adopted in the treatment of metastatic disease. In recent years image guided ablation has seen increased use in the treatment of thoracic malignancies. The objective of this study was to evaluate oncological outcomes following percutaneous ablation (PA) of pulmonary metastasis.

METHODS

A comprehensive search of the PubMed, MEDLINE and EMBASE databases from January 2000 to August 2021 was performed to identify studies evaluating patient survival following ablation of lung metastasis. Pooled outcomes have been presented with a random effects model to assess primary outcomes of overall survival, progression free survival and 1-year local control. Secondary outcomes included procedural mortality, major complications, and the incidence of pneumothorax.

RESULTS

A total of 24 studies were identified. The pooled median overall survival was 5.13 [95% confidence interval (CI): 4.37-6.84] years, and the 1-, 3-, 5-year progression free survival rates were 53%, 26% and 20% respectively. The 1-year local control rate was 91% (95%CI: 86-95%). Periprocedural mortality was rare (0%; 95%CI: 0-1%), as were major complications excluding pneumothorax (1%; 95%CI: 1-2%). Pneumothorax developed in 44% of ablation sessions, although only half of these required chest tube placement. Most patients were able to be discharged day one post-procedurally.

CONCLUSION

PA demonstrates high overall, progression free and local tumour survival in patients with lung metastasis. Complications and mortality are also rare. Consideration of its use should be made in a tumour board meeting in conjunction with surgical and radiotherapy perspectives for targeted local control of metastases.

Heterogeneity and tumor evolution reflected in liquid biopsy in metastatic breast cancer patients: a review

Breast cancer is a spatially and temporally dynamic disease in which differently evolving genetic clones are responsible for progression and clinical outcome. We review tumor heterogeneity and clonal evolution from studies comparing primary tumors and metastasis and discuss plasma circulating tumor DNA as a powerful real-time approach for monitoring the clonal landscape of breast cancer during treatment and recurrence. We found only a few early studies exploring clonal evolution and heterogeneity through analysis of multiregional tissue biopsies of different progression steps in comparison with circulating tumor DNA (ctDNA) from blood plasma. The model of linear progression seemed to be more often reported than the model of parallel progression. The results show complex routes to metastasis, however, and plasma most often reflected metastasis more than primary tumor. The described patterns of evolution and the polyclonal nature of breast cancer have clinical consequences and should be considered during patient diagnosis and treatment selection. Current studies focusing on the relevance of clonal evolution in the clinical setting illustrate the role of liquid biopsy as a noninvasive biomarker for monitoring clonal progression and response to treatment. In the clinical setting, circulating tumor DNA may be an ideal support for tumor biopsies to characterize the genetic landscape of the metastatic disease and to improve longitudinal monitoring of disease dynamics and treatment effectiveness through detection of residual tumor after resection, relapse, or metastasis within a particular patient.

Deciphering Tumour Heterogeneity: From Tissue to Liquid Biopsy

Simple Summary

Most malignant tumours are highly heterogeneous at molecular and phenotypic levels. Tumour variability poses challenges for the management of patients, as it arises between patients and even evolves in space and time within a single patient. Currently, treatment-decision making usually relies on the molecular characteristics of a limited tumour tissue sample at the time of diagnosis or disease progression but does not take into account the complexity of the bulk tumours and their constant evolution over time. In this review, we explore the extent of tumour heterogeneity and report the mechanisms that promote and sustain this diversity in cancers. We summarise the clinical strikes of tumour diversity in the management of patients with cancer. Finally, we discuss the current material and technological approaches that are relevant to adequately appreciate tumour heterogeneity.

Abstract

Human solid malignancies harbour a heterogeneous set of cells with distinct genotypes and phenotypes. This heterogeneity is installed at multiple levels. A biological diversity is commonly observed between tumours from different patients (inter-tumour heterogeneity) and cannot be fully captured by the current consensus molecular classifications for specific cancers. To extend the complexity in cancer, there are substantial differences from cell to cell within an individual tumour (intra-tumour heterogeneity, ITH) and the features of cancer cells evolve in space and time. Currently, treatment-decision making usually relies on the molecular characteristics of a limited tumour tissue sample at the time of diagnosis or disease progression but does not take into account the complexity of the bulk tumours and their constant evolution over time. In this review, we explore the extent of tumour heterogeneity with an emphasis on ITH and report the mechanisms that promote and sustain this diversity in cancers. We summarise the clinical strikes of ITH in the management of patients with cancer. Finally, we discuss the current material and technological approaches that are relevant to adequately appreciate ITH.

Molecular Pathology of Lung Cancer

This overview of the molecular pathology of lung cancer includes a review of the most salient molecular alterations of the genome, transcriptome, and the epigenome. The insights provided by the growing use of next-generation sequencing (NGS) in lung cancer will be discussed, and interrelated concepts such as intertumor heterogeneity, intratumor heterogeneity, tumor mutational burden, and the advent of liquid biopsy will be explored. Moreover, this work describes how the evolving field of molecular pathology refines the understanding of different histologic phenotypes of non-small-cell lung cancer (NSCLC) and the underlying biology of small-cell lung cancer. This review will provide an appreciation for how ongoing scientific findings and technologic advances in molecular pathology are crucial for development of biomarkers, therapeutic agents, clinical trials, and ultimately improved patient care.

Beyond Sequencing: Prioritizing and Delivering Neoantigens for Cancer Vaccines

Neoantigens are tumor-specific proteins and peptides that can be highly immunogenic. Immune-mediated tumor rejection is strongly associated with cytotoxic responses to neoantigen-derived peptides in noncovalent association with self-HLA molecules. Neoantigen-based therapies, such as adoptive T cell transfer, have shown the potential to induce remission of treatment-resistant metastatic disease in select patients. Cancer vaccines are similarly designed to elicit or amplify antigen-specific T cell populations and stimulate directed antitumor immunity, but the selection and prioritization of the neoantigens remains a challenge. Bioinformatic algorithms can predict tumor neoantigens from somatic mutations, insertion-deletions, and other aberrant peptide products, but this often leads to hundreds of potential neoepitopes, all unique for that tumor. Selecting neoantigens for cancer vaccines is complicated by the technical challenges of neoepitope discovery, the diversity of HLA molecules, and intratumoral heterogeneity of passenger mutations leading to immune escape. Despite strong preclinical evidence, few neoantigen cancer vaccines tested in vivo have generated epitope-specific T cell populations, suggesting suboptimal immune system activation. In this chapter, we review factors affecting the prioritization and delivery of candidate neoantigens in the design of therapeutic and preventive cancer vaccines and consider synergism with standard chemotherapies.

APOBEC mediated mutagenesis drives genomic heterogeneity in endometriosis

Endometriosis is a benign gynecologic condition, acting as a precursor of certain histological subtypes of ovarian cancers. The epithelial cells of endometriotic tissues and normal uterine endometrium accumulated somatic mutations in cancer-associated genes such as phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) and Kirsten rat sarcoma (KRAS) proto-oncogene. To determine the genomic characteristic of endometriotic epithelial cells and normal uterine endometrium and to identify the predominant mutational process acting on them, we studied the somatic mutation profiles obtained from whole exome sequencing of 14 endometriotic epithelium and 11 normal uterine endometrium tissues and classified them into mutational signatures. We observed that single base substitutions 2/13 (SBS), attributed to Apolipoprotein B mRNA Editing Enzyme Catalytic Subunit (APOBEC) induced mutagenesis, were significant in endometriotic tissues, but not in the normal uterine endometrium. Additionally, the larger number and wider allele frequency distribution of APOBEC signature mutations, compared to cancer-associated driver mutations in endometriotic epithelium suggested APOBEC mutagenesis as an important source of mutational burden and heterogeneity in endometriosis. Further, the relative risk of enriched APOBEC signature mutations was higher in endometriosis patients who were carriers of APOBEC3A/3B germline deletion, a common polymorphism in East Asians which involves the complete loss of APOBEC3B coding region. Our results illustrate the significance of APOBEC induced mutagenesis in driving the genomic heterogeneity of endometriosis.

Classification of lung adenocarcinoma based on stemness scores in bulk and single cell transcriptomes

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This study explores tumor stemness based on both bulk tumor and single cell transcriptome datasets.

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High-stemness tumors are less responsive to immunotherapy and targeted therapy compared with low-stemness tumors.

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Many high-stemness cells are at the beginning of the cell evolution trajectory, while most low-stemness cells are in the terminal or later phase.

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The correlations of tumor stemness with intratumor heterogeneity and tumor immunity were in the opposite direction between bulk tumors and single cells.

Tumor stemness is associated with tumor progression and therapy resistance. The recent advances in sequencing, genomics, and computational technologies have facilitated investigation into the tumor stemness cell-like characteristics. We identified subtypes of lung adenocarcinoma (LUAD) in bulk tumors or single cells based on the enrichment scores of 12 stemness signatures by clustering analysis of their transcriptomic profiles. Three stemness subtypes of LUAD were identified: St-H, St-M, and St-L, having high, medium, and low stemness signatures, respectively, consistently in six different datasets. Among the three subtypes, St-H was the most enriched in epithelial-mesenchymal transition, invasion, and metastasis signaling, genomically instable, irresponsive to immunotherapies and targeted therapies, and hence had the worst prognosis. We observed that intratumor heterogeneity was significantly higher in high-stemness than in low-stemness bulk tumors, but significantly lower in high-stemness than in low-stemness single cancer cells. Moreover, tumor immunity was stronger in high-stemness than in low-stemness cancer cells, but weaker in high-stemness than in low-stemness bulk tumors. These differences between bulk tumors and single cancer cells could be attributed to the non-tumor cells in bulk tumors that confounded the results of correlation analysis. Furthermore, pseudotime analysis showed that many St-H cells were at the beginning of the cell evolution trajectory, compared to most St-L cells in the terminal or later phase, suggesting that many low-stemness cells are originated from high-stemness cells. The stemness-based classification of LUAD may provide novel insights into the tumor biology as well as precise clinical management of this disease.

Dynamic Monitoring of EMT in CTCs as an Indicator of Cancer Metastasis

Epithelial to mesenchymal transition (EMT) results in the genesis of circulating tumor cells (CTCs) from tumor sites and promotes the metastatic capability of CTCs in circulation. In this study, we develop a multiplex surface-enhanced Raman scattering nanotechnology for comprehensive characterization of EMT-associated phenotypes in CTCs, to monitor cancer metastasis. We observe the downregulation of the CTC marker (EpCAM) and the epithelial marker (E-cadherin), as well as the upregulation of a mesenchymal marker (N-cadherin) and a stem cell marker (ABCB5) during the transforming growth factor-β-induced EMT process in breast cancer cell line models. Additionally, we also find changes in the heterogeneity levels of these selected markers in cells. With this method, we successfully detect the presence of disease in samples from breast cancer patients and characterize EMT-associated phenotypes in their CTCs. Overall, this approach and findings provide a new means for monitoring the EMT process in cancer, insights into the detailed mechanistic progress of the diseases, and have potential for detecting the early occurrence of cancer metastasis.

Major Evolutionary Transitions and the Roles of Facilitation and Information in Ecosystem Transformations

A small number of extraordinary “Major Evolutionary Transitions” (METs) have attracted attention among biologists. They comprise novel forms of individuality and information, and are defined in relation to organismal complexity, irrespective of broader ecosystem-level effects. This divorce between evolutionary and ecological consequences qualifies unicellular eukaryotes, for example, as a MET although they alone failed to significantly alter ecosystems. Additionally, this definition excludes revolutionary innovations not fitting into either MET type (e.g., photosynthesis). We recombine evolution with ecology to explore how and why entire ecosystems were newly created or radically altered – as Major System Transitions (MSTs). In doing so, we highlight important morphological adaptations that spread through populations because of their immediate, direct-fitness advantages for individuals. These are Major Competitive Transitions, or MCTs. We argue that often multiple METs and MCTs must be present to produce MSTs. For example, sexually-reproducing, multicellular eukaryotes (METs) with anisogamy and exoskeletons (MCTs) significantly altered ecosystems during the Cambrian. Therefore, we introduce the concepts of Facilitating Evolutionary Transitions (FETs) and Catalysts as key events or agents that are insufficient themselves to set a MST into motion, but are essential parts of synergies that do. We further elucidate the role of information in MSTs as transitions across five levels: (I) Encoded; (II) Epigenomic; (III) Learned; (IV) Inscribed; and (V) Dark Information. The latter is ‘authored’ by abiotic entities rather than biological organisms. Level IV has arguably allowed humans to produce a MST, and V perhaps makes us a FET for a future transition that melds biotic and abiotic life into one entity. Understanding the interactive processes involved in past major transitions will illuminate both current events and the surprising possibilities that abiotically-created information may produce.

Tumor glycolytic heterogeneity improves detection of regional nodal metastasis in patients with lung adenocarcinoma

OBJECTIVE

The diagnostic performance of ¹⁸F-FDG PET for detecting regional lymph node metastasis in resectable lung cancer is variable, and its sensitivity for adenocarcinoma is even lower. We aimed to evaluate the value of ¹⁸F-FDG PET-derived features in predicting pathological lymph node metastasis in patients with lung adenocarcinoma.

METHODS

We retrospectively analyzed pretreatment ¹⁸F-FDG PET-derived features of 126 lung adenocarcinoma patients who underwent curative surgery. A logistic regression model was used to analyze the association between study variables and pathological regional lymph node status obtained from the curative surgery. Furthermore, Cox regression analysis was used to test the effect of the study variables on survival outcomes, including disease-free survival (DFS) and overall survival (OS).

RESULTS

The primary tumor entropy (OR = 1.7, p = 0.014) and visual interpretation of regional nodes via ¹⁸F-FDG PET (OR = 2.5, p = 0.026) independently predicted pathological regional lymph node metastasis. The areas under the receiver-operating-characteristic curves were 0.631, 0.671, and 0.711 for visual interpretation, primary tumor entropy, and their combination, respectively. Based on visual interpretation, a primary tumor entropy ≥ 3.0 improved the positive predictive value of positive visual interpretation from 51.2% to 63.0%, whereas an entropy < 3.0 improved the negative predictive value of negative visual interpretation from 75.3% to 82.6%. In cases with positive visual interpretation and low entropy, or negative visual interpretation and high entropy, the nodal metastasis rates were approximately 30%. In the survival analyses, the primary tumor entropy was also independently associated with DFS (HR = 2.7, p = 0.001) and OS (HR = 4.8, p = 0.001).

CONCLUSIONS

Our preliminary results show that the primary tumor entropy may improve ¹⁸F-FDG PET visual interpretation in predicting pathological nodal metastasis in lung adenocarcinoma, and may also show a survival prognostic value. This versatile biomarker may facilitate tailored therapeutic strategies for patients with resectable lung adenocarcinoma.

Cell Membrane-Coated Mimics: A Methodological Approach for Fabrication, Characterization for Therapeutic Applications, and Challenges for Clinical Translation

Cell membrane-coated (CMC) mimics are micro/nanosystems that combine an isolated cell membrane and a template of choice to mimic the functions of a cell. The design exploits its physicochemical and biological properties for therapeutic applications. The mimics demonstrate excellent biological compatibility, enhanced biointerfacing capabilities, physical, chemical, and biological tunability, ability to retain cellular properties, immune escape, prolonged circulation time, and protect the encapsulated drug from degradation and active targeting. These properties and the ease of adapting them for personalized clinical medicine have generated a significant research interest over the past decade. This review presents a detailed overview of the recent advances in the development of cell membrane-coated (CMC) mimics. The primary focus is to collate and discuss components, fabrication methodologies, and the significance of physiochemical and biological characterization techniques for validating a CMC mimic. We present a critical analysis of the two main components of CMC mimics: the template and the cell membrane and mapped their use in therapeutic scenarios. In addition, we have emphasized on the challenges associated with CMC mimics in their clinical translation. Overall, this review is an up to date toolbox that researchers can benefit from while designing and characterizing CMC mimics.

Application of single-cell RNA sequencing technology in liver diseases: a narrative review

Objective

This review aimed to summarize the application of single-cell transcriptome sequencing technology in liver diseases.

Background

The increasing application of single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) in life science and biomedical research has greatly improved our understanding of cellular heterogeneity in immunology, oncology, and developmental biology. scRNA-seq has proven to be a powerful tool for identifying and classifying cell subsets, characterizing rare or small cell subsets and tracking cell differentiation along the dynamic cell stages. Globally, liver disease has high rates of morbidity and mortality, and its exact pathological mechanism remains unclear, current treatment options are limited to clearance of the underlying cause or liver transplantation, which cannot overwhelm and cure liver diseases. scRNA-seq provides many novel insights for healthy and diseased livers.

Methods

In this review, we searched for related articles in the PubMed database and summarized the advances of scRNA-seq in revealing the molecular mechanisms of liver development, regeneration, and disease. We also discussed the challenges and future application potential of scRNA-seq, which is expected to enhance the ability to explore the field of liver research and accelerate the clinical application of liver precision medicine.

Conclusions

With the continuous improvement of scRNA-seq technology, scRNA-seq is expected to unlock new avenues for liver biology exploration, liver disease diagnosis, and personalized treatment, which will pave the way for breakthrough innovation in personalized medicine.