Tumor heterogeneity: causes and consequences

Single-cell morphological and topological atlas reveals the ecosystem diversity of human breast cancer

Digital pathology allows computerized analysis of tumor ecosystem using whole slide images (WSIs). Here, we present single-cell morphological and topological profiling (sc-MTOP) to characterize tumor ecosystem by extracting the features of nuclear morphology and intercellular spatial relationship for individual cells. We construct a single-cell atlas comprising 410 million cells from 637 breast cancer WSIs and dissect the phenotypic diversity within tumor, inflammatory and stroma cells respectively. Spatially-resolved analysis identifies recurrent micro-ecological modules representing locoregional multicellular structures and reveals four breast cancer ecotypes correlating with distinct molecular features and patient prognosis. Further analysis with multiomics data uncovers clinically relevant ecosystem features. High abundance of locally-aggregated inflammatory cells indicates immune-activated tumor microenvironment and favorable immunotherapy response in triple-negative breast cancers. Morphological intratumor heterogeneity of tumor nuclei correlates with cell cycle pathway activation and CDK inhibitors responsiveness in hormone receptor-positive cases. sc-MTOP enables using WSIs to characterize tumor ecosystems at the single-cell level.

The effect of tumor vascular remodeling and immune microenvironment activation induced by radiotherapy: quantitative evaluation with magnetic resonance/photoacoustic dual-modality imaging

Background

Tumor radiotherapy combined with immunotherapy for solid tumors has been proposed, but tumor vascular structure abnormalities and immune microenvironment often affect the therapeutic effect of tumor, and multimodal imaging technology can provide more accurate and comprehensive information in tumor research. The purpose of this study was to evaluate the dynamic monitoring of tumor blood vessels and microenvironment induced by radiotherapy by magnetic resonance/photoacoustic (MR/PA) imaging, and to explore its application value in radiotherapy combined with immunotherapy.

Methods

The tumor-bearing mice were randomly allocated into six groups, which received different doses of radiation therapy (2 Gy ×14 or 8 Gy ×3) and anti-programmed death ligand-1 (PD-L1) antibody for two consecutive weeks. MR/PA imaging was used to noninvasively evaluate the response of tumor to different doses of radiotherapy, combined with histopathological techniques to observe the tumor vessels and microenvironment.

Results

The inhibitory effect of high-dose radiotherapy on tumors was significantly greater than that of low-dose radiotherapy, with the MR images revealing that the signal intensity decreased significantly (P<0.05). Compared with those in the other groups, the tumor vascular density decreased significantly (P<0.01), and the vascular maturity index increased significantly in the low-dose group (P<0.05). The PA images showed that the deoxyhemoglobin and total hemoglobin levels decreased and the SO2 level increased after radiation treatment (P<0.05). In addition, the high-dose group had an increased number of tumor-infiltrating lymphocytes (CD4+ T and CD8+ T cells) (P<0.01, P<0.05) and natural killer cells (P<0.001) and increased PD-L1 expression in the tumors (P<0.05). The combination of radiotherapy and immunotherapy increased the survival rate of the mice (P<0.05), and a regimen of an 8 Gy dose of radiation combined with immunotherapy inhibited tumor growth and increased the survival rate of the mice to a greater degree than the 2 Gy radiation dose with immunotherapy combination (P=0.002).

Conclusions

Differential fractionation radiotherapy doses exert biological effects on tumor vascular and the immune microenvironment, and MR/PA can be used to evaluate tumor vascular remodeling after radiotherapy, which has certain value for the clinical applications of radiotherapy combined with immunotherapy.

Dynamic contrast-enhanced magnetic resonance imaging radiomics analysis based on intratumoral subregions for predicting luminal and nonluminal breast cancer

Background

Breast cancer is a heterogeneous disease with different morphological and biological characteristics. The molecular subtypes of breast cancer are closely related to the treatment and prognosis of patients. In order to predict the luminal type of breast cancer in a noninvasive manner, our study developed and validated a radiomics nomogram combining clinical factors with a radiomics score based on the features of the intratumoral subregion to distinguish between luminal and nonluminal breast cancer.

Methods

From January 2018 to January 2020, 153 women with clinically and pathologically diagnosed breast cancer with an average age of 50.08 years were retrospectively analyzed. Using a semiautomatic segmentation method, the whole tumor was divided into 3 subregions on the basis of the time required for the contrast agent to reach its peak; 540 features were extracted from 3 subregions and the whole tumor region. Subsequently, 2 machine learning classifiers were developed. The least absolute shrinkage and selection operator method was used for feature selection and radiomics score (Rad-score) construction. Moreover, multivariable logistic regression analysis was applied to select independent factors from the Rad-score and clinical factors to establish a prediction model in the form of a nomogram. The performance of the nomogram was evaluated through calibration, discrimination, and clinical usefulness.

Results

The prediction performance of texture features from the rapid subregion was the best in the 3 intratumoral subregions, and the area under the receiver operating characteristic curve (AUC) values in the training and validation cohort were 0.805 (95% CI: 0.719-0.892) and 0.737 (95% CI: 0.581-0.893), respectively. The Rad-score, consisting of 5 features from the rapid subregion, was associated with the luminal type of breast cancer (P=0.001 and P=0.035 in the training and validation cohorts, respectively). The predictors included in the personalized prediction nomogram included Rad-score, human epidermal growth factor receptor 2 (HER2) status, and tumor histological grade. The nomogram showed good discrimination, with an area under the receiver operating characteristic curve in the training and validation cohorts of 0.830 (95% CI: 0.746-0.896) and 0.879 (95% CI: 0.748-0.957), respectively. The calibration curve of the 2 cohorts and decision curve analysis demonstrated that the nomogram had good calibration and clinical usefulness.

Conclusions

We proposed a nomogram model that combined clinical factors and Rad-score, which showed good performance in predicting the luminal type of breast cancer.

Dual receptor specific nanoparticles targeting EGFR and PD-L1 for enhanced delivery of docetaxel in cancer therapy

Dual-receptor targeted (DRT) nanoparticles which contain two distinct targeting agents may exhibit higher cell selectivity, cellular uptake, and cytotoxicity toward cancer cells than single-ligand targeted nanoparticle systems without additional functionality. The purpose of this study is to prepare DRT poly(lactic-co-glycolic acid) (PLGA) nanoparticles for targeting the delivery of docetaxel (DTX) to the EGFR and PD-L1 receptor positive cancer cells such as human glioblastoma multiform (U87-MG) and human non-small cell lung cancer (A549) cell lines. Anti-EGFR and anti-PD-L1 antibody were decorated on DTX loaded PLGA nanoparticles to prepare DRT-DTX-PLGA via. single emulsion solvent evaporation method. Physicochemical characterizations of DRT-DTX-PLGA, such as particle size, zeta-potential, morphology, and in vitro DTX release were also evaluated. The average particle size of DRT-DTX-PLGA was 124.2 ± 1.1 nm with spherical and smooth morphology. In the cellular uptake study, the DRT-DTX-PLGA endocytosed by the U87-MG and A549 cells was single ligand targeting nanoparticle. From the in vitro cell cytotoxicity, and apoptosis studies, we reported that DRT-DTX-PLGA exhibited high cytotoxicity and enhanced the apoptotic cell compared to the single ligand-targeted nanoparticle. The dual receptor mediated endocytosis of DRT-DTX-PLGA showed a high binding affinity effect that leads to high intracellular DTX concentration and exhibited high cytotoxic properties. Thus, DRT nanoparticles have the potential to improve cancer therapy by providing selectivity over single-ligand-targeted nanoparticles.

Chimeric antigen receptor-modified T cells therapy in prostate cancer: A comprehensive review on the current state and prospects

Recent immunotherapy research has focused on chimeric antigen receptor-modified T cells (CAR-Ts). CAR-T therapies have been clinically applied to manage hematologic malignancies with satisfactory effectiveness. However, the application of CAR-T immunotherapy in solid tumors remains challenging. Even so, current CAR-T immunotherapies for prostate cancer (PCa) have shown some promise, giving hope to patients with advanced metastatic PCa. This review aimed to elucidate different types of prostate tumor-associated antigen targets, such as prostate-specific membrane antigen and prostate stem cell antigen, and their effects. The current status of the corresponding targets in clinical research through their applications was also discussed. To improve the efficacy of CAR-T immunotherapy, we addressed the possible applications of multimodal immunotherapy, chemotherapy, and CAR-T combined therapies. The obstacles of solid tumors were concisely elaborated. Further studies should aim to discover novel potential targets and establish new models by overcoming the inherent barriers of solid tumors, such as tumor heterogeneity and the immunosuppressive nature of the tumor microenvironment.

Spermatogonial fate in mice with increased activin A bioactivity and testicular somatic cell tumours

Adult male fertility depends on spermatogonial stem cells (SSCs) which undergo either self-renewal or differentiation in response to microenvironmental signals. Activin A acts on Sertoli and Leydig cells to regulate key aspects of testis development and function throughout life, including steroid production. Recognising that activin A levels are elevated in many pathophysiological conditions, this study investigates effects of this growth factor on the niche that determines spermatogonial fate. Although activin A can promote differentiation of isolated spermatogonia in vitro, its impacts on SSC and spermatogonial function in vivo are unknown. To assess this, we examined testes of Inha KO mice, which feature elevated activin A levels and bioactivity, and develop gonadal stromal cell tumours as adults. The GFRA1+ SSC-enriched population was more abundant and proliferative in Inha KO compared to wildtype controls, suggesting that chronic elevation of activin A promotes a niche which supports SSC self-renewal. Intriguingly, clusters of GFRA1+/EOMES+/LIN28A- cells, resembling a primitive SSC subset, were frequently observed in tubules adjacent to tumour regions. Transcriptional analyses of Inha KO tumours, tubules adjacent to tumours, and tubules distant from tumour regions revealed disrupted gene expression in each KO group increased in parallel with tumour proximity. Modest transcriptional changes were documented in Inha KO tubules with complete spermatogenesis. Importantly, tumours displaying upregulation of activin responsive genes were also enriched for factors that promote SSC self-renewal, including Gdnf, Igf1, and Fgf2, indicating the tumours generate a supportive microenvironment for SSCs. Tumour cells featured some characteristics of adult Sertoli cells but lacked consistent SOX9 expression and exhibited an enhanced steroidogenic phenotype, which could arise from maintenance or acquisition of a fetal cell identity or acquisition of another somatic phenotype. Tumour regions were also heavily infiltrated with endothelial, peritubular myoid and immune cells, which may contribute to adjacent SSC support. Our data show for the first time that chronically elevated activin A affects SSC fate in vivo. The discovery that testis stromal tumours in the Inha KO mouse create a microenvironment that supports SSC self-renewal but not differentiation offers a strategy for identifying pathways that improve spermatogonial propagation in vitro.

Growth exponents reflect evolutionary processes and treatment response in brain metastases

Tumor growth is the result of the interplay of complex biological processes in huge numbers of individual cells living in changing environments. Effective simple mathematical laws have been shown to describe tumor growth in vitro, or simple animal models with bounded-growth dynamics accurately. However, results for the growth of human cancers in patients are scarce. Our study mined a large dataset of 1133 brain metastases (BMs) with longitudinal imaging follow-up to find growth laws for untreated BMs and recurrent treated BMs. Untreated BMs showed high growth exponents, most likely related to the underlying evolutionary dynamics, with experimental tumors in mice resembling accurately the disease. Recurrent BMs growth exponents were smaller, most probably due to a reduction in tumor heterogeneity after treatment, which may limit the tumor evolutionary capabilities. In silico simulations using a stochastic discrete mesoscopic model with basic evolutionary dynamics led to results in line with the observed data.

Tumor matrix stiffness provides fertile soil for cancer stem cells

Matrix stiffness is a mechanical characteristic of the extracellular matrix (ECM) that increases from the tumor core to the tumor periphery in a gradient pattern in a variety of solid tumors and can promote proliferation, invasion, metastasis, drug resistance, and recurrence. Cancer stem cells (CSCs) are a rare subpopulation of tumor cells with self-renewal, asymmetric cell division, and differentiation capabilities. CSCs are thought to be responsible for metastasis, tumor recurrence, chemotherapy resistance, and consequently poor clinical outcomes. Evidence suggests that matrix stiffness can activate receptors and mechanosensor/mechanoregulator proteins such as integrin, FAK, and YAP, modulating the characteristics of tumor cells as well as CSCs through different molecular signaling pathways. A deeper understanding of the effect of matrix stiffness on CSCs characteristics could lead to development of innovative cancer therapies. In this review, we discuss how the stiffness of the ECM is sensed by the cells and how the cells respond to this environmental change as well as the effect of matrix stiffness on CSCs characteristics and also the key malignant processes such as proliferation and EMT. Then, we specifically focus on how increased matrix stiffness affects CSCs in breast, lung, liver, pancreatic, and colorectal cancers. We also discuss how the molecules responsible for increased matrix stiffness and the signaling pathways activated by the enhanced stiffness can be manipulated as a therapeutic strategy for cancer.

Development and validation of a postoperative pulmonary infection prediction model for patients with primary hepatic carcinoma

BACKGROUND

There are factors that significantly increase the risk of postoperative pulmonary infections in patients with primary hepatic carcinoma (PHC). Previous reports have shown that over 10% of patients with PHC experience postoperative pulmonary infections. Thus, it is crucial to prioritize the prevention and treatment of postoperative pulmonary infections in patients with PHC.

AIM

To identify the risk factors for postoperative pulmonary infection in patients with PHC and develop a prediction model to aid in postoperative management.

METHODS

We retrospectively collected data from 505 patients who underwent hepatobiliary surgery between January 2015 and February 2023 in the Department of Hepatobiliary and Pancreaticospleen Surgery. Radiomics data were selected for statistical analysis, and clinical pathological parameters and imaging data were included in the screening database as candidate predictive variables. We then developed a pulmonary infection prediction model using three different models: An artificial neural network model; a random forest model; and a generalized linear regression model. Finally, we evaluated the accuracy and robustness of the prediction model using the receiver operating characteristic curve and decision curve analyses.

RESULTS

Among the 505 patients, 86 developed a postoperative pulmonary infection, resulting in an incidence rate of 17.03%. Based on the gray-level co-occurrence matrix, we identified 14 categories of radiomic data for variable screening of pulmonary infection prediction models. Among these, energy, contrast, the sum of squares (SOS), the inverse difference (IND), mean sum (MES), sum variance (SUV), sum entropy (SUE), and entropy were independent risk factors for pulmonary infection after hepatectomy and were listed as candidate variables of machine learning prediction models. The random forest model algorithm, in combination with IND, SOS, MES, SUE, SUV, and entropy, demonstrated the highest prediction efficiency in both the training and internal verification sets, with areas under the curve of 0.823 and 0.801 and a 95% confidence interval of 0.766-0.880 and 0.744-0.858, respectively. The other two types of prediction models had prediction efficiencies between areas under the curve of 0.734 and 0.815 and 95% confidence intervals of 0.677-0.791 and 0.766-0.864, respectively.

CONCLUSION

Postoperative pulmonary infection in patients undergoing hepatectomy may be related to risk factors such as IND, SOS, MES, SUE, SUV, energy, and entropy. The prediction model in this study based on diffusion-weighted images, especially the random forest model algorithm, can better predict and estimate the risk of pulmonary infection in patients undergoing hepatectomy, providing valuable guidance for postoperative management.

Single-Cell Transcriptomics for Unlocking Personalized Cancer Immunotherapy: Toward Targeting the Origin of Tumor Development Immunogenicity

Cancer immunotherapy is a promising approach for treating malignancies through the activation of anti-tumor immunity. However, the effectiveness and safety of immunotherapy can be limited by tumor complexity and heterogeneity, caused by the diverse molecular and cellular features of tumors and their microenvironments. Undifferentiated tumor cell niches, which we refer to as the "Origin of Tumor Development" (OTD) cellular population, are believed to be the source of these variations and cellular heterogeneity. From our perspective, the existence of distinct features within the OTD is expected to play a significant role in shaping the unique tumor characteristics observed in each patient. Single-cell transcriptomics is a high-resolution and high-throughput technique that provides insights into the genetic signatures of individual tumor cells, revealing mechanisms of tumor development, progression, and immune evasion. In this review, we explain how single-cell transcriptomics can be used to develop personalized cancer immunotherapy by identifying potential biomarkers and targets specific to each patient, such as immune checkpoint and tumor-infiltrating lymphocyte function, for targeting the OTD. Furthermore, in addition to offering a possible workflow, we discuss the future directions of, and perspectives on, single-cell transcriptomics, such as the development of powerful analytical tools and databases, that will aid in unlocking personalized cancer immunotherapy through the targeting of the patient's cellular OTD.

Learning models for classifying Raman spectra of genomic DNA from tumor subtypes

An early and accurate detection of different subtypes of tumors is crucial for an effective guidance to personalized therapy and in predicting the ability of tumor to metastasize. Here we exploit the Surface Enhanced Raman Scattering (SERS) platform, based on disordered silver coated silicon nanowires (Ag/SiNWs), to efficiently discriminate genomic DNA of different subtypes of melanoma and colon tumors. The diagnostic information is obtained by performing label free Raman maps of the dried drops of DNA solutions onto the Ag/NWs mat and leveraging the classification ability of learning models to reveal the specific and distinct physico-chemical interaction of tumor DNA molecules with the Ag/NW, here supposed to be partly caused by a different DNA methylation degree.

Characteristics of DNA macro-alterations in breast cancer with liver metastasis before treatment

BACKGROUND

Whole-genome doubling (WGD) has been observed in 30% of cancers, followed by a highly complex rearranged karyotype unfavourable to breast cancer's outcome. However, the macro-alterations that characterise liver metastasis in breast cancer(BC) are poorly understood. Here, we conducted a whole-genome sequencing analysis of liver metastases to explore the status and the time frame model of these macro-alterations in pre-treatment patients with metastatic breast cancer.

RESULTS

Whole-genome sequencing was conducted in 11 paired primary tumours, lymph node metastasis, and liver metastasis fresh samples from four patients with late-stage breast cancer. We also chose five postoperative frozen specimens from patients with early-stage breast cancer before any treatment as control. Surprisingly, all four liver metastasis samples were classified as WGD + . However, the previous study reported that WGD happened in 30% of cancers and 2/5 in our early-stage samples. WGD was not observed in the two separate primary tumours and one lymph node metastasis of one patient with metastatic BC, but her liver metastasis showed an early burst of bi-allelic copy number gain. The phylogenetic tree proves her 4 tumour samples were the polyclonal origin and only one WGD + clone metastasis to the liver. Another 3 metastatic BC patients' primary tumour and lymph node metastasis experienced WGD as well as liver metastasis, and they all showed similar molecular time-frame of copy number(CN) gain across locations within the same patient. These patients' tumours were of monoclonal origin, and WGD happened in a founding clone before metastasis, explaining that all samples share the CN-gain time frame. After WGD, the genomes usually face instability to evolve other macro-alterations. For example, a greater quantity and variety of complex structural variations (SVs) were detected in WGD + samples. The breakpoints were enriched in the chr17: 39 Mb-40 Mb tile, which contained the HER2 gene, resulting in the formation of tyfonas, breakage-fusion-bridge cycles, and double minutes. These complex SVs may be involved in the evolutionary mechanisms of the dramatic increase of HER2 copy number.

CONCLUSION

Our work revealed that the WGD + clone might be a critical evolution step for liver metastasis and favoured following complex SV of breast cancer.

Single-center study on clinicopathological and typical molecular pathologic features of metastatic brain tumor

BACKGROUND

The metastatic brain tumor is the most common brain tumor. The aim of this study was to demonstrate the clinicopathological and molecular pathologic features of brain metastases (BM).

METHODS

A total of 269 patients were diagnosed with BM through surgical resection at Seoul St. Mary's Hospital from January 2010 to March 2020. We reviewed the clinicopathological features and molecular status of primary and metastatic brain tissues using immunohistochemistry and molecular pathology results.

RESULTS

Among 269 patients, 139 males and 130 females were included. The median age of primary tumor was 58 years (range, 13 to 87 years) and 86 patients (32.0%) had BM at initial presentation. Median BM free interval was 28.0 months (range, 1 to 286 months). The most frequent primary site was lung 46.5% (125/269), and followed by breast 15.6% (42/269), colorectum 10.0% (27/269). Epidermal growth factor receptor (EGFR) mutation was found in 50.8% (32/63) and 58.0% (40/69) of lung primary and BM, respectively. In both breast primary and breast cancer with BM, luminal B was the most frequent subtype at 37.9% (11/29) and 42.9% (18/42), respectively, followed by human epidermal growth factor receptor 2 with 31.0% (9/29) and 33.3% (14/42). Triple-negative was 20.7% (6/29) and 16.7% (7/42), and luminal A was 10.3% (3/29) and 7.1% (3/42) of breast primary and BM, respectively. In colorectal primary and colorectal cancer with BM, KRAS mutation was found in 76.9% (10/13) and 66.7% (2/3), respectively.

CONCLUSIONS

We report the clinicopathological and molecular pathologic features of BM that can provide useful information for understanding the pathogenesis of metastasis and for clinical trials based on the tumor's molecular pathology.

The paradox of cooperation among selfish cancer cells

It is traditionally assumed that during cancer development, tumor cells abort their initially cooperative behavior (i.e., cheat) in favor of evolutionary strategies designed solely to enhance their own fitness (i.e., a "selfish" life style) at the expense of that of the multicellular organism. However, the growth and progress of solid tumors can also involve cooperation among these presumed selfish cells (which, by definition, should be noncooperative) and with stromal cells. The ultimate and proximate reasons behind this paradox are not fully understood. Here, in the light of current theories on the evolution of cooperation, we discuss the possible evolutionary mechanisms that could explain the apparent cooperative behaviors among selfish malignant cells. In addition to the most classical explanations for cooperation in cancer and in general (by-product mutualism, kin selection, direct reciprocity, indirect reciprocity, network reciprocity, group selection), we propose the idea that "greenbeard" effects are relevant to explaining some cooperative behaviors in cancer. Also, we discuss the possibility that malignant cooperative cells express or co-opt cooperative traits normally expressed by healthy cells. We provide examples where considerations of these processes could help understand tumorigenesis and metastasis and argue that this framework provides novel insights into cancer biology and potential strategies for cancer prevention and treatment.

CD168+ macrophages promote hepatocellular carcinoma tumor stemness and progression through TOP2A/β-catenin/YAP1 axis

Liver cancer stem-like cells (LCSCs) are the main cause of heterogeneity and poor prognosis in hepatocellular carcinoma (HCC). In this study, we aimed to explore the origin of LCSCs and the role of the TOP2A/β-catenin/YAP1 axis in tumor stemness and progression. Using single-cell RNA-seq analysis, we identified TOP2A+CENPF+ LCSCs, which were mainly regulated by CD168+ M2-like macrophages. Furthermore, spatial location analysis and fluorescent staining confirmed that LCSCs were enriched at tumor margins, constituting the spatial heterogeneity of HCC. Mechanistically, TOP2A competitively binds to β-catenin, leading to disassociation of β-catenin from YAP1, promoting HCC stemness and overgrowth. Our study provides valuable insights into the spatial transcriptome heterogeneity of the HCC microenvironment and the critical role of TOP2A/β-catenin/YAP1 axis in HCC stemness and progression.

A 3D-printed SERS bionic taster for dynamic tumor metabolites detection

The variation of tumor-associated metabolites in extracellular microenvironment timely reflects the development, the progression and the treatment of cancers. Conventional methods for metabolite detection lack the efficiency to grasp the dynamic metabolic alterations. Herein, we developed a SERS bionic taster which enabled real-time analysis of extracellular metabolites. The instant information of cell metabolism was provided by the responsive Raman reporters, which experienced SERS spectral changes upon metabolite activation. Such a SERS sensor was integrated into a 3D-printed fixture which fits the commercial-standard cell culture dishes, allowing in-situ acquisition of the vibrational spectrum. The SERS taster can not only accomplish simultaneous and quantitative analysis of multiple tumor-associated metabolites, but also fulfill the dynamic monitoring of cellular metabolic reprogramming, which is expected to become a promising tool for investigating cancer biology and therapeutics.

Nanotechnology-based radiation therapy to cure cancer and the challenges in its clinical applications

Radiation therapy against cancer frequently fails to attain the desired outcomes because of several restricting aspects. Radiation therapy is not a targeted antitumor treatment, and it poses serious threats to normal tissues as well. In many cases, some inherent features of tumors make them resistant to radiation therapy. Several nanoparticles have shown the capacity to upgrade the viability of radiation treatment because they can directly interact with ionizing radiation to increase cellular radiation sensitivity. Several types of nanomaterials have been investigated as radio-sensitizers, to improve the efficacy of radiotherapy and overcome radio-resistance including, metal-based nanoparticles, quantum dots, silica-based nanoparticles, polymeric nanoparticles, etc. Despite all this research and development, certain challenges associated with the exploitation of nanoparticles to enhance and improve radiation therapy for cancer treatment are encountered. Potential applications of nanoparticles as radiosensitizers is hindered by the difficulties associated with ensuring their production at a large scale with improved characterizations and because of certain biological challenges. By overcoming the shortcomings of nanoparticles like working on the pharmacokinetics, and physical and chemical characterization, the therapy can be improved. It is expected that in the future more knowledge will be available regarding nanoparticles and their clinical efficacy, leading to the successful development of nanotechnology-based radiation therapies for a variety of cancers. This review highlights the limitations of conventional radiotherapy in cancer treatment and explores the potential of nanotechnology, specifically the use of nanomaterials, to overcome these challenges. It discusses the concept of using nanomaterials to enhance the effectiveness of radiation therapy and provides an overview of different types of nanomaterials and their beneficial properties. The review emphasizes the need to address the obstacles and limitations associated with the application of nanotechnology in cancer radiation therapy for successful clinical translation.

Advances in PET imaging of cancer

Molecular imaging has experienced enormous advancements in the areas of imaging technology, imaging probe and contrast development, and data quality, as well as machine learning-based data analysis. Positron emission tomography (PET) and its combination with computed tomography (CT) or magnetic resonance imaging (MRI) as a multimodality PET-CT or PET-MRI system offer a wealth of molecular, functional and morphological data with a single patient scan. Despite the recent technical advances and the availability of dozens of disease-specific contrast and imaging probes, only a few parameters, such as tumour size or the mean tracer uptake, are used for the evaluation of images in clinical practice. Multiparametric in vivo imaging data not only are highly quantitative but also can provide invaluable information about pathophysiology, receptor expression, metabolism, or morphological and functional features of tumours, such as pH, oxygenation or tissue density, as well as pharmacodynamic properties of drugs, to measure drug response with a contrast agent. It can further quantitatively map and spatially resolve the intertumoural and intratumoural heterogeneity, providing insights into tumour vulnerabilities for target-specific therapeutic interventions. Failure to exploit and integrate the full potential of such powerful imaging data may lead to a lost opportunity in which patients do not receive the best possible care. With the desire to implement personalized medicine in the cancer clinic, the full comprehensive diagnostic power of multiplexed imaging should be utilized.

Synergistic inter-clonal cooperation involving crosstalk, co-option and co-dependency can enhance the invasiveness of genetically distant cancer clones

BACKGROUND

Despite intensive research, cancer remains a major health problem. The difficulties in treating cancer reflect the complex nature of this disease, including high levels of heterogeneity within tumours. Intra-tumour heterogeneity creates the conditions for inter-clonal competition and selection, which could result in selective sweeps and a reduction in levels of heterogeneity. However, in addition to competing, cancer clones can also cooperate with each other, and the positive effects of these interactions on the fitness of clones could actually contribute to maintaining the heterogeneity of tumours. Consequently, understanding the evolutionary mechanisms and pathways involved in such activities is of great significance for cancer treatment. This is particularly relevant for metastasis (i.e., tumor cell migration, invasion, dispersal and dissemination), which is the most lethal phase during cancer progression. To explore if and how genetically distant clones can cooperate during migration and invasion, this study used three distinct cancer cell lines with different metastatic potentials.

RESULTS

We found that (i) the conditioned media from two invasive lines (breast and lung) increased the migration and invasion potential of a poorly metastatic line (breast), and (ii) this inter-clonal cooperative interaction involved the TGF-β1 signalling pathway. Furthermore, when the less aggressive line was co-cultured with the highly metastatic breast line, the invasive potential of both lines was enhanced, and this outcome was dependent on the co-option (through TGF-β1 autocrine-paracrine signalling) of the weakly metastatic clone into expressing an enhanced malignant phenotype that benefited both clones (i.e., a "help me help you" strategy).

CONCLUSIONS

Based on our findings, we propose a model in which crosstalk, co-option, and co-dependency can facilitate the evolution of synergistic cooperative interactions between genetically distant clones. Specifically, we suggest that synergistic cooperative interactions can easily emerge, regardless of the degree of overall genetic/genealogical relatedness, via crosstalk involving metastatic clones able to constitutively secrete molecules that induce and maintain their own malignant state (producer-responder clones) and clones that have the ability to respond to those signals (responder clones) and express a synergistic metastatic behaviour. Taking into account the lack of therapies that directly affect the metastatic process, interfering with such cooperative interactions during the early steps in the metastatic cascade could provide additional strategies to increase patient survival.

Intratumor heterogeneity and cell secretome promote chemotherapy resistance and progression of colorectal cancer

The major underlying cause for the high mortality rate in colorectal cancer (CRC) relies on its drug resistance, to which intratumor heterogeneity (ITH) contributes substantially. CRC tumors have been reported to comprise heterogeneous populations of cancer cells that can be grouped into 4 consensus molecular subtypes (CMS). However, the impact of inter-cellular interaction between these cellular states on the emergence of drug resistance and CRC progression remains elusive. Here, we explored the interaction between cell lines belonging to the CMS1 (HCT116 and LoVo) and the CMS4 (SW620 and MDST8) in a 3D coculture model, mimicking the ITH of CRC. The spatial distribution of each cell population showed that CMS1 cells had a preference to grow in the center of cocultured spheroids, while CMS4 cells localized at the periphery, in line with observations in tumors from CRC patients. Cocultures of CMS1 and CMS4 cells did not alter cell growth, but significantly sustained the survival of both CMS1 and CMS4 cells in response to the front-line chemotherapeutic agent 5-fluorouracil (5-FU). Mechanistically, the secretome of CMS1 cells exhibited a remarkable protective effect for CMS4 cells against 5-FU treatment, while promoting cellular invasion. Secreted metabolites may be responsible for these effects, as demonstrated by the existence of 5-FU induced metabolomic shifts, as well as by the experimental transfer of the metabolome between CMS1 and CMS4 cells. Overall, our results suggest that the interplay between CMS1 and CMS4 cells stimulates CRC progression and reduces the efficacy of chemotherapy.

Osteosarcoma tumors maintain intra-tumoral transcriptional heterogeneity during bone and lung colonization

BACKGROUND

Tumors are complex tissues containing collections of phenotypically diverse malignant and nonmalignant cells. We know little of the mechanisms that govern heterogeneity of tumor cells nor of the role heterogeneity plays in overcoming stresses, such as adaptation to different microenvironments. Osteosarcoma is an ideal model for studying these mechanisms-it exhibits widespread inter- and intra-tumoral heterogeneity, predictable patterns of metastasis, and a lack of clear targetable driver mutations. Understanding the processes that facilitate adaptation to primary and metastatic microenvironments could inform the development of therapeutic targeting strategies.

RESULTS

We investigated single-cell RNA-sequencing profiles of 47,977 cells obtained from cell line and patient-derived xenograft models as cells adapted to growth within primary bone and metastatic lung environments. Tumor cells maintained phenotypic heterogeneity as they responded to the selective pressures imposed during bone and lung colonization. Heterogenous subsets of cells defined by distinct transcriptional profiles were maintained within bone- and lung-colonizing tumors, despite high-level selection. One prominent heterogenous feature involving glucose metabolism was clearly validated using immunofluorescence staining. Finally, using concurrent lineage tracing and single-cell transcriptomics, we found that lung colonization enriches for multiple clones with distinct transcriptional profiles that are preserved across cellular generations.

CONCLUSIONS

Response to environmental stressors occurs through complex and dynamic phenotypic adaptations. Heterogeneity is maintained, even in conditions that enforce clonal selection. These findings likely reflect the influences of developmental processes promoting diversification of tumor cell subpopulations, which are retained, even in the face of selective pressures.

New insights into the combined toxicity of aflatoxin B1 and fumonisin B1 in HepG2 cells using Seahorse respirometry analysis and RNA transcriptome sequencing

Aflatoxin B1 (AFB1) and fumonisin B1 (FB1) are widely (co-)detected in food and known for their hepatotoxicity in humans. Still, their combined toxicity needs to be investigated, especially the impact on mitochondria. In our previous work, we examined the effect of short-term exposure to different doses of AFB1, FB1, and their binary mixture (MIX) on the bioenergetic status of HepG2 cells, a well-recognized in vitro model system for studying liver cell function. In the current work, we further investigated the (combined) effect of AFB1 and FB1 on the mitochondrial and glycolytic activity of HepG2 cells using Seahorse respirometry analysis and RNA transcriptome sequencing. The results showed that the co-exposure, especially at high doses, is more toxic due to a more inhibition of all parameters of mitochondrial respiration. However, FB1 contributes more to the MIX effects than AFB1. RNA transcriptome sequencing showed that the p53 signaling pathway, a major orchestrator of mitochondrial apoptosis, was differentially expressed. Moreover, the co-exposure significantly downregulated the genes encoding for Complexes I, II, III, and IV, representing the onset of the suppressed mitochondrial respiration in HepG2 cells.

Evolution-Informed Strategies for Combating Drug Resistance in Cancer

The ever-changing nature of cancer poses the most difficult challenge oncologists face today. Cancer's remarkable adaptability has inspired many to work toward understanding the evolutionary dynamics that underlie this disease in hopes of learning new ways to fight it. Eco-evolutionary dynamics of a tumor are not accounted for in most standard treatment regimens, but exploiting them would help us combat treatment-resistant effectively. Here, we outline several notable efforts to exploit these dynamics and circumvent drug resistance in cancer.

Radiomic tumor phenotypes augment molecular profiling in predicting recurrence free survival after breast neoadjuvant chemotherapy

BACKGROUND

Early changes in breast intratumor heterogeneity during neoadjuvant chemotherapy may reflect the tumor's ability to adapt and evade treatment. We investigated the combination of precision medicine predictors of genomic and MRI data towards improved prediction of recurrence free survival (RFS).

METHODS

A total of 100 women from the ACRIN 6657/I-SPY 1 trial were retrospectively analyzed. We estimated MammaPrint, PAM50 ROR-S, and p53 mutation scores from publicly available gene expression data and generated four, voxel-wise 3-D radiomic kinetic maps from DCE-MR images at both pre- and early-treatment time points. Within the primary lesion from each kinetic map, features of change in radiomic heterogeneity were summarized into 6 principal components.

RESULTS

We identify two imaging phenotypes of change in intratumor heterogeneity (p < 0.01) demonstrating significant Kaplan-Meier curve separation (p < 0.001). Adding phenotypes to established prognostic factors, functional tumor volume (FTV), MammaPrint, PAM50, and p53 scores in a Cox regression model improves the concordance statistic for predicting RFS from 0.73 to 0.79 (p = 0.002).

CONCLUSIONS

These results demonstrate an important step in combining personalized molecular signatures and longitudinal imaging data towards improved prognosis.

Deep learning image analysis quantifies tumor heterogeneity and identifies microsatellite instability in colon cancer

BACKGROUND AND OBJECTIVES

Deep learning utilizing convolutional neural networks (CNNs) applied to hematoxylin & eosin (H&E)-stained slides numerically encodes histomorphological tumor features. Tumor heterogeneity is an emerging biomarker in colon cancer that is, captured by these features, whereas microsatellite instability (MSI) is an established biomarker traditionally assessed by immunohistochemistry or polymerase chain reaction.

METHODS

H&E-stained slides from The Cancer Genome Atlas (TCGA) colon cohort are passed through the CNN. Resulting imaging features are used to cluster morphologically similar slide regions. Tile-level pairwise similarities are calculated and used to generate a tumor heterogeneity score (THS). Patient-level THS is then correlated with TCGA-reported biomarkers, including MSI-status.

RESULTS

H&E-stained images from 313 patients generated 534 771 tiles. Deep learning automatically identified and annotated cells by type and clustered morphologically similar slide regions. MSI-high tumors demonstrated significantly higher THS than MSS/MSI-low (p < 0.001). THS was higher in MLH1-silent versus non-silent tumors (p < 0.001). The sequencing derived MSIsensor score also correlated with THS (r = 0.51, p < 0.0001).

CONCLUSIONS

Deep learning provides spatially resolved visualization of imaging-derived biomarkers and automated quantification of tumor heterogeneity. Our novel THS correlates with MSI-status, indicating that with expanded training sets, translational tools could be developed that predict MSI-status using H&E-stained images alone.

Mathematical modelling of the dynamics of image-informed tumor habitats in a murine model of glioma

Tumors exhibit high molecular, phenotypic, and physiological heterogeneity. In this effort, we employ quantitative magnetic resonance imaging (MRI) data to capture this heterogeneity through imaging-based subregions or "habitats" in a murine model of glioma. We then demonstrate the ability to model and predict the growth of the habitats using coupled ordinary differential equations (ODEs) in the presence and absence of radiotherapy. Female Wistar rats (N = 21) were inoculated intracranially with 10⁶ C6 glioma cells, a subset of which received 20 Gy (N = 5) or 40 Gy (N = 8) of radiation. All rats underwent diffusion-weighted and dynamic contrast-enhanced MRI at up to seven time points. All MRI data at each visit were subsequently clustered using k-means to identify physiological tumor habitats. A family of four models consisting of three coupled ODEs were developed and calibrated to the habitat time series of control and treated rats and evaluated for predictive capability. The Akaike Information Criterion was used for model selection, and the normalized sum-of-square-error (SSE) was used to evaluate goodness-of-fit in model calibration and prediction. Three tumor habitats with significantly different imaging data characteristics (p < 0.05) were identified: high-vascularity high-cellularity, low-vascularity high-cellularity, and low-vascularity low-cellularity. Model selection resulted in a five-parameter model whose predictions of habitat dynamics yielded SSEs that were similar to the SSEs from the calibrated model. It is thus feasible to mathematically describe habitat dynamics in a preclinical model of glioma using biology-based ODEs, showing promise for forecasting heterogeneous tumor behavior.

Red-Shifted Coumarin Luciferins for Improved Bioluminescence Imaging

Multicomponent bioluminescence imaging in vivo requires an expanded collection of tissue-penetrant probes. Toward this end, we generated a new class of near-infrared (NIR) emitting coumarin luciferin analogues (CouLuc-3s). The scaffolds were easily accessed from commercially available dyes. Complementary mutant luciferases for the CouLuc-3 analogues were also identified. The brightest probes enabled sensitive imaging in vivo. The CouLuc-3 scaffolds are also orthogonal to popular bioluminescent reporters and can be used for multicomponent imaging applications. Collectively, this work showcases a new set of bioluminescent tools that can be readily implemented for multiplexed imaging in a variety of biological settings.

Low-Density Lipoprotein Pathway Is a Ubiquitous Metabolic Vulnerability in High Grade Glioma Amenable for Nanotherapeutic Delivery

Metabolic reprogramming, through increased uptake of cholesterol in the form of low-density lipoproteins (LDL), is one way by which cancer cells, including high grade gliomas (HGG), maintain their rapid growth. In this study, we determined LDL receptor (LDLR) expression in HGGs using immunohistochemistry on tissue microarrays from intra- and inter tumour regions of 36 adult and 133 paediatric patients to confirm LDLR as a therapeutic target. Additionally, we analysed expression levels in three representative cell line models to confirm their future utility to test LDLR-targeted nanoparticle uptake, retention, and cytotoxicity. Our data show widespread LDLR expression in adult and paediatric cohorts, but with significant intra-tumour variation observed between the core and either rim or invasive regions of adult HGG. Expression was independent of paediatric tumour grade or identified clinicopathological factors. LDLR-expressing tumour cells localized preferentially within perivascular niches, also with significant adult intra-tumour variation. We demonstrated variable levels of LDLR expression in all cell lines, confirming their suitability as models to test LDLR-targeted nanotherapy delivery. Overall, our study reveals the LDLR pathway as a ubiquitous metabolic vulnerability in high grade gliomas across all ages, amenable to future consideration of LDL-mediated nanoparticle/drug delivery to potentially circumvent tumour heterogeneity.

Integration of quantitative methods and mathematical approaches for the modeling of cancer cell proliferation dynamics

Physiological processes rely on the control of cell proliferation, and the dysregulation of these processes underlies various pathological conditions, including cancer. Mathematical modeling can provide new insights into the complex regulation of cell proliferation dynamics. In this review, we first examine quantitative experimental approaches for measuring cell proliferation dynamics in vitro and compare the various types of data that can be obtained in these settings. We then explore the toolbox of common mathematical modeling frameworks that can describe cell behavior, dynamics, and interactions of proliferation. We discuss how these wet-laboratory studies may be integrated with different mathematical modeling approaches to aid the interpretation of the results and to enable the prediction of cell behaviors, specifically in the context of cancer.

Predictive value of intratumor metabolic and heterogeneity parameters on [18F]FDG PET/CT for EGFR mutations in patients with lung adenocarcinoma

PURPOSE

This study aimed to investigate the value of metabolic and heterogeneity parameters of 2-deoxy-2[¹⁸F]fluoro-D-glucose ([¹⁸F]FDG) positron emission tomography/computed tomography (PET/CT) in predicting epidermal growth factor receptor (EGFR) mutations in patients with lung adenocarcinoma (ADC).

MATERIALS AND METHODS

A retrospective analysis was performed on 157 patients with lung ADC between September 2015 and June 2021, who had undergone both EGFR mutation testing and [¹⁸F]FDG PET/CT examination. Metabolic and heterogeneity parameters were measured and calculated, including maximum diameter (Dmax), maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and heterogeneity factor (HF). Relationships between PET/CT parameters and EGFR mutation status were evaluated and a multivariate logistic regression analysis was analyzed to establish a combined prediction model.

RESULTS

108 (68.8%) patients exhibited EGFR mutations. EGFR mutations were more likely to occur in females (51.9% vs. 48.1%, P = 0.007), non-smokers (83.3% vs. 16.7%, P < 0.001) and right lobes (55.6% vs. 44.4%, P = 0.017). High Dmax, MTV and HF and low SUVmean were significantly correlated with EGFR mutations, and the areas under the ROC curve (AUCs) measuring 0.647, 0.701, 0.757, and 0.661, respectively. Multivariate logistic regression analysis suggested that non-smokers (OR = 0.30, P = 0.034), low SUVmean (≤ 7.75, OR = 0.63, P < 0.001) and high HF (≥ 4.21, OR = 1.80, P = 0.027) were independent predictors of EGFR mutations. The AUC of the combined prediction model measured up to 0.863, significantly higher than that of a single parameter.

CONCLUSIONS

EGFR mutant in lung ADC patients showed more intratumor heterogeneity (HF) than EGFR wild type, which was combined clinical feature (non-smokers), and metabolic parameter (SUVmean) may be helpful in predicting EGFR mutation status, thus playing a guiding role in EGFR-tyrosine kinase inhibitors (EGFR-TKIs) targeted therapies.

Extrachromosomal circular DNA in cancer drug resistance and its potential clinical implications

Chemotherapy is widely used to treat patients with cancer. However, resistance to chemotherapeutic drugs remains a major clinical concern. The mechanisms of cancer drug resistance are extremely complex and involve such factors such as genomic instability, DNA repair, and chromothripsis. A recently emerging area of interest is extrachromosomal circular DNA (eccDNA), which forms owing to genomic instability and chromothripsis. eccDNA exists widely in physiologically healthy individuals but also arises during tumorigenesis and/or treatment as a drug resistance mechanism. In this review, we summarize the recent progress in research regarding the role of eccDNA in the development of cancer drug resistance as well as the mechanisms thereof. Furthermore, we discuss the clinical applications of eccDNA and propose some novel strategies for characterizing drug-resistant biomarkers and developing potential targeted cancer therapies.

Application of individualized multimodal radiotherapy combined with immunotherapy in metastatic tumors

Radiotherapy is one of the mainstays of cancer treatment. More than half of cancer patients receive radiation therapy. In addition to the well-known direct tumoricidal effect, radiotherapy has immunomodulatory properties. When combined with immunotherapy, radiotherapy, especially high-dose radiotherapy (HDRT), exert superior systemic effects on distal and unirradiated tumors, which is called abscopal effect. However, these effects are not always effective for cancer patients. Therefore, many studies have focused on exploring the optimized radiotherapy regimens to further enhance the antitumor immunity of HDRT and reduce its immunosuppressive effect. Several studies have shown that low-dose radiotherapy (LDRT) can effectively reprogram the tumor microenvironment, thereby potentially overcoming the immunosuppressive stroma induced by HDRT. However, bridging the gap between preclinical commitment and effective clinical delivery is challenging. In this review, we summarized the existing studies supporting the combined use of HDRT and LDRT to synergistically enhance antitumor immunity, and provided ideas for the individualized clinical application of multimodal radiotherapy (HDRT+LDRT) combined with immunotherapy.

POSEA: A novel algorithm to evaluate the performance of multi-object instance image segmentation

Many techniques and software packages have been developed to segment individual cells within microscopy images, necessitating a robust method to evaluate images segmented into a large number of unique objects. Currently, segmented images are often compared with ground-truth images at a pixel level; however, this standard pixel-level approach fails to compute errors due to pixels incorrectly assigned to adjacent objects. Here, we define a per-object segmentation evaluation algorithm (POSEA) that calculates segmentation accuracy metrics for each segmented object relative to a ground truth segmented image. To demonstrate the performance of POSEA, precision, recall, and f-measure metrics are computed and compared with the standard pixel-level evaluation for simulated images and segmented fluorescence microscopy images of three different cell samples. POSEA yields lower accuracy metrics than the standard pixel-level evaluation due to correct accounting of misclassified pixels of adjacent objects. Therefore, POSEA provides accurate evaluation metrics for objects with pixels incorrectly assigned to adjacent objects and is robust for use across a variety of applications that require evaluation of the segmentation of unique adjacent objects.

Radiogenomic markers enable risk stratification and inference of mutational pathway states in head and neck cancer

PURPOSE

Head and neck squamous cell carcinomas (HNSCCs) are a molecularly, histologically, and clinically heterogeneous set of tumors originating from the mucosal epithelium of the oral cavity, pharynx, and larynx. This heterogeneous nature of HNSCC is one of the main contributing factors to the lack of prognostic markers for personalized treatment. The aim of this study was to develop and identify multi-omics markers capable of improved risk stratification in this highly heterogeneous patient population.

METHODS

In this retrospective study, we approached this issue by establishing radiogenomics markers to identify high-risk individuals in a cohort of 127 HNSCC patients. Hybrid in vivo imaging and whole-exome sequencing were employed to identify quantitative imaging markers as well as genetic markers on pathway-level prognostic in HNSCC. We investigated the deductibility of the prognostic genetic markers using anatomical and metabolic imaging using positron emission tomography combined with computed tomography. Moreover, we used statistical and machine learning modeling to investigate whether a multi-omics approach can be used to derive prognostic markers for HNSCC.

RESULTS

Radiogenomic analysis revealed a significant influence of genetic pathway alterations on imaging markers. A highly prognostic radiogenomic marker based on cellular senescence was identified. Furthermore, the radiogenomic biomarkers designed in this study vastly outperformed the prognostic value of markers derived from genetics and imaging alone.

CONCLUSION

Using the identified markers, a clinically meaningful stratification of patients is possible, guiding the identification of high-risk patients and potentially aiding in the development of effective targeted therapies.

Expression heterogeneity of ABC-transporter family genes and chemosensitivity genes in gastric tumor, carcinomatosis and lymph node metastases

Introduction. Metastatic tumors (particularly gastric cancer) have been found to be characterized by heterogeneity between the primary tumor and metastases. This type of heterogeneity comes to the fore when treating primary-metastatic forms of tumor and is an important reason for the low effectiveness of their treatment. In this regard, comparative analysis of ABC-transporter gene expression and chemosensitivity genes will allow to characterize to a certain extent the resistance and sensitivity of primary tumor, carcinomatosis and metastases to therapy and provide the basis for personalized treatment approach.Aim. To evaluate expression heterogeneity of ABC-transporter genes and chemosensitivity genes in gastric tumor, carcinomatosis and lymph node metastases.Materials and methods. Overall 41 patients with disseminated gastric cancer stage IV with carcinomatosis of peritoneum were included in the investigation. All patients underwent surgery according to Roux palliative gastrectomy. After surgery patients underwent chemotherapy depending on indications. RNA was isolated using RNeasy Plus mini kit (Qiagen, Germany). The expression level of ABC transporter genes (ABCB1, ABCC1, ABCC2, ABCC5, ABCG1, ABCG2) and chemosensitivity genes (BRCA1, RRM1, ERCC1, TOP1, TOP2α, TUBβ3, TYMS, GSTP1) was assessed by reverse transcription polymerase chain reaction (RT-PCR) in primary tumor, carcinomatosis and lymph node metastases.Results. The expression levels of the genes under study were shown to vary widely. For ABC transporter genes, ABCG1 (3.1 ± 1.1; max 32.0), ABCG2 (7.9 ± 2.3; max 54.1), ABCG2 (9.6 ± 3.8; max 101.0) were the most expressed genes in gastric tumor tissue, carcinomatosis and lymph node metastasis, respectively. Hyperexpression among chemosensitivity genes at all three sites was characteristic only of TOP2α (17.2 ± 6.0; max. 161.9; 10.8 ± 4.1; max. 105.1; 35.3 ± 0.8; max. 439.6, respectively). We found that TOP2α and BRCA1 gene expression levels were higher in lymph node metastasis compared with gastric tumor tissue and carcinomatosis (at p = 0.005 and p = 0.001). Whereas ABCC1 gene expression was statistically significantly higher in carcinomatosis (p = 0.03).Conclusion. Thus, a high level of expression heterogeneity is observed in gastric cancer, which affects the expression patterns of various genes in different localizations. The expression profile can be used to determine the level of heterogeneity and approach to personalized therapy tactics.

Evaluating the Accuracy of FUCCI Cell Cycle In Vivo Fluorescent Imaging to Assess Tumor Proliferation in Preclinical Oncology Models

PURPOSE

The primary goal of this study is to evaluate the accuracy of the fluorescence ubiquitination cell cycle indicator (FUCCI) system with fluorescence in vivo imaging compared to 3'-deoxy-3'-[18F]fluorothymidine ([18F]-FLT) positron emission tomography (PET)/computed tomography (CT) and biological validation through histology. Imaging with [18F]-FLT PET/CT can be used to noninvasively assess cancer cell proliferation and has been utilized in both preclinical and clinical studies. However, a cost-effective and straightforward method for in vivo, cell cycle targeted cancer drug screening is needed prior to moving towards translational imaging methods such as PET/CT.

PROCEDURES

In this study, fluorescent MDA-MB-231-FUCCI tumor growth was monitored weekly with caliper measurements and fluorescent imaging. Seven weeks post-injection, [18F]-FLT PET/CT was performed with a preclinical PET/CT, and tumors samples were harvested for histological analysis.

RESULTS

RFP fluorescent signal significantly correlated with tumor volume (r = 0.8153, p < 0.0001). Cell proliferation measured by GFP fluorescent imaging was correlated with tumor growth rate (r = 0.6497, p < 0.001). Also, GFP+ cells and [18F]-FLT regions of high uptake were both spatially located in the tumor borders, indicating that the FUCCI-IVIS method may provide an accurate assessment of tumor heterogeneity of cell proliferation. The quantification of total GFP signal was correlated with the sum of tumor [18F]-FLT standard uptake value (SUV) (r = 0.5361, p = 0.0724). Finally, histological analysis confirmed viable cells in the tumor and the correlation of GFP + and Ki67 + cells (r = 0.6368, p = 0.0477).

CONCLUSION

Fluorescent imaging of the cell cycle provides a noninvasive accurate depiction of tumor progression and response to therapy, which may benefit in vivo testing of novel cancer therapeutics that target the cell cycle.

Yeast-based evolutionary modeling of androgen receptor mutations and natural selection

Cancer progression is associated with the evolutionary accumulation of genetic mutations that are biologically significant. Mutations of the androgen receptor (AR) are associated with the development of prostate cancer (PCa) by responding to non-androgenic hormones, and the lack of annotations in their responsiveness to hormone ligands remains a daunting challenge. Here, we have used a yeast reporter system to quickly evaluate the responsiveness of all fifty clinical AR mutations to a variety of steroidal ligands including dihydrotestosterone (DHT), 17β-estradiol (E2), progesterone (PROG), and cyproterone acetate (CPA). Based on an AR-driven reporter that synthesizes histidine, a basic amino acid required for yeast survival and propagation, the yeast reporter system enabling clonal selection was further empowered by combining with a random DNA mutagenesis library to simulate the natural evolution of AR gene under the selective pressures of steroidal ligands. In a time-frame of 1-2 weeks, 19 AR mutants were identified, in which 11 AR mutants were validated for activation by tested steroidal compounds. The high efficiency of our artificial evolution strategy was further evidenced by a sequential selection that enabled the discovery of multipoint AR mutations and evolution directions under the pressure of steroidal ligands. In summary, our designer yeast is a portable reporter module that can be readily adapted to streamline high-throughput AR-compound screening, used as a PCa clinical reference, and combined with additional bioassay systems to further extend its potential.

Hybrid chalcogen bonds in prodrug nanoassemblies provides dual redox-responsivity in the tumor microenvironment

Sulfur bonds, especially trisulfide bond, have been found to ameliorate the self-assembly stability of homodimeric prodrug nanoassemblies and could trigger the sensitive reduction-responsive release of active drugs. However, the antitumor efficacy of homodimeric prodrug nanoassemblies with single reduction-responsivity may be restricted due to the heterogeneous tumor redox microenvironment. Herein, we replace the middle sulfur atom of trisulfide bond with an oxidizing tellurium atom or selenium atom to construct redox dual-responsive sulfur-tellurium-sulfur and sulfur-selenium-sulfur hybrid chalcogen bonds. The hybrid chalcogen bonds, especially the sulfur-tellurium-sulfur bond, exhibit ultrahigh dual-responsivity to both oxidation and reduction conditions, which could effectively address the heterogeneous tumor microenvironment. Moreover, the hybrid sulfur-tellurium-sulfur bond promotes the self-assembly of homodimeric prodrugs by providing strong intermolecular forces and sufficient steric hindrance. The above advantages of sulfur-tellurium-sulfur bridged homodimeric prodrug nanoassemblies result in the improved antitumor efficacy of docetaxel with satisfactory safety. The exploration of hybrid chalcogen bonds in drug delivery deepened insight into the development of prodrug-based chemotherapy to address tumor redox heterogeneity, thus enriching the design theory of prodrug-based nanomedicines.

Cancer metastasis is related to normal tissue stemness

The occurrence of cancer metastasis may be related to stem cells in normal tissues. We searched for patient IDs with both normal tissue stem cell values and TCGA (The Cancer Genome Atlas) clinical data for pairing and obtained 639 sets of data (stemness index of normal tissue, stemness index of tumor tissue, cancer stage, distant metastasis, tumor size) and invasion, and lymph node involvement). However, clinical data on cancer metastasis are of only four stages (e.g., Stage I, II, III, and IV), which cannot show subtle changes continuously. We need to find an effective data mining method to transform this four-valued clinical description into a numerical curve. We data-mine this data through numericalization, sorting, and noise reduction filtering. The results showed that: as the normal tissue stemness value (NS) increased, the tumor tissue stemness value (TS) increased proportionally (1.26 times NS). When NS >0.5, the rate of change in TS decelerated (0.43 times NS), and tumor metastasis began to occur. Clinical indicators, such as cancer stage, distant metastasis, tumor size and invasion, and lymph node involvement, showed that tumor metastasis became more and more severe with the increase of NS. This study suggests that tumor metastasis is triggered when the NS in the patient's body is more significant than 0.5.

Recent advances of bioresponsive polymeric nanomedicine for cancer therapy

A bioresponsive polymeric nanocarrier for drug delivery is able to alter its physical and physicochemical properties in response to a variety of biological signals and pathological changes, and can exert its therapeutic efficacy within a confined space. These nanosystems can optimize the biodistribution and subcellular location of therapeutics by exploiting the differences in biochemical properties between tumors and normal tissues. Moreover, bioresponsive polymer-based nanosystems could be rationally designed as precision therapeutic platforms by optimizing the combination of responsive elements and therapeutic components according to the patient-specific disease type and stage. In this review, recent advances in smart bioresponsive polymeric nanosystems for cancer chemotherapy and immunotherapy will be summarized. We mainly discuss three categories, including acidity-sensitive, redox-responsive, and enzyme-triggered polymeric nanosystems. The important issues regarding clinical translation such as reproducibility, manufacture, and probable toxicity, are also commented.

Mastering the use of cellular barcoding to explore cancer heterogeneity

Tumours are often composed of a multitude of malignant clones that are genomically unique, and only a few of them may have the ability to escape cancer therapy and grow as symptomatic lesions. As a result, tumours with a large degree of genomic diversity have a higher chance of leading to patient death. However, clonal fate can be driven by non-genomic features. In this context, new technologies are emerging not only to track the spatiotemporal fate of individual cells and their progeny but also to study their molecular features using various omics analysis. In particular, the recent development of cellular barcoding facilitates the labelling of tens to millions of cancer clones and enables the identification of the complex mechanisms associated with clonal fate in different microenvironments and in response to therapy. In this Review, we highlight the recent discoveries made using lentiviral-based cellular barcoding techniques, namely genetic and optical barcoding. We also emphasize the strengths and limitations of each of these technologies and discuss some of the key concepts that must be taken into consideration when one is designing barcoding experiments. Finally, we suggest new directions to further improve the use of these technologies in cancer research.

Advances in Promoting the Efficacy of Chimeric Antigen Receptor T Cells in the Treatment of Hepatocellular Carcinoma

HCC, one of the most common and deadly cancers worldwide, develops from hepatocytes and accounts for more than 90% of primary liver cancers. The current widely used treatment modalities are far from meeting the needs of liver cancer patients. CAR-T cell therapy, which has recently emerged, has shown promising efficacy in lymphoma and hematologic cancers, but there are still many challenges to overcome in its application to the clinical treatment of HCC, including osmotic barriers, the inhibition of hepatocellular carcinoma microenvironment activity, the limited survival and killing ability of CAR-T cells, and inevitable side effects, among others. As a result, a number of studies have begun to address the suboptimal efficacy of CAR-T cells in HCC, and many of these schemes hold good promise. This review focuses on advances in the past five years aimed at promoting the efficacy of CAR-T cell therapy for treatment of HCC.

Stimuli-responsive image-guided nanocarriers as smart drug delivery platforms

INTRODUCTION

In recent years, to improve the precision of drug delivery and reduce toxicity associated from the uncontrolled drug release at off-target locations, extensive efforts have been paid to develop stimuli-responsive nanocarriers, which enable precise control over on-demand drug release due to internal stimuli like pH, redox, enzyme and external stimuli like light, magnetic field, and ultrasound. Moreover, some stimuli-responsive nanocarriers have been strategically incorporated with imaging probes for simultaneous monitoring of the drug delivery process and region of interest for treatment optimization.

AREAS COVERED

In this review, the state-of-art progress in developing stimuli-responsive image-guided nanocarriers are summarized, including their designed strategies, synergistic mechanism, and biomedical applications in cancer therapy, and the current challenges and new perspectives are discussed.

EXPERT OPINION

The stimuli-responsive nanocarriers provide assurance for precise release of drugs and imaging probes, and the molecular imaging techniques can monitor the pharmacokinetics, biodistribution and bioavailability of drugs in vivo, and feedback the drug delivery profile. Therefore, stimuli-responsive image-guided nanocarriers can integrate diagnosis and therapy in one nanoplatform and facilitate optimal therapeutic efficacy.

FDG PET and CT radiomics in diagnosis and prognosis of non-small-cell lung cancer

BACKGROUND

18F-FDG PET and CT radiomics has been the object of a wide research for over 20 years but its contribution to clinical practice remains not yet well established. We have investigated its impact versus that of only histo-clinical data, for the routine management of non-small-cell lung cancer (NSCLC).

METHODS

Our patients were retrospectively considered. They all had a FDG PET-CT and immuno-histo-chemistry (IHC) to assess PD-L1 expression at the beginning of the disease. A prognosis univariate and multivariate Cox survival analyses was performed for overall survival (OS) and progression free survival (PFS) prediction, including a training/testing procedure. Two sets of 47 PET and 47 CT radiomics features (RFs) were extracted. Difference between RFs according to PD-L1 expression, the histology status and the stage level were tested using suited non parametric statistical tests and the receiver operating characteristics (ROC) curve and the area under curve (AUC).

RESULTS

From 2017 to 2019, 212 NSCLC patients treated in our institution were included. The main conventional prognostic variables were stage and gender with a low added prognostic value in the models including PET and CT RFs. Neither PET nor CT RFs were significant to separate the different levels of PD-L1 expression. Several RFs differ between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) tumours and a large number of PET and CT RFs are significantly linked to patient stage.

CONCLUSIONS

In our population, PET and CT RFs show their intrinsic power to predict survival but do not significantly improve OS and PFS prediction in the different multivariate models, in comparison to conventional data. It would seem necessary to carry out one's own survival analysis before determining a radiomics signature.

A pan-cancer analysis of collagen VI family on prognosis, tumor microenvironment, and its potential therapeutic effect

Background

Collagen VI family (COL6A) is a major member of extracellular matrix protein. There is accumulating evidence that COL6A is involved in tumorigenesis and tumor progression. In this study, we performed a systematic analysis of COL6A in pan-cancer based on their molecular features and clinical significance.

Methods

Based on updated public databases, we integrated several bioinformatics analysis methods to investigate the expression levels of COL6A as well as the relationship between their expression and patient survival, immune subtypes, tumor microenvironment, stemness scores, drug sensitivity, and DNA methylation.

Results

The expression levels of COL6A members varied in different cancers, suggesting their expression was cancer-dependent. Among COL6A members, COL6A1/2/3 were predicted poor prognosis in specific cancers. Furthermore, COL6A1/2/3 expression levels revealed a clear correlation with immune subtypes, and COL6A1/2/3 were associated with tumor purity, that is, gene expression levels were generally higher in tumors with higher stromal scores and immune scores. COL6A1/2/3 had a significantly negative correlation with RNA stemness scores, and meanwhile they were also related to DNA stemness scores in different degrees. In addition, the expression of COL6A1/2/3 was significantly related to drug sensitivity of cancer cells. Finally, our study revealed that COL6A1/2/3 expression was mainly negatively correlated with gene methylation, and the methylation levels showed remarkable differences in various cancers.

Conclusions

These findings highlight both the similarities and differences in the molecular characteristics of COL6A members in pan-cancer, and provide comprehensive insights for further investigation into the mechanism of COL6A.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04951-0.

Elevated CDK5R1 expression associated with poor prognosis, proliferation, and drug resistance in colorectal and breast malignancies: CDK5R1 as an oncogene in cancers

BACKGROUND

Studies have shown that the CDK5R1 gene could have a part in some types of cancer. This study sought to examine the relationship between CDK5R1 expression and prognosis and medication resistance in 13 commonly occurring cancers.

METHOD

The cancer genome atlas data and clinical data were utilized to assess the role of CDK5R1 in malignancies. The expression data of 13 cancers were also integrated and used for the co-expression network. The relationship between CDK5R1 expression and drug resistance and sensitivity was evaluated using pharmacogenomics data. The colorectal cancer (CRC) and breast cancer (BC) were used to confirm the results through the RT-qPCR method.

RESULTS

With the exception of gastric cancer, all common malignancies showed an increase in CDK5R1 expression. Also, outcomes of sensitivity and specificity showed that CDK5R1 level could be a really good potential biomarker. Additionally, CDK5R1 expression was higher in CRC and BC samples compared to adjacent normal, according to RT-qPCR data. In six types of tumors and combined data, a poor prognosis was associated with increased CDK5R1 expression. The CDK5R1-associated genes were connected to the primary oncogenic pathways in cancer cells, according to the co-expression network. Also, CDK5R1 level was significantly linked to the resistance and sensitivity of several chemotherapy drugs and caused the highest resistance to cyclophosphamide.

CONCLUSION

CDK5R1 expression is upregulated in 12 prevalent cancers and can play an oncogenic role. Also, this gene's expression could be used as a biomarker to predict patient survival and medication resistance.

Three-dimensional in vitro culture models in oncology research

Cancer is a multifactorial disease that is responsible for 10 million deaths per year. The intra- and inter-heterogeneity of malignant tumors make it difficult to develop single targeted approaches. Similarly, their diversity requires various models to investigate the mechanisms involved in cancer initiation, progression, drug resistance and recurrence. Of the in vitro cell-based models, monolayer adherent (also known as 2D culture) cell cultures have been used for the longest time. However, it appears that they are often less appropriate than the three-dimensional (3D) cell culture approach for mimicking the biological behavior of tumor cells, in particular the mechanisms leading to therapeutic escape and drug resistance. Multicellular tumor spheroids are widely used to study cancers in 3D, and can be generated by a multiplicity of techniques, such as liquid-based and scaffold-based 3D cultures, microfluidics and bioprinting. Organoids are more complex 3D models than multicellular tumor spheroids because they are generated from stem cells isolated from patients and are considered as powerful tools to reproduce the disease development in vitro. The present review provides an overview of the various 3D culture models that have been set up to study cancer development and drug response. The advantages of 3D models compared to 2D cell cultures, the limitations, and the fields of application of these models and their techniques of production are also discussed.

Liquid biopsy for the assessment of adrenal cancer heterogeneity: where do we stand?

Almost 10 years have passed since the first attempts of liquid biopsy aimed at the characterisation of tumor cells present in the bloodstream from a regular sample of peripheral blood were performed. Liquid biopsy has been used to characterise tumor heterogeneity in various types of solid tumors including adrenocortical carcinoma. The development of molecular biology, genetics, and methodological advances such as digital PCR and next-generation sequencing allowed us to use besides circulating tumor cells a variety of circulating cell-free nucleic acids, DNAs, RNAs and microRNAs secreted by tumors into blood and other body fluids as specific molecular markers. These markers are used for diagnosis, to check tumor development, selecting efficient therapies, therapy monitoring and even possess prognostic power. In adrenocortical carcinoma, there are some studies reporting analysis of circulating tumor cells, circulating cell free DNA and microRNAs for assessing tumor heterogeneity. Among microRNAs, hsa-miR-483-5p seems to be the most important player. Combined with other microRNAs like hsa-miR-195, their expression correlates with recurrence-free survival. Most studies support the applicability of liquid biopsy for assessing temporal tumor heterogeneity (i.e. tumor progression) in adrenocortical cancer. In this mini-review, the available findings of liquid biopsy for assessing tumor heterogeneity in adrenocortical cancer are presented.