Unveiling functional heterogeneity in breast cancer multicellular tumor spheroids through single-cell RNA-seq

Intratumoral Heterogeneity and Metabolic Cross-Feeding in a Three-Dimensional Breast Cancer Culture: An In Silico Perspective

Today, the intratumoral composition is a relevant factor associated with the progression and aggression of cancer. Although it suggests a metabolic interdependence among the subpopulations inside the tumor, a detailed map of how this interdependence contributes to the malignant phenotype is still lacking. To address this issue, we developed a systems biology approach integrating single-cell RNASeq and genome-scale metabolic reconstruction to map the metabolic cross-feeding among the subpopulations previously identified in the spheroids of MCF7 breast cancer. By calibrating our model with expression profiles and the experimental growth rate, we concluded that the reverse Warburg effect emerges as a mechanism to optimize community growth. Furthermore, through an in silico analysis, we identified lactate, alpha-ketoglutarate, and some amino acids as key metabolites whose disponibility alters the growth rate of the spheroid. Altogether, this work provides a strategy for assessing how space and intratumoral heterogeneity influence the metabolic robustness of cancer, issues suggesting that computational strategies should move toward the design of optimized treatments.

Single-Cell RNA-Sequencing: Opening New Horizons for Breast Cancer Research

Breast cancer is the most prevalent malignant tumor among women with high heterogeneity. Traditional techniques frequently struggle to comprehensively capture the intricacy and variety of cellular states and interactions within breast cancer. As global precision medicine rapidly advances, single-cell RNA sequencing (scRNA-seq) has become a highly effective technique, revolutionizing breast cancer research by offering unprecedented insights into the cellular heterogeneity and complexity of breast cancer. This cutting-edge technology facilitates the analysis of gene expression profiles at the single-cell level, uncovering diverse cell types and states within the tumor microenvironment. By dissecting the cellular composition and transcriptional signatures of breast cancer cells, scRNA-seq provides new perspectives for understanding the mechanisms behind tumor therapy, drug resistance and metastasis in breast cancer. In this review, we summarized the working principle and workflow of scRNA-seq and emphasized the major applications and discoveries of scRNA-seq in breast cancer research, highlighting its impact on our comprehension of breast cancer biology and its potential for guiding personalized treatment strategies.

Diffusion on PCA-UMAP Manifold: The Impact of Data Structure Preservation to Denoise High-Dimensional Single-Cell RNA Sequencing Data

Single-cell transcriptomics (scRNA-seq) is revolutionizing biological research, yet it faces challenges such as inefficient transcript capture and noise. To address these challenges, methods like neighbor averaging or graph diffusion are used. These methods often rely on k-nearest neighbor graphs from low-dimensional manifolds. However, scRNA-seq data suffer from the 'curse of dimensionality', leading to the over-smoothing of data when using imputation methods. To overcome this, sc-PHENIX employs a PCA-UMAP diffusion method, which enhances the preservation of data structures and allows for a refined use of PCA dimensions and diffusion parameters (e.g., k-nearest neighbors, exponentiation of the Markov matrix) to minimize noise introduction. This approach enables a more accurate construction of the exponentiated Markov matrix (cell neighborhood graph), surpassing methods like MAGIC. sc-PHENIX significantly mitigates over-smoothing, as validated through various scRNA-seq datasets, demonstrating improved cell phenotype representation. Applied to a multicellular tumor spheroid dataset, sc-PHENIX identified known extreme phenotype states, showcasing its effectiveness. sc-PHENIX is open-source and available for use and modification.

Live Microscopy of Multicellular Spheroids with the Multimodal Near-Infrared Nanoparticles Reveals Differences in Oxygenation Gradients

Assessment of hypoxia, nutrients, metabolite gradients, and other hallmarks of the tumor microenvironment within 3D multicellular spheroid and organoid models represents a challenging analytical task. Here, we report red/near-infrared (NIR) emitting cell staining with O2-sensitive nanoparticles, which enable measurements of spheroid oxygenation on a conventional fluorescence microscope. Nanosensor probes, termed "MMIR" (multimodal infrared), incorporate an NIR O2-sensitive metalloporphyrin (PtTPTBPF) and deep red aza-BODIPY reference dyes within a biocompatible polymer shell, allowing for oxygen gradient quantification via fluorescence ratio and phosphorescence lifetime readouts. We optimized staining techniques and evaluated the nanosensor probe characteristics and cytotoxicity. Subsequently, we applied nanosensors to the live spheroid models based on HCT116, DPSCs, and SKOV3 cells, at rest, and treated with drugs affecting cell respiration. We found that the growth medium viscosity, spheroid size, and formation method influenced spheroid oxygenation. Some spheroids produced from HCT116 and dental pulp stem cells exhibited "inverted" oxygenation gradients, with higher core oxygen levels than the periphery. This contrasted with the frequently encountered "normal" gradient of hypoxia toward the core caused by diffusion. Further microscopy analysis of spheroids with an "inverted" gradient demonstrated metabolic stratification of cells within spheroids: thus, autofluorescence FLIM of NAD(P)H indicated the formation of a glycolytic core and localization of OxPhos-active cells at the periphery. Collectively, we demonstrate a strong potential of NIR-emitting ratiometric nanosensors for advanced microscopy studies targeting live and quantitative real-time monitoring of cell metabolism and hypoxia in complex 3D tissue models.

Genomic analysis of plasma circulating tumor DNA in patients with heavily pretreated HER2 + metastatic breast cancer

We explored accumulated genomic alterations in patients with heavily treated HER2 + metastatic breast cancer enrolled in the KCSG BR18-14/KM10B trial. Targeted sequencing was performed with circulating tumor DNAs (ctDNAs) collected before the treatment of 92 patients. ctDNAs collected at the time of disease progression from seven patients who had a durable response for > 12 months were also analyzed. Sixty-five genes were identified as pathogenic alterations in 99 samples. The most frequently altered genes were TP53 (n = 48), PIKCA (n = 21) and ERBB3 (n = 19). TP53 and PIK3CA mutations were significantly related with shorter progression free survival (PFS), and patients with a higher ctDNA fraction showed a worse PFS. The frequency of homologous recombination deficiency (HRD)-related gene mutations was higher than that in matched tumor tissues, and these mutations tended to be associated with shorter PFS. New pathogenic variants were found at the end of treatment in all seven patients, including BRCA2, VHL, RAD50, RB1, BRIP1, ATM, FANCA, and PIK3CA mutations. In conclusion, TP53 and PIK3CA mutations, as well as a higher ctDNA fraction, were associated with worse PFS with trastuzumab and cytotoxic chemotherapy. The enrichment of HRD-related gene mutations and newly detected variants in ctDNA may be related to resistance to treatment.

Microscopy-based phenotypic monitoring of MDA-MB-231 spheroids allows the evaluation of phenotype-directed therapy

Breast cancer (BC) is the most commonly diagnosed cancer among women. Prognosis has improved over the years, to a large extent, owing to personalized therapy informed by molecular profiling of hormone receptors. However, there is a need for new therapeutic approaches for a subgroup of BCs lacking molecular markers, the Triple Negative Breast Cancer (TNBC) subgroup. TNBC is the most aggressive type of BC, lacks an effective standard of care, shows high levels of resistance and relapse is often inevitable. High resistance to therapy has been hypothesized to be associated with high intratumoral phenotypic heterogeneity. To characterize and treat this phenotypic heterogeneity, we optimized a whole-mount staining and image analysis protocol for three-dimensions (3D) spheroids. Applying this protocol to TNBC spheroids located in the outer region of the spheroid the cells with selected phenotypes: dividing, migrating, and high mitochondrial mass phenotypes. To evaluate the relevance of phenotype-based targeting these cell populations were targeted with Paclitaxel, Trametinib, and Everolimus, respectively, in a dose-dependent manner. Single agents cannot specifically target all phenotypes at the same time. Therefore, we combined drugs that should target independent phenotype. With this rationale we observed that combining Trametinib and Everolimus achieves the highest cytotoxicity at lower doses from all the tested combinations. These findings suggest a rational approach to design treatments can be evaluated in spheroids prior to pre-clinical models and potentially reduce adverse effects.

MUG CCArly: A Novel Autologous 3D Cholangiocarcinoma Model Presents an Increased Angiogenic Potential

Cholangiocarcinoma (CCA) are characterized by their desmoplastic and hypervascularized tumor microenvironment (TME), which is mainly composed of tumor cells and cancer-associated fibroblasts (CAFs). CAFs play a pivotal role in general and CCA tumor progression, angiogenesis, metastasis, and the development of treatment resistance. To our knowledge, no continuous human in vivo-like co-culture model is available for research. Therefore, we aimed to establish a new model system (called MUG CCArly) that mimics the desmoplastic microenvironment typically seen in CCA. Proteomic data comparing the new CCA tumor cell line with our co-culture tumor model (CCTM) indicated a higher gene expression correlation of the CCTM with physiological CCA characteristics. A pro-angiogenic TME that is typically observed in CCA could also be better simulated in the CCTM group. Further analysis of secreted proteins revealed CAFs to be the main source of these angiogenic factors. Our CCTM MUG CCArly represents a new, reproducible, and easy-to-handle 3D CCA model for preclinical studies focusing on CCA-stromal crosstalk, tumor angiogenesis, and invasion, as well as the immunosuppressive microenvironment and the involvement of CAFs in the way that drug resistance develops.

How to build a tumor: An industry perspective

During the past 15 years, a plethora of innovative 3D in vitro systems has been developed. They offer the possibility of identifying crucial cellular and molecular contributors to the disease by permitting manipulation of each in isolation. However, improvements are needed particularly with respect to the predictivity and validity of those models. The major challenge now is to identify which assay and readout combination(s) best suits the current scientific question(s). A deep understanding of the different platforms along with their pros and cons is a prerequisite to make this decision. This review aims to give an overview of the most prominent systems with a focus on applications, translational relevance and adoption drivers from an industry perspective.

Estimating intraclonal heterogeneity and subpopulation changes from bulk expression profiles in CMap

Premnas is a computational framework that provides a new perspective to interpret perturbational data in LINC L1000 CMap by learning an ad hoc subpopulation representation from scRNA-seq and performing the digital cytometry to estimate the abundance of undetermined subpopulations.

The connectivity among signatures upon perturbations curated in the CMap library provides a valuable resource for understanding therapeutic pathways and biological processes associated with the drugs and diseases. However, because of the nature of bulk-level expression profiling by the L1000 assay, intraclonal heterogeneity and subpopulation compositional change that could contribute to the responses to perturbations are largely neglected, hampering the interpretability and reproducibility of the connections. In this work, we proposed a computational framework, Premnas, to estimate the abundance of undetermined subpopulations from L1000 profiles in CMap directly according to an ad hoc subpopulation representation learned from a well-normalized batch of single-cell RNA-seq datasets by the archetypal analysis. By recovering the information of subpopulation changes upon perturbation, the potentials of drug-resistant/susceptible subpopulations with CMap L1000 were further explored and examined. The proposed framework enables a new perspective to understand the connectivity among cellular signatures and expands the scope of the CMAP and other similar perturbation datasets limited by the bulk profiling technology.

Predictive Biomarkers of Response to Neoadjuvant Chemotherapy in Breast Cancer: Current and Future Perspectives for Precision Medicine

Pathological complete response (pCR) after neoadjuvant chemotherapy in patients with early breast cancer is correlated with better survival. Meanwhile, an expanding arsenal of post-neoadjuvant treatment strategies have proven beneficial in the absence of pCR, leading to an increased use of neoadjuvant systemic therapy in patients with early breast cancer and the search for predictive biomarkers of response. The better prediction of response to neoadjuvant chemotherapy could enable the escalation or de-escalation of neoadjuvant treatment strategies, with the ultimate goal of improving the clinical management of early breast cancer. Clinico-pathological prognostic factors are currently used to estimate the potential benefit of neoadjuvant systemic treatment but are not accurate enough to allow for personalized response prediction. Other factors have recently been proposed but are not yet implementable in daily clinical practice or remain of limited utility due to the intertumoral heterogeneity of breast cancer. In this review, we describe the current knowledge about predictive factors for response to neoadjuvant chemotherapy in breast cancer patients and highlight the future perspectives that could lead to the better prediction of response, focusing on the current biomarkers used for clinical decision making and the different gene signatures that have recently been proposed for patient stratification and the prediction of response to therapies. We also discuss the intratumoral phenotypic heterogeneity in breast cancers as well as the emerging techniques and relevant pre-clinical models that could integrate this biological factor currently limiting the reliable prediction of response to neoadjuvant systemic therapy.

Development of a Multicellular 3D Tumor Model to Study Cellular Heterogeneity and Plasticity in NSCLC Tumor Microenvironment

Heterogeneity is a characteristic feature of solid tumors. Intra-tumor heterogeneity includes phenotypic diversity, epigenetic abnormalities, cell proliferation, and plasticity that eventually drives disease progression. Studying tumor heterogeneity in 2D culture is challenging as it cannot simulate the microenvironmental features, such as hypoxia, nutrient unavailability, and cell-ECM interactions. We propose the development of multicellular (tri-culture) 3D spheroids using a hanging drop method to study the non-tumorigenic (BEAS-2B) vs. tumorigenic NSCLC (A549/NCI-H460)cells’ interaction with lung fibroblasts (MRC-5) and monocytes (THP-1). Unlike the non-tumorigenic model, the tumorigenic 3D spheroids show significant induction of cell proliferation, hypoxia, pluripotency markers, notable activation of cancer-associated fibroblasts, and tumor-associated macrophages. CD68+ macrophages isolated from tumorigenic spheroids exhibited profound induction of phenotypic endothelial characteristics. The results are zebrafish tumor xenograft model and by using human patient samples. This multicellular 3D tumor model is a promising tool to study tumor-stroma interaction and cellular plasticity, targeting tumor heterogeneity, and facilitating cancer therapy success against NSCLC.

Longitudinal Shifts of Solid Tumor and Liquid Biopsy Sequencing Concordance in Metastatic Breast Cancer

Tissue-based next-generation sequencing (NGS) in metastatic breast cancer (mBC) is limited by the inability to noninvasively track tumor evolution. Cell-free DNA (cfDNA) NGS has made sequential testing feasible; however, the relationship between cfDNA and tissue-based testing in mBC is not well understood. Here, we evaluate concordance between tissue and cfDNA NGS relative to cfDNA sampling frequency in a large, clinically annotated mBC data set.

METHODS

Tempus LENS was used to analyze deidentified records of mBC cases with Tempus xT (tissue) and xF (cfDNA) sequencing results. Then, various metrics of concordance were assessed within overlapping probe regions of the tissue and cfDNA assays (104 genes), focusing on pathogenic variants. Variant allele frequencies of discordant and concordant pathogenic variants were also compared. Analyses were stratified by mBC subtype and time between tests.

RESULTS

Records from 300 paired tissue and liquid biopsies were analyzed. Median time between tissue and blood collection was 78.5 days (standard deviation = 642.99). The median number of pathogenic variants/patient was one for cfDNA and two for tissue. Across the cohort, 77.8% of pathogenic tissue variants were found in cfDNA and 75.7% of pathogenic cfDNA variants were found in tissue when tests were ≤ 7 days apart, which decreased to 50.3% and 51.8%, respectively, for > 365 days. Furthermore, the median patient-level variant concordance was 67% when tests were ≤7 days apart and 30%-37% when > 30 days. The median variant allele frequencies of discordant variants were generally lower than those of concordant variants within the same time frame.

CONCLUSION

We observed high concordances between tissue and cfDNA results that generally decreased with longer durations between tests. Thus, cfDNA NGS reliably measures tissue genomics and is likely beneficial for longitudinal monitoring of molecular changes in mBC.

Ultrasound Triggers Hypericin Activation Leading to Multifaceted Anticancer Activity

The use of ultrasound (US) in combination with a responsive chemical agent (sonosensitizer) can selectively trigger the agent's anticancer activity in a process called sonodynamic therapy (SDT). SDT shares some properties with photodynamic therapy (PDT), which has been clinically approved, but sets itself apart because of its use of US rather than light to achieve better tissue penetration. SDT provides anticancer effects mainly via the sonosensitizer-mediated generation of reactive oxygen species (ROS), although the precise nature of the underpinning mechanism is still under debate. This work investigates the SDT anticancer activity of hypericin (Hyp) in vitro in two- (2D) and three-dimensional (3D) HT-29 colon cancer models, and uses PDT as a yardstick due to its well-known Hyp phototoxicity. The cancer cell uptake and cellular localization of Hyp were investigated first to determine the proper noncytotoxic concentration and incubation time of Hyp for SDT. Furthermore, ROS production, cell proliferation, and cell death were evaluated after Hyp was exposed to US. Since cancer relapse and transporter-mediated multidrug resistance (MDR) are important causes of cancer treatment failure, the US-mediated ability of Hyp to elicit immunogenic cell death (ICD) and overcome MDR was also investigated. SDT showed strong ROS-mediated anticancer activity 48 h after treatment in both the HT-29 models. Specific damage-associated molecular patterns that are consistent with ICD, such as calreticulin (CRT) exposure and high-mobility group box 1 protein (HMGB1) release, were observed after SDT with Hyp. Moreover, the expression of the ABC transporter, P-glycoprotein (P-gp), in HT-29/MDR cells was not able to hinder cancer cell responsiveness to SDT with Hyp. This work reveals, for the first time, the US responsiveness of Hyp with significant anticancer activity being displayed, making it a full-fledged sonosensitizer for the SDT of cancer.

Metabolic flux analysis of 3D spheroids reveals significant differences in glucose metabolism from matched 2D cultures of colorectal cancer and pancreatic ductal adenocarcinoma cell lines

BACKGROUND

Most in vitro cancer cell experiments have been performed using 2D models. However, 3D spheroid cultures are increasingly favored for being more representative of in vivo tumor conditions. To overcome the translational challenges with 2D cell cultures, 3D systems better model more complex cell-to-cell contact and nutrient levels present in a tumor, improving our understanding of cancer complexity. Despite this need, there are few reports on how 3D cultures differ metabolically from 2D cultures.

METHODS

Well-described cell lines from colorectal cancer (HCT116 and SW948) and pancreatic ductal adenocarcinoma (Panc-1 and MIA-Pa-Ca-2) were used to investigate metabolism in 3D spheroid models. The metabolic variation under normal glucose conditions were investigated comparing 2D and 3D cultures by metabolic flux analysis and expression of key metabolic proteins.

RESULTS

We find significant differences in glucose metabolism of 3D cultures compared to 2D cultures, both related to glycolysis and oxidative phosphorylation. Spheroids have higher ATP-linked respiration in standard nutrient conditions and higher non-aerobic ATP production in the absence of supplemented glucose. In addition, ATP-linked respiration is significantly inversely correlated with OCR/ECAR (p = 0.0096). Mitochondrial transport protein, TOMM20, expression decreases in all spheroid models compared to 2D, and monocarboxylate transporter (MCT) expression increases in 3 of the 4 spheroid models.

CONCLUSIONS

In this study of CRC and PDAC cell lines, we demonstrate that glucose metabolism in 3D spheroids differs significantly from 2D cultures, both in terms of glycolytic and oxidative phosphorylation metrics. The metabolic phenotype shift from 2D to 3D culture in one cell line is greater than the phenotypic differences between each cell line and tumor source. The results herein emphasize the need to use 3D cell models for investigating nutrient utilization and metabolic flux for a better understanding of tumor metabolism and potential metabolic therapeutic targets.

Enriched transcriptome analysis of laser capture microdissected populations of single cells to investigate intracellular heterogeneity in immunostained FFPE sections

To investigate intracellular heterogeneity, cell capture of particular cell populations followed by transcriptome analysis has been highly effective in freshly isolated tissues. However, this approach has been quite challenging in immunostained formalin-fixed paraffin-embedded (FFPE) sections. This study aimed at combining the standard pathology techniques, immunostaining and laser capture microdissection, with whole RNA-sequencing and bioinformatics analysis to characterize FFPE breast cancer cell populations with heterogeneous expression of progesterone receptor (PR). Immunocytochemical analysis revealed that 60% of MCF-7 cells admixture highly express PR. Immunocytochemistry-based targeted RNA-seq (ICC-RNAseq) and in silico functional analysis revealed that the PR-high cell population is associated with upregulation in transcripts implicated in immunomodulatory and inflammatory pathways (e.g. NF-κB and interferon signaling). In contrast, the PR-low cell population is associated with upregulation of genes involved in metabolism and mitochondrial processes as well as EGFR and MAPK signaling. These findings were cross-validated and confirmed in FACS-sorted PR high and PR-low MCF-7 cells and in MDA-MB-231 cells ectopically overexpressing PR. Significantly, ICC-RNAseq could be extended to analyze samples captured at specific spatio-temporal states to investigate gene expression profiles using diverse biomarkers. This would also facilitate our understanding of cell population-specific molecular events driving cancer and potentially other diseases.

Single cell organization and cell cycle characterization of DNA stained multicellular tumor spheroids

Multicellular tumor spheroids (MCTSs) can serve as in vitro models for solid tumors and have become widely used in basic cancer research and drug screening applications. The major challenges when studying MCTSs by optical microscopy are imaging and analysis due to light scattering within the 3-dimensional structure. Herein, we used an ultrasound-based MCTS culture platform, where A498 renal carcinoma MCTSs were cultured, DAPI stained, optically cleared and imaged, to connect nuclear segmentation to biological information at the single cell level. We show that DNA-content analysis can be used to classify the cell cycle state as a function of position within the MCTSs. We also used nuclear volumetric characterization to show that cells were more densely organized and perpendicularly aligned to the MCTS radius in MCTSs cultured for 96 h compared to 24 h. The method presented herein can in principle be used with any stochiometric DNA staining protocol and nuclear segmentation strategy. Since it is based on a single counter stain a large part of the fluorescence spectrum is free for other probes, allowing measurements that correlate cell cycle state and nuclear organization with e.g., protein expression or drug distribution within MCTSs.

Effects of collagen membranes and bone substitute differ in periodontal ligament cell microtissues and monolayers

BACKGROUND

Barrier membranes and bone substitute are major tools of guided tissue regeneration (GTR) after periodontal disease. Integrity of the periodontal ligament plays a key role in periodontal health, but its functionality fails to be fully re-established by GTR after disease or trauma. Microtissue models suggest an in vivo-like model to develop novel GTR approaches due to its three-dimensionality. This study aims to assess the effects of collagen membranes and bone substitute on cell viability, adhesion and gene expression of regenerative and inflammatory biomarkers by periodontal ligament cell (PDLC) microtissues.

METHODS

Human PDLC microtissues and monolayers were cultured on collagen membranes or bone substitute. After 24 hours incubation, metabolic activity, focal adhesion, mRNA and protein production of collagen-type-I (COL1A1), periostin (POSTN), vascular endothelial growth factor (VEGF), angiogenin (ANG), interleukin (IL)6 and IL8 were measured by resazurin-based toxicity assay, focal adhesion staining, quantitative polymerase chain reaction and enzyme-linked immunosorbent assay, respectively.

RESULTS

PDLC microtissues and monolayers were viable on collagen membranes and bone substitute, but microtissues were less metabolically active. Dominant staining of actin filaments was found in PDLC microtissues on collagen membranes. COL1A1, POSTN, VEGF, ANG and IL6 were modulated in PDLC microtissues on bone substitute, while there were no significant changes on collagen membranes. PDLC monolayers showed a different character of gene expression changes.

CONCLUSIONS

PDLC microtissues and monolayers react diversely to collagen membranes and bone substitute. Further descriptive and mechanistic tests will be required to clarify the potential of PDLC microtissues as in vivo-like model for GTR.

Activity of trastuzumab emtansine (T-DM1) in 3D cell culture

Background

Cell spheroids and aggregates generated from three-dimensional (3D) cell culture methods are similar to in vivo tumors in terms of tissue morphology, biology, and gene expression, unlike cells grown in 2D cell cultures. Breast cancer heterogeneity is one of the main drug resistant mechanisms and needs to be overcome in order to increase the efficacy of drug activity in its treatments.

Methods

We performed a unique 3D cell culture and drug efficacy study with trastuzumab emtansine (Kadcyla®, T-DM1) across five breast cancer cell lines (BT-474, SK-BR-3, MDA-MB-361, MDA-MB-175, and MCF-7) that were previously investigated in 2D cell culture. We performed HER2 IHC staining, cell viability experiments, Gene-protein-assay (GPA), and T-DM1 internalization studies.

Results

We obtained significantly different results including higher IC₅₀ for some of the cell lines. Our GPA showed some significant heterogeneous HER2 gene and protein expression in 3D cultured spheroids or aggregates. The fluorescent images also showed that a longer incubation time is needed for T-DM1 to be internalized effectively into 3D cultured spheroids or aggregates.

Conclusion

Our study demonstrated that the difference of T-DM1 drug activity in 3D spheroids or aggregates might be due to tumor heterogeneity and less efficient internalization of T-DM1 that is not seen using 2D cell culture models. Drug studies using 3D cell culture are expected to provide biologically relevant models for determining drug activity in tumor tissue in future drug response and resistance research.

Advances in removing mass transport limitations for more physiologically relevant in vitro 3D cell constructs

Spheroids and organoids are promising models for biomedical applications ranging from human disease modeling to drug discovery. A main goal of these 3D cell-based platforms is to recapitulate important physiological parameters of their in vivo organ counterparts. One way to achieve improved biomimetic architectures and functions is to culture cells at higher density and larger total numbers. However, poor nutrient and waste transport lead to low stability, survival, and functionality over extended periods of time, presenting outstanding challenges in this field. Fortunately, important improvements in culture strategies have enhanced the survival and function of cells within engineered microtissues/organs. Here, we first discuss the challenges of growing large spheroids/organoids with a focus on mass transport limitations, then highlight recent tools and methodologies that are available for producing and sustaining functional 3D in vitro models. This information points toward the fact that there is a critical need for the continued development of novel cell culture strategies that address mass transport in a physiologically relevant human setting to generate long-lasting and large-sized spheroids/organoids.

A liquid culture cancer spheroid model reveals low PI3K/Akt pathway activity and low adhesiveness to the extracellular matrix

Three-dimensional (3D) cultures of cancer cells in liquid without extracellular matrix (ECM) offer in vitro models for metastasising conditions such as those in vessels and effusion. However, liquid culturing is often hindered by cell adhesiveness, which causes large cell clumps. We previously described a liquid culture material, LA717, which prevents nonclonal cell adhesion and subsequent clumping, thus allowing clonal growth of spheroids in an anchorage-independent manner. Here, we examined such liquid culture cancer spheroids for the acquisition of apical-basal polarity, sensitivity to an Akt inhibitor (anticancer drug MK-2206) and interaction with ECM. The spheroids present apical plasma membrane on the surface, which originated from the failure of polarisation at the single-cell stage and subsequent defects in phosphorylated ezrin accumulation at the cell boundary during the first cleavage, failing internal lumen formation. At the multicellular stage, liquid culture spheroids presented bleb-like protrusion on the surface, which was enhanced by the activation of the PI3K/Akt pathway and reduced by PI3K/Akt inhibitors. Liquid culture spheroids exhibited slow proliferation speed and low endogenous pAkt levels compared with gel-cultured spheroids and 2D-cultured cells, explaining the susceptibility to the Akt-inhibiting anticancer drug. Subcutaneous xenografting and in vitro analysis demonstrated low viability and adhesive property of liquid culture spheroids to ECM, while migratory and invasive capacities were comparable with gel-cultured spheroids. These features agree with the low efficacy of circulating tumour spheroids in the settling step of metastasis. This study demonstrates the feature of anchorage-independent spheroids and validates liquid cultures as a useful method in cancer spheroid research.

An interplay of resource availability, population size and mutation rate potentiates the evolution of metabolic signaling

Background

Asexually reproducing populations of single cells evolve through mutation, natural selection, and genetic drift. Environmental conditions in which the evolution takes place define the emergent fitness landscapes. In this work, we used Avida—a digital evolution framework—to uncover a hitherto unexplored interaction between mutation rates, population size, and the relative abundance of metabolizable resources, and its effect on evolutionary outcomes in small populations of digital organisms.

Results

Over each simulation, the population evolved to one of several states, each associated with a single dominant phenotype with its associated fitness and genotype. For a low mutation rate, acquisition of fitness by organisms was accompanied with, and dependent on, an increase in rate of genomic replication. At an increased mutation rate, phenotypes with high fitness values were similarly achieved through enhanced genome replication rates. In addition, we also observed the frequent emergence of suboptimal fitness phenotype, wherein neighboring organisms signaled to each other information relevant to performing metabolic tasks. This metabolic signaling was vital to fitness acquisition and was correlated with greater genotypic and phenotypic heterogeneity in the population. The frequency of appearance of signaling populations increased with population size and with resource abundance.

Conclusions

Our results reveal a minimal set of environment–genotype interactions that lead to the emergence of metabolic signaling within evolving populations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12862-021-01782-0.

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

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

MicroRNAs Regulate Metabolic Phenotypes During Multicellular Tumor Spheroids Progression

During tumor progression, cancer cells rewire their metabolism to face their bioenergetic demands. In recent years, microRNAs (miRNAs) have emerged as regulatory elements that inhibit the translation and stability of crucial mRNAs, some of them causing direct metabolic alterations in cancer. In this study, we investigated the relationship between miRNAs and their targets mRNAs that control metabolism, and how this fine-tuned regulation is diversified depending on the tumor stage. To do so, we implemented a paired analysis of RNA-seq and small RNA-seq in a breast cancer cell line (MCF7). The cell line was cultured in multicellular tumor spheroid (MCTS) and monoculture conditions. For MCTS, we selected two-time points during their development to recapitulate a proliferative and quiescent stage and contrast their miRNA and mRNA expression patterns associated with metabolism. As a result, we identified a set of new direct putative regulatory interactions between miRNAs and metabolic mRNAs representative for proliferative and quiescent stages. Notably, our study allows us to suggest that miR-3143 regulates the carbon metabolism by targeting hexokinase-2. Also, we found that the overexpression of several miRNAs could directly overturn the expression of mRNAs that control glycerophospholipid and N-Glycan metabolism. While this set of miRNAs downregulates their expression in the quiescent stage, the same set is upregulated in proliferative stages. This last finding suggests an additional metabolic switch of the above mentioned metabolic pathways between the quiescent and proliferative stages. Our results contribute to a better understanding of how miRNAs modulate the metabolic landscape in breast cancer MCTS, which eventually will help to design new strategies to mitigate cancer phenotype.