A Unique Morphological Phenotype in Chemoresistant Triple-Negative Breast Cancer Reveals Metabolic Reprogramming and PLIN4 Expression as a Molecular Vulnerability

Lipid droplets provide metabolic flexibility for cancer progression

A hallmark of cancer cells is their remarkable ability to efficiently adapt to favorable and hostile environments. Due to a unique metabolic flexibility, tumor cells can grow even in the absence of extracellular nutrients or in stressful scenarios. To achieve this, cancer cells need large amounts of lipids to build membranes, synthesize lipid-derived molecules, and generate metabolic energy in the absence of other nutrients. Tumor cells potentiate strategies to obtain lipids from other cells, metabolic pathways to synthesize new lipids, and mechanisms for efficient storage, mobilization, and utilization of these lipids. Lipid droplets (LDs) are the organelles that collect and supply lipids in eukaryotes and it is increasingly recognized that the accumulation of LDs is a new hallmark of cancer cells. Furthermore, an active role of LD proteins in processes underlying tumorigenesis has been proposed. Here, by focusing on three major classes of LD-resident proteins (perilipins, lipases, and acyl-CoA synthetases), we provide an overview of the contribution of LDs to cancer progression and discuss the role of LD proteins during the proliferation, invasion, metastasis, apoptosis, and stemness of cancer cells.

Cancer radioresistance is characterized by a differential lipid droplet content along the cell cycle

BACKGROUND

Cancer radiation treatments have seen substantial advancements, yet the biomolecular mechanisms underlying cancer cell radioresistance continue to elude full understanding. The effectiveness of radiation on cancer is hindered by various factors, such as oxygen concentrations within tumors, cells' ability to repair DNA damage and metabolic changes. Moreover, the initial and radiation-induced cell cycle profiles can significantly influence radiotherapy responses as radiation sensitivity fluctuates across different cell cycle stages. Given this evidence and our prior studies establishing a correlation between cancer radiation resistance and an increased number of cytoplasmic Lipid Droplets (LDs), we investigated if LD accumulation was modulated along the cell cycle and if this correlated with differential radioresistance in lung and bladder cell lines.

RESULTS

Our findings identified the S phase as the most radioresistant cell cycle phase being characterized by an increase in LDs. Analysis of the expression of perilipin genes (a family of proteins involved in the LD structure and functions) throughout the cell cycle also uncovered a unique gene cell cycle pattern.

CONCLUSIONS

In summary, although these results require further molecular studies about the mechanisms of radioresistance, the findings presented here are the first evidence that LD accumulation could participate in cancer cells' ability to better survive X-Ray radiation when cells are in the S phase. LDs can represent new players in the radioresistance processes associated with cancer metabolism. This could open new therapeutic avenues in which the use of LD-interfering drugs might enhance cancer sensitivity to radiation.

Optical imaging reveals chemotherapy-induced metabolic reprogramming of residual disease and recurrence

Fewer than 20% of triple-negative breast cancer patients experience long-term responses to mainstay chemotherapy. Resistant tumor subpopulations use alternative metabolic pathways to escape therapy, survive, and eventually recur. Here, we show in vivo, longitudinal metabolic reprogramming in residual disease and recurrence of triple-negative breast cancer xenografts with varying sensitivities to the chemotherapeutic drug paclitaxel. Optical imaging coupled with metabolomics reported an increase in non-glucose-driven mitochondrial metabolism and an increase in intratumoral metabolic heterogeneity during regression and residual disease in resistant MDA-MB-231 tumors. Conversely, sensitive HCC-1806 tumors were primarily reliant on glucose uptake and minimal changes in metabolism or heterogeneity were observed over the tumors' therapeutic life cycles. Further, day-matched resistant HCC-1806 tumors revealed a higher reliance on mitochondrial metabolism and elevated metabolic heterogeneity compared to sensitive HCC-1806 tumors. Together, metabolic flexibility, increased reliance on mitochondrial metabolism, and increased metabolic heterogeneity are defining characteristics of persistent residual disease, features that will inform the appropriate type and timing of therapies.

Hypoxia induced cell dormancy of salivary adenoid cystic carcinoma through miR-922/DEC2 axis

BACKGROUND

Hypoxia has been shown to induce cancer cells to become dormant meanwhile these cells inclined to disseminate and eventually cause metastasis. However, the molecular mechanism is still elusive. The purpose is to explore whether dormancy-associated microRNAs (DmiRs) get involved in hypoxia-induced cell dormancy of salivary adenoid cystic carcinoma (SACC).

MATERIAL AND METHODS

This study performed multi-perspective investigation of the biological effects of miR-922/DEC2 on SACC based on clinical samples, 2D and 3D in vitro model and nude mice in vivo model, based on our previous study of overexpression of DEC2 inducing SACC cellular dormancy.

RESULTS

According to the existing microRNA array of SACC tissue, we found that miR-922 was upregulated in SACC tissue and was inversely correlated with DEC2, suggesting that miR-922 might participate in the activation of SACC cell dormancy as a DmiR. Then, we found miR-922 low SACC cells exhibited cell dormancy and a low level of fatty acid oxidation with propensity for lipid droplets accumulation through DEC2. Moreover, HIF1a downregulated the level of miR-922 to induce SACC cell dormancy. In addition, in xenografts of nude mice the inhibition of miR-922 attenuated the growth of primary tumor and the lung metastasis of SACC.

CONCLUSIONS

miR-922/DEC2 axis was necessary to hypoxia-induced cell dormancy and played an important role in the lipid metabolism reprogramming of SACC.

The lipid metabolism remodeling: A hurdle in breast cancer therapy

Lipids, as one of the three primary energy sources, provide energy for all cellular life activities. Lipids are also known to be involved in the formation of cell membranes and play an important role as signaling molecules in the intracellular and microenvironment. Tumor cells actively or passively remodel lipid metabolism, using the function of lipids in various important cellular life activities to evade therapeutic attack. Breast cancer has become the leading cause of cancer-related deaths in women, which is partly due to therapeutic resistance. It is necessary to fully elucidate the formation and mechanisms of chemoresistance to improve breast cancer patient survival rates. Altered lipid metabolism has been observed in breast cancer with therapeutic resistance, indicating that targeting lipid reprogramming is a promising anticancer strategy.

Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer

BACKGROUND

Pathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30-40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60-70% show residual disease (RD). The role of the tumor microenvironment in NAC response in patients with TNBC remains unclear. In this study, we developed a machine learning-based two-step pipeline to distinguish between various histological components in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of TNBC tissue biopsies and to identify histological features that can predict NAC response.

METHODS

H&E-stained WSIs of treatment-naïve biopsies from 85 patients (51 with pCR and 34 with RD) of the model development cohort and 79 patients (41 with pCR and 38 with RD) of the validation cohort were separated through a stratified eightfold cross-validation strategy for the first step and leave-one-out cross-validation strategy for the second step. A tile-level histology label prediction pipeline and four machine-learning classifiers were used to analyze 468,043 tiles of WSIs. The best-trained classifier used 55 texture features from each tile to produce a probability profile during testing. The predicted histology classes were used to generate a histology classification map of the spatial distributions of different tissue regions. A patient-level NAC response prediction pipeline was trained with features derived from paired histology classification maps. The top graph-based features capturing the relevant spatial information across the different histological classes were provided to the radial basis function kernel support vector machine (rbfSVM) classifier for NAC treatment response prediction.

RESULTS

The tile-level prediction pipeline achieved 86.72% accuracy for histology class classification, while the patient-level pipeline achieved 83.53% NAC response (pCR vs. RD) prediction accuracy of the model development cohort. The model was validated with an independent cohort with tile histology validation accuracy of 83.59% and NAC prediction accuracy of 81.01%. The histological class pairs with the strongest NAC response predictive ability were tumor and tumor tumor-infiltrating lymphocytes for pCR and microvessel density and polyploid giant cancer cells for RD.

CONCLUSION

Our machine learning pipeline can robustly identify clinically relevant histological classes that predict NAC response in TNBC patients and may help guide patient selection for NAC treatment.

The Mutational Spectrum of Pre- and Post-Neoadjuvant Chemotherapy Triple-Negative Breast Cancers

The response of triple-negative breast cancer (TNBC) patients to pre-operative (neoadjuvant chemotherapy) is a critical factor of their outcome. To determine the effects of chemotherapy on the tumor genome and to identify mutations associated with chemoresistance and sensitivity, we performed whole exome sequencing on pre/post-chemotherapy tumors and matched lymphocytes from 26 patients. We observed great inter-tumoral heterogeneity with no gene mutated recurrently in more than four tumors besides TP53. Although the degree of response to chemotherapy in residual tumors was associated with more subclonal changes during chemotherapy, there was minimal evolution between pre/post-tumors. Indeed, gene sets enriched for mutations in pre- and post-chemotherapy tumors were very similar and reflected genes involved in the biological process of neurogenesis. Somatically mutated genes present in chemosensitive tumors included COL1A2, PRMD15, APOBEC3B, PALB2 and histone protein encoding genes, while BRCA1, ATR, ARID1A, XRCC3 and genes encoding for tubulin-associated proteins were present in the chemoresistant tumors. We also found that the mutational spectrum of post-chemotherapy tumors was more reflective of matching metastatic tumor biopsies than pre-chemotherapy samples. These findings support a portrait of modest ongoing genomic instability with respect to single-nucleotide variants induced by or selected for by chemotherapy in TNBCs.

Metabolic Imaging as a Tool to Characterize Chemoresistance and Guide Therapy in Triple-Negative Breast Cancer (TNBC)

After an initial response to chemotherapy, tumor relapse is frequent. This event is reflective of both the spatiotemporal heterogeneities of the tumor microenvironment as well as the evolutionary propensity of cancer cell populations to adapt to variable conditions. Because the cause of this adaptation could be genetic or epigenetic, studying phenotypic properties such as tumor metabolism is useful as it reflects molecular, cellular, and tissue-level dynamics. In triple-negative breast cancer (TNBC), the characteristic metabolic phenotype is a highly fermentative state. However, during treatment, the spatial and temporal dynamics of the metabolic landscape are highly unstable, with surviving populations taking on a variety of metabolic states. Thus, longitudinally imaging tumor metabolism provides a promising approach to inform therapeutic strategies, and to monitor treatment responses to understand and mitigate recurrence. Here we summarize some examples of the metabolic plasticity reported in TNBC following chemotherapy and review the current metabolic imaging techniques available in monitoring chemotherapy responses clinically and preclinically. The ensemble of imaging technologies we describe has distinct attributes that make them uniquely suited for a particular length scale, biological model, and/or features that can be captured. We focus on TNBC to highlight the potential of each of these technological advances in understanding evolution-based therapeutic resistance.

Cytoplasmic SIRT1 promotes paclitaxel resistance in ovarian carcinoma through increased formation and survival of polyploid giant cancer cells

Therapeutic resistance is a notable cause of death in patients with ovarian carcinoma. Polyploid giant cancer cells (PGCCs), commonly arising in tumor tissues following chemotherapy, have recently been considered to contribute to drug resistance. As a type III deacetylase, Sirtuin1 (SIRT1) plays essential roles in the cell cycle, cellular senescence, and drug resistance. Accumulating evidence has suggested that alteration in its subcellular localization via nucleocytoplasmic shuttling is a critical process influencing the functions of SIRT1. However, the roles of SIRT1 subcellular localization in PGCC formation and subsequent senescence escape remain unclear. In this study, we compared the differences in the polyploid cell population and senescence state of PGCCs following paclitaxel treatment between tumor cells overexpressing wild-type SIRT1 (WT SIRT1) and those expressing nuclear localization sequence (NLS)-mutated SIRT1 (SIRT1NLSmt ). We investigated the involvement of cytoplasmic SIRT1 in biological processes and signaling pathways, including the cell cycle and cellular senescence, in ovarian carcinoma cells' response to paclitaxel treatment. We found that the SIRT1NLSmt tumor cell population contained more polyploid cells and fewer senescent PGCCs than the SIRT1-overexpressing tumor cell population. Comparative proteomic analyses using co-immunoprecipitation (Co-IP) combined with liquid chromatography-mass spectrometry (LC-MS)/MS showed the differences in the differentially expressed proteins related to PGCC formation, cell growth, and death, including CDK1 and CDK2, between SIRT1NLSmt and SIRT1 cells or PGCCs. Our results suggested that ovarian carcinoma cells utilize polyploidy formation as a survival mechanism during exposure to paclitaxel-based treatment via the effect of cytoplasmic SIRT1 on PGCC formation and survival, thereby boosting paclitaxel resistance. © 2023 The Pathological Society of Great Britain and Ireland.

Rewiring of mitochondrial metabolism in therapy-resistant cancers: permanent and plastic adaptations

Deregulation of tumor cell metabolism is widely recognized as a "hallmark of cancer." Many of the selective pressures encountered by tumor cells, such as exposure to anticancer therapies, navigation of the metastatic cascade, and communication with the tumor microenvironment, can elicit further rewiring of tumor cell metabolism. Furthermore, phenotypic plasticity has been recently appreciated as an emerging "hallmark of cancer." Mitochondria are dynamic organelles and central hubs of metabolism whose roles in cancers have been a major focus of numerous studies. Importantly, therapeutic approaches targeting mitochondria are being developed. Interestingly, both plastic (i.e., reversible) and permanent (i.e., stable) metabolic adaptations have been observed following exposure to anticancer therapeutics. Understanding the plastic or permanent nature of these mechanisms is of crucial importance for devising the initiation, duration, and sequential nature of metabolism-targeting therapies. In this review, we compare permanent and plastic mitochondrial mechanisms driving therapy resistance. We also discuss experimental models of therapy-induced metabolic adaptation, therapeutic implications for targeting permanent and plastic metabolic states, and clinical implications of metabolic adaptations. While the plasticity of metabolic adaptations can make effective therapeutic treatment challenging, understanding the mechanisms behind these plastic phenotypes may lead to promising clinical interventions that will ultimately lead to better overall care for cancer patients.

Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer

Background

Pathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30-40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60-70% show residual disease (RD). The role of the tumor microenvironment (TME) in NAC response in patients with TNBC remains unclear. In this study, we developed a machine learning-based two-step pipeline to distinguish between various histological components in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of TNBC tissue biopsies and to identify histological features that can predict NAC response.

Methods

H&E-stained WSIs of treatment-naïve biopsies from 85 patients (51 with pCR and 34 with RD) were separated through a stratified 8-fold cross validation strategy for the first step and leave one out cross validation strategy for the second step. A tile-level histology label prediction pipeline and four machine learning classifiers were used to analyze 468,043 tiles of WSIs. The best-trained classifier used 55 texture features from each tile to produce a probability profile during testing. The predicted histology classes were used to generate a histology classification map of the spatial distributions of different tissue regions. A patient-level NAC response prediction pipeline was trained with features derived from paired histology classification maps. The top graph-based features capturing the relevant spatial information across the different histological classes were provided to the radial basis function kernel support vector machine (rbfSVM) classifier for NAC treatment response prediction.

Results

The tile-level prediction pipeline achieved 86.72% accuracy for histology class classification, while the patient-level pipeline achieved 83.53% NAC response (pCR vs. RD) prediction accuracy. The histological class pairs with the strongest NAC response predictive ability were tumor and tumor tumor-infiltrating lymphocytes for pCR and microvessel density and polyploid giant cancer cells for RD.

Conclusion

Our machine learning pipeline can robustly identify clinically relevant histological classes that predict NAC response in TNBC patients and may help guide patient selection for NAC treatment.

Challenges in Pharmacological Intervention in Perilipins (PLINs) to Modulate Lipid Droplet Dynamics in Obesity and Cancer

Perilipins (PLINs) are the most abundant proteins in lipid droplets (LD). These LD-associated proteins are responsible for upgrading LD from inert lipid storage structures to fully functional organelles, fundamentally integrated in the lipid metabolism. There are five distinct perilipins (PLIN1-5), each with specific expression patterns and metabolic activation, but all capable of regulating the activity of lipases on LD. This plurality creates a complex orchestrated mechanism that is directly related to the healthy balance between lipogenesis and lipolysis. Given the essential role of PLINs in the modulation of the lipid metabolism, these proteins can become interesting targets for the treatment of lipid-associated diseases. Since reprogrammed lipid metabolism is a recognized cancer hallmark, and obesity is a known risk factor for cancer and other comorbidities, the modulation of PLINs could either improve existing treatments or create new opportunities for the treatment of these diseases. Even though PLINs have not been, so far, directly considered for pharmacological interventions, there are many established drugs that can modulate PLINs activity. Therefore, the aim of this study is to assess the involvement of PLINs in diseases related to lipid metabolism dysregulation and whether PLINs can be viewed as potential therapeutic targets for cancer and obesity.

Targeting polyploid giant cancer cells potentiates a therapeutic response and overcomes resistance to PARP inhibitors in ovarian cancer

To understand the mechanism of acquired resistance to poly(ADP-ribose) polymerase inhibitors (PARPi) olaparib, we induced the formation of polyploid giant cancer cells (PGCCs) in ovarian and breast cancer cell lines, high-grade serous cancer (HGSC)-derived organoids, and patient-derived xenografts (PDXs). Time-lapse tracking of ovarian cancer cells revealed that PGCCs primarily developed from endoreplication after exposure to sublethal concentrations of olaparib. PGCCs exhibited features of senescent cells but, after olaparib withdrawal, can escape senescence via restitutional multipolar endomitosis and other noncanonical modes of cell division to generate mitotically competent resistant daughter cells. The contraceptive drug mifepristone blocked PGCC formation and daughter cell formation. Mifepristone/olaparib combination therapy substantially reduced tumor growth in PDX models without previous olaparib exposure, while mifepristone alone decreased tumor growth in PDX models with acquired olaparib resistance. Thus, targeting PGCCs may represent a promising approach to potentiate the therapeutic response to PARPi and overcome PARPi-induced resistance.

Polyploid giant cancer cells, cytokines and cytomegalovirus in breast cancer progression

BACKGROUND

Breast cancer is the most common cancer among women. Accumulated evidence over the past decades indicates a very high prevalence of human cytomegalovirus (HCMV) in breast cancer. High-risk HCMV strains possess a direct oncogenic effect displayed by cellular stress, polyploid giant cancer cells (PGCCs) generation, stemness, and epithelial-to-mesenchymal transition (EMT) leading to cancer of aggressive phenotype. Breast cancer development and progression have been regulated by several cytokines where the latter can promote cancer cell survival, help in tumor immune evasion, and initiate the EMT process, thereby resulting in invasion, angiogenesis, and breast cancer metastasis. In the present study, we screened cytokines expression in cytomegalovirus-transformed HMECs (CTH cells) cultures infected with HCMV high-risk strains namely, HCMV-DB and BL, as well as breast cancer biopsies, and analyzed the association between cytokines production, PGCCs count, and HCMV presence in vitro and in vivo.

METHODS

In CTH cultures and breast cancer biopsies, HCMV load was quantified by real-time qPCR. PGCCs count in CTH cultures and breast cancer biopsies was identified based on cell morphology and hematoxylin and eosin staining, respectively. CTH supernatants were evaluated for the production of TGF-β, IL-6, IL1-β, and IL-10 by ELISA assays. The above-mentioned cytokines expression was assessed in breast cancer biopsies using reverse transcription-qPCR. The correlation analyses were performed using Pearson correlation test.

RESULTS

The revealed PGCCs/cytokine profile in our in vitro CTH model matched that of the breast cancer biopsies, in vivo. Pronounced cytokine expression and PGCCs count were detected in particularly CTH-DB cultures and basal-like breast cancer biopsies.

CONCLUSIONS

The analysis of cytokine profiles in PGCCs present mostly in basal-like breast cancer biopsies and derived from CTH cells chronically infected with the high-risk HCMV strains might have the potential to provide novel therapies such as cytokine-based immunotherapy which is a promising field in cancer treatments.

Targeting HMG-CoA synthase 2 suppresses tamoxifen-resistant breast cancer growth by augmenting mitochondrial oxidative stress-mediated cell death

AIMS

In this study, we aimed to investigate previously unrecognized lipid metabolic perturbations in tamoxifen-resistant breast cancer (BC) by conducting comprehensive metabolomics and transcriptomics analysis. We identified the role of 3-hydroxy-3-methylglutary-coenzyme-A-synthase 2 (HMGCS2), a key enzyme responsible for ketogenesis, in tamoxifen-resistant BC growth.

MAIN METHODS

Comprehensive metabolomics (CE-TOFMS, LC-TOFMS) and transcriptiomics analysis were performed to characterize metabolic pathways in tamoxifen-resistant BC cells. The upregulation of HMGCS2 were verified thorugh immunohistochemistry (IHC) in clinical samples obtained from patients with recurrent BC. HMGCS2 inhibitor was discovered through surface plasmon resonance analysis, enzyme assay, and additional molecular docking studies. The effect of HMGCS2 suppression on tumor growth was studied thorugh BC xenograft model, and intratumoral lipid metabolites were analyzed via MALDI-TOFMS imaging.

KEY FINDINGS

We revealed that the level of HMGCS2 was highly elevated in both tamoxifen-resistant T47D sublines (T47D/TR) and clinical refractory tumor specimens from patients with ER+ breast cancer, who had been treated with adjuvant tamoxifen. Suppression of HMGCS2 in T47D/TR resulted in the accumulation of mitochondrial reactive oxygen species (mtROS) and apoptotic cell death. Further, we identified alphitolic acid, a triterpenoid natural product, as a novel HMGCS2-specific inhibitor that elevated mtROS levels and drastically retarded the growth of T47D/TR in in vitro and in vivo experiments.

SIGNIFICANCE

Enhanced ketogenesis with upregulation of HMGCS2 is a potential metabolic vulnerability of tamoxifen-resistant BC that offers a new therapeutic opportunity for treating patients with ER+ BC that are refractory to tamoxifen treatment.

Convolutional neural network for biomarker discovery for triple negative breast cancer with RNA sequencing data

Triple negative breast cancers (TNBCs) are tumors with a poor treatment response and prognosis. In this study, we propose a new approach, candidate extraction from convolutional neural network (CNN) elements (CECE), for discovery of biomarkers for TNBCs. We used the GSE96058 and GSE81538 datasets to build a CNN model to classify TNBCs and non-TNBCs and used the model to make TNBC predictions for two additional datasets, the cancer genome atlas (TCGA) breast cancer RNA sequencing data and the data from Fudan University Shanghai Cancer Center (FUSCC). Using correctly predicted TNBCs from the GSE96058 and TCGA datasets, we calculated saliency maps for these subjects and extracted the genes that the CNN model used to separate TNBCs from non-TNBCs. Among the TNBC signature patterns that the CNN models learned from the training data, we found a set of 21 genes that can classify TNBCs into two major classes, or CECE subtypes, with distinct overall survival rates (P = 0.0074). We replicated this subtype classification in the FUSCC dataset using the same 21 genes, and the two subtypes had similar differential overall survival rates (P = 0.0490). When all TNBCs were combined from the 3 datasets, the CECE II subtype had a hazard ratio of 1.94 (95% CI, 1.25-3.01; P = 0.0032). The results demonstrate that the spatial patterns learned by the CNN models can be utilized to discover interacting biomarkers otherwise unlikely to be identified by traditional approaches.

Involvement of cell shape and lipid metabolism in glioblastoma resistance to temozolomide

Temozolomide (TMZ) has been used as standard-of-care for glioblastoma multiforme (GBM), but the resistance to TMZ develops quickly and frequently. Thus, more studies are needed to elucidate the resistance mechanisms. In the current study, we investigated the relationship among the three important phenotypes, namely TMZ-resistance, cell shape and lipid metabolism, in GBM cells. We first observed the distinct difference in cell shapes between TMZ-sensitive (U87) and resistant (U87R) GBM cells. We then conducted NMR-based lipid metabolomics, which revealed a significant increase in cholesterol and fatty acid synthesis as well as lower lipid unsaturation in U87R cells. Consistent with the lipid changes, U87R cells exhibited significantly lower membrane fluidity. The transcriptomic analysis demonstrated that lipid synthesis pathways through SREBP were upregulated in U87R cells, which was confirmed at the protein level. Fatostatin, an SREBP inhibitor, and other lipid pathway inhibitors (C75, TOFA) exhibited similar or more potent inhibition on U87R cells compared to sensitive U87 cells. The lower lipid unsaturation ratio, membrane fluidity and higher fatostatin sensitivity were all recapitulated in patient-derived TMZ-resistant primary cells. The observed ternary relationship among cell shape, lipid composition, and TMZ-resistance may be applicable to other drug-resistance cases. SREBP and fatostatin are suggested as a promising target-therapeutic agent pair for drug-resistant glioblastoma.

Spatiotemporal view of malignant histogenesis and macroevolution via formation of polyploid giant cancer cells

To understand how malignant tumors develop, we tracked cell membrane, nuclear membrane, spindle, and cell cycle dynamics in polyploid giant cancer cells (PGCCs) during the formation of high-grade serous carcinoma organoids using long-term time-lapse imaging. Single cells underwent traditional mitosis to generate tissue with uniform nuclear size, while others formed PGCCs via asymmetric mitosis, endoreplication, multipolar endomitosis, nuclear fusion, and karyokinesis without cytokinesis. PGCCs underwent restitution multipolar endomitosis, nuclear fragmentation, and micronuclei formation to increase nuclear contents and heterogeneity. At the cellular level, the development of PGCCs was associated with forming transient intracellular cells, termed fecundity cells. The fecundity cells can be decellularized to facilitate nuclear fusion and synchronized with other nuclei for subsequent nuclear replication. PGCCs can undergo several rounds of entosis to form complex tissue structures, termed fecundity structures. The formation of PGCCs via multiple modes of nuclear replication in the absence of cytokinesis leads to an increase in the nuclear-to-cytoplasmic (N/C) ratio and intracellular cell reproduction, which is remarkably similar to the mode of nuclear division during pre-embryogenesis. Our data support that PGCCs may represent a central regulator in malignant histogenesis, intratumoral heterogeneity, immune escape, and macroevolution via the de-repression of suppressed pre-embryogenic program in somatic cells.

Zoledronic acid targets chemo-resistant polyploid giant cancer cells

Although polyploid giant cancer cells (PGCCs) are known as a key source of failure of current therapies, sufficient drugs to target these cells are not yet introduced. Considering the similarities of polyploid cells in regeneration and cancer, we hypothesized that zoledronic acid (ZA), an osteoclast-targeting agent, might be used to eliminate PGCCs. The 5637-bladder cancer cell line was treated with various doses of cisplatin to enrich polyploid cells and the efficacy of different concentrations of ZA in reducing this population was assessed. The metabolic profile of PGCCs was investigated with gas chromatography-mass spectrometry. Lipid profiles, mitochondrial density, and ROS content were also measured to assess the response of the cells to ZA. Cancer cells surviving after three days of exposure with 6 μM cisplatin were mainly polyploid. These cells demonstrated special morphological features such as fusion with diploid or other polyploid cells and originated in daughter cells through budding. ZA could substantially eradicate PGCCs with the maximal effect observed with 50 μM which resulted in the drop of PGCC fraction from 60 ± 7.5 to 19 ± 1.7%. Enriched PGCCs after cisplatin-treatment demonstrated a drastic metabolic shift compared to untreated cancer cells with an augmentation of lipids. Further assays confirmed the high content of lipid droplets and cholesterol in these cells which were reduced after ZA administration. Additionally, the mitochondrial density and ROS increased in PGCCs both of which declined in response to ZA. Taken together, we propose that ZA is a potent inhibitor of PGCCs which alters the metabolism of PGCCs. Although this drug has been successfully exploited as adjuvant therapy for some malignancies, the current evidence on its effects on PGCCs justifies further trials to assess its potency for improving the success of current therapies for tackling tumor resistance and relapse.

Potentiation of novel porphyrin based photodynamic therapy against colon cancer with low dose doxorubicin and elucidating the molecular signalling pathways responsible for relapse

Photodynamic therapy (PDT) is a promising non-invasive treatment modality for cancer and can be potentiated by combination with chemotherapy. Here, we combined PDT of novel porphyrin-based photosensitizers with low dose doxorubicin (Dox) to get maximum outcome. Dox potentiated and showed synergism with PDT under in vitro conditions on CT26.WT cells. The current colon cancer treatment strategies assure partial or even complete tumour regression but loco-regional relapse or distant metastasis is the major cause of death despite combination therapy. The spared cells after the treatment contribute to relapse and it is important to study their behaviour in host environment. Hence, we developed relapse models for PDT, Dox and combination treatments by transplanting respectively treated equal number of live cells to mice (n = 5) for tumour formation. Most of the treated cells lost tumour forming ability, but some treatment resistant cells developed tumours in few mice. These tumours served as relapse models and Western blot analysis of tumour samples provided clinically relevant information to delineate resistance strategies of individual as well as combination therapies at molecular level. Our results showed that low dose Dox helped in increasing the tumour inhibiting effect of PDT in combination therapy, but still there are indeed possibilities of relapse at later stages due to chemoresistance and immune suppression that may occur post-treatment. We observed that the combination therapy may also lead to the development of multidrug resistant (MDR) phenotype during relapse. Thus, this study provided clinically relevant information to further strengthen and improve PDT-drug combination therapy in order to avoid relapse and to treat cancer more effectively.

Construction and validation of a novel Ferroptosis-related gene signature predictive model in rectal Cancer

Background

Rectal cancer (RC) is one of the most common malignant tumors. Ferroptosis is an iron-dependent form of cell death, which plays an important role in various cancers. However, the correlation between ferroptosis-related genes (FRGs) and prognosis in RC remains unclear.

Methods

Gene expression data from The Cancer Genome Atlas Rectum adenocarcinoma (TCGA-READ) and GSE87211 were downloaded. Clustering and functional enrichment were evaluated. A FRGs risk score was established based on the univariate Cox analysis and the Least absolute shrinkage and selection operator (LASSO) analysis. K-M analysis and ROC analysis were conducted to determine prognostic values. qRT-PCR was performed to validate levels of mRNA expression. Multivariate Cox analysis was used to build a prognostic prediction model based on the risk score.

Results

Based on FRGs, RC patients were grouped into two clusters. In the functional enrichment of differentially expressed genes between the two clusters, immune-related pathways dominated. A novel FRGs signature with 14 genes related to the overall survival (OS) of RC was established. qRT-PCR of the 14 genes identified TP63, ISCU, PLIN4, MAP3K5, OXSR, FANCD2 and ATM were overexpressed in RC tissue; HSPB1, MAPK1, ABCC1, PANX1, MAPK9 and ATG7 were underexpressed; TUBE1 had no difference. The high-risk group had a significantly lower OS than the low-risk group (P < 0.001), and ROC curve analysis confirmed the signature’s predictive capacity. Multivariate analysis demonstrated that the risk score and age were independent prognostic factors.

Conclusion

A novel FRGs model can be used to predict the prognosis in RC, as well as to guide individual treatment.

Identification of the hub genes related to adipose tissue metabolism of bovine

Due to the demand for high-quality animal protein, there has been consistent interest in how to obtain more high-quality beef. As well-known, the adipose content of beef has a close connection with the taste and quality of beef, and cattle with different energy or protein diet have corresponding effects on the lipid metabolism of beef. Thus, we performed weighted gene co-expression network analysis (WGCNA) with subcutaneous adipose genes from Norwegian red heifers fed different diets to identify hub genes regulating bovine lipid metabolism. For this purpose, the RNA sequencing data of subcutaneous adipose tissue of 12-month-old Norwegian red heifers (n = 48) with different energy or protein levels were selected from the GEO database, and 7,630 genes with the largest variation were selected for WGCNA analysis. Then, three modules were selected as hub genes candidate modules according to the correlation between modules and phenotypes, including pink, magenta and grey60 modules. GO and KEGG enrichment analysis showed that genes were related to metabolism, and participated in Rap, MAPK, AMPK, VEGF signaling pathways, and so forth. Combined gene interaction network analysis using Cytoscape software, eight hub genes of lipid metabolism were identified, including TIA1, LOC516108, SNAPC4, CPSF2, ZNF574, CLASRP, MED15 and U2AF2. Further, the expression levels of hub genes in the cattle tissue were also measured to verify the results, and we found hub genes in higher expression in muscle and adipose tissue in adult cattle. In summary, we predicted the key genes of lipid metabolism in the subcutaneous adipose tissue that were affected by the intake of various energy diets to find the hub genes that coordinate lipid metabolism, which provide a theoretical basis for regulating beef quality.

Genomic landscape of lymphatic malformations: a case series and response to the PI3Kα inhibitor alpelisib in an N-of-1 clinical trial

Background

Lymphatic malformations (LMs) often pose treatment challenges due to a large size or a critical location that could lead to disfigurement, and there are no standardized treatment approaches for either refractory or unresectable cases.

Methods

We examined the genomic landscape of a patient cohort of LMs (n = 30 cases) that underwent comprehensive genomic profiling using a large-panel next-generation sequencing assay. Immunohistochemical analyses were completed in parallel.

Results

These LMs had low mutational burden with hotspot PIK3CA mutations (n = 20) and NRAS (n = 5) mutations being most frequent, and mutually exclusive. All LM cases with Kaposi sarcoma-like (kaposiform) histology had NRAS mutations. One index patient presented with subacute abdominal pain and was diagnosed with a large retroperitoneal LM harboring a somatic PIK3CA gain-of-function mutation (H1047R). The patient achieved a rapid and durable radiologic complete response, as defined in RECIST1.1, to the PI3Kα inhibitor alpelisib within the context of a personalized N-of-1 clinical trial (NCT03941782). In translational correlative studies, canonical PI3Kα pathway activation was confirmed by immunohistochemistry and human LM-derived lymphatic endothelial cells carrying an allele with an activating mutation at the same locus were sensitive to alpelisib treatment in vitro, which was demonstrated by a concentration-dependent drop in measurable impedance, an assessment of cell status.

Conclusions

Our findings establish that LM patients with conventional or kaposiform histology have distinct, yet targetable, driver mutations.

Funding

R.P. and W.A. are supported by awards from the Levy-Longenbaugh Fund. S.G. is supported by awards from the Hugs for Brady Foundation. This work has been funded in part by the NCI Cancer Center Support Grants (CCSG; P30) to the University of Arizona Cancer Center (CA023074), the University of New Mexico Comprehensive Cancer Center (CA118100), and the Rutgers Cancer Institute of New Jersey (CA072720). B.K.M. was supported by National Science Foundation via Graduate Research Fellowship DGE-1143953.

Clinical trial number

NCT03941782.

Development and Validation of a Prognostic Classifier Based on Lipid Metabolism-Related Genes for Breast Cancer

Background

The changes of lipid metabolism have been implicated in the development of many tumors, but its role in breast invasive carcinoma (BRCA) remains to be fully established. Here, we attempted to ascertain the prognostic value of lipid metabolism-related genes in BRCA.

Methods

We obtained RNA expression data and clinical information for BRCA and normal samples from public databases and downloaded a lipid metabolism-related gene set. Ingenuity Pathway Analysis (IPA) was applied to identify the potential pathways and functions of Differentially Expressed Genes (DEGs) related to lipid metabolism. Subsequently, univariate and multivariate Cox regression analyses were utilized to construct the prognostic gene signature. Functional enrichment analysis of prognostic genes was achieved by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Kaplan-Meier analysis, Receiver Operating Characteristic (ROC) curves, clinical follow-up results were employed to assess the prognostic potency. Potential compounds targeting prognostic genes were screened by Connectivity Map (CMap) database and a prognostic gene-drug interaction network was constructed using Comparative Toxicogenomics Database (CTD). Furthermore, we separately validated the selected marker genes in BRCA samples and human breast cancer cell lines (MCF-7, MDA-MB-231).

Results

IPA and functional enrichment analysis demonstrated that the 162 lipid metabolism-related DEGs we obtained were involved in many lipid metabolism and BRCA pathological signatures. The prognostic classifier we constructed comprising SDC1 and SORBS1 can serve as an independent prognostic marker for BRCA. CMap filtered 37 potential compounds against prognostic genes, of which 16 compounds could target both two prognostic genes were identified by CTD. The functions of the two prognostic genes in breast cancer cells were verified by cell function experiments.

Conclusion

Within this study, we identified a novel prognostic classifier based on two lipid metabolism-related genes: SDC1 and SORBS1. This result highlighted a new perspective on the metabolic exploration of BRCA.

Cholesterol and Its Derivatives: Multifaceted Players in Breast Cancer Progression

Cholesterol is an essential lipid primarily synthesized in the liver through the mevalonate pathway. Besides being a precursor of steroid hormones, bile acid, and vitamin D, it is an essential structural component of cell membranes, is enriched in membrane lipid rafts, and plays a key role in intracellular signal transduction. The lipid homeostasis is finely regulated end appears to be impaired in several types of tumors, including breast cancer. In this review, we will analyse the multifaceted roles of cholesterol and its derivatives in breast cancer progression. As an example of the bivalent role of cholesterol in the cell membrane of cancer cells, on the one hand, it reduces membrane fluidity, which has been associated with a more aggressive tumor phenotype in terms of cell motility and migration, leading to metastasis formation. On the other hand, it makes the membrane less permeable to small water-soluble molecules that would otherwise freely cross, resulting in a loss of chemotherapeutics permeability. Regarding cholesterol derivatives, a lower vitamin D is associated with an increased risk of breast cancer, while steroid hormones, coupled with the overexpression of their receptors, play a crucial role in breast cancer progression. Despite the role of cholesterol and derivatives molecules in breast cancer development is still controversial, the use of cholesterol targeting drugs like statins and zoledronic acid appears as a challenging promising tool for breast cancer treatment.

Analysis of the genomic landscapes of Barbadian and Nigerian women with triple negative breast cancer

PURPOSE

Triple negative breast cancer (TNBC) is an aggressive breast cancer subtype that disproportionately affects women of African ancestry (WAA) and is often associated with poor survival. Although there is a high prevalence of TNBC across West Africa and in women of the African diaspora, there has been no comprehensive genomics study to investigate the mutational profile of ancestrally related women across the Caribbean and West Africa.

METHODS

This multisite cross-sectional study used 31 formalin-fixed paraffin-embedded (FFPE) samples from Barbadian and Nigerian TNBC participants. High-resolution whole exome sequencing (WES) was performed on the Barbadian and Nigerian TNBC samples to identify their mutational profiles and comparisons were made to African American, European American and Asian American sequencing data obtained from The Cancer Genome Atlas (TCGA). Whole exome sequencing was conducted on tumors with an average of 382 × coverage and 4335 × coverage for pooled germline non-tumor samples.

RESULTS

Variants detected at high frequency in our WAA cohorts were found in the following genes NBPF12, PLIN4, TP53 and BRCA1. In the TCGA TNBC cases, these genes had a lower mutation rate, except for TP53 (32% in our cohort; 63% in TCGA-African American; 67% in TCGA-European American; 63% in TCGA-Asian). For all altered genes, there were no differences in frequency of mutations between WAA TNBC groups including the TCGA-African American cohort. For copy number variants, high frequency alterations were observed in PIK3CA, TP53, FGFR2 and HIF1AN genes.

CONCLUSION

This study provides novel insights into the underlying genomic alterations in WAA TNBC samples and shines light on the importance of inclusion of under-represented populations in cancer genomics and biomarker studies.

Targeting tumor cell senescence and polyploidy as potential therapeutic strategies

Senescence is a unique state of growth arrest that develops in response to a plethora of cellular stresses, including replicative exhaustion, oxidative injury, and genotoxic insults. Senescence has been implicated in the pathogenesis of multiple aging-related pathologies, including cancer. In cancer, senescence plays a dual role, initially acting as a barrier against tumor progression by enforcing a durable growth arrest in premalignant cells, but potentially promoting malignant transformation in neighboring cells through the secretion of pro-tumorigenic drivers. Moreover, senescence is induced in tumor cells upon exposure to a wide variety of conventional and targeted anticancer drugs (termed Therapy-Induced Senescence-TIS), representing a critical contributing factor to therapeutic outcomes. As with replicative or oxidative senescence, TIS manifests as a complex phenotype of macromolecular damage, energetic dysregulation, and altered gene expression. Senescent cells are also frequently polyploid. In vitro studies have suggested that polyploidy may confer upon senescent tumor cells the ability to escape from growth arrest, thereby providing an additional avenue whereby tumor cells escape the lethality of anticancer treatment. Polyploidy in tumor cells is also associated with persistent energy production, chromatin remodeling, self-renewal, stemness and drug resistance - features that are also associated with escape from senescence and conversion to a more malignant phenotype. However, senescent cells are highly heterogenous and can present with variable phenotypes, where polyploidy is one component of a complex reversion process. Lastly, emerging efforts to pharmacologically target polyploid tumor cells might pave the way towards the identification of novel targets for the elimination of senescent tumor cells by the incorporation of senolytic agents into cancer therapeutic strategies.

DNA damage and metabolic mechanisms of cancer drug resistance

Cancer drug resistance is one of the main barriers to overcome to ensure durable treatment responses. While many pivotal advances have been made in first combination therapies, then targeted therapies, and now broadening out to immunomodulatory drugs or metabolic targeting compounds, drug resistance is still ultimately universally fatal. In this brief review, we will discuss different strategies that have been used to fight drug resistance from synthetic lethality to tumor microenvironment modulation, focusing on the DNA damage response and tumor metabolism both within tumor cells and their surrounding microenvironment. In this way, with a better understanding of both targetable mutations in combination with the metabolism, smarter drugs may be designed to combat cancer drug resistance.

A Spectroscopic Technique to Simultaneously Characterize Fatty Acid Uptake, Mitochondrial Activity, Vascularity, and Oxygen Saturation for Longitudinal Studies In Vivo

Aggressive breast cancer has been shown to shift its metabolism towards increased lipid catabolism as the primary carbon source for oxidative phosphorylation. In this study, we present a technique to longitudinally monitor lipid metabolism and oxidative phosphorylation in pre-clinical tumor models to investigate the metabolic changes with mammary tissue development and characterize metabolic differences between primary murine breast cancer and normal mammary tissue. We used optical spectroscopy to measure the signal of two simultaneously injected exogenous fluorescent metabolic reporters: TMRE (oxidative phosphorylation surrogate) and Bodipy FL C16 (lipid catabolism surrogate). We leverage an inverse Monte Carlo algorithm to correct for aberrations resulting from tissue optical properties and to extract vascular endpoints relevant to oxidative metabolism, specifically oxygen saturation (SO2) and hemoglobin concentration ([Hb]). We extensively validated our optical method to demonstrate that our two fluorescent metabolic endpoints can be measured without chemical or optical crosstalk and that dual measurements of both fluorophores in vivo faithfully recapitulate the measurements of each fluorophore independently. We then applied our method to track the metabolism of growing 4T1 and 67NR breast tumors and aging mammary tissue, all highly metabolic tissue types. Our results show the changes in metabolism as a function of mammary age and tumor growth, and these changes can be best distinguished through the combination of endpoints measured with our system. Clustering analysis incorporating both Bodipy FL C16 and TMRE endpoints combined with either SO2 or [Hb] proved to be the most effective in minimizing intra-group variance and maximizing inter-group differences. Our platform can be extended to applications in which long-term metabolic flexibility is important to study, for example in tumor regression, recurrence following dormancy, and responses to cancer treatment.

Tumor Glucose and Fatty Acid Metabolism in the Context of Anthracycline and Taxane-Based (Neo)Adjuvant Chemotherapy in Breast Carcinomas

Breast cancer is characterized by considerable metabolic diversity. A relatively high percentage of patients diagnosed with breast carcinoma do not respond to standard-of-care treatment, and alteration in metabolic pathways nowadays is considered one of the major mechanisms responsible for therapeutic resistance. Consequently, there is an emerging need to understand how metabolism shapes therapy response, therapy resistance and not ultimately to analyze the metabolic changes occurring after different treatment regimens. The most commonly applied neoadjuvant chemotherapy regimens in breast cancer contain an anthracycline (doxorubicin or epirubicin) in combination or sequentially administered with taxanes (paclitaxel or docetaxel). Despite several efforts, drug resistance is still frequent in many types of breast cancer, decreasing patients’ survival. Understanding how tumor cells rapidly rewire their signaling pathways to persist after neoadjuvant cancer treatment have to be analyzed in detail and in a more complex system to enable scientists to design novel treatment strategies that target different aspects of tumor cells and tumor resistance. Tumor heterogeneity, the rapidly changing environmental context, differences in nutrient use among different cell types, the cooperative or competitive relationships between cells pose additional challenges in profound analyzes of metabolic changes in different breast carcinoma subtypes and treatment protocols. Delineating the contribution of metabolic pathways to tumor differentiation, progression, and resistance to different drugs is also the focus of research. The present review discusses the changes in glucose and fatty acid pathways associated with the most frequently applied chemotherapeutic drugs in breast cancer, as well the underlying molecular mechanisms and corresponding novel therapeutic strategies.

AMPK-mTOR-Mediated Activation of Autophagy Promotes Formation of Dormant Polyploid Giant Cancer Cells

Dormant cancer cells that survive anticancer therapy can lead to cancer recurrence and disseminated metastases that prove fatal in most cases. Recently, specific dormant polyploid giant cancer cells (PGCC) have drawn our attention because of their association with the clinical risk of nasopharyngeal carcinoma (NPC) recurrence, as demonstrated by previous clinical data. In this study, we report the biological properties of PGCC, including mitochondrial alterations, and reveal that autophagy is a critical mechanism of PGCC induction. Moreover, pharmacologic or genetic inhibition of autophagy greatly impaired PGCC formation, significantly suppressing metastasis and improving survival in a mouse model. Mechanistically, chemotherapeutic drugs partly damaged mitochondria, which then produced low ATP levels and activated autophagy via the AMPK-mTOR pathway to promote PGCC formation. Analysis of the transcriptional and epigenetic landscape of PGCC revealed overexpression of RIPK1, and the scaffolding function of RIPK1 was required for AMPK-mTOR pathway-induced PGCC survival. High numbers of PGCCs correlated with shorter recurrence time and worse survival outcomes in patients with NPC. Collectively, these findings suggest a therapeutic approach of targeting dormant PGCCs in cancer.

SIGNIFICANCE

Pretreatment with an autophagy inhibitor before chemotherapy could prevent formation of therapy-induced dormant polyploid giant cancer cells, thereby reducing recurrence and metastasis of nasopharyngeal carcinoma.

HSP90 inhibitors induce GPNMB cell-surface expression by modulating lysosomal positioning and sensitize breast cancer cells to glembatumumab vedotin

Transmembrane glycoprotein NMB (GPNMB) is a prognostic marker of poor outcome in patients with triple-negative breast cancer (TNBC). Glembatumumab Vedotin, an antibody drug conjugate targeting GPNMB, exhibits variable efficacy against GPNMB-positive metastatic TNBC as a single agent. We show that GPNMB levels increase in response to standard-of-care and experimental therapies for multiple breast cancer subtypes. While these therapeutic stressors induce GPNMB expression through differential engagement of the MiTF family of transcription factors, not all are capable of increasing GPNMB cell-surface localization required for Glembatumumab Vedotin inhibition. Using a FACS-based genetic screen, we discovered that suppression of heat shock protein 90 (HSP90) concomitantly increases GPNMB expression and cell-surface localization. Mechanistically, HSP90 inhibition resulted in lysosomal dispersion towards the cell periphery and fusion with the plasma membrane, which delivers GPNMB to the cell surface. Finally, treatment with HSP90 inhibitors sensitizes breast cancers to Glembatumumab Vedotin in vivo, suggesting that combination of HSP90 inhibitors and Glembatumumab Vedotin may be a viable treatment strategy for patients with metastatic TNBC.

Analysis of Non-Relapsed and Relapsed Adult Type Granulosa Cell Tumors Suggests Stable Transcriptomes during Tumor Progression

Adult-type granulosa cell tumor (AGCT) is a rare ovarian malignancy characterized by slow growth and hormonal activity. The prognosis of AGCT is generally favorable, but one-third of patients with low-stage disease experience a late relapse, and over half of them die of AGCT. To identify markers that would distinguish patients at risk for relapse, we performed Lexogen QuantSeq 3′ mRNA sequencing on formalin-fixed paraffin-embedded, archival AGCT tissue samples tested positive for the pathognomonic Forkhead Box L2 (FOXL2) mutation. We compared the transcriptomic profiles of 14 non-relapsed archival primary AGCTs (follow-up time 17–26 years after diagnosis) with 13 relapsed primary AGCTs (follow-up time 1.7–18 years) and eight relapsed tumors (follow-up time 2.8–18.9 years). Non-relapsed and relapsed primary AGCTs had similar transcriptomic profiles. In relapsed tumors three genes were differentially expressed: plasmalemma vesicle associated protein (PLVAP) was upregulated (p = 0.01), whereas argininosuccinate synthase 1 (ASS1) (p = 0.01) and perilipin 4 (PLIN4) (p = 0.02) were downregulated. PLVAP upregulation was validated using tissue microarray RNA in situ hybridization. In our patient cohort with extremely long follow-up, we observed similar gene expression patterns in both primary AGCT groups, suggesting that relapse is not driven by transcriptomic changes. These results reinforce earlier findings that molecular markers do not predict AGCT behavior or risk of relapse.

Impact of cancer metabolism on therapy resistance - Clinical implications

Despite an increasing arsenal of anticancer therapies, many patients continue to have poor outcomes due to the therapeutic failures and tumor relapses. Indeed, the clinical efficacy of anticancer therapies is markedly limited by intrinsic and/or acquired resistance mechanisms that can occur in any tumor type and with any treatment. Thus, there is an urgent clinical need to implement fundamental changes in the tumor treatment paradigm by the development of new experimental strategies that can help to predict the occurrence of clinical drug resistance and to identify alternative therapeutic options. Apart from mutation-driven resistance mechanisms, tumor microenvironment (TME) conditions generate an intratumoral phenotypic heterogeneity that supports disease progression and dismal outcomes. Tumor cell metabolism is a prototypical example of dynamic, heterogeneous, and adaptive phenotypic trait, resulting from the combination of intrinsic [(epi)genetic changes, tissue of origin and differentiation dependency] and extrinsic (oxygen and nutrient availability, metabolic interactions within the TME) factors, enabling cancer cells to survive, metastasize and develop resistance to anticancer therapies. In this review, we summarize the current knowledge regarding metabolism-based mechanisms conferring adaptive resistance to chemo-, radio-and immunotherapies as well as targeted therapies. Furthermore, we report the role of TME-mediated intratumoral metabolic heterogeneity in therapy resistance and how adaptations in amino acid, glucose, and lipid metabolism support the growth of therapy-resistant cancers and/or cellular subpopulations. We also report the intricate interplay between tumor signaling and metabolic pathways in cancer cells and discuss how manipulating key metabolic enzymes and/or providing dietary changes may help to eradicate relapse-sustaining cancer cells. Finally, in the current era of personalized medicine, we describe the strategies that may be applied to implement metabolic profiling for tumor imaging, biomarker identification, selection of tailored treatments and monitoring therapy response during the clinical management of cancer patients.

Tumour fatty acid metabolism in the context of therapy resistance and obesity

Fatty acid metabolism is known to support tumorigenesis and disease progression as well as treatment resistance through enhanced lipid synthesis, storage and catabolism. More recently, the role of membrane fatty acid composition, for example, ratios of saturated, monounsaturated and polyunsaturated fatty acids, in promoting cell survival while limiting lipotoxicity and ferroptosis has been increasingly appreciated. Alongside these insights, it has become clear that tumour cells exhibit plasticity with respect to fatty acid metabolism, responding to extratumoural and systemic metabolic signals, such as obesity and cancer therapeutics, to promote the development of aggressive, treatment-resistant disease. Here, we describe cellular fatty acid metabolic changes that are connected to therapy resistance and contextualize obesity-associated changes in host fatty acid metabolism that likely influence the local tumour microenvironment to further modify cancer cell behaviour while simultaneously creating potential new vulnerabilities.

Oncogene-mediated metabolic gene signature predicts breast cancer outcome

Breast cancer remains the second most lethal cancer among women in the United States and triple-negative breast cancer is the most aggressive subtype with limited treatment options. Trop2, a cell membrane glycoprotein, is overexpressed in almost all epithelial cancers. In this study, we demonstrate that Trop2 is overexpressed in triple-negative breast cancer (TNBC), and downregulation of Trop2 delays TNBC cell and tumor growth supporting the oncogenic role of Trop2 in breast cancer. Through proteomic profiling, we discovered a metabolic signature comprised of TALDO1, GPI, LDHA, SHMT2, and ADK proteins that were downregulated in Trop2-depleted breast cancer tumors. The identified oncogene-mediated metabolic gene signature is significantly upregulated in TNBC patients across multiple RNA-expression clinical datasets. Our study further reveals that the metabolic gene signature reliably predicts poor survival of breast cancer patients with early stages of the disease. Taken together, our study identified a new five-gene metabolic signature as an accurate predictor of breast cancer outcome.

The life cycle of polyploid giant cancer cells and dormancy in cancer: Opportunities for novel therapeutic interventions

Recent data suggest that most genotoxic agents in cancer therapy can lead to shock of genome and increase in cell size, which leads whole genome duplication or multiplication, formation of polyploid giant cancer cells, activation of an early embryonic program, and dedifferentiation of somatic cells. This process is achieved via the giant cell life cycle, a recently proposed mechanism for malignant transformation of somatic cells. Increase in both cell size and ploidy allows cells to completely or partially restructures the genome and develop into a blastocyst-like structure, similar to that observed in blastomere-stage embryogenesis. Although blastocyst-like structures with reprogrammed genome can generate resistant or metastatic daughter cells or benign cells of different lineages, they also acquired ability to undergo embryonic diapause, a reversible state of suspended embryonic development in which cells enter dormancy for survival in response to environmental stress. Therapeutic agents can activate this evolutionarily conserved developmental program, and when cells awaken from embryonic diapause, this leads to recurrence or metastasis. Understanding of the key mechanisms that regulate the different stages of the giant cell life cycle offers new opportunities for therapeutic intervention.

Growth Inhibition of Triple-Negative Breast Cancer: The Role of Spatiotemporal Delivery of Neoadjuvant Doxorubicin and Cisplatin

Combinations of platinum-based compounds with doxorubicin in free and/or in liposomal form for improved safety are currently being evaluated in the neoadjuvant setting on patients with advanced triple-negative breast cancer (TNBC). However, TNBC may likely be driven by chemotherapy-resistant cells. Additionally, established TNBC tumors may also exhibit diffusion-limited transport, resulting in heterogeneous intratumoral delivery of the administered therapeutics; this limits therapeutic efficacy in vivo. We studied TNBC cells with variable chemosensitivities, in the absence (on monolayers) and presence (in 3D multicellular spheroids) of transport barriers; we compared the combined killing effect of free doxorubicin and free cisplatin to the killing effect (1) of conventional liposomal forms of the two chemotherapeutics, and (2) of tumor-responsive lipid nanoparticles (NP), specifically engineered to result in more uniform spatiotemporal microdistributions of the agents within solid tumors. This was enabled by the NP properties of interstitial release, cell binding/internalization, and/or adhesion to the tumors’ extracellular matrix. The synergistic cell kill by combinations of the agents (in all forms), compared to the killing effect of each agent alone, was validated on monolayers of cells. Especially for spheroids formed by cells exhibiting resistance to doxorubicin combination treatments with both agents in free and/or in tumor-responsive NP-forms were comparably effective; we not only observed greater inhibition of outgrowth compared to the single agent(s) but also compared to the conventional liposome forms of the combined agents. We correlated this finding to more uniform spatiotemporal microdistributions of agents by the tumor-responsive NP. Our study shows that combinations of NP with properties specifically optimized to improve the spatiotemporal uniformity of the delivery of their corresponding therapeutic cargo can improve treatment efficacy while keeping favorable safety profiles.

IL-6 promotes drug resistance through formation of polyploid giant cancer cells and stromal fibroblast reprogramming

To understand the role of polyploid giant cancer cells (PGCCs) in drug resistance and disease relapse, we examined the mRNA expression profile of PGCCs following treatment with paclitaxel in ovarian cancer cells. An acute activation of IL-6 dominated senescence-associated secretory phenotype lasted 2–3 weeks and declined during the termination phase of polyploidy. IL-6 activates embryonic stemness during the initiation of PGCCs and can reprogram normal fibroblasts into cancer-associated fibroblasts (CAFs) via increased collagen synthesis, activation of VEGF expression, and enrichment of CAFs and the GPR77 + /CD10 + fibroblast subpopulation. Blocking the IL-6 feedback loop with tocilizumab or apigenin prevented PGCC formation, attenuated embryonic stemness and the CAF phenotype, and inhibited tumor growth in a patient-derived xenograft high-grade serous ovarian carcinoma model. Thus, IL-6 derived by PGCCs is capable of reprogramming both cancer and stromal cells and contributes to the evolution and remodeling of cancer. Targeting IL-6 in PGCCs may represent a novel approach to combating drug resistance.

Lipid droplet evolution gives insight into polyaneuploid cancer cell lipid droplet functions

Lipid droplets (LDs) are found throughout all phyla across the tree of life. Originating as pure energy stores in the most basic organisms, LDs have evolved to fill various roles as regulators of lipid metabolism, signaling, and trafficking. LDs have been noted in cancer cells and have shown to increase tumor aggressiveness and chemotherapy resistance. A certain transitory state of cancer cell, the polyaneuploid cancer cell (PACC), appears to have higher LD levels than the cancer cell from which they are derived. PACCs are postulated to be the mediators of metastasis and resistance in many different cancers. Utilizing the evolutionarily conserved roles of LDs to protect from cellular lipotoxicity allows PACCs to survive otherwise lethal stressors. By better understanding how LDs have evolved throughout different phyla we will identify opportunities to target LDs in PACCs to increase therapeutic efficiency in cancer cells.

Stress-Induced Polyploid Giant Cancer Cells: Unique Way of Formation and Non-Negligible Characteristics

Polyploidy is a conserved mechanism in cell development and stress responses. Multiple stresses of treatment, including radiation and chemotherapy drugs, can induce the polyploidization of tumor cells. Through endoreplication or cell fusion, diploid tumor cells convert into giant tumor cells with single large nuclei or multiple small nucleuses. Some of the stress-induced colossal cells, which were previously thought to be senescent and have no ability to proliferate, can escape the fate of death by a special way. They can remain alive at least before producing progeny cells through asymmetric cell division, a depolyploidization way named neosis. Those large and danger cells are recognized as polyploid giant cancer cells (PGCCs). Such cells are under suspicion of being highly related to tumor recurrence and metastasis after treatment and can bring new targets for cancer therapy. However, differences in formation mechanisms between PGCCs and well-accepted polyploid cancer cells are largely unknown. In this review, the methods used in different studies to induce polyploid cells are summarized, and several mechanisms of polyploidization are demonstrated. Besides, we discuss some characteristics related to the poor prognosis caused by PGCCs in order to provide readers with a more comprehensive understanding of these huge cells.

Bidirectional Genetic Control of Phenotypic Heterogeneity and Its Implication for Cancer Drug Resistance

Negative genetic regulators of phenotypic heterogeneity, or phenotypic capacitors/stabilizers, elevate population average fitness by limiting deviation from the optimal phenotype and increase the efficacy of natural selection by enhancing the phenotypic differences among genotypes. Stabilizers can presumably be switched off to release phenotypic heterogeneity in the face of extreme or fluctuating environments to ensure population survival. This task could, however, also be achieved by positive genetic regulators of phenotypic heterogeneity, or "phenotypic diversifiers," as shown by recently reported evidence that a bacterial divisome factor enhances antibiotic resistance. We hypothesized that such active creation of phenotypic heterogeneity by diversifiers, which is functionally independent of stabilizers, is more common than previously recognized. Using morphological phenotypic data from 4,718 single-gene knockout strains of Saccharomyces cerevisiae, we systematically identified 324 stabilizers and 160 diversifiers and constructed a bipartite network between these genes and the morphological traits they control. Further analyses showed that, compared with stabilizers, diversifiers tended to be weaker and more promiscuous (regulating more traits) regulators targeting traits unrelated to fitness. Moreover, there is a general division of labor between stabilizers and diversifiers. Finally, by incorporating NCI-60 human cancer cell line anticancer drug screening data, we found that human one-to-one orthologs of yeast diversifiers/stabilizers likely regulate the anticancer drug resistance of human cancer cell lines, suggesting that these orthologs are potential targets for auxiliary treatments. Our study therefore highlights stabilizers and diversifiers as the genetic regulators for the bidirectional control of phenotypic heterogeneity as well as their distinct evolutionary roles and functional independence.

Polyaneuploid Cancer Cell Dormancy: Lessons From Evolutionary Phyla

Dormancy is a key survival strategy in many organisms across the tree of life. Organisms that utilize some type of dormancy (hibernation, aestivation, brumation, diapause, and quiescence) are able to survive in habitats that would otherwise be uninhabitable. Induction into dormant states is typically caused by environmental stress. While organisms are dormant, their physical activity is minimal, and their metabolic rates are severely depressed (hypometabolism). These metabolic reductions allow for the conservation and distribution of energy while conditions in the environment are poor. When conditions are more favorable, the organisms are then able to come out of dormancy and reengage in their environment. Polyaneuploid cancer cells (PACCs), proposed mediators of cancer metastasis and resistance, access evolutionary programs and employ dormancy as a survival mechanism in response to stress. Quiescence, the type of dormancy observed in PACCs, allows these cells the ability to survive stressful conditions (e.g., hypoxia in the microenvironment, transiting the bloodstream during metastasis, and exposure to chemotherapy) by downregulating and altering metabolic function, but then increasing metabolic activities again once stress has passed. We can gain insights regarding the mechanisms underlying PACC dormancy by looking to the evolution of dormancy in different organisms.

Ceramide Synthase 6 Maximizes p53 Function to Prevent Progeny Formation from Polyploid Giant Cancer Cells

Simple Summary

One mechanism that contributes to cancer recurrence is the ability of some malignant cells to temporarily halt cell division and accumulate multiple nuclei that are later released as progeny, which resume cell division. The release of progeny occurs via primitive cleavage and is highly dependent on the sphingolipid enzyme acid ceramidase but the role of sphingolipid metabolism in this process remains to be elucidated. This study highlights differences in sphingolipid metabolism between non-polyploid and polyploid cancer cells and shows that ceramide synthase 6, which preferentially generates C₁₆-ceramide maximizes the ability of the tumor suppressor p53 to inhibit progeny formation in polyploid cancer cells. These results offer an explanation as to why non-cancerous polyploid cells, which express wildtype p53, do not generate progeny and suggest that cancer cells with deregulated p53 function pose a higher risk of evading therapy especially if enzymes that generate C₁₆-ceramide are also dysregulated.

Abstract

Polyploid giant cancer cells (PGCC) constitute a transiently senescent subpopulation of cancer cells that arises in response to stress. PGCC are capable of generating progeny via a primitive, cleavage-like cell division that is dependent on the sphingolipid enzyme acid ceramidase (ASAH1). The goal of this study was to understand differences in sphingolipid metabolism between non-polyploid and polyploid cancer cells to gain an understanding of the ASAH1-dependence in the PGCC population. Steady-state and flux analysis of sphingolipids did not support our initial hypothesis that the ASAH1 product sphingosine is rapidly converted into the pro-survival lipid sphingosine-1-phosphate. Instead, our results suggest that ASAH1 activity is important for preventing the accumulation of long chain ceramides such as C₁₆-ceramide. We therefore determined how modulation of C₁₆-ceramide, either through CerS6 or p53, a known PGCC suppressor and enhancer of CerS6-derived C₁₆-ceramide, affected PGCC progeny formation. Co-expression of the CerS6 and p53 abrogated the ability of PGCC to form offspring, suggesting that the two genes form a positive feedback loop. CerS6 enhanced the effect of p53 by significantly increasing protein half-life. Our results support the idea that sphingolipid metabolism is of functional importance in PGCC and that targeting this signaling pathway has potential for clinical intervention.

Human cell polyploidization: The good and the evil

Therapeutic resistance represents a major cause of death for most lethal cancers. However, the underlying mechanisms of such resistance have remained unclear. The polyploid cells are due to an increase in DNA content, commonly associated with cell enlargement. In human, they play a variety of roles in physiology and pathologic conditions and perform the specialized functions during development, inflammation, and cancer. Recent work shows that cancer cells can be induced into polyploid giant cancer cells (PGCCs) that leads to reprogramming of surviving cancer cells to acquire resistance. In this article, we will review the polyploidy involved in development and inflammation, and the process of PGCCs formation and propagation that benefits to cell survival. We will discuss the potential opportunities in fighting resistant cancers. The increased knowledge of PGCCs will offer a completely new paradigm to explore the therapeutic intervention for lethal cancers.

Polyploid giant cancer cell characterization: New frontiers in predicting response to chemotherapy in breast cancer

Although polyploid cells were first described nearly two centuries ago, their ability to proliferate has only recently been demonstrated. It also becomes increasingly evident that a subset of tumor cells, polyploid giant cancer cells (PGCCs), play a critical role in the pathophysiology of breast cancer (BC), among other cancer types. In BC, PGCCs can arise in response to therapy-induced stress. Their progeny possess cancer stem cell (CSC) properties and can repopulate the tumor. By modulating the tumor microenvironment (TME), PGCCs promote BC progression, chemoresistance, metastasis, and relapse and ultimately impact the survival of BC patients. Given their pro- tumorigenic roles, PGCCs have been proposed to possess the ability to predict treatment response and patient prognosis in BC. Traditionally, DNA cytometry has been used to detect PGCCs.. The field will further derive benefit from the development of approaches to accurately detect PGCCs and their progeny using robust PGCC biomarkers. In this review, we present the current state of knowledge about the clinical relevance of PGCCs in BC. We also propose to use an artificial intelligence-assisted image analysis pipeline to identify PGCC and map their interactions with other TME components, thereby facilitating the clinical implementation of PGCCs as biomarkers to predict treatment response and survival outcomes in BC patients. Finally, we summarize efforts to therapeutically target PGCCs to prevent chemoresistance and improve clinical outcomes in patients with BC.

Proteomic analysis reveals rotator cuff injury caused by oxidative stress

Background and aims:

Rotator cuff tendinopathy is common and is related to pain and dysfunction. However, the pathological mechanism of rotator cuff injury and shoulder pain is unclear. Objective: to investigate the pathological mechanism of rotator cuff injury and shoulder pain, and screen out the marker proteins related to rotator cuff injury by proteomics.

Methods:

Subacromial synovium specimens were collected from patients undergoing shoulder arthroscopic surgery. The experimental group were patients with rotator cuff repair surgery, and the control group were patients with habitual dislocation of the shoulder joint. Pathological examination was performed, and then followed by non-labeled quantitative proteomic detection. Finally, from analysis of the biological information of the samples, specific proteins related to rotator cuff injury and shoulder pain were deduced by functional analysis of differential proteins.

Results:

All the patients in experimental groups were representative. A large number of adipocytes and inflammatory cells were found in the pathological sections of the experimental group; the proteomics analysis screen identified 80 proteins with significant differences, and the analysis of protein function revealed that S100A11 (p = 0.011), PLIN4 (p = 0.017), HYOU1 (p = 0.002) and CLIC1 (p = 0.007) were closely related to oxidative stress and chronic inflammation.

Conclusion:

Rotator cuff injury is closely related to oxidative stress and chronic inflammatory response, and the results suggest that the expression of S100A11, PLIN4, HYOU1 and CLIC1 in the synovium of rotator cuff injury provides a new marker for the study of its pathological mechanism.

Laboratory Models for Investigating Breast Cancer Therapy Resistance and Metastasis

While numerous therapies are highly efficacious in early-stage breast cancers and in particular subsets of breast cancers, therapeutic resistance and metastasis unfortunately arise in many patients. In many cases, tumors that are resistant to standard of care therapies, as well as tumors that have metastasized, are treatable but incurable with existing clinical strategies. Both therapy resistance and metastasis are multi-step processes during which tumor cells must overcome diverse environmental and selective hurdles. Mechanisms by which tumor cells achieve this are numerous and include acquisition of invasive and migratory capabilities, cell-intrinsic genetic and/or epigenetic adaptations, clonal selection, immune evasion, interactions with stromal cells, entering a state of dormancy or senescence, and maintaining self-renewal capacity. To overcome therapy resistance and metastasis in breast cancer, the ability to effectively model each of these mechanisms in the laboratory is essential. Herein we review historic and the current state-of-the-art laboratory model systems and experimental approaches used to investigate breast cancer metastasis and resistance to standard of care therapeutics. While each model system has inherent limitations, they have provided invaluable insights, many of which have translated into regimens undergoing clinical evaluation. We will discuss the limitations and advantages of a variety of model systems that have been used to investigate breast cancer metastasis and therapy resistance and outline potential strategies to improve experimental modeling to further our knowledge of these processes, which will be crucial for the continued development of effective breast cancer treatments.

The Metabolic Mechanisms of Breast Cancer Metastasis

Breast cancer is one of the most common malignancy among women worldwide. Metastasis is mainly responsible for treatment failure and is the cause of most breast cancer deaths. The role of metabolism in the progression and metastasis of breast cancer is gradually being emphasized. However, the regulatory mechanisms that conduce to cancer metastasis by metabolic reprogramming in breast cancer have not been expounded. Breast cancer cells exhibit different metabolic phenotypes depending on their molecular subtypes and metastatic sites. Both intrinsic factors, such as MYC amplification, PIK3CA, and TP53 mutations, and extrinsic factors, such as hypoxia, oxidative stress, and acidosis, contribute to different metabolic reprogramming phenotypes in metastatic breast cancers. Understanding the metabolic mechanisms underlying breast cancer metastasis will provide important clues to develop novel therapeutic approaches for treatment of metastatic breast cancer.