Targeting Metabolic Deregulation Landscapes in Breast Cancer Subtypes

Potential Drug Synergy Through the ERBB2 Pathway in HER2+ Breast Tumors

HER2-positive (HER2+) breast cancer is characterized by the overexpression of the ERBB2 (HER2) gene, which promotes aggressive tumor growth and poor prognosis. Targeting the ERBB2 pathway with single-agent therapies has shown limited efficacy due to resistance mechanisms and the complexity of gene interactions within the tumor microenvironment. This study aims to explore potential drug synergies by analyzing gene-drug interactions and combination therapies that target the ERBB2 pathway in HER2+ breast tumors. Using gene co-expression network analysis, we identified 23 metabolic pathways with significant cross-linking of gene interactions, including those involving EGFR tyrosine kinase inhibitors, PI3K, mTOR, and others. We visualized these interactions using Cytoscape to generate individual and combined drug-gene networks, focusing on frequently used drugs such as Erlotinib, Gefitinib, Lapatinib, and Cetuximab. Individual networks highlighted the direct effects of these drugs on their target genes and neighboring genes within the ERBB2 pathway. Combined drug networks, such as those for Cetuximab with Lapatinib, Cetuximab with Erlotinib, and Erlotinib with Lapatinib, revealed potential synergies that could enhance therapeutic efficacy by simultaneously influencing multiple genes and pathways. Our findings suggest that a network-based approach to analyzing drug combinations provides valuable insights into the molecular mechanisms of HER2+ breast cancer and offers promising strategies for overcoming drug resistance and improving treatment outcomes.

Single-Sample Networks Reveal Intra-Cytoband Co-Expression Hotspots in Breast Cancer Subtypes

Breast cancer is a heterogeneous disease comprising various subtypes with distinct molecular characteristics, clinical outcomes, and therapeutic responses. This heterogeneity evidences significant challenges for diagnosis, prognosis, and treatment. Traditional genomic co-expression network analyses often overlook individual-specific interactions critical for personalized medicine. In this study, we employed single-sample gene co-expression network analysis to investigate the structural and functional genomic alterations across breast cancer subtypes (Luminal A, Luminal B, Her2-enriched, and Basal-like) and compared them with normal breast tissue. We utilized RNA-Seq gene expression data to infer gene co-expression networks. The LIONESS algorithm allowed us to construct individual networks for each patient, capturing unique co-expression patterns. We focused on the top 10,000 gene interactions to ensure consistency and robustness in our analysis. Network metrics were calculated to characterize the topological properties of both aggregated and single-sample networks. Our findings reveal significant fragmentation in the co-expression networks of breast cancer subtypes, marked by a change from interchromosomal (TRANS) to intrachromosomal (CIS) interactions. This transition indicates disrupted long-range genomic communication, leading to localized genomic regulation and increased genomic instability. Single-sample analyses confirmed that these patterns are consistent at the individual level, highlighting the molecular heterogeneity of breast cancer. Despite these pronounced alterations, the proportion of CIS interactions did not significantly correlate with patient survival outcomes across subtypes, suggesting limited prognostic value. Furthermore, we identified high-degree genes and critical cytobands specific to each subtype, providing insights into subtype-specific regulatory networks and potential therapeutic targets. These genes play pivotal roles in oncogenic processes and may represent important keys for targeted interventions. The application of single-sample co-expression network analysis proves to be a powerful tool for uncovering individual-specific genomic interactions.

Cholesterol metabolism in pancreatic cancer and associated therapeutic strategies

Pancreatic cancer remains one of the most lethal cancers due to late diagnosis and high chemoresistance. Despite recent progression in the development of chemotherapies, immunotherapies, and potential nanoparticles-based approaches, the success rate of therapeutic response is limited which is further compounded by cancer drug resistance. Understanding of emerging biological and molecular pathways causative of pancreatic cancer's aggressive and chemoresistance is vital to improve the effectiveness of existing therapeutics and to develop new therapies. One such under-investigated and relatively less explored area of research is documenting the effect that lipids, specifically cholesterol, and its metabolism, impose on pancreatic cancer. Dysregulated cholesterol metabolism has a profound role in supporting cellular proliferation, survival, and promoting chemoresistance and this has been well established in various other cancers. Thus, we aimed to provide an in-depth review focusing on the significance of cholesterol metabolism in pancreatic cancer and relevant genes at play, molecular processes contributing to cellular cholesterol homeostasis, and current research efforts to develop new cholesterol-targeting therapeutics. We highlight the caveats, weigh in different experimental therapeutic strategies, and provide possible suggestions for future research highlighting cholesterol's importance as a therapeutic target against pancreatic cancer resistance and cancer progression.

GPR81-mediated reprogramming of glucose metabolism contributes to the immune landscape in breast cancer

BACKGROUND

Local tumor microenvironment (TME) plays a crucial role in immunotherapy for breast cancer (BC). Whereas, the molecular mechanism responsible for the crosstalk between BC cells and surrounding immune cells remains unclear. The present study aimed to determine the interplay between GPR81-mediated glucometabolic reprogramming of BC and the immune landscape in TME.

MATERIALS AND METHODS

Immunohistochemistry (IHC) assay was first performed to evaluate the association between GPR81 and the immune landscape. Then, several stable BC cell lines with down-regulated GPR81 expression were established to directly identify the role of GPR81 in glucometabolic reprogramming, and western blotting assay was used to detect the underlying molecular mechanism. Finally, a transwell co-culture system confirmed the crosstalk between glucometabolic regulation mediated by GPR81 in BC and induced immune attenuation.

RESULTS

IHC analysis demonstrated that the representation of infiltrating CD8⁺ T cells and FOXP3⁺ T cells were dramatically higher in BC with a triple negative (TN) subtype in comparison with that with a non-TN subtype (P < 0.001). Additionally, the ratio of infiltrating CD8⁺ to FOXP3⁺ T cells was significantly negatively associated with GPR81 expression in BC with a TN subtype (P < 0.001). Furthermore, GPR81 was found to be substantially correlated with the glycolytic capability (P < 0.001) of BC cells depending on a Hippo-YAP signaling pathway (P < 0.001). In the transwell co-culture system, GPR81-mediated reprogramming of glucose metabolism in BC significantly contributed to a decreased proportion of CD8⁺ T (P < 0.001) and an increased percentage of FOXP3⁺ T (P < 0.001) in the co-cultured lymphocytes.

CONCLUSION

Glucometabolic reprogramming through a GPR81-mediated Hippo-YAP signaling pathway was responsible for the distinct immune landscape in BC. GPR81 was a potential biomarker to stratify patients before immunotherapy to improve BC's clinical prospect.

Molecular mechanisms of multi-omic regulation in breast cancer

Breast cancer is a complex disease that is influenced by the concurrent influence of multiple genetic and environmental factors. Recent advances in genomics and other high throughput biomolecular techniques (-omics) have provided numerous insights into the molecular mechanisms underlying breast cancer development and progression. A number of these mechanisms involve multiple layers of regulation. In this review, we summarize the current knowledge on the role of multiple omics in the regulation of breast cancer, including the effects of DNA methylation, non-coding RNA, and other epigenomic changes. We comment on how integrating such diverse mechanisms is envisioned as key to a more comprehensive understanding of breast carcinogenesis and cancer biology with relevance to prognostics, diagnostics and therapeutics. We also discuss the potential clinical implications of these findings and highlight areas for future research. Overall, our understanding of the molecular mechanisms of multi-omic regulation in breast cancer is rapidly increasing and has the potential to inform the development of novel therapeutic approaches for this disease.

FASN multi-omic characterization reveals metabolic heterogeneity in pancreatic and prostate adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) and prostate cancer (PCa) are among the most prevalent malignant tumors worldwide. There is now a comprehensive understanding of metabolic reprogramming as a hallmark of cancer. Fatty acid synthase (FASN) is a key regulator of the lipid metabolic network, providing energy to favor tumor proliferation and development. Whereas the biological role of FASN is known, its response and sensitivity to inhibition have not yet been fully established in these two cancer settings.

METHODS

To evaluate the association between FASN expression, methylation, prognosis, and mutational profile in PDAC and PCa, we interrogated public databases and surveyed online platforms using TCGA data. The STRING database was used to investigate FASN interactors, and the Gene Set Enrichment Analysis platform Reactome database was used to perform an enrichment analysis using data from RNA sequencing public databases of PDAC and PCa. In vitro models using PDAC and PCa cell lines were used to corroborate the expression of FASN, as shown by Western blot, and the effects of FASN inhibition on cell proliferation/cell cycle progression and mitochondrial respiration were investigated with MTT, colony formation assay, cell cycle analysis and MitoStress Test.

RESULTS

The expression of FASN was not modulated in PDAC compared to normal pancreatic tissues, while it was overexpressed in PCa, which also displayed a different level of promoter methylation. Based on tumor grade, FASN expression decreased in advanced stages of PDAC, but increased in PCa. A low incidence of FASN mutations was found for both tumors. FASN was overexpressed in PCa, despite not reaching statistical significance, and was associated with a worse prognosis than in PDAC. The biological role of FASN interactors correlated with lipid metabolism, and GSEA indicated that lipid-mediated mitochondrial respiration was enriched in PCa. Following validation of FASN overexpression in PCa compared to PDAC in vitro, we tested TVB-2640 as a FASN inhibitor. PCa proliferation arrest was modulated by FASN inhibition in a dose- and time-dependent manner, whereas PDAC proliferation was not altered. In line with this finding, mitochondrial respiration was found to be more affected in PCa than in PDAC. FASN inhibition interfered with metabolic signaling causing lipid accumulation and affecting cell viability with an impact on the replicative processes.

CONCLUSIONS

FASN exhibited differential expression patterns in PDAC and PCa, suggesting a different evolution during cancer progression. This was corroborated by the fact that both tumors responded differently to FASN inhibition in terms of proliferative potential and mitochondrial respiration, indicating that its use should reflect context specificity.

The Role of Copy Number Variants in Gene Co-Expression Patterns for Luminal B Breast Tumors

Gene co-expression networks have become a usual approach to integrate the vast amounts of information coming from gene expression studies in cancer cohorts. The reprogramming of the gene regulatory control and the molecular pathways depending on such control are central to the characterization of the disease, aiming to unveil the consequences for cancer prognosis and therapeutics. There is, however, a multitude of factors which have been associated with anomalous control of gene expression in cancer. In the particular case of co-expression patterns, we have previously documented a phenomenon of loss of long distance co-expression in several cancer types, including breast cancer. Of the many potential factors that may contribute to this phenomenology, copy number variants (CNVs) have been often discussed. However, no systematic assessment of the role that CNVs may play in shaping gene co-expression patterns in breast cancer has been performed to date. For this reason we have decided to develop such analysis. In this study, we focus on using probabilistic modeling techniques to evaluate to what extent CNVs affect the phenomenon of long/short range co-expression in Luminal B breast tumors. We analyzed the co-expression patterns in chromosome 8, since it is known to be affected by amplifications/deletions during cancer development. We found that the CNVs pattern in chromosome 8 of Luminal B network does not alter the co-expression patterns significantly, which means that the co-expression program in this cancer phenotype is not determined by CNV structure. Additionally, we found that region 8q24.3 is highly dense in interactions, as well as region p21.3. The most connected genes in this network belong to those cytobands and are associated with several manifestations of cancer in different tissues. Interestingly, among the most connected genes, we found MAF1 and POLR3D, which may constitute an axis of regulation of gene transcription, in particular for non-coding RNA species. We believe that by advancing on our knowledge of the molecular mechanisms behind gene regulation in cancer, we will be better equipped, not only to understand tumor biology, but also to broaden the scope of diagnostic, prognostic and therapeutic interventions to ultimately benefit oncologic patients.

Expression of HK2, PKM2, and PFKM Is Associated with Metastasis and Late Disease Onset in Breast Cancer Patients

The reprogramming of energy metabolism is one of the hallmarks of cancer and is crucial for tumor progression. Altered aerobic glycolysis is a well-known characteristic of cancer cell metabolism. In the present study, the expression profiles of key metabolic genes (HK2, PFKM, and PKM2) were assessed in the breast cancer cohort of Pakistan using quantitative polymerase chain reaction (qPCR) and IHC. Expression patterns were correlated with molecular subtypes and clinical parameters in the patients. A significant upregulation of key glycolytic genes was observed in tumor samples in comparison to their adjacent controls (p < 0.0001). The expression of the studied glycolytic genes was significantly increased in late clinical stages, positive nodal involvement, and distant metastasis (p < 0.05). HK2 and PKM2 were found to be upregulated in luminal B, whereas PFKM was overexpressed in the luminal A subtype of breast cancer. The genes were positively correlated with the proliferation marker Ki67 (p < 0.001). Moreover, moderate positive linear correlations between HK2 and PKM2 (r = 0.476), HK2 and PFKM (r = 0.473), and PKM2 and PFKM (r = 0.501) were also observed (p < 0.01). These findings validate that the key regulatory genes in glycolysis can serve as potential biomarkers and/or molecular targets for breast cancer management. However, the clinical significance of these molecules needs to be further validated through in vitro and in vivo experiments.

Metabolic Flexibility Is a Determinant of Breast Cancer Heterogeneity and Progression

Breast cancer progression is characterized by changes in cellular metabolism that contribute to enhanced tumour growth and adaptation to microenvironmental stresses. Metabolic changes within breast tumours are still poorly understood and are not as yet exploited for therapeutic intervention, in part due to a high level of metabolic heterogeneity within tumours. The metabolic profiles of breast cancer cells are flexible, providing dynamic switches in metabolic states to accommodate nutrient and energy demands and further aggravating the challenges of targeting metabolic dependencies in cancer. In this review, we discuss the intrinsic and extrinsic factors that contribute to metabolic heterogeneity of breast tumours. Next, we examine how metabolic flexibility, which contributes to the metabolic heterogeneity of breast tumours, can alter epigenetic landscapes and increase a variety of pro-tumorigenic functions. Finally, we highlight the difficulties in pharmacologically targeting the metabolic adaptations of breast tumours and provide an overview of possible strategies to sensitize heterogeneous breast tumours to the targeting of metabolic vulnerabilities.

Multi-Omic Approaches to Breast Cancer Metabolic Phenotyping: Applications in Diagnosis, Prognosis, and the Development of Novel Treatments

Breast cancer (BC) is characterized by high disease heterogeneity and represents the most frequently diagnosed cancer among women worldwide. Complex and subtype-specific gene expression alterations participate in disease development and progression, with BC cells known to rewire their cellular metabolism to survive, proliferate, and invade. Hence, as an emerging cancer hallmark, metabolic reprogramming holds great promise for cancer diagnosis, prognosis, and treatment. Multi-omics approaches (the combined analysis of various types of omics data) offer opportunities to advance our understanding of the molecular changes underlying metabolic rewiring in complex diseases such as BC. Recent studies focusing on the combined analysis of genomics, epigenomics, transcriptomics, proteomics, and/or metabolomics in different BC subtypes have provided novel insights into the specificities of metabolic rewiring and the vulnerabilities that may guide therapeutic development and improve patient outcomes. This review summarizes the findings of multi-omics studies focused on the characterization of the specific metabolic phenotypes of BC and discusses how they may improve clinical BC diagnosis, subtyping, and treatment.

Integration of Metabolomics and Gene Expression Profiling Elucidates IL4I1 as Modulator of Ibrutinib Resistance in ABC-Diffuse Large B Cell Lymphoma

Simple Summary

In this study, we present a workflow to understand the modulator of ibrutinib resistance in ABC diffuse large B cell lymphoma by integrating Metabolomics and Gene expression profiling as shown in the graphical abstract. We performed an untargeted metabolomics analysis using a Q-Exactive high-resolution mass spectrometer to dissect the metabolic reprogramming associated with acquired ibrutinib resistance in paired ibrutinib-sensitive and ibrutinib-resistant DLBCL cell lines. Further, we identified common denominators, integrating metabolome and transcriptome data, confirming clinical significance, integrating pathways, and identifying the candidate gene driving ibrutinib resistance and metabolic reprogramming. Our work demonstrates that a multi-omics approach can be a robust and impartial strategy to uncover genes and pathways that cause metabolic deregulation in cancer cells.

Abstract

Diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma (NHL). B-cell NHLs rely on Bruton’s tyrosine kinase (BTK) mediated B-cell receptor signaling for survival and disease progression. However, they are often resistant to BTK inhibitors or soon acquire resistance after drug exposure resulting in the drug-tolerant form. The drug-tolerant clones proliferate faster, have increased metabolic activity, and shift to oxidative phosphorylation; however, how this metabolic programming occurs in the drug-resistant tumor is poorly understood. In this study, we explored for the first time the metabolic regulators of ibrutinib-resistant activated B-cell (ABC) DLBCL using a multi-omics analysis that integrated metabolomics (using high-resolution mass spectrometry) and transcriptomic (gene expression analysis). Overlay of the unbiased statistical analyses, genetic perturbation, and pharmaceutical inhibition was further used to identify the key players contributing to the metabolic reprogramming of the drug-resistant clone. Gene-metabolite integration revealed interleukin four induced 1 (IL4I1) at the crosstalk of two significantly altered metabolic pathways involved in producing various amino acids. We showed for the first time that drug-resistant clones undergo metabolic reprogramming towards oxidative phosphorylation and are modulated via the BTK-PI3K-AKT-IL4I1 axis. Our report shows how these cells become dependent on PI3K/AKT signaling for survival after acquiring ibrutinib resistance and shift to sustained oxidative phosphorylation; additionally, we outline the compensatory pathway that might regulate this metabolic reprogramming in the drug-resistant cells. These findings from our unbiased analyses highlight the role of metabolic reprogramming during drug resistance development. Our work demonstrates that a multi-omics approach can be a robust and impartial strategy to uncover genes and pathways that drive metabolic deregulation in cancer cells.

Luminal A Breast Cancer Co-expression Network: Structural and Functional Alterations

Luminal A is the most common breast cancer molecular subtype in women worldwide. These tumors have characteristic yet heterogeneous alterations at the genomic and transcriptomic level. Gene co-expression networks (GCNs) have contributed to better characterize the cancerous phenotype. We have previously shown an imbalance in the proportion of intra-chromosomal (cis-) over inter-chromosomal (trans-) interactions when comparing cancer and healthy tissue GCNs. In particular, for breast cancer molecular subtypes (Luminal A included), the majority of high co-expression interactions connect gene-pairs in the same chromosome, a phenomenon that we have called loss of trans- co-expression. Despite this phenomenon has been described, the functional implication of this specific network topology has not been studied yet. To understand the biological role that communities of co-expressed genes may have, we constructed GCNs for healthy and Luminal A phenotypes. Network modules were obtained based on their connectivity patterns and they were classified according to their chromosomal homophily (proportion of cis-/trans- interactions). A functional overrepresentation analysis was performed on communities in both networks to observe the significantly enriched processes for each community. We also investigated possible mechanisms for which the loss of trans- co-expression emerges in cancer GCN. To this end we evaluated transcription factor binding sites, CTCF binding sites, differential gene expression and copy number alterations (CNAs) in the cancer GCN. We found that trans- communities in Luminal A present more significantly enriched categories than cis- ones. Processes, such as angiogenesis, cell proliferation, or cell adhesion were found in trans- modules. The differential expression analysis showed that FOXM1, CENPA, and CIITA transcription factors, exert a major regulatory role on their communities by regulating expression of their target genes in other chromosomes. Finally, identification of CNAs, displayed a high enrichment of deletion peaks in cis- communities. With this approach, we demonstrate that network topology determine, to at certain extent, the function in Luminal A breast cancer network. Furthermore, several mechanisms seem to be acting together to avoid trans- co-expression. Since this phenomenon has been observed in other cancer tissues, a remaining question is whether the loss of long distance co-expression is a novel hallmark of cancer.

Loss of Long Distance Co-Expression in Lung Cancer

Lung cancer is one of the deadliest, most aggressive cancers. Abrupt changes in gene expression represent an important challenge to understand and fight the disease. Gene co-expression networks (GCNs) have been widely used to study the genomic regulatory landscape of human cancer. Here, based on 1,143 RNA-Seq experiments from the TCGA collaboration, we constructed GCN for the most common types of lung tumors: adenocarcinoma (TAD) and squamous cells (TSCs) as well as their respective control networks (NAD and NSC). We compared the number of intra-chromosome (cis-) and inter-chromosome (trans-) co-expression interactions in normal and cancer GCNs. We compared the number of shared interactions between TAD and TSC, as well as in NAD and NSC, to observe which phenotypes were more alike. By means of an over-representation analysis, we associated network topology features with biological functions. We found that TAD and TSC present mostly cis- small disconnected components, whereas in control GCNs, both types have a giant trans- component. In both cancer networks, we observed cis- components in which genes not only belong to the same chromosome but to the same cytoband or to neighboring cytobands. This supports the hypothesis that in lung cancer, gene co-expression is constrained to small neighboring regions. Despite this loss of distant co-expression observed in TAD and TSC, there are some remaining trans- clusters. These clusters seem to play relevant roles in the carcinogenic processes. For instance, some clusters in TAD and TSC are associated with the immune system, response to virus, or control of gene expression. Additionally, other non-enriched trans- clusters are composed of one gene and several associated pseudo-genes, as in the case of the FTH1 gene. The appearance of those common trans- clusters reflects that the gene co-expression program in lung cancer conserves some aspects for cell maintenance. Unexpectedly, 0.48% of the edges are shared between control networks; conversely, 35% is shared between lung cancer GCNs, a 73-fold larger intersection. This suggests that in lung cancer a process of de-differentiation may be occurring. To further investigate the implications of the loss of distant co-expression, it will become necessary to broaden the investigation with other omic-based approaches. However, the present approach provides a basis for future work toward an integrative perspective of abnormal transcriptional regulatory programs in lung cancer.

Gene Expression and Co-expression Networks Are Strongly Altered Through Stages in Clear Cell Renal Carcinoma

Clear cell renal carcinoma (ccRC) is a highly heterogeneous and progressively malignant disease. Analyzing ccRC progression in terms of modifications at the molecular and genetic level may help us to develop a broader understanding of its patho-physiology and may give us a glimpse toward improved therapeutics. In this work, by using TCGA data, we studied the molecular progression of the four main ccRC stages (i, ii, iii, iv) in two different yet complementary approaches: (a) gene expression and (b) gene co-expression. For (a) we analyzed the differential gene expression between each stage and the control non-cancer group. We compared the progression molecular signature between stages, and observed those genes that change their expression patterns through progression stages. For (b) we constructed and analyzed co-expression networks for the four ccRC progression stages, as well as for the control phenotype, to observe whether and how the co-expression landscape changes with progression. We separated genomic interactions into intra-chromosome (cis-) and inter-chromosome (trans-). Finally, we intersected those networks and performed functional enrichment analysis. All calculations were made over different network sizes, from the top 100 edges to top 1,000,000. We show that differential expression is quite similar between ccRC progression stages. However, interestingly, two genes, namely SLC6A19 and PLG show a significant progressive decrease in their expression according to ccRC stage, meanwhile two other genes, SAA2-SAA4 and CXCL13 show progressive increase. Despite the high similarity between gene expression profiles, all networks are substantially different between them in terms of their topological features. Control network has a larger proportion of trans- interactions, meanwhile for any stage, the amount of cis- interactions is higher, independent of the network cut-off. The majority of interactions in any network are phenotype-specific. Only 189 interactions are shared between the five networks, and 533 edges are ccRC-specific, independent of the stage. The small resulting connected components in both cases are formed by genes with the same differential expression trend, and are associated with important biological processes, such as cell cycle or immune system, suggesting that activity of these categories follows the differential expression trend. With this approach we have shown that, even if the expression program is similar during ccRC progression, the co-expression programs strongly differ. More research is needed to understand the delicate interplay between expression and co-expression, but this is a first approach to enclose both approaches in an integrative view aimed at a deeper understanding in gene regulation in tumor evolution.