Robust inflammatory breast cancer gene signature using nonparametric random forest analysis

Comparing the BAD Protein Interactomes in 2D and 3D Cell Culture Using Proximity Labeling

Protein-protein interaction studies using proximity labeling techniques, such as biotin ligase-based BioID, have become integral in understanding cellular processes. Most studies utilize conventional 2D cell culture systems, potentially missing important differences in protein behavior found in 3D tissues. In this study, we investigated the protein-protein interactions of a protein, Bcl-2 Agonist of cell death (BAD), and compared conventional 2D culture conditions to a 3D system, wherein cells were embedded within a 3D extracellular matrix (ECM) mimic. Using BAD fused to the engineered biotin ligase miniTurbo (BirA*), we identified both overlapping and distinct BAD interactomes under 2D and 3D conditions. The known BAD binding proteins 14-3-3 isoforms and Bcl-XL interacted with BAD in both 2D and 3D. Of the 131 BAD-interactors identified, 56% were specific to 2D, 14% were specific to 3D, and 30% were common to both conditions. Interaction network analysis demonstrated differential associations between 2D and 3D interactomes, emphasizing the impact of the culture conditions on protein interactions. The 2D-3D overlap interactome encapsulated the apoptotic program, which is a well-known role of BAD. The 3D unique pathways were enriched in ECM signaling, suggestive of hitherto unknown functions for BAD. Thus, exploring protein-protein interactions in 3D provides novel clues into cell behavior. This exciting approach has the potential to bridge the knowledge gap between tractable 2D cell culture and organoid-like 3D systems.

DNA methylation profile of inflammatory breast cancer and its impact on prognosis and outcome

BACKGROUND

Inflammatory breast cancer (IBC) is a rare disease characterized by rapid progression, early metastasis, and a high mortality rate.

METHODS

Genome-wide DNA methylation analysis (EPIC BeadChip platform, Illumina) and somatic gene variants (105 cancer-related genes) were performed in 24 IBCs selected from a cohort of 140 cases.

RESULTS

We identified 46,908 DMPs (differentially methylated positions) (66% hypomethylated); CpG islands were predominantly hypermethylated (39.9%). Unsupervised clustering analysis revealed three clusters of DMPs characterized by an enrichment of specific gene mutations and hormone receptor status. The comparison among DNA methylation findings and external datasets (TCGA-BRCA stages III-IV) resulted in 385 shared DMPs mapped in 333 genes (264 hypermethylated). 151 DMPs were associated with 110 genes previously detected as differentially expressed in IBC (GSE45581), and 68 DMPs were negatively correlated with gene expression. We also identified 4369 DMRs (differentially methylated regions) mapped on known genes (2392 hypomethylated). BCAT1, CXCL12, and TBX15 loci were selected and evaluated by bisulfite pyrosequencing in 31 IBC samples. BCAT1 and TBX15 had higher methylation levels in triple-negative compared to non-triple-negative, while CXCL12 had lower methylation levels in triple-negative than non-triple-negative IBC cases. TBX15 methylation level was associated with obesity.

CONCLUSIONS

Our findings revealed a heterogeneous DNA methylation profile with potentially functional DMPs and DMRs. The DNA methylation data provided valuable insights for prognostic stratification and therapy selection to improve patient outcomes.

DREAM On, DREAM Off: A Review of the Estrogen Paradox in Luminal A Breast Cancers

It is generally assumed that all estrogen-receptor-positive (ER+) breast cancers proliferate in response to estrogen and, therefore, examples of the estrogen-induced regression of ER+ cancers are paradoxical. This review re-examines the estrogen regression paradox for the Luminal A subtype of ER+ breast cancers. The proliferative response to estrogen is shown to depend on the level of ER. Mechanistically, a window of opportunity study of pre-operative estradiol suggested that with higher levels of ER, estradiol could activate the DREAM-MMB (Dimerization partner, Retinoblastoma-like proteins, E2F4, and MuvB-MYB-MuvB) pathway to decrease proliferation. The response of breast epithelium and the incidence of breast cancers during hormonal variations that occur during the menstrual cycle and at the menopausal transition, respectively, suggest that a single hormone, either estrogen, progesterone or androgen, could activate the DREAM pathway, leading to reversible cell cycle arrest. Conversely, the presence of two hormones could switch the DREAM-MMB complex to a pro-proliferative pathway. Using publicly available data, we examine the gene expression changes after aromatase inhibitors and ICI 182,780 to provide support for the hypothesis. This review suggests that it might be possible to integrate all current hormonal therapies for Luminal A tumors within a single theoretical schema.

A new methodology to predict the oncotype scores based on clinico-pathological data with similar tumor profiles

PURPOSE

The Oncotype DX (ODX) test is a commercially available molecular test for breast cancer assay that provides prognostic and predictive breast cancer recurrence information for hormone positive, HER2-negative patients. The aim of this study is to propose a novel methodology to assist physicians in their decision-making.

METHODS

A retrospective study between 2012 and 2020 with 333 cases that underwent an ODX assay from three hospitals in the Bourgogne Franche-Comté region (France) was conducted. Clinical and pathological reports were used to collect the data. A methodology based on distributional random forest was developed to predict the ODX score classes (ODX [Formula: see text] and ODX [Formula: see text]) using 9 clinico-pathological characteristics. This methodology can be used particularly to identify the patients of the training cohort that share similarities with the new patient and to predict an estimate of the distribution of the ODX score.

RESULTS

The mean age of participants is 56.9 years old. We have correctly classified [Formula: see text] of patients in low risk and [Formula: see text] of patients in high risk. The overall accuracy is [Formula: see text]. The proportion of low risk correct predicted value (PPV) is [Formula: see text]. The percentage of high risk correct predicted value (NPV) is approximately [Formula: see text]. The F1-score and the Area Under Curve (AUC) are of 0.87 and 0.759, respectively.

CONCLUSION

The proposed methodology makes it possible to predict the distribution of the ODX score for a patient. This prediction is reinforced by the determination of a family of known patients with follow-up of identical scores. The use of this methodology with the pathologist's expertise on the different histological and immunohistochemical characteristics has a clinical impact to help oncologist in decision-making regarding breast cancer therapy.

Bilateral inflammatory recurrence of HER-2 positive breast cancer: a unique case report and literature review

Inflammatory breast cancer (IBC) is an aggressive and rare form of breast cancer with a poor prognosis. The occurrence of bilateral IBC in a short period of time is extremely rare. In this case report, a 54-year-old woman diagnosed with invasive ductal carcinoma of the left breast underwent lumpectomy, lymph node dissection, chemotherapy, and radiotherapy but opted against trastuzumab treatment. Four years later, she experienced bilateral breast inflammation, skin changes, edema, and heat (calor). Biopsies confirmed breast cancer metastasis to both breasts. Whole-Exome Sequencing revealed genetic mutations, including PIK3CA and C4orf54, in both primary and recurrent tumors, with significant downregulation in the recurrent tumors. KEGG analysis suggested potential enrichment of axon guidance signal pathways in both tumors. The patient showed a partial response after treatment with liposome paclitaxel, along with targeted therapy using trastuzumab and pertuzumab. This case report sheds light on the rare occurrence of bilateral inflammatory breast cancer post-HER-2 treatment and highlights the importance of genetic profiling in understanding the disease. Further research on clinical targets for breast cancer management is warranted.

Immune, metabolic landscapes of prognostic signatures for lung adenocarcinoma based on a novel deep learning framework

Lung adenocarcinoma (LUAD) is a malignant tumor with high lethality, and the aim of this study was to identify promising biomarkers for LUAD. Using the TCGA-LUAD dataset as a discovery cohort, a novel joint framework VAEjMLP based on variational autoencoder (VAE) and multilayer perceptron (MLP) was proposed. And the Shapley Additive Explanations (SHAP) method was introduced to evaluate the contribution of feature genes to the classification decision, which helped us to develop a biologically meaningful biomarker potential scoring algorithm. Nineteen potential biomarkers for LUAD were identified, which were involved in the regulation of immune and metabolic functions in LUAD. A prognostic risk model for LUAD was constructed by the biomarkers HLA-DRB1, SCGB1A1, and HLA-DRB5 screened by Cox regression analysis, dividing the patients into high-risk and low-risk groups. The prognostic risk model was validated with external datasets. The low-risk group was characterized by enrichment of immune pathways and higher immune infiltration compared to the high-risk group. While, the high-risk group was accompanied by an increase in metabolic pathway activity. There were significant differences between the high- and low-risk groups in metabolic reprogramming of aerobic glycolysis, amino acids, and lipids, as well as in angiogenic activity, epithelial-mesenchymal transition, tumorigenic cytokines, and inflammatory response. Furthermore, high-risk patients were more sensitive to Afatinib, Gefitinib, and Gemcitabine as predicted by the pRRophetic algorithm. This study provides prognostic signatures capable of revealing the immune and metabolic landscapes for LUAD, and may shed light on the identification of other cancer biomarkers.

Cancer invasion and metastasis: Insights from murine pubertal mammary gland morphogenesis

Cancer invasion and metastasis accounts for the majority of cancer related mortality. A better understanding of the players that drive the aberrant invasion and migration of tumors cells will provide critical targets to inhibit metastasis. Postnatal pubertal mammary gland morphogenesis is characterized by highly proliferative, invasive, and migratory normal epithelial cells. Identifying the molecular regulators of pubertal gland development is a promising strategy since tumorigenesis and metastasis is postulated to be a consequence of aberrant reactivation of developmental stages. In this review, we summarize the pubertal morphogenesis regulators that are involved in cancer metastasis and revisit pubertal mammary gland transcriptome profiling to uncover both known and unknown metastasis genes. Our updated list of pubertal morphogenesis regulators shows that most are implicated in invasion and metastasis. This review highlights molecular linkages between development and metastasis and provides a guide for exploring novel metastatic drivers.

Establishment and analysis of a disease risk prediction model for the systemic lupus erythematosus with random forest

Systemic lupus erythematosus (SLE) is a latent, insidious autoimmune disease, and with the development of gene sequencing in recent years, our study aims to develop a gene-based predictive model to explore the identification of SLE at the genetic level. First, gene expression datasets of SLE whole blood samples were collected from the Gene Expression Omnibus (GEO) database. After the datasets were merged, they were divided into training and validation datasets in the ratio of 7:3, where the SLE samples and healthy samples of the training dataset were 334 and 71, respectively, and the SLE samples and healthy samples of the validation dataset were 143 and 30, respectively. The training dataset was used to build the disease risk prediction model, and the validation dataset was used to verify the model identification ability. We first analyzed differentially expressed genes (DEGs) and then used Lasso and random forest (RF) to screen out six key genes (OAS3, USP18, RTP4, SPATS2L, IFI27 and OAS1), which are essential to distinguish SLE from healthy samples. With six key genes incorporated and five iterations of 10-fold cross-validation performed into the RF model, we finally determined the RF model with optimal mtry. The mean values of area under the curve (AUC) and accuracy of the models were over 0.95. The validation dataset was then used to evaluate the AUC performance and our model had an AUC of 0.948. An external validation dataset (GSE99967) with an AUC of 0.810, an accuracy of 0.836, and a sensitivity of 0.921 was used to assess the model's performance. The external validation dataset (GSE185047) of all SLE patients yielded an SLE sensitivity of up to 0.954. The final high-throughput RF model had a mean value of AUC over 0.9, again showing good results. In conclusion, we identified key genetic biomarkers and successfully developed a novel disease risk prediction model for SLE that can be used as a new SLE disease risk prediction aid and contribute to the identification of SLE.

DREAM, a possible answer to the estrogen paradox of the Women's Health Initiative Trial

Estrogen is thought to cause proliferation of all estrogen receptor positive (ER+) breast cancers. Paradoxically, in the Women's Health Initiative Trial, estrogen-only hormone replacement therapy reduced the incidence and mortality of low grade, ER+, HER2- breast cancer. We gave estradiol to 19 post-menopausal women with newly diagnosed low-grade, ER+, HER2- breast cancer in a prospective window of opportunity clinical trial and examined the changes in proliferation and gene expression before and after estradiol treatment. Ki67 decreased in 13/19 (68%) patients and 8/13 (62%) showed a decrease in Risk of Recurrence Score. We chose three prototypical estrogen responders (greatest decrease in ROR) and non-responders (no/minimal change in ROR) and applied a differential gene expression analysis to develop pre-treatment (PRESTO-30^core) and post-treatment (PRESTO-45^surg) gene expression profiles. The PRESTO-30^core predicted adjuvant benefit in a published series of tamoxifen, the partial estrogen agonist. Of the 45 genes in the PRESTO-45^surg, thirty contain the Cell cycle genes Homology Region (CHR) motif that binds the class B multi-vulva complex (MuvB) a member of the DREAM (Dimerization partner, retinoblastoma-like proteins, E2F, MuvB) complex responsible for reversible cell cycle arrest or quiescence. There was also near uniform suppression (89%) of the remaining DREAM genes consistent with estrogen induced activation of the DREAM complex to mediate cell cycle block after a short course of estrogens. To our knowledge, this is the first report to show estrogen modulation of DREAM genes and suggest involvement of DREAM pathway associated quiescence in endocrine responsive post-menopausal ER+ breast cancers.

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WHI trial found that estrogen decreases some ER+ post-menopausal breast cancers.

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Our prospective clinical trial confirmed an anti-proliferative effect of estradiol.

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DREAM pathway genes were downregulated during the anti-proliferative response.

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This suggests that estrogen may activate DREAM quiescence of ER+ breast cancers.