Breast cancer in the era of integrating “Omics” approaches

Transcriptomic and proteomic profiling of bi-partite and tri-partite murine iPSC-derived neurospheroids under steady-state and inflammatory condition

induced-pluripotent stem cell (iPSC)-derived neurospheroid (NSPH) models are an emerging in vitro toolkit to study the influence of inflammatory triggers on neurodegeneration and repair in a 3D neural environment. In contrast to their human counterpart, the absence of murine iPSC-derived NSPHs for profound characterisation and validation studies is a major experimental research gap, even though they offer the only possibility to truly compare or validate in vitro NSPH responses with in vivo brain responses. To contribute to these developments, we here describe the generation and characterisation of 5-week-old CX3CR1eGFP+/- CCR2RFP+/- murine (m)iPSC-derived bi-partite (neurons + astrocytes) and tri-partite (neurons + astrocytes + microglia) NSPH models that can be subjected to cellular activation following pro-inflammatory stimulation. First, cytokine analysis demonstrates that both bi-partite and tri-partite NSPHs can be triggered to release IL6 and CXCL10 following three days of stimulation with, respectively, TNFα + IL1β + IFNγ and LPS + IFNγ. Additionally, immunocytochemical analysis for G3BP1 and PABPC1 revealed the development of stress granules in both bi-partite and tri-partite NSPHs after 3 days of stimulation. To further investigate the observed signs of inflammatory response and cellular stress, we performed an untargeted transcriptomic and proteomic analysis of bi- and tri-partite NSPHs under steady-state and inflammatory conditions. Here, using the combined differential gene and protein expression profiles between unstimulated and stimulated NSPHs, Ingenuity Pathway Analysis (IPA) confirms the activation of canonical pathways associated with inflammation and cellular stress in both bi-partite and tri-partite NSPHs. Moreover, our multi-omics analysis suggests a higher level of downstream inflammatory responses, impairment of homeostatic and developmental processes, as well as activation of cell death processes in stimulated tri-partite NSPHs compared to bi-partite NSPHs. Concluding, these results emphasise the advantages of including microglia in NSPH research to study inflammation-induced neurodegeneration in a 3D neural environment.

The Crucial Role of Hereditary Cancer Panel Testing in Unaffected Individuals with a Strong Family History of Cancer: A Retrospective Study of a Cohort of 103 Healthy Subjects

Hereditary cancer syndromes caused by germline mutations account for 5-10% of all cancers. The finding of a genetic mutation could have far-reaching consequences for pharmaceutical therapy, personalized prevention strategies, and cascade testing. According to the National Comprehensive Cancer Network's (NCCN) and the Italian Association of Medical Oncology (AIOM) guidelines, unaffected family members should be tested only if the affected one is unavailable. This article explores whether germline genetic testing may be offered to high-risk families for hereditary cancer even if a living affected relative is missing. A retrospective study was carried out on 103 healthy subjects tested from 2017 to 2023. We enrolled all subjects with at least two first- or second-degree relatives affected by breast, ovarian, pancreatic, gastric, prostate, or colorectal cancer. All subjects were tested by Next Generation Sequencing (NGS) multi-gene panel of 27 cancer-associated genes. In the study population, 5 (about 5%) pathogenic/likely pathogenic variants (PVs/LPVs) were found, while 40 (42%) had a Variant of Uncertain Significance (VUS). This study highlights the importance of genetic testing for individuals with a strong family history of hereditary malignancies. This approach would allow women who tested positive to receive tailored treatment and prevention strategies based on their personal mutation status.

Comprehensive multi-omics analysis of breast cancer reveals distinct long-term prognostic subtypes

Breast cancer (BC) is a leading cause of cancer-related death worldwide. The diverse nature and heterogeneous biology of BC pose challenges for survival prediction, as patients with similar diagnoses often respond differently to treatment. Clinically relevant BC intrinsic subtypes have been established through gene expression profiling and are implemented in the clinic. While these intrinsic subtypes show a significant association with clinical outcomes, their long-term survival prediction beyond 5 years often deviates from expected clinical outcomes. This study aimed to identify naturally occurring long-term prognostic subgroups of BC based on an integrated multi-omics analysis. This study incorporates a clinical cohort of 335 untreated BC patients from the Oslo2 study with long-term follow-up (>12 years). Multi-Omics Factor Analysis (MOFA+) was employed to integrate transcriptomic, proteomic, and metabolomic data obtained from the tumor tissues. Our analysis revealed three prominent multi-omics clusters of BC patients with significantly different long-term prognoses (p = 0.005). The multi-omics clusters were validated in two independent large cohorts, METABRIC and TCGA. Importantly, a lack of prognostic association to long-term follow-up above 12 years in the previously established intrinsic subtypes was shown for these cohorts. Through a systems-biology approach, we identified varying enrichment levels of cell-cycle and immune-related pathways among the prognostic clusters. Integrated multi-omics analysis of BC revealed three distinct clusters with unique clinical and biological characteristics. Notably, these multi-omics clusters displayed robust associations with long-term survival, outperforming the established intrinsic subtypes.

Enhanced efficacy of β-carotene loaded solid lipid nanoparticles optimized and developed via central composite design on breast cancer cell lines

β-carotene is obtained from both plants and animals and has been the subject of intense research because of its provitamin-A, antioxidant, and anticancer effects. Its limited absorption and oxidative degradation significantly reduce its antitumor efficacy when taken orally. In our study, we utilize a central composite design to develop "bio-safe and highly bio-compatible" solid lipid nanoparticles (SLNs) by using only the combination of palmitic acid and poloxamer-407, a block co-polymer as a surfactant. The current research aim to develop and characterize SLNs loaded with β-carotene to improve their bioavailability and therapeutic efficacy. In addition, the improved cytotoxicity of solid lipid nanoparticles loaded with β-carotene was screened in-vitro in human breast cancer cell lines (MCF-7). The nanoparticles exhibits good stability, as indicated by their mean zeta potential of -26.3 ± 1.3 mV. The particles demonstrated high drug loading and entrapment capabilities. The fabricated nanoparticle's prolonged release potential was shown by the in-vitro release kinetics, which showed a first-order release pattern that adhered to the Higuchi model and showed a slow, linear, and steady release over 48 h. Moreover, a diffusion-type release mechanism was used to liberate β-carotene from the nanoparticles. For six months, the nanoparticles also showed a notable degree of physical stability. Lastly, using the MTT assay, the anti-cancer properties of β-carotene-loaded solid lipid nanoparticles were compared with intact β-carotene on MCF-7 cell lines. The cytotoxicity tests have shown that the encapsulation of β-carotene in the lipid bilayers of the optimized formulation does not interfere with the anti-cancer activity of the drug. When compared to standard β-carotene, β-carotene loaded SLNs showed enhanced anticancer efficacy and it is a plausible therapeutic candidate for enhancing the solubility of water-insoluble and degradation-sensitive biotherapeutics like β-carotene.

Minocycline induced apoptosis and suppressed expression of matrix metalloproteinases 2 and 9 in the breast cancer MCF-7 cells

BACKGROUND

In women, breast cancer is the second most frequent type of cancer. Looking for new and effective cancer-specific therapies with little to no adverse effects on healthy cells is critical.

OBJECTIVE

Minocycline, a second-generation tetracycline, has shown anticancer effects by targeting multiple pathways in various cancers. This study aimed to determine minocycline effects on the cell proliferation, apoptosis, and invasion of the human MCF-7 cells.

METHODS

MTT assay was used to evaluate the cytotoxicity of minocycline on the cells. Flow cytometry was performed to investigate the induction of apoptosis and the cell cycle progression. The expression levels of apoptotic and migration proteins and genes were assessed by western blotting and qRT-PCR. The scratch test was performed to evaluate the anti-migration effect of the drug.

RESULTS

The results indicated that the IC50 value of minocycline for MCF-7 cells was 36.10 µM. Minocycline treatment caused sub-G1 cell accumulation, indicating a significant apoptotic effect on the MCF-7 cells. Annexin-V/PI staining revealed a significant rise in early and late apoptotic cell percentages. Minocycline up-regulated Bax and Caspase-3 expression and down-regulated Bcl-2 and Pro-Cas3. The scratch test revealed significant anti-migration effects for minocycline. Furthermore, it caused down-regulation of MMP-2 and MMP-9 in a concentration-dependent method.

CONCLUSION

These findings further confirmed the anticancer effect of minocycline and highlighted that minocycline maybe considered as potential therapeutic agent for breast cancer treatment.

Hype or hope? A review of challenges in balancing tumor control and treatment toxicity in breast cancer from the perspective of the radiation oncologist

The aim of this article is to discuss the challenges and new strategies in managing breast cancer patients, with a specific focus on radiation oncology and the importance of balancing oncologic outcomes with quality of life and post-treatment morbidity. A comprehensive literature review was conducted to identify advances in the management of breast cancer, exploring de-escalation strategies, hypofractionation schemes, predictors and tools for reducing toxicity (radiation-induced lymphocyte apoptosis, deep inspiration breath-hold, adaptive radiotherapy), enhancer treatments (hyperthermia, immunotherapy) and innovative diagnostic modalities (PET-MRI, omics). Balancing oncologic outcomes with quality of life and post-treatment morbidity is crucial in the era of personalized medicine. Radiotherapy plays a critical role in the management of breast cancer patients. Large randomized trials are necessary to generalize some practices and cost remains the main obstacle for many innovations that are already applicable.

Recent Contributions of Mass Spectrometry-Based "Omics" in the Studies of Breast Cancer

Breast cancer (BC) is one of the most heterogeneous groups of cancer. As every biotype of BC is unique and presents a particular "omic" signature, they are increasingly characterized nowadays with novel mass spectrometry (MS) strategies. BC therapeutic approaches are primarily based on the two features of human epidermal growth factor receptor 2 (HER2) and estrogen receptor (ER) positivity. Various strategic MS implementations are reported in studies of BC also involving data independent acquisitions (DIAs) of MS which report novel differential proteomic, lipidomic, proteogenomic, phosphoproteomic, and metabolomic characterizations associated with the disease and its therapeutics. Recently many "omic" studies have aimed to identify distinct subsidiary biotypes for diagnosis, prognosis, and targets of treatment. Along with these, drug-induced-resistance phenotypes are characterized by "omic" changes. These identifying aspects of the disease may influence treatment outcomes in the near future. Drug quantifications and characterizations are also done regularly and have implications in therapeutic monitoring and in drug efficacy assessments. We report these studies, mentioning their implications toward the understanding of BC. We briefly provide the MS instrumentation principles that are adopted in such studies as an overview with a brief outlook on DIA-MS strategies. In all of these, we have chosen a model cancer for its revelations through MS-based "omics".

Image-guided metabolomics and transcriptomics reveal tumour heterogeneity in luminal A and B human breast cancer beyond glucose tracer uptake

BACKGROUND

Breast cancer is a metabolically heterogeneous disease, and although the concept of heterogeneous cancer metabolism is known, its precise role in human breast cancer is yet to be fully elucidated.

METHODS

We investigated in an explorative approach a cohort of 42 primary mamma carcinoma patients with positron emission tomography/magnetic resonance imaging (PET/MR) prior to surgery, followed by histopathology and molecular diagnosis. From a subset of patients, which showed high metabolic heterogeneity based on tracer uptake and pathology classification, tumour centre and periphery specimen tissue samples were further investigated by a targeted breast cancer gene expression panel and quantitative metabolomics by nuclear magnetic resonance (NMR) spectroscopy. All data were analysed in a combinatory approach.

RESULTS

[18 F]FDG (2-deoxy-2-[fluorine-18]fluoro-d-glucose) tracer uptake confirmed dominance of glucose metabolism in the breast tumour centre, with lower levels in the periphery. Additionally, we observed differences in lipid and proliferation related genes between luminal A and B subtypes in the centre and periphery. Tumour periphery showed elevated acetate levels and enrichment in lipid metabolic pathways genes especially in luminal B. Furthermore, serine was increased in the periphery and higher expression of thymidylate synthase (TYMS) indicated one-carbon metabolism increased in tumour periphery. The overall metabolic activity based on [18 F]FDG uptake of luminal B subtype was higher than that of luminal A and the difference between the periphery and centre increased with tumour grade.

CONCLUSION

Our analysis indicates variations in metabolism among different breast cancer subtypes and sampling locations which details the heterogeneity of the breast tumours. Correlation analysis of [18 F]FDG tracer uptake, transcriptome and tumour metabolites like acetate and serine facilitate the search for new candidates for metabolic tracers and permit distinguishing luminal A and B. This knowledge may help to differentiate subtypes preclinically or to provide patients guide for neoadjuvant therapy and optimised surgical protocols based on individual tumour metabolism.

PaCMAP-embedded convolutional neural network for multi-omics data integration

Aims

The multi-omics data integration has emerged as a prominent avenue within the healthcare industry, presenting substantial potential for enhancing predictive models. The main motivation behind this study stems from the imperative need to advance prognostic methodologies in cancer diagnosis, an area where precision is pivotal for effective clinical decision-making. In this context, the present study introduces an innovative methodology that integrates copy number alteration (CNA), DNA methylation, and gene expression data.

Methods

The three omics data were successfully merged into a two-dimensional (2D) map using the PaCMAP dimensionality reduction technique. Utilizing the RGB coloring scheme, a visual representation of the integration was produced utilizing the values of the three omics of each sample. Then, the colored 2D maps were fed into a convolutional neural network (CNN) to forecast the Gleason score.

Results

Our proposed model outperforms the cutting-edge i-SOM-GSN model by integrating multi-omics data and the CNN architecture with an accuracy of 98.89, and AUC of 0.9996.

Conclusion

This study demonstrates the effectiveness of multi-omics data integration in predicting health outcomes. The proposed methodology, combining PaCMAP for dimensionality reduction, RGB coloring for visualization, and CNN for prediction, offers a comprehensive framework for integrating heterogeneous omics data and improving predictive accuracy. These findings contribute to the advancement of personalized medicine and have the potential to aid in clinical decision-making for prostate cancer patients.

Profiling regulatory T lymphocytes within the tumor microenvironment of breast cancer via radiomics

OBJECTIVE

To generate an image-driven biomarker (Rad_score) to predict tumor-infiltrating regulatory T lymphocytes (Treg) in breast cancer (BC).

METHODS

Overall, 928 BC patients were enrolled from the Cancer Genome Atlas (TCGA) for survival analysis; MRI (n = 71 and n = 30 in the training and validation sets, respectively) from the Cancer Imaging Archive (TCIA) were retrieved and subjected to repeat least absolute shrinkage and selection operator for feature reduction. The radiomic scores (rad_score) for Treg infiltration estimation were calculated via support vector machine (SVM) and logistic regression (LR) algorithms, and validated on the remaining patients.

RESULTS

Landmark analysis indicated Treg infiltration was a risk factor for BC patients in the first 5 years and after 10 years of diagnosis (p = 0.007 and 0.018, respectively). Altogether, 108 radiomic features were extracted from MRI images, 4 of which remained for model construction. Areas under curves (AUCs) of the SVM model were 0.744 (95% CI 0.622-0.867) and 0.733 (95% CI 0.535-0.931) for training and validation sets, respectively, while for the LR model, AUCs were 0.771 (95% CI 0.657-0.885) and 0.724 (95% CI 0.522-0.926). The calibration curves indicated good agreement between prediction and true value (p > 0.05), and DCA shows the high clinical utility of the radiomic model. Rad_score was significantly correlated with immune inhibitory genes like CTLA4 and PDCD1.

CONCLUSIONS

High Treg infiltration is a risk factor for patients with BC. The Rad_score formulated on radiomic features is a novel tool to predict Treg abundance in the tumor microenvironment.

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.

Integrating Proteomics and Lipidomics for Evaluating the Risk of Breast Cancer Progression: A Pilot Study

Metastasis is a serious and often life-threatening condition, representing the leading cause of death among women with breast cancer (BC). Although the current clinical classification of BC is well-established, the addition of minimally invasive laboratory tests based on peripheral blood biomarkers that reflect pathological changes in the body is of utmost importance. In the current study, the serum proteome and lipidome profiles for 50 BC patients with (25) and without (25) metastasis were studied. Targeted proteomic analysis for concertation measurements of 125 proteins in the serum was performed via liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM MS) using the BAK 125 kit (MRM Proteomics Inc., Victoria, BC, Canada). Untargeted label-free lipidomic analysis was performed using liquid chromatography coupled to tandem mass-spectrometry (LC-MS/MS), in both positive and negative ion modes. Finally, 87 serum proteins and 295 lipids were quantified and showed a moderate correlation with tumor grade, histological and biological subtypes, and the number of lymph node metastases. Two highly accurate classifiers that enabled distinguishing between metastatic and non-metastatic BC were developed based on proteomic (accuracy 90%) and lipidomic (accuracy 80%) features. The best classifier (91% sensitivity, 89% specificity, AUC = 0.92) for BC metastasis diagnostics was based on logistic regression and the serum levels of 11 proteins: alpha-2-macroglobulin, coagulation factor XII, adiponectin, leucine-rich alpha-2-glycoprotein, alpha-2-HS-glycoprotein, Ig mu chain C region, apolipoprotein C-IV, carbonic anhydrase 1, apolipoprotein A-II, apolipoprotein C-II and alpha-1-acid glycoprotein 1.

DNA repair pathways in breast cancer: from mechanisms to clinical applications

BACKGROUND

Breast cancer (BC) is a complex disease with various subtypes and genetic alterations that impact DNA repair pathways. Understanding these pathways is essential for developing effective treatments and improving patient outcomes.

AREA COVERED

This study investigates the significance of DNA repair pathways in breast cancer, specifically focusing on various pathways such as nucleotide excision repair, base excision repair, mismatch repair, homologous recombination repair, non-homologous end joining, fanconi anemia pathway, translesion synthesis, direct repair, and DNA damage tolerance. The study also examines the role of these pathways in breast cancer resistance and explores their potential as targets for cancer treatment.

CONCLUSION

Recent advances in targeted therapies have shown promise in exploiting DNA repair pathways for BC treatment. However, much research is needed to improve the efficacy of these therapies and identify new targets. Additionally, personalized treatments that target specific DNA repair pathways based on tumor subtype or genetic profile are being developed. Advances in genomics and imaging technologies can potentially improve patient stratification and identify biomarkers of treatment response. However, many challenges remain, including toxicity, resistance, and the need for more personalized treatments. Continued research and development in this field could significantly improve BC treatment.

New Biomarkers and Treatment Advances in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is a specific subtype of breast cancer lacking hormone receptor expression and HER2 gene amplification. TNBC represents a heterogeneous subtype of breast cancer, characterized by poor prognosis, high invasiveness, high metastatic potential, and a tendency to relapse. In this review, the specific molecular subtypes and pathological aspects of triple-negative breast cancer are illustrated, with particular attention to the biomarker characteristics of TNBC, namely: regulators of cell proliferation and migration and angiogenesis, apoptosis-regulating proteins, regulators of DNA damage response, immune checkpoints, and epigenetic modifications. This paper also focuses on omics approaches to exploring TNBC, such as genomics to identify cancer-specific mutations, epigenomics to identify altered epigenetic landscapes in cancer cells, and transcriptomics to explore differential mRNA and protein expression. Moreover, updated neoadjuvant treatments for TNBC are also mentioned, underlining the role of immunotherapy and novel and targeted agents in the treatment of TNBC.

Targeting breast cancer stem cells directly to treat refractory breast cancer

For patients with refractory breast cancer (BC), integrative immunotherapies are emerging as a critical component of treatment. However, many patients remain unresponsive to treatment or relapse after a period. Different cells and mediators in the tumor microenvironment (TME) play important roles in the progression of BC, and cancer stem cells (CSCs) are deemed the main cause of relapse. Their characteristics depend on their interactions with their microenvironment as well as on the inducing factors and elements in this environment. Strategies to modulate the immune system in the TME of BC that are aimed at reversing the suppressive networks within it and eradicating residual CSCs are, thus, essential for improving the current therapeutic efficacy of BC. This review focuses on the development of immunoresistance in BCs and discusses the strategies that can modulate the immune system and target breast CSCs directly to treat BC including immunotherapy with immune checkpoint blockades.

Novel computational biology modeling system can accurately forecast response to neoadjuvant therapy in early breast cancer

BACKGROUND

Generalizable population-based studies are unable to account for individual tumor heterogeneity that contributes to variability in a patient's response to physician-chosen therapy. Although molecular characterization of tumors has advanced precision medicine, in early-stage and locally advanced breast cancer patients, predicting a patient's response to neoadjuvant therapy (NAT) remains a gap in current clinical practice. Here, we perform a study in an independent cohort of early-stage and locally advanced breast cancer patients to forecast tumor response to NAT and assess the stability of a previously validated biophysical simulation platform.

METHODS

A single-blinded study was performed using a retrospective database from a single institution (9/2014-12/2020). Patients included: ≥ 18 years with breast cancer who completed NAT, with pre-treatment dynamic contrast enhanced magnetic resonance imaging. Demographics, chemotherapy, baseline (pre-treatment) MRI and pathologic data were input into the TumorScope Predict (TS) biophysical simulation platform to generate predictions. Primary outcomes included predictions of pathological complete response (pCR) versus residual disease (RD) and final volume for each tumor. For validation, post-NAT predicted pCR and tumor volumes were compared to actual pathological assessment and MRI-assessed volumes. Predicted pCR was pre-defined as residual tumor volume ≤ 0.01 cm³ (≥ 99.9% reduction).

RESULTS

The cohort consisted of eighty patients; 36 Caucasian and 40 African American. Most tumors were high-grade (54.4% grade 3) invasive ductal carcinomas (90.0%). Receptor subtypes included hormone receptor positive (HR+)/human epidermal growth factor receptor 2 positive (HER2+, 30%), HR+/HER2- (35%), HR-/HER2+ (12.5%) and triple negative breast cancer (TNBC, 22.5%). Simulated tumor volume was significantly correlated with post-treatment radiographic MRI calculated volumes (r = 0.53, p = 1.3 × 10^-7, mean absolute error of 6.57%). TS prediction of pCR compared favorably to pathological assessment (pCR: TS n = 28; Path n = 27; RD: TS n = 52; Path n = 53), for an overall accuracy of 91.2% (95% CI: 82.8% - 96.4%; Clopper-Pearson interval). Five-year risk of recurrence demonstrated similar prognostic performance between TS predictions (Hazard ratio (HR): - 1.99; 95% CI [- 3.96, - 0.02]; p = 0.043) and clinically assessed pCR (HR: - 1.76; 95% CI [- 3.75, 0.23]; p = 0.054).

CONCLUSION

We demonstrated TS ability to simulate and model tumor in vivo conditions in silico and forecast volume response to NAT across breast tumor subtypes.

Controlling the confounding effect of metabolic gene expression to identify actual metabolite targets in microsatellite instability cancers

BACKGROUND

The metabolome is the best representation of cancer phenotypes. Gene expression can be considered a confounding covariate affecting metabolite levels. Data integration across metabolomics and genomics to establish the biological relevance of cancer metabolism is challenging. This study aimed to eliminate the confounding effect of metabolic gene expression to reflect actual metabolite levels in microsatellite instability (MSI) cancers.

METHODS

In this study, we propose a new strategy using covariate-adjusted tensor classification in high dimensions (CATCH) models to integrate metabolite and metabolic gene expression data to classify MSI and microsatellite stability (MSS) cancers. We used datasets from the Cancer Cell Line Encyclopedia (CCLE) phase II project and treated metabolomic data as tensor predictors and data on gene expression of metabolic enzymes as confounding covariates.

RESULTS

The CATCH model performed well, with high accuracy (0.82), sensitivity (0.66), specificity (0.88), precision (0.65), and F1 score (0.65). Seven metabolite features adjusted for metabolic gene expression, namely, 3-phosphoglycerate, 6-phosphogluconate, cholesterol ester, lysophosphatidylethanolamine (LPE), phosphatidylcholine, reduced glutathione, and sarcosine, were found in MSI cancers. Only one metabolite, Hippurate, was present in MSS cancers. The gene expression of phosphofructokinase 1 (PFKP), which is involved in the glycolytic pathway, was related to 3-phosphoglycerate. ALDH4A1 and GPT2 were associated with sarcosine. LPE was associated with the expression of CHPT1, which is involved in lipid metabolism. The glycolysis, nucleotide, glutamate, and lipid metabolic pathways were enriched in MSI cancers.

CONCLUSIONS

We propose an effective CATCH model for predicting MSI cancer status. By controlling the confounding effect of metabolic gene expression, we identified cancer metabolic biomarkers and therapeutic targets. In addition, we provided the possible biology and genetics of MSI cancer metabolism.

Breast Cancer: How Hippotherapy Bridges the Gap between Healing and Recovery-A Randomized Controlled Clinical Trial

BACKGROUND

Breast cancer is the most diagnosed women's cancer, and has a high survival rate. Despite great progress in detection and treatment, life reconstruction requires comprehensive cross-sectoral approaches between different disciplines and deeper consideration of the patient's challenges. Hippotherapy is an emerging specialized rehabilitation approach, performed by accredited health professionals and equine specialists, on specially trained horses via their movement, activating major paths for physical, mental, psychic and social reinforcement, and is synergistic to rehabilitative and supportive care.

METHODS

We conducted a randomized open, prospective, two-armed, controlled trial on the effectiveness of hippotherapy versus conventional supportive care on adult women with a diagnosis of breast cancer, after the period of primary treatment (surgery, chemotherapy, radiotherapy). The 6-month program included, in the treated group, an initial 1-week daily hippotherapy session, followed by three short 2-day sessions with an interval of 2 months between each, where the patients received conventional supportive care. The control group received 6 months of conventional supportive care. The primary end point was quality of life. Cognitive performances, fatigue, anxiety, depression, and body image were the secondary end points. Measurements were done through self-reported questionnaires.

RESULTS

We observed statistical differences in the evolution of the measured parameters over time between the two groups. The hippotherapy group showed a much faster, favorable and continuous improvement until the end of the program for each function assessed. The most striking improvements were observed in global quality of life, and fatigue, while breast cancer-specific quality of life, cognitive performance, anxiety and depression and body image showed a less marked but still statistically significant difference at the final post-treatment evaluation.

CONCLUSIONS

We demonstrate the therapeutic relevance of hippotherapy, a one-health approach, as a key initial stage after cancer diagnosis and treatment to foster recovery. Furthermore, hippotherapy has a strong impact on cancer treatments' efficiency and reconstruction of patient's life and ecosystem. This work reveals a layer of complexity that needs to be broadly considered.

TRIAL REGISTRATION

ClincalTrials.gov NCT04350398 accessed on 1 January 2022. Registered 17 April 2020, retrospectively registered; French Clinical Trials in Cancer Register RECF3818. Registered 18 March 2019, retrospectively registered.

Clinical usefulness of NGS multi-gene panel testing in hereditary cancer analysis

Introduction: A considerable number of families with pedigrees suggestive of a Mendelian form of Breast Cancer (BC), Ovarian Cancer (OC), or Pancreatic Cancer (PC) do not show detectable BRCA1/2 mutations after genetic testing. The use of multi-gene hereditary cancer panels increases the possibility to identify individuals with cancer predisposing gene variants. Our study was aimed to evaluate the increase in the detection rate of pathogenic mutations in BC, OC, and PC patients when using a multi-gene panel.

Methods: 546 patients affected by BC (423), PC (64), or OC (59) entered the study from January 2020 to December 2021. For BC patients, inclusion criteria were i) positive cancer family background, ii) early onset, and iii) triple negative BC. PC patients were enrolled when affected by metastatic cancer, while OC patients were all submitted to genetic testing without selection. The patients were tested using a Next-Generation Sequencing (NGS) panel containing 25 genes in addition to BRCA1/2.

Results: Forty-four out of 546 patients (8%) carried germline pathogenic/likely pathogenic variants (PV/LPV) on BRCA1/2 genes, and 46 (8%) presented PV or LPV in other susceptibility genes.

Discussion: Our findings demonstrate the utility of expanded panel testing in patients with suspected hereditary cancer syndromes, since this approach increased the mutation detection rate of 15% in PC, 8% in BC and 5% in OC cases. In absence of multi-gene panel analysis, a considerable percentage of mutations would have been lost.

Circulating metabolites in the early stage of breast cancer were not related to cancer stage or subtypes but associated with ki67 level. Promising statistical discrimination from controls

It was documented that the presence of malignancy in an organism causes metabolomic alterations in blood plasma which applies also to breast cancer. Breast cancer is a heterogeneous disease and there are only limited known relations of plasma metabolomic signatures with the tumour characteristics in early BC and knowing them would be of great advantage in noninvasive diagnostics. In this study, we focused on the metabolic alterations in early BC in blood plasma with the aim to identify metabolomic characteristics of BC subtypes. We used 50 early BC patients (FIGO stage I and II), where no additional metabolomic changes from metastatically changed remote organs were to be expected. We compared plasma levels of metabolites against controls and among various molecular and histological BC subtypes. BC patients showed decreased plasma levels of branched-chain amino acids BCAAs (and related keto-acids), histidine pyruvate and alanine balanced with an increased level of 3-hydroxybutyrate. The levels of circulating metabolites were not related to BC molecular subtypes (luminal A/luminal B), histological finding or grade, eventually stage, which indicate that in early BC, the BC patients share common metabolomics fingerprint in blood plasma independent of grade, stage or molecular subtype of BC. We observed statistically significant correlations between tumour proliferation marker Ki-67 level and circulating metabolites: alanine, citrate, tyrosine, glutamine, histidine and proline. This may point out the metabolites those levels could be associated with tumour growth, and conversely, the rate of tumour proliferation could be potentially estimated from plasma metabolites. When analyzing metabolomic changes in BC, we concluded that some of them could be associated with the metabolomic features of cancer cells, but the other observed alterations in blood plasma are the results of the complex mutual biochemical pathways in the comprehensive inter-organ metabolic exchange and communication. In the end, statistical discrimination against controls performed with AUC >0.91 showed the very promising potential of plasma metabolomics in the search for biomarkers for oncologic diseases.

Influence of the Microbiome Metagenomics and Epigenomics on Gastric Cancer

Gastric cancer (GC) is one of the major causes of cancer deaths worldwide. The disease is seldomly detected early and this limits treatment options. Because of its heterogeneous and complex nature, the disease remains poorly understood. The literature supports the contribution of the gut microbiome in the carcinogenesis and chemoresistance of GC. Drug resistance is the major challenge in GC therapy, occurring as a result of rewired metabolism. Metabolic rewiring stems from recurring genetic and epigenetic factors affecting cell development. The gut microbiome consists of pathogens such as H. pylori, which can foster both epigenetic alterations and mutagenesis on the host genome. Most of the bacteria implicated in GC development are Gram-negative, which makes it challenging to eradicate the disease. Gram-negative bacterium co-infections with viruses such as EBV are known as risk factors for GC. In this review, we discuss the role of microbiome-induced GC carcinogenesis. The disease risk factors associated with the presence of microorganisms and microbial dysbiosis are also discussed. In doing so, we aim to emphasize the critical role of the microbiome on cancer pathological phenotypes, and how microbiomics could serve as a potential breakthrough in determining effective GC therapeutic targets. Additionally, consideration of microbial dysbiosis in the GC classification system might aid in diagnosis and treatment decision-making, taking the specific pathogen/s involved into account.

Proteomics-Based Identification of Dysregulated Proteins in Breast Cancer

Immunohistochemistry (IHC) is still widely used as a morphology-based assay for in situ analysis of target proteins as specific tumor antigens. However, as a very heterogeneous collection of neoplastic diseases, breast cancer (BC) requires an accurate identification and characterization of larger panels of candidate biomarkers, beyond ER, PR, and HER2 proteins, for diagnosis and personalized treatment, without the limited availability of antibodies that are required to identify specific proteins. Top-down, middle-down, and bottom-up mass spectrometry (MS)-based proteomics approaches complement traditional histopathological tissue analysis to examine expression, modification, and interaction of hundreds to thousands of proteins simultaneously. In this review, we discuss the proteomics-based identification of dysregulated proteins in BC that are essential for the following issues: discovery and validation of new biomarkers by analysis of solid and liquid/non-invasive biopsies, cell lines, organoids and xenograft models; identification of panels of biomarkers for early detection and accurate discrimination between cancer, benign and normal tissues; identification of subtype-specific and stage-specific protein expression profiles in BC grading and measurement of disease progression; characterization of new subtypes of BC; characterization and quantitation of post-translational modifications (PTMs) and aberrant protein-protein interactions (PPI) involved in tumor development; characterization of the global remodeling of BC tissue homeostasis, diagnosis and prognostic information; and deciphering of molecular functions, biological processes and mechanisms through which the dysregulated proteins cause tumor initiation, invasion, and treatment resistance.

Metabolomics of Breast Cancer: A Review

Breast cancer is the most commonly diagnosed cancer in women worldwide. Major advances have been made towards breast cancer prevention and treatment. Unfortunately, the incidence of breast cancer is still increasing globally. Metabolomics is the field of science which studies all the metabolites in a cell, tissue, system, or organism. Metabolomics can provide information on dynamic changes occurring during cancer development and progression. The metabolites identified using cutting-edge metabolomics techniques will result in the identification of biomarkers for the early detection, diagnosis, and treatment of cancers. This review briefly introduces the metabolic changes in cancer with particular focus on breast cancer.