Toward precision medicine of breast cancer

Predicting Patterns of Distant Metastasis in Breast Cancer Patients following Local Regional Therapy Using Machine Learning

Up to 30% of breast cancer (BC) patients will develop distant metastases (DM), for which there is no cure. Here, statistical and machine learning (ML) models were developed to estimate the risk of site-specific DM following local-regional therapy. This retrospective study cohort included 175 patients diagnosed with invasive BC who later developed DM. Clinicopathological information was collected for analysis. Outcome variables were the first site of metastasis (brain, bone or visceral) and the time interval (months) to developing DM. Multivariate statistical analysis and ML-based multivariable gradient boosting machines identified factors associated with these outcomes. Machine learning models predicted the site of DM, demonstrating an area under the curve of 0.74, 0.75, and 0.73 for brain, bone and visceral sites, respectively. Overall, most patients (57%) developed bone metastases, with increased odds associated with estrogen receptor (ER) positivity. Human epidermal growth factor receptor-2 (HER2) positivity and non-anthracycline chemotherapy regimens were associated with a decreased risk of bone DM, while brain metastasis was associated with ER-negativity. Furthermore, non-anthracycline chemotherapy alone was a significant predictor of visceral metastasis. Here, clinicopathologic and treatment variables used in ML prediction models predict the first site of metastasis in BC. Further validation may guide focused patient-specific surveillance practices.

Chemotherapy-Induced Toxicities and Their Associations with Clinical and Non-Clinical Factors among Breast Cancer Patients in Vietnam

Understanding the burden and factors related to chemotherapy-induced toxicity is important in treatment planning for breast cancer patients. We conducted a prospective study among 396 newly diagnosed and chemotherapy-treated breast cancer patients recruited in two major cancer hospitals in northern Vietnam. Toxicities were captured through medical chart reviews and patient self-reports and graded using NCI CTCAE classification. Associations for sociodemographic and clinical factors with chemotherapy-induced toxicities during first-line chemotherapy were evaluated via multivariable logistic regression. Severe (i.e., grade ≥ 3) hematological (38.6%), and gastrointestinal (12.9%) toxicities were common. A pre-existing nephrological condition was significantly associated with the risk of severe hematological toxicity with adjusted odds ratios (OR) and 95% confidence intervals (CIs) of 2.30 (1.32-4.01). Patients living in rural areas had a lower risk of severe hematological toxicity (OR = 0.48; 95% CI, 0.30-0.77). Patients diagnosed with stage II and stage III-IV had a lower risk of severe gastrointestinal toxicity with ORs and 95% CIs of 0.26 (0.12-0.59) and 0.47 (0.20-1.10), respectively. Triple-negative/basal-like subtype was associated with a higher risk of severe hematological (OR = 3.15; 95% CI, 1.56-6.34) and gastrointestinal toxicities (OR = 3.60; 95% CI, 1.45-8.95) comparing to hormone receptor (HR)-positive HER2-negative subtype. Further research investigating underlying mechanisms would facilitate the development and delivery of personalized treatment and care plans.

iCEMIGE: Integration of CEll-morphometrics, MIcrobiome, and GEne biomarker signatures for risk stratification in breast cancers

BACKGROUND

The development of precision medicine is essential for personalized treatment and improved clinical outcome, whereas biomarkers are critical for the success of precision therapies.

AIM

To investigate whether iCEMIGE (integration of CEll-morphometrics, MIcro biome, and GEne biomarker signatures) improves risk stratification of breast cancer (BC) patients.

METHODS

We used our recently developed machine learning technique to identify cellular morphometric biomarkers (CMBs) from the whole histological slide images in The Cancer Genome Atlas (TCGA) breast cancer (TCGA-BRCA) cohort. Multivariate Cox regression was used to assess whether cell-morphometrics prognosis score (CMPS) and our previously reported 12-gene expression prognosis score (GEPS) and 15-microbe abundance prognosis score (MAPS) were independent prognostic factors. iCEMIGE was built upon the sparse representation learning technique. The iCEMIGE scoring model performance was measured by the area under the receiver operating characteristic curve compared to CMPS, GEPS, or MAPS alone. Nomogram models were created to predict overall survival (OS) and progress-free survival (PFS) rates at 5- and 10-year in the TCGA-BRCA cohort.

RESULTS

We identified 39 CMBs that were used to create a CMPS system in BCs. CMPS, GEPS, and MAPS were found to be significantly independently associated with OS. We then established an iCEMIGE scoring system for risk stratification of BC patients. The iGEMIGE score has a significant prognostic value for OS and PFS independent of clinical factors (age, stage, and estrogen and progesterone receptor status) and PAM50-based molecular subtype. Importantly, the iCEMIGE score significantly increased the power to predict OS and PFS compared to CMPS, GEPS, or MAPS alone.

CONCLUSION

Our study demonstrates a novel and generic artificial intelligence framework for multimodal data integration toward improving prognosis risk stratification of BC patients, which can be extended to other types of cancer.

CancerBERT: a cancer domain-specific language model for extracting breast cancer phenotypes from electronic health records

OBJECTIVE

Accurate extraction of breast cancer patients' phenotypes is important for clinical decision support and clinical research. This study developed and evaluated cancer domain pretrained CancerBERT models for extracting breast cancer phenotypes from clinical texts. We also investigated the effect of customized cancer-related vocabulary on the performance of CancerBERT models.

MATERIALS AND METHODS

A cancer-related corpus of breast cancer patients was extracted from the electronic health records of a local hospital. We annotated named entities in 200 pathology reports and 50 clinical notes for 8 cancer phenotypes for fine-tuning and evaluation. We kept pretraining the BlueBERT model on the cancer corpus with expanded vocabularies (using both term frequency-based and manually reviewed methods) to obtain CancerBERT models. The CancerBERT models were evaluated and compared with other baseline models on the cancer phenotype extraction task.

RESULTS

All CancerBERT models outperformed all other models on the cancer phenotyping NER task. Both CancerBERT models with customized vocabularies outperformed the CancerBERT with the original BERT vocabulary. The CancerBERT model with manually reviewed customized vocabulary achieved the best performance with macro F1 scores equal to 0.876 (95% CI, 0.873-0.879) and 0.904 (95% CI, 0.902-0.906) for exact match and lenient match, respectively.

CONCLUSIONS

The CancerBERT models were developed to extract the cancer phenotypes in clinical notes and pathology reports. The results validated that using customized vocabulary may further improve the performances of domain specific BERT models in clinical NLP tasks. The CancerBERT models developed in the study would further help clinical decision support.

Quercus acutissima Carruth. root extract triggers apoptosis, autophagy and inhibits cell viability in breast cancer cells

ETHNOPHARMACOLOGICAL RELEVANCE

The bark of Quercus acutissima Carruth. (QA) has long been used by Chinese people to treat noncancerous growths and cancerous ailments. It was traditionally used by Chinese folk to inhibit tumor proliferation in cancerous treatment, but the specific mechanism remain to be elucidated.

AIM OF THE STUDY

This study investigated the anticancer activities of QA root extract and its regulatory pathways in two human breast cancer cell lines (MCF-7 and SUM159).

MATERIALS AND METHODS

Dried QA root barks were extracted by ethanol and used to treat human breast cancer MCF-7 and SUM159 cells with varying concentrations. The CCK-8 assay, Hoechst 33342 staining assay and wound healing assay were used to detect the cell proliferation, apoptotic cell morphology, and cell migration in each group, respectively. Caspase 3 activity assay kit was used to determine caspase 3 activity. Western blot was used to measure proteins expression level in apoptosis and autophagy pathways (Bcl-W, caspase 3, Beclin1, LC3 and Atg5). LC-MS was performed to determine the chemical components in QA root extract.

RESULTS

CCK-8 assay showed that QA root extract significantly inhibited cell viability and proliferation in breast cancer cells by a concentration-dependent manner. Cell wound healing assay indicated that it had high suppression ability on cell migration both in MCF-7 and SUM159 cells. QA root extract treatment induced the morphological and nuclear structural changes in breast cancer cells including rounded appearance and shrunken nucleus with several nuclear body fragments. Western blot indicated that QA root extract induced mitochondria-mediated apoptosis by up-regulating caspase 3 and down-regulating Bcl-W. Moreover, QA root extract up-regulated Beclin1 and Atg5, and activated LC3 in two human breast cancer cell lines. LC-MS results showed that QA root extract contains high content of bioactive compounds like coumarins and derivatives, prenol lipids, flavonoids and tannins.

CONCLUSIONS

QA root extract inhibited cell proliferation and migration in MCF-7 and SUM159 cells, and it also induced cell morphology changes and regulated mitochondria-mediated apoptotic cell death and autophagic cell death.

The anticancer activity of root extract from Quercus acutissima Carruth. Via regulating apoptosis and autophagy in breast cancer cells

ETHNOPHARMACOLOGICAL RELEVANCE

The bark of Quercus acutissima Carruth. (QA) has long been used by Chinese people to treat noncancerous growths and cancerous ailments. It was traditionally used by Chinese folk to inhibit tumor proliferation in cancerous treatment, but the specific mechanism remain to be elucidated.

AIM OF THE STUDY

Breast cancer is the most common form of cancer in women and the leading cause of mortality around the globe. This study investigated the anticancer activities of QA root extract and its regulatory pathways in two human breast cancer cell lines (MCF-7 and SUM159).

MATERIALS AND METHODS

Dried QA root barks were extracted by ethanol and used to treat human breast cancer MCF-7 and SUM159 cells with varying concentrations. The CCK-8 assay, Hoechst 33342 staining assay and wound healing assay were used to detect the cell proliferation, apoptotic cell morphology, and cell migration in each group, respectively. Caspase 3 activity assay kit was used to determine caspase 3 activity. Western blot was used to measure proteins expression level in apoptosis and autophagy pathways (Bcl-W, caspase 3, Beclin1, LC3 and Atg5).

RESULTS

CCK-8 assay showed that QA root extract significantly inhibited cell viability and proliferation in breast cancer cells by a hormone receptor independent manner. Cell wound healing assay indicated that it had high suppression ability on cell migration both in MCF-7 and SUM159 cells. QA root extract treatment induced the morphological and nuclear structural changes in breast cancer cells including rounded appearance and shrunken nucleus with several nuclear body fragments. Western blot indicated that QA root extract induced mitochondria-mediated apoptosis by up-regulating caspase 3 and down-regulating Bcl-W. Moreover, QA root extract up-regulated Beclin1 and Atg5, and activated LC3 in two human breast cancer cell lines.

CONCLUSIONS

QA root extract inhibited cell proliferation and migration in MCF-7 and SUM159 cells, and it also induced cell morphology changes and regulated mitochondria-mediated apoptotic cell death and autophagic cell death.

Molecular Spectra and Frequency Patterns of Somatic Mutations in Arab Women with Breast Cancer

BACKGROUND

The role of somatic mutations in breast cancer prognosis and management continues to be recognized. However, data on the molecular profiles of Arab women are limited.

MATERIALS AND METHODS

This was a cross-sectional study based on medical chart review of all Arab women diagnosed with breast cancer at a single institution between 2010 and 2018 who underwent next-generation sequencing with Ampliseq 46-Gene or 50-Gene.

RESULTS

A total of 78 Arab women were identified, with a median age at diagnosis of 52.3 years (range: 37-82 years; 38.5% ≤50 years). The majority of patients had stage III or IV disease (74.4%). Next-generation sequencing revealed the following somatic mutation rates: TP53, 23.1%; ATM, 2.6%; IDH1, 2.6%; IDH2, 3.8%; PTEN, 7.7%; PIK3CA, 15.4%; APC, 7.7%; NPM1, 2.5%; MPL, 1.3%; JAK2, 2.5%; KIT, 7.7%; KRAS, 3.8%; and NRAS, 3.8%.

CONCLUSION

Our study illustrates frequencies of somatic mutations in Arab women with breast cancer and suggests potential variations from estimates reported in the Western population. These data calls for larger epidemiologic studies considering the evolving role of such mutations in prognostication and personalized management.

Human Plasma Metabolomics for Biomarker Discovery: Targeting the Molecular Subtypes in Breast Cancer

Simple Summary

Breast cancer is the leading cause of female cancer-related deaths worldwide. New technologies with enhanced sensitivity and specificity for early diagnosis and tailored monitoring are in critical demand. Thus, metabolomics appears to be a growing tool in order to detect molecular differences between distinct groups. In this case, an untargeted analytical approach was used to identify metabolomics differences between molecular subtypes of breast cancer in comparison with healthy matched controls. Footprints for each breast cancer subtype provided diagnostic capacities with an area under the receiver-operating characteristic curve above 0.85, which suggests that our results may represent a major advance towards the improvement of personalized medicine and precise targeted therapies for the various breast cancer phenotypes. To validate these molecular profiling as potential therapeutic strategies for the different breast cancer subtypes, further analysis and larger cohorts would be necessary in the near future.

Abstract

Purpose: The aim of this study is to identify differential metabolomic signatures in plasma samples of distinct subtypes of breast cancer patients that could be used in clinical practice as diagnostic biomarkers for these molecular phenotypes and to provide a more individualized and accurate therapeutic procedure. Methods: Untargeted LC-HRMS metabolomics approach in positive and negative electrospray ionization mode was used to analyze plasma samples from LA, LB, HER2+ and TN breast cancer patients and healthy controls in order to determine specific metabolomic profiles through univariate and multivariate statistical data analysis. Results: We tentatively identified altered metabolites displaying concentration variations among the four breast cancer molecular subtypes. We found a biomarker panel of 5 candidates in LA, 7 in LB, 5 in HER2 and 3 in TN that were able to discriminate each breast cancer subtype with a false discovery range corrected p-value < 0.05 and a fold-change cutoff value > 1.3. The model clinical value was evaluated with the AUROC, providing diagnostic capacities above 0.85. Conclusion: Our study identifies metabolic profiling differences in molecular phenotypes of breast cancer. This may represent a key step towards therapy improvement in personalized medicine and prioritization of tailored therapeutic intervention strategies.

Systematic Identification of Molecular Targets and Pathways Related to Human Organ Level Toxicity

The mechanisms leading to organ level toxicities are poorly understood. In this study, we applied an integrated approach to deduce the molecular targets and biological pathways involved in chemically induced toxicity for eight common human organ level toxicity end points (carcinogenicity, cardiotoxicity, developmental toxicity, hepatotoxicity, nephrotoxicity, neurotoxicity, reproductive toxicity, and skin toxicity). Integrated analysis of in vitro assay data, molecular targets and pathway annotations from the literature, and toxicity-molecular target associations derived from text mining, combined with machine learning techniques, were used to generate molecular targets for each of the organ level toxicity end points. A total of 1516 toxicity-related genes were identified and subsequently analyzed for biological pathway coverage, resulting in 206 significant pathways (p-value <0.05), ranging from 3 (e.g., developmental toxicity) to 101 (e.g., skin toxicity) for each toxicity end point. This study presents a systematic and comprehensive analysis of molecular targets and pathways related to various in vivo toxicity end points. These molecular targets and pathways could aid in understanding the biological mechanisms of toxicity and serve as a guide for the design of suitable in vitro assays for more efficient toxicity testing. In addition, these results are complementary to the existing adverse outcome pathway (AOP) framework and can be used to aid in the development of novel AOPs. Our results provide abundant testable hypotheses for further experimental validation.

Bioluminescence Imaging-Based Assessment of the Anti-Triple-Negative Breast Cancer and NF-Kappa B Pathway Inhibition Activity of Britanin

Britanin has been reported to have therapeutic effects on neurodegenerative and inflammation-based diseases. However, whether it is involved in the regulation of triple-negative breast cancer development has not been elucidated. In this study, we investigated the anti-tumor activity against triple-negative breast cancer tumor of Britanin by bioluminescence imaging in vivo using athymic (nu/nu) mice implanted with MDA-MB-231 and SUM-159 cells expressing a luciferase reporter gene, and explored the anti-tumor mechanism of Britanin. The results showed that Britanin treatment inhibited triple-negative breast cancer cell proliferation in vivo, and Cell Counting Kit-8 (IC₅₀ values are 4.27 and 5.05 μM) and colony formation tests (P < 0.001) confirmed this result. Transwell assays were conducted to verify that Britanin treatment inhibited cell migration and invasion (P < 0.001). Apoptosis was determined by TdT-mediated dUTP nick-end labeling method. Western blot and qRT-PCR analysis showed that Britanin treatment caused a decrease in the member expression of NF-kappa B signaling pathway. Computational modeling showed that Britanin could directly bind to a p-65 core region composed of Cys38, Cys120, and Gln128 residues. The results showed that the inhibitory mechanisms of Britanin on cancer cells may be by ways of inhibiting the NF-kappa B pathway. In addition, bioluminescence imaging screening system is useful for accelerating the application of Britanin in the antitumor field, and provides a useful tool for evaluating the phytochemicals efficacy in inhibiting cancer cell proliferation in animal models.

Toxicity and Pharmacogenomic Biomarkers in Breast Cancer Chemotherapy

Breast cancer (BC) is one of the most prevalent types of cancer worldwide with high morbidity and mortality rates. Treatment modalities include systemic therapy, in which chemotherapy is a major component in many cases. Several chemotherapeutic agents are used in combinations or as single agents with many adverse events occurring in variable frequencies. These events can be a significant barrier in completing the treatment regimens. Germline genomic variants are thought of as potential determinants in chemotherapy response and the development of side effects. Some pharmacogenomic studies were designed to explore germline variants that can be used as biomarkers for predicting developing toxicity or adverse events during chemotherapy in BC. In this review, we reassess and summarize the major findings of pharmacogenomic studies of chemotherapy toxicity during BC management. In addition, deficiencies hampering utilizing these findings and the potential targets of future research are emphasized. Main insufficiencies in toxicity pharmacogenomics studies originate from study design, sample limitations, heterogeneity of selected genes, variants, and toxicity definitions. With the advent of high throughput genotyping techniques, researchers are expected to explore the identified as well as the potential genetic biomarkers of toxicity and efficacy to improve BC management. However, to achieve this, the limitations of previous work should be evaluated and avoided to reach more conclusive and translatable evidence for personalizing BC chemotherapy.

The Expression, Clinicopathologic Characteristics, and Prognostic Value of Androgen Receptor in Breast Cancer: A Bioinformatics Analysis Using Public Databases

The role of androgen receptor (AR) in breast cancer has been unveiled in succession for the past few years. In this study, we conducted a comprehensive analysis based on four online public databases of data from many previous studies. We found that the expression of AR is significantly related to age, histological grade, and subtype but not to lymph node status. The low expression level of AR is strongly associated with poor recurrence-free survival, especially with poor distance metastasis-free survival in luminal A patients, but inverse in HER2 (human epidermal growth factor receptor-2) enriched patients. AR might be a biomarker of chemosensitivity in the basal subtype. Besides, the expression of melanophilin (MLPH) is distinctly in accordance with that of AR. AR could play diverse roles in different subtypes of breast cancer.

A Novel System for Functional Determination of Variants of Uncertain Significance using Deep Convolutional Neural Networks

Many drugs are developed for commonly occurring, well studied cancer drivers such as vemurafenib for BRAF V600E and erlotinib for EGFR exon 19 mutations. However, most tumors also harbor mutations which have an uncertain role in disease formation, commonly called Variants of Uncertain Significance (VUS), which are not studied or characterized and could play a significant role in drug resistance and relapse. Therefore, the determination of the functional significance of VUS and their response to Molecularly Targeted Agents (MTA) is essential for developing new drugs and predicting response of patients. Here we present a multi-scale deep convolutional neural network (DCNN) architecture combined with an in-vitro functional assay to investigate the functional role of VUS and their response to MTA's. Our method achieved high accuracy and precision on a hold-out set of examples (0.98 mean AUC for all tested genes) and was used to predict the oncogenicity of 195 VUS in 6 genes. 63 (32%) of the assayed VUS's were classified as pathway activating, many of them to a similar extent as known driver mutations. Finally, we show that responses of various mutations to FDA approved MTAs are accurately predicted by our platform in a dose dependent manner. Taken together this novel system can uncover the treatable mutational landscape of a drug and be a useful tool in drug development.

Overcome trastuzumab resistance of breast cancer using anti-HER2 chimeric antigen receptor T cells and PD1 blockade

Trastuzumab-resistance is still a major challenge in treating patients with HER2 positive breast cancer. In this study, we tried to overcome transtuzumab-resistance by examining the therapeutic efficacy of third generation anti-HER2 chimeric antigen receptor (CAR)-T cells alone and in combination with PD1 blockade against HER2 positive and trastuzumab-resistance breast cancer cells in vitro and xenograft model. The anti-HER2 CAR-T cells were generated by infecting CD3/CD28 activated peripheral blood mononuclear cells with lentivirus expressing third generation anti-HER2 CAR. Anti-HER2 CAR-T cells were specifically targeted to HER2 positive BT474 and trastuzumab resistant HCC1954 cells compared with HER2 negative breast cancer cells. Results from ELISA revealed that the secretion of IL-2 and IFN-γ was increased in anti-HER2 CAR-T cells after being co-cultured with HCC1954 cells, and was further increased with the addition of anti-PD1 antibody in the co-culture system. Furthermore, data from lactate dehydrogenase assay showed that anti-HER2 CAR-T cells displayed a potent cytotoxicity against HCC1954 and BT474 cells. Addition of anti-PD1 antibody further enhanced the cytotoxicity of anti-HER2 CAR-T cells against HCC1954 cells. Lastly, injection of anti-HER2 CAR-T cells significantly reduced the growth of HCC1954 xenograft tumors. Combining anti-HER2 CAR-T cells with anti-PD1 antibody further impaired the growth of HCC1954 tumors. The present results indicate that anti-HER2 CAR-T cells have therapeutic efficacy against trastuzumab resistant breast tumors and addition of the PD1 antibody can further enhance the therapeutic effect of anti-HER2 CAR-T cells. Thus, third generation anti-HER2 CAR-T cells along with PD1 blockade is a potential therapy to overcome trastuzumab resistance of breast cancer.

Arylamide as Potential Selective Inhibitor for Matrix Metalloproteinase 9 (MMP9): Design, Synthesis, Biological Evaluation, and Molecular Modeling

Previous studies have reported that compounds bearing an arylamide linked to a heterocyclic planar ring have successfully inhibited the hemopexin-like domain (PEX9) of matrix metalloproteinase 9 (MMP9). PEX9 has been suggested to be more selectively targeted than MMP9's catalytic domain in a degrading extracellular matrix under some pathologic conditions, especially in cancer. In this study, we aim to synthesize and evaluate 10 arylamide compounds as MMP9 inhibitors through an enzymatic assay as well as a cellular assay. The mechanism of inhibition for the most active compounds was investigated via molecular dynamics simulation (MD). Molecular docking was performed using AutoDock4.0 with PEX9 as the protein model to predict the binding of the designed compounds. The synthesis was carried out by reacting aniline derivatives with 3-bromopropanoyl chloride using pyridine as the catalyst at room temperature. The MMP9 assay was conducted using the FRET-based MMP9 kits protocol and gelatin zymography assay. The cytotoxicity assay was done using the MTT method, and the MD simulation was performed using AMBER16. Assay on MMP9 demonstrated activities of three compounds (2, 7, and 9) with more than 50% inhibition. Further inhibition on MMP9 expressed by 4T1 showed that two compounds (7 and 9) inhibited its gelatinolytic activity more than 50%. The cytotoxicity assay against 4T1 cells results in the inhibition of the cell growth with an EC₅₀ of 125 μM and 132 μM for 7 and 9, respectively. The MD simulation explained a stable interaction of 7 and 9 in PEX9 at 100 ns with a free energy of binding of -8.03 kcal/mol and -6.41 kcal/mol, respectively. Arylamides have potential effects as selective MMP9 inhibitors in inhibiting breast cancer cell progression.

Electronic Health Record Phenotypes for Precision Medicine: Perspectives and Caveats From Treatment of Breast Cancer at a Single Institution

Precision medicine is at the forefront of biomedical research. Cancer registries provide rich perspectives and electronic health records (EHRs) are commonly utilized to gather additional clinical data elements needed for translational research. However, manual annotation is resource‐intense and not readily scalable. Informatics‐based phenotyping presents an ideal solution, but perspectives obtained can be impacted by both data source and algorithm selection. We derived breast cancer (BC) receptor status phenotypes from structured and unstructured EHR data using rule‐based algorithms, including natural language processing (NLP). Overall, the use of NLP increased BC receptor status coverage by 39.2% from 69.1% with structured medication information alone. Using all available EHR data, estrogen receptor‐positive BC cases were ascertained with high precision (P = 0.976) and recall (R = 0.987) compared with gold standard chart‐reviewed patients. However, status negation (R = 0.591) decreased 40.2% when relying on structured medications alone. Using multiple EHR data types (and thorough understanding of the perspectives offered) are necessary to derive robust EHR‐based precision medicine phenotypes.

In silico signaling modeling to understand cancer pathways and treatment responses

Precision medicine has changed thinking in cancer therapy, highlighting a better understanding of the individual clinical interventions. But what role do the drivers and pathways identified from pan-cancer genome analysis play in the tumor? In this letter, we will highlight the importance of in silico modeling in precision medicine. In the current era of big data, tumor engines and pathways derived from pan-cancer analysis should be integrated into in silico models to understand the mutational tumor status and individual molecular pathway mechanism at a deeper level. This allows to pre-evaluate the potential therapy response and develop optimal patient-tailored treatment strategies which pave the way to support precision medicine in the clinic of the future.

Anticancer activity of Adiantum capillus veneris and Pteris quadriureta L. in human breast cancer cell lines

Breast cancer is the most common cancer worldwide that costs lives of millions of people every year. Plant products with potential anticancer activities have become a vital source of novel agents in treating cancer. Adiantum capillus veneris (ACV) and Pteris Quadriureta (PQ) are such traditional herbs with potential pharmacological properties. In this study, both crude methanol extract and gold nanoparticles of ACV and PQ were tested for their anticancer activities in MCF7 and BT47 cell lines. By using GC-MS, we have identified 23 and 28 bioactive compounds in ACV and PQ respectively. We analysed the effects of ACV and PQ nanoparticles on various proteins involved in cell cycle and apoptosis using western blotting and PCR. With the help of flow cytometry, we measured number of cells undergoing apoptosis. We found that both the crude extract and nanoparticles have anti-proliferative and apoptosis inducing properties against MCF7 and BT47 cell lines. We also performed molecular docking to check whether there were any interactions between proteins involved in apoptosis and cell cycle and bioactive compounds present in the plant extracts. By using docking analysis, we also showed that phytol and eicosapentaenoic acid present in ACV and PQ interact with Bcl2 and cyclin D1. These findings demonstrate that ACV and PQ possess anticancer activities by modulating proteins involved in cell cycle and apoptosis. ACV and PQ that effectively modulate various oncogenic molecules can be used as promising agent for cancer therapy.

Positive crosstalk between EGFR and the TF-PAR2 pathway mediates resistance to cisplatin and poor survival in cervical cancer

Cisplatin-based chemoradiation is the standard treatment for cervical cancer, but chemosensitizing strategies are needed to improve patient survival. EGFR (Epidermal Growth Factor Receptor) is an oncogene overexpressed in cervical cancer that is involved in chemoresistance. Recent studies showed that EGFR upregulates multiple elements of the coagulation cascade, including tissue factor (TF) and the protease-activated receptors (PAR) 1 and 2. Moreover, many G protein-coupled receptors, including PARs, have been implicated in EGFR transactivation. However, the role of coagulation proteins in the progression of cervical cancer has been poorly investigated. Herein we employed cervical cancer cell lines and The Cancer Genome Atlas (TCGA) database to evaluate the role of EGFR, TF and PAR2 in chemoresistance. The SLIGKL-NH2 peptide (PAR2-AP) and coagulation factor VIIa (FVIIa) were used as PAR2 agonists, while cetuximab was used to inhibit EGFR. The more aggressive cell line CASKI showed higher expression levels of EGFR, TF and PAR2 than that of C33A. PAR2 transactivated EGFR, which further upregulated cyclooxygenase-2 (COX2) expression. PAR2-AP decreased cisplatin-induced apoptosis through an EGFR- and COX2-dependent mechanism. Furthermore, treatment of CASKI cells with EGF upregulated TF expression, while treatment with cetuximab decreased the TF protein levels. The RNA-seq data from 309 TCGA samples showed a strong positive correlation between EGFR and TF expression (P = 0.0003). In addition, the increased expression of EGFR, PAR2 or COX2 in cervical cancer patients was significantly correlated with poor overall survival. Taken together, our results suggest that EGFR and COX2 are effectors of the TF/FVIIa/PAR2 signaling pathway, promoting chemoresistance.

Precision Medicine for Breast Cancer: The Paths to Truly Individualized Diagnosis and Treatment

Precision medicine in oncology seeks to individualize each patient's treatment regimen based on an accurate assessment of the risk of recurrence or progression of that person's cancer. Precision will be achieved at each phase of care, from detection to diagnosis to surgery, systemic therapy, and radiation therapy, to survivorship and follow-up care. The precision arises from detailed knowledge of the inherent biological propensities of each tumor, rather than generalizing treatment approaches based on phenotypic, or even genotypic, categories. Extensive research is being conducted in multiple disciplines, including radiology, pathology, molecular biology, and surgical, medical, and radiation oncology. Clinical trial design is adapting to the new paradigms and moving away from grouping heterogeneous patient populations into limited treatment comparison arms. This review touches on several areas invested in clinical research. This special issue highlights the specific work of a number of groups working on precision medicine for breast cancer.

Validation of a network-based strategy for the optimization of combinatorial target selection in breast cancer therapy: siRNA knockdown of network targets in MDA-MB-231 cells as an in vitro model for inhibition of tumor development

Network-based strategies provided by systems biology are attractive tools for cancer therapy. Modulation of cancer networks by anticancer drugs may alter the response of malignant cells and/or drive network re-organization into the inhibition of cancer progression. Previously, using systems biology approach and cancer signaling networks, we identified top-5 highly expressed and connected proteins (HSP90AB1, CSNK2B, TK1, YWHAB and VIM) in the invasive MDA-MB-231 breast cancer cell line. Here, we have knocked down the expression of these proteins, individually or together using siRNAs. The transfected cell lines were assessed for in vitro cell growth, colony formation, migration and invasion relative to control transfected MDA-MB-231, the non-invasive MCF-7 breast carcinoma cell line and the non-tumoral mammary epithelial cell line MCF-10A. The knockdown of the top-5 upregulated connectivity hubs successfully inhibited the in vitro proliferation, colony formation, anchorage independence, migration and invasion in MDA-MB-231 cells; with minimal effects in the control transfected MDA-MB-231 cells or MCF-7 and MCF-10A cells. The in vitro validation of bioinformatics predictions regarding optimized multi-target selection for therapy suggests that protein expression levels together with protein-protein interaction network analysis may provide an optimized combinatorial target selection for a highly effective anti-metastatic precision therapy in triple-negative breast cancer. This approach increases the ability to identify not only druggable hubs as essential targets for cancer survival, but also interactions most susceptible to synergistic drug action. The data provided in this report constitute a preliminary step toward the personalized clinical application of our strategy to optimize the therapeutic use of anti-cancer drugs.

In silico cancer research towards 3R

BACKGROUND

Improving our understanding of cancer and other complex diseases requires integrating diverse data sets and algorithms. Intertwining in vivo and in vitro data and in silico models are paramount to overcome intrinsic difficulties given by data complexity. Importantly, this approach also helps to uncover underlying molecular mechanisms. Over the years, research has introduced multiple biochemical and computational methods to study the disease, many of which require animal experiments. However, modeling systems and the comparison of cellular processes in both eukaryotes and prokaryotes help to understand specific aspects of uncontrolled cell growth, eventually leading to improved planning of future experiments. According to the principles for humane techniques milestones in alternative animal testing involve in vitro methods such as cell-based models and microfluidic chips, as well as clinical tests of microdosing and imaging. Up-to-date, the range of alternative methods has expanded towards computational approaches, based on the use of information from past in vitro and in vivo experiments. In fact, in silico techniques are often underrated but can be vital to understanding fundamental processes in cancer. They can rival accuracy of biological assays, and they can provide essential focus and direction to reduce experimental cost.

MAIN BODY

We give an overview on in vivo, in vitro and in silico methods used in cancer research. Common models as cell-lines, xenografts, or genetically modified rodents reflect relevant pathological processes to a different degree, but can not replicate the full spectrum of human disease. There is an increasing importance of computational biology, advancing from the task of assisting biological analysis with network biology approaches as the basis for understanding a cell's functional organization up to model building for predictive systems.

CONCLUSION

Underlining and extending the in silico approach with respect to the 3Rs for replacement, reduction and refinement will lead cancer research towards efficient and effective precision medicine. Therefore, we suggest refined translational models and testing methods based on integrative analyses and the incorporation of computational biology within cancer research.

Incidence of isolated local breast cancer recurrence and contralateral breast cancer: A systematic review

An increasing number of women is surviving breast cancer and due to that at risk of developing an isolated ipsilateral breast tumor recurrence (IBTR) or a contralateral breast cancer (CBC). Patients' main concern is cancer recurrence. Patient counseling on breast cancer recurrence is challenging. In order to provide healthcare professionals and patients more guidance, a systematic literature review of the incidence of isolated IBTR and CBC in women diagnosed with early invasive breast cancer was performed. Medline, EMBASE and the Cochrane Library were searched from 2000 until October 2015. Multicenter studies reporting an IBTR or CBC rate in curatively treated adult females diagnosed with invasive breast cancer were included. The initial search yielded 6998 potentially relevant articles. Twenty were eligible for inclusion, representing 25 recurrence incidence rates. Both isolated IBTR and CBC incidence rates steadily increased with the length of follow-up, indicating that IBTR and CBC occur even more than 15 years after diagnosis. The annual incidence rate of isolated IBTR and CBC in women diagnosed with an early invasive breast cancer was 0.6% (range: 0.4-1.1%) and 0.5% (range: 0.2-0.7%), respectively. Analyzed data were lacking information about important risk factors and given treatment with regard to the incidence of recurrence, which hampers the prediction of patient tailored recurrence risks. The presented rates are therefore the best available estimates of isolated IBTR and CBC annual incidence rates based on the current literature. Healthcare professionals could use these rates in their communication with patients diagnosed with early invasive breast cancer.

Phytochemicals: Current strategies for treating breast cancer

Females with early-stage metastatic, estrogen-dependent breast cancer are generally treated with surgery, radiation and chemotherapy, or with more targeted approaches such as aromatase inhibitors (anastrozole or letrozole) or anti-estrogens (tamoxifen). Despite widespread successful usage of these agents for the treatment of breast cancer, resistance, tumor relapse and metastasis remain the principal causes of mortality for patients with breast cancer. While numerous groups have made major contributions toward an improved understanding of resistance mechanisms, the currently insufficient grasp of the most critical pathways involved in resistance is evident in the inability to adequately treat and drastically improve patient outcomes in females with hormone-refractory breast cancer, including triple negative breast cancer. Therefore, further investigation of novel therapeutic approaches is paramount to reveal previously unconsidered agents that could be utilized to treat metastatic disease. Numerous naturally occurring phytochemicals have recently gained interest as potential therapeutic breast cancer agents appear to directly affect estrogen-dependent and estrogen-independent breast cancer cell proliferation, potentially via affecting breast cancer stem cell populations. While numerous natural compounds have exhibited promise, they are limited by their bioavailability. Therefore, to effectively treat future hormone-refractory breast tumors, it is critical to adequately refine and formulate these agents for effective therapeutic use and delivery. Herein, the literature on the current state of phytochemicals is reviewed, including their limitations and potential as targeted therapies for breast cancer.

Lecithin–chitosan–TPGS nanoparticles as nanocarriers of (−)-epicatechin enhanced its anticancer activity in breast cancer cells

Natural compounds such as (−)-epicatechin show a variety of biological properties including anticancer activity. Nonetheless, (−)-epicatechin's therapeutic application is limited due to its low water solubility and sensitivity to oxygen and light. Additionally, previous studies have reported that the encapsulation of flavonoids in nanoparticles might generate stable deliverable forms, which improves the availability and solubility of the bioactive compounds. The aims of this study were to generate (−)-epicatechin-loaded lecithin–chitosan nanoparticles (EC-LCT-NPs) by molecular self-assembly and to assess their cytotoxic potential against breast cancer cells. Various parameters were measured to characterize the EC-LCT-NPs including size, polydispersity index (PdI), zeta potential, morphology and entrapment efficiency. The results showed that the mean particle size of the EC-CLT-NPs was 159 ± 2.23 nm (PdI, 0.189), and the loading and entrapment efficiencies of (−)-epicatechin were 3.42 ± 0.85% and 56.1 ± 3.9%, respectively. The cytotoxic effect of the EC-CLT-NPs was greater than that of free (−)-epicatechin on breast cancer cell lines (MCF-7, MDA-MB-231, MDA-MB-436 and SK-Br3). Indeed, EC-LCT-NPs showed an IC₅₀ that was four-fold lower (85 μM) than free (−)-epicatechin (350 μM) and showed selectivity to cancerous cells. This study demonstrated that encapsulating (−)-epicatechin into lecithin–chitosan nanoparticles opens new options for breast cancer treatment.

Natural compounds such as (−)-epicatechin show a variety of biological properties including anticancer activity.

Dual mTOR Kinase Inhibitor MLN0128 Sensitizes HR+/HER2+ Breast Cancer Patient-Derived Xenografts to Trastuzumab or Fulvestrant

Purpose: Therapeutic strategies against hormonal receptor-positive (HR⁺)/HER2⁺ breast cancers with poor response to trastuzumab need to be optimized.Experimental Design: Two HR⁺/HER2⁺ patient-derived xenograft (PDX) models named as COH-SC1 and COH-SC31 were established to explore targeted therapies for HER2⁺ breast cancers. RNA sequencing and RPPA (reverse phase protein array) analyses were conducted to decipher molecular features of the two PDXs and define the therapeutic strategy of interest, validated by in vivo drug efficacy examination and in vitro cell proliferation analysis.Results: Estrogen acted as a growth driver of trastuzumab-resistant COH-SC31 tumors but an accelerator in the trastuzumab-sensitive COH-SC1 model. In vivo trastuzumab efficacy examination further confirmed the consistent responses between PDXs and the corresponding tumors. Integrative omics analysis revealed that mammalian target of rapamycin (mTOR) and ERα signaling predominantly regulate tumor growth of the two HR⁺/HER2⁺ PDXs. Combination of the dual mTOR complex inhibitor MLN0128 and anti-HER2 trastuzumab strongly suppressed tumor growth of COH-SC1 PDX accompanied by increasing ER-positive cell population in vivo Instead, MLN0128 in combination with antiestrogen fulvestrant significantly halted the growth of HR⁺/HER2⁺ cancer cells in vitro and trastuzumab-resistant COH-SC31 as well as trastuzumab-sensitive COH-SC1 tumors in vivoConclusions: Compared with the standard trastuzumab treatment, this study demonstrates alternative therapeutic strategies against HR⁺/HER2⁺ tumors through establishment of two PDXs coupled with integrative omics analyses and in vivo drug efficacy examination. This work presents a prototype of future "co-clinical" trials to tailor personalized medicine in clinical practice. Clin Cancer Res; 24(2); 395-406. ©2017 AACR.

Personomics: The Missing Link in the Evolution from Precision Medicine to Personalized Medicine

Clinical practice guidelines have been developed for many common conditions based on data from randomized controlled trials. When medicine is informed solely by clinical practice guidelines, however, the patient is not treated as an individual, but rather as member of a group. Precision medicine, as defined herein, characterizes unique biological characteristics of the individual or of specimens obtained from an individual to tailor diagnostics and therapeutics to a specific patient. These unique biological characteristics are defined by the tools of precision medicine: genomics, proteomics, metabolomics, epigenomics, pharmacogenomics, and other “-omics.” Personalized medicine, as defined herein, uses additional information about the individual derived from knowing the patient as a person. These unique personal characteristics are defined by the tools of personalized medicine—personomics—which take into account an individual’s personality, preferences, values, goals, health beliefs, social support network, financial resources, and unique life circumstances that affect how and when a given health condition will manifest in that person and how that condition will respond to treatment. In this paradigm, precision medicine may be considered a necessary step in the evolution of medical care to personalized medicine, with personomics as the missing link.

Human CD3+ T-Cells with the Anti-ERBB2 Chimeric Antigen Receptor Exhibit Efficient Targeting and Induce Apoptosis in ERBB2 Overexpressing Breast Cancer Cells

Breast cancer is a common malignancy among women. The innate and adaptive immune responses failed to be activated owing to immune modulation in the tumour microenvironment. Decades of scientific study links the overexpression of human epidermal growth factor receptor 2 (ERBB2) antigen with aggressive tumours. The Chimeric Antigen Receptor (CAR) coding for specific tumour-associated antigens could initiate intrinsic T-cell signalling, inducing T-cell activation, and cytotoxic activity without the need for major histocompatibility complex recognition. This renders CAR as a potentially universal immunotherapeutic option. Herein, we aimed to establish CAR in CD3+ T-cells, isolated from human peripheral blood mononucleated cells that could subsequently target and induce apoptosis in the ERBB2 overexpressing human breast cancer cell line, SKBR3. Constructed CAR was inserted into a lentiviral plasmid containing a green fluorescent protein tag and produced as lentiviral particles that were used to transduce activated T-cells. Transduced CAR-T cells were then primed with SKBR3 cells to evaluate their functionality. Results showed increased apoptosis in SKBR3 cells co-cultured with CAR-T cells compared to the control (non–transduced T-cells). This study demonstrates that CAR introduction helps overcome the innate limitations of native T-cells leading to cancer cell apoptosis. We recommend future studies should focus on in vivo cytotoxicity of CAR-T cells against ERBB2 expressing tumours.

The AAV-mediated and RNA-guided CRISPR/Cas9 system for gene therapy of DMD and BMD

Mutations in the dystrophin gene (Dmd) result in Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD), which afflict many newborn boys. In 2016, Brain and Development published several interesting articles on DMD treatment with antisense oligonucleotide, kinase inhibitor, and prednisolone. Even more strikingly, three articles in the issue 6271 of Science in 2016 provide new insights into gene therapy of DMD and BMD via the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9). In brief, adeno-associated virus (AAV) vectors transport guided RNAs (gRNAs) and Cas9 into mdx mouse model, gRNAs recognize the mutated Dmd exon 23 (having a stop codon), and Cas9 cut the mutated exon 23 off the Dmd gene. These manipulations restored expression of truncated but partially functional dystrophin, improved skeletal and cardiac muscle function, and increased survival of mdx mice significantly. This review concisely summarized the related advancements and discussed their primary implications in the future gene therapy of DMD, including AAV-vector selection, gRNA designing, Cas9 optimization, dystrophin-restoration efficiency, administration routes, and systemic and long-term therapeutic efficacy. Future orientations, including off-target effects, safety concerns, immune responses, precision medicine, and Dmd-editing in the brain (potentially blocked by the blood-brain barrier) were also elucidated briefly. Collectively, the AAV-mediated and RNA-guided CRISPR/Cas9 system has major superiorities compared with traditional gene therapy, and might contribute to the treatment of DMD and BMD substantially in the near future.

Rapid evaporative ionisation mass spectrometry of electrosurgical vapours for the identification of breast pathology: towards an intelligent knife for breast cancer surgery

BACKGROUND

Re-operation for positive resection margins following breast-conserving surgery occurs frequently (average = 20-25%), is cost-inefficient, and leads to physical and psychological morbidity. Current margin assessment techniques are slow and labour intensive. Rapid evaporative ionisation mass spectrometry (REIMS) rapidly identifies dissected tissues by determination of tissue structural lipid profiles through on-line chemical analysis of electrosurgical aerosol toward real-time margin assessment.

METHODS

Electrosurgical aerosol produced from ex-vivo and in-vivo breast samples was aspirated into a mass spectrometer (MS) using a monopolar hand-piece. Tissue identification results obtained by multivariate statistical analysis of MS data were validated by histopathology. Ex-vivo classification models were constructed from a mass spectral database of normal and tumour breast samples. Univariate and tandem MS analysis of significant peaks was conducted to identify biochemical differences between normal and cancerous tissues. An ex-vivo classification model was used in combination with bespoke recognition software, as an intelligent knife (iKnife), to predict the diagnosis for an ex-vivo validation set. Intraoperative REIMS data were acquired during breast surgery and time-synchronized to operative videos.

RESULTS

A classification model using histologically validated spectral data acquired from 932 sampling points in normal tissue and 226 in tumour tissue provided 93.4% sensitivity and 94.9% specificity. Tandem MS identified 63 phospholipids and 6 triglyceride species responsible for 24 spectral differences between tissue types. iKnife recognition accuracy with 260 newly acquired fresh and frozen breast tissue specimens (normal n = 161, tumour n = 99) provided sensitivity of 90.9% and specificity of 98.8%. The ex-vivo and intra-operative method produced visually comparable high intensity spectra. iKnife interpretation of intra-operative electrosurgical vapours, including data acquisition and analysis was possible within a mean of 1.80 seconds (SD ±0.40).

CONCLUSIONS

The REIMS method has been optimised for real-time iKnife analysis of heterogeneous breast tissues based on subtle changes in lipid metabolism, and the results suggest spectral analysis is both accurate and rapid. Proof-of-concept data demonstrate the iKnife method is capable of online intraoperative data collection and analysis. Further validation studies are required to determine the accuracy of intra-operative REIMS for oncological margin assessment.

'To test or not to test', the arguments for and against thrombophilia testing in obstetrics

Clinicians increasingly investigate women for thrombophilias due to their associations with venous thromboembolism and placenta-mediated pregnancy complication. These associations, however, are modest and based largely on retrospective data from studies with heterogeneous classifications and populations, leading to discordance between evidence and guidelines. Current evidence suggests a contributory rather than causative role for thrombophilia in placenta-mediated pregnancy complication and venous thromboembolism. With little evidence of benefit from antithrombotic therapy in placenta-mediated pregnancy complication, thrombophilia screening remains controversial. Given the low absolute risk of placenta-mediated pregnancy complication and gestational venous thromboembolism with heritable thrombophilia, universal screening is inappropriate. Selective screening for antiphospholipid syndrome is supported by robust evidence of benefit. Conversely, selective screening for heritable thrombophilia has not been shown to effectively manage placenta-mediated pregnancy complication. Therefore, at present heritable thrombophilia screening is not warranted for placenta-mediated pregnancy complication. Until we have better evidence from better stratified patient groups, caution should remain if we wish to practice evidence-based medicine.