Can machines learn the mutation signatures of SARS-CoV-2 and enable viral-genotype guided predictive prognosis?

MotivationContinuous emergence of new variants through appearance/accumulation/disappearance of mutations is a hallmark of many viral diseases. SARS-CoV-2 variants have particularly exerted tremendous pressure on global healthcare system owing to their life threatening and debilitating implications. The sheer plurality of variants and huge scale of genomic data have added to the challenges of tracing the mutations/variants and their relationship to infection severity (if any).ResultsWe explored the suitability of virus-genotype guided machine-learning in infection prognosis and identification of features/mutations-of-interest. Total 199,519 outcome-traced genomes, representing 45,625 nucleotide-mutations, were employed. Among these, post data-cleaning, Low and High severity genomes were classified using an integrated model (employing virus genotype, epitopic-influence and patient-age) with consistently high ROC-AUC (Asia:0.97 ± 0.01, Europe:0.94 ± 0.01, N.America:0.92 ± 0.02, Africa:0.94 ± 0.07, S.America:0.93 ± 03). Although virus-genotype alone could enable high predictivity (0.97 ± 0.01, 0.89 ± 0.02, 0.86 ± 0.04, 0.95 ± 0.06, 0.9 ± 0.04), the performance was not found to be consistent and the models for a few geographies displayed significant improvement in predictivity when the influence of age and/or epitope was incorporated with virus-genotype (Wilcoxon p_BH < 0.05). Neither age or epitopic-influence or clade information could out-perform the integrated features. A sparse model (6 features), developed using patient-age and epitopic-influence of the mutations, performed reasonably well (>0.87 ± 0.03, 0.91 ± 0.01, 0.87 ± 0.03, 0.84 ± 0.08, 0.89 ± 0.05). High-performance models were employed for inferring the important mutations-of-interest using Shapley Additive exPlanations (SHAP). The changes in HLA interactions of the mutated epitopes of reference SARS-CoV-2 were then subsequently probed. Notably, we also describe the significance of a ‘temporal-modeling approach’ to benchmark the models linked with continuously evolving pathogens. We conclude that while machine learning can play a vital role in identifying relevant mutations and factors driving the severity, caution should be exercised in using the genotypic signatures for predictive prognosis.     

Identification of prognostic genes for recurrent risk prediction in triple negative breast cancer patients in Taiwan

Discrepancies in the prognosis of triple negative breast cancer exist between Caucasian and Asian populations. Yet, the gene signature of triple negative breast cancer specifically for Asians has not become available. Therefore, the purpose of this study is to construct a prediction model for recurrence of triple negative breast cancer in Taiwanese patients. Whole genome expression profiling of breast cancers from 185 patients in Taiwan from 1995 to 2008 was performed, and the results were compared to the previously published literature to detect differences between Asian and Western patients. Pathway analysis and Cox proportional hazard models were applied to construct a prediction model for the recurrence of triple negative breast cancer. Hierarchical cluster analysis showed that triple negative breast cancers from different races were in separate sub-clusters but grouped in a bigger cluster. Two pathways, cAMP-mediated signaling and ephrin receptor signaling, were significantly associated with the recurrence of triple negative breast cancer. After using stepwise model selection from the combination of the initial filtered genes, we developed a prediction model based on the genes SLC22A23, PRKAG3, DPEP3, MORC2, GRB7, and FAM43A. The model had 91.7% accuracy, 81.8% sensitivity, and 94.6% specificity under leave-one-out support vector regression. In this study, we identified pathways related to triple negative breast cancer and developed a model to predict its recurrence. These results could be used for assisting with clinical prognosis and warrant further investigation into the possibility of targeted therapy of triple negative breast cancer in Taiwanese patients.     

CXCL12 chemokine and CXCR4 receptor: association with susceptibility and prognostic markers in triple negative breast cancer

CXCL12/CXCR4 signaling has been implicated in breast carcinogenesis, and genetic polymorphisms in these molecules have been associated with different types of cancer. The present study analyzed genetic polymorphisms in CXCL12 (rs1801157, G?>?A) and CXCR4 (rs2228014, C?>?T) and CXCR4 immunostaining in tumor tissues from patients with triple negative breast cancer (TNBC) aiming to evaluate their possible role in its’ susceptibility and prognosis. Genetic polymorphisms were analyzed in 59 TNBC patients and 150 control women; age-adjusted logistic regression showed no association when variants were considered in isolation; however, a statistically significant interaction was noted for heterozygosis for both allelic variants increasing the odds for TNBC (CXCL12-GA by CXCR4-CT: OR 7.23; 95% CI 1.15–45.41; p?=?0.035). CXCL12 polymorphism was correlated negatively with proliferation index (Ki67) (Tau-b?=???0.406; p?=?0.006). CXCR4 immunostaining was evaluated in 37 TNBC patients (22 with paired tumor-normal adjacent tissue). CXCR4 was detected more intensely in cell cytoplasm than in membrane, and was more expressed in tumor than in normal adjacent tissues, although not statistically significant. CXCR4 expression on the membrane of tumor cells was correlated positively with histopathological grade (Tau-b?=?0.271; p?=?0.036) and negatively with lymph node metastasis (Tau-b?=???0.478; p?=?0.036). The present study indicates that CXCL12 and CXCR4 polymorphisms and CXCR4 immunostaining might have susceptibility and prognostic roles in TNBC pathogenesis.     

Single peptides and combination modalities for triple negative breast cancer

Unlike other types of breast cancers (BCs), no specific therapeutic targets have been established for triple negative breast cancer (TNBC). Therefore, chemotherapy and radiotherapy are the only available adjuvant therapeutic choices for TNBC. New emerging reports show that TNBC is associated with higher numbers of intratumoral tumor infiltrating lymphocytes. This is indicative of host anti-TNBC immune surveillance and suggesting that immunotherapy can be considered as a therapeutic approach for TNBC management. Recent progress in molecular mechanisms of tumor-immune system interaction and cancer vaccine development studies, fast discoveries and FDA approvals of immune checkpoint inhibitors, chimeric antigen receptor T-cells, and oncolytic virotherapy have significantly attracted attention and research directions toward the immunotherapeutic approach to TNBC. Here in this review different aspects of TNBC immunotherapies including the host immune system-tumor interactions, the tumor microenvironment, the relevant molecular targets for immunotherapy, and clinical trials in the field are discussed.     

Comprehensive analysis of novel three-long noncoding RNA signatures as a diagnostic and prognostic biomarkers of human triple-negative breast cancer

Currently, traditional predictors of prognosis (tumor size, nodal status, progesterone receptor [PR], estrogen receptor [ER], or human epidermal growth factor receptor-2 [HER2]) are insufficient for precise survival prediction for triple-negative breast cancer (TNBC). Long noncoding RNAs (lncRNAs) have been observed to exert critical functions in cancer, including in TNBC. Nevertheless, systematically tracking expression-based lncRNA biomarkers based on the sequence data for the prediction of prognosis in TNBC has not yet been investigated. To ascertain whether biomarkers exist that can distinguish TNBC from adjacent normal tissue or nTNBC, we implemented a comprehensive analysis of lncRNA expression profiles and clinical data of 1097 BC samples from The Cancer Genome Atlas database. A total of 1510 differentially expressed lncRNAs in normal and TNBC samples were extracted. Similarly, 672 differentially expressed lncRNAs between nTNBC and TNBC samples were detected. The receiver operating characteristic curve analysis indicated that three upregulated lncRNAs (AC091043.1, AP000924.1, and FOXCUT) may be of strong diagnostic value for predicting the existence of TNBC in the training and validation sets (area under the curve (AUC > 0.85). Kaplan-Meier analysis demonstrated that the other three lncRNAs (AC010343.3, AL354793.1, and FGF10-AS1) were associated with the prognosis of TNBC patients (P < 0.05). We used the three overall survival (OS)-related lncRNAs to establish a three-lncRNA signature. Multivariate Cox regression analysis suggested that the three-lncRNA signature was a prognostic factor independent of other clinical variables ( P < 0.01) for predicting OS in TNBC patients that could be utilized to classify patients into high- or low-risk subgroups. Our results might provide efficient signatures for clinical diagnosis and prognostic evaluation of TNBC.     

Dormancy signatures and metastasis in estrogen receptor positive and negative breast cancer

Breast cancers can recur after removal of the primary tumor and treatment to eliminate remaining tumor cells. Recurrence may occur after long periods of time during which there are no clinical symptoms. Tumor cell dormancy may explain these prolonged periods of asymptomatic residual disease and treatment resistance. We generated a dormancy gene signature from published experimental models and applied it to both breast cancer cell line expression data as well as four published clinical studies of primary breast cancers. We found that estrogen receptor (ER) positive breast cell lines and primary tumors have significantly higher dormancy signature scores (P<0.0000001) than ER- cell lines and tumors. In addition, a stratified analysis combining all ER+ tumors in four studies indicated 2.1 times higher hazard of recurrence among patients whose tumors had low dormancy scores (LDS) compared to those whose tumors had high dormancy scores (HDS) (p<0.000005). The trend was shown in all four individual studies. Suppression of two dormancy genes, BHLHE41 and NR2F1, resulted in increased in vivo growth of ER positive MCF7 cells. The patient data analysis suggests that disseminated ER positive tumor cells carrying a dormancy signature are more likely to undergo prolonged dormancy before resuming metastatic growth. Furthermore, genes identified with this approach might provide insight into the mechanisms of dormancy onset and maintenance as well as dormancy models using human breast cancer cell lines.     

Network analysis of gene lists for finding reproducible prognostic breast cancer gene signatures

Many genome-scale studies in molecular biology deliver results in the form of a ranked list of gene names, accordingly to some scoring method. There is always the question how many top-ranked genes to consider for further analysis, for example, in order creating a diagnostic or predictive gene signature for a disease. This question is usually approached from a statistical point of view, without considering any biological properties of top-ranked genes or how they are related to each other functionally. Here we suggest a new method for selecting a number of genes in a ranked gene list such that this set forms the Optimally Functionally Enriched Network (OFTEN), formed by known physical interactions between genes or their products. The method allows associating a network with the gene list, providing easier interpretation of the results and classifying the genes or proteins accordingly to their position in the resulting network. We demonstrate the method on four breast cancer datasets and show that 1) the resulting gene signatures are more reproducible from one dataset to another compared to standard statistical procedures and 2) the overlap of these signatures has significant prognostic potential. The method is implemented in BiNoM Cytoscape plugin (http://binom.curie.fr).     

Anti-tumor activity of novel biisoquinoline derivatives against breast cancers

Breast cancer is classified into three groups according to its expression of hormone/growth factor receptors: (i) estrogen receptor (ER) and progesterone receptor (PR)-positive; (ii) human epidermal growth factor receptor 2 (HER2)-positive; and (iii) ER, PR, and HER2-negative (triple-negative). A series of methoxy-substituted biisoquinoline compounds have been synthesized as a potential chemotherapeutic agent for the triple-negative breast cancers which are especially challenging to manage. Structure activity relationship study revealed that rigid 6,6′-dimethoxy biisoquinoline imidazolium compound (1c, DH20931) exhibited the significant growth inhibitory effects on both triple-positive and triple-negative human breast cancer cell lines with IC₅₀ in the range of 0.3–3.9 μM. The 1c (DH20931) is more potent than structurally related noscapine for growth inhibition of MCF7 cell line (IC₅₀ = 1.3 vs 57 μM) and MDA-MB231 cell line (IC₅₀ = 3.9 vs 64 μM).     

Metformin induces unique biological and molecular responses in triple negative breast cancer cells

Triple negative (TN) breast cancer is more frequent in women who are obese or have type II diabetes, as well as young Women of Color. These cancers do not express receptors for the steroid hormones estrogen or progesterone, or the type II receptor tyrosine kinase (RTK) Her-2 but do have upregulation of basal cytokeratins and the epidermal growth factor (EGFR). These data suggest that aberrations of glucose and fatty acid metabolism, signaling through EGFR and genetic factors may promote the development of TN cancers. The anti-type II diabetes drug metformin has been associated with a decreased incidence of breast cancer, although the specific molecular subtypes that may be reduced by metformin have not been reported. Our data indicates that metformin has unique anti-TN breast cancer effects both in vitro and in vivo. It inhibits cell proliferation (with partial S phase arrest), colony formation and induces apoptosis via activation of the intrinsic and extrinsic signaling pathways only in TN breast cancer cell lines. At the molecular level, metformin increases P-AMPK, reduces P-EGFR, EGFR, P-MAPK, P-Src, cyclin D1 and cyclin E (but not cyclin A or B, p27 or p21), and induces PARP cleavage in a dose- and time-dependent manner. These data are in stark contrast to our previously published biological and molecular effects of metformin on luminal A and B, or Her-2 type breast cancer cells. Nude mice bearing tumor xenografts of the TN line MDA-MB-231, treated with metformin, show significant reductions in tumor growth (p=0.0066) and cell proliferation (p=0.0021) as compared to untreated controls. Metformin pre-treatment, before injection of MDA-MB-231 cells, results in a significant decrease in tumor outgrowth and latency. Given the unique anti-cancer activity of metformin against TN disease, both in vitro and in vivo, it should be explored as a therapeutic agent against this aggressive form of breast cancer.     

Nanobiopolymer for direct targeting and inhibition of EGFR expression in triple negative breast cancer

Treatment options for triple negative breast cancer (TNBC) are generally limited to cytotoxic chemotherapy. Recently, anti-epidermal growth factor receptor (EGFR) therapy has been introduced for TNBC patients. We engineered a novel nanobioconjugate based on a poly(β-L-malic acid) (PMLA) nanoplatform for TNBC treatment. The nanobioconjugate carries anti-tumor nucleosome-specific monoclonal antibody (mAb) 2C5 to target breast cancer cells, anti-mouse transferrin receptor (TfR) antibody for drug delivery through the host endothelial system, and Morpholino antisense oligonucleotide (AON) to inhibit EGFR synthesis. The nanobioconjugates variants were: (1) P (BioPolymer) with AON, 2C5 and anti-TfR for tumor endothelial and cancer cell targeting, and EGFR suppression (P/AON/2C5/TfR), and (2) P with AON and 2C5 (P/AON/2C5). Controls included (3) P with 2C5 but without AON (P/2C5), (4) PBS, and (5) P with PEG and leucine ester (LOEt) for endosomal escape (P/mPEG/LOEt). Drugs were injected intravenously to MDA-MB-468 TNBC bearing mice. Tissue accumulation of injected nanobioconjugates labeled with Alexa Fluor 680 was examined by Xenogen IVIS 200 (live imaging) and confocal microscopy of tissue sections. Levels of EGFR, phosphorylated and total Akt in tumor samples were detected by western blotting. In vitro western blot showed that the leading nanobioconjugate P/AON/2C5/TfR inhibited EGFR synthesis significantly better than naked AON. In vivo imaging revealed that 2C5 increased drug-tumor accumulation. Significant tumor growth inhibition was observed in mice treated with the lead nanobioconjugate (1) [P = 0.03 vs. controls; P<0.05 vs. nanobioconjugate variant (2)]. Lead nanobioconjugate (1) also showed stronger inhibition of EGFR expression and Akt phosphorylation than other treatments. Treatment of TNBC with the new nanobioconjugate results in tumor growth arrest by inhibiting EGFR and its downstream signaling intermediate, phosphorylated Akt. The nanobioconjugate represents a new generation of nanodrugs for treatment of TNBC.     

Exploration of a novel prognostic risk signatures and immune checkpoint molecules in endometrial carcinoma microenvironment

In this study, we devoted to investigate immune-related genes and tumor microenvironment (TME) in EC based on The Cancer Genome Atlas (TCGA) database. In total 799 up-regulated and 139 down-regulated immune-related and differentially expressed genes in EC were investigated for functional annotations and prognosis. By a conjoint Cox regression analysis, we built two risk models for OS and DFS, as well as the consistent nomograms. Immune-related pathways were revealed mostly enriched in the low-risk group. By further analyzing TME based on the risk signatures, the higher immune cell infiltration and activation, lower tumor purity and higher tumor mutational burden were found in low-risk group, which presented a better prognosis. Both the expression and immunophenoscore of immune checkpoints PD-1, CTLA4, PD-L1 and PD-L2 increased significantly in low-risk group. These findings may provide new ideas for novel biomarkers and immunotherapy targets in EC.     

Antitumor activity of Noscapine in combination with Doxorubicin in triple negative breast cancer

Background

The aim of this study was to investigate the anticancer activity and mechanism of action of Noscapine alone and in combination with Doxorubicin against triple negative breast cancer (TNBC).

Methods

TNBC cells were pretreated with Noscapine or Doxorubicin or combination and combination index values were calculated using isobolographic method. Apoptosis was assessed by TUNEL staining. Female athymic Nu/nu mice were xenografted with MDA-MB-231 cells and the efficacy of Noscapine, Doxorubicin and combination was determined. Protein expression, immunohistochemical staining were evaluated in harvested tumor tissues.

Results

Noscapine inhibited growth of MDA-MB-231 and MDA-MB-468 cells with the IC₅₀ values of 36.16±3.76 and 42.7±4.3 µM respectively. The CI values (<0.59) were suggestive of strong synergistic interaction between Noscapine and Doxorubicin and combination treatment showed significant increase in apoptotic cells. Noscapine showed dose dependent reduction in the tumor volumes at a dose of 150–550 mg/kg/day compared to controls. Noscapine (300 mg/kg), Doxorubicin (1.5 mg/kg) and combination treatment reduced tumor volume by 39.4±5.8, 34.2±5.7 and 82.9±4.5 percent respectively and showed decreased expression of NF-KB pathway proteins, VEGF, cell survival, and increased expression of apoptotic and growth inhibitory proteins compared to single-agent treatment and control groups.

Conclusions

Noscapine potentiated the anticancer activity of Doxorubicin in a synergistic manner against TNBC tumors via inactivation of NF-KB and anti-angiogenic pathways while stimulating apoptosis. These findings suggest potential benefit for use of oral Noscapine and Doxorubicin combination therapy for treatment of more aggressive TNBC.     

A serum protein profile predictive of the resistance to neoadjuvant chemotherapy in advanced breast cancers

Prediction of the responses to neoadjuvant chemotherapy (NACT) can improve the treatment of patients with advanced breast cancer. Genes and proteins predictive of chemoresistance have been extensively studied in breast cancer tissues. However, noninvasive serum biomarkers capable of such prediction have been rarely exploited. Here, we performed profiling of N-glycosylated proteins in serum from fifteen advanced breast cancer patients (ten patients sensitive to and five patients resistant to NACT) to discover serum biomarkers of chemoresistance using a label-free liquid chromatography-tandem MS method. By performing a series of statistical analyses of the proteomic data, we selected thirteen biomarker candidates and tested their differential serum levels by Western blotting in 13 independent samples (eight patients sensitive to and five patients resistant to NACT). Among the candidates, we then selected the final set of six potential serum biomarkers (AHSG, APOB, C3, C9, CP, and ORM1) whose differential expression was confirmed in the independent samples. Finally, we demonstrated that a multivariate classification model using the six proteins could predict responses to NACT and further predict relapse-free survival of patients. In summary, global N-glycoproteome profile in serum revealed a protein pattern predictive of the responses to NACT, which can be further validated in large clinical studies.