The landscape of cancer genes and mutational processes in breast cancer

Identification of a centrosome-related prognostic signature for breast cancer

Background

As the major microtubule organizing center in animal cells, the centrosome is implicated with human breast tumor in multiple ways, such as promotion of tumor cell immune evasion. Here, we aimed to detect the expression of centrosome-related genes (CRGs) in normal and malignant breast tissues, and construct a novel centrosome-related prognostic model to discover new biomarkers and screen drugs for breast cancer.

Methods

We collected CRGs from the public databases and literature. The differentially expressed CRGs between normal and malignant breast tissues were identified by the DESeq2. Univariate Cox and LASSO regression analyses were conducted to screen candidate prognostic CRGs and develop a centrosome-related signature (CRS) to score breast cancer patients. We further manipulated and visualized data from TCGA, GEO, IMvigor210, TCIA and TIMER to explore the correlation between CRS and patient outcomes, clinical manifestations, mutational landscapes, tumor immune microenvironments, and responses to diverse therapies. Single cell analyses were performed to investigate the difference of immune cell landscape between high- and low-risk group patients. In addition, we constructed a nomogram to guide clinicians in precise treatment.

Results

A total of 726 CRGs were collected from the public databases and literature. PSME2, MAPK10, EIF4EBP1 were screened as the prognostic genes in breast cancer. Next, we constructed a centrosome-related prognostic signature and validated its efficacy based on the genes for predicting the survival of breast cancer patients. The high-risk group patients had poor prognoses, the area under the ROC curve for 1-, 3-, and 5-year overall survival (OS) was 0.77, 0.67, and 0.65, respectively. The predictive capacity of CRS was validated by other datasets from GEO dataset. In addition, high-risk group patients exhibited elevated level of mutational landscapes and decreased level of immune infiltration, especially T and B lymphocytes. In terms of treatment responses, patients in the high-risk group were found to be resistant to immunotherapy but sensitive to chemotherapy. Moreover, we screened a series of candidate anticancer drugs with high sensitivity in the high-risk group.

Conclusion

Our work exploited a centrosome-related prognostic signature and developed a predictive nomogram capable of accurately predicting breast cancer OS. The above discoveries provide deeper insights into the vital roles of the centrosome and contribute to the development of personalized treatment for breast cancer.

Mechanisms of Endocrine Resistance in Hormone Receptor-Positive Breast Cancer

Hormone receptor-positive (HR+) breast cancer (BC) accounts for approximately 70% of all breast invasive tumors. Endocrine therapy (ET) represents the standard treatment for HR + BC. Most patients, however, eventually develop resistance to ET, which limits their effectiveness and poses a major challenge for the management of HR + BC. Several mechanisms that contribute to ET resistance have been described. One of the most common mechanisms is the upregulation of alternative signaling pathways that can bypass estrogen dependency, such as activation of the PI3K/Akt/mTOR as well as mitogen-activated protein kinase (MAPK) and the insulin-like growth factor 1 receptor (IGF-1R) pathways. Another common mechanism of endocrine resistance is the acquisition of activating mutations of ESR1, which encodes for the estrogen receptor, that lead to structural changes of the receptor, prevent the binding to anti-estrogen drugs and result in constitutive activation of the receptor, even in the absence of estrogens. Epigenetic changes, such as DNA methylation and histone modifications, can also contribute to ET resistance by altering the expression of genes that are involved in estrogen signaling. Understanding the mechanisms of resistance to ET is crucial for the development of new therapies that can overcome resistance and improve outcomes for patients with HR + BC.

Multiomics in primary and metastatic breast tumors from the AURORA US network finds microenvironment and epigenetic drivers of metastasis

The AURORA US Metastasis Project was established with the goal to identify molecular features associated with metastasis. We assayed 55 females with metastatic breast cancer (51 primary cancers and 102 metastases) by RNA sequencing, tumor/germline DNA exome and low-pass whole-genome sequencing and global DNA methylation microarrays. Expression subtype changes were observed in ~30% of samples and were coincident with DNA clonality shifts, especially involving HER2. Downregulation of estrogen receptor (ER)-mediated cell-cell adhesion genes through DNA methylation mechanisms was observed in metastases. Microenvironment differences varied according to tumor subtype; the ER+/luminal subtype had lower fibroblast and endothelial content, while triple-negative breast cancer/basal metastases showed a decrease in B and T cells. In 17% of metastases, DNA hypermethylation and/or focal deletions were identified near HLA-A and were associated with reduced expression and lower immune cell infiltrates, especially in brain and liver metastases. These findings could have implications for treating individuals with metastatic breast cancer with immune- and HER2-targeting therapies.

The complex nature of heterogeneity and its roles in breast cancer biology and therapeutic responsiveness

Heterogeneity is a complex feature of cells and tissues with many interacting components. Depending on the nature of the research context, interacting features of cellular, drug response, genetic, molecular, spatial, temporal, and vascular heterogeneity may be present. We describe the various forms of heterogeneity with examples of their interactions and how they play a role in affecting cellular phenotype and drug responses in breast cancer. While cellular heterogeneity may be the most widely described and invoked, many forms of heterogeneity are evident within the tumor microenvironment and affect responses to the endocrine and cytotoxic drugs widely used in standard clinical care. Drug response heterogeneity is a critical determinant of clinical response and curative potential and also is multifaceted when encountered. The interactive nature of some forms of heterogeneity is readily apparent. For example, the process of metastasis has the properties of both temporal and spatial heterogeneity within the host, whereas each individual metastatic deposit may exhibit cellular, genetic, molecular, and vascular heterogeneity. This review describes the many forms of heterogeneity, their integrated activities, and offers some insights into how heterogeneity may be understood and studied in the future.

Whole Genome Sequencing of Single-Circulating Tumor Cell Ameliorates Unraveling Breast Cancer Heterogeneity

Objective

Because tumor tissues are most frequently heterogeneous and hard to characterize, the resulting therapeutic strategy could be misled. The most active and invasive tumor cells are circulating tumor cells (CTCs). In this study, we investigated the feasibility of individualized treatment of breast cancer patients based on whole genome sequencing (WGS) of single cell CTC.

Methods

Twenty-four CTCs were identified in three breast cancer patients. For each patient, one polyploid CTC was captured, and on which the WGS was performed. WGS was considered due to its sequencing robustness compared to conventional sequencing approaches. Based on the histopathological Her-2 status in tumor tissue and the HER2 gene status in WGS results of CTC, we adjusted treatment strategies and monitored disease progression.

Results

Patients ID1 and ID2 are found to be Her-2 positive in primary tumors and HER2 gene amplification in the DNA of CTCs. In-patient ID3, histopathological examination of the primary tumor and liver metastases revealed Her-2 negative, but the WGS analysis of CTC showed HER2 gene amplification. After adjusting treatment by adding Her-2 inhibitors according to the results of CTC sequencing, liver metastases and pleural effusion were significantly reduced two months later, CTC number and ctDNA burden were decreased, and 18-months progression-free survival (PFS) was recorded. In addition, some potential therapeutic targets and mutations in drug-resistant genes were detected.

Conclusion

The results of CTC sequencing effectively guided the treatment of a patient with HER2 gene amplification in CTC but with Her-2 negative on tumor tissue. Therefore, CTC sequencing could help resolve the heterogeneity of tumors and provide precision medicine for patients.

A network-based method for identifying cancer driver genes based on node control centrality

Cancer is one of the major contributors to human mortality and has a serious influence on human survival and health. In biomedical research, the identification of cancer driver genes (cancer drivers for short) is an important task; cancer drivers can promote the progression and generation of cancer. To identify cancer drivers, many methods have been developed. These computational models only identify coding cancer drivers; however, non-coding drivers likewise play significant roles in the progression of cancer. Hence, we propose a Network-based Method for identifying cancer Driver Genes based on node Control Centrality (NMDGCC), which can identify coding and non-coding cancer driver genes. The process of NMDGCC for identifying driver genes mainly includes the following two steps. In the first step, we construct a gene interaction network by using mRNAs and miRNAs expression data in the cancer state. In the second step, the control centrality of the node is used to identify cancer drivers in the constructed network. We use the breast cancer dataset from The Cancer Genome Atlas (TCGA) to verify the effectiveness of NMDGCC. Compared with the existing methods of cancer driver genes identification, NMDGCC has a better performance. NMDGCC also identifies 295 miRNAs as non-coding cancer drivers, of which 158 are related to tumorigenesis of BRCA. We also apply NMDGCC to identify driver genes related to the different breast cancer subtypes. The result shows that NMDGCC detects many cancer drivers of specific cancer subtypes.

SETD7 Expression Is Associated with Breast Cancer Survival Outcomes for Specific Molecular Subtypes: A Systematic Analysis of Publicly Available Datasets

SETD7 is a lysine N-methyltransferase that targets many proteins important in breast cancer (BC). However, its role and clinical significance remain unclear. Here, we used online tools and multiple public datasets to explore the predictive potential of SETD7 expression (high or low quartile) considering BC subtype, grade, stage, and therapy. We also investigated overrepresented biological processes associated with its expression using TCGA-BRCA data. SETD7 expression was highest in the Her2 (ERBB2)-enriched molecular subtype and lowest in the basal-like subtype. For the basal-like subtype specifically, higher SETD7 was consistently correlated with worse recurrence-free survival (p < 0.009). High SETD7-expressing tumours further exhibited a higher rate of ERBB2 mutation (20% vs. 5%) along with a poorer response to anti-Her2 therapy. Overall, high SETD7-expressing tumours showed higher stromal and lower immune scores. This was specifically related to higher counts of cancer-associated fibroblasts and endothelial cells, but lower B and T cell signatures, especially in the luminal A subtype. Genes significantly associated with SETD7 expression were accordingly overrepresented in immune response processes, with distinct subtype characteristics. We conclude that the prognostic value of SETD7 depends on the BC subtype and that SETD7 may be further explored as a potential treatment-predictive marker for immune checkpoint inhibitors.

Phosphoproteomic analysis of neoadjuvant breast cancer suggests that increased sensitivity to paclitaxel is driven by CDK4 and filamin A

Precision oncology research is challenging outside the contexts of oncogenic addiction and/or targeted therapies. We previously showed that phosphoproteomics is a powerful approach to reveal patient subsets of interest characterized by the activity of a few kinases where the underlying genomics is complex. Here, we conduct a phosphoproteomic screening of samples from HER2-negative female breast cancer receiving neoadjuvant paclitaxel (N = 130), aiming to find candidate biomarkers of paclitaxel sensitivity. Filtering 11 candidate biomarkers through 2 independent patient sets (N = 218) allowed the identification of a subgroup of patients characterized by high levels of CDK4 and filamin-A who had a 90% chance of achieving a pCR in response to paclitaxel. Mechanistically, CDK4 regulates filamin-A transcription, which in turn forms a complex with tubulin and CLIP-170, which elicits increased binding of paclitaxel to microtubules, microtubule acetylation and stabilization, and mitotic catastrophe. Thus, phosphoproteomics allows the identification of explainable factors for predicting response to paclitaxel.

MicroRNA-dependent mechanisms of taxane resistance in breast cancer

Breast cancer (BC) has a leading position in the statistics of oncological morbidity and mortality among women. Taxan-based polychemotherapy regimens are an essential component of the complex therapy of the BC. However, currently used algorithms of taxan-based regimens application do not always provide with desire effect. It indicates the need to identify new prognostic markers and to develop new approaches to modify response of BC cells to standard therapeutic regimens. MicroRNAs, small RNA molecules regulating protein synthesis, are considered as promising markers and potential modulators of the BC cells sensitivity to taxanes.

The review includes a brief summary of the molecular mechanisms of action of the taxanes and the mechanism BC resistance to the process of microtubules depolymerization, provides with analysis of recent experimental and observational studies of the role of microRNAs in control of these mechanisms, and evaluates prospects for the development of new approaches to predict and to improve the cytostatic effects of taxanes through the analysis and modification of cellular microRNAs.

High SURF4 expression is associated with poor prognosis of breast cancer

SURF4 has been suggested as an oncogene in cancer. However, the role of SURF4 in breast cancer has not been demonstrated yet. The data were obtained from TCGA database and 1104 patients were analyzed using bioinformatics analysis. SURF4 is significantly (P < 0.001) highly expressed in tumor. High expression of SURF4 was observed in T4, infiltrating ductal carcinoma, ER negative, PR negative, and HER2 positive, female, patients without lymph node metastasis, HER2 overexpression type, and deceased patients. As for characteristics correlated with high expression of SURF4, gender, histological type, molecular subtype, ER, PR, HER2, and vital status exhibited significant differences. The age (HR: 2.317, P < 0.001), stage (HR: 2.090, P < 0.001), and SURF4 expression (HR: 1.958, P = 0.005) exhibited independent prognostic value for overall survival (OS). Patients with high SURF4 expression, higher age, equivocal HER2, higher stages, or positive margin status had shorter OS. The stage (HR: 1.579, P < 0.001), and margin status (HR: 1.463, P = 0.006) exhibited independent prognostic value for relapse-free survival of breast cancer. High expression of SURF4 was first found in breast cancer. High SURF4 expression was observed in breast cancer tissue and cell. SURF4 promoted the proliferation and migration of 4T1 cells. SURF4 may be a biomarker in diagnosis and prognosis of breast cancer.

dTMP imbalance through thymidylate 5'-phosphohydrolase activity induces apoptosis in triple-negative breast cancers

Immunotherapy has a number of advantages over traditional anti-tumor therapy but can cause severe adverse reactions due to an overactive immune system. In contrast, a novel metabolic treatment approach can induce metabolic vulnerability through multiple cancer cell targets. Here, we show a therapeutic effect by inducing nucleotide imbalance and apoptosis in triple negative breast cancer cells (TNBC), by treating with cytosolic thymidylate 5'-phosphohydrolase (CT). We show that a sustained consumption of dTMP by CT could induce dNTP imbalance, leading to apoptosis as tricarboxylic acid cycle intermediates were depleted to mitigate this imbalance. These cytotoxic effects appeared to be different, depending on substrate specificity of the 5' nucleotide or metabolic dependency of the cancer cell lines. Using representative TNBC cell lines, we reveal how the TNBC cells were affected by CT-transfection through extracellular acidification rate (ECAR)/oxygen consumption rate (OCR) analysis and differential transcription/expression levels. We suggest a novel approach for treating refractory TNBC by an mRNA drug that can exploit metabolic dependencies to exacerbate cell metabolic vulnerability.

The Analysis of Relevant Gene Networks Based on Driver Genes in Breast Cancer

BACKGROUND

The occurrence and development of breast cancer has a strong correlation with a person's genetics. Therefore, it is important to analyze the genetic factors of breast cancer for future development of potential targeted therapies from the genetic level.

METHODS

In this study, we complete an analysis of the relevant protein-protein interaction network relating to breast cancer. This includes three steps, which are breast cancer-relevant genes selection using mutual information method, protein-protein interaction network reconstruction based on the STRING database, and vital genes calculating by nodes centrality analysis.

RESULTS

The 230 breast cancer-relevant genes were chosen in gene selection to reconstruct the protein-protein interaction network and some vital genes were calculated by node centrality analyses. Node centrality analyses conducted with the top 10 and top 20 values of each metric found 19 and 39 statistically vital genes, respectively. In order to prove the biological significance of these vital genes, we carried out the survival analysis and DNA methylation analysis, inquired about the prognosis in other cancer tissues and the RNA expression level in breast cancer. The results all proved the validity of the selected genes.

CONCLUSIONS

These genes could provide a valuable reference in clinical treatment among breast cancer patients.

Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor

Mutational signature analysis is commonly performed in cancer genomic studies. Here, we present SigProfilerExtractor, an automated tool for de novo extraction of mutational signatures, and benchmark it against another 13 bioinformatics tools by using 34 scenarios encompassing 2,500 simulated signatures found in 60,000 synthetic genomes and 20,000 synthetic exomes. For simulations with 5% noise, reflecting high-quality datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true-positive signatures while yielding 5-fold less false-positive signatures. Applying SigProfilerExtractor to 4,643 whole-genome- and 19,184 whole-exome-sequenced cancers reveals four novel signatures. Two of the signatures are confirmed in independent cohorts, and one of these signatures is associated with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting signatures, and several novel mutational signatures, including one putatively attributed to direct tobacco smoking mutagenesis in bladder tissues.

A Case Series Exploration of Multi-Regional Expression Heterogeneity in Triple-Negative Breast Cancer Patients

Extensive intratumoral heterogeneity (ITH) is believed to contribute to therapeutic failure and tumor recurrence, as treatment-resistant cell clones can survive and expand. However, little is known about ITH in triple-negative breast cancer (TNBC) because of the limited number of single-cell sequencing studies on TNBC. In this study, we explored ITH in TNBC by evaluating gene expression-derived and imaging-derived multi-region differences within the same tumor. We obtained tissue specimens from 10 TNBC patients and conducted RNA sequencing analysis of 2-4 regions per tumor. We developed a novel analysis framework to dissect and characterize different types of variability: between-patients (inter-tumoral heterogeneity), between-patients across regions (inter-tumoral and region heterogeneity), and within-patient, between-regions (regional intratumoral heterogeneity). We performed a Bayesian changepoint analysis to assess and classify regional variability as low (convergent) versus high (divergent) within each patient feature (TNBC and PAM50 subtypes, immune, stroma, tumor counts and tumor infiltrating lymphocytes). Gene expression signatures were categorized into three types of variability: between-patients (108 genes), between-patients across regions (183 genes), and within-patients, between-regions (778 genes). Based on the between-patient gene signature, we identified two distinct patient clusters that differed in menopausal status. Significant intratumoral divergence was observed for PAM50 classification, tumor cell counts, and tumor-infiltrating T cell abundance. Other features examined showed a representation of both divergent and convergent results. Lymph node stage was significantly associated with divergent tumors. Our results show extensive intertumoral heterogeneity and regional ITH in gene expression and image-derived features in TNBC. Our findings also raise concerns regarding gene expression based TNBC subtyping. Future studies are warranted to elucidate the role of regional heterogeneity in TNBC as a driver of treatment resistance.

K-RAS Associated Gene-Mutation-Based Algorithm for Prediction of Treatment Response of Patients with Subtypes of Breast Cancer and Especially Triple-Negative Cancer

Purpose: There is an urgent need for developing new biomarker tools to accurately predict treatment response of breast cancer, especially the deadly triple-negative breast cancer. We aimed to develop gene-mutation-based machine learning (ML) algorithms as biomarker classifiers to predict treatment response of first-line chemotherapy with high precision. Methods: Random Forest ML was applied to screen the algorithms of various combinations of gene mutation profiles of primary tumors at diagnosis using a TCGA Cohort (n = 399) with up to 150 months follow-up as a training set and validated in a MSK Cohort (n = 807) with up to 220 months follow-up. Subtypes of breast cancer including triple-negative and luminal A (ER+, PR+ and HER2−) were also assessed. The predictive performance of the candidate algorithms as classifiers was further assessed using logistic regression, Kaplan−Meier progression-free survival (PFS) plot, and univariate/multivariate Cox proportional hazard regression analyses. Results: A novel algorithm termed the 12-Gene Algorithm based on mutation profiles of KRAS, PIK3CA, MAP3K1, MAP2K4, PTEN, TP53, CDH1, GATA3, KMT2C, ARID1A, RunX1, and ESR1, was identified. The performance of this algorithm to distinguish non-progressed (responder) vs. progressed (non-responder) to treatment in the TCGA Cohort as determined using AUC was 0.96 (95% CI 0.94−0.98). It predicted progression-free survival (PFS) with hazard ratio (HR) of 21.6 (95% CI 11.3−41.5) (p < 0.0001) in all patients. The algorithm predicted PFS in the triple-negative subgroup with HR of 19.3 (95% CI 3.7−101.3) (n = 42, p = 0.000). The 12-Gene Algorithm was validated in the MSK Cohort with a similar AUC of 0.97 (95% CI 0.96−0.98) to distinguish responder vs. non-responder patients, and had a HR of 18.6 (95% CI 4.4−79.2) to predict PFS in the triple-negative subgroup (n = 75, p < 0.0001). Conclusions: The novel 12-Gene algorithm based on multitude gene-mutation profiles identified through ML has a potential to predict breast cancer treatment response to therapies, especially in triple-negative subgroups patients, which may assist personalized therapies and reduce mortality.

Interpretation of the role of germline and somatic non-coding mutations in cancer: expression and chromatin conformation informed analysis

BACKGROUND

There has been extensive scrutiny of cancer driving mutations within the exome (especially amino acid altering mutations) as these are more likely to have a clear impact on protein functions, and thus on cell biology. However, this has come at the neglect of systematic identification of regulatory (non-coding) variants, which have recently been identified as putative somatic drivers and key germline risk factors for cancer development. Comprehensive understanding of non-coding mutations requires understanding their role in the disruption of regulatory elements, which then disrupt key biological functions such as gene expression.

MAIN BODY

We describe how advancements in sequencing technologies have led to the identification of a large number of non-coding mutations with uncharacterized biological significance. We summarize the strategies that have been developed to interpret and prioritize the biological mechanisms impacted by non-coding mutations, focusing on recent annotation of cancer non-coding variants utilizing chromatin states, eQTLs, and chromatin conformation data.

CONCLUSION

We believe that a better understanding of how to apply different regulatory data types into the study of non-coding mutations will enhance the discovery of novel mechanisms driving cancer.

Immune landscape and risk prediction based on pyroptosis-related molecular subtypes in triple-negative breast cancer

The survival outcome of triple-negative breast cancer (TNBC) remains poor, with difficulties still existing in prognosis assessment and patient stratification. Pyroptosis, a newly discovered form of programmed cell death, is involved in cancer pathogenesis and progression. The role of pyroptosis in the tumor microenvironment (TME) of TNBC has not been fully elucidated. In this study, we disclosed global alterations in 58 pyroptosis-related genes at somatic mutation and transcriptional levels in TNBC samples collected from The Cancer Genome Atlas and Gene Expression Omnibus databases. Based on the expression patterns of genes related to pyroptosis, we identified two molecular subtypes that harbored different TME characteristics and survival outcomes. Then, based on differentially expressed genes between two subtypes, we established a 12-gene score with robust efficacy in predicting short- and long-term overall survival of TNBC. Patients at low risk exhibited a significantly better prognosis, more antitumor immune cell infiltration, and higher expression of immune checkpoints including PD-1, PD-L1, CTLA-4, and LAG3. The comprehensive analysis of the immune landscape in TNBC indicated that alterations in pyroptosis-related genes were closely related to the formation of the immune microenvironment and the intensity of the anticancer response. The 12-gene score provided new information on the risk stratification and immunotherapy strategy for highly heterogeneous patients with TNBC.

Combination of AURKA inhibitor and HSP90 inhibitor to treat breast cancer with AURKA overexpression and TP53 mutations

Breast cancer is the most common cancer among women worldwide. Researches show that Aurora kinase A (AURKA) is highly expressed in approximately 73% of breast cancer patients, which induces drug resistance in breast cancer patients and decreases the median survival time. AURKA regulates spindle assembly, centrosome maturation, and chromosome alignment. AURKA overexpression affects the occurrence and development of breast cancer. Besides AURKA overexpression, heat shock protein 90 (HSP90) maintains the survival and proliferation of tumor cells by stabilizing the structure of oncoproteins, including P53 mutants (mtP53). TP53 mutations accounted for approximately 13%, 40%, 80%, 33%, 71%, and 82% of luminal A, Luminal B, Luminal C, normal basal-like, HER2-amplified, and basal-like breast cancers, respectively. TP53 mutation can aggravate cell genome instability and enhance the invasion, migration, and resistance of cancer cell. This review describes the research status of AURKA and HSP90 in breast cancer, summarizes the structure, function, and the chaperone cycle of HSP90, elaborates the interrelation between HSP90, mtP53, P53, and AURKA, and proposes the combination of HSP90 inhibitor and AURKA inhibitor to treat breast cancer. Targeting AURKA and HSP90 to treat cancer with AURKA overexpression and TP53 mutations will help improve the specificity and efficiency of breast cancer treatment and solve the problem of drug resistance.

Comprehensive analysis of the prognostic value and immune infiltration of FGFR family members in gastric cancer

Background

Fibroblast growth factor receptors (FGFRs) modulate numerous cellular processes in tumor cells and tumor microenvironment. However, the effect of FGFRs on tumor prognosis and tumor-infiltrating lymphocytes in gastric cancer (GC) remains controversial.

Methods

The expression of four different types of FGFRs was analyzed via GEPIA, TCGA-STAD, and GTEX databases and our 27 pairs of GC tumor samples and the adjacent normal tissue. Furthermore, the Kaplan–Meier plot and the TCGA database were utilized to assess the association of FGFRs with clinical prognosis. The R software was used to evaluate FGFRs co-expression genes with GO/KEGG Pathway Enrichment Analysis. In vitro and in vivo functional analyses and immunoblotting were performed to verify FGFR4 overexpression consequence. Moreover, the correlation between FGFRs and cancer immune infiltrates was analyzed by TIMER and TCGA databases. And the efficacy of anti-PD-1 mAb treatment was examined in NOG mouse models with overexpressed FGFR1 or FGFR4.

Results

The expression of FGFRs was considerably elevated in STAD than in the normal gastric tissues and was significantly correlated with poor OS and PFS. ROC curve showed the accuracy of the FGFRs in tumor diagnosis, among which FGFR4 had the highest ROC value. Besides, univariate and multivariate analysis revealed that FGFR4 was an independent prognostic factor for GC patients. According to a GO/KEGG analysis, the FGFRs were implicated in the ERK/MAPK, PI3K-AKT and extracellular matrix (ECM) receptor signaling pathways. In vivo and in vitro studies revealed that overexpression of FGFR4 stimulated GC cell proliferation, invasion, and migration. In addition, FGFR1 expression was positively correlated with infiltrating levels of CD8+ T-cells, CD4+ T-cells, macrophages, and dendritic cells in STAD. In contrast, FGFR4 expression was negatively correlated with tumor-infiltrating lymphocytes. Interestingly, overexpression of FGFR1 in the NOG mouse model improved the immunotherapeutic impact of GC, while overexpression of FGFR4 impaired the effect. When combined with an FGFR4 inhibitor, the anti-tumor effect of anti-PD-1 treatment increased significantly in a GC xenograft mouse model with overexpressed FGFR4.

Conclusions

FGFRs has critical function in GC and associated with immune cell infiltration, which might be a potential prognosis biomarker and predictor of response to immunotherapy in GC.

Biological Random Walks: multi-omics integration for disease gene prioritization

MOTIVATION

Over the past decade, network-based approaches have proven useful in identifying disease modules within the human interactome, often providing insights into key mechanisms and guiding the quest for therapeutic targets. This is all the more important, since experimental investigation of potential gene candidates is an expensive task, thus not always a feasible option. On the other hand, many sources of biological information exist beyond the interactome and an important research direction is the design of effective techniques for their integration.

RESULTS

In this work, we introduce the Biological Random Walks (BRW) approach for disease gene prioritization in the human interactome. The proposed framework leverages multiple biological sources within an integrated framework. We perform an extensive, comparative study of BRW's performance against well-established baselines.

AVAILABILITY AND IMPLEMENTATION

All codes are publicly available and can be downloaded at https://github.com/LeoM93/BiologicalRandomWalks. We used publicly available datasets, details on their retrieval and preprocessing are provided in the Supplementary Material.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Clinical utility of whole-genome sequencing in precision oncology

Precision diagnostics is one of the two pillars of precision medicine. Sequencing efforts in the past decade have firmly established cancer as a primarily genetically driven disease. This concept is supported by therapeutic successes aimed at particular pathways that are perturbed by specific driver mutations in protein-coding domains and reflected in three recent FDA tissue agnostic cancer drug approvals. In addition, there is increasing evidence from studies that interrogate the entire genome by whole-genome sequencing that acquired global and complex genomic aberrations including those in non-coding regions of the genome might also reflect clinical outcome. After addressing technical, logistical, financial and ethical challenges, national initiatives now aim to introduce clinical whole-genome sequencing into real-world diagnostics as a rational and potentially cost-effective tool for response prediction in cancer and to identify patients who would benefit most from 'expensive' targeted therapies and recruitment into clinical trials. However, so far, this has not been accompanied by a systematic and prospective evaluation of the clinical utility of whole-genome sequencing within clinical trials of uniformly treated patients of defined clinical outcome. This approach would also greatly facilitate novel predictive biomarker discovery and validation, ultimately reducing size and duration of clinical trials and cost of drug development. This manuscript is the third in a series of three to review and critically appraise the potential and challenges of clinical whole-genome sequencing in solid tumors and hematological malignancies.

Harnessing gene fusion-derived neoantigens for 'cold' breast and prostate tumor immunotherapy

Breast and prostate cancers are generally considered immunologically 'cold' tumors due to multiple mechanisms rendering them unresponsive to immune checkpoint blockade therapies. With little success in garnering positive outcomes in modern immunotherapeutic clinical trials, it is prudent to re-examine the role of immunogenic neoantigens in these cold tumors. Gene fusions are driver mutations in hormone-driven cancers that can result in alternative mutation-specific neoantigens to promote immunotherapy sensitivity. This review focuses on 1) gene fusion formation mechanisms in neoantigen generation; 2) gene fusion neoantigens in cancer immunotherapeutic strategies and associated clinical trials; and 3) challenges and opportunities in computational and liquid biopsy technologies. This review is anticipated to initiate further research into gene fusion neoantigens of cold tumors for further experimental validation.

ABL kinases regulate translation in HER2+ cells through Y-box-binding protein 1 to facilitate colonization of the brain

Patients with human epidermal growth factor receptor 2-positive (HER2+/ERBB2) breast cancer often present with brain metastasis. HER2-targeted therapies have not been successful to treat brain metastases in part due to poor blood-brain barrier (BBB) penetrance and emergence of resistance. Here, we report that Abelson (ABL) kinase allosteric inhibitors improve overall survival and impair HER2+ brain metastatic outgrowth in vivo. Mechanistically, ABL kinases phosphorylate the RNA-binding protein Y-box-binding protein 1 (YB-1). ABL kinase inhibition disrupts binding of YB-1 to the ERBB2 mRNA and impairs translation, leading to a profound decrease in HER2 protein levels. ABL-dependent tyrosine phosphorylation of YB-1 promotes HER2 translation. Notably, loss of YB-1 inhibits brain metastatic outgrowth and impairs expression of a subset of ABL-dependent brain metastatic targets. These data support a role for ABL kinases in the translational regulation of brain metastatic targets through YB-1 and offer a therapeutic target for HER2+ brain metastasis patients.

The Multi-Omic Landscape of Primary Breast Tumors and Their Metastases: Expanding the Efficacy of Actionable Therapeutic Targets

Breast cancer (BC) mortality is almost exclusively due to metastasis, which is the least understood aspect of cancer biology and represents a significant clinical challenge. Although we have witnessed tremendous advancements in the treatment for metastatic breast cancer (mBC), treatment resistance inevitably occurs in most patients. Recently, efforts in characterizing mBC revealed distinctive genomic, epigenomic and transcriptomic (multi-omic) landscapes to that of the primary tumor. Understanding of the molecular underpinnings of mBC is key to understanding resistance to therapy and the development of novel treatment options. This review summarizes the differential molecular landscapes of BC and mBC, provides insights into the genomic heterogeneity of mBC and highlights the therapeutically relevant, multi-omic features that may serve as novel therapeutic targets for mBC patients.

Prevalence of Estrogen Receptor Alpha (ESR1) Somatic Mutations in Breast Cancer

Estrogen receptor–positive breast tumors, which initially respond effectively to endocrine therapy, progress due to acquired endocrine therapy resistance, including genomic alterations in estrogen receptor alpha (ESR1). A recent study has suggested that there is a sufficient number of preexisting ESR1 mutations acting as an intrinsic resistance mechanism to warrant primary screening. We determined the incidence of de novo ESR1 mutations in hormone-positive treatment-naïve primary breast tumors using 12 publicly available international datasets in the cBioPortal. The prevalence of mutation was statistically significantly lower in treatment-naïve primary tumors (n = 6 of 3682, 0.16%) than in metastatic (n = 156 of 1089, 14.3%, 2-sided P < .001) or previously treated primary tumors (n = 11 of 92, 12.0%, 2-sided P < .001). Pathogenic ESR1 mutations are a common mechanism of acquired but not intrinsic resistance to endocrine therapy and may not warrant universal testing of primary breast cancer populations.

Onepot-Seq: capturing single-cell transcriptomes simultaneously in a continuous medium via transient localization of mRNA

The development of single-cell RNA-seq has broadened the spectrum for biological research by providing a high-resolution analysis of cellular heterogeneity. However, the requirement for sophisticated devices for the compartmentalization of cells has limited its widespread applicability. Here, we develop Onepot-Seq, a device-free method, that harnesses the transient localization of mRNA after lysis to capture single-cell transcriptomes simultaneously in a continuous fluid medium. In mixed-species experiments, we obtained high-quality single-cell profiles. Further, cell type-specific poly(A)-conjugated antibodies allow Onepot-Seq to effectively capture target cells in complex populations. Chemical perturbations to cells can be profiled by Onepot-Seq at single-cell resolution. Onepot-Seq should allow routine transcriptional profiling at single-cell resolution, accelerating clinical and scientific discoveries in many fields of science.

Unified classification and risk-stratification in Acute Myeloid Leukemia

Clinical recommendations for Acute Myeloid Leukemia (AML) classification and risk-stratification remain heavily reliant on cytogenetic findings at diagnosis, which are present in <50% of patients. Using comprehensive molecular profiling data from 3,653 patients we characterize and validate 16 molecular classes describing 100% of AML patients. Each class represents diverse biological AML subgroups, and is associated with distinct clinical presentation, likelihood of response to induction chemotherapy, risk of relapse and death over time. Secondary AML-2, emerges as the second largest class (24%), associates with high-risk disease, poor prognosis irrespective of flow Minimal Residual Disease (MRD) negativity, and derives significant benefit from transplantation. Guided by class membership we derive a 3-tier risk-stratification score that re-stratifies 26% of patients as compared to standard of care. This results in a unified framework for disease classification and risk-stratification in AML that relies on information from cytogenetics and 32 genes. Last, we develop an open-access patient-tailored clinical decision support tool.

The spatio-temporal evolution of multiple myeloma from baseline to relapse-refractory states

Deciphering Multiple Myeloma evolution in the whole bone marrow is key to inform curative strategies. Here, we perform spatial-longitudinal whole-exome sequencing, including 140 samples collected from 24 Multiple Myeloma patients during up to 14 years. Applying imaging-guided sampling we observe three evolutionary patterns, including relapse driven by a single-cell expansion, competing/co-existing sub-clones, and unique sub-clones at distinct locations. While we do not find the unique relapse sub-clone in the baseline focal lesion(s), we show a close phylogenetic relationship between baseline focal lesions and relapse disease, highlighting focal lesions as hotspots of tumor evolution. In patients with ≥3 focal lesions on positron-emission-tomography at diagnosis, relapse is driven by multiple distinct sub-clones, whereas in other patients, a single-cell expansion is typically seen (p < 0.01). Notably, we observe resistant sub-clones that can be hidden over years, suggesting that a prerequisite for curative therapies would be to overcome not only tumor heterogeneity but also dormancy.

Association of Pathway Mutations With Survival in Taiwanese Breast Cancers

Breast cancer is the most common invasive cancer in women worldwide. Next-generation sequencing (NGS) provides a high-resolution profile of cancer genome. Our study ultimately gives the insight for genetic screening to identify the minority of patients with breast cancer with a poor prognosis, who might benefit from the most intensive possible treatment. The detection of mutations can polish the traditional method to detect high-risk patients who experience poor prognosis, recurrence and death early. In total, 147 breast cancer tumors were sequenced with targeted sequencing using a RainDance Cancer Hotspot Panel. The average age of all 147 breast cancer patients in the study was 51.7 years, with a range of 21-77 years. The average sequencing depth was 5,222x (range 2,900x-8,633x), and the coverage was approximately 100%. A total of 235 variants in 43 genes were detected in 147 patients by high-depth Illumina sequencing. A total of 219 single nucleotide variations were found in 42 genes from 147 patients, and 16 indel mutations were found in 13 genes from 84 patients. After filtering with the 1000 Genomes database and for synonymous SNPs, we focused on 54 somatic functional point mutations. The functional point mutations contained 54 missense mutations in 22 genes. Additionally, mutation of genes within the RET, PTEN, CDH1, MAP2K4, NF1, ERBB2, RUNX1, PIK3CA, FGFR3, KIT, KDR, APC, SMO, NOTCH1, and FBXW7 in breast cancer patients were with poor prognosis. Moreover, TP53 and APC mutations were enriched in triple-negative breast cancer. APC mutations were associated with a poor prognosis in human breast cancer (log-rank P<0.001). Our study identified tumor mutation hotspot profiles in Taiwanese breast cancer patients, revealing new targetable gene mutations in Asian breast cancer patients.

Machine learning-based tissue of origin classification for cancer of unknown primary diagnostics using genome-wide mutation features

Cancers of unknown primary (CUP) origin account for ∼3% of all cancer diagnoses, whereby the tumor tissue of origin (TOO) cannot be determined. Using a uniformly processed dataset encompassing 6756 whole-genome sequenced primary and metastatic tumors, we develop Cancer of Unknown Primary Location Resolver (CUPLR), a random forest TOO classifier that employs 511 features based on simple and complex somatic driver and passenger mutations. CUPLR distinguishes 35 cancer (sub)types with ∼90% recall and ∼90% precision based on cross-validation and test set predictions. We find that structural variant derived features increase the performance and utility for classifying specific cancer types. With CUPLR, we could determine the TOO for 82/141 (58%) of CUP patients. Although CUPLR is based on machine learning, it provides a human interpretable graphical report with detailed feature explanations. The comprehensive output of CUPLR complements existing histopathological procedures and can enable improved diagnostics for CUP patients.

Multiomics technologies: role in disease biomarker discoveries and therapeutics

 

Medical research has been revolutionized after the publication of the full human genome. This was the major landmark that paved the way for understanding the biological functions of different macro and micro molecules. With the advent of different high-throughput technologies, biomedical research was further revolutionized. These technologies constitute genomics, transcriptomics, proteomics, metabolomics, etc. Collectively, these high-throughputs are referred to as multi-omics technologies. In the biomedical field, these omics technologies act as efficient and effective tools for disease diagnosis, management, monitoring, treatment and discovery of certain novel disease biomarkers. Genotyping arrays and other transcriptomic studies have helped us to elucidate the gene expression patterns in different biological states, i.e. healthy and diseased states. Further omics technologies such as proteomics and metabolomics have an important role in predicting the role of different biological molecules in an organism. It is because of these high throughput omics technologies that we have been able to fully understand the role of different genes, proteins, metabolites and biological pathways in a diseased condition. To understand a complex biological process, it is important to apply an integrative approach that analyses the multi-omics data in order to highlight the possible interrelationships of the involved biomolecules and their functions. Furthermore, these omics technologies offer an important opportunity to understand the information that underlies disease. In the current review, we will discuss the importance of omics technologies as promising tools to understand the role of different biomolecules in diseases such as cancer, cardiovascular diseases, neurodegenerative diseases and diabetes.

SUMMARY POINTS

The crucial role of epigenetic regulation in breast cancer anti-estrogen resistance: Current findings and future perspectives

Breast cancer (BC) cell de-sensitization to Tamoxifen (TAM) or other selective estrogen receptor (ER) modulators (SERM) is a complex process associated with BC heterogeneity and the transformation of ER signalling. The most influential resistance-related mechanisms include modifications in ER expression and gene regulation patterns. During TAM/SERM treatment, epigenetic mechanisms can effectively silence ER expression and facilitate the development of endocrine resistance. ER status is efficiently regulated by specific epigenetic tools including hypermethylation of CpG islands within ER promoters, increased histone deacetylase activity in the ER promoter, and/or translational repression by miRNAs. Over-methylation of the ER α gene (ESR1) promoter by DNA methyltransferases was associated with poor prognosis and indicated the development of resistance. Moreover, BC progression and spreading were marked by transformed chromatin remodelling, post-translational histone modifications, and expression of specific miRNAs and/or long non-coding RNAs. Therefore, targeted inhibition of histone acetyltransferases (e.g. MYST3), deacetylases (e.g. HDAC1), and/or demethylases (e.g. lysine-specific demethylase LSD1) was shown to recover and increase BC sensitivity to anti-estrogens. Indicated as a powerful molecular instrument, the administration of epigenetic drugs can regain ER expression along with the activation of tumour suppressor genes, which can in turn prevent selection of resistant cells and cancer stem cell survival. This review examines recent advances in the epigenetic regulation of endocrine drug resistance and evaluates novel anti-resistance strategies. Underlying molecular mechanisms of epigenetic regulation will be discussed, emphasising the utilization of epigenetic enzymes and their inhibitors to re-program irresponsive BCs.

High prevalence of somatic PIK3CA and TP53 pathogenic variants in the normal mammary gland tissue of sporadic breast cancer patients revealed by duplex sequencing

The mammary gland undergoes hormonally stimulated cycles of proliferation, lactation, and involution. We hypothesized that these factors increase the mutational burden in glandular tissue and may explain high cancer incidence rate in the general population, and recurrent disease. Hence, we investigated the DNA sequence variants in the normal mammary gland, tumor, and peripheral blood from 52 reportedly sporadic breast cancer patients. Targeted resequencing of 542 cancer-associated genes revealed subclonal somatic pathogenic variants of: PIK3CA, TP53, AKT1, MAP3K1, CDH1, RB1, NCOR1, MED12, CBFB, TBX3, and TSHR in the normal mammary gland at considerable allelic frequencies (9 × 10-2- 5.2 × 10-1), indicating clonal expansion. Further evaluation of the frequently damaged PIK3CA and TP53 genes by ultra-sensitive duplex sequencing demonstrated a diversified picture of multiple low-level subclonal (in 10-2-10-4 alleles) hotspot pathogenic variants. Our results raise a question about the oncogenic potential in non-tumorous mammary gland tissue of breast-conserving surgery patients.

Breast Cancer Genomics: Primary and Most Common Metastases

Specific genomic alterations have been found in primary breast cancer involving driver mutations that result in tumorigenesis. Metastatic breast cancer, which is uncommon at the time of disease onset, variably impacts patients throughout the course of their disease. Both the molecular profiles and diverse genomic pathways vary in the development and progression of metastatic breast cancer. From the most common metastatic site (bone), to the rare sites such as orbital, gynecologic, or pancreatic metastases, different levels of gene expression indicate the potential involvement of numerous genes in the development and spread of breast cancer. Knowledge of these alterations can, not only help predict future disease, but also lead to advancement in breast cancer treatments. This review discusses the somatic landscape of breast primary and metastatic tumors.

Immunology and immunotherapy in breast cancer

Immuno-oncology is a rapidly developing field in medicine. Drug combination therapies have already been studied in many clinical trials on various tumor types. In recent years, a checkpoint inhibition therapy with monoclonal antibodies targeting PD-1 and its ligand PD-L1 has been developed. Breast cancer had been examined in the field of immune-oncology relatively recently. This review focuses on clinical evidence regarding immune checkpoint inhibition for curative treatment of various breast cancer subtypes. In addition, we present the results of studies demonstrating the prognostic and predictive value of levels of tumorinfiltrating lymphocytes (CD4 and CD8), their quantitative ratios, and their correlation with regulatory genes (PD-1, PD-L1, and FOX-P3).

Hormonal and Genetic Regulatory Events in Breast Cancer and Its Therapeutics: Importance of the Steroidogenic Acute Regulatory Protein

Estrogen promotes the development and survival of the majority of breast cancers (BCs). Aromatase is the rate-limiting enzyme in estrogen biosynthesis, and it is immensely expressed in both cancerous and non-cancerous breast tissues. Endocrine therapy based on estrogen blockade, by aromatase inhibitors, has been the mainstay of BC treatment in post-menopausal women; however, resistance to hormone therapy is the leading cause of cancer death. An improved understanding of the molecular underpinnings is the key to develop therapeutic strategies for countering the most prevalent hormone receptor positive BCs. Of note, cholesterol is the precursor of all steroid hormones that are synthesized in a variety of tissues and play crucial roles in diverse processes, ranging from organogenesis to homeostasis to carcinogenesis. The rate-limiting step in steroid biosynthesis is the transport of cholesterol from the outer to the inner mitochondrial membrane, a process that is primarily mediated by the steroidogenic acute regulatory (StAR) protein. Advances in genomic and proteomic technologies have revealed a dynamic link between histone deacetylases (HDACs) and StAR, aromatase, and estrogen regulation. We were the first to report that StAR is abundantly expressed, along with large amounts of 17β-estradiol (E2), in hormone-dependent, but not hormone-independent, BCs, in which StAR was also identified as a novel acetylated protein. Our in-silico analyses of The Cancer Genome Atlas (TCGA) datasets, for StAR and steroidogenic enzyme genes, revealed an inverse correlation between the amplification of the StAR gene and the poor survival of BC patients. Additionally, we reported that a number of HDAC inhibitors, by altering StAR acetylation patterns, repress E2 synthesis in hormone-sensitive BC cells. This review highlights the current understanding of molecular pathogenesis of BCs, especially for luminal subtypes, and their therapeutics, underlining that StAR could serve not only as a prognostic marker, but also as a therapeutic candidate, in the prevention and treatment of this life-threatening disease.

Panel Informativity Optimizer: An R Package to Improve Cancer Next-Generation Sequencing Panel Informativity

Mutation detection by next-generation sequencing is routinely used for cancer diagnosis. Selecting an optimal set of genes for a given cancer is not trivial as it has to optimize informativity (ie, the number of patients with at least one mutation in the panel), while minimizing panel length to reduce sequencing costs and increase sensitivity. We propose herein Panel Informativity Optimizer (PIO), an open-source software developed as an R package with a user-friendly graphical interface to help optimize cancer next-generation sequencing panel informativity. Using patient-level mutational data from either private data sets or preloaded data set of 91 independent cohorts from 31 different cancer types, PIO selects an optimal set of genomic intervals to maximize informativity and panel size in a given cancer type. Different options are offered, such as the definition of genomic intervals at the gene or exon level and the use of optimization strategy at the patient or patient per kilobase level. PIO can also propose an optimal set of genomic intervals to increase informativity of custom panels. A panel tester function is also available for panel benchmarking. Using public databases, as well as data from real-life settings, we demonstrate that PIO allows panel size reduction of up to 1000 kb, and accurately predicts the performance of custom or commercial panels.

ROS1 altered breast cancers - a distinctive molecular subtype of PR- metastatic breast cancers: Expanding the scope of targeted therapeutics

BACKGROUND

Breast cancer, one of the leading causes of cancer-related mortality in women worldwide, exhibits wide-ranging histo-morphologic, clinical and molecular diversity.

OBJECTIVE

This study compares the genetic alterations of breast tumors with the histo-morphological, hormone receptor status and metastatic "organotropism".

MATERIALS AND METHODS

Twenty-two cases of primary invasive breast carcinoma with local/distant metastasis were retrieved from the pathology archives. The status of estrogen and progesterone receptors by immunohistochemistry was recorded along with other pertinent case data. Next generation sequencing was performed on formalin-fixed paraffin embedded blocks of tumor.

RESULTS

The mean age of the study subjects was 57.9 ± 13.3 years. TP53 mutation was the most common gene alteration in this study and was seen in 40.9% cases. ROS1 gene was mutated in 44.4% PR negative breast cancers while being wild type in the twelve PR positive tumors. (p = 0.021).STRING interaction network constructed with ROS1 and PR revealed a significantly higher number of interactions in this network than expected (p-value 0.000973).

CONCLUSION

This study highlights the significantly higher incidence of ROS1 gene alterations in metastatic PR- breast cancers, with STRING network analysis revealing higher nodal interaction in the nodal network comprised of PR and ROS1 exclusive of ER.

Heritable genomic diversity in breast cancer driver genes and associations with risk in a Chilean population

BACKGROUND

Driver mutations are the genetic components responsible for tumor initiation and progression. These variants, which may be inherited, influence cancer risk and therefore underlie many familial cancers. The present study examines the potential association between SNPs in driver genes SF3B1 (rs4685), TBX3 (rs12366395, rs8853, and rs1061651) and MAP3K1 (rs72758040) and BC in BRCA1/2-negative Chilean families.

METHODS

The SNPs were genotyped in 486 BC cases and 1258 controls by TaqMan Assay.

RESULTS

Our data do not support an association between rs4685:C > T, rs8853:T > C, or rs1061651:T > C and BC risk. However, the rs12366395-G allele (A/G + G/G) was associated with risk in families with a strong history of BC (OR = 1.2 [95% CI 1.0-1.6] p = 0.02 and OR = 1.5 [95% CI 1.0-2.2] p = 0.02, respectively). Moreover, rs72758040-C was associated with increased risk in cases with a moderate-to-strong family history of BC (OR = 1.3 [95% CI 1.0-1.7] p = 0.02 and OR = 1.3 [95% CI 1.0-1.8] p = 0.03 respectively). Finally, risk was significantly higher in homozygous C/C cases from families with a moderate-to-strong BC history (OR = 1.8 [95% CI 1.0-3.1] p = 0.03 and OR = 1.9 [95% CI 1.1-3.4] p = 0.01, respectively). We also evaluated the combined impact of rs12366395-G and rs72758040-C. Familial BC risk increased in a dose-dependent manner with risk allele count, reflecting an additive effect (p-trend = 0.0002).

CONCLUSIONS

Our study suggests that germline variants in driver genes TBX3 (rs12366395) and MAP3K1 (rs72758040) may influence BC risk in BRCA1/2-negative Chilean families. Moreover, the presence of rs12366395-G and rs72758040-C could increase BC risk in a Chilean population.

Kindlin-2 promotes Src-mediated tyrosine phosphorylation of androgen receptor and contributes to breast cancer progression

Androgen receptor (AR) signaling plays important roles in breast cancer progression. We show here that Kindlin-2, a focal adhesion protein, is critically involved in the promotion of AR signaling and breast cancer progression. Kindlin-2 physically associates with AR and Src through its two neighboring domains, namely F1 and F0 domains, resulting in formation of a Kindlin-2-AR-Src supramolecular complex and consequently facilitating Src-mediated AR Tyr-534 phosphorylation and signaling. Depletion of Kindlin-2 was sufficient to suppress Src-mediated AR Tyr-534 phosphorylation and signaling, resulting in diminished breast cancer cell proliferation and migration. Re-expression of wild-type Kindlin-2, but not AR-binding-defective or Src-binding-defective mutant forms of Kindlin-2, in Kindlin-2-deficient cells restored AR Tyr-534 phosphorylation, signaling, breast cancer cell proliferation and migration. Furthermore, re-introduction of phosphor-mimic mutant AR-Y534D, but not wild-type AR reversed Kindlin-2 deficiency-induced inhibition of AR signaling and breast cancer progression. Finally, using a genetic knockout strategy, we show that ablation of Kindlin-2 from mammary tumors in mouse significantly reduced AR Tyr-534 phosphorylation, breast tumor progression and metastasis in vivo. Our results suggest a critical role of Kindlin-2 in promoting breast cancer progression and shed light on the molecular mechanism through which it functions in this process.

Frequent 4EBP1 Amplification Induces Synthetic Dependence on FGFR Signaling in Cancer

The eIF4E translation initiation factor has oncogenic properties and concordantly, the inhibitory eIF4E-binding protein (4EBP1) is considered a tumor suppressor. The exact molecular effects of 4EBP1 activation in cancer are still unknown. Surprisingly, 4EBP1 is a target of genomic copy number gains (Chr. 8p11) in breast and lung cancer. We noticed that 4EBP1 gains are genetically linked to gains in neighboring genes, including WHSC1L1 and FGFR1. Our results show that FGFR1 gains act to attenuate the function of 4EBP1 via PI3K-mediated phosphorylation at Thr37/46, Ser65, and Thr70 sites. This implies that not 4EBP1 but instead FGFR1 is the genetic target of Chr. 8p11 gains in breast and lung cancer. Accordingly, these tumors show increased sensitivity to FGFR1 and PI3K inhibition, and this is a therapeutic vulnerability through restoring the tumor-suppressive function of 4EBP1. Ribosome profiling reveals genes involved in insulin signaling, glucose metabolism, and the inositol pathway to be the relevant translational targets of 4EBP1. These mRNAs are among the top 200 translation targets and are highly enriched for structure and sequence motifs in their 5'UTR, which depends on the 4EBP1-EIF4E activity. In summary, we identified the translational targets of 4EBP1-EIF4E that facilitate the tumor suppressor function of 4EBP1 in cancer.

Computational Screening of Anti-Cancer Drugs Identifies a New BRCA Independent Gene Expression Signature to Predict Breast Cancer Sensitivity to Cisplatin

Simple Summary

Using a collection of publicly available drug screening resources, we identified different partners of genes associated with either sensitivity or resistance to 90 anti-cancer therapies. When subsequently applying these signatures to multiple datasets, we found that these predictive models could predict a large range of drug responses in patient samples. In particular, we discovered a new gene signature to identify breast cancer tumors that are likely to respond to cisplatin in the absence of BRCA1 mutations. This work constitutes an important advance to accelerate the application of platinum-based therapies in patient groups that are not routinely treated with these drugs. In the future, this approach may help to guide the choice of drugs based on the molecular profile of the tumors.

Abstract

The development of therapies that target specific disease subtypes has dramatically improved outcomes for patients with breast cancer. However, survival gains have not been uniform across patients, even within a given molecular subtype. Large collections of publicly available drug screening data matched with transcriptomic measurements have facilitated the development of computational models that predict response to therapy. Here, we generated a series of predictive gene signatures to estimate the sensitivity of breast cancer samples to 90 drugs, comprising FDA-approved drugs or compounds in early development. To achieve this, we used a cell line-based drug screen with matched transcriptomic data to derive in silico models that we validated in large independent datasets obtained from cell lines and patient-derived xenograft (PDX) models. Robust computational signatures were obtained for 28 drugs and used to predict drug efficacy in a set of PDX models. We found that our signature for cisplatin can be used to identify tumors that are likely to respond to this drug, even in absence of the BRCA-1 mutation routinely used to select patients for platinum-based therapies. This clinically relevant observation was confirmed in multiple PDXs. Our study foreshadows an effective delivery approach for precision medicine.

Serial Tumor Molecular Profiling of Newly Diagnosed HER2-Negative Breast Cancers During Chemotherapy in Combination with Angiogenesis Inhibitors

Background

Breast cancers are heterogeneous with variable clinical courses and treatment responses.

Objective

We sought to evaluate dynamic changes in the molecular landscape of HER2-negative tumors treated with chemotherapy and anti-angiogenic agents.

Patients and Methods

Newly diagnosed HER2-negative breast cancer patients received low-dose sunitinib or bevacizumab prior to four 2-weekly cycles of dose-dense doxorubicin and cyclophosphamide. Tumor biopsies were obtained at baseline, after 2 weeks and after 8 weeks of chemotherapy. Next-generation sequencing was performed to assess for single nucleotide variants (SNVs) and copy number alterations (CNAs) of 440 cancer-related genes (ACTOnco^®). Observed genomic changes were correlated with the Miller-Payne histological response to treatment.

Results

Thirty-four patients received sunitinib and 18 received bevacizumab. In total, 77% were hormone receptor positive (HER2−/HR+) and 23% were triple negative breast cancers (TNBC). New therapy-induced mutations were infrequent, occurring only in 13%, and appeared early after a single cycle of treatment. Seventy-two percent developed changes in the variant allele frequency (VAF) of pathogenic SNVs; the majority (51%) of these changes occurred early at 2 weeks and were sustained for 8 weeks. Changes in VAF of SNVs were most commonly seen in the PI3K/mTOR/AKT pathway; 13% developed changes in pathogenic mutations, which potentially confer sensitivity to PIK3CA inhibitors. Tumors with poor Miller-Payne response to treatment were less likely to experience changes in VAF of SNVs compared with those with good response (50% [7/14] vs 15% [4/24] had no changes observed at any timepoint, p = 0.029).

Conclusions

Serial molecular profiling identifies early therapy-induced genomic alterations, which may guide future selection of targeted therapies in breast cancer patients who progress after standard chemotherapy.

Clinical trial registration

ClinicalTrials.gov: NCT02790580 (first posted June 6, 2016).

Supplementary Information

The online version contains supplementary material available at 10.1007/s11523-022-00886-x.

Mechanistic Effects of Estrogens on Breast Cancer

PURPOSE

Current concepts regarding estrogen and its mechanistic effects on breast cancer in women are evolving. This article reviews studies that address estrogen-mediated breast cancer development, the prevalence of occult tumors at autopsy, and the natural history of breast cancer as predicted by a newly developed tumor kinetic model.

METHODS

This article reviews previously published studies from the authors and articles pertinent to the data presented.

RESULTS

We discuss the concepts of adaptive hypersensitivity that develops in response to long-term deprivation of estrogen and results in both increased cell proliferation and apoptosis. The effects of menopausal hormonal therapy on breast cancer in postmenopausal women are interpreted based on the tumor kinetic model. Studies of the administration of a tissue selective estrogen complex in vitro, in vivo, and in patients are described. We review the various clinical studies of breast cancer prevention with selective estrogen receptor modulators and aromatase inhibitors. Finally, the effects of the underlying risk of breast cancer on the effects of menopausal hormone therapy are outlined.

DISCUSSION

The overall intent of this review is to present data supporting recent concepts, discuss pertinent literature, and critically examine areas of controversy.

Circulating Glycan Monosaccharide Composite-Based Biomarker Diagnoses Colorectal Cancer at Early Stages and Predicts Prognosis

Introduction

Early diagnosis could lead to a cure of colorectal cancer (CRC). Since CRC is related to aging and lifestyles, we tested if the environmental information-enriched monosaccharide composite (MC) of circulating glycans could serve as an early diagnostic biomarker for CRC. Meanwhile, we evaluated its role in predicting prognosis.

Methods

HPAEC-PAD was used to quantify glycan monosaccharide compositions from a total of 467 serum samples including CRC patients, colorectal adenoma (CRA) patients and healthy individuals. Two diagnostic model was constructed by logistic regression analysis. The diagnostic performance of the two models was verified in the retrospective validation group and the prospective validation group. The prognostic performance of the model was assessed by survival analysis.

Results

The concentrations of monosaccharides in serum were significantly higher in CRA and CRC patients than in healthy individuals. Two diagnostic models were constructed: MC1 was used to distinguish between healthy individuals and CRC; MC2 was used to distinguish between healthy individuals and CRA. Area under receptor operating characteristic curve (AUC) of MC2 and MC1 was 0.8025 and 0.9403 respectively. However, the AUC of CEA between healthy individuals and CRC was 0.7384. Moreover, in early stage of CRC (without lymph node metastasis), the positive rates of CEA and MC1 were 28% and 80%, respectively. The follow-up data showed that the increased MC1 value was associated with poor survival in patients with CRC (p=0.0010, HR=5.30).

Discussion

The MC1 model is superior to CEA in the diagnosis of CRC, especially in the early diagnosis. MC1 can be used for predicting prognosis of CRC patients, and elevated MC1 values indicate poor survival.

‘Breast Cancer Resistance Likelihood and Personalized Treatment Through Integrated Multiomics’

In recent times, enormous progress has been made in improving the diagnosis and therapeutic strategies for breast carcinoma, yet it remains the most prevalent cancer and second highest contributor to cancer-related deaths in women. Breast cancer (BC) affects one in eight females globally. In 2018 alone, 1.4 million cases were identified worldwide in postmenopausal women and 645,000 cases in premenopausal females, and this burden is constantly increasing. This shows that still a lot of efforts are required to discover therapeutic remedies for this disease. One of the major clinical complications associated with the treatment of breast carcinoma is the development of therapeutic resistance. Multidrug resistance (MDR) and consequent relapse on therapy are prevalent issues related to breast carcinoma; it is due to our incomplete understanding of the molecular mechanisms of breast carcinoma disease. Therefore, elucidating the molecular mechanisms involved in drug resistance is critical. For management of breast carcinoma, the treatment decision not only depends on the assessment of prognosis factors but also on the evaluation of pathological and clinical factors. Integrated data assessments of these multiple factors of breast carcinoma through multiomics can provide significant insight and hope for making therapeutic decisions. This omics approach is particularly helpful since it identifies the biomarkers of disease progression and treatment progress by collective characterization and quantification of pools of biological molecules within and among the cancerous cells. The scrupulous understanding of cancer and its treatment at the molecular level led to the concept of a personalized approach, which is one of the most significant advancements in modern oncology. Likewise, there are certain genetic and non-genetic tests available for BC which can help in personalized therapy. Genetically inherited risks can be screened for personal predisposition to BC, and genetic changes or variations (mutations) can also be identified to decide on the best treatment. Ultimately, further understanding of BC at the molecular level (multiomics) will define more precise choices in personalized medicine. In this review, we have summarized therapeutic resistance associated with BC and the techniques used for its management.

Response of Leptomeningeal Metastasis of Breast Cancer With a HER2/neu Activating Variant to Tucatinib: A Case Report

Metastatic breast cancer demonstrates HER2/neu amplification approximately 15% of the time. However, HER2 mutations, which often stimulate tumor growth, occur in only 3% to 5% of patients, and are seen more frequently in metastatic versus primary tumors. They are more frequent in lobular carcinoma, including triple-negative lobular cancer. Many of these variants are resistant to trastuzumab and lapatinib. However, neratinib can be efficacious, and recent data suggest that antibody-drug conjugates (ADCs) such as ado-trastuzumab emtansine (T-DM1) and trastuzumab deruxtecan may also be helpful. Laboratory and clinical data raise the possibility that simultaneous treatment with ADCs plus neratinib may be even more efficacious. Tucatinib, which has demonstrated significant activity in the central nervous system, has also been shown in vitro to be active against a number of these HER2 variants. This report describes a patient with metastatic estrogen receptor-positive, HER2-nonamplified breast cancer with an activating HER2 mutation whose tumor became resistant to neratinib as well as capecitabine, but whose subsequent leptomeningeal disease had a dramatically successful response to tucatinib plus capecitabine. As the frequency of HER2 mutations increases during the evolution of metastatic breast cancer, it is important to obtain genomic evaluation on these tumors with either repeat tissue or liquid biopsy as they progress over time.

A Comprehensive Survey of Genomic Mutations in Breast Cancer Reveals Recurrent Neoantigens as Potential Therapeutic Targets

Neoantigens are mutated antigens specifically generated by cancer cells but absent in normal cells. With high specificity and immunogenicity, neoantigens are considered as an ideal target for immunotherapy. This study was aimed to investigate the signature of neoantigens in breast cancer. Somatic mutations, including SNVs and indels, were obtained from cBioPortal of 5991 breast cancer patients. 738 non-silent somatic variants present in at least 3 patients for neoantigen prediction were selected. PIK3CA (38%), the highly mutated gene in breast cancer, could produce the highest number of neoantigens per gene. Some pan-cancer hotspot mutations, such as PIK3CA E545K (6.93%), could be recognized by at least one HLA molecule. Since there are more SNVs than indels in breast cancer, SNVs are the major source of neoantigens. Patients with hormone receptor-positive or HER2 negative are more competent to produce neoantigens. Age, but not the clinical stage, is a significant contributory factor of neoantigen production. We believe a detailed description of breast cancer neoantigen signatures could contribute to neoantigen-based immunotherapy development.