Prognosis and Genomic Landscape of Liver Metastasis in Patients With Breast Cancer

A prognostic model for triple-negative breast cancer patients with liver metastasis: A population-based study

However, it is still difficult for clinicians to establish prognostic stratifications and therapeutic strategies because of the lack of tools for predicting the survival of triple-negative breast cancer patients with liver metastases (TNBC-LM). Based on clinical data from large populations, a sensitive and discriminative nomogram was developed and validated to predict the prognosis of TNBC patients with LM at initial diagnosis or at the later course.

Introduction/background

Liver metastasis (LM) in TNBC patients is associated with significant morbidity and mortality. The objective of this study was to construct a clinical model to predict the survival of TNBC-LM patients.

Materials and methods

Clinicopathologic data were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database and the Fifth Affiliated Hospital of Sun Yat-Sen University (FAFSYU). Based on patients with newly diagnosed TNBC with LM (nTNBC-LM) from the SEER database, a predictive nomogram was established and validated. Its predictive effect on TNBC patients with LM at later disease course by enrolling TNBC patients from FAFSYU who developed LM later. The prognostic effect of different treatment for nTNBC-LM was further assessed.

Results

A prognostic model was developed and validated to predict the prognosis of TNBC-LM patients. For LM patients diagnosed at the initial or later treatment stage, the C-index (0.712, 0.803 and 0.699 in the training, validation and extended groups, respectively) and calibration plots showed the acceptable prognostic accuracy and clinical applicability of the nomogram. Surgical resection on the primary tumour and chemotherapy were found to be associated with significantly better overall survival (OS).

Conclusion

A sensitive and discriminative model was developed to predict OS in TNBC-LM patients both at and after initial diagnosis.

Multiomics insights on the onset, progression, and metastatic evolution of breast cancer

Breast cancer is the most common malignant neoplasm in women. Despite progress to date, 700,000 women worldwide died of this disease in 2020. Apparently, the prognostic markers currently used in the clinic are not sufficient to determine the most appropriate treatment. For this reason, great efforts have been made in recent years to identify new molecular biomarkers that will allow more precise and personalized therapeutic decisions in both primary and recurrent breast cancers. These molecular biomarkers include genetic and post-transcriptional alterations, changes in protein expression, as well as metabolic, immunological or microbial changes identified by multiple omics technologies (e.g., genomics, epigenomics, transcriptomics, proteomics, glycomics, metabolomics, lipidomics, immunomics and microbiomics). This review summarizes studies based on omics analysis that have identified new biomarkers for diagnosis, patient stratification, differentiation between stages of tumor development (initiation, progression, and metastasis/recurrence), and their relevance for treatment selection. Furthermore, this review highlights the importance of clinical trials based on multiomics studies and the need to advance in this direction in order to establish personalized therapies and prolong disease-free survival of these patients in the future.

The dual-crosslinked prospective values of RAI14 for the diagnosis and chemosurveillance in triple negative breast cancer

OBJECTIVE

The exploration of non-invasive biomarkers for assessing tumor response is critical to optimize treatment decisions. In this study, we aimed at determining the potential role of RAI14 in the early diagnosis and evaluation of chemotherapy efficacy in triple-negative breast cancer (TNBC).

METHODS

We recruited 116 patients newly diagnosed with breast cancer, 30 patients with benign breast disease and 30 healthy controls. In addition, 57 TNBC patients were collected in serum at different time points (C0, C2 and C4) for chemotherapy monitoring. The expression of serum RAI14 and CA15-3 were quantified by Elisa and electrochemiluminescence assay, respectively. Then we compared the performances of markers with the chemotherapy efficacy assessed by imaging.

RESULTS

RAI14 is significantly overexpressed in TNBC and is linked to adverse clinicopathological features such as tumor burden, CA15-3 levels and the ER, PR, and HER2 status of the patients. ROC curve analysis showed that RAI14 improves the diagnostic performance for CA15-3(AUCRAI14 = 0.934 vs. AUCCA15-3 = 0.836), especially embodied in early-stage breast cancer diagnosis and patients with CA15-3 negativity. Furthermore, RAI14 behaves well in reproducing treatment response which was consistent with clinical Imaging assessment.

CONCLUSIONS

Recent studies showed that RAI14 has a complementary effect to CA15-3 and a test combining the two parameters can improve the detection rate of early triple-negative breast cancer. At the same time, RAI14 plays a more important role in chemotherapy monitoring than CA15-3 as the change in its concentration is in line with the tumor volume variation. Taken together, RAI14 is a reliable novel marker in the early diagnosis and chemotherapy monitoring of triple-negative breast cancer.

Organ-Specificity of Breast Cancer Metastasis

Breast cancer (BC) remains one of the most common malignancies among women worldwide. Breast cancer shows metastatic heterogeneity with priority to different organs, which leads to differences in prognosis and response to therapy among patients. The main targets for metastasis in BC are the bone, lung, liver and brain. The molecular mechanism of BC organ-specificity is still under investigation. In recent years, the appearance of new genomic approaches has led to unprecedented changes in the understanding of breast cancer metastasis organ-specificity and has provided a new platform for the development of more effective therapeutic agents. This review summarises recent data on molecular organ-specific markers of metastasis as the basis of a possible therapeutic approach in order to improve the diagnosis and prognosis of patients with metastatically heterogeneous breast cancer.

Interplay between ESR1/PIK3CA codon variants, oncogenic pathway alterations and clinical phenotype in patients with metastatic breast cancer (MBC): comprehensive circulating tumor DNA (ctDNA) analysis

BACKGROUND

although being central for the biology and druggability of hormone-receptor positive, HER2 negative metastatic breast cancer (MBC), ESR1 and PIK3CA mutations are simplistically dichotomized as mutated or wild type in current clinical practice.

METHODS

The study analyzed a multi-institutional cohort comprising 703 patients with luminal-like MBC characterized for circulating tumor DNA through next generation sequencing (NGS). Pathway classification was defined based on previous work (i.e., RTK, RAS, RAF, MEK, NRF2, ER, WNT, MYC, P53, cell cycle, notch, PI3K). Single nucleotide variations (SNVs) were annotated for their oncogenicity through OncoKB. Only pathogenic variants were included in the models. Associations among clinical characteristics, pathway classification, and ESR1/PIK3CA codon variants were explored.

RESULTS

The results showed a differential pattern of associations for ESR1 and PIK3CA codon variants in terms of co-occurring pathway alterations patterns of metastatic dissemination, and prognosis. ESR1 537 was associated with SNVs in the ER and RAF pathways, CNVs in the MYC pathway and bone metastases, while ESR1 538 with SNVs in the cell cycle pathway and liver metastases. PIK3CA 1047 and 542 were associated with CNVs in the PI3K pathway and with bone metastases.

CONCLUSIONS

The study demonstrated how ESR1 and PIK3CA codon variants, together with alterations in specific oncogenic pathways, can differentially impact the biology and clinical phenotype of luminal-like MBC. As novel endocrine therapy agents such as selective estrogen receptor degraders (SERDS) and PI3K inhibitors are being developed, these results highlight the pivotal role of ctDNA NGS to describe tumor evolution and optimize clinical decision making.

Breast cancer liver metastasis: Pathogenesis and clinical implications

Breast cancer is the most common malignant disease in female patients worldwide and can spread to almost every place in the human body, most frequently metastasizing to lymph nodes, bones, lungs, liver and brain. The liver is a common metastatic location for solid cancers as a whole, and it is also the third most common metastatic site for breast cancer. Breast cancer liver metastasis (BCLM) is a complex process. Although the hepatic microenvironment and liver sinusoidal structure are crucial factors for the initial arrest of breast cancer and progression within the liver, the biological basis of BCLM remains to be elucidated. Importantly, further understanding of the interaction between breast cancer cells and hepatic microenvironment in the liver metastasis of breast cancer will suggest ways for the development of effective therapy and prevention strategies for BCLM. In this review, we provide an overview of the recent advances in the understanding of the molecular mechanisms of the hepatic microenvironment in BCLM formation and discuss current systemic therapies for treating patients with BCLM as well as potential therapeutic development based on the liver microenvironment-associated signaling proteins governing BCLM.

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.

Growth Pattern of Hepatic Metastasis as a Prognostic Index Reflecting Liver Metastasis-Associated Survival in Breast Cancer Liver Metastasis

Breast cancer with liver metastasis (BCLM) frequently cause hepatic failure owing to extensive liver metastasis compared to other cancers; however, there are no clinicopathologic or radiologic parameters for estimating BCLM prognosis. We analyzed the relationship between radiologic and clinicopathologic characteristics with survival outcomes in BCLM. During 2009-2019, baseline and final abdomen computed tomography or liver magnetic resonance imaging of BCLM patients were reviewed. Liver metastasis patterns were classified as oligometastasis (≤3 metastatic lesions), non-confluent or confluent mass formation, infiltration, and pseudocirrhosis. Thirty-one surgical or biopsy specimens for liver metastasis were immunostained for L1 adhesion molecule (L1CAM), Yes-associated protein 1/Transcriptional co-activator with PDZ-binding motif (YAP/TAZ), and β1-integrin. Out of 156 patients, 77 initially had oligometastasis, 58 had nonconfluent mass formation, 14 had confluent mass formation, and 7 had infiltrative liver metastasis. Confluent or infiltrative liver metastasis showed inferior liver metastasis-associated survival (LMOS) compared to others (p = 0.001). Positive staining for L1CAM and YAP/TAZ was associated with inferior survival, and YAP/TAZ was related to final liver metastasis. Initial hepatic metastasis was associated with LMOS, especially confluent mass formation, and infiltrative liver metastasis pattern was associated with poor survival. Positive staining for YAP/TAZ and L1CAM was associated with inferior LMOS, and YAP/TAZ was related to final liver metastasis.

Clinical, Pathological, and Molecular Features of Breast Carcinoma Cutaneous Metastasis

Cutaneous metastases (CMs) account for 2% of all skin malignancies, and nearly 70% of CMs in women originate from breast cancer (BC). CMs are usually associated with poor prognosis, are difficult to treat, and can pose diagnostic problems, such as in histopathological diagnosis when occurring long after development of the primary tumor. In addition, the molecular differences between the primary tumors and their CMs, and between CMs and metastases in other organs, are not well defined. Here, we review the main clinical, pathological, and molecular characteristics of breast cancer CMs. Identifying molecular markers in primary BC that predict CM and can be used to determine the molecular differences between primary tumors and their metastases is of great interest for the design of new therapeutic approaches.

Systematic Construction and Validation of a Prognostic Model for Hepatocellular Carcinoma Based on Immune-Related Genes

Hepatocellular carcinoma (HCC), a highly aggressive tumor, has high incidence and mortality rates. Recently, immunotherapies have been shown to be a promising treatment in HCC. The results of either the CheckMate-040 or IMbrave 150 trials demonstrate the importance of immunotherapy in the systemic treatment of liver cancer. Thus, in this study, we tried to establish a reliable prognostic model for liver cancer based on immune-related genes (IRGs) and to provide a new insight for immunotherapy of HCC. In this study, we used four datasets that incorporated 851 HCC samples, including 340 samples with complete clinical information from the cancer genome atlas (TCGA) database, to establish an effective model for predicting the prognosis of HCC patients based on the differential expression of IRGs and validated the prognostic model using the data from International Cancer Genome Consortium (ICGC). The top 6 characteristic IRGs identified by protein-protein interaction (PPI) network analysis, MMP9, FOS, CAT, ESR1, ANGPTL3, and KLKB1, were selected for further study. In addition, we assessed the correlations of the six characteristic IRGs with the tumor immune microenvironment, clinical stage, and sensitivity to anti-cancer drugs. We also explored whether the differential expression of the characteristic IRGs was specific to HCC or present in pan-cancer. The expression levels of the six characteristic IRGs were significantly different between most tumor tissues and adjacent normal tissues. In addition, these characteristic IRGs showed a strong association with immune cell infiltration in HCC patients. We found that MMP9 and ESR1 were independent prognostic factors for HCC, while CAT, ESR1, and KLKB1 were associated with the clinical stage. We collected HCC paraffin sections from 24 patients from Xijing hospital to identify the differential expression of the five genes (MMP9, ESR1, CAT, FOS, and KLKB1). Finally, the results of decision curve analysis (DCA) and nomogram revealed that our models provided a prognostic benefit for most HCC patients and the predicted overall survival (OS) was consistent with the actual OS. In conclusion, we systemically constructed a novel prognostic model that provides new insights into HCC.

Functional Genomic Analysis of Breast Cancer Metastasis: Implications for Diagnosis and Therapy

Simple Summary

Metastasis remains the greatest cause of fatalities in breast cancer patients world-wide. The process of metastases is highly complex, and the current research efforts in this area are still rather fragmented. The revolution of genomic profiling methods to analyze samples from human and animal models dramatically improved our understanding of breast cancer metastasis. This article summarizes the recent breakthroughs in genomic analyses of breast cancer metastasis and discusses their implications for prognostic and therapeutic applications.

Abstract

Breast cancer (BC) is one of the most diagnosed cancers worldwide and is the second cause of cancer related death in women. The most frequent cause of BC-related deaths, like many cancers, is metastasis. However, metastasis is a complicated and poorly understood process for which there is a shortage of accurate prognostic indicators and effective treatments. With the rapid and ever-evolving development and application of genomic sequencing technologies, many novel molecules were identified that play previously unappreciated and important roles in the various stages of metastasis. In this review, we summarize current advancements in the functional genomic analysis of BC metastasis and discuss about the potential prognostic and therapeutic implications from the recent genomic findings.