Novel immunohistochemistry-based signatures to predict metastatic site of triple-negative breast cancers

Development and external validation of a prediction model for brain metastases in patients with metastatic breast cancer

BACKGROUND

Breast cancer patients with brain metastasis (BM) have a poor prognosis. This study aims to identify the risk factors of BM in patients with metastatic breast cancer (MBC) and establish a competing risk model for predicting the risk of brain metastases at different time points along the course of disease.

METHODS

Patients with MBC admitted to the breast disease center of Peking University First Hospital from 2008 to 2019 were selected and retrospectively analyzed to establish a risk prediction model for brain metastases. Patients with MBC admitted to eight breast disease centers from 2015 to 2017 were selected for external validation of the competing risk model. The competing risk approach was used to estimate cumulative incidence. Univariate Fine-Gray competing risk regression, optimal subset regression, and LASSO Cox regression were used to screen potential predictors of brain metastases. Based on the results, a competing risk model for predicting brain metastases was established. The discrimination of the model was evaluated using AUC, Brier score, and C-index. The calibration was evaluated by the calibration curves. The model was assessed for clinical utility by decision curve analysis (DCA), as well as by comparing the cumulative incidence of brain metastases between groups with different predicted risks.

RESULTS

From 2008 to 2019, a total of 327 patients with MBC in the breast disease center of Peking University First Hospital were admitted into the training set for this study. Among them, 74 (22.6%) patients developed brain metastases. From 2015 to 2017, a total of 160 patients with MBC in eight breast disease centers were admitted into the validation set for this study. Among them, 26 (16.3%) patients developed brain metastases. BMI, age, histological type, breast cancer subtype, and extracranial metastasis pattern were included in the final competing risk model for BM. The C-index of the prediction model in the validation set was 0.695, and the AUCs for predicting the risk of brain metastases within 1, 3, and 5 years were 0.674, 0.670, and 0.729, respectively. Time-dependent DCA curves demonstrated a net benefit of the prediction model with thresholds of 9-26% and 13-40% when predicting the risk of brain metastases at 1 and 3 years, respectively. Significant differences were observed in the cumulative incidence of brain metastases between groups with different predicted risks (P < 0.05 by Gray's test).

CONCLUSIONS

In this study, a competing risk model for BM was innovatively established, with the multicenter data being used as an independent external validation set to confirm the predictive efficiency and universality of the model. The C-index, calibration curves, and DCA of the prediction model indicated good discrimination, calibration, and clinical utility, respectively. Considering the high risk of death in patients with metastatic breast cancer, the competing risk model of this study is more accurate in predicting the risk of brain metastases compared with the traditional Logistic and Cox regression models.

Risk of metastasis in BRCA2 carriers diagnosed with triple-negative breast cancer

BACKGROUND

Triple-negative breast cancer (TNBC) is the neoplasia most associated with BRCA1 germline pathogenic variants (PV) and is more likely to develop metastases than the other breast cancer (BC) subtypes, mainly in the lungs and the central nervous system (CNS). Recently, BRCA2 carriers were shown to have a higher risk for developing CNS metastases. However, the patterns of recurrence and metastases of BRCA2 carriers with TNBC are unknown.

METHODS

TNBC patient data attending the A.C. Camargo Cancer Center, from 1998 through 2020, were verified either by medical records or by BRCA1/2 genetic testing carried out. Multivariable logistic regression models were fit to the data to assess the independent factors for bone and CNS metastases. Adjustment was done using all independent variables with p < 0.2 in the univariable Cox model to describe the relationship between the independent variables until time of death.

RESULTS

A total of 388 TNBC patients were evaluated. We identified PV in BRCA1/2 genes in 21% (82/388), being 17.7% (69/388) in BRCA1 and only 3.3% (13/388) in BRCA2. A total of 120 patients (31%) developed distant metastases. Bone or CNS metastases were observed in 40% and 60% of BRCA2 PV carriers (p = 0.155), respectively. The BRCA2 carriers tended to have a higher likelihood of developing bone metastases (OR, 4.06; 95% CI, 0.82-20.01; p = 0.085), when compared to BRCA1 carriers (OR, 0.6; 95% CI, 0.12-2.87; p = 0.528). BRCA2 carriers had an OR of 1.75 (95% CI, 0.33-9.14; p = 0.503) for CNS metastasis development, while BRCA1 carriers had an OR of 0.72 (95% CI, 0.23-2.23; p = 0.574).

CONCLUSIONS

Patients with TNBC and PV in the BRCA2 gene had higher frequencies of secondary bone involvement and CNS in the course of the disease. However, the BRCA2 PV did not represent an independent outcome predictor of metastases and overall survival. Efforts to increase the number of BRCA2 carriers among TNBC patients are crucial for determining their risk of developing bone and CNS metastases compared to BRCA2 noncarriers.

Immunotherapy in triple-negative breast cancer: Insights into tumor immune landscape and therapeutic opportunities

Triple-negative breast cancer (TNBC) is a clinically aggressive subtype of breast cancer that represents 15-20% of breast tumors and is more prevalent in young pre-menopausal women. It is the subtype of breast cancers with the highest metastatic potential and recurrence at the first 5 years after diagnosis. In addition, mortality increases when a complete pathological response is not achieved. As TNBC cells lack estrogen, progesterone, and HER2 receptors, patients do not respond well to hormone and anti-HER2 therapies, and conventional chemotherapy remains the standard treatment. Despite efforts to develop targeted therapies, this disease continues to have a high unmet medical need, and there is an urgent demand for customized diagnosis and therapeutics. As immunotherapy is changing the paradigm of anticancer treatment, it arises as an alternative treatment for TNBC patients. TNBC is classified as an immunogenic subtype of breast cancer due to its high levels of tumor mutational burden and presence of immune cell infiltrates. This review addresses the implications of these characteristics for the diagnosis, treatment, and prognosis of the disease. Herein, the role of immune gene signatures and tumor-infiltrating lymphocytes as biomarkers in TNBC is reviewed, identifying their application in patient diagnosis and stratification, as well as predictors of efficacy. The expression of PD-L1 expression is already considered to be predictive of response to checkpoint inhibitor therapy, but the challenges regarding its value as biomarker are described. Moreover, the rationales for different formats of immunotherapy against TNBC currently under clinical research are discussed, and major clinical trials are highlighted. Immune checkpoint inhibitors have demonstrated clinical benefit, particularly in early-stage tumors and when administered in combination with chemotherapy, with several regimens approved by the regulatory authorities. The success of antibody-drug conjugates and research on other emerging approaches, such as vaccines and cell therapies, will also be addressed. These advances give hope on the development of personalized, more effective, and safe treatments, which will improve the survival and quality of life of patients with TNBC.

Nuclear import of PTPN18 inhibits breast cancer metastasis mediated by MVP and importin β2

Distant metastasis is the primary cause of breast cancer-associated death. The existing information, such as the precise molecular mechanisms and effective therapeutic strategies targeting metastasis, is insufficient to combat breast cancer. This study demonstrates that the protein tyrosine phosphatase PTPN18 is downregulated in metastatic breast cancer tissues and is associated with better metastasis-free survival. Ectopic expression of PTPN18 inhibits breast cancer cell metastasis. PTPN18 is translocated from the cytoplasm to the nucleus by MVP and importin β2 in breast cancer. Then, nuclear PTPN18 dephosphorylates ETS1 and promotes its degradation. Moreover, nuclear PTPN18 but not cytoplasmic PTPN18 suppresses transforming growth factor-β signaling and epithelial-to-mesenchymal transition by targeting ETS1. Our data highlight PTPN18 as a suppressor of breast cancer metastasis and provide an effective antimetastatic therapeutic strategy.

Breast cancer-derived DAMPs enhance cell invasion and metastasis, while nucleic acid scavengers mitigate these effects

Breast cancer (BC) is the most common malignancy in women. Particular subtypes with aggressive behavior are major contributors to poor outcomes. Triple-negative breast cancer (TNBC) is difficult to treat, pro-inflammatory, and highly metastatic. We demonstrate that TNBC cells express TLR9 and are responsive to TLR9 ligands, and treatment of TNBC cells with chemotherapy increases the release of nucleic-acid-containing damage-associated molecular patterns (NA DAMPs) in cell culture. Such culture-derived and breast cancer patient-derived NA DAMPs increase TLR9 activation and TNBC cell invasion in vitro. Notably, treatment with the polyamidoamine dendrimer generation 3.0 (PAMAM-G3) behaved as a nucleic acid scavenger (NAS) and significantly mitigates such effects. In mice that develop spontaneous BC induced by polyoma middle T oncoprotein (MMTV-PyMT), treatment with PAMAM-G3 significantly reduces lung metastasis. Thus, NAS treatment mitigates cancer-induced inflammation and metastasis and represents a novel therapeutic approach for combating breast cancer.

A pre-operative MRI-based brain metastasis risk-prediction model for triple-negative breast cancer

BACKGROUND

Triple-negative breast cancer (TNBC) patients have a high 2-year post-operative incidence of brain metastasis (BM). Currently, there is no early prediction tool to predict the risk of BM in TNBC patients.

METHODS

Data of breast cancer patients, who had been scanned, resected, and pathologically diagnosed at a local hospital from May 2012 to June 2018 were collected. Primary and radiological secondary exclusion criteria were used to determine patients' eligibility for inclusion in the study. Data for the TNBC cohort included qualified 2-year post-operative follow-up information, BM status, and pre-operative MRI data. Age-based propensity score matching (PSM) was used to build a comparable study cohort. The tumor regions of interest were segmented and used for lattice radiomics feature extraction. The filtered and normalized lattice radiomics features were then trained with BM status using the random forest (RF), support vector machine (SVM), k-nearest neighbor, least absolute shrinkage and selection operator regression, naïve Bayesian, and neural network algorithms. The generated prediction models were evaluated using 10-fold cross verification, and the areas under the curve (AUCs), accuracy, sensitivity, and specificity were reported.

RESULTS

Data from 643 breast cancer patients were collected. Among these, 84 TNBC cases (comprising 42 pairs) were included in this study after primary exclusion, radiological secondary exclusion, and PSM. We extracted 3,854 lattice radiomics features from the pre-operative MRI. Of these, 2,480 were used for model training after filtration. The 10-fold verification results showed that the BM risk-prediction model, which was based on the normalized and filtered lattice radiomics features of collected cases trained by naïve Bayesian algorithm, had a high AUC (0.878), accuracy (0.786), specificity (81.0%), and sensitivity (76.2%).

CONCLUSIONS

The pre-operative MRI data of TNBC patients can be used to predict 2-year BM risk. This application could help to achieve better early stratification, BM screening, and the overall prognosis.

An Immune Model to Predict Prognosis of Breast Cancer Patients Receiving Neoadjuvant Chemotherapy Based on Support Vector Machine

Tumor microenvironment has been increasingly proved to be crucial during the development of breast cancer. The theory about the conversion of cold and hot tumor attracted the attention to the influences of traditional therapeutic strategies on immune system. Various genetic models have been constructed, although the relation between immune system and local microenvironment still remains unclear. In this study, we tested and collected the immune index of 262 breast cancer patients before and after neoadjuvant chemotherapy. Five indexes were selected and analyzed to form the prediction model, including the ratio values between after and before neoadjuvant chemotherapy of CD4⁺/CD8⁺ T cell ratio; lymphosum of T, B, and natural killer (NK) cells; CD3⁺CD8⁺ cytotoxic T cell percent; CD16⁺CD56⁺ NK cell absolute value; and CD3⁺CD4⁺ helper T cell percent. Interestingly, these characters are both the ratio value of immune status after neoadjuvant chemotherapy to the baseline. Then the prediction model was constructed by support vector machine (accuracy rate = 75.71%, area under curve = 0.793). Beyond the prognostic effect and prediction significance, the study instead emphasized the importance of immune status in traditional systemic therapies. The result provided new evidence that the dynamic change of immune status during neoadjuvant chemotherapy should be paid more attention.

Omission of Chemotherapy in HR+/HER2- Early Invasive Breast Cancer Based on Combined 6-IHC Score?

PURPOSE

Current methods of judging whether HR+/HER2- breast cancer (BC) require adjuvant therapy, such as Ki67 and multigene prognostic tests, cannot balance accuracy with the price most patients can afford.

METHODS

A retrospective analysis of 330 HR+/HER2- BC patients was conducted. Six BC-related genes (Cathepsin L2, MMP11, CyclinB1, Aurora A, Survivin, and Ki67) were screened using univariate and multivariate COX regression, and correlate clinical follow-up with immunohistochemical expression (designated as 6-IHC). All the included patients were divided randomly at a 7:3 ratio into training and testing cohorts. The cutoff prognosis index (PI) of 6-IHC was determined by multivariate Cox risk regression analysis after calculating the PI of each patient in training cohort and confirmed in testing cohort. Kaplan-Meier (KM) method was used to analyze Disease-free survival (DFS) and overall survival (OS). Six-IHC score and other factors associated with survival benefit of adjuvant chemotherapy were compared with Ki67 index.

RESULTS

The receiver operating characteristic curve analysis showed that the patients can be divided into 6-IHC score "High" and "Low" risk groups. The 8-year DFS and OS of the KM curves showed that chemotherapy did not significantly improve the DFS in the 6-IHC score "Low" risk group (P= 0.830), but significantly improved the DFS in the 6-IHC score "High" risk group (P = 0.012).

CONCLUSIONS

Combined 6-IHC score could be a reliable tool in predicting cancer-specific recurrences and survival in HR+/HER2-breast cancer patients, with additional advantages over using immunohistochemical expression of Ki67.

Risk factors for breast cancer brain metastases: a systematic review

Background: Brain metastasis (BM) is an increasingly common and devastating complication of breast cancer (BC).

Methods: A systematic literature search of EMBASE and MEDLINE was conducted to elucidate the current state of knowledge on known and novel prognostic factors associated with 1) the risk for BCBM and 2) the time to brain metastases (TTBM).

Results: A total of 96 studies involving institutional records from 28 countries were identified. Of these, 69 studies reported risk factors of BCBM, 46 factors associated with the TTBM and twenty studies examined variables for both outcomes. Young age, estrogen receptor negativity (ER-), overexpression of human epidermal factor (HER2+), and higher presenting stage, histological grade, tumor size, Ki67 labeling index and nodal involvement were consistently found to be independent risk factors of BCBM. Of these, triple-negative BC (TNBC) subtype, ER-, higher presenting histological grade, tumor size, and nodal involvement were also reported to associate with shorter TTBM. In contrast, young age, hormone receptor negative (HR-) status, higher presenting stage, nodal involvement and development of liver metastasis were the most important risk factors for BM in HER2-positive patients.

Conclusions: The study provides a comprehensive and individual evaluation of the risk factors that could support the design of screening tools and interventional trials for early detection of BCBM.

Added value of histogram analysis of apparent diffusion coefficient maps for differentiating triple-negative breast cancer from other subtypes of breast cancer on standard MRI

Background

Triple-negative breast cancers generally occur in young women with remarkable potential to be aggressive. It will be of great help to detect this subtype of tumor early. To retrospectively evaluate the performance of histogram analysis of apparent diffusion coefficient (ADC) maps in distinguishing triple-negative breast cancer (TNBC) from other subtypes of breast cancer (non-TNBC), when combined with magnetic resonance imaging (MRI) features.

Materials and methods

From February 2014 to December 2018, 192 patients were included in this study taking preoperative standard MRI (s-MRI) and DWI. Seventy-six of them were pathologically confirmed with TNBC and rest 116 with other subtypes. First, their clinical-pathological features and morphological characteristics on MRI were assessed, including tumor size, foci quantity, tumor shape, margin, internal enhancement, and time-signal intensity curve types, in addition to the signal intensity on T2-weighted images. Second, whole-lesion apparent diffusion coefficient (ADC) histogram analysis was executed. Finally, both univariate and multivariate regression analyses were applied to identify the most useful variables in separating TNBCs from non-TNBCs, and then their effects were evaluated following receiver operating characteristic curve analysis.

Result

Multivariate regression analysis indicated that circumscribed margin, rim enhancement, and ADC₉₀ were important predictors for TNBC. Increased area under curve (AUC) and improved specificity can be obtained when combined s-MRI and DWI (circumscribed margin+rim enhancement+ADC₉₀>1.47×10⁻³ mm²/s) is taken as the criterion, other than s-MRI (circumscribed margin+rim enhancement) alone (s-MRI+DWI vs s-MRI; AUC, 0.833 vs 0.797; specificity, 98.3% vs 89.7%; sensitivity, 68.4% vs 69.7%).

Conclusion

Circumscribed margin and rim enhancement on s-MRI and ADC₉₀ are three important elements in detecting TNBC, while ADC histogram analysis can provide additional value in this detection.

Machine learning for tissue diagnostics in oncology: brave new world

Machine learning is an exciting technology with broad application in big data analysis, as well as increasingly in specialised healthcare. As a diagnostic tool in tissue workup and pathology, it has the potential for personalised and stratified approaches, but the limitations and pitfalls need to be better understood and characterised especially in this critical area of medical care.

Aberrant miRNAs expressed in HER-2 negative breast cancers patient

Background

Breast cancer is a highly heterogeneous pathology, exhibiting a number of subtypes commonly associated with a poor outcome. Due to their high stability, microRNAs are often regarded as non-invasive cancer biomarkers, having an expression pattern specific for their ‘cell of origin’.

Method

Triple negative breast cancer (TNBC: ER-, PR-, Her-2-) and double positive breast cancer (DPBC: ER+, PR+, Her-2) miRNA expression patterns were obtained by analysis of the TCGA (The Cancer Genome Atlas) data, followed by PCR-array analysis on plasma samples from 20 TNBC patients, 14 DPBC patients and 11 controls.

Results

Three downregulated and nine upregulated miRNAs were obtained from the TNBC analysis. Five overexpressed miRNAs were identified in the DPBC group. Four of the dysregulated miRNAs (miR-10a, miR-125b, miR-210 and miR-489) were common for both groups. The cluster miR-17-92 (miR-17, miR-20a, miR-20b, and miR-93), along with miR-130, miR-22 and miR-29a/c, were found to differentiate between TNBC and DPBC. A panel of five transcripts (miR-10a, miR-125, miR-193b, miR-200b and miR-489) was validated in a new set of plasma samples. The overlapping of TCGA and plasma profiling data revealed miR-200b, miR-200c, miR-210 and miR-29c as common signature. MiR-200b was validated on additional normal and tumor tissue samples. The expression level of this transcript from the TCGA data was correlated with lung and bone metastatic genes.

Conclusion

The miR-200b presents a great potential for the future advancements in the diagnostic/prognostic and therapeutic approach of TNBC, along with other coding or non-coding transcripts. However, this needs to be further integrated in a regulatory network that acts in conjunction with other markers that affect the patients’ prognosis or response to therapy.

Electronic supplementary material

The online version of this article (10.1186/s13046-018-0920-2) contains supplementary material, which is available to authorized users.