Bim may be a poor prognostic biomarker in breast cancer patients especially in those with luminal A tumors

Purpose: Older breast cancer patients are underrepresented in cancer research even though the majority (81.4%) of women dying of breast cancer are 55 years and older. Here we study a common phenomenon observed in breast cancer which is a large inter- and intratumor heterogeneity; this poses a tremendous clinical challenge, for example with respect to treatment stratification. To further elucidate genomic instability and tumor heterogeneity in older patients, we analyzed the genetic aberration profiles of 39 breast cancer patients aged 50 years and older (median 67 years) with either short (median 2.4 years) or long survival (median 19 years). The analysis was based on copy number enumeration of eight breast cancer-associated genes using multiplex interphase fluorescence in situ hybridization (miFISH) of single cells, and by targeted next-generation sequencing of 563 cancer-related genes. Results: We detected enormous inter- and intratumor heterogeneity, yet maintenance of common cancer gene mutations and breast cancer specific chromosomal gains and losses. The gain of COX2 was most common (72%), followed by MYC (69%); losses were most prevalent for CDH1 (74%) and TP53 (69%). The degree of intratumor heterogeneity did not correlate with disease outcome. Comparing the miFISH results of diploid with aneuploid tumor samples significant differences were found: aneuploid tumors showed significantly higher average signal numbers, copy number alterations (CNAs) and instability indices. Mutations in PIKC3A were mostly restricted to luminal A tumors. Furthermore, a significant co-occurrence of CNAs of DBC2/MYC, HER2/DBC2 and HER2/TP53 and mutual exclusivity of CNAs of HER2 and PIK3CA mutations and CNAs of CCND1 and PIK3CA mutations were revealed. Conclusion: Our results provide a comprehensive picture of genome instability profiles with a large variety of inter- and intratumor heterogeneity in breast cancer patients aged 50 years and older. In most cases, the distribution of chromosomal aneuploidies was consistent with previous results; however, striking exceptions, such as tumors driven by exclusive loss of chromosomes, were identified.

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Prognostic biomarker identification and tumor classification in breast cancer patients by methylation and transcriptome analysis

FEBS Open Bio ◽

10.1002/2211-5463.13211 ◽

2021 ◽

Author(s):

Xiongdong Zhong ◽

Guoying Zhong

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Transcriptome Analysis ◽

Prognostic Biomarker ◽

Tumor Classification ◽

Breast Cancer Patients ◽

Biomarker Identification

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Relationship between tumor size and metastatic site in patients with stage IV breast cancer: A large SEER-based study.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e13065 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e13065-e13065

Author(s):

Qian Dong ◽

Mi Zhang ◽

Da Jiang

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Tumor Size ◽

Stage Iv ◽

Breast Cancer Patients ◽

Luminal A ◽

Chi Square ◽

Metastatic Site ◽

Luminal B ◽

Stage Iv Breast Cancer

e13065 Background: To analyze the correlation between tumor size and metastatic site in first-diagnosed stage IV breast cancer patients. Methods: Stage IV breast cancer patients diagnosed from 2010 to 2015 were screened by the Surveillance, Epidemiology, and End Results (SEER) database. The characteristics of clinical variables were represented by a frequency table, and the Chi-square test was used for comparison. At the same time, the Chi-square test was used to analyze the relationship between tumor size and organ metastasis. Correlation between tumor size and the prognosis of patients was contributed by KM curve and Log-rank test. Results: Regardless of tumor size, the proportion of bone metastasis was higher and brain metastasis was lower in breast cancer patients. There were significant differences in the site of metastases based on different subtype. Luminal A and Luminal B breast cancer had the highest proportion of bone metastases; brain metastasis accounted for the highest proportion in triple-negative breast cancer (TNBC); while the incidence of liver metastasis was the highest in Her-2(+) breast cancer. At the same time, the results indicated that Luminal A breast cancer with a tumor size > 5 cm was more likely to develop multi-site metastasis and lung metastasis, while Luminal B breast cancer with a tumor size ≤ 5 cm was more likely to develop liver metastasis. The results also revealed that TNBC patients with a tumor size of 0 - 2cm were more likely to develop bone metastasis than those with a tumor size > 5 cm, and the incidence of lung metastasis in triple-negative patients showed an increasing trend with the increase of tumor size. Conclusions: Based on subtype, we found that there was a significant difference between tumor size and metastatic site in patients with stage IV breast cancer, and the difference was statistically significant. This study provided evidence-based basis for decision-making of stage IV breast cancer treatment.

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Áp dụng hóa bảng phân loại của ST GALLEN 2013 trong phân nhóm phân tử ung thư vú biểu mô tuyến vú

Journal of Clinical Medicine- Hue Central Hospital ◽

10.38103/jcmhch.2020.65.16 ◽

2020 ◽

Author(s):

Chu Nguyen Van

Keyword(s):

Breast Cancer ◽

Adjuvant Therapy ◽

Cancer Patients ◽

Molecular Classification ◽

Breast Cancer Patients ◽

Luminal A ◽

Prognostic Information ◽

Patient Groups

Molecular classification of breast cancer is target to category patient groups who need to treat by the appropriate adjuvant therapy and provide more exact prognostic information. Purpose: Determining the proportion of molecular types and commenting on some association with clinicpathological characteristics of breast cancer. Methods: 521 operated breast cancer patients were stained by immunohistochemistry with markes such as: ER, PR, HER2, and Ki67 for classifying into 5 molecular categories and follow up assessment. Results: Type LUMBH- accounted for the highest proportion of 26.5%, followed by luminal A (22.5%). Typically, LUMA was the highest rate in good NPI (35.0%), whereas in poor NPI group, HER2 type was the highest rate (36.4%) (p<0.001). The LUMBH - group has the OS rate during the 5-year follow-up of 94.6% and LUMA is 93.5%; In contrast, the HER2 group showed the lowest OS ratio (72.6%) (p<0.05). Conclusion: Molecular classification of breast cancer according to St Gallen 2013 classification can provide the important information for treatment and prognosis.

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Abstract P4-11-43: Ki-67 and p53 are useful factors to predict long term survival in low-risk luminal A breast cancer patients

10.1158/1538-7445.sabcs14-p4-11-43 ◽

2015 ◽

Author(s):

Ha Woo Yi ◽

Se Kyung Lee ◽

Soo Youn Bae ◽

Jun Ho Lee ◽

Hyun-Chul Lee ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Low Risk ◽

Breast Cancer Patients ◽

Luminal A ◽

Ki 67 ◽

Term Survival ◽

Long Term Survival

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Prolactin receptor expression as a novel prognostic biomarker for triple negative breast cancer patients

Annals of Diagnostic Pathology ◽

10.1016/j.anndiagpath.2020.151507 ◽

2020 ◽

Vol 46 ◽

pp. 151507 ◽

Cited By ~ 1

Author(s):

Behnaz Motamedi ◽

Hossain-Ali Rafiee-Pour ◽

Mohammad-Reza Khosravi ◽

Amirhosein Kefayat ◽

Azar Baradaran ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Prognostic Biomarker ◽

Prolactin Receptor ◽

Receptor Expression ◽

Breast Cancer Patients

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Apoptotic caspases inhibit abscopal responses to radiation and identify a new prognostic biomarker for breast cancer patients

OncoImmunology ◽

10.1080/2162402x.2019.1655964 ◽

2019 ◽

Vol 8 (11) ◽

pp. e1655964 ◽

Cited By ~ 32

Author(s):

Maria Esperanza Rodriguez-Ruiz ◽

Aitziber Buqué ◽

Michal Hensler ◽

Jonathan Chen ◽

Norma Bloy ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Prognostic Biomarker ◽

Breast Cancer Patients

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Compliance with consensus recommendations for systemic therapy, breast cancer intrinsic subtype, and clinical outcome.

Journal of Clinical Oncology ◽

10.1200/jco.2011.29.27_suppl.262 ◽

2011 ◽

Vol 29 (27_suppl) ◽

pp. 262-262

Author(s):

S. Takayama ◽

J. Matsui ◽

N. Ando

Keyword(s):

Breast Cancer ◽

Clinical Outcome ◽

Cancer Patients ◽

The Other ◽

Breast Cancer Patients ◽

Luminal A ◽

Consensus Recommendations ◽

Time To Recurrence ◽

Kaplan Meier

262 Background: Consensus recommendations for breast cancer are come into wide use, and we believe clinical guidelines leads our patients to improved outcome. Recently, gene profiling can be available, and immunohistochemical surrogates for molecular subtyping can provide much of prognostic and predictive factors. We investigated the relationship between compliance with consensus recommendations and clinical outcome of each subtype on the hypothesis that conformity with consensus recommendations is associated with increased survival among all intrinsic subtypes. Methods: We investigated breast cancer patients operated in our hospital from 2002 to 2004. DCIS, Stage 4 and male breast cancer were excluded. Patients were divided into luminal A (ER+ and/or PgR+, HER2-, low nuclear grade (NG)), luminal B (ER+ and/or PgR+, HER2-, high NG), luminal-HER2 (ER+ and/or PgR+, HER2+), HER2 (ER-, PgR-, HER2+) and triple negative (TN), (ER-, PgR-, HER2-). All patients were divided into compliance group or non-compliance group according to their conformity with consensus recommendations. Kaplan-Meier analysis was used in statistical studies. Results: 121 breast cancer patients were eligible and investigated in this study. Median follow-up period was 6.7 years. Fifteen relapse cases (12.4%) were identified. Among luminal A patients, compliance group was significantly associated with better recurrence free survival compared with non-compliance group with Kaplan-Meier analysis (p<0.001). In contrast, there was no difference between compliance group and non-compliance group among the other subtypes. Median time to recurrence (4.2 year) in non-compliance group among luminal A was apparently longer than that (1.7 year) in compliance group among the other subtypes. Conclusions: Compliance group was significantly associated with better clinical outcome compared with non-compliance group among luminal A. This study suggests that cautious follow-up is required against non-compliance patients among luminal A, because time to recurrence of them was longer than that of both compliance and non-compliance subgroups among the other subtypes.

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Prognosis of Treatment Response (Pathological Complete Response) in Breast Cancer

Biomarker Insights ◽

10.4137/bmi.s9387 ◽

2012 ◽

Vol 7 ◽

pp. BMI.S9387 ◽

Cited By ~ 5

Author(s):

Jason B. Nikas ◽

Walter C. Low ◽

Paul A. Burgio

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Sensitivity And Specificity ◽

Pathological Complete Response ◽

Prognostic Biomarker ◽

Complete Response ◽

Mathematical Function ◽

Breast Cancer Patients ◽

Model Complex ◽

Clinical Stages

Pertaining to the female population in the USA, breast cancer is the leading cancer in terms of annual incidence rate and, in terms of mortality, the second most lethal cancer. There are currently no biomarkers available that can predict which breast cancer patients will respond to chemotherapy with both sensitivity and specificity > 80%, as mandated by the latest FDA requirements. In this study, we have developed a prognostic biomarker model (complex mathematical function) that–-based on global gene expression analysis of tumor tissue collected during biopsy and prior to the commencement of chemotherapy–-can identify with a high accuracy those patients with breast cancer (clinical stages I–III) who will respond to the paclitaxel-fluorouracil-doxorubicin-cyclophosphamide chemotherapy and will experience pathological complete response (Responders), as well as those breast cancer patients (clinical stages I–III) who will not do so (Non-Responders). Most importantly, both the application and the accuracy of our breast cancer prognostic biomarker model are independent of the status of the hormone receptors ER, PR, and HER2, as well as of the ethnicity and age of the subjects. We developed our prognostic biomarker model with 50 subjects [10 responders (R) and 40 non-responders (NR)], and we validated it with 43 unknown (new and different) subjects [10 responders (R) and 33 non-responders (NR)]. All 93 subjects were recruited at five different clinical centers around the world. The overall sensitivity and specificity of our prognostic biomarker model were 90.0% and 91.8%, respectively. The nine most significant genes identified, which comprise the input variables to the mathematical function, are involved in regulation of transcription; cell proliferation, invasion, and migration; oncogenesis; suppression of immune response; and drug resistance and cancer recurrence.

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