| 61. |
- Wang, Chengshi, et al.
(författare)
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Cardiovascular mortality among cancer survivors who developed breast cancer as a second primary malignancy
- 2020
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Ingår i: Cancer Research. - : American Association for Cancer Research. - 0008-5472 .- 1538-7445. ; 80:16
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Background: Patients with breast cancer are at increased risk of cardiovascular diseases and cardiovascular mortality, potentially due to treatment-related cardiotoxicity, early menopause, and psychological distress. Cancer survivors who developed breast cancer as a second malignancy (BCa-2) are common and may experience cancer treatment and distress for a second time. It is however unknown whether BCa-2 patients have increased risk of cardiovascular mortality compared to patients with breast cancer as the first malignancy (BCa-1) and the cancer-free female population.Methods: Using the Surveillance, Epidemiology, and End Results database, we conducted a cohort study including 1,024,047 patients with BCa-1 and 41,744 with BCa-2 diagnosed at age of 30 years and older during 1975-2016, and the corresponding female population of 994,415,911 person-years in the U.S.. Compared with patients with BCa-1 and the general population, we calculated incidence rate ratios (IRRs) of cardiovascular deaths among patients with BCa-2 by using multivariable Poisson regression.Results: During the median follow-up of 5.9 years (interquartile range, 2.4-11.3 years), 3,550 and 71,298 cardiovascular deaths were observed in BCa-2 and BCa-1 patients. Overall, BCa-2 patients had a mildly increased risk of cardiovascular mortality compared with the general population (IRR 1.07, 95% CI 1.03-1.10) and BCa-1 patients (IRR 1.13, 95% CI 1.10-1.17) with the adjustment of demographic factors. When comparing BCa-2 with BCa-1, the association was further independent of tumor characteristics and treatment modes (IRR 1.15, 95% CI 1.11-1.19). The elevated risk of cardiovascular mortality was mostly pronounced at age of follow-up between 30 and 79 years (IRR 1.31, 95% CI 1.23-1.40 compared with the general population; IRR 1.34, 95% CI 1.25-1.43 compared with BCa-1), whereas the risk was mildly increased, if any, at age of 80+ years compared with BCa-1 (IRR 1.07, 95% CI 1.02-1.11). The increased risk at age of 30-79 years in BCa-2 was particularly greater within the first month after cancer diagnosis compared with the general population (IRR 3.20, 95% CI 2.34-4.38). In contrast, cardiovascular mortality rate in BCa-2 was comparable to that in BCa-1 within the first month (IRR 0.87, 95% CI 0.63-1.21).Conclusions: Our findings suggest that patients with BCa-2, particularly in patients younger than 80 years, are at increased risk of cardiovascular mortality, compared with the general population and patients with BCa-1.
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| 64. |
- Bekele, Maheteme, et al.
(författare)
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Tumor and immune cell profiling in breast cancer using highly multiplexed imaging mass cytometry single-cell technology demonstrates tumor heterogeneity and immune phenotypic abnormality in Ethiopian women
- 2020
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Ingår i: Cancer Research. - : American Association for Cancer Research. - 0008-5472 .- 1538-7445. ; 80:21 Suppl.
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Background: Tumor heterogeneity represents a complex challenge to cancer treatment, disease recurrence, and patient survival. Imaging mass cytometry (IMC) is an emerging proteomic tool for cancer profiling in tumor tissue samples. IMC enables digital image analysis by multiplexed immunostaining of cells and proteins within tissue and preserves spatial relations within tumor environment. We have applied IMC based approach to study the heterogeneity of invasive breast carcinoma protein expression pattern in formalin fixed paraffin embedded tissues.Methods: A total of 10 region of interest (ROI) derived from 5 patients with primary invasive breast carcinoma representing three molecular subclasses (HR+/HER2-,HER2+/HR- and TNBC) were stained with a 30-marker IMC metal labeled antibody panel (α-SMA, EGFR, p53, CD33, CD16, CD163, CD11b, PDL1, CD31, CD45, D44,Vimentin, FoxP3, CD4, ECadherin, CD68, CD20, CD8a, Cytokeratin7, PD1, GranzymeB, Ki67, ColTypeI, CD3, CD45RO, HLADR, DARC & CD11c). Tissue imaging was done by quantifying the abundance of bound antibody with a Hyperion IMC. MCD Viewer was used for visualization purpose and to export raw 16-bit tiff images for segmentation on CellProfiler. Segmentation masks were combined with the individual tiff files to extract single-cell information from each individual image. HistoCAT was applied to perform unbiased clustering of cell populations using the PhenoGraph algorithm and clustered cell populations was overlaid on t-SNE plot. The relative marker expression was used to generate heat-maps and each cluster was manually assigned a phenotype based on its expression profile.Results: The t-SNE generated from each ROI revealed different distinct cell populations and we report the presence of diverse tumor and immune cell populations in our samples. The (min, max) number of PhenoGraph clustered tumor cell populations in HR+/HER2-, HER2+ and TNBC Cases were (5,8) (7,9) and (5,7) respectively. Similarly, the (min, max) number of PhenoGraph clustered immune cell populations in HR+/HER2-, HER2+ and TNBC Cases were (5,8) (7,9) and (5,7) respectively. We also document the presence of inter and intra-tumor heterogeniety in expression of PD1 and PDL1 in all the tumor subtypes studied. Additionally, we report a phenotypic abnormality in the immune cell populations identified with dual or triple markers expression of the canonical CD antigens of T-Cells, B-Cells and macrophages.Conclusion: The current study demonstrates high-dimensional visualization with the simultaneous analysis of epithelial, immune, and stromal components using IMC can be used to explore cell populations in tumor tissue to quantify tumor heterogeneity or identification of novel clustering patterns that has potential for translational research and clinical practice. Significance: This study presents the potential of Imaging Mass Cytometry and single cell analysis algorithms in multiplex high throughput tumor tissue studies.
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