A5061942734- Cancer Immunotherapy and Biomarkers
- Immunotherapy and Immune Responses
- Cancer, Stress, Anesthesia, and Immune Response
- Cancer Genomics and Diagnostics
- Cancer Diagnosis and Treatment
- Medical Imaging Techniques and Applications
- Cancer Cells and Metastasis
- Epigenetics and DNA Methylation
- Cancer Treatment and Pharmacology
- CAR-T cell therapy research
- Reproductive System and Pregnancy
- Radiomics and Machine Learning in Medical Imaging
- Breast Cancer Treatment Studies
- Brain Metastases and Treatment
- Cancer-related molecular mechanisms research
- Immune Cell Function and Interaction
- Circular RNAs in diseases
- Pancreatic and Hepatic Oncology Research
- Advanced Breast Cancer Therapies
- Ethics in Clinical Research
- Medical Imaging and Pathology Studies
City Of Hope National Medical Center
2024-2025
City of Hope
2020-2024
While there is a great clinical need to understand the biology of metastatic cancer in order treat it more effectively, research hampered by limited sample availability. Research autopsy programmes can crucially advance field through synchronous, extensive, and high-volume collection. However, remains an underused strategy translational research. Via extensive questionnaire, we collected information on study design, enrolment strategy, conduct, data management, challenges opportunities...
Abstract The biology of metastatic breast cancer (MBC) is understudied, primarily due to the difficulty procuring multiple samples from patients with oligometastatic cancer. We developed a rapid postmortem tissue procurement program that allows collection and analysis numerous lesions, subclinical locations, potential pre-metastatic niches fall within this scope. conducted study on 9 MBC. Patients their families consented donate tissues immediately after death in an IRB-approved study....
End stage breast cancer often presents with oligometastatic tumors and lacks effective treatment options. Previous studies leveraged post-mortem tissue procurement (also called rapid autopsy) programs to collect shortly after death for profiling by bulk whole exome sequencing (WES). This has revealed substantial heterogeneity in the genomes of metastatic estrogen receptor-positive (ER+) triple-negative cancers. In order understand both genotypes, transcriptional phenotypes, evolution these...
Abstract Metastatic breast cancers show variable clinical responses due to inherent heterogeneity, both within tumors and among patients. Studies mainly examine molecular diversity across different patients' genetic variance a single tumor. Yet, the variability multiple or metastases in patient remains underexplored. Our study investigates this intrapatient examining cell tumor evolution from source metastatic cancer. In warm procurement trial involving 6 patients with multi-site tumors,...
Abstract Immune composition within the tumor microenvironment (TME) plays a central role in propensity of cancer cells to metastasize and respond therapy. Previous studies have suggested that metastatic TME is immune-suppressed. However, limited accessibility multiple sites patients has made assessing immune difficult context multiorgan metastases. We utilized rapid postmortem tissue collection protocol assess numerous breast metastasis paired tumor-free tissues. Metastases had comparable...
<p>Immune composition differences between tumor-free and tumor-involved tissues. Matchstick plots depict p-values of significant A, bone B, brain C, skin lung tissues for all immune subsets densities frequencies assessed. Negative log10 transformed are displayed sorted from most to least within the Yellow lines indicate a greater density/frequency in tissue as compared tissues, while green Frequencies complex cell D, CD8+ T cells, E, CD4+ F, monocytes. G, HLA-DR low cells amongst...
<p>Statistical comparison of immune subset densities across metastatic and non-metastatic tissues. Densities cell subsets compared organ sites within A, general B, complex subsets. Statistics generated by Wilcoxon rank sum tests.</p>
<p>Evaluation of immune densities in segmented tissue areas across different organ sites. Densities phenotype cells within were compared tumor-involved sites as shown (A, B). Statistics generated by Kruskal–Wallis test ranks (A ) and Wilcoxon rank sum tests (B). Calculated p values are displayed *, < 0.05.</p>
<p>Detailed comparison of immune densities between metastatic and non-metastatic tissue samples the same organ type. Densities cell subsets within A, CD8+ T cells, B, CD4+ C, B D, NK E, DCs, F, myeloid cells in tumor-free (blue) tumor-involved (red) tissues assayed by flow cytometry. Statistics generated Wilcoxon rank sum tests. Calculated p values are displayed as *, < 0.05</p>
<p>Comparison of immune subset composition between organ sites. Frequencies complex cell subsets compared across tumor-free and tumor-involved sites within A, CD8+ T cells, B, CD4+ C, B D, NK E, monocytes. Statistics generated by Wilcoxon rank sum tests. Calculated p values are displayed as *, < 0.05; **, 0.01; ***, 0.001.</p>
<p>Paired analysis of immune densities between tumor-free and tumor-involved tissues. Densities cell subsets as determined by flow cytometry are plotted connecting matched (blue) (red) Statistics generated paired student t-tests. Calculated p values displayed.</p>
<p>Detailed comparison of immune densities between different organ sites metastatic or non-metastatic tissue. Densities cell subsets within A, CD8+ T cells, B, CD4+ C, B D, NK E, DCs, and F, myeloid cells in tumor-free (blue) tumor-involved (red) tissues assayed by flow cytometry. Statistics generated Kruskal–Wallis test ranks. Calculated p values are displayed as *, < 0.05; **, 0.01; ***, 0.001; ****, 0.0001.</p>
<p>PD-L1 expression assessment in metastatic tumor tissues. A, PD-L1 tumor-involved tissues as assessed by a pathologist. B, Representative images of chromogenic staining percentages are shown. C, Densities immune cell subsets were compared across PD-L1+ (>1%) and PD-L1- Capacity for production IFN-γ, IL-2, TNF-α D, CD8+ T cells E, CD4+ between Frequencies individual that compose all F, tumor-free G, Statistics generated Kruskal–Wallis test ranks (a,f,g) Wilcoxon rank sum tests...
<p>Multiplex immunofluorescence quantification of immune densities in metastatic tissues. A, Densities phenotype cells were compared between tumor-free and tumor-involved tissues across different organ sites. B, Pathologist evaluation percentage tissue area considered tumor-involved. C, Percentages ‘clean tumor’ within identified to be composed by stroma. total stroma as contributed 100um proximal (D) 200um (E) tumor alone. F, Representative composite images are shown for each type....
<div>Abstract<p>Immune composition within the tumor microenvironment (TME) plays a central role in propensity of cancer cells to metastasize and respond therapy. Previous studies have suggested that metastatic TME is immune-suppressed. However, limited accessibility multiple sites patients has made assessing immune difficult context multiorgan metastases. We utilized rapid postmortem tissue collection protocol assess numerous breast metastasis paired tumor-free tissues....
<p>Whole slide multiplex immunofluorescence representative images. Whole composite images (left) of staining for CD3 (green), CD8 (orange), FOXP3 (yellow), CD20 (cyan), CD68 (magenta), pan-CK (gray), DAPI (blue). Tissue segmentation (right) each whole image is shown cancer islands (red), stroma extending 100um bands (multiple colors) and adjacent tumor-free tissue. Representative slides are tumor-involved bone (A, B), brain (C, D), liver (E, F), lung (G, H), skin (I, J). Scale bars...
<p>Multiplex immunofluorescence representative images and analysis strategy. Representative of a tumor-involved liver are shown. A, A composite image, B, phenotyped C, tissue segmented image. Individual markers shown at higher magnification for pan-CK (D), CD3 (E), CD8 (F), CD68 (G), CD20 (H), FOXP3 (I), DAPI alone (J), as image (K). L, Segmented cells were CD3+ CD8- T (green), CD8+ (orange), FOXP3+ Tregs (yellow), CD20+ B (cyan), CD68+ macrophages (magenta). Unclassified not Scale...
<p>Paired analysis of tissue immune cell densities between organ sites. Densities subsets as determined by flow cytometry are plotted connecting matched A, lung and liver, B, skin, C, liver skin tissues across tumor-free tumor-involved tissues. Statistics generated paired student t-tests. Calculated p values displayed.</p>
<p>Differences in T cell effector features between tumor-free and tumor-involved tissues. Frequencies of A, non-naïve CD8+ cells B, CD4+ expressing PD-1, CD69, CD127, KLRG1, TIGIT, CTLA-4 were compared tissue sites. The capacity for C, D, to produce IFN-γ, TNF-α, IL-2 was also assessed. Statistics generated by Wilcoxon rank sum tests.</p>
<p>Evaluation of dendritic cell enrichment and PD-L1 expression across organ sites. A, Densities conventional DC subsets, B, frequencies subsets amongst total DCs, C, percentage each subset expressing PD-L1, D, monocyte were compared tumor-free tumor-involved Statistics generated by Wilcoxon rank sum tests.</p>
<p>Immune subset composition across different organ sites. Frequencies of complex immune cell subsets within A, CD8+ T cells, B, CD4+ C, B D, NK and E, monocytes. Statistics generated by Kruskal–Wallis test ranks. Calculated p values are displayed as *, < 0.05; **, 0.01; ***, 0.001; ****, 0.0001.</p>