Project description:In this study we use expression data from breast cancer tumors to define immune clusters in breast cancer.
Immune clusters have gradual levels of immune infiltration. In the intermediate immune infiltration cluster, we found a worse prognosis which is independent of known clinicopathological features. We also found the immune clusters associated with treatment response. Further using gene expression data and deconvolution algorithms to dissect the immune contexture of the clusters.
Project description:We use expression data from breast cancer tumors to define immune clusters in breast cancer. Immune clusters have gradual levels of immune infiltration. In the intermediate immune infiltration cluster, we found a worse prognosis which is independent of known clinicopathological features. We also found the immune clusters associated with treatment response. Further we use gene expression data and deconvolution algorithms to dissect the immune contexture of the clusters.
Project description:Clusters of circulating tumor cells (CTC-clusters) are present in the blood of patients with cancer but their contribution to metastasis is not well defined. Here, we first use mouse models to demonstrate that breast cancer cells injected intravascularly as clusters are more prone to survive and colonize the lungs than single cells. Primary mammary tumors comprised of tagged cells give rise to oligoclonal CTC-clusters, with 50-fold increased metastatic potential, compared with single CTCs. Using intravital imaging and in vivo flow cytometry, CTC-clusters are visualized in the tumor circulation, and they demonstrate rapid clearance in peripheral vessels. In patients with breast cancer, presence of CTC-clusters is correlated with decreased progression-free survival. RNA sequencing identifies the cell junction protein plakoglobin as most differentially expressed between clusters and single human breast CTCs. Expression of plakoglobin is required for efficient CTC-cluster formation and breast cancer metastasis in mice, while its expression is associated with diminished metastasis-free survival in breast cancer patients. Together, these observations suggest that plakoglobin-enriched primary tumor cells break off into the vasculature as CTC-clusters, with greatly enhanced metastasis propensity. RNA-seq from 29 samples (15 pools of single CTCs and 14 CTC-clusters) isolated from 10 breast cancer patients
Project description:Clusters of circulating tumor cells (CTC-clusters) are present in the blood of patients with cancer but their contribution to metastasis is not well defined. Here, we first use mouse models to demonstrate that breast cancer cells injected intravascularly as clusters are more prone to survive and colonize the lungs than single cells. Primary mammary tumors comprised of tagged cells give rise to oligoclonal CTC-clusters, with 50-fold increased metastatic potential, compared with single CTCs. Using intravital imaging and in vivo flow cytometry, CTC-clusters are visualized in the tumor circulation, and they demonstrate rapid clearance in peripheral vessels. In patients with breast cancer, presence of CTC-clusters is correlated with decreased progression-free survival. RNA sequencing identifies the cell junction protein plakoglobin as most differentially expressed between clusters and single human breast CTCs. Expression of plakoglobin is required for efficient CTC-cluster formation and breast cancer metastasis in mice, while its expression is associated with diminished metastasis-free survival in breast cancer patients. Together, these observations suggest that plakoglobin-enriched primary tumor cells break off into the vasculature as CTC-clusters, with greatly enhanced metastasis propensity.
Project description:RNA-seq was performed on breast cancer cell lines and primary tumors RNA-seq was performed on 28 breast cancer cell lines, 42 Triple Negative Breast Cancer (TNBC) primary tumors, and 42 Estrogen Receptor Positive (ER+) and HER2 Negative Breast Cancer primary tumors, 30 uninovlved breast tissue samples that were adjacent to ER+ primary tumors, 5 breast tissue samples from reduction mammoplasty procedures performed on patients with no known cancer, and 21 uninvolved breast tissue samples that were adjacent to TNBC primary tumors.
Project description:Immunotherapy is applied to breast cancer to resolve the limitations of survival gain in existing treatment modalities. With immunotherapy, a tumor can be classified into immune-inflamed, excluded and desert based on the distribution of immune cells. We assessed the clinicopathological features, each subtype’s prognostic value and differentially expressed proteins between immune subtypes.
Project description:RNA-seq was performed on breast cancer cell lines and primary tumors RNA-seq was performed on 28 breast cancer cell lines, 42 Triple Negative Breast Cancer (TNBC) primary tumors, and 42 Estrogen Receptor Positive (ER+) and HER2 Negative Breast Cancer primary tumors, 30 uninovlved breast tissue samples that were adjacent to ER+ primary tumors, 5 breast tissue samples from reduction mammoplasty procedures performed on patients with no known cancer, and 21 uninvolved breast tissue samples that were adjacent to TNBC primary tumors.
Project description:Mufudza2012 - Estrogen effect on the dynamics
of breast cancer
This deterministic model shows the
dynamics of breast cancer with immune response. The effects of
estrogen are incorporated to study its effects as a risk factor for
the disease.
This model is described in the article:
Assessing the effects of
estrogen on the dynamics of breast cancer.
Mufudza C, Sorofa W, Chiyaka
ET.
Comput Math Methods Med 2012; 2012:
473572
Abstract:
Worldwide, breast cancer has become the second most common
cancer in women. The disease has currently been named the most
deadly cancer in women but little is known on what causes the
disease. We present the effects of estrogen as a risk factor on
the dynamics of breast cancer. We develop a deterministic
mathematical model showing general dynamics of breast cancer
with immune response. This is a four-population model that
includes tumor cells, host cells, immune cells, and estrogen.
The effects of estrogen are then incorporated in the model. The
results show that the presence of extra estrogen increases the
risk of developing breast cancer.
This model is hosted on
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and identified by:
BIOMD0000000642.
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