Breast Cancer Heterogeneity Analysis

Variations in the characteristics of a tumor across different individuals or cancer types.

Differences in the characteristics of a tumor within the same individual.

The surrounding environment of a tumor, including blood vessels, immune cells, and extracellular matrix, which can influence tumor growth and response to treatment.

The fact that different cancer types have different characteristics and behaviors, making them unique.

Focusing on the specific characteristics of the epithelial cells, from which most solid tumors arise, to target cancer therapies.

Targeting treatment based on a patient's specific cancer type and individual circumstances, recognizing that one drug does not fit all patients.

The difficulty of treating cancer patients effectively due to the complexity and variation of cancer types and the potential for severe side effects from drugs.

Treating cancer patients based on the type and stage of their cancer, with the goal of extending life while considering the side effects of treatment.

The likelihood that a patient will respond to a specific treatment, such as hormone therapy.

Indicates the overall outcome or progression of a disease, regardless of treatment.

Estrogen Receptor (ER) status is more useful in predicting a patient's response to hormone therapy than in determining the overall course of the disease.

Around 50% of patients with ER+ breast cancer respond to hormone therapy like tamoxifen.

ER- breast cancer cells don't have estrogen receptors and typically do not respond to hormone therapy.

ER status is a useful tool to help determine if a patient is likely to respond to hormone therapy.

While ER status is a helpful indicator, it's not a perfect predictor. Other molecular biomarkers may be needed to better predict response to hormone therapy.

ER+ patients generally have better survival rates compared to ER- patients, possibly due to the effectiveness of hormone therapies.

A gene that influences a specific trait, such as aggression, cell communication, or metastasis.

Understanding how a specific gene contributes to cancer spread helps predict which cancers are more likely to spread.

Biological markers found in blood, body fluids, or tissues that provide information about a disease's presence, progression, or response to treatment.

Tests used to identify specific predictive biomarkers before administering a treatment.

A specific subtype of disease that indicates that a particular treatment will not be effective.

Treatment that is designed to target a specific mechanism involving a biomarker, which results in treatment effectiveness only when the biomarker is present.

The process of measuring the amount of RNA present for each gene in a sample, which reflects the activity of these genes within the tumor cells.

Classifying patients into groups based on their gene expression profiles to personalize treatment plans.

The likelihood of a disease recurring after treatment.

A set of biological markers, often genes, used to predict disease risk, diagnose conditions, or guide treatment choices.

Treating patients with unnecessary therapies based on limited information.

Tailoring treatment strategies to individual patients based on their unique characteristics, especially their genetic profile.

A breast cancer diagnostic tool that uses gene expression analysis to predict the risk of metastasis.

A breast cancer diagnostic tool that uses gene expression analysis to help decide if chemotherapy is necessary.

NGS allows the sequencing of the entire genome, including all mutations, identifying single nucleotide changes, insertions, deletions, and large deletions.

NGS helps identify specific mutations linked to diseases, aiding in understanding disease mechanisms and developing targeted therapies.

NGS has significantly improved cancer subtyping, allowing for the identification of more than 10 subtypes based on their molecular characteristics.

Integrating NGS with transcription profiling provides more accurate prognostic indicators for each cancer subtype, leading to better treatment planning.

NGS helps identify patients at the highest risk of disease progression by detecting genetic variations associated with more aggressive disease.

NGS enables clinicians to make more informed treatment decisions by understanding the molecular basis of different subtypes, potentially improving outcomes and reducing unnecessary treatments.

Patients with different cancer subtypes may have vastly different prognoses, with some having a significantly lower survival rate than others.

NGS helps identify genetic factors and molecular profiles that predict patient outcomes, enabling doctors to adjust treatment plans based on individual needs.

A type of graph used to visualize survival probabilities over time, often used in medical research to understand disease progression and treatment effectiveness.

Distinct groups of patients categorized based on specific molecular or clinical characteristics, often identified through analysis of gene expression or other factors.

A statistical test used to compare the survival curves of different groups, helping to determine if there are significant differences in survival outcomes between these groups.

Genes that drive cancer development and progression. They are often targeted by cancer treatments.

Genes that modify the behavior of cancer cells but don't always directly cause the disease. They can influence how cancer cells respond to therapy.

The process of classifying cancer into subtypes based on specific molecular characteristics, leading to more precise diagnosis and targeted treatment options.

Understanding the molecular mechanisms that drive cancer progression, providing insights into how cancer cells grow, spread, and respond to treatment.