Measuring tumour heterogeneity

Heterogeneity in Breast Cancer: Tumour Heterogeneity Pre-reading

Heterogeneity is the diversity seen within tumours and between patients, which affects diagnosis, prognosis and therapy choices. It is crucial for personalised medicine (tailoring of medical treatment to individual patient characteristics).

Breast cancer is often stratified into sub-types based on specific biomarkers e.g. Hormone receptor status or HER2 expression, leading to different treatment plans. This recognition is vital, as some sub-types may respond better to hormone therapy but others may require more aggressive treatment such as chemotherapy.

Key reviews to summarise:

·       Yersal and Barutca (2014)

·       Cho et al (2012)

·       Arpino et al (2013)

·       Vargo-Gagola + Rosen (2007)

·       Xenograft/Allograft models and GEM models

Drug response statistics: Each year, 7% of hospitalised patients in the US die due to adverse drug reactions, 50% being linked to genetic differences. This highlights the importance of understanding patients’ genetic makeup to avoid harmful side effects and optimise treatment efficiency.

Intra-tumour heterogeneity (differences within a single tumour): Refers to genetic, epigenetics and phenotypic variations within a single tumour, leading to different subpopulations of cells that carry different genetic variations. This can cause treatment resistance because as one part of the tumour is sensitive to the drug, another part may be resistant.

Inter-tumour heterogeneity (differences between different tumours): Differences between tumours from different patients, even if they are the same sub-type of breast cancer. This diversity means treatments need to be tailored for individual patients.

Tumour characteristics: Breast cancer can arise from different cell types ‘’cells of origin’’ (e.g. ductal or lobular cells), resulting in different behaviours and treatment responses. Tumours exhibit genetic mutations, chromosomal alterations and variations in protein expression. Treatments don’t work on all tumours or all cells in the tumour, it is a heterogenous disease (different gene mutations causing the same disease or condition.)

Personalised medicine approach:

Biomarkers: Biological indicators used to guide treatment decisions.

Molecular biomarkers: Measurable indicators of biological states, used for diagnosis, prognosis and monitoring treatment responses. E.g. hormone receptor status, HER2 expression, tumour size and nodal status.

Prognostic biomarkers: Indicate the likely course or outcome of the disease, independent of treatment. For example, tumour size would be a prognostic indicator. Offer information on the overall outcome e.g. likelihood of recurrence or survival. Needs to be easy to measure and has to have consequences for therapy.

Predictive biomarkers: Provide information on the likelihood of response to a specific therapy. For example, HER2 amplification predicts the responsiveness to HER2-targeted therapies. Choose effective treatment e.g. HER2 positivity indicates the use of Herceptin. Statistical tests must be applied to determine overall clinical benefit.

Stratification: The process of categorising patients into subgroups based on genetic and molecular characteristics to optimise treatment strategies.

Breast cancer is most common among young women in the UK, with 1 in 9 women diagnosed during their lifetime. High prevalence underscores the need for better diagnostic tools and personalised treatments.

Subtypes:

·       DCIS (Ductal carcinoma in situ): Non- invasive cancer confined to the ducts

·       LCIS (Lobular carcinoma in situ): Indicates increased risk of invasive breast cancer but isn’t cancer itself

·       Infiltrating types: Includes invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC) which have spread beyond the ducts or lobules.

Tumour grading: Reflects how much tumour cells differ from normal cells, indicating how aggressive a tumour is likely to be. G1: Well differentiated (similar to normal cells). G4: Undifferentiated (very abnormal and aggressive).

Tumour microenvironment components:

Epithelium: The layer where cancer originates (95%). Made of tubular ducts and epithelial sacks, interconnected to a central duct. Elongated cells secrete milk into lumen during pregnancy and don’t exist in non-pregnant.

Stroma: Connective tissue providing structural support

Immune cells: May either attack or support tumour growth

Cancer stem cells: Potentially drive tumour growth and resistance to treatment.

Classical histology is limited in predicting treatment response because it doesn’t capture the molecular complexity and heterogeneity of tumours.

The estrogen receptor is a protein that drives the growth of many breast cancers and is the first targetable biomarker, leading to hormone therapies like tamoxifen. ER positive indicates the need for hormone therapy.

The HER2 protein receptor is a growth promoting protein that is overexpressed in around 20% of breast cancers. Cell surface receptor, signals once activated it signals for cell proliferation.

 HER2-positive cancers are aggressive but respond well to HER2-targeted treatments. HER2 positive indicates the need for HER2-targeted therapy.

Immunohistochemistry is a technique used to measure receptor levels (ER and HER2), guiding treatment options

Breast cancers are subdivided into molecular subtypes (e.g. luminal A, luminal B, HER2-enriched and basal-like) based on gene expressions.

Biomarker panel assays:

 Oncotype DX and MammaPrint: These tests analyse multiple genes to assess recurrence risk and help to guide treatment decisions. They help to avoid overtreatment by identifying patients who may not benefit from chemotherapy, but require careful interpretation.

Overall the goals include prevention (identifying at-risk individuals) and prediction (determining treatment response.)