DOI: 10.25934/pr00009004
title: Predicting which breast cancer patients will benefit from Palbiclib treatment through a machine learning model

description: Breast cancer is one of the most common cancers affecting millions of women worldwide. It occurs when cells in the breast grow uncontrollably. The risk of breast cancer increases with age, and older patients may respond differently to treatments compared to younger patients. One treatment for breast cancer is a drug called Palbociclib, which works by blocking the growth of cancer cells. However, how well this treatment works can vary depending on a patient's age and genetic characteristics.

Our study focuses on understanding how age affects the expression of genes (the instructions that tell cells how to function) and how these differences may impact the effectiveness of Palbociclib. We are investigating how age-related differences in gene activity could influence the success of treatment for breast cancer patients. By focusing on this, we hope to provide more personalized treatment strategies, where the treatment is tailored to both the age and genetic makeup of the patient, increasing its chances of success.

We chose Palbociclib because it is an important treatment for breast cancer that targets cancer cell growth. It is particularly relevant for our study because the way it works could be influenced by how genes are expressed in patients of different ages. Palbociclib is already in use for breast cancer treatment, so understanding its varying effectiveness could help improve its use in clinical settings.

To rule out other factors that might influence the results, we will account for several potential confounding factors. These include tumor stage, molecular subtype (the specific characteristics of the cancer), treatment history, and other health-related factors such as the type of chemotherapy (is a treatment that uses strong medicines to kill or stop the growth of cancer cells) used. We will analyze how these factors interact with gene expression and age to ensure that we focus specifically on the impact of age-related gene expression differences on treatment outcomes.

In our study, we will divide breast cancer patients into 3 age groups: Young Adults (20-39), Middle-Aged (40-59), Seniors (60+). By comparing their gene expression profiles (the pattern of genes that are turned on or off), we aim to identify "differential genes"—genes that behave differently in the two groups. These differences could help explain how patients of different ages respond to Palbociclib. For example, if a specific gene is more active in older patients and this affects how well they respond to the drug, we can use that information to adjust their treatment plan.

To analyze this, we will use statistical techniques like “unsupervised clustering analysis.” This means grouping patients based on patterns in their gene expression data without any prior knowledge of which group they belong to. By comparing these groups, we hope to identify key genes and pathways that influence treatment success. We will also use "expression matrices" of these genes, which are like tables that show how active different genes are in each patient. By looking at these tables, we can identify which genes are linked to better or worse treatment outcomes.

The findings from this study will help improve the way breast cancer is treated by providing more personalized treatment recommendations. For example, if we find that certain genes are linked to better outcomes for younger patients but not for older ones, doctors may adjust the treatment plan depending on the patient’s age. Pharmaceutical companies could also use our findings to develop new drugs or drug combinations that work better for specific age groups.

Ultimately, this research will help ensure that breast cancer patients receive the most effective treatments based on both their genetic profile and age, improving survival rates and the quality of life for women affected by the disease.