Electronic Thesis and Dissertation Repository

Thesis Format

Integrated Article

Degree

Master of Science

Program

Surgery

Supervisor

Brackstone, M

Abstract

Axillary lymph nodes (ALNs) are the primary site of metastasis in breast cancer, and their involvement has implications in disease staging, prognostication, and treatment decisions. A non-invasive modality of assessing the risk of ALN metastasis can improve care in patients with early-stage breast cancer by omitting the morbidity and costs associated with axillary surgery.

This thesis explores the molecular landscape of early-stage breast cancers with ALN metastasis and shows the potential of tumour molecular signatures in predicting ALN involvement. After a systematic review of the literature, we use data from The Cancer Genome Atlas (TCGA) to develop molecular signatures correlated with ALN metastasis. We then use machine-learning to develop predictive models. We show that the predictive performance of models may be improved by accounting for the intrinsic molecular subtype of breast cancer. If validated externally, these models can reduce the rates of axillary surgery in patients with early-stage breast cancer.

Summary for Lay Audience

The lymph nodes underneath the armpit are the most common site of spread in breast cancer. In each patient, it is important to determine if these lymph nodes contain cancer as this information helps clinicians assign a stage to the cancer and suggest appropriate treatments. Clinical examination is not enough to rule out the presence of cancer in these lymph nodes. Most patients require surgery to remove several representative lymph nodes from the armpit area, so that these lymph nodes can be examined by a pathologist underneath a microscope for the presence of breast cancer. There is an opportunity to improve care, as surgery has risks for patients and costs for healthcare system. A solution to this problem could be a computer-generated predictive model that uses the genetic information from the cancer biopsy sample and provides an estimation for risk of cancer spread to lymph nodes for each patient.

We first searched the literature for available evidence on the topic of lymph node spread prediction in early-stage breast cancers. We included 59 articles and discussed the various patient and tumour factors studied in connection to the lymph node spread of breast cancer. We then used the publicly available genetic databases from The Cancer Genome Atlas (TCGA) collaborative to find the differences in the genetic information of early-stage breast tumours with lymph node spread, compared to those without. Our study also highlights that the genetic differences seen in cancers with lymph node spread are not consistent between the four previously established subgroups of breast cancer, known as the “intrinsic molecular subtypes”, and emphasizes the heterogeneity in the genetic information of breast cancers.

Based on the discovered molecular differences, we use computer-generated predictive models of lymph node spread in early-stage breast cancer. We show that the accuracy of these predictive models can be improved using a new approach that takes into account the intrinsic molecular subtype of the cancer. If validated in other populations, these models can be useful in reducing the rates of lymph node surgery and improve care in early-stage breast cancer.

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License.

Available for download on Friday, December 01, 2023

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