Defining the landscape of antibody-drug conjugates for maintenance therapy in neoadjuvant chemotherapy-treated ovarian cancer

This project evaluates whether key targets for antibody-drug conjugates remain expressed in high-grade serous ovarian cancer following chemotherapy. Using a unique WA tissue biobank and AI-powered digital pathology, the study will identify promising maintenance therapy targets and inform future clinical trials to improve survival outcomes.

Research overview


High-grade serous ovarian cancer (HGSOC) is the most lethal gynaecological malignancy, often diagnosed at an advanced stage. In Western Australia, many women undergo neoadjuvant chemotherapy (NACT) to shrink tumours before surgery. While initial responses are often positive, recurrence occurs in many women within two years. There is an urgent need to develop effective maintenance therapies.

Antibody-drug conjugates (ADCs) represent a revolutionary new class of cancer therapy that links a tumour-targeting antibody to a potent chemotherapy agent, enabling precision drug delivery directly to cancer cells. The first ADC for ovarian cancer was approved in 2024, and several others have shown remarkable response rates in early-phase clinical trials. However, a critical question must be answered before these agents can be confidently deployed as maintenance therapies: are the protein targets they rely upon such as folate receptor alpha (FOLR1), human epidermal growth factor receptor 2 (HER2), tumour-associated calcium signal transducer 2 (TROP2), cadherin 6 (CDH6), and mesothelin (MSLN) expressed on tumour cells after women have received standard first-line chemotherapy?

This project will address this question using a unique Western Australian biobank: two tissue microarrays (TMAs) containing 282 cores from 76 patients who received NACT. Using immunohistochemistry, the team will systematically measure the expression of five key ADC targets across this cohort. In parallel, they will develop a digital pathology scoring workflow using HALO. HALO is an AI-powered platform that applies machine learning algorithms to high-resolution digitised tissue slide images. Once trained on expert-annotated examples, HALO automatically identifies tumour regions, detects individual cells, and quantifies protein expression across many slides, delivering objective, reproducible scoring at a scale and consistency that manual pathologist review cannot match.

Findings will directly inform the design of ADC-based maintenance therapy trials in WA, with real potential to improve survival outcomes for Western Australian women with ovarian cancer.

Project researchers

Dr Weitao Lin

Funders

Women and Infants Research Foundation

Project timeline

2026 – 2027

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