Predicting and understanding outcomes on first-line immunochemotherapy for oesophageal adenocarcinoma: Deep molecular profiling of the LUD2015-005 trial

Oesophageal adenocarcinoma (OAC) is a significant cause of cancer mortality that is rapidly rising in prevalence in many parts of the world. OAC is often inoperable at the time of diagnosis, and for these patients, the median survival on current first-line palliative treatment options is less than one year. Therefore, these patients represent a sizable population in need of additional treatment options. Recent phase III studies have shown that the addition of immune checkpoint inhibitors (ICI) to standard of care chemotherapy provides survival benefits for previously untreated oesophageal cancers. Selection of patients ideally suited for immunochemotherapy would be best informed by predictive biomarkers of patient outcome, but as this is a novel therapy in this first-line setting, these markers have not yet been fully investigated.
In this thesis, I undertake to deeply profile predictive biomarkers and mechanisms of response and resistance for immunochemotherapy in OAC using a comprehensive analysis of the LUD2015-005 clinical trial. LUD2015-005 is a unique phase I/II study of immunochemotherapy; the trial design includes an initial four-week window before immunochemotherapy is initiated where only ICI is given in order to disambiguate the effects attributable to the ICI agents.
Patient outcomes on the LUD2015-005 study showed reasonable agreement with reported outcomes from the immunochemotherapy arms in the recently reported phase III studies, with 41% of patients in this trial remaining alive and without reported progression at 12 months. The adverse event profile was generally acceptable, and interestingly, both immune-related and chemotherapy-related adverse events appeared to predict better outcomes.
To understand why some patients had better outcomes than others, tumours were assessed with whole genome sequencing and bulk and single-cell RNA-sequencing. The coding total mutational burden was shown to be strongly predictive of better outcome in this cohort, similar to what has been shown in other ICI settings. However, while T cell inflammation signatures are predictive of outcome on ICI in other cancers, they were not in the LUD2015-005 cohort. Instead, an innate immune signature specific to infiltrating monocytes was a strong predictor of better outcomes.
While T cell signatures did not have predictive power at the pre-treatment timepoint, sequential sampling revealed that patients with strong on-therapy upregulation of a novel T cell inflammation signature (INCITE) showed better radiological responses during the ICI-only phase of treatment. Development of durable responses was also seen during the immunochemotherapy period even in patients who did not show strong T cell responses during the initial ICI-only window.
A droplet-based single-cell RNA-sequencing dataset is also presented in this thesis, the first reported large-scale effort to profile OAC with this methodology. This dataset was used to demonstrate a novel workflow for cryopreservation of small endoscopic biopsies, and the transcriptional profiles of all identified cell types were leveraged in a deconvolution approach to reveal the potential contribution of tumour cell composition to immunochemotherapy outcome. These data were also applied to investigate how tumour cells evolve transcriptionally in response to ICI at single-cell resolution and the implications for ICI resistance mechanisms.
In summary, this thesis deeply profiles a first-line immunochemotherapy cohort and identifies a number of predictive biomarkers and potential mechanisms of response and resistance before, during, and after treatment. If validated, these findings could be used to help improve management and outcomes of patients with inoperable OAC.

Keywords: oesophageal squamous cell carcinoma
WGS
immunotherapy
LUD2015-005
durvalumab
oesophageal adenocarcinoma
RNA-seq
biomarkers
immunochemotherapy
clinical trial
tremelimumab
scRNA-seq
oesophageal cancer
predictive biomarkers
multiomics
Advisor: Lu, X
Middleton, M