Circulating tumour DNA in metastatic breast cancer to guide clinical trial enrolment and precision oncology: A cohort study.

BACKGROUND: Metastatic breast cancer (mBC) is a heterogenous disease with increasing availability of targeted therapies as well as emerging genomic markers of therapeutic resistance, necessitating timely and accurate molecular characterization of disease. As a minimally invasive test, analysis of circulating tumour DNA (ctDNA) is well positioned for real-time genomic profiling to guide treatment decisions. Here, we report the results of a prospective testing program established to assess the feasibility of ctDNA analysis to guide clinical management of mBC patients. METHODS AND FINDINGS: Two hundred thirty-four mBC patients (median age 54 years) were enrolled between June 2015 and October 2018 at the Peter MacCallum Cancer Centre, Melbourne, Australia. Median follow-up was 15 months (range 1-46). All patient samples at the time of enrolment were analysed in real time for the presence of somatic mutations. Longitudinal plasma testing during the course of patient management was also undertaken in a subset of patients (n = 67, 28.6%), according to clinician preference, for repeated molecular profiling or disease monitoring. Detection of somatic mutations from patient plasma was performed using a multiplexed droplet digital PCR (ddPCR) approach to identify hotspot mutations in PIK3CA, ESR1, ERBB2, and AKT1. In parallel, subsets of samples were also analysed via next-generation sequencing (targeted panel sequencing and low-coverage whole-genome sequencing [LC-WGS]). The sensitivity of ddPCR and targeted panel sequencing to identify actionable mutations was compared. Results were discussed at a multidisciplinary breast cancer meeting prior to treatment decisions. ddPCR and targeted panel sequencing identified at least 1 actionable mutation at baseline in 80/234 (34.2%) and 62/159 (39.0%) of patients tested, respectively. Combined, both methods detected an actionable alteration in 104/234 patients (44.4%) through baseline or serial ctDNA testing. LC-WGS was performed on 27 patients from the cohort, uncovering several recurrently amplified regions including 11q13.3 encompassing CCND1. Increasing ctDNA levels were associated with inferior overall survival, whether assessed by ddPCR, targeted sequencing, or LC-WGS. Overall, the ctDNA results changed clinical management in 40 patients including the direct recruitment of 20 patients to clinical trials. Limitations of the study were that it was conducted at a single site and that 31.3% of participants were lost to follow-up. CONCLUSION: In this study, we found prospective ctDNA testing to be a practical and feasible approach that can guide clinical trial enrolment and patient management in mBC.

Modelling aggressive prostate cancers of young men in immune-competent mice, driven by isogenic Trp53 alterations and Pten loss.

Understanding prostate cancer onset and progression in order to rationally treat this disease has been critically limited by a dire lack of relevant pre-clinical animal models. We have generated a set of genetically engineered mice that mimic human prostate cancer, initiated from the gland epithelia. We chose driver gene mutations that are specifically relevant to cancers of young men, where aggressive disease poses accentuated survival risks. An outstanding advantage of our models are their intact repertoires of immune cells. These mice provide invaluable insight into the importance of immune responses in prostate cancer and offer scope for studying treatments, including immunotherapies. Our prostate cancer models strongly support the role of tumour suppressor p53 in functioning to critically restrain the emergence of cancer pathways that drive cell cycle progression; alter metabolism and vasculature to fuel tumour growth; and mediate epithelial to mesenchymal-transition, as vital to invasion. Importantly, we also discovered that the type of p53 alteration dictates the specific immune cell profiles most significantly disrupted, in a temporal manner, with ramifications for disease progression. These new orthotopic mouse models demonstrate that each of the isogenic hotspot p53 amino acid mutations studied (R172H and R245W, the mouse equivalents of human R175H and R248W respectively), drive unique cellular changes affecting pathways of proliferation and immunity. Our findings support the hypothesis that individual p53 mutations confer their own particular oncogenic gain of function in prostate cancer.

A Deep Learning Model to Automate Skeletal Muscle Area Measurement on Computed Tomography Images.

BACKGROUND: Muscle wasting (Sarcopenia) is associated with poor outcomes in cancer patients. Early identification of sarcopenia can facilitate nutritional and exercise intervention. Cross-sectional skeletal muscle (SM) area at the third lumbar vertebra (L3) slice of a computed tomography (CT) image is increasingly used to assess body composition and calculate SM index (SMI), a validated surrogate marker for sarcopenia in cancer. Manual segmentation of SM requires multiple steps, which limits use in routine clinical practice. This project aims to develop an automatic method to segment L3 muscle in CT scans. METHODS: Attenuation correction CTs from full body PET-CT scans from patients enrolled in two prospective trials were used. The training set consisted of 66 non-small cell lung cancer (NSCLC) patients who underwent curative intent radiotherapy. An additional 42 NSCLC patients prescribed curative intent chemo-radiotherapy from a second trial were used for testing. Each patient had multiple CT scans taken at different time points prior to and post- treatment (147 CTs in the training and validation set and 116 CTs in the independent testing set). Skeletal muscle at L3 vertebra was manually segmented by two observers, according to the Alberta protocol to serve as ground truth labels. This included 40 images segmented by both observers to measure inter-observer variation. An ensemble of 2.5D fully convolutional neural networks (U-Nets) was used to perform the segmentation. The final layer of U-Net produced the binary classification of the pixels into muscle and non-muscle area. The model performance was calculated using Dice score and absolute percentage error (APE) in skeletal muscle area between manual and automated contours. RESULTS: We trained five 2.5D U-Nets using 5-fold cross validation and used them to predict the contours in the testing set. The model achieved a mean Dice score of 0.92 and an APE of 3.1% on the independent testing set. This was similar to inter-observer variation of 0.96 and 2.9% for mean Dice and APE respectively. We further quantified the performance of sarcopenia classification using computer generated skeletal muscle area. To meet a clinical diagnosis of sarcopenia based on Alberta protocol the model achieved a sensitivity of 84% and a specificity of 95%. CONCLUSIONS: This work demonstrates an automated method for accurate and reproducible segmentation of skeletal muscle area at L3. This is an efficient tool for large scale or routine computation of skeletal muscle area in cancer patients which may have applications on low quality CTs acquired as part of PET/CT studies for staging and surveillance of patients with cancer.

TP53 Status, Patient Sex, and the Immune Response as Determinants of Lung Cancer Patient Survival.

Lung cancer poses the greatest cancer-related death risk and males have poorer outcomes than females, for unknown reasons. Patient sex is not a biological variable considered in lung cancer standard of care. Correlating patient genetics with outcomes is predicted to open avenues for improved management. Using a bioinformatics approach across non-small cell lung cancer (NSCLC) subtypes, we identified where patient sex, mutation of the major tumor suppressor gene, Tumour protein P53 (TP53), and immune signatures stratified outcomes in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), among datasets of The Cancer Genome Atlas (TCGA). We exposed sex and TP53 gene mutations as prognostic for LUAD survival. Longest survival in LUAD occurred among females with wild-type (wt) TP53 genes, high levels of immune infiltration and enrichment for pathway signatures of Interferon Gamma (INF-γ), Tumour Necrosis Factor (TNF) and macrophages-monocytes. In contrast, poor survival in men with LUAD and wt TP53 genes corresponded with enrichment of Transforming Growth Factor Beta 1 (TGFB1, hereafter TGF-ß) and wound healing signatures. In LUAD with wt TP53 genes, elevated gene expression of immune checkpoint CD274 (hereafter: PD-L1) and also protein 53 (p53) negative-regulators of the Mouse Double Minute (MDM)-family predict novel avenues for combined immunotherapies. LUSC is dominated by male smokers with TP53 gene mutations, while a minor population of TCGA LC patients with wt TP53 genes unexpectedly had the poorest survival, suggestive of a separate etiology. We conclude that advanced approaches to LUAD and LUSC therapy lie in the consideration of patient sex, TP53 gene mutation status and immune signatures.

E6AP Promotes a Metastatic Phenotype in Prostate Cancer.

Although primary prostate cancer is largely curable, progression to metastatic disease is associated with very poor prognosis. E6AP is an E3 ubiquitin ligase and a transcriptional co-factor involved in normal prostate development. E6AP drives prostate cancer when overexpressed. Our study exposed a role for E6AP in the promotion of metastatic phenotype in prostate cells. We revealed that elevated levels of E6AP in primary prostate cancer correlate with regional metastasis and demonstrated that E6AP promotes acquisition of mesenchymal features, migration potential, and ability for anchorage-independent growth. We identified the metastasis suppressor NDRG1 as a target of E6AP and showed it is key in E6AP induction of mesenchymal phenotype. We showed that treatment of prostate cancer cells with pharmacological agents upregulated NDRG1 expression suppressed E6AP-induced cell migration. We propose that the E6AP-NDRG1 axis is an attractive therapeutic target for the treatment of E6AP-driven metastatic prostate cancer.