There is an urgent need to identify biomarkers of early response that can accurately predict the benefit of immune checkpoint inhibitors (ICI). Patients receiving durvalumab/tremelimumab had tumor samples sequenced before treatment (baseline) to identify variants for the design of a personalized circulating tumor (ctDNA) assay. ctDNA was assessed at baseline and at 4 and/or 8 weeks into treatment. Correlations between ctDNA changes to radiographic response and overall survival (OS) were made to assess potential clinical benefit. 35/40 patients (87.5%) had personalized ctDNA assays designed, and 29/35 (82.9%) had plasma available for baseline analysis, representing 16 unique solid tumor histologies. As early as 4 weeks after treatment, decline in ctDNA from baseline predicted improved OS (P = 0.0144; HR = 9.98) and ctDNA changes on treatment-supported and refined radiographic response calls. ctDNA clearance at any time through week 8 identified complete responders by a median lead time of 11.5 months ahead of radiographic imaging. ctDNA response monitoring is emerging as a dynamic, personalized biomarker method that may predict survival outcomes in patients with diverse solid tumor histologies, complementing and sometimes preceding standard-of-care imaging assessments.

While several resources exist that interpret therapeutic significance of genomic alterations in cancer, many regional real-world issues limit access to drugs. There is a need for a pragmatic, evidence-based, context-adapted tool to guide clinical management based on molecular biomarkers. To this end, we have structured a compendium of approved and experimental therapies with associated biomarkers following a survey of drug regulatory databases, existing knowledge bases, and published literature. Each biomarker-disease-therapy triplet was categorised using a tiering system reflective of key therapeutic considerations: approved and reimbursed therapies with respect to a jurisdiction (Tier 1), evidence of efficacy or approval in another jurisdiction (Tier 2), evidence of antitumour activity (Tier 3), and plausible biological rationale (Tier 4). Two resistance categories were defined: lack of efficacy (Tier R1) or antitumor activity (Tier R2). Based on this framework, we curated a digital resource focused on drugs relevant in the Australian healthcare system (TOPOGRAPH: Therapy Oriented Precision Oncology Guidelines for Recommending Anticancer Pharmaceuticals). As of November 2020, TOPOGRAPH comprised 2810 biomarker-disease-therapy triplets in 989 expert-appraised entries, including 373 therapies, 199 biomarkers, and 106 cancer types. In the 345 therapies catalogued, 84 (24%) and 65 (19%) were designated Tiers 1 and 2, respectively, while 271 (79%) therapies were supported by preclinical studies, early clinical trials, retrospective studies, or case series (Tiers 3 and 4). A companion algorithm was also developed to support rational, context-appropriate treatment selection informed by molecular biomarkers. This framework can be readily adapted to build similar resources in other jurisdictions to support therapeutic decision-making.

With rapid advances in our understanding of cancer, there is an expanding number of potential novel combination therapies, including novel-novel combinations. Identifying which combinations are appropriate and in which subpopulations are among the most difficult questions in medical research. We conducted a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided systematic review of trials of novel-novel combination therapies involving immunotherapies or molecular targeted therapies in advanced solid tumors. A MEDLINE search was conducted using a modified Cochrane Highly Sensitive Search Strategy for published clinical trials between July 1, 2017, and June 30, 2020, in the top-ranked medical and oncology journals. Trials were evaluated according to a criterion adapted from previously published Food and Drug Administration guidance and other key considerations in designing trials of combinations. This included the presence of a strong biological rationale, the use of a new established or emerging predictive biomarker prospectively incorporated into the clinical trial design, appropriate comparator arms of monotherapy or supportive external data sources and a primary endpoint demonstrating a clinically meaningful benefit. Of 32 identified trials, there were 11 (34%) trials of the novel-novel combination of anti-programmed death 1 (PD-1)/programmed death ligand 1 (PD-L1) and anti-cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) therapy, and 10 (31%) trials of anti-PD-1/PD-L1 and anti-vascular endothelial growth factor (VEGF) combination therapy. 20 (62.5%) trials were phase II trials, while 12 (37.5%) were phase III trials. Most (72%) trials lacked significant preclinical evidence supporting the development of the combination in the given indication. A majority of trials (69%) were conducted in biomarker unselected populations or used pre-existing biomarkers within the given indication for patient selection. Most studies (66%) were considered to have appropriate comparator arms or had supportive external data sources such as prior studies of monotherapy. All studies were evaluated as selecting a clinically meaningful primary endpoint. In conclusion, designing trials to evaluate novel-novel combination therapies presents numerous challenges to demonstrate efficacy in a comprehensive manner. A greater understanding of biological rationale for combinations and incorporating predictive biomarkers may improve effective evaluation of combination therapies. Innovative statistical methods and increasing use of external data to support combination approaches are potential strategies that may improve the efficiency of trial design. Designing trials to evaluate novel-novel combination therapies presents numerous challenges to demonstrate efficacy in a comprehensive manner. A greater understanding of biological rationale for combinations and incorporating predictive biomarkers may improve effective evaluation of combination therapies. Innovative statistical methods and increasing use of external data to support combination approaches are potential strategies that may improve the efficiency of trial design.

Oesophageal adenocarcinoma is a poor prognosis cancer and the molecular features underpinning response to treatment remain unclear. We investigate whole genome, transcriptomic and methylation data from 115 oesophageal adenocarcinoma patients mostly from the DOCTOR phase II clinical trial (Australian New Zealand Clinical Trials Registry-ACTRN12609000665235), with exploratory analysis pre-specified in the study protocol of the trial. We report genomic features associated with poorer overall survival, such as the APOBEC mutational and RS3-like rearrangement signatures. We also show that positron emission tomography non-responders have more sub-clonal genomic copy number alterations. Transcriptomic analysis categorises patients into four immune clusters correlated with survival. The immune suppressed cluster is associated with worse survival, enriched with myeloid-derived cells, and an epithelial-mesenchymal transition signature. The immune hot cluster is associated with better survival, enriched with lymphocytes, myeloid-derived cells, and an immune signature including CCL5, CD8A, and NKG7. The immune clusters highlight patients who may respond to immunotherapy and thus may guide future clinical trials.

Statin therapy reduces the risk of occlusive vascular events, but uncertainty remains about potential effects on cancer. We sought to provide a detailed assessment of any effects on cancer of lowering LDL cholesterol (LDL-C) with a statin using individual patient records from 175,000 patients in 27 large-scale statin trials.

Progression-free survival (PFS) rate at 6 months has been proposed as a potential surrogate for overall survival (OS) rate at 12 months for immune checkpoint inhibitor (ICI) trials but requires further assessment for validation.

Background The plasma concentration of B-type natriuretic peptide (BNP) is a strong predictor of adverse cardiovascular events. The aim of this study was to determine whether the association between plasma BNP concentration and cardiovascular mortality is sustained or diminishes with increasing time after BNP is measured. Methods and Results Six thousand seven hundred forty patients with a history of myocardial infarction or unstable angina who participated in the LIPID (Long-Term Intervention with Pravastatin in Ischemic Disease) trial had plasma BNP concentration measured at baseline and after 1 year. Associations with cardiovascular mortality were evaluated in landmark analyses 1 to <5, 5 to <10, and 10 to 16 years after randomization. There were 1640 cardiovascular deaths. The cardiovascular mortality rate increased progressively from 10.2 to 19.1 to 26.3/1000 patient-years from 1 to <5, 5 to <10, and 10 to 16 years after baseline, respectively. The average of baseline and 1-year BNP concentration was more strongly associated with cardiovascular mortality compared with baseline or 1-year BNP only. The hazard ratio (HR) for cardiovascular death associated with each doubling of average BNP concentration was similar during years 1 to <5 (HR, 1.53 [95% CI, 1.44-1.63]), years 5 to <10 (HR, 1.52 [95% CI, 1.44-1.60]), and years 10-16 (HR, 1.43 [95% CI, 1.36-1.50]), P<0.0001 for all. Conclusions BNP concentration remains an independent predictor of cardiovascular mortality more than a decade after it is measured. Because of random variation in plasma concentrations, the average of >1 BNP measurement improves long-term risk prediction.

To determine the safety and efficacy of PARP plus PD-L1 inhibition (olaparib + durvalumab, O + D) in patients with advanced solid, predominantly rare cancers harbouring homologous recombination repair (HRR) defects.

Background Elevated plasma cystatin C levels reflect reduced renal function and increased cardiovascular risk. Less is known about whether the increased risk persists long-term or is independent of renal function and other important biomarkers. Methods and Results Cystatin C and other biomarkers were measured at baseline (in 7863 patients) and 1 year later (in 6106 patients) in participants in the LIPID (Long-Term Intervention with Pravastatin in Ischemic Disease) study, who had a previous acute coronary syndrome. Outcomes were ascertained during the study (median follow-up, 6 years) and long-term (median follow-up, 16 years). Glomerular filtration rate (GFR) was estimated using Chronic Kidney Disease Epidemiology Collaboration equations (first GFR-creatinine, then GFR-creatinine-cystatin C). Over 6 years, in fully adjusted multivariable time-to-event models, with respect to the primary end point of coronary heart disease mortality or nonfatal myocardial infarction, for comparison of Quartile 4 versus 1 of baseline cystatin C, the hazard ratio was 1.37 (95% CI, 1.07-1.74; P=0.01), and for major cardiovascular events was 1.47 (95% CI, 1.19-1.82; P<0.001). Over 16 years, the association of baseline cystatin C with coronary heart disease, cardiovascular, and all-cause mortality persisted (each P<0.001) and remained significant after adjustment for estimated GFR-creatinine-cystatin C. Cystatin C also predicted the development of chronic kidney disease for 6 years (odds ratio, 6.61; 95% CI, 4.28-10.20) independently of estimated GFR-creatinine and other risk factors. However, this association was no longer significant after adjustment for estimated GFR-creatinine-cystatin C. Conclusions Cystatin C independently predicted major cardiovascular events, development of chronic kidney disease, and cardiovascular and all-cause mortality. Prediction of long-term mortality was independent of improved estimation of GFR. Registration URL: https://anzctr.org.au; Unique identifier: ACTRN12616000535471.

Lung cancer continues to be the most common cause of cancer-related death and the leading cause of morbidity and burden of disease across Australia. There is an ongoing need to identify and reduce unwarranted clinical variation that may contribute to these poor outcomes for patients with lung cancer. An Australian national strategy acknowledges clinical quality outcome data as a critical component of a continuously improving healthcare system but there is a need to ensure clinical quality indicators adequately measure evidence-based contemporary care, including novel and emerging treatments. This study aimed to develop a suite of lung cancer-specific, evidence-based, clinically acceptable quality indicators to measure quality of care and outcomes, and an associated comparative feedback dashboard to provide performance data to clinicians and hospital administrators.

UK cardiovascular disease (CVD) incidence and mortality have declined in recent decades but socioeconomic inequalities persist.

The optimal management of patients with resectable gastric cancer continues to evolve in Western countries. Following publication of the US Intergroup 0116 and UK Medical Research Council MAGIC trials, there are now two standards of care for adjuvant therapy in resectable gastric cancer, at least in the Western world: postoperative chemoradiotherapy and perioperative epirubicin/cisplatin/fluorouracil (ECF) chemotherapy. We hypothesize that adding chemoradiation to standard perioperative ECF chemotherapy will achieve further survival gains. We also believe there are advantages to administering chemoradiation in the preoperative rather than postoperative setting. In this article, we describe the TOPGEAR trial, which is a randomised phase III trial comparing control arm therapy of perioperative ECF chemotherapy with experimental arm therapy of preoperative chemoradiation plus perioperative ECF chemotherapy.

Timing of cord clamping and other cord management strategies may improve outcomes at preterm birth. However, it is unclear whether benefits apply to all preterm subgroups. Previous and current trials compare various policies, including time-based or physiology-based deferred cord clamping, and cord milking. Individual participant data (IPD) enable exploration of different strategies within subgroups. Network meta-analysis (NMA) enables comparison and ranking of all available interventions using a combination of direct and indirect comparisons.

The aim of this study was to develop prediction models for the individual-level impacts of cardiovascular events on UK healthcare costs.

Recent advancements in plasma lipidomic profiling methodology have significantly increased specificity and accuracy of lipid measurements. This evolution, driven by improved chromatographic and mass spectrometric resolution of newer platforms, has made it challenging to align datasets created at different times, or on different platforms. Here we present a framework for harmonising such plasma lipidomic datasets with different levels of granularity in their lipid measurements. Our method utilises elastic-net prediction models, constructed from high-resolution lipidomics reference datasets, to predict unmeasured lipid species in lower-resolution studies. The approach involves (1) constructing composite lipid measures in the reference dataset that map to less resolved lipids in the target dataset, (2) addressing discrepancies between aligned lipid species, (3) generating prediction models, (4) assessing their transferability into the targe dataset, and (5) evaluating their prediction accuracy. To demonstrate our approach, we used the AusDiab population-based cohort (747 lipid species) as the reference to impute unmeasured lipid species into the LIPID study (342 lipid species). Furthermore, we compared measured and imputed lipids in terms of parameter estimation and predictive performance, and validated imputations in an independent study. Our method for harmonising plasma lipidomic datasets will facilitate model validation and data integration efforts.

When rates of uptake of other drugs differ between treatment arms in long-term trials, the true benefit or harm of the treatment may be underestimated. Methods to allow for such contamination have often been limited by failing to preserve the randomization comparisons. In the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) study, patients were randomized to fenofibrate or placebo, but during the trial many started additional drugs, particularly statins, more so in the placebo group. The effects of fenofibrate estimated by intention-to-treat were likely to have been attenuated. We aimed to quantify this effect and to develop a method for use in other long-term trials.

Analyzing longitudinal cancer quality-of-life (QoL) measurements and their impact on clinical outcomes may improve our understanding of patient trajectories during systemic therapy. We applied an unsupervised growth mixture modeling (GMM) approach to identify unobserved subpopulations ("patient clusters") in the CO.20 clinical trial longitudinal QoL data. Classes were then evaluated for differences in clinico-epidemiologic characteristics and overall survival (OS).

Cancer is increasingly classified according to biomarkers that drive tumour growth and therapies developed to target them. In rare biomarker-defined cancers, randomised controlled trials to adequately assess targeted therapies may be infeasible. Extrapolating existing evidence of targeted therapy from common cancers to rare cancers sharing the same biomarker may reduce evidence requirements for regulatory approval in rare cancers. It is unclear whether guidelines exist for extrapolation. We sought to identify methodological guidance for extrapolating evidence from targeted therapies used for common cancers to rare biomarker-defined cancers.