Changes in cancer incidence and mortality in Australia over the period 1996-2015

Did the COVID-19 pandemic delay treatment for localized breast cancer patients? A multicenter study

BACKGROUND

Longer times between diagnosis and treatments of cancer patients have been estimated as effects of the COVID-19 pandemic. However, relatively few studies attempted to estimate actual delay to treatment at the patient level.

OBJECTIVE

To assess changes in delays to first treatment and surgery among newly diagnosed patients with localized breast cancer (BC) during the COVID-19 pandemic.

METHODS

We used data from the PAPESCO-19 multicenter cohort study, which included patients from 4 French comprehensive cancer centers. We measured the delay to first treatment as the number of days between diagnosis and the first treatment regardless of whether this was neoadjuvant chemotherapy or surgery. COVID-19 pandemic exposure was estimated with a composite index that considered both the severity of the pandemic and the level of lockdown restrictions. We ran generalized linear models with a log link function and a gamma distribution to model the association between delay and the pandemic.

RESULTS

Of the 187 patients included in the analysis, the median delay to first treatment was 42 (IQR:32-54) days for patients diagnosed before and after the start of the 1st lockdown (N = 99 and 88, respectively). After adjusting for age and centers of inclusion, a higher composite pandemic index (> = 50 V.S. <50) had only a small, non-significant effect on times to treatment. Longer delays were associated with factors other than the COVID-19 pandemic.

CONCLUSION

We found evidence of no direct impact of the pandemic on the actual delay to treatment among patients with localized BC.

Trends and projections of cause-specific premature mortality in Australia to 2044: a statistical modelling study

Background

Long-term projections of premature mortality (defined as deaths age <75 years) help to inform decisions about public health priorities. This study aimed to project premature mortality rates in Australia to 2044, and to estimate numbers of deaths and potential years of life lost (PYLL) due to premature mortality overall and for 59 causes.

Methods

We examined the past trends in premature mortality rates using Australian mortality data by sex, 5-year age group and 5-year calendar period up to 2019. Cigarette smoking exposure data (1945-2019) were included to project lung cancer mortality. Age-period-cohort or generalised linear models were developed and validated for each cause to project premature mortality rates to 2044.

Findings

Over the 25-year period from 1990-1994 to 2015-2019, there was a 44.4% decrease in the overall age-standardised premature mortality rate. This decline is expected to continue, from 162.4 deaths/100,000 population in 2015-2019 to 141.7/100,000 in 2040-2044 (12.7% decrease). Despite declining rates, total numbers of premature deaths are projected to increase by 22.8%, rising from 272,815 deaths in 2015-2019 to 334,894 deaths in 2040-2044. This is expected to result in 1.58 million premature deaths over the 25-year period 2020-2044, accounting for 24.5 million PYLL. Of the high-level cause categories, cancer is projected to remain the most common cause of premature death in Australia by 2044, followed by cardiovascular disease, external causes (including injury, poisoning, and suicide), and respiratory diseases.

Interpretation

Despite continuously declining overall premature mortality rates, the total number of premature deaths in Australia is projected to remain substantial, and cancer will continue to be the leading cause. These projections can inform the targeting of public health efforts and can serve as benchmarks against which to measure the impact of future interventions. They emphasise the ongoing importance of accelerating the prevention, early detection, and treatment of key health conditions.

Funding

No funding was provided for this study.

Identifying cancer driver genes in individual tumours

Cancer is a heterogeneous disease with a strong genetic component making it suitable for precision medicine approaches aimed at identifying the underlying molecular drivers within a tumour. Large scale population-level cancer sequencing consortia have identified many actionable mutations common across both cancer types and sub-types, resulting in an increasing number of successful precision medicine programs. Nonetheless, such approaches fail to consider the effects of mutations unique to an individual patient and may miss rare driver mutations, necessitating personalised approaches to driver-gene prioritisation. One approach is to quantify the functional importance of individual mutations in a single tumour based on how they affect the expression of genes in a gene interaction network (GIN). These GIN-based approaches can be broadly divided into those that utilise an existing reference GIN and those that construct de novo patient-specific GINs. These single-tumour approaches have several limitations that likely influence their results, such as use of reference cohort data, network choice, and approaches to mathematical approximation, and more research is required to evaluate the in vitro and in vivo applicability of their predictions. This review examines the current state of the art methods that identify driver genes in single tumours with a focus on GIN-based driver prioritisation.

Survival of primary liver cancer for people from culturally and linguistically diverse backgrounds in Australia

BACKGROUND

Survival for Primary Liver Cancer (PLC) has been investigated in Australia, but limited work has been conducted on the burden for people with different socioeconomic status, region of residence, causes of PLC, and culturally and linguistically diverse (CALD) backgrounds. This study aimed to cover this gap in the literature by investigating PLC survival with the aforementioned factors.

METHODS

This study linked four administrative datasets: Victorian Cancer Registry, Admitted Episodes Dataset, Emergency Minimum Dataset, and Death Index. The cohort was all cases with a PLC notification within the Victorian Cancer Registry between 01/01/2008 and 01/01/2016. The Kaplan-Meier method was used to estimate survival probabilities and the log-rank test was used to compare the difference in survival between subgroups. The Cox proportional hazard model was used to explore factors associated with PLC survival.

RESULTS

The 1-, 3- and 5-year survival rates were 50.0%, 28.1% and 20.6%, respectively, with a median survival of 12.0 months (95% confidence interval (CI): 11.0 - 12.9 months). Higher survival was associated with younger age, hepatocellular carcinoma, and higher socio-economic status. People born in Asian, African, and American regions had higher survival than those born in Australia and New Zealand. Cases with viral hepatitis as an identified aetiology had higher survival than those whose PLC was related to alcohol consumption (hazard ratio=1.52, 95% CI: 1.19 - 1.96), diabetes and fatty liver disease (hazard ratio=1.35, 95% CI: 1.08 - 1.68).

CONCLUSION

Survival outcomes for people diagnosed with PLC were still poor and affected by many factors. Asian and African cases had better survival than Australian and New Zealand patients as PLC in Asian and African cases was mostly caused by viral hepatitis. Metropolitan areas were associated with a higher survival than rural areas, not only due to accessibility to surveillance and healthcare services but also because the majority of overseas-born patients reside in metropolitan areas.

Projections of smoking-related cancer mortality in Australia to 2044

BACKGROUND

While many high-income countries including Australia have successfully implemented a range of tobacco control policies, smoking remains the leading preventable cause of cancer death in Australia. We have projected Australian mortality rates for cancer types, which have been shown to have an established relationship with cigarette smoking and estimated numbers of cancer deaths attributable to smoking to 2044.

METHODS

Cancer types were grouped according to the proportion of cases currently caused by smoking: 8%-30% and >30%. For each group, an age-period- cohort model or generalised linear model with cigarette smoking exposure as a covariate was selected based on the model fit statistics and validation using observed data. The smoking-attributable fraction (SAF) was calculated for each smoking-related cancer using Australian smoking prevalence data and published relative risks.

RESULTS

Despite the decreasing mortality rates projected for the period 2015-2019 to 2040-2044 for both men and women, the overall number of smoking-related cancer deaths is estimated to increase by 28.7% for men and 35.8% for women: from 138 707 (77 839 men and 60 868 women) in 2015-2019 to 182 819 (100 153 men and 82 666 women) in 2040-2044. Over the period 2020-2044, there will be 254 583 cancer deaths (173 943 men and 80 640 women) directly attributable to smoking, with lung, larynx, oesophagus and oral (comprising lip, oral cavity and pharynx) cancers having the largest SAFs.

INTERPRETATION

Cigarette smoking will cause over 250 000 cancer deaths in Australia from 2020 to 2044. Continued efforts in tobacco control remain a public health priority, even in countries where smoking prevalence has substantially declined.

Cancer incidence and mortality in Australia from 2020 to 2044 and an exploratory analysis of the potential effect of treatment delays during the COVID-19 pandemic: a statistical modelling study

BACKGROUND

Long-term projections of cancer incidence and mortality estimate the future burden of cancer in a population, and can be of great use in informing the planning of health services and the management of resources. We aimed to estimate incidence and mortality rates and numbers of new cases and deaths up until 2044 for all cancers combined and for 21 individual cancer types in Australia. We also illustrate the potential effect of treatment delays due to the COVID-19 pandemic on future colorectal cancer mortality rates.

METHODS

In this statistical modelling study, cancer incidence and mortality rates in Australia from 2020 to 2044 were projected based on data up to 2017 and 2019, respectively. Cigarette smoking exposure (1945-2019), participation rates in the breast cancer screening programme (1996-2019), and prostate-specific antigen testing rates (1994-2020) were included where relevant. The baseline projection model using an age-period-cohort model or generalised linear model for each cancer type was selected based on model fit statistics and validation with pre-COVID-19 observed data. To assess the impact of treatment delays during the COVID-19 pandemic on colorectal cancer mortality, we obtained data on incidence, survival, prevalence, and cancer treatment for colorectal cancer from different authorities. The relative risks of death due to system-caused treatment delays were derived from a published systematic review. Numbers of excess colorectal cancer deaths were estimated using the relative risk of death per week of treatment delay and different durations of delay under a number of hypothetical scenarios.

FINDINGS

Projections indicate that in the absence of the COVID-19 pandemic effects, the age-standardised incidence rate for all cancers combined for males would decline over 2020-44, and for females the incidence rate would be relatively stable in Australia. The mortality rates for all cancers combined for both males and females are expected to continuously decline during 2020-44. The total number of new cases are projected to increase by 47·4% (95% uncertainty interval [UI] 35·2-61·3) for males, from 380 306 in 2015-19 to 560 744 (95% UI 514 244-613 356) in 2040-44, and by 54·4% (95% UI 40·2-70·5) for females, from 313 263 in 2015-19 to 483 527 (95% UI 439 069-534 090) in 2040-44. The number of cancer deaths are projected to increase by 36·4% (95% UI 15·3-63·9) for males, from 132 440 in 2015-19 to 180 663 (95% UI 152 719-217 126) in 2040-44, and by 36·6% (95% UI 15·8-64·1) for females, from 102 103 in 2015-19 to 139 482 (95% UI 118 186-167 527) in 2040-44, due to population ageing and growth. The example COVID-19 pandemic scenario of a 6-month health-care system disruption with 16-week treatment delays for colorectal cancer patients could result in 460 (95% UI 338-595) additional deaths and 437 (95% UI 314-570) deaths occurring earlier than expected in 2020-44.

INTERPRETATION

These projections can inform health service planning for cancer care and treatment in Australia. Despite the continuous decline in cancer mortality rates, and the decline or plateau in incidence rates, our projections suggest an overall 51% increase in the number of new cancer cases and a 36% increase in the number of cancer deaths over the 25-year projection period. This means that continued efforts to increase screening uptake and to control risk factors, including smoking exposure, obesity, physical inactivity, alcohol use, and infections, must remain public health priorities.

FUNDING

Partly funded by Cancer Council Australia.

Pyrimidyn-Based Dynamin Inhibitors as Novel Cytotoxic Agents

Five focused libraries of pyrimidine-based dynamin GTPase inhibitors, in total 69 compounds were synthesised, and their dynamin inhibition and broad-spectrum cytotoxicity examined. Dynamin plays a crucial role in mitosis, and as such inhibition of dynamin was expected to broadly correlate with the observed cytotoxicity. The pyrimidines synthesised ranged from mono-substituted to trisubstituted. The highest levels of dynamin inhibition were noted with di- and tri- substituted pyrimidines, especially those with pendent amino alkyl chains. Short chains and simple heterocyclic rings reduced dynamin activity. There were three levels of dynamin activity noted: 1-10, 10-25 and 25-60 μM. Screening of these compounds in a panel of cancer cell lines: SW480 (colon), HT29 (colon), SMA (spontaneous murine astrocytoma), MCF-7 (breast), BE2-C (glioblastoma), SJ-G2 (neuroblastoma), MIA (pancreas), A2780 (ovarian), A431 (skin), H460 (lung), U87 (glioblastoma) and DU145 (prostate) cell lines reveal a good correlation between the observed dynamin inhibition and the observed cytotoxicity. The most active analogues (31 a,b) developed returned average GI₅₀ values of 1.0 and 0.78 μM across the twelve cell lines examined. These active analogues were: N² -(3-dimethylaminopropyl)-N⁴ -dodecyl-6-methylpyrimidine-2,4-diamine (31 a) and N⁴ -(3-dimethylaminopropyl)-N² -dodecyl-6-methylpyrimidine-2,4-diamine (31 b).