Silent theatre 2 – Monday 4 November

10:30 am-11:00 am

Room: Hall 4


Programme session type(s): Silent theatre

Meat intake and cancer risk: prospective analyses in UK Biobank
Speaker: Anika Knuppel
Affiliation: Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford

Abstract:

Red and processed meat has been consistently found to be associated with colorectal cancer but evidence for other cancer sites is limited and few studies have examined the association with poultry intake. We examined associations between red, processed meat and poultry intake and incidence for 20 cancer sites.

Method

We analysed prospective data from 475,023 participants (54 % women) in UK Biobank, who were cancer-free at baseline and reported their meat intake in a touchscreen dietary questionnaire. Trends in risk across baseline meat intake categories were calculated by assigning a mean value to each category using re-measured meat intakes in a subsample (15 %) that completed ≥3 web-based 24h recalls. Multivariable-adjusted Cox regressions were used to determine the association between baseline meat intake and cancer incidence.

Results

During a mean follow-up of 6.9 years, 28,955 participants were diagnosed with any type of cancer. Red and processed meat intake was positively associated with risk of colorectal cancer (hazard ratio (HR) per 70 g/day higher intake of red and processed meat 1.31, 95%-confidence interval (CI) 1.14-1.52). Red meat intake was positively associated with risk of breast cancer (HR per 50 g/day higher intake 1.12, 95%-CI 1.01-1.24) and risk of prostate cancer (HR per 50 g/day higher intake 1.15, 95%-CI 1.03-1.29). Poultry intake was positively associated with risk of cancers of the lymphatic and haematopoietic tissues (HR per 30g/day higher intake 1.16, 95%-CI 1.03-1.32). Only the association with colorectal cancer risk was robust to Bonferroni correction for multiple comparisons (adjusted p-value ≤0.0025).

Conclusion

Higher intake of red and processed meat was associated with a higher risk of colorectal cancer. Although the positive associations of red meat with breast and prostate cancer and poultry with cancers of the lymphatic and haematopoietic tissues did not survive correction for multiple testing, they require further investigation.

Renal cell carcinoma patients under 60 should be screened for colorectal carcinoma
Speaker: Joseph John
Affiliation: Taunton and Somerset NHS Foundation Trust, Taunton, UK

Abstract:

The link between renal cell carcinoma (RCC) and colorectal cancer (CRC) is widely recognised but poorly quantified. This systematic review describes the current available evidence and considers whether we should take additional steps to screen for CRC in patients diagnosed with RCC. 

Method

Literature searches were performed using Pubmed and Web of science. Six hundred and twelve papers were returned, of which papers were selected which quantified the risk of antecedent, synchronous or subsequent CRC in RCC patients. 

Results

Seven studies met our inclusion criteria. The inclusion criteria used in each study were heterogenous. Three papers were single centre studies assessing their institutional records, giving risks of CRC in the presence of RCC between 3.67 and 4.70%. Four were national or regional population-based studies from the USA (3) and Norway (1). Standardised incidence ratios (SIR) were calculated in three of these. The largest study (n = 194,329 urological cancer patients) identified a modestly elevated SIR of CRC in patients with RCC (1.14, 95% confidence interval 1.04 – 1.25), with an increasing SIR with decreasing age of RCC diagnosis. Of the further two studies reporting SIRs, one indicated an increased SIR of 3.1 – 3.4 (p < 0.05, n = 551 RCCs), and one reported no significantly different SIR of colon cancer in RCC patients, but excluded rectal cancer from analysis. 

Conclusion

The highest quality available evidence suggests an association between RCC and CRC, with a possible stronger association in younger patients. The advent of highly sensitive and specific quantitative faecal immunochemical testing (QFIT) means we should consider non-invasive screening for CRC in RCC patients who fall below the current age for national bowel cancer screening. Such screening would constitute effective resource allocation. 

Development and validation of a risk prediction model for lung cancer with common health examination indexes
Speaker: Zhangyan Lyu
Affiliation: National Cancer Center, Beijing, China, National Clinical Research Center for Cancer, Beijing, China, Cancer Hospital, Beijing, China, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

Abstract:

Lung cancer has been the most common cancer and leading cause of cancer-related death for several decades worldwide, especially in China, the most populous country. Low-dose computed tomography (LDCT) has been proven to reduce lung cancer mortality. A user-friendly lung cancer risk perdition model could help standardize the selection of high-risk population for LDCT screening and alter individuals’ lifestyle factors to lower their risk. We thus sought to develop and internally validate a simple model for lung cancer based on a prospective cohort study in China.

Method

A total of 138,150 people was prospectively observed from 2006 to 2015 for lung cancer incidence. Stepwise multivariable-adjusted logistic regressions with Pentry=0.15 and Pstay=0.20 were conducted to select the candidate variables included in the prediction model. Concordance statistics (C-statistics) and Hosmer–Lemeshow tests were used to evaluate discrimination and calibration, respectively. Ten-fold cross-validation was used for internal validation.

Results

During a median of 9-year follow-up, a total of 1088 (0.79 %) lung cancer cases were identified. The simple model including age and smoking generated a C-statistics of 0.71. The full model additionally included sex, alcohol consumption, body mass index (BMI), low-density lipoprotein cholesterol (LDL-C), and C-reactive protein (CRP) showed significantly better predictive performance regarding discrimination (C-statistics=0.73, P<0.01). In 10-fold cross-validation, the average C-statistic across the 10 test sets was similar (0.73). Model calibrated well across deciles of predicted risk (PHL=0.48). The predicted risk of lung cancer in the top decile was 0.04% vs. 2.36% in the bottom decile (Odds ratio [OR]=98.16).

Conclusion

We developed and internally validated an easy-to-use risk prediction model for lung cancer among the Chinese population that could provide guidance for LDCT screening and early detection of lung cancer.

Real world treatment sequencing patterns in secondary breast cancer (SBC): Pathway visualisation using national datasets.
Speaker: Ashley Horne
Affiliation: Edinburgh Cancer Centre, Western General Hospital, Edinburgh, UK

Abstract:

Treatment pathways in metastatic breast cancer are complex. The accelerated adoption of new medicines has resulted in an uncertain evidence base supporting their use. Uncertainties are related to the mismatch between trial-recruited and real-world populations and variation in the order of sequential drugs.

Published examples describing real-world practice in SBC are scarce, mainly due to the complexity of the clinical pathways that rely on a mixture of chemotherapy, endocrine therapy and biologicals, often over a long period. We demonstrate how new opportunities in routine healthcare data allow a highly granular description of real-world treatment pathways and how this varies in light of patient (pt) case-mix.

Method

Scottish nationally available data source datasets for linkage included the National Cancer Registry, Scottish Morbidity Record, the National Cancer Quality Audit and the national Prescribing Information System. Scottish CHI number was the universal identifier for linkage. Key baseline characteristics included age, de-novo presentation, prior adjuvant treatments, co-morbidities, concomitant medications and socioeconomic status. Targeted and random sampling manual review was used to quantify missing data. R version 3.6 was used for analysis.

Results

345 pts were identified of which 276 had ER+HER2- SBC between 2012-2017. First line therapy included 68% (235 patients) endocrine therapy, 17% (59 pts) chemotherapy, 14% (50 pts) received no treatment. Subsequent treatment decisions, including best supportive care and death, have been tracked to identify 70 unique pathways with up to 8 lines of treatment. Graphical representation of treatment pathways is made using Sankey plots. Detailed data quality reports describe missing data rates over time and a comprehensive guide for analysts has been produced as a wiki [https://blogs.ed.ac.uk/canceroutcomes/edinburgh-cancer-informatics-wiki/].

Conclusion

It is now possible to describe treatment sequences using routine, nationally available administrative healthcare data. Pathways are complex and do not always conform to standard guidelines. Interpretation requires modern graphical visualisation methods.