The International Lung Cancer Consortium (ILCCO) is an international group of lung cancer researchers, established in 2004 with the aim of sharing comparable data from ongoing lung cancer case-control and cohort studies. Questionnaire data from a approximately 100,000 case-control pairs, over 1 million cohort participants, and the biological samples from the majority of the subjects would be available. These studies are from different geographical areas and ethnicities. The overall objectives are to achieve greater power, especially for subgroup analyses, reduce duplication of research effort, replicate novel findings, and afford substantial cost savings through large collaborative efforts.

The annual ILCCO meeting ran from 24th-25th October 2022 in Los Angeles, USA. The talks fell into 7 broad areas:

1. risk prediction; 2. early detection; 3. functional genomics; 4. new genotyping and sequencing initiatives in ILCCO; 5. cancer progression (conventional epi and GWAS); 6. epigenetics; and 7. aetiology of lung cancer in never smokers.

Key highlights are summarised below: 

One of the most interesting talks was by Dr Linda Kachuri, who demonstrated that deMendelian randomization of PSA can be used to improve prostate cancer risk prediction. The genetic variance component of PSA is not associated with prostate cancer risk and hence its removal improves the use of PSA in risk prediction. Dr Chris Amos presented new genotyping and sequencing plans in ILCCO and showed that low pass sequencing now costs the same as array genotyping. PhD student Mei Dong presented a GWAS of lung cancer survival in ILCCO, identifying two potential associations that appear to replicate in TCGA. Dr Philip Haycock presented a GWAS of pan-cancer survival in Genomics England, identifying one potential hit that he will try to replicate in UK Biobank. The outcomes working group in ILCCO, lead by Dr Geoff Liu and Cathy Brown, is trying to collect more information on lung cancer outcomes and somatic mutations from the participating cohorts, which will be used to increase the power of future lung cancer survival GWAS. In the early detection theme, separate talks by Dr Denise Aberle and Matthew Warkentin presented the use of machine learning to improve early detection and characterisation of lung cancer prognosis using imaging data. An interesting takeaway is that machine learning is likely to become routine in the processing of imaging data for lung cancer early detection and prognosis in future. In another talk, Dr Elham Moez addressed the question of whether 36 protein biomarkers (measured before lung cancer diagnosis) can be used to differentiate between malignant and benign nodules, in the context of nodules detected by low-dose CT lung cancer screening. The 36 protein biomarkers were found to improve prediction of malignancy over-and-above existing prognostic factors. Furthermore, the additive contribution of the protein biomarkers was greater for smaller nodules and for nodules with a longer time to diagnosis from protein measurement.