The replicative immortality of human cancer cells is achieved by activation of a telomere maintenance mechanism (TMM). To achieve this, cancer cells utilise either the enzyme telomerase, or the Alternative Lengthening of Telomeres (ALT) pathway. These distinct molecular pathways are incompletely understood with respect to activation and propagation, as well as their associations with clinical outcomes. We have identified significant differences in the telomere repeat composition of tumours that use ALT compared to tumours that do not. We then employed a machine learning approach to stratify tumours according to telomere repeat content with an accuracy of 91.6%. Importantly, this classification approach is applicable across all tumour types. Analysis of pathway mutations that were under-represented in ALT tumours, across 1,075 tumour samples, revealed that the autophagy, cell cycle control of chromosomal replication, and transcriptional regulatory network in embryonic stem cells pathways are involved in the survival of ALT tumours. Overall, our approach demonstrates that telomere sequence content can be used to stratify ALT activity in cancers, and begin to define the molecular pathways involved in ALT activation.