The prognosis for extensive-stage small cell lung cancer (ES-SCLC) is poor. Real-world evidence can highlight the unmet clinical need within this population. We conducted a population-based cohort study of ES-SCLC patients diagnosed in a large Canadian province (2010–2018) using electronic medical records and administrative claims data. In all, 1941 ES-SCLC patients were included, of which 476 (25%) were recurrent cases. Median age at diagnosis was 70 years (range: 39–94) and 50.2% were men. Of the 1941 ES-SCLC patients, 29.5% received chemotherapy and radiotherapy, 17.0% chemotherapy alone, 8.7% radiotherapy alone, and 44.8% received best supportive care. Chemotherapy was initiated by 46.5%, 8.5%, and 1.4% of first-, second-, and third-line patients, with lower uptake for recurrent cases. Median survival from first-, second-, and third-line chemotherapy was 7.82 months (95% CI: 7.50–8.22), 5.72 months (95% CI: 4.90–6.87), and 3.83 months (95% CI: 2.99–4.60). Among patients who received first-line therapy, the 2-year and 5-year survival was 7.3% (95% CI: 5.7–9.2) and 2.9% (95% CI: 1.8–4.5). In conclusion, initiation of first-line treatment in ES-SCLC was low with significant attrition in subsequent lines. These results underscore the need for effective front-line treatments and highlight the potential for novel therapies to improve patient outcomes.
1 November 2018 in Cancer Epidemiology, Biomarkers & Prevention
Background: The incidence of breast cancer among young women (aged ≤40 years) has increased in North America and Europe. Fewer than 10% of cases among young women are attributable to inheritedBRCA1orBRCA2mutations, suggesting an important role for somatic mutations. This study investigated genomic differences between young- and older-onset breast tumours.Methods: In this study we characterized the mutational landscape of 89 young-onset breast tumours (≤40 years) and examined differences with 949 older-onset tumours (> 40 years) using data from The Cancer Genome Atlas. We examined mutated genes, mutational load, and types of mutations. We used complementary R packages “deconstructSigs” and “SomaticSignatures” to extract mutational signatures. A recursively partitioned mixture model was used to identify whether combinations of mutational signatures were related to age of onset.Results: Older patients had a higher proportion of mutations inPIK3CA,CDH1, andMAP3K1genes, while young-onset patients had a higher proportion of mutations inGATA3andCTNNB1. Mutational load was lower for young-onset tumours, and a higher proportion of these mutations were C > A mutations, but a lower proportion were C > T mutations compared to older-onset tumours. The most common mutational signatures identified in both age groups were signatures 1 and 3 from the COSMIC database. Signatures resembling COSMIC signatures 2 and 13 were observed among both age groups. We identified a class of tumours with a unique combination of signatures that may be associated with young age of onset.Conclusions: The results of this exploratory study provide some evidence that the mutational landscape and mutational signatures among young-onset breast cancer are different from those of older-onset patients. The characterization of young-onset tumours could provide clues to their etiology which may inform future prevention. Further studies are required to confirm our findings.
Genetic and epigenetic alterations are required for carcinogenesis and the mutation burden across tumor types has been investigated. Here, we investigate epigenetic alterations with a novel measure of global DNA methylation dysregulation, the methylation dysregulation index (MDI), across 14 cancer types in The Cancer Genome Atlas (TCGA) database. DNA methylation data—obtained using Illumina HumanMethylation450 BeadChip—was accessed from TCGA. We calculated the MDI in 14 tumor types (n = 5,592 tumors), using adjacent normal tissues (n = 701) from each tumor site. Copy number alteration, and mutation burden were retrieved from cBioportal (n = 5,152). We tested the relation of subject MDI across tumors and with age, gender, tumor stage, estimated tumor purity, and copy number alterations for both overall MDI and genomic-context-specific MDI. We also investigated the top most dysregulated loci shared across tumor types. There was a broad range of extent in methylation dysregulation across tumor types (P < 2.2E-16). However, a consistent pattern of methylation dysregulation stratified by genomic context was observed across tumor types where the highest dysregulation occurred at non-CpG island regions. Considering other summary measures of somatic alteration, MDI was correlated with copy number alterations but not with mutation burden. Using the top dysregulated CpG sites in common across tumors, 4 classes of cancer types were observed, and the functional consequences of these alterations to gene expression were confirmed. This work identified the global DNA methylation dysregulation patterns across 14 cancer types showing a higher impact for the non-CpG island areas. The most dysregulated loci across cancer types identified common clusters across cancer types that may have implications for future treatment and prevention measures.