Fountain of youth for squamous cell carcinomas? On the epigenetic age of non‐small cell lung cancer and corresponding tumor‐free lung tissues

Abstract

Aging affects the core processes of almost every organism, and the functional decline at the cellular and tissue levels influences disease development. Recently, it was shown that the methylation of certain CpG dinucleotides correlates with chronological age and that this epigenetic clock can be applied to study aging‐related effects. We investigated these molecular age loci in non‐small cell lung cancer (NSCLC) tissues from patients with adenocarcinomas (AC) and squamous cell carcinomas (SQC) as well as in matched tumor‐free lung tissue. In both NSCLC subtypes, the calculated epigenetic age did not correlate with the chronological age. In particular, SQC exhibited rejuvenation compared to the corresponding normal lung tissue as well as with the chronological age of the donor. Moreover, the younger epigenetic pattern was associated with a trend toward stem cell‐like gene expression patterns. These findings show deep phenotypic differences between the tumor entities AC and SQC, which might be useful for novel therapeutic and diagnostic approaches.

Dysfunctional epigenetic aging of the normal colon and colorectal cancer risk

“…Our results are consistent with this possibility. Recently, Marwitz et al observed epigenetic age deceleration in squamous cell carcinoma of the lungs and that stem cellrelated gene expression was increased in these cancers [51]. This raises the possibility that epigenetic age deceleration in the high-risk normal colons may reflect expansions of the stem cell pool, which could increase CRC risk.…”

DNA methylation age as a biomarker for cancer

“…66 Additionally, tumour subtypes and menopause status may also influence cancer prognosis prediction, as tumour Horvath's DNAm age was found higher in postmenopausal, oestrogen receptor positive, progestogen receptor positive or HER2-negative patients in the study. 66 A more recent study 67 Although the regulation of DNA methylation patterns at ageassociated CpG sites has been shown to be consistent across multiple cancer tissue types, 21,68 69 In addition, a colorectal cancer study of over 300 patients has found Horvath' clock to be associated with chronological age in normal colonic tissue, but not in tumour tissues. 70 The discrepancies across tissue organs, and between tumour and normal tissues, are thought to be related to differences in stem cell population and cell type compositions in tissues, 71 which could be altered as cancer disease develops and progresses.…”

“…For example, two studies have independently shown that Horvath's DNAm age in nontumour breast tissues to be strongly correlated with chronological age, Hofstatter et al reported a Spearman's r = 0.712 from a sample of 88 individuals, which included 35 breast cancer patients, 64 and Ren et al also reported a Pearson's r of 0.85 in a sample of 122 cancer patients 66 . In contrast, Ren et al found that breast cancer tissues were poorly correlated with patients' chronological age (Pearson's r = 0.30) in a sample of >1000 breast cancer patients, 66 and no correlation was found between Horvath's DNAm age and chronological age in adenocarcinomas and squamous cell carcinomas tissue of nonsmall cell tissues in lung cancer patients 69 . In addition, a colorectal cancer study of over 300 patients has found Horvath' clock to be associated with chronological age in normal colonic tissue, but not in tumour tissues 70 .…”

Envisioning Arti's ‘Cadillac study’ to assess aging trajectories in cancer survivors

“…While, overall, there is a paucity of validated and reliable biological measures of aging, Arti believed that the current state of the science was sufficient to move forward with the use of biological markers that have been more recently described in the scientific literature and utilized in aging studies. Such promising measures discussed at the July 2018 meeting include: 31p recovery time [18][19][20], p16INK4a [21][22][23][24], DNA methylation age [25][26][27][28][29], Phenotypic Age [30], and the Pace of Aging [12,31,32]. Optimally, data for each of these aging measures would be captured repeatedly at critical time points after enrollment.…”