Chromatin organisation and cancer prognosis: a pan-cancer study

Abstract

Background: Chromatin organisation affects gene expression and regional mutation frequencies and contributes to carcinogenesis. Aberrant organisation of DNA has been correlated with cancer prognosis in analyses of the chromatin component of tumour cell nuclei using image texture analysis. As yet, the methodology has not been sufficiently validated to permit its clinical application. We aimed to define and validate a novel prognostic biomarker for the automatic detection of heterogeneous chromatin organisation.

Methods: Machine learning algorithms analysed the chromatin organisation in 461 000 images of tumour cell nuclei stained for DNA from 390 patients (discovery cohort) treated for stage I or II colorectal cancer at the Aker University Hospital (Oslo, Norway). The resulting marker of chromatin heterogeneity, termed Nucleotyping, was subsequently independently validated in six patient cohorts: 442 patients with stage I or II colorectal cancer in the Gloucester Colorectal Cancer Study (UK); 391 patients with stage II colorectal cancer in the QUASAR 2 trial; 246 patients with stage I ovarian carcinoma; 354 patients with uterine sarcoma; 307 patients with prostate carcinoma; and 791 patients with endometrial carcinoma. The primary outcome was cancer-specific survival.

Findings: In all patient cohorts, patients with chromatin heterogeneous tumours had worse cancer-specific survival than patients with chromatin homogeneous tumours (univariable analysis hazard ratio [HR] 1·7, 95% CI 1·2-2·5, in the discovery cohort; 1·8, 1·0-3·0, in the Gloucester validation cohort; 2·2, 1·1-4·5, in the QUASAR 2 validation cohort; 3·1, 1·9-5·0, in the ovarian carcinoma cohort; 2·5, 1·8-3·4, in the uterine sarcoma cohort; 2·3, 1·2-4·6, in the prostate carcinoma cohort; and 4·3, 2·8-6·8, in the endometrial carcinoma cohort). After adjusting for established prognostic patient characteristics in multivariable analyses, Nucleotyping was prognostic in all cohorts except for the prostate carcinoma cohort (HR 1·7, 95% CI 1·1-2·5, in the discovery cohort; 1·9, 1·1-3·2, in the Gloucester validation cohort; 2·6, 1·2-5·6, in the QUASAR 2 cohort; 1·8, 1·1-3·0, for ovarian carcinoma; 1·6, 1·0-2·4, for uterine sarcoma; 1·43, 0·68-2·99, for prostate carcinoma; and 1·9, 1·1-3·1, for endometrial carcinoma). Chromatin heterogeneity was a significant predictor of cancer-specific survival in microsatellite unstable (HR 2·9, 95% CI 1·0-8·4) and microsatellite stable (1·8, 1·2-2·7) stage II colorectal cancer, but microsatellite instability was not a significant predictor of outcome in chromatin homogeneous (1·3, 0·7-2·4) or chromatin heterogeneous (0·8, 0·3-2·0) stage II colorectal cancer.

Interpretation: The consistent prognostic prediction of Nucleotyping in different biological and technical circumstances suggests that the marker of chromatin heterogeneity can be reliably assessed in routine clinical practice and could be used to objectively assist decision making in a range of clinical settings. An immediate application would be to identify high-risk patients with stage II colorectal cancer who might have greater absolute benefit from adjuvant chemotherapy. Clinical trials are warranted to evaluate the survival benefit and cost-effectiveness of using Nucleotyping to guide treatment decisions in multiple clinical settings.

Funding: The Research Council of Norway, the South-Eastern Norway Regional Health Authority, the National Institute for Health Research, and the Wellcome Trust.

Figure 1

CONSORT diagrams showing the origin of each patient cohort (A) Colorectal cancer discovery cohort. (B) Gloucester colorectal cancer validation cohort. (C) QUASAR 2 colorectal cancer validation cohort. (D) Ovarian carcinoma cohort. (E) Uterine sarcoma cohort. (F) Prostate cancer cohort. (G) Endometrial carcinoma cohort.

Figure 2

Computation of the grey level entropy matrix (GLEM) and visualisation of nuclear images (A) Illustration of GLEM computation. (1) A nuclear image. (2) Each nuclear pixel is taken to be the centre of a square subregion, here with a side length of nine pixels. (3) For each subregion, two quantities are extracted (the grey level of the centre pixel [here 22] and the entropy of the grey levels in the subregion [here 3·2]); the entropy H is a variability characteristic of the probability mass function P(i) (ie, the histogram that gives the probability P that grey level i occurs in the subregion). (4) The two quantities extracted from the subregion will together identify a position in a two-way table. The table cell position corresponding to the subregion in figure part 3 of panel A is marked by a green circle in part 4 of panel A. The occurrence is counted by incrementing the value at the table cell position (initially, all table cell values are 0), and the computation of the two quantities and incrementation of the corresponding table cell value is performed for every subregion of the nuclear image. The resulting table describes the frequency of each pair of centre grey level and surrounding entropy and is normalised by its total count to provide the bivariate probability mass function called the GLEM. The two-way table visualised in part A4 is the GLEM of the nuclear image in part A1. (B) Depiction of five nuclear images and their chromatin value. The threshold applied to dichotomise the chromatin value was 0·044.

Figure 3

Kaplan-Meier analysis of cancer-specific survival in patients with chromatin homogeneous and chromatin heterogeneous tumours (A) Discovery cohort for colorectal cancer. (B) Gloucester validation cohort for colorectal cancer. (C) QUASAR 2 validation cohort for colorectal cancer. (D) Ovarian carcinoma cohort. (E) Uterine sarcoma cohort. (F) Prostate carcinoma cohort. (G) Endometrial carcinoma cohort. HR=hazard ratio.

Figure 4

Forest plot of chromatin heterogeneity for all stage II colorectal cancer patients in analysis of cancer-specific survival *Microsatellite stability data were not available for the Gloucester validation cohort. †Surgery type data were not available for the QUASAR 2 validation cohort.

Figure 5

Cancer-specific survival of stage II colorectal cancer patients according to Nucleotyping and microsatellite stability Kaplan-Meier curves according to (A) Nucleotyping, (B) microsatellite stability, (C) Nucleotyping in microsatellite unstable tumours, (D) microsatellite stability in chromatin homogeneous tumours, (E) Nucleotyping in microsatellite stable tumours, and (F) microsatellite stability in chromatin heterogeneous tumours. HR=hazard ratio.