Implications of all the available evidence It is possible to utilise deep learning to develop biomarkers for automatic prediction of patient outcome directly from conventional histopathology images. In colorectal cancer, the marker was found to be a clinically useful prognostic marker in analysis of a large series of patients who received consistent, modern cancer treatment.

Chromosome instability (CIN) is gaining increasing interest as a central process in cancer. CIN, either past or present, is indicated whenever tumour cells harbour an abnormal quantity of DNA, termed 'aneuploidy'. At present, the most widely used approach to detecting aneuploidy is DNA cytometry - a well-known research assay that involves staining of DNA in the nuclei of cells from a tissue sample, followed by analysis using quantitative flow cytometry or microscopic imaging. Aneuploidy in cancer tissue has been implicated as a predictor of a poor prognosis. In this Review, we have explored this hypothesis by surveying the current landscape of peer-reviewed research in which DNA cytometry has been applied in studies with disease-appropriate clinical follow up. This area of research is broad, however, and we restricted our survey to results published since 2000 relating to seven common epithelial cancers (those of the breast; endometrium, ovary, and uterine cervix; oesophagus; colon and rectum; lung; prostate; and bladder). We placed particular emphasis on results from multivariate analyses to pinpoint situations in which the prognostic value of aneuploidy as a biomarker is strong compared with that of existing indicators, such as clinical stage, histological grade, and specific molecular markers. We summarize the implications of our findings for the prognostic use of ploidy analysis in the clinic and for the theoretical understanding of the role of CIN in carcinogenesis.

Background:The high degree of genomic diversity in cancer represents a challenge for identifying objective prognostic markers. We aimed to examine the extent of tumour heterogeneity and its effect on the evaluation of a selected prognostic marker using prostate cancer as a model.Methods:We assessed Gleason Score (GS), DNA ploidy status and phosphatase and tensin homologue (PTEN) expression in radical prostatectomy specimens (RP) from 304 patients followed for a median of 10 years (interquartile range 6–12). GS was assessed for every tumour-containing block and DNA ploidy for a median of four samples for each RP. In a subgroup of 40 patients we assessed DNA ploidy and PTEN status in every tumour-containing block. In 102 patients assigned to active surveillance (AS), GS and DNA ploidy were studied in needle biopsies.Results:Extensive heterogeneity was observed for GS (89% of the patients) and DNA ploidy (40% of the patients) in the cohort, and DNA ploidy (60% of the patients) and PTEN expression (75% of the patients) in the subgroup. DNA ploidy was a significant prognostic marker when heterogeneity was taken into consideration. In the AS cohort we found heterogeneity in GS (24%) and in DNA ploidy (25%) specimens.Conclusions:Multi-sample analysis should be performed to support clinical treatment decisions.

SummaryBackgroundChromatin 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.MethodsMachine 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.FindingsIn 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.InterpretationThe 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 patie...