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Key Points Academic interest in the role of chromosomal instability (CIN) in cancer development and the influence of the resultant large-scale genomic alterations on clinical outcomes is increasing Aneuploidy — that is, the presence of an abnormal amount of cellular DNA, is an inevitable result of CIN, and this characteristic can be detected and quantified using DNA cytometry DNA ploidy (cellular DNA quantity) is an independent prognostic marker in patients with node-negative invasive breast, early stage endometrioid endometrial, early stage ovarian, prostate, or colorectal cancers In patients with Barrett oesophagus, DNA ploidy can be combined with other biomarkers to identify disease that will progress to high-grade dysplasia and/or carcinoma, and to improve the diagnostic sensitivity of pulmonary cytology In cervical screening tests, detection of aneuploid cells in Pap smears or using liquid-based cytology is a reliable, cost-effective indicator of the early stages of neoplastic progression toward squamous-cell carcinoma Chromosome instability (CIN) is gaining increasing interest as a central process in cancer, and is indicated whenever tumour cells harbour an abnormal quantity of DNA, termed 'aneuploidy'. In this Review, the authors review the literature published since 2000 that support the hypothesis that aneuploidy is a predictor of a poor prognosis in patients with cancer, focusing on the evidence from studies of seven common epithelial cancer types that performed multivariate analyses. The implications of ploidy analysis with regard to our theoretical understanding of the role of CIN in carcinogenesis, as well as its prognostic use in the clinic, are discussed. 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.

The number of publications on deep learning for cancer diagnostics is rapidly increasing, and systems are frequently claimed to perform comparable with or better than clinicians. However, few systems have yet demonstrated real-world medical utility. In this Perspective, we discuss reasons for the moderate progress and describe remedies designed to facilitate transition to the clinic. Recent, presumably influential, deep learning studies in cancer diagnostics, of which the vast majority used images as input to the system, are evaluated to reveal the status of the field. By manipulating real data, we then exemplify that much and varied training data facilitate the generalizability of neural networks and thus the ability to use them clinically. To reduce the risk of biased performance estimation of deep learning systems, we advocate evaluation in external cohorts and strongly advise that the planned analyses, including a predefined primary analysis, are described in a protocol preferentially stored in an online repository. Recommended protocol items should be established for the field, and we present our suggestions.The number of publications on deep learning for cancer diagnostics is rapidly increasing, but clinical translation is slow. This Perspective advocates performance estimation in external cohorts and strongly advises that a primary analysis is predefined in a standardized protocol preferentially stored in an online repository.

The current standard-of-care adjuvant treatment for patients with colorectal cancer (CRC) comprises a fluoropyrimidine (5-fluorouracil or capecitabine) as a single agent or in combination with oxaliplatin, for either 3 or 6 months. Selection of therapy depends on conventional histopathological staging procedures, which constitute a blunt tool for patient stratification. Given the relatively marginal survival benefits that patients can derive from adjuvant treatment, improving the safety of chemotherapy regimens and identifying patients most likely to benefit from them is an area of unmet need. Patient stratification should enable distinguishing those at low risk of recurrence and a high chance of cure by surgery from those at higher risk of recurrence who would derive greater absolute benefits from chemotherapy. To this end, genetic analyses have led to the discovery of germline determinants of toxicity from fluoropyrimidines, the identification of patients at high risk of life-threatening toxicity, and enabling dose modulation to improve safety. Thus far, results from analyses of resected tissue to identify mutational or transcriptomic signatures with value as prognostic biomarkers have been rather disappointing. In the past few years, the application of artificial intelligence-driven models to digital images of resected tissue has identified potentially useful algorithms that stratify patients into distinct prognostic groups. Similarly, liquid biopsy approaches involving measurements of circulating tumour DNA after surgery are additionally useful tools to identify patients at high and low risk of tumour recurrence. In this Perspective, we provide an overview of the current landscape of adjuvant therapy for patients with CRC and discuss how new technologies will enable better personalization of therapy in this setting.The current standard-of-care adjuvant treatment for patients with colorectal cancer is chemotherapy selected on the basis of conventional histopathological staging criteria; however, the clinical benefit from these regimens is limited. The authors of this Perspective discuss strategies to minimize toxicity and monitor efficacy of these regimens, and propose new tools for disease staging that could enable more personalized treatment decisions.

Improved markers of prognosis are needed to stratify patients with early-stage colorectal cancer to refine selection of adjuvant therapy. The aim of the present study was to develop a biomarker of patient outcome after primary colorectal cancer resection by directly analysing scanned conventional haematoxylin and eosin stained sections using deep learning. More than 12 000 000 image tiles from patients with a distinctly good or poor disease outcome from four cohorts were used to train a total of ten convolutional neural networks, purpose-built for classifying supersized heterogeneous images. A prognostic biomarker integrating the ten networks was determined using patients with a non-distinct outcome. The marker was tested on 920 patients with slides prepared in the UK, and then independently validated according to a predefined protocol in 1122 patients treated with single-agent capecitabine using slides prepared in Norway. All cohorts included only patients with resectable tumours, and a formalin-fixed, paraffin-embedded tumour tissue block available for analysis. The primary outcome was cancer-specific survival. 828 patients from four cohorts had a distinct outcome and were used as a training cohort to obtain clear ground truth. 1645 patients had a non-distinct outcome and were used for tuning. The biomarker provided a hazard ratio for poor versus good prognosis of 3·84 (95% CI 2·72–5·43; p<0·0001) in the primary analysis of the validation cohort, and 3·04 (2·07–4·47; p<0·0001) after adjusting for established prognostic markers significant in univariable analyses of the same cohort, which were pN stage, pT stage, lymphatic invasion, and venous vascular invasion. A clinically useful prognostic marker was developed using deep learning allied to digital scanning of conventional haematoxylin and eosin stained tumour tissue sections. The assay has been extensively evaluated in large, independent patient populations, correlates with and outperforms established molecular and morphological prognostic markers, and gives consistent results across tumour and nodal stage. The biomarker stratified stage II and III patients into sufficiently distinct prognostic groups that potentially could be used to guide selection of adjuvant treatment by avoiding therapy in very low risk groups and identifying patients who would benefit from more intensive treatment regimes. The Research Council of Norway.

The DoMore-v1-CRC marker was recently developed using deep learning and conventional haematoxylin and eosin-stained tissue sections, and was observed to outperform established molecular and morphological markers of patient outcome after primary colorectal cancer resection. The aim of the present study was to develop a clinical decision support system based on DoMore-v1-CRC and pathological staging markers to facilitate individualised selection of adjuvant treatment. We estimated cancer-specific survival in subgroups formed by pathological tumour stage (pT<4 or pT4), pathological nodal stage (pN0, pN1, or pN2), number of lymph nodes sampled (≤12 or >12) if not pN2, and DoMore-v1-CRC classification (good, uncertain, or poor prognosis) in 997 patients with stage II or III colorectal cancer considered to have no residual tumour (R0) from two community-based cohorts in Norway and the UK, and used these data to define three risk groups. An external cohort of 1075 patients with stage II or III R0 colorectal cancer from the QUASAR 2 trial was used for validation; these patients were treated with single-agent capecitabine. The proposed risk stratification system was evaluated using Cox regression analysis. We similarly evaluated a risk stratification system intended to reflect current guidelines and clinical practice. The primary outcome was cancer-specific survival. The new risk stratification system provided a hazard ratio of 10·71 (95% CI 6·39–17·93; p<0·0001) for high-risk versus low-risk patients and 3·06 (1·73–5·42; p=0·0001) for intermediate versus low risk in the primary analysis of the validation cohort. Estimated 3-year cancer-specific survival was 97·2% (95% CI 95·1–98·4; n=445 [41%]) for the low-risk group, 94·8% (91·7–96·7; n=339 [32%]) for the intermediate-risk group, and 77·6% (72·1–82·1; n=291 [27%]) for the high-risk group. The guideline-based risk grouping was observed to be less prognostic and informative (the low-risk group comprised only 142 [13%] of the 1075 patients). Integrating DoMore-v1-CRC and pathological staging markers provided a clinical decision support system that risk stratifies more accurately than its constituent elements, and identifies substantially more patients with stage II and III colorectal cancer with similarly good prognosis as the low-risk group in current guidelines. Avoiding adjuvant chemotherapy in these patients might be safe, and could reduce morbidity, mortality, and treatment costs. The Research Council of Norway.

Abstract Within the last decade, the science of molecular testing has evolved from single gene and single protein analysis to broad molecular profiling as a standard of care, quickly transitioning from research to practice. Terms such as genomics, transcriptomics, proteomics, circulating omics, and artificial intelligence are now commonplace, and this rapid evolution has left us with a significant knowledge gap within the medical community. In this paper, we attempt to bridge that gap and prepare the physician in oncology for multiomics, a group of technologies that have gone from looming on the horizon to become a clinical reality. The era of multiomics is here, and we must prepare ourselves for this exciting new age of cancer medicine. Through multiomics, the combined use many available technologies, a more complete and dynamic vision of cancer can be obtained. This article bridges the gap between multiomics technology and oncology practice.

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

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.

Sentinel lymph nodes are the first nodes draining the lymph from a breast and could reveal early changes in the host immune system upon dissemination of breast cancer cells. To investigate this, we performed single‐cell immune profiling of lymph nodes with and without metastatic cells. Whereas no significant changes were observed for B‐cell and natural killer (NK)‐cell subsets, metastatic lymph nodes had a significantly increased frequency of CD8 T cells and a skewing toward an effector/memory phenotype of CD4 and CD8 T cells, suggesting an ongoing immune response. Additionally, metastatic lymph nodes had an increased frequency of TIGIT (T‐cell immunoreceptor with Ig and ITIM domains)‐positive T cells with suppressed TCR signaling compared with non‐metastatic nodes, indicating exhaustion of effector T cells, and an increased frequency of regulatory T cells (Tregs) with an activated phenotype. T‐cell alterations correlated with the percentage of metastatic tumor cells, reflecting the presence of metastatic tumor cells driving T effector cells toward exhaustion and promoting immunosuppression by recruitment or increased differentiation toward Tregs. These results show that immune suppression occurs already in early stages of tumor progression. Sentinel lymph nodes (SLN) are the first nodes draining the lymph from a breast and could reveal early changes in the host immune system upon dissemination of breast cancer (BC) cells. We used single‐cell mass cytometry and functional analyses of metastatic and non‐metastatic LN and revealed a dysfunctional and suppressed immune response. Metastatic LN had an increased frequency of TIGIT‐positive T cells with suppressed TCR signaling compared with non‐metastatic nodes and an increased frequency of Tregs with an activated phenotype. The alterations were correlated with metastatic tumor burden in the LN of patients with BC.

Background Current risk stratification tools for prostate cancer patients under active surveillance (AS) may inadequately identify those needing treatment. We investigated DNA ploidy and PTEN as potential biomarkers to predict aggressive disease in AS patients. Methods We assessed DNA ploidy by image cytometry and PTEN protein expression by immunohistochemistry in 3197 tumour-containing tissue blocks from 558 patients followed in AS at a Norwegian local hospital. The primary endpoint was treatment, with treatment failure (biochemical recurrence or initiation of salvage therapy) as the secondary endpoint. Results The combined DNA ploidy and PTEN (DPP) status at diagnosis was associated with treatment-free survival in univariable- and multivariable analysis, with a HR for DPP-aberrant vs. DPP-normal tumours of 2.12 ( p  < 0.0001) and 1.94 ( p  < 0.0001), respectively. Integration of DNA ploidy and PTEN status with the Cancer of the Prostate Risk Assessment (CAPRA) score improved risk stratification (c-index difference = 0.025; p  = 0.0033). Among the treated patients, those with DPP-aberrant tumours exhibited a significantly higher likelihood of treatment failure (HR 2.01; p  = 0.027). Conclusions DNA ploidy and PTEN could serve as additional biomarkers to identify AS patients at increased risk of developing aggressive disease, enabling earlier intervention for nearly 50% of the patients that will eventually receive treatment with current protocol.