Impact of molecular profile on prognosis and relapse pattern in low and intermediate risk endometrial cancer.

INTRODUCTION: The role of molecular classification in patients with low/intermediate risk endometrial cancer (EC) is uncertain. Higher precision in diagnostics will inform the unsettled debate on optimal adjuvant treatment. We aimed to determine the association of molecular profiling with patterns of relapse and survival. MATERIAL AND METHODS: This retrospective cohort study included patients referred to The Norwegian Radium Hospital, Oslo University Hospital from 2006-2017. Patients with low/intermediate risk EC were molecularly classified as pathogenic polymerase epsilon (POLE)-mutated, mismatch repair deficient (MMRd), p53 abnormal, or no specific molecular profile (NSMP). The main outcomes were time to recurrence (TTR) and cancer-specific survival (CSS). RESULTS: Of 626 patients, 610 could be molecularly classified. Fifty-seven patients (9%) had POLE-mutated tumors, 202 (33%) had MMRd tumors, 34 (6%) had p53 abnormal tumors and 317 (52%) had NSMP tumors. After median follow-up time of 8.9 years, there was a statistically significant difference in TTR and CSS by molecular groups. Patients with p53 abnormal tumors had poor prognosis, with 10 of the 12 patients with relapse presenting with para-aortic/distant metastases. Patients with POLE mutations had excellent prognosis. In the NSMP group, L1CAM expression was associated with shorter CSS but not TTR. CONCLUSIONS: The differences in outcome by molecular groups are driven by differences in relapse frequency and -patterns and demand a higher precision in diagnostics, also in patients with low/intermediate risk EC. Tailored adjuvant treatment strategies need to consider systemic treatment for patients with p53 abnormal tumors and de-escalated treatment for patients with POLE mutated tumors.

Personalizing adjuvant therapy for patients with colorectal cancer.

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.

Patient-reported nausea after implementation of an enhanced recovery after surgery protocol for gynae-oncology patients.

OBJECTIVES: This study aimed to analyze the adherence to strategies to prevent post-operative nausea and vomiting after implementation of an enhanced recovery after surgery (ERAS) protocol for gynae-oncology patients. Patient-reported nausea before and after ERAS was also studied. METHODS: This prospective observational study included all patients undergoing laparotomy for a suspicious pelvic mass or confirmed advanced ovarian cancer before (pre-ERAS) and after the implementation of ERAS (post-ERAS) at Oslo University Hospital, Norway. Patients were a priori stratified according to the planned extent of surgery into two cohorts (Cohort 1: Surgery of advanced disease; Cohort 2: Surgery for a suspicious pelvic tumor). Clinical data including baseline characteristics and outcome data were prospectively collected. RESULTS: A total of 439 patients were included, 243 pre-ERAS and 196 post-ERAS. At baseline, 27% of the patients reported any grade of nausea. In the post-ERAS cohort, statistically significantly more patients received double post-operative nausea and vomiting prophylaxis (64% pre-ERAS vs 84% post-ERAS, p<0.0001). There was no difference in the need for rescue medication (82% pre-ERAS vs 79% post-ERAS; p=0.17) and no statistically significant difference between pre- and post-ERAS or between the surgical cohorts in patient-reported nausea of any grade on day 2. Patients who reported none/mild nausea on day 2 had significantly less peri-operative fluid administered during surgery than those who reported moderate or severe nausea (median 12.5 mL/kg/hour vs 16.5 mL/kg/hour, p=0.045) but, in multivariable analysis, fluid management did not remain significantly associated with nausea. CONCLUSION: Implementation of an ERAS protocol increased the adherence to post-operative nausea and vomiting prevention guidelines. Nausea, both before and after laparotomy, remains an unmet clinical need of gynae-oncology patients also in an ERAS program. Patient-reported outcome measures warrant further investigation in the evaluation of ERAS.

Prospective evaluation of an enhanced recovery after surgery (ERAS) pathway in a Norwegian cohort of patients with suspected or advanced ovarian cancer.

OBJECTIVE: This prospective cohort study evaluated the introduction of an enhanced recovery after surgery (ERAS) pathway in a tertiary gynecologic oncology referral center. Compliance and clinical outcomes were studied in two separate surgical cohorts. METHODS: Patients undergoing laparotomy for suspected or verified advanced ovarian cancer at Oslo University Hospital were prospectively included in a pre- and post-implementation cohort. A priori, patients were stratified into: cohort 1, patients planned for surgery of advanced disease; and cohort 2, patients undergoing surgery for suspicious pelvic tumor. Baseline characteristics, adherence to the pathway, and clinical outcomes were assessed. RESULTS: Of the 439 included patients, 235 (54%) underwent surgery for advanced ovarian cancer in cohort 1 and 204 (46%) in cohort 2. In cohort 1, 53% of the patients underwent surgery with an intermediate/high Aletti complexity score. Post-ERAS, median fasting times for solids (13.1 hours post-ERAS vs 16.0 hours pre-ERAS, p<0.001) and fluids (3.7 hours post-ERAS vs 11.0 hours pre-ERAS, p<0.001) were significantly reduced. Peri-operative fluid management varied less and was reduced from median 15.8 mL/kg/hour (IQR 10.8-22.5) to 11.5 mL/kg/hour (IQR 9.0-15.4) (p<0.001). In cohort 2 only there was a statistically significant reduction in length of stay (mean (SD) 4.3±1.5 post-ERAS vs 4.6±1.2 pre-ERAS, p=0.026). Despite stable readmission rates, there were significantly more serious complications reported in cohort 1 post-ERAS. CONCLUSIONS: ERAS increased adherence to current standards in peri-operative management with significant reduction in fasting times for both solids and fluids, and peri-operative fluid administration. Length of stay was reduced in patients with suspicious pelvic tumor. Despite serious complications being common in patients with advanced disease undergoing debulking surgery, a causal relationship with the ERAS protocol could not be established. Implementing ERAS and continuous performance auditing are crucial to advancing peri-operative care of patients with ovarian cancer.

A clinical decision support system optimising adjuvant chemotherapy for colorectal cancers by integrating deep learning and pathological staging markers: a development and validation study.

BACKGROUND: 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. METHODS: 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. FINDINGS: 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). INTERPRETATION: 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. FUNDING: The Research Council of Norway.

PTEN and DNA Ploidy Status by Machine Learning in Prostate Cancer.

Machine learning (ML) is expected to improve biomarker assessment. Using convolution neural networks, we developed a fully-automated method for assessing PTEN protein status in immunohistochemically-stained slides using a radical prostatectomy (RP) cohort (n = 253). It was validated according to a predefined protocol in an independent RP cohort (n = 259), alone and by measuring its prognostic value in combination with DNA ploidy status determined by ML-based image cytometry. In the primary analysis, automatically assessed dichotomized PTEN status was associated with time to biochemical recurrence (TTBCR) (hazard ratio (HR) = 3.32, 95% CI 2.05 to 5.38). Patients with both non-diploid tumors and PTEN-low had an HR of 4.63 (95% CI 2.50 to 8.57), while patients with one of these characteristics had an HR of 1.94 (95% CI 1.15 to 3.30), compared to patients with diploid tumors and PTEN-high, in univariable analysis of TTBCR in the validation cohort. Automatic PTEN scoring was strongly predictive of the PTEN status assessed by human experts (area under the curve 0.987 (95% CI 0.968 to 0.994)). This suggests that PTEN status can be accurately assessed using ML, and that the combined marker of automatically assessed PTEN and DNA ploidy status may provide an objective supplement to the existing risk stratification factors in prostate cancer.

Immune checkpoint B7-H3 protein expression is associated with poor outcome and androgen receptor status in prostate cancer.

BACKGROUND: Novel immune checkpoint-based immunotherapies may benefit specific groups of prostate cancer patients who are resistant to other treatments. METHODS: We analyzed by immunohistochemistry the expression of B7-H3, PD-L1/B7-H1, and androgen receptor (AR) in tissue samples from 120 prostate adenocarcinoma patients treated with radical prostatectomy in Spain, and from 206 prostate adenocarcinoma patients treated with radical prostatectomy in Norway. RESULTS: B7-H3 expression correlated positively with AR expression and was associated with biochemical recurrence in the Spanish cohort, but PD-L1 expression correlated with neither of them. Findings for B7-H3 were validated in the Norwegian cohort, where B7-H3 expression correlated positively with Gleason grade, surgical margins, seminal vesicle invasion, and CAPRA-S risk group, and was associated with clinical recurrence. High B7-H3 expression in the Norwegian cohort was also consistent with positive AR expression. CONCLUSION: These results suggest distinct clinical relevance of the two immune checkpoint proteins PD-L1 and B7-H3 in prostate cancer. Our findings highlight B7-H3 as an actionable novel immune checkpoint protein in prostate cancer.

Long-term oncological outcomes and recurrence patterns in early-stage cervical cancer treated with minimally invasive versus abdominal radical hysterectomy: The Norwegian Radium Hospital experience.

OBJECTIVE: To compare long-term oncological outcomes in early-stage cervical cancer (CC) patients treated with minimally invasive radical hysterectomy (MIRH) versus abdominal radical hysterectomy (ARH), with a focus on recurrence patterns, tumor sizes, and conization. METHODS: This single-institution, retrospective study consisted of stage IA1-IB1 (FIGO 2009) squamous cell carcinoma, adenocarcinoma, and adenosquamous carcinoma of the cervix, who underwent radical hysterectomy between 2000 and 2017. RESULTS: Of the 582 patients included, 353 (60.7%) underwent ARH, and 229 (39.3%) MIRH. The median follow-up was 14.4 years in the ARH group and 6.1 years in the MIRH group (p < 0.0001). Among the 96 stage IA patients, only 3 (3.1%) experienced recurrence. Among stage IB1 patients, the risk of recurrence, after adjusting for standard prognostic variables, was twofold higher in the MIRH group versus the ARH group (HR 2.73, 95% CI: 1.56-4.80), and the relative difference was similar in terms of risk of cancer-specific survival (CSS) (HR 3.04, 95% CI: 1.28-7.20) and overall survival (OS) (HR 2.35, 95% CI: 1.21-4.59). In stage IB1 ≤ 2 cm patients without conization MIRH was associated with reduced time to recurrence (TTR) (HR 4.00, 95% CI: 1.67-9.57), CSS (HR 3.71, 95% CI: 1.19-11.58) and OS (HR 3.02, 95% CI: 1.24-7.34). Intraperitoneal combined recurrences accounted for 12 of 30 (40.0%) recurrences in the MIRH group but were not identified after ARH (p = 0.0001). CONCLUSIONS: MIRH was associated with reduced TTR, CSS and OS versus ARH in stage IB1 CC patients. The risk of peritoneal recurrence was high, even for tumors ≤2 cm without conization.

Designing deep learning studies in cancer diagnostics.

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.

Prognostic Value of the Diversity of Nuclear Chromatin Compartments in Gynaecological Carcinomas.

Statistical texture analysis of cancer cell nuclei stained for DNA has recently been used to develop a pan-cancer prognostic marker of chromatin heterogeneity. In this study, we instead analysed chromatin organisation by automatically quantifying the diversity of chromatin compartments in cancer cell nuclei. The aim was to investigate the prognostic value of such an assessment in relation to chromatin heterogeneity and as a potential supplement to pathological risk classifications in gynaecological carcinomas. The diversity was quantified by calculating the entropy of both chromatin compartment sizes and optical densities within compartments. We analysed a median of 281 nuclei (interquartile range (IQR), 273 to 289) from 246 ovarian carcinoma patients and a median of 997 nuclei (IQR, 502 to 1452) from 791 endometrial carcinoma patients. The prognostic value of the entropies and chromatin heterogeneity was moderately strongly correlated (r ranged from 0.68 to 0.73), but the novel marker was observed to provide additional prognostic information. In multivariable analysis with clinical and pathological markers, the hazard ratio associated with the novel marker was 2.1 (95% CI, 1.3 to 3.5) in ovarian carcinoma and 2.4 (95% CI, 1.5 to 3.9) in endometrial carcinoma. Integration with pathological risk classifications gave three risk groups with distinctly different prognoses. This suggests that the novel marker of diversity of chromatin compartments might possibly contribute to the selection of high-risk stage I ovarian carcinoma patients for adjuvant chemotherapy and low-risk endometrial carcinoma patients for less extensive surgery.

Deep learning for prediction of colorectal cancer outcome: a discovery and validation study.

BACKGROUND: 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. METHODS: 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. FINDINGS: 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. INTERPRETATION: 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. FUNDING: The Research Council of Norway.

Prognostic value of DNA ploidy and automated assessment of stroma fraction in prostate cancer.

The combination of DNA ploidy and automatically estimated stroma fraction has been shown to correlate with recurrence and cancer death in colorectal cancer. We aimed to extend this observation and evaluate the prognostic importance of this combined marker in prostate cancer. DNA ploidy status was determined by image cytometry and the stroma fraction was estimated automatically on hematoxylin and eosin stained sections in three tumor samples from each patient to account for tumor heterogeneity. The optimal threshold for low (≤56%) and high (>56%) stroma fraction was identified in a discovery cohort (n = 253). The combined marker was validated in an independent patient cohort (n = 259) with biochemical recurrence as endpoint. The combined marker predicted biochemical recurrence independently in the validation cohort. Multivariable analysis showed that the highest risk of recurrence was observed for patients with samples that had both non-diploid ploidy status and a high stroma fraction (hazard ratio: 2.51, 95% confidence interval: 1.18-5.34). In conclusion, we suggest the combination of DNA ploidy and automatically estimated stroma fraction as a prognostic marker for the risk stratification of prostate cancer patients. It may also be a potential generic marker as concurrent results have been described in colorectal cancer.

Prognostic value of mitotic checkpoint protein BUB3, cyclin B1, and pituitary tumor-transforming 1 expression in prostate cancer.

The mitotic checkpoint protein BUB3, cyclin B1 (CCNB1) and pituitary tumor-transforming 1 (PTTG1) regulates cell division, and are sparsely studied in prostate cancer. Deregulation of these genes can lead to genomic instability, a characteristic of more aggressive tumors. We aimed to determine the expression levels of BUB3, CCNB1, and PTTG1 as potential prognostic markers of recurrence after radical prostatectomy. Protein levels were determined by immunohistochemistry on three formalin-fixed paraffin-embedded tissue sections from each of the 253 patients treated with radical prostatectomy. Immunohistochemistry scores were obtained by automated image analysis for CCNB1 and PTTG1. Recurrence, defined as locoregional recurrence, distant metastasis or death from prostate cancer, was used as endpoint for survival analysis. Tumors having both positive and negative tumor areas for cytoplasmic BUB3 (30%), CCNB1 (28%), or PTTG1 (35%) were considered heterogeneous. Patients with ≥1 positive tumor area had significantly increased risk of disease recurrence in univariable analysis compared with patients where all tumor areas were negative for cytoplasmic BUB3 (hazard ratio [HR] = 2.18, 95% confidence interval [CI] 1.41-3.36), CCNB1 (HR = 2.98, 95% CI 1.93-4.61) and PTTG1 (HR = 1.91, 95% CI 1.23-2.97). Combining the scores of cytoplasmic BUB3 and CCNB1 improved risk stratification when integrated with the Cancer of the Prostate Risk Assessment post-Surgical (CAPRA-S) score (difference in concordance index = 0.024, 95% CI 0.001-0.05). In analysis of multiple tumor areas, prognostic value was observed for cytoplasmic BUB3, CCNB1, and PTTG1.

Association Between Proportion of Nuclei With High Chromatin Entropy and Prognosis in Gynecological Cancers.

Background: Nuclear texture analysis measuring differences in chromatin structure has provided prognostic biomarkers in several cancers. There is a need for improved cell-by-cell chromatin analysis to detect nuclei with highly disorganized chromatin. The purpose of this study was to develop a method for detecting nuclei with high chromatin entropy and to evaluate the association between the presence of such deviating nuclei and prognosis. Methods: A new texture-based biomarker that characterizes each cancer based on the proportion of high-chromatin entropy nuclei (<25% vs ≥25%) was developed on a discovery set of 175 uterine sarcomas. The prognostic impact of this biomarker was evaluated on a validation set of 179 uterine sarcomas, as well as on independent validation sets of 246 early-stage ovarian carcinomas and 791 endometrial carcinomas. More than 1 million images of nuclei stained for DNA were included in the study. All statistical tests were two-sided. Results: An increased proportion of high-chromatin entropy nuclei was associated with poor clinical outcome. The biomarker predicted five-year overall survival for uterine sarcoma patients with a hazard ratio (HR) of 2.02 (95% confidence interval [CI] = 1.43 to 2.84), time to recurrence for ovarian cancer patients (HR = 2.91, 95% CI = 1.74 to 4.88), and cancer-specific survival for endometrial cancer patients (HR = 3.74, 95% CI = 2.24 to 6.24). Chromatin entropy was an independent prognostic marker in multivariable analyses with clinicopathological parameters (HR = 1.81, 95% CI = 1.21 to 2.70, for sarcoma; HR = 1.71, 95% CI = 1.01 to 2.90, for ovarian cancer; and HR = 2.03, 95% CI = 1.19 to 3.45, for endometrial cancer). Conclusions: A novel method detected high-chromatin entropy nuclei, and an increased proportion of such nuclei was associated with poor prognosis. Chromatin entropy supplemented existing prognostic markers in multivariable analyses of three gynecological cancer cohorts.

Chromatin organisation and cancer prognosis: a pan-cancer study.

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.

Tumour heterogeneity poses a significant challenge to cancer biomarker research.

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.

Chromatin changes predict recurrence after radical prostatectomy.

BACKGROUND: Pathological evaluations give the best prognostic markers for prostate cancer patients after radical prostatectomy, but the observer variance is substantial. These risk assessments should be supported and supplemented by objective methods for identifying patients at increased risk of recurrence. Markers of epigenetic aberrations have shown promising results in several cancer types and can be assessed by automatic analysis of chromatin organisation in tumour cell nuclei. METHODS: A consecutive series of 317 prostate cancer patients treated with radical prostatectomy at a national hospital between 1987 and 2005 were followed for a median of 10 years (interquartile range, 7-14). On average three tumour block samples from each patient were included to account for tumour heterogeneity. We developed a novel marker, termed Nucleotyping, based on automatic assessment of disordered chromatin organisation, and validated its ability to predict recurrence after radical prostatectomy. RESULTS: Nucleotyping predicted recurrence with a hazard ratio (HR) of 3.3 (95% confidence interval (CI), 2.1-5.1). With adjustment for clinical and pathological characteristics, the HR was 2.5 (95% CI, 1.5-4.1). An updated stratification into three risk groups significantly improved the concordance with patient outcome compared with a state-of-the-art risk-stratification tool (P<0.001). The prognostic impact was most evident for the patients who were high-risk by clinical and pathological characteristics and for patients with Gleason score 7. CONCLUSION: A novel assessment of epigenetic aberrations was capable of improving risk stratification after radical prostatectomy.