215 Background: The decision to offer adjuvant chemotherapy (ACT) for patients with high-risk stage II and stage III colon cancer is fraught with challenges as most patients do not benefit from potentially toxic therapy. Histotype Px Colorectal stratifies patients into distinct risk groups by combining staging parameters with the DoMore v1 marker, a novel artificial intelligence-based digital biomarker that analyzes routine H&E-stained FFPE whole-slide images. The purpose of this study was to externally validate its prognostic performance and investigate its potential to predict adjuvant chemotherapy (ACT) benefit. Methods: This was a retrospective analysis of patients diagnosed with pathological stage II/III colon adenocarcinoma treated at Ohio State University in 2011-2024. Clinical parameters and outcome data were extracted from patient records. Blinded to clinical outcomes, anonymized digitized slides were analyzed by Histotype Px. Logrank test and Cox proportional hazards regression were used to analyze cancer-specific death (CSD). The multivariable models included age, sex, pN stage, pT stage, number of lymph nodes sampled, tumor perforation, lymphovascular invasion, perineural invasion, MSI status, and receipt of ACT. Competing risk analysis was used to calculate CSD rates. Results: Baseline characteristics of the 503 eligible patients included median age of 63 years (22-97), 54% male, 55% stage III, 61% right-sided tumors and 23% MSI, with 51% of patients receiving ACT and a median follow-up of 44 months. The DoMore v1 marker was statistically significant in both univariable (p<0.0001) and multivariable analysis with the clinicopathological markers (p=0.0010). Among the 298 patients in the Histotype Px low-risk group, 41.3% received ACT and their 5-year CSD rate was 6.1% (2.2%-12.8%) compared to 5.9% (2.7%-10.9%) for those that did not receive ACT. For the 123 Histotype Px intermediate-risk patients, 58.5% received ACT with a 5-year CSD rate of 17.5% (8.3%-29.5%) compared to 15.7% (6.2%-29.2%) without ACT. For the 81 Histotype Px high-risk patients, 76.5% received ACT with a 5-year CSD rate of 42.3% (21.9%-61.5%) compared to 58.3% (30.8%-78.1%) without ACT. In multivariable analyses, ACT benefit was observed in the high-risk group (HR 0.17, 95% CI 0.06-0.47; p=0.0007) but neither in the intermediate-risk (p=0.84) nor low-risk (p=0.16) group. Conclusions: Histotype Px Colorectal was able to improve risk stratification and showed promise as a predictive biomarker for ACT benefit in patients with stage II/III colon adenocarcinoma. Current standard-of-care ACT might not be sufficient for Histotype Px high-risk patients, while ACT might not be beneficial for Histotype Px low-risk patients.

Mitotic figure counting is an established measure of cell proliferation that is included in grading systems. We developed a deep learning method for mitotic figure counting and evaluated its prognostic impact in multiple external validation datasets. The deep learning method was trained in whole slide images of tissue sections stained with haematoxylin and eosin from a publicly available breast cancer dataset where mitotic figures have been annotated by expert pathologists. The final model was externally validated according to a protocol with predefined analyses of 14 571 patient samples from 13 patient cohorts from seven different cancer types. The predefined primary analysis was univariable Cox survival analysis of the number of mitotic figures detected per mm 2 . Automatic mitotic figure counting correlated well with known proliferation rates, and patients with more mitotic figures per mm 2 had significantly worse patient outcome in all the studied cancer types except colorectal cancer. This study demonstrates the practical potential of automated, deep learning‐based mitotic figure counting, both by automating pathology work and by suggesting expanded use in more cancer types, such as prostate cancer.

OBJECTIVE:Endometrial carcinoma is molecularly classified into sub-types with distinct prognoses. The no specific molecular profile sub-group is prognostically heterogeneous. This study evaluates the prognostic significance of ER (estrogen receptor) and L1CAM (L1 cell adhesion molecule) expression in no specific molecular profile tumors individually, in combination with tumor grade, and as a combined biomarker. METHODS:Immunohistochemistry was used to assess expression of ER and L1CAM in tumors from patients with endometrial carcinoma referred to Oslo University Hospital (2006-2017). ER and L1CAM expression were grouped as negative (<10%) or positive (≥10%). Survival analyses were performed using Cox regression with cancer-specific survival as the end point. RESULTS:In this study of 465 no specific molecular profile patients, ER negativity (78/446, 17.5%) and L1CAM positivity (76/454, 14.5%) were positively correlated with adverse clinicopathologic features, particularly non-endometrioid histology. Both markers, individually and in combination, were associated with shorter cancer-specific survival, especially in FIGO (International Federation of Gynecology and Obstetrics) stage I (2009) patients (n = 360). Combining ER and/or L1CAM with tumor grade (grade 1-2 vs grade 3/non-endometrioid) increased the hazard ratio in multi-variable analyses of cancer-specific survival, with hazard ratios of 10.3 (95% confidence interval [CI] 3.43 to 31.14) for ER-negative and/or high grade, 9.43 (95% CI 3.17 to 28.00) for L1CAM-positive and/or high grade, and 15.08 (95% CI 4.13 to 55.14) for ER-negative and/or L1CAM-positive and/or high grade, compared with their corresponding low-risk groups. Estimated 5-year cancer-specific survival rates among FIGO stage I patients were 98.5% for ER-positive/low grade, 98.6% for L1CAM-negative/low grade, and 99.2% for ER-positive/L1CAM-negative/low grade, and 80.5%, 79.5%, and 82.6% for the corresponding high-risk groups. CONCLUSIONS:ER and L1CAM appear to have comparable prognostic value in no specific molecular profile endometrial cancer, and their combination with tumor grade enhances risk stratification. A combined marker including ER, L1CAM, and grade may improve risk stratification beyond either pair of markers and may be used to tailor treatment.

3604 Background: The current standard of care for patients with stage III colon cancer (CC) is resection followed by adjuvant chemotherapy (ACT). About half of patients are cured by surgery and hence overtreated with ACT, whereas ~30% experience recurrence despite ACT. Several studies show that patients with no detectable circulating tumor DNA (ctDNA) after surgery are at a lower risk of recurrence (RR), although false negative ctDNA results remain a concern. Other studies show prognostic value of digital pathology biomarkers on resected CC tissue, like the Combined Analysis of Pathologists and Artificial Intelligence (CAPAI; Kleppe Lancet Oncol 2022). This study aimed to explore the potential added value of CAPAI to post-surgery ctDNA in risk stratification of patients with stage III CC receiving ACT. Methods: Patients were selected from the Prospective Dutch ColoRectal Cancer (PLCRC) cohort substudy PROVENC3 (Rubio-Alarcon AACR 2024), based on stage III CC treated with radical resection and adjuvant capecitabine or CAPOX. Post-surgery ctDNA status was determined using Labcorp Plasma Detect. From the resected tumor, a representative H&E slide was digitalized to generate a DoMore-v1-CE-CRC score, which was combined with the pT and pN stage and number of assessed lymph nodes for classification as CAPAI high-, intermediate- or low-risk. Three-year RR and Cox proportional hazard ratios (HR) were reported for ctDNA-based and CAPAI-based risk groups. Time to recurrence was compared between risk groups using the log-rank test. Results: Post-surgery ctDNA status and CAPAI risk classification were available for 163 patients. The 20 patients (12%) with detectable ctDNA had a higher recurrence risk (3-year RR 60% [32-77], HR 4.9 [2.5-9.6], p < 0.001) than patients with no detectable ctDNA (N = 143, 3-year RR 18% [11-25]). Within the subgroup with no detectable post-surgery ctDNA, 50 patients (35%) were classified as CAPAI high-risk. These CAPAI high-risk patients had a higher recurrence risk (3-year RR 35% [20-48], HR 4.2 [2.0-9.1], p < 0.001) than patients classified as CAPAI low/intermediate-risk, who were combined based on their observed similar RR (N = 93, 3-year RR 9% [3-15%]). Conclusions: In patients with stage III colon cancer treated with adjuvant CAP(OX), CAPAI risk classification has potential to further stratify RR in the subgroup with no detectable post-surgery ctDNA. These preliminary results suggest that CAPAI high-risk may help identify patients with false negative post-surgery ctDNA results. Over half of all patients had both no detectable ctDNA and were CAPAI low/intermediate-risk. Given their low RR in our preliminary results, future studies on larger patient cohorts should focus on the ability to combine biomarkers to select very low-risk patients and evaluate whether these patients can potentially be spared ACT.

We aimed to develop deep learning (DL) models to detect protein expression in immunohistochemically (IHC) stained tissue-sections, and to compare their accuracy and performance with manually scored clinically relevant proteins in common cancer types.Five cancer patient cohorts (colon, two prostate, breast, and endometrial) were included. We developed separate DL models for scoring IHC-stained tissue-sections with nuclear, cytoplasmic, and membranous staining patterns. For training, we used images with annotations of cells with positive and negative staining from the colon cohort stained for Ki-67 and PMS2 (nuclear model), the prostate cohort 1 stained for PTEN (cytoplasmic model) and β-catenin (membranous model). The nuclear DL model was validated for MSH6 in the colon, MSH6 and PMS2 in the endometrium, Ki-67 and CyclinB1 in prostate, and oestrogen and progesterone receptors in the breast cancer cohorts. The cytoplasmic DL model was validated for PTEN and Mapre2, and the membranous DL model for CD44 and Flotillin1, all in prostate cohorts. When comparing the results of manual and DL scores in the validation sets, using manual scores as the ground truth, we observed an average correct classification rate of 91.5 % (76.9–98.5 %) for the nuclear model, 85.6 % (73.3–96.6 %) for the cytoplasmic model, and 78.4 % (75.5–84.3 %) for the membranous model. In survival analyses, manual and DL scores showed similar prognostic impact, with similar hazard ratios and p-values for all DL models. Our findings demonstrate that DL models offer a promising alternative to manual IHC scoring, providing efficiency and reproducibility across various data sources and markers.

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.

Gastrointestinal stromal tumor (GIST) is a mesenchymal neoplasm with variable behavior. An increased understanding of the tumor pathogenesis may improve clinical decision-making. Our aim was to obtain more data about the overall chromosome aberrations and intratumor cytogenetic heterogeneity in GIST. We analyzed 306 GIST samples from 291 patients using G-banding, direct sequencing, and statistics. Clonal chromosome aberrations were found in 81% of samples, with 34% of 226 primary tumors demonstrating extensive cytogenetic heterogeneity. 135 tumors had simple (≤5 changes) and 91 had complex (>5 changes) karyotypes. The karyotypically complex tumors more often were non-gastric (P < 0.001), larger (P < 0.001), more mitotically active (P = 0.009) and had a higher risk of rupture (P < 0.001) and recurrence (P < 0.001). Significant differences between gastric and non-gastric tumors were found also in the frequency of main chromosome losses: of 14q (79% vs. 63%), 22q (38% vs. 67%), 1p (23% vs. 88%), and 15q (18% vs. 77%). Gastric PDGFRA-mutated tumors, compared with gastric KIT-mutated, had a lower incidence of 22q losses (18% vs. 43%) but a higher rate of 1p losses (42% vs. 22%). The present, largest by far karyotypic study of GISTs provides further evidence for the existence of variable pathogenetic pathways operating in these tumors' development.

Endometrial carcinomas (ECs) are histologically classified as endometrioid and nonendometrioid tumors, with each subgroup displaying different molecular profiles and clinical outcomes. Considerable biological and clinical heterogeneity exists within this scheme, however, reflecting its imperfection. We aimed to gather additional data that might help clarify the tumors' pathogenesis and contribute toward a more meaningful classification scheme. In total, 33 ECs were examined for the presence of chromosomal aberrations, genomic imbalances, pathogenic variants, microsatellite instability, and expression profiles at both gene and miRNA levels. Chromosome 1 was the most frequently rearranged chromosome, showing a gain of all or part of the long arm. Pathogenic variants were found for PTEN (53%), PDGFRA (37%), PIK3CA (34%), and KIT (31%). High microsatellite instability was identified in 15 ECs. Comparing tumors and controls, we identified 23 differentially expressed genes of known importance in carcinogenesis, 15 genes involved in innate and adaptative immune responses, and altered expression of 7 miRNAs. miR-32-5p was the most upregulated. Our series showed a high degree of heterogeneity. Tumors were well-separated from controls, but there was no clear-cut separation between endometrioid and nonendometrioid ECs. Whether this means that the current phenotypic classification is of little relevance or if one still has not detected which genomic parameters to enter into correlation analyses remains unknown.

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.

Background: Tumor development is critically dependent on the supporting stroma consisting of inflammatory cells and fibroblasts. This study intended to improve prognostic prediction for early colorectal cancer (CRC) by combined estimation of T-lymphocyte and stroma fractions with conventional markers. Methods: In total 509 and 1041 stage II/ΙΙΙ CRC from the VICTOR and QUASAR 2 trials were included as a training set and a validation set, respectively. Intratumoral CD8+ T-lymphocytes and stroma were identified and quantified by machine-based learning on digital sections. The primary endpoint was to evaluate the prognostic value of the combined marker for time to recurrence (TTR). Findings: For low-risk patients (n = 598; stage Ⅱ, and stage ΙΙΙ pT1-3 pN1 with neither lymphatic (L−) nor vascular (V−) invasion), low stroma fraction (n = 511) identified a good prognostic subgroup with 5-year TTR of 86% (95% CI 83–89), versus the high stroma subgroup TTR of 78% (HR = 1.75, 95% CI 1.05–2.92; P = 0.029). For high-risk patients (n = 394; stage ΙΙΙ pT3 pN1 L+/V+, pT4, or pN2), combined low CD8+ and high stroma fraction identified a poor prognostic subgroup (n = 34) with 5-year TTR of 29% (95% CI 17-50), versus the high CD8+ fraction and low stroma fraction subgroup (n = 138) of 64% (HR = 2.86, 95% CI 1.75–4.69; P < 0.001). Interpretation: Quantification of intratumoral CD8+ T-lymphocyte and stroma fractions can be combined with conventional prognostic markers to improve patient stratification.

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.

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. Copyright (C) 2020 Elsevier Ltd. All rights reserved.

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.

Many cases of acute lymphoblastic leukemia (ALL) carry visible acquired chromosomal changes of pathogenetic, diagnostic, and prognostic importance. Nevertheless, from one-fourth to half of newly diagnosed ALL patients have no visible chromosomal changes detectable by G-banding analysis at diagnosis. The introduction of powerful molecular methodologies has shown that many karyotypically normal ALLs carry clinically important submicroscopic aberrations. We used fluorescence in situ hybridization (FISH), array comparative genomic hybridization (aCGH), RNA sequencing, reverse transcription (RT) and genomic polymerase chain reaction (PCR), as well as Sanger sequencing to investigate a case of pediatric ALL with a normal karyotype. FISH with a commercial PDGFRB breakapart probe showed loss of the distal part of the probe suggesting a breakpoint within the PDGFRB locus. aCGH revealed submicroscopic deletions in chromosome bands 5q32q35.3 (about 30 Mb long, starting within PDGFRB and finishing in the CANX locus), 7q34 (within TCRB), 9p13 (PAX5), 10q26.13 (DMBT1), 14q11.2 (TRAC), and 14q32.33 (within the IGH locus). RNA sequencing detected an in-frame GTF2I–PDGFRB and an out-of-frame IKZF1–TYW1 fusion transcript. Both fusion transcripts were verified by RT-PCR together with Sanger sequencing and interphase FISH. The GTF2I–PDGFRB fusion was also verified by genomic PCR and FISH. The corresponding GTF2I–PDGFRB fusion protein would consist of almost the entire GTF2I and that part of PDGFRB which harbors the catalytic domain of the tyrosine kinase. It would therefore seem to lead to abnormal tyrosine kinase activity in a manner similar to what has been seen for other PDGFRB fusion proteins. The examined pediatric leukemia is a Ph-like ALL which carries novel GTF2I–PDGFRB and IKZF1–TYW1 fusion genes together with additional submicroscopic deletions. Because hematologic neoplasms with PDGFRB-fusion genes can be treated with tyrosine kinase inhibitors, the detection of such novel fusions may be clinically important. Since the GTF2I–PDGFRB could be detected only after molecular studies of the leukemic cells, further investigations of ALL-cases, perhaps especially but not exclusively with a normal karyotype, are needed in order to determine the frequency of GTF2I–PDGFRB in leukemia, and also to find out which clinical impact the fusion may have.

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] 1⁄4 1.43 to 2.84), time to recurrence for ovarian cancer patients (HR 1⁄4 2.91, 95% CI 1⁄4 1.74 to 4.88), and cancer-specific survival for endometrial cancer patients (HR 1⁄4 3.74, 95% CI 1⁄4 2.24 to 6.24). Chromatin entropy was an independent prognostic marker in multivariable analyses with clinicopathological parameters (HR 1⁄4 1.81, 95% CI 1⁄4 1.21 to 2.70, for sarcoma; HR 1⁄4 1.71, 95% CI 1⁄4 1.01 to 2.90, for ovarian cancer; and HR 1⁄4 2.03, 95% CI 1⁄4 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. Genomic instability is central in the multistep development of cancer (1,2), and the assessment of large-scale genomic alterations in cancer cell nuclei is useful for predicting clinical outcomes in cancer patients (3). There is a complex relation between genomic alterations and large-scale rearrangement of interphase nuclear chromatin. Chromatin structure is central in both transcriptional regulation and maintenance of genomic stability (4). Chromatin is continually remodeled, and targeted chromatin remodeling determines transcriptional control (5). Modification of chromatin structure also has a regulatory function in DNA repair, replication, and chromosome segregation (5). Nuclear texture analysis (Nucleotyping) refers to the characterization of chromatin structure based on digital microscope images of cell nuclei (6). Prior to imaging, the nuclei are stained with a DNA-specific stain, and the gray levels in the images thus correspond to DNA content. Nucleotyping describes the changes in chromatin structure in cancer nuclei by measuring A R T IC LE Received: June 23, 2017; Revised: December 6, 2017; Accepted: March 13, 2018 © The Author(s) 2018. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/ licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com 1400 JNCI J Natl Cancer Inst (2018) 110(12): djy063 doi: 10.1093/jnci/djy063 First published online April 18, 2018

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, .68-2.9, for prostate carcinoma; and 1.9, 1.1-3 0.1, 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. Copyright (C) The Author(s). Published by Elsevier Ltd.

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.

Abnormalities of cellular DNA content have long been associated with tumorigenesis. Such abnormalities were originally implicated in cancer development over 100 years ago by German biologist Boveri (1), and during the last years it has become clear that cellular aneuploidy is a driving force in the process of 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.

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.