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Objectives: Accurate risk stratification of patients with stage II and III colorectal cancer (CRC) prior to treatment selection enables limited health resources to be efficiently allocated to patients who are likely to benefit from adjuvant chemotherapy. We aimed to investigate the cost-effectiveness of a recently developed deep learning-based prognostic method, Histotyping, from the perspective of the Norwegian healthcare system. Methods: Two partitioned survival models were developed to assess the cost-effectiveness of Histotyping for two treatment cohorts: patients with CRC stage II and III. For each of the two cohorts, Histotyping was used for risk stratification to assign adjuvant chemotherapy and was compared with the standard of care (SOC) (adjuvant chemotherapy to all patients). Health outcomes measured in the model were quality-adjusted life years (QALYs) and life years (LYs) gained. Deterministic and probabilistic sensitivity analyses were performed to determine the impact of uncertainty. Scenario analyses were performed to assess the impact of the parameters with the greatest uncertainty. Results: Risk-stratifying patients with CRC stage II and III using Histotyping was dominant (less costly and more effective) compared to SOC. In patients with CRC stage II, the net monetary benefit of Histotyping was 270,934 Norwegian kroners (NOK) (year of valuation is 2021), and the net health benefit of Histotyping was 0.99. In stage III, the net monetary benefit of Histotyping was 195,419 NOK, and the net health benefit of Histotyping was 0.71. Conclusions: Risk-stratifying patients with CRC using Histotyping prior to the administration of adjuvant chemotherapy is likely to be a cost-effective strategy in Norway. [ABSTRACT FROM AUTHOR]

Aims: After local excision of early rectal cancer, definitive lymph node status is not available. An alternative means for accurate assessment of recurrence risk is required to determine the most appropriate subsequent management. Currently used measures are suboptimal. We assess three measures of tumour stromal content to determine their predictive value after local excision in a well‐characterised cohort of rectal cancer patients without prior radiotherapy. Methods and results: A total of 143 patients were included. Haematoxylin and eosin (H&E) sections were scanned for (i) deep neural network (DNN, a machine‐learning algorithm) tumour segmentation into compartments including desmoplastic stroma and inflamed stroma; and (ii) digital assessment of tumour stromal fraction (TSR) and optical DNA ploidy analysis. 3′ mRNA sequencing was performed to obtain gene expression data from which stromal and immune scores were calculated using the ESTIMATE method. Full results were available for 139 samples and compared with disease‐free survival. All three methods were prognostic. Most strongly predictive was a DNN‐determined ratio of desmoplastic to inflamed stroma >5.41 (P < 0.0001). A ratio of ESTIMATE stromal to immune score <1.19 was also predictive of disease‐free survival (P = 0.00051), as was stromal fraction >36.5% (P = 0.037). Conclusions: The DNN‐determined ratio of desmoplastic to inflamed ratio is a novel and powerful predictor of disease recurrence in locally excised early rectal cancer. It can be assessed on a single H&E section, so could be applied in routine clinical practice to improve the prognostic information available to patients and clinicians to inform the decision concerning further management. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. 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). 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. 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. • Clarifying the role of molecular classification in low/intermediate risk endometrial cancer. • Time to relapse and survival differed signifi cantly by molecular groups. • roboPatients with p53 abnormal tumors had poor outcome. • roboPatients with POLE mutated tumors had excellent survival. • pmMolecular classification is crucial for tailoring of adjuvant treatment. [ABSTRACT FROM AUTHOR]

Abstract The effect of poly(2-ethyl-butyl cyanoacrylate) nanoparticles containing the cytotoxic drug cabazitaxel was studied in three breast cancer cell lines and one basal-like patient-derived xenograft model grown in the mammary fat pad of immunodeficient mice. Nanoparticle-encapsulated cabazitaxel had a much better efficacy than similar concentrations of free drug in the basal-like patient-derived xenograft and resulted in complete remission of 6 out of 8 tumors, whereas free drug gave complete remission only with 2 out of 9 tumors. To investigate the different efficacies obtained with nanoparticle-encapsulated versus free cabazitaxel, mass spectrometry quantification of cabazitaxel was performed in mice plasma and selected tissue samples. Nanoparticle-encapsulated drug had a longer circulation time in blood. There was approximately a three times higher drug concentration in tumor tissue 24 h after injection, and two times higher 96 h after injection of nanoparticles with drug compared to the free drug. The tissue biodistribution obtained after 24 h using mass spectrometry analyses correlates well with biodistribution data obtained using IVIS® Spectrum in vivo imaging of nanoparticles labeled with the fluorescent substance NR668, indicating that these data also are representative for the nanoparticle distribution. Furthermore, immunohistochemistry was used to estimate infiltration of macrophages into the tumor tissue following injection of nanoparticle-encapsulated and free cabazitaxel. The higher infiltration of anti-tumorigenic versus pro-tumorigenic macrophages in tumors treated with the nanoparticles might also contribute to the improved effect obtained with the nanoparticle-encapsulated drug. Tumor infiltration of pro-tumorigenic macrophages was four times lower when using nanoparticles containing cabazitaxel than when using particles without drug, and we speculate that the very good therapeutic efficacy obtained with our cabazitaxel-containing particles may be due to their ability to reduce the level of pro-tumorigenic macrophages in the tumor. In summary, encapsulation of cabazitaxel in poly(2-ethyl-butyl cyanoacrylate) nanoparticles seems promising for treatment of breast cancer. Graphical abstract Unlabelled Image Highlights • Efficient treatment of patient-derived basal-like breast cancer xenograft with NPs. • PEBCA NPs with cabazitaxel gave complete remission of 6 out of 8 tumors. • NPs induced higher infiltration in tumors of anti- than pro-tumorigenic macrophages. • Infiltration of pro-tumorigenic macrophages strongly reduced by drug-containing NPs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Simple Summary: Molecular tissue-based prognostic biomarkers are anticipated to complement the current risk stratification systems in prostate cancer, but their manual assessment is subjective and time-consuming. Objective assessment of such biomarkers by machine learning-based methods could advance their adoption in a clinical workflow. PTEN and DNA ploidy status are well-studied biomarkers, which can provide clinically relevant information in prostate cancer at a low cost. Using a cohort of 253 patients who received radical prostatectomy, we developed a novel, fully-automated PTEN scoring in immunohistochemically-stained tissue slides, which could be used to assess PTEN status in a reliable and reproducible manner. In an independent validation cohort of 259 patients, automatically assessed PTEN status was significantly associated with time to biochemical recurrence after radical prostatectomy, and the combination of PTEN and DNA ploidy status further improved risk stratification. These results demonstrate the utility of machine learning in biomarker assessment. 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. [ABSTRACT FROM AUTHOR]