Prognostic and therapeutic implication of molecular classification including L1CAM expression in high-risk endometrial cancer.

Introduction: The role of molecular classification and L1CAM in high-risk endometrial cancer is uncertain. We aimed to determine the association of molecular profiling and L1CAM with patterns of relapse and survival.

Material And Methods: This retrospective cohort study included patients referred to Department for Gynecologic Oncology, Oslo University Hospital between January 1, 2006 and December 31, 2017.

Mirror, mirror on my screen: Focus on self-presentation on social media is associated with perfectionism and disordered eating among adolescents. Results from the "LifeOnSoMe"-study.

Background: Social media use, perfectionism, and disordered eating have all increased over the last decades. Some studies indicate that there is a relationship between self-presentation behaviors and being exposed to others' self-presentation on social media, and disordered eating. Studies also show that the relationship between focus on self-presentation and highly visual social media is stronger than for non-visual social media, hence facilitating upward social comparison.

Deep learning for automated scoring of immunohistochemically stained tumour tissue sections - Validation across tumour types based on patient outcomes.

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.

DNA ploidy and PTEN as biomarkers for predicting aggressive disease in prostate cancer patients under active surveillance.

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.

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.

A Thermoplasmonic Approach for Investigating Plasma Membrane Repair in Living Cells and Model Membranes.

The cell membrane is crucial for cell survival, and ensuring its integrity is essential as the cell experiences injuries throughout its entire life cycle. To prevent damage to the membrane, cells have developed efficient plasma membrane repair mechanisms. These repair mechanisms can be studied by combining confocal microscopy and nanoscale thermoplasmonics to identify and investigate the role of key proteins, such as annexins, involved in surface repair in living cells and membrane model systems.

Multiplex analysis of intratumoural immune infiltrate and prognosis in patients with stage II-III colorectal cancer from the SCOT and QUASAR 2 trials: a retrospective analysis.

Background: Tumour-infiltrating CD8 cytotoxic T cells confer favourable prognosis in colorectal cancer. The added prognostic value of other infiltrating immune cells is unclear and so we sought to investigate their prognostic value in two large clinical trial cohorts.

Methods: We used multiplex immunofluorescent staining of tissue microarrays to assess the densities of CD8, CD20, FoxP3, and CD68 cells in the intraepithelial and intrastromal compartments from tumour samples of patients with stage II-III colorectal cancer from the SCOT trial (ISRCTN59757862), which examined 3 months versus 6 months of adjuvant oxaliplatin-based chemotherapy, and from the QUASAR 2 trial (ISRCTN45133151), which compared adjuvant capecitabine with or without bevacizumab.

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.

Accounting for intensity variation in image analysis of large-scale multiplexed clinical trial datasets.

Multiplex immunofluorescence (mIF) imaging can provide comprehensive quantitative and spatial information for multiple immune markers for tumour immunoprofiling. However, application at scale to clinical trial samples sourced from multiple institutions is challenging due to pre-analytical heterogeneity. This study reports an analytical approach to the largest multi-parameter immunoprofiling study of clinical trial samples to date.

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.

The Essentials of Multiomics.

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.

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 ( = 253). It was validated according to a predefined protocol in an independent RP cohort ( = 259), alone and by measuring its prognostic value in combination with DNA ploidy status determined by ML-based image cytometry.

Automated assessment of CD8 T-lymphocytes and stroma fractions complement conventional staging of colorectal 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.

Stress-Mediated Reprogramming of Prostate Cancer One-Carbon Cycle Drives Disease Progression.

One-carbon (1C) metabolism has a key role in metabolic programming with both mitochondrial (m1C) and cytoplasmic (c1C) components. Here we show that activating transcription factor 4 (ATF4) exclusively activates gene expression involved in m1C, but not the c1C cycle in prostate cancer cells. This includes activation of methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) expression, the central player in the m1C cycle.

Stromal composition predicts recurrence of early rectal cancer after local excision.

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.

Breast cancer metastasis: immune profiling of lymph nodes reveals exhaustion of effector T cells and immunosuppression.

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.

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.

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.

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.