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Image-based deep learning models are used to extract new information from standard hematoxylin and eosin pathology slides; however, biological interpretation of the features detected by artificial intelligence (AI) remains a challenge. High-grade serous carcinoma of the ovary (HGSC) is characterized by aggressive behavior and chemotherapy resistance, but also exhibits striking variability in outcome. Our understanding of this disease is limited, partly due to considerable tumor heterogeneity. We previously trained an AI model to identify HGSC tumor regions that are highly associated with outcome status but are indistinguishable by conventional morphologic methods. Here, we applied spatially resolved transcriptomics to further profile the AI-identified tumor regions in 16 patients (8 per outcome group) and identify molecular features related to disease outcome in patients who underwent primary debulking surgery and platinum-based chemotherapy. We examined formalin-fixed paraffin-embedded tissue from (1) regions identified by the AI model as highly associated with short or extended chemotherapy response, and (2) background tumor regions (not identified by the AI model as highly associated with outcome status) from the same tumors. We show that the transcriptomic profiles of AI-identified regions are more distinct than background regions from the same tumors, are superior in predicting outcome, and differ in several pathways including those associated with chemoresistance in HGSC. Further, we find that poor outcome and good outcome regions are enriched by different tumor subpopulations, suggesting distinctive interaction patterns. In summary, our work presents proof of concept that AI-guided spatial transcriptomic analysis improves recognition of biologic features relevant to patient outcomes., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

High-grade extrauterine serous carcinoma (HGSC) is an aggressive tumor with high rates of recurrence, frequent chemotherapy resistance, and overall 5-year survival of less than 50%. Beyond determining and confirming the diagnosis itself, pathologist review of histologic slides provides no prognostic or predictive information, which is in sharp contrast to almost all other carcinoma types. Deep-learning based image analysis has recently been able to predict outcome and/or identify morphology-based representations of underlying molecular alterations in other tumor types, such as colorectal carcinoma, lung carcinoma, breast carcinoma, and melanoma. Using a carefully stratified HGSC patient cohort consisting of women (n = 30) with similar presentations who experienced very different treatment responses (platinum free intervals of either ≤ 6 months or ≥ 18 months), we used whole slide images (WSI, n = 205) to train a convolutional neural network. The neural network was trained, in three steps, to identify morphologic regions (digital biomarkers) that are highly associating with one or the other treatment response group. We tested the classifier using a separate 22 slide test set, and 18/22 slides were correctly classified. We show that a neural network based approach can discriminate extremes in patient response to primary platinum-based chemotherapy with high sensitivity (73%) and specificity (91%). These proof-of-concept results are novel, because for the first time, prospective prognostic information is identified specifically within HGSC tumor morphology., (© 2021. The Author(s).)

Background: The incidence of venous thromboembolism (VTE) is 1-2/1000 individuals. Patients with cancer, especially during chemotherapy, are at enhanced risk, but real-world data on factors associated with VTE events are still scarce., Aim: The aim of this retrospective study was to survey the incidence of VTE based on a large hospital database, and to identify comorbidities and features associated with VTE events. We focused on cancer-related VTE events and on factors indicating increased VTE risk during chemotherapy., Methods: The cohort included patients treated at Turku University Hospital during years 2005-2013. Health information was derived and analysed from multiple electronic databases. The diagnoses of VTE and all comorbidities, including type of cancer, were based on International Classification of Diseases 10th Revision coding. For further analysis, we focused on 16 common types of cancers treated with chemotherapy. Age, gender, surgery, radiotherapy, distant metastasis, available laboratory values and platinum-based chemotherapy were evaluated for VTE group, and associations were estimated by Cox regression analyses., Results: The entire database contained information from 495 089 patients, of whom 5452 (1.1%) had a VTE diagnosis. Among individuals with VTE, 1437 (26.4%) had diagnosis of coronary heart disease and 1467 (26.9%) had cancer diagnosis. Among 7778 patients with cancer treated with chemotherapy, 282 (3.6%) had a VTE, platinum-based chemotherapy being a major risk factor (HR 1.77, 95% CI 1.40 to 2.24, p<0.001). In multivariate analysis, elevated blood neutrophil counts (>3.25×10 9  cells/L, HR 1.96, 95% CI 1.33 to 2.89, p<0.001) and plasma creatinine (>62.5 μmol/L; HR 1.60, 95% CI 1.21 to 2.13, p=0.001) values were independent indicators of increased VTE risk during chemotherapy., Conclusions: Longitudinal electronic health record analysis provides a powerful tool to gather meaningful real-world information to study clinical associations, like comorbidities, and to identify markers associated with VTE. The combination of various clinical and laboratory variables could be used for VTE risk evaluation and targeted prevention., Competing Interests: Competing interests: None declared.

The cancer stem cell marker aldehyde dehydrogenase 1 (ALDH1) associates with treatment resistance and adverse outcome in several human cancers. We studied ALDH1 expression in rectal cancer, with special emphasis on its association with treatment response and disease outcome. Immunohistochemical staining for ALDH1 was conducted for 64 biopsies and 209 operative samples from rectal cancer patients treated with short- (n = 89) or long-course (n = 46) (chemo)radiotherapy plus surgery, or with surgery only (n = 74). The staining results were compared to clinicopathological variables, tumor regression grade (TRG) and disease outcome. Nuclear β-catenin expression pattern was analyzed from 197 operative samples. Positive ALDH1 expression was present in 149 operative samples (71%), correlating with deficient nuclear β-catenin regulation (P = .018). In a pairwise comparison of respective biopsy and operative samples, ALDH1 expression remained stable or increased after preoperative (chemo)radiotherapy in most of the cases, while it decreased in few cases only (P = .02 for positive/negative category; P <.001 for intensity). ALDH1 expression did not, however, relate to tumor regression grade. In node-negative rectal cancer, ALDH1 expression was an independent predictor of short disease-free and disease-specific survival (P = .044; P = .049), specifically among patients treated with adjuvant chemotherapy. We conclude that ALDH1 associates with deregulated β-catenin signaling, supporting the role of ALDH1 in rectal cancer stemness. ALDH1 expression relates to poor outcome in early stage rectal cancer, a group where new prognostic tools are particularly needed, and may indicate chemo- and radioresistance., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Contractile myofibroblasts are responsible for remodeling of extracellular matrix during wound healing; however, their continued activity results in various fibrocontractive diseases. Conversion of fibroblasts into myofibroblasts is induced by transforming growth factor-beta1 (TGF-beta1) and is hallmarked by the neo-expression of alpha-smooth muscle actin (alpha-SMA), a commonly used myofibroblast marker. Moreover, myofibroblast differentiation and acquisition of the contractile phenotype involves functionally important alterations in the expression of actin-organizing proteins. We investigated whether myofibroblast differentiation is accompanied by changes in the expression of palladin, a cytoskeletal protein that controls stress fiber integrity. Palladin is expressed as several isoforms, including major 3Ig (90 kDa) and 4Ig (140 kDa) forms that differ in their N-terminal sequence. Expression of the 4Ig isoform is strongly induced in fibroblast stress fibers upon TGF-beta1 treatment preceding alpha-SMA upregulation. TGF-beta1 induced upregulation of palladin is mediated both by Smad and mitogen-activated protein kinase pathways. Furthermore, palladin 4Ig-isoform is co-expressed with alpha-SMA in vivo in experimental rat wounds and in human myofibroblast-containing lesions. Taken together these results identify palladin 4Ig as a novel marker of myofibroblast conversion in vitro and in vivo. They also provide for the first time information about the signaling cascades involved in the regulation of palladin expression.