Multilingual RECIST classification of radiology reports using supervised learning.

OBJECTIVES The objective of this study is the exploration of Artificial Intelligence and Natural Language Processing techniques to support the automatic assignment of the four Response Evaluation Criteria in Solid Tumors (RECIST) scales based on radiology reports. We also aim at evaluating how languages and institutional specificities of Swiss teaching hospitals are likely to affect the quality of the classification in French and German languages. METHODS In our approach, 7 machine learning methods were evaluated to establish a strong baseline. Then, robust models were built, fine-tuned according to the language (French and German), and compared with the expert annotation. RESULTS The best strategies yield average F1-scores of 90% and 86% respectively for the 2-classes (Progressive/Non-progressive) and the 4-classes (Progressive Disease, Stable Disease, Partial Response, Complete Response) RECIST classification tasks. CONCLUSIONS These results are competitive with the manual labeling as measured by Matthew's correlation coefficient and Cohen's Kappa (79% and 76%). On this basis, we confirm the capacity of specific models to generalize on new unseen data and we assess the impact of using Pre-trained Language Models (PLMs) on the accuracy of the classifiers

Pathway level subtyping identifies a slow-cycling biological phenotype associated with poor clinical outcomes in colorectal cancer

Molecular stratification using gene-level transcriptional data has identified subtypes with distinctive genotypic and phenotypic traits, as exemplified by the consensus molecular subtypes (CMS) in colorectal cancer (CRC). Here, rather than gene-level data, we make use of gene ontology and biological activation state information for initial molecular class discovery. In doing so, we defined three pathway-derived subtypes (PDS) in CRC: PDS1 tumors, which are canonical/LGR5+ stem-rich, highly proliferative and display good prognosis; PDS2 tumors, which are regenerative/ANXA1+ stem-rich, with elevated stromal and immune tumor microenvironmental lineages; and PDS3 tumors, which represent a previously overlooked slow-cycling subset of tumors within CMS2 with reduced stem populations and increased differentiated lineages, particularly enterocytes and enteroendocrine cells, yet display the worst prognosis in locally advanced disease. These PDS3 phenotypic traits are evident across numerous bulk and single-cell datasets, and demark a series of subtle biological states that are currently under-represented in pre-clinical models and are not identified using existing subtyping classifiers

Oncogenic BRAF, unrestrained by TGFβ-receptor signalling, drives right-sided colonic tumorigenesis

Right-sided (proximal) colorectal cancer (CRC) has a poor prognosis and a distinct mutational profile, characterized by oncogenic BRAF mutations and aberrations in mismatch repair and TGFβ signalling. Here, we describe a mouse model of right-sided colon cancer driven by oncogenic BRAF and loss of epithelial TGFβ-receptor signalling. The proximal colonic tumours that develop in this model exhibit a foetal-like progenitor phenotype (Ly6a/Sca1+) and, importantly, lack expression of Lgr5 and its associated intestinal stem cell signature. These features are recapitulated in human BRAF-mutant, right-sided CRCs and represent fundamental differences between left- and right-sided disease. Microbial-driven inflammation supports the initiation and progression of these tumours with foetal-like characteristics, consistent with their predilection for the microbe-rich right colon and their antibiotic sensitivity. While MAPK-pathway activating mutations drive this foetal-like signature via ERK-dependent activation of the transcriptional coactivator YAP, the same foetal-like transcriptional programs are also initiated by inflammation in a MAPK-independent manner. Importantly, in both contexts, epithelial TGFβ-receptor signalling is instrumental in suppressing the tumorigenic potential of these foetal-like progenitor cells

Dynamic and adaptive cancer stem cell population admixture in colorectal neoplasia

Intestinal homeostasis is underpinned by LGR5+ve crypt-base columnar stem cells (CBCs), but following injury, dedifferentiation results in the emergence of LGR5−ve regenerative stem cell populations (RSCs), characterized by fetal transcriptional profiles. Neoplasia hijacks regenerative signaling, so we assessed the distribution of CBCs and RSCs in mouse and human intestinal tumors. Using combined molecular-morphological analysis, we demonstrate variable expression of stem cell markers across a range of lesions. The degree of CBC-RSC admixture was associated with both epithelial mutation and microenvironmental signaling disruption and could be mapped across disease molecular subtypes. The CBC-RSC equilibrium was adaptive, with a dynamic response to acute selective pressure, and adaptability was associated with chemoresistance. We propose a fitness landscape model where individual tumors have equilibrated stem cell population distributions along a CBC-RSC phenotypic axis. Cellular plasticity is represented by position shift along this axis and is influenced by cell-intrinsic, extrinsic, and therapeutic selective pressures

Oncogenic BRAF, unrestrained by TGFβ-receptor signalling, drives right-sided colonic tumorigenesis

Right-sided colorectal cancer (rCRC) has a different mutational spectrum to the left-sided counterpart. Here the authors develop a mouse model of rCRC that recapitulates human BRAF-mutant rCRC and show that loss of TGFβ-receptor signalling and inflammation induce the development of colonic tumours with a foetal-like phenotype

Erratum: Dynamic and adaptive cancer stem cell population admixture in colorectal neoplasia (Cell Stem Cell (2022) 29(8) (1213–1228.e8), (S1934590922003034), (10.1016/j.stem.2022.07.008))

(Cell Stem Cell 29, 1213–1228, August 4, 2022) In the version of our manuscript that was accepted by the Cell Press editorial office, we mistakenly included a misspelling of the first author's surname. We did not catch this unfortunate error during the production process, and the manuscript published with the error included. We have now corrected the spelling, and the correct author list appears here and in the online version of our article. We apologize for the oversight