Atezolizumab plus anthracycline-based chemotherapy in metastatic triple-negative breast cancer: the randomized, double-blind phase 2b ALICE trial

Røssevold, Andreas Hagen; Andresen, NK; Bjerre, Christina Annette; Gilje, Bjørnar; Jakobsen, Erik; Raj, Sunil Xavier; Falk, Ragnhild Sørum; Russnes, Hege G.; Jahr, Thea; Mathiesen, Randi R.; Lømo, Jon; Garred, Øystein; Chauhan, Sudhir Kumar; Lereim, Ragnhild Reehorst; Dunn, Claire; Naume, Bjørn; Kyte, Jon Amund

doi:10.1038/s41591-022-02126-1

Cited by 47 publications

(43 citation statements)

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“…“Atezolizumab plus anthracycline-based chemotherapy in metastatic triple-negative breast cancer: the randomized, double-blind phase 2b ALICE trial” in the style of Nature Medicine; original article 32…”

Section: Discussionmentioning

confidence: 99%

“…"Atezolizumab plus anthracycline-based chemotherapy in metastatic triple-negative breast cancer: the randomized, double-blind phase 2b ALICE trial" in the style of Nature Medicine; original article 32 Triple-negative breast cancer (TNBC) is a subtype of breast cancer that is aggressive and difficult to treat. Atezolizumab is a monoclonal antibody that targets the protein PD-L1, which is expressed on some cancer cells and can help them evade the immune system.…”

Section: Contributionsmentioning

confidence: 99%

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Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers

Gao

Howard

Markov

et al. 2022

Preprint

268

155

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Background: Large language models such as ChatGPT can produce increasingly realistic text, with unknown information on the accuracy and integrity of using these models in scientific writing. Methods: We gathered ten research abstracts from five high impact factor medical journals (n=50) and asked ChatGPT to generate research abstracts based on their titles and journals. We evaluated the abstracts using an artificial intelligence (AI) output detector, plagiarism detector, and had blinded human reviewers try to distinguish whether abstracts were original or generated. Results: All ChatGPT-generated abstracts were written clearly but only 8% correctly followed the specific journal's formatting requirements. Most generated abstracts were detected using the AI output detector, with scores (higher meaning more likely to be generated) of median [interquartile range] of 99.98% [12.73, 99.98] compared with very low probability of AI-generated output in the original abstracts of 0.02% [0.02, 0.09]. The AUROC of the AI output detector was 0.94. Generated abstracts scored very high on originality using the plagiarism detector (100% [100, 100] originality). Generated abstracts had a similar patient cohort size as original abstracts, though the exact numbers were fabricated. When given a mixture of original and general abstracts, blinded human reviewers correctly identified 68% of generated abstracts as being generated by ChatGPT, but incorrectly identified 14% of original abstracts as being generated. Reviewers indicated that it was surprisingly difficult to differentiate between the two, but that the generated abstracts were vaguer and had a formulaic feel to the writing. Conclusion: ChatGPT writes believable scientific abstracts, though with completely generated data. These are original without any plagiarism detected but are often identifiable using an AI output detector and skeptical human reviewers. Abstract evaluation for journals and medical conferences must adapt policy and practice to maintain rigorous scientific standards; we suggest inclusion of AI output detectors in the editorial process and clear disclosure if these technologies are used. The boundaries of ethical and acceptable use of large language models to help scientific writing remain to be determined.

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Section: Discussionmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers

Gao

Howard

Markov

et al. 2022

Preprint

268

155

View full text Add to dashboard Cite

show abstract

“…The present study shows that TT is also preclinically active in another type of otherwise ICI-resistant neoplastic disease, a high-grade, Myc-driven B-cell lymphoma. It should be noted that, in randomized clinical trials, TNBC patients seem to benefit from the association of ICIs with several chemotherapeutics including cyclophosphamide [ 17 ], whereas so far high-grade B-cell non-Hodgkin’s lymphoma patients have not been reported to receive a significant clinical benefit from ICIs, alone or in combinatorial therapies, apart from the case of mediastinal large-B cell lymphoma where the anti-PD-1 antibody is clinically active [ 18 ]. Thus, our data might be used to design future clinical trials in the non-Hodgkin’s lymphoma field.…”

Section: Discussionmentioning

confidence: 99%

Vinorelbine and Intermittent Cyclophosphamide Sensitize an Aggressive Myc-Driven B-Cell Lymphoma to Anti-PD-1 by an Immunological Memory Effective against Tumor Re-Challenge

Orecchioni

Falvo

Talarico

et al. 2023

JCM

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We have previously shown in triple-negative breast cancer (TNBC) models that a triple therapy (TT) including intermittent cyclophosphamide (C), vinorelbine (V), and anti-PD-1 activates antigen-presenting cells (APC) and generates stem like-T cells able to control local and metastatic tumor progression. In the present manuscript, we report the generation of a highly aggressive, anti-PD-1 resistant model of a high-grade, Myc-driven B-cell non-Hodgkin’s lymphoma (NHL) that can be controlled in vivo by TT but not by other chemotherapeutic agents, including cytarabine (AraC), platinum (P), and doxorubicin (D). The immunological memory elicited in tumor-bearing mice by TT (but not by other treatments) can effectively control NHL re-challenge even at very high inoculum doses. TT re-shaped the landscape of circulating innate NK cells and adaptive immune cells, including B and T cells, and significantly reduced exhausted CD4+ and CD8+ TIM3+PD-1+ T cells in the spleens of treated mice.

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“…The spatial distribution and interactions of Treg, PD‐L1 + , and CD8 + T cells in the TC, SA, or invasive margin (IM) of the TIME were demonstrated to be important prognostic markers for TNBC prognosis. In recent years, increasing attention has been given to changes in the TIME because of the effectiveness of immunomodulatory therapy targeting the PD‐L1/PD‐1 pathway in the treatment of various cancers 2,3,20 . Moreover, the m‐IHC method and quantitative multiplex immunofluorescence platform have become increasingly useful for the analysis of the spatial distribution and interactions of specific cells in the TIME.…”

Section: Discussionmentioning

confidence: 99%

“…2,3,20 Moreover, T A B L E 2 Differences of components of TIME between DM and non-DM, non-death and death, non-relapse and relapse in TNBC cases.…”

mentioning

confidence: 99%

Components of the tumor immune microenvironment based on m‐IHC correlate with prognosis and subtype of triple‐negative breast cancer

Lin,

Li,

Wang

et al. 2023

Cancer Medicine

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Background and AimThe spatial distribution and interactions of cells in the tumor immune microenvironment (TIME) might be related to the different responses of triple‐negative breast cancer (TNBC) to immunomodulators. The potential of multiplex IHC (m‐IHC) in evaluating the TIME has been reported, but the efficacy is insufficient. We aimed to research whether m‐IHC results could be used to reflect the TIME, and thus to predict prognosis and complement the TNBC subtyping system.MethodsThe clinical, imaging, and prognosis data for 86 TNBC patients were retrospectively reviewed. CD3, CD4, CD8, Foxp3, PD‐L1, and Pan‐CK markers were stained by m‐IHC. Particular cell spatial distributions and interactions in the TIME were evaluated with the HALO multispectral analysis platform. Then, we calculated the prognostic value of components of the TIME and their correlations with TNBC transcriptomic subtypes and MRI radiomic features reflecting TNBC subtypes.ResultsThe components of the TIME score were established by m‐IHC and demonstrated positive prognostic value for TNBC (p = 0.0047, 0.039, <0.0001 for DMFS, RFS, and OS). The score was calculated from several indicators, including Treg% in the tumor core (TC) or stromal area (SA), PD‐L1+ cell% in the SA, CD3 + cell% in the TC, and PD‐L1+/CD8+ cells in the invasive margin and SA. According to the TNBC subtyping system, a few TIME indicators were significantly different in different subtypes and significantly correlated with MRI radiomic features reflecting TNBC subtypes.ConclusionWe demonstrated that the m‐IHC‐based quantitative score and indicators related to the spatial distribution and interactions of cells in the TIME can aid in the accurate diagnosis of TNBC in terms of prognosis and classification.

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Atezolizumab plus anthracycline-based chemotherapy in metastatic triple-negative breast cancer: the randomized, double-blind phase 2b ALICE trial

Cited by 47 publications

References 45 publications

Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers

Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers

Vinorelbine and Intermittent Cyclophosphamide Sensitize an Aggressive Myc-Driven B-Cell Lymphoma to Anti-PD-1 by an Immunological Memory Effective against Tumor Re-Challenge

Components of the tumor immune microenvironment based on m‐IHC correlate with prognosis and subtype of triple‐negative breast cancer

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