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  1. Article: Biological insights and novel biomarker discovery through deep learning approaches in breast cancer histopathology.

    Mandair, Divneet / Reis-Filho, Jorge S / Ashworth, Alan

    NPJ breast cancer

    2023  Volume 9, Issue 1, Page(s) 21

    Abstract: Breast cancer remains a highly prevalent disease with considerable inter- and intra-tumoral heterogeneity complicating prognostication and treatment decisions. The utilization and depth of genomic, transcriptomic and proteomic data for cancer has ... ...

    Abstract Breast cancer remains a highly prevalent disease with considerable inter- and intra-tumoral heterogeneity complicating prognostication and treatment decisions. The utilization and depth of genomic, transcriptomic and proteomic data for cancer has exploded over recent times and the addition of spatial context to this information, by understanding the correlating morphologic and spatial patterns of cells in tissue samples, has created an exciting frontier of research, histo-genomics. At the same time, deep learning (DL), a class of machine learning algorithms employing artificial neural networks, has rapidly progressed in the last decade with a confluence of technical developments - including the advent of modern graphic processing units (GPU), allowing efficient implementation of increasingly complex architectures at scale; advances in the theoretical and practical design of network architectures; and access to larger datasets for training - all leading to sweeping advances in image classification and object detection. In this review, we examine recent developments in the application of DL in breast cancer histology with particular emphasis of those producing biologic insights or novel biomarkers, spanning the extraction of genomic information to the use of stroma to predict cancer recurrence, with the aim of suggesting avenues for further advancing this exciting field.
    Language English
    Publishing date 2023-04-06
    Publishing country United States
    Document type Journal Article ; Review
    ISSN 2374-4677
    ISSN 2374-4677
    DOI 10.1038/s41523-023-00518-1
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Large language models should be used as scientific reasoning engines, not knowledge databases.

    Truhn, Daniel / Reis-Filho, Jorge S / Kather, Jakob Nikolas

    Nature medicine

    2023  Volume 29, Issue 12, Page(s) 2983–2984

    MeSH term(s) Language ; Problem Solving ; Data Management
    Language English
    Publishing date 2023-10-16
    Publishing country United States
    Document type Letter
    ZDB-ID 1220066-9
    ISSN 1546-170X ; 1078-8956
    ISSN (online) 1546-170X
    ISSN 1078-8956
    DOI 10.1038/s41591-023-02594-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 4212: Show issues Location:
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  3. Article ; Online: Overcoming the challenges to implementation of artificial intelligence in pathology.

    Reis-Filho, Jorge S / Kather, Jakob Nikolas

    Journal of the National Cancer Institute

    2023  Volume 115, Issue 6, Page(s) 608–612

    Abstract: Pathologists worldwide are facing remarkable challenges with increasing workloads and lack of time to provide consistently high-quality patient care. The application of artificial intelligence (AI) to digital whole-slide images has the potential of ... ...

    Abstract Pathologists worldwide are facing remarkable challenges with increasing workloads and lack of time to provide consistently high-quality patient care. The application of artificial intelligence (AI) to digital whole-slide images has the potential of democratizing the access to expert pathology and affordable biomarkers by supporting pathologists in the provision of timely and accurate diagnosis as well as supporting oncologists by directly extracting prognostic and predictive biomarkers from tissue slides. The long-awaited adoption of AI in pathology, however, has not materialized, and the transformation of pathology is happening at a much slower pace than that observed in other fields (eg, radiology). Here, we provide a critical summary of the developments in digital and computational pathology in the last 10 years, outline key hurdles and ways to overcome them, and provide a perspective for AI-supported precision oncology in the future.
    MeSH term(s) Humans ; Artificial Intelligence ; Neoplasms/diagnosis ; Neoplasms/pathology ; Precision Medicine/methods ; Medical Oncology/methods ; Prognosis
    Language English
    Publishing date 2023-03-16
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2992-0
    ISSN 1460-2105 ; 0027-8874 ; 0198-0157
    ISSN (online) 1460-2105
    ISSN 0027-8874 ; 0198-0157
    DOI 10.1093/jnci/djad048
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  4. Article ; Online: Accelerating Drug Development Using Spatial Multi-omics.

    Goodwin, Richard J A / Platz, Stefan J / Reis-Filho, Jorge S / Barry, Simon T

    Cancer discovery

    2024  Volume 14, Issue 4, Page(s) 620–624

    Abstract: Summary: Spatial biology approaches enabled by innovations in imaging biomarker platforms and artificial intelligence-enabled data integration and analysis provide an assessment of patient and disease heterogeneity at ever-increasing resolution. The ... ...

    Abstract Summary: Spatial biology approaches enabled by innovations in imaging biomarker platforms and artificial intelligence-enabled data integration and analysis provide an assessment of patient and disease heterogeneity at ever-increasing resolution. The utility of spatial biology data in accelerating drug programs, however, requires balancing exploratory discovery investigations against scalable and clinically applicable spatial biomarker analysis.
    MeSH term(s) Humans ; Artificial Intelligence ; Multiomics ; Drug Development ; Biomarkers
    Chemical Substances Biomarkers
    Language English
    Publishing date 2024-04-05
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2625242-9
    ISSN 2159-8290 ; 2159-8274
    ISSN (online) 2159-8290
    ISSN 2159-8274
    DOI 10.1158/2159-8290.CD-24-0101
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 6916: Show issues Location:
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  5. Article: A framework for artificial intelligence in cancer research and precision oncology.

    Perez-Lopez, Raquel / Reis-Filho, Jorge S / Kather, Jakob Nikolas

    NPJ precision oncology

    2023  Volume 7, Issue 1, Page(s) 43

    Language English
    Publishing date 2023-05-17
    Publishing country England
    Document type Editorial
    ISSN 2397-768X
    ISSN 2397-768X
    DOI 10.1038/s41698-023-00383-y
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Clinical Trials and Digital Pathology-Toward Quantitative Therapeutic Immunohistochemistry and Tissue Hybridization.

    Salto-Tellez, Manuel / Reis-Filho, Jorge S

    JAMA oncology

    2022  Volume 9, Issue 2, Page(s) 168–169

    MeSH term(s) Humans ; Immunohistochemistry ; Image Processing, Computer-Assisted
    Language English
    Publishing date 2022-12-14
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
    ISSN 2374-2445
    ISSN (online) 2374-2445
    DOI 10.1001/jamaoncol.2022.5826
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Pathogenesis of Triple-Negative Breast Cancer.

    Derakhshan, Fatemeh / Reis-Filho, Jorge S

    Annual review of pathology

    2022  Volume 17, Page(s) 181–204

    Abstract: Triple-negative breast cancer (TNBC) encompasses a heterogeneous group of fundamentally different diseases with different histologic, genomic, and immunologic profiles, which are aggregated under this term because of their lack of estrogen receptor, ... ...

    Abstract Triple-negative breast cancer (TNBC) encompasses a heterogeneous group of fundamentally different diseases with different histologic, genomic, and immunologic profiles, which are aggregated under this term because of their lack of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 expression. Massively parallel sequencing and other omics technologies have demonstrated the level of heterogeneity in TNBCs and shed light into the pathogenesis of this therapeutically challenging entity in breast cancer. In this review, we discuss the histologic and molecular classifications of TNBC, the genomic alterations these different tumor types harbor, and the potential impact of these alterations on the pathogenesis of these tumors. We also explore the role of the tumor microenvironment in the biology of TNBCs and its potential impact on therapeutic response. Dissecting the biology and understanding the therapeutic dependencies of each TNBC subtype will be essential to delivering on the promise of precision medicine for patients with triple-negative disease.
    MeSH term(s) Genomics ; High-Throughput Nucleotide Sequencing ; Humans ; Receptors, Estrogen/genetics ; Receptors, Estrogen/metabolism ; Receptors, Estrogen/therapeutic use ; Triple Negative Breast Neoplasms/genetics ; Triple Negative Breast Neoplasms/metabolism ; Triple Negative Breast Neoplasms/therapy ; Tumor Microenvironment/genetics
    Chemical Substances Receptors, Estrogen
    Language English
    Publishing date 2022-01-24
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Review
    ZDB-ID 2227429-7
    ISSN 1553-4014 ; 1553-4006
    ISSN (online) 1553-4014
    ISSN 1553-4006
    DOI 10.1146/annurev-pathol-042420-093238
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  8. Article: Rare subtypes of triple negative breast cancer: Current understanding and future directions.

    Thomas, Alexandra / Reis-Filho, Jorge S / Geyer, Charles E / Wen, Hannah Y

    NPJ breast cancer

    2023  Volume 9, Issue 1, Page(s) 55

    Abstract: Rare subtypes of triple-negative breast cancers (TNBC) are a heterogenous group of tumors, comprising 5-10% of all TNBCs. Despite accounting for an absolute number of cases in aggregate approaching that of other less common, but well studied solid tumors, ...

    Abstract Rare subtypes of triple-negative breast cancers (TNBC) are a heterogenous group of tumors, comprising 5-10% of all TNBCs. Despite accounting for an absolute number of cases in aggregate approaching that of other less common, but well studied solid tumors, rare subtypes of triple-negative disease remain understudied. Low prevalence, diagnostic challenges and overlapping diagnoses have hindered consistent categorization of these breast cancers. Here we review epidemiology, histology and clinical and molecular characteristics of metaplastic, triple-negative lobular, apocrine, adenoid cystic, secretory and high-grade neuroendocrine TNBCs. Medullary pattern invasive ductal carcinoma no special type, which until recently was a considered a distinct subtype, is also discussed. With this background, we review how applying biological principals often applied to study TNBC no special type could improve our understanding of rare TNBCs. These could include the utilization of targeted molecular approaches or disease agnostic tools such as tumor mutational burden or germline mutation-directed treatments. Burgeoning data also suggest that pathologic response to neoadjuvant therapy and circulating tumor DNA have value in understanding rare subtypes of TNBC. Finally, we discuss a framework for advancing disease-specific knowledge in this space. While the conduct of randomized trials in rare TNBC subtypes has been challenging, re-envisioning trial design and technologic tools may offer new opportunities. These include embedding rare TNBC subtypes in umbrella studies of rare tumors, retrospective review of contemporary trials, prospective identification of patients with rare TNBC subtypes entering on clinical trials and querying big data for outcomes of patients with rare breast tumors.
    Language English
    Publishing date 2023-06-23
    Publishing country United States
    Document type Journal Article ; Review
    ISSN 2374-4677
    ISSN 2374-4677
    DOI 10.1038/s41523-023-00554-x
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Metaplastic Breast Cancer: Current Understanding and Future Directions.

    Thomas, Alexandra / Douglas, Emily / Reis-Filho, Jorge S / Gurcan, Metin N / Wen, Hannah Y

    Clinical breast cancer

    2023  Volume 23, Issue 8, Page(s) 775–783

    Abstract: Metaplastic breast cancers (MBC) encompass a group of highly heterogeneous tumors which share the ability to differentiate into squamous, mesenchymal or neuroectodermal components. While often termed rare breast tumors, given the relatively high ... ...

    Abstract Metaplastic breast cancers (MBC) encompass a group of highly heterogeneous tumors which share the ability to differentiate into squamous, mesenchymal or neuroectodermal components. While often termed rare breast tumors, given the relatively high prevalence of breast cancer, they are seen with some frequency. Depending upon the definition applied, MBC represents 0.2% to 1% of breast cancers diagnosed in the United States. Less is known about the epidemiology of MBC globally, though a growing number of reports are providing information on this. These tumors are often more advanced at presentation relative to breast cancer broadly. While more indolent subtypes exist, the majority of MBC subtypes are associated with inferior survival. MBC is most commonly of triple-negative phenotype. In less common hormone receptor positive MBCs, hormone receptor status appears not to be prognostic. In contrast, relatively rare HER2-positive MBCs are associated with superior outcomes. Multiple potentially targetable molecular features are overrepresented in MBC including DNA repair deficiency signatures and PIK3/AKT/mTOR and WNT pathways alterations. Data on the prevalence of targets for novel antibody-drug conjugates is also emerging. While chemotherapy appears to be less active in MBC than in other breast cancer subtypes, efficacy is seen in some MBCs. Disease-specific trials, as well as reports of exceptional responses, may provide clues for novel approaches to this often hard-to-treat breast cancer. Strategies which harness newer research tools, such as large data and artificial intelligence hold the promise of overcoming historic barriers to the study of uncommon tumors and could markedly advance disease-specific understanding in MBC.
    MeSH term(s) Humans ; Female ; Breast Neoplasms/epidemiology ; Breast Neoplasms/therapy ; Breast Neoplasms/genetics ; Artificial Intelligence ; Biomarkers, Tumor/metabolism ; Prognosis ; Wnt Signaling Pathway
    Chemical Substances Biomarkers, Tumor
    Language English
    Publishing date 2023-04-19
    Publishing country United States
    Document type Journal Article ; Review ; Research Support, N.I.H., Extramural
    ZDB-ID 2106734-X
    ISSN 1938-0666 ; 1526-8209
    ISSN (online) 1938-0666
    ISSN 1526-8209
    DOI 10.1016/j.clbc.2023.04.004
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    Zs.MO 498: Show issues

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  10. Article: Post-therapy emergence of an NBN reversion mutation in a patient with pancreatic acinar cell carcinoma.

    Pelster, Meredith S / Silverman, Ian M / Schonhoft, Joseph D / Johnson, Adrienne / Selenica, Pier / Ulanet, Danielle / Rimkunas, Victoria / Reis-Filho, Jorge S

    NPJ precision oncology

    2024  Volume 8, Issue 1, Page(s) 82

    Abstract: Pancreatic acinar cell carcinoma (PACC) is a rare form of pancreatic cancer that commonly harbors targetable alterations, including activating fusions in the MAPK pathway and loss-of-function (LOF) alterations in DNA damage response/homologous ... ...

    Abstract Pancreatic acinar cell carcinoma (PACC) is a rare form of pancreatic cancer that commonly harbors targetable alterations, including activating fusions in the MAPK pathway and loss-of-function (LOF) alterations in DNA damage response/homologous recombination DNA repair-related genes. Here, we describe a patient with PACC harboring both somatic biallelic LOF of NBN and an activating NTRK1 fusion. Upon disease progression following 13 months of treatment with folinic acid, fluorouracil, irinotecan, and oxaliplatin (FOLFIRINOX), genomic analysis of a metastatic liver biopsy revealed the emergence of a novel reversion mutation restoring the reading frame of NBN. To our knowledge, genomic reversion of NBN has not been previously reported as a resistance mechanism in any tumor type. The patient was treated with, but did not respond to, targeted treatment with a selective NTRK inhibitor. This case highlights the complex but highly actionable genomic landscape of PACC and underlines the value of genomic profiling of rare tumor types such as PACC.
    Language English
    Publishing date 2024-04-01
    Publishing country England
    Document type Journal Article
    ISSN 2397-768X
    ISSN 2397-768X
    DOI 10.1038/s41698-024-00497-x
    Database MEDical Literature Analysis and Retrieval System OnLINE

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