ORCID Profile
0000-0002-4411-4435
Current Organisation
The Institute of Cancer Research
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Publisher: American Association for Cancer Research (AACR)
Date: 03-04-2023
DOI: 10.1158/2159-8290.22536929.V1
Abstract: Gene lists used in this analysis.
Publisher: American Association for Cancer Research (AACR)
Date: 03-04-2023
DOI: 10.1158/2159-8290.22536941
Abstract: Details of molecular investigations performed on each s le. Clinical and outcome data is also presented. In all cases with a previous history of lung cancer, this was of squamous histology.
Publisher: American Association for Cancer Research (AACR)
Date: 03-04-2023
DOI: 10.1158/2159-8290.22536926.V1
Abstract: Pathway analysis comparing the illumina and Affymetrix datasets as described in Supplementary Methods.
Publisher: American Association for Cancer Research (AACR)
Date: 03-04-2023
DOI: 10.1158/2159-8290.22536935.V1
Abstract: Immune cell deconvolution from molecular data. Relative proportions of immune cell subtypes are predicted from gene expression data using the Danaher method, and from methylation data using methylCIBERSORT. Differences between progressive and regressive groups are reported here.
Publisher: American Association for Cancer Research (AACR)
Date: 03-04-2023
DOI: 10.1158/2159-8290.22536938.V1
Abstract: Markers used to define cell types in quantitative multiplex immunohistochemistry experiments.
Publisher: American Association for Cancer Research (AACR)
Date: 03-04-2023
DOI: 10.1158/2159-8290.22536941.V1
Abstract: Details of molecular investigations performed on each s le. Clinical and outcome data is also presented. In all cases with a previous history of lung cancer, this was of squamous histology.
Publisher: American Association for Cancer Research (AACR)
Date: 03-04-2023
DOI: 10.1158/2159-8290.22536944.V1
Abstract: Supplementary Methods
Publisher: American Association for Cancer Research (AACR)
Date: 03-04-2023
DOI: 10.1158/2159-8290.22536929
Abstract: Gene lists used in this analysis.
Publisher: Cold Spring Harbor Laboratory
Date: 07-03-2022
DOI: 10.1101/2022.03.05.482261
Abstract: Cancers occur across species. Understanding what is consistent and varies across species can provide new insights into cancer initiation and evolution, with significant implications for animal welfare and wildlife conservation. We built the pan-species cancer digital pathology atlas (PANCAD) and conducted the first pan-species study of computational comparative pathology using a supervised convolutional neural network algorithm trained on human s les. The artificial intelligence algorithm achieves high accuracy in measuring immune response through single-cell classification for two transmissible cancers (canine transmissible venereal tumour, 0.94 Tasmanian devil facial tumour disease, 0.88). Furthermore, in 18 other vertebrate species (mammalia=11, reptilia=4, aves=2, and hibia=1), accuracy (0.57-0.94) was influenced by cell morphological similarity preserved across different taxonomic groups, tumour sites, and variations in the immune compartment. A new metric, named morphospace overlap, was developed to guide veterinary pathologists towards rational deployment of this technology on new s les. This study provides the foundation and guidelines for transferring artificial intelligence technologies to veterinary pathology based on a new understanding of morphological conservation, which could vastly accelerate new developments in veterinary medicine and comparative oncology.
Publisher: American Association for Cancer Research (AACR)
Date: 03-04-2023
DOI: 10.1158/2159-8290.22536938
Abstract: Markers used to define cell types in quantitative multiplex immunohistochemistry experiments.
Publisher: American Association for Cancer Research (AACR)
Date: 03-04-2023
DOI: 10.1158/2159-8290.C.6548276.V1
Abstract: Abstract Before squamous cell lung cancer develops, precancerous lesions can be found in the airways. From longitudinal monitoring, we know that only half of such lesions become cancer, whereas a third spontaneously regress. Although recent studies have described the presence of an active immune response in high-grade lesions, the mechanisms underpinning clinical regression of precancerous lesions remain unknown. Here, we show that host immune surveillance is strongly implicated in lesion regression. Using bronchoscopic biopsies from human subjects, we find that regressive carcinoma i in situ /i lesions harbor more infiltrating immune cells than those that progress to cancer. Moreover, molecular profiling of these lesions identifies potential immune escape mechanisms specifically in those that progress to cancer: antigen presentation is impaired by genomic and epigenetic changes, i CCL27–CCR10 /i signaling is upregulated, and the immunomodulator i TNFSF9 /i is downregulated. Changes appear intrinsic to the carcinoma i in situ /i lesions, as the adjacent stroma of progressive and regressive lesions are transcriptomically similar. Significance: Immune evasion is a hallmark of cancer. For the first time, this study identifies mechanisms by which precancerous lesions evade immune detection during the earliest stages of carcinogenesis and forms a basis for new therapeutic strategies that treat or prevent early-stage lung cancer. i See related commentary by Krysan et al., p. 1442 /i . i This article is highlighted in the In This Issue feature, p. 1426 /i /
Publisher: American Association for Cancer Research (AACR)
Date: 03-04-2023
DOI: 10.1158/2159-8290.22536926
Abstract: Pathway analysis comparing the illumina and Affymetrix datasets as described in Supplementary Methods.
Publisher: American Association for Cancer Research (AACR)
Date: 10-2020
DOI: 10.1158/2159-8290.CD-19-1366
Abstract: Immune evasion is a hallmark of cancer. For the first time, this study identifies mechanisms by which precancerous lesions evade immune detection during the earliest stages of carcinogenesis and forms a basis for new therapeutic strategies that treat or prevent early-stage lung cancer. See related commentary by Krysan et al., p. 1442. This article is highlighted in the In This Issue feature, p. 1426
Publisher: American Association for Cancer Research (AACR)
Date: 03-04-2023
DOI: 10.1158/2159-8290.22536935
Abstract: Immune cell deconvolution from molecular data. Relative proportions of immune cell subtypes are predicted from gene expression data using the Danaher method, and from methylation data using methylCIBERSORT. Differences between progressive and regressive groups are reported here.
Publisher: Springer Science and Business Media LLC
Date: 27-05-2020
Publisher: American Association for Cancer Research (AACR)
Date: 03-04-2023
DOI: 10.1158/2159-8290.22536944
Abstract: Supplementary Methods
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Khalid AbdulJabbar.