Joshua McMichael | Washington University in St. Louis (original) (raw)
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Papers by Joshua McMichael
Science immunology, Apr 14, 2023
Neoantigens are tumor-specific peptide sequences resulting from sources such as somatic DNA mutat... more Neoantigens are tumor-specific peptide sequences resulting from sources such as somatic DNA mutations. Upon loading onto major histocompatibility complex (MHC) molecules, they can trigger recognition by T cells. Accurate neoantigen identification is thus critical for both designing cancer vaccines and predicting response to immunotherapies. Neoantigen identification and prioritization relies on correctly predicting whether the presenting peptide sequence can successfully induce an immune response. Because most somatic mutations are single-nucleotide variants, changes between wild-type and mutated peptides are typically subtle and require cautious interpretation. A potentially underappreciated variable in neoantigen prediction pipelines is the mutation position within the peptide relative to its anchor positions for the patient’s specific MHC molecules. Whereas a subset of peptide positions are presented to the T cell receptor for recognition, others are responsible for anchoring to the MHC, making these positional considerations critical for predicting T cell responses. We computationally predicted anchor positions for different peptide lengths for 328 common HLA alleles and identified unique anchoring patterns among them. Analysis of 923 tumor samples shows that 6 to 38% of neoantigen candidates are potentially misclassified and can be rescued using allele-specific knowledge of anchor positions. A subset of anchor results were orthogonally validated using protein crystallography structures. Representative anchor trends were experimentally validated using peptide-MHC stability assays and competition binding assays. By incorporating our anchor prediction results into neoantigen prediction pipelines, we hope to formalize, streamline, and improve the identification process for relevant clinical studies.
Cancer Research
The comprehensive evaluation of somatic variants in cancer requires consensus interpretation of t... more The comprehensive evaluation of somatic variants in cancer requires consensus interpretation of their potential clinical significance (diagnosis, prognosis, and treatment response) and oncogenicity. To aid precision medicine through public interpretations, a multifaceted collaborative effort is required to bring together a community, structured guidance, and a public platform. The over 200 multi-disciplinary experts in the Clinical Genome Resource (ClinGen) Somatic Cancer Clinical Domain Working Group (CDWG) provide a community that develops data curation guidelines and standards and curates evidence to determine the clinical significance and oncogenicity of somatic alterations in cancer. The Somatic CDWG established the Pediatric Cancer, Hematological Cancer, and Solid Tumor Taskforces to facilitate membership growth and targeted curation projects. Within these Taskforces, Somatic Cancer Variant Curation Expert Panels (SC-VCEPs) are developed through a 4-step approval process adapt...
Genetics in Medicine Open
Nature Cancer
The material cannot be used for any other purpose without further permission of the publisher and... more The material cannot be used for any other purpose without further permission of the publisher and is for private use only. There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.
Cancer Research, 2021
The Clinical Interpretation of Variants in Cancer (CIViC) knowledgebase (civicdb.org) is an open ... more The Clinical Interpretation of Variants in Cancer (CIViC) knowledgebase (civicdb.org) is an open access, centralized hub for structured, community curated and expertly moderated relationships between genomic variants and cancer. Evidence is curated from peer-reviewed, published literature and is classified into one of five Types: Predisposing, Diagnostic, Prognostic, Predictive (therapeutic), or Functional. The robustness of the Evidence is conveyed through the assignment of Levels with the first three derived from patient studies (Validated, Clinical, Case Study), Preclinical, generated from in vivo or in vitro data, and Inferential, which describes indirect associations. Each Evidence Item requires an Evidence Statement written in the curator's own words summarizing the source's results regarding the variant's clinical impact. Collaborations with groups like ClinGen have generated a significant influx of new curators, increasing the demand for detailed principles regar...
Cancer Research, 2021
CIViC (civicdb.org) is an open access, expertly moderated knowledgebase for crowdsourcing Clinica... more CIViC (civicdb.org) is an open access, expertly moderated knowledgebase for crowdsourcing Clinical Interpretations of Variants in Cancer. Stakeholders globally-including those in government, academia, industry and medicine-use CIViC to find and curate actionable interpretations of genomic variants in their therapeutic, prognostic, predisposing, diagnostic and functional contexts. Through engagement with curators and leaders in the field, CIViC has implemented several features including Assertions, Organizations and expanded help documentation. The foundational unit of CIViC is the Evidence Item, which describes the clinical relevance of a specific variant curated from a single published source within peer-reviewed literature or ASCO abstract. Assertions aggregate Evidence Items for a given variant-disease or variant-disease-therapy combination. In response to the 2017 AMP-ASCO-CAP guidelines and collaborations with ClinGen, Assertions were modified to integrate ACMG variant pathogen...
Figure S1. Fusions in the TCGA cohort. Figure S2. Druggable protein expression outliers using mas... more Figure S1. Fusions in the TCGA cohort. Figure S2. Druggable protein expression outliers using mass spectrometry. Figure S3. Co-occurring druggable mutations represent opportunities for combinational and alternative therapy. Figure S4. Druggability and Demographics. Figure S5. Potential Druggability by Cancer Type. (PDF 514Â KB) (PDF 501 kb)
Supplementary Materials and Methods. (PDF 33 kb)
Analysis Pipeline example: an example that uses the sciClone, clonevol, and fishplot packages to ... more Analysis Pipeline example: an example that uses the sciClone, clonevol, and fishplot packages to take data from raw somatic variant calls through subclonal detection, phylogeny inference, and fishplot creation. (ZIP 29Â kb)
Tagging master as 1.0.0 to mint our first DOI for the CIViC server.
Cancer Genetics, 2018
of a disease-specific panel or a gene list from single external source, and directs the analyst t... more of a disease-specific panel or a gene list from single external source, and directs the analyst to appropriate disease-specific resources, which may not be apparent from the information given by the care provider. Data generated from use of this database can offer relevant diagnostic, prognostic, and/or predictive value for patients. Here we describe several examples of how our pan-cancer database has aided in the interpretation of complex genomic test results that may have otherwise been misinterpreted by using only a disease-specific or single-reference approach.
Cancer Medicine, 2019
This is an open access article under the terms of the Creative Commons Attribution License, which... more This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Molecular & Cellular Proteomics, 2019
Highlights • Our search identifies 2,134 kinase-substrate phosphosite pairs in breast cancer. • C... more Highlights • Our search identifies 2,134 kinase-substrate phosphosite pairs in breast cancer. • CDKs and MAPKs are dominant regulators of trans substrate-phosphorylation. • Druggability, outcomes, and immune signatures related to kinase-substrates. • Experimentally validated activated phosphosites of ERBB2, EIF4EBP1, and EGFR.
Cancer Research, 2013
Deeper understanding the mechanisms by which luminal-type breast cancer develops resistance to en... more Deeper understanding the mechanisms by which luminal-type breast cancer develops resistance to endocrine therapy and development of novel strategies to treat these patients requires model systems recapitulate human breast cancer as accurately as possible. An increasing body of work suggests patient derived xenografts (PDX) may represent an informative model for development of novel therapeutics. We therefore established seven xenograft tumor lines from late-stage breast cancer patients with estrogen positive (ER+) disease. To date five ER+ PDX lines have been tested for responses to estradiol treatment in overiectomized NOD/SCID mice. Three showed estradiol independent-growth, one estrogen-stimulated growth and in one estradiol-induced a regression. These patterns mimicked the clinical phenotypes of each patient, tracking survival and responses to serial endocrine treatments. To define new mechanisms for resistance, whole genome DNA sequencing, RNA sequencing and Reverse Phase Prote...
New England Journal of Medicine, 2009
* Plus-minus values are means ±SD. Percentages may not total 100 because of rounding. AML-M3 deno... more * Plus-minus values are means ±SD. Percentages may not total 100 because of rounding. AML-M3 denotes acute promyelocytic leukemia, FLT3 FMS-related tyrosine kinase 3, and IDH1 isocitrate dehydrogenase 1. † The P value was calculated with the use of the two-sided t-test. ‡ Race was self-reported. § The P value was calculated with the use of Fisher's exact test. ¶ The definitions for cytogenetic risk groups are from the Medical Research Council and the Cancer and Leukemia Group B. 4,5 Adequate cytogenetic data were not available for 3 of the 172 patients who did not have an IDH1 mutation.
Science immunology, Apr 14, 2023
Neoantigens are tumor-specific peptide sequences resulting from sources such as somatic DNA mutat... more Neoantigens are tumor-specific peptide sequences resulting from sources such as somatic DNA mutations. Upon loading onto major histocompatibility complex (MHC) molecules, they can trigger recognition by T cells. Accurate neoantigen identification is thus critical for both designing cancer vaccines and predicting response to immunotherapies. Neoantigen identification and prioritization relies on correctly predicting whether the presenting peptide sequence can successfully induce an immune response. Because most somatic mutations are single-nucleotide variants, changes between wild-type and mutated peptides are typically subtle and require cautious interpretation. A potentially underappreciated variable in neoantigen prediction pipelines is the mutation position within the peptide relative to its anchor positions for the patient’s specific MHC molecules. Whereas a subset of peptide positions are presented to the T cell receptor for recognition, others are responsible for anchoring to the MHC, making these positional considerations critical for predicting T cell responses. We computationally predicted anchor positions for different peptide lengths for 328 common HLA alleles and identified unique anchoring patterns among them. Analysis of 923 tumor samples shows that 6 to 38% of neoantigen candidates are potentially misclassified and can be rescued using allele-specific knowledge of anchor positions. A subset of anchor results were orthogonally validated using protein crystallography structures. Representative anchor trends were experimentally validated using peptide-MHC stability assays and competition binding assays. By incorporating our anchor prediction results into neoantigen prediction pipelines, we hope to formalize, streamline, and improve the identification process for relevant clinical studies.
Cancer Research
The comprehensive evaluation of somatic variants in cancer requires consensus interpretation of t... more The comprehensive evaluation of somatic variants in cancer requires consensus interpretation of their potential clinical significance (diagnosis, prognosis, and treatment response) and oncogenicity. To aid precision medicine through public interpretations, a multifaceted collaborative effort is required to bring together a community, structured guidance, and a public platform. The over 200 multi-disciplinary experts in the Clinical Genome Resource (ClinGen) Somatic Cancer Clinical Domain Working Group (CDWG) provide a community that develops data curation guidelines and standards and curates evidence to determine the clinical significance and oncogenicity of somatic alterations in cancer. The Somatic CDWG established the Pediatric Cancer, Hematological Cancer, and Solid Tumor Taskforces to facilitate membership growth and targeted curation projects. Within these Taskforces, Somatic Cancer Variant Curation Expert Panels (SC-VCEPs) are developed through a 4-step approval process adapt...
Genetics in Medicine Open
Nature Cancer
The material cannot be used for any other purpose without further permission of the publisher and... more The material cannot be used for any other purpose without further permission of the publisher and is for private use only. There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.
Cancer Research, 2021
The Clinical Interpretation of Variants in Cancer (CIViC) knowledgebase (civicdb.org) is an open ... more The Clinical Interpretation of Variants in Cancer (CIViC) knowledgebase (civicdb.org) is an open access, centralized hub for structured, community curated and expertly moderated relationships between genomic variants and cancer. Evidence is curated from peer-reviewed, published literature and is classified into one of five Types: Predisposing, Diagnostic, Prognostic, Predictive (therapeutic), or Functional. The robustness of the Evidence is conveyed through the assignment of Levels with the first three derived from patient studies (Validated, Clinical, Case Study), Preclinical, generated from in vivo or in vitro data, and Inferential, which describes indirect associations. Each Evidence Item requires an Evidence Statement written in the curator's own words summarizing the source's results regarding the variant's clinical impact. Collaborations with groups like ClinGen have generated a significant influx of new curators, increasing the demand for detailed principles regar...
Cancer Research, 2021
CIViC (civicdb.org) is an open access, expertly moderated knowledgebase for crowdsourcing Clinica... more CIViC (civicdb.org) is an open access, expertly moderated knowledgebase for crowdsourcing Clinical Interpretations of Variants in Cancer. Stakeholders globally-including those in government, academia, industry and medicine-use CIViC to find and curate actionable interpretations of genomic variants in their therapeutic, prognostic, predisposing, diagnostic and functional contexts. Through engagement with curators and leaders in the field, CIViC has implemented several features including Assertions, Organizations and expanded help documentation. The foundational unit of CIViC is the Evidence Item, which describes the clinical relevance of a specific variant curated from a single published source within peer-reviewed literature or ASCO abstract. Assertions aggregate Evidence Items for a given variant-disease or variant-disease-therapy combination. In response to the 2017 AMP-ASCO-CAP guidelines and collaborations with ClinGen, Assertions were modified to integrate ACMG variant pathogen...
Figure S1. Fusions in the TCGA cohort. Figure S2. Druggable protein expression outliers using mas... more Figure S1. Fusions in the TCGA cohort. Figure S2. Druggable protein expression outliers using mass spectrometry. Figure S3. Co-occurring druggable mutations represent opportunities for combinational and alternative therapy. Figure S4. Druggability and Demographics. Figure S5. Potential Druggability by Cancer Type. (PDF 514Â KB) (PDF 501 kb)
Supplementary Materials and Methods. (PDF 33 kb)
Analysis Pipeline example: an example that uses the sciClone, clonevol, and fishplot packages to ... more Analysis Pipeline example: an example that uses the sciClone, clonevol, and fishplot packages to take data from raw somatic variant calls through subclonal detection, phylogeny inference, and fishplot creation. (ZIP 29Â kb)
Tagging master as 1.0.0 to mint our first DOI for the CIViC server.
Cancer Genetics, 2018
of a disease-specific panel or a gene list from single external source, and directs the analyst t... more of a disease-specific panel or a gene list from single external source, and directs the analyst to appropriate disease-specific resources, which may not be apparent from the information given by the care provider. Data generated from use of this database can offer relevant diagnostic, prognostic, and/or predictive value for patients. Here we describe several examples of how our pan-cancer database has aided in the interpretation of complex genomic test results that may have otherwise been misinterpreted by using only a disease-specific or single-reference approach.
Cancer Medicine, 2019
This is an open access article under the terms of the Creative Commons Attribution License, which... more This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Molecular & Cellular Proteomics, 2019
Highlights • Our search identifies 2,134 kinase-substrate phosphosite pairs in breast cancer. • C... more Highlights • Our search identifies 2,134 kinase-substrate phosphosite pairs in breast cancer. • CDKs and MAPKs are dominant regulators of trans substrate-phosphorylation. • Druggability, outcomes, and immune signatures related to kinase-substrates. • Experimentally validated activated phosphosites of ERBB2, EIF4EBP1, and EGFR.
Cancer Research, 2013
Deeper understanding the mechanisms by which luminal-type breast cancer develops resistance to en... more Deeper understanding the mechanisms by which luminal-type breast cancer develops resistance to endocrine therapy and development of novel strategies to treat these patients requires model systems recapitulate human breast cancer as accurately as possible. An increasing body of work suggests patient derived xenografts (PDX) may represent an informative model for development of novel therapeutics. We therefore established seven xenograft tumor lines from late-stage breast cancer patients with estrogen positive (ER+) disease. To date five ER+ PDX lines have been tested for responses to estradiol treatment in overiectomized NOD/SCID mice. Three showed estradiol independent-growth, one estrogen-stimulated growth and in one estradiol-induced a regression. These patterns mimicked the clinical phenotypes of each patient, tracking survival and responses to serial endocrine treatments. To define new mechanisms for resistance, whole genome DNA sequencing, RNA sequencing and Reverse Phase Prote...
New England Journal of Medicine, 2009
* Plus-minus values are means ±SD. Percentages may not total 100 because of rounding. AML-M3 deno... more * Plus-minus values are means ±SD. Percentages may not total 100 because of rounding. AML-M3 denotes acute promyelocytic leukemia, FLT3 FMS-related tyrosine kinase 3, and IDH1 isocitrate dehydrogenase 1. † The P value was calculated with the use of the two-sided t-test. ‡ Race was self-reported. § The P value was calculated with the use of Fisher's exact test. ¶ The definitions for cytogenetic risk groups are from the Medical Research Council and the Cancer and Leukemia Group B. 4,5 Adequate cytogenetic data were not available for 3 of the 172 patients who did not have an IDH1 mutation.