5 research outputs found
Proteogenetic drug response profiling elucidates targetable vulnerabilities of myelofibrosis
Myelofibrosis is a hematopoietic stem cell disorder belonging to the myeloproliferative neoplasms. Myelofibrosis patients frequently carry driver mutations in either JAK2 or Calreticulin (CALR) and have limited therapeutic options. Here, we integrate ex vivo drug response and proteotype analyses across myelofibrosis patient cohorts to discover targetable vulnerabilities and associated therapeutic strategies. Drug sensitivities of mutated and progenitor cells were measured in patient blood using high-content imaging and single-cell deep learning-based analyses. Integration with matched molecular profiling revealed three targetable vulnerabilities. First, CALR mutations drive BET and HDAC inhibitor sensitivity, particularly in the absence of high Ras pathway protein levels. Second, an MCM complex-high proliferative signature corresponds to advanced disease and sensitivity to drugs targeting pro-survival signaling and DNA replication. Third, homozygous CALR mutations result in high endoplasmic reticulum (ER) stress, responding to ER stressors and unfolded protein response inhibition. Overall, our integrated analyses provide a molecularly motivated roadmap for individualized myelofibrosis patient treatment
Theoretical Fundamentals of Computational Proteomics and Deep Learning- Based Identification of Chimeric Mass Spectrometry Data
A complicating factor for peptide identification by MS/MS experiments is the presence of âchimericâ spectra where at least two precursor ions with similar retention time and mass co- elute in the mass spectrometer. This results in a spectrum that is a superposition of the spectra of the individual peptides. These chimeric spectra make peptide identification more difficult, so chimeric detection tools are needed to improve peptide identification rates. GLEAMS is a learned embedding algorithm for efficient joint analysis of millions of mass spectra. In this work, we first simulate chimeric spectra. Then we present a deep neural network- based classifier that learns to distinguish between chimeras and pure spectra. The result shows that GLEAMS captures a spectrumâs chimericness, which can lead to a higher protein identification rate in samples or support biomarker development processes and the like. En komplicerande faktor för peptididentifiering genom MS / MS- experiment Ă€r nĂ€rvaron av âchimĂ€raâ spektra eller âchimeraâ, dĂ€r Ă„tminstone tvĂ„ föregĂ„ngare med liknande retentionstid och massa sameluerar in i masspektrometern och resulterar i ett spektrum som Ă€r en superposition av individuella spektra. Eftersom dessa chimĂ€ra spektra gör identifieringen av peptider mer utmanande behövs ett detekteringsverktyg för att förbĂ€ttra identifieringsgraden för peptider. I detta arbete fokuserade vi pĂ„ GLEAMS, en lĂ€rd inbĂ€ddning för effektiv gemensam analys av miljontals masspektrum. Först simulerade vi chimĂ€ra spektra. Sedan presenterar vi en ensembleklassificering baserad pĂ„ olika maskininlĂ€rnings- och djupinlĂ€rningsmetoder som lĂ€r sig att skilja pĂ„ simulerad chimera och rena spektra. Resultatet visar att GLEAM fĂ„ngar âchimĂ€rhetenâ i ett spektrum, vilket kan leda till högre identifieringsgrad av protein samt ge stöd till utvecklingsprocesser för biomarkörer
Theoretical Fundamentals of Computational Proteomics and Deep Learning- Based Identification of Chimeric Mass Spectrometry Data
A complicating factor for peptide identification by MS/MS experiments is the presence of âchimericâ spectra where at least two precursor ions with similar retention time and mass co- elute in the mass spectrometer. This results in a spectrum that is a superposition of the spectra of the individual peptides. These chimeric spectra make peptide identification more difficult, so chimeric detection tools are needed to improve peptide identification rates. GLEAMS is a learned embedding algorithm for efficient joint analysis of millions of mass spectra. In this work, we first simulate chimeric spectra. Then we present a deep neural network- based classifier that learns to distinguish between chimeras and pure spectra. The result shows that GLEAMS captures a spectrumâs chimericness, which can lead to a higher protein identification rate in samples or support biomarker development processes and the like. En komplicerande faktor för peptididentifiering genom MS / MS- experiment Ă€r nĂ€rvaron av âchimĂ€raâ spektra eller âchimeraâ, dĂ€r Ă„tminstone tvĂ„ föregĂ„ngare med liknande retentionstid och massa sameluerar in i masspektrometern och resulterar i ett spektrum som Ă€r en superposition av individuella spektra. Eftersom dessa chimĂ€ra spektra gör identifieringen av peptider mer utmanande behövs ett detekteringsverktyg för att förbĂ€ttra identifieringsgraden för peptider. I detta arbete fokuserade vi pĂ„ GLEAMS, en lĂ€rd inbĂ€ddning för effektiv gemensam analys av miljontals masspektrum. Först simulerade vi chimĂ€ra spektra. Sedan presenterar vi en ensembleklassificering baserad pĂ„ olika maskininlĂ€rnings- och djupinlĂ€rningsmetoder som lĂ€r sig att skilja pĂ„ simulerad chimera och rena spektra. Resultatet visar att GLEAM fĂ„ngar âchimĂ€rhetenâ i ett spektrum, vilket kan leda till högre identifieringsgrad av protein samt ge stöd till utvecklingsprocesser för biomarkörer
Proteogenetic drug response profiling elucidates targetable vulnerabilities of myelofibrosis
Myelofibrosis is a hematopoietic stem cell disorder belonging to the myeloproliferative neoplasms. Myelofibrosis patients frequently carry driver mutations in either JAK2 or Calreticulin (CALR) and have limited therapeutic options. Here, we integrate ex vivo drug response and proteotype analyses across myelofibrosis patient cohorts to discover targetable vulnerabilities and associated therapeutic strategies. Drug sensitivities of mutated and progenitor cells were measured in patient blood using high-content imaging and single-cell deep learning-based analyses. Integration with matched molecular profiling revealed three targetable vulnerabilities. First, CALR mutations drive BET and HDAC inhibitor sensitivity, particularly in the absence of high Ras pathway protein levels. Second, an MCM complex-high proliferative signature corresponds to advanced disease and sensitivity to drugs targeting pro-survival signaling and DNA replication. Third, homozygous CALR mutations result in high endoplasmic reticulum (ER) stress, responding to ER stressors and unfolded protein response inhibition. Overall, our integrated analyses provide a molecularly motivated roadmap for individualized myelofibrosis patient treatment.ISSN:2041-172