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Acute Myeloid Leukemia (AML) affects 20,000 patients in the US annually with a five-year survival rate of approximately 25%. One reason for the low survival rate is the high prevalence of clonal evolution that gives rise to heterogeneous sub-populations of leukemic cells with diverse mutation spectra, which eventually leads to disease relapse. This genetic heterogeneity drives the activation of complex signaling pathways that is reflected at the protein level. This diversity makes it difficult to treat AML with targeted therapy, requiring custom patient treatment protocols tailored to each individual’s leukemia. Toward this end, the Beat AML research program prospectively collected genomic and transcriptomic data from over 1000 AML patients and carried out ex vivo drug sensitivity assays to identify genomic signatures that could predict patient-specific drug responses. However, there are inherent weaknesses in using only genetic and transcriptomic measurements as surrogates of drug response, particularly the absence of direct information about phosphorylation-mediated signal transduction. As a member of the Clinical Proteomic Tumor Analysis Consortium, we have extended the molecular characterization of this cohort by collecting proteomic and phosphoproteomic measurements from a subset of these patient samples (38 in total) to evaluate the hypothesis that proteomic signatures can improve the ability to predict response to 26 drugs in AML ex vivo samples. In this work we describe our systematic, multi-omic approach to evaluate proteomic signatures of drug response and compare protein levels to other markers of drug response such as mutational patterns. We explore the nuances of this approach using two drugs that target key pathways activated in AML: quizartinib (FLT3) and trametinib (Ras/MEK), and show how patient-derived signatures can be interpreted biologically and validated in cell lines. In conclusion, this pilot study demonstrates strong promise for proteomics-based patient stratification to assess drug sensitivity in AML.

Isobaric labeling via tandem mass tag (TMT) reagents enables sample multiplexing prior to LC-MS/MS, facilitating high-throughput large-scale quantitative proteomics. Consistent and efficient labeling reactions are essential to achieve robust quantification; therefore, embedded in our clinical proteomic protocol is a quality control (QC) sample that contains a small aliquot from each sample within a TMT set, referred to as "Mixing QC." This Mixing QC enables the detection of TMT labeling issues by LC-MS/MS before combining the full samples to allow for salvaging of poor TMT labeling reactions. While TMT labeling is a valuable tool, factors leading to poor reactions are not fully studied. We observed that relabeling does not necessarily rescue TMT reactions and that peptide samples sometimes remained acidic after resuspending in 50 mM HEPES buffer (pH 8.5), which coincided with low labeling efficiency (LE) and relatively low median reporter ion intensities (MRIIs). To obtain a more resilient TMT labeling procedure, we investigated LE, reporter ion missingness, the ratio of mean TMT set MRII to individual channel MRII, and the distribution of log 2 reporter ion ratios of Mixing QC samples. We discovered that sample pH is a critical factor in LE, and increasing the buffer concentration in poorly labeled samples before relabeling resulted in the successful rescue of TMT labeling reactions. Moreover, resuspending peptides in 500 mM HEPES buffer for TMT labeling resulted in consistently higher LE and lower missing data. By better controlling the sample pH for labeling and implementing multiple methods for assessing labeling quality before combining samples, we demonstrate that robust TMT labeling for large-scale quantitative studies is achievable.

Acute myeloid leukemia (AML) is a deadly blood cancer that remains largely classified by genetic aberrations, which inform therapy stratification. However, therapeutic response cannot be predicted or explained by genetic abnormalities alone. The integration of multiple omics, consisting of genomic, transcriptomic, proteomic, and phosphoproteomic measurements, offers a holistic view to resolve the underlying pathophysiology of AML that influences response to therapy. In this work, we pair multi-omic characterization together with ex vivo drug sensitivity assays accrued with 145 small molecule inhibitors for 210 AML patient samples (Bottomly et al., Cancer Cell 2022). We showcase how the integration of these data can guide drug sensitivity exploration and prediction. We first expanded the dataset by generating matching comprehensive proteomics and phosphoproteomics data for the Beat AML samples and integrated these data using non-negative matrix factorization. This analysis identified four distinct proteogenomic subtypes of AML, each representing distinct clinical and biological features including differences in survival and biological pathway activation. We then sought distinct patterns of drug sensitivity across the subtypes of the patient cohort and found one pair of drugs, venetoclax and panobinostat, to be sensitive in complementary sets of patients, suggesting that they could be more effective in combination. Lastly, we further enhanced the proteogenomic subtypes by a building machine learning based model of distinct drug response that we then evaluated in vitro. Our results show that the four proteogenomic subtypes are independent yet complementary to existing mutational profiles, and can be used to improve drug treatment stratification. We tested the combination of panobinostat and venetoclax in patient samples and show that they are more effective in combination than as single agents. We then tested drug-specific machine learning models to predict drug response on AML cell lines that were in varying stages of resistance to the FLT3 inhibitor quizartinib. The models predicted a change across the proteogenomic landscape as quizartinib resistance evolves, resulting in a shift in drug sensitivities that we experimentally validated. This work represents a seminal effort in the integration of proteogenomic and ex vivo drug sensitivity datasets. In summary, we show how multi-omic characterization of AML maps a proteogenomic landscape that enables improved exploration of patient drug response and ultimately patient treatment. Citation Format: James C. Pino, Camilo Posso, Sunil K. Joshi, Michael Nestor, Jamie Moon, Joshua R. Hansen, Marina A. Gritsenko, Chelsea Hutchinson-Bunch, Karl K. Weitz, Kevin Watanabe-Smith, Jason E. McDermott, Brian J. Druker, Tao Liu, Jeffrey W. Tyner, Anupriya Agarwal, Elie Traer, Paul D. Piehowski, Cristina E. Tognon, Karin D. Rodland, Sara J. Gosline. Mapping the molecular landscape of acute myeloid leukemia enables prediction of drug response from proteogenomic data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3172.

Acute Myeloid Leukemia (AML) affects 20,000 patients in the US annually with a five-year survival rate of approximately 25%. One reason for the low survival rate is the high prevalence of clonal evolution that gives rise to heterogeneous sub-populations of leukemic cells with diverse mutation spectra, which eventually leads to disease relapse. This genetic heterogeneity drives the activation of complex signaling pathways that is reflected at the protein level. This diversity makes it difficult to treat AML with targeted therapy, requiring custom patient treatment protocols tailored to each individual’s leukemia. Toward this end, the Beat AML research program prospectively collected genomic and transcriptomic data from over 1000 AML patients and carried out ex vivo drug sensitivity assays to identify genomic signatures that could predict patient-specific drug responses. However, there are inherent weaknesses in using only genetic and transcriptomic measurements as surrogates of drug response, particularly the absence of direct information about phosphorylation-mediated signal transduction. As a member of the Clinical Proteomic Tumor Analysis Consortium, we have extended the molecular characterization of this cohort by collecting proteomic and phosphoproteomic measurements from a subset of these patient samples to evaluate the hypothesis that proteomic signatures can improve the ability to predict drug response in AML patients. In this work we describe our systematic, multi-omic approach to evaluate proteomic signatures of drug response and compare protein levels to other markers of drug response such as mutational patterns. We explore the nuances of this approach using two drugs that target key pathways activated in AML: quizartinib (FLT3) and trametinib (Ras/MEK), and show how patient-derived signatures can be interpreted biologically and validated in cell lines. In conclusion, this pilot study demonstrates strong promise for proteomics-based patient stratification to assess drug sensitivity in AML.