The protein landscape of chronic lymphocytic leukemia.

Many functional consequences of mutations on tumor phenotypes in chronic lymphocytic leukemia (CLL) are unknown. This may be in part due to a scarcity of information on the proteome of CLL. We profiled the proteome of 117 CLL patient samples with data-independent acquisition mass spectrometry and integrated the results with genomic, transcriptomic, ex vivo drug response, and clinical outcome data. We found trisomy 12, IGHV mutational status, mutated SF3B1, trisomy 19, del(17)(p13), del(11)(q22.3), mutated DDX3X and MED12 to influence protein expression (false discovery rate [FDR] = 5%). Trisomy 12 and IGHV status were the major determinants of protein expression variation in CLL as shown by principal-component analysis (1055 and 542 differentially expressed proteins, FDR = 5%). Gene set enrichment analyses of CLL with trisomy 12 implicated B-cell receptor (BCR)/phosphatidylinositol 3-kinase (PI3K)/AKT signaling as a tumor driver. These findings were supported by analyses of protein abundance buffering and protein complex formation, which identified limited protein abundance buffering and an upregulated protein complex involved in BCR, AKT, MAPK, and PI3K signaling in trisomy 12 CLL. A survey of proteins associated with trisomy 12/IGHV-independent drug response linked STAT2 protein expression with response to kinase inhibitors, including Bruton tyrosine kinase and mitogen-activated protein kinase kinase (MEK) inhibitors. STAT2 was upregulated in unmutated IGHV CLL and trisomy 12 CLL and required for chemokine/cytokine signaling (interferon response). This study highlights the importance of protein abundance data as a nonredundant layer of information in tumor biology and provides a protein expression reference map for CLL.

BRAF inhibitor treatment of classical hairy cell leukemia allows successful vaccination against SARS-CoV-2.

In classical hairy cell leukemia (HCL), standard treatments including purine analogs achieve a durable response (up to 90%), but lead to severe immunosuppression and long-lasting depletion of CD4 + T lymphocytes. The BRAF inhibitor vemurafenib is effective in HCL, but its use in first-line treatment is restricted to select clinical situations (e.g. active infection). Its impact on immune function or response to vaccines in HCL is unclear. We treated four HCL patients with vemurafenib during the COVID-19 pandemic and monitored immune reconstitution and response to SARS-CoV-2 immunization. All patients responded to HCL treatment with normalization of peripheral blood counts. No severe infections occurred. As an indication of limited immunosuppression by vemurafenib, stable CD4 + and CD8 + T lymphocyte counts and immunoglobulin levels were observed. Three out of four patients received SARS-CoV-2 vaccination (Pfizer-BioNTech) during treatment with vemurafenib. IgG antibody levels against the spike-protein of SARS-CoV-2 were detected (40-818 AE/ml). Our data suggest that vemurafenib has limited effects on cellular and humoral immune function in HCL, which allows for successful SARS-CoV-2 vaccination. These data support the use of BRAF inhibitors during the current pandemic where continued immune response is necessary for minimizing the COVID-19-related risk of non-vaccinated patients.

Generating Actionable Insights from Patient Medical Records and Structured Clinical Knowledge.

While adherence to clinical guidelines improves the quality and consistency of care, personalized healthcare also requires a deep understanding of individual disease models and treatment plans. The structured preparation of medical routine data in a certain clinical context, e.g. a treatment pathway outlined in a medical guideline, is currently a challenging task. Medical data is often stored in diverse formats and systems, and the relevant clinical knowledge defining the context is not available in machine-readable formats. We present an approach to extract information from medical free text documentation by using structured clinical knowledge to guide information extraction into a structured and encoded format, overcoming the known challenges for natural language processing algorithms. Preliminary results have been encouraging, as one of our methods managed to extract 100% of all data-points with 85% accuracy in details. These advancements show the potential of our approach to effectively use unstructured clinical data to elevate the quality of patient care and reduce the workload of medical personnel.

Targeting CD157 in AML using a novel, Fc-engineered antibody construct.

Antibody-based immunotherapy represents a promising strategy to eliminate chemorefractory leukemic cells in acute myeloid leukemia (AML). In this study, we evaluated a novel Fc-engineered antibody against CD157 (MEN1112) for its suitability as immunotherapy in AML. CD157 was expressed in 97% of primary AML patient samples. A significant, albeit lower expression level of CD157 was observed within the compartment of leukemia-initiating cells, which are supposed to be the major source of relapse. In healthy donor bone marrow, CD157 was expressed on CD34+ cells. In ex vivo assays, MEN1112 triggered natural killer (NK) cell-mediated cytotoxicity against AML cell lines and primary AML cells. Compared to its parental analogue, the Fc-engineered antibody exhibited higher antibody dependent cellular cytotoxicity responses. Using NK cells from AML patients, we observed heterogeneous MEN1112-mediated cytotoxicity against AML cells, most likely due to well-documented defects in AML-NK cells and corresponding inter-patient variations in NK cell function. Cytotoxicity could not be correlated to the time after completion of chemotherapy. In summary, we could demonstrate that CD157 is strongly expressed in AML. MEN1112 is a promising antibody construct that showed high cytotoxicity against AML cells and warrants further clinical testing. Due to variability in NK-cell function of AML patients, the time of application during the course of the disease as well as combinatorial strategies might influence treatment results.