scholarly journals Single-Cell Proteomics Identifies Leukemia Landscape Associated with Clinical Outcomes in R/R AML Treated with MDM2i (Milademetan) and FLT3i (Quizartinib): Putative Role of CD68 and Diversity Index

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 3443-3443
Author(s):  
Muharrem Muftuoglu ◽  
Li Li ◽  
Mahesh Basyal ◽  
Shaoheng Liang ◽  
Carissa J. Ball ◽  
...  
Keyword(s):  
Decision Making ◽  
Single Cell ◽  
Research Funding ◽  
Clinical Decision Making ◽  
Therapy Response ◽  
Clinical Decision ◽  
Response To Therapy ◽  
Leukemia Cells ◽  
Npm1 Mutation ◽  
Pre Treatment

Abstract Distinct mutations could differentially regulate cellular programs and alter the proteomic landscape in AML. Sequential acquisition of various mutations not only leads to clonal diversification but also alters the leukemia proteomic landscape through activation of mutation-specific gene programs. Characterization of AML proteomic profiles and diversity could be utilized as a measure of genetic imprint on leukemia proteome to inform clinical decision-making. We reasoned that diverse leukemia-specific proteomic profile could be indicative of the presence of multiple mutations activating numerous pathways, thus leading to a heterogenous clonal composition and more likely to therapy resistance. We aimed to test this hypothesis by assessing the proteomic profiles of FLT3-ITD AML patients treated with MDM2i (Milademetan) plus FLT3i (Quizartinib) (NCT03552029) and interrogate the association between proteomic landscape and therapy response. We assessed single-cell proteomic profiles of 35 sequentially collected samples for six selected patients treated with MDM2i+FLT3i using CyTOF, enabling us to assess expression of 51-parameters across leukemia compartments and identify leukemic clones with distinct proteomic profiles. Three patients achieved CRi while three patients did not respond. This allowed us to start interrogating proteomic signatures for their ability to predict response to therapy. We performed single-cell analysis and interrogated the phenotypic profiles of leukemia compartments to assess leukemia hierarchies, defined by spatial organization of leukemic subpopulations, and whether mutations in AML were associated with unique phenotypes. Notably, we found that NPM1-mutant (Mt) leukemia cells lacked CD34 expression, expressed high levels of CD99 and had patchy c-kit expression. Despite lacking a canonical marker, CD34, high-dimensional analysis positioned NPM1-Mt leukemia cells spatially in close proximity to CD34+ leukemia cells (NPM1 WT), indicating that NPM1 WT and Mt leukemia cells are closely related. CD34+ expressing cells most likely serve as the founding clone and acquisition of NPM1 mutation led to emergence of CD34- leukemia clones. As expected, all three patients who achieved CRi were NPM1-Mt and NPM1-Mt leukemia cells in CRi patients expressed CD68. Importantly, we also observed that CD68+ leukemia cells were eradicated in a NR patient where only a fraction of leukemia cells expressed CD68. This suggests that NPM1 mutations could activate unique cellular programs and induce distinct differentiation states (CD68), which could sensitize leukemia cells to MDM2+FLT3 inhibition. Altogether, NPM1 mutation status and CD68 expression level were associated with therapy response. Next, we mapped the response kinetics and quantified survived leukemia cells across multiple timepoints. Strikingly, MDM2i+FLT3i almost completely eliminated circulating blasts in responders (R) by day 8 while leukemia blasts persisted in NR (median blast %: 0.11 in R vs 19.8 in NR). This indicates that assessment of therapy response as early as day 8 could provide insights into the overall response and identify patients who will fail to achieve CR. Importantly, patients with reduced leukemia blasts at day 8 were also leukemia-free in BM at the end of cycle 1. Lastly, we sought to investigate the association between proteomic landscape diversity and therapy response, and quantified the number of leukemia subpopulations by unsupervised clustering. The median number of subpopulations detected in R vs NR at baseline were 3 and 9, respectively. We also utilized the inverse Simpson index to quantify the proteomic diversity of leukemia compartments and to further investigate the association between proteomic diversity and therapy outcome in an unbiased manner. The median diversity indices in R vs NR were 64 vs 212, revealing that patients with CR had restricted pre-treatment proteomic diversity. These findings suggest that a pre-treatment diverse phenotypic landscape could portend poor therapeutic outcome. Altogether, single-cell proteomic analysis identified correlates associated with overall clinical response in AML patients treated with MDM2i+FLT3i. Further validation is needed in a larger cohort of patients. Such approaches could be utilized in clinical-trial settings to predict therapy response with targeted agents and inform clinical decision-making. Disclosures Lesegretain: Daiichi-Sankyo Inc.: Current Employment. Daver: Amgen: Consultancy, Research Funding; Glycomimetics: Research Funding; Trovagene: Consultancy, Research Funding; Hanmi: Research Funding; Genentech: Consultancy, Research Funding; Trillium: Consultancy, Research Funding; Novimmune: Research Funding; ImmunoGen: Consultancy, Research Funding; Abbvie: Consultancy, Research Funding; FATE Therapeutics: Research Funding; Astellas: Consultancy, Research Funding; Sevier: Consultancy, Research Funding; Gilead Sciences, Inc.: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Bristol Myers Squibb: Consultancy, Research Funding; Daiichi Sankyo: Consultancy, Research Funding; Novartis: Consultancy; Jazz Pharmaceuticals: Consultancy, Other: Data Monitoring Committee member; Dava Oncology (Arog): Consultancy; Celgene: Consultancy; Syndax: Consultancy; Shattuck Labs: Consultancy; Agios: Consultancy; Kite Pharmaceuticals: Consultancy; SOBI: Consultancy; STAR Therapeutics: Consultancy; Karyopharm: Research Funding; Newave: Research Funding. Andreeff: AstraZeneca: Research Funding; Glycomimetics: Consultancy; Reata, Aptose, Eutropics, SentiBio; Chimerix, Oncolyze: Current holder of individual stocks in a privately-held company; Breast Cancer Research Foundation: Research Funding; Aptose: Consultancy; ONO Pharmaceuticals: Research Funding; Oxford Biomedica UK: Research Funding; Medicxi: Consultancy; Syndax: Consultancy; Karyopharm: Research Funding; Novartis, Cancer UK; Leukemia & Lymphoma Society (LLS), German Research Council; NCI-RDCRN (Rare Disease Clin Network), CLL Foundation; Novartis: Membership on an entity's Board of Directors or advisory committees; Daiichi-Sankyo: Consultancy, Research Funding; Senti-Bio: Consultancy; Amgen: Research Funding.

scholarly journals Feasibility study of in vitro drug sensitivity assay of advanced non-small cell lung adenocarcinomas

2020 ◽  
Vol 7 (1) ◽  
pp. e000505
Author(s):  
Emoke Papp ◽  
Anita Steib ◽  
Elhusseiny MM Abdelwahab ◽  
Judit Meggyes-Rapp ◽  
Laszlo Jakab ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Therapy Response ◽  
Clinical Decision ◽  
Response To Therapy ◽  
Solid Tumours ◽  
Chemosensitivity Test ◽  
Pleural Effusions ◽  
In Vitro Chemosensitivity ◽  
Lung Adenocarcinomas

Background Despite improved screening techniques, diagnosis of lung cancer is often late and its prognosis is poor. In the present study, in vitro chemosensitivity of solid tumours and pleural effusions of lung adenocarcinomas were analysed and compared with clinical drug response.Methods Tumour cells were isolated from resected solid tumours or pleural effusions, and cryopreserved. Three-dimensional (3D) tissue aggregate cultures were set up when the oncoteam reached therapy decision for individual patients. The aggregates were then treated with the selected drug or drug combination and in vitro chemosensitivity was tested individually measuring ATP levels. The clinical response to therapy was assessed by standard clinical evaluation over an 18 months period.Results Based on the data, the in vitro chemosensitivity test results correlate well with clinical treatment response.Conclusions Such tests if implemented into the clinical decision making process might allow the selection of an even more individualised chemotherapy protocol which could lead to better therapy response.

scholarly journals Echocardiography in the intensive care unit: an essential tool for diagnosis, monitoring and guiding clinical decision-making

Imaging ◽  
2021 ◽  
Author(s):  
Hatem Soliman-Aboumarie ◽  
Maria Concetta Pastore ◽  
Eftychia Galiatsou ◽  
Luna Gargani ◽  
Nicola Riccardo Pugliese ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Decision Making ◽  
Mechanical Ventilation ◽  
Intensive Care ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Response To Therapy ◽  
Circulatory Support ◽  
Positive Pressure ◽  
Essential Tool

AbstractIn the last years, new trends on patient diagnosis for admission in cardiac intensive care unit (CICU) have been observed, shifting from acute myocardial infarction or acute heart failure to non-cardiac diseases such as sepsis, acute respiratory failure or acute kidney injury. Moreover, thanks to the advances in scientific knowledge and higher availability, there has been increasing use of positive pressure mechanical ventilation which has its implications on the heart. Therefore, there is a growing need for Cardiac intensivists to quickly, noninvasively and repeatedly evaluate various hemodynamic conditions and the response to therapy.Transthoracic critical care echocardiography (CCE) currently represents an essential tool in CICU, as it is used to evaluate biventricular function and complications following acute coronary syndromes, identify the mechanisms of circulatory failure, acute valvular pathologies, tailoring and titrating intravenous treatment or mechanical circulatory support. This could be completed with trans-oesophageal echocardiography (TOE), advanced echocardiography and lung ultrasound to provide a thorough evaluation and monitoring of CICU patients. However, CCE could sometimes be challenging as the acquisition of good-quality images is limited by mechanical ventilation, suboptimal patient position or recent surgery with drains on the chest. Moreover, there are some technical caveats that one should bear in mind while performing CCE in order to optimize its use and avoid misleading findings. The aim of this review is to highlight the key role of CCE, providing an updated overview of its main applications and possible pitfalls in order to facilitate its use in CICU for clinical decision-making.

scholarly journals Impact of a 40-Gene Targeted Panel Test on Physician Decision Making for Patients With Acute Myeloid Leukemia

2021 ◽  
pp. 191-203
Author(s):  
Erica K. Barnell ◽  
Kenneth F. Newcomer ◽  
Zachary L. Skidmore ◽  
Kilannin Krysiak ◽  
Sydney R. Anderson ◽  
...  
Keyword(s):  
Decision Making ◽  
Acute Myeloid Leukemia ◽  
Genetic Variants ◽  
Myeloid Leukemia ◽  
Clinical Decision Making ◽  
Clinical Care ◽  
Clinical Decision ◽  
Hematologic Malignancies ◽  
Response To Therapy ◽  
Acute Myeloid

PURPOSE Physicians treating hematologic malignancies increasingly order targeted sequencing panels to interrogate recurrently mutated genes. The precise impact of these panels on clinical decision making is not well understood. METHODS Here, we report our institutional experience with a targeted 40-gene panel (MyeloSeq) that is used to generate a report for both genetic variants and variant allele frequencies for the treating physician (the limit of mutation detection is approximately one AML cell in 50). RESULTS In total, 346 sequencing reports were generated for 325 patients with suspected hematologic malignancies over an 8-month period (August 2018 to April 2019). To determine the influence of genomic data on clinical care for patients with acute myeloid leukemia (AML), we analyzed 122 consecutive reports from 109 patients diagnosed with AML and surveyed the treating physicians with a standardized questionnaire. The panel was ordered most commonly at diagnosis (61.5%), but was also used to assess response to therapy (22.9%) and to detect suspected relapse (15.6%). The panel was ordered at multiple timepoints during the disease course for 11% of patients. Physicians self-reported that 50 of 114 sequencing reports (44%) influenced clinical care decisions in 44 individual patients. Influences were often nuanced and extended beyond identifying actionable genetic variants with US Food and Drug Administration–approved drugs. CONCLUSION This study provides insights into how physicians are currently using multigene panels capable of detecting relatively rare AML cells. The most influential way to integrate these tools into clinical practice will be to perform prospective clinical trials that assess patient outcomes in response to genomically driven interventions.

Childhood Apraxia of Speech: Clinical Decision Making From a Motor-Based Perspective

2020 ◽  
Vol 5 (4) ◽  
pp. 831-842 ◽  
Author(s):  
Ruth Stoeckel ◽  
Susan Caspari
Keyword(s):  
Decision Making ◽  
Best Practice ◽  
Clinical Decision Making ◽  
Personal Information ◽  
Clinical Decision ◽  
Response To Therapy ◽  
Apraxia Of Speech ◽  
Childhood Apraxia Of Speech ◽  
Assessment And Treatment

Purpose This article uses two case studies to illustrate clinical decision making using the best available evidence to approach the assessment and intervention for children with childhood apraxia of speech. The cases represent children seen in the authors' clinical practice, with personal information altered or omitted to protect the identity of the individuals. The case discussions exemplify choices that may be made for children of different ages, highlighting common elements across ages, as well as treatment aspects that may differ by age. Conclusions While research regarding best practice for assessment and treatment for childhood apraxia of speech has not been conclusive and, in fact, at times has been equivocal, there is empirical evidence from which to develop a rationale for assessment and treatment decisions. Accountability is important even as decisions are being made based on the best available evidence. In each case study, modifications in treatment depended on data that allowed the clinician to evaluate the children's response to therapy and adapt accordingly.

scholarly journals Patient-derived organoid (PDO) platforms to facilitate clinical decision making

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Lisa Liu ◽  
Lei Yu ◽  
Zhichao Li ◽  
Wujiao Li ◽  
WeiRen Huang
Keyword(s):  
Decision Making ◽  
Cell Lines ◽  
Drug Screening ◽  
Clinical Decision Making ◽  
Therapy Response ◽  
Clinical Decision ◽  
Chip Technology ◽  
Vitro Model ◽  
Therapeutic Decision Making

AbstractBased on recent advances in organoid research as well as the need to find more accurate models for drug screening in cancer research, patient-derived organoids have emerged as an effective in vitro model system to study cancer. Showing numerous advantages over 2D cell lines, 3D cell lines, and primary cell culture, organoids have been applied in drug screening to demonstrate the correlation between genetic mutations and sensitivity to targeted therapy. Organoids have also been used in co-clinical trials to compare drug responses in organoids to clinical responses in the corresponding patients. Numerous studies have reported the successful use of organoids to predict therapy response in cancer patients. Recently, organoids have been adopted to predict treatment response to radiotherapy and immunotherapy. The development of high throughput drug screening and organoids-on-a-chip technology can advance the use of patient-derived organoids in clinical practice and facilitate therapeutic decision-making.

scholarly journals Deep learning in cancer pathology: a new generation of clinical biomarkers

2020 ◽  
Author(s):  
Amelie Echle ◽  
Niklas Timon Rindtorff ◽  
Titus Josef Brinker ◽  
Tom Luedde ◽  
Alexander Thomas Pearson ◽  
...  
Keyword(s):  
Decision Making ◽  
Image Analysis ◽  
Deep Learning ◽  
Clinical Decision Making ◽  
External Validation ◽  
Therapy Response ◽  
Clinical Decision ◽  
Tumour Tissue ◽  
Molecular Features ◽  
Clinical Biomarkers

AbstractClinical workflows in oncology rely on predictive and prognostic molecular biomarkers. However, the growing number of these complex biomarkers tends to increase the cost and time for decision-making in routine daily oncology practice; furthermore, biomarkers often require tumour tissue on top of routine diagnostic material. Nevertheless, routinely available tumour tissue contains an abundance of clinically relevant information that is currently not fully exploited. Advances in deep learning (DL), an artificial intelligence (AI) technology, have enabled the extraction of previously hidden information directly from routine histology images of cancer, providing potentially clinically useful information. Here, we outline emerging concepts of how DL can extract biomarkers directly from histology images and summarise studies of basic and advanced image analysis for cancer histology. Basic image analysis tasks include detection, grading and subtyping of tumour tissue in histology images; they are aimed at automating pathology workflows and consequently do not immediately translate into clinical decisions. Exceeding such basic approaches, DL has also been used for advanced image analysis tasks, which have the potential of directly affecting clinical decision-making processes. These advanced approaches include inference of molecular features, prediction of survival and end-to-end prediction of therapy response. Predictions made by such DL systems could simplify and enrich clinical decision-making, but require rigorous external validation in clinical settings.

Debates in Dysphagia Management: How Do You Use Evidence-Based Practice in Your Dysphagia Patient Care?

2011 ◽  
Vol 20 (4) ◽  
pp. 121-123
Author(s):  
Jeri A. Logemann
Keyword(s):  
Decision Making ◽  
Patient Care ◽  
Clinical Outcomes ◽  
Clinical Judgment ◽  
Clinical Decision Making ◽  
Evidence Based Practice ◽  
Clinical Decision ◽  
Evidence Based ◽  
Clinical Method ◽  
Do So

Evidence-based practice requires astute clinicians to blend our best clinical judgment with the best available external evidence and the patient's own values and expectations. Sometimes, we value one more than another during clinical decision-making, though it is never wise to do so, and sometimes other factors that we are unaware of produce unanticipated clinical outcomes. Sometimes, we feel very strongly about one clinical method or another, and hopefully that belief is founded in evidence. Some beliefs, however, are not founded in evidence. The sound use of evidence is the best way to navigate the debates within our field of practice.

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