JAK2 (and other genes) be nimble with MPN diagnosis, prognosis, and therapy

Abstract Now that the spectrum of somatic mutations that initiate, propagate, and drive the progression of myeloproliferative neoplasms (MPNs) has largely been defined, recent efforts have focused on integrating this information into clinical decision making. In this regard, the greatest progress has been made in myelofibrosis, in which high-molecular-risk mutations have been identified and incorporated into prognostic models to help guide treatment decisions. In this chapter, we focus on advances in 4 main areas: (1) What are the MPN phenotypic driver mutations? (2) What constitutes high molecular risk in MPN (focusing on ASXL1)? (3) How do we risk-stratify patients with MPN? And (4) What is the significance of molecular genetics for MPN treatment? Although substantial progress has been made, we still have an incomplete understanding of the molecular basis for phenotypic diversity in MPN, and few rationally designed therapeutic approaches to target high-risk mutations are available. Ongoing research efforts in these areas are critical to understanding the biological consequences of genetic heterogeneity in MPN and to improving outcomes for patients.

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Driver somatic mutations identify distinct disease entities within myeloid neoplasms with myelodysplasia

Blood ◽

10.1182/blood-2014-03-560227 ◽

2014 ◽

Vol 124 (9) ◽

pp. 1513-1521 ◽

Cited By ~ 158

Author(s):

Luca Malcovati ◽

Elli Papaemmanuil ◽

Ilaria Ambaglio ◽

Chiara Elena ◽

Anna Gallì ◽

...

Keyword(s):

Somatic Mutations ◽

Clinical Decision Making ◽

Myeloproliferative Neoplasms ◽

Clinical Decision ◽

Driver Mutations ◽

Myeloid Neoplasms ◽

Key Points ◽

Distinct Disease ◽

Disease Entities

Key Points Different driver mutations have distinct effects on phenotype of myelodysplastic syndromes (MDS) and myelodysplastic/myeloproliferative neoplasms (MDS/MPN). Accounting for driver mutations may allow a classification of these disorders that is considerably relevant for clinical decision-making.

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ALK inhibitors, resistance development, clinical trials

Current Oncology ◽

10.3747/co.25.3760 ◽

2018 ◽

Vol 25 ◽

pp. 59 ◽

Cited By ~ 22

Author(s):

J.M. Rothenstein ◽

N. Chooback

Keyword(s):

Clinical Trials ◽

Patient Outcomes ◽

Clinical Decision Making ◽

Clinical Decision ◽

Current Treatment ◽

Resistance Development ◽

Ongoing Research ◽

Alk Positive ◽

Emergence Of Resistance ◽

Key Questions

The treatment of advanced non-small-cell lung cancer (nsclc) has undergone a paradigm shift since the early 2000s. The identification of molecular subtypes of the disease, based on oncogenic drivers, has led to the development of personalized medicine and the ability to deliver molecularly targeted therapies to patients. In the 10 years that have elapsed since the discovery of the ALK gene in a patient with nsclc, several active drugs have moved rapidly from bench to bedside, and multiple others are currently in clinical trials. Those developments have led to important improvements in patient outcomes, while simultaneously raising key questions about the optimal treatment for ALK-positive nsclc. The inevitable emergence of resistance to alk-directed therapy is central to ongoing research and daily clinical practice for affected patients. In the present review, we highlight the current treatment landscape, the available and emerging clinical trials, and the evolving clinical decision-making in ALK-positive nsclc, with a focus on Canadian practice.

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Understanding the usefulness of prognostic models in clinical decision-making

Journal of Physiotherapy ◽

10.1016/j.jphys.2017.01.003 ◽

2017 ◽

Vol 63 (2) ◽

pp. 121-125 ◽

Cited By ~ 1

Author(s):

Adrian C Traeger ◽

Markus Hübscher ◽

James H McAuley

Keyword(s):

Decision Making ◽

Clinical Decision Making ◽

Clinical Decision ◽

Prognostic Models

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Validation of parsimonious prognostic models for patients infected with COVID-19

BMJ Health & Care Informatics ◽

10.1136/bmjhci-2020-100267 ◽

2021 ◽

Vol 28 (1) ◽

pp. e100267

Author(s):

Keerthi Harish ◽

Ben Zhang ◽

Peter Stella ◽

Kevin Hauck ◽

Marwa M Moussa ◽

...

Keyword(s):

Clinical Decision Making ◽

Validation Cohort ◽

External Validation ◽

Clinical Decision ◽

Prognostic Models ◽

Clinical Prediction ◽

Inclusion Criteria ◽

Local Data ◽

External Data ◽

Operator Curve

ObjectivesPredictive studies play important roles in the development of models informing care for patients with COVID-19. Our concern is that studies producing ill-performing models may lead to inappropriate clinical decision-making. Thus, our objective is to summarise and characterise performance of prognostic models for COVID-19 on external data.MethodsWe performed a validation of parsimonious prognostic models for patients with COVID-19 from a literature search for published and preprint articles. Ten models meeting inclusion criteria were either (a) externally validated with our data against the model variables and weights or (b) rebuilt using original features if no weights were provided. Nine studies had internally or externally validated models on cohorts of between 18 and 320 inpatients with COVID-19. One model used cross-validation. Our external validation cohort consisted of 4444 patients with COVID-19 hospitalised between 1 March and 27 May 2020.ResultsMost models failed validation when applied to our institution’s data. Included studies reported an average validation area under the receiver–operator curve (AUROC) of 0.828. Models applied with reported features averaged an AUROC of 0.66 when validated on our data. Models rebuilt with the same features averaged an AUROC of 0.755 when validated on our data. In both cases, models did not validate against their studies’ reported AUROC values.DiscussionPublished and preprint prognostic models for patients infected with COVID-19 performed substantially worse when applied to external data. Further inquiry is required to elucidate mechanisms underlying performance deviations.ConclusionsClinicians should employ caution when applying models for clinical prediction without careful validation on local data.

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Myeloproliferative neoplasms: from origins to outcomes

Hematology ◽

10.1182/asheducation-2017.1.470 ◽

2017 ◽

Vol 2017 (1) ◽

pp. 470-479 ◽

Cited By ~ 4

Author(s):

Jyoti Nangalia ◽

Anthony R. Green

Keyword(s):

Somatic Mutations ◽

Myeloproliferative Neoplasms ◽

Myeloproliferative Neoplasm ◽

Genomic Profiling ◽

Jak Inhibitors ◽

Major Goal ◽

Disease Biology ◽

Substantial Progress ◽

Prognostic Implications ◽

Made In

Abstract Substantial progress has been made in our understanding of the pathogenetic basis of myeloproliferative neoplasms. The discovery of mutations in JAK2 over a decade ago heralded a new age for patient care as a consequence of improved diagnosis and the development of therapeutic JAK inhibitors. The more recent identification of mutations in calreticulin brought with it a sense of completeness, with most patients with myeloproliferative neoplasm now having a biological basis for their excessive myeloproliferation. We are also beginning to understand the processes that lead to acquisition of somatic mutations and the factors that influence subsequent clonal expansion and emergence of disease. Extended genomic profiling has established a multitude of additional acquired mutations, particularly prevalent in myelofibrosis, where their presence carries prognostic implications. A major goal is to integrate genetic, clinical, and laboratory features to identify patients who share disease biology and clinical outcome, such that therapies, both existing and novel, can be better targeted.

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Genetic alteration in melanoma development

Proceedings of the Latvian Academy of Sciences Section B Natural Exact and Applied Sciences ◽

10.2478/v10046-009-0029-2 ◽

2009 ◽

Vol 63 (3) ◽

pp. 73-80

Author(s):

Dace Pjanova ◽

Kristīne Azarjana ◽

Ingrīda Čēma ◽

Olita Heisele

Keyword(s):

Neoplastic Transformation ◽

Neoplastic Cell ◽

Genetic Alteration ◽

Cancer Development ◽

Therapeutic Approaches ◽

Resistance To Therapy ◽

Complex Process ◽

Substantial Progress ◽

Genetic Events ◽

Made In

Genetic alteration in melanoma developmentThe transition from a normal to a neoplastic cell is a complex process and involves many sequential genetic events. Over the last several years, substantial progress has been made in the understanding of molecular biology of melanoma including its development, progression, and resistance to therapy. An important step in cancer development appears to be senescence — a crucial barrier that prevents the proliferation of cells that are at different stages of malignancy. This review is mainly focused on patterns of molecular changes in the different steps of neoplastic transformation in melanoma and provides an up-to-date view on our understanding of the molecular genetics of melanoma development as well as therapeutic approaches based on this knowledge.

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Diagnostic conversations: Clinical Decision Making in surgery – Part 2

Diagnosis ◽

10.1515/dx-2014-0009 ◽

2014 ◽

Vol 1 (2) ◽

pp. 189-193 ◽

Cited By ~ 2

Author(s):

David Allan Watters ◽

Spencer Wynyard Beasley ◽

Wendy Crebbin

Keyword(s):

Decision Making ◽

Clinical Decision Making ◽

Clinical Decision ◽

Learning Opportunities ◽

Clinical Decisions ◽

Decision Making Processes ◽

Royal Australasian College ◽

Insight Into ◽

Made In

AbstractProceduralists who fail to review their decision making are unlikely to learn from their experiences, irrespective of whether the operative outcome is successful or not. Teaching junior surgeons to develop ‘insight’ into their own decision making has long been a challenge. Surgeons and staff of the Royal Australasian College of Surgeons worked together to develop a model to help explain the processes around clinical decision making and incorporated this model into a Clinical Decision Making (CDM) training course. In this course, faculty apply the model to specific surgical cases, within the model’s framework of how clinical decisions are made; thus providing an opportunity to identify specific decision making processes as they occur and to highlight some of the learning opportunities they provide. The conversation in this paper illustrates the kinds of case-based interactions which typically occur in the development and teaching of the CDM course.The focus in this, the second of two papers, is on reviewing post-operative clinical decisions made in relation to one case, to improve the quality of subsequent decision making.

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Chronic Eosinophilic Leukaemia Associated with JAK2 Exon 13 Insertion/Deletion Mutations

Acta Haematologica ◽

10.1159/000518737 ◽

2021 ◽

pp. 1-6

Author(s):

Nicholas Lafferty ◽

Matthew Salmon ◽

Nicholas C.P. Cross ◽

Iain Singer ◽

Aaron Cooney ◽

...

Keyword(s):

Myeloproliferative Neoplasms ◽

Driver Mutations ◽

Rare Mutation ◽

Indel Mutation ◽

Not Otherwise Specified ◽

Disease Subtypes ◽

Specific Association ◽

Eosinophilic Disorders ◽

Made In

Chronic eosinophilic leukaemia, not otherwise specified (CEL, NOS), is a diagnosis of exclusion made in cases in which there is clonal eosinophilia but an absence of genetic aberrations that define other disease subtypes. There is a need for further characterization of these cases in order to inform risk stratification and management. The importance of JAK2 mutations in myeloproliferative neoplasms (MPN) as a whole is well established, although their role specifically in eosinophilic disorders is less clear, with only a minority of cases demonstrating JAK2 abnormalities. Here, we report 2 cases with an exon 13 insertion-deletion (indel) mutation in JAK2: one with CEL-NOS and the second with an unspecified eosinophilic disorder. JAK2 indels were not detected in a screen of suspected MPN cases (n = 592) without eosinophilia that tested negative for common MPN driver mutations. Our findings thus provide further evidence for a specific association between this rare mutation and clonal eosinophilic disorders.

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NLP Methods for Extraction of Symptoms from Unstructured Data for Use in Prognostic COVID-19 Analytic Models

Journal of Artificial Intelligence Research ◽

10.1613/jair.1.12631 ◽

2021 ◽

Vol 72 ◽

pp. 429-474

Author(s):

Greg M. Silverman ◽

Himanshu S. Sahoo ◽

Nicholas E. Ingraham ◽

Monica Lupei ◽

Michael A. Puskarich ◽

...

Keyword(s):

Hospital Mortality ◽

Clinical Decision Making ◽

Quality Care ◽

Vital Signs ◽

Clinical Decision ◽

Prognostic Models ◽

P Value ◽

Rule Based ◽

Presenting Symptoms ◽

Increased Risk

Statistical modeling of outcomes based on a patient's presenting symptoms (symptomatology) can help deliver high quality care and allocate essential resources, which is especially important during the COVID-19 pandemic. Patient symptoms are typically found in unstructured notes, and thus not readily available for clinical decision making. In an attempt to fill this gap, this study compared two methods for symptom extraction from Emergency Department (ED) admission notes. Both methods utilized a lexicon derived by expanding The Center for Disease Control and Prevention's (CDC) Symptoms of Coronavirus list. The first method utilized a word2vec model to expand the lexicon using a dictionary mapping to the Uni ed Medical Language System (UMLS). The second method utilized the expanded lexicon as a rule-based gazetteer and the UMLS. These methods were evaluated against a manually annotated reference (f1-score of 0.87 for UMLS-based ensemble; and 0.85 for rule-based gazetteer with UMLS). Through analyses of associations of extracted symptoms used as features against various outcomes, salient risks among the population of COVID-19 patients, including increased risk of in-hospital mortality (OR 1.85, p-value < 0.001), were identified for patients presenting with dyspnea. Disparities between English and non-English speaking patients were also identified, the most salient being a concerning finding of opposing risk signals between fatigue and in-hospital mortality (non-English: OR 1.95, p-value = 0.02; English: OR 0.63, p-value = 0.01). While use of symptomatology for modeling of outcomes is not unique, unlike previous studies this study showed that models built using symptoms with the outcome of in-hospital mortality were not significantly different from models using data collected during an in-patient encounter (AUC of 0.9 with 95% CI of [0.88, 0.91] using only vital signs; AUC of 0.87 with 95% CI of [0.85, 0.88] using only symptoms). These findings indicate that prognostic models based on symptomatology could aid in extending COVID-19 patient care through telemedicine, replacing the need for in-person options. The methods presented in this study have potential for use in development of symptomatology-based models for other diseases, including for the study of Post-Acute Sequelae of COVID-19 (PASC).

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Prognostic and Therapeutic Implications of Tumor Biology in Colorectal Liver Metastases

Cancers ◽

10.3390/cancers14010088 ◽

2021 ◽

Vol 14 (1) ◽

pp. 88

Author(s):

Carsten Kamphues ◽

Katharina Beyer ◽

Georgios Antonios Margonis

Keyword(s):

Liver Metastases ◽

Colorectal Liver Metastases ◽

Clinical Decision Making ◽

Tumor Biology ◽

Clinical Decision ◽

Prognostic Models ◽

Treatment Benefit ◽

Colorectal Cancer Liver Metastases ◽

Therapeutic Implications ◽

Prognostic Stratification

Prognostic models allow clinicians to predict survival outcomes, facilitate patient–physician discussions, and identify subgroups with potentially distinct prognoses. Although such prognostic stratification cannot directly predict treatment benefit, it can help to inform clinical decision making. This editorial will discuss potential avenues for these topics in the context of colorectal cancer liver metastases (CRLM)[...]

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