Biomedical subjects

Cheryl Willman

Publications and source records attributed to Cheryl Willman.

6 recordsLinked to original sources

PubMed2025

Optimizing participant and community engagement in cancer genomic sequencing research.

PURPOSE: We describe strategies implemented across research centers of the Participant Engagement and Cancer Genome Sequencing (PE-CGS) Network to optimize engagement of participants and communities in cancer genomics research. We also present consensus definitions of engagement and engagement optimization, informed by our shared experiences in the Network. METHODS: Key informant interviews and a document review identified engagement and optimization strategies across PE-CGS research centers. Findings were synthesized using qualitative content analysis. Consensus on definitions of engagement and optimization were developed through iterative review by PE-CGS members. RESULTS: PE-CGS research centers adopted tailored strategies based on community needs and scientific gaps. Engagement strategies included community-based efforts (eg, advisory boards and newsletters) and participant-focused approaches (eg, enhanced informed consent and decision support tools). Optimization strategies leveraged scientific methods (eg, randomized controlled trials and surveys) to evaluate engagement. Engagement was described as the sustained and meaningful interactions between researchers, participants, and communities. Optimization was described as the application of scientific methods to refine and improve engagement and research processes and outcomes. CONCLUSION: Engagement and optimization strategies have informed research planning, conduct, and dissemination across PE-CGS. These approaches and definitions provide a foundation for developing evidence-based practices to strengthen participant and community involvement in cancer genomics research.

Humans↗

PubMed2004

A Bayesian network classification methodology for gene expression data.

We present new techniques for the application of a Bayesian network learning framework to the problem of classifying gene expression data. The focus on classification permits us to develop techniques that address in several ways the complexities of learning Bayesian nets. Our classification model reduces the Bayesian network learning problem to the problem of learning multiple subnetworks, each consisting of a class label node and its set of parent genes. We argue that this classification model is more appropriate for the gene expression domain than are other structurally similar Bayesian network classification models, such as Naive Bayes and Tree Augmented Naive Bayes (TAN), because our model is consistent with prior domain experience suggesting that a relatively small number of genes, taken in different combinations, is required to predict most clinical classes of interest. Within this framework, we consider two different approaches to identifying parent sets which are supported by the gene expression observations and any other currently available evidence. One approach employs a simple greedy algorithm to search the universe of all genes; the second approach develops and applies a gene selection algorithm whose results are incorporated as a prior to enable an exhaustive search for parent sets over a restricted universe of genes. Two other significant contributions are the construction of classifiers from multiple, competing Bayesian network hypotheses and algorithmic methods for normalizing and binning gene expression data in the absence of prior expert knowledge. Our classifiers are developed under a cross validation regimen and then validated on corresponding out-of-sample test sets. The classifiers attain a classification rate in excess of 90% on out-of-sample test sets for two publicly available datasets. We present an extensive compilation of results reported in the literature for other classification methods run against these same two datasets. Our results are comparable to, or better than, any we have found reported for these two sets, when a train-test protocol as stringent as ours is followed.

Bayes Theorem↗

PubMed2003

Induction of apoptosis in retinoid-refractory acute myelogenous leukemia by a novel AHPN analog.

Acute myelogenous leukemia (AML) is a heterogeneous disease consisting of a variety of different leukemic subtypes. While acute promyelocytic leukemia displays marked sensitivity to the differentiating effects of trans-retinoic acid (tRA), other subtypes of AML display resistance. We now describe a novel compound (E)-4-[3-(1-adamantyl)-4-hydroxyphenyl]-3-chlorocinnamic acid (3-Cl-AHPC/MM002) that induces apoptosis in the tRA-resistant leukemia cell lines M07e, KG-1, and HL-60R, and in tRA-resistant patient leukemic blasts. The 3-Cl-AHPC totally inhibits leukemia colony formation at concentrations that inhibit committed human bone marrow stem cell proliferation, that is, granulocyte/macrophage colony-forming units (CFU-GMs) by only 30%. Exposure to 3-Cl-AHPC results in caspase activation and the cleavage of poly(adenosine diphosphate) (poly(ADP)) ribose polymerase. While activation of the extracellular signal-regulated kinase (ERK) and p38 pathways is not necessary for 3-Cl-AHPC-mediated apoptosis, maximal apoptosis requires c-Jun N-terminal kinase (JNK) activation. The 3-Cl-AHPC-mediated cleavage of the antiapoptotic B-cell leukemia XL (Bcl-XL) protein to a proapoptotic 18-kDa product is found in both the M07e cell line and patient leukemic blasts. The 3-Cl-AHPC treatment of mice bearing the AML 1498 cell line results in a 3.3-log kill in the leukemic blasts. While 3-Cl-AHPC does not activate retinoic nuclear receptors, it is a potent inducer of apoptosis in AML cells and may represent a novel therapy in the treatment of this disease.

Adamantane↗

PubMed2002

All-trans retinoic acid in acute promyelocytic leukemia: long-term outcome and prognostic factor analysis from the North American Intergroup protocol.

We previously reported a benefit for all-trans retinoic acid (ATRA) in both induction and maintenance therapy in patients with acute promyelocytic leukemia (APL). To determine the durability of this benefit and identify important prognostic factors, long-term follow-up of the North American Intergroup APL trial is reported. A total of 350 patients with newly diagnosed APL were randomized to either daunorubicin and cytarabine (DA) or ATRA for induction and then either ATRA maintenance or observation following consolidation chemotherapy. The complete remission (CR) rates were not significantly different between the ATRA and DA groups (70% and 73%, respectively). However, the 5-year disease-free survival (DFS) and overall survival (OS) were longer with ATRA than with DA for induction (69% vs 29% and 69% vs 45%, respectively). Based on both induction and maintenance randomizations, the 5-year DFS was 16% for patients randomized to DA and observation, 47% for DA and ATRA, 55% for ATRA and observation, and 74% for ATRA and ATRA. There was no advantage of either induction regimen among any subgroups when CR alone was considered. However, female sex, classical M3 morphology (vs the microgranular variant [M3v]), and treatment-white blood cell count (WBC) interaction (ATRA/WBC below 2 x 10(9)/L [2000/microL] best, DA/WBC above 2 x 10(9)/L worst) were each significantly associated with improved DFS (P <.05). Treatment with ATRA, WBC below 2 x 10(9)/L, and absence of bleeding disorder were each significantly associated with improved OS. Age more than 15 years, female sex, and treatment-morphology interaction (DA/M3v worst, ATRA best regardless of morphology) were each significantly associated with improved DFS based on maintenance randomization. The improvement in outcome with ATRA in APL was maintained with long-term follow-up.

Adolescent↗

PubMed2002

Identification of gene expression profiles that segregate patients with childhood leukemia.

To identify genes whose expression correlated with biological features of childhood leukemia, we prospectively analyzed the expression profiles of 4608 genes using cDNA microarrays in 51 freshly processed bone marrow samples from children with acute leukemia, over a 24-month period, at a single institution. Two supervised methods of analysis were used to identify the 20 best discriminating genes between the following cohorts: acute myelogenous leukemia (AML) versus acute lymphoblastic leukemia (ALL); B-lineage versus T-lineage ALL; newly diagnosed B-lineage standard-risk versus high-risk ALL; and B-lineage leukemia harboring the TEL-AML 1 fusion versus patients without a molecularly characterized translocation. These methods identified overlapping sets of genes that segregated patients within described subgroups. Cross-validation demonstrated that the majority of patients could be correctly classified based on these genes alone, and hierarchical clustering grouped patients with similar clinical and biological disease features. The potential for select genes to discriminate patients was validated using real-time PCR in samples that were analyzed by microarray profiling and in other uniformly processed leukemic marrow samples. As expected, microarray technology can successfully segregate patients defined by traditional measures such as immunophenotype and cytogenetic alterations. However, among specific subgroups, this preliminary analysis also suggests that microarrays can identify unanticipated similarities and diversity in individual patients and thus may be useful in augmenting risk-group stratification in the future.

Child↗

PubMed2000

Achievements in Understanding and Treatment of Myelodysplastic Syndromes.

The myelodysplastic syndromes (MDS) constitute a challenge for the biologist as well as for the treating physician. In Section I, Dr. Willman reviews the current classifications and disease mechanisms involved in this heterogeneous clonal hematopoietic stem cell disorder. A stepwise genetic progression model is proposed in which inherited or acquired genetic lesions promote the acquisition of "secondary" genetic events mainly characterized by gains and losses of specific chromosome regions. The genetic risk to develop MDS is likely multifactorial and dependent on various constellations of risk-producing and -protecting alleles. In Section II Dr. Barrett with Dr. Saunthararajah addresses the immunologic factors that may act as important secondary events in the development of severe pancytopenia. T cells from patients with MDS may suppress autologous erythroid and granulocytic growth in vitro, and T cell suppression by antithymocyte globulin or cyclosporine may significantly improve cytopenia, especially in refractory anemia. Recent studies have also demonstrated an increased vessel density in MDS bone marrow, and a phase II trial of thalidomide showed responses in a subgroup of MDS patients especially in those with low blast counts. In Section III Dr. Hellström-Lindberg presents results of allogeneic and autologous stem cell transplantation (SCT), intensive and low-dose chemotherapy. The results of allogeneic SCT in MDS are slowly improving but are still poor for patients with unfavorable cytogenetics and/or a high score according to the International Prognostic Scoring System. A recently published study of patients between 55-65 years old showed a disease-free survival (DFS) at 3 years of 39%. Consolidation treatment with autologous SCT after intensive chemotherapy may result in long-term DFS in a proportion of patients with high-risk MDS. Low-dose treatment with 5-azacytidine has been shown to significantly prolong the time to leukemic transformation or death in patients with high-risk MSA. Erythropoietin and granulocyte colony-stimulating factor may synergistically improve hemoglobin levels, particularly in sideroblastic anemia. Recent therapeutic advances have made it clear that new biological information may lead to new treatment modalities and, in combination with statistically developed predictive models, help select patients for different therapeutic options.

Journal Article↗