Cancer patients with clonal hematopoiesis die from primary malignancy or comorbidities despite higher rates of transformation to myeloid neoplasms

BACKGROUND

The occurrence of somatic mutations in patients with no evidence of hematological disorders is called clonal hematopoiesis (CH). CH, whose subtypes include CH of indeterminate potential and clonal cytopenia of undetermined significance, has been associated with both hematologic cancers and systemic comorbidities. However, CH's effect on patients, especially those with concomitant malignancies, is not fully understood.

METHODS

We performed a retrospective evaluation of all patients with CH at a tertiary cancer center. Patient characteristics, mutational data, and outcomes were collected and analyzed.

RESULTS

Of 78 individuals included, 59 (76%) had a history of cancer and 60 (77%) had moderate to severe comorbidity burdens. DNMT3A, TET2, TP53, and ASXL1 were the most common mutations. For the entire cohort, the 2-year overall survival rate was 79% (95% CI: 70, 90), while the median survival was not reached. Of 20 observed deaths, most were related to primary malignancies (n = 7, 35%), comorbidities (n = 4, 20%), or myeloid neoplasms (n = 4, 20%). Twelve patients (15%) experienced transformation to a myeloid neoplasm. According to the clonal hematopoiesis risk score, the 3-year transformation rate was 0% in low-risk, 15% in intermediate-risk (p = 0.098), and 28% in high-risk (p = 0.05) patients. By multivariate analysis, transformation was associated with variant allele frequency ≥0.2 and hemoglobin <10 g/dL.

CONCLUSIONS

In a population including mostly cancer patients, CH was associated with comorbidities and myeloid transformation in patients with higher mutational burdens and anemia. Nevertheless, such patients were less likely to die of their myeloid neoplasm than of primary malignancy or comorbidities.

The LEukemia Artificial Intelligence Program (LEAP) in chronic myeloid leukemia in chronic phase: A model to improve patient outcomes

Extreme gradient boosting methods outperform conventional machine-learning models. Here, we have developed the LEukemia Artificial intelligence Program (LEAP) with the extreme gradient boosting decision tree method for the optimal treatment recommendation of tyrosine kinase inhibitors (TKIs) in patients with chronic myeloid leukemia in chronic phase (CML-CP). A cohort of CML-CP patients was randomly divided into training/validation (N = 504) and test cohorts (N = 126). The training/validation cohort was used for 3-fold cross validation to develop the LEAP CML-CP model using 101 variables at diagnosis. The test cohort was then applied to the LEAP CML-CP model and an optimum TKI treatment was suggested for each patient. The area under the curve in the test cohort was 0.81899.Backward multivariate analysis identified age at diagnosis, the degree of comorbidities, and TKI recommended therapy by the LEAP CML-CP model as independent prognostic factors for overall survival. The bootstrapping method internally validated the association of the LEAP CML-CP recommendation with overall survival as an independent prognostic for overall survival. Selecting treatment according to the LEAP CML-CP personalized recommendations, in this model, is associated with better survival probability compared to treatment with a LEAP CML-CP non-recommended therapy. This approach may pave a way of new era of personalized treatment recommendations for patients with cancer.

Myelodysplastic syndromes: 2021 update on diagnosis, risk stratification and management

DISEASE OVERVIEW

The myelodysplastic syndromes (MDS) are a very heterogeneous group of myeloid disorders characterized by peripheral blood cytopenias and increased risk of transformation to acute myelogenous leukemia (AML). Myelodysplastic syndromes occur more frequently in older males and in individuals with prior exposure to cytotoxic therapy.

DIAGNOSIS

Diagnosis of MDS is based on morphological evidence of dysplasia upon visual examination of a bone marrow aspirate and biopsy. Information obtained from additional studies such as karyotype, flow cytometry and molecular genetics is usually complementary and may help refine diagnosis.

RISK-STRATIFICATION

Prognosis of patients with MDS can be calculated using a number of scoring systems. In general, all these scoring systems include analysis of peripheral cytopenias, percentage of blasts in the bone marrow and cytogenetic characteristics. The most commonly accepted system is the Revised International Prognostic Scoring System (IPSS-R). Somatic mutations can help define prognosis and therapy.

RISK-ADAPTED THERAPY

Therapy is selected based on risk, transfusion needs, percent of bone marrow blasts, cytogenetic and mutational profiles, comorbidities, potential for allogeneic stem cell transplantation (alloSCT) and prior exposure to hypomethylating agents (HMA). Goals of therapy are different in lower-risk patients than in higher-risk individuals and in those with HMA failure. In lower-risk MDS, the goal is to decrease transfusion needs and transformation to higher risk disease or AML, as well as to improve survival. In higher-risk disease, the goal is to prolong survival. In 2020, we witnessed an explosion of new agents and investigational approaches. Current available therapies include growth factor support, lenalidomide, HMAs, intensive chemotherapy and alloSCT. Novel therapeutics approved in 2020 are luspatercept and the oral HMA ASTX727. At the present time, there are no approved interventions for patients with progressive or refractory disease particularly after HMA-based therapy. Options include participation in a clinical trial, cytarabine-based therapy or alloSCT.