Cell-Free DNA for Genomic Analysis in Primary Mediastinal Large B-Cell Lymphoma

High-throughput sequencing of cell-free DNA (cfDNA) has emerged as a promising noninvasive approach in lymphomas, being particularly useful when a biopsy specimen is not available for molecular analysis, as it frequently occurs in primary mediastinal large B-cell lymphoma (PMBL). We used cfDNA for genomic characterization in 20 PMBL patients by means of a custom NGS panel for gene mutations and low-pass whole-genome sequencing (WGS) for copy number analysis (CNA) in a real-life setting. Appropriate cfDNA to perform the analyses was obtained in 18/20 cases. The sensitivity of cfDNA to detect the mutations present in paired FFPE samples was 69% (95% CI: 60-78%). The mutational landscape found in cfDNA samples was highly consistent with that of the tissue, with the most frequently mutated genes being B2M (61%), SOCS1 (61%), GNA13 (44%), STAT6 (44%), NFKBIA (39%), ITPKB (33%), and NFKBIE (33%). Overall, we observed a 75% concordance to detect CNA gains/losses between DNA microarray and low-pass WGS. The sensitivity of low-pass WGS was remarkably higher for clonal CNA (18/20, 90%) compared to subclonal alterations identified by DNA microarray. No significant associations between cfDNA amount and tumor burden or outcome were found. cfDNA is an excellent alternative source for the accurate genetic characterization of PMBL cases

In silico validation of RNA-Seq results can identify gene fusions with oncogenic potential in glioblastoma

RNA-Sequencing (RNA-Seq) can identify gene fusions in tumors, but not all these fusions have functional consequences. Using multiple data bases, we have performed an in silico analysis of fusions detected by RNA-Seq in tumor samples from 139 newly diagnosed glioblastoma patients to identify in-frame fusions with predictable oncogenic potential. Among 61 samples with fusions, there were 103 different fusions, involving 167 different genes, including 20 known oncogenes or tumor suppressor genes (TSGs), 16 associated with cancer but not oncogenes or TSGs, and 32 not associated with cancer but previously shown to be involved in fusions in gliomas. After selecting in-frame fusions able to produce a protein product and running Oncofuse, we identified 30 fusions with predictable oncogenic potential and classified them into four non-overlapping categories: six previously described in cancer; six involving an oncogene or TSG; four predicted by Oncofuse to have oncogenic potential; and 14 other in-frame fusions. Only 24 patients harbored one or more of these 30 fusions, and only two fusions were present in more than one patient: FGFR3::TACC3 and EGFR::SEPTIN14. This in silico study provides a good starting point for the identification of gene fusions with functional consequences in the pathogenesis or treatment of glioblastoma

Palbociclib Rechallenge for Hormone Receptor–Positive/HER-Negative Advanced Breast Cancer: Findings from the Phase II BioPER Trial

Purpose: To assess the efficacy and exploratory biomarkers of continuing palbociclib plus endocrine therapy (ET) beyond pro-gression on prior palbociclib-based regimen in patients with hor-mone receptor-positive/HER2-negative (HR+/HER2-) advanced breast cancer (ABC).Patients and Methods: The multicenter, open-label, phase II BioPER trial included women who had experienced a progressive disease (PD) after having achieved clinical benefit on the immedi-ately prior palbociclib plus ET regimen. Palbociclib (125 mg, 100 mg, or 75 mg daily orally for 3 weeks and 1 week off as per prior palbociclib-based regimen) plus ET of physician's choice were administered in 4-week cycles until PD or unacceptable toxicity. Coprimary endpoints were clinical benefit rate (CBR) and percent-age of tumors with baseline loss of retinoblastoma (Rb) protein expression. Additional endpoints included safety and biomarker analysis.Results: Among 33 patients enrolled, CBR was 34.4% [95% confidence interval (CI), 18.6-53.2; P < 0.001] and 13.0% of tumors (95% CI, 5.2-27.5) showed loss of Rb protein expression, meeting both coprimary endpoints. Median progression-free survival was 2.6 months (95% CI, 1.8-6.7). No new safety signals were reported. A signature that included baseline mediators of therapeutic resistance to palbociclib and ET (low Rb score, high cyclin E1 score, ESR1 mutation) was independently associated with shorter median progression-free survival (HR, 22.0; 95% CI, 1.71-282.9; P = 0.018). Conclusions: Maintaining palbociclib after progression on prior palbociclib-based regimen seems to be a reasonable, investigational approach for selected patients. A composite biomarker signature predicts a subset of patients who may not derive a greater benefit from palbociclib rechallenge, warranting further validation in larger randomized controlled trials

Machine Learning Improves Risk Stratification in Myelofibrosis: An Analysis of the Spanish Registry of Myelofibrosis

Myelofibrosis (MF) is a myeloproliferative neoplasm (MPN) with heterogeneous clinical course. Allogeneic hematopoietic cell transplantation remains the only curative therapy, but its morbidity and mortality require careful candidate selection. Therefore, accurate disease risk prognostication is critical for treatment decision-making. We obtained registry data from patients diagnosed with MF in 60 Spanish institutions (N = 1386). These were randomly divided into a training set (80%) and a test set (20%). A machine learning (ML) technique (random forest) was used to model overall survival (OS) and leukemia-free survival (LFS) in the training set, and the results were validated in the test set. We derived the AIPSS-MF (Artificial Intelligence Prognostic Scoring System for Myelofibrosis) model, which was based on 8 clinical variables at diagnosis and achieved high accuracy in predicting OS (training set c-index, 0.750; test set c-index, 0.744) and LFS (training set c-index, 0.697; test set c-index, 0.703). No improvement was obtained with the inclusion of MPN driver mutations in the model. We were unable to adequately assess the potential benefit of including adverse cytogenetics or high-risk mutations due to the lack of these data in many patients. AIPSS-MF was superior to the IPSS regardless of MF subtype and age range and outperformed the MYSEC-PM in patients with secondary MF. In conclusion, we have developed a prediction model based exclusively on clinical variables that provides individualized prognostic estimates in patients with primary and secondary MF. The use of AIPSS-MF in combination with predictive models that incorporate genetic information may improve disease risk stratification

Real-world analysis of main clinical outcomes in patients with polycythemia vera treated with ruxolitinib or best available therapy after developing resistance/intolerance to hydroxyurea

Background: Ruxolitinib is approved for patients with polycythemia vera (PV) who are resistant/intolerant to hydroxyurea, but its impact on preventing thrombosis or disease-progression is unknown. Methods: A retrospective, real-world analysis was performed on the outcomes of 377 patients with resistance/intolerance to hydroxyurea from the Spanish Registry of Polycythemia Vera according to subsequent treatment with ruxolitinib (n = 105) or the best available therapy (BAT; n = 272). Survival probabilities and rates of thrombosis, hemorrhage, acute myeloid leukemia, myelofibrosis, and second primary cancers were calculated according to treatment. To minimize biases in treatment allocation, all results were adjusted by a propensity score for receiving ruxolitinib or BAT. Results: Patients receiving ruxolitinib had a significantly lower rate of arterial thrombosis than those on BAT (0.4% vs 2.3% per year; P = .03), and this persisted as a trend after adjustment for the propensity to have received the drug (incidence rate ratio, 0.18; 95% confidence interval, 0.02-1.3; P = .09). There were no significant differences in the rates of venous thrombosis (0.8% and 1.1% for ruxolitinib and BAT, respectively; P = .7) and major bleeding (0.8% and 0.9%, respectively; P = .9). Ruxolitinib exposure was not associated with a higher rate of second primary cancers, including all types of neoplasia, noncutaneous cancers, and nonmelanoma skin cancers. After a median follow-up of 3.5 years, there were no differences in survival or progression to acute leukemia or myelofibrosis between the 2 groups. Conclusions: The results suggest that ruxolitinib treatment for PV patients with resistance/intolerance to hydroxyurea may reduce the incidence of arterial thrombosis. Lay summary: Ruxolitinib is better than other available therapies in achieving hematocrit control and symptom relief in patients with polycythemia vera who are resistant/intolerant to hydroxyurea, but we still do not know whether ruxolitinib provides an additional benefit in preventing thrombosis or disease progression. We retrospectively studied the outcomes of 377 patients with resistance/intolerance to hydroxyurea from the Spanish Registry of Polycythemia Vera according to whether they subsequently received ruxolitinib (n = 105) or the best available therapy (n = 272). Our findings suggest that ruxolitinib could reduce the incidence of arterial thrombosis, but a disease-modifying effect could not be demonstrated for ruxolitinib in this patient population

Long-term follow-up of recovered MPN patients with COVID-19

The study was supported by a research grant by the COVID “3×1 project”, BREMBO S.p.A., Bergamo, Italy (TB) and by AIRC 5×1000 call “Metastatic disease: the key unmet need in oncology” to MYNERVA project, #21267 (MYeloid NEoplasms Research Venture AIRC). A detailed description of the MYNERVA project is available at https://progettomynerva.it (AMV, PG). The study was also supported by HARMONY PLUS, which is funded through the Innovative Medicines Initiative (IMI), Europe’s largest public-private initiative aiming to speed up the development of better and safer medicines for patients. The HARMONY Alliance has received funding from IMI 2 Joint Undertaking and is listed under grant agreement No. 945406. This Joint Undertaking receives support from the European Union’s Horizon 2020 Research and Innovation Programme and the European Federation of Pharmaceutical Industries and Associations (EFPIA). IMI supports collaborative research projects and builds networks of industrial and academic experts in order to boost pharmaceutical innovation in Europe

Low-risk polycythemia vera treated with phlebotomies: clinical characteristics, hematologic control and complications in 453 patients from the Spanish Registry of Polycythemia Vera

Hematological control, incidence of complications, and need for cytoreduction were studied in 453 patients with low-risk polycythemia vera (PV) treated with phlebotomies alone. Median hematocrit value decreased from 54% at diagnosis to 45% at 12 months, and adequate hematocrit control over time ( 60 years, and microvascular symptoms constituted the main indications for starting cytoreduction. Median duration without initiating cytoreduction was significantly longer in patients younger than 50 years (< 0.0001). The incidence rate of thrombosis under phlebotomies alone was 0.8% per year and the estimated probability of thrombosis at 10 years was 8.5%. The probability of arterial thrombosis was significantly higher in patients with arterial hypertension whereas there was a trend to higher risk of venous thrombosis in cases with high JAK2V617F allele burden. Rates of major bleeding and second primary neoplasm were low. With a median follow-up of 9 years, survival probability at 10 years was 97%, whereas the probability of myelofibrosis at 10 and 20 years was 7% and 20%, respectively. Progression to acute myeloid leukemia was documented in 3 cases (1%). Current management of low-risk PV patients is associated with low rate of thrombosis and long survival. New treatment strategies are needed for improving hematological control and, in the long term, reducing progression to myelofibrosis

Effect of mutation order on myeloproliferative neoplasms.

BACKGROUND: Cancers result from the accumulation of somatic mutations, and their properties are thought to reflect the sum of these mutations. However, little is known about the effect of the order in which mutations are acquired. METHODS:/nWe determined mutation order in patients with myeloproliferative neoplasms by genotyping hematopoietic colonies or by means of next-generation sequencing. Stem cells and progenitor cells were isolated to study the effect of mutation order on mature and immature hematopoietic cells. RESULTS: The age at which a patient presented with a myeloproliferative neoplasm, acquisition of JAK2 V617F homozygosity, and the balance of immature progenitors were all influenced by mutation order. As compared with patients in whom the TET2 mutation was acquired first (hereafter referred to as "TET2-first patients"), patients in whom the Janus kinase 2 (JAK2) mutation was acquired first ("JAK2-first patients") had a greater likelihood of presenting with polycythemia vera than with essential thrombocythemia, an increased risk of thrombosis, and an increased sensitivity of JAK2-mutant progenitors to ruxolitinib in vitro. Mutation order influenced the proliferative response to JAK2 V617F and the capacity of double-mutant hematopoietic cells and progenitor cells to generate colony-forming cells. Moreover, the hematopoietic stem-and-progenitor-cell compartment was dominated by TET2 single-mutant cells in TET2-first patients but by JAK2-TET2 double-mutant cells in JAK2-first patients. Prior mutation of TET2 altered the transcriptional consequences of JAK2 V617F in a cell-intrinsic manner and prevented JAK2 V617F from up-regulating genes associated with proliferation. CONCLUSIONS: The order in which JAK2 and TET2 mutations were acquired influenced clinical features, the response to targeted therapy, the biology of stem and progenitor cells, and clonal evolution in patients with myeloproliferative neoplasms. (Funded by Leukemia and Lymphoma Research and others.).Work in the Green lab is supported by Leukemia and Lymphoma Research, Cancer Research UK, the Kay Kendall Leukaemia Fund, the NIHR Cambridge Biomedical Research Centre, the Cambridge Experimental Cancer Medicine Centre, and the Leukemia & Lymphoma Society of America. DGK was supported by a postdoctoral fellowship from the Canadian Institutes of Health Research (Ottawa, ON), and a Lady Tata Memorial Trust International Award for Research in Leukaemia (London, UK). CAO was supported by a research fellowship from the Deutsche Forschungsgemeinschaft (DFG, OR255/1-1)

Machine learning improves risk stratification in myelofibrosis: An analysis of the spanish registry of myelofibrosis

Myelofibrosis (MF) is a myeloproliferative neoplasm (MPN) with heterogeneous clinical course. Allogeneic hematopoietic cell transplantation remains the only curative therapy, but its morbidity and mortality require careful candidate selection. Therefore, accurate disease risk prognostication is critical for treatment decision-making. We obtained registry data from patients diagnosed with MF in 60 Spanish institutions (N = 1386). These were randomly divided into a training set (80%) and a test set (20%). A machine learning (ML) technique (random forest) was used to model overall survival (OS) and leukemia-free survival (LFS) in the training set, and the results were validated in the test set. We derived the AIPSS-MF (Artificial Intelligence Prognostic Scoring System for Myelofibrosis) model, which was based on 8 clinical variables at diagnosis and achieved high accuracy in predicting OS (training set c-index, 0.750; test set c-index, 0.744) and LFS (training set c-index, 0.697; test set c-index, 0.703). No improvement was obtained with the inclusion of MPN driver mutations in the model. We were unable to adequately assess the potential benefit of including adverse cytogenetics or high-risk mutations due to the lack of these data in many patients. AIPSS-MF was superior to the IPSS regardless of MF subtype and age range and outperformed the MYSEC-PM in patients with secondary MF. In conclusion, we have developed a prediction model based exclusively on clinical variables that provides individualized prognostic estimates in patients with primary and secondary MF. The use of AIPSS-MF in combination with predictive models that incorporate genetic information may improve disease risk stratification