Chimeric Antigen Receptor T-cell Therapy in Hematologic Malignancies and Patient-reported Outcomes: A Scoping Review

The inclusion of patient-reported outcome (PRO) measures in chimeric antigen receptor (CAR) T-cell therapy research is critical for understanding the impact of this novel approach from a unique patient standpoint. We performed a scoping review to map the available literature on the use of PRO measures in CAR T-cell therapy studies of patients with hematologic malignancies published between January 2015 and July 2022. Fourteen studies were identified, of which 7 (50%) were investigational early-phase trials, 6 (42.9%) were observational studies, and 1 (7.1%) was a pilot study. The EQ-5D and the PROMIS-29 were the 2 most frequently used PRO measures, being included in 6 (42.9%) and 5 (35.7%) studies, respectively. Despite differences in study designs, there seems to be evidence of improvements over time since CAR T-cell infusion in important domains such as physical functioning and fatigue, at least in patients who respond to therapy. Overall, the studies identified in our review have shown the added value of PRO assessment in CAR T-cell therapy research by providing novel information that complements the knowledge on safety and efficacy. However, there are several questions which remain to be answered in future research. For example, limited evidence exists regarding patient experience during important phases of the disease trajectory as only 4 (28.6%) and 5 (35.7%) studies provided information on PROs during the first 2 weeks from CAR T-cell infusion and after the first year, respectively. Time is ripe for a more systematic implementation of high-quality PRO assessment in future clinical trials and in real-life settings of patients treated with CAR T-cell therapy

Rapid identification of BCR/ABL1-like acute lymphoblastic leukaemia patients using a predictive statistical model based on quantitative real time-polymerase chain reaction: clinical, prognostic and therapeutic implications.

BCR/ABL1-like acute lymphoblastic leukaemia (ALL) is a subgroup of B-lineage acute lymphoblastic leukaemia that occurs within cases without recurrent molecular rearrangements. Gene expression profiling (GEP) can identify these cases but it is expensive and not widely available. Using GEP, we identified 10 genes specifically overexpressed by BCR/ABL1-like ALL cases and used their expression values - assessed by quantitative real time-polymerase chain reaction (Q-RT-PCR) in 26 BCR/ABL1-like and 26 non-BCR/ABL1-like cases to build a statistical "BCR/ABL1-like predictor", for the identification of BCR/ABL1-like cases. By screening 142 B-lineage ALL patients with the "BCR/ABL1-like predictor", we identified 28/142 BCR/ABL1-like patients (19·7%). Overall, BCR/ABL1-like cases were enriched in JAK/STAT mutations (P < 0·001), IKZF1 deletions (P < 0·001) and rearrangements involving cytokine receptors and tyrosine kinases (P = 0·001), thus corroborating the validity of the prediction. Clinically, the BCR/ABL1-like cases identified by the BCR/ABL1-like predictor achieved a lower rate of complete remission (P = 0·014) and a worse event-free survival (P = 0·0009) compared to non-BCR/ABL1-like ALL. Consistently, primary cells from BCR/ABL1-like cases responded in vitro to ponatinib. We propose a simple tool based on Q-RT-PCR and a statistical model that is capable of easily, quickly and reliably identifying BCR/ABL1-like ALL cases at diagnosis

Exploring cost-benefit analysis of research, development and innovation infrastructures: an evaluation framework

Governments, funding agencies and policy makers have high expectations on research, development and innovation (RDI) infrastructures in the context of science and innovation policies aimed at sustaining economic growth in the long term. The stakes associated with their selection and evaluation are therefore high. Cost-benefit analysis of RDI infrastructures is a new field. The intangible nature of some benefits and the uncertainty associated to the achievement of research results have often discouraged the use of a proper CBA for RDI infrastructures. Recently, some attempts to develop a CBA theoretical framework for RDI infrastructures have been made in the context of the use of Structural Funds by the Czech government and JASPERS. Moreover, the new Guide for the CBA of investment projects in the context of Cohesion Policy, recently adopted by the European Commission (2014) provides guidelines to appraise RDI projects, but also admits that \u2013 due to lack of experience and best practices \u2013 further steps are needed to improve the evaluation framework. This paper presents the results and the lessons learned on how to apply ex-ante CBA for major RDI infrastructures by a team of economists and scientists at the University of Milan and CSIL during a three-year research project supported by a EIBURS grant of the European Investment Bank Institute. Albeit the comprehensive conceptual framework presented in the paper builds on principles firmly rooted in CBA tradition, their application to the RDI sector is still in its infancy. So far, the model has been applied on two cases in physics involving particle accelerators (the Large Hadron Collider (LHC) at CERN and the National Centre for Oncological Treatment (CNAO) in Italy)). In a nutshell, the model presented break down benefits into two broad classes: i) use benefits, held by different categories of infrastructure\u2019s users such as scientists, firms, students and general public visitors, and ii) non-use benefits, denoting the social value for the discovery potential of the RDI infrastructure regardless of its actual or future use. We argue that the social value of discovery can be estimated with contingent valuation techniques. Another significant feature of our approach is the stochastic nature of the CBA model, intended to deal with the uncertainty and risk of optimism bias in the estimates

Cost-benefit analysis of applied research infrastructure : Evidence from health care

The present study aims at offering empirical evidence to improve existing knowledge and theory building on research infrastructure evaluation. Through an inductive case study research strategy, an innovative cost-benefit analysis framework has been used to assess the impact of an applied research infrastructure. The case study is the National Hadrontherapy Centre for Cancer Treatment (CNAO) located in Pavia (Italy). CNAO is an applied research facility specialised in hadrontherapy, an advanced oncological treatment showing clinical advantages as compared to traditional radiotherapy, at the same time being more expensive as it exploits non-commercial accelerators technology and sophisticated control and dose delivery systems. The analysis shows that with a fairly high probability the Centre provides a positive net contribution to society's welfare. Source of benefits are mainly health treatments to patients, for whom gains in terms of longer or better lives are guaranteed as compared to a counterfactual situation where they are treated with conventional therapies or they have no alternatives. Such benefits are the direct consequences of the application to end users of the knowledge developed in the Centre with research activities and are quantified and assessed on the basis of conventional cost-benefit analysis (CBA) approaches for health benefits. Additional benefits generated by the Centre are typical of research infrastructures in different scientific domains and refer to technological spillovers (namely creation of spin-offs, technological transfer to companies in the supply chain and to other similar facilities), knowledge creation (production of scientific outputs), human capital formation (training of doctoral students, technicians and professionals in the field of hadrontherapy) and cultural outreach (students, researchers and wider public visiting the facilities). Evidences show that the adopted CBA framework is a promising avenue as compared to existing alternative methodologies informing decision-making. Further research is however needed to fine tune the methodology, in particular for what concerns technological spillovers and knowledge creation benefits

Prognostic impact of KMT2A-AFF1-positivity in 926 BCR-ABL1-negative B-lineage acute lymphoblastic leukemia patients treated in GIMEMA clinical trials since 1996

The impact of KMT2A-AFF1 rearrangement in pediatric-like, minimal residual disease (MRD)-based clinical trials and the effect of transplant in KMT2A-AFF1 ALL are still debated

Fatigue in newly diagnosed acute myeloid leukaemia: General population comparison and predictive factors

Objectives: This study compared the burden of fatigue between treatment-naïve patients with newly diagnosed acute myeloid leukaemia (AML) and the general population and investigated patient factors associated with fatigue severity. Methods: Pretreatment patient-reported fatigue was assessed with the Functional Assessment of Chronic Illness Therapy-Fatigue questionnaire in a sample of 463 newly diagnosed patients with AML who were enrolled in a clinical trial. Multivariable linear regression models were used to estimate the adjusted mean differences in fatigue between patients with AML and adults from the general population (n=847) by AML disease risk categories. A clinically meaningful difference in fatigue was defined as ≥3 points. Univariable and multivariable linear regression models were used to identify sociodemographic, clinical and molecular correlates of worse fatigue in patients with AML. Results: Patients with AML reported adjusted mean fatigue scores that were 7.5 points worse than the general population (95% CI -8.6 to -6.4, p&lt;0.001). Across AML disease risk categories, adjusted mean differences in fatigue compared with the general population ranged from 6.7 points worse (patients with favourable risk: 95% CI -8.6 to -4.8, p&lt;0.001) to 8.9 points worse (patients with poor risk, 95% CI -10.5 to -7.2, p&lt;0.001). Overall, 91% of patients with AML reported fatigue that was equal to or worse than the general population's median fatigue score. Higher pretreatment fatigue was independently associated with female sex, WHO performance status ≥1 and lower platelet levels. Conclusions: Patients with newly diagnosed AML reported worse fatigue than the general population, and mean differences exceeded twice the threshold for clinical significance. Our findings may help to identify patients with AML most likely to benefit from supportive care interventions to reduce fatigue

Fatigue in newly diagnosed acute myeloid leukaemia: General population comparison and predictive factors

Objectives: This study compared the burden of fatigue between treatment-naïve patients with newly diagnosed acute myeloid leukaemia (AML) and the general population and investigated patient factors associated with fatigue severity. Methods: Pretreatment patient-reported fatigue was assessed with the Functional Assessment of Chronic Illness Therapy-Fatigue questionnaire in a sample of 463 newly diagnosed patients with AML who were enrolled in a clinical trial. Multivariable linear regression models were used to estimate the adjusted mean differences in fatigue between patients with AML and adults from the general population (n=847) by AML disease risk categories. A clinically meaningful difference in fatigue was defined as ≥3 points. Univariable and multivariable linear regression models were used to identify sociodemographic, clinical and molecular correlates of worse fatigue in patients with AML. Results: Patients with AML reported adjusted mean fatigue scores that were 7.5 points worse than the general population (95% CI -8.6 to -6.4, p&lt;0.001). Across AML disease risk categories, adjusted mean differences in fatigue compared with the general population ranged from 6.7 points worse (patients with favourable risk: 95% CI -8.6 to -4.8, p&lt;0.001) to 8.9 points worse (patients with poor risk, 95% CI -10.5 to -7.2, p&lt;0.001). Overall, 91% of patients with AML reported fatigue that was equal to or worse than the general population's median fatigue score. Higher pretreatment fatigue was independently associated with female sex, WHO performance status ≥1 and lower platelet levels. Conclusions: Patients with newly diagnosed AML reported worse fatigue than the general population, and mean differences exceeded twice the threshold for clinical significance. Our findings may help to identify patients with AML most likely to benefit from supportive care interventions to reduce fatigue

Validation and reference values of the EORTC QLQ-CML24 questionnaire to assess health-related quality of life in patients with chronic myeloid leukemia

Health-related quality of life (HRQOL) assessment is important to facilitate decisions in the current treatment landscape of chronic myeloid leukemia (CML). Therefore, the availability of a validated HRQOL questionnaire, specifically developed for CML patients treated with tyrosine kinase inhibitors (TKIs), may enhance quality of research in this area. We performed an international study including 782 CML patients to assess the validity of the EORTC QLQ-CML 24 questionnaire, and to generate HRQOL reference values to facilitate interpretation of results in future studies. Internal consistency, assessed with Cronbach’s alpha coefficients, ranged from 0.66 to 0.83. In the confirmatory factor analysis, all standardized factor loadings exceeded the threshold of 0.40 (range 0.49–0.97), confirming the hypothesized scale structure. Reference values stratified by age and sex were also generated. Our findings support the use of the EORTC QLQ-CML 24, in conjunction with the EORTC QLQ-C30, as a valuable measure to assess HRQOL in CML patients

Complex karyotype in unfit patients with CLL treated with ibrutinib and rituximab: the GIMEMA LLC1114 phase 2 study

In chronic lymphocytic leukemia (CLL), the presence of a complex karyotype, as defined by ≥3 chromosomal abnormalities in the neoplastic clone, has been shown to confer an adverse prognosis in retrospective series of untreated patients and in patients treated with chemoimmunotherap

ELN2017 risk stratification improves outcome prediction when applied to the prospective GIMEMA AML1310 protocol

The 2017 version of the European LeukemiaNet (ELN) recommendations, by integrating cytogenetics and mutational status of specific genes, divides patients with acute myeloid leukemia into 3 prognostically distinct risk categories: favorable (ELN2017-FR), intermediate (ELN2017-IR), and adverse (ELN2017-AR). We performed a post hoc analysis of the GIMEMA (Gruppo Italiano Malattie EMatologiche dell’Adulto) AML1310 trial to investigate the applicability of the ELN2017 risk stratification to our study population. In this trial, after induction and consolidation, patients in complete remission were to receive an autologous stem cell transplant (auto-SCT) if categorized as favorable risk or an allogeneic stem cell transplant (allo-SCT) if adverse risk. Intermediate-risk patients were to receive auto-SCT or allo-SCT based on the postconsolidation levels of measurable residual disease as measured by using flow cytometry. Risk categorization was originally conducted according to the 2009 National Comprehensive Cancer Network recommendations. Among 500 patients, 445 (89%) were reclassified according to the ELN2017 criteria: ELN2017-FR, 186 (41.8%) of 455; ELN2017-IR, 179 (40.2%) of 445; and ELN2017-AR, 80 (18%) of 455. In 55 patients (11%), ELN2017 was not applicable. Two-year overall survival (OS) was 68.8%, 51.3%, 45.8%, and 42.8% for the ELN2017-FR, ELN2017-IR, ELN2017-not classifiable, and ELN2017-AR groups, respectively (P, .001). When comparing the 2 different transplant strategies in each ELN2017 risk category, a significant benefit of auto-SCT over allo-SCT was observed among ELN2017-FR patients (2-year OS of 83.3% vs 66.7%; P 5 .0421). The 2 transplant procedures performed almost equally in the ELN2017-IR group (2-year OS of 73.9% vs 70.8%; P 5 .5552). This post hoc analysis of the GIMEMA AML1310 trial confirms that the ELN2017 classification is able to accurately discriminate patients with different outcomes and who may benefit from different transplant strategies. This trial was registered as EudraCT number 2010-023809-36 and at www.clinicaltrials.gov as #NCT01452646