How comparable are patient outcomes in the "real-world" with populations studied in pivotal AML trials?

Despite an increasing desire to use historical cohorts as "synthetic" controls for new drug evaluation, limited data exist regarding the comparability of real-world outcomes to those in clinical trials. Governmental cancer data often lacks details on treatment, response, and molecular characterization of disease sub-groups. The Australasian Leukaemia and Lymphoma Group National Blood Cancer Registry (ALLG NBCR) includes source information on morphology, cytogenetics, flow cytometry, and molecular features linked to treatment received (including transplantation), response to treatment, relapse, and survival outcome. Using data from 942 AML patients enrolled between 2012-2018, we assessed age and disease-matched control and interventional populations from published randomized trials that led to the registration of midostaurin, gemtuzumab ozogamicin, CPX-351, oral azacitidine, and venetoclax. Our analyses highlight important differences in real-world outcomes compared to clinical trial populations, including variations in anthracycline type, cytarabine intensity and scheduling during consolidation, and the frequency of allogeneic hematopoietic cell transplantation in first remission. Although real-world outcomes were comparable to some published studies, notable differences were apparent in others. If historical datasets were used to assess the impact of novel therapies, this work underscores the need to assess diverse datasets to enable geographic differences in treatment outcomes to be accounted for.

The path to approval for oral hypomethylating agents in acute myeloid leukemia and myelodysplastic syndromes.

Two oral hypomethylating agents, oral azacitidine (CC-486) and decitabine/cedazuridine (ASTX727), have recently entered the clinical domain. CC-486 has been shown to improve overall survival as maintenance therapy for older patients with acute myeloid leukemia in complete remission, whereas the combination of decitabine with cedazuridine, a cytidine deaminase inhibitor, is indicated for the treatment of adult patients with myelodysplastic syndromes and chronic myelomonocytic leukemia with intermediate-1, or higher, International Prognostic Scoring System risk. This article briefly summarizes the clinical development of both drugs, the pivotal studies that led to their approval and some of the issues faced in extending the use of these drugs to other indications.

Lay abstract One of the key challenges in treating acute myeloid leukemia is to prevent relapse after remission has been achieved. This means that developing an effective maintenance treatment is very important. Maintenance treatment is given for a prolonged period and so it needs to be easy to give and well tolerated. Oral azacitidine is an example of this type of treatment and is the first drug that has been shown to improve survival as maintenance therapy for acute myeloid leukemia patients. This article describes the key studies that led to the approval of this important therapy.

Pralatrexate in relapsed/refractory T-cell lymphoma: a retrospective multicenter study.

We present a retrospective multicenter study of pralatrexate treatment outcomes in an Australian practice setting for patients with relapsed/refractory T-cell lymphoma who had failed 1+ systemic therapies, treated via a compassionate access program. Endpoints assessed included response rates, toxicities, and subsequent therapies. Progression-free survival (PFS), time to next treatment (TTNT), event-free survival (EFS), overall survival (OS), and time to best response, were assessed by Kaplan-Meier analysis. The study included 31 patients, with median age 69 years. We demonstrated ORR of 35.5% (n = 11), including 4 complete responses (13%) and 7 partial responses (23%). The predicted median OS was 10 months, with EFS of 9 months, and PFS of 9 months. Median TTNT was 8 months. Mucositis was the most commonly observed toxicity. This study - the second largest real-world cohort reported to date - underscores the importance of pralatrexate in relapsed/refractory T-cell lymphoma, as well as its acceptable toxicity profile.

Effectiveness of Evenup™ Shoe-Lift Use Among Individuals Prescribed a Walking Boot.

BACKGROUND: Walking boots are prescribed after foot and ankle injuries, allowing immediate ambulation for patients. However, temporary limb-length inequality (LLI) may result, causing dysfunction and pain, including low back pain. The Evenup™ shoe-lift was designed to eliminate joint pathology, pain, and gait deviations resulting from walking-boot-induced LLI, yet no clinical trials have been reported on its effectiveness. METHODS: Thirty-four subjects undergoing unilateral lower-extremity orthopedic medical and rehabilitative care were recruited for this study. Seventeen subjects were assigned to an intervention group using a walking boot on the foot of their involved side and the Evenup™ on the other foot, while the control group used a walking boot only on the involved side. Outcome measures included the lower extremity functional scale (LEFS), modified Oswestry low back pain disability questionnaire (OSW), numeric pain rating scale, ankle range of motion (ROM) and strength. RESULTS: All subjects, regardless of the intervention, demonstrated improved function, decreased pain, increased ROM, and increased strength. Additionally, a clinically relevant difference was found between the intervention and control groups for the OSW and LEFS. CONCLUSION: LEFS and OSW results suggest Evenup™ use added value in the form of improved patient function.

ERG signaling in prostate cancer is driven through PRMT5-dependent methylation of the Androgen Receptor.

The TMPRSS2:ERG gene fusion is common in androgen receptor (AR) positive prostate cancers, yet its function remains poorly understood. From a screen for functionally relevant ERG interactors, we identify the arginine methyltransferase PRMT5. ERG recruits PRMT5 to AR-target genes, where PRMT5 methylates AR on arginine 761. This attenuates AR recruitment and transcription of genes expressed in differentiated prostate epithelium. The AR-inhibitory function of PRMT5 is restricted to TMPRSS2:ERG-positive prostate cancer cells. Mutation of this methylation site on AR results in a transcriptionally hyperactive AR, suggesting that the proliferative effects of ERG and PRMT5 are mediated through attenuating AR's ability to induce genes normally involved in lineage differentiation. This provides a rationale for targeting PRMT5 in TMPRSS2:ERG positive prostate cancers. Moreover, methylation of AR at arginine 761 highlights a mechanism for how the ERG oncogene may coax AR towards inducing proliferation versus differentiation.

Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry.

OBJECTIVES: Using the prediction of cancer outcome as a model, we have tested the hypothesis that through analysing routinely collected digital data contained in an electronic administrative record (EAR), using machine-learning techniques, we could enhance conventional methods in predicting clinical outcomes. SETTING: A regional cancer centre in Australia. PARTICIPANTS: Disease-specific data from a purpose-built cancer registry (Evaluation of Cancer Outcomes (ECO)) from 869 patients were used to predict survival at 6, 12 and 24 months. The model was validated with data from a further 94 patients, and results compared to the assessment of five specialist oncologists. Machine-learning prediction using ECO data was compared with that using EAR and a model combining ECO and EAR data. PRIMARY AND SECONDARY OUTCOME MEASURES: Survival prediction accuracy in terms of the area under the receiver operating characteristic curve (AUC). RESULTS: The ECO model yielded AUCs of 0.87 (95% CI 0.848 to 0.890) at 6 months, 0.796 (95% CI 0.774 to 0.823) at 12 months and 0.764 (95% CI 0.737 to 0.789) at 24 months. Each was slightly better than the performance of the clinician panel. The model performed consistently across a range of cancers, including rare cancers. Combining ECO and EAR data yielded better prediction than the ECO-based model (AUCs ranging from 0.757 to 0.997 for 6 months, AUCs from 0.689 to 0.988 for 12 months and AUCs from 0.713 to 0.973 for 24 months). The best prediction was for genitourinary, head and neck, lung, skin, and upper gastrointestinal tumours. CONCLUSIONS: Machine learning applied to information from a disease-specific (cancer) database and the EAR can be used to predict clinical outcomes. Importantly, the approach described made use of digital data that is already routinely collected but underexploited by clinical health systems.