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At least 55 records · Page 3

Association of Short-term Patient-reported Outcomes With Long-term Oncologic Outcomes in Localized Prostate Cancer Patients Treated With Radiation Therapy and Androgen Deprivation Therapy in a Randomized Controlled Trial

Both oncologic outcomes and patient-reported outcomes are pivotal in prostate cancer (PCa). However, it remains unknown if there is any association between these 2 outcomes. In this secondary analysis of a randomized controlled trial, we investigated the association of short-term changes in patient-reported outcome with long-term event-free survival (EFS) and metastasis-free survival (MFS) in localized PCa.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Expected individual benefit of prophylactic platelet transfusions in hemato‐oncology patients based on bleeding risks

Abstract Background Prophylactic platelet transfusions prevent bleeding in hemato‐oncology patients, but it is unclear how any benefit varies between patients. Our aim was to assess if patients with different baseline risks for bleeding benefit differently from a prophylactic platelet transfusion strategy. Study design and methods Using the data from the randomized controlled TOPPS trial (Trial of Platelet Prophylaxis), we developed a prediction model for World Health Organization grades 2, 3, and 4 bleeding risk (defined as at least one bleeding episode in a 30 days period) and grouped patients in four risk‐quartiles based on this predicted baseline risk. Predictors in the model were baseline platelet count, age, diagnosis, disease modifying treatment, disease status, previous stem cell transplantation, and the randomization arm. Results The model had a c‐statistic of 0.58 (95% confidence interval [CI] 0.54–0.64). There was little variation in predicted risks (quartiles 46%, 47%, and 51%), but prophylactic platelet transfusions gave a risk reduction in all risk quartiles. The absolute risk difference (ARD) was 3.4% (CI −12.2 to 18.9) in the lowest risk quartile (quartile 1), 7.4% (95% CI −8.4 to 23.3) in quartile 2, 6.8% (95% CI −9.1 to 22.9) in quartile 3, and 12.8% (CI −3.1 to 28.7) in the highest risk quartile (quartile 4). Conclusion In our study, generally accepted bleeding risk predictors had limited predictive power (expressed by the low c‐statistic), and, given the wide confidence intervals of predicted ARD, could not aid in identifying subgroups of patients who might benefit more (or less) from prophylactic platelet transfusion.

Cornelissen, Loes L.↗

A vision of immuno-oncology: the Siena think tank of the Italian Network for Tumor Biotherapy (NIBIT) foundation

The yearly Think Tank Meeting of the Italian Network for Tumor Biotherapy (NIBIT) Foundation, brings together in Siena, Tuscany (Italy), experts in immuno-oncology to review the learnings from current immunotherapy treatments, and to propose new pre-clinical and clinical investigations in selected research areas. While immunotherapies in non-small cell lung cancer and melanoma led to practice changing therapies, the same therapies had only modest benefit for patients with other malignancies, such as mesothelioma and glioblastoma. One way to improve on current immunotherapies is to alter the sequence of each combination agent. Matching the immunotherapy to the host’s immune response may thus improve the activity of the current treatments. A second approach is to combine current immunotherapies with novel agents targeting complementary mechanisms. Identifying the appropriate novel agents may require different approaches than the traditional laboratory-based discovery work. For example, artificial intelligence-based research may help focusing the search for innovative and most promising combination partners. Novel immunotherapies are needed in cancer patients with resistance to or relapse after current immunotherapeutic drugs. Such new treatments may include targeted agents or monoclonal antibodies to overcome the immune-suppressive tumor microenvironment. The mode of combining the novel treatments, including vaccines, needs to be matched to the patient’s immune status for achieving the maximum benefit. In this scenario, specific attention should be also paid nowadays to the immune intersection between COVID-19 and cancer.

60 APPLIED LIFE SCIENCES↗

Utility of Physiologically Based Pharmacokinetic Modeling to Investigate the Impact of Physiological Changes of Pregnancy and Cancer on Oncology Drug Pharmacokinetics

The treatment of cancer during pregnancy remains challenging with knowledge gaps in drug dosage, safety, and efficacy due to the under-representation of this population in clinical trials. Our aim was to investigate physiological changes reported in both pregnancy and cancer populations into a PBPK modeling framework that allows for a more accurate estimation of PK changes in pregnant patients with cancer. Methods: Paclitaxel and docetaxel were selected to validate a population model using clinical data from pregnant patients with cancer. The validated population model was subsequently used to predict the PK of acalabrutinib in pregnant patients with cancer. Results: The Simcyp pregnancy population model reasonably predicted the PK of docetaxel in pregnant patients with cancer, while a modified model that included a 2.5-fold increase in CYP2C8 abundance, consistent with the increased expression during pregnancy, was needed to reasonably predict the PK of paclitaxel in pregnant patients with cancer. Changes in protein binding levels of patients with cancer had a minimal impact on the predicted clearance of paclitaxel and docetaxel. PBPK modeling predicted approximately 60% lower AUC and Cmax for acalabrutinib in pregnant versus non-pregnant patients with cancer. Conclusions: Our results suggest that PBPK modeling is a promising approach to investigate the effects of pregnancy and cancer on the PK of oncology drugs and potentially inform dosing for pregnant patients with cancer. Further evaluation and refinement of the population model are needed for pregnant patients with cancer with additional compounds and clinical PK data.

60 APPLIED LIFE SCIENCES↗

FrESCO: Framework for Exploring Scalable Computational Oncology

The National Cancer Institute (NCI) monitors population level cancer trends as part of its Surveillance, Epidemiology, and End Results (SEER) program. This program consists of state or regional level cancer registries which collect, analyze, and annotate cancer pathology reports. From these annotated pathology reports, each individual registry aggregates cancer phenotype information from electronic health records. This data is then used to create summary statistics about cancer incidence and mortality to facilitate population health monitoring. Extracting phenotypic information from these reports is a labor intensive task, requiring specialized knowledge about the reports and cancer. Automating the information extraction process from cancer pathology reports has the potential to improve data quality by extracting information in a consistent manner across registries. It can also improve patient outcomes by reducing the time from diagnosis, enabling rapid case ascertainment for clinical trials. Here we present FrESCO, a modular deep-learning natural language processing (NLP) library initially designed for extracting pathology information from clinical text documents. This repository is not solely limited to clinical medical text, but may also be used by researchers just getting started with NLP methods and those looking for a robust solution for their classification problems.

60 APPLIED LIFE SCIENCES↗

Impact of Diabetes and Insulin Use on Prognosis in Patients With Resected Pancreatic Cancer: An Ancillary Analysis of NRG Oncology RTOG 9704

Diabetes mellitus (DM) has been proposed to be tumorigenic; however, prior studies of the association between DM and survival are conflicting. The goal of this ancillary analysis of RTOG 9704, a randomized controlled trial of adjuvant chemotherapy in pancreatic cancer, was to determine the prognostic effects of DM and insulin use on survival.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Local Control For High-Grade Nonrhabdomyosarcoma Soft Tissue Sarcoma Assigned to Radiation Therapy on ARST0332: A Report From the Childrens Oncology Group

The ARST0332 trial for pediatric and young adults with nonrhabdomyosarcoma soft tissue sarcoma (NRSTS) used risk-based treatment including primary resection with lower-than-standard radiation doses to optimize local control (LC) while minimizing long-term toxicity in those requiring radiation therapy (RT). RT for high-grade NRSTS was based on extent of resection (R0: negative margins, R1: microscopic margins, R2/U: gross disease/unresectable); those with >5 cm tumors received chemotherapy (CT; ifosfamide/doxorubicin). This analysis evaluates LC for patients assigned to RT and prognostic factors associated with local recurrence (LR).

62 RADIOLOGY AND NUCLEAR MEDICINE↗