Survival In Clinical Trials Research Articles

Immunophenotypic Profile and Measurable Residual Disease Monitoring in Multiple Myeloma: A Prospective Study From a Tertiary Care Centre in Southern India.

Multiple myeloma (MM) immunophenotyping (IPT) and measurable residual disease (MRD) monitoring by flow cytometry is a surrogate for progression-free survival and overall survival in clinical trials. However, plasma cell enumeration is challenging owing to morphological discrepancies and plasma cell (PC) loss during the sample processing. In (n=87) newly diagnosed MM patients, we evaluated the immunophenotype of PCs at baseline, and for a subset of 35 patients MRD at post-induction was quantified and analyzed for association with outcomes and survival.The software Statistical Package for Social Sciences (SPSS), version 16.0 (SPSS Inc., Chicago, IL, USA) was used for all the statistical analysis. Immunophenotyping showed strong positive expression of CD56 (83%), CD200 (94%), CD38 (92%), and CD117 (91%) and negative/weak expression of CD19 (83%), CD45 (89%), CD27 (74%), and CD81 (90%) respectively. Negative/weak expression of CD19 was significantly associated with age ≥56 years (p<0.048), with lower albumin (<3.4g/dL, p<0.001). Strong positive CD56 expression was significantly associated with the presence of M-protein (p<0.03). Strong positive CD117 expression was significantly associated with lower albumin (p<0.02). Strong positive CD200 expression was significantly associated with a good response (p<0.02). The median (IQR) value of bone marrow (BM)-MRD% was 0.005 (0.002-0.034). We found that there was no significant difference in the correlation, association, and survival outcomes with MRD%. This study sheds light on the utility of IPT as an invaluable diagnostic tool in disease management. The findings of this study could be important when it comes to modifying the criteria for high-risk diseases and implementing a risk-adapted first therapy in clinical practice.

Molecular and Clinicopathological Biomarkers in the Neoadjuvant Treatment of Patients with Advanced Resectable Melanoma.

Neoadjuvant systemic therapy is emerging as the best medical practice in patients with resectable stage III melanoma. As different regimens are expected to become available in this approach, the improved optimization of treatment strategies is required. Personalization of care in each individual patient-by precisely determining the disease-related risk and the most efficient therapeutic approach-is expected to minimize disease recurrence, but also the incidence of treatment-related adverse events and the extent of surgical intervention. This can be achieved through validation and clinical application of predictive and prognostic biomarkers. For immune checkpoint inhibitors, there are no validated predictive biomarkers until now. Promising predictive molecular biomarkers for neoadjuvant immunotherapy are tumor mutational burden and the interferon-gamma pathway expression signature. Pathological response to neoadjuvant treatment is a biomarker of a favorable prognosis and surrogate endpoint for recurrence-free survival in clinical trials. Despite the reliability of these biomarkers, risk stratification and response prediction in the neoadjuvant setting are still unsatisfactory and represent a critical knowledge gap, limiting the development of optimized personalized strategies in everyday practice.

Abstract 639: Prevention of pancreatitis and subsequent pancreatic cancer and fibrosis development by PI3Kγ inhibition

Abstract Anti-inflammatory, immunosuppressive macrophages play significant roles in promoting both tumorigenesis and fibrosis. Wound-healing type macrophages accumulate in sites of tissue injury, such as the cirrhotic liver, where they can induce oxidative damage and secrete immunosuppressive and pro-fibrotic factors that lead to oncogenesis. These innate immune cells are also major cellular producers of PDGFB and TGF-β1, which stimulate fibroblasts to secrete extracellular matrix proteins, such as collagen, and which also promote metastatic progression of neoplastic diseases. Chronic liver and pancreatic inflammation are significant risk factors for the development of hepatocellular carcinoma (HCC) and pancreatic ductal adenocarcinoma (PDAC), respectively. We have determined that the myeloid cell specific PI3K isoform PI3Kγ promotes the accumulation of myeloid cells and their immune suppressive polarization within the tumor microenvironment (TME), and that this kinase thereby promotes tumor growth in animal models of cancer, including PDAC. Genetic and pharmacological inhibition of PI3Kγ suppresses tumor growth and reduces fibrosis that is associated with PDAC and other tumors. Targeting immune suppressive myeloid cells represent an emerging approach within the field of cancer immunotherapy and fibrosis treatment; indeed, inhibition of PI3Kγ by the therapeutic eganelisib has shown beneficial effects, including increased overall and progression-free survival in clinical trials for melanoma, head and neck carcinoma, urothelial carcinoma and triple negative breast carcinoma. In this project, we are exploringWe examined the effect of whether PI3Kγ inhibition can in cancer prevention using mouse models of prevent cancer development by suppressing damaging chronic hepatic and pancreatic inflammation, associated fibrosis, and progression towards tumorigenesis using models of HCC and PDAC. We found that PI3Kγ inhibition prevented prevents acute and chronic inflammation and that promotes normal tissue loss and development of fibrosis in mouse models of chronic pancreatitis and liver inflammation. Studies are underway to determine iOur studies indicate thatf PI3Kγ inhibition can also prevent the development of pancreatic and liver carcinoma that results fromin response to chronic inflammation and fibrosis. Our These studies thus far suggest the a potential role of for PI3Kγ inhibition in the prevention of cancer development by preventing the chronic inflammation that leads towards fibrosis and carcinogenesis. The results of these studies could be applied to the future design of clinical trials with using clinical PI3Kγ inhibitors. Citation Format: Anghesom Ghebremedhin, Judith Varner, Marc Paradise. Prevention of pancreatitis and subsequent pancreatic cancer and fibrosis development by PI3Kγ inhibition [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 639.

Abstract 1174: Enhancing the utilization of deep learning to predict patient response in small immunotherapy cohorts using real-world data

Abstract Real-world evidence (RWE) can provide useful insights to support the design of clinical trials by, for example, helping to identify biomarkers associated with patient response to a given treatment. Because RWE comprises large, diverse, and incomplete data entries (e.g., missing treatments, survival endpoints, response), however, its impact can be limited. We introduce a deep learning AI framework designed to utilize diverse types and qualities of available RWE data, demonstrating its potential application in the clinical setting. The deep learning framework was built on top of transformer architecture to maximize the value of large, unlabeled RWE data for predicting patient survival. The framework has two stages. (1) Pre-training, an oracle model is trained by using the GENIE v.11 data set (N = 136,000), including 2,290 variables via standard masked self-supervised learning. (2) Transfer learning, the oracle’s weights are transferred to a survival model that has the same architecture but outputs a survival score. This model is fine-tuned to predict patient survival and is optimized using the sigmoid approximation of Harrell’s concordance index. The survival model is benchmarked independently across four different immunooncology (IO) and hormone therapy (HT) data sets: pan-cancer (IO, N = 1610), lung cancer from MSK MIND (IO, N = 246) and the MYSTIC trial (IO, N = 325), and melanoma (IO, N = 110) and breast cancer from METABRIC (HT, N = 1981). We built two survival models per data set, with (+) and without (-) transfer learning. Models were evaluated using the concordance index (CI) on 10 training (80%)/testing (20%) splits. Transfer learning from the GENIE data set consistently improved patient survival prediction in almost all the data sets evaluated in this study: pan-cancer data set: from CI(-) = 0.63 ± 0.015 in 86 epochs to CI(+) = 0.65 ± 0.023 in 23 epochs, patient stratification from HR(-) = 0.52 (0.35-0.78) to HR(+) = 0.34 (0.22-0.53; MSK MIND from a CI(-) = 0.56 ± 0.023 in 57 epochs to CI(+) = 0.59 ± 0.031 in 23 epochs; MYSTIC trial: from a CI(-) = 0.56 ± 0.052 in 95 epochs to CI(+) = 0.60 ± 0.05 in 38 epochs; melanoma from a CI(-) = 0.59 ± 0.1 in 85 epochs to CI(+) = 0.63 ± 0.07 in 38 epochs; and METABRIC from a CI(-) = 0.60 ± 0.016 in 62 epochs to CI(+) = 0.59 ± 0.012 in 15 epochs. In addition, the models with transfer learning trained 2.5 times faster. Our framework leverages the value of RWE data sets, such as GENIE, to enhance the utilization of complex modeling approaches for small clinical data sets. In systems where multiple players, such as, host immune system, patient health, and tumor biology are all contributing to patient’s response to treatment, complex modeling such as deep learning is essential to capture these convoluted relationships. We anticipate that our framework has the potential to set the path for improved predictions of patient survival in clinical trials. Citation Format: Gustavo Arango-Argoty, Etai Jacob. Enhancing the utilization of deep learning to predict patient response in small immunotherapy cohorts using real-world data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1174.

Venetoclax with decitabine or azacitidine in the first-line treatment of acute myeloid leukemia.

Treatment paradigms for acute myeloid leukemia (AML) have evolved at a rapid pace in recent years. The combination of venetoclax with a hypomethylating agent prolonged survival in clinical trials when compared to hypomethylating agent monotherapy. However, little is known about the performance of venetoclax-based regimens outside of clinical trials, given conflicting safety and efficacy data. Even less is known about the impact of the hypomethylating agent backbone. In this study, we demonstrate that decitabine-venetoclax is associated with a significantly higher rate of grade three or higher thrombocytopenia, but lower rates of lymphocytopenia compared to azacitidine-venetoclax. There was no difference in response or survival across ELN 2017 cytogenetic risk categories in the overall cohort. Significantly more patients succumb to relapsed or refractory disease than death from any other cause. We demonstrated that a Charlson comorbidity index score threshold of seven identifies exceptionally high-risk patients, providing evidence for clinical use to reduce the risk of early treatment-related mortality. Lastly, we provide evidence that measurable residual disease negativity and an IDH mutation predict a significant survival benefit outside clinical trials. Taken together, these data illuminate the real-world performance of venetoclax and decitabine or azacitidine in the treatment of AML.

Correlation of Anticancer Drug Prices with Outcomes of Overall Survival and Progression-Free Survival in Clinical Trials in Japan.

Drug pricing methods vary extensively across countries. Japan calculates drug prices using cost accounting and based on the efficacy of similar drugs. This study investigated the relationship between drug prices and their clinical efficacy and usefulness using public information on anticancer drugs reimbursed by the National Health Insurance price listing between January 2009 and March 2020. We investigated drug characteristics, prices, and clinical benefits based on overall survival (OS) and progression-free survival (PFS). Eighty anticancer drugs were approved in Japan during the study period. The largest number (28 drugs, 35.0%) was approved based on PFS, 18 (22.5%) were approved based on OS, and 13 (16.3%) based on the response rate. The mean (±SD) drug price was JPY 88,416.2 (±148,974.7), while the median drug price (with quartiles) was JPY 21,694 (JPY 4855.0-JPY 93,396.8). Drug prices were significantly higher for PFS than for OS, while cost index-the drug price to extend PFS or OS by one day-did not differ significantly between PFS and OS. The relationship between the 46 drugs approved based on OS or PFS and their prices was examined. A correlation was found between drug prices and their clinical usefulness in terms of OS but not PFS.

Uptake of novel systemic therapy: Real world patterns among adults with advanced non-small cell lung cancer

Introduction/backgroundSystemic treatment for advanced non-small cell lung cancer (NSCLC) is shifting from platinum-based chemotherapy to immunotherapy and targeted therapies associated with improved survival in clinical trials. As new therapies are approved for use, examining variations in use for treating patients in community practice can generate additional evidence as to the magnitude of their benefit. Patients and methodsWe identified 1,442 patients diagnosed with de novo stage IV NSCLC between 3/1/2012 and 12/31/2020. Patient characteristics and treatment patterns are described overall and by type of first- and second-line systemic therapy received. Prevalence ratios estimate the association of patient and tumor characteristics with receipt of first-line therapy. ResultsWithin 180 days of diagnosis, 949 (66%) patients received first-line systemic therapy, increasing from 53% in 2012 to 71% in 2020 (p = 0.0004). The proportion of patients receiving first-line immunotherapy+/-chemotherapy (IMO) increased from 14%-66% (p<0.0001). Overall, 380 (26%) patients received both first- and second-line treatment, varying by year between 16%-36% (p = 0.18). The proportion of patients receiving second-line IMO increased from 13%-37% (p<0.0001). Older age and current smoking status were inversely associated with receipt of first-line therapy. Higher BMI, receipt of radiation, and diagnosis year were positively associated with receipt of first-line therapy. No association was found for race, ethnicity, or socioeconomic status. ConclusionThe proportion of advanced NSCLC patients receiving first- and second-line treatment increased over time, particularly for IMO treatments. Additional research is needed to better understand the impact of these therapies on patient outcomes, including short-term, long-term, and financial toxicities. MicroabstractSystemic treatment for non-small cell lung cancer (NSCLC) is shifting from platinum-based therapies to immunotherapy and targeted therapies. Using de novo stage IV NSCLC patients identified from 4 healthcare systems, we examine trends in systemic therapy. We saw an increase in the portion of patients receiving any systemic therapy and a sharp increase in the proportion of patients receiving immunotherapy.

Drug Sensitivity Testing for Cancer Therapy, Key Areas.

Cancer is a high-mortality disease (9.6 million deaths in 2018 worldwide). Given various anticancer drugs, drug selection plays a key role in patient survival in clinical trials. Drug Sensitivity Testing (DST), one of the leading drug selective systems, was widely practiced for therapeutic promotion in the clinic. Notably, DSTs assist in drug selection that benefits drug responses against cancer from 20-22% to 30-35% over the past two decades. The relationship between drug resistance in vitro and drug treatment benefits was associated with different tumor origins and subtypes. Medical theory and underlying DST mechanisms remain poorly understood until now. The study of the clinical scenario, sustainability and financial support for mechanism and technical promotions is indispensable. Despite the great technical advance, therapeutic prediction and drug selection by DST needs to be miniature, versatility and cost-effective in the clinic. Multi-parameters and automation of DST should be a future trend. Advanced biomedical knowledge and clinical approaches to translating oncologic profiles into drug selection were the main focuses of DST developments. With a great technical stride, the clinical architecture of the DST platform was entering higher levels (drug response testing at any stage of cancer patients and miniaturization of tumor samples). The cancer biology and pharmacology for drug selection mutually benefit the clinic. New proposals to reveal more therapeutic information and drug response prediction at genetic, molecular and omics levels should be estimated overall. By upholding this goal of non-invasive, versatility and automation, DST could save the life of several thousand annually worldwide. In this article, new insights into DST novelty and development are highlighted.

Major Pathologic Response and Prognostic Score Predict Survival in Patients With Lung Cancer Receiving Neoadjuvant Chemotherapy

IntroductionComplete pathologic response (CPR) is an acceptable surrogate for survival in clinical trials but it occurs infrequently in patients with NSCLC receiving neoadjuvant chemotherapy (NCT). Therefore, we studied the impact of major pathologic response (MPR) for predicting survival of patients with NSCLC receiving NCT. We also tested a newly reported scoring system—the prognostic score (PRSC)—which combines T category, lymph node status, and MPR status. MethodsWe analyzed CPR and MPR, defined as 0% and less than or equal to 10% viable tumor cells, respectively, in 339 patients with NSCLC with various histologic types who had been treated with NCT followed by complete surgical resection. We evaluated the relationships between CPR, MPR, or PRSC and overall survival using the Kaplan-Meier method and Cox regression multivariate models, accounting for known prognostic factors, such as age, gender, histologic subtype, and pathologic stage. ResultsAmong all 339 patients, the Kaplan-Meier method revealed that patients with CPR and MPR had better survival. MPR identified a favorable group of patients who experienced survival similar to patients with CPR. Nevertheless, patients with no MPR had a significantly reduced probability of survival. Furthermore, univariate and multivariate Cox proportional hazards regression analysis revealed that MPR and PRSC were significantly associated with overall survival. ConclusionsOur data suggest that MPR can be used as an end point for overall survival in different histologic types for evaluation of therapeutic agents in clinical trials exploring NCT. We also confirmed that PRSC had a prognostic impact, differentiating patients into three prognostic groups, but not superior compared with MPR alone or the TNM8 systems.

MRD Monitoring by Multiparametric Flow Cytometry in AML: Is It Time to Incorporate Immune Parameters?

Simple SummaryMeasurable residual disease (MRD) is emerging as an important prognostic and predictive biomarker in acute myeloid leukemia (AML). However, its use is currently hampered by the disparity and lack of harmonization between the available MRD methodologies. In addition, the current assessment of MRD in AML focuses only on the quantification of the residual leukemic burden, without addressing the parallel alterations of the antineoplastic immune response that can critically affect the course and outcome of AML, often despite MRD persistence. Incorporating parameters of immune competence provides more consistency with the biological concept of MRD and may lead to higher accuracy. Multiparameter flow cytometry (MFC) is a highly efficacious and sensitive technology for the thorough and synchronous investigation of the kinetics of both antitumor immunity and the leukemic clone. MFC-based MRD provides the platform for the development of a composite leukemia- and immune-based biomarker which can outcompete the current MRD assessment.Acute myeloid leukemia (AML) is a heterogeneous group of clonal myeloid disorders characterized by intrinsic molecular variability. Pretreatment cytogenetic and mutational profiles only partially inform prognosis in AML, whereas relapse is driven by residual leukemic clones and mere morphological evaluation is insensitive for relapse prediction. Measurable residual disease (MRD), an independent post-diagnostic prognosticator, has recently been introduced by the European Leukemia Net as a new outcome definition. However, MRD techniques are not yet standardized, thus precluding its use as a surrogate endpoint for survival in clinical trials and MRD-guided strategies in real-life clinical practice. AML resistance and relapse involve a complex interplay between clonal and immune cells, which facilitates the evasion of the leukemic clone and which is not taken into account when merely quantifying the residual leukemia. Multiparameter flow cytometry (MFC) offers the possibility of capturing an overall picture of the above interactions at the single cell level and can simultaneously assess the competence of anticancer immune response and the levels of residual clonal cells. In this review, we focus on the current status of MFC-based MRD in diverse AML treatment settings and introduce a novel perspective of combined immune and leukemia cell profiling for MRD assessment in AML.

A drug-disease model for predicting survival in an Ebola outbreak.

REGN‐EB3 (Inmazeb) is a cocktail of three human monoclonal antibodies approved for treatment of Ebola infection. This paper describes development of a mathematical model linking REGN‐EB3’s inhibition of Ebola virus to survival in a non‐human primate (NHP) model, and translational scaling to predict survival in humans. Pharmacokinetic/pharmacodynamic data from single‐ and multiple‐dose REGN‐EB3 studies in infected rhesus macaques were incorporated. Using discrete indirect response models, the antiviral mechanism of action was used as a forcing function to drive the reversal of key Ebola disease hallmarks over time, for example, liver and kidney damage (elevated alanine [ALT] and aspartate aminotransferases [AST], blood urea nitrogen [BUN], and creatinine), and hemorrhage (decreased platelet count). A composite disease characteristic function was introduced to describe disease severity and integrated with the ordinary differential equations estimating the time course of clinical biomarkers. Model simulation results appropriately represented the concentration‐dependence of the magnitude and time course of Ebola infection (viral and pathophysiological), including time course of viral load, ALT and AST elevations, platelet count, creatinine, and BUN. The model estimated the observed survival rate in rhesus macaques and the dose of REGN‐EB3 required for saturation of the pharmacodynamic effects of viral inhibition, reversal of Ebola pathophysiology, and survival. The model also predicted survival in clinical trials with appropriate scaling to humans. This mathematical investigation demonstrates that drug‐disease modeling can be an important translational tool to integrate preclinical data from an NHP model recapitulating disease progression to guide future translation of preclinical data to clinical study design.

Development of a 27-color panel for the detection of measurable residual disease in patients diagnosed with acute myeloid leukemia.

Acute myeloid leukemia (AML) measurable residual disease (MRD) evaluated by multiparametric flow cytometry (MFC) is a surrogate for progression-free and overall survival in clinical trials and patient management. Due to the limited number of detection channels available in conventional flow cytometers, panels used for assessing AML MRD are typically split into multiple tubes. This cripples the simultaneous and correlated assessment of all myeloblast measurements. In response, we prototyped a single-tube 27-color MFC assay for the evaluation of AML MRD, incorporating all recommended markers. Marrow aspirates from 22 patients were processed for analysis using full spectrum flow cytometry (FSFC). The signal resolution of each marker was compared between samples stained with single antibody vs. the fully stained panel. The analytical accuracy for quantifying hematopoietic cells between our established 8-color assay and the new 27-color method were compared. Variations within an operator and between separate operators were assessed to evaluate the assays reproducibility. The limited of blank (LOB), limit of detection (LOD), and lower limit of quantification (LLOQ) of the 27-color method were empirically determined using limiting dilution experiments. The stability of antibody cocktails over a period of 120 h was also studied using cryopreserved marrow cells. The stain indices for all antibodies were lower in the fully stained panel compared to cells stained with one antibody but clear separations between negative and positive signals were achieved for all antibodies. Our results demonstrated a high concordance between the established 8-color method and the new 27-color assay for enumerating myeloblasts and MRD interpretation within and between operators. The data further showed that the single-tube 27-color assay easily achieved the minimum required detection sensitivity of 0.1%. When antibodies were combined, however, expression intensity of some antigens deteriorated significantly when stored. Our single-tube 27-color panel is a suitable, high sensitivity flow cytometric approach that can be used for AML MRD testing, which improves the correlation of aberrant antigens and detection of asynchronous differentiation patterns. Based on the stability study, we recommend the full panel be made prior to staining.

Recent updates on innovative approaches to overcome drug resistance for better outcomes in cancer.

The multi-dimensional challenge of drug resistance is one of the pivotal hindrances for cancer chemotherapy. A reductive approach to define and distinguish the main aspects of drug resistance, such as tumor growth kinetics about tumor micro-environment (tumor multifariousness), therapeutic pressure, physical barricades, irreversible genetic mutation, as well as role of the immune system, are the main causes of failure in cancer therapy are presented systematically. We are focusing on general approaches to reduce drug resistance: earlier diagnosis of tumors allowing for cancer halting; dynamic surveillance throughout treatment; the adding of new therapeutic strategies and improve pharmacodynamics precepts resulting in profound effects; and identification of cancerous cells repositories using high-throughput monitoring, as well as the interoperability of clinical- gene mapping statics are described in detail. These strategies could be potentially constructed for any tumor at any precise moment and used to guide therapy selection. Chemotherapeutic agents results in mild improved survival in clinical trials owing to several pathophysiologic obstacles, such as intra-tumoral dispersion, invasion & intra-cellular transportation. This review highlights recent advancements in developing new therapeutic innovations to combat drug resistance in cancer therapy by overcoming various barricades in the tumor microenvironment.

Impact of advances in therapy on survival in patients with de novo metastatic prostate cancer.

e17052 Background: The approval of newer therapies including novel androgen pathway inhibitors (abiraterone [2011], enzalutamide [2012], apalutamide [2018]), and radium 223 dichloride [2013]) in the past decade has expanded the therapeutic landscape for patients with metastatic prostate cancer (mPCa). Such therapies have demonstrated a consistent benefit on survival in clinical trials including highly selected patients. However, the benefit on survival at the population level is unknown. This study aims to evaluate the impact of therapeutic advancements in the past decade on survival in patients with de novo mPCa using the SEER database. Methods: We included males with de novo mPCa ≥18 years of age in the SEER database from 2000 to 2018. Patients with missing survival data and those diagnosed at autopsy were excluded. Using the year of diagnosis, patients were categorized into two therapeutic periods: 2000-2010 (period A) and 2011-2018 (period B). The period 2011 to 2018 corresponds to the timeline of approval for novel antiandrogens and radium 223. Kaplan-Meier was used to estimate prostate cancer-specific survival (pCS) and overall survival (OS). Log-rank test was used to compare pCS and OS between the two periods. Univariate and multivariate cox regression models were performed to determine the significant covariates for pCS and OS. Results: A total of 33,969 patients were included in the analysis; 15,433 in period A (45.4%) and 18,536 in period B (54.6%). The mean age was 70.9(SD:10.5) and 61% were Caucasian. No significant difference was noted in the clinicopathologic and socioeconomic characteristics in both periods. The median pCS (36 vs. 30 months, p &lt; 0.001) and OS (30 vs. 25 months, p &lt; 0.001) were longer in period B compared to period A. In the multivariate model for pCS, diagnosis during period B was associated with a significantly decreased hazard of death from prostate cancer (HR:0.89, CI: 0.85-0.94, p &lt; 0.001). Age &gt; 80 years (HR:1.6, p &lt; 0.001), PSA &gt; 50 ng/mL (HR:1.3, p &lt; 0.001), and poorly differentiated histology (HR:2.0, p &lt; 0.001) were significant predictors of worse pCS. Hispanic origin (HR:0.94, p = 0.02) and median household income &gt; $75000 (HR:0.86, p = 0.007) were associated with better pCS. In the multivariate model for OS, diagnosis in period B was associated with a decreased hazard of death from any cause (HR:0.89, CI:0.85-0.93, p &lt; 0.001). Age &gt; 65 years (HR:1.26, p &lt; 0.001), PSA &gt; 50 ng/mL (HR:1.30, p &lt; 0.001), and poorly differentiated histology (HR:1.6, p &lt; 0.001) were associated with worse OS. Hispanic origin (HR:0.94, p = 0.01) and median household income &gt; $75000 (HR:0.81, p &lt; 0.001) were associated with better OS. Conclusions: The diagnosis of de novo mPCa during the period of the therapeutic advancement (2011-2018) was associated with improved pCS and OS. However, the observed 11% reduction in the risk of death from any cause in this population-based study was smaller than the reported 20 to 38% in clinical trials.

Real-World Time on Treatment with First-Line Pembrolizumab Monotherapy for Advanced NSCLC with PD-L1 Expression ≥ 50%: 3-Year Follow-Up Data.

Simple SummaryLung cancer is the leading cause of cancer-related death in the United States (US), and real-world studies are needed to understand effectiveness of cancer therapies for patients treated outside of cancer clinical trials. Pembrolizumab, an immunotherapy agent that aids the body’s immune system in fighting cancer, is administered for up to 2 years when treating advanced non-small cell lung cancer (NSCLC). We evaluated the real-world time on treatment (rwToT), a surrogate indicator that has been associated with survival in NSCLC studies, for over 1000 patients with advanced NSCLC treated initially with pembrolizumab at US oncology clinics. The median rwToT for patients with good performance status (similar to those in clinical trials) was 7.4 months, consistent with the median treatment duration in the KEYNOTE-024 trial (7.9 months). Our findings suggest long-term benefit of first-line pembrolizumab for patients with advanced NSCLC and good performance status at the start of therapy who are treated in real-world settings.Our aim was to evaluate real-world time on treatment (rwToT), overall and by KRAS mutation status, with first-line pembrolizumab monotherapy for advanced non-small cell lung cancer (NSCLC) in real-world oncology practice in the US. rwToT is a readily available, intermediate-range endpoint that is moderately to highly correlated with overall survival in clinical trials and real-world data. Using deidentified electronic medical record data, we studied patients with ECOG performance status (PS) of 0–2 who initiated pembrolizumab (1 November 2016 to 31 March 2020) for advanced NSCLC with programmed death-ligand 1 (PD-L1) expression ≥ 50% and without EGFR/ALK/ROS1 genomic alterations. The data cutoff was 31 March 2021, and the median study follow-up was 34 months. The Kaplan–Meier median rwToT with first-line pembrolizumab monotherapy was 7.4 months (95% CI, 6.3–8.1) for 807 patients with PS 0–1, which was consistent with the median treatment duration in the KEYNOTE-024 trial (7.9 months). The median rwToT for 237 patients with PS 2 was 2.1 months (95% CI, 1.4–2.8). For those with KRAS-mutated and KRAS wild-type nonsquamous NSCLC and PS 0–1, the median rwToT was 7.6 months and 7.0 months, respectively. Our findings suggest long-term benefit of first-line pembrolizumab monotherapy for advanced NSCLC with PD-L1 expression ≥ 50% in real-world settings in the US, particularly for patients with good performance status at the start of therapy, irrespective of KRAS status.

Evaluation of multiple myeloma measurable residual disease by high sensitivity flow cytometry: An international harmonized approach for data analysis.

Multiple myeloma (MM) measurable residual disease (MRD) evaluated by flow cytometry is a surrogate for progression-free and overall survival in clinical trials. However, analysis and reporting between centers lack uniformity. We designed and evaluated a consensus protocol for MM MRD analysis to reduce inter-laboratory variation in MM MRD reporting. Seventeen participants from 13 countries performed blinded analysis of the same eight de-identified flow cytometry files from patients with/without MRD using their own method (Stage 1). A consensus gating protocol was then designed following survey and discussions, and the data re-analyzed for MRD and other bone marrow cells (Stage 2). Inter-laboratory variation using the consensus strategy was reassessed for another 10 cases and compared with earlier results (Stage 3). In Stage 1, participants agreed on MRD+/MRD- status 89% and 68% of the time respectively. Inter-observer variation was high for total numbers of analyzed cells, total and normal plasma cells (PCs), limit of detection, lower limit of quantification, and enumeration of cell populations that determine sample adequacy. The identification of abnormal PCs remained relatively consistent. By consensus method, average agreement on MRD- status improved to 74%. Better consistency enumerating all parameters among operators resulted in near-unanimous agreement on sample adequacy. Uniform flow cytometry data analysis substantially reduced inter-laboratory variation in reporting multiple components of the MM MRD assay. Adoption of a harmonized approach would meet an important need for conformity in reporting MM MRD for clinical trials, and wider acceptance of MM MRD as a surrogate clinical endpoint.

Impact of the timing of tumor assessments on median progression-free survival in clinical trials in advanced cancer patients

BackgroundSurvival-based surrogate endpoints such as progression-free survival (PFS) are commonly used in oncology clinical trials. The evaluation-time bias in the assessment of median disease progression in randomized trials has been suggested by several simulation studies, but never demonstrated in the clinic. We aimed to demonstrate the existence of potential evaluation-time bias by assessing the impact of the timing of tumor assessments on median PFS from control arms without any active treatment of randomized controlled trials involving advanced cancer patients.Materials and methodsA systematic literature search of English language publications from 1 January 2000 to 7 January 2021 was performed using MEDLINE (PubMed). Eligible trials for our meta-analysis included all randomized clinical trials evaluating anticancer drugs in adult patients with advanced cancers with a control arm without any anticancer drug consisting of best supportive care with or without a placebo. We performed a meta-regression analysis to analyze the correlation between the timing of the first tumor assessment and median PFS in patients randomized in the control arms without any active treatment.ResultsOf 3551 studies screened, 97 eligible trials were retrieved involving 36 747 patients, including 14 229 patients randomized into the control arms. A later first tumor assessment correlated with a prolonged median PFS (R2 = 0.44, P < 10−5).ConclusionsOur results confirm the existence of potential evaluation-time bias in clinical research that had been suggested by simulation studies. The timing of tumor assessments should be kept the same in precision medicine trials using the PFS ratio as an efficacy endpoint.

Machine Learning-Based Prediction of Early Recurrence in Glioblastoma Patients: A Glance Towards Precision Medicine.

Ability to thrive and time-to-recurrence following treatment are important parameters to assess in patients with glioblastoma multiforme (GBM), given its dismal prognosis. Though there is an ongoing debate whether it can be considered an appropriate surrogate endpoint for overall survival in clinical trials, progression-free survival (PFS) is routinely used for clinical decision-making. To investigate whether machine learning (ML)-based models can reliably stratify newly diagnosed GBM patients into prognostic subclasses on PFS basis, identifying those at higher risk for an early recurrence (≤6 mo). Data were extracted from a multicentric database, according to the following eligibility criteria: histopathologically verified GBM and follow-up >12mo: 474 patients met our inclusion criteria and were included in the analysis. Relevant demographic, clinical, molecular, and radiological variables were selected by a feature selection algorithm (Boruta) and used to build a ML-based model. Random forest prediction model, evaluated on an 80:20 split ratio, achieved an AUC of 0.81 (95% CI: 0.77; 0.83) demonstrating high discriminative ability. Optimizing the predictive value derived from the linear and nonlinear combinations of the selected input features, our model outperformed across all performance metrics multivariable logistic regression. A robust ML-based prediction model that identifies patients at high risk for early recurrence was successfully trained and internally validated. Considerable effort remains to integrate these predictions in a patient-centered care context.

Estimands for overall survival in clinical trials with treatment switching in oncology

An addendum of the ICH E9 guideline on Statistical Principles for Clinical Trials was released in November 2019 introducing the estimand framework. This new framework aims to align trial objectives and statistical analyses by requiring a precise definition of the inferential quantity of interest, that is, the estimand. This definition explicitly accounts for intercurrent events, such as switching to new anticancer therapies for the analysis of overall survival (OS), the gold standard in oncology. Traditionally, OS in confirmatory studies is analyzed using the intention-to-treat (ITT) approach comparing treatment groups as they were initially randomized regardless of whether treatment switching occurred and regardless of any subsequent therapy (treatment-policy strategy). Regulatory authorities and other stakeholders often consider ITT results as most relevant. However, the respective estimand only yields a clinically meaningful comparison of two treatment arms if subsequent therapies are already approved and reflect clinical practice. We illustrate different scenarios where subsequent therapies are not yet approved drugs and thus do not reflect clinical practice. In such situations the hypothetical strategy could be more meaningful from patient's and prescriber's perspective. The cross-industry Oncology Estimand Working Group (www.oncoestimand.org) was initiated to foster a common understanding and consistent implementation of the estimand framework in oncology clinical trials. This paper summarizes the group's recommendations for appropriate estimands in the presence of treatment switching, one of the key intercurrent events in oncology clinical trials. We also discuss how different choices of estimands may impact study design, data collection, trial conduct, analysis, and interpretation.

Inhibition of Janus Kinase 1 synergizes docetaxel sensitivity in prostate cancer cells.

Prostate cancer (PCa) is the second most common malignancy and is the fifth leading cause of cancer mortality among men globally. Docetaxel‐based therapy remains the first‐line treatment for metastatic castration‐resistant prostate cancer. However, dose‐limiting toxicity including neutropenia, myelosuppression and neurotoxicity is the major reason for docetaxel dose reductions and fewer cycles administered, despite a recent study showing a clear survival benefit with increased total number of docetaxel cycles in PCa patients. Although previous studies have attempted to improve the efficacy and reduce docetaxel toxicity through drug combination, no drug has yet demonstrated improved overall survival in clinical trial, highlighting the challenges of improving the activity of docetaxel monotherapy in PCa. Herein, we identified 15 lethality hits for which inhibition could enhance docetaxel sensitivity in PCa cells via a high‐throughput kinome‐wide loss‐of‐function screen. Further drug‐gene interactions analyses identified Janus kinase 1 (JAK1) as a viable druggable target with existing experimental inhibitors and FDA‐approved drugs. We demonstrated that depletion of endogenous JAK1 enhanced docetaxel‐induced apoptosis in PCa cells. Furthermore, inhibition of JAK1/2 by baricitinib and ruxolitinib synergizes docetaxel sensitivity in both androgen receptor (AR)–negative DU145 and PC3 cells, but not in the AR‐positive LNCaP cells. In contrast, no synergistic effects were observed in cells treated with JAK2‐specific inhibitor, fedratinib, suggesting that the synergistic effects are mainly mediated through JAK1 inhibition. In conclusion, the combination therapy with JAK1 inhibitors and docetaxel could be a useful therapeutic strategy in the treatment of prostate cancers.