Showing papers by "Malcolm J. Moore published in 2022"
TL;DR: In this paper , the authors compared the efficacy and safety of adjuvant nab-paclitaxel + gemcitabine with those of gemitabine for resected pancreatic ductal adenocarcinoma.
Abstract: PURPOSE This randomized, open-label trial compared the efficacy and safety of adjuvant nab-paclitaxel + gemcitabine with those of gemcitabine for resected pancreatic ductal adenocarcinoma (ClinicalTrials.gov identifier: NCT01964430). METHODS We assigned 866 treatment-naive patients with pancreatic ductal adenocarcinoma to nab-paclitaxel (125 mg/m2) + gemcitabine (1,000 mg/m2) or gemcitabine alone to one 30-40 infusion on days 1, 8, and 15 of six 28-day cycles. The primary end point was independently assessed disease-free survival (DFS). Additional end points included investigator-assessed DFS, overall survival (OS), and safety. RESULTS Two hundred eighty-seven of 432 patients and 310 of 434 patients completed nab-paclitaxel + gemcitabine and gemcitabine treatment, respectively. At primary data cutoff (December 31, 2018; median follow-up, 38.5 [interquartile range [IQR], 33.8-43 months), the median independently assessed DFS was 19.4 (nab-paclitaxel + gemcitabine) versus 18.8 months (gemcitabine; hazard ratio [HR], 0.88; 95% CI, 0.729 to 1.063; P = .18). The median investigator-assessed DFS was 16.6 (IQR, 8.4-47.0) and 13.7 (IQR, 8.3-44.1) months, respectively (HR, 0.82; 95% CI, 0.694 to 0.965; P = .02). The median OS (427 events; 68% mature) was 40.5 (IQR, 20.7 to not reached) and 36.2 (IQR, 17.7-53.3) months, respectively (HR, 0.82; 95% CI, 0.680 to 0.996; P = .045). At a 16-month follow-up (cutoff, April 3, 2020; median follow-up, 51.4 months [IQR, 47.0-57.0]), the median OS (511 events; 81% mature) was 41.8 (nab-paclitaxel + gemcitabine) versus 37.7 months (gemcitabine; HR, 0.82; 95% CI, 0.687 to 0.973; P = .0232). At the 5-year follow-up (cutoff, April 9, 2021; median follow-up, 63.2 months [IQR, 60.1-68.7]), the median OS (555 events; 88% mature) was 41.8 versus 37.7 months, respectively (HR, 0.80; 95% CI, 0.678 to 0.947; P = .0091). Eighty-six percent (nab-paclitaxel + gemcitabine) and 68% (gemcitabine) of patients experienced grade ≥ 3 treatment-emergent adverse events. Two patients per study arm died of treatment-emergent adverse events. CONCLUSION The primary end point (independently assessed DFS) was not met despite favorable OS seen with nab-paclitaxel + gemcitabine.
14 citations
TL;DR: In this article , a 70-year-old male was diagnosed with advanced pancreatic acinar cell carcinoma (PACC) and was treated with modified FOLFIRINOX (mFFX) with the knowledge of the germline BRCA2 likely pathogenic variant (LPV).
Abstract: BACKGROUND Pancreatic acinar cell carcinoma (PACC) is a rare tumor. Up to 45% of PACCs have alterations in the DNA damage repair pathway and 23% harbor rearrangements in the BRAF or RAF1 genes. We present a PACC case with a germline BRCA2 likely pathogenic variant (LPV) to highlight the impact of genomic testing on treatment decisions and patient outcomes. In our larger case series, we provide clinic-based information on additional 10 PACC patients treated in our center. CASE SUMMARY A 70-year-old male was diagnosed with advanced PACC. At presentation, he was cachectic with severe arthralgia despite prednisolone and a skin rash that was later confirmed to be panniculitis. He was treated with modified FOLFIRINOX (mFFX) with the knowledge of the germline BRCA2 LPV. Following 11 cycles of mFFX, a computed tomography (CT) scan demonstrated significant tumor response in the pancreatic primary and hepatic metastases, totaling 70% from baseline as per Response Evaluation Criteria in Solid Tumors. Resolution of the skin panniculitis was also noted. We identified two additional PACCs with druggable targets in our case series. Our data contribute to practical evidence for the value of germline and somatic profiling in the management of rare diseases like PACC. CONCLUSION This patient and others in our larger case series highlight the importance of genomic testing in PACC with potential utility in personalized treatment.
1 citations
TL;DR: GMM can be used to analyze longitudinal QoL data in cancer studies, by identifying unobserved subpopulations (patient clusters) that can have important implications, including clinical prognostication as demonstrated by CO.20 data.
Abstract: Analyzing longitudinal cancer quality‐of‐life (QoL) measurements and their impact on clinical outcomes may improve our understanding of patient trajectories during systemic therapy. We applied an unsupervised growth mixture modeling (GMM) approach to identify unobserved subpopulations (“patient clusters”) in the CO.20 clinical trial longitudinal QoL data. Classes were then evaluated for differences in clinico‐epidemiologic characteristics and overall survival (OS).
1 citations
TL;DR: Assessment of time toxicity in a completed RCT that evaluated weekly cetuximab infusions vs supportive care alone in 572 patients with advanced colorectal cancer found time toxicity measures can supplement traditional survival endpoints in RCTs to guide patient-oncologist decision-making.
Abstract: 248 Background: Most individual treatments for advanced cancer are associated with modest survival benefits. The time spent in pursuing these treatments can be substantial. We have previously developed a pragmatic and patient-centered metric of these time costs, which we term ‘’time toxicity’’, as any day with physical healthcare system contact. This includes outpatient visits (e.g., for bloodwork, scans, infusions, urgent care), emergency room visits, and overnight stays in a healthcare facility. Herein we sought to assess time toxicity in a completed RCT. Methods: We conducted a secondary analysis of the Canadian Cancer Trials Group CO.17 RCT that evaluated weekly cetuximab infusions vs supportive care alone in 572 patients with advanced colorectal cancer. CO.17 recruited participants in Canada, Australia and New Zealand. Initial results reported a 6-week improvement in median overall survival (OS) with cetuximab (6.1 vs 4.6 months, p = 0.005). Subsequent analyses reported that OS benefit was restricted to and significantly more in patients with K-ras wild-type tumors. We calculated patient-level time toxicity by analyzing treatment, follow-up, and resource utilization forms. We considered a day without physical healthcare contact as a ‘’home day’’. Thus, for a patient, OS was time toxic days + home days. We compared medians of time measures across arms, and stratified results by K-ras status. Results: In the overall population, median time toxic days were higher in the cetuximab arm (28, vs 10, p < 0.001), although median home days were not statistically different (140, vs 121, p = 0.09). The proportion of time toxic days (time toxic days/OS) were significantly more with cetuximab (18%, vs 6%, p < 0.001). Of the 28 time toxic days in the overall cetuximab arm, 14 (50%) were protocol-related (e.g., scheduled infusions etc). Stratified results are in the table. Conclusions: Time toxicity can be extracted through secondary analyses of RCTs. In CO.17, despite an overall OS-benefit with cetuximab, home days were statistically similar across arms. In the K-ras-mutated group, cetuximab was associated with numerically similar OS but more time toxic days. In the K-ras-wild type group, cetuximab was associated with higher home days. Thus, time toxicity data need not always be sobering. Time toxicity measures can supplement traditional survival endpoints in RCTs to guide patient-oncologist decision-making. Cooperative group RCTs are well positioned for such analyses. Clinical trial information: NCT00079066. [Table: see text]