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PURPOSE The US Food and Drug Administration has granted regulatory approval for the use of nivolumab—an immune checkpoint inhibitor (ICI)—in the first-line treatment of advanced gastric or esophageal adenocarcinoma (GEAC), regardless of programmed death-ligand 1 (PD-L1) expression. However, the efficacy of ICIs in low PD-L1–expressing tumors remains unclear. MATERIALS AND METHODS This study aims to reconstruct unreported Kaplan-Meier (KM) plots of PD-L1 combined positive score (CPS) subgroups of randomized phase III trials comparing the addition of ICIs with conventional chemotherapy in the first-line treatment of GEAC. A graphical reconstructive algorithm was adopted to estimate time-to-event outcomes from reported overall survival and progression-free survival (OS and PFS) KM plots describing overall or subgroup cohorts. Using reconstructed time-to-event outcomes, KMSubtraction conducts bipartite matching of patients from the reported subgroup among the overall cohort. By excluding matched patients, KM plots and survival analyses of the unreported subgroups were retrieved. RESULTS CheckMate-649, KEYNOTE-062, and KEYNOTE-590 were included. Two PD-L1 subgroups were identified with data unreported in the primary manuscripts: PD-L1 CPS 1-4 from CheckMate-649 and PD-L1 CPS 1-9 from KEYNOTE-062. No significant differences in OS and PFS were demonstrated in ICI-chemotherapy combinations when compared with chemotherapy among CheckMate-649 PD-L1 CPS 1-4 (OS: hazard ratio [HR] = 0.950, 95% CI, 0.747 to 1.209, P = .678; PFS: HR = 0.958, 95% CI, 0.743 to 1.236, P = .743) and KEYNOTE-062 PD-L1 CPS 1-9 subgroups. In the KEYNOTE-062 PD-L1 CPS 1-9 subgroup, patients treated with pembrolizumab had an increased hazard of tumor progression (HR = 2.092, 95% CI, 1.661 to 2.635, P < .001). CONCLUSION Using KMSubtraction, data of PD-L1 subgroups previously unreported by primary manuscripts of pivotal clinical trials were retrieved. These data suggest the lack of benefit in the addition of ICI to chemotherapy in low PD-L1–expressing GEAC tumors.

BackgroundSerum electrolyte imbalances are highly prevalent in COVID-19 patients. However, their associations with COVID-19 outcomes are inconsistent, and of unknown prognostic value.ObjectivesTo systematically clarify the associations and prognostic accuracy of electrolyte imbalances (sodium, calcium, potassium, magnesium, chloride and phosphate) in predicting poor COVID-19 clinical outcome.MethodsPubMed, Embase and Cochrane Library were searched. Odds of poor clinical outcome (a composite of mortality, intensive-care unit (ICU) admission, need for respiratory support and acute respiratory distress syndrome) were pooled using mixed-effects models. The associated prognostic sensitivity, positive and negative likelihood ratios (LR+, LR-) and predictive values (PPV, NPV; assuming 25% pre-test probability), and area under the curve (AUC) were computed.ResultsWe included 28 observational studies from 953 records with low to moderate risk-of-bias. Hyponatremia (OR=2.08, 95%CI=1.48-2.94, I2=93%, N=8), hypernatremia (OR=4.32, 95%CI=3.17-5.88, I2=45%, N=7) and hypocalcemia (OR=3.31, 95%CI=2.24-4.88, I2=25%, N=6) were associated with poor COVID-19 outcome. These associations remained significant on adjustment for covariates such as demographics and comorbidities. Hypernatremia was 97% specific in predicting poor outcome (LR+ 4.0, PPV=55%, AUC=0.80) despite no differences in CRP and IL-6 levels between hypernatremic and normonatremic patients. Hypocalcemia was 76% sensitive in predicting poor outcome (LR- 0.44, NPV=87%, AUC=0.71). Overall quality of evidence ranged from very low to moderate.ConclusionHyponatremia, hypernatremia and hypocalcemia are associated with poor COVID-19 clinical outcome. Hypernatremia is 97% specific for a poor outcome and the association is independent of inflammatory marker levels. Further studies should evaluate if correcting these imbalances help improve clinical outcome.

ObjectiveTo compare the efficacy of COVID 19 vaccines between those with immunocompromised medical conditions and those who are immunocompetentDesignSystematic review and meta-analysisData sourcesPubMed, EMBASE, CENTRAL, CORD-19 and WHO COVID-19 research databases were searched for eligible comparative studies published between 1 December 2020 and 3 September 2021. ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform were searched in September 2021 to identify registered yet unpublished or ongoing studies.Study selectionProspective observational studies which compared the efficacy of COVID-19 vaccination between those with immunocompromising medical conditions and those who were immunocompetent were included. Two reviewers independently screened for potentially eligible studies.Data extractionThe primary outcomes of interest were cumulative incidence of seroconversion after first and second doses of COVID vaccination. Secondary outcomes included SARS-CoV-2 antibody titre level after first and second doses of COVID-19 vaccination. After duplicate data abstraction, a frequentist random effects meta-analysis was conducted. Risk of bias was assessed using the ROBINS-I tool. Certainty of evidence was assessed using the GRADE approach.ResultsAfter screening 3283 studies, 42 studies that met our inclusion criteria were identified. 18 immunocompromised cohorts from 17 studies reported seroconversion in immunocompromised patients compared to healthy controls after the first dose and 30 immunocompromised cohorts in 28 studies reporting data after the second dose.Among immunocompromised groups, in incremental order, transplant recipients had the lowest pooled risk ratio of 0.06 (95%CI: 0.04 to 0.09, I^2=0%, p=0.81) (GRADE=Moderate) followed by haematological cancer patients at 0.36 (95%CI: 0.21 to 0.62, I^2 = 89%, pAfter the second dose, the lowest pooled risk ratio was again seen in transplant recipients at 0.29 (95%CI: 0.21 to 0.40, I^2=91%, pConclusionSeroconversion rates and serological titres are significantly lower in immunocompromised patients with transplant recipients having the poorest outcomes. Additional strategies on top of the conventional 2-dose regimen will likely be warranted, such as a booster dose of the vaccine.Systematic review registrationPROSPERO CRD42021272088

Background: Faced with a trade-off between efficacy and toxicity, oncologists have conventionally administered the maximum tolerated doses in chemotherapy on an assumption that higher doses increase efficacy. However, multiple studies have shown that this method of toxicity-guided dosing may result in more frequent toxicities and potentially suboptimal efficacy. With the advent of artificial intelligence (AI), personalized dosing in chemotherapy may be considered to optimize patient care. CURATE.AI is an efficacy-driven, indication-agnostic and mechanism-independent personalized dosing platform that may offer an optimal solution. In contrast to traditional AI approaches based on massive volumes of population data, CURATE.AI requires only the individual patient's medical profile for dose recommendations. Based on the observation that the relationship between a drug dose and a phenotypic response in a human system can be modelled by a dynamic quadratic surface, CURATE.AI continually guides dosing throughout the treatment duration to optimize efficacy. While CURATE.AI has been used in various clinical settings, there are no prior randomized controlled trials (RCTs) on CURATE.AI-guided chemotherapy dosing for solid tumors. Therefore, we are conducting a pilot study to assess the technical and logistical feasibility of an RCT for CURATE.AI-guided solid tumor chemotherapy dosing. We aim to collect exploratory data on efficacy and toxicity, and on the use of longitudinal blood tumor marker measurements, including ctDNA, to inform dose guidance decision. Methods: PRECISE is an open-label, single-arm, multi-centre, prospective pilot clinical trial on using CURATE.AI to achieve personalized, efficacy-driven and dynamically optimized chemotherapy dosing for solid tumors (NCT04522284). Adults with metastatic solid tumors and raised baseline tumor marker levels who are planned for palliative-intent, capecitabine-based chemotherapy will be recruited. CURATE.AI will guide drug dosing for each participant based only on their own tumor marker levels and drug doses as input data. The primary outcome is the proportion of participants in whom CURATE.AI is successfully applied. Secondary outcomes include the timeliness of dose recommendations, participant and physician adherence to CURATE.AI-recommended doses, and the proportion of clinically significant dose changes. As an exploratory outcome, we will analyze the utility of tumor markers including CEA, CA19-9 and ctDNA in high frequency serial measurements. We aim to initially enroll 10 participants from 2 hospitals in Singapore, perform an interim analysis, and consider either cohort expansion or a RCT based on initial pilot data. Recruitment of patients began in August 2020. As of December 2020, 2 participants have been enrolled with recruitment planned for 1 year. Citation Format: Chong Boon Teo, Benjamin Kye Jyn Tan, Xavier Tadeo, Siyu Peng, Hazel Pei Lin Soh, Sherry De Xuan Du, Vilianty Wen Ya Luo, Aishwarya Bandla, Raghav Sundar, Dean Ho, Theodore Kee, Agata Blasiak. Personalized, rational, efficacy-driven chemotherapy dosing via an artificial intelligence system (PRECISE): A protocol for the PRECISE CURATE.AI pilot clinical trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr CT211.