First-in-man application of a novel therapeutic cancer vaccine formulation with the capacity to induce multi-functional T cell responses in ovarian, breast and prostate cancer patients

BACKGROUND: DepoVax(TM) is a novel non-emulsion depot-forming vaccine platform with the capacity to significantly enhance the immunogenicity of peptide cancer antigens. Naturally processed HLA-A2 restricted peptides presented by breast, ovarian and prostate cancer cells were used as antigens to create a therapeutic cancer vaccine, DPX-0907. METHODS: A phase I clinical study was designed to examine the safety and immune activating potential of DPX-0907 in advanced stage breast, ovarian and prostate cancer patients. A total of 23 late stage cancer patients were recruited and were divided into two dose/volume cohorts in a three immunization protocol. RESULTS: DPX-0907 was shown to be safe with injection site reactions being the most commonly reported adverse event. All breast cancer patients (3/3), most of ovarian (5/6) and one third of prostate (3/9) cancer patients exhibited detectable immune responses, resulting in a 61% immunological response rate. Immune responses were generally observed in patients with better disease control after their last prior treatment. Antigen-specific responses were detected in 73% of immune responders (44% of evaluable patients) after the first vaccination. In 83% of immune responders (50% of evaluable patients), peptide-specific T cell responses were detected at ≥2 time points post vaccination with 64% of the responders (39% of evaluable patients) showing evidence of immune persistence. Immune monitoring also demonstrated the generation of antigen-specific T cell memory with the ability to secrete multiple Type 1 cytokines. CONCLUSIONS: The novel DepoVax formulation promotes multifunctional effector memory responses to peptide-based tumor associated antigens. The data supports the capacity of DPX-0907 to elicit Type-1 biased immune responses, warranting further clinical development of the vaccine. This study underscores the importance of applying vaccines in clinical settings in which patients are more likely to be immune competent. TRIAL REGISTRATION: ClinicalTrials.gov NCT0109584

Federated learning enables big data for rare cancer boundary detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing