Using Structural Equation Modeling to Understand Prescription Stimulant Misuse: A Test of the Theory of Triadic Influence

OBJECTIVE: To test a theory-driven model of health behavior to predict the illicit use of prescription stimulants (IUPS) among college students. PARTICIPANTS: A probability sample of 554 students from one university located in California (response rate = 90.52%). METHODS: Students completed a paper-based survey developed with guidance from the Theory of Triadic Influence. We first assessed normality of measures and checked for multicollinearity. A single structural equation model of frequency of IUPS in college was then tested using constructs from the theory’s three streams of influence (i.e., intrapersonal, social situation/context, and sociocultural environment) and four levels of causation (i.e., ultimate causes, distal influences, proximal predictors, and immediate precursors). RESULTS: Approximately 18% of students reported engaging in IUPS during college, with frequency of use ranging from never to 40 or more times per academic term. The model tested had strong fit and the majority of paths specified within and across streams were significant at the p<0.01 level. Additionally, 46% of the variance in IUPS frequency was explained by the tested model. CONCLUSIONS: Results suggest the utility of the TTI as an integrative model of health behavior, specifically in predicting IUPS, and provide insight on the need for multifaceted prevention and intervention efforts.Keywords: prevention, health behavior theory, prescription stimulants, substance us

Fully human IgG and IgM antibodies directed against the carcinoembryonic antigen (CEA) Gold 4 epitope and designed for radioimmunotherapy (RIT) of colorectal cancers

BACKGROUND: Human monoclonal antibodies (MAbs) are needed for colon cancer radioimmunotherapy (RIT) to allow for repeated injections. Carcinoembryonic antigen (CEA) being the reference antigen for immunotargeting of these tumors, we developed human anti-CEA MAbs. METHODS: XenoMouse(®)-G2 animals were immunized with CEA. Among all the antibodies produced, two of them, VG-IgG2κ and VG-IgM, were selected for characterization in vitro in comparison with the human-mouse chimeric anti-CEA MAb X4 using flow cytometry, surface plasmon resonance, and binding to radiolabeled soluble CEA and in vivo in human colon carcinoma LS174T bearing nude mice. RESULTS: Flow cytometry analysis demonstrated binding of MAbs on CEA-expressing cells without any binding on NCA-expressing human granulocytes. In a competitive binding assay using five reference MAbs, directed against the five Gold CEA epitopes, VG-IgG2κ and VG-IgM were shown to be directed against the Gold 4 epitope. The affinities of purified VG-IgG2κ and VG-IgM were determined to be 0.19 ± 0.06 × 10(8 )M(-1 )and 1.30 ± 0.06 × 10(8 )M(-1), respectively, as compared with 0.61 ± 0.05 × 10(8 )M(-1 )for the reference MAb X4. In a soluble phase assay, the binding capacities of VG-IgG2κ and VG-IgM to soluble CEA were clearly lower than that of the control chimeric MAb X4. A human MAb concentration of about 10(-7 )M was needed to precipitate approximatively 1 ng (125)I-rhCEA as compared with 10(-9 )M for MAb X4, suggesting a preferential binding of the human MAbs to solid phase CEA. In vivo, 24 h post-injection, (125)I-VG-IgG2κ demonstrated a high tumor uptake (25.4 ± 7.3%ID/g), close to that of (131)I-X4 (21.7 ± 7.2%ID/g). At 72 h post-injection, (125)I-VG-IgG2κ was still concentrated in the tumor (28.4 ± 11.0%ID/g) whereas the tumor concentration of (131)I-X4 was significantly reduced (12.5 ± 4.8%ID/g). At no time after injection was there any accumulation of the radiolabeled MAbs in normal tissues. A pertinent analysis of VG-IgM biodistribution was not possible in this mouse model in which IgM displays a very short half-life due to poly-Ig receptor expression in the liver. CONCLUSION: Our human anti-CEA IgG2κ is a promising candidate for radioimmunotherapy in intact form, as F(ab')(2 )fragments, or as a bispecific antibody

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

A polymorphism of EGFR extracellular domain is associated with progression free-survival in metastatic colorectal cancer patients receiving cetuximab-based treatment

International audienceBackground: Cetuximab, a monoclonal antibody targeting Epidermal Growth Factor Receptor (EGFR), is currently used in metastatic colorectal cancer (mCRC), but predictive factors for therapeutic response are lacking. Mutational status of KRAS and EGFR, and EGFR copy number are potential determinants of cetuximab activity.Methods: We analyzed tumor tissues from 32 EGFR-positive mCRC patients receiving cetuximab/irinotecan combination and evaluable for treatment response. EGFR copy number was quantified by fluorescence in situ hybridization (FISH). KRAS exon 1 and EGFR exons coding for extracellular regions were sequenced.Results: Nine patients experienced an objective response (partial response) and 23 were considered as nonresponders (12 with stable disease and 11 with progressive disease). There was no EGFR amplification found, but high polysomy was noted in 2 patients, both of which were cetuximab responders. No EGFR mutations were found but a variant of exon 13 (R521K) was observed in 12 patients, 11 of which achieved objective response or stable disease. Progression-free and overall survivals were significantly better in patients with this EGFR exon 13 variant. KRAS mutations were found in 14 cases. While there was a trend for an increased KRAS mutation frequency in nonresponder patients (12 mutations out of 23, 52%) as compared to responder patients (2 out of 9, 22%), authentic tumor response or long-term disease stabilization was found in KRAS mutated patients.Conclusion: This preliminary study suggests that: an increase in EGFR copy number may be associated with cetuximab response but is a rare event in CRC, KRAS mutations are associated with low response rate but do not preclude any cetuximab-based combination efficacy and EGFR exon 13 variant (R521K) may predict for cetuximab benefit

Association of cetuximab with adverse pulmonary events in cancer patients: a comprehensive review

Compounds derived from biologic sources, or biologicals, are increasingly utilized as therapeutic agents in malignancy. Development of anti-cancer targeted therapies from biologics is increasingly being utilized. Cetuximab, a chimeric monoclonal antibody, is one such anti-cancer targeted therapeutic that has shown efficacy in quelling the rate of patient decline in colorectal, head/neck, and non-small cell lung cancer. However, due to the relatively recent addition of biologic compounds to the therapeutic arsenal, information related to adverse reactions is less well known than those seen in traditional chemotherapeutics. Dermatologic reactions have been demonstrated as the most frequent side effect cited during cetuximab therapy for malignancy; however, other effects may lead to greater morbidity. In general, pulmonary complications of therapeutics can lead to significant morbidity and mortality. The purpose of this review is to compile the various pulmonary side effects seen in patients treated with cetuximab for various malignancies, and to compare the incidence of these adverse reactions to standard therapies

Anti-angiogenic therapy for cancer: Current progress, unresolved questions and future directions

Tumours require a vascular supply to grow and can achieve this via the expression of pro-angiogenic growth factors, including members of the vascular endothelial growth factor (VEGF) family of ligands. Since one or more of the VEGF ligand family is overexpressed in most solid cancers, there was great optimism that inhibition of the VEGF pathway would represent an effective anti-angiogenic therapy for most tumour types. Encouragingly, VEGF pathway targeted drugs such as bevacizumab, sunitinib and aflibercept have shown activity in certain settings. However, inhibition of VEGF signalling is not effective in all cancers, prompting the need to further understand how the vasculature can be effectively targeted in tumours. Here we present a succinct review of the progress with VEGF-targeted therapy and the unresolved questions that exist in the field: including its use in different disease stages (metastatic, adjuvant, neoadjuvant), interactions with chemotherapy, duration and scheduling of therapy, potential predictive biomarkers and proposed mechanisms of resistance, including paradoxical effects such as enhanced tumour aggressiveness. In terms of future directions, we discuss the need to delineate further the complexities of tumour vascularisation if we are to develop more effective and personalised anti-angiogenic therapies. © 2014 The Author(s)

Effect of Angiogenesis Inhibitor Bevacizumab on Survival in Patients with Cancer: A Meta-Analysis of the Published Literature

Bevacizumab is a recombinant humanized monoclonal antibody against vascular endothelial growth factor which has been used in conjunction with other anti-cancer agents in the treatment of patients with many cancers. It remains controversial whether bevacizumab can prolong survival in cancer patients. This meta-analysis was therefore performed to evaluate effect of bevacizumab on survival in cancer patients. PubMed, EMBASE, and Web of Science databases were searched for English-language studies of randomized controlled trials comparing bevacizumab with control therapy published through February 8, 2012. Progression-free survival, overall survival, and one-year survival rate were analyzed using random- or fixed-effects model. Thirty one assessable randomized controlled trials were identified. A significant improvement in progression-free survival in cancer patients was attributable to bevacizumab compared with control therapy (hazard ratio, 0.72; 95% confidence interval, 0.68 to 0.76; p<0.001). Overall survival was also significantly longer in patients were treated with bevacizumab (hazard ratio, 0.87; 95% confidence interval, 0.83 to 0.91; p<0.001). The significant benefit in one-year survival rate was further seen in cancer patients receiving bevacizumab (odds ratio, 1.30; 95% confidence interval, 1.20 to 1.41; p<0.001). Current evidences showed that bevacizumab prolong progression-free survival and overall survival, and increase one-year survival rate in cancer patients as compared with control therapy

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

Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials

Funder: Breast Cancer Research Foundation (BCRF); doi: https://doi.org/10.13039/100001006Abstract: Stromal tumor-infiltrating lymphocytes (sTILs) are a potential predictive biomarker for immunotherapy response in metastatic triple-negative breast cancer (TNBC). To incorporate sTILs into clinical trials and diagnostics, reliable assessment is essential. In this review, we propose a new concept, namely the implementation of a risk-management framework that enables the use of sTILs as a stratification factor in clinical trials. We present the design of a biomarker risk-mitigation workflow that can be applied to any biomarker incorporation in clinical trials. We demonstrate the implementation of this concept using sTILs as an integral biomarker in a single-center phase II immunotherapy trial for metastatic TNBC (TONIC trial, NCT02499367), using this workflow to mitigate risks of suboptimal inclusion of sTILs in this specific trial. In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting