Biology subjects

Pineda, J. M. B.

Publications and source records attributed to Pineda, J. M. B..

4 recordsLinked to original sources

bioRxiv2025

Chorionic Gonadotropin Beta 7 is a marker of immune evasion in cancer

Human chorionic gonadotropin beta (beta-hCG) is an oncofetal antigen expressed by trophoblast cells of the placenta, with minimal expression in adult somatic tissues. Numerous studies have demonstrated that beta-hCG-encoding genes are expressed in various cancers, but expression of these genes (CGB3, CGB5, CGB7, and CGB8) across diverse cancers has not been systematically evaluated. Here, we report that CGB genes are more widely expressed across diverse cancer types than previously appreciated and that secreted beta-hCG is readily detected. In particular, CGB genes are expressed in the majority of urothelial bladder cancers, where CGB7 is most frequently expressed and significantly associated with an immunosuppressed tumor microenvironment, including decreased CD8+ T cell infiltration. Multiple CGB genes are associated with failure to respond to immune checkpoint inhibitor (ICI) therapy, and CGB7 is particularly strongly predictive of poor prognosis. Overall, our findings indicate that beta-hCG is a clinically accessible, predictive biomarker of immunotherapeutic response.

cancer biology↗

bioRxiv2023

DUX4 is a common driver of immune evasion and immunotherapy failure in metastatic cancers

Cancer immune evasion contributes to checkpoint immunotherapy failure in many patients with metastatic cancers. The embryonic transcription factor DUX4 was recently characterized as a suppressor of interferon-{gamma} signaling and antigen presentation that is aberrantly expressed in a small subset of primary tumors. Here, we report that DUX4 expression is a common feature of metastatic tumors, with [~]10-50% of advanced bladder, breast, kidney, prostate, and skin cancers expressing DUX4. DUX4 expression is significantly associated with immune cell exclusion and decreased objective response to PD-L1 blockade in a large cohort of urothelial carcinoma patients. DUX4 expression is a significant predictor of survival even after accounting for tumor mutational burden and other molecular and clinical features in this cohort, with DUX4 expression associated with a median reduction in survival of over one year. Our data motivate future attempts to develop DUX4 as a biomarker and therapeutic target for checkpoint immunotherapy resistance.

cancer biology↗

bioRxiv2019

Disentangling strictly self-serving mutations from win-win mutations in a mutualistic microbial community

Mutualisms can be promoted by win-win mutations which directly benefit self (self-serving) and partner (partner-serving). Intuitively, partner-serving phenotype could be quantified as the benefit supply rate to partner by an individual. Here, we demonstrate the inadequacy of this thinking, and propose an alternative measure. Specifically, we evolved well-mixed mutualistic communities where two engineered yeast strains exchanged essential metabolites lysine and hypoxanthine. Among cells that consumed lysine and released hypoxanthine, a chromosome duplication mutation seemed win-win: it improved cells affinity for lysine, and increased hypoxanthine release rate per cell. However, increased release rate was due to increased cell size accompanied by increased lysine consumption per birth. Consequently this mutation is solely self-serving, since a fixed amount of intake lysine leads to an identical total hypoxanthine release rate - either by more numerous lower-releasing ancestors or fewer higher-releasing mutants. By extension, individuals with reduced benefit production rates may not be cheaters.

microbiology↗

bioRxiv2018

Resolving challenges in quantitative modeling of microbial community dynamics

Microbial communities can perform biochemical activities that monocultures cannot. Controlling communities requires an understanding of community dynamics. Here, we mathematically predict the growth rate of an engineered community consisting of two S. cerevisiae strains, each releasing a metabolite required and consumed by the partner. Initial model parameters were based on strain phenotypes measured in batch mono-cultures with zero or excess metabolite, and failed to quantitatively predict experimental results. To resolve model-experiment discrepancy, we chemically identified the correct exchanged metabolites, but this did not improve model performance. We then re-measured strain phenotypes in chemostats mimicking the metabolite-limited community environments, while mitigating or incorporating effects of rapid evolution. Almost all phenotypes we measured varied significantly with the metabolite environment. Once we used parameters measured in community-like chemostat environments, prediction agreed with experimental results. In summary, using a simplified community, we uncovered, and devised means to resolve, modeling challenges that are likely general.

microbiology↗