Cancer Therapeutics Research Articles

Engineered bacteria for near-infrared light-inducible expression of cancer therapeutics

Engineered bacteria for near-infrared light-inducible expression of cancer therapeutics

Synergistic therapeutic efficacy of Selective Anticancer Complex in triple-negative breast cancer.

Synergistic therapeutic efficacy of Selective Anticancer Complex in triple-negative breast cancer.

Deciphering the Intricacies of Breast Cancer Signaling Network and the Potential of Soy-derived Isoflavones on Cancer Therapeutics

Deciphering the Intricacies of Breast Cancer Signaling Network and the Potential of Soy-derived Isoflavones on Cancer Therapeutics

CD44 is a nexus between prognosis and therapeutics for brain cancer management.

CD44 is a nexus between prognosis and therapeutics for brain cancer management.

Leveraging space innovations for cancer breakthroughs on Earth.

Leveraging space innovations for cancer breakthroughs on Earth.

Abstract B007: The organotypic brain slice culture platform as a functional biomarker for preclinical drug performance testing

Abstract Of the roughly 1% of brain cancer therapeutics that show promise in preclinical testing, about 85% fail in clinical trials, indicating that drugs should be further scrutinized in preclinical settings. This failure is partially attributed to the inability of many preclinical models to recapitulate relevant aspects of disease and response to treatment, thereby misrepresenting a drug's therapeutic potential. Hence, we developed an Organotypic Brain Slice Culture (OBSC)-based platform that allows the direct testing of drugs on cell lines and living, uncultured patient brain tumor tissues to obtain functional readouts that capture both anti-tumor efficacy and off-target toxicity in a summarized drug sensitivity score (DSS). In this rapid, four-day assay, cells or tumor tissues are seeded atop coronal sections of postnatal day 8 rat pup brains just after slicing, therapeutics are added the next day, and live tumor bioluminescence is quantified three days after treatment initiation. In a parallel assay, off-target toxicity to non-tumor-bearing OBSCs after treatment is assessed using a propidium iodide assay that quantifies OBSC-associated cell death. Here, we investigated the antitumor activity of two experimental compounds, TR-107, a potent ONC201 derivative, and EdU, a newly repurposed DNA labeling agent, against an array of cell lines and brain tumor tissues. We then compared tumor survival on OBSCs to the standard of care and/or other relevant drugs tested in our system. The potency and selectivity of TR-107 in our assay is unsurprising as its parent compound, ONC 201, is the first imipridone in Phase 3 trials for treating H3- K27M gliomas. When investigating EdU against patient tumor tissues on OBSCs, we observed that intertumoral differences in Ki-67 abundance, a proliferation marker measured by clinical pathology, correlated with the effective doses necessary to eliminate up to 50% of these heterogeneous tumors. Importantly, while many of our results correlate with expected outcomes, we also identified non-responding tumor tissues whose “static” biomarkers predicted otherwise, reinforcing the need for functional approaches to drug development. In summary, this data supports the use of our OBSC platform as a functional biomarker for drug performance against cell lines and heterogeneous patient tumor tissues, and as an assay that can provide insight into discordance between genomic predictions and functional outcomes long before a drug fails in the clinic. Citation Format: Adebimpe Adefolaju, Humeyra Kaanoglu, Breanna Mann, Xiaopei Zhang, Noah Bell, Morrent Thang, Rajaneekar Dasari, Nichole Artz, Andrew Buckley, Lee Graves, Shawn Hingtgen, Aziz Sancar, Andrew Satterlee. The organotypic brain slice culture platform as a functional biomarker for preclinical drug performance testing [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr B007.

Abstract B043: A fully transparent and automatable form of AI for multi-omics precision medicine

Abstract Acquired drug resistance and heterogeneity of patient responses to cancer therapeutics represent major challenges to delivering effective treatments to patients. Predictive biomarkers provide valuable information for increasing drug responses among treatment cohorts by guiding the delivery of drugs to patients most likely to benefit. However, the identification of biomarkers of drug sensitivity and resistance remains challenging, and their mechanistic basis is difficult to discern. Large-scale multi-omics methods represent a promising approach; however, they capture far more data than can be digested in human readable form, creating an opportunity for machine-based technologies to significantly expand the boundaries of scientific knowledge. Artificial intelligence (AI) methods promise to fill this gap, however significant challenges remain to adapt machine learning (ML) based approaches to data that is noisy, complex, and bespoke while still preserving the transparency required for scientific and regulatory decision making. Herein we describe a novel approach, which combines knowledge graphs and centrality algorithms, allowing the construction of systems that are scalable, robust to noise, concise, and perhaps most importantly, highly transparent. The central construct, which we’ve termed a “focal graph”, can seamlessly integrate information across multiple, diverse, complex data sets, including large-scale chemical biology and multi-omics data. Focal graphs can be combined with large language models (LLMs) as part of a retrieval-augmented generation (RAG) strategy to build intelligent, autonomous drug discovery workflows that preserve the provenance of the underlying experimental data, and whose processes and conclusions can be examined and evaluated in tremendous detail. Focal graphs can be paired with LLMs to autonomously plan and execute research programs, potentially resulting in discoveries that are entirely novel, and derive their support from multiple, diverse, large-scale experimental data sets. As such autonomous systems become more sophisticated and are given access to more data and computational power, they can be expected to generate insights with increasing levels of novelty and experimental support. Here we present the theoretical underpinnings of focal graphs and their automation, as well as selected examples highlighting their application to biomarker development and the investigation of mechanisms underlying the heterogeneity of therapeutic responses in cancer. More information is available at www.plexresearch.com. Citation Format: Douglas W Selinger, Timothy R Wall, Eleni Stylianou, Ehab Khalil, Jedidiah Gaetz, Oren Levy. A fully transparent and automatable form of AI for multi-omics precision medicine [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr B043.

Abstract IA017: Single mouse testing (SMT): An alternative approach to encompass clinical heterogeneity of pediatric cancers

Abstract Patient derived xenografts (PDXs) have become the predominant models for testing novel therapeutics, however the failure rate for new cancer therapeutics remains high around 95%. Several publications have analyzed at what stage failure occurs, some due to unrecognized toxicity in preclinical models, but predominantly failure occurs due to lack of efficacy. For many cancer drugs failure to elicit adequate therapeutic activity may be a consequence of how we do preclinical studies. Through the Pediatric Preclinical Testing Program (PPTP) its successor PPT/Consortium we conducted over 2100 tumor/drug studies that included 68 drugs and up to 83 PDX models of childhood cancer. These studies used conventional testing approaches with 10 mice per treatment group for solid tumors and 8 mice/group for leukemia models. Mi imal statistical differences between control and treatment groups was defined as a P value of 0.05. However, such statistical difference does not imply biologically meaningful activity. Limitations of conventional testing include 1) a failure to recapitulate the genomic/epigenomic heterogeneity of a human cancer type, 2) use of activity criterial in preclinical studies that are less stringent than in clinical trials and 3) differences in species tolerance to drugs has not been considered critical for accurate translation. Here we consider the first two limitations. We analyzed 2106 tumor model drug studies. These studies we used 10 mice/group (olid tumors) or 8 mice/group (leukemias), that allowed inclusion of 6-8 models representing a pediatiric cancer type (neuroblastoma, osteosarcom ALL etc.) and asked a simple question: would 1 mouse per group, chosen at random, predict the group median response? The result showed that in 78% of drug studies the single mouse accurately predicted the group response. If some latitude was allowed (+/- 1 response criteria (PR vs CR) etc.) the accuracy was 95%. Further, if we used the same approach to objective responses the single mouse accurately predicted group response for 67 of 68 drugs tested over a range of antitumor activities (0-100%). We have validated the SMT results in prospective testing with different childhood cancer models (solid tumors and leukemias) using PDX models largely not included in the initial analysis, with similar concordance beteeen SMT and conventional testing (CT). Based on these data we consider that SMT allows for inclusion of a greater number of PDX models that more accurately simulate the genetic/epigenetic heterogeneity of pediatric cancers and potentially allows identification of 1) truly acive drugs and 2) biomarkers of response. The SMT approach is now being used by the NCI supported Preclinical Pediatric In Vivo Testing (PIVOT) program. Citation Format: Peter J Houghton, Samson Ghilu, Andrew Robles, Raushan T Kurmasheva, Richard B Lock. Single mouse testing (SMT): An alternative approach to encompass clinical heterogeneity of pediatric cancers [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr IA017.

Exosomes in Precision Oncology and Beyond: From Bench to Bedside in Diagnostics and Therapeutics

Exosomes have emerged as pivotal players in precision oncology, offering innovative solutions to longstanding challenges such as metastasis, therapeutic resistance, and immune evasion. These nanoscale extracellular vesicles facilitate intercellular communication by transferring bioactive molecules that mirror the biological state of their parent cells, positioning them as transformative tools for cancer diagnostics and therapeutics. Recent advancements in exosome engineering, artificial intelligence (AI)-driven analytics, and isolation technologies are breaking barriers in scalability, reproducibility, and clinical application. Bioengineered exosomes are being leveraged for CRISPR-Cas9 delivery, while AI models are enhancing biomarker discovery and liquid biopsy accuracy. Despite these advancements, key obstacles such as heterogeneity in exosome populations and the lack of standardized isolation protocols persist. This review synthesizes pioneering research on exosome biology, molecular engineering, and clinical translation, emphasizing their dual roles as both mediators of tumor progression and tools for intervention. It also explores emerging areas, including microbiome–exosome interactions and the integration of machine learning in exosome-based precision medicine. By bridging innovation with translational strategies, this work charts a forward-looking path for integrating exosomes into next-generation cancer care, setting it apart as a comprehensive guide to overcoming clinical and technological hurdles in this rapidly evolving field.

Development of Novel Peptides That Target the Ninjurin 1 and 2 Pathways to Inhibit Cell Growth and Survival via p53

Ninjurin 1 and 2 (NINJ1, NINJ2) belong to the homophilic cell adhesion family and play significant roles in cellular communication and tissue development. While both NINJ1 and NINJ2 are found to be over-expressed in several types of cancers, it remains unclear whether they can be targeted for cancer treatment. In this study, we aimed to develop NINJ1/2 peptides derived from the N-terminal extracellular domain that can elicit growth suppression and thus possess therapeutic potentials. We found that peptide NINJ1-A, which is derived from the N-terminal adhesion motif of NINJ1, was able to inhibit cell growth in a NINJ1- or p53-dependent manner. Similarly, peptide NINJ2-A, which is derived from the N-terminal adhesion motif of NINJ2, was able to inhibit cell growth in a NINJ2- or p53-dependent manner. We also found that NINJ1 and NINJ2 physically interact via their respective N-terminal domains. Interestingly, NINJ1-B and NINJ2-B peptides, which were derived from the N-terminal amphipathic helix domains of NINJ1 and NINJ2, respectively, were able to disrupt NINJ1-NINJ2 interaction and inhibit cell growth in a NINJ1/NINJ2-dependent manner. Notably, NINJ1-B and NINJ2-B peptides demonstrated greater potency in growth suppression than NINJ1-A and NINJ2-A peptides, respectively. Mechanistically, we found that NINJ1-B and NINJ2-B peptides were able to induce p53 expression and suppress cell growth in a p53-dependent manner. Together, our findings provide valuable insights into the development of NINJ1/NINJ2 peptides as potential cancer therapeutics, particularly for cancers harboring wild-type p53.

Biological Functions and Therapeutic Potential of UBE2T in Human Cancer.

The ubiquitin-proteasome system is a fundamental regulatory mechanism that governs protein stability and intracellular signaling in eukaryotic cells. This system relies on a coordinated cascade of enzymatic activities involving activating enzymes, conjugating enzymes, and ligases to assemble distinct ubiquitin signals. These signals are subsequently edited, removed, or interpreted by deubiquitinases and ubiquitin-binding proteins. While E3 ligases have traditionally been recognized as the primary determinants of substrate specificity in the ubiquitination process, recent studies have revealed that the dysregulation of E2 enzymes can also lead to significant pathological outcomes, including chromatin instability, immune dysregulation, metabolic dysfunction, and an elevated risk of cancer. Consequently, E2 enzymes have emerged as promising therapeutic targets for the treatment of various dis-eases. This review provides a comprehensive examination of the roles and mechanisms of the ubiquitin-conjugating enzyme E2T (UBE2T) in cancer initiation, progression, and therapy resistance, highlighting its potential as a compelling target for cancer therapeutics.

Cancer Immunotherapies: Navigating the Immune Landscape.

Biotechnology has paved the way for the development of cancer therapeutics that harness biological systems. Cancer immunotherapy (CI) is a pivotal and swiftly progressing therapeutic modality, alongside surgical intervention, cytotoxic chemo-therapy, radiation therapy, and targeted therapy. Therefore, this is the fifth cornerstone of cancer management. Biotechnological pharmaceuticals are superior modalities for combat-ing neoplastic conditions when juxtaposed with conventional chemical therapeutics. Consid-ering empirical evidence, it can be posited that biotechnology exhibits a heightened level of precision in targeting malignant cells associated with cancer, thereby minimizing collateral damage compared to conventional chemotherapeutic approaches. Furthermore, this ap-proach harnesses the inherent capabilities of the immune system to impede cancer recur-rence, thereby facilitating a more proactive therapeutic intervention, as opposed to a mere deleterious remedy. Novel cancer immunotherapeutic medications, along with uncomplicat-ed methodologies that enable the quantitative evaluation of the effectiveness of compounds capable of modifying T cell-mediated, tumor antigen-specific immune responses, play a pivotal role in the assessment process. This highlights the considerable promise of immuno-therapies, monoclonal antibodies, and an array of biotechnological products in a relentless battle against cancer.

A novel anti-mouse CCR7 monoclonal antibody, C7Mab-7, demonstrates high sensitivity in flow cytometry, western blot, and immunohistochemistry.

A novel anti-mouse CCR7 monoclonal antibody, C7Mab-7, demonstrates high sensitivity in flow cytometry, western blot, and immunohistochemistry.

HVEM as a tumor-intrinsic regulator in non-small cell lung cancer: Suppression of metastasis via glycolysis inhibition and modulation of macrophage polarization.

HVEM as a tumor-intrinsic regulator in non-small cell lung cancer: Suppression of metastasis via glycolysis inhibition and modulation of macrophage polarization.

Bioorthogonal SERS-Bioluminescence Dual-Modal Imaging for Real-Time Tracking of Triple-Negative Breast Cancer Metastasis.

Bioorthogonal SERS-Bioluminescence Dual-Modal Imaging for Real-Time Tracking of Triple-Negative Breast Cancer Metastasis.

Emerging roles of EGFL family members in neoplastic diseases: Molecular mechanisms and targeted therapies.

Emerging roles of EGFL family members in neoplastic diseases: Molecular mechanisms and targeted therapies.

Two-dimensional (2D) materials for biomedical applications

Two-dimensional (2D) materials with unique structural and physicochemical properties have emerged as a powerful toolbox in biomedical applications. Nanoscale inorganic, organic, and hybrid 2D materials have shown exceptional potential in biosensing, drug delivery, tissue engineering, and cancer therapeutics. Their ultrahigh specific surface area, remarkable functionalization capabilities, and distinctive optoelectronic characteristics enable sophisticated interactions with biological systems. This Editorial covers the synthesis methods, characteristic properties, and emerging biomedical applications of 2D materials, with a particular focus on their potential in cancer theranostics and biosensing. It also highlights the critical challenges in clinical translation.

SiRNA-based therapeutics in hormone-driven cancers: Advancements and benefits over conventional treatments

siRNA-based therapeutics in hormone-driven cancers: Advancements and benefits over conventional treatments

Pharmacological strategies to bridge the gap between cancer and cardiovascular therapeutics

Pharmacological strategies to bridge the gap between cancer and cardiovascular therapeutics

Lanthanides and their complexes/nanoparticles: A review of cancer diagnosis, therapeutics, toxicity considerations, and clinical trials

Lanthanides and their complexes/nanoparticles: A review of cancer diagnosis, therapeutics, toxicity considerations, and clinical trials