Personalized Therapy Research Articles

U-shaped relationship found between fibrinogen-to-albumin ratio and systemic inflammation response index in osteoporotic fracture patients.

The relationship between the Systemic Inflammatory Response Index (SIRI) and the Fibrinogen-to-albumin ratio (FAR) has not been extensively investigated. The objective of this study was to determine the independent relationship between FAR and SIRI in people with osteoporotic fractures (OPF). A cross-sectional study was conducted using retrospective data from 3431 hospitalized OPF patients. The exposure variable in this study was the baseline FAR, while the outcome variable was the SIRI. Covariates, including age, gender, BMI, and other clinical and laboratory factors, were adjusted. Cross-correlation analysis and linear regression models were applied. The generalized additive model (GAM) investigated non-linear relationships. Adjusted analysis revealed an independent negative association between FAR and SIRI in OPF patients (β = -0.114, p = 0.00064, 95% CI -0.180, -0.049). A substantial U-shaped association between FAR and SIRI was shown using GAM analysis (p < 0.001). FAR and SIRI indicated a negative association for FAR below 6.344% and a positive correlation for FAR over 6.344%. The results of our study revealed a U-shaped relationship between SIRI and FAR. The lowest conceivable FAR for a bone-loose inflammatory disease might be 6.344%, suggesting that this has particular significance for the medical diagnosis and therapy of persons with OPF. Consequently, the term "inflammatory trough" is proposed. These results offer fresh perspectives on controlling inflammation in individuals with OPF and preventing inflammatory osteoporosis.

Multimodal data integration for enhanced longitudinal prediction for cardiac and cerebrovascular events following initial diagnosis of obstructive sleep apnea syndrome.

Obstructive sleep apnea syndrome (OSAS), a prevalent condition, often coexists with intricate metabolic issues and is frequently associated with negative cardiovascular outcomes. We developed a longitudinal prediction model integrating multimodal data for cardiovascular risk stratification of patients with an initial diagnosis of OSAS. We reviewed the data of patients with new-onset OSAS who underwent diagnostic polysomnography between 2018-19. Patients were treated using standard treatment regimens according to clinical practice guidelines. Over a median follow-up of 32 months, 98/729 participants (13.4%) experienced our composite outcome. At a ratio of 7:3, cases were randomly divided into development (n = 510) and validation (n = 219) cohorts. A prediction nomogram was created using six clinical factors - sex, age, diabetes mellitus, history of coronary artery disease, triglyceride-glucose index, and apnea-hypopnea index. The prediction nomogram showed excellent discriminatory power, based on Harrell's C-index values of 0.826 (95% confidence interval (CI) = 0.779-0.873) for the development cohort and 0.877 (95% CI = 0.824-0.93) for the validation cohort. Moreover, comparing the predicted and observed major adverse cardiac and cerebrovascular events in both development and validation cohorts indicated that the prediction nomogram was well-calibrated. Decision curve analysis demonstrated the good clinical applicability of the prediction nomogram. Our findings demonstrated the construction of an innovative visualisation tool that utilises various types of data to predict poor outcomes in Chinese patients diagnosed with OSAS, providing accurate and personalised therapy. Chinese Clinical Trial Registry ChiCTR2300075727.

From rare events to systematic data collection: the RESCUED registry for sudden cardiac death in the young in Germany.

Approximately one-third of sudden cardiac deaths in the young (SCDY) occur due to a structural cardiac disease. Forty to fifty percent of SCDY cases remain unexplained after autopsy (including microscopic and forensic-toxicological analyses), suggesting arrhythmia syndromes as a possible cause of death. Due to the possible inheritability of these diseases, blood relatives of the deceased may equally be carriers of the causative genetic variations and therefore may have an increased cardiac risk profile. A better understanding of the forensic, clinical, and genetic data might help identify a subset of the general population that is at increased risk of sudden cardiac death. The German registry RESCUED (REgistry for Sudden Cardiac and UnExpected Death) comprises information about SCDY fatalities and clinical and genetic data of both the deceased and their biological relatives. The datasets collected in the RESCUED registry will allow for the identification of leading causes of SCDY in Germany and offer unique possibilities of scientific analyses with the aim of detecting unrecognized trends, risk factors, and clinical warning signs of SCDY. In a pilot phase of 24months, approximately 180 SCDY cases (< 50years of age) and 500 family members and clinical patients will be included. RESCUED is the first registry in Germany collecting comprehensive data of SCDY cases and clinical data of the biological relatives reviewed by cardiac experts. RESCUED aims to improve individual risk assessment and public health approaches by directing resources towards early diagnosis and evidence-based, personalized therapy and prevention in affected families. Trial registration number (TRN): DRKS00033543.

Versatile Biomass‐Based Injectable Photothermal Hydrogel for Integrated Regenerative Wound Healing and Skin Bioelectronics

AbstractThe continuously growing utilization of wound healing materials and skin bioelectronics urges the development of flexible hydrogels for personal therapy and health management. Versatile conductive hydrogels prepared from natural biomass are ideal candidates as one of the promising solutions for chronic wound management. Here, the study proposes a kind of robust (strain: 1560.8%), adhesive, self‐healing, injectable, antibacterial (sterilization rate: 99%), near‐infrared (NIR) photothermal responsive, biocompatible, and conductive hydrogel (CPPFe@TA) composed of carboxymethyl cellulose and tannic acid/iron ion complex (TA@Fe3+), featuring rapid self‐assembly and tunable crosslinking time. TA@Fe3+ facilitated the self‐catalysis of the polymerization reaction, and the crosslinking time could be controlled by adjusting Fe3+ concentration. Under NIR irradiation, the hydrogel exhibited remarkable photothermal antibacterial performance. In the full‐thickness skin defect repair experiment on mice, the prepared hydrogel dressing significantly enhanced wound healing. After 14 days, the wound healing rate (95.49%) of CPPFe@TA3 hydrogel + NIR treatment greatly exceeded that of commercial dressings. Meanwhile, the hydrogels has good electrical conductivity and thermo‐responsiveness, making them promising for skin bioelectronics in physiological signal monitoring and rehabilitation exercise management. This work therefore offers a promising strategy for developing versatile biomass‐based hydrogels, which is expected to be applicable to integrated regenerative wound healing and skin bioelectronics.

Noninvasive Atherosclerotic Phenotyping: The Next Frontier into Understanding the Pathobiology of Coronary Artery Disease.

Despite recent advances, coronary artery disease remains one of the leading causes of mortality worldwide. Noninvasive imaging allows atherosclerotic phenotyping by measurement of plaque burden, morphology, activity and inflammation, which has the potential to refine patient risk stratification and guide personalized therapy. This review describes the current and emerging roles of advanced noninvasive cardiovascular imaging methods for the assessment of coronary artery disease. Cardiac computed tomography enables comprehensive, noninvasive imaging of the coronary vasculature, and is used to assess luminal stenoses, coronary calcifications, and distinct adverse plaque characteristics, helping to identify patients prone to future events. Novel software tools, implementing artificial intelligence solutions, can automatically quantify and characterize atherosclerotic plaque from standard computed tomography datasets. These quantitative imaging biomarkers have been shown to improve patient risk stratification beyond clinical risk scores and current clinical interpretation of cardiac computed tomography. In addition, noninvasive molecular imaging in higher risk patients can be used to assess plaque activity and plaque thrombosis. Noninvasive imaging allows unique insight into the burden, morphology and activity of atherosclerotic coronary plaques. Such phenotyping of atherosclerosis can potentially improve individual patient risk prediction, and in the near future has the potential for clinical implementation.

Randomized controlled trial of an Acceptance and Commitment Therapy and compassion-based group intervention for persons with inflammatory bowel disease: the LIFEwithIBD intervention

ObjectivesThis study tested the acceptability and efficacy of an Acceptance and Commitment Therapy and compassion-based intervention (LIFEwithIBD) in people with IBD through a two-arm RCT.MethodsParticipants were recruited at the Gastroenterology Department of the Coimbra University Hospital between June and September 2019. Of the 355 patients screened, those who accepted to participate were randomly assigned to one of two conditions: experimental group (LIFEwithIBD; n = 25) or control group (waitlist; n = 29). Participants completed self-report measures at baseline (T0), post-intervention (T1), and 3-month (T2) and 12-month (T3) follow-ups. Intervention acceptability was assessed. Efficacy was examined using intent-to-treat ANCOVA at post-intervention after adjusting for baseline values of depressive, anxiety, and stress symptoms (primary outcomes). Linear mixed models for all longitudinal outcomes were also analysed. Inflammatory and disease biomarkers were determined at T0 and T3.ResultsAcceptability results revealed a high level of satisfaction and perceived usefulness regarding the intervention. Both groups experienced a significant decrease in stress symptoms and IBD symptom perception at T1. No significant differences were observed at follow-up for the primary outcomes. The experimental group reported significantly lower Crohn’s disease Symptom severity at T2 than the control group. Post-hoc analyses designed to mitigate floor effects revealed substantial treatment effects for the experimental group regarding anxiety symptoms. No significant differences were observed in clinical biomarkers from T0 to T3.ConclusionThe LIFEwithIBD intervention shows promising, although preliminary, benefits for managing disease activity and reducing anxiety symptoms in IBD patients with high severity of psychological distress.Clinical trial registration: https://www.clinicaltrials.gov/ct2/show/NCT03840707, identifier NCT03840707.

Case report: complete long-lasting response to multimodal third line treatment with neurosurgical resection, carmustine wafer implantation and dabrafenib plus trametinib in a BRAFV600E mutated high-grade glioma

Dabrafenib plus trametinib is a promising new therapy for patients affected by BRAFV600E-mutant glioma, with high overall response and manageable toxicity. We described a complete and long-lasting response in a case of recurrent anaplastic pleomorphic xanthoastrocytoma CNS WHO-grade 3 BRAFV600E mutated. Due to very poor prognosis, there are a few described cases of high-grade glioma (HGG) patients treated with the combined target therapy as third-line treatment. The emergence of optimized sequencing strategies and targeted agents, including multimodal and systemic therapy with dabrafenib plus trametinib, will continue to broaden personalized therapy in HGG improving patient outcomes.

Thermoluminescence dosimetry (TLD) in a 3 T magnetic resonance imaging (MRI) environment: implications for personnel exposure monitoring.

Thermoluminescent dosimeters (TLDs) serve as compact and user-friendly tools for various applications, including personal radiation dosimetry and radiation therapy. This study explores the potential of utilizing TLD-100 personal dosimetry, conventionally applied in PET/CT (positron emission tomography/computed tomography) settings, in the PET/MRI (magnetic resonance imaging) environment. The integration of MRI into conventional radiotherapy and PET systems necessitates ionizing radiation dosimetry in the presence of static magnetic fields. In this study, TLD-100 dosimeters were exposed on the surface of a water-filled cylindrical phantom containing PET-radioisotope and positioned on the patient table of a 3 T PET/MRI, where the magnetic field strength is around 0.2 T, aiming to replicate real-world scenarios experienced by personnel in PET/MRI environments. . Results indicate that the modified MR-safe TLD-100 personal dosimeters exhibit no significant impact from the static magnetic field of the 3 T PET/MRI, supporting their suitability for personal dosimetry in PET/MRI settings. This study addresses a notable gap in existing literature on the effect of MRI static magnetic field on TLDs.

Abstract PO5-14-06: Transcriptomics-based drug screening in 3D ex vivo patient-drived breast cancer model and patient biopsy for personalized therapy

Abstract Breast cancer is a leading cause of cancer-related mortality for women worldwide. Hormone receptor-positive (HR+) breast tumors, which represent 70% of all breast cancer cases, are treated with endocrine therapy. However, not all patients benefit from this treatment regimen because of patient-to-patient variation. Therefore, high throughput drug screening system is warranted to enable personalized medicine. Patient-derived organoids, which serve as a screening platform, lose their hormone receptors and response upon ex vivo culturing, and may not be adequate models for HR+ breast tumors. Moreover, unidentified biological components in the widely-used basement membrane matrix, Matrigel, result in high batch-to-batch variations and poor reproducibility in organoid cultures. Here, we propose a hydrogel-based 3D ex vivo model with defined structural and chemical properties to test hormone and drug sensitivity of HR+ breast tumors from patient-derived xenografts (PDXs) and patient tumor biopsies using microfluidics. Our data demonstrate the feasibility of this model to preserve cell proliferation and hormone receptor expression over 7 days. We also demonstrate that responses to hormones and FDA-approved drugs are faithfully maintained in this model. Finally, to establish a high throughput hormone and drug testing workflow with transcriptomic readout, we multiplexed barcoded- and drug-treated tumor samples in a single experiment with bulk RNA-sequencing. Our preliminary data demonstrate patient-specific responses to hormones and drugs that correspond to patient genetic mutation profiles, treatment history, disease stages and subtypes. This platform also enables testing of drugs in clinical trials that shows promising therapeutic outcomes for breast cancer, such as CDK4/6i, AKTi, PARPi, mTORi and their combination with endocrine therapy, thanks to the high throughput of the screening system and the low consumption of patient-derived or patient tissue. Given the capability of combining this physiologically-relevant 3D ex vivo model with RNA-seq for HR+ breast tumors, this platform holds potential for high throughput compound testing and transcriptomic profiling of patient biopsies for personalized medicine. Citation Format: Yueyun Zhang, Carlos Henrique Venturi Ronchi, Giovanna Ambrosini, Yuanlong Liu, Patrick Aouad, Daria Matvienko, Christoph Merten, Cathrin Brisken. Transcriptomics-based drug screening in 3D ex vivo patient-drived breast cancer model and patient biopsy for personalized therapy [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-14-06.

Abstract PS03-08: Spatial biomechanics determines fate in breast cancer

Abstract Background: With the recent advancements of spatial omics, it has become increasingly recognized that spatial analysis the of tumor architecture is key to decipher the heterogenous intratumoral relationships between different tumor components and provide better understanding of cancer therapy resistance, as well as help to identify potential targets for personalized therapy. The challenge of the discovery and implementation of such biomarkers still lays in the technological and methodological difficulties, and their translation into clinical practice. In parallel a major advancement in cancer research has been the recognition of the importance of cancer biomechanical properties in cancer progression, metastasis, and therapy response. Methods: We developed a new computational platform to identify biomechanical drivers of cancer outcome from spatial omics data. We used it to identify recurring spatial patterns of these markers within the tumor microenvironment, and define the spatial scale of heterogeneity of such patterns. Our dataset consists in 700 primary breast cancer baseline samples analyzed with two 30-marker imaging mass cytometry panels to identify cancer, immune, and stromal cells and structures and their biomechanical states. Our patient cohort includes 20+ years of follow up, with up to 6 samples per patient from 64 patients alive 12 years post diagnosis, and 49 patients lost to breast cancer deaths. Patients were treated with surgery, radiation, chemotherapy, endocrine therapy, or combinations of these post-surgery. Based on this approach, we derived a new three-parameter quantitative metric of preferential spatial co-localization between different cells or structures, and we use this metric to stratify patients into responders and non-responders with the aid of single and multivariate survival analysis. Finally, we include a correlation with Atomic Force Microscopy to identify the mechanical signature of these driving patterns in breast cancer. Results: Our work enabled the identification of tumor-immune-stromal spatial patterns that drive breast cancer outcome and their spatial scale. Among our results, we were able to further define spatial regions of hypoxia-driven EMT. We found these regions to be usually on the scale of 50 mm radius, and enriched in presence of unspecified immune cells. They are also more frequent in ER+ areas, with high NaKATPase activity. Our analysis also shed light on the controversy on the role of fibroblasts. We have found that the presence of a strong, spatially structured stroma, in fibroblast-rich regions is a strong predictor of positive outcome. Similarly, in our analysis collagen cross-linking emerges as a positive control factor in Vimentin-rich microenvironments, offering new interpretation to the role of the ECM structure. Importantly, our first principle approach allows for the identification of driving structures beyond heterogeneity, and thus enables easy extension to additional datasets. Finally, when correlating these patterns with Atomic force microscopy measurements, we can clearly see that patterns predictive of poor survival present a clear heterogeneous stiffness signature, as opposed to a very homogenous good survival pattern signature. Conclusion: These results confirm the importance of spatial distribution of biomechanical drivers in cancer, offering new avenues of physics-based therapeutic targets. Our results also offer a solid base to inform machine learning algorithms on how to significantly parametrize spatial patterns in breast cancer for clinical significance. Furthermore, we demonstrate for the first time the use of Atomic Force Microscopy as a single biomarker for these patterns in breast cancer with a direct correlation based on pathology matching. Citation Format: Sara Nizzero, Maria Pelaez Soni, Gregory Zaugg, Mariam Gachechiladze, Yitian Xu, Licheng Zhang, Junjun Zheng, Brian Menegaz, Lee B Jordan, Colin A Purdie, Philip R Quinlan, Chandandeep Nagi, Karla A Sepulveda, Philipp Oertle, Tobias A Appenzeller, Marko Loparic, Zhihui Wang, Shu-Hsia Chen, Vittorio Cristini, Marija Plodinec, Alastair Thompson. Spatial biomechanics determines fate in breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS03-08.

Abstract PO5-01-05: An Open-label, Single-center Study Assessing the Efficacy of PH-based Regimen as Neoadjuvant Therapy for HER2-positive Early-stage Breast Cancer: Integrating Genomic Features and Functional Imaging

Abstract Background: Pathological complete response (pCR) has been demonstrated as a surrogate endpoint for disease-free and overall survival in HER2-positive early-stage breast cancer (EBC). Trastuzumab (H) plus pertuzumab (P) improved pCR rates in patients with HER2-positive early-stage breast cancer. Nevertheless, biomarkers to assess the effectiveness of this treatment in the neoadjuvant setting are still unclear. This study aims to evaluate the effectiveness of fluorodeoxyglucose positron emission tomography (18F-FDG-PET) and 68 Ga-Affibody HER2 Imaging PET (HER2-PET) in HER2 EBC neoadjuvant therapy, and to identify novel biomarkers for this regimen. Methods: HER2-positive EBC patients were prospectively recruited at Fudan University Shanghai Cancer Center between April 2020 and March 2022. All patients received the 1 dose of HP, followed by 6 cycles of docetaxel (T), carboplatin (Cb), and HP. We use FoundationOne to evaluate the genomic alterations before treatment. In addition, 18F-FDG-PET and HER2 PET were performed at baseline, after 1 cycle of HP, and after 6 cycles of TCbHP, to assess the effectiveness of these examinations in the prediction of pCR and correlation to disease burden. All patients were received surgery and completed 1 year of HP-based regimen in the adjuvant setting. Results: A total of 61 patients with a median age of 49 years were included. Among them, 93.5% of patients are node positive. 75.8% of the patients reported adverse events (AEs), primarily grade 1 and 2. The incidence of serious AEs was 6.6%. Patients exhibiting a greater than 40% reduction in SUVmax of 18-FDG-PET or HER2-PET scans from baseline to the fifth day after the initial HP treatment were classified as the treatment-sensitive group. The pCR rate for the 18-FDG-PET-sensitive group was 79.3%, while for the insensitive group was 48.1%. For HER2-PET scans, the pCR rate was 70.0% in the sensitive group and 59.3% in the non-sensitive group. To evaluate the potential biomarkers, 25 patients (13 pCR and 12 non-pCR) were evaluated with FoundationOne, with the identification of 1,321 somatic alterations. The most frequently altered genes were ERBB2 (100%), TP53 (96%), CDK12 (76%), MYC (56%), CCND1 (44%), PIK3CA (40%), and SRSF2 (36%). In the pCR group, we found PPM1D (38.5%) and RAD21 (38.5%) were altered more frequently than the non-pCR group. PIK3CA alterations were significantly reduced in pCR group, only 23% of cases are affected compared to 58.3% in non-pCR group. Patients were non-pCR had more MDM4 (41.7%) and ZNF217 (41.7%) alterations than pCR patients. Conclusion: The SUVmax value of 18-FDG-PET and HER2-PET scans are correlated well with the tumor burden and the residual disease in the HER2 EBCs. The reduction of SUVmax could predict pCR but need further exploration. Patients with alterations in PPM1D and RAD21 have a favorable response to the TCbHP regimen. Conversely, alterations in PIK3CA, MDM4, and ZNF217 are associated with treatment resistance. These results indicate the significance of 18-FDG-PET and HER2-PET in monitoring treatment effectiveness. The biomarker analysis in this study will help us understand the mechanism of HER2 positive breast cancer treatment resistance and assisting in tailoring personalized therapy. Clinical trial information: NCT04281641. Citation Format: Ming Chen, Benlong Yang, Sun Yuyun, Linxiaoxi Ma, Song Shaoli, Min He, Zhi-Ming Shao, Bingqiu Xiu, Jiong Wu. An Open-label, Single-center Study Assessing the Efficacy of PH-based Regimen as Neoadjuvant Therapy for HER2-positive Early-stage Breast Cancer: Integrating Genomic Features and Functional Imaging [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-01-05.

Abstract PO4-10-12: Value of Molecular Targets and Genome-Targeted Therapies FDA-Approved for Metastatic Breast Cancer, 2006-2023

Abstract Title: Value of Molecular Targets and Genome-Targeted Therapies FDA-Approved for Metastatic Breast Cancer, 2006-2023 Background: The number of FDA-approved genome-targeted cancer drugs for metastatic breast cancer has increased, providing the potential for personalized therapy. To help physicians, patients, and policymakers differentiate meaningful from trivial innovation in this field, we assessed the validity of the targets and value of the outcomes used in the pivotal trials supporting approval. Methods: We analyzed trials supporting genome-targeted breast cancer drugs FDA-approved between 2006-2023, defined as those using a genomic test in which the drug targeted a given genomic alteration. From FDA drug labels and trial reports, we extracted characteristics of pivotal trials. For Accelerated Approvals—a special FDA program allowing approval based on unvalidated surrogate measures—if the drug later received traditional approval based on a confirmatory trial, only the latter was analyzed. Strength of evidence supporting molecular targetability was evaluated using the European Society for Medical Oncology (ESMO) Scale for Clinical Actionability of molecular Targets (ESCAT). Clinical benefit for approved indications was assessed using the ESMO-Magnitude of Clinical Benefit Scale (ESMO-MCBS). Molecular targets qualifying for ESCAT category level I-A or I-B associated with ESMO-MCBS grade 4 or 5 were rated as high-benefit genomic-targeted breast cancer treatments. Results: Fifteen genome-targeted drugs covered 17 indications and targeted 8 driver alterations. Among the 17 pivotal trials supporting these indications, most were randomized (11, 65%), phase 3 (11, 65%) and open-label (14, 82%). The most common primary endpoint leading to approval (10, 59%) was progression-free survival. Eleven trials (65%) had a I-A ESCAT targetability, 5 (29%) had a I-C targetability score and 1 (6%) was categorized as II-A. ERBB2 amplification, germline BRCA1/2 mutations, PIK3CA mutations, and ESR1 mutation were classified as tier I-A due to randomized trials demonstrating the effectiveness of approved targeted therapies in patients with these alterations. RET fusions, NTRK fusions, and microsatellite instability were classified as tier I-C, while high-tumor mutational burden was categorized as tier II-A. Eighteen percent of trials (3/17) demonstrated ESMO-MCBS grades 4-5. Overall, 3 of 17 (18%) indications had high-benefit genomic-based cancer treatments. Conclusions: Among molecular-targeted cancer therapies approved for metastatic breast cancer, fewer than a fifth demonstrated substantial patient benefits at approval. Benefit frameworks like ESCAT and ESMO-MCBS can help stakeholders identify therapies with the greatest potential. Citation Format: Ariadna Tibau, Thomas J. Hwang, Consolacion Molto Valiente, Jerry Avorn, Aaron Kesselheim. Value of Molecular Targets and Genome-Targeted Therapies FDA-Approved for Metastatic Breast Cancer, 2006-2023 [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-10-12.

Abstract PS09-08: Genomic characterization of endocrine resistance in ER+HER2+ breast cancers in the POETIC Trial

Abstract Background: Mechanisms of resistance to endocrine therapy are not well understood within ER+HER2+ breast cancer (BC). Our prior work suggested that intrinsic HER2-Enriched (HER2E) molecular subtype predicts early resistance to aromatase inhibitors (AI) (Bergamino eBioMedicine 2022) and high on-treatment (on-Txt) Ki67 levels predict poor survival (Smith Lancet Oncol 2020). Improved early detection of persistent proliferating tumor cells with endocrine resistance pathways could be targeted by pre-emptive personalized therapy and reduction in recurrence. In this study, we proposed to further identify additional alterations/features from genomic and spatial data to provide unprecedented new insight into intrinsic and adaptive resistant pathways in tumor cells that may assist to identify molecular targets for treatment. Materials: POETIC was a phase III trial of post-menopausal patients with ER/PR+ invasive BC (n = 4480) randomized 2:1 to 2 weeks of peri-operative AI (POAI) vs control, followed by standard-of-care treatment. Ki67 was assessed by IHC and intra-tumor heterogeneity was evaluated (5-15 regions) for all the POETIC POAI samples (N = 2487). ER+HER2+ samples were classified as good responders (GR) or poor responders (PR) based on a reduction in Ki67 between pre-treatment (pre-Txt) and 2-week on-Txt samples. Tumor-infiltrating lymphocytes were assessed; multiplex Immunofluorescence (mIF) was performed to measure immune cell densities in tumor and stroma compartments (CD3, CD20, CD68, FOXP3, and CD3 FOXP3 co-expression). Gene expression profiles by BC360™ (Nanostring) on all 210 pairs of POAI treated ER+/HER2+; whole exome sequencing (WES, 100X) were performed on pre-Txt tumor and blood samples from 13 GR, 17 PR, and 9 HER2E GR. We performed GeoMx Whole Transcriptome on 4 pairs (pre-Txt and on-Txt) of GR and GeoMx Proteins (77 including IO proteins) on 6 pairs of GRs and 6 pairs of PRs. Results: The most frequently mutated genes were TP53, PIK3CA, GATA3, and CHD4. Only TP53 was associated with PR (Fisher’s exact p=0.01). TP53 mutated cases had higher expression of TP53 mutant-like gene expression signature compared to wild-type cases (Wilcoxon test p=0.001), mIF FOXP3 (Wilcoxon test p = 0.0005), and CD68 (Wilcoxon test p = 0.019) density score. However, within the HER2-E subset, we found that TP53 mutations were associated with GR (Fisher’s exact p=0.02). We found spatial heterogeneity of Ki67 IHC levels across POAI samples. Examining IHC, while there was higher heterogeneity of Ki67 in the ER+HER2- samples (n = 2264) with 3% of pre-Txt and 9% on-Txt, 6% of ER+HER2+ samples (13/223, 6 LumA, 5 LumB, and 2 HER2E) showed heterogeneity of Ki67 exclusively on-Txt. Even in GR tumors with Ki67 &amp;lt; 10% on-Txt, we identified hotspots with retained proliferating Ki67+ cells after 2 weeks of POAI. The lobular tumors were GR and had characteristic CDH1 mutations. Importantly, cases with persistent areas of Ki67+ cells, regardless of Her2 status, were associated with late relapse. To further explore intratumoral heterogeneity, we performed spatial whole transcriptomics profiling on 95 regions from 4 pairs of GR samples (Ki67 &amp;gt; 10% at baseline and Ki67 &amp;lt; 10% on-Txt) and found low intratumoral heterogeneity in the pre-Txt samples that increased at 2 weeks on-Txt. In a larger set of samples including both GR and PR with the GeoMx protein method, we found increased intratumoral heterogeneity in the PR vs GR. Conclusion: While TP53 mutation was generally a predictor of poor response; in HER2-E it paradoxically was associated with a good early response to aromatase inhibitor which warrants further investigation. Ki67 levels in ER+HER2+ showed higher intratumoral heterogeneity in a subset of patients on treatment suggesting the potential of persistent, proliferating cells leading to later recurrence. Our spatial RNA and protein data further observe the intratumoral heterogeneity that identifies pathways for use as potential spatial biomarkers. Citation Format: Maggie Chon U Cheang, Xixuan Zhu, Orsolya Sipos, Anastasia Alataki, Mikayla Feldbauer, Elena López-Knowles, Holly Tovey, Lucy Kilburn, Milana Bergamino Sirvén, Dhrusti Patel, Hui Xiao, Perry Maxwell, Anthony Skene, Chris Holcombe, Manuel Salto-Tellez, Nicholas Turner, Andrew Dodson, Ian Smith, John Robertson, Judith Bliss, Gene Schuster, Roberto Salgado, Mitch Dowsett, Katherine A Hoadley. Genomic characterization of endocrine resistance in ER+HER2+ breast cancers in the POETIC Trial [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS09-08.

Characterization of a predictive signature for tumor microenvironment and immunotherapy response in hepatocellular carcinoma involving neutrophil extracellular traps

Neutrophil extracellular traps (NETs) and other factors play a significant role in impacting the prognosis of patients with Hepatocellular carcinoma (HCC). Nevertheless, further research is warranted to fully elucidate the prognostic implications of NETs in patients with HCC. We employed a hierarchical clustering technique to examine the Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) data and identified subtypes associated with NETs. Subsequently, we utilized LASSO regression analysis to identify a distinct gene expression pattern within these subtypes. The strength of this signature was further validated through analysis of TCGA-LIHC and International Cancer Genome Consortium-Liver Cancer (ICGC-LIRI-JP) data. Our findings resulted in the construction of a six-gene signature related to NETs, which can predict survival outcomes in HCC patients. To enhance the predictive accuracy of our tool, we developed a nomogram that integrates the NETs signature with clinicopathological characteristics. We validated the significance of NETs in HCC patients using qRT-PCR and immunohistochemistry assays, along with in vitro experiments targeting high-risk genes. Furthermore, our exploration of the immune microenvironment uncovered augmented immune-specific metrics within the low-risk cohort, implying potential disparities in immune-related attributes between the high-risk and low-risk contingents. In summary, the NETs signature we discovered serves as a valuable biomarker and provides guidance for personalized therapy in HCC patients.

Antiobesity Drug Discovery Research: in vitro Models for Shortening the Drug Discovery Pipeline.

Obesity is a growing global health problem, leading to various chronic diseases. Despite standard treatment options, the prevalence of obesity continues to rise, emphasizing the need for new drugs. in vitro methods of drug discovery research provide a time and cost-saving platform to identify new antiobesity drugs. The review covers various aspects of obesity and drug discovery research using in vitro models. Besides discussing causes, diagnosis, prevention, and treatment, the review focuses on the advantages and limitations of in vitro studies and exhaustively covers models based on enzymes and cell lines from different animal species and humans. In contrast to conventional in vivo animal investigations, in vitro preclinical tests using enzyme- and cell line-based assays provide several advantages in development of antiobesity drugs. These methods are quick, affordable, and provide high-throughput screening. They can also yield insightful information about drug-target interactions, modes of action, and toxicity profiles. By shedding light on the factors that lead to obesity, in vitro tests can also present a chance for personalized therapy. Technology will continue to evolve, leading to the creation of more precise and trustworthy in vitro assays, which will become more and more crucial in the search for novel antiobesity medications.

CDK4/6 inhibitors and endocrine therapy in the treatment of metastatic breast cancer: A real-world and Propensity Score-Adjusted comparison

Introduction/BackgroundHormone Receptor-positive (HR+) and Human Epidermal Growth Factor Receptor 2-negative (HER2-) breast cancer is the most common subtype, predominantly treated with endocrine therapy. The efficacy of CDK4/6 inhibitors combined with endocrine therapy in this context remains to be fully evaluated. Materials (or Patients) and MethodsThis study compared the effectiveness of CDK4/6 inhibitors (palbociclib and ribociclib) in combination with an aromatase inhibitor or fulvestrant against endocrine therapy alone in patients with HR+/HER2- advanced breast cancer. The main focus was on progression-free survival (PFS) and overall survival (OS). The study involved a population treated exclusively with endocrine therapy for bone involvement, examining median OS and PFS, and adjusting for variables like stage, visceral metastasis, age, and treatment line. ResultsThe study found no significant OS difference between treatments with palbociclib, ribociclib, and endocrine therapy alone. However, ribociclib combined with letrozole significantly improved PFS over letrozole alone. Propensity score weighting indicated a potential 50% reduction in death risk with ribociclib compared to palbociclib, though this was not confirmed by cox regression. ConclusionCDK4/6 inhibitors, particularly ribociclib in combination with letrozole, show promise in improving outcomes for HR+/HER2- breast cancer patients. While palbociclib may not be superior to traditional endocrine therapy, the results underscore the need for further research. These findings could influence future treatment protocols, emphasizing the importance of personalized therapy in this patient group.

Application of PLGA in Tumor Immunotherapy.

Biodegradable polymers have been extensively researched in the field of biomedicine. Polylactic-co-glycolic acid (PLGA), a biodegradable polymer material, has been widely used in drug delivery systems and has shown great potential in various medical fields, including vaccines, tissue engineering such as bone regeneration and wound healing, and 3D printing. Cancer, a group of diseases with high mortality rates worldwide, has recently garnered significant attention in the field of immune therapy research. In recent years, there has been growing interest in the delivery function of PLGA in tumor immunotherapy. In tumor immunotherapy, PLGA can serve as a carrier to load antigens on its surface, thereby enhancing the immune system's ability to attack tumor cells. Additionally, PLGA can be used to formulate tumor vaccines and immunoadjuvants, thereby enhancing the efficacy of tumor immunotherapy. PLGA nanoparticles (NPs) can also enhance the effectiveness of tumor immunotherapy by regulating the activity and differentiation of immune cells, and by improving the expression and presentation of tumor antigens. Furthermore, due to the diverse physical properties and surface modifications of PLGA, it has a wider range of potential applications in tumor immunotherapy through the loading of various types of drugs or other innovative substances. We aim to highlight the recent advances and challenges of plga in the field of oncology therapy to stimulate further research and development of innovative PLGA-based approaches, and more effective and personalized cancer therapies.

Realizing the Promise of Artificial Intelligence in Hepatocellular Carcinoma through Opportunities and Recommendations for Responsible Translation

This study aims to provide an overview of the current state-of-the-art applications of artificial intelligence (AI) and machine learning in the management of hepatocellular carcinoma (HCC), and to explore future directions for continued progress in this emerging field. This study is a comprehensive literature review that synthesizes recent findings and advancements in the application of AI and machine learning techniques across various aspects of HCC care, including screening and early detection, diagnosis and staging, prognostic modeling, treatment planning, interventional guidance, and monitoring of treatment response. The review draws upon a wide range of published research studies, focusing on the integration of AI and machine learning with diverse data sources, such as medical imaging, clinical data, genomics, and other multimodal information. The results demonstrate that AI-based systems have shown promise in improving the accuracy and efficiency of HCC screening, diagnosis, and tumor characterization compared to traditional methods. Machine learning models integrating clinical, imaging, and genomic data have outperformed conventional staging systems in predicting survival and recurrence risk. AI-based recommendation systems have the potential to optimize personalized therapy selection, while augmented reality techniques can guide interventional procedures in real-time. Moreover, longitudinal application of AI may enhance the assessment of treatment response and recurrence monitoring. Despite these promising findings, the review highlights the need for rigorous multicenter prospective validation studies, standardized multimodal datasets, and thoughtful consideration of ethical implications before widespread clinical implementation of AI technologies in HCC management.

A prognostic model for hepatocellular carcinoma patients based on polyunsaturated fatty acid-related genes.

Polyunsaturated fatty acids (PUFAs) have attracted increasing attention for their role in liver cancer development. The objective of this study is to develop a prognosis prediction model for patients with liver cancer based on PUFA-related metabolic gene characteristics. Transcriptome data and clinical data were obtained from public databases, while gene sets related to PUFAs were acquired from the gene set enrichment analysis (GSEA) database. Univariate Cox analysis was conducted on the training set, followed by LASSO logistic regression and multivariate Cox analysis on genes with p < .05. Subsequently, the stepwise Akaike information criterion method was employed to construct the model. The high- and low-risk groups were divided based on the median score, and the model's survival prediction ability, diagnostic efficiency, and risk score distribution of clinical features were validated. The above procedures were also validated in the validation set. Immune infiltration levels were evaluated using four algorithms, and the immunotherapeutic potential of different groups was explored. Significant enrichment pathways among different groups were selected based on the GSEA algorithm, and mutation analyses were conducted. Nomogram prognostic models were constructed by incorporating clinical factors and risk scores using univariate and multivariate Cox regression analysis, validated through calibration curves and clinical decision curves. Additionally, sensitivity analysis of drugs was performed to screen potential targeted drugs. We constructed a prognostic model comprising eight genes (PLA2G12A, CYP2C8, ABCCI, CD74, CCR7, P2RY4, P2RY6, and YY1). Validation across multiple datasets indicated the model's favorable prognostic prediction ability and diagnostic efficiency, with poorer grading and staging observed in the high-risk group. Variations in mutation status and pathway enrichment were noted among different groups. Incorporating Stage, Grade, T.Stage, and RiskScore into the nomogram prognostic model demonstrated good accuracy and clinical decision benefits. Multiple immune analyses suggested greater benefits from immunotherapy in the low-risk group. We predicted multiple targeted drugs, providing a basis for drug development. Our study's multifactorial prognostic model across multiple datasets demonstrates good applicability, offering a reliable tool for personalized therapy. Immunological and mutation-related analyses provide theoretical foundations for further research. Drug predictions offer important insights for future drug development and treatment strategies. Overall, this study provides comprehensive insights into tumor prognosis assessment and personalized treatment planning.

CNS Germ Cell Tumors: Molecular Advances, Significance in Risk Stratification and Future Directions

Central Nervous System Germ Cell Tumors (CNS GCTs) represent a subtype of intracranial malignant tumors characterized by highly heterogeneous histology. Current diagnostic methods in clinical practice have notable limitations, and treatment strategies struggle to achieve personalized therapy based on patient risk stratification. Advances in molecular genetics, biology, epigenetics, and understanding of the tumor microenvironment suggest the diagnostic potential of associated molecular alterations, aiding risk subgroup identification at diagnosis. Furthermore, they suggest the existence of novel therapeutic approaches targeting chromosomal alterations, mutated genes and altered signaling pathways, methylation changes, microRNAs, and immune checkpoints. Moving forward, further research is imperative to explore the pathogenesis of CNS GCTs and unravel the intricate interactions among various molecular alterations. Additionally, these findings require validation in clinical cohorts to assess their role in the diagnosis, risk stratification, and treatment of patients.