Precision medicine for cancer with next-generation functional diagnostics

Unveiling racial disparities in prostate cancer using an integrative genomic and transcriptomic analysis

Prostate cancer exhibits significant racial disparities, with African American (AA) individuals showing ∼64% higher incidence and 2.3 times greater mortality rates compared to their Caucasian (CA) counterparts. Understanding the complex interplay of genetic, environmental, lifestyle, socioeconomic, and healthcare access factors is crucial for developing effective interventions to reduce this disproportionate burden. This study aims to uncover the genetic and transcriptomic differences driving these disparities through a comprehensive analysis using RNA sequencing (RNA-seq) and exome sequencing of prostate cancer tissues from both Black and White patients. Our transcriptomics analysis revealed enhanced activity in pathways linked to immune response and cellular interactions in AA prostate cancer samples, with notable regulation by histone-associated transcription factors (HIST1H1A, HIST1H1D, and HIST1H1B) suggests potential involvement of histone modification mechanisms. Additionally, pseudogenes and long non-coding RNAs (lncRNAs) among the regulated genes indicate non-coding elements' role in these disparities. Exome sequencing identified unique variants in AA patient samples within key genes, including TP73 (tumor suppression), XYLB (metabolism), ALDH4A1 (oxidative stress), PTPRB (cellular signaling), and HLA-DRB5 (immune response). These genetic variations likely contribute to disease progression and therapy response disparities. This study highlights the importance of considering genetic and epigenetic variations in developing tailored therapeutic approaches to improve treatment efficacy and reduce mortality rates across diverse populations.

Functional combinatorial precision medicine for predicting and optimizing soft tissue sarcoma treatments

Soft tissue sarcomas (STS) are rare, heterogeneous tumors with poor survival outcomes, primarily due to reliance on cytotoxic chemotherapy and lack of targeted therapies. Given the uniquely individualized nature of STS, we hypothesized that the ex vivo drug sensitivity platform, quadratic phenotypic optimization platform (QPOP), can predict treatment response and enhance combination therapy design for STS. Using QPOP, we screened 45 primary STS patient samples, and showed improved or concordant patient outcomes that are attributable to QPOP predictions. From a panel of approved and investigational agents, QPOP identified AZD5153 (BET inhibitor) and pazopanib (multi-kinase blocker) as the most effective combination with superior efficacy compared to standard regimens. Validation in a panel of established patient lines and in vivo models supported its synergistic interaction, accompanied by repressed oncogenic MYC and related pathways. These findings provide preliminary clinical evidence for QPOP to predict STS treatment outcomes and guide the development of novel therapeutic strategies for STS patients.

Comprehensive immunophenotyping of gastric adenocarcinoma identifies an inflamed class of tumors amenable to immunotherapies

BACKGROUND

Gastric adenocarcinoma (GAC) imposes a considerable global health burden. Molecular profiling of GAC from the tumor microenvironment perspective through a multi-omics approach is eagerly awaited in order to allow a more precise application of novel therapies in the near future.

METHODS

To better understand the tumor-immune interface of GAC, we identified an internal cohort of 82 patients that allowed an integrative molecular analysis including mutational profiling by whole-exome sequencing, RNA gene expression of 770 genes associated with immune response, and multiplex protein expression at spatial resolution of 34 immuno-oncology targets at different compartments (tumorous cells and immune cells). Molecular findings were validated in 595 GAC from the TCGA and ACRG external cohorts with available multiomics data. Prediction of response to immunotherapies of the discovered immunophenotypes was assessed in 1039 patients with cancer from external cohorts with available transcriptome data.

RESULTS

Unsupervised clustering by gene expression identified a subgroup of GAC that includes 52% of the tumors, the so-called Inflamed class, characterized by high tumor immunogenicity and cytotoxicity, particularly in the tumor center at protein level, with enrichment of PIK3CA and ARID1A mutations and increased presence of exhausted CD8+ T cells as well as co-inhibitory receptors such as PD1, CTLA4, LAG3, and TIGIT. The remaining 48% of tumors were called non-inflamed based on the observed exclusion of T cell infiltration, with an overexpression of VEGFA and higher presence of TP53 mutations, resulting in a worse clinical outcome. A 10-gene RNA signature was developed for the identification of tumors belonging to these classes, demonstrating in evaluated datasets comparable clinical utility in predicting response to current immunotherapies when tested against other published gene signatures.

CONCLUSIONS

Comprehensive immunophenotyping of GAC identifies an inflamed class of tumors that complements previously proposed tumor-based molecular clusters. Such findings may provide the rationale for exploring novel immunotherapeutic approaches for biomarker-enriched populations in order to improve GAC patient's survival.

Hallmarks of artificial intelligence contributions to precision oncology

The integration of artificial intelligence (AI) into oncology promises to revolutionize cancer care. In this Review, we discuss ten AI hallmarks in precision oncology, organized into three groups: (1) cancer prevention and diagnosis, encompassing cancer screening, detection and profiling; (2) optimizing current treatments, including patient outcome prediction, treatment planning and monitoring, clinical trial design and matching, and developing response biomarkers; and (3) advancing new treatments by identifying treatment combinations, discovering cancer vulnerabilities and designing drugs. We also survey AI applications in interventional clinical trials and address key challenges to broader clinical adoption of AI: data quality and quantity, model accuracy, clinical relevance and patient benefit, proposing actionable solutions for each.

Neural Network-Enabled Multiparametric Impedance Signal Templating for High throughput Single-Cell Deformability Cytometry Under Viscoelastic Extensional Flows

Cellular biophysical metrics exhibit systematic alterations during processes, such as metastasis and immune cell activation, which can be used to identify and separate live cell subpopulations for targeting drug screening. Image-based biophysical cytometry under extensional flows can accurately quantify cell deformability based on cell shape alterations but needs extensive image reconstruction, which limits its inline utilization to activate cell sorting. Impedance cytometry can measure these cell shape alterations based on electric field screening, while its frequency response offers functional information on cell viability and interior structure, which are difficult to discern by imaging. Furthermore, 1-D temporal impedance signal trains exhibit characteristic shapes that can be rapidly templated in near real-time to extract single-cell biophysical metrics to activate sorting. We present a multilayer perceptron neural network signal templating approach that utilizes raw impedance signals from cells under extensional flow, alongside its training with image metrics from corresponding cells to derive net electrical anisotropy metrics that quantify cell deformability over wide anisotropy ranges and with minimal errors from cell size distributions. Deformability and electrical physiology metrics are applied in conjunction on the same cell for multiparametric classification of live pancreatic cancer cells versus cancer associated fibroblasts using the support vector machine model.

MGATAF: multi-channel graph attention network with adaptive fusion for cancer-drug response prediction

BACKGROUND

Drug response prediction is critical in precision medicine to determine the most effective and safe treatments for individual patients. Traditional prediction methods relying on demographic and genetic data often fall short in accuracy and robustness. Recent graph-based models, while promising, frequently neglect the critical role of atomic interactions and fail to integrate drug fingerprints with SMILES for comprehensive molecular graph construction.

RESULTS

We introduce multimodal multi-channel graph attention network with adaptive fusion (MGATAF), a framework designed to enhance drug response predictions by capturing both local and global interactions among graph nodes. MGATAF improves drug representation by integrating SMILES and fingerprints, resulting in more precise predictions of drug effects. The methodology involves constructing multimodal molecular graphs, employing multi-channel graph attention networks to capture diverse interactions, and using adaptive fusion to integrate these interactions at multiple abstraction levels. Empirical results demonstrate MGATAF's superior performance compared to traditional and other graph-based techniques. For example, on the GDSC dataset, MGATAF achieved a 5.12% improvement in the Pearson correlation coefficient (PCC), reaching 0.9312 with an RMSE of 0.0225. Similarly, in new cell-line tests, MGATAF outperformed baselines with a PCC of 0.8536 and an RMSE of 0.0321 on the GDSC dataset, and a PCC of 0.7364 with an RMSE of 0.0531 on the CCLE dataset.

CONCLUSIONS

MGATAF significantly advances drug response prediction by effectively integrating multiple molecular data types and capturing complex interactions. This framework enhances prediction accuracy and offers a robust tool for personalized medicine, potentially leading to more effective and safer treatments for patients. Future research can expand on this work by exploring additional data modalities and refining the adaptive fusion mechanisms.

Zebrafish patient-derived xenograft system for predicting carboplatin resistance and metastasis of ovarian cancer

AIMS

Ovarian cancer (OC) remains a significant challenge in oncology due to high rates of drug resistance and disease relapse following standard treatment with surgery and platinum-based chemotherapy. Despite the widespread use of these treatments, no effective biomarkers currently exist to identify which patients will respond favorably to therapy. This study introduces a zebrafish patient-derived xenograft (PDX) system, capable of replicating both the carboplatin response and metastatic behavior observed in OC patients, within a rapid 3-day assay period.

METHODS

Two OC cell lines: carboplatin-sensitive (A2780) and resistant (OVCAR8) were used to assess differential responses to treatment in murine and zebrafish xenograft models. Tumor tissues from 16 OC patients were implanted into zebrafish embryos to test carboplatin responses and predict metastasis. Additionally, eight clinical OC samples were directly implanted into zebrafish embryos as part of a proof-of-concept demonstration.

RESULTS

The zebrafish xenografts accurately reflected the carboplatin sensitivity and resistance patterns seen in in vitro and murine models. The zebrafish PDX model demonstrated a 67 % success rate for implantation and a 100 % success rate for engraftment. Notably, the model effectively distinguished between metastatic and non-metastatic disease, with an area under the ROC curve (AUC) of 0.818. Furthermore, the zebrafish PDX model showed a high concordance with patient-specific responses to carboplatin.

CONCLUSIONS

This zebrafish PDX model offers a fast, accurate, and clinically relevant platform for evaluating carboplatin response and predicting metastasis in OC patients. It holds significant potential for advancing personalized medicine, allowing for more precise therapeutic outcome predictions and individualized treatment strategies.

Gastric biopsy-derived cell line and its utility in assessing tumor cell drug sensitivity

Gastric cancer (GC) has benefited from treatment improvements such as minimally invasive surgery, molecular-targeted drugs, and immune check point inhibitors. However, the prognosis of advanced GC is still unfavorable. Minimally invasive pre-treatment detection of drug sensitivity (MI-PDDS) has increasing importance in view of improved chemotherapy. Gastric biopsy specimens are obtained with relative ease but have not been considered an appropriate source for generating cell lines because of their minute amounts. We therefore materialized the idea of MI-PDDS using biopsy-derived cell lines obtained from endoscopic biopsy specimens. Here, a cell line designated TCC-GC1-C1 was established from a biopsy specimen of a histologically confirmed adenocarcinoma of the stomach. The cell line showed the ability of forming spheroid with deeply stained nuclei and disturbed cellular morphology indicative of malignancy. Single-nucleotide polymorphism (SNP) genotyping of the cell line revealed a duplication of chromosome19q and a deletion of chromosome 8p. A drug screening test with 221 anticancer drugs showed that the cell line had high sensitivity to the proteasome inhibitor (Carfilzomib) and the fibroblast growth factor receptor inhibitor (Erdafitinib), with a low IC50 value of under 0.1 μM. Our MI-PDDS approach holds promise in making a treatment decision for advanced GC.

Pharmacometabolomics in Drug Disposition, Toxicity, and Precision Medicine

The precision medicine initiative has driven a substantial change in the way scientists and health care practitioners think about diagnosing and treating disease. While it has long been recognized that drug response is determined by the intersection of genetic, environmental, and disease factors, improvements in technology have afforded precision medicine guided dosing of drugs to improve efficacy and reduce toxicity. Pharmacometabolomics aims to evaluate small molecule metabolites in plasma and/or urine to help evaluate mechanisms that predict and/or reflect drug efficacy and toxicity. In this mini review, we provide an overview of pharmacometabolomic approaches and methodologies. Relevant examples where metabolomic techniques have been used to better understand drug efficacy and toxicity in major depressive disorder and cancer chemotherapy are discussed. In addition, the utility of metabolomics in drug development and understanding drug metabolism, transport, and pharmacokinetics is reviewed. Pharmacometabolomic approaches can help describe factors mediating drug disposition, efficacy, and toxicity. While important advancements in this area have been made, there remain several challenges that must be overcome before this approach can be fully implemented into clinical drug therapy. SIGNIFICANCE STATEMENT: Pharmacometabolomics has emerged as an approach to identify metabolites that allow for implementation of precision medicine approaches to pharmacotherapy. This review article provides an overview of pharmacometabolomics including highlights of important examples.

Patient-derived melanoma models

Melanoma is a very aggressive, rapidly metastasizing tumor that has been studied intensively in the past regarding the underlying genetic and molecular mechanisms. More recently developed treatment modalities have improved response rates and overall survival of patients. However, the majority of patients suffer from secondary treatment resistance, which requires in depth analyses of the underlying mechanisms. Here, melanoma models based on patients-derived material may play an important role. Consequently, a plethora of different experimental techniques have been developed in the past years. Among these are 3D and 4D culture techniques, organotypic skin reconstructs, melanoma-on-chip models and patient-derived xenografts, Every technique has its own strengths but also weaknesses regarding throughput, reproducibility, and reflection of the human situation. Here, we provide a comprehensive overview of currently used techniques and discuss their use in different experimental settings.

Drug screening on digital microfluidics for cancer precision medicine

Drug screening based on in-vitro primary tumor cell culture has demonstrated potential in personalized cancer diagnosis. However, the limited number of tumor cells, especially from patients with early stage cancer, has hindered the widespread application of this technique. Hence, we developed a digital microfluidic system for drug screening using primary tumor cells and established a working protocol for precision medicine. Smart control logic was developed to increase the throughput of the system and decrease its footprint to parallelly screen three drugs on a 4 × 4 cm2 chip in a device measuring 23 × 16 × 3.5 cm3. We validated this method in an MDA-MB-231 breast cancer xenograft mouse model and liver cancer specimens from patients, demonstrating tumor suppression in mice/patients treated with drugs that were screened to be effective on individual primary tumor cells. Mice treated with drugs screened on-chip as ineffective exhibited similar results to those in the control groups. The effective drug identified through on-chip screening demonstrated consistency with the absence of mutations in their related genes determined via exome sequencing of individual tumors, further validating this protocol. Therefore, this technique and system may promote advances in precision medicine for cancer treatment and, eventually, for any disease.

MOINER: A Novel Multiomics Early Integration Framework for Biomedical Classification and Biomarker Discovery

In the context of precision medicine, multiomics data integration provides a comprehensive understanding of underlying biological processes and is critical for disease diagnosis and biomarker discovery. One commonly used integration method is early integration through concatenation of multiple dimensionally reduced omics matrices due to its simplicity and ease of implementation. However, this approach is seriously limited by information loss and lack of latent feature interaction. Herein, a novel multiomics early integration framework (MOINER) based on information enhancement and image representation learning is thus presented to address the challenges. MOINER employs the self-attention mechanism to capture the intrinsic correlations of omics-features, which make it significantly outperform the existing state-of-the-art methods for multiomics data integration. Moreover, visualizing the attention embedding and identifying potential biomarkers offer interpretable insights into the prediction results. All source codes and model for MOINER are freely available https://github.com/idrblab/MOINER.

Fibroblast growth factor receptors 1 and 4 combined with lymph node metastasis predicts poor prognosis in oral cancer

OBJECTIVES

The fibroblast growth factor receptor (FGFR) members including FGFR1-4 have been identified as promising novel therapeutic targets and prognostic markers in multiple solid tumors. However, the predictive role of the expression of FGFR proteins in oral squamous cell carcinoma (OSCC) requires further exploration.

MATERIALS AND METHODS

Immunohistochemical evaluation of FGFR1-4 was performed on 161 paired OSCC samples. The associations of FGFRs with clinicopathologic and prognostic parameters were analyzed. To further assess the contribution of FGFRs to OSCC proliferation, cell lines, and one PDX model was utilized to examine the anti-tumor effect of the pan-FGFR inhibitor AZD4547.

RESULTS

All FGFR members were found to be overexpressed in OSCC tumors when compared to normal tissues, and their expression was significantly associated with poor overall survival and disease-free survival. Multivariate Cox regression analysis revealed high expression of FGFR1 (p = 0.014) and FGFR4 (p = 0.009) were independent prognostic factors and co-overexpression of FGFR1 and FGFR4 with lymph node metastasis increased HR for death (p = 0.02). The pan-FGFR inhibitor AZD4547 showed anti-tumor activity in cell lines and in a patient-derived xenograft of OSCC.

CONCLUSIONS

This study highlights the co-overexpression of FGFR1 and FGFR4 as a significantly poor prognosis indicator in OSCC when combined with lymph node metastasis.

Innovative strategies for measuring kinase activity to accelerate the next wave of novel kinase inhibitors

The development of protein kinase inhibitors (PKIs) has gained significance owing to their therapeutic potential for diseases like cancer. In addition, there has been a rise in refining kinase activity assays, each possessing unique biological and analytical characteristics crucial for PKI development. However, the PKI development pipeline experiences high attrition rates and approved PKIs exhibit unexploited potential because of variable patient responses. Enhancing PKI development efficiency involves addressing challenges related to understanding the PKI mechanism of action and employing biomarkers for precision medicine. Selecting appropriate kinase activity assays for these challenges can overcome these attrition rate issues. This review delves into the current obstacles in kinase inhibitor development and elucidates kinase activity assays that can provide solutions.

Zebrafish tumour xenograft models: a prognostic approach to epithelial ovarian cancer

Epithelial ovarian cancer (EOC) is the gynaecological malignancy with highest mortality. Although adjuvant treatment with carboplatin and paclitaxel leads to an objective response in ~80% of these patients, a majority will relapse within two years. Better methods for assessing long-term treatment outcomes are needed. To address this, we established safe and efficacious doses of carboplatin and paclitaxel using IGROV-1 zebrafish-CDX models. Then fluorescently-labelled cell suspensions from 83 tumour biopsies collected at exploratory laparotomy of women with suspected EOC were generated and 37 (45%) were successfully implanted in zebrafish larvae. Among these 19 of 27 pathology-confirmed EOC samples (70%) engrafted. These zebrafish patient-derived tumour xenograft (ZTX) models were treated with carboplatin or paclitaxel and tumour growth/regression and metastatic dissemination were recorded. In a subgroup of nine patients, four ZTX models regressed during carboplatin treatment. All four corresponding patients had >24 months PFS. Furthermore, both ZTX models established from two patients having <24 months PFS failed to regress during carboplatin treatment. Seven of eight models seeding <6 metastatic cells were established from patients having >24 months PFS. In eleven of fourteen patients, FIGO stage I + II or III tumours gave rise to ZTX models seeding <4 or >4 metastatic cells, respectively. In conclusion, ZTX models predicted patients having >24 or <24 months PFS, based on response/no response to carboplatin. Furthermore, high metastatic dissemination in ZTX models correlated to shorter PFS and more advanced disease at diagnosis. These preliminary results suggest that ZTX models could become a useful prognostic tool in EOC treatment planning.

Delineating functional and molecular impact of ex vivo sample handling in precision medicine

Consistent handling of samples is crucial for achieving reproducible molecular and functional testing results in translational research. Here, we used 229 acute myeloid leukemia (AML) patient samples to assess the impact of sample handling on high-throughput functional drug testing, mass spectrometry-based proteomics, and flow cytometry. Our data revealed novel and previously described changes in cell phenotype and drug response dependent on sample biobanking. Specifically, myeloid cells with a CD117 (c-KIT) positive phenotype decreased after biobanking, potentially distorting cell population representations and affecting drugs targeting these cells. Additionally, highly granular AML cell numbers decreased after freezing. Secondly, protein expression levels, as well as sensitivity to drugs targeting cell proliferation, metabolism, tyrosine kinases (e.g., JAK, KIT, FLT3), and BH3 mimetics were notably affected by biobanking. Moreover, drug response profiles of paired fresh and frozen samples showed that freezing samples can lead to systematic errors in drug sensitivity scores. While a high correlation between fresh and frozen for the entire drug library was observed, freezing cells had a considerable impact at an individual level, which could influence outcomes in translational studies. Our study highlights conditions where standardization is needed to improve reproducibility, and where validation of data generated from biobanked cohorts may be particularly important.

Translational bioinformatics approach to combat cardiovascular disease and cancers

Bioinformatics is an interconnected subject of science dealing with diverse fields including biology, chemistry, physics, statistics, mathematics, and computer science as the key fields to answer complicated physiological problems. Key intention of bioinformatics is to store, analyze, organize, and retrieve essential information about genome, proteome, transcriptome, metabolome, as well as organisms to investigate the biological system along with its dynamics, if any. The outcome of bioinformatics depends on the type, quantity, and quality of the raw data provided and the algorithm employed to analyze the same. Despite several approved medicines available, cardiovascular disorders (CVDs) and cancers comprises of the two leading causes of human deaths. Understanding the unknown facts of both these non-communicable disorders is inevitable to discover new pathways, find new drug targets, and eventually newer drugs to combat them successfully. Since, all these goals involve complex investigation and handling of various types of macro- and small- molecules of the human body, bioinformatics plays a key role in such processes. Results from such investigation has direct human application and thus we call this filed as translational bioinformatics. Current book chapter thus deals with diverse scope and applications of this translational bioinformatics to find cure, diagnosis, and understanding the mechanisms of CVDs and cancers. Developing complex yet small or long algorithms to address such problems is very common in translational bioinformatics. Structure-based drug discovery or AI-guided invention of novel antibodies that too with super-high accuracy, speed, and involvement of considerably low amount of investment are some of the astonishing features of the translational bioinformatics and its applications in the fields of CVDs and cancers.

Platform trial for off-label oncology drugs using comprehensive genomic profiling under the universal public healthcare system: the BELIEVE trial

BACKGROUND

Precision medicine has transformed cancer treatment by focusing on personalized approaches based on genomic abnormalities. However, comprehensive genomic profiling (CGP) and access to targeted therapies are limited in Japan. This study investigates the BELIEVE trial, which aims to improve drug accessibility for patients with actionable genetic abnormalities through off-label drug administration.

METHODS

The BELIEVE trial is a platform trial with a single master protocol, conducted under the Clinical Trials Act and the patient-proposed health services (PPHS) scheme. Eligible patients with solid tumors exhibiting actionable alterations were enrolled, and CGP tests covered by national health insurance were employed. Treatment selection, study drugs from collaborating pharmaceutical companies, and treatment schedules adhered to predefined protocols. Primary and secondary endpoints were evaluated, and statistical analysis was conducted based on patient response rates.

RESULTS

The BELIEVE trial offered treatment opportunities for patients with relapse/refractory disease who lacked standard therapies or clinical trial options. This study addresses unmet medical needs and contributes to the establishment of precision medicine systems. Similar trials like NCI-MATCH and TAPUR are being conducted globally. The BELIEVE trial provides a platform for off-label drug administration, collects essential clinical data, and contributes to drug approval applications.

CONCLUSION

The BELIEVE trial provides hope for patients with actionable genetic abnormalities by facilitating access to targeted therapies through off-label drug administration. It establishes a regulatory framework and promotes collaboration between industry and academia by expanding organ-specific and cross-organ biomarker-based treatments.

Culture and expansion of murine proximal airway basal stem cells

BACKGROUND

The stem cell characteristic makes basal cells desirable for ex vivo modeling of airway diseases. However, to date, approaches allowing them extensively in vitro serial expansion and maintaining bona fide stem cell property are still awaiting to be established. This study aims to develop a feeder-free culture system of mouse airway basal stem cells (ABSCs) that sustain their stem cell potential in vitro, providing an experimental basis for further in-depth research and mechanism exploration.

METHODS

We used ROCK inhibitor Y-27632-containing 3T3-CM, MEF-CM, and RbEF-CM to determine the proper feeder-free culture system that could maintain in vitro stem cell morphology of mouse ABSCs. Immunocytofluorescence was used to identify the basal cell markers of obtained cells. Serial propagation was carried out to observe whether the stem cell morphology and basal cell markers could be preserved in this cultivation system. Next, we examined the in vitro expansion and self-renewal ability by evaluating population doubling time and colony-forming efficiency. Moreover, the differentiation potential was detected by an in vitro differentiation culture and a 3D tracheosphere assay.

RESULTS

When the mouse ABSCs were cultured using 3T3-CM containing ROCK inhibitor Y-27632 in combination with Matrigel-coated culture dishes, they could stably expand and maintain stem cell-like clones. We confirmed that the obtained clones comprised p63/Krt5 double-positive ABSCs. In continuous passage and maintenance culture, we found that it could be subculture to at least 15 passages in vitro, stably maintaining its stem cell morphology, basal cell markers, and in vitro expansion and self-renewal capabilities. Meanwhile, through in vitro differentiation culture and 3D tracheosphere culture, we found that in addition to maintaining self-renewal, mouse ABSCs could differentiate into other airway epithelial cells such as acetylated tubulin (Act-Tub) + ciliated and MUC5AC + mucus-secreting cells. However, they failed to differentiate into alveoli epithelial cells, including alveolar type I and alveolar type II.

CONCLUSION

We established an in vitro feeder-free culture system that allows mouse ABSCs to maintain their stem cell characteristics, including self-renewal and airway epithelium differentiation potential, while keeping up in vitro expansion stability.

Dielectrophoretic enrichment of live chemo-resistant circulating-like pancreatic cancer cells from media of drug-treated adherent cultures of solid tumors

Due to low numbers of circulating tumor cells (CTCs) in liquid biopsies, there is much interest in enrichment of alternative circulating-like mesenchymal cancer cell subpopulations from in vitro tumor cultures for utilization within molecular profiling and drug screening. Viable cancer cells that are released into the media of drug-treated adherent cancer cell cultures exhibit anoikis resistance or anchorage-independent survival away from their extracellular matrix with nutrient sources and waste sinks, which serves as a pre-requisite for metastasis. The enrichment of these cell subpopulations from tumor cultures can potentially serve as an in vitro source of circulating-like cancer cells with greater potential for scale-up in comparison with CTCs. However, these live circulating-like cancer cell subpopulations exhibit size overlaps with necrotic and apoptotic cells in the culture media, which makes it challenging to selectively enrich them, while maintaining them in their suspended state. We present optimization of a flowthrough high frequency (1 MHz) positive dielectrophoresis (pDEP) device with sequential 3D field non-uniformities that enables enrichment of the live chemo-resistant circulating cancer cell subpopulation from an in vitro culture of metastatic patient-derived pancreatic tumor cells. Central to this strategy is the utilization of single-cell impedance cytometry with gates set by supervised machine learning, to optimize the frequency for pDEP, so that live circulating cells are selected based on multiple biophysical metrics, including membrane physiology, cytoplasmic conductivity and cell size, which is not possible using deterministic lateral displacement that is solely based on cell size. Using typical drug-treated samples with low levels of live circulating cells (<3%), we present pDEP enrichment of the target subpopulation to ∼44% levels within 20 minutes, while rejecting >90% of dead cells. This strategy of utilizing single-cell impedance cytometry to guide the optimization of dielectrophoresis has implications for other complex biological samples.

Machine Learning and Health Science Research: Tutorial

Machine learning (ML) has seen impressive growth in health science research due to its capacity for handling complex data to perform a range of tasks, including unsupervised learning, supervised learning, and reinforcement learning. To aid health science researchers in understanding the strengths and limitations of ML and to facilitate its integration into their studies, we present here a guideline for integrating ML into an analysis through a structured framework, covering steps from framing a research question to study design and analysis techniques for specialized data types.

Patient-Derived Xenograft Models in Cancer Research: Methodology, Applications, and Future Prospects

Patient-derived xenografts (PDXs) have emerged as a pivotal tool in translational cancer research, addressing limitations of traditional methods and facilitating improved therapeutic interventions. These models involve engrafting human primary malignant cells or tissues into immunodeficient mice, allowing for the investigation of cancer mechanobiology, validation of therapeutic targets, and preclinical assessment of treatment strategies. This chapter provides an overview of PDXs methodology and their applications in both basic cancer research and preclinical studies. Despite current limitations, ongoing advancements in humanized xenochimeric models and autologous immune cell engraftment hold promise for enhancing PDX model accuracy and relevance. As PDX models continue to refine and extend their applications, they are poised to play a pivotal role in shaping the future of translational cancer research.

Cellular Mass Response to Therapy Correlates With Clinical Response for a Range of Malignancies

PURPOSE

Cancer patients with advanced-stage disease have poor prognosis, typically having limited options for efficacious treatment, and genomics-based therapy guidance continues to benefit only a fraction of patients. Next-generation ex vivo approaches, such as cell mass-based response testing (MRT), offer an alternative precision medicine approach for a broader population of patients with cancer, but validation of clinical feasibility and potential impact remain necessary.

MATERIALS AND METHODS

We evaluated the clinical feasibility and accuracy of using live-cell MRT to predict patient drug sensitivity. Using a unified measurement workflow with a 48-hour result turnaround time, samples were subjected to MRT after treatment with a panel of drugs in vitro. After completion of therapeutic course, clinical response data were correlated with MRT-based predictions of outcome. Specimens were collected from 104 patients with solid (n = 69) and hematologic (n = 35) malignancies, using tissue formats including needle biopsies, malignant fluids, bone marrow aspirates, and blood samples. Of the 81 (78%) specimens qualified for MRT, 41 (51%) patients receiving physician-selected therapies had treatments matched to MRT.

RESULTS

MRT demonstrated high concordance with clinical responses with an odds ratio (OR) of 14.80 (P = .0003 [95% CI, 2.83 to 102.9]). This performance held for both solid and hematologic malignances with ORs of 20.67 (P = .0128 [95% CI, 1.45 to 1,375.57]) and 8.20 (P = .045 [95% CI, 0.77 to 133.56]), respectively. Overall, these results had a predictive accuracy of 80% (P = .0026 [95% CI, 65 to 91]).

CONCLUSION

MRT showed highly significant correlation with clinical response to therapy. Routine clinical use is technically feasible and broadly applicable to a wide range of samples and malignancy types, supporting the need for future validation studies.

Big data in breast cancer: Towards precision treatment

Breast cancer is the most prevalent and deadliest cancer among women globally, representing a major threat to public health. In response, the World Health Organization has established the Global Breast Cancer Initiative framework to reduce breast cancer mortality through global collaboration. The integration of big data analytics (BDA) and precision medicine has transformed our understanding of breast cancer's biological traits and treatment responses. By harnessing large-scale datasets - encompassing genetic, clinical, and environmental data - BDA has enhanced strategies for breast cancer prevention, diagnosis, and treatment, driving the advancement of precision oncology and personalised care. Despite the increasing importance of big data in breast cancer research, comprehensive studies remain sparse, underscoring the need for more systematic investigation. This review evaluates the contributions of big data to breast cancer precision medicine while addressing the associated opportunities and challenges. Through the application of big data, we aim to deepen insights into breast cancer pathogenesis, optimise therapeutic approaches, improve patient outcomes, and ultimately contribute to better survival rates and quality of life. This review seeks to provide a foundation for future research in breast cancer prevention, treatment, and management.

Next-Generation Preclinical Functional Testing Models in Cancer Precision Medicine: CTC-Derived Organoids

Basic and clinical cancer research requires tumor models that consistently recapitulate the characteristics of prima tumors. As ex vivo 3D cultures of patient tumor cells, patient-derived tumor organoids possess the biological properties of primary tumors and are therefore excellent preclinical models for cancer research. Patient-derived organoids can be established using primary tumor tissues, peripheral blood, pleural fluid, ascites, and other samples containing tumor cells. Circulating tumor cells acquired by non-invasive sampling feature dynamic circulation and high heterogeneity. Circulating tumor cell-derived organoids are prospective tools for the dynamic monitoring of tumor mutation evolution profiles because they reflect the heterogeneity of the original tumors to a certain extent. This review discusses the advantages and applications of patient-derived organoids. Meanwhile, this work highlights the biological functions of circulating tumor cells, the latest advancement in research of circulating tumor cell-derived organoids, and potential application and challenges of this technology.

The LMIT: Light-mediated minimally-invasive theranostics in oncology

Minimally-invasive diagnosis and therapy have gradually become the trend and research hotspot of current medical applications. The integration of intraoperative diagnosis and treatment is a development important direction for real-time detection, minimally-invasive diagnosis and therapy to reduce mortality and improve the quality of life of patients, so called minimally-invasive theranostics (MIT). Light is an important theranostic tool for the treatment of cancerous tissues. Light-mediated minimally-invasive theranostics (LMIT) is a novel evolutionary technology that integrates diagnosis and therapeutics for the less invasive treatment of diseased tissues. Intelligent theranostics would promote precision surgery based on the optical characterization of cancerous tissues. Furthermore, MIT also requires the assistance of smart medical devices or robots. And, optical multimodality lay a solid foundation for intelligent MIT. In this review, we summarize the important state-of-the-arts of optical MIT or LMIT in oncology. Multimodal optical image-guided intelligent treatment is another focus. Intraoperative imaging and real-time analysis-guided optical treatment are also systemically discussed. Finally, the potential challenges and future perspectives of intelligent optical MIT are discussed.

Nrf2, A Target for Precision Oncology in Cancer Prognosis and Treatment

Activating nuclear factor-erythroid 2-related factor (Nrf2), a master regulator of redox homeostasis, has been shown to suppress initiation of carcinogenesis in normal cells. However, this transcription factor has recently been reported to promote proliferation of some transformed or cancerous cells. In tumor cells, Nrf2 is prone to mutations that result in stabilization and concurrent accumulation of its protein product. A hyperactivated mutant form of Nrf2 could support the cancer cells for enhanced proliferation, invasiveness, and resistance to chemotherapeutic agents and radiotherapy, which are associated with a poor clinical outcome. Hence understanding mutations in Nrf2 would have a significant impact on the prognosis and treatment of cancer in the era of precision medicine. This perspective would provide an insight into the genetic alterations in Nrf2 and suggest the application of small molecules, RNAi, and genome editing technologies, particularly CRISR-Cas9, in therapeutic intervention of cancer in the context of the involvement of Nrf2 mutations.

Pharmacological approaches to understanding protein kinase signaling networks

Protein kinases play vital roles in controlling cell behavior, and an array of kinase inhibitors are used successfully for treatment of disease. Typical drug development pipelines involve biological studies to validate a protein kinase target, followed by the identification of small molecules that effectively inhibit this target in cells, animal models, and patients. However, it is clear that protein kinases operate within complex signaling networks. These networks increase the resilience of signaling pathways, which can render cells relatively insensitive to inhibition of a single kinase, and provide the potential for pathway rewiring, which can result in resistance to therapy. It is therefore vital to understand the properties of kinase signaling networks in health and disease so that we can design effective multi-targeted drugs or combinations of drugs. Here, we outline how pharmacological and chemo-genetic approaches can contribute to such knowledge, despite the known low selectivity of many kinase inhibitors. We discuss how detailed profiling of target engagement by kinase inhibitors can underpin these studies; how chemical probes can be used to uncover kinase-substrate relationships, and how these tools can be used to gain insight into the configuration and function of kinase signaling networks.

Dual network analysis of transcriptome data for discovery of new therapeutic targets in non-small cell lung cancer

The drug therapy for non-small cell lung cancer (NSCLC) have always been issues of poisonous side effect, acquired drug resistance and narrow applicable population. In this study, we built a novel network analysis method (difference- correlation- enrichment- causality- node), which was based on the difference analysis, Spearman correlation network analysis, biological function analysis and Bayesian causality network analysis to discover new therapeutic target of NSCLC in the sequencing data of BEAS-2B and 7 NSCLC cell lines. Our results showed that, as a proteasome subunit coding gene in the central of cell cycle network, PSMD2 was associated with prognosis and was an independent prognostic factor for NSCLC patients. Knockout of PSMD2 inhibited the proliferation of NSCLC cells by inducing cell cycle arrest, and exhibited marked increase of cell cycle blocking protein p21, p27 and decrease of cell cycle driven protein CDK4, CDK6, CCND1 and CCNE1. IPA and molecular docking suggested bortezomib has stronger affinity to PSMD2 compared with reported targets PSMB1 and PSMB5. In vitro and In vivo experiments demonstrated the inhibitory effect of bortezomib in NSCLC with different driven mutations or with tyrosine kinase inhibitors resistance. Taken together, bortezomib could target PSMD2, PSMB1 and PSMB5 to inhibit the proteasome degradation of cell cycle check points, to block cell proliferation of NSCLC, which was potential optional drug for NSCLC patients.

Colorectal cancer tissue-originated spheroids reveal tumor intrinsic signaling pathways and mimic patient clinical chemotherapeutic response as a rapid and valid model

Locally advanced colorectal cancer requires preoperative chemotherapy to reduce local recurrence and metastasis rates, but it remains difficult to predict the tumor will be sensitive to which treatments. The patient-derived organoids (PDOs) are considered an effective platform for predicting tumor drug responses in precision oncology. However, it has the limitation of being time-consuming in practical applications, especially in neoadjuvant treatment. Here we used cancer tissue-originated spheroids (CTOS) method to establish organoids from a heterogeneous population of colorectal cancer specimens, and evaluated the capacity of CTOS to predict clinical drug responses. By analyzing the relationship of the activities of drug-treated CTOS, drug targets and target-related pathways, tumor intrinsic effective-target-related pathways can be identified. These pathways were highly matched to the abnormal pathways indicated by whole-exome sequencing. Based on this, we used half effective concentration gradients to classify CTOS as sensitive or resistant to chemotherapy regimens within a week, for predicting neoadjuvant treatment outcomes for colorectal cancer patients. The drug sensitivity test results are highly matched to the clinical responses to treatment in individual patients. Thus, our data suggested that CTOS models can be effectively screened ex vivo to identify pathways sensitive to chemotherapies. These data also supported organoid research for personalized clinical medication guidance immediately after diagnosis in patients with advanced colorectal cancer.

The drug efficacy testing in 3D cultures platform identifies effective drugs for ovarian cancer patients

Most patients with advanced ovarian cancer (OC) relapse and progress despite systemic therapy, pointing to the need for improved and tailored therapy options. Functional precision medicine can help to identify effective therapies for individual patients in a clinically relevant timeframe. Here, we present a scalable functional precision medicine platform: DET3Ct (Drug Efficacy Testing in 3D Cultures), where the response of patient cells to drugs and drug combinations are quantified with live-cell imaging. We demonstrate the delivery of individual drug sensitivity profiles in 20 samples from 16 patients with ovarian cancer in both 2D and 3D culture formats, achieving over 90% success rate in providing results six days after operation. In this cohort all patients received carboplatin. The carboplatin sensitivity scores were significantly different for patients with a progression free interval (PFI) less than or equal to 12 months and those with more than 12 months (p < 0.05). We find that the 3D culture format better retains proliferation and characteristics of the in vivo setting. Using the DET3Ct platform we evaluate 27 tailored combinations with results available 10 days after operation. Notably, carboplatin and A-1331852 (Bcl-xL inhibitor) showed an additive effect in four of eight OC samples tested, while afatinib and A-1331852 led to synergy in five of seven OC models. In conclusion, our 3D DET3Ct platform can rapidly define potential, clinically relevant data on efficacy of existing drugs in OC for precision medicine purposes, as well as provide insights on emerging drugs and drug combinations that warrant testing in clinical trials.

Microphysiological model reveals the promise of memory-like natural killer cell immunotherapy for HIV± cancer

Numerous studies are exploring the use of cell adoptive therapies to treat hematological malignancies as well as solid tumors. However, there are numerous factors that dampen the immune response, including viruses like human immunodeficiency virus. In this study, we leverage human-derived microphysiological models to reverse-engineer the HIV-immune system interaction and evaluate the potential of memory-like natural killer cells for HIV+ head and neck cancer, one of the most common tumors in patients living with human immunodeficiency virus. Here, we evaluate multiple aspects of the memory-like natural killer cell response in human-derived bioengineered environments, including immune cell extravasation, tumor penetration, tumor killing, T cell dependence, virus suppression, and compatibility with retroviral medication. Overall, these results suggest that memory-like natural killer cells are capable of operating without T cell assistance and could simultaneously destroy head and neck cancer cells as well as reduce viral latency.

Novel biomolecules in targeted cancer therapy: a new approach towards precision medicine

Cancer is a major threat to human life around the globe, and the discovery of novel biomolecules continue to be an urgent therapeutic need that is still unmet. Precision medicine relies on targeted therapeutic strategies. Researchers are better equipped to develop therapies that target proteins as they understand more about the genetic alterations and molecules that cause progression of cancer. There has been a recent diversification of the sorts of targets exploited in treatment. Therapeutic antibody and biotechnology advancements enabled curative treatments to reach previously inaccessible sites. New treatment strategies have been initiated for several undruggable targets. The application of tailored therapy has been proven to have efficient results in controlling cancer progression. Novel biomolecules like SMDCs, ADCs, mABs, and PROTACS has gained vast attention in the recent years. Several studies have shown that using these novel technology helps in reducing the drug dosage as well as to overcome drug resistance in different cancer types. Therefore, it is crucial to fully untangle the mechanism and collect evidence to understand the significance of these novel drug targets and strategies. This review article will be discussing the importance and role of these novel biomolecules in targeted cancer therapies.

HMM-GDAN: Hybrid multi-view and multi-scale graph duplex-attention networks for drug response prediction in cancer

Precision medicine is devoted to discovering personalized therapy for complex and difficult diseases like cancer. Many machine learning approaches have been developed for drug response prediction towards precision medicine. Notwithstanding, genetic profiles based multi-view graph learning schemes have not yet been explored for drug response prediction in previous works. Furthermore, multi-scale latent feature fusion is not considered sufficiently in the existing frameworks of graph neural networks (GNNs). Previous works on drug response prediction mainly depend on sequence data or single-view graph data. In this paper, we propose to construct multi-view graph by means of multi-omics data and STRING protein-protein association data, and develop a new architecture of GNNs for drug response prediction in cancer. Specifically, we propose hybrid multi-view and multi-scale graph duplex-attention networks (HMM-GDAN), in which both multi-view self-attention mechanism and view-level attention mechanism are devised to capture the complementary information of views and emphasize on the importance of each view collaboratively, and rich multi-scale features are constructed and integrated to further form high-level representations for better prediction. Experiments on GDSC2 dataset verify the superiority of the proposed HMM-GDAN when compared with state-of-the-art baselines. The effectiveness of multi-view and multi-scale strategies is demonstrated by the ablation study.

Molecular biomarkers for facilitating genome‑directed precision medicine in gynecological cancer (Review)

Prominent recent advancements in cancer treatment include the development and clinical application of next-generation sequencing (NGS) technologies, alongside a diverse array of novel molecular targeting therapeutics. NGS has enabled the high-speed and low-cost sequencing of whole genomes in individual patients, which has opened the era of genome-based precision medicine. The development of numerous molecular targeting agents, including anti-VEGF antibodies, poly (ADP-ribose) polymerase inhibitors and immune checkpoint inhibitors, have all improved the efficacy of systemic cancer therapy. Accumulating bench and translational research evidence has led to identification of various cancer-related biomarker profiles. In particular, companion diagnostics have been developed for some of these biomarkers, which can be clinically applied and are now widely used for guiding cancer therapies. Selecting biomarkers accurately will improve therapeutic efficacy, avoid overtreatment, enable earlier diagnosis and reduce the cost of preventing and treating gynecological cancer. Therefore, biomarkers are fast becoming indispensable tools in the practice of genome-directed precision medicine. In the present review, the current evidence of cancer-related biomarkers in the field of gynecological oncology, their molecular interpretations and future perspectives are outlined. The aim of the present review is to provide potentially useful information for the formulation of clinical trials.

Multifunctional polysaccharide nanoprobes for biological imaging

Imaging and tracking biological targets or processes play an important role in revealing molecular mechanisms and disease states. Bioimaging via optical, nuclear, or magnetic resonance techniques enables high resolution, high sensitivity, and high depth imaging from the whole animal down to single cells via advanced functional nanoprobes. To overcome the limitations of single-modality imaging, multimodality nanoprobes have been engineered with a variety of imaging modalities and functionalities. Polysaccharides are sugar-containing bioactive polymers with superior biocompatibility, biodegradability, and solubility. The combination of polysaccharides with single or multiple contrast agents facilitates the development of novel nanoprobes with enhanced functions for biological imaging. Nanoprobes constructed with clinically applicable polysaccharides and contrast agents hold great potential for clinical translations. This review briefly introduces the basics of different imaging modalities and polysaccharides, then summarizes the recent progress of polysaccharide-based nanoprobes for biological imaging in various diseases, emphasizing bioimaging with optical, nuclear, and magnetic resonance techniques. The current issues and future directions regarding the development and applications of polysaccharide nanoprobes are further discussed.

A clinical evaluation of an ex vivo organ culture system to predict patient response to cancer therapy

Introduction

Ex vivo organ cultures (EVOC) were recently optimized to sustain cancer tissue for 5 days with its complete microenvironment. We examined the ability of an EVOC platform to predict patient response to cancer therapy.

Methods

A multicenter, prospective, single-arm observational trial. Samples were obtained from patients with newly diagnosed bladder cancer who underwent transurethral resection of bladder tumor and from core needle biopsies of patients with metastatic cancer. The tumors were cut into 250 μM slices and cultured within 24 h, then incubated for 96 h with vehicle or intended to treat drug. The cultures were then fixed and stained to analyze their morphology and cell viability. Each EVOC was given a score based on cell viability, level of damage, and Ki67 proliferation, and the scores were correlated with the patients' clinical response assessed by pathology or Response Evaluation Criteria in Solid Tumors (RECIST).

Results

The cancer tissue and microenvironment, including endothelial and immune cells, were preserved at high viability with continued cell division for 5 days, demonstrating active cell signaling dynamics. A total of 34 cancer samples were tested by the platform and were correlated with clinical results. A higher EVOC score was correlated with better clinical response. The EVOC system showed a predictive specificity of 77.7% (7/9, 95% CI 0.4-0.97) and a sensitivity of 96% (24/25, 95% CI 0.80-0.99).

Conclusion

EVOC cultured for 5 days showed high sensitivity and specificity for predicting clinical response to therapy among patients with muscle-invasive bladder cancer and other solid tumors.

Impact of Opioids on Cellular Metabolism: Implications for Metabolic Pathways Involved in Cancer

Opioid utilization for pain management is prevalent among cancer patients. There is significant evidence describing the many effects of opioids on cancer development. Despite the pivotal role of metabolic reprogramming in facilitating cancer growth and metastasis, the specific impact of opioids on crucial oncogenic metabolic pathways remains inadequately investigated. This review provides an understanding of the current research on opioid-mediated changes to cellular metabolic pathways crucial for oncogenesis, including glycolysis, the tricarboxylic acid cycle, glutaminolysis, and oxidative phosphorylation (OXPHOS). The existing literature suggests that opioids affect energy production pathways via increasing intracellular glucose levels, increasing the production of lactic acid, and reducing ATP levels through impediment of OXPHOS. Opioids modulate pathways involved in redox balance which may allow cancer cells to overcome ROS-mediated apoptotic signaling. The majority of studies have been conducted in healthy tissue with a predominant focus on neuronal cells. To comprehensively understand the impact of opioids on metabolic pathways critical to cancer progression, research must extend beyond healthy tissue and encompass patient-derived cancer tissue, allowing for a better understanding in the context of the metabolic reprogramming already undergone by cancer cells. The current literature is limited by a lack of direct experimentation exploring opioid-induced changes to cancer metabolism as they relate to tumor growth and patient outcome.

Liquid Chromatography-Mass Spectrometry Screening of Cyclophosphamide DNA Damage In Vitro and in Patients Undergoing Chemotherapy Treatment

DNA alkylating drugs have been used as frontline medications to treat cancer for decades. Their chemical reaction with DNA leads to the blockage of DNA replication, which impacts cell replication. While this impacts rapidly dividing cancerous cells, this process is not selective and results in highly variable and often severe side effects in patients undergoing alkylating-drug based therapies. The development of biomarkers to identify patients who effectively respond with tolerable toxicities vs patients who develop serious side effects is needed. Cyclophosphamide (CPA) is a commonly used chemotherapeutic drug and lacks biomarkers to evaluate its therapeutic effect and toxicity. Upon administration, CPA is metabolically activated and converted to phosphoramide mustard and acrolein, which are responsible for its efficacy and toxicity, respectively. Previous studies have explored the detection of the major DNA adduct of CPA, the interstrand DNA-DNA cross-link G-NOR-G, finding differences in the cross-link amount between Fanconi Anemia and non-Fanconi Anemia patients undergoing chemotherapy treatment. In this study, we take advantage of our DNA adductomic approach to comprehensively profile CPA's and its metabolites' reactions with DNA in vitro and in patients undergoing CPA-based chemotherapy. This investigation led to the detection of 40 DNA adducts in vitro and 20 DNA adducts in patients treated with CPA. Moreover, acrolein-derived DNA adducts were quantified in patient samples. The results suggest that CPA-DNA damage is very complex, and an evaluation of DNA adduct profiles is necessary when evaluating the relationship between CPA-DNA damage and patient outcome.

Cancer drug response prediction with surrogate modeling-based graph neural architecture search

MOTIVATION

Understanding drug-response differences in cancer treatments is one of the most challenging aspects of personalized medicine. Recently, graph neural networks (GNNs) have become state-of-the-art methods in many graph representation learning scenarios in bioinformatics. However, building an optimal handcrafted GNN model for a particular drug sensitivity dataset requires manual design and fine-tuning of the hyperparameters for the GNN model, which is time-consuming and requires expert knowledge.

RESULTS

In this work, we propose AutoCDRP, a novel framework for automated cancer drug-response predictor using GNNs. Our approach leverages surrogate modeling to efficiently search for the most effective GNN architecture. AutoCDRP uses a surrogate model to predict the performance of GNN architectures sampled from a search space, allowing it to select the optimal architecture based on evaluation performance. Hence, AutoCDRP can efficiently identify the optimal GNN architecture by exploring the performance of all GNN architectures in the search space. Through comprehensive experiments on two benchmark datasets, we demonstrate that the GNN architecture generated by AutoCDRP surpasses state-of-the-art designs. Notably, the optimal GNN architecture identified by AutoCDRP consistently outperforms the best baseline architecture from the first epoch, providing further evidence of its effectiveness.

AVAILABILITY AND IMPLEMENTATION

https://github.com/BeObm/AutoCDRP.

Mapping the Potential of Microfluidics in Early Diagnosis and Personalized Treatment of Head and Neck Cancers

Head and neck cancers (HNCs) account for ~4% of all cancers in North America and encompass cancers affecting the oral cavity, pharynx, larynx, sinuses, nasal cavity, and salivary glands. The anatomical complexity of the head and neck region, characterized by highly perfused and innervated structures, presents challenges in the early diagnosis and treatment of these cancers. The utilization of sub-microliter volumes and the unique phenomenon associated with microscale fluid dynamics have facilitated the development of microfluidic platforms for studying complex biological systems. The advent of on-chip microfluidics has significantly impacted the diagnosis and treatment strategies of HNC. Sensor-based microfluidics and point-of-care devices have improved the detection and monitoring of cancer biomarkers using biological specimens like saliva, urine, blood, and serum. Additionally, tumor-on-a-chip platforms have allowed the creation of patient-specific cancer models on a chip, enabling the development of personalized treatments through high-throughput screening of drugs. In this review, we first focus on how microfluidics enable the development of an enhanced, functional drug screening process for targeted treatment in HNCs. We then discuss current advances in microfluidic platforms for biomarker sensing and early detection, followed by on-chip modeling of HNC to evaluate treatment response. Finally, we address the practical challenges that hinder the clinical translation of these microfluidic advances.

Next generation pan-cancer blood proteome profiling using proximity extension assay

A comprehensive characterization of blood proteome profiles in cancer patients can contribute to a better understanding of the disease etiology, resulting in earlier diagnosis, risk stratification and better monitoring of the different cancer subtypes. Here, we describe the use of next generation protein profiling to explore the proteome signature in blood across patients representing many of the major cancer types. Plasma profiles of 1463 proteins from more than 1400 cancer patients are measured in minute amounts of blood collected at the time of diagnosis and before treatment. An open access Disease Blood Atlas resource allows the exploration of the individual protein profiles in blood collected from the individual cancer patients. We also present studies in which classification models based on machine learning have been used for the identification of a set of proteins associated with each of the analyzed cancers. The implication for cancer precision medicine of next generation plasma profiling is discussed.

Cancer chemotherapy and beyond: Current status, drug candidates, associated risks and progress in targeted therapeutics

Cancer is an abnormal state of cells where they undergo uncontrolled proliferation and produce aggressive malignancies that causes millions of deaths every year. With the new understanding of the molecular mechanism(s) of disease progression, our knowledge about the disease is snowballing, leading to the evolution of many new therapeutic regimes and their successive trials. In the past few decades, various combinations of therapies have been proposed and are presently employed in the treatment of diverse cancers. Targeted drug therapy, immunotherapy, and personalized medicines are now largely being employed, which were not common a few years back. The field of cancer discoveries and therapeutics are evolving fast as cancer type-specific biomarkers are progressively being identified and several types of cancers are nowadays undergoing systematic therapies, extending patients' disease-free survival thereafter. Although growing evidence shows that a systematic and targeted approach could be the future of cancer medicine, chemotherapy remains a largely opted therapeutic option despite its known side effects on the patient's physical and psychological health. Chemotherapeutic agents/pharmaceuticals served a great purpose over the past few decades and have remained the frontline choice for advanced-stage malignancies where surgery and/or radiation therapy cannot be prescribed due to specific reasons. The present report succinctly reviews the existing and contemporary advancements in chemotherapy and assesses the status of the enrolled drugs/pharmaceuticals; it also comprehensively discusses the emerging role of specific/targeted therapeutic strategies that are presently being employed to achieve better clinical success/survival rate in cancer patients.

Supervised learning on impedance cytometry data for label-free biophysical distinction of pancreatic cancer cells versus their associated fibroblasts under gemcitabine treatment

Chemotherapy failure in pancreatic cancer patients is widely attributed to cancer cell reprogramming towards drug resistance by cancer associated fibroblasts (CAFs), which are the abundant cell type in the tumor microenvironment. Association of drug resistance to specific cancer cell phenotypes within multicellular tumors can advance isolation protocols for enabling cell-type specific gene expression markers to identify drug resistance. This requires the distinction of drug resistant cancer cells versus CAFs, which is challenging since permeabilization of CAF cells during drug treatment can cause non-specific uptake of cancer cell-specific stains. Cellular biophysical metrics, on the other hand, can provide multiparametric information to assess the gradual alteration of target cancer cells towards drug resistance, but these phenotypes need to be distinguished versus CAFs. Using pancreatic cancer cells and CAFs from a metastatic patient-derived tumor that exhibits cancer cell drug resistance under CAF co-culture, the biophysical metrics from multifrequency single-cell impedance cytometry are utilized for distinction of the subpopulation of viable cancer cells versus CAFs, before and after gemcitabine treatment. This is accomplished through supervised machine learning after training the model using key impedance metrics for cancer cells and CAFs from transwell co-cultures, so that an optimized classifier model can recognize each cell type and predict their respective proportions in multicellular tumor samples, before and after gemcitabine treatment, as validated by their confusion matrix and flow cytometry assays. In this manner, an aggregate of the distinguishing biophysical metrics of viable cancer cells after gemcitabine treatment in co-cultures with CAFs can be used in longitudinal studies, to classify and isolate the drug resistant subpopulation for identifying markers.

Precision Medicine Is Changing the Roles of Healthcare Professionals, Scientists, and Research Staff: Learnings from a Childhood Cancer Precision Medicine Trial

Precision medicine programs aim to utilize novel technologies to identify personalized treatments for children with cancer. Delivering these programs requires interdisciplinary efforts, yet the many groups involved are understudied. This study explored the experiences of a broad range of professionals delivering Australia's first precision medicine trial for children with poor-prognosis cancer: the PRecISion Medicine for Children with Cancer (PRISM) national clinical trial of the Zero Childhood Cancer Program. We conducted semi-structured interviews with 85 PRISM professionals from eight professional groups, including oncologists, surgeons, clinical research associates, scientists, genetic professionals, pathologists, animal care technicians, and nurses. We analyzed interviews thematically. Professionals shared that precision medicine can add complexity to their role and result in less certain outcomes for families. Although many participants described experiencing a greater emotional impact from their work, most expressed very positive views about the impact of precision medicine on their profession and its future potential. Most reported navigating precision medicine without formal training. Each group described unique challenges involved in adapting to precision medicine in their profession. Addressing training gaps and meeting the specific needs of many professional groups involved in precision medicine will be essential to ensure the successful implementation of standard care.

Plasmonic Functional Assay Platform Determines the Therapeutic Profile of Cancer Cells

Functional assay platforms could identify the biophysical properties of cells and their therapeutic response to drug treatments. Despite their strong ability to assess cellular pathways, functional assays require large tissue samples, long-term cell culture, and bulk measurements. Even though such a drawback is still valid, these limitations did not hinder the interest in these platforms for their capacity to reveal drug susceptibility. Some of the limitations could be overcome with single-cell functional assays by identifying subpopulations using small sample volumes. Along this direction, in this article, we developed a high-throughput plasmonic functional assay platform to identify the growth profile of cells and their therapeutic profile under therapies using mass and growth rate statistics of individual cells. Our technology could determine populations' growth profiles using the growth rate data of multiple single cells of the same population. Evaluating spectral variations based on the plasmonic diffraction field intensity images in real time, we could simultaneously monitor the mass change for the cells within the field of view of a camera with the capacity of > ∼500 cells/h scanning rate. Our technology could determine the therapeutic profile of cells under cancer drugs within few hours, while the classical techniques require days to show reduction in viability due to antitumor effects. The platform could reveal the heterogeneity within the therapeutic profile of populations and determine subpopulations showing resistance to drug therapies. As a proof-of-principle demonstration, we studied the growth profile of MCF-7 cells and their therapeutic behavior to standard-of-care drugs that have antitumor effects as shown in the literature, including difluoromethylornithine (DFMO), 5-fluorouracil (5-FU), paclitaxel (PTX), and doxorubicin (Dox). We successfully demonstrated the resistant behavior of an MCF-7 variant that could survive in the presence of DFMO. More importantly, we could precisely identify synergic and antagonistic effects of drug combinations based on the order of use in cancer therapy. Rapidly assessing the therapeutic profile of cancer cells, our plasmonic functional assay platform could be used to reveal personalized drug therapies for cancer patients.

Precision medicine applied to metastatic colorectal cancer using tumor-derived organoids and in-vitro sensitivity testing: a phase 2, single-center, open-label, and non-comparative study

BACKGROUND

Patients with colorectal metastatic disease have a poor prognosis, limited therapeutic options, and frequent development of resistance. Strategies based on tumor-derived organoids are a powerful tool to assess drug sensitivity at an individual level and to suggest new treatment options or re-challenge. Here, we evaluated the method's feasibility and clinical outcome as applied to patients with no satisfactory treatment options.

METHODS

In this phase 2, single-center, open-label, non-comparative study (ClinicalTrials.gov, register NCT03251612), we enrolled 90 patients with metastatic colorectal cancer following progression on or after standard therapy. Participants were 18 years or older with an Eastern Cooperative Oncology Group performance status of 0-2, adequate organ function, and metastasis available for biopsy. Biopsies from the metastatic site were cultured using organoids model. Sensitivity testing was performed with a panel of drugs with proven activity in phase II or III trials. At the discretion of the investigator considering toxicity, the drug with the highest relative activity was offered. The primary endpoint was the proportion of patients alive without disease progression at two months per local assessment.

RESULTS

Biopsies available from 82 to 90 patients were processed for cell culture, of which 44 successfully generated organoids with at least one treatment suggested. The precision cohort of 34 patients started treatment and the primary endpoint, progression-free survival (PFS) at two months was met in 17 patients (50%, 95% CI 32-68), exceeding the pre-defined level (14 of 45; 31%). The median PFS was 67 days (95% CI 51-108), and the median overall survival was 189 days (95% CI 103-277).

CONCLUSIONS

Patient-derived organoids and in-vitro sensitivity testing were feasible in a cohort of metastatic colorectal cancer. The primary endpoint was met, as half of the patients were without progression at two months. Cancer patients may benefit from functional testing using tumor-derived organoids.

TRIAL REGISTRATION

ClinicalTrials.gov, register NCT03251612.

Patient Derived Organoids (PDOs), Extracellular Matrix (ECM), Tumor Microenvironment (TME) and Drug Screening: State of the Art and Clinical Implications of Ovarian Cancer Organoids in the Era of Precision Medicine

Ovarian cancer (OC) has the highest mortality rate of all gynecological malignancies due to the high prevalence of advanced stages of diagnosis and the high rate of recurrence. Furthermore, the heterogeneity of OC tumors contributes to the rapid development of resistance to conventional chemotherapy. In recent years, in order to overcome these problems, targeted therapies have been introduced in various types of tumors, including gynecological cancer. However, the lack of predictive biomarkers showing different clinical benefits limits the effectiveness of these therapies. This requires the development of preclinical models that can replicate the histological and molecular characteristics of OC subtypes. In this scenario, organoids become an important preclinical model for personalized medicine. In fact, patient-derived organoids (PDO) recapture tumor heterogeneity with the possibility of performing drug screening. However, to best reproduce the patient's characteristics, it is necessary to develop a specific extracellular matrix (ECM) and introduce a tumor microenvironment (TME), which both represent an actual object of study to improve drug screening, particularly when used in targeted therapy and immunotherapy to guide therapeutic decisions. In this review, we summarize the current state of the art for the screening of PDOs, ECM, TME, and drugs in the setting of OC, as well as discussing the clinical implications and future perspectives for the research of OC organoids.