As precision medicine such as targeted therapy and immunotherapy often have limited accessibility, low response rate, and evolved resistance, it is urgent to develop simple, low-cost, and quick-turnaround personalized diagnostic technologies for drug response prediction with high sensitivity, speed, and accuracy. The major challenges of drug response prediction strategies employing digital database modeling are the scarcity of labeled clinical data, applicability only to a few classes of drugs, and losing the resolution at the individual patient level. Although these challenges have been partially addressed by large-scale cancer cell line datasets and more patient-relevant cell-based systems, the integration of different data types and data translation from pre-clinical to clinical utilities are still far-fetched. To overcome the current limitations of precision medicine with a clinically proven drug response prediction assay, we have developed an innovative and proprietary technology based on in vitro patient testing and in silico data analytics. First, a patient-derived gene expression signature was established via the transcriptomic profiling of cell-free mRNA (cfmRNA) from the patient’s blood. Second, a gene-to-drug data fusion and overlaying mechanism to transfer data were performed. Finally, a semi-supervised method was used for the database searching, matching, annotation, and ranking of drug efficacies from a pool of ~700 approved, investigational, or clinical trial drug candidates. A personalized drug response report can be delivered to inform clinical decisions within a week. The PGA (patient-derived gene expression-informed anticancer drug efficacy) test has significantly improved patient outcomes when compared to the treatment plans without PGA support. The implementation of PGA, which combines patient-unique cfmRNA fingerprints with drug mapping power, has the potential to identify treatment options when patients are no longer responding to therapy and when standard-of-care is exhausted.

Background: Many patients harbor minimal residual disease (MRD)—small clusters of residual tumor cells that survive therapy and evade conventional detection but drive recurrence. Although advances in molecular and computational methods have improved circulating tumor DNA (ctDNA)-based MRD detection, these approaches face challenges: ctDNA shedding fluctuates widely across tumor types, disease stages, and histological features. Additionally, low levels of driver mutations originating from healthy tissues can create background noise, complicating the accurate identification of bona fide tumor-specific signals. These limitations underscore the need for refined technologies to further enhance MRD detection beyond DNA sequences in solid malignancies. Methods: Profiling circulating cell-free mRNA (cfmRNA), which is hyperactive in tumor and non-tumor microenvironments, could address these limitations to inform postoperative surveillance and treatment strategies. This study reported the development of OncoMRD BREAST, a customized, gene signature-informed cfmRNA assay for residual disease monitoring in breast cancer. OncoMRD BREAST introduces several advanced technologies that distinguish it from the existing ctDNA-MRD tests. It builds on the patient-derived gene signature for capturing tumor activities while introducing significant upgrades to its liquid biopsy transcriptomic profiling, digital scoring systems, and tracking capabilities. Results: The OncoMRD BREAST test processes inputs from multiple cutting-edge biomarkers—tumor and non-tumor microenvironment—to provide enhanced awareness of tumor activities in real time. By fusing data from these diverse intra- and inter-cellular networks, OncoMRD BREAST significantly improves the sensitivity and reliability of MRD detection and prognosis analysis, even under challenging and complex conditions. In a proof-of-concept real-world pilot trial, OncoMRD BREAST’s rapid quantification of potential tumor activity helped reduce the risk of incorrect treatment strategies, while advanced predictive analytics contributed to the overall benefits and improved outcomes of patients. Conclusions: By tailoring the assay to individual tumor profiles, we aimed to enhance early identification of residual disease and optimize therapeutic decision-making. OncoMRD BREAST is the world’s first and only gene signature-powered test for monitoring residual disease in solid tumors.

Many patients harbor minimal residual disease (MRD)—small clusters of residual tumor cells that survive therapy and evade conventional detection but drive recurrence. Although advances in molecular and computational methods have improved circulating tumor DNA (ctDNA)-based MRD detection, these approaches face challenges: ctDNA shedding fluctuates widely across tumor types, disease stages, and histological features. Additionally, low levels of driver mutations originating from healthy tissues can create background noise, complicating the accurate identification of bona fide tumor-specific signals. These limitations underscore the need for refined technologies to further enhance MRD detection beyond DNA sequences in solid malignancies. Profiling of circulating cell-free mRNA (cfmRNA), hyperactive in tumor and non-tumor microenvironment, could address these limitations to inform postoperative surveillance and treatment strategies. This study reported the development of OncoMRD BREAST, a customized, gene signature-informed cfmRNA assay for residual disease monitoring in breast cancer. OncoMRD BREAST introduces several advanced technologies that distinguish itself from the existing ctDNA-MRD tests. It builds on the patient-derived gene signature for capturing tumor activities while introducing significant upgrades to its liquid biopsy transcriptomic profiling, digital scoring systems, and tracking capabilities. The OncoMRD BREAST test processes inputs from multiple cutting-edge biomarkers—tumor and non-tumor microenvironment—to provide enhanced awareness of tumor activities in real time. By fusing data from these diverse intra- and inter-cellular networks, OncoMRD BREAST significantly improves the sensitivity and reliability of MRD detection and prognosis analysis, even under challenging and complex conditions. In a proof-of-concept real-world pilot trial, OncoMRD BREAST’s rapid quantification of potential tumor activity helped reduce the risk of incorrect treatment strategies while advanced predictive analytics, contributing to the overall benefits and improved outcomes of patients. By tailoring the assay to individual tumor profiles, we aimed to enhance early identification of residual disease and optimize therapeutic decision-making. OncoMRD BREAST is the world-first and only gene signature-powered test for monitoring residual disease in solid tumor.

For years, valuable clinical samples preserved in formalin-fixed paraffin-embedded tissues were underutilized. However, with advanced spatial multiomics profiling tools, crucial information has become increasingly accessible. Integrating genomic data with spatial information has unveiled crucial insights into cellular activities, enhancing our comprehension of biology. Measuring cellular gene expression while capturing spatial context – including morphology and intercellular relationships – is vital for understanding both normal and diseased biological processes. To date, this approach has illuminated the mechanisms of complex diseases, such as cancer and has facilitated the discovery of biomarkers for early disease detection and new therapeutic targets, accelerating progress in cancer immunotherapies. Cutting-edge single-cell analysis tools are rapidly emerging as the gold standard for investigating intricate biological systems and medical specimens, fueling a multi-billion-dollar industry. Single-cell spatial research, in particular, is inherently cross-disciplinary and addresses questions that remain hidden when focusing solely on the genome or transcriptome of large cell populations. Leveraging advances in single-cell spatial profiling can offer insights into improving cancer immunotherapy and other modern medical treatments. This review will delve into the diverse applications of spatial profiling technology, showcasing examples that demonstrate its ability to provide a detailed picture of the underlying molecular and cellular mechanisms within cells. As a comprehensive reference, this review empowers researchers and industry leaders to harness single-cell and spatial omics for breakthroughs in biomedicine and translational science.

Background: In 1937, President Franklin Roosevelt initiated a nationwide campaign against cancer by signing the National Cancer Act. Eighty-eight years later, while significant strides have been made, cancer treatment still faces challenges. With 50 to 80 percent of patients not responding to treatment and over 600,000 cancer-related deaths annually in the United States, the need for improvement is evident. Imagine if clinicians could accurately predict the success of any cancer treatment, guaranteeing that each patient receives the most effective care possible. Although biomarker tests offer unprecedented capabilities, they ultimately qualify the minority of patients for precision medicine, while excluding the majority. Drug efficacy/response prediction is thus an important prospect for precision oncology, we need a disruptive technology to extend precision treatments and bring benefit to each and every cancer patient. Methods: The revolutionary technology PGA (Patient-derived Gene expression-informed Anticancer drug efficacy) has been documented previously, which integrates a patient’s own gene signature to identify the top-ranking drugs that individual patients most likely will respond to, especially for those unresponsive to targeted therapy or immunotherapy. In this study, we further conducted a benchmarking reality check for evaluating the clinical application of PGA in real-world settings. The framework incorporates three publicly accessible drug utilization datasets for NSCLC – TCGA, NCCN and MEDLINE. To enable a comprehensive assessment of PGA clinical utility, we define evaluation metrics by capturing drug usage trends in the best clinical practice. Results: Aligned with TCGA treatment landscapes and real-world NSCLC treatment trends in the US, the PGA LUNG test could significantly help oncologists by providing them with reliable predictions to choose the most suitable drugs for their patients. PGA LUNG also highlights the potential for off-label cancer drugs to overcome the challenges of ongoing therapy exhaustion and drug resistance. Conclusions: PGA LUNG’s ability to predict drug efficacies/responses for those cancer patient non-responders at a high accuracy provides a critical clinical advantage.

Background: ctDNA-based NGS MRD testing in breast cancer faces a critical but overlooked limitation: low-frequency somatic mutations from normal aging non-malignant tissues—beyond clonal hematopoiesis—contaminate plasma ctDNA at levels indistinguishable from true MRD signals, creating a specificity ceiling that increased sequencing depth cannot resolve. The OncoMRD BREAST panel, an 11-gene transcriptomic index, offers an orthogonal liquid biopsy approach measuring tumor gene hyperactivity rather than mutant DNA, addressing limitations of ctDNA NGS. This study performed the first comprehensive multi-dataset retrospective clinical validation of the OncoMRD BREAST gene signature. Methods: OncoMRD BREAST gene overexpression index (GOI) was computed as the mean z-score composite of 11 genes across six public datasets (n>5,600). Correlations were assessed against: validated ctDNA MRD from I-SPY2 (n=253); genomic proxy-simulated MRD (FGA/TMB) from TCGA-BRCA (n=1,218); tumor activity metrics (MKI67, ER, HER2, survival) from METABRIC (n=2,114); RECIST responses across four neoadjuvant cohorts (n=727); and nine validated breast cancer transcriptomic assays including Oncotype DX and MammaPrint panels in TCGA-BREAST (n=1,018). Statistical methods included Spearman/Pearson correlation with bootstrap CIs, Wilcoxon, Kruskal-Wallis, Cox, Kaplan-Meier, and FDR correction. Results: In TCGA-BRCA, GOI correlated significantly with FGA and composite simulated MRD score. In I-SPY2, subtype-stratified analysis revealed significant GOI-ctDNA MRD correlation in TNBC patients with strengthening associations at post-NAC timepoints. In METABRIC, GOI significantly correlated with MKI67, ER status, HER2 status, and overall survival. RECIST response analyses yielded significant correlations in the GSE20194 neoadjuvant cohort. External benchmark concordance analyses demonstrated significant Pearson correlations with all nine validated transcriptomic panels: 17-gene CTC MRD signature (R=0.76), 26-gene FDG-PET metabolic signature (R=0.73), 3q metastasis signature (R=0.52), MammaPrint (R=0.52), 6-gene chemotherapy persistence signature (R=0.44), Oncotype DX 21-gene (R=0.46), Prosigna/PAM50 (R=0.36), EndoPredict (R=0.29), Breast Cancer Index (R=0.19); all p<2.2×10⁻⁸. Conclusions: The comprehensive mega-dataset retrospective validation presented here provides the most rigorous pre-prospective evidence base yet assembled for a novel breast cancer liquid biopsy MRD panel, strongly supporting immediate advancement of OncoMRD BREAST to prospective interventional clinical validation in breast cancer MRD monitoring trials.