Machine learning identifies prognostic subtypes of the tumor microenvironment of NSCLC.

The tumor microenvironment (TME) plays a fundamental role in tumorigenesis, tumor progression, and anti-cancer immunity potential of emerging cancer therapeutics. Understanding inter-patient TME heterogeneity, however, remains a challenge to efficient drug development. This article applies recent advances in machine learning (ML) for survival analysis to a retrospective study of NSCLC patients who received definitive surgical resection and immune pathology following surgery. ML methods are compared for their effectiveness in identifying prognostic subtypes. Six survival models, including Cox regression and five survival machine learning methods, were calibrated and applied to predict survival for NSCLC patients based on PD-L1 expression, CD3 expression, and ten baseline patient characteristics. Prognostic subregions of the biomarker space are delineated for each method using synthetic patient data augmentation and compared between models for overall survival concordance. A total of 423 NSCLC patients (46% female; median age [inter quantile range]: 67 [60-73]) treated with definite surgical resection were included in the study. And 219 (52%) patients experienced events during the observation period consisting of a maximum follow-up of 10 years and median follow up 78 months. The random survival forest (RSF) achieved the highest predictive accuracy, with a C-index of 0.84. The resultant biomarker subtypes demonstrate that patients with high PD-L1 expression combined with low CD3 counts experience higher risk of death within five-years of surgical resection.

Genomic, immunologic, and prognostic associations of TROP2 (TACSTD2) expression in solid tumors.

BACKGROUND: TROP2 (TACSTD2) expression is associated with decreased overall survival (OS) in some solid tumors, and the TROP2-targeting antibody-drug conjugate (ADC) sacituzumab govitecan has been approved in breast and urothelial carcinomas. We aimed to explore the multi-omic landscape associated with TACSTD2 gene expression in various solid tumors to identify patients most likely to benefit from this approach. METHODS: Breast (N = 11 246), colorectal (N = 15 425), hepatocellular (N = 433), pancreatic (N = 5488), and urothelial (N = 4125) tumors were stratified into quartiles by TACSTD2 gene expression, analyzed by next-generation DNA sequencing, whole transcriptome sequencing, and immunohistochemistry at Caris Life Sciences (Phoenix, AZ). Survival data were obtained from insurance claims, and Kaplan-Meier estimates were calculated for molecularly defined cohorts. RESULTS: Several pathogenic mutations were associated with TACSTD2-high tumors, including TP53 in breast, colorectal (CRC), pancreatic, and hepatocellular cancers; KRAS in pancreatic and CRC cancers; ARID1A and FGFR3 in urothelial cancer; and CTNNB1 in hepatocellular cancer. TACSTD2-low breast tumors were enriched for copy number amplifications in CCND1 and FGF/R family member genes. TACSTD2 high was generally associated with more immune cell infiltration and greater T-cell inflammation scores. Patients with TACSTD2-high breast, CRC, and pancreatic cancers demonstrated a significantly shorter OS than TACSTD2-low tumors. This was restricted to CRC with microsatellite stable tumors and patients with pancreatic cancer with KRAS-mutant tumors. Patients with breast cancer with TACSTD2-high tumors also experienced significantly worse OS following immune checkpoint inhibitors. CONCLUSIONS: TACSTD2 expression is associated with key driver alterations and a more active immune microenvironment, suggesting possible combinatorial strategies with TROP2-targeting ADCs plus immunotherapy in various solid tumors.

Understanding homologous recombination repair deficiency in biliary tract cancers: clinical implications and correlation with platinum sensitivity.

BACKGROUND: Biliary tract cancers (BTCs) exhibit high mortality rates and significant heterogeneity in both clinical and molecular characteristics. This study aims to molecularly characterize a cohort of patients with BTC, with a specific focus on genomic alterations within homologous recombination repair (HRR) genes in a real-world setting. PATIENTS AND METHODS: We carried out a retrospective analysis on 256 patients with BTC treated at five Austrian centers and one German comprehensive cancer center between 2016 and 2023 utilizing comprehensive genomic profiling platforms to assess HRR status and its correlation with clinical outcomes after platinum-based chemotherapy. RESULTS: A total of 67 patients (27.5%) exhibited HRR gene mutations (HRRm), with the most common pathogenic alterations in BAP1 (9%), ARID1A (7.8%), and ATM (6.1%). Time to failure of the first-line strategy (TFS) between patients with HRRm and non-HRRm treated with platinum agents was 7.9 and 6.7 months, respectively [hazard ratio (HR) 0.89; P = 0.49]. The overall survival (OS) estimates at 6, 18, and 24 months were 82%, 45%, and 39% in the HRRm group (median 16.01 months) and 81%, 42%, and 22% in the HRR group (median 15.68 months), respectively (Fleming-Harrington test P = 0.0004; log-rank P = 0.022). Significance did not persist in the multivariate analysis (HR 0.72; 95% confidence interval 0.489-1.059; P = 0.095). An interaction between HRRm status and molecular-informed therapeutic strategies in later lines was noted. In the second-line treatment, OS following an irinotecan-based regimen was comparable to re-exposure to platinum-based agents (12.36 versus 10.13 months; HR 0.92; P = 0.85). No better outcome was noted for patients with HRRm versus patients with non-HRRm with second-line platinum agents (HR 1.45; P = 0.35). CONCLUSIONS: Patients with HRRm with BTC showed a potential advantage in OS following platinum-based first-line chemotherapy, presumably attributed to enhanced opportunities for targetable coalterations. Further investigation is needed to outline HRR within the scope of BTCs and detail a clinically meaningful sensitivity to platinum agents or targeted approaches with poly (ADP-ribose) polymerase (PARP) inhibitors.

Real-world cost-effectiveness of panel-based genomic testing to inform therapeutic decisions for metastatic colorectal cancer.

BACKGROUND: Mutations in KRAS and NRAS are associated with a lack of response to cetuximab and panitumumab, two biologics used for third-line therapy of metastatic colorectal cancer (mCRC). In British Columbia, Canada, eligibility for cetuximab or panitumumab was first based on single-gene KRAS testing. OncoPanel, a multi-gene next-generation sequencing panel with both KRAS and NRAS, was introduced in 2016. Our objective was to estimate the real-world cost-effectiveness of OncoPanel versus to single-gene KRAS testing to inform eligibility for cetuximab or panitumumab in mCRC. METHODS: Using population-based administrative health data, we identified a cohort of mCRC patients who had received a KRAS or OncoPanel test, and completed prior chemotherapy in 2010-2019. We matched KRAS- and OncoPanel-tested patients (1:1) using genetic matching to balance baseline covariates. Mean and incremental 3-year costs, survival, and quality-adjusted survival were estimated using inverse-probability-of-censoring weighting and bootstrapping. We conducted scenario-based sensitivity analysis for key costs and assumptions. FINDINGS: All OncoPanel-tested cases (n=371) were matched to a KRAS-tested comparator. In the KRAS and OncoPanel groups, respectively, 55·8 % and 41·2 % of patients were potentially eligible for cetuximab or panitumumab based on mutation status. Incremental cost and effectiveness of OncoPanel were $72 (95 % CI: -6387, 6107), -0·004 life-years (95 % CI: -0·119, 0·113), and -0·011 quality-adjusted life-years (95 % CI: -0·094, 0·075). Reductions in systemic therapy costs were offset by increased costs in other resources. Results were moderately sensitive to time horizon and changes in testing or treatment cost. INTERPRETATION: The use of OncoPanel resulted in more precise targeting of cetuximab and panitumumab, but there was no change in incremental cost or quality-adjusted survival. Understanding the balance of costs achieved in practice can provide insight into the effect of future changes in testing policy, test cost, treatment eligibility, or drug prices in this setting.

Precision oncology: current and future platforms for treatment selection.

Genomic profiling of hundreds of cancer-associated genes is now a component of routine cancer care. DNA sequencing can identify mutations, mutational signatures, and structural alterations predictive of therapy response and assess for heritable cancer risk, but it has been less useful for identifying predictive biomarkers of sensitivity to cytotoxic chemotherapies, antibody drug conjugates, and immunotherapies. The clinical adoption of molecular profiling platforms such as RNA sequencing better suited to identifying those patients most likely to respond to immunotherapies and drug combinations will be critical to expanding the benefits of precision oncology. This review discusses the potential advantages of innovative molecular and functional profiling platforms designed to replace or complement targeted DNA sequencing and the major hurdles to their clinical adoption.

Targeting the multifaceted BRAF in cancer: New directions.

Activating mutations in the mitogen-activated protein kinase (MAPK) pathway represent driver alterations governing tumorigenesis, metastasis, and therapy resistance. MAPK activation predominantly occurs through genomic alterations in RAS and BRAF. BRAF is an effector kinase that functions downstream of RAS and propagates this oncogenic activity through MEK and ERK. Across cancers, BRAF alterations include gain-of-function mutations, copy-number alterations, and structural rearrangements. In cancer patients, BRAF-targeting precision therapeutics are effective against Class I BRAF alterations (p.V600 hotspot mutations) in tumors such as melanomas, thyroid cancers, and colorectal cancers. However, numerous non-Class I BRAF inhibitors are also in development and have been explored in some cancers. Here we discuss the diverse forms of BRAF alterations found in human cancers and the strategies to inhibit them in patients harboring cancers of distinct origins.

Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study.

BACKGROUND: Providing appropriate specialized treatment to the right patient at the right time is considered necessary in cancer management. Targeted therapy tailored to the genetic changes of each breast cancer patient is a desirable feature of precision oncology, which can not only reduce disease progression but also potentially increase patient survival. The use of artificial intelligence alongside precision oncology can help physicians by identifying and selecting more effective treatment factors for patients. METHOD: A systematic review was conducted using the PubMed, Embase, Scopus, and Web of Science databases in September 2023. We performed the search strategy with keywords, namely: Breast Cancer, Artificial intelligence, and precision Oncology along with their synonyms in the article titles. Descriptive, qualitative, review, and non-English studies were excluded. The quality assessment of the articles and evaluation of bias were determined based on the SJR journal and JBI indices, as well as the PRISMA2020 guideline. RESULTS: Forty-six studies were selected that focused on personalized breast cancer management using artificial intelligence models. Seventeen studies using various deep learning methods achieved a satisfactory outcome in predicting treatment response and prognosis, contributing to personalized breast cancer management. Two studies utilizing neural networks and clustering provided acceptable indicators for predicting patient survival and categorizing breast tumors. One study employed transfer learning to predict treatment response. Twenty-six studies utilizing machine-learning methods demonstrated that these techniques can improve breast cancer classification, screening, diagnosis, and prognosis. The most frequent modeling techniques used were NB, SVM, RF, XGBoost, and Reinforcement Learning. The average area under the curve (AUC) for the models was 0.91. Moreover, the average values for accuracy, sensitivity, specificity, and precision were reported to be in the range of 90-96% for the models. CONCLUSION: Artificial intelligence has proven to be effective in assisting physicians and researchers in managing breast cancer treatment by uncovering hidden patterns in complex omics and genetic data. Intelligent processing of omics data through protein and gene pattern classification and the utilization of deep neural patterns has the potential to significantly transform the field of complex disease management.

Trastuzumab-deruxtecan in solid tumors with HER2 alterations: from early phase development to the first agnostic approval of an antibody-drug conjugate.

INTRODUCTION: Antibody-drug conjugates (ADCs) represent a revolutionary approach in the systemic treatment for both solid and hematologic tumors. Constituted by an antibody, a cytotoxic payload, and a linker, ADCs aim to selectively deliver cytotoxic agents to tumors while sparing normal tissues. Various ADCs have been tested and approved for multiple solid tumors so far, but if there is one that had a major impact on clinical practice, this is Trastuzumab-deruxtecan (T-DXd). Notably, T-DXd was approved for HER2-positive and HER2-low metastatic breast cancer (MBC), HER2-positive gastric cancer (GC), HER2-mutant non-small cell lung cancer (NSCLC) and HER2 3+ solid tumors. Moreover, it received Breakthrough Therapy Designation for HER2-positive colorectal cancer (CRC). AREAS COVERED: We review preclinical and clinical data of T-DXd, focusing on early-phase ongoing trials exploring combination therapies to enhance the activity of T-DXd in HER2-expressing solid tumors. EXPERT OPINION: The clinical use of T-DXd still raises questions about selection of patients, treatment duration, prioritization over other approved ADCs, and management of resistance. Concerns regarding the toxicity of T-DXd remain, particularly with combinations involving potentially toxic drugs. Advancements in biomarker identification and combination therapies offer promising avenues to enhance efficacy and overcome resistance to T-DXd, ultimately improving outcomes for patients with cancer.

The future of collaborative precision oncology approaches in sub-Saharan Africa: learnings from around the globe.

As the projected incidence and mortality of cancer in Sub-Saharan Africa (SSA) rises to epidemic proportions, it is imperative that more is done to identify the genomic differences and commonalities between patients of African and European ancestry to fulfil the promise of precision oncology. Here, we summarize the utility of precision oncology approaches, with a focus on comprehensive genomic profiling (CGP) and consolidate examples of national and international consortia that are driving the field forward. We describe the importance of genomic diversity and its relevance in cancer, and propose recommendations, success factors and desired outcomes for precision oncology consortia to adopt in SSA. Through this, we hope to catalyze the initiation of such projects and to contribute to improving cancer patient outcomes in the region.

Lung Adenocarcinoma Systems Biomarker and Drug Candidates Identified by Machine Learning, Gene Expression Data, and Integrative Bioinformatics Pipeline.

Lung adenocarcinoma (LUAD) is a significant planetary health challenge with its high morbidity and mortality rate, not to mention the marked interindividual variability in treatment outcomes and side effects. There is an urgent need for robust systems biomarkers that can help with early cancer diagnosis, prediction of treatment outcomes, and design of precision/personalized medicines for LUAD. The present study aimed at systems biomarkers of LUAD and deployed integrative bioinformatics and machine learning tools to harness gene expression data. Predictive models were developed to stratify patients based on prognostic outcomes. Importantly, we report here several potential key genes, for example, PMEL and BRIP1, and pathways implicated in the progression and prognosis of LUAD that could potentially be targeted for precision/personalized medicine in the future. Our drug repurposing analysis and molecular docking simulations suggested eight drug candidates for LUAD such as heat shock protein 90 inhibitors, cardiac glycosides, an antipsychotic agent (trifluoperazine), and a calcium ionophore (ionomycin). In summary, this study identifies several promising leads on systems biomarkers and drug candidates for LUAD. The findings also attest to the importance of integrative bioinformatics, structural biology and machine learning techniques in biomarker discovery, and precision oncology research and development.

Case report: Precision guided reactive cancer management: molecular complete response in heavily pretreated metastatic CRC by dual immunotherapy and sorafenib.

Background: Metastatic colon adenocarcinoma presents significant challenges in treatment, particularly when resistant to standard therapies. Precision oncology, guided by multidisciplinary tumor boards (MTBs), offers a promising way for individualized therapeutic approaches. Integration of comprehensive genomic profiling (CGP) and minimal residual disease (MRD) testing strengthens treatment decision-making, yet challenges persist in identifying and overcoming resistance mechanisms. FLT3 amplification can be one of those resistance/escape mechanisms that needs to be targeted. Case presentation: This case report presents a 58-year-old male diagnosed with metastatic colon adenocarcinoma with liver metastasis, resistant to conventional treatments. Utilizing CGP and MRD testing, our multidisciplinary MTB identified a complex mutational profile, including APC, DAXX, TP53 mutations, and CDK8 and FLT3 amplifications. With a tumor mutational burden of 10 muts/mb and TPS, CPS scores of 0, immunotherapy was considered, employing dual immune checkpoint inhibitors alongside mebendazole and Lenvatinib targeting the WNT and VEGF/angiogenesis pathways. MRD testing revealed early treatment failure. Re-evaluation identified high copied FLT3 amplification (62 copies) as a resistance mechanism, prompting modification to incorporate sorafenib and dual immunotherapy with mebendazole. Subsequent MRD assessments and radiological scans demonstrated a remarkable therapeutic response, with sustained efficacy and absence of detectable residual disease. Conclusion: This case highlights the successful application of precision oncology principles, facilitated by dynamic MTB-guided treatment strategies. Integration of MRD testing provided early detection of treatment inefficacy, allowing for timely intervention and adaptation of the treatment plan. Additionally, the case highlights the educational value of rare molecular alterations, emphasizing continual learning and refinement of treatment approaches in precision oncology.

Single-cell somatic copy number alteration profiling of vitreous humor seeds in retinoblastoma.

BACKGROUND: Heterogeneity can impact biomarker identification. Thus, we investigated the somatic copy number alterations (SCNAs) of individual tumor cells in the vitreous humor of a retinoblastoma patient using single-cell whole-genome profiling and explored the genomic concordance among vitreous and aqueous humor, vitreous seeds, and tumor. METHODS: Aqueous humor (AH), vitreous humor (VH), and tumor biopsy were obtained from an enucleated globe with retinoblastoma and vitreous seeding. Micromanipulation was used to manually isolate 39 live single tumor cells from vitreous seeds harvested from the VH. The SCNA profiles of these individual cells were generated via whole-genome sequencing and analyzed alongside profiles from the tumor mass and cell-free DNA (cfDNA) from AH and VH. RESULTS: Heatmap of VH single-cell SCNA profiles demonstrates heterogeneity among individual vitreous seeds with one clearly dominant subclone (23 of 37 cells). The SCNA profiles from the cells in this subclone demonstrate an average concordance of 98% with cfDNA profiles from acellular AH and VH and with the tumor profile. CONCLUSIONS: Our findings reveal some heterogeneity among single-cell SCNA profiles in individual VH seeds. Despite this heterogeneity, the dominant vitreous subclone exhibits extremely (>98%) high concordance with the SCNA profile from tumor and AH, suggesting AH cfDNA is representative of the dominant genomic subclone. This may facilitate tumoral biomarker identification via the AH. This preliminary work supports the potential of applying single-cell technology to VH seeds in retinoblastoma as a platform to study tumor subclones, which may provide insight into the genomic complexity of disease.

Changing the Landscape of Solid Tumor Therapy from Apoptosis-Promoting to Apoptosis-Inhibiting Strategies.

The many limitations of implementing anticancer strategies under the term "precision oncology" have been extensively discussed. While some authors propose promising future directions, others are less optimistic and use phrases such as illusion, hype, and false hypotheses. The reality is revealed by practicing clinicians and cancer patients in various online publications, one of which has stated that "in the quest for the next cancer cure, few researchers bother to look back at the graveyard of failed medicines to figure out what went wrong". The message is clear: Novel therapeutic strategies with catchy names (e.g., synthetic "lethality") have not fulfilled their promises despite decades of extensive research and clinical trials. The main purpose of this review is to discuss key challenges in solid tumor therapy that surprisingly continue to be overlooked by the Nomenclature Committee on Cell Death (NCCD) and numerous other authors. These challenges include: The impact of chemotherapy-induced genome chaos (e.g., multinucleation) on resistance and relapse, oncogenic function of caspase 3, cancer cell anastasis (recovery from late stages of apoptosis), and pitfalls of ubiquitously used preclinical chemosensitivity assays (e.g., cell "viability" and tumor growth delay studies in live animals) that score such pro-survival responses as "lethal" events. The studies outlined herein underscore the need for new directions in the management of solid tumors.

Exploring the Potential of Antibody-Drug Conjugates in Targeting Non-small Cell Lung Cancer Biomarkers.

Antibody-drug conjugates (ADCs), combining the cytotoxicity of the drug payload with the specificity of monoclonal antibodies, are one of the rapidly evolving classes of anti-cancer agents. These agents have been successfully incorporated into the treatment paradigm of many malignancies, including non-small cell lung cancer (NSCLC). The NSCLC is the most prevalent subtype of lung cancer, having a considerable burden on the cancer-related mortality and morbidity rates globally. Several ADC molecules are currently approved by the Food and Drug Administration (FDA) to be used in patients with NSCLC. However, the successful management of NSCLC patients using these agents was met with several challenges, including the development of resistance and toxicities. These shortcomings resulted in the exploration of novel therapeutic targets that can be targeted by the ADCs. This review aims to explore the recently identified ADC targets along with their oncologic mechanisms. The ADC molecules targeting these biomarkers are further discussed along with the evidence from clinical trials.

Precision Oncology, Artificial Intelligence, and Novel Therapeutic Advancements in the Diagnosis, Prevention, and Treatment of Cancer: Highlights from the 59th Irish Association for Cancer Research (IACR) Annual Conference.

Advancements in oncology, especially with the era of precision oncology, is resulting in a paradigm shift in cancer care. Indeed, innovative technologies, such as artificial intelligence, are paving the way towards enhanced diagnosis, prevention, and personalised treatments as well as novel drug discoveries. Despite excellent progress, the emergence of resistant cancers has curtailed both the pace and extent to which we can advance. By combining both their understanding of the fundamental biological mechanisms and technological advancements such as artificial intelligence and data science, cancer researchers are now beginning to address this. Together, this will revolutionise cancer care, by enhancing molecular interventions that may aid cancer prevention, inform clinical decision making, and accelerate the development of novel therapeutic drugs. Here, we will discuss the advances and approaches in both artificial intelligence and precision oncology, presented at the 59th Irish Association for Cancer Research annual conference.

Whole genome sequencing of HER2-positive metastatic extramammary Paget's disease: a case report.

BACKGROUND: Extramammary Paget's disease (EMPD) is a rare cancer that occurs within the epithelium of the skin, arising predominantly in areas with high apocrine gland concentration such as the vulva, scrotum, penis and perianal regions. Here, we aim to integrate clinicopathological data with genomic analysis of aggressive, rapidly-progressing de novo metastatic EMPD responding to HER2-directed treatment in combination with other agents, to attain a more comprehensive understanding of the disease landscape. METHODS: Immunohistochemical staining on the scrotal wall tumor and bone marrow metastasis demonstrated HER2 overexpression. Whole genome sequencing of the tumor and matched blood was performed. RESULTS: Notable copy number gains (log2FC > 0.9) on chromosomes 7 and 8 were detected (n = 81), with 92.6% of these unique genes specifically located on chromosome 8. Prominent cancer-associated genes include ZNF703, HOOK3, DDHD2, LSM1, NSD3, ADAM9, BRF2, KAT6A and FGFR1. Interestingly, ERBB2 gene did not exhibit high copy number gain (log2FC = 0.4) although 90% of tumor cells stained HER2-positive. Enrichment in pathways associated with transforming growth factor-beta (TGFß) (FDR = 0.0376, Enrichment Ratio = 8.12) and fibroblast growth factor receptor (FGFR1) signaling (FDR = 0.0082, Enrichment Ratio = 2.3) was detected. Amplicon structure analysis revealed that this was a simple-linear amplification event. CONCLUSION: Whole genome sequencing revealed the underlying copy number variation landscape in HER2-positive metastatic EMPD. The presence of alternative signalling pathways and genetic variants suggests potential interactions with HER2 signalling, which possibly contributed to the HER2 overexpression and observed response to HER2-directed therapy combined with other agents in a comprehensive treatment regimen.

iGenSig-Rx: an integral genomic signature based white-box tool for modeling cancer therapeutic responses using multi-omics data.

Multi-omics sequencing is poised to revolutionize clinical care in the coming decade. However, there is a lack of effective and interpretable genome-wide modeling methods for the rational selection of patients for personalized interventions. To address this, we present iGenSig-Rx, an integral genomic signature-based approach, as a transparent tool for modeling therapeutic response using clinical trial datasets. This method adeptly addresses challenges related to cross-dataset modeling by capitalizing on high-dimensional redundant genomic features, analogous to reinforcing building pillars with redundant steel rods. Moreover, it integrates adaptive penalization of feature redundancy on a per-sample basis to prevent score flattening and mitigate overfitting. We then developed a purpose-built R package to implement this method for modeling clinical trial datasets. When applied to genomic datasets for HER2 targeted therapies, iGenSig-Rx model demonstrates consistent and reliable predictive power across four independent clinical trials. More importantly, the iGenSig-Rx model offers the level of transparency much needed for clinical application, allowing for clear explanations as to how the predictions are produced, how the features contribute to the prediction, and what are the key underlying pathways. We anticipate that iGenSig-Rx, as an interpretable class of multi-omics modeling methods, will find broad applications in big-data based precision oncology. The R package is available: https://github.com/wangxlab/iGenSig-Rx .

Hybrid Nanoparticles for Cancer Theranostics: A Critical Review on Design, Synthesis, and Multifunctional Capabilities.

Theranostics, a method that combines targeted therapy and diagnostic imaging, has emerged as a viable route for enhancing cancer treatment, and hybrid nanoparticles (HNPs) are at the forefront of this field. Metallic, polymeric, lipid-based, and silica- based HNPs are studied for targeting and biocompatibility. Using HNPs, chemotherapeutic drugs, small interfering RNA, and therapeutic genes can be given precisely and controlled. This enhances therapeutic efficacy and reduces adverse effects. With fluorescence dyes, MRI contrast agents, and PET tracers, real-time therapy response monitoring is conceivable. A nano platform with therapeutic and diagnostic capabilities holds great promise for personalized medicine and precision oncology. The present study discusses HNPs' biocompatibility, stability, immunogenicity, and long-term biosafety, which are crucial to the clinical translation of cancer theranostics. Further, in this in- -depth investigation, we investigated the design, synthesis, and multifunctional activities of HNPs for use in cancer theranostics.

From Detection to Cure - Emerging Roles for Urinary Tumor DNA (utDNA) in Bladder Cancer.

PURPOSE OF REVIEW: This review sought to define the emerging roles of urinary tumor DNA (utDNA) for diagnosis, monitoring, and treatment of bladder cancer. Building from early landmark studies the focus is on recent studies, highlighting how utDNA could aid personalized care. RECENT FINDINGS: Recent research underscores the potential for utDNA to be the premiere biomarker in bladder cancer due to the constant interface between urine and tumor. Many studies find utDNA to be more informative than other biomarkers in bladder cancer, especially in early stages of disease. Points of emphasis include superior sensitivity over traditional urine cytology, broad genomic and epigenetic insights, and the potential for non-invasive, real-time analysis of tumor biology. utDNA shows promise for improving all phases of bladder cancer care, paving the way for personalized treatment strategies. Building from current research, future comprehensive clinical trials will validate utDNA's clinical utility, potentially revolutionizing bladder cancer management.

Metastatic Castration-Resistant Prostate Cancer: Advances in Treatment and Symptom Management.

OPINION STATEMENT: The management of metastatic castrate-resistant prostate cancer (mCRPC) has evolved in the past decade due to substantial advances in understanding the genomic landscape and biology underpinning this form of prostate cancer. The implementation of various therapeutic agents has improved overall survival but despite the promising advances in therapeutic options, mCRPC remains incurable. The focus of treatment should be not only to improve survival but also to preserve the patient's quality of life (QoL) and ameliorate cancer-related symptoms such as pain. The choice and sequence of therapy for mCRPC patients are complex and influenced by various factors, such as side effects, disease burden, treatment history, comorbidities, patient preference and, more recently, the presence of actionable genomic alterations or biomarkers. Docetaxel is the first-line treatment for chemo-naïve patients with good performance status and those who have yet to progress on docetaxel in the castration-sensitive setting. Novel androgen agents (NHAs), such as abiraterone and enzalutamide, are effective treatment options that are utilized as second-line options. These medications can be considered upfront in frail patients or patients who are NHA naïve. Current guidelines recommend genetic testing in mCRPC for mutations in DNA repair deficiency genes to inform treatment decisions, as for example in breast cancer gene mutation testing. Other potential biomarkers being investigated include phosphatase and tensin homologues and homologous recombination repair genes. Despite a growing number of studies incorporating biomarkers in their trial designs, to date, only olaparib in the PROFOUND study and lutetium-177 in the VISION trial have improved survival. This is an unmet need, and future trials should focus on biomarker-guided treatment strategies. The advent of novel noncytotoxic agents has enhanced targeted drug delivery and improved treatment responses with favourable toxicity profiling. Trials should continue to incorporate and report health-related QoL scores and functional assessments into their trial designs.