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In the era of precision oncology, the management of triple-negative breast cancer (TNBC) is rapidly changing and becoming more complicated with a variety of chemotherapy, immunotherapy, and targeted treatment options. Currently, TNBC treatment is based on prognostic and predictive factors including immunohistochemical biomarkers [e.g. programmed death-ligand 1 (PD-L1)] and germline BRCA mutations. Given the current limitation of existing biomarkers, liquid biopsies may serve as clinically useful tools to determine treatment efficacy and response in both the (neo)adjuvant and metastatic settings, for detecting early relapse, and for monitoring clonal evolution during treatment. In this review, we comprehensively summarize current and future liquid biopsy applications. Specifically, we highlight the role of circulating tumor cell characterization, circulating tumor DNA, and other preclinical liquid biopsy technologies including circulating exosomes, RNA liquid biopsy, and circulating immune-based biomarkers. In the near future, these biomarkers may serve to identify early disease relapse, therapeutic targets, and disease clonality for patients with TNBC in the clinical setting.

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Objectives: Human epidermal growth factor receptor 2 (HER2)-targeted therapies have demonstrated potential benefits for metastatic colorectal cancer (mCRC) patients with HER2 amplification, but are not satisfactory in cases of HER2 mutant CRCs. Methods: Consequently, further elucidation of amplifications and somatic mutations in erythroblastic oncogene B-2 (ERBB2) is imperative. Comprehensive genomic profiling was conducted on 2454 Chinese CRC cases to evaluate genomic alterations in 733 cancer-related genes, tumor mutational burden, microsatellite instability, and programmed death ligand 1 (PD-L1) expression. Results: Among 2454 CRC patients, 85 cases (3.46%) exhibited ERBB2 amplification, and 55 cases (2.24%) carried ERBB2 mutation. p.R678Q (28%), p.V8421 (24%), and p.S310F/Y (12%) were the most prevalent of the 16 detected mutation sites. In comparison to the ERBB2 altered (alt) group, KRAS/BRAF mutations were more prevalent in ERBB2 wild-type (wt) samples (ERBB2wt vs. ERBB2alt, KRAS: 50.9% vs. 25.6%, p < 0.05; BRAF: 8.5% vs. 2.3%, p < 0.05). 32.7% (18/55) of CRCs with ERBB2 mutation exhibited microsatellite instability high (MSI-H), while no cases with HER2 amplification displayed MSI-H. Mutant genes varied between ERBB2 copy number variation (CNV) and ERBB2 single nucleotide variant (SNV); TP53 alterations tended to co-occur with ERBB2 amplification (92.3%) as opposed to ERBB2 mutation (58.3%). KRAS and PIK3CA alterations were more prevalent in ERBB2 SNV cases (KRAS/PIK3CA: 45.8%/31.2%) compared to ERBB2 amplification cases (KRAS/PIK3CA: 14.1%/7.7%). Conclusion: Our study delineates the landscape of HER2 alterations in a large-scale cohort of CRC patients from China. These findings enhance our understanding of the molecular features of Chinese CRC patients and offer valuable implications for further investigation.

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Rationale: PSMA-targeting radioligand therapy (PSMA-RLT) has shown promise in metastatic castration-resistant prostate cancer (mCRPC), particularly in PSMA-avid tumours. However, predicting response remains challenging. Preclinical data suggests aberrant p53-signalling as a predictor of poor response. Methods: The patient population of this pre-planned retrospective cohort study consists of 96 patients with mCRPC who underwent treatment with PSMA-RLT and were molecularly profiled by whole-genome sequencing and or targeted next-generation sequencing. Response to PSMA-RLT was assessed per molecular subtype, including TP53-mutational status. Results: Patients with TP53 loss-of-function alterations had a shorter median progression-free survival (3.7 versus 6.2 months, P<0.001), a lower median PSA change (-55% vs. -75%, P=0.012) and shorter overall survival from initiation of PMSA-RLT (7.6 vs. 13.9 months, P=0.003) compared to TP53-wildtype patients. Pathogenic alterations in AR, MYC, BRCA1, or BRCA2 as well as in genes linked to the PI3K or MAPK pathways or genes involved in homologous recombination repair, were not associated with response. Only lactate dehydrogenase was, alongside TP53-status, significantly associated with response. Transcriptome analysis of 21 patients, identified six p53 signalling genes whose low expression was associated to a shorter progression-free survival (P<0.05). Conclusion: TP53 loss-of-function may serve as a prognostic factor for PSMA-RLT outcomes in patients with mCRPC.

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A multitude of exogenous and endogenous processes have the potential to result in DNA damage. While the repair mechanisms are typically capable of correcting this damage, errors in the repair process can result in mutations. The findings of research conducted in 2012 indicate that mutations do not occur randomly but rather follow specific patterns that can be attributed to known or inferred mutational processes. The process of mutational signature analysis allows for the inference of the predominant mutational process for a given cancer sample, with significant potential for clinical applications. A deeper comprehension of these mutational signatures in CRC could facilitate enhanced prevention strategies, facilitate the comprehension of genotoxic drug activity, predict responses to personalized treatments, and, in the future, inform the development of targeted therapies in the context of precision oncology. The efforts of numerous researchers have led to the identification of several mutational signatures, which can be categorized into different mutational signature references. In CRC, distinct mutational signatures are identified as correlating with mismatch repair deficiency, polymerase mutations, and chemotherapy treatment. In this context, a mutational signature analysis offers considerable potential for enhancing minimal residual disease (MRD) tests in stage II (high-risk) and stage III CRC post-surgery, stratifying CRC based on the impacts of genetic and epigenetic alterations for precision oncology, identifying potential therapeutic vulnerabilities, and evaluating drug efficacy and guiding therapy, as illustrated in a proof-of-concept clinical trial.

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The search for dependable molecular biomarkers to enhance routine clinical practice is a compelling challenge across all oncology fields. Urothelial bladder carcinoma, known for its significant heterogeneity, presents difficulties in predicting responses to systemic therapies and outcomes post-radical cystectomy. Recent advancements in molecular cancer biology offer promising avenues to understand the disease's biology and identify emerging predictive biomarkers. Stratifying patients based on their recurrence risk post-curative treatment or predicting the efficacy of conventional and targeted therapies could catalyze personalized treatment selection and disease surveillance. Despite progress, reliable molecular biomarkers to forecast responses to systemic agents, in neoadjuvant, adjuvant, or palliative treatment settings, are still lacking, underscoring an urgent unmet need. This review aims to delve into the utilization of current and emerging molecular signatures across various stages of urothelial bladder carcinoma to predict responses to systemic therapy.

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Lung cancer (LC) is a significant global health issue, particularly among smokers, and is characterized by high rates of incidence and mortality. This comprehensive review offers detailed insights into the potential of artificial intelligence (AI) to revolutionize early detection and personalized treatment strategies for LC. By critically evaluating the limitations of conventional methodologies, we emphasize the innovative potential of AI-driven risk prediction models and imaging analyses to enhance diagnostic precision and improve patient outcomes. Our in-depth analysis of the current state of AI integration in LC care highlights the achievements and challenges encountered in real-world applications, thereby shedding light on practical implementation. Furthermore, we examined the profound implications of AI on treatment response, survival rates, and patient satisfaction, addressing ethical considerations to ensure responsible deployment. In the future, we will outline emerging technologies, anticipate potential barriers to their adoption, and identify areas for further research, emphasizing the importance of collaborative efforts to fully harness the transformative potential of AI in reshaping LC therapy. Ultimately, this review underscores the transformative impact of AI on LC care and advocates for a collective commitment to innovation, collaboration, and ethical stewardship in healthcare.

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Ovarian cancer, predominantly epithelial ovarian cancer (EOC), is a global health concern due to its high mortality rate. Despite the progress made during the last two decades in the surgery and chemotherapy of ovarian cancer, more than 70% of advanced patients are with recurrent cancer and disease. Bevacizumab is a humanized monoclonal antibody, which blocks VEGF signaling in cancer, inhibits angiogenesis and causes tumor shrinkage, and has been recently approved by the FDA as a monotherapy for advanced ovarian cancer in combination with chemotherapy. Unfortunately, Bevacizumab may also induce harmful adverse effects, such as hypertension, bleeding, arterial thromboembolism, poor wound healing and gastrointestinal perforation. Given the expensive cost and unwanted toxicities, there is an urgent need for predictive methods to identify who could benefit from bevacizumab. Of the 18 (approved) requests from 5 countries, 6 teams using 284 whole section WSIs for training to develop fully automated systems submitted their predictions on a test set of 180 tissue core images, with the corresponding ground truth labels kept private. This paper summarizes the 5 qualified methods successfully submitted to the international challenge of automated prediction of treatment effectiveness in ovarian cancer using the histopathologic images (ATEC23) held at the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2023 and evaluates the methods in comparison with 5 state of the art deep learning approaches. This study further assesses the effectiveness of the presented prediction models as indicators for patient selection utilizing both Cox proportional hazards analysis and Kaplan-Meier survival analysis. A robust and cost-effective deep learning pipeline for digital histopathology tasks has become a necessity within the context of the medical community. This challenge highlights the limitations of current MIL methods, particularly within the context of prognosis-based classification tasks, and the importance of DCNNs like inception that has nonlinear convolutional modules at various resolutions to facilitate processing the data in multiple resolutions, which is a key feature required for pathology related prediction tasks. This further suggests the use of feature reuse at various scales to improve models for future research directions. In particular, this paper releases the labels of the testing set and provides applications for future research directions in precision oncology to predict ovarian cancer treatment effectiveness and facilitate patient selection via histopathological images.

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Background: Transcriptomics can reveal much about cellular activity, and cancer transcriptomics have been useful in investigating tumor cell behaviors. Patterns in transcriptome-wide gene expression can be used to investigate biological mechanisms and pathways that can explain the variability in patient response to cancer therapies. Methods: We identified gene expression patterns related to patient drug response by clustering tumor gene expression data and selecting from the resulting gene clusters those where expression of cluster genes was related to patient survival on specific drugs. We then investigated these gene clusters for biological meaning using several approaches, including identifying common genomic locations and transcription factors whose targets were enriched in these clusters and performing survival analyses to support these candidate transcription factor-drug relationships. Results: We identified gene clusters related to drug-specific survival, and through these, we were able to associate observed variations in patient drug response to specific known biological phenomena. Specifically, our analysis implicated 2 stem cell-related transcription factors, HOXB4 and SALL4, in poor response to temozolomide in brain cancers. In addition, expression of SNRNP70 and its targets were implicated in cetuximab response by 3 different analyses, although the mechanism remains unclear. We also found evidence that 2 cancer-related chromosomal structural changes may impact drug efficacy. Conclusion: In this study, we present the gene clusters identified and the results of our systematic analysis linking drug efficacy to specific transcription factors, which are rich sources of potential mechanistic relationships impacting patient outcomes. We also highlight the most promising of these results, which were supported by multiple analyses and by previous research. We report these findings as promising avenues for independent validation and further research into cancer treatments and patient response.

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Nephroblastoma or Wilms' tumor (WT) is the most common pediatric renal malignancy but rare in adults. Treatment protocols for adults are typically extrapolated from pediatric guidelines, but there are no standard guidelines for adults due to the rarity of the disease. However, next-generation sequencing has led to new therapeutic options for adult WT patients. We present the first case to our knowledge of a recurrent adult WT treated with dual BRAF/MEK-targeted therapy, which showed initial robust clinical response and was well tolerated.

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Outcomes from pancreatic ductal adenocarcinoma (PDAC) remain poor and better methods of prognostication and therapeutic approaches are needed. Recent advances in cancer genomics have led to the development of molecular subtypes of PDAC associated with clinical outcomes. Current evidence also suggests that the subtypes have differential response to first-line chemotherapy regimens. PDAC is also characterized by different stroma and immune environments. Further work is needed to confirm the utility of these subtypes to predicting response to different systemic therapies.

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Modern generative artificial intelligence techniques like retrieval-augmented generation (RAG) may be applied in support of precision oncology treatment discussions. Experts routinely review published literature for evidence and recommendations of treatments in a labor-intensive process. A RAG pipeline may help reduce this effort by providing chunks of text from these publications to an off-the-shelf large language model (LLM), allowing it to answer related questions without any fine-tuning. This potential application is demonstrated by retrieving treatment relationships from a trusted data source (OncoKB) and reproducing over 80% of them by asking simple questions to an untrained Llama 2 model with access to relevant abstracts.

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Advances in radiology are crucial not only to the future of the field but to medicine as a whole. Here, we present three emerging areas of medicine that are poised to change how health care is delivered-hospital at home, artificial intelligence, and precision medicine-and illustrate how advances in radiological tools and technologies are helping to fuel the growth of these markets in the United States and across the globe.

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The Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)/Cas9 system, a groundbreaking innovation in genetic engineering, has revolutionized our approach to surmounting complex diseases, culminating in CASGEVY™ approved for sickle cell anemia. Derived from a microbial immune defense mechanism, CRISPR/Cas9, characterized as precision, maneuverability and universality in gene editing, has been harnessed as a versatile tool for precisely manipulating DNA in mammals. In the process of applying it to practice, the consecutive exploitation of novel orthologs and variants never ceases. It's conducive to understanding the essentialities of diseases, particularly cancer, which is crucial for diagnosis, prevention, and treatment. CRISPR/Cas9 is used not only to investigate tumorous genes functioning but also to model disparate cancers, providing valuable insights into tumor biology, resistance, and immune evasion. Upon cancer therapy, CRISPR/Cas9 is instrumental in developing individual and precise cancer therapies that can selectively activate or deactivate genes within tumor cells, aiming to cripple tumor growth and invasion and sensitize cancer cells to treatments. Furthermore, it facilitates the development of innovative treatments, enhancing the targeting efficiency of reprogrammed immune cells, exemplified by advancements in CAR-T regimen. Beyond therapy, it is a potent tool for screening susceptible genes, offering the possibility of intervening before the tumor initiative or progresses. However, despite its vast potential, the application of CRISPR/Cas9 in cancer research and therapy is accompanied by significant efficacy, efficiency, technical, and safety considerations. Escalating technology innovations are warranted to address these issues. The CRISPR/Cas9 system is revolutionizing cancer research and treatment, opening up new avenues for advancements in our understanding and management of cancers. The integration of this evolving technology into clinical practice promises a new era of precision oncology, with targeted, personalized, and potentially curative therapies for cancer patients.

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Skin cancer comprises one-third of all diagnosed cancer cases and remains a major health concern. Genetic and environmental parameters serve as the two main risk factors associated with the development of skin cancer, with ultraviolet radiation being the most common environmental risk factor. Studies have also found fair complexion, arsenic toxicity, indoor tanning, and family history among the prevailing causes of skin cancer. Prevention and early diagnosis play a crucial role in reducing the frequency and ensuring effective management of skin cancer. Recent studies have focused on exploring minimally invasive or non-invasive diagnostic technologies along with artificial intelligence to facilitate rapid and accurate diagnosis. The treatment of skin cancer ranges from traditional surgical excision to various advanced methods such as phototherapy, radiotherapy, immunotherapy, targeted therapy, and combination therapy. Recent studies have focused on immunotherapy, with the introduction of new checkpoint inhibitors and personalized immunotherapy enhancing treatment efficacy. Advancements in multi-omics, nanotechnology, and artificial intelligence have further deepened the understanding of the mechanisms underlying tumoral growth and their interaction with therapeutic effects, which has paved the way for precision oncology. This review aims to highlight the recent advancements in the understanding and management of skin cancer, and provide an overview of existing and emerging diagnostic, prognostic, and therapeutic modalities, while highlighting areas that require further research to bridge the existing knowledge gaps.

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INTRODUCTION: High-throughput sequencing techniques have revolutionized oncology. Paired germline-tumor DNA analysis has emerged as a comprehensive strategy to uncover actionable alterations in advanced cancer patients (ACP) enrolled in precision oncology trials. However, challenges persist in variant interpretation and managing incidental germline findings. METHODS: We conducted a study involving 288 ACP from MOSCATO (NCT01566019) and MATCHR (NCT02517892) trials to assess germline variants impacting cancer-related genes. Germline DNA sequencing was performed using a panel of 250 cancer-related genes, and the results were discussed during tumor molecular board sessions. RESULTS: Germline pathogenic variants (PV) were classified according to the ESCAT classification. Lung cancer (36.8 %), followed by prostate (18.4 %) and breast cancer (17.7 %), comprised the most prevalent tumor types. PVs were found in 12.5 % of patients. Most PVs were classified as ESCAT X (63.9 %), highlighting limited therapeutic actionability. Notably,2 % of patients had actionable variants (ESCAT I-A/II-A). Incidental findings included 7.3 % of patients with PVs in cancer-predisposition genes, with 2.4 % having very high-risk potential, necessitating mandatory oncogenetic counseling. Nearly one in five patients (21.9 %) had at least one VUS. DISCUSSION: Our study underscores the significance of germline sequencing in identifying actionable alterations and the need for improved variant interpretation as well as pretest counseling plans in precision oncology trials.

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Sarcomatoid carcinoma of the pancreas (SCP) is a rare and aggressive subtype of undifferentiated pancreatic ductal adenocarcinoma, with a generally poor prognosis and only sporadic cases reported worldwide. Histologically, the most notable feature of SCP is the presence of abundant of mesenchymatoid spindle tumor cells in the tumor, which lack glandular differentiation. Immunohistochemically, SCP is characterized by the expression of both mesenchymal and epithelial markers. With only a few reported cases, there is limited knowledge about its molecular and clinicopathological characteristics. Therefore, the present study performed a literature search to identify all relevant published studies. The present review provides an overview of the histogenesis, diagnosis, genetic features, prognosis and treatment of SCP, specifically focusing on the molecular alterations. Furthermore, a single-center experience is reported, which adds to the limited evidence available in the literature.

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INTRODUCTION: Next-generation sequencing (NGS) identifies genetic variants to inform personalized treatment plans. Insufficient evidence of cost-effectiveness impedes the integration of NGS into routine cancer care. The complexity of personalized treatment challenges conventional economic evaluation. Clearly delineating challenges informs future cost-effectiveness analyses to better value and contextualize health, preference-, and equity-based outcomes. AREAS COVERED: We conducted a scoping review to characterize the applied methods and outcomes of economic evaluations of NGS in oncology and identify existing challenges. We included 27 articles published since 2016 from a search of PubMed, Embase, and Web of Science. Identified challenges included defining the evaluative scope, managing evidentiary limitations including lack of causal evidence, incorporating preference-based utility, and assessing distributional and equity-based impacts. These challenges reflect the difficulty of generating high-quality clinical effectiveness and real-world evidence (RWE) for NGS-guided interventions. EXPERT OPINION: Adapting methodological approaches and developing life-cycle health technology assessment (HTA) guidance using RWE is crucial for implementing NGS in oncology. Healthcare systems, decision-makers, and HTA organizations are facing a pivotal opportunity to adapt to an evolving clinical paradigm and create innovative regulatory and reimbursement processes that will enable more sustainable, equitable, and patient-oriented healthcare.

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The Victorian Precision Oncology Summit, convened in 2023, was a joint initiative between the Victorian Comprehensive Cancer Centre Alliance (VCCC Alliance) and the Monash Partners Comprehensive Cancer Consortium (MPCCC) and was proposed to guide a coordinated state-wide conversation about how the oncology sector can overcome some of the current obstacles in achieving equity of access to clinical cancer genomics for Victorian patients. Themes that emerged from discussion groups at the Summit include standardisation, centralisation, funding, education and communication and insights across those themes are outlined in this manuscript. The event served as a large consultation piece for the development of a broader precision oncology roadmap, which explores equitable access to molecular testing for Victorian patients, currently in development by the VCCC Alliance and MPCCC in collaboration with other key Victorian and national stakeholders. While this symposium was a Victorian initiative, it is felt that the insights garnered from this consultation piece will be of interest to consumer groups, clinicians, researchers, educators, policy makers and other key stakeholders in other states of Australia as well as in other countries implementing comprehensive genomic profiling within complex health systems.

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The liver is a frequent site of metastasis in advanced gastric cancer (GC). Despite significant advancements in diagnostic and therapeutic techniques, the overall survival rate for patients afflicted with gastric cancer liver metastasis (GCLM) remains dismally low. Precision oncology has made significant progress in identifying therapeutic targets and enhancing our understanding of metastasis mechanisms through genome sequencing and molecular characterization. Therefore, it is crucial to have a comprehensive understanding of the various molecular processes involved in GCLM and the fundamental principles of systemic therapy to develop new treatment approaches. This paper aims to review recent findings on the diagnosis, potential biomarkers, and therapies targeting the multiple molecular processes of GCLM, with the goal of improving treatment strategies for patients with GCLM.

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