A case of treatment-resistant advanced gastric cancer with FGFR2 gene alteration successfully treated with pemigatinib.

Comprehensive genome profiling (CGP) is expected to widen the scope of cancer drug options by identifying the genes involved in carcinogenesis. However, a few patients can access recommended treatments following CGP. Herein, we report a case in which pemigatinib, a selective fibroblast growth factor receptor (FGFR) inhibitor, was used as last-line therapy to treat a patient with advanced gastric cancer exhibiting FGFR2 genomic alterations, as determined by CGP testing. The patient (male, 52 years old) was diagnosed with advanced gastric cancer (cStage IV, cT4aN3M1 [LYM], por, HER2 0, microsatellite stable) and received docetaxel + cisplatin + S-1 (7 cycles), irinotecan + ramucirumab (11 cycles), and nivolumab (3 cycles), but experienced progressive disease (PD). Subsequently, FoundationOne Liquid CDx testing was conducted, revealing FGFR2 rearrangement and amplification; however, no clinical trials on genotype-matched therapies for FGFR2 alterations were available. After three cycles of TAS-102, the patient experienced PD and provided consent for the off-label use of pemigatinib. The Cancer Genomics Medical Committee of our hospital approved the self-funded treatment. The patient had markedly decreased CEA and CA19-9 levels after treatment initiation, but experienced PD after five courses. Over the treatment course, grade 1 hyperphosphatemia and onychomadesis were observed. To the best of our knowledge, this is the first reported case of pemigatinib therapy employed in a patient with advanced gastric cancer exhibiting FGFR2 gene alterations. This case could serve as a notable example of tumor-agnostic therapy to broaden treatment options for gastric cancer patients with rare genetic alterations.

Guilt by association: Plant-based foods can be incorporated into both healthy and unhealthy plant-based diet indices associated with coronary heart disease.

Background: One approach to test for differential associations between plant foods with health uses a scoring approach: foods categorized into animal or 'healthy' plant-based or 'unhealthy' plant-based groups to construct a plant-based diet index (PDI), healthy PDI (hPDI), and unhealthy PDI (uPDI). Objective: To evaluate robustness of associations between diet indices and incident coronary heart disease (CHD) risk when recategorizing food groups in indices. Methods: Using REasons for Geographic and Racial Differences in Stroke (REGARDS) data, we replicated a published use of the scoring approach. Using Cox proportional hazards regression, we assessed ramifications of the following on associations between diet indices and CHD risk: 1) reconfiguring foods within and among food groups, using potatoes as an example, 2) leave-one-out analysis for each of 12 plant-based food groups, and 3) agnostically redefining each food group as 'healthy' or 'unhealthy'. Results: Over 153,286 person-years of follow-up, there were 868 cases of CHD. Replication analyses did not reach statistical significance. General patterns of magnitude of hazard ratios (HRs) in replication and reconfiguration models were PDI HRs < hPDI HRs < uPDI HRs for women, and hPDI < PDI < uPDI for men. Five models reconfiguring potatoes resulted in small, varied differences in PDI, hPDI, and uPDI associations. Leave-one-out analyses resulted in greater variation of associations between indices and CHD. In agnostic models, each plant-based food group was classified in indices as 'healthy' and 'unhealthy' with statistically significant beneficial or deleterious associations with CHD. Averaged over 4,096 models, HRs' shifts were small when food groups were moved between 'healthy' and 'unhealthy'. Conclusion: Statistically significant associations between hPDI, uPDI, and PDI and incident CHD were not replicated. Small perturbations of the scoring approach had varied impacts on HRs. Agnostically constructing diet indices demonstrated the potential for guilt (or benefit) by association: any of the food groups we studied could be categorized with others in an index showing beneficial or deleterious associations.

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.

Dual domain distribution disruption with semantics preservation: Unsupervised domain adaptation for medical image segmentation.

Recent unsupervised domain adaptation (UDA) methods in medical image segmentation commonly utilize Generative Adversarial Networks (GANs) for domain translation. However, the translated images often exhibit a distribution deviation from the ideal due to the inherent instability of GANs, leading to challenges such as visual inconsistency and incorrect style, consequently causing the segmentation model to fall into the fixed wrong pattern. To address this problem, we propose a novel UDA framework known as Dual Domain Distribution Disruption with Semantics Preservation (DDSP). Departing from the idea of generating images conforming to the target domain distribution in GAN-based UDA methods, we make the model domain-agnostic and focus on anatomical structural information by leveraging semantic information as constraints to guide the model to adapt to images with disrupted distributions in both source and target domains. Furthermore, we introduce the inter-channel similarity feature alignment based on the domain-invariant structural prior information, which facilitates the shared pixel-wise classifier to achieve robust performance on target domain features by aligning the source and target domain features across channels. Without any exaggeration, our method significantly outperforms existing state-of-the-art UDA methods on three public datasets (i.e., the heart dataset, the brain dataset, and the prostate dataset). The code is available at https://github.com/MIXAILAB/DDSPSeg.

Novel insights toward diagnosis and treatment of glioneuronal and neuronal tumors in young adults.

Aim: Glioneuronal and neuronal tumors are rare primary central nervous system malignancies with heterogeneous features. Due to the rarity of these malignancies diagnosis and treatment remains a clinical challenge. Methods: Here we performed a narrative review aimed to investigate the principal issues concerning the diagnosis, pathology, and clinical management of glioneuronal tumors. Results: Diagnostic criteria have been recently overturned thanks to a better characterization on a histological and molecular biology level. The study of genomic alterations occurring within these tumors has allowed us to identify potential therapeutic targets including BRAF, FGFR, and PDGFRA. Conclusion: Techniques allowing molecular sequencing DNA methylation assessment of the disease are essential diagnostic tools. Targeting agents should be included in the therapeutic armamentarium after loco-regional treatment failure.

[Box: see text].

Tumor-Agnostic Therapy-The Final Step Forward in the Cure for Human Neoplasms?

Cancer accounted for 10 million deaths in 2020, nearly one in every six deaths annually. Despite advancements, the contemporary clinical management of human neoplasms faces a number of challenges. Surgical removal of tumor tissues is often not possible technically, while radiation and chemotherapy pose the risk of damaging healthy cells, tissues, and organs, presenting complex clinical challenges. These require a paradigm shift in developing new therapeutic modalities moving towards a more personalized and targeted approach. The tumor-agnostic philosophy, one of these new modalities, focuses on characteristic molecular signatures of transformed cells independently of their traditional histopathological classification. These include commonly occurring DNA aberrations in cancer cells, shared metabolic features of their homeostasis or immune evasion measures of the tumor tissues. The first dedicated, FDA-approved tumor-agnostic agent's profound progression-free survival of 78% in mismatch repair-deficient colorectal cancer paved the way for the accelerated FDA approvals of novel tumor-agnostic therapeutic compounds. Here, we review the historical background, current status, and future perspectives of this new era of clinical oncology.

Basket Trials: Past, Present, and Future.

Large-scale tumor molecular profiling has revealed that diverse cancer histologies are driven by common pathways with unifying biomarkers that can be exploited therapeutically. Disease-agnostic basket trials have been increasingly utilized to test biomarker-driven therapies across cancer types. These trials have led to drug approvals and improved the lives of patients while simultaneously advancing our understanding of cancer biology. This review focuses on the practicalities of implementing basket trials, with an emphasis on molecularly targeted trials. We examine the biologic subtleties of genomic biomarker and patient selection, discuss previous successes in drug development facilitated by basket trials, describe certain novel targets and drugs, and emphasize practical considerations for participant recruitment and study design. This review also highlights strategies for aiding patient access to basket trials. As basket trials become more common, steps to ensure equitable implementation of these studies will be critical for molecularly targeted drug development.

[Expectations and challenges for clinical application of cancer genome panels in acute myeloid leukemia].

The blood cancer field has played a pioneering role in advancing precision medicine, with milestones such as development of ABL1 inhibitors for chronic myeloid leukemia. The significance of gene mutation information in AML treatment has increased, evident in classifications and guidelines from organizations such as WHO and ELN. This article examines the anticipated roles of cancer genome panels (CGPs) in AML treatment from three perspectives: diagnosis, risk stratification, and treatment selection. Use of CGPs enables more accurate diagnosis and risk stratification. In treatment selection, CGPs not only complements but also substitutes existing companion diagnostics, and is expected to be a crucial information source for future drug adoption and investigation of tumor-agnostic therapies. However, various challenges remain to be addressed, including the purpose and timing of CGPs, the time required for the tests, and how to utilize expert panels.

Framework for Ranking Machine Learning Predictions of Limited, Multimodal, and Longitudinal Behavioral Passive Sensing Data: Combining User-Agnostic and Personalized Modeling.

BACKGROUND: Passive mobile sensing provides opportunities for measuring and monitoring health status in the wild and outside of clinics. However, longitudinal, multimodal mobile sensor data can be small, noisy, and incomplete. This makes processing, modeling, and prediction of these data challenging. The small size of the data set restricts it from being modeled using complex deep learning networks. The current state of the art (SOTA) tackles small sensor data sets following a singular modeling paradigm based on traditional machine learning (ML) algorithms. These opt for either a user-agnostic modeling approach, making the model susceptible to a larger degree of noise, or a personalized approach, where training on individual data alludes to a more limited data set, giving rise to overfitting, therefore, ultimately, having to seek a trade-off by choosing 1 of the 2 modeling approaches to reach predictions. OBJECTIVE: The objective of this study was to filter, rank, and output the best predictions for small, multimodal, longitudinal sensor data using a framework that is designed to tackle data sets that are limited in size (particularly targeting health studies that use passive multimodal sensors) and that combines both user agnostic and personalized approaches, along with a combination of ranking strategies to filter predictions. METHODS: In this paper, we introduced a novel ranking framework for longitudinal multimodal sensors (FLMS) to address challenges encountered in health studies involving passive multimodal sensors. Using the FLMS, we (1) built a tensor-based aggregation and ranking strategy for final interpretation, (2) processed various combinations of sensor fusions, and (3) balanced user-agnostic and personalized modeling approaches with appropriate cross-validation strategies. The performance of the FLMS was validated with the help of a real data set of adolescents diagnosed with major depressive disorder for the prediction of change in depression in the adolescent participants. RESULTS: Predictions output by the proposed FLMS achieved a 7% increase in accuracy and a 13% increase in recall for the real data set. Experiments with existing SOTA ML algorithms showed an 11% increase in accuracy for the depression data set and how overfitting and sparsity were handled. CONCLUSIONS: The FLMS aims to fill the gap that currently exists when modeling passive sensor data with a small number of data points. It achieves this through leveraging both user-agnostic and personalized modeling techniques in tandem with an effective ranking strategy to filter predictions.

ntsm: an alignment-free, ultra-low-coverage, sequencing technology agnostic, intraspecies sample comparison tool for sample swap detection.

BACKGROUND: Due to human error, sample swapping in large cohort studies with heterogeneous data types (e.g., mix of Oxford Nanopore Technologies, Pacific Bioscience, Illumina data, etc.) remains a common issue plaguing large-scale studies. At present, all sample swapping detection methods require costly and unnecessary (e.g., if data are only used for genome assembly) alignment, positional sorting, and indexing of the data in order to compare similarly. As studies include more samples and new sequencing data types, robust quality control tools will become increasingly important. FINDINGS: The similarity between samples can be determined using indexed k-mer sequence variants. To increase statistical power, we use coverage information on variant sites, calculating similarity using a likelihood ratio-based test. Per sample error rate, and coverage bias (i.e., missing sites) can also be estimated with this information, which can be used to determine if a spatially indexed principal component analysis (PCA)-based prescreening method can be used, which can greatly speed up analysis by preventing exhaustive all-to-all comparisons. CONCLUSIONS: Because this tool processes raw data, is faster than alignment, and can be used on very low-coverage data, it can save an immense degree of computational resources in standard quality control (QC) pipelines. It is robust enough to be used on different sequencing data types, important in studies that leverage the strengths of different sequencing technologies. In addition to its primary use case of sample swap detection, this method also provides information useful in QC, such as error rate and coverage bias, as well as population-level PCA ancestry analysis visualization.

Non-Targeted RNA Sequencing: Towards the Development of Universal Clinical Diagnosis Methods for Human and Veterinary Infectious Diseases.

Metagenomics offers the potential to replace and simplify classical methods used in the clinical diagnosis of human and veterinary infectious diseases. Metagenomics boasts a high pathogen discovery rate and high specificity, advantages absent in most classical approaches. However, its widespread adoption in clinical settings is still pending, with a slow transition from research to routine use. While longer turnaround times and higher costs were once concerns, these issues are currently being addressed by automation, better chemistries, improved sequencing platforms, better databases, and automated bioinformatics analysis. However, many technical options and steps, each producing highly variable outcomes, have reduced the technology's operational value, discouraging its implementation in diagnostic labs. We present a case for utilizing non-targeted RNA sequencing (NT-RNA-seq) as an ideal metagenomics method for the detection of infectious disease-causing agents in humans and animals. Additionally, to create operational value, we propose to identify best practices for the "core" of steps that are invariably shared among many human and veterinary protocols. Reference materials, sequencing procedures, and bioinformatics standards should accelerate the validation processes necessary for the widespread adoption of this technology. Best practices could be determined through "implementation research" by a consortium of interested institutions working on common samples.

Evaluation of Molecular Residual Disease by a Fixed Panel in Resectable Colorectal Cancer.

Purpose: Molecular residual disease (MRD) is a promising biomarker in colorectal cancer (CRC) for prognosis and guiding treatment, while the whole-exome sequencing (WES) based tumor-informed assay is standard for evaluating MRD based on circulating tumor DNA (ctDNA). In this study, we assessed the feasibility of a fixed-panel for evaluating MRD in CRC. Materials and Methods: 75 patients with resectable stage I-III CRC were enrolled. Tumor tissues obtained by surgery, and pre-operative and post-operative day 7 blood samples were collected. The ctDNA was evaluated using the tumor-agnostic and tumor-informed fixed assays, as well as the WES-based and panel-based personalized assays in randomly selected patients. Results: The tumor-informed fixed assay had a higher pre-operative positive rate than the tumor-agnostic assay (73.3% vs 57.3%). The pre-op ctDNA status failed to predict disease-free survival (DFS) in either of the fixed assays, while the tumor-informed fixed assay-determined post-op ctDNA positivity was significantly associated with worse DFS (HR, 20.74, 95%CI 7.19-59.83; p<0.001), which was an independent predictor by multivariable analysis (HR, 28.57, 95%CI 7.10-114.9; p<0.001). Sub-cohort analysis indicated the WES-based personalized assay had the highest pre-operative positive rate (95.1%). The two personalized assays and the tumor-informed fixed assay demonstrated same results in post-op landmark (HR, 26.34, 95%CI, 6.01-115.57; p<0.001), outperforming the tumor-agnostic fixed panel (HR, 3.04, 95%CI, 0.94-9.89; p=0.052). Conclusion: Our study confirmed the prognostic value of the ctDNA positivity at post-op day 7 by the tumor-informed fixed panel. The tumor-informed fixed panel may be a cost-effective method to evaluate MRD, which warrants further studies in future.

Facilitators of and barriers to County Behavioral Health System Transformation and Innovation: an interview study.

BACKGROUND: Inadequate and inequitable access to quality behavioral health services and high costs within the mental health systems are long-standing problems. System-level (e.g., fee-for-service payment model, lack of a universal payor) and individual factors (e.g., lack of knowledge of existing resources) contribute to difficulties in accessing resources and services. Patients are underserved in County behavioral health systems in the United States. Orange County's (California) Behavioral Health System Transformation project sought to improve access by addressing two parts of their system: developing a template for value-based contracts that promote payor-agnostic care (Part 1); developing a digital platform to support resource navigation (Part 2). Our aim was to evaluate facilitators of and barriers to each of these system changes. METHODS: We collected interview data from County or health care agency leaders, contracted partners, and community stakeholders. Themes were informed by the Consolidated Framework for Implementation Research. RESULTS: Five themes were identified related to behavioral health system transformation, including 1) aligning goals and values, 2) addressing fit, 3) fostering engagement and partnership, 4) being aware of implementation contexts, and 5) promoting communication. A lack of fit into incentive structures and changing state guidelines and priorities were barriers to contract development. Involving diverse communities to inform design and content facilitated the process of developing digital tools. CONCLUSIONS: The study highlights the multifaceted factors that help facilitate or hinder behavioral health system transformation, such as the need for addressing systematic and process behaviors, leveraging the knowledge of leadership and community stakeholders, fostering collaboration, and adapting to implementation contexts.

Healthcare decision-making for tumour-agnostic therapies in Europe: lessons learned.

The tumour-agnostic authorisations of larotrectinib and entrectinib shifted the paradigm for indication setting. European healthcare decision-makers agreed on their therapeutic potential but diverged primarily in identified uncertainties concerning basket trial designs and endpoints, prognostic value of neurotrophic tropomyosin receptor kinase (NTRK) gene fusions, and resistance mechanisms. In addition, assessments of relevant comparators, unmet medical needs (UMNs), and implementation of NTRK-testing strategies diverged. In particular, the tumour-specific reimbursement recommendations and guidelines do not reflect tumour-agnostic thinking. These differences indicate difficulties experienced in these assessments and provide valuable lessons for future disruptive therapies. As we discuss here, early multistakeholder dialogues concerning minimum evidence requirements and involving clinicians are essential.

Explainable AI: Machine Learning Interpretation in Blackcurrant Powders.

Recently, explainability in machine and deep learning has become an important area in the field of research as well as interest, both due to the increasing use of artificial intelligence (AI) methods and understanding of the decisions made by models. The explainability of artificial intelligence (XAI) is due to the increasing consciousness in, among other things, data mining, error elimination, and learning performance by various AI algorithms. Moreover, XAI will allow the decisions made by models in problems to be more transparent as well as effective. In this study, models from the 'glass box' group of Decision Tree, among others, and the 'black box' group of Random Forest, among others, were proposed to understand the identification of selected types of currant powders. The learning process of these models was carried out to determine accuracy indicators such as accuracy, precision, recall, and F1-score. It was visualized using Local Interpretable Model Agnostic Explanations (LIMEs) to predict the effectiveness of identifying specific types of blackcurrant powders based on texture descriptors such as entropy, contrast, correlation, dissimilarity, and homogeneity. Bagging (Bagging_100), Decision Tree (DT0), and Random Forest (RF7_gini) proved to be the most effective models in the framework of currant powder interpretability. The measures of classifier performance in terms of accuracy, precision, recall, and F1-score for Bagging_100, respectively, reached values of approximately 0.979. In comparison, DT0 reached values of 0.968, 0.972, 0.968, and 0.969, and RF7_gini reached values of 0.963, 0.964, 0.963, and 0.963. These models achieved classifier performance measures of greater than 96%. In the future, XAI using agnostic models can be an additional important tool to help analyze data, including food products, even online.

The evolving landscape of tissue-agnostic therapies in precision oncology.

Tumor-agnostic therapies represent a paradigm shift in oncology by altering the traditional means of characterizing tumors based on their origin or location. Instead, they zero in on specific genetic anomalies responsible for fueling malignant growth. The watershed moment for tumor-agnostic therapies arrived in 2017, with the US Food and Drug Administration's historic approval of pembrolizumab, an immune checkpoint inhibitor. This milestone marked the marriage of genomics and immunology fields, as an immunotherapeutic agent gained approval based on genomic biomarkers, specifically, microsatellite instability-high or mismatch repair deficiency (dMMR). Subsequently, the approval of NTRK inhibitors, designed to combat NTRK gene fusions prevalent in various tumor types, including pediatric cancers and adult solid tumors, further underscored the potential of tumor-agnostic therapies. The US Food and Drug Administration approvals of targeted therapies (BRAF V600E, RET fusion), immunotherapies (tumor mutational burden ≥10 mutations per megabase, dMMR) and an antibody-drug conjugate (Her2-positive-immunohistochemistry 3+ expression) with pan-cancer efficacy have continued, offering newfound hope to patients grappling with advanced solid tumors that harbor particular biomarkers. In this comprehensive review, the authors delve into the expansive landscape of tissue-agnostic targets and drugs, shedding light on the rationale underpinning this approach, the hurdles it faces, presently approved therapies, voices from the patient advocacy perspective, and the tantalizing prospects on the horizon. This is a welcome advance in oncology that transcends the boundaries of histology and location to provide personalized options.

Patient-reported outcomes with selpercatinib treatment in patients with RET-driven cancers in the phase I/II LIBRETTO-001 trial.

BACKGROUND: This post-hoc retrospective study describes long-term patient-reported outcomes (PROs) for REarranged during Transfection (RET)-altered non-small-cell lung cancer (NSCLC), medullary thyroid cancer (MTC), non-MTC thyroid cancer (TC), and tumor agnostic (TA) patients (Data cut-off: January 2023) from the LIBRETTO-001 trial. PATIENTS AND METHODS: Patients completed the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire-Core 30 (QLQ-C30). Patients with MTC also completed a modified version of the Systemic Therapy-Induced Diarrhea Assessment Tool (mSTIDAT). The proportion of patients with improved, stable, or worsened status after baseline was reported. PROs were summarized at 3 years (cycle 37) post-baseline for the NSCLC and MTC cohorts, and at 2 years (cycle 25) post-baseline for the TC and TA cohorts. Time-to-event outcomes (time to first improvement or worsening and duration of improvement) were reported. RESULTS: The baseline assessment was completed by 200 (63.3%), 209 (70.8%), 50 (76.9%), and 38 (73.1%) patients in the NSCLC, MTC, TC, and TA cohorts, respectively. The total compliance rate was 80%, 82%, 70%, and 85%, respectively. Approximately 75% (NSCLC), 81% (MTC), 75% (TC), and 40% (TA) of patients across all cohorts reported improved or stable QLQ-C30 scores at year 3 (NSCLC and MTC) or year 2 (TC and TA) with continuous selpercatinib use. Across cohorts, the median time to first improvement ranged from 2.0 to 19.4 months, the median duration of improvement ranged from 1.9 to 28.2 months, and the median time to first worsening ranged from 5.6 to 44.2 months. The total compliance rate for the mSTIDAT was 83.7% and the proportion of patients with MTC who reported diarrhea on the mSTIDAT was reduced from 80.8% at baseline to 35.6% at year 3. CONCLUSIONS: A majority of patients with RET-driven cancers improved or remained stable on most QLQ-C30 domains, demonstrating favorable health-related quality of life as measured by the QLQ-C30 during long-term treatment with selpercatinib.

Agnostic Administration of Targeted Anticancer Drugs: Looking for a Balance between Hype and Caution.

Many tumors have well-defined vulnerabilities, thus potentially allowing highly specific and effective treatment. There is a spectrum of actionable genetic alterations which are shared across various tumor types and, therefore, can be targeted by a given drug irrespective of tumor histology. Several agnostic drug-target matches have already been approved for clinical use, e.g., immune therapy for tumors with microsatellite instability (MSI) and/or high tumor mutation burden (TMB), NTRK1-3 and RET inhibitors for cancers carrying rearrangements in these kinases, and dabrafenib plus trametinib for BRAF V600E mutated malignancies. Multiple lines of evidence suggest that this histology-independent approach is also reasonable for tumors carrying ALK and ROS1 translocations, biallelic BRCA1/2 inactivation and/or homologous recombination deficiency (HRD), strong HER2 amplification/overexpression coupled with the absence of other MAPK pathway-activating mutations, etc. On the other hand, some well-known targets are not agnostic: for example, PD-L1 expression is predictive for the efficacy of PD-L1/PD1 inhibitors only in some but not all cancer types. Unfortunately, the individual probability of finding a druggable target in a given tumor is relatively low, even with the use of comprehensive next-generation sequencing (NGS) assays. Nevertheless, the rapidly growing utilization of NGS will significantly increase the number of patients with highly unusual or exceptionally rare tumor-target combinations. Clinical trials may provide only a framework for treatment attitudes, while the decisions for individual patients usually require case-by-case consideration of the probability of deriving benefit from agnostic versus standard therapy, drug availability, associated costs, and other circumstances. The existing format of data dissemination may not be optimal for agnostic cancer medicine, as conventional scientific journals are understandably biased towards the publication of positive findings and usually discourage the submission of case reports. Despite all the limitations and concerns, histology-independent drug-target matching is certainly feasible and, therefore, will be increasingly utilized in the future.