Generating and using real-world data: A worthwhile uphill battle.

The precision oncology paradigm challenges the feasibility and data generalizability of traditional clinical trials. Consequently, an unmet need exists for practical approaches to test many subgroups, evaluate real-world drug value, and gather comprehensive, accessible datasets to validate novel biomarkers. Real-world data (RWD) are increasingly recognized to have the potential to fill this gap in research methodology. Established applications of RWD include informing disease epidemiology, pharmacovigilance, and healthcare quality assessment. Currently, concerns regarding RWD quality and comprehensiveness, privacy, and biases hamper their broader application. Nonetheless, RWD may play a pivotal role in supplementing clinical trials, enabling conditional reimbursement and accelerated drug access, and innovating trial conduct. Moreover, purpose-built RWD repositories may support the extension or refinement of drug indications and facilitate the discovery and validation of new biomarkers. This perspective explores the potential of leveraging RWD to advance oncology, highlights its benefits and challenges, and suggests a path forward in this evolving field.

LAG-3 and PD-1 synergize on CD8+ T cells to drive T cell exhaustion and hinder autocrine IFN-γ-dependent anti-tumor immunity.

Overcoming immune-mediated resistance to PD-1 blockade remains a major clinical challenge. Enhanced efficacy has been demonstrated in melanoma patients with combined nivolumab (anti-PD-1) and relatlimab (anti-LAG-3) treatment, the first in its class to be FDA approved. However, how these two inhibitory receptors synergize to hinder anti-tumor immunity remains unknown. Here, we show that CD8+ T cells deficient in both PD-1 and LAG-3, in contrast to CD8+ T cells lacking either receptor, mediate enhanced tumor clearance and long-term survival in mouse models of melanoma. PD-1- and LAG-3-deficient CD8+ T cells were transcriptionally distinct, with broad TCR clonality and enrichment of effector-like and interferon-responsive genes, resulting in enhanced IFN-γ release indicative of functionality. LAG-3 and PD-1 combined to drive T cell exhaustion, playing a dominant role in modulating TOX expression. Mechanistically, autocrine, cell-intrinsic IFN-γ signaling was required for PD-1- and LAG-3-deficient CD8+ T cells to enhance anti-tumor immunity, providing insight into how combinatorial targeting of LAG-3 and PD-1 enhances efficacy.

Interpreting and integrating genomic tests results in clinical cancer care: Overview and practical guidance.

The last decade has seen rapid progress in the use of genomic tests, including gene panels, whole-exome sequencing, and whole-genome sequencing, in research and clinical cancer care. These advances have created expansive opportunities to characterize the molecular attributes of cancer, revealing a subset of cancer-associated aberrations called driver mutations. The identification of these driver mutations can unearth vulnerabilities of cancer cells to targeted therapeutics, which has led to the development and approval of novel diagnostics and personalized interventions in various malignancies. The applications of this modern approach, often referred to as precision oncology or precision cancer medicine, are already becoming a staple in cancer care and will expand exponentially over the coming years. Although genomic tests can lead to better outcomes by informing cancer risk, prognosis, and therapeutic selection, they remain underutilized in routine cancer care. A contributing factor is a lack of understanding of their clinical utility and the difficulty of results interpretation by the broad oncology community. Practical guidelines on how to interpret and integrate genomic information in the clinical setting, addressed to clinicians without expertise in cancer genomics, are currently limited. Building upon the genomic foundations of cancer and the concept of precision oncology, the authors have developed practical guidance to aid the interpretation of genomic test results that help inform clinical decision making for patients with cancer. They also discuss the challenges that prevent the wider implementation of precision oncology.

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.

Cancer screening with multicancer detection tests: A translational science review.

Multicancer detection (MCD) tests use a single, easily obtainable biospecimen, such as blood, to screen for more than one cancer concurrently. MCD tests can potentially be used to improve early cancer detection, including cancers that currently lack effective screening methods. However, these tests have unknown and unquantified benefits and harms. MCD tests differ from conventional cancer screening tests in that the organ responsible for a positive test is unknown, and a broad diagnostic workup may be necessary to confirm the location and type of underlying cancer. Among two prospective studies involving greater than 16,000 individuals, MCD tests identified those who had some cancers without currently recommended screening tests, including pancreas, ovary, liver, uterus, small intestine, oropharyngeal, bone, thyroid, and hematologic malignancies, at early stages. Reported MCD test sensitivities range from 27% to 95% but differ by organ and are lower for early stage cancers, for which treatment toxicity would be lowest and the potential for cure might be highest. False reassurance from a negative MCD result may reduce screening adherence, risking a loss in proven public health benefits from standard-of-care screening. Prospective clinical trials are needed to address uncertainties about MCD accuracy to detect different cancers in asymptomatic individuals, whether these tests can detect cancer sufficiently early for effective treatment and mortality reduction, the degree to which these tests may contribute to cancer overdiagnosis and overtreatment, whether MCD tests work equally well across all populations, and the appropriate diagnostic evaluation and follow-up for patients with a positive test.

Aspiring toward equitable benefits from genomic advances to individuals of ancestrally diverse backgrounds.

Advancements in genomic technologies have shown remarkable promise for improving health trajectories. The Human Genome Project has catalyzed the integration of genomic tools into clinical practice, such as disease risk assessment, prenatal testing and reproductive genomics, cancer diagnostics and prognostication, and therapeutic decision making. Despite the promise of genomic technologies, their full potential remains untapped without including individuals of diverse ancestries and integrating social determinants of health (SDOHs). The NHGRI launched the 2020 Strategic Vision with ten bold predictions by 2030, including "individuals from ancestrally diverse backgrounds will benefit equitably from advances in human genomics." Meeting this goal requires a holistic approach that brings together genomic advancements with careful consideration to healthcare access as well as SDOHs to ensure that translation of genetics research is inclusive, affordable, and accessible and ultimately narrows rather than widens health disparities. With this prediction in mind, this review delves into the two paramount applications of genetic testing-reproductive genomics and precision oncology. When discussing these applications of genomic advancements, we evaluate current accessibility limitations, highlight challenges in achieving representativeness, and propose paths forward to realize the ultimate goal of their equitable applications.

Single-nuclei sequencing of uterine serous carcinoma reveals racial differences in immune signaling.

Significant racial disparities exist between Black and White patients with uterine serous carcinoma (USC). While the reasons for these disparities are unclear, several studies have demonstrated significantly different rates of driver mutations between racial groups, including TP53. However, limited research has investigated the transcriptional differences of tumors or the composition of the tumor microenvironment (TME) between these groups. Here, we report the single-nuclei RNA-sequencing profiles of primary USC tumors from diverse racial backgrounds. We find that there are significant differences between the tumors of Black and White patients. Tumors from Black patients exhibited higher expression of specific genes associated with aggressiveness, such as PAX8, and axon guidance and synaptic signaling pathways. We also demonstrated that T cell populations are reduced in the tumor tissue compared to matched benign, while anti-inflammatory macrophage populations are retained within the TME. Furthermore, we investigated the connection between PAX8 overexpression and immunosuppression in USC through regulation of several cytokines and chemokines. Notably, we show that PAX8 activity can influence macrophage gene expression and protein secretion. These studies provide a detailed understanding of the USC transcriptome and TME, and identify differences in tumor biology from patients of different racial backgrounds.

Cancer-on-a-chip model shows that the adenomatous polyposis coli mutation impairs T cell engagement and killing of cancer spheroids.

Evaluating the ability of cytotoxic T lymphocytes (CTLs) to eliminate tumor cells is crucial, for instance, to predict the efficiency of cell therapy in personalized medicine. However, the destruction of a tumor by CTLs involves CTL migration in the extra-tumoral environment, accumulation on the tumor, antigen recognition, and cooperation in killing the cancer cells. Therefore, identifying the limiting steps in this complex process requires spatio-temporal measurements of different cellular events over long periods. Here, we use a cancer-on-a-chip platform to evaluate the impact of adenomatous polyposis coli (APC) mutation on CTL migration and cytotoxicity against 3D tumor spheroids. The APC mutated CTLs are found to have a reduced ability to destroy tumor spheroids compared with control cells, even though APC mutants migrate in the extra-tumoral space and accumulate on the spheroids as efficiently as control cells. Once in contact with the tumor however, mutated CTLs display reduced engagement with the cancer cells, as measured by a metric that distinguishes different modes of CTL migration. Realigning the CTL trajectories around localized killing cascades reveals that all CTLs transition to high engagement in the 2 h preceding the cascades, which confirms that the low engagement is the cause of reduced cytotoxicity. Beyond the study of APC mutations, this platform offers a robust way to compare cytotoxic cell efficiency of even closely related cell types, by relying on a multiscale cytometry approach to disentangle complex interactions and to identify the steps that limit the tumor destruction.

Prostate-Specific Membrane Antigen: Gateway to Management of Advanced Prostate Cancer.

Prostate-specific membrane antigen (PSMA) as a transmembrane protein is overexpressed by prostate cancer (PC) cells and is accessible for binding antibodies or low-molecular-weight radioligands due to its extracellular portion. Successful targeting of PSMA began with the development of humanized J591 antibody. Due to their faster clearance compared to antibodies, small-molecule radioligands for targeted imaging and therapy of PC have been favored in recent development efforts. PSMA positron emission tomography (PET) imaging has higher diagnostic performance than conventional imaging for initial staging of high-risk PC and biochemical recurrence detection/localization. However, it remains to be demonstrated how to integrate PSMA PET imaging for therapy response assessment and as an outcome endpoint measure in clinical trials. With the recent approval of 177Lu-PSMA-617 by the US Food and Drug Administration for metastatic castration-resistant PC progressing after chemotherapy, the high value of PSMA-targeted therapy was confirmed. Compared to standard of care, PSMA-based radioligand therapy led to a better outcome and a higher quality of life. This review, focusing on the advanced PC setting, provides an overview of different approved and nonapproved PSMA-targeted imaging and therapeutic modalities and discusses the future of PSMA-targeted theranostics, also with an outlook on non-radiopharmaceutical-based PSMA-targeted therapies.

Leveraging multi-omics data to empower quantitative systems pharmacology in immuno-oncology.

Understanding the intricate interactions of cancer cells with the tumor microenvironment (TME) is a pre-requisite for the optimization of immunotherapy. Mechanistic models such as quantitative systems pharmacology (QSP) provide insights into the TME dynamics and predict the efficacy of immunotherapy in virtual patient populations/digital twins but require vast amounts of multimodal data for parameterization. Large-scale datasets characterizing the TME are available due to recent advances in bioinformatics for multi-omics data. Here, we discuss the perspectives of leveraging omics-derived bioinformatics estimates to inform QSP models and circumvent the challenges of model calibration and validation in immuno-oncology.

Mutational signatures of colorectal cancers according to distinct computational workflows.

Tumor mutational signatures have gained prominence in cancer research, yet the lack of standardized methods hinders reproducibility and robustness. Leveraging colorectal cancer (CRC) as a model, we explored the influence of computational parameters on mutational signature analyses across 230 CRC cell lines and 152 CRC patients. Results were validated in three independent datasets: 483 endometrial cancer patients stratified by mismatch repair (MMR) status, 35 lung cancer patients by smoking status and 12 patient-derived organoids (PDOs) annotated for colibactin exposure. Assessing various bioinformatic tools, reference datasets and input data sizes including whole genome sequencing, whole exome sequencing and a pan-cancer gene panel, we demonstrated significant variability in the results. We report that the use of distinct algorithms and references led to statistically different results, highlighting how arbitrary choices may induce variability in the mutational signature contributions. Furthermore, we found a differential contribution of mutational signatures between coding and intergenic regions and defined the minimum number of somatic variants required for reliable mutational signature assignment. To facilitate the identification of the most suitable workflows, we developed Comparative Mutational Signature analysis on Coding and Extragenic Regions (CoMSCER), a bioinformatic tool which allows researchers to easily perform comparative mutational signature analysis by coupling the results from several tools and public reference datasets and to assess mutational signature contributions in coding and non-coding genomic regions. In conclusion, our study provides a comparative framework to elucidate the impact of distinct computational workflows on mutational signatures.

Small Extracellular Vesicles From Infarcted and Failing Heart Accelerate Tumor Growth.

BACKGROUND: Myocardial infarction (MI) and heart failure are associated with an increased incidence of cancer. However, the mechanism is complex and unclear. Here, we aimed to test our hypothesis that cardiac small extracellular vesicles (sEVs), particularly cardiac mesenchymal stromal cell-derived sEVs (cMSC-sEVs), contribute to the link between post-MI left ventricular dysfunction (LVD) and cancer. METHODS: We purified and characterized sEVs from post-MI hearts and cultured cMSCs. Then, we analyzed cMSC-EV cargo and proneoplastic effects on several lines of cancer cells, macrophages, and endothelial cells. Next, we modeled heterotopic and orthotopic lung and breast cancer tumors in mice with post-MI LVD. We transferred cMSC-sEVs to assess sEV biodistribution and its effect on tumor growth. Finally, we tested the effects of sEV depletion and spironolactone treatment on cMSC-EV release and tumor growth. RESULTS: Post-MI hearts, particularly cMSCs, produced more sEVs with proneoplastic cargo than nonfailing hearts did. Proteomic analysis revealed unique protein profiles and higher quantities of tumor-promoting cytokines, proteins, and microRNAs in cMSC-sEVs from post-MI hearts. The proneoplastic effects of cMSC-sEVs varied with different types of cancer, with lung and colon cancers being more affected than melanoma and breast cancer cell lines. Post-MI cMSC-sEVs also activated resting macrophages into proangiogenic and protumorigenic states in vitro. At 28-day follow-up, mice with post-MI LVD developed larger heterotopic and orthotopic lung tumors than did sham-MI mice. Adoptive transfer of cMSC-sEVs from post-MI hearts accelerated the growth of heterotopic and orthotopic lung tumors, and biodistribution analysis revealed accumulating cMSC-sEVs in tumor cells along with accelerated tumor cell proliferation. sEV depletion reduced the tumor-promoting effects of MI, and adoptive transfer of cMSC-sEVs from post-MI hearts partially restored these effects. Finally, spironolactone treatment reduced the number of cMSC-sEVs and suppressed tumor growth during post-MI LVD. CONCLUSIONS: Cardiac sEVs, specifically cMSC-sEVs from post-MI hearts, carry multiple protumorigenic factors. Uptake of cMSC-sEVs by cancer cells accelerates tumor growth. Treatment with spironolactone significantly reduces accelerated tumor growth after MI. Our results provide new insight into the mechanism connecting post-MI LVD to cancer and propose a translational option to mitigate this deadly association.

OX40/OX40 ligand and its role in precision immune oncology.

Immune checkpoint inhibitors have changed the treatment landscape for various malignancies; however, their benefit is limited to a subset of patients. The immune machinery includes both mediators of suppression/immune evasion, such as PD-1, PD-L1, CTLA-4, and LAG-3, all of which can be inhibited by specific antibodies, and immune-stimulatory molecules, such as T-cell co-stimulatory receptors that belong to the tumor necrosis factor receptor superfamily (TNFRSF), including OX40 receptor (CD134; TNFRSF4), 4-1BB (CD137; TNFRSF9), and glucocorticoid-induced TNFR-related (GITR) protein (CD357; TNFRSF18). In particular, OX40 and its binding ligand OX40L (CD134L; TNFSF4; CD252) are critical for immunoregulation. When OX40 on activated T cells binds OX40L on antigen-presenting cells, T-cell activation and immune stimulation are initiated via enhanced T-cell survival, proliferation and cytotoxicity, memory T-cell formation, and abrogation of regulatory T cell (Treg) immunosuppressive functions. OX40 agonists are in clinical trials both as monotherapy and in combination with other immunotherapy agents, in particular specific checkpoint inhibitors, for cancer treatment. To date, however, only a minority of patients respond. Transcriptomic profiling reveals that OX40 and OX40L expression vary between and within tumor types, and that only ~ 17% of cancer patients have high OX40 and low OX40L, one of the expression patterns that might be theoretically amenable to OX40 agonist enhancement. Taken together, the data suggest that the OX40/OX40L machinery is a critical part of the immune stimulatory system and that understanding endogenous expression patterns of these molecules and co-existing checkpoints merits further investigation in the context of a precision immunotherapy strategy for cancer therapy.

Advancing Point-of-Care Applications with Droplet Microfluidics: From Single-Cell to Multicellular Analysis.

Recent advances in single-cell and multicellular microfluidics technology have provided powerful tools for studying cancer biology and immunology. The ability to create controlled microenvironments, perform high-throughput screenings, and monitor cellular interactions at the single-cell level has significantly advanced our understanding of tumor biology and immune responses. We discuss cutting-edge multicellular and single-cell microfluidic technologies and methodologies utilized to investigate cancer-immune cell interactions and assess the effectiveness of immunotherapies. We explore the advantages and limitations of the wide range of 3D spheroid and single-cell microfluidic models recently developed, highlighting the various approaches in device generation and applications in immunotherapy screening for potential opportunities for point-of-care approaches.

Patient-Specific, Mechanistic Models of Tumor Growth Incorporating Artificial Intelligence and Big Data.

Despite the remarkable advances in cancer diagnosis, treatment, and management over the past decade, malignant tumors remain a major public health problem. Further progress in combating cancer may be enabled by personalizing the delivery of therapies according to the predicted response for each individual patient. The design of personalized therapies requires the integration of patient-specific information with an appropriate mathematical model of tumor response. A fundamental barrier to realizing this paradigm is the current lack of a rigorous yet practical mathematical theory of tumor initiation, development, invasion, and response to therapy. We begin this review with an overview of different approaches to modeling tumor growth and treatment, including mechanistic as well as data-driven models based on big data and artificial intelligence. We then present illustrative examples of mathematical models manifesting their utility and discuss the limitations of stand-alone mechanistic and data-driven models. We then discuss the potential of mechanistic models for not only predicting but also optimizing response to therapy on a patient-specific basis. We describe current efforts and future possibilities to integrate mechanistic and data-driven models. We conclude by proposing five fundamental challenges that must be addressed to fully realize personalized care for cancer patients driven by computational models.

Liquid Biopsy Based on Cell-Free DNA and RNA.

This review delves into the rapidly evolving landscape of liquid biopsy technologies based on cell-free DNA (cfDNA) and cell-free RNA (cfRNA) and their increasingly prominent role in precision medicine. With the advent of high-throughput DNA sequencing, the use of cfDNA and cfRNA has revolutionized noninvasive clinical testing. Here, we explore the physical characteristics of cfDNA and cfRNA, present an overview of the essential engineering tools used by the field, and highlight clinical applications, including noninvasive prenatal testing, cancer testing, organ transplantation surveillance, and infectious disease testing. Finally, we discuss emerging technologies and the broadening scope of liquid biopsies to new areas of diagnostic medicine.

Upcycling HOXB13: enhancing prostate cancer detection with a novel antibody†.

Prostate cancer is one of the most prevalent and, upon metastasis, deadliest cancers in men. Timely identification is essential for effective treatment. Furthermore, accurate determination of prostatic origin is crucial for personalized therapy once the cancer has spread. However, current prostate cancer screening methods are lacking. A recent article in The Journal of Pathology addresses this issue by utilizing an improved antibody to reevaluate HOXB13 as a lineage marker for prostate cancer. The study's findings support the concept that, despite decreased expression in advanced prostate cancer, HOXB13 remains highly suitable for determining prostatic origin due to its androgen receptor independence, high specificity, and sensitivity. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Emulation of Quantitative Systems Pharmacology models to accelerate virtual population inference in immuno-oncology.

Quantitative Systems Pharmacology (QSP) models are increasingly being applied for target discovery and dose selection in immuno-oncology (IO). Typical application involves virtual trial, a simulation of a virtual population of hundreds of model instances with model inputs reflecting individual variability. While the structure of the model and initial parameterisation are based on literature describing the underlying biology, calibration of the virtual population by existing clinical data is frequently required to create tumour and patient population specific model instances. Since comparison of a virtual trial with clinical output requires hundreds of large-scale, non-linear model evaluations, the inference of a virtual population is computationally expensive, frequently becoming a bottleneck. Here, we present novel approach to virtual population inference in IO using emulation of the QSP model and an objective function based on Kolmogorov-Smirnov statistics to maximise congruence of simulated and observed clinical tumour size distributions. We sample the parameter space of a QSP IO model to collect a set of tumour growth time profiles. We evaluate performance of several machine learning approaches in interpolating these time profiles and create a surrogate model, which computes tumor growth profiles faster than the original model and allows examination of tens of millions of virtual patients. We use the surrogate model to infer a virtual population maximising congruence with the waterfall plot of a pembrolizumab clinical trial. We believe that our approach is applicable not only in QSP IO, but also in other applications where virtual populations need to be inferred for computationally expensive mechanistic models.

Genetics of prostate cancer: a review of latest evidence.

Prostate cancer (PrCa) is a largely heritable and polygenic disease. It is the most common cancer in people with prostates (PwPs) in Europe and the USA, including in PwPs of African descent. In the UK in 2020, 52% of all cancers were diagnosed at stage I or II. The National Health Service (NHS) long-term plan is to increase this to 75% by 2028, to reduce absolute incidence of late-stage disease. In the absence of a UK PrCa screening programme, we should explore how to identify those at increased risk of clinically significant PrCa.Incorporating genomics into the PrCa screening, diagnostic and treatment pathway has huge potential for transforming patient care. Genomics can increase efficiency of PrCa screening by focusing on those with genetic predisposition to cancer-which when combined with risk factors such as age and ethnicity, can be used for risk stratification in risk-based screening (RBS) programmes. The goal of RBS is to facilitate early diagnosis of clinically significant PrCa and reduce overdiagnosis/overtreatment in those unlikely to experience PrCa-related symptoms in their lifetime. Genetic testing can guide PrCa management, by identifying those at risk of lethal PrCa and enabling access to novel targeted therapies.PrCa is curable if diagnosed below stage III when most people do not experience symptoms. RBS using genetic profiling could be key here if we could show better survival outcomes (or reduction in cancer-specific mortality accounting for lead-time bias), in addition to more cost efficiency than age-based screening alone. Furthermore, PrCa outcomes in underserved communities could be optimised if genetic testing was accessible, minimising health disparities.

Ischaemic heart disease in patients with cancer.

Cardiologists are encountering a growing number of cancer patients with ischaemic heart disease (IHD). Several factors account for the interrelationship between these two conditions, in addition to improving survival rates in the cancer population. Established cardiovascular (CV) risk factors, such as hypercholesterolaemia and obesity, predispose to both IHD and cancer, through specific mechanisms and via low-grade, systemic inflammation. This latter is also fuelled by clonal haematopoiesis of indeterminate potential. Furthermore, experimental work indicates that IHD and cancer can promote one another, and the CV or metabolic toxicity of anticancer therapies can lead to IHD. The connections between IHD and cancer are reinforced by social determinants of health, non-medical factors that modify health outcomes and comprise individual and societal domains, including economic stability, educational and healthcare access and quality, neighbourhood and built environment, and social and community context. Management of IHD in cancer patients is often challenging, due to atypical presentation, increased bleeding and ischaemic risk, and worse outcomes as compared to patients without cancer. The decision to proceed with coronary revascularization and the choice of antithrombotic therapy can be difficult, particularly in patients with chronic coronary syndromes, necessitating multidisciplinary discussion that considers both general guidelines and specific features on a case by case basis. Randomized controlled trial evidence in cancer patients is very limited and there is urgent need for more data to inform clinical practice. Therefore, coexistence of IHD and cancer raises important scientific and practical questions that call for collaborative efforts from the cardio-oncology, cardiology, and oncology communities.