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Immune checkpoint inhibitors have shaped the landscape of cancer treatment. However, many patients either do not respond or suffer from later progression. Numerous proteins can control immune system activity, including multiple tumor necrosis factor (TNF) superfamily (TNFSF) and TNF receptor superfamily (TNFRSF) members; these proteins play a complex role in regulating cell survival and death, cellular differentiation, and immune system activity. Notably, TNFSF/TNFRSF molecules may display either pro-tumoral or anti-tumoral activity, or even both, depending on tumor type. Therefore, TNF is a prototype of an enigmatic two-faced mediator in oncogenesis. To date, multiple anti-TNF agents have been approved and/or included in guidelines for treating autoimmune disorders and immune-related toxicities after immune checkpoint blockade for cancer. A confirmed role for the TNFSF/TNFRSF members in treating cancer has proven more elusive. In this review, we highlight the cancer-relevant TNFSF/TNFRSF family members, focusing on the death domain-containing and co-stimulation members and their signaling pathways, as well as their complicated role in the life and death of cancer cells. [ABSTRACT FROM AUTHOR]

Background: Early cancer detection is crucial for improving patient outcomes. Molecular imaging biomarkers offer the potential for non-invasive, early-stage cancer diagnosis. Objectives: To evaluate the effectiveness and accuracy of molecular imaging biomarkers for early cancer detection across various imaging modalities and cancer types. Methods: A comprehensive search of PubMed/MEDLINE, Embase, Web of Science, Cochrane Library, and Scopus was performed, covering the period from January 2010 to December 2023. Eligibility criteria included original research articles published in English on molecular imaging biomarkers for early cancer detection in humans. The risk of bias for included studies was evaluated using the QUADAS-2 tool. The findings were synthesized through narrative synthesis, with quantitative analysis conducted where applicable. Results: In total, 50 studies were included. Positron emission tomography (PET)-based biomarkers showed the highest sensitivity (mean: 89.5%, range: 82–96%) and specificity (mean: 91.2%, range: 85–100%). Novel tracers such as [68Ga]-PSMA for prostate cancer and [18F]-FES for breast cancer demonstrated promising outcomes. Optical imaging techniques showed high specificity in intraoperative settings. Conclusions: Molecular imaging biomarkers show significant potential for improving early cancer detection. Integration into clinical practice could lead to earlier interventions and improved outcomes. Further research is needed to address standardization and cost-effectiveness. [ABSTRACT FROM AUTHOR]

Background: Continuous breakthroughs in computational algorithms have positioned AI-based models as some of the most sophisticated technologies in the healthcare system. AI shows dynamic contributions in advancing various medical fields involving data interpretation and monitoring, imaging screening and diagnosis, and treatment response and survival prediction. Despite advances in clinical oncology, more effort must be employed to tailor therapeutic plans based on each patient's unique transcriptomic profile within the precision/personalized oncology frame. Furthermore, the standard analysis method is not compatible with the comprehensive deciphering of significant data streams, thus precluding the prediction of accurate treatment options. Methodology: We proposed a novel approach that includes obtaining different tumour tissues and preparing RNA samples for comprehensive transcriptomic interpretation using specifically trained, programmed, and optimized AI-based models for extracting large data volumes, refining, and analyzing them. Next, the transcriptomic results will be scanned against an expansive drug library to predict the response of each target to the tested drugs. The obtained target-drug combination/s will be then validated using in vitro and in vivo experimental models. Finally, the best treatment combination option/s will be introduced to the patient. We also provided a comprehensive review discussing AI models' recent innovations and implementations to aid in molecular diagnosis and treatment planning. Results: The expected transcriptomic analysis generated by the AI-based algorithms will provide an inclusive genomic profile for each patient, containing statistical and bioinformatics analyses, identification of the dysregulated pathways, detection of the targeted genes, and recognition of molecular biomarkers. Subjecting these results to the prediction and pairing AI-based processes will result in statistical graphs presenting each target's likely response rate to various treatment options. Different in vitro and in vivo investigations will further validate the selection of the target drug/s pairs. Conclusions: Leveraging AI models will provide more rigorous manipulation of large-scale datasets on specific cancer care paths. Such a strategy would shape treatment according to each patient's demand, thus fortifying the avenue of personalized/precision medicine. Undoubtedly, this will assist in improving the oncology domain and alleviate the burden of clinicians in the coming decade. [ABSTRACT FROM AUTHOR]

Abstract Background Tumor-immune interactions shape a developing tumor and its tumor immune microenvironment (TIME) resulting in either well-infiltrated, immunologically inflamed tumor beds, or immune deserts with low levels of infiltration. The pre-treatment immune make-up of the TIME is associated with treatment outcome; immunologically inflamed tumors generally exhibit better responses to radio- and immunotherapy than non-inflamed tumors. However, radiotherapy is known to induce opposing immunological consequences, resulting in both immunostimulatory and inhibitory responses. In fact, it is thought that the radiation-induced tumoricidal immune response is curtailed by subsequent applications of radiation. It is thus conceivable that spatially fractionated radiotherapy (SFRT), administered through GRID blocks (SFRT-GRID) or lattice radiotherapy to create areas of low or high dose exposure, may create protective reservoirs of the tumor immune microenvironment, thereby preserving anti-tumor immune responses that are pivotal for radiation success. Methods We have developed an agent-based model (ABM) of tumor-immune interactions to investigate the immunological consequences and clinical outcomes after $$2\,\text{Gy} \times 35$$ 2 Gy × 35 whole tumor radiation therapy (WTRT) and SFRT-GRID. The ABM is conceptually calibrated such that untreated tumors escape immune surveillance and grow to clinical detection. Individual ABM simulations are initialized from four distinct multiplex immunohistochemistry (mIHC) slides, and immune related parameter rates are generated using Latin Hypercube Sampling. Results In silico simulations suggest that radiation-induced cancer cell death alone is insufficient to clear a tumor with WTRT. However, explicit consideration of radiation-induced anti-tumor immunity synergizes with radiation cytotoxicity to eradicate tumors. Similarly, SFRT-GRID is successful with radiation-induced anti-tumor immunity, and, for some pre-treatment TIME compositions and modeling parameters, SFRT-GRID might be superior to WTRT in providing tumor control. Conclusion This study demonstrates the pivotal role of the radiation-induced anti-tumor immunity. Prolonged fractionated treatment schedules may counteract early immune recruitment, which may be protected by SFRT-facilitated immune reservoirs. Different biological responses and treatment outcomes are observed based on pre-treatment TIME composition and model parameters. A rigorous analysis and model calibration for different tumor types and immune infiltration states is required before any conclusions can be drawn for clinical translation.

Background: Tumor-immune interactions shape a developing tumor and its tumor immune microenvironment (TIME) resulting in either well-infiltrated, immunologically inflamed tumor beds, or immune deserts with low levels of infiltration. The pre-treatment immune make-up of the TIME is associated with treatment outcome; immunologically inflamed tumors generally exhibit better responses to radio- and immunotherapy than non-inflamed tumors. However, radiotherapy is known to induce opposing immunological consequences, resulting in both immunostimulatory and inhibitory responses. In fact, it is thought that the radiation-induced tumoricidal immune response is curtailed by subsequent applications of radiation. It is thus conceivable that spatially fractionated radiotherapy (SFRT), administered through GRID blocks (SFRT-GRID) or lattice radiotherapy to create areas of low or high dose exposure, may create protective reservoirs of the tumor immune microenvironment, thereby preserving anti-tumor immune responses that are pivotal for radiation success. Methods: We have developed an agent-based model (ABM) of tumor-immune interactions to investigate the immunological consequences and clinical outcomes after 2 Gy × 35 whole tumor radiation therapy (WTRT) and SFRT-GRID. The ABM is conceptually calibrated such that untreated tumors escape immune surveillance and grow to clinical detection. Individual ABM simulations are initialized from four distinct multiplex immunohistochemistry (mIHC) slides, and immune related parameter rates are generated using Latin Hypercube Sampling. Results: In silico simulations suggest that radiation-induced cancer cell death alone is insufficient to clear a tumor with WTRT. However, explicit consideration of radiation-induced anti-tumor immunity synergizes with radiation cytotoxicity to eradicate tumors. Similarly, SFRT-GRID is successful with radiation-induced anti-tumor immunity, and, for some pre-treatment TIME compositions and modeling parameters, SFRT-GRID might be superior to WTRT in providing tumor control. Conclusion: This study demonstrates the pivotal role of the radiation-induced anti-tumor immunity. Prolonged fractionated treatment schedules may counteract early immune recruitment, which may be protected by SFRT-facilitated immune reservoirs. Different biological responses and treatment outcomes are observed based on pre-treatment TIME composition and model parameters. A rigorous analysis and model calibration for different tumor types and immune infiltration states is required before any conclusions can be drawn for clinical translation. [ABSTRACT FROM AUTHOR]

Purpose of Review: In contemporary urological practice, managing rare genitourinary (GU) malignancies presents significant challenges, necessitating a comprehensive understanding of their unique characteristics and tailored treatment approaches. Recent Findings: Rare GU malignancies, whether per se, variants of common histologies, or common tumors in uncommon locations, often lack widely available clinical guidelines. Consequently, treatment decisions are frequently based on empirical evidence, risking suboptimal outcomes. However, recent advances in molecular profiling, targeted therapies, and immunotherapy offer promising avenues for improving management strategies and patient outcomes. Summary: This review provides a comprehensive overview of some rare GU malignancies encountered in clinical practice, including their distinct pathological features, current management approaches, and ongoing research directions. Understanding the complexities of these rare tumors and implementing multidisciplinary treatment strategies are essential for optimizing patient care and improving survival outcomes. [ABSTRACT FROM AUTHOR]

Simple Summary: This review article explores the forefront of research and technology in the detection and isolation of circulating tumor cells (CTCs) for liquid biopsy applications. Traditional biopsies, which necessitate tissue samples, are invasive and often challenging for patients. In contrast, liquid biopsies provide a less invasive alternative, enabling the continuous monitoring of the cancer's progression and the effectiveness of treatments. Recent advancements have significantly enhanced our understanding of CTCs, underscoring their critical role in cancer research and patient management. This article aims to highlight these technological innovations and their potential to transform cancer diagnosis, prognosis, and personalized therapy, offering new hope for improved patient outcomes in the fight against cancer. Cancer remains a leading cause of mortality worldwide, with metastasis significantly contributing to its lethality. The metastatic spread of tumor cells, primarily through the bloodstream, underscores the importance of circulating tumor cells (CTCs) in oncological research. As a critical component of liquid biopsies, CTCs offer a non-invasive and dynamic window into tumor biology, providing invaluable insights into cancer dissemination, disease progression, and response to treatment. This review article delves into the recent advancements in CTC research, highlighting their emerging role as a biomarker in various cancer types. We explore the latest technologies and methods for CTC isolation and detection, alongside novel approaches to characterizing their biology through genomics, transcriptomics, proteomics, and epigenetic profiling. Additionally, we examine the clinical implementation of these findings, assessing how CTCs are transforming the landscape of cancer diagnosis, prognosis, and management. By offering a comprehensive overview of current developments and potential future directions, this review underscores the significance of CTCs in enhancing our understanding of cancer and in shaping personalized therapeutic strategies, particularly for patients with metastatic disease. [ABSTRACT FROM AUTHOR]

Background: Early cancer detection is crucial for improving patient outcomes. Molecular imaging biomarkers offer the potential for non-invasive, early-stage cancer diagnosis. Objectives: To evaluate the effectiveness and accuracy of molecular imaging biomarkers for early cancer detection across various imaging modalities and cancer types. Methods: A comprehensive search of PubMed/MEDLINE, Embase, Web of Science, Cochrane Library, and Scopus was performed, covering the period from January 2010 to December 2023. Eligibility criteria included original research articles published in English on molecular imaging biomarkers for early cancer detection in humans. The risk of bias for included studies was evaluated using the QUADAS-2 tool. The findings were synthesized through narrative synthesis, with quantitative analysis conducted where applicable. Results: In total, 50 studies were included. Positron emission tomography (PET)-based biomarkers showed the highest sensitivity (mean: 89.5%, range: 82–96%) and specificity (mean: 91.2%, range: 85–100%). Novel tracers such as [68Ga]-PSMA for prostate cancer and [18F]-FES for breast cancer demonstrated promising outcomes. Optical imaging techniques showed high specificity in intraoperative settings. Conclusions: Molecular imaging biomarkers show significant potential for improving early cancer detection. Integration into clinical practice could lead to earlier interventions and improved outcomes. Further research is needed to address standardization and cost-effectiveness.

Early detection of disease progression is a crucial issue in the management of cancer patients, especially in metastatic settings. Currently, treatment selection mostly relies on criteria based on radiologic evaluations (RECIST). The aim of the present retrospective study is to evaluate the potential inclusion of circulating tumor cells (CTCs) in hybrid criteria. CTC counts from a total of 160 patients with different metastatic tumors were analyzed for this purpose. In our cohort, 73 patients were affected by breast cancer, 69 by colorectal cancer and 18 by prostate cancer. PFS and OS were evaluated according to the corresponding prediction of disease progression by CTCs and RECIST criteria. In breast cancer, CTC-I has an important impact on the progression-free survival (PFS) and overall survival (OS) values. When CTC-I predicted earlier than RECIST-I, the disease progression, the PFS and OS were shorter with respect to the opposite case. In particular, PFS was 11 (5–16) vs. 34 (23–45)—with p < 0.001—and OS was 80 (22–138) vs. 116 (43–189), p = 0.33. The results suggest a promising role of CTCs as complementary information which could significantly improve the clinical outcomes, and they encourage consideration of future trials to evaluate new hybrid criteria, particularly for patients with breast cancer. [ABSTRACT FROM AUTHOR]

The progression and the metastatic potential of colorectal cancer (CRC) are intricately linked to the epithelial–mesenchymal transition (EMT) process. The present study harnesses the power of machine learning combined with multi‐omics data to develop a risk stratification model anchored on EMT‐associated genes. The aim is to facilitate personalized prognostic assessments in CRC. We utilized publicly accessible gene expression datasets to pinpoint EMT‐associated genes, employing a CoxBoost algorithm to sift through these genes for prognostic significance. The resultant model, predicated on gene expression levels, underwent rigorous independent validation across various datasets. Our model demonstrated a robust capacity to segregate CRC patients into distinct high‐ and low‐risk categories, each correlating with markedly different survival probabilities. Notably, the risk score emerged as an independent prognostic indicator for CRC. High‐risk patients were characterized by an immunosuppressive tumor milieu and a heightened responsiveness to certain chemotherapeutic agents, underlining the model's potential in steering tailored oncological therapies. Moreover, our research unearthed a putative repressive interaction between the long non‐coding RNA PVT1 and the EMT‐associated genes TIMP1 and MMP1, offering new insights into the molecular intricacies of CRC. In essence, our research introduces a sophisticated risk model, leveraging machine learning and multi‐omics insights, which accurately prognosticates outcomes for CRC patients, paving the way for more individualized and effective oncological treatment paradigms. [ABSTRACT FROM AUTHOR]

Background and purpose: In this manuscript we describe the academic French multicentric molecular analysis platforms including PROFILER, promoted by Centre Léon Berard, and the multicentric personalized medicine trials MOST, MOST Plus and MEGAMOST. Patients/material and methods: MOST, MOST Plus and MEGAMOST comprise 14 cohorts with different targeted agents and immunotherapies. Results and interpretation: PROFILER has recruited 5,991 patients in 10 years, MOST and MOST Plus 875 patients since 2014 and MEGAMOST 172 patients since 2020, and are still ongoing. We provide a description of the local, national and international implications of these initiatives, and we review the results of the sorafenib and olaparib cohorts.

This article explores the advancements in radiotherapy for head and neck cancer (HNC). It discusses the use of more precise delivery techniques, hadrontherapy, and artificial intelligence (AI) support in modern radiotherapy, which have improved the safety and effectiveness of treatments. The article also covers topics such as de-escalation in treatment, multidisciplinary care paths, and advancements in machine learning and radiomics. The research includes 14 articles that discuss clinical and technological advancements in radiotherapy for HNC. The article specifically highlights the use of radiomic and dosiomic analysis in predicting the risk of recurrence and treatment-related toxicity in HNC patients. [Extracted from the article]

Background: Continuous breakthroughs in computational algorithms have positioned AI-based models as some of the most sophisticated technologies in the healthcare system. AI shows dynamic contributions in advancing various medical fields involving data interpretation and monitoring, imaging screening and diagnosis, and treatment response and survival prediction. Despite advances in clinical oncology, more effort must be employed to tailor therapeutic plans based on each patient’s unique transcriptomic profile within the precision/personalized oncology frame. Furthermore, the standard analysis method is not compatible with the comprehensive deciphering of significant data streams, thus precluding the prediction of accurate treatment options. Methodology: We proposed a novel approach that includes obtaining different tumour tissues and preparing RNA samples for comprehensive transcriptomic interpretation using specifically trained, programmed, and optimized AI-based models for extracting large data volumes, refining, and analyzing them. Next, the transcriptomic results will be scanned against an expansive drug library to predict the response of each target to the tested drugs. The obtained target-drug combination/s will be then validated using in vitro and in vivo experimental models. Finally, the best treatment combination option/s will be introduced to the patient. We also provided a comprehensive review discussing AI models’ recent innovations and implementations to aid in molecular diagnosis and treatment planning. Results: The expected transcriptomic analysis generated by the AI-based algorithms will provide an inclusive genomic profile for each patient, containing statistical and bioinformatics analyses, identification of the dysregulated pathways, detection of the targeted genes, and recognition of molecular biomarkers. Subjecting these results to the prediction and pairing AI-based processes will result in statistical graphs presenting each target’s likely response rate to various treatment options. Different in vitro and in vivo investigations will further validate the selection of the target drug/s pairs. Conclusions: Leveraging AI models will provide more rigorous manipulation of large-scale datasets on specific cancer care paths. Such a strategy would shape treatment according to each patient’s demand, thus fortifying the avenue of personalized/precision medicine. Undoubtedly, this will assist in improving the oncology domain and alleviate the burden of clinicians in the coming decade.

Abstract Background Inflammatory myofibroblastic tumor (IMTs) are rare mesenchymal neoplasms with slow growth. Resection is considered as therapeutic standard, with chemotherapy being insufficiently effective in advanced disease. ALK translocations are present in 50% of cases, ROS1 fusions (YWHAE::ROS1, TFG::ROS1) are extremely rare. Here, we present a case with TFG::ROS1 fusion and highlight the significance of molecular tumor boards (MTBs) in clinical precision oncology for post‐last‐line therapy. Case Presentation A 32‐year‐old woman presented with IMT diagnosed at age 27 for biopsy and treatment evaluation. Previous treatments included multiple resections and systemic therapy with vinblastine, cyclophosphamide, and methotrexate. A computed tomography scan showed extensive tumor infiltration of the psoas muscles and the posterior abdomen. Next generation sequencing revealed an actionable ROS1 fusion (TFG::ROS1) with breakpoints at exon 4/35 including the kinase domain and activating the RAS‐pathway. TFG, the Trk‐fused gene, exerts functions such as intracellular trafficking and exhibits high sequence homology between species. Based on single reports about efficacy of ROS1‐targeting in ROS1 translocation positive IMTs the patient was started on crizotinib, an ATP‐competitive small molecule c‐MET, ALK and ROS1‐inhibitor. With a follow‐up of more than 9 months, the patient continues to show a profound response with major tumor regression, improved quality of life and no evidence for severe adverse events. Conclusion This case underscores the importance of the availability of modern molecular diagnostics and interdisciplinarity in precision oncology to identify rare, disease‐defining genotypes that make an otherwise difficult‐to‐treat disease targetable.

Fusions and mutations of anaplastic lymphoma kinase (ALK), a tyrosine kinase receptor, have been identified in several neoplastic diseases. Rearranged ALK is a driver of tumorigenesis, which activates various signaling pathway associated with proliferation and survival. To date, several agents that target and inhibit ALK have been developed. The most studied ALK-positive disease is non-small cell lung cancer, and three generations of ALK tyrosine kinase inhibitors (TKIs) have been approved for the treatment of metastatic disease. Nevertheless, the use of ALK-TKIs is associated with acquired resistance (resistance mutations, bypass signaling), which leads to disease progression and may require a substitution or introduction of other treatment agents. Understanding of the complex nature and network of resistance mutations may allow to introduce sequential and targeted therapies. In this review, we aim to summarize the efficacy and safety profile of ALK inhibitors, describe off-target anticancer effects, and discuss resistance mechanisms in the context of personalized oncology. [ABSTRACT FROM AUTHOR]

Introduction: Radiation therapy (RT) is one of the most common anticancer therapies. Yet, current radiation oncology practice does not adapt RT dose for individual patients, despite wide interpatient variability in radiosensitivity and accompanying treatment response. We have previously shown that mechanistic mathematical modeling of tumor volume dynamics can simulate volumetric response to RT for individual patients and estimation personalized RT dose for optimal tumor volume reduction. However, understanding the implications of the choice of the underlying RT response model is critical when calculating personalized RT dose. Methods: In this study, we evaluate the mathematical implications and biological effects of 2 models of RT response on dose personalization: (1) cytotoxicity to cancer cells that lead to direct tumor volume reduction (DVR) and (2) radiation responses to the tumor microenvironment that lead to tumor carrying capacity reduction (CCR) and subsequent tumor shrinkage. Tumor growth was simulated as logistic growth with pre-treatment dynamics being described in the proliferation saturation index (PSI). The effect of RT was simulated according to each respective model for a standard schedule of fractionated RT with 2 Gy weekday fractions. Parameter sweeps were evaluated for the intrinsic tumor growth rate and the radiosensitivity parameter for both models to observe the qualitative impact of each model parameter. We then calculated the minimum RT dose required for locoregional tumor control (LRC) across all combinations of the full range of radiosensitvity and proliferation saturation values. Results: Both models estimate that patients with higher radiosensitivity will require a lower RT dose to achieve LRC. However, the two models make opposite estimates on the impact of PSI on the minimum RT dose for LRC: the DVR model estimates that tumors with higher PSI values will require a higher RT dose to achieve LRC, while the CCR model estimates that higher PSI values will require a lower RT dose to achieve LRC. Discussion: Ultimately, these results show the importance of understanding which model best describes tumor growth and treatment response in a particular setting, before using any such model to make estimates for personalized treatment recommendations. [ABSTRACT FROM AUTHOR]

Precision cancer medicine is a multidisciplinary team effort that requires involvement and commitment of many stakeholders including the society at large. Building on the success of significant advances in precision therapy for oncological patients over the last two decades, future developments will be significantly shaped by improvements in scalable molecular diagnostics in which increasingly complex multilayered datasets require transformation into clinically useful information guiding patient management at fast turnaround times. Adaptive profiling strategies involving tissue‐ and liquid‐based testing that account for the immense plasticity of cancer during the patient's journey and also include early detection approaches are already finding their way into clinical routine and will become paramount. A second major driver is the development of smart clinical trials and trial concepts which, complemented by real‐world evidence, rapidly broaden the spectrum of therapeutic options. Tight coordination with regulatory agencies and health technology assessment bodies is crucial in this context. Multicentric networks operating nationally and internationally are key in implementing precision oncology in clinical practice and support developing and improving the ecosystem and framework needed to turn invocation into benefits for patients. The review provides an overview of the diagnostic tools, innovative clinical studies, and collaborative efforts needed to realize precision cancer medicine. [ABSTRACT FROM AUTHOR]

Targeted next-generation sequencing (NGS) enables the identification of genomic variants in cancer patients with high sensitivity at relatively low costs, and has thus opened the era to personalized human oncology. Veterinary medicine tends to adopt new technologies at a slower pace compared to human medicine due to lower funding, nonetheless it embraces technological advancements over time. Hence, it is reasonable to assume that targeted NGS will be incorporated into routine veterinary practice in the foreseeable future. Many animal diseases have well-researched human counterparts and hence, insights gained from the latter might, in principle, be harnessed to elucidate the former. Here, we present the TiHoCL targeted NGS panel as a proof of concept, exemplifying how functional genomics and network approaches can be effectively used to leverage the wealth of information available for human diseases in the development of targeted sequencing panels for veterinary medicine. Specifically, the TiHoCL targeted NGS panel is a molecular tool for characterizing and stratifying canine lymphoma (CL) patients designed based on human non-Hodgkin lymphoma (NHL) research outputs. While various single nucleotide polymorphisms (SNPs) have been associated with high risk of developing NHL, poor prognosis and resistance to treatment in NHL patients, little is known about the genetics of CL. Thus, the ~100 SNPs featured in the TiHoCL targeted NGS panel were selected using functional genomics and network approaches following a literature and database search that shielded ~500 SNPs associated with, in nearly all cases, human hematologic malignancies. The TiHoCL targeted NGS panel underwent technical validation and preliminary functional assessment by sequencing DNA samples isolated from blood of 29 lymphoma dogs using an Ion Torrent™ PGM System achieving good sequencing run metrics. Our design framework holds new possibilities for the design of similar molecular tools applied to other diseases for which limited knowledge is available and will improve drug target discovery and patient care.

Fusions and mutations of anaplastic lymphoma kinase (ALK), a tyrosine kinase receptor, have been identified in several neoplastic diseases. Rearranged ALK is a driver of tumorigenesis, which activates various signaling pathway associated with proliferation and survival. To date, several agents that target and inhibit ALK have been developed. The most studied ALK-positive disease is non-small cell lung cancer, and three generations of ALK tyrosine kinase inhibitors (TKIs) have been approved for the treatment of metastatic disease. Nevertheless, the use of ALK-TKIs is associated with acquired resistance (resistance mutations, bypass signaling), which leads to disease progression and may require a substitution or introduction of other treatment agents. Understanding of the complex nature and network of resistance mutations may allow to introduce sequential and targeted therapies. In this review, we aim to summarize the efficacy and safety profile of ALK inhibitors, describe off-target anticancer effects, and discuss resistance mechanisms in the context of personalized oncology.

IntroductionRadiation therapy (RT) is one of the most common anticancer therapies. Yet, current radiation oncology practice does not adapt RT dose for individual patients, despite wide interpatient variability in radiosensitivity and accompanying treatment response. We have previously shown that mechanistic mathematical modeling of tumor volume dynamics can simulate volumetric response to RT for individual patients and estimation personalized RT dose for optimal tumor volume reduction. However, understanding the implications of the choice of the underlying RT response model is critical when calculating personalized RT dose.MethodsIn this study, we evaluate the mathematical implications and biological effects of 2 models of RT response on dose personalization: (1) cytotoxicity to cancer cells that lead to direct tumor volume reduction (DVR) and (2) radiation responses to the tumor microenvironment that lead to tumor carrying capacity reduction (CCR) and subsequent tumor shrinkage. Tumor growth was simulated as logistic growth with pre-treatment dynamics being described in the proliferation saturation index (PSI). The effect of RT was simulated according to each respective model for a standard schedule of fractionated RT with 2 Gy weekday fractions. Parameter sweeps were evaluated for the intrinsic tumor growth rate and the radiosensitivity parameter for both models to observe the qualitative impact of each model parameter. We then calculated the minimum RT dose required for locoregional tumor control (LRC) across all combinations of the full range of radiosensitvity and proliferation saturation values. ResultsBoth models estimate that patients with higher radiosensitivity will require a lower RT dose to achieve LRC. However, the two models make opposite estimates on the impact of PSI on the minimum RT dose for LRC: the DVR model estimates that tumors with higher PSI values will require a higher RT dose to achieve LRC, while the CCR model estimates that higher PSI values will require a lower RT dose to achieve LRC.DiscussionUltimately, these results show the importance of understanding which model best describes tumor growth and treatment response in a particular setting, before using any such model to make estimates for personalized treatment recommendations.

Simple Summary: Cancer cells are dependent on normal cells for their survival and functionality because they can use nanoscale tubes to steal the mitochondria from immune cells. It also highlights the significance of mitochondria in the biology of cancer cells as the key organelles for cellular metabolism and energy generation. Recent research has shown that mitochondria are critical for cancer cell survival in the hostile tumor microenvironments, immune system evasion, acquisition of more aggressive characteristics, and treatment resistance. This article discusses the role of mitochondrial metabolism in cancer biology, customized cancer therapy, and how it affects cancer resistance to chemotherapy, immunotherapy, and radiation. For instance, by scavenging the produced reactive oxygen species, functioning mitochondria might enhance cancer resistance to radiation. According to this hypothesis, targeting mitochondria may improve oncological results. The tumors can respond completely to anticancer therapies or even experience malignant progression while receiving them. As a result, individualized cancer treatment is essential. Up until now, genetic analysis has been the foundation for customized cancer treatment. There is evidence that cancers with a high mitochondrial concentration are more difficult to cure. Evaluation of mitochondrial metabolism before therapy may supplement genetic data and enhance the personalization of oncological interventions. Energy is needed by cancer cells to stay alive and communicate with their surroundings. The primary organelles for cellular metabolism and energy synthesis are mitochondria. Researchers recently proved that cancer cells can steal immune cells' mitochondria using nanoscale tubes. This finding demonstrates the dependence of cancer cells on normal cells for their living and function. It also denotes the importance of mitochondria in cancer cells' biology. Emerging evidence has demonstrated how mitochondria are essential for cancer cells to survive in the harsh tumor microenvironments, evade the immune system, obtain more aggressive features, and resist treatments. For instance, functional mitochondria can improve cancer resistance against radiotherapy by scavenging the released reactive oxygen species. Therefore, targeting mitochondria can potentially enhance oncological outcomes, according to this notion. The tumors' responses to anticancer treatments vary, ranging from a complete response to even cancer progression during treatment. Therefore, personalized cancer treatment is of crucial importance. So far, personalized cancer treatment has been based on genomic analysis. Evidence shows that tumors with high mitochondrial content are more resistant to treatment. This paper illustrates how mitochondrial metabolism can participate in cancer resistance to chemotherapy, immunotherapy, and radiotherapy. Pretreatment evaluation of mitochondrial metabolism can provide additional information to genomic analysis and can help to improve personalized oncological treatments. This article outlines the importance of mitochondrial metabolism in cancer biology and personalized treatments. [ABSTRACT FROM AUTHOR]

Introduction: Radiotherapy is a mainstay of cancer treatment. Clinical studies revealed a heterogenous response to radiotherapy, from a complete response to even disease progression. To that end, finding the relative prognostic factors of disease outcomes and predictive factors of treatment efficacy and toxicity is essential. It has been demonstrated that radiation response depends on DNA damage response, cell cycle phase, oxygen concentration, and growth rate. Emerging evidence suggests that altered mitochondrial metabolism is associated with radioresistance. Methods: This article provides a comprehensive evaluation of the role of mitochondria in radiotherapy efficacy and toxicity. In addition, it demonstrates how mitochondria might be involved in the famous 6Rs of radiobiology. Results: In terms of this idea, decreasing the mitochondrial metabolism of cancer cells may increase radiation response, and enhancing the mitochondrial metabolism of normal cells may reduce radiation toxicity. Enhancing the normal cells (including immune cells) mitochondrial metabolism can potentially improve the tumor response by enhancing immune reactivation. Future studies are invited to examine the impacts of mitochondrial metabolism on radiation efficacy and toxicity. Improving radiotherapy response with diminishing cancer cells' mitochondrial metabolism, and reducing radiotherapy toxicity with enhancing normal cells' mitochondrial metabolism. [ABSTRACT FROM AUTHOR]

Abstract Background Personalized oncology represents a shift in cancer treatment from conventional methods to target specific therapies where the decisions are made based on the patient specific tumor profile. Selection of the optimal therapy relies on a complex interdisciplinary analysis and interpretation of these variants by experts in molecular tumor boards. With up to hundreds of somatic variants identified in a tumor, this process requires visual analytics tools to guide and accelerate the annotation process. Results The Personal Cancer Network Explorer (PeCaX) is a visual analytics tool supporting the efficient annotation, navigation, and interpretation of somatic genomic variants through functional annotation, drug target annotation, and visual interpretation within the context of biological networks. Starting with somatic variants in a VCF file, PeCaX enables users to explore these variants through a web-based graphical user interface. The most protruding feature of PeCaX is the combination of clinical variant annotation and gene-drug networks with an interactive visualization. This reduces the time and effort the user needs to invest to get to a treatment suggestion and helps to generate new hypotheses. PeCaX is being provided as a platform-independent containerized software package for local or institution-wide deployment. PeCaX is available for download at https://github.com/KohlbacherLab/PeCaX-docker .

Abstract Background Cancer-associated fibroblasts (CAFs) are considered to play a fundamental role in pancreatic ductal adenocarcinoma (PDAC) progression and chemoresistance. Patient-derived organoids have demonstrated great potential as tumor avatars for drug response prediction in PDAC, yet they disregard the influence of stromal components on chemosensitivity. Methods We established direct three-dimensional (3D) co-cultures of primary PDAC organoids and patient-matched CAFs to investigate the effect of the fibroblastic compartment on sensitivity to gemcitabine, 5-fluorouracil and paclitaxel treatments using an image-based drug assay. Single-cell RNA sequencing was performed for three organoid/CAF pairs in mono- and co-culture to uncover transcriptional changes induced by tumor-stroma interaction. Results Upon co-culture with CAFs, we observed increased proliferation and reduced chemotherapy-induced cell death of PDAC organoids. Single-cell RNA sequencing data evidenced induction of a pro-inflammatory phenotype in CAFs in co-cultures. Organoids showed increased expression of genes associated with epithelial-to-mesenchymal transition (EMT) in co-cultures and several potential receptor-ligand interactions related to EMT were identified, supporting a key role of CAF-driven induction of EMT in PDAC chemoresistance. Conclusions Our results demonstrate the potential of personalized PDAC co-cultures models not only for drug response profiling but also for unraveling the molecular mechanisms involved in the chemoresistance-supporting role of the tumor stroma.

Early detection of disease progression is a crucial issue in the management of cancer patients, especially in metastatic settings. Currently, treatment selection mostly relies on criteria based on radiologic evaluations (RECIST). The aim of the present retrospective study is to evaluate the potential inclusion of circulating tumor cells (CTCs) in hybrid criteria. CTC counts from a total of 160 patients with different metastatic tumors were analyzed for this purpose. In our cohort, 73 patients were affected by breast cancer, 69 by colorectal cancer and 18 by prostate cancer. PFS and OS were evaluated according to the corresponding prediction of disease progression by CTCs and RECIST criteria. In breast cancer, CTC-I has an important impact on the progression-free survival (PFS) and overall survival (OS) values. When CTC-I predicted earlier than RECIST-I, the disease progression, the PFS and OS were shorter with respect to the opposite case. In particular, PFS was 11 (5–16) vs. 34 (23–45)—with p < 0.001—and OS was 80 (22–138) vs. 116 (43–189), p = 0.33. The results suggest a promising role of CTCs as complementary information which could significantly improve the clinical outcomes, and they encourage consideration of future trials to evaluate new hybrid criteria, particularly for patients with breast cancer.

Background: Personalized oncology represents a shift in cancer treatment from conventional methods to target specific therapies where the decisions are made based on the patient specific tumor profile. Selection of the optimal therapy relies on a complex interdisciplinary analysis and interpretation of these variants by experts in molecular tumor boards. With up to hundreds of somatic variants identified in a tumor, this process requires visual analytics tools to guide and accelerate the annotation process. Results: The Personal Cancer Network Explorer (PeCaX) is a visual analytics tool supporting the efficient annotation, navigation, and interpretation of somatic genomic variants through functional annotation, drug target annotation, and visual interpretation within the context of biological networks. Starting with somatic variants in a VCF file, PeCaX enables users to explore these variants through a web-based graphical user interface. The most protruding feature of PeCaX is the combination of clinical variant annotation and gene-drug networks with an interactive visualization. This reduces the time and effort the user needs to invest to get to a treatment suggestion and helps to generate new hypotheses. PeCaX is being provided as a platform-independent containerized software package for local or institution-wide deployment. PeCaX is available for download at https://github.com/KohlbacherLab/PeCaX-docker. [ABSTRACT FROM AUTHOR]

Engineering and computational advances have opened many new avenues in cancer research, particularly when being exploited in interdisciplinary approaches. For example, the combination of microfluidics, novel sequencing technologies, and computational analyses has been crucial to enable single-cell assays, giving a detailed picture of tumor heterogeneity for the very first time. In a similar way, these 'tech' disciplines have been elementary for generating large data sets in multidimensional cancer 'omics' approaches, cell–cell interaction screens, 3D tumor models, and tissue level analyses. In this review we summarize the most important technology and computational developments that have been or will be instrumental for transitioning classical cancer research to a large data-driven, high-throughput, high-content discipline across all biological scales. Advanced sequencing and barcoding technologies allow the analysis of tumor heterogeneity and evolution by combining multi-omics data sets and including spatial information. Miniaturized microfluidic assay formats can be exploited to obtain detectable concentrations of analytes from single cells and to perform large-scale drug screens on very limited patient material. Microfluidics allows the analysis of the tumor microenvironment (TME) by pairing cells for interaction studies and by generating highly complex and geometrically defined tumor models in vitro. Advances in computational analysis provide essential tools to integrate different data modalities, allowing a holistic description of the tumor. Machine learning facilitates the identification of relevant patterns in high-throughput data, thus enabling their use for cancer diagnosis and prognosis. [ABSTRACT FROM AUTHOR]

Background: Cancer-associated fibroblasts (CAFs) are considered to play a fundamental role in pancreatic ductal adenocarcinoma (PDAC) progression and chemoresistance. Patient-derived organoids have demonstrated great potential as tumor avatars for drug response prediction in PDAC, yet they disregard the influence of stromal components on chemosensitivity. Methods: We established direct three-dimensional (3D) co-cultures of primary PDAC organoids and patient-matched CAFs to investigate the effect of the fibroblastic compartment on sensitivity to gemcitabine, 5-fluorouracil and paclitaxel treatments using an image-based drug assay. Single-cell RNA sequencing was performed for three organoid/CAF pairs in mono- and co-culture to uncover transcriptional changes induced by tumor-stroma interaction. Results: Upon co-culture with CAFs, we observed increased proliferation and reduced chemotherapy-induced cell death of PDAC organoids. Single-cell RNA sequencing data evidenced induction of a pro-inflammatory phenotype in CAFs in co-cultures. Organoids showed increased expression of genes associated with epithelial-to-mesenchymal transition (EMT) in co-cultures and several potential receptor-ligand interactions related to EMT were identified, supporting a key role of CAF-driven induction of EMT in PDAC chemoresistance. Conclusions: Our results demonstrate the potential of personalized PDAC co-cultures models not only for drug response profiling but also for unraveling the molecular mechanisms involved in the chemoresistance-supporting role of the tumor stroma. [ABSTRACT FROM AUTHOR]

Immune checkpoint inhibitors have shaped the landscape of cancer treatment. However, many patients either do not respond or suffer from later progression. Numerous proteins can control immune system activity, including multiple tumor necrosis factor (TNF) superfamily (TNFSF) and TNF receptor superfamily (TNFRSF) members; these proteins play a complex role in regulating cell survival and death, cellular differentiation, and immune system activity. Notably, TNFSF/TNFRSF molecules may display either pro-tumoral or anti-tumoral activity, or even both, depending on tumor type. Therefore, TNF is a prototype of an enigmatic two-faced mediator in oncogenesis. To date, multiple anti-TNF agents have been approved and/or included in guidelines for treating autoimmune disorders and immune-related toxicities after immune checkpoint blockade for cancer. A confirmed role for the TNFSF/TNFRSF members in treating cancer has proven more elusive. In this review, we highlight the cancer-relevant TNFSF/TNFRSF family members, focusing on the death domain-containing and co-stimulation members and their signaling pathways, as well as their complicated role in the life and death of cancer cells., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Over the past two decades, cancer treatment has benefited from having a significant increase in the number of targeted drugs approved by the United States Food and Drug Administration. With the introduction of targeted therapy, a great shift towards a new era has taken place that is characterized by reduced cytotoxicity and improved clinical outcomes compared to traditional chemotherapeutic drugs. At present, targeted therapies and other systemic anti-cancer therapies available (immunotherapy, cytotoxic, endocrine therapies and others) are used alone or in combination in different settings (neoadjuvant, adjuvant, and metastatic). As a result, it is not uncommon for patients affected by an advanced malignancy to receive subsequent anti-cancer therapies. In this challenging complexity of cancer treatment, the clinical pathways of real-life patients are often not as direct as predicted by standard guidelines and clinical trials, and cross-resistance among sequential anti-cancer therapies represents an emerging issue. In this review, we summarize the main cross-resistance events described in the diverse tumor types and provide insight into the molecular mechanisms involved in this process. We also discuss the current challenges and provide perspectives for the research and development of strategies to overcome cross-resistance and proceed towards a personalized approach. [ABSTRACT FROM AUTHOR]

Simple Summary: Usually, for dose planning in radiotherapy, the tumor is delimited as a volume on the image of the patient together with other clinical considerations based on populational evidence. However, the same prescription dose can provide different results, depending on the patient. Unfortunately, the biological aspects of the tumor are hardly considered in dose planning. Boron Neutron Capture Radiotherapy enables targeted treatment by incorporating boron-10 at the cellular level and irradiating with neutrons of a certain energy so that they produce nuclear reactions locally and almost exclusively damage the tumor cell. This technique is not new, but modern neutron generators and more efficient boron carriers have reactivated the clinical interest of this technique in the pursuit of more precise treatments. In this work, we review the latest technological facilities and future possibilities for the clinical implementation of BNCT and for turning it into a personalized therapy. Boron Neutron Capture Therapy (BNCT) is a promising binary disease-targeted therapy, as neutrons preferentially kill cells labeled with boron (10B), which makes it a precision medicine treatment modality that provides a therapeutic effect exclusively on patient-specific tumor spread. Contrary to what is usual in radiotherapy, BNCT proposes cell-tailored treatment planning rather than to the tumor mass. The success of BNCT depends mainly on the sufficient spatial biodistribution of 10B located around or within neoplastic cells to produce a high-dose gradient between the tumor and healthy tissue. However, it is not yet possible to precisely determine the concentration of 10B in a specific tissue in real-time using non-invasive methods. Critical issues remain to be resolved if BNCT is to become a valuable, minimally invasive, and efficient treatment. In addition, functional imaging technologies, such as PET, can be applied to determine biological information that can be used for the combined-modality radiotherapy protocol for each specific patient. Regardless, not only imaging methods but also proteomics and gene expression methods will facilitate BNCT becoming a modality of personalized medicine. This work provides an overview of the fundamental principles, recent advances, and future directions of BNCT as cell-targeted cancer therapy for personalized radiation treatment. [ABSTRACT FROM AUTHOR]

An obstacle to effective uniform treatment of glioblastoma, especially at recurrence, is genetic and cellular intertumoral heterogeneity. Hence, personalized strategies are necessary, as are means to stratify potential targeted therapies in a clinically relevant timeframe. Functional profiling of drug candidates against patient-derived glioblastoma organoids (PD-GBO) holds promise as an empirical method to preclinically discover potentially effective treatments of individual tumors. Here, we describe our establishment of a PD-GBO-based functional profiling platform and the results of its application to four patient tumors. We show that our PD-GBO model system preserves key features of individual patient glioblastomas in vivo. As proof of concept, we tested a panel of 41 FDA-approved drugs and were able to identify potential treatment options for three out of four patients; the turnaround from tumor resection to discovery of treatment option was 13, 14, and 15 days, respectively. These results demonstrate that this approach is a complement and, potentially, an alternative to current molecular profiling efforts in the pursuit of effective personalized treatment discovery in a clinically relevant time period. Furthermore, these results warrant the use of PD-GBO platforms for preclinical identification of new drugs against defined morphological glioblastoma features. [ABSTRACT FROM AUTHOR]

In Molecular Tumor Boards, a team of experts discuss the individual therapy options of a cancer patient based on their individual molecular profile. The process—from recommendation request, through molecular diagnosis, to a personalized therapy recommendation—is complex and time-consuming. Therefore, process optimization is needed to decrease the workload of physicians and to standardize the process. For this purpose, we modeled the current workflow of the Molecular Tumor Board at the University Hospital Hamburg-Eppendorf on Service-Oriented Architecture using Business Process Modeling and Notation to highlight areas for improvement. This identified many manual tasks and an extensive workload for the physician. We then created a novel, simplified, more efficient workflow in which the physician is supported by additional software. In summary, we show that the use of Service-Oriented Architecture using Business Process Modeling and Notation for Molecular Tumor Board processes promotes rapid adaptability, standardization, interoperability, quality assurance, and facilitates collaboration. [ABSTRACT FROM AUTHOR]

Over the past two decades, cancer treatment has benefited from having a significant increase in the number of targeted drugs approved by the United States Food and Drug Administration. With the introduction of targeted therapy, a great shift towards a new era has taken place that is characterized by reduced cytotoxicity and improved clinical outcomes compared to traditional chemotherapeutic drugs. At present, targeted therapies and other systemic anti-cancer therapies available (immunotherapy, cytotoxic, endocrine therapies and others) are used alone or in combination in different settings (neoadjuvant, adjuvant, and metastatic). As a result, it is not uncommon for patients affected by an advanced malignancy to receive subsequent anti-cancer therapies. In this challenging complexity of cancer treatment, the clinical pathways of real-life patients are often not as direct as predicted by standard guidelines and clinical trials, and cross-resistance among sequential anti-cancer therapies represents an emerging issue. In this review, we summarize the main cross-resistance events described in the diverse tumor types and provide insight into the molecular mechanisms involved in this process. We also discuss the current challenges and provide perspectives for the research and development of strategies to overcome cross-resistance and proceed towards a personalized approach.

Radiotherapy is a primary therapeutic modality widely utilized with curative intent. Traditionally tumor response was hypothesized to be due to high levels of cell death induced by irreparable DNA damage. However, the immunomodulatory aspect of radiation is now widely accepted. As such, interest into the combination of radiotherapy and immunotherapy is increasing, the synergy of which has the potential to improve tumor regression beyond that observed after either treatment alone. However, questions regarding the timing (sequential vs concurrent) and dose fractionation (hyper-, standard-, or hypo-fractionation) that result in improved anti-tumor immune responses, and thus potentially enhanced tumor inhibition, remain. Here we discuss the biological response to radiotherapy and its immunomodulatory properties before giving an overview of pre-clinical data and clinical trials concerned with answering these questions. Finally, we review published mathematical models of the impact of radiotherapy on tumor-immune interactions. Ranging from considering the impact of properties of the tumor microenvironment on the induction of anti-tumor responses, to the impact of choice of radiation site in the setting of metastatic disease, these models all have an underlying feature in common: the push towards personalized therapy.

Correctly identifying the true driver mutations in a patient's tumor is a major challenge in precision oncology. Most efforts address frequent mutations, leaving medium- and low-frequency variants mostly unaddressed. For TP53 , this identification is crucial for both somatic and germline mutations, with the latter associated with the Li-Fraumeni syndrome (LFS), a multiorgan cancer predisposition. We present TP53_PROF (prediction of functionality), a gene specific machine learning model to predict the functional consequences of every possible missense mutation in TP53 , integrating human cell- and yeast-based functional assays scores along with computational scores. Variants were labeled for the training set using well-defined criteria of prevalence in four cancer genomics databases. The model's predictions provided accuracy of 96.5%. They were validated experimentally, and were compared to population data, LFS datasets, ClinVar annotations and to TCGA survival data. Very high accuracy was shown through all methods of validation. TP53_PROF allows accurate classification of TP53 missense mutations applicable for clinical practice. Our gene specific approach integrated machine learning, highly reliable features and biological knowledge, to create an unprecedented, thoroughly validated and clinically oriented classification model. This approach currently addresses TP53 mutations and will be applied in the future to other important cancer genes. [ABSTRACT FROM AUTHOR]

Advanced high‐grade serous ovarian cancer continues to be a therapeutic challenge for those affected using the current therapeutic interventions. There is an increasing interest in personalized cancer immunotherapy using activated natural killer (NK) cells. NK cells account for approximately 15% of circulating white blood cells. They are also an important element of the tumor microenvironment (TME) and the body's immune response to cancers. In the present study, DeepNEU‐C2Rx, a machine learning platform, was first used to create validated artificially induced pluripotent stem cell simulations. These simulations were then used to generate wild‐type artificially induced NK cells (aiNK‐WT) and TME simulations. Once validated, the aiNK‐WT simulations were exposed to artificially induced high‐grade serous ovarian cancer represented by aiOVCAR3. Cytolytic activity of aiNK was evaluated in presence and absence of aiOVCAR3 and data were compared with the literature for validation. The TME simulations suggested 26 factors that could be evaluated based on their ability to enhance aiNK‐WT cytolytic activity in the presence of aiOVCAR3. The addition of programmed cell death‐1 inhibitor leads to significant reinvigoration of aiNK cytolytic activity. The combination of programmed cell death‐1 and glycogen synthase kinase 3 inhibitors showed further improvement. Further addition of ascitic fluid factor inhibitors leads to optimal aiNK activation. Our data showed that NK cell simulations could be used not only to pinpoint novel immunotherapeutic targets to reinvigorate the activity of NK cells against cancers, but also to predict the outcome of targeting tumors with specific genetic expression and mutation profiles. [ABSTRACT FROM AUTHOR]

Globally, metastatic colorectal cancer is one of the leading causes for cancer-related death. Treatment limited to conventional chemotherapeutics extended life for only a few months. However, advances in surgical approaches and medical treatment regimens have greatly increased survival, even leading to long-term remission in selected patients. Advances in multiomics analysis of tumors have built a foundation for molecular-targeted therapies. Furthermore, immunotherapies are on the edge of revolutionizing oncological practice. This review summarizes recent advances in the growing toolbox of personalized treatment for patients with metastatic colorectal cancer. We provide an overview of current multimodal therapy and explain novel immunotherapy and targeted therapy approaches in detail. We emphasize clinically relevant therapies, such as inhibitors of MAPK signaling, and give recommendations for clinical practice. Finally, we describe the potential predictive impact of molecular subtypes and provide an outlook on novel concepts, such as functional precision medicine. [ABSTRACT FROM AUTHOR]

Abstract Background Machine learning has been utilized to predict cancer drug response from multi-omics data generated from sensitivities of cancer cell lines to different therapeutic compounds. Here, we build machine learning models using gene expression data from patients’ primary tumor tissues to predict whether a patient will respond positively or negatively to two chemotherapeutics: 5-Fluorouracil and Gemcitabine. Results We focused on 5-Fluorouracil and Gemcitabine because based on our exclusion criteria, they provide the largest numbers of patients within TCGA. Normalized gene expression data were clustered and used as the input features for the study. We used matching clinical trial data to ascertain the response of these patients via multiple classification methods. Multiple clustering and classification methods were compared for prediction accuracy of drug response. Clara and random forest were found to be the best clustering and classification methods, respectively. The results show our models predict with up to 86% accuracy; despite the study’s limitation of sample size. We also found the genes most informative for predicting drug response were enriched in well-known cancer signaling pathways and highlighted their potential significance in chemotherapy prognosis. Conclusions Primary tumor gene expression is a good predictor of cancer drug response. Investment in larger datasets containing both patient gene expression and drug response is needed to support future work of machine learning models. Ultimately, such predictive models may aid oncologists with making critical treatment decisions.