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Inhibiting protein kinases (PKs) that cause cancers has been an important topic in cancer therapy for years. So far, almost 8% of >530 PKs have been targeted by FDA-approved medications, and around 150 protein kinase inhibitors (PKIs) have been tested in clinical trials. We present an approach based on natural language processing and machine learning to investigate the relations between PKs and cancers, predicting PKs whose inhibition would be efficacious to treat a certain cancer. Our approach represents PKs and cancers as semantically meaningful 100-dimensional vectors based on word and concept neighborhoods in PubMed abstracts. We use information about phase I-IV trials in ClinicalTrials.gov to construct a training set for random forest classification. Our results with historical data show that associations between PKs and specific cancers can be predicted years in advance with good accuracy. Our tool can be used to predict the relevance of inhibiting PKs for specific cancers and to support the design of well-focused clinical trials to discover novel PKIs for cancer therapy.

PURPOSE: The Targeted Agent and Profiling Utilization Registry Study is a phase II basket trial evaluating the antitumor activity of commercially available targeted agents in patients with advanced cancer and genomic alterations known to be drug targets. Results from cohorts of patients with metastatic breast cancer (BC) with FGFR1 and FGFR2 alterations treated with sunitinib are reported. METHODS: Eligible patients had measurable disease, Eastern Cooperative Oncology Group performance status 0-2, adequate organ function, and no standard treatment options. Simon's two-stage design was used with a primary end point of disease control (DC), defined as objective response (OR) or stable disease of at least 16 weeks duration (SD16+) according to RECIST v1.1. Secondary end points included OR, progression-free survival, overall survival, duration of response, duration of stable disease, and safety. RESULTS: Forty patients with BC with FGFR1 (N = 30; amplification only n = 26, mutation only n = 1, both n = 3) or FGFR2 (N = 10; amplification only n = 2, mutation only n = 6, both n = 2) alterations were enrolled. Three patients in the FGFR1 cohort were not evaluable for efficacy; all patients in the FGFR2 cohort were evaluable. For the FGFR1 cohort, two patients with partial response and four with SD16+ were observed for DC and OR rates of 27% (90% CI, 13 to 100) and 7% (95% CI, 1 to 24), respectively. The null hypothesis of 15% DC rate was not rejected (P =.169). No patients achieved DC in the FGFR2 cohort (P = 1.00). Thirteen of the 40 total patients across both cohorts had at least one grade 3-4 adverse event or serious adverse event at least possibly related to sunitinib. CONCLUSION: Sunitinib did not meet prespecified criteria to declare a signal of antitumor activity in patients with BC with either FGFR1 or FGFR2 alterations. Other treatments and clinical trials should be considered for these patient populations. Sunitinib did not demonstrate antitumor activity in patients with FGFR1-/2-amplified or -mutated breast cancer. @ASCO #TAPUR [ABSTRACT FROM AUTHOR]

Real-world evidence regarding the value of integrating genomic profiling (GP) in managing cancer of unknown primary (CUP) is limited. We assessed this clinical utility using a prospective trial of 158 patients with CUP (October 2016-September 2019) who underwent GP using next-generation sequencing designed to identify genomic alterations (GAs). Only 61 (38.6%) patients had sufficient tissue for successful profiling. GAs were seen in 55 (90.2%) patients of which GAs with US Food and Drug Administration–approved genomically matched therapy were seen in 25 (40.9%) patients. A change in therapy was recommended and implemented (primary endpoint of the study) in 16 (10.1%) and 4 (2.5%) patients of the entire study cohort, respectively. The most common reason for inability to implement the profiling-guided therapy was worsening of performance status (56.3%). Integrating GP in management of CUP is feasible but challenging because of paucity of tissue and aggressive natural history of the disease and requires innovative precision strategies. [ABSTRACT FROM AUTHOR]

PURPOSE: The Maine Cancer Genomics Initiative (MCGI) aimed to overcome patient- and provider-level barriers to using genomic tumor testing (GTT) in rural practices by providing genomic tumor boards (GTBs), clinician education, and access to comprehensive large-panel next-generation sequencing to all patients with cancer in Maine. This paper describes the successful implementation of the initiative and three key services made operative between 2016 and 2020. METHODS: A community-inclusive, hub-and-spoke approach was taken to implement the three program components: (1) a centralized GTB program; (2) a modular online education program, designed using an iterative approach with broad clinical stakeholders; and (3) GTT free of charge to clinicians and patients. Implementation timelines, participation metrics, and survey data were used to describe the rollout. RESULTS: The MCGI was launched over an 18-month period at all 19 oncology practices in the State. Seventy-nine physicians (66 medical oncologists, 5 gynecologic oncologists, 1 neuro-oncologist, and 7 pediatric oncologists) enrolled on the study, representing 100% of all practicing oncologists in Maine. Between July 2017 and September 2020, 1610 patients were enrolled. A total of 515 cases were discussed by 47 (73%) clinicians in 196 GTBs. Clinicians who participated in the GTBs enrolled significantly more patients on the study, stayed in Maine, and reported less time spent in clinical patient care. CONCLUSION: The MCGI was able to engage geographically and culturally disparate cancer care practices in a precision oncology program using a hub-and-spoke model. By facilitating access to GTT, structured education, and GTBs, we narrowed the gap in the implementation of precision oncology in one of the most rural states in the country. MCGI aimed to overcome patient- and provider-level barriers to GTT in rural practices by providing GTBs, clinician education, and access to large-panel NGS to all oncologists and their patients in Maine. [ABSTRACT FROM AUTHOR]

PURPOSE: Social determinants of health, such as rurality, income, and education, may widen health disparities by driving variation in patients' knowledge and perceptions of medical interventions. This effect may be greatest for medical technologies that are hard to understand and less accessible. This study explored whether knowledge and perceptions (expectations and attitudes) of patients with cancer toward large-panel genomic tumor testing (GTT), an emerging cancer technology, vary by patient rurality independent of other socioeconomic characteristics (education and income). METHODS: Patients with cancer enrolled in a large precision oncology initiative completed surveys measuring rurality, sociodemographic characteristics, and knowledge and perceptions of GTT. We used multivariable linear models to examine differences in GTT knowledge, expectations, and attitudes by patient rurality, education, and income level. Models controlled for age, sex and clinical cancer stage and type. RESULTS: Rural patients had significantly lower knowledge of GTT than urban patients using bivariate models (P =.025). However, this association disappeared when adjusting for education and income level: patients with lower educational attainment and lower income had lower knowledge and higher expectations (P ≤.002), whereas patients with higher income had more positive attitudes (P =.005). Urban patients had higher expectations of GTT compared with patients living in large rural areas (P =.011). Rurality was not associated with attitudes. CONCLUSION: Patients' education and income level are associated with knowledge, expectations, and attitudes toward GTT, whereas rurality is associated with patient expectations. These findings suggest that efforts to promote adoption of GTT should focus on improving knowledge and awareness among individuals with low education and income. These differences may lead to downstream disparities in GTT utilization, which should be explored in future research. [ABSTRACT FROM AUTHOR]

Purpose: Large-panel genomic tumor testing (GTT) is an emerging technology with great promise but uncertain clinical value. Previous research has documented variability in academic oncologists' perceptions and use of GTT, but little is known about community oncologists' perceptions of GTT and how perceptions relate to clinicians' intentions to use GTT.Methods: Community oncology physicians (N = 58) participating in a statewide initiative aimed at improving access to large-panel GTT completed surveys assessing their confidence in using GTT, attitudes regarding the value of GTT, perceptions of barriers to GTT implementation, and future intentions to use GTTs. Descriptive and multivariable regression analyses were conducted to characterize these perceptions and to explore the relationships between them.Results: There was substantial variability in clinicians' perceptions of GTT. Clinicians generally had moderate confidence in their ability to use GTT, but lower confidence in patients' ability to understand test results and access targeted treatment. Clinicians had positive attitudes regarding the value of GTT. Clinicians' future intentions to use GTT were associated with greater confidence in using GTT and greater perceived barriers to implementing GTT, but not with attitudes about the value of GTT.Conclusions: Community oncologists' perceptions of large-panel genomic tumor testing are variable, and their future intentions to use GTT are associated with both their confidence in and perceived barriers to its use, but not with their attitudes towards GTT. More research is needed to understand other factors that determine how oncologists perceive and use GTT in clinical practice. [ABSTRACT FROM AUTHOR]

Novel medical technologies, like large-panel genomic tumor testing (GTT), offer great promise but also substantial uncertainty regarding their clinical value and appropriate use. The goal of this study was to understand how clinicians' perceived uncertainty about GTT, and uncertainty tolerance (UT), a construct that describes trait-level differences in individuals' responses to uncertainty, influence attitudes and self-efficacy regarding GTT. Community-based oncologists participating in a study of large-panel GTT completed surveys assessing their perceptions of uncertainty about GTT, and their attitudes and self-efficacy regarding GTT. Multivariable regression analyses examined the relationship between oncologists' perceived uncertainty of GTT and their GTT-related attitudes and self-efficacy, and the potential moderating effect of individual differences in UT. Fifty-seven oncologists completed surveys. Greater perceived uncertainty about GTT was associated with more negative attitudes towards it. This association was moderated by UT, such that lower UT was associated with a stronger negative relationship between perceived uncertainty and attitudes. That is, oncologists who perceive GTT as uncertain, tended to have more negative attitudes, particularly if they were low in the trait of uncertainty tolerance. More research is warranted to understand how uncertainty and uncertainty tolerance influence clinicians' responses to GTT and other novel medical interventions. [ABSTRACT FROM AUTHOR]

Cancer immunotherapy relies upon the ability of T cells to infiltrate tumors. The endothelium constitutes a barrier between the tumor and effector T cells, and the ability to manipulate local vascular permeability could be translated into effective immunotherapy. Here, we show that in the context of adoptive T cell therapy, antitumor T cells, delivered at high enough doses, can overcome the endothelial barrier and infiltrate tumors, a process that requires local production of C3, complement activation on tumor endothelium and release of C5a. C5a, in turn, acts on endothelial cells promoting the upregulation of adhesion molecules and T-cell homing. Genetic deletion of C3 or the C5a receptor 1 (C5aR1), and pharmacological blockade of C5aR1, impaired the ability of T cells to overcome the endothelial barrier, infiltrate tumors, and control tumor progressionin vivo, while genetic chimera mice demonstrated that C3 and C5aR1 expression by tumor stroma, and not leukocytes, governs T cell homing, acting on the local endothelium.In vitro, endothelial C3 and C5a expressions were required for endothelial activation by type 1 cytokines. Our data indicate that effective immunotherapy is a consequence of successful homing of T cells in response to local complement activation, which disrupts the tumor endothelial barrier. [ABSTRACT FROM PUBLISHER]

Purpose: Emerging cancer treatments are often most available to socially advantaged individuals. This study examines the relationship of patient educational attainment, income level, and rurality to the receipt of genome-matched treatment (GMT) and overall survival., Methods: Survey and clinical data were collected from patients with cancer (n = 1258) enrolled in the Maine Cancer Genomics Initiative. Logistic regression models examined whether receipt of GMT differed by patient education, income, and rurality. Kaplan-Meier curves and Cox regression were conducted to evaluate 12-month mortality. We completed additional exploratory analyses using Kaplan-Meier curves and Cox models stratified by receipt of GMT. Both logistic and Cox regression models were adjusted for age and gender., Results: Educational attainment, income level, and rurality were not associated with GMT receipt. Of 1258 patients, 462 (36.7%) died within 365 days of consent. Mortality risk was associated with lower educational attainment (hazard ratio (HR): 1.30; 95% CI: 1.06 to 1.59; p = .013). No statistically significant differences in mortality risk were observed for income level or rurality. Exploratory models suggest that patients who did not receive GMT with lower educational attainment had higher mortality risk (HR = 1.36, 95% CI: 1.09 to 1.69, p = .006). For patients who did receive GMT, there was no difference in mortality risk between the education groups (HR: 1.01, 95% CI: 0.56 to 1.81, p > .9)., Conclusion: While there were no disparities in who received GMT, we found a disparity in mortality associated with education level, which was more pronounced for patients who did not receive GMT. Future research is warranted to investigate the intersectionality of social disadvantage with clinical outcomes to address survival disparities., (© The Author(s) 2024. Published by Oxford University Press.)

Real-world evidence regarding the value of integrating genomic profiling (GP) in managing cancer of unknown primary (CUP) is limited. We assessed this clinical utility using a prospective trial of 158 patients with CUP (October 2016-September 2019) who underwent GP using next-generation sequencing designed to identify genomic alterations (GAs). Only 61 (38.6%) patients had sufficient tissue for successful profiling. GAs were seen in 55 (90.2%) patients of which GAs with US Food and Drug Administration-approved genomically matched therapy were seen in 25 (40.9%) patients. A change in therapy was recommended and implemented (primary endpoint of the study) in 16 (10.1%) and 4 (2.5%) patients of the entire study cohort, respectively. The most common reason for inability to implement the profiling-guided therapy was worsening of performance status (56.3%). Integrating GP in management of CUP is feasible but challenging because of paucity of tissue and aggressive natural history of the disease and requires innovative precision strategies., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Cancer of unknown primary (CUP), representing approximately 3-5% of all malignancies, is defined as metastatic cancer where a primary site of origin cannot be found despite a standard diagnostic workup. Because knowledge of a patient's primary cancer remains fundamental to their treatment, CUP patients are significantly disadvantaged and most have a poor survival outcome. Developing robust and accessible diagnostic methods for resolving cancer tissue of origin, therefore, has significant value for CUP patients., Methods: We developed an RNA-based classifier called CUP-AI-Dx that utilizes a 1D Inception convolutional neural network (1D-Inception) model to infer a tumor's primary tissue of origin. CUP-AI-Dx was trained using the transcriptional profiles of 18,217 primary tumours representing 32 cancer types from The Cancer Genome Atlas project (TCGA) and International Cancer Genome Consortium (ICGC). Gene expression data was ordered by gene chromosomal coordinates as input to the 1D-CNN model, and the model utilizes multiple convolutional kernels with different configurations simultaneously to improve generality. The model was optimized through extensive hyperparameter tuning, including different max-pooling layers and dropout settings. For 11 tumour types, we also developed a random forest model that can classify the tumour's molecular subtype according to prior TCGA studies. The optimised CUP-AI-Dx tissue of origin classifier was tested on 394 metastatic samples from 11 tumour types from TCGA and 92 formalin-fixed paraffin-embedded (FFPE) samples representing 18 cancer types from two clinical laboratories. The CUP-AI-Dx molecular subtype was also independently tested on independent ovarian and breast cancer microarray datasets FINDINGS: CUP-AI-Dx identifies the primary site with an overall top-1-accuracy of 98.54% in cross-validation and 96.70% on a test dataset. When applied to two independent clinical-grade RNA-seq datasets generated from two different institutes from the US and Australia, our model predicted the primary site with a top-1-accuracy of 86.96% and 72.46% respectively., Interpretation: The CUP-AI-Dx predicts tumour primary site and molecular subtype with high accuracy and therefore can be used to assist the diagnostic work-up of cancers of unknown primary or uncertain origin using a common and accessible genomics platform., Funding: NIH R35 GM133562, NCI P30 CA034196, Victorian Cancer Agency Australia., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)