Clinical Significance of Extracellular Vesicles in Prostate and Renal Cancer

Extracellular vesicles (EVs)-including apoptotic bodies, microvesicles, and exosomes-are released by almost all cell types and contain molecular footprints from their cell of origin, including lipids, proteins, metabolites, RNA, and DNA. They have been successfully isolated from blood, urine, semen, and other body fluids. In this review, we discuss the current understanding of the predictive value of EVs in prostate and renal cancer. We also describe the findings supporting the use of EVs from liquid biopsies in stratifying high-risk prostate/kidney cancer and advanced disease, such as castration-resistant (CRPC) and neuroendocrine prostate cancer (NEPC) as well as metastatic renal cell carcinoma (RCC). Assays based on EVs isolated from urine and blood have the potential to serve as highly sensitive diagnostic studies as well as predictive measures of tumor recurrence in patients with prostate and renal cancers. Overall, we discuss the biogenesis, isolation, liquid-biopsy, and therapeutic applications of EVs in CRPC, NEPC, and RCC.

Patient portal access for caregivers of adult and geriatric patients: reframing the ethics of digital patient communication

Patient portals are poised to transform health communication by empowering patients with rapid access to their own health data. The 21st Century Cures Act is a US federal law that, among other provisions, prevents health entities from engaging in practices that disrupt the exchange of electronic health information-a measure that may increase the usage of patient health portals. Caregiver access to patient portals, however, may lead to breaches in patient privacy and confidentiality if not managed properly through proxy accounts. We present an ethical framework that guides policy and clinical workflow development for healthcare institutions to support the best use of patient portals. Caregivers are vital members of the care team and should be supported through novel forms of health information technology (IT). Patients, however, may not want all information to be shared with their proxies so healthcare institutions must support the development and use of separate proxy accounts as opposed to using the patient's own account as well provide controls for limiting the scope of information displayed in the proxy accounts. Lastly, as socioeconomic barriers to adoption of health IT persist, healthcare providers must work to ensure multiple streams of patient communication, to prevent further propagating health inequities.

Oncologist-driven development of an electronic health record (EHR) clinical data visualization tool for prostate cancer

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Background: Clinical information management is a burdensome process for oncology providers owing to the complexity of modern cancer data. The results that a clinician needs to review are often spread across several areas within the EHR, making it cumbersome to sense a broad overview of patient status. The purpose of this study is to describe a framework for oncologist-driven development of a data visualization tool to trend cancer biomarkers and assess the feasibility of this tool to query and display EHR data relevant for the treatment of prostate cancer. Methods: A clinical sponsor is selected to identify data elements necessary to make treatment decisions for patients receiving therapy for prostate cancer and to provide rapid feedback for the clinical tool interface. The commercial EHR database is queried to determine identifying codes for relevant laboratory tests, medications, and procedures. Data elements are assembled using the EHR platform for clinical synopsis and the clinical tool is made available to five genitourinary medical oncologists for initial pilot. Results: Oral prostate cancer medications were queried based on medication therapeutic and pharmaceutical class. In addition, androgen deprivation therapy (ADT) injections were separately identified based on route of administration. Prostate specific antigen (PSA) and testosterone result values were queried using laboratory base and/or common name codes. However, many duplicate entities are found varying by hospital/laboratory site and test assay. The assembled clinical data visualization tool can overlay temporal trends in PSA and testosterone over medication start/stop dates to convey treatment response and signs of early medication resistance. The clinician also has the option to overlay vital signs or other laboratory information to visualize treatment related adverse events (ex. weight gain related to ADT, anemia related to PARP inhibitor therapy, etc.) Lastly, the tool can also highlight the dates of the patient’s last imaging tests to allow clinicians to determine if the patient is due for any follow up imaging. Conclusions: The EHR can support novel data visualization tools for cancer biomarkers that can reasonably support clinical workflows. Development requires an intimate knowledge of EHR data but may still be limited by duplicate or erroneous codes for laboratory results. This issue may be addressed by using standard nomenclature for laboratory results such as LOINC codes but is not currently supported by the commercial EHR. Future work in this area will include formal usability testing from the perspective of oncology providers and patients.

Local and systemic therapies for renal cell carcinoma with brain metastases: A single-institution series

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Background: Current practice guidelines for the management of renal cell carcinoma (RCC) with brain metastases (BM) recommend a multimodal approach with systemic therapy, radiation, and/or surgery. The purpose of this study is to assess treatment patterns and identify prognostic factors for survival in this population. Methods: This retrospective, single-center study evaluated patients with RCC who were diagnosed with metastatic disease between 2015-2020 and were identified to have BM at any point in their care. Fisher's exact test and Mann-Whitney test were used to compare differences among groups. Kaplan-Meier curves were used to estimate time to events from the date of BM diagnosis. Univariate Cox proportional hazards model was used to estimate the effect of factors on survival. Results: 29 patients are included. Median follow-up was 25 months (range: 0.5-73). 16 (55%) patients presented with BM at the time of diagnosis of metastatic disease. 17 (58%) were treated with radiation. 8 (28%) were treated with surgery of which 7 also received radiation. Median overall survival was 10 months (95% CI: 7-not reached). Patients received a median of one (range 0-4) system therapy regimen after diagnosis of BM. 19 (66%) patients were exposed to at least one class of medication therapy for management of BM while 10 (34%) were exposed to two or more classes. 10 patients experienced long term survival (defined as survival ≥12 months). Patients with long term survival were more likely to have received radiation (90% vs 42%; P=0.02). However, use of radiation was not associated with survival (HR 0.47, 95% CI 0.1-1.3). Conclusions: The study is limited by sample size but suggests there is a population of patients with RCC with BM who can experience long term survival. Optimal selection for patients with BM that can be feasibly treated within radiation fields appears favorable. Most patients are not exposed to more than one systemic medication class during their care. Additional research is needed to compare efficacy in BM to guide first line systemic therapy selection. [Table: see text]

Human-centered design to improve clinical decision support systems (CDSS) to engage in serious illness communication (SIC) with patients with cancer in a gastrointestinal oncology clinic

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Background: We previously reported the implementation of a machine learning (ML) model for mortality prediction that was integrated into a CDSS encouraging clinicians to have a SIC with at-risk cancer patients. The clinical utility of a ML model can change after implementation due to fluctuations in the organization’s patient population and clinical practices. It is important to establish a workflow to monitor and continually reinforce ML-powered CDSS to ensure that it continues to benefit patients. We report a workgroup structure that incorporates data driven evaluation of ML model performance and feedback from CDSS end users to optimize the acceptability of the CDSS. Methods: The workflow was piloted in the gastrointestinal (GI) oncology clinic from 11/2021-5/2022. A workgroup including members of the implementation team and end-users of the CDSS met monthly to review 1) a dashboard that displays model performance, 2) an electronic health record (EHR) report that summarizes use of the CDSS, 3) feedback from end users regarding their opinion of the CDSS and any barriers to implementation. We evaluated the accuracy of model predictions among subgroups as defined by mortality and unplanned hospital admissions or ED visit rates. Fisher’s Exact Test was used to identify differences between categorical variables. Numeric values including incidence rate ratios (IRRs) adjusted for age, sex, race, and gender with 95% confidence intervals (CIs) were calculated using Poisson regression. Results: 119 patients were evaluated by the model and 50 (42%) were assessed as high-risk. In the high-risk group, the oncology team evaluated 39 (78%) patients for appropriateness of a SIC; SIC was completed with 5 (10%) patients. During workgroup meetings, physicians shared that some of the high-risk predictions were for patients undergoing curative intent therapy. 0 out of 24 patients who received curative treatment died and 5 out of 26 patients who receive palliative treatment died. The log-rank p-value of 0.03 indicates that the survival distribution differs significantly over time between two groups. The adjusted IRR for unplanned hospital visits (palliative vs curative) was 2.55 (1.3-5.0). Adjusted mean hospital visits per month were 0.34 (0.21-0.51) vs 0.13 (0.06-0.21). Conclusions: The workgroup format is a feasible method to continuously review acceptability of a ML-powered CDSS. It may evaluate critical feedback from end users in a holistic manner that can augment a data driven evaluation of the model performance. The data implies that patients undergoing curative therapy have a decreased risk for mortality and unplanned hospital admissions or ED visits. The CDSS may be optimized by excluding these patients; however, longer follow up of this sub-population is needed to confirm that they have no additional risk factors.

Effect of adrenergic receptor antagonists on clinical outcomes in metastatic renal cell carcinoma

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Background: Adrenergic receptors (ADR) have a well-established role in kidney physiology, suggesting a possible role of ADR signaling in renal cell carcinoma (RCC). Preclinical studies have shown a possible anti-tumor effect of both alpha-adrenergic blockers (AB) and beta-adrenergic blockers (BB) on renal cancer cells. However, the literature is conflicting regarding the effect on clinical outcomes. In addition, there is no comprehensive analysis of what role these medications have specifically in RCC patients with metastatic disease. In this single health system retrospective study, we evaluate the effect of AB or BB prescription on cancer-specific outcomes in patients with metastatic RCC. Methods: Clinical data was abstracted from the electronic health record and the institutional cancer registry at The Mount Sinai Health System to include patients with RCC diagnosed with metastatic disease ( de novo or relapsed) between 2016-2020. Follow up data was recorded until a data lock of 4/1/2021. Multivariate Cox regression was used to evaluate the association of AB and BB prescription on overall survival (OS) for the entire cohort and progression free survival (PFS) for patients who received any first line therapy. Multivariate logistic regression was used to evaluate association of these medications with overall response rate (ORR) of first line therapies with available data on initial response. Analyses were controlled for clinical risk factors including age at diagnosis, sex, race, treatment plan including immunotherapy, and medical history of benign prostatic hyperplasia, hypertension, or heart failure. Data was analyzed using R version 4.1.0. Results: 133 patients were identified for inclusion in the study. 61% of cases are de novo metastatic and 39% are relapsed from previously resected disease. 71% are clear cell histology, 3.8 % are papillary, and the rest are unspecified/other. 84 patients (63%) received at least a 1st line systemic therapy. AB or BB prescription alone is not associated with OS, PFS, or ORR. However, prescription of both AB and BB is associated with worse OS (HR 2.94 95% CI 1.13-7.68), worse PFS (HR 7.70 95% CI 1.80-33.0), and decreased odds of responding to 1st line therapy (OR 0.06 95% CI 0.00-0.55) on multivariate analysis. Conclusions: The data suggests that the concurrent use of both an AB and BB in patients with metastatic RCC leads to worse clinical outcomes. What remains unclear is if there is a negative synergistic effect of these medications on RCC pathophysiology or if the prescription of these medications is associated with another negative confounding clinical factor. Since these medications are commonly utilized for cardiovascular disease, multivariate analyses attempted to control for these comorbidities. Future studies may add to this data by cataloging the severity of heart failure and doses of AB and BB medications used in a RCC cohort.

Acceptability of a machine learning-powered clinical decision support system aiding serious illness conversation and its impact on clinical outcomes: A pilot study

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Background: Patients with advanced cancer that utilize end of life planning see benefits including better quality of life and medical care that is more consistent with their values. We developed a 30-day mortality predictive model using a machine learning algorithm and integrated it into a clinical decision support system (CDSS) that encourages clinicians to use the serious illness conversation (SIC) guide—a standardized questionnaire and conversational tool that facilitates end-of-life planning. The CDSS was piloted in the thoracic oncology clinic. We evaluated clinicians’ use of this system and its impact on patient outcomes. Methods: Between 4/14/21-1/15/22, information about patients identified by the model was sent to clinical teams via the electronic health record (EHR) to assess eligibility for a SIC. We reviewed the EHR for patients identified, SIC completion, and level of agreement by oncologists with the model. We evaluated the SIC guide responses using descriptive statistics and assessed differences in rates of hospice referral, hospital visits, and 30-day mortality by SIC completion status. Chi-squared test was used for testing association. Results: 94 patients were evaluated for SIC eligibility. Of these, oncologists agreed with 48 (51%) model predictions and SIC was completed for 28 (58%) of those patients. A median of 2.5 SIC eligibility assessments per week were completed, with a median time of 4 days from prediction to assessment. Likewise, a median of 1 SIC per week was completed, with a median time of 20 days from SIC eligibility assessment to conversation. Regarding the responses to the SIC guide, out of 28 patients, 75% have an appropriate understanding of their illness; 64% want to be fully informed of their medical information while 21% prefer information to be limited. Common patient goals were “being comfortable” (54%), “being at home” (29%) and “being independent” (25%). The most prevalent patient fears were “family concerns” (29%) or “physical suffering” (25%). The clinician who performs the SIC most often recommended an “additional conversation with physician” (39%), “conversation with family” (36%), or “referral to palliative care” (18%). SIC completion was associated with an increased rate of enrollment in hospice (33% vs 14%, P= 0.03) on univariate analysis. SIC was not associated with a difference in 30-day mortality or hospital visits. Multivariable analysis is ongoing. Conclusions: The machine-learning powered CDSS was adopted by the oncology care team within a reasonable timeframe. However, even if an oncologist used and agreed with the CDSS, the rate of eventual completion of SIC was not 100%. Additional barriers to SIC will be studied to optimize the CDSS. SIC completion may lead to increased enrollment in hospice and should continue to be studied as a standard component of comprehensive cancer care.

Clinical Outcomes and Toxic Effects of Single-Agent Immune Checkpoint Inhibitors Among Patients Aged 80 Years or Older With Cancer: A Multicenter International Cohort Study

Importance

Geriatric (aged ≥80 years) patients are historically underrepresented in cancer clinical trials. Little is known about the efficacy of immune checkpoint inhibitors (ICIs) in geriatric patients. These agents are associated with immune-related adverse events (irAEs), which may be particularly associated with morbidity in this population.

Objective

To provide insight into the clinical outcomes and safety of ICIs among geriatric patients (aged ≥80 years) with cancer.

Design, Setting, and Participants

A Multicenter, international retrospective study of 928 geriatric patients with different tumors treated with single-agent ICIs between 2010 to 2019 from 18 academic centers in the US and Europe. Analyses were conducted from January 2021 to April 2021.

Main Outcomes and Measures

Clinical outcomes and irAE patterns in geriatric patients treated with single-agent ICIs.

Results

Median (range) age of the 928 patients at ICI initiation was 83.0 (75.8-97.0) years. Most patients (806 [86.9%]) were treated with anti-programmed cell death 1 therapy. Among the full cohort, the 3 most common tumors were non-small cell lung cancer (NSCLC, 345 [37.2%]), melanoma (329 [35.5%]), and genitourinary (GU) tumors (153 [16.5%]). Objective response rates for patients with NSCLC, melanoma, and GU tumors were 32.2%, 39.3%, and 26.2%, respectively. Median PFS and OS, respectively, were 6.7 and 10.9 months (NSCLC), 11.1 and 30.0 months (melanoma), and 6.0 and 15.0 months (GU). Within histologically specific subgroups (NSCLC, melanoma, and GU), clinical outcomes were similar across age subgroups (aged <85 vs ≥85 years). Among all 928 patients, 383 (41.3%) experienced ≥1 irAE(s), including 113 (12.2%) that were reported to be grade (G) 3 to 4 based on Common Terminology Criteria for Adverse Events (version 5.0). The median time to irAE onset was 9.8 weeks; 219 (57%) occurred within the first 3 months after ICI initiation. Discontinuation of treatment with ICIs owing to irAEs occurred in 137 (16.1%) patients. There was no significant difference in the rate of irAEs among patients aged younger than 85, 85 to 89, and 90 years or older. Despite the similar rate of G3 or higher irAEs, ICIs were discontinued owing to irAEs more than twice as often among patients aged 90 years or older compared with patients younger than 90 years (30.9% vs 15.1%, P = .008).

Conclusions and Relevance

The findings of this international cohort study suggest that treatment with ICIs may be effective and generally well tolerated among older patients with cancer, though ICI discontinuation owing to irAEs was more frequent with increasing age.

239 Efficacy and toxicity of single agent immune checkpoint inhibitors among adults with cancer aged ≥80 years: a multicenter international cohort study

Background

Immune checkpoint inhibitors (ICIs) are approved by the U.S. Food&Drug Administration in over 17 tumor types. Older adult patients make up about a quarter of all cancer patients but are historically understudied in cancer clinical trials. ICIs are associated with immune-related adverse events (irAEs), which may be particularly morbid for older adult patients with underlying comorbidities and impaired functional status. In this study, we provide insight into the real-world safety and efficacy of ICIs among older adult patients (≥80 years) with cancer.

Methods

This is a multicenter, international retrospective study of tumor-agnostic older adult patients with cancer treated with single-agent ICIs between 2010–2019 from 18 academic centers in the U.S. and Europe. A cohort of 928 patients aged ≥80 years during treatment with ICI was assembled and analyzed to evaluate clinical outcomes and irAE patterns in older adult patients treated with single-agent ICIs.

Results

Median age at ICI initiation was 83.0 years (range 75.8–97.0). Most patients (86.9%) were treated with anti-PD-1 therapy. Among the full cohort, the three most common tumors were non-small cell lung cancer (NSCLC, 37.2%,n=345), melanoma (35.5%,n=329), and genitourinary (GU) tumors (16.5%,n=153). Objective response rates for patients with NSCLC, melanoma, and GU tumors were 32.2%, 39.3%, and 26.2%, respectively. Median progression-free survival (PFS) was 6.7 months (95%CI, 5.2–8.6) for patients with NSCLC, 11.1 months (95%CI, 8.9–16.0) for patients with melanoma, and 6.0 months (95% CI, 5.0–10.7) for patients with GU malignancy. Median overall survival (OS) was 10.9 months (95%CI, 8.6–13.1) for patients with NSCLC, 30.0 months (95%CI, 23.6–46.4) for patients with melanoma, and 15.0 months (95%CI 9.1–25.4) for GU patients (Figure 1A-C). Within histology-specific cohorts (NSCLC, melanoma and GU), clinical outcomes were similar across age subgroups (<85,85–89,>90). Among all patients (N=928), 41.3% experienced ≥1 irAE(s), including 12.2% reported to be grade (G)3–4. No irAE-related deaths occurred. The median time to irAE onset was 9.8 weeks; 57% occurred within the first 3 months after ICI initiation. ICI was discontinued due to irAEs in 16.1% patients. There was no significant difference in the rate of irAEs among patients age <85, 85–89, and ≥90 years (p=0.15). Despite similar rates of G3+ irAEs, ICIs were discontinued due to irAE more than twice as often among patients ≥90 years compared to patients <90 years (30.9% vs. 15.1%, p=0.008) (table 1).

Conclusions

ICIs are effective and generally well-tolerated among older patients with cancer. However, ICI discontinuation due to irAE is more frequent with increasing age.

Implementing clinical decision support for oncology advanced care planning: A systems engineering framework to optimize the usability and utility of a machine learning predictive model in clinical practice

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Background: Machine learning models are well-positioned to transform cancer care delivery by providing oncologists with more accurate or accessible information to augment clinical decisions. Many machine learning projects, however, focus on model accuracy without considering the impact of using the model in real-world settings and rarely carry forward to clinical implementation. We present a human-centered systems engineering approach to address clinical problems with workflow interventions utilizing machine learning algorithms. Methods: We aimed to develop a mortality predictive tool, using a Random Forest algorithm, to identify oncology patients at high risk of death within 30 days to move advance care planning (ACP) discussions earlier in the illness trajectory. First, a project sponsor defined the clinical need and requirements of an intervention. The data scientists developed the predictive algorithm using data available in the electronic health record (EHR). A multidisciplinary workgroup was assembled including oncology physicians, advanced practice providers, nurses, social workers, chaplain, clinical informaticists, and data scientists. Meeting bi-monthly, the group utilized human-centered design (HCD) methods to understand clinical workflows and identify points of intervention. The workgroup completed a workflow redesign workshop, a 90-minute facilitated group discussion, to integrate the model in a future state workflow. An EHR (Epic) analyst built the user interface to support the intervention per the group’s requirements. The workflow was piloted in thoracic oncology and bone marrow transplant with plans to scale to other cancer clinics. Results: Our predictive model performance on test data was acceptable (sensitivity 75%, specificity 75%, F-1 score 0.71, AUC 0.82). The workgroup identified a “quality of life coordinator” who: reviews an EHR report of patients scheduled in the upcoming 7 days who have a high risk of 30-day mortality; works with the oncology team to determine ACP clinical appropriateness; documents the need for ACP; identifies potential referrals to supportive oncology, social work, or chaplain; and coordinates the oncology appointment. The oncologist receives a reminder on the day of the patient’s scheduled visit. Conclusions: This workgroup is a viable approach that can be replicated at institutions to address clinical needs and realize the full potential of machine learning models in healthcare. The next steps for this project are to address end-user feedback from the pilot, expand the intervention to other cancer disease groups, and track clinical metrics.

Role of α- and β-adrenergic signaling in phenotypic targeting: significance in benign and malignant urologic disease

The urinary tract is highly innervated by autonomic nerves which are essential in urinary tract development, the production of growth factors, and the control of homeostasis. These neural signals may become dysregulated in several genitourinary (GU) disease states, both benign and malignant. Accordingly, the autonomic nervous system is a therapeutic target for several genitourinary pathologies including cancer, voiding dysfunction, and obstructing nephrolithiasis. Adrenergic receptors (adrenoceptors) are G-Protein coupled-receptors that are distributed throughout the body. The major function of α1-adrenoceptors is signaling smooth muscle contractions through GPCR and intracellular calcium influx. Pharmacologic intervention of α-and β-adrenoceptors is routinely and successfully implemented in the treatment of benign urologic illnesses, through the use of α-adrenoceptor antagonists. Furthermore, cell-based evidence recently established the antitumor effect of α1-adrenoceptor antagonists in prostate, bladder and renal tumors by reducing neovascularity and impairing growth within the tumor microenvironment via regulation of the phenotypic epithelial-mesenchymal transition (EMT). There has been a significant focus on repurposing the routinely used, Food and Drug Administration-approved α1-adrenoceptor antagonists to inhibit GU tumor growth and angiogenesis in patients with advanced prostate, bladder, and renal cancer. In this review we discuss the current evidence on (a) the signaling events of the autonomic nervous system mediated by its cognate α- and β-adrenoceptors in regulating the phenotypic landscape (EMT) of genitourinary organs; and (b) the therapeutic significance of targeting this signaling pathway in benign and malignant urologic disease. Video abstract.

Stress related to electronic health record (EHR) use among oncology trainees: A quality improvement study to optimize information gathering

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Background: Poor EHR usability is associated with physician burnout [1]. Oncology providers are tasked with coordinating data from multiple sources/tabs within an EHR (biomarkers, genetic data, imaging, etc.) to make treatment plan decisions. It is hypothesized that oncology trainees will have decreased stress using an EHR tool to optimize data gathering. Methods: This single-institution quality improvement study aims to decrease the time and stress associated with navigating the EHR to review/document laboratory data among first-year clinical oncology fellows rotating on consult services at The Mount Sinai Hospital as measured by self-reported survey data. An EHR tool was built that pulls into the note a drop-down list of user-selectable laboratory panels grouped according to hematologic/oncologic disease presentations. A survey was sent to all fellows in the program to assess attitudes toward the EHR. All 1st-year fellows were given access to the EHR tool as they are the ones who primarily rotate on consult services. After 3 months, a follow-up survey was sent to 1st-year clinical fellows to assess satisfaction with the EHR intervention. Results: For the baseline survey, there are a total of 17 respondents (response rate 77%). 70.6% of fellows believe using the EHR increases their level of stress. The most prevalent reasons cited for frustrations with the EHR are “Hard to read other providers’ notes” (82.4%), “Gathering information from multiple different pages” (76.5%), “Increased documentation requirements” (76.5%), “Inability to quickly navigate the system” (64.7%). For the follow-up survey targeting 1st-year fellows, there are a total of 6 respondents (response rate 100%). 5 fellows tried the EHR tool at least once. Of those 5 fellows, 100% use it daily in clinical practice while on consult services, believe it makes notes more legible, believe it saves time spent in chart review, and believe it decreases their stress associated with navigating the EHR. Conclusions: A majority of clinical oncology fellows believe that the EHR contributes to their stress. The reasons cited suggest that interventions that optimize note-writing and chart-reviewing may decrease stress. The majority of fellows who used this EHR tool believe the tool improved their efficiency and stress levels. This suggests more widespread use of this tool among oncology clinicians may contribute to wellness although further studies to increase utilization must be taken.

Clinical efficacy of immunotherapy for the treatment of solid tumors in patients with chronic kidney disease

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Background: Regulatory T cells play a key role in protecting kidney cells from ischemic injury. Immune checkpoint inhibitors (ICIs) may increase the risk of acute kidney injury via inhibition of regulatory T cells [1, 2]. Prospective clinical trials have largely excluded patients with chronic kidney disease (CKD); thus, we have limited knowledge of the safety and efficacy of ICI in these patients. Herein, we hypothesize that patients with CKD receiving ICIs have worse clinical outcomes. Methods: This single-institution retrospective cohort study included adult patients with solid tumors who were treated with ICIs at The Mount Sinai Hospital between 2011 and 2017. Clinical endpoints [response to treatment, progression of disease (POD) on treatment, mortality] were compared between patients with and without CKD using multivariate logistic regression. Odds ratios were controlled for demographics, primary tumor type, presence of cardiovascular comorbidities, smoking status, incidence of renal adverse events, and a composite of stage of illness with indication for treatment [localized—neoadjuvant, localized—adjuvant, regionally advanced, metastatic disease]. Data were analyzed using R version 3.5.1 with the following packages: readr, dplyr, broom, lubridate, tableone. Results: 420 patients met inclusion criteria: 399 patients without CKD and 21 patients with CKD. Cohorts are well matched for demographics, smoking status, stage/indication for treatment. The CKD cohort has a higher proportion of patients with urothelial cancer compared to patients without CKD (33% vs 11%) as well as a higher proportion of patients with HTN (81% vs 53%), HF (14% vs 3%), and DM (48% vs 21%). There was no statistical difference in odds of response to treatment [OR 0.76, 95% CI 0.26-2.23], POD [OR 0.42, 95% CI 0.15-1.17], or mortality [OR 2.05, 95% CI 0.71-5.96] between the CKD and non-CKD cohort. Conclusions: The data suggest the presence of CKD is not associated with worse clinical outcomes in cancer patients treated with ICIs. As a small retrospective study, the conclusions are hypothesis-generating but support continued use of immunotherapy in CKD in clinical practice and the inclusion of patients with CKD in immunotherapy clinical trials to further clarify safety and efficacy. [Table: see text]

Smoking status and immunotherapy outcomes in smoking-associated cancers

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Background: Improved immunotherapy (IO) outcomes have been observed among non-small cell lung cancer patients with a current or former smoking history. This is thought to be a consequence of increased immunogenic mutation burden among smoking-related cancers. We set out to explore the association between smoking status and immunotherapy outcomes in lung and other smoking-associated cancers. Methods: This was a retrospective analysis of 200 consecutive patients with advanced, smoking-associated solid tumor types, treated with single-agent anti-PD1/PDL1 therapy at a single center between July 2014 and February 2018. The primary outcome was overall survival from date of IO initiation. The secondary outcome was overall response, defined as radiographic complete response or partial response, by RECIST 1.1 criteria. The primary predictor was smoking status (former/current smoker vs. never smoker). The primary and secondary outcomes were analyzed using multivariable Cox proportional hazards models and multivariable logistic regression models, respectively. Models were adjusted for age and sex, and stratified by cancer type. Results: The majority of patients were male (64%) with a history of smoking (72%); the average age was 67.1 ± 11.4 years. Cancer types represented were: non-small cell lung cancer (NSCLC, N = 81), hepatocellular carcinoma (HCC, N = 41), urothelial carcinoma (BLCA, N = 39), head and neck squamous cell carcinoma (HNSC, N = 21), and renal cell carcinoma (RCC, N = 18). Over a median follow-up of 11.3 months (range 0.5-53.2), there were 96 deaths and 27% of evaluable patients achieved radiographic response. Response was not evaluable in 27 patients. In multivariable regression analysis, smoking status was not significantly associated with overall survival nor overall response in any cancer type examined (Table). Conclusions: Smoking status was not associated with outcomes in our cohort of IO-treated patients with smoking-associated cancers, though sample size was limited. [Table: see text]

The RNA-binding protein Musashi 1 stabilizes the oncotachykinin 1 mRNA in breast cancer cells to promote cell growth

Substance P and its truncated receptor exert oncogenic effects. The high production of substance P in breast cancer cells (BCCs) is caused by the enhancement of tachykinin (TAC)1 translation by cytosolic factor. In vitro translational studies and mRNA stabilization analyses indicate that BCCs contain the factor needed to increase TAC1 translation and to stabilize the mRNA. Prediction of protein folding, RNA-shift analysis, and proteomic analysis identified a 40 kDa molecule that interacts with the noncoding exon 7. Western blot analysis and RNA supershift identified Musashi 1 (Msi1) as the binding protein. Ectopic expression of TAC1 in nontumorigenic breast cells (BCs) indicates that TAC1 regulates its stability by increasing Msi1. Using a reporter gene system, we showed that Msi1 competes with microRNA (miR)130a and -206 for the 3' UTR of exon 7/TAC1. In the absence of Msi1 and miR130a and -206, reporter gene activity decreased, indicating that Msi1 expression limits TAC1 expression. Tumor growth was significantly decreased when nude BALB/c mice were injected with Msi1-knockdown BCCs. In summary, the RNA-binding protein Msi1 competes with miR130a and -206 for interaction with TAC1 mRNA, to stabilize and increase its translation. Consequently, these interactions increase tumor growth.