Current Status and Application of Proton Therapy for Esophageal Cancer

Esophageal cancer remains one of the leading causes of death from cancer across the world despite advances in multimodality therapy. Although early-stage disease can often be treated surgically, the current state of the art for locally advanced disease is concurrent chemoradiation, followed by surgery whenever possible. The uniform midline tumor location puts a strong importance on the need for precise delivery of radiation that would minimize dose to the heart and lungs, and the biophysical properties of proton beam makes this modality potential ideal for esophageal cancer treatment. This review covers the current state of knowledge of proton therapy for esophageal cancer, focusing on published retrospective single- and multi-institutional clinical studies, and emerging data from prospective clinical trials, that support the benefit of protons vs photon-based radiation in reducing postoperative complications, cardiac toxicity, and severe radiation induced immune suppression, which may improve survival outcomes for patients. In addition, we discuss the incorporation of immunotherapy to the curative management of esophageal cancers in the not-too-distant future. However, there is still a lack of high-level evidence to support proton therapy in the treatment of esophageal cancer, and proton therapy has its limitations in clinical application. It is expected to see the results of future large-scale randomized clinical trials and the continuous improvement of proton radiotherapy technology

Effect of Dexamethasone on Dyspnoea in Patients With Cancer (Abcd): A Parallel-Group, Double-Blind, Randomised, Controlled Trial

BACKGROUND: Systemic corticosteroids are commonly prescribed for palliation of dyspnoea in patients with cancer, despite scarce evidence to support their use. We aimed to assess the effect of high-dose dexamethasone versus placebo on cancer-related dyspnoea. METHODS: This double-blind, multi-site, parallel group randomized trial enrolled ambulatory patients with cancer, age ≥18, average dyspnea intensity over the past week ≥4/10 in a 0–10 point numeric rating scale and randomly assigned them to receive dexamethasone 8 mg orally every 12 hours for 7 days followed by 4 mg orally every 12 hours for 7 days or matching placebo capsules. Pharmacists conducted permuted block randomization (block size=6, 2:1) stratified by baseline dyspnea and study site. Patients, research staff and clinicians were blinded. The primary outcome was change in dyspnea intensity assessed with a 0–10 numeric rating scale (0=none, 10=worst) between baseline and day 7. Comparisons between groups were done by modified intention-totreat analysis. This study is registered with ClinicalTrials.gov, NCT03367156. Enrollment was stopped after second pre-planned interim analysis when futility criterion was met. FINDINGS: Between Jan 11, 2018, and April 23, 2021, we screened 2867 patients, enrolled 149 patients, and randomly assigned 128 to dexamethasone (n=85) or placebo (n=43). The mean change in dyspnoea NRS intensity from baseline to day 7 (±2 days) was -1·6 (95% CI -2·0 to -1·2) in the dexamethasone group and -1·6 (-2·3 to -0·9) in the placebo group, with no significant between-group difference (mean 0 [95% CI -0·8 to 0·7]; p=0·48). The most common all-cause grade 3-4 adverse events were infections (nine [11%] of 85 patients in the dexamethasone group vs three [7%] of 43 in the placebo group), insomnia (seven [8%] vs one [2%]), and neuropsychiatric symptoms (three [4%] vs none [0%]). Serious adverse events, all resulting in hospital admissions, were reported in 24 (28%) of 85 patients in the dexamethasone group and in three (7%) of 43 patients in the placebo group. No treatment-related deaths occurred in either group. INTERPRETATION: High-dose dexamethasone did not improve dyspnoea in patients with cancer more effectively than placebo and was associated with a higher frequency of adverse events. These data suggest that dexamethasone should not be routinely given to unselected patients with cancer for palliation of dyspnoea. FUNDING: US National Cancer Institute

Distinct Patterns of Auto-Reactive Antibodies Associated With Organ-Specific Immune-Related Adverse Events

UNLABELLED: The roles of preexisting auto-reactive antibodies in immune-related adverse events (irAEs) associated with immune checkpoint inhibitor therapy are not well defined. Here, we analyzed plasma samples longitudinally collected at predefined time points and at the time of irAEs from 58 patients with immunotherapy naïve metastatic non-small cell lung cancer treated on clinical protocol with ipilimumab and nivolumab. We used a proteomic microarray system capable of assaying antibody reactivity for IgG and IgM fractions against 120 antigens for systemically evaluating the correlations between auto-reactive antibodies and certain organ-specific irAEs. We found that distinct patterns of auto-reactive antibodies at baseline were associated with the subsequent development of organ-specific irAEs. Notably, ACHRG IgM was associated with pneumonitis, anti-cytokeratin 19 IgM with dermatitis, and anti-thyroglobulin IgG with hepatitis. These antibodies merit further investigation as potential biomarkers for identifying high-risk populations for irAEs and/or monitoring irAEs during immunotherapy treatment. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT03391869

Benchmarking Outcomes for Molecularly Characterized Synchronous Oligo metastatic Non-Small-Cell Lung Cancer Reveals EGFR Mutations to Be Associated With Longer Overall Survival

PURPOSE: Local consolidative therapy (LCT) for patients with synchronous oligometastatic non-small-cell lung cancer is an evolving treatment strategy, but outcomes following LCT stratified by genetic mutations have not been reported. We sought to identify genomic associations with overall survival (OS) and progression-free survival (PFS) for these patients. METHODS: We identified all patients presenting between 2000 and 2017 with stage IV non-small-cell lung cancer and ≤ 3 synchronous metastatic sites. Patients were grouped according to mutational statuses. Primary outcomes included OS and PFS following initial diagnosis. RESULTS: Of 194 included patients, 121 received comprehensive LCT to all sites of disease with either surgery or radiation. CONCLUSION: When compared with wild-type patients, those wit

Incidence and Risk Factors for Pneumonitis Associated With Checkpoint Inhibitors in Advanced Non-Small Cell Lung Cancer: A Single Center Experience

INTRODUCTION: Immune checkpoint inhibitor (ICI) pneumonitis causes substantial morbidity and mortality. Estimates of real-world incidence and reported risk factors vary substantially. METHODS: We conducted a retrospective review of 419 patients with advanced non-small cell lung cancer (NSCLC) who were treated with anti-PD-(L)1 with or without anti-CTLA-4 therapy. Clinical, imaging, and microbiological data were evaluated by multidisciplinary adjudication teams. The primary outcome of interest was grade ≥2 (CTCAEv5) pneumonitis. Clinicopathologic variables, tobacco use, cancer therapies, and preexisting lung disease were assessed for univariate effects using Cox proportional hazards models. We created multivariate Cox proportional hazards models to assess risk factors for pneumonitis and mortality. Pneumonitis, pneumonia, and progression were modeled as time-dependent variables in mortality models. RESULTS: We evaluated 419 patients between 2013 and 2021. The cumulative incidence of pneumonitis was 9.5% (40/419). In a multivariate model, pneumonitis increased the risk for mortality (HR 1.6, 95% CI, 1.0-2.5), after adjustment for disease progression (HR 1.6, 95% CI, 1.4-1.8) and baseline shortness of breath (HR 1.5, 95% CI, 1.2-2.0). Incomplete resolution was more common with more severe pneumonitis. Interstitial lung disease was associated with higher risk for pneumonitis (HR 5.4, 95% CI, 1.1-26.6), particularly in never smokers (HR 26.9, 95% CI, 2.8-259.0). CONCLUSION: Pneumonitis occurred at a high rate and significantly increased mortality. Interstitial lung disease, particularly in never smokers, increased the risk for pneumonitis

Efficacy and Clinicogenomic Correlates of Response to Immune Checkpoint Inhibitors Alone or With Chemotherapy in Non-Small Cell Lung Cancer

The role of combination chemotherapy with immune checkpoint inhibitors (ICI) (ICI-chemo) over ICI monotherapy (ICI-mono) in non-small cell lung cancer (NSCLC) remains underexplored. In this retrospective study of 1133 NSCLC patients, treatment with ICI-mono vs ICI-chemo associate with higher rates of early progression, but similar long-term progression-free and overall survival. Sequential vs concurrent ICI and chemotherapy have similar long-term survival, suggesting no synergism from combination therapy. Integrative modeling identified PD-L1, disease burden (Stage IVb; liver metastases), and STK11 and JAK2 alterations as features associate with a higher likelihood of early progression on ICI-mono. CDKN2A alterations associate with worse long-term outcomes in ICI-chemo patients. These results are validated in independent external (n = 89) and internal (n = 393) cohorts. This real-world study suggests that ICI-chemo may protect against early progression but does not influence overall survival, and nominates features that identify those patients at risk for early progression who may maximally benefit from ICI-chemo

Predicting Benefit From Immune Checkpoint Inhibitors in Patients With Non-Small-Cell Lung Cancer by CT-Based Ensemble Deep Learning: A Retrospective Study

BACKGROUND: Only around 20-30% of patients with non-small-cell lung cancer (NCSLC) have durable benefit from immune-checkpoint inhibitors. Although tissue-based biomarkers (eg, PD-L1) are limited by suboptimal performance, tissue availability, and tumour heterogeneity, radiographic images might holistically capture the underlying cancer biology. We aimed to investigate the application of deep learning on chest CT scans to derive an imaging signature of response to immune checkpoint inhibitors and evaluate its added value in the clinical context. METHODS: In this retrospective modelling study, 976 patients with metastatic, EGFR/ALK negative NSCLC treated with immune checkpoint inhibitors at MD Anderson and Stanford were enrolled from Jan 1, 2014, to Feb 29, 2020. We built and tested an ensemble deep learning model on pretreatment CTs (Deep-CT) to predict overall survival and progression-free survival after treatment with immune checkpoint inhibitors. We also evaluated the added predictive value of the Deep-CT model in the context of existing clinicopathological and radiological metrics. FINDINGS: Our Deep-CT model demonstrated robust stratification of patient survival of the MD Anderson testing set, which was validated in the external Stanford set. The performance of the Deep-CT model remained significant on subgroup analyses stratified by PD-L1, histology, age, sex, and race. In univariate analysis, Deep-CT outperformed the conventional risk factors, including histology, smoking status, and PD-L1 expression, and remained an independent predictor after multivariate adjustment. Integrating the Deep-CT model with conventional risk factors demonstrated significantly improved prediction performance, with overall survival C-index increases from 0·70 (clinical model) to 0·75 (composite model) during testing. On the other hand, the deep learning risk scores correlated with some radiomics features, but radiomics alone could not reach the performance level of deep learning, indicating that the deep learning model effectively captured additional imaging patterns beyond known radiomics features. INTERPRETATION: This proof-of-concept study shows that automated profiling of radiographic scans through deep learning can provide orthogonal information independent of existing clinicopathological biomarkers, bringing the goal of precision immunotherapy for patients with NSCLC closer

A clinical Raman spectroscopy system for real-time disease diagnosis

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references.by Saumil J. Gandhi.M.Eng

Real-time Raman system for in vivo disease diagnosis

Raman spectroscopy has been well established as a powerful in vitro method for studying biological tissue and diagnosing disease. The recent development of efficient, high-throughput, low-background optical fiber Raman probes provides, for the first time, the opportunity to obtain real-time performance in the clinic. We present an instrument for in vivo tissue analysis which is capable of collecting and processing Raman spectra in less than 2 s. This is the first demonstration that data acquisition, analysis, and diagnostics can be performed in clinically relevant times. The instrument is designed to work with the new Raman probes and includes custom written LabVIEW and Matlab programs to provide accurate spectral calibration, analysis, and diagnosis along with important safety features related to laser exposure. The real-time capabilities of the system were demonstrated in vivo during femoral bypass and breast lumpectomy surgeries. Such a system will greatly facilitate the adoption of Raman spectroscopy into clinical research and practice.National Institutes of Health (U.S.) (Grant R01-HL-64675)National Center for Research Resources (U.S.) (Grant P41-RR-02594)Pfizer Inc.Cameron and Hayden Lord Foundatio