Brief report on the efficacy of sotorasib in KRAS-Mutated NSCLC patients with brain metastases.

INTRODUCTION: Sotorasib has emerged as a treatment option for patients with KRAS-mutated non-small cell lung cancer (NSCLC); however, its effect in patients with brain metastases is not well described. We assessed the intracranial response of sotorasib in a retrospective case series of patients with brain metastases (BMs) at a single institution. METHODS: Patients with KRAS-mutated NSCLC with BMs who received sotorasib at Mass General Brigham Hospitals were included. Patients were stratified into three groups: patients with active BM without local therapy within one month of sotorasib initiation (group 1), patients with active BM with local therapy (surgery or radiation) within one month of sotorasib initiation (group 2), and patients with stable BM (group 3). Intracranial progression-free survival (ICPFS) and overall survival (OS) were explored using Kaplan Meier curves that were compared through log-rank test. RESULTS: Thirty patients were included (five in group 1; seven in group 2; 18 in group 3). Mean age at sotorasib initiation was 60 years. Most (67 %) patients had between one and four BMs at sotorasib initiation. Median ICPFS was three months (95 % CI: 0- 7.7) from start of sotorasib for group 1, two months (0-5.7) for group 2, and 15 months (6.0-24.0) for group 3 (p-value = 0.02). Median OS was four months (1.9-6.1) for group 1, six months (0-13.7) for group 2, and 12 months (3.5-20.5) for group 3 (p-value = 0.13). 57 % of patients experienced intracranial progression, including 44 % of patients who had stable BM at sotorasib initiation. CONCLUSION: While sotorasib may have some intracranial activity, a multidisciplinary approach to BM therapy is still warranted, as are future studies with larger patient samples, controls, and extended follow-up.

A Multidimensional Connectomics- and Radiomics-Based Advanced Machine-Learning Framework to Distinguish Radiation Necrosis from True Progression in Brain Metastases.

We introduce tumor connectomics, a novel MRI-based complex graph theory framework that describes the intricate network of relationships within the tumor and surrounding tissue, and combine this with multiparametric radiomics (mpRad) in a machine-learning approach to distinguish radiation necrosis (RN) from true progression (TP). Pathologically confirmed cases of RN vs. TP in brain metastases treated with SRS were included from a single institution. The region of interest was manually segmented as the single largest diameter of the T1 post-contrast (T1C) lesion plus the corresponding area of T2 FLAIR hyperintensity. There were 40 mpRad features and 6 connectomics features extracted, as well as 5 clinical and treatment factors. We developed an Integrated Radiomics Informatics System (IRIS) based on an Isomap support vector machine (IsoSVM) model to distinguish TP from RN using leave-one-out cross-validation. Class imbalance was resolved with differential misclassification weighting during model training using the IRIS. In total, 135 lesions in 110 patients were analyzed, including 43 cases (31.9%) of pathologically proven RN and 92 cases (68.1%) of TP. The top-performing connectomics features were three centrality measures of degree, betweenness, and eigenvector centralities. Combining these with the 10 top-performing mpRad features, an optimized IsoSVM model was able to produce a sensitivity of 0.87, specificity of 0.84, AUC-ROC of 0.89 (95% CI: 0.82-0.94), and AUC-PR of 0.94 (95% CI: 0.87-0.97).

Predicting leptomeningeal disease spread after resection of brain metastases using machine learning.

OBJECTIVE: The incidence of leptomeningeal disease (LMD) has increased as treatments for brain metastases (BMs) have improved and patients with metastatic disease are living longer. Sample sizes of individual studies investigating LMD after surgery for BMs and its risk factors have been limited, ranging from 200 to 400 patients at risk for LMD, which only allows the use of conventional biostatistics. Here, the authors used machine learning techniques to enhance LMD prediction in a cohort of surgically treated BMs. METHODS: A conditional survival forest, a Cox proportional hazards model, an extreme gradient boosting (XGBoost) classifier, an extra trees classifier, and logistic regression were trained. A synthetic minority oversampling technique (SMOTE) was used to train the models and handle the inherent class imbalance. Patients were divided into an 80:20 training and test set. Fivefold cross-validation was used on the training set for hyperparameter optimization. Patients eligible for study inclusion were adults who had consecutively undergone neurosurgical BM treatment, had been admitted to Brigham and Women's Hospital from January 2007 through December 2019, and had a minimum of 1 month of follow-up after neurosurgical treatment. RESULTS: A total of 1054 surgically treated BM patients were included in this analysis. LMD occurred in 168 patients (15.9%) at a median of 7.05 months after BM diagnosis. The discrimination of LMD occurrence was optimal using an XGboost algorithm (area under the curve = 0.83), and the time to LMD was prognosticated evenly by the random forest algorithm and the Cox proportional hazards model (C-index = 0.76). The most important feature for both LMD classification and regression was the BM proximity to the CSF space, followed by a cerebellar BM location. Lymph node metastasis of the primary tumor at BM diagnosis and a cerebellar BM location were the strongest risk factors for both LMD occurrence and time to LMD. CONCLUSIONS: The outcomes of LMD patients in the BM population are predictable using SMOTE and machine learning. Lymph node metastasis of the primary tumor at BM diagnosis and a cerebellar BM location were the strongest LMD risk factors.

Clinical validation of deep learning algorithms for radiotherapy targeting of non-small-cell lung cancer: an observational study.

BACKGROUND: Artificial intelligence (AI) and deep learning have shown great potential in streamlining clinical tasks. However, most studies remain confined to in silico validation in small internal cohorts, without external validation or data on real-world clinical utility. We developed a strategy for the clinical validation of deep learning models for segmenting primary non-small-cell lung cancer (NSCLC) tumours and involved lymph nodes in CT images, which is a time-intensive step in radiation treatment planning, with large variability among experts. METHODS: In this observational study, CT images and segmentations were collected from eight internal and external sources from the USA, the Netherlands, Canada, and China, with patients from the Maastro and Harvard-RT1 datasets used for model discovery (segmented by a single expert). Validation consisted of interobserver and intraobserver benchmarking, primary validation, functional validation, and end-user testing on the following datasets: multi-delineation, Harvard-RT1, Harvard-RT2, RTOG-0617, NSCLC-radiogenomics, Lung-PET-CT-Dx, RIDER, and thorax phantom. Primary validation consisted of stepwise testing on increasingly external datasets using measures of overlap including volumetric dice (VD) and surface dice (SD). Functional validation explored dosimetric effect, model failure modes, test-retest stability, and accuracy. End-user testing with eight experts assessed automated segmentations in a simulated clinical setting. FINDINGS: We included 2208 patients imaged between 2001 and 2015, with 787 patients used for model discovery and 1421 for model validation, including 28 patients for end-user testing. Models showed an improvement over the interobserver benchmark (multi-delineation dataset; VD 0·91 [IQR 0·83-0·92], p=0·0062; SD 0·86 [0·71-0·91], p=0·0005), and were within the intraobserver benchmark. For primary validation, AI performance on internal Harvard-RT1 data (segmented by the same expert who segmented the discovery data) was VD 0·83 (IQR 0·76-0·88) and SD 0·79 (0·68-0·88), within the interobserver benchmark. Performance on internal Harvard-RT2 data segmented by other experts was VD 0·70 (0·56-0·80) and SD 0·50 (0·34-0·71). Performance on RTOG-0617 clinical trial data was VD 0·71 (0·60-0·81) and SD 0·47 (0·35-0·59), with similar results on diagnostic radiology datasets NSCLC-radiogenomics and Lung-PET-CT-Dx. Despite these geometric overlap results, models yielded target volumes with equivalent radiation dose coverage to those of experts. We also found non-significant differences between de novo expert and AI-assisted segmentations. AI assistance led to a 65% reduction in segmentation time (5·4 min; p<0·0001) and a 32% reduction in interobserver variability (SD; p=0·013). INTERPRETATION: We present a clinical validation strategy for AI models. We found that in silico geometric segmentation metrics might not correlate with clinical utility of the models. Experts' segmentation style and preference might affect model performance. FUNDING: US National Institutes of Health and EU European Research Council.

Multiparametric radiomic tissue signature and machine learning for distinguishing radiation necrosis from tumor progression after stereotactic radiosurgery.

BACKGROUND: Stereotactic radiosurgery (SRS) may cause radiation necrosis (RN) that is difficult to distinguish from tumor progression (TP) by conventional MRI. We hypothesize that MRI-based multiparametric radiomics (mpRad) and machine learning (ML) can differentiate TP from RN in a multi-institutional cohort. METHODS: Patients with growing brain metastases after SRS at 2 institutions underwent surgery, and RN or TP were confirmed by histopathology. A radiomic tissue signature (RTS) was selected from mpRad, as well as single T1 post-contrast (T1c) and T2 fluid-attenuated inversion recovery (T2-FLAIR) radiomic features. Feature selection and supervised ML were performed in a randomly selected training cohort (N = 95) and validated in the remaining cases (N = 40) using surgical pathology as the gold standard. RESULTS: One hundred and thirty-five discrete lesions (37 RN, 98 TP) from 109 patients were included. Radiographic diagnoses by an experienced neuroradiologist were concordant with histopathology in 67% of cases (sensitivity 69%, specificity 59% for TP). Radiomic analysis indicated institutional origin as a significant confounding factor for diagnosis. A random forest model incorporating 1 mpRad, 4 T1c, and 4 T2-FLAIR features had an AUC of 0.77 (95% confidence interval [CI]: 0.66-0.88), sensitivity of 67% and specificity of 86% in the training cohort, and AUC of 0.71 (95% CI: 0.51-0.91), sensitivity of 52% and specificity of 90% in the validation cohort. CONCLUSIONS: MRI-based mpRad and ML can distinguish TP from RN with high specificity, which may facilitate the triage of patients with growing brain metastases after SRS for repeat radiation versus surgical intervention.

Analysis of Spatial Dose-Volume Relationships and Decline in Sexual Function Following Permanent Brachytherapy for Prostate Cancer.

OBJECTIVE: To explore relationships between dose to periprostatic anatomic structures and erectile dysfunction (ED) outcomes in an institutional cohort treated with prostate brachytherapy. METHODS: The Sexual Health Inventory for Men (SHIM) instrument was administered for stage cT1-T2 prostate cancer patients treated with Pd-103 brachytherapy over a 10-year interval. Dose volume histograms for regional organs at risk and periprostatic regions were calculated with and without expansions to account for contouring uncertainty. Regression tree analysis clustered patients into ED risk groups. RESULTS: We identified 115 men treated with definitive prostate brachytherapy who had 2 years of complete follow-up. On univariate analysis, the subapical region (SAR) caudal to prostate was the only defined region with dose volume histograms parameters significant for potency outcomes. Regression tree analysis separated patients into low ED risk (mean 2-year SHIM 20.03), medium ED risk (15.02), and high ED risk (5.54) groups. Among patients with good baseline function (SHIM ≥ 17), a dose ≥72.75 Gy to 20% of the SAR with 1 cm expansion was most predictive for 2-year potency outcome. On multivariate analysis, regression tree risk group remained significant for predicting potency outcomes even after adjustment for baseline SHIM and age. CONCLUSION: Dose to the SAR immediately caudal to prostate was predictive for potency outcomes in patients with good baseline function. Minimization of dose to this region may improve potency outcomes following prostate brachytherapy.

Updated risk models demonstrate low risk of symptomatic radionecrosis following stereotactic radiosurgery for brain metastases.

BACKGROUND: Improvements in systemic therapy continue to increase survival for patients with brain metastases. Updated dosimetric models are required to optimize long-term safety of stereotactic radiosurgery (SRS) for this indication. METHODS: Patients at a single institution receiving SRS from December 2011 to December 2014 were retrospectively reviewed. Patients with radiographic progression of at least one lesion, and with at least 6 months of follow-up from the start of SRS were included. Grade 3 necrosis was defined as requiring surgical intervention. This data were combined with two additional published datasets to construct logistic models describing necrosis risk as a function of dose and volume. RESULTS: From our institution, 294 brain metastases across 57 patients in 139 treatment plans met inclusion criteria. Primary histologies included non-small cell lung cancer (n = 19), melanoma (n = 13), breast carcinoma (n = 9), renal cell carcinoma (n = 7), and other (n = 9). Median follow-up from SRS of first cranial metastasis was 21.7 months (range: 6.3-56.6) and median overall survival was 25.6 months (range: 6.5-56.6). There were eight cases of Grade 1-2 and two cases of Grade 3 necrosis. As a useful clinical reference point, 20 cc of total brain receiving a single-fraction equivalent dose ≥14 Gy corresponded to 12.1% risk for Grade 1-3 (P < 0.003) and 3.4% risk for Grade 3 necrosis (P < 0.001). CONCLUSIONS: These results compare favorably with the QUANTEC brain tolerance estimates for radiosurgery, providing optimism for lower toxicity in the modern era. Additional studies are needed to determine dose tolerance parameters across a broad spectrum of patients.

Predicting acute radiation induced xerostomia in head and neck Cancer using MR and CT Radiomics of parotid and submandibular glands.

PURPOSE: To analyze baseline CT/MR-based image features of salivary glands to predict radiation-induced xerostomia 3-months after head-and-neck cancer (HNC) radiotherapy. METHODS: A retrospective analysis was performed on 266 HNC patients who were treated using radiotherapy at our institution between 2009 and 2018. CT and T1 post-contrast MR images along with NCI-CTCAE xerostomia grade (3-month follow-up) were prospectively collected at our institution. CT and MR images were registered on which parotid/submandibular glands were contoured. Image features were extracted for ipsilateral/contralateral parotid and submandibular glands relative to the location of the primary tumor. Dose-volume-histogram (DVH) parameters were also acquired. Features were pre-selected based on Spearman correlation before modelling by examining the correlation with xerostomia (p < 0.05). A shrinkage regression analysis of the pre-selected features was performed using LASSO. The internal validity of the variable selection was estimated by repeating the entire variable selection procedure using a leave-one-out-cross-validation. The most frequently selected variables were considered in the final model. A generalized linear regression with repeated ten-fold cross-validation was developed to predict radiation-induced xerostomia at 3-months after radiotherapy. This model was tested in an independent dataset (n = 50) of patients who were treated at the same institution in 2017-2018. We compared the prediction performances under eight conditions (DVH-only, CT-only, MR-only, CT + MR, DVH + CT, DVH + CT + MR, Clinical+CT + MR, and Clinical+DVH + CT + MR) using the area under the receiver operating characteristic curve (ROC-AUC). RESULTS: Among extracted features, 7 CT, 5 MR, and 2 DVH features were selected. The internal cohort (n = 216) ROC-AUC values for DVH, CT, MR, and Clinical+DVH + CT + MR features were 0.73 ± 0.01, 0.69 ± 0.01, 0.70 ± 0.01, and 0.79 ± 0.01, respectively. The validation cohort (n = 50) ROC-AUC values for DVH, CT, MR, and Clinical+DVH + CT + MR features were 0.63, 0.57, 0.66, and 0.68, respectively. The DVH-ROC was not significantly different than the CT-ROC (p = 0.8) or MR-ROC (p = 0.4). However, the CT + MR-ROC was significantly different than the CT-ROC (p = 0.03), but not the Clinical+DVH + CT + MR model (p = 0.5). CONCLUSION: Our results suggest that baseline CT and MR image features may reflect baseline salivary gland function and potential risk for radiation injury. The integration of baseline image features into prediction models has the potential to improve xerostomia risk stratification with the ultimate goal of truly personalized HNC radiotherapy.

Assessing the Effectiveness of Systemic Therapy after Stereotactic Radiosurgery on Cancer Recurrence and All-Cause Mortality.

BACKGROUND: Patients with cancer often present with brain metastases in the setting of controlled extracranial disease, for which they receive stereotactic radiosurgery (SRS) and surgical resection. The role of systemic therapy after SRS is unclear. Brain metastasis indicates active cancer dissemination, and microscopic systemic disease may be present despite absence of gross disease as assessed by conventional imaging modalities. OBJECTIVE: The aim was to determine if post-SRS systemic therapy reduces the risk of brain relapse, systemic relapse, and death in patients with brain metastases and controlled extracranial disease. METHODS: We retrospectively reviewed the medical records of 67 patients with controlled extracranial disease who received SRS for brain metastases. Kaplan-Meier analysis and Cox proportional hazards regression were used to assess how post-SRS systemic therapy affected the risk of brain relapse, systemic relapse, and all-cause mortality. RESULTS: In our sample, 31% of patients received systemic therapy after SRS. Post-SRS systemic therapy did not affect median time to brain relapse (P = 0.43), systemic relapse (P = 0.16), or death (P = 0.33) by univariate analysis. After accounting for confounding factors such as cancer histology and age, post-SRS systemic therapy significantly reduced the risk of brain relapse (hazard ratio [HR], 0.22; P = 0.002) but not systemic relapse (HR, 0.38; P = 0.09) or all-cause mortality (HR, 2.16; P = 0.09). CONCLUSIONS: Only a minority of patients with brain metastases and controlled extracranial disease receive adjuvant systemic therapy after SRS, but those that do have a reduced risk of brain relapse. Post-SRS systemic therapy may act prophylactically to reduce the risk of intracranial cancer recurrence.

Distinguishing True Progression From Radionecrosis After Stereotactic Radiation Therapy for Brain Metastases With Machine Learning and Radiomics.

PURPOSE: Treatment effect or radiation necrosis after stereotactic radiosurgery (SRS) for brain metastases is a common phenomenon often indistinguishable from true progression. Radiomics is an emerging field that promises to improve on conventional imaging. In this study, we sought to apply a radiomics-based prediction model to the problem of diagnosing treatment effect after SRS. METHODS AND MATERIALS: We included patients in the Johns Hopkins Health System who were treated with SRS for brain metastases who subsequently underwent resection for symptomatic growth. We also included cases of likely treatment effect in which lesions grew but subsequently regressed spontaneously. Lesions were segmented semiautomatically on preoperative T1 postcontrast and T2 fluid-attenuated inversion recovery magnetic resonance imaging, and radiomic features were extracted with software developed in-house. Top-performing features on univariate logistic regression were entered into a hybrid feature selection/classification model, IsoSVM, with parameter optimization and further feature selection performed using leave-one-out cross-validation. Final model performance was assessed by 10-fold cross-validation with 100 repeats. All cases were independently reviewed by a board-certified neuroradiologist for comparison. RESULTS: We identified 82 treated lesions across 66 patients, with 77 lesions having pathologic confirmation. There were 51 radiomic features extracted per segmented lesion on each magnetic resonance imaging sequence. An optimized IsoSVM classifier based on top-ranked radiomic features had sensitivity and specificity of 65.38% and 86.67%, respectively, with an area under the curve of 0.81 on leave-one-out cross-validation. Only 73% of cases were classifiable by the neuroradiologist, with a sensitivity of 97% and specificity of 19%. CONCLUSIONS: Radiomics holds promise for differentiating between treatment effect and true progression in brain metastases treated with SRS. A predictive model built on radiomic features from an institutional cohort performed well on cross-validation testing. These results warrant further validation in independent datasets. Such work could prove invaluable for guiding management of individual patients and assessing outcomes of novel interventions.

Adjuvant radiotherapy and outcomes of presumed hemorrhagic melanoma brain metastases without malignant cells.

BACKGROUND: Patients with melanoma can present with a hemorrhagic intracranial lesion. Upon resection, pathology reports may not detect any malignant cells. However, the hemorrhage may obscure their presence and so physicians may still decide whether adjuvant radiotherapy should be applied. Here, we report on the outcomes of a series of patients with melanoma with hemorrhagic brain lesions that returned with no tumor cells. METHODS: All melanoma patients who had craniotomies from 2008 to 2017 at a single institution for hemorrhagic brain lesions were identified through retrospective chart review. Those who had pathology reports with no malignant cells were analyzed. Recurrence at the former site of hemorrhage and resection was the primary outcome. RESULTS: Ten patients met inclusion criteria, and the median follow-up time was 8.5 (1.8-27.3) months. At the time of craniotomy, the median number of brain lesions was 3 (1-25). Two patients had prior craniotomies, eight had prior radiation, and six had prior immunotherapy to the lesion of interest. After surgery, one patient received stereotactic radiosurgery (SRS) to the resection bed. Only one patient developed subsequent melanoma at the resection site; this patient developed the lesion recurrence once and had not received postoperative SRS. CONCLUSION: Although small foci of metastatic disease as a source of bleeding for some patients cannot be excluded, melanoma patients with a suspected hemorrhagic brain metastasis that shows no tumor cells on pathology may benefit from close observation. The local recurrence risk in such cases appears to be low, even without adjuvant radiation.

Prospective evaluation of patient reported swallow function with the Functional Assessment of Cancer Therapy (FACT), MD Anderson Dysphagia Inventory (MDADI) and the Sydney Swallow Questionnaire (SSQ) in head and neck cancer patients.

OBJECTIVES: The Functional Assessment of Cancer Therapy (FACT) instrument is comprised of a group of related and overlapping quality of life (QoL) questionnaires including a core general form, head and neck cancer (HNC)-specific items, and an expert-selected index (FACT-HNSI). Understanding how these relate to more HNC-specific instruments such as the MD Anderson Dysphagia Inventory (MDADI) and Sydney Swallow Questionnaire (SSQ) is vital for guiding their use in clinical trials. MATERIALS AND METHODS: HNC patients concurrently completed MDADI, SSQ, and FACT questionnaires at radiation oncology clinic visits (2015-2016). Spearman correlation coefficients were calculated between each FACT instrument and MDADI or SSQ. Unsupervised k-means cluster analyses were performed to identify clusters of similar QoL responses. Principal component analysis (PCA) identified the degree of variability explained by each instrument. RESULTS: We identified 631 instances (363 patients) where the questionnaires were completed concurrently. Correlations between the various FACT measures and SSQ or MDADI were all significant (p < 0.001), but FACT HNC-specific subscale and FACT-HNSI showed the strongest correlation with MDADI and SSQ. Clustering identified 3 distinct groups of responses when combining instruments either pairwise or three-way. PCA revealed that MDADI and FACT HNC-specific subscale provide similar and likely redundant information. CONCLUSION: FACT HNC-subscale and FACT-HNSI may be preferable over other FACT measures for use in clinical trials where patient-reported swallow function is evaluated. MDADI and FACT provide similar insights into HNC patient QoL while SSQ provides additional, complementary information which could serve to better stratify patients into groups with high, medium, and low QoL outcomes.

Effects of perineural invasion on biochemical recurrence and prostate cancer-specific survival in patients treated with definitive external beam radiotherapy.

OBJECTIVES: Perineural invasion (PNI) has not yet gained universal acceptance as an independent predictor of adverse outcomes for prostate cancer treated with external beam radiotherapy (EBRT). We analyzed the prognostic influence of PNI for a large institutional cohort of prostate cancer patients who underwent EBRT with and without androgen deprivation therapy (ADT). MATERIAL AND METHODS: We, retrospectively, reviewed prostate cancer patients treated with EBRT from 1993 to 2007 at our institution. The primary endpoint was biochemical failure-free survival (BFFS), with secondary endpoints of metastasis-free survival (MFS), prostate cancer-specific survival (PCSS), and overall survival (OS). Univariate and multivariable Cox proportional hazards models were constructed for all survival endpoints. Hazard ratios for PNI were analyzed for the entire cohort and for subsets defined by NCCN risk level. Additionally, Kaplan-Meier survival curves were generated for all survival endpoints after stratification by PNI status, with significant differences computed using the log-rank test. RESULTS: Of 888 men included for analysis, PNI was present on biopsy specimens in 187 (21.1%). PNI was associated with clinical stage, pretreatment PSA level, biopsy Gleason score, and use of ADT (all P<0.01). Men with PNI experienced significantly inferior 10-year BFFS (40.0% vs. 57.8%, P = 0.002), 10-year MFS (79.7% vs. 89.0%, P = 0.001), and 10-year PCSS (90.9% vs. 95.9%, P = 0.009), but not 10-year OS (67.5% vs. 77.5%, P = 0.07). On multivariate analysis, PNI was independently associated with inferior BFFS (P<0.001), but not MFS, PCSS, or OS. In subset analysis, PNI was associated with inferior BFFS (P = 0.04) for high-risk patients and with both inferior BFFS (P = 0.01) and PCSS (P = 0.05) for low-risk patients. Biochemical failure occurred in 33% of low-risk men with PNI who did not receive ADT compared to 8% for low-risk men with PNI treated with ADT (P = 0.01). CONCLUSION: PNI was an independently significant predictor of adverse survival outcomes in this large institutional cohort, particularly for patients with NCCN low-risk disease. PNI should be carefully considered along with other standard prognostic factors when treating these patients with EBRT. Supplementing EBRT with ADT may be beneficial for select low-risk patients with PNI though independent validation with prospective studies is recommended.

Quantitative Evaluation of Head and Neck Cancer Treatment-Related Dysphagia in the Development of a Personalized Treatment Deintensification Paradigm.

PURPOSE: To test the hypothesis that quantifying swallow function with multiple patient-reported outcome (PRO) instruments is an important strategy to yield insights in the development of personalized deintensified therapies seeking to reduce the risk of head and neck cancer (HNC) treatment-related dysphagia (HNCTD). METHODS AND MATERIALS: Irradiated HNC subjects seen in follow-up care (April 2015 to December 2015) who prospectively completed the Sydney Swallow Questionnaire (SSQ) and the MD Anderson Dysphagia Inventory (MDADI) concurrently on the web interface to our Oncospace database were evaluated. A correlation matrix quantified the relationship between the SSQ and MDADI. Machine-learning unsupervised cluster analysis using the elbow criterion and CLUSPLOT analysis to establish its validity was performed. RESULTS: We identified 89 subjects. The MDADI and SSQ scores were moderately but significantly correlated (correlation coefficient -0.69). K-means cluster analysis demonstrated that 3 unique statistical cohorts (elbow criterion) could be identified with CLUSPLOT analysis, confirming that 100% of variances were accounted for. Correlation coefficients between the individual items in the SSQ and the MDADI demonstrated weak to moderate negative correlation, except for SSQ17 (quality of life question). CONCLUSIONS: Pilot analysis demonstrates that the MDADI and SSQ are complementary. Three unique clusters of patients can be defined, suggesting that a unique dysphagia signature for HNCTD may be definable. Longitudinal studies relying on only a single PRO, such as MDADI, may be inadequate for classifying HNCTD.

Phase I/II study of resection and intraoperative cesium-131 radioisotope brachytherapy in patients with newly diagnosed brain metastases.

OBJECT: Resected brain metastases have a high rate of local recurrence without adjuvant therapy. Adjuvant whole-brain radiotherapy (WBRT) remains the standard of care with a local control rate > 90%. However, WBRT is delivered over 10-15 days, which can delay other therapy and is associated with acute and long-term toxicities. Permanent cesium-131 ((131)Cs) implants can be used at the time of metastatic resection, thereby avoiding the need for any additional therapy. The authors evaluated the safety, feasibility, and efficacy of a novel therapeutic approach with permanent (131)Cs brachytherapy at the resection for brain metastases. METHODS: After institutional review board approval was obtained, 24 patients with a newly diagnosed metastasis to the brain were accrued to a prospective protocol between 2010 and 2012. There were 10 frontal, 7 parietal, 4 cerebellar, 2 occipital, and 1 temporal metastases. Histology included lung cancer (16), breast cancer (2), kidney cancer (2), melanoma (2), colon cancer (1), and cervical cancer (1). Stranded (131)Cs seeds were placed as permanent volume implants. The prescription dose was 80 Gy at a 5-mm depth from the resection cavity surface. Distant metastases were treated with stereotactic radiosurgery (SRS) or WBRT, depending on the number of lesions. The primary end point was local (resection cavity) freedom from progression (FFP). Secondary end points included regional FFP, distant FFP, median survival, overall survival (OS), and toxicity. RESULTS: The median follow-up was 19.3 months (range 12.89-29.57 months). The median age was 65 years (range 45-84 years). The median size of resected tumor was 2.7 cm (range 1.5-5.5 cm), and the median volume of resected tumor was 10.31 cm(3) (range 1.77-87.11 cm(3)). The median number of seeds used was 12 (range 4-35), with a median activity of 3.82 mCi per seed (range 3.31-4.83 mCi) and total activity of 46.91 mCi (range 15.31-130.70 mCi). Local FFP was 100%. There was 1 adjacent leptomeningeal recurrence, resulting in a 1-year regional FFP of 93.8% (95% CI 63.2%-99.1%). One-year distant FFP was 48.4% (95% CI 26.3%-67.4%). Median OS was 9.9 months (95% CI 4.8 months, upper limit not estimated) and 1-year OS was 50.0% (95% CI 29.1%-67.8%). Complications included CSF leak (1), seizure (1), and infection (1). There was no radiation necrosis. CONCLUSIONS: The use of postresection permanent (131)Cs brachytherapy implants resulted in no local recurrences and no radiation necrosis. This treatment was safe, well tolerated, and convenient for patients, resulting in a short radiation treatment course, high response rate, and minimal toxicity. These findings merit further study with a multicenter trial.

Surveillance, epidemiology, and end results-based analysis of the impact of preoperative or postoperative radiotherapy on survival outcomes for T3N0 rectal cancer.

PURPOSE: Preoperative chemoradiation has been established as standard of care for T3/T4 node-positive rectal cancer. Recent work, however, has called into question the overall benefit of radiation for tumors with lower risk characteristics, particularly T3N0 rectal cancers. We retrospectively analyzed T3N0 rectal cancer patients and examined how outcomes differed according to the sequence of treatment received. METHODS: The Surveillance, Epidemiology, and End Results (SEER) database was used to analyze T3N0 rectal cancer cases diagnosed between 1998 and 2008. Treatment consisted of surgery alone (No RT), preoperative radiation followed by surgery (Neo-Adjuvant RT), or surgery followed by postoperative radiation (Adjuvant RT). Demographic and tumor characteristics of the three groups were compared using t-tests for the comparison of means. Survival information from the SEER database was utilized to estimate cause-specific survival (CSS) and to generate Kaplan-Meier survival curves. Multivariate analysis (MVA) of features associated with outcomes was conducted using Cox proportional hazards regression models with Adjuvant RT, Neo-Adjuvant RT, No RT, histological grade, tumor size, year of diagnosis, and demographic characteristics as covariates. RESULTS: 10-Year CSS estimates were 66.1% (95% CI 62.3-69.6%; P=0.02), 73.5% (95% CI 68.9-77.5%; P=0.02), and 76.1% (95% CI 72.4-79.4%; P=0.02), for No RT, Neo-Adjuvant RT, and Adjuvant RT, respectively. On MVA, Adjuvant RT (HR=0.688; 95% CI, 0.578-0.819; P<0.001) was associated with significantly decreased risk for cancer death. By contrast, Neo-Adjuvant RT was not significantly associated with improved cancer survival (HR=0.863; 95% CI, 0.715-1.043; P=0.127). CONCLUSION: Adjuvant RT was associated with significantly higher CSS when compared with surgery alone, while the benefit of Neo-Adjuvant RT was not significant. This indicates that surgery followed by Adjuvant RT may still be an important treatment plan for T3N0 rectal cancer with potentially significant survival advantages over other treatment sequences.

The mutational landscape of adenoid cystic carcinoma.

Adenoid cystic carcinomas (ACCs) are among the most enigmatic of human malignancies. These aggressive salivary gland cancers frequently recur and metastasize despite definitive treatment, with no known effective chemotherapy regimen. Here we determined the ACC mutational landscape and report the exome or whole-genome sequences of 60 ACC tumor-normal pairs. These analyses identified a low exonic somatic mutation rate (0.31 non-silent events per megabase) and wide mutational diversity. Notably, we found mutations in genes encoding chromatin-state regulators, such as SMARCA2, CREBBP and KDM6A, suggesting that there is aberrant epigenetic regulation in ACC oncogenesis. Mutations in genes central to the DNA damage response and protein kinase A signaling also implicate these processes. We observed MYB-NFIB translocations and somatic mutations in MYB-associated genes, solidifying the role of these aberrations as critical events in ACC. Lastly, we identified recurrent mutations in the FGF-IGF-PI3K pathway (30% of tumors) that might represent new avenues for therapy. Collectively, our observations establish a molecular foundation for understanding and exploring new treatments for ACC.

Recurrent somatic mutation of FAT1 in multiple human cancers leads to aberrant Wnt activation.

Aberrant Wnt signaling can drive cancer development. In many cancer types, the genetic basis of Wnt pathway activation remains incompletely understood. Here, we report recurrent somatic mutations of the Drosophila melanogaster tumor suppressor-related gene FAT1 in glioblastoma (20.5%), colorectal cancer (7.7%), and head and neck cancer (6.7%). FAT1 encodes a cadherin-like protein, which we found is able to potently suppress cancer cell growth in vitro and in vivo by binding ß-catenin and antagonizing its nuclear localization. Inactivation of FAT1 via mutation therefore promotes Wnt signaling and tumorigenesis and affects patient survival. Taken together, these data strongly point to FAT1 as a tumor suppressor gene driving loss of chromosome 4q35, a prevalent region of deletion in cancer. Loss of FAT1 function is a frequent event during oncogenesis. These findings address two outstanding issues in cancer biology: the basis of Wnt activation in non-colorectal tumors and the identity of a 4q35 tumor suppressor.

Genome sequencing identifies a basis for everolimus sensitivity.

Cancer drugs often induce dramatic responses in a small minority of patients. We used whole-genome sequencing to investigate the genetic basis of a durable remission of metastatic bladder cancer in a patient treated with everolimus, a drug that inhibits the mTOR (mammalian target of rapamycin) signaling pathway. Among the somatic mutations was a loss-of-function mutation in TSC1 (tuberous sclerosis complex 1), a regulator of mTOR pathway activation. Targeted sequencing revealed TSC1 mutations in about 8% of 109 additional bladder cancers examined, and TSC1 mutation correlated with everolimus sensitivity. These results demonstrate the feasibility of using whole-genome sequencing in the clinical setting to identify previously occult biomarkers of drug sensitivity that can aid in the identification of patients most likely to respond to targeted anticancer drugs.

An integrative probabilistic model for identification of structural variation in sequencing data.

Paired-end sequencing is a common approach for identifying structural variation (SV) in genomes. Discrepancies between the observed and expected alignments indicate potential SVs. Most SV detection algorithms use only one of the possible signals and ignore reads with multiple alignments. This results in reduced sensitivity to detect SVs, especially in repetitive regions. We introduce GASVPro, an algorithm combining both paired read and read depth signals into a probabilistic model which can analyze multiple alignments of reads. GASVPro outperforms existing methods with a 50-90% improvement in specificity on deletions and a 50% improvement on inversions.