Molecular profiling of single circulating tumor cells from lung cancer patients.

Circulating tumor cells (CTCs) are established cancer biomarkers for the "liquid biopsy" of tumors. Molecular analysis of single CTCs, which recapitulate primary and metastatic tumor biology, remains challenging because current platforms have limited throughput, are expensive, and are not easily translatable to the clinic. Here, we report a massively parallel, multigene-profiling nanoplatform to compartmentalize and analyze hundreds of single CTCs. After high-efficiency magnetic collection of CTC from blood, a single-cell nanowell array performs CTC mutation profiling using modular gene panels. Using this approach, we demonstrated multigene expression profiling of individual CTCs from non-small-cell lung cancer (NSCLC) patients with remarkable sensitivity. Thus, we report a high-throughput, multiplexed strategy for single-cell mutation profiling of individual lung cancer CTCs toward minimally invasive cancer therapy prediction and disease monitoring.

Automated model versus treating physician for predicting survival time of patients with metastatic cancer.

OBJECTIVE: Being able to predict a patient's life expectancy can help doctors and patients prioritize treatments and supportive care. For predicting life expectancy, physicians have been shown to outperform traditional models that use only a few predictor variables. It is possible that a machine learning model that uses many predictor variables and diverse data sources from the electronic medical record can improve on physicians' performance. For patients with metastatic cancer, we compared accuracy of life expectancy predictions by the treating physician, a machine learning model, and a traditional model. MATERIALS AND METHODS: A machine learning model was trained using 14 600 metastatic cancer patients' data to predict each patient's distribution of survival time. Data sources included note text, laboratory values, and vital signs. From 2015-2016, 899 patients receiving radiotherapy for metastatic cancer were enrolled in a study in which their radiation oncologist estimated life expectancy. Survival predictions were also made by the machine learning model and a traditional model using only performance status. Performance was assessed with area under the curve for 1-year survival and calibration plots. RESULTS: The radiotherapy study included 1190 treatment courses in 899 patients. A total of 879 treatment courses in 685 patients were included in this analysis. Median overall survival was 11.7 months. Physicians, machine learning model, and traditional model had area under the curve for 1-year survival of 0.72 (95% CI 0.63-0.81), 0.77 (0.73-0.81), and 0.68 (0.65-0.71), respectively. CONCLUSIONS: The machine learning model's predictions were more accurate than those of the treating physician or a traditional model.

Predicting Survival for Patients With Metastatic Disease.

PURPOSE: This prospective study aimed to determine the accuracy of radiation oncologists in predicting the survival of patients with metastatic disease receiving radiation therapy and to understand factors associated with their accuracy. METHODS AND MATERIALS: This single-institution study surveyed 22 attending radiation oncologists to estimate patient survival. Survival predictions were defined as accurate if the observed survival (OS) was within the correct survival prediction category (0-6 months, >6-12 months, >12-24 months, and >24 months). The physicians made survival estimates for each course of radiation, yielding 877 analyzable predictions for 689 unique patients. Data analysis included Stuart's Tau C, logistic regression models, ordinal logistic regression models, and stepwise selection to examine variable interactions. RESULTS: Of the 877 radiation oncologists' predictions, 39.7% were accurate, 26.5% were underestimations, and 33.9% were overestimations. Stuart's Tau C showed low correlation between OS and survival estimates (0.3499), consistent with the inaccuracy reported in the literature. However, results showed less systematic overprediction than reported in the literature. Karnofsky performance status was the most significant predictor of accuracy, with greater accuracy for patients with shorter OS. Estimates were also more accurate for patients with lower Karnofsky performance status. Accuracy by patient age varied by primary site and race. Physician years of experience did not correlate with accuracy. CONCLUSIONS: The sampled radiation oncologists have a 40% accuracy in predicting patient survival. Future investigation should explore how survival estimates influence treatment decisions and how to improve survival prediction accuracy.

18F-EF5 PET-based Imageable Hypoxia Predicts Local Recurrence in Tumors Treated With Highly Conformal Radiation Therapy.

PURPOSE: Tumor hypoxia contributes to radiation resistance. A noninvasive assessment of tumor hypoxia would be valuable for prognostication and possibly selection for hypoxia-targeted therapies. 18F-pentafluorinated etanidazole (18F-EF5) is a nitroimidazole derivative that has demonstrated promise as a positron emission tomography (PET) hypoxia imaging agent in preclinical and clinical studies. However, correlation of imageable hypoxia by 18F-EF5 PET with clinical outcomes after radiation therapy remains limited. METHODS AND MATERIALS: Our study prospectively enrolled 28 patients undergoing radiation therapy for localized lung or other tumors to receive pretreatment 18F-EF5 PET imaging. Depending on the level of 18F-EF5 tumor uptake, patients underwent functional manipulation of tumor oxygenation with either carbogen breathing or oral dichloroacetate followed by repeated 18F-EF5 PET. The hypoxic subvolume of tumor was defined as the proportion of tumor voxels exhibiting higher 18F-EF5 uptake than the 95th percentile of 18F-EF5 uptake in the blood pool. Tumors with a hypoxic subvolume ≥ 10% on baseline 18F-EF5 PET imaging were classified as hypoxic by imaging. A Cox model was used to assess the correlation between imageable hypoxia and clinical outcomes after treatment. RESULTS: At baseline, imageable hypoxia was demonstrated in 43% of all patients (12 of 28), including 6 of 16 patients with early-stage non-small cell lung cancer treated with stereotactic ablative radiation therapy and 6 of 12 patients with other cancers. Carbogen breathing was significantly associated with decreased imageable hypoxia, while dichloroacetate did not result in a significant change under our protocol conditions. Tumors with imageable hypoxia had a higher incidence of local recurrence at 12 months (30%) than those without (0%) (P < .01). CONCLUSIONS: Noninvasive hypoxia imaging by 18F-EF5 PET identified imageable hypoxia in about 40% of tumors in our study population. Local tumor recurrence after highly conformal radiation therapy was higher in tumors with imageable hypoxia.

Role of KEAP1/NRF2 and TP53 Mutations in Lung Squamous Cell Carcinoma Development and Radiation Resistance.

Lung squamous cell carcinoma (LSCC) pathogenesis remains incompletely understood, and biomarkers predicting treatment response remain lacking. Here, we describe novel murine LSCC models driven by loss of Trp53 and Keap1, both of which are frequently mutated in human LSCCs. Homozygous inactivation of Keap1 or Trp53 promoted airway basal stem cell (ABSC) self-renewal, suggesting that mutations in these genes lead to expansion of mutant stem cell clones. Deletion of Trp53 and Keap1 in ABSCs, but not more differentiated tracheal cells, produced tumors recapitulating histologic and molecular features of human LSCCs, indicating that they represent the likely cell of origin in this model. Deletion of Keap1 promoted tumor aggressiveness, metastasis, and resistance to oxidative stress and radiotherapy (RT). KEAP1/NRF2 mutation status predicted risk of local recurrence after RT in patients with non-small lung cancer (NSCLC) and could be noninvasively identified in circulating tumor DNA. Thus, KEAP1/NRF2 mutations could serve as predictive biomarkers for personalization of therapeutic strategies for NSCLCs. SIGNIFICANCE: We developed an LSCC mouse model involving Trp53 and Keap1, which are frequently mutated in human LSCCs. In this model, ABSCs are the cell of origin of these tumors. KEAP1/NRF2 mutations increase radioresistance and predict local tumor recurrence in radiotherapy patients. Our findings are of potential clinical relevance and could lead to personalized treatment strategies for tumors with KEAP1/NRF2 mutations. Cancer Discov; 7(1); 86-101. ©2016 AACR.This article is highlighted in the In This Issue feature, p. 1.

Early Detection of Molecular Residual Disease in Localized Lung Cancer by Circulating Tumor DNA Profiling.

Identifying molecular residual disease (MRD) after treatment of localized lung cancer could facilitate early intervention and personalization of adjuvant therapies. Here, we apply cancer personalized profiling by deep sequencing (CAPP-seq) circulating tumor DNA (ctDNA) analysis to 255 samples from 40 patients treated with curative intent for stage I-III lung cancer and 54 healthy adults. In 94% of evaluable patients experiencing recurrence, ctDNA was detectable in the first posttreatment blood sample, indicating reliable identification of MRD. Posttreatment ctDNA detection preceded radiographic progression in 72% of patients by a median of 5.2 months, and 53% of patients harbored ctDNA mutation profiles associated with favorable responses to tyrosine kinase inhibitors or immune checkpoint blockade. Collectively, these results indicate that ctDNA MRD in patients with lung cancer can be accurately detected using CAPP-seq and may allow personalized adjuvant treatment while disease burden is lowest.Significance: This study shows that ctDNA analysis can robustly identify posttreatment MRD in patients with localized lung cancer, identifying residual/recurrent disease earlier than standard-of-care radiologic imaging, and thus could facilitate personalized adjuvant treatment at early time points when disease burden is lowest. Cancer Discov; 7(12); 1394-403. ©2017 AACR.See related commentary by Comino-Mendez and Turner, p. 1368This article is highlighted in the In This Issue feature, p. 1355.

Severe Chest Wall Toxicity From Cryoablation in the Setting of Prior Stereotactic Ablative Radiotherapy.

We present the case of a 42-year-old woman with metastatic synovial sarcoma of parotid origin, treated definitively with chemoradiation, who subsequently developed oligometastatic disease limited to the lungs. She underwent multiple left and right lung wedge resections and left lower lobectomy, followed by right lower lobe stereotactic ablative radiotherapy (SABR), 54 Gy in three fractions to a right lower lobe lesion abutting the chest wall. Two years later, she was treated with cryoablation for a separate right upper lobe nodule abutting the chest wall. Two months later, she presented with acute shortness of breath, pleuritic chest pain, decreased peripheral blood O2 saturation, and productive cough. A computed tomography (CT) scan demonstrated severe chest wall necrosis in the area of recent cryoablation that, in retrospect, also received a significant radiation dose from her prior SABR. This case demonstrates that clinicians should exercise caution in using cryoablation when treating lung tumors abutting a previously irradiated chest wall. Note: Drs. Loo and Shah contributed equally as co-senior authors.

Tracheal Diverticulum Following Paratracheal Hypofractionated Radiotherapy in the Setting of Prior and Subsequent Bevacizumab.

We present the case of a 63-year-old woman with limited metastatic colorectal cancer to the lungs and liver treated with FOLFIRI-bevacizumab, followed by consolidative hypofractionated radiotherapy to right paratracheal metastatic lymphadenopathy. We treated the right paratracheal site with 60 Gy in 15 fractions (70 Gy equivalent dose in 2 Gy fractions). The patient tolerated the treatment well, and six months later started a five-month course of FOLFIRI-bevacizumab for new metastatic disease. She presented to our clinic six months after completing this, complaining of productive cough with scant hemoptysis, and was found to have localized tracheal wall breakdown and diverticulum in the region of prior high-dose radiation therapy, threatening to progress to catastrophic tracheovascular fistula. This was successfully repaired surgically after a lack of response to conservative measures. We urge caution in treating patients with vascular endothelial growth factor (VEGF) inhibitors in the setting of hypofractionated radiotherapy involving the mucosa of tubular organs, even when these treatments are separated by months. Though data is limited as to the impact of sequence, this may be particularly an issue when VEGF inhibitors follow prior radiotherapy.

Integrated digital error suppression for improved detection of circulating tumor DNA.

High-throughput sequencing of circulating tumor DNA (ctDNA) promises to facilitate personalized cancer therapy. However, low quantities of cell-free DNA (cfDNA) in the blood and sequencing artifacts currently limit analytical sensitivity. To overcome these limitations, we introduce an approach for integrated digital error suppression (iDES). Our method combines in silico elimination of highly stereotypical background artifacts with a molecular barcoding strategy for the efficient recovery of cfDNA molecules. Individually, these two methods each improve the sensitivity of cancer personalized profiling by deep sequencing (CAPP-Seq) by about threefold, and synergize when combined to yield ∼15-fold improvements. As a result, iDES-enhanced CAPP-Seq facilitates noninvasive variant detection across hundreds of kilobases. Applied to non-small cell lung cancer (NSCLC) patients, our method enabled biopsy-free profiling of EGFR kinase domain mutations with 92% sensitivity and >99.99% specificity at the variant level, and with 90% sensitivity and 96% specificity at the patient level. In addition, our approach allowed monitoring of NSCLC ctDNA down to 4 in 10(5) cfDNA molecules. We anticipate that iDES will aid the noninvasive genotyping and detection of ctDNA in research and clinical settings.

Pre-treatment non-target lung FDG-PET uptake predicts symptomatic radiation pneumonitis following Stereotactic Ablative Radiotherapy (SABR).

PURPOSE: To determine if pre-treatment non-target lung FDG-PET uptake predicts for symptomatic radiation pneumonitis (RP) following lung stereotactic ablative radiotherapy (SABR). METHODS: We reviewed a 258 patient database from our institution to identify 28 patients who experienced symptomatic (grade â©¾ 2) RP after SABR, and compared them to 57 controls who did not develop symptomatic RP. We compared clinical, dosimetric and functional imaging characteristics between the 2 cohorts including pre-treatment non-target lung FDG-PET uptake. RESULTS: Median follow-up time was 26.9 months. Patients who experienced symptomatic RP had significantly higher non-target lung FDG-PET uptake as measured by mean SUV (p < 0.0001) than controls. ROC analysis for symptomatic RP revealed area under the curve (AUC) of 0.74, with sensitivity 82.1% and specificity 57.9% with cutoff mean non-target lung SUV > 0.56. Predictive value increased (AUC of 0.82) when mean non-target lung SUV was combined with mean lung dose (MLD). We developed a 0-2 point model using these 2 variables, 1 point each for SUV > 0.56 or MLD > 5.88 Gy equivalent dose in 2 Gy per fraction (EQD2), predictive for symptomatic RP in our cohort with hazard ratio 10.01 for score 2 versus 0 (p < 0.001). CONCLUSIONS: Patients with elevated pre-SABR non-target lung FDG-PET uptake are at increased risk of symptomatic RP after lung SABR. Our predictive model suggests patients with mean non-target lung SUV > 0.56 and MLD > 5.88 Gy EQD2 are at highest risk. Our predictive model should be validated in an external cohort before clinical implementation.