Overall Survival Among Patients With De Novo Stage IV Metastatic and Distant Metastatic Recurrent Non-Small Cell Lung Cancer.

Importance: Despite recent breakthroughs in therapy, advanced lung cancer still poses a therapeutic challenge. The survival profile of patients with metastatic lung cancer remains poorly understood by metastatic disease type (ie, de novo stage IV vs distant recurrence). Objective: To evaluate the association of metastatic disease type on overall survival (OS) among patients with non-small cell lung cancer (NSCLC) and to identify potential mechanisms underlying any survival difference. Design, Setting, and Participants: Cohort study of a national US population based at a tertiary referral center in the San Francisco Bay Area using participant data from the National Lung Screening Trial (NLST) who were enrolled between 2002 and 2004 and followed up for up to 7 years as the primary cohort and patient data from Stanford Healthcare (SHC) for diagnoses between 2009 and 2019 and followed up for up to 13 years as the validation cohort. Participants from NLST with de novo metastatic or distant recurrent NSCLC diagnoses were included. Data were analyzed from January 2021 to March 2023. Exposures: De novo stage IV vs distant recurrent metastatic disease. Main Outcomes and Measures: OS after diagnosis of metastatic disease. Results: The NLST and SHC cohort consisted of 660 and 180 participants, respectively (411 men [62.3%] vs 109 men [60.6%], 602 White participants [91.2%] vs 111 White participants [61.7%], and mean [SD] age of 66.8 [5.5] vs 71.4 [7.9] years at metastasis, respectively). Patients with distant recurrence showed significantly better OS than patients with de novo metastasis (adjusted hazard ratio [aHR], 0.72; 95% CI, 0.60-0.87; P < .001) in NLST, which was replicated in SHC (aHR, 0.64; 95% CI, 0.43-0.96; P = .03). In SHC, patients with de novo metastasis more frequently progressed to the bone (63 patients with de novo metastasis [52.5%] vs 19 patients with distant recurrence [31.7%]) or pleura (40 patients with de novo metastasis [33.3%] vs 8 patients with distant recurrence [13.3%]) than patients with distant recurrence and were primarily detected through symptoms (102 patients [85.0%]) as compared with posttreatment surveillance (47 patients [78.3%]) in the latter. The main finding remained consistent after further adjusting for metastasis sites and detection methods. Conclusions and Relevance: In this cohort study, patients with distant recurrent NSCLC had significantly better OS than those with de novo disease, and the latter group was associated with characteristics that may affect overall survival. This finding can help inform future clinical trial designs to ensure a balance for baseline patient characteristics.

Predictive Model to Guide Brain Magnetic Resonance Imaging Surveillance in Patients With Metastatic Lung Cancer: Impact on Real-World Outcomes.

PURPOSE: Brain metastasis is common in lung cancer, and treatment of brain metastasis can lead to significant morbidity. Although early detection of brain metastasis may improve outcomes, there are no prediction models to identify high-risk patients for brain magnetic resonance imaging (MRI) surveillance. Our goal is to develop a machine learning-based clinicogenomic prediction model to estimate patient-level brain metastasis risk. METHODS: A penalized regression competing risk model was developed using 330 patients diagnosed with lung cancer between January 2014 and June 2019 and followed through June 2021 at Stanford HealthCare. The main outcome was time from the diagnosis of distant metastatic disease to the development of brain metastasis, death, or censoring. RESULTS: Among the 330 patients, 84 (25%) developed brain metastasis over 627 person-years, with a 1-year cumulative brain metastasis incidence of 10.2% (95% CI, 6.8 to 13.6). Features selected for model inclusion were histology, cancer stage, age at diagnosis, primary site, and RB1 and ALK alterations. The prediction model yielded high discrimination (area under the curve 0.75). When the cohort was stratified by risk using a 1-year risk threshold of > 14.2% (85th percentile), the high-risk group had increased 1-year cumulative incidence of brain metastasis versus the low-risk group (30.8% v 6.1%, P < .01). Of 48 high-risk patients, 24 developed brain metastasis, and of these, 12 patients had brain metastasis detected more than 7 months after last brain MRI. Patients who missed this 7-month window had larger brain metastases (58% v 33% largest diameter > 10 mm; odds ratio, 2.80, CI, 0.51 to 13) versus those who had MRIs more frequently. CONCLUSION: The proposed model can identify high-risk patients, who may benefit from more intensive brain MRI surveillance to reduce morbidity of subsequent treatment through early detection.

Evaluation of Absolute Lymphocyte Count at Diagnosis and Mortality Among Patients With Localized Bone or Soft Tissue Sarcoma.

Importance: Host-related immune factors have been implicated in the development and progression of diverse malignant neoplasms. Identifying associations between immunologic laboratory parameters and overall survival may inform novel prognostic biomarkers and mechanisms of antitumor immunity in localized bone and soft tissue sarcoma. Objective: To assess whether lymphopenia at diagnosis is associated with overall survival among patients with localized bone and soft tissue sarcoma. Design, Setting, and Participants: This retrospective cohort study analyzed patients from the Stanford Cancer Institute with localized bone and soft tissue sarcoma between September 1, 1998, and November 1, 2018. Patients were included if laboratory values were available within 60 days of diagnosis and, if applicable, prior to the initiation of chemotherapy and/or radiotherapy. Statistical analysis was performed from January 1, 2019, to November 1, 2020. Exposures: Absolute lymphocyte count within 60 days of diagnosis and antimicrobial exposure, defined by the number of antimicrobial agent prescriptions and the cumulative duration of antimicrobial administration within 60 days of diagnosis. Main Outcomes and Measures: The association between minimum absolute lymphocyte count at diagnosis and 5-year overall survival probability was characterized with the Kaplan-Meier method and multivariate Cox proportional hazards regression models. Multivariable logistic regressions were fitted to evaluate whether patients with lymphopenia were at greater risk of increased antimicrobial exposure. Results: Among 634 patients, the median age at diagnosis was 53.7 years (interquartile range, 37.5-66.8 years), and 290 patients (45.7%) were women, with a 5-year survival probability of 67.9%. There was a significant inverse association between lymphopenia at diagnosis and overall survival (hazard ratio [HR], 1.82; 95% CI, 1.39-1.40), resulting in a 13.5% 5-year survival probability difference compared with patients who did not have lymphopenia at diagnosis (60.2% vs 73.7% for those who never had lymphopenia). In addition, poorer survival was observed with higher-grade lymphopenia (grades 3 and 4: HR, 2.44; 95% CI, 1.68-3.55; grades 1 and 2: HR, 1.60; 95% CI, 1.18-2.18). In an exploratory analysis, patients with increased antibiotic exposure were more likely to have lymphopenia (odds ratio, 1.96; 95% CI, 1.26-3.07 for total number of antimicrobial agents; odds ratio, 1.70; 95% CI, 1.10-2.57 for antimicrobial duration) than antimicrobial-naive patients. Conclusions and Relevance: This study suggests that an abnormally low absolute lymphocyte count at diagnosis is associated with higher mortality among patients with localized bone and soft tissue sarcoma; therefore, lymphopenia may serve as a reliable prognostic biomarker. Potential mechanisms associated with host immunity and overall survival include a suppressed antitumor response and increased infectious complications, which merit future investigation.

Changes in Cancer Management due to COVID-19 Illness in Patients with Cancer in Northern California.

PURPOSE: The response to the COVID-19 pandemic has affected the management of patients with cancer. In this pooled retrospective analysis, we describe changes in management patterns for patients with cancer diagnosed with COVID-19 in two academic institutions in the San Francisco Bay Area. MATERIALS AND METHODS: Adult and pediatric patients diagnosed with COVID-19 with a current or historical diagnosis of malignancy were identified from the electronic medical record at the University of California, San Francisco, and Stanford University. The proportion of patients undergoing active cancer management whose care was affected was quantified and analyzed for significant differences with regard to management type, treatment intent, and the time of COVID-19 diagnosis. The duration and characteristics of such changes were compared across subgroups. RESULTS: A total of 131 patients were included, of whom 55 were undergoing active cancer management. Of these, 35 of 55 (64%) had significant changes in management that consisted primarily of delays. An additional three patients not undergoing active cancer management experienced a delay in management after being diagnosed with COVID-19. The decision to change management was correlated with the time of COVID-19 diagnosis, with more delays identified in patients treated with palliative intent earlier in the course of the pandemic (March/April 2020) compared with later (May/June 2020) (OR, 4.2; 95% CI, 1.03 to 17.3; P = .0497). This difference was not seen among patients treated with curative intent during the same timeframe. CONCLUSION: We found significant changes in the management of cancer patients with COVID-19 treated with curative and palliative intent that evolved over time. Future studies are needed to determine the impact of changes in management and treatment on cancer outcomes for patients with cancer and COVID-19.

Utility of Routine Surveillance Laboratory Testing in Detecting Relapse in Patients With Classic Hodgkin Lymphoma in First Remission: Results From a Large Single-Institution Study.

PURPOSE: Classic Hodgkin lymphoma is highly curable with contemporary therapy. Although the limited role of surveillance imaging to detect early relapse for patients in complete remission at the end of therapy is well established, there is a paucity of data regarding role of laboratory testing in this setting. METHODS: Patients with newly diagnosed classic Hodgkin lymphoma uniformly treated with the Stanford V regimen from 1998-2014 and in complete remission for at least 3 months were identified in a single-center institutional database. Laboratory tests categorized by Common Terminology Criteria for Adverse Events v4.03 as grade 2 or higher were considered abnormal. Primary analysis included sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of surveillance laboratory tests for predicting relapse in the first 3 years after end of treatment. RESULTS: Among 235 eligible patients, 24 (10.2%) patients ultimately relapsed. In the first 3 years after end of therapy, the mean number of surveillance blood draws per patient was 7.1, (range, 1-13). These 1,661 surveillance blood draws included 4,684 individual laboratory tests, comprising 1,609 CBCs, 1,578 metabolic panels, and 1,497 erythrocyte sedimentation rates. None of the biopsies confirming relapses were prompted by any abnormal laboratory finding. The sensitivity of any surveillance laboratory test for detecting relapse within 3 years of end of treatment was 72.7% (95% CI, 49.8% to 89.3%), specificity 22.6% (95% CI, 17.2% to 28.9%), yielding a PPV of 8.9% (95% CI, 7.0% to 11.3%) and NPV of 88.9% (95% CI, 79% to 94%). CONCLUSION: Our study found limited clinically meaningful utility for routine surveillance laboratory testing in detecting relapse in patients with complete remission at end of treatment. Our results warrant consideration of modifications to current practice guidelines.

Natural Disease History, Outcomes, and Co-mutations in a Series of Patients With BRAF-Mutated Non-small-cell Lung Cancer.

BACKGROUND: BRAF mutations occur in 1% to 4% of non-small-cell lung cancer (NSCLC) cases. Previous retrospective studies have reported similar outcomes for BRAF-mutated NSCLC as compared with wild-type tumors without a known driver mutation or tumors harboring other mutations. However, select cases of prolonged survival have also been described, and thus, the natural history of BRAF-mutated NSCLC remains an area of ongoing study. The aim of this series was to describe the natural history, clinical outcomes, and occurrence of co-mutations in patients with BRAF-mutated NSCLC. PATIENTS AND METHODS: Patients with BRAF-mutated NSCLC seen at Stanford University Medical Center from January 1, 2006 through July 31, 2015 were reviewed. The Kaplan-Meier method was used to calculate median overall survival, and the generalized Wilcoxon test was used to compare median survivals across subgroups of patients. RESULTS: Within a cohort of 18 patients with BRAF-mutated NSCLC, V600E mutations were most common (72%; 13/18). Clinicopathologic features were similar between patients with V600E versus non-V600E mutations, although there was a trend toward more patients with non-V600E mutations being heavy smokers (80% vs. 31%; P = .12). Co-occurring mutations in TP53 were identified most commonly (28%; 5/18). The median overall survival for the entire cohort was 40.1 months, and the median survival from the onset of metastases (n = 16) was 28.1 months. Survival rates at 2 and 5 years from the onset of metastases were 56% and 13%, respectively. CONCLUSION: The clinical behavior of BRAF-mutated NSCLC is variable, but favorable outcomes can be seen in a subset of patients.

New models and online calculator for predicting non-sentinel lymph node status in sentinel lymph node positive breast cancer patients.

BACKGROUND: Current practice is to perform a completion axillary lymph node dissection (ALND) for breast cancer patients with tumor-involved sentinel lymph nodes (SLNs), although fewer than half will have non-sentinel node (NSLN) metastasis. Our goal was to develop new models to quantify the risk of NSLN metastasis in SLN-positive patients and to compare predictive capabilities to another widely used model. METHODS: We constructed three models to predict NSLN status: recursive partitioning with receiver operating characteristic curves (RP-ROC), boosted Classification and Regression Trees (CART), and multivariate logistic regression (MLR) informed by CART. Data were compiled from a multicenter Northern California and Oregon database of 784 patients who prospectively underwent SLN biopsy and completion ALND. We compared the predictive abilities of our best model and the Memorial Sloan-Kettering Breast Cancer Nomogram (Nomogram) in our dataset and an independent dataset from Northwestern University. RESULTS: 285 patients had positive SLNs, of which 213 had known angiolymphatic invasion status and 171 had complete pathologic data including hormone receptor status. 264 (93%) patients had limited SLN disease (micrometastasis, 70%, or isolated tumor cells, 23%). 101 (35%) of all SLN-positive patients had tumor-involved NSLNs. Three variables (tumor size, angiolymphatic invasion, and SLN metastasis size) predicted risk in all our models. RP-ROC and boosted CART stratified patients into four risk levels. MLR informed by CART was most accurate. Using two composite predictors calculated from three variables, MLR informed by CART was more accurate than the Nomogram computed using eight predictors. In our dataset, area under ROC curve (AUC) was 0.83/0.85 for MLR (n = 213/n = 171) and 0.77 for Nomogram (n = 171). When applied to an independent dataset (n = 77), AUC was 0.74 for our model and 0.62 for Nomogram. The composite predictors in our model were the product of angiolymphatic invasion and size of SLN metastasis, and the product of tumor size and square of SLN metastasis size. CONCLUSION: We present a new model developed from a community-based SLN database that uses only three rather than eight variables to achieve higher accuracy than the Nomogram for predicting NSLN status in two different datasets.

Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) Utilizing Free-Text Clinical Narratives.

We propose a deep learning model - Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) for estimating short-term life expectancy (>3 months) of the patients by analyzing free-text clinical notes in the electronic medical record, while maintaining the temporal visit sequence. In a single framework, we integrated semantic data mapping and neural embedding technique to produce a text processing method that extracts relevant information from heterogeneous types of clinical notes in an unsupervised manner, and we designed a recurrent neural network to model the temporal dependency of the patient visits. The model was trained on a large dataset (10,293 patients) and validated on a separated dataset (1818 patients). Our method achieved an area under the ROC curve (AUC) of 0.89. To provide explain-ability, we developed an interactive graphical tool that may improve physician understanding of the basis for the model's predictions. The high accuracy and explain-ability of the PPES-Met model may enable our model to be used as a decision support tool to personalize metastatic cancer treatment and provide valuable assistance to the physicians.

Dynamin impacts homology-directed repair and breast cancer response to chemotherapy.

After the initial responsiveness of triple-negative breast cancers (TNBCs) to chemotherapy, they often recur as chemotherapy-resistant tumors, and this has been associated with upregulated homology-directed repair (HDR). Thus, inhibitors of HDR could be a useful adjunct to chemotherapy treatment of these cancers. We performed a high-throughput chemical screen for inhibitors of HDR from which we obtained a number of hits that disrupted microtubule dynamics. We postulated that high levels of the target molecules of our screen in tumors would correlate with poor chemotherapy response. We found that inhibition or knockdown of dynamin 2 (DNM2), known for its role in endocytic cell trafficking and microtubule dynamics, impaired HDR and improved response to chemotherapy of cells and of tumors in mice. In a retrospective analysis, levels of DNM2 at the time of treatment strongly predicted chemotherapy outcome for estrogen receptor-negative and especially for TNBC patients. We propose that DNM2-associated DNA repair enzyme trafficking is important for HDR efficiency and is a powerful predictor of sensitivity to breast cancer chemotherapy and an important target for therapy.