Joy Chen

Combined Inactivation of MYC and K-Ras Oncogenes Reverses Tumorigenesis in Lung Adenocarcinomas and Lymphomas

Background Conditional transgenic models have established that tumors require sustained oncogene activation for tumor maintenance, exhibiting the phenomenon known as “oncogene-addiction.” However, most cancers are caused by multiple genetic events making it difficult to determine which oncogenes or combination of oncogenes will be the most effective targets for their treatment. Methodology/Principal Findings To examine how the MYC and K-rasG12D oncogenes cooperate for the initiation and maintenance of tumorigenesis, we generated double conditional transgenic tumor models of lung adenocarcinoma and lymphoma. The ability of MYC and K-rasG12D to cooperate for tumorigenesis and the ability of the inactivation of these oncogenes to result in tumor regression depended upon the specific tissue context. MYC-, K-rasG12D- or MYC/K-rasG12D-induced lymphomas exhibited sustained regression upon the inactivation of either or both oncogenes. However, in marked contrast, MYC-induced lung tumors failed to regress completely upon oncogene inactivation; whereas K-rasG12D-induced lung tumors regressed completely. Importantly, the combined inactivation of both MYC and K-rasG12D resulted more frequently in complete lung tumor regression. To account for the different roles of MYC and K-rasG12D in maintenance of lung tumors, we found that the down-stream mediators of K-rasG12D signaling, Stat3 and Stat5, are dephosphorylated following conditional K-rasG12D but not MYC inactivation. In contrast, Stat3 becomes dephosphorylated in lymphoma cells upon inactivation of MYC and/or K-rasG12D. Interestingly, MYC-induced lung tumors that failed to regress upon MYC inactivation were found to have persistent Stat3 and Stat5 phosphorylation. Conclusions/Significance Taken together, our findings point to the importance of the K-Ras and associated down-stream Stat effector pathways in the initiation and maintenance of lymphomas and lung tumors. We suggest that combined targeting of oncogenic pathways is more likely to be effective in the treatment of lung cancers and lymphomas.

Twist1 Suppresses Senescence Programs and Thereby Accelerates and Maintains Mutant Kras-Induced Lung Tumorigenesis

KRAS mutant lung cancers are generally refractory to chemotherapy as well targeted agents. To date, the identification of drugs to therapeutically inhibit K-RAS have been unsuccessful, suggesting that other approaches are required. We demonstrate in both a novel transgenic mutant Kras lung cancer mouse model and in human lung tumors that the inhibition of Twist1 restores a senescence program inducing the loss of a neoplastic phenotype. The Twist1 gene encodes for a transcription factor that is essential during embryogenesis. Twist1 has been suggested to play an important role during tumor progression. However, there is no in vivo evidence that Twist1 plays a role in autochthonous tumorigenesis. Through two novel transgenic mouse models, we show that Twist1 cooperates with KrasG12D to markedly accelerate lung tumorigenesis by abrogating cellular senescence programs and promoting the progression from benign adenomas to adenocarcinomas. Moreover, the suppression of Twist1 to physiological levels is sufficient to cause Kras mutant lung tumors to undergo senescence and lose their neoplastic features. Finally, we analyzed more than 500 human tumors to demonstrate that TWIST1 is frequently overexpressed in primary human lung tumors. The suppression of TWIST1 in human lung cancer cells also induced cellular senescence. Hence, TWIST1 is a critical regulator of cellular senescence programs, and the suppression of TWIST1 in human tumors may be an effective example of pro-senescence therapy.

Survival and Death Signals Can Predict Tumor Response to Therapy After Oncogene Inactivation

Modeling survival and death signaling in tumors by combining quantitative imaging and in situ biomarker analysis can be used to predict responses to therapy after oncogene inactivation. Predicting Oncogene Addiction with Math Some cancer cells are dependent or “addicted” to the continued activity of oncoproteins such as Myc. Drugs that target these oncoproteins induce the addicted cancer cells to die rapidly, a phenomenon called “oncogene addiction.” Marked clinical responses have been reported in some cancer patients, particularly those with lung cancer, after treatment with drugs targeting oncoproteins. However, only a distinct subset of human cancer patients have tumors that exhibit this behavior of oncogene addiction. The ability to predict when a tumor will exhibit oncogene addiction would be useful not only for developing new oncoprotein-targeted therapies but also for selecting which cancer patients are likely to respond best to such drugs. In a new study, Tran et al. use a systems approach to mathematically model the behavior of oncogene-addicted lung and lymphoma tumors. Starting from the simple premise that oncogene addiction could be deduced from the balance between converging signals from cell survival and cell death pathways in the tumors, Tran and colleagues were able to predict the clinical behavior of lung and lymphoma cells in mice treated with oncoprotein-targeted therapies. They also showed that their mathematical model for oncogene addiction could predict the distinct temporal relationships of signaling molecules from converging survival and death pathways. The group then used quantitative imaging of mouse tumors to train an algorithm called support vector machine to accurately differentiate mice that responded to oncoprotein-targeted therapy from those mice that did not respond. They then extended this quantitative imaging approach to humans with lung cancer who had been treated with oncoprotein-targeted therapy. Their model was able to differentiate patients who had lung tumors that were oncogene-addicted and hence were most likely to benefit from this treatment from those who did not. Although much more work needs to be done, the Tran et al. study demonstrates that mathematical modeling could be a useful tool for helping guide the management of cancer patients. Qualitative approaches alone are not sufficient given the complexity and heterogeneity of tumors, so the ability to quantify complex systems will be of benefit to the field of oncology. Finally, quantitative imaging combined with classifier algorithms may be useful clinically for distinguishing those cancer patients who are most likely to derive clinical benefit from oncoprotein-targeted therapeutics. Cancers can exhibit marked tumor regression after oncogene inhibition through a phenomenon called “oncogene addiction.” The ability to predict when a tumor will exhibit oncogene addiction would be useful in the development of targeted therapeutics. Oncogene addiction is likely the consequence of many cellular programs. However, we reasoned that many of these inputs may converge on aggregate survival and death signals. To test this, we examined conditional transgenic models of K-rasG12D– or MYC-induced lung tumors and lymphoma combined with quantitative imaging and an in situ analysis of biomarkers of proliferation and apoptotic signaling. We then used computational modeling based on ordinary differential equations (ODEs) to show that oncogene addiction could be modeled as differential changes in survival and death intracellular signals. Our mathematical model could be generalized to different imaging methods (computed tomography and bioluminescence imaging), different oncogenes (K-rasG12D and MYC), and several tumor types (lung and lymphoma). Our ODE model could predict the differential dynamics of several putative prosurvival and prodeath signaling factors [phosphorylated extracellular signal–regulated kinase 1 and 2, Akt1, Stat3/5 (signal transducer and activator of transcription 3/5), and p38] that contribute to the aggregate survival and death signals after oncogene inactivation. Furthermore, we could predict the influence of specific genetic lesions (p53−/−, Stat3-d358L, and myr-Akt1) on tumor regression after oncogene inactivation. Then, using machine learning based on support vector machine, we applied quantitative imaging methods to human patients to predict both their EGFR genotype and their progression-free survival after treatment with the targeted therapeutic erlotinib. Hence, the consequences of oncogene inactivation can be accurately modeled on the basis of a relatively small number of parameters that may predict when targeted therapeutics will elicit oncogene addiction after oncogene inactivation and hence tumor regression.

Association Between Weekend Discharge and Hospital Readmission Rates Following Major Surgery

IMPORTANCE Although evidence suggests worse outcomes for patients admitted to the hospital on a weekend, to our knowledge, no previous studies have investigated the effects of weekend discharge. OBJECTIVE To determine whether weekend discharge would be associated with an increased rate of 30- and 90-day hospital readmission. DESIGN, SETTING, AND PARTICIPANTS Retrospective review of discharge abstracts from the California Office of State Health Planning and Development from 2012 identifying all patients who underwent abdominal aortic aneurysm (AAA) repair, colectomy, total hip arthroplasty, and pancreatectomy. This study was conducted from January to December 2012. MAIN OUTCOMES AND MEASURES Thirty- and 90-day readmission rates were compared between patients discharged on a weekend vs weekday. RESULTS Of 128 057 patients, 5225 patients (4.1%) underwent AAA repair; 29 388 (22.9%), colectomy; 91 168 (71.2%), hip replacement; and 2276 (1.8%), pancreatectomy. Overall, 29 883 (23.3%) were discharged on a weekend. Although there were no significant differences with respect to sex, age, race/ethnicity, insurance status, or type of admission, patients discharged on a weekend had shorter length of stays and were less often discharged to a skilled nursing facility. Overall, the 30-day readmission rate was 9.4% after AAA repair, 13.6% after colectomy, 7.5% after hip replacement, and 16.3% after pancreatectomy. Hospital readmission rates were similar for those discharged on a weekend vs weekday after AAA repair (8.8% vs 9.3%; P = .55) and pancreatectomy (17.5% vs 15.9%; P = .40). However, weekend discharge was associated with a lower 30-day readmission rate for patients undergoing colectomy (12.1% vs 14.1%; P < .001) and hip replacement (6.9% vs 7.7%; P < .001). On multivariable analysis, weekend discharge was inversely associated with readmission after colectomy (odds ratio [OR], 0.86; 95% CI, 0.79-0.93) but not AAA repair (OR, 0.93; 95% CI, 0.73-1.19), hip replacement (OR, 0.97; 95% CI, 0.91-1.03), or pancreatectomy (OR, 1.02; 95% CI, 0.76-1.36). Finally, a substantial percentage of 30-day readmissions occurred at a different hospital (AAA repair: 40.5%; colectomy: 25.8%; hip replacement: 32.5%; and pancreatectomy: 19.7%) compared with the index hospitalization. Similar results were seen for 90-day readmissions. CONCLUSIONS AND RELEVANCE Weekend discharge after major surgery is not associated with higher 30- or 90-day readmission rates.

The role of the hospital and health care system characteristics in readmissions after major surgery in California

BACKGROUND Hospital readmission after major surgery is a costly problem that has been associated with patient characteristics. Because hospitals are incentivized to join accountable care organizations, interventions on a hospital or health care system level may help reduce readmissions. Our objective was to identify hospital- and systems-level characteristics associated with readmissions after major operative procedures. METHODS Retrospective analysis of California discharge abstracts with record linkage numbers for adult patients undergoing coronary artery bypass graft (CABG), colectomy or total hip/knee arthroplasty (TJA) in California acute, nonfederal hospitals in 2011. The record linkage number showed where patients were readmitted. Hierarchic logistic regression estimated the odds of readmission by hospital characteristics. RESULTS There were 91,205 records analyzed: CABG (6.4%), colectomy (12.0%), and TJA (82.3%). There were 120 hospitals that performed CABG surgery; 296 performed colectomy; and 298 performed TJA. Readmission rates after CABG was 9.7%, colectomy 7.7%, and TJA 3.9%. After adjustment for patient factors, rural location was predictive of readmission after colectomy (odds ratio [OR] 2.08, 95% confidence interval [CI] 1.40-3.08). Low-volume (OR 1.54, 95% CI 1.13-2.10) and minority-serving hospitals (OR 1.18, 95% CI 1.05-1.33) were associated with greater odds of readmission after TJA. CONCLUSION Select hospital characteristics are associated with readmissions after major operative procedures. Because financial penalties may worsen performance in vulnerable or low-resource settings, policies aimed at reducing readmissions should be attentive to the potential unintended consequences.

Is weekend discharge associated with hospital readmission

BACKGROUND Although recent evidence suggests worse outcomes for patients admitted to the hospital on a weekend, the impact of weekend discharge is less understood. METHODS Utilizing the 2012 California Office of Statewide Health Planning and Development database, the impact of weekend discharge on 30-day hospital readmission rates for patients admitted with acute myocardial infarction (AMI), congestive heart failure (CHF), or pneumonia (PNA) was investigated. RESULTS Out of 266,519 patients, 60,097 (22.5%) were discharged on a weekend. Unadjusted 30-day hospital readmission rates were similar between weekend and weekday discharges (AMI: 21.9% vs 21.9%; CHF: 15.4% vs 16.0%; PNA: 12.1% vs 12.4%). Patients discharged on a weekday had a longer length of stay and were more often discharged to a skilled nursing facility. However, in multivariable logistic regression models, weekend discharge was not associated with readmission (AMI: odds ratio [OR] 1.02 [95% CI: 0.98-1.06]; CHF: OR 0.99 [95% CI: 0.94-1.03]; PNA: OR 1.02 (95% CI: 0.98-1.07)). CONCLUSIONS Among patients in California with AMI, CHF, and PNA, discharge on a weekend was not associated with an increased hospital readmission rate.

Management of Central Compartment Lymph Nodes in Patients with Papillary Thyroid Carcinoma

Central compartment neck dissection (CCND) in patients with papillary thyroid cancer (PTC) for macroscopic lymph node metastases has been shown to reduce recurrence and improve survival. As a result, CCND is uniformly accepted for treatment of patients with macroscopic lymph node metastases in the central compartment of the neck. Controversy exists regarding the benefit of prophylactic central compartment neck dissection (pCCND) in PTC. Microscopic lymph node metastases are common in PTC, but their presence is not associated with a reduction in survival or an increase in disease recurrence. The only potential benefit of pCCND may be for precise lymph node staging to help determine if postoperative radioactive iodine therapy is necessary and what the dose should be for patients with intermediate-risk PTC; this remains controversial. Patients undergoing pCCND experience a higher rate of temporary hypocalcemia with no reduction in disease recurrence. There is also evidence to suggest that the rate of permanent hypoparathyroidism is higher. Furthermore, there is no clear survival benefit for pCCND. To date, there are a limited number of prospective randomized trials and a limited amount of high-level evidence to guide decision-making in the management of central compartment lymph nodes in PTC. The only clear indication for CCND is for treatment of clinically node-positive PTC. Overall, there is no proven benefit for pCCND in the treatment of clinically node-negative PTC.