The efficacy of angiogenesis inhibitors in cancer is limited by resistance mechanisms that are poorly understood. Notably, instead of inducing angiogenesis, some cancers vascularize by the non-angiogenic mechanism of vessel co-option. Here we show that vessel co-option is associated with a poor response to the anti-angiogenic agent bevacizumab in patients with colorectal cancer liver metastases. Moreover, we find that vessel co-option prevails in human breast cancer liver metastases, a setting where results with anti-angiogenic therapy have been disappointing. In our preclinical mechanistic studies, we show that cancer cell motility mediated by the Arp2/3 complex is required for vessel co-option in liver metastases in vivo and that combined inhibition of angiogenesis and vessel co-option is more effective than inhibiting angiogenesis alone in this setting. Vessel co-option is therefore a clinically relevant mechanism of resistance to anti-angiogenic therapy and combined inhibition of angiogenesis and vessel co-option may be a warranted therapeutic strategy.

Although prediction of class membership from observed variables in latent class analysis is well understood, predicting an observed distal outcome from latent class membership is more complicated. A flexible model-based approach is proposed to empirically derive and summarize the class-dependent density functions of distal outcomes with categorical, continuous, or count distributions. A Monte Carlo simulation study is conducted to compare the performance of the new technique to two commonly used classify-analyze techniques: maximum-probability assignment and multiple pseudo-class draws. Simulation results show that the model-based approach produces substantially less biased estimates of the effect compared to either classify-analyze technique, particularly when the association between the latent class variable and the distal outcome is strong. In addition, we show that only the model-based approach is consistent. The approach is demonstrated empirically: latent classes of adolescent depression are used to predict smoking, grades, and delinquency. SAS syntax for implementing this approach using PROC LCA and a corresponding macro are provided.

Understanding temporal change in human behavior and psychological processes is a central issue in the behavioral sciences. With technological advances, intensive longitudinal data (ILD) are increasingly generated by studies of human behavior that repeatedly administer assessments over time. ILD offer unique opportunities to describe temporal behavioral changes in detail and identify related environmental and psychosocial antecedents and consequences. Traditional analytical approaches impose strong parametric assumptions about the nature of change in the relationship between time-varying covariates and outcomes of interest. This paper introduces time-varying effect models (TVEM) that explicitly model changes in the association between ILD covariates and ILD outcomes over time in a flexible manner. In this article, we describes unique research questions that the TVEM addresses, outline the model-estimation procedure, share a SAS macro for implementing the model, demonstrate model utility with a simulated example, and illustrate model applications in ILD collected as part of a smoking-cessation study to explore the relationship between smoking urges and self-efficacy during the course of the pre- and post- cessation period.

Purpose: To identify a profile of circulating tumor human papilloma virus (HPV) DNA (ctHPVDNA) clearance kinetics that is associated with disease control after chemoradiotherapy (CRT) for HPV-associated oropharyngeal squamous cell carcinoma (OPSCC). Experimental Design: A multi-institutional prospective biomarker trial was conducted in 103 patients with (i) p16positive OPSCC, (ii) M0 disease, and (iii) receipt of definitive CRT. Blood specimens were collected at baseline, weekly during CRT, and at follow-up visits. Optimized multianalyte digital PCR assays were used to quantify ctHPVDNA (types 16/18/31/33/35) in plasma. A control cohort of 55 healthy volunteers and 60 patients with non-HPV-associated malignancy was also analyzed. Results: Baseline plasma ctHPVDNA had high specificity (97%) and high sensitivity (89%) for detecting newly diagnosed HPV-associated OPSCC. Pretreatment ctHPV16DNA copy number correlated with disease burden, tumor HPV copy number, and HPV integration status. We define a ctHPV16DNA favorable clearance profile as having high baseline copy number (>200 copies/mL) and >95% clearance of ctHPV16DNA by day 28 of CRT. Nineteen of 67 evaluable patients had a ctHPV16DNA favorable clearance profile, and none had persistent or recurrent regional disease after CRT. In contrast, patients with adverse clinical risk factors (T4 or >10 pack years) and an unfavorable ctHPV16DNA clearance profile had a 35% actuarial rate of persistent or recurrent regional disease after CRT (P ¼ 0.0049). Conclusions: A rapid clearance profile of ctHPVDNA may predict likelihood of disease control in patients with HPVassociated OPSCC patients treated with definitive CRT and may be useful in selecting patients for deintensified therapy.

PURPOSE Plasma circulating tumor human papillomavirus DNA (ctHPVDNA) is a sensitive and specific biomarker of human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC). We investigated whether longitudinal monitoring of ctHPVDNA during post-treatment surveillance could accurately detect clinical disease recurrence. METHODS AND MATERIALS A prospective biomarker clinical trial was conducted among patients with nonmetastatic HPV-associated (p16-positive) OPSCC. All patients were treated with curative-intent chemoradiotherapy (CRT). Patients underwent a 3-month post-CRT positron emission tomography/computed tomography scan and were thereafter clinically evaluated every 2-4 months (years 1-2), then every 6 months (years 3-5). Chest imaging was performed every 6 months. Blood specimens were collected every 6-9 months for analysis of plasma ctHPVDNA using a multianalyte digital polymerase chain reaction assay. The primary endpoint was to estimate the negative predictive value (NPV) and positive predictive value (PPV) of ctHPVDNA surveillance. RESULTS One hundred fifteen patients were enrolled, and 1,006 blood samples were analyzed. After a median follow-up time of 23 months (range, 6.1-54.7 months), 15 patients (13%) developed disease recurrence. Eighty-seven patients had undetectable ctHPVDNA at all post-treatment time points, and none developed recurrence (NPV, 100%; 95% CI, 96% to 100%). Twenty-eight patients developed a positive ctHPVDNA during post-treatment surveillance, 15 of whom were diagnosed with biopsy-proven recurrence. Sixteen patients had 2 consecutively positive ctHPVDNA blood tests, 15 of whom developed biopsy-proven recurrence. Two consecutively positive ctHPVDNA blood tests had a PPV of 94% (95% CI, 70% to 99%). Median lead time between ctHPVDNA positivity and biopsy-proven recurrence was 3.9 months (range, 0.37-12.9 months). CONCLUSION Detection of ctHPVDNA in two consecutive plasma samples during post-treatment surveillance has high PPV and NPV for identifying disease recurrence in patients with HPV-associated oropharyngeal cancer and may facilitate earlier initiation of salvage therapy.