Andrew Nixon

<jats:p>&lt;p&gt;Supplementary Figure 4B Kaplan-Meier OS curves by treatment and dichotomized angiokines groups for following angiokines: TGFb-R3, HGF, TSP-2, OPN, IL-6, SDF-1, Ang-2, ICAM-1, TGF-b2, TIMP-1, BMP-9, PDGF-BB, VCAM-1, PDGF-AA, VEGF-D, TGF-b1, VEGF-R1, HER-3, VEGF, PlGF, CD-73, VEGF-R3, and VEGF-R2&lt;/p&gt;</jats:p>

Cells respond to many stressors by senescing, acquiring stable growth arrest, morphologic and metabolic changes, and a proinflammatory senescence-associated secretory phenotype. The heterogeneity of senescent cells (SnCs) and senescence-associated secretory phenotype are vast, yet ill characterized. SnCs have diverse roles in health and disease and are therapeutically targetable, making characterization of SnCs and their detection a priority. The Cellular Senescence Network (SenNet), a National Institutes of Health Common Fund initiative, was established to address this need. The goal of SenNet is to map SnCs across the human lifespan to advance diagnostic and therapeutic approaches to improve human health. State-of-the-art methods will be applied to identify, define and map SnCs in 18 human tissues. A common coordinate framework will integrate data to create four-dimensional SnC atlases. Other key SenNet deliverables include innovative tools and technologies to detect SnCs, new SnC biomarkers and extensive public multi-omics datasets. This Perspective lays out the impetus, goals, approaches and products of SenNet.

<jats:p>&lt;div&gt;AbstractPurpose:&lt;p&gt;CALGB 90206 was a phase III trial of 732 patients with metastatic renal cell carcinoma (mRCC) comparing bevacizumab plus IFNα (BEV + IFN) with IFNα alone (IFN). No difference in overall survival (OS) was observed. Baseline samples were analyzed to identify predictive biomarkers for survival benefit.&lt;/p&gt;Patients and Methods:&lt;p&gt;A total of 32 biomarkers were assessed in 498 consenting patients randomly assigned into training (&lt;i&gt;n&lt;/i&gt; = 279) and testing (&lt;i&gt;n&lt;/i&gt; = 219) sets. The proportional hazards model was used to test for treatment arm and biomarker interactions of OS. The estimated coefficients from the training set were used to compute a risk score for each patient and to classify patients by risk in the testing set. The resulting model was assessed for predictive accuracy using the time-dependent area under the ROC curve (tAUROC).&lt;/p&gt;Results:&lt;p&gt;A statistically significant three-way interaction between IL6, hepatocyte growth factor (HGF), and bevacizumab treatment was observed in the training set and confirmed in the testing set (&lt;i&gt;P&lt;/i&gt; &lt; 0.0001). The model based on IL6, HGF, and bevacizumab treatment was predictive of OS (&lt;i&gt;P&lt;/i&gt; &lt; 0.001), with the high- and low-risk groups having a median OS of 10.2 [95% confidence interval (CI), 8.0–13.8] and 34.3 (95% CI, 28.5–40.5) months, respectively. The average tAUROC for the final model of OS based on 100 randomly split testing sets was 0.78 (first, third quartiles = 0.77, 0.79).&lt;/p&gt;Conclusions:&lt;p&gt;IL6 and HGF are potential predictive biomarkers of OS benefit from BEV + IFN in patients with mRCC. The model based on key biological and clinical factors demonstrated predictive efficacy for OS. These markers warrant further validation in future anti-VEGF and immunotherapy in mRCC trials.&lt;/p&gt;&lt;p&gt;&lt;i&gt;&lt;a href="http://dx.doi.org/10.1158/1078-0432.CCR-22-0750" target="_blank"&gt;See related commentaries by Mishkin and Kohn, p. 2722&lt;/a&gt; and &lt;a href="http://dx.doi.org/10.1158/1078-0432.CCR-22-0027" target="_blank"&gt;George and Bertagnolli, p. 2725&lt;/a&gt;&lt;/i&gt;&lt;/p&gt;&lt;/div&gt;</jats:p>

<jats:p>&lt;div&gt;AbstractPurpose:&lt;p&gt;Preclinical studies suggest PARP inhibition (PARPi) induces immunostimulatory micromilieu in ovarian cancer thus complementing activity of immune checkpoint blockade. We conducted a phase II trial of PARPi olaparib and anti–PD-L1 durvalumab and collected paired fresh core biopsies and blood samples to test this hypothesis.&lt;/p&gt;Patients and Methods:&lt;p&gt;In a single-center, proof-of-concept phase II study, we enrolled women aged ≥18 with recurrent ovarian cancer. All patients were immune checkpoint inhibitor–naïve and had measurable disease per RECISTv1.1, ECOG performance status 0–2, and adequate organ and marrow function. Patients received olaparib 300 mg twice daily and durvalumab 1,500 mg intravenously every 4 weeks until disease progression, unacceptable toxicity, or withdrawal of consent. Primary endpoint was overall response rate (ORR). Secondary objectives were safety and progression-free survival (PFS). Translational objectives included biomarker evaluation for relationships with clinical response and immunomodulatory effects by treatment.&lt;/p&gt;Results:&lt;p&gt;Thirty-five patients with ovarian cancer [median, four prior therapies (IQR, 2–5.5), predominantly platinum-resistant (86%), &lt;i&gt;BRCA&lt;/i&gt; wild-type (77%)] received at least one full cycle of treatment. ORR was 14% [5/35; 95% confidence interval (CI), 4.8%–30.3%]. Disease control rate (PR+SD) was 71% (25/35; 95% CI, 53.7%–85.4%). Treatment enhanced &lt;i&gt;IFNγ&lt;/i&gt; and &lt;i&gt;CXCL9/CXCL10&lt;/i&gt; expression, systemic IFNγ/TNFα production, and tumor-infiltrating lymphocytes, indicating an immunostimulatory environment. Increased IFNγ production was associated with improved PFS [HR, 0.37 (95% CI, 0.16–0.87), &lt;i&gt;P&lt;/i&gt; = 0.023], while elevated VEGFR3 levels were associated with worse PFS (HR, 3.22 (95% CI, 1.23–8.40), &lt;i&gt;P&lt;/i&gt; = 0.017].&lt;/p&gt;Conclusions:&lt;p&gt;The PARPi and anti–PD-L1 combination showed modest clinical activity in recurrent ovarian cancer. Our correlative study results suggest immunomodulatory effects by olaparib/durvalumab in patients and indicate that VEGF/VEGFR pathway blockade would be necessary for improved efficacy of the combination.&lt;/p&gt;&lt;/div&gt;</jats:p>

<jats:p>&lt;div&gt;AbstractPurpose:&lt;p&gt;CALGB 90206 was a phase III trial of 732 patients with metastatic renal cell carcinoma (mRCC) comparing bevacizumab plus IFNα (BEV + IFN) with IFNα alone (IFN). No difference in overall survival (OS) was observed. Baseline samples were analyzed to identify predictive biomarkers for survival benefit.&lt;/p&gt;Patients and Methods:&lt;p&gt;A total of 32 biomarkers were assessed in 498 consenting patients randomly assigned into training (&lt;i&gt;n&lt;/i&gt; = 279) and testing (&lt;i&gt;n&lt;/i&gt; = 219) sets. The proportional hazards model was used to test for treatment arm and biomarker interactions of OS. The estimated coefficients from the training set were used to compute a risk score for each patient and to classify patients by risk in the testing set. The resulting model was assessed for predictive accuracy using the time-dependent area under the ROC curve (tAUROC).&lt;/p&gt;Results:&lt;p&gt;A statistically significant three-way interaction between IL6, hepatocyte growth factor (HGF), and bevacizumab treatment was observed in the training set and confirmed in the testing set (&lt;i&gt;P&lt;/i&gt; &lt; 0.0001). The model based on IL6, HGF, and bevacizumab treatment was predictive of OS (&lt;i&gt;P&lt;/i&gt; &lt; 0.001), with the high- and low-risk groups having a median OS of 10.2 [95% confidence interval (CI), 8.0–13.8] and 34.3 (95% CI, 28.5–40.5) months, respectively. The average tAUROC for the final model of OS based on 100 randomly split testing sets was 0.78 (first, third quartiles = 0.77, 0.79).&lt;/p&gt;Conclusions:&lt;p&gt;IL6 and HGF are potential predictive biomarkers of OS benefit from BEV + IFN in patients with mRCC. The model based on key biological and clinical factors demonstrated predictive efficacy for OS. These markers warrant further validation in future anti-VEGF and immunotherapy in mRCC trials.&lt;/p&gt;&lt;p&gt;&lt;i&gt;&lt;a href="http://dx.doi.org/10.1158/1078-0432.CCR-22-0750" target="_blank"&gt;See related commentaries by Mishkin and Kohn, p. 2722&lt;/a&gt; and &lt;a href="http://dx.doi.org/10.1158/1078-0432.CCR-22-0027" target="_blank"&gt;George and Bertagnolli, p. 2725&lt;/a&gt;&lt;/i&gt;&lt;/p&gt;&lt;/div&gt;</jats:p>