calculating the window of opportunity to screen for ovarian cancer during early stages: a...

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Fig. 1. doi:10.1016/j.ygyno.2011.12.225 225 Should bevacizumab be continued after progression on bevacizumab in recurrent ovarian cancer? F. Backes 1 , D. Richardson 2 , G. McCann 1 , R. Salani 1 , E. Eisenhauer 1 , J. Fowler 1 , L. Copeland 1 , D. Cohn 1 , D. O'Malley 1 . 1 The Ohio State University, Columbus, OH, 2 University of Texas Soutwestern, Dallas, TX. Objective: To compare response rates (RR), progression-free survival (PFS), and overall survival (OS) between patients who were treated with chemotherapy and bevacizumab (Bev) after progression on bev (BAB) versus patients who were treated with cytotoxic chemotherapy without bev (CWB). Methods: We conducted a retrospective chart review of all patients who received treatment with bev (with or without cytotoxic chemotherapy) for recurrent ovarian cancer at a single institution. Patients who received additional therapy (cytotoxic with or without bev) after progression while on bev were included. RR (complete and partial) was assessed using RECIST criteria, CA125 levels, or progressive disease symptoms. PFS was defined as the period from initiation of the next treatment regimen after initial progression on bev until progression or date of last contact. OS was defined as the period from initiation of the next treatment regimen until death or date of last contact. Results: Forty-six patients were included, 16 patient in the CWB group and 30 in the BAB group. The median number of previous chemotherapy regimens was 2.5 (range 17) for CWB compared to 4 (range 18) for BAB (p=.11). 52% of patients had an objective response to the first bev regimen prior to progressing on bev. Objective RR for the regimen after progression on bev was 19% (3/16) in the CWB group, and 23% (7/30) in the BAB group (p = 1). 25% of the patients who responded to the first bev regimen, and 18% of those who did not respond to the first bev regimen responded to the second bev regimen (p = .72). The median PFS for patients in the CWB group was 2.6 months (95% CI 1.3-5 mo), compared to 5.0 months (3.5-7.3 mo) for patients in the BAB group (p=.008). OS was similar, 9.4 months (95% CI 5.0-12.0) for CWB versus 8.6 months (95% CI 5.8-15.5 months) for BAB (p = .19). One patient in the BAB group died of a bowel perforation. Conclusions: In patients previously treated with bevacizumab for recurrent ovarian cancer, the subsequent addition of bevacizumab to cytotoxic chemotherapy significantly increased the PFS compared with patients not receiving a second course of bevacizumab, but does so without an impact on OS. The response to the first bevacizumab regimen did not predict whether a patient would respond again to the next bevacizumab regimen. Future studies should focus on identifying biomarkers that could predict whether a patient will respond to bevacizumab, especially in light of the additional costs and toxicity of bevacizumab. doi:10.1016/j.ygyno.2011.12.226 226 Calculating the window of opportunity to screen for ovarian cancer during early stages: A mathematical model K. Danesh, L. Havrilesky, E. Myers, R. Durrett. Duke University Medical Center, Durham, NC. Objective: To estimate the window of opportunity for ovarian cancer screening using a mathematical model of tumor growth and metastasis that is based on the underlying biological events that occur during tumorigenesis. Methods: We created a stochastic model of three classes of ovarian tumor cells: Primary (cells in the ovary or fallopian tube), Peritoneal (viable cells in peritoneal fluid), and Metastatic (cells implanted on other intra-abdominal surfaces). Using parameters for cell division and migration derived from the literature, we started with a single Primary cell and simulated exponential tumor growth, subsequent migration from the surface of the primary tumor into a subpopulation of Peritoneal cells, followed by attachment to other intra-abdominal sites (Metastatic). In our calculations of the window of opportunity for screening, we assumed that the minimally detectable tumor size using current technologies was 0.5 cm in diameter. Results: Figure 1 depicts the relative proportion of each cell class (expressed as the logarithm of the number of cells) over time. The model predicted the mean time between the primary tumor reaching minimally detectable size and the onset of Stage III to be 2.6 years; the mean diameter of the primary tumor at the onset of stage III was 2.0 cm. Conclusions: Population-level models suggest that the rapid progres- sion of most ovarian cancers from early to late stages, rather than the lack of test sensitivity, may play a role in the failure of clinical trials to identify an effective screening strategy for ovarian cancer. Our simple model of tumor growth and dissemination is consistent with these findings: given the relatively narrow window of opportunity to detect a tumor prior to progression to Stage III and the likely small tumor volume, an effective screening test would need to be both highly sensitive and performed frequently in order to be effective in preventing mortality. Further refinement of this model to incorporate more detailed features of ovarian cancer biology at the cellular and tissue level, as well as incorporation of its results into population-level models of screening, can aid with planning, designing, and evaluating novel strategies for prevention of ovarian cancer morbidity and mortality. doi:10.1016/j.ygyno.2011.12.227 Abstracts / Gynecologic Oncology 125 (2012) S3S167 S94

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Fig. 1.

doi:10.1016/j.ygyno.2011.12.225

225Should bevacizumab be continued after progression onbevacizumab in recurrent ovarian cancer?F. Backes1, D. Richardson2, G. McCann1, R. Salani1, E. Eisenhauer1, J.Fowler1, L. Copeland1, D. Cohn1, D. O'Malley1. 1The Ohio StateUniversity, Columbus, OH, 2University of Texas Soutwestern, Dallas, TX.

Objective: To compare response rates (RR), progression-free survival (PFS),and overall survival (OS) between patients who were treated withchemotherapy and bevacizumab (Bev) after progression on bev (BAB)versus patients who were treated with cytotoxic chemotherapy withoutbev (CWB).Methods:Weconducted a retrospective chart reviewof all patientswhoreceived treatmentwith bev (with or without cytotoxic chemotherapy)for recurrent ovarian cancer at a single institution. Patientswho receivedadditional therapy (cytotoxic with or without bev) after progressionwhile on bev were included. RR (complete and partial) was assessedusing RECIST criteria, CA125 levels, or progressive disease symptoms.PFS was defined as the period from initiation of the next treatmentregimen after initial progression on bev until progression or date of lastcontact. OS was defined as the period from initiation of the nexttreatment regimen until death or date of last contact.Results: Forty-six patients were included, 16 patient in the CWB groupand 30 in the BAB group. The median number of previous chemotherapyregimenswas 2.5 (range 1–7) for CWBcompared to 4 (range 1–8) for BAB(p=.11). 52% of patients had an objective response to the first bevregimen prior to progressing on bev. Objective RR for the regimen afterprogression on bev was 19% (3/16) in the CWB group, and 23% (7/30) inthe BAB group (p=1). 25% of the patients who responded to the first bevregimen, and 18% of those who did not respond to the first bev regimenresponded to the second bev regimen (p=.72). The median PFS forpatients in theCWBgroupwas2.6 months (95%CI1.3-5mo), compared to5.0 months (3.5-7.3mo) for patients in the BAB group (p=.008). OSwassimilar, 9.4 months (95% CI 5.0-12.0) for CWB versus 8.6 months (95% CI5.8-15.5 months) for BAB (p=.19). One patient in the BAB groupdied of abowel perforation.Conclusions: In patients previously treated with bevacizumab forrecurrent ovarian cancer, the subsequent addition of bevacizumab tocytotoxic chemotherapy significantly increased the PFS compared withpatients not receiving a second course of bevacizumab, but does sowithout an impact on OS. The response to the first bevacizumab regimendid not predict whether a patient would respond again to the next

bevacizumab regimen. Future studies should focus on identifyingbiomarkers that could predict whether a patient will respond tobevacizumab, especially in light of the additional costs and toxicity ofbevacizumab.

doi:10.1016/j.ygyno.2011.12.226

226Calculating the window of opportunity to screen for ovariancancer during early stages: A mathematical modelK. Danesh, L. Havrilesky, E. Myers, R. Durrett. Duke University MedicalCenter, Durham, NC.

Objective: To estimate the window of opportunity for ovarian cancerscreening using a mathematical model of tumor growth andmetastasis that is based on the underlying biological events thatoccur during tumorigenesis.Methods: We created a stochastic model of three classes of ovarian tumorcells: Primary (cells in theovaryor fallopian tube), Peritoneal (viable cells inperitoneal fluid), andMetastatic (cells implanted on other intra-abdominalsurfaces). Usingparameters for cell division andmigrationderived fromtheliterature, we started with a single Primary cell and simulated exponentialtumorgrowth, subsequentmigration fromthesurfaceof theprimary tumorinto a subpopulation of Peritoneal cells, followed by attachment to otherintra-abdominal sites (Metastatic). In our calculations of the window ofopportunity for screening, we assumed that the minimally detectabletumor size using current technologies was 0.5 cm in diameter.Results: Figure 1 depicts the relative proportion of each cell class(expressed as the logarithm of the number of cells) over time. Themodelpredicted themean timebetween theprimary tumor reachingminimallydetectable size and the onset of Stage III to be 2.6 years; the meandiameter of the primary tumor at the onset of stage III was 2.0 cm.Conclusions: Population-level models suggest that the rapid progres-sion of most ovarian cancers from early to late stages, rather than thelack of test sensitivity, may play a role in the failure of clinical trials toidentify an effective screening strategy for ovarian cancer. Our simplemodel of tumor growth and dissemination is consistent with thesefindings: given the relatively narrowwindowof opportunity to detect atumor prior to progression to Stage III and the likely small tumorvolume, an effective screening test would need to be both highlysensitive and performed frequently in order to be effective in preventingmortality. Further refinementof thismodel to incorporatemore detailedfeatures of ovarian cancer biology at the cellular and tissue level, as wellas incorporation of its results into population-level models of screening,can aid with planning, designing, and evaluating novel strategies forprevention of ovarian cancer morbidity and mortality.

doi:10.1016/j.ygyno.2011.12.227

Abstracts / Gynecologic Oncology 125 (2012) S3–S167S94