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  • PR007/#569  Neoadjuvant chemotherapy for locally advanced cervical cancer. Can clinical-pathologic factors and biomarkers expression combined model predict the chemoresponsivity?

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AS03. Cervical cancer
PR007/#569  Neoadjuvant chemotherapy for locally advanced cervical cancer. Can clinical-pathologic factors and biomarkers expression combined model predict the chemoresponsivity?
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  1. Antonino Ditto1,
  2. Mariangela Longo2,
  3. Umberto Leone Roberti Maggiore1,
  4. Giulia Chiarello3,
  5. Giorgio Bogani1,
  6. Valentina Chiappa1,
  7. Fabio Martinelli1 and
  8. Francesco Raspagliesi1
  1. 1Fondazione IRCCS Istituto Nazionale dei Tumori, Gynecological Oncology Unit, Milan, Italy
  2. 2Filippo Del Ponte Hospital, University of Insubria, Department of Obstetrics and Gynecology, Varese, Italy
  3. 3University of Bari, Department of Biomedical Sciences and Human Oncology, Bari, Italy

Abstract

Introduction To evaluate the prognostic outcomes of NACT (neoadjuvant chemotherapy) in LACC (locally advanced cervical cancer) patients describing the predictive potential of biomarkers (p53, Bcl1 and Bcl2) and clinical-pathologic factors on chemoresponsivity.

Methods Clinical-pathologic data of 88 consecutive patients with LACC who underwent NACT followed by nerve-sparing surgery with retroperitoneal lymphadenectomy at National Cancer Institute of Milan, between January 2000 and June 2013 were retrieved from the institutional database. Biomarkers were evaluated before and after NACT in the specimen. To investigate their role as predictors of response, we tried several statistical machine learning algorithms.

Results Responders to NACT (55.7%) showed a 5 years survival between 100% (complete response) and 85.7% (partial response). Clinical factors (age, body mass index (BMI) and grade) were the most important predictors of response at random forest analysis. Area under the curve was 0.8676. Tree based boosting analysis revealed a significant trend towards worse response with p53 expression. Whereas Bcl-1 and Bcl-2, were not predictors for response to NACT. It confirmed that after adjusting for other prognostic factors, age, grade, BMI and tumor size were independent predictor of response to NACT, while p53 was moderately related to response to NACT. The final logistic regression reported that age and grade were significant factors unlike p53.

Conclusion/Implications Combined model including clinical pathologic variables plus p53 did not predict NACT response. Although prognosticate chemoresponsitivity is still an ongoing problem in LACC patients, NACT followed by surgery remain a safe treatment in young patients with brilliant oncologic outcome without clinical sequalae related to radio-chemotherapy.

Abstract PR007/#569 Figure 1
Abstract PR007/#569 Figure 1

Combined model. Importance of Clinical factors plus biomarkers pre-treatment as predictors of response to NACT at the tree based boosting analysis. Area under the curve (crude estimate): 0.8676

View this table:
Abstract PR007/#569 Table 1

Variables of importance with logistic regression model. Model including important clinical variables (age, grade, BMI) and P53

https://doi.org/10.1136/ijgc-2023-IGCS.50

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