Abstract
Objective:
Antibiotics (ABX) have been linked to reduced survival in ovarian cancer (OC) when administered before or during platinum-based chemotherapy. This study evaluates the impact of ABX, proton pump inhibitors (PPI), H2-receptor antagonists (H2RA), and steroids on progression-free survival (PFS) in OC patients receiving poly-ADP-ribose polymerase inhibitors (PARPi).
Methods:
This retrospective study examined OC patients who used ABX, PPI, H2RA, or steroids before or during PARPi therapy. Demographics, clinicopathologic characteristics, and treatment outcomes were analyzed. Cox proportional hazards models assessed risk factors for PFS, and Kaplan-Meier analysis with log-rank testing evaluated survival differences based on ABX use.
Results:
Among 237 patients treated with 269 PARPi regimens, most received PARPi in the recurrent setting (79.6%). At the time of PARPi therapy, 21.6% used ABX, 15.6% PPI, 11.2% H2RA, and 63.9% steroids. Patients with HRP/unknown status had significantly worse median PFS than those with BRCA-mutated/HRD (6 vs. 11 months; p<0.001). ABX use correlated with improved median PFS (10 vs. 7 months; p=0.049) but lost significance in multivariate analysis. PPI, H2RA, and steroids had no impact on PFS.
Conclusion:
In this retrospective analysis, concurrent medications were not associated with worsened survival outcomes in OC patients receiving PARPi. Further, while ABX use during PARPi therapy was associated with improved survival this was not significant after adjusting for confounders.
Keywords: PARP inhibitors, ovarian cancer, proton pump inhibitors, antibiotics, steroids
Introduction
Among gynecologic cancers, ovarian cancer (OC) remains with the highest death-to-case ratio, despite uterine cancer surpassing it in overall mortality rate1. Despite front-line treatment with a combination of cytoreductive surgery and platinum-based chemotherapy, most patients will develop recurrent disease2. Poly (ADP-ribose) polymerase inhibitors (PARPi) may be considered for front-line maintenance in patients with advanced OC after a complete or partial response to therapy to improve progression-free survival (PFS), with the most pronounced benefit seen in those with germline or somatic BRCA mutations or homologous recombination deficient (HRD) tumors3–5. Despite the significant therapeutic benefit seen in many patients, innate and acquired resistance to PARPi remains a relevant clinical challenge for patients with OC and other non-gynecologic malignancies6. Advancing our understanding of strategies to prevent and combat PARPi resistance is essential for improving outcomes in women with OC and other malignancies.
In recent years, the clinical relevance of drug-drug interactions has been increasingly studied in cancer care7,8. Many commonly prescribed medications, like antibiotics (ABX), proton pump inhibitors (PPI), H2-receptor antagonists (H2RA), and steroids, can impact response to cancer treatments. Several possible theories exist for possible drug-drug interactions including altered pharmacokinetics, effects on the tumor microenvironment and medication-induced gut microbiome changes7–9. Patients with OC often use concurrent medications for the treatment of infections, symptom management, and co-morbid medical conditions during treatment10. Notably, decreased survival has been demonstrated in both gynecologic and non-gynecologic malignancies across various types of therapy, including immune checkpoint inhibitors and platinum-based regimens, when certain medications are used, including ABX and PPI11–17. At present, our understanding of how concurrent medication use may impact PARPi response is limited in gynecologic and non-gynecologic cancer patients. This study aims to investigate how the use of concurrent medications, including ABX, PPI, H2RA, and steroids, impact oncologic outcomes in patients with OC undergoing therapy with PARPi.
Methods
Study Design and Patient Population
This study was an institutional review board (IRB) approved, single-institution retrospective cohort study. A retrospective analysis was performed in patients diagnosed with OC between January 1, 2015, and January 1, 2022. Eligible patients underwent cytoreductive surgery, adjuvant platinum-based chemotherapy with carboplatin and paclitaxel, and subsequent treatment with PARPi therapy (either in the front-line or recurrent setting) at the James Cancer Hospital at The Ohio State University Comprehensive Cancer Center (OSUCCC).
Data was abstracted through electronic medical record review by study investigators (QK, HW, ML) and stored in an encrypted RedCAP database [Redcap reference]. Demographic and clinical information collected, including age, race, ECOG status at PARPi initiation, tumor histology, BRCA/HRD status based on available germline and somatic testing results, residual disease at cytoreduction, current disease status, timing of PARPi use, and medication use before or during treatment (ABX, PPI, H2RA, steroids). We additionally recorded ABX indication, type, and those with anti-gram-positive activity. Gram positive activity was defined as those with primarily anti-gram-positive activity, including linezolid, vancomycin, and daptomycin. The timing of medication use was defined as before PARPi (within 30 days of initiation), during (any time during treatment), or both. Data collection was stopped at the time of cessation of PARPi therapy.
Data analysis
Demographics, clinicopathologic factors, and treatment outcomes were compared among patients receiving PARPi therapy based on medication use before or during treatment. Categorical variables were compared utilizing Fishers’ exact tests and Chi-square tests. Continuous variables were compared using Wilcoxon rank sum tests or t-tests where appropriate. Progression-free survival (PFS) was defined as the date of PARPi initiation until documented recurrence; patients who did not recur after PARPi therapy were censored at their last physician encounter. Overall survival (OS) was defined as the time from PARPi initiation until death from any cause. A Cox proportional hazards model was utilized where significant variables (timing of PARPi therapy (front-line versus recurrent), BRCA-mut/HRD status, any ABX use) on univariate analysis were entered into multivariable regression analysis where hazard ratios (HRs) were used to evaluate risk factors associated with PFS. Kaplan-Meier curves were generated to compare survival outcomes based on medication use, and differences were estimated using the log-rank test. Statistical analysis was performed using JMP software version 17 (SAS Institute Inc., Cary, NC, 1989-2023).
Results
Of 237 patients with OC treated with PARPi from 2015-2022, most received PARPi in the recurrent setting (79.6%, n=213). The majority had advanced-stage disease at diagnosis (88.6%, n=210), high-grade serous histology (93.2%, n=221), BRCA-mut or HRD tumors (58.2%, n=138), R0 or optimal cytoreduction (44.7%, n=106 and 34.2%, n=81, respectively), and an ECOG score 0-1 (85.7%, n=108) (Table 1). The median follow-up time of patients receiving PARPi therapy from initiation was 8 months. During PARPi therapy, 21.6% (n=58) of patients were treated with ABX. The most common indications for antibiotics were UTI (n=23), upper respiratory infection (n=11), cellulitis (n=8), and infection prophylaxis (n=7), with all indications displayed in Table 1. Of those who received ABX, 29.3% (n=17) received anti-gram-positive ABX. Additionally, 15% of patients were treated with PPIs (n=42), 11.2% with H2RA (n=30), and 36.1% with steroids (n=96).
Table 1.
Demographic and clinicopathologic information of patients who underwent PARPi for ovarian cancer
|
| ||
|---|---|---|
| Variable | Median | Range |
|
| ||
| Age | 60 | 25-90 |
|
| ||
| Variable | N=237 | (%) |
|
| ||
| ECOG* | ||
| 0-1 | 108 | 85.7% |
| > 1 | 18 | 14.3% |
|
| ||
| Stage | ||
| Early (FIGO I-II) | 27 | 11.4% |
| Advanced (FIGO III-IV) | 210 | 88.6% |
|
| ||
| Histology | ||
| High grade serous | 221 | 93.2% |
| Low grade serous | 6 | 2.5% |
| Clear cell | 5 | 2.1% |
| Endometrioid | 2 | 0.8% |
| Mixed | 3 | 1.3% |
|
| ||
| BRCA/HRD status | ||
| BRCA-mut/HRD | 138 | 58.2% |
| HRP/unknown | 99 | 41.8% |
|
| ||
| Degree of cytoreduction* | ||
| R0 | 106 | 44.7% |
| Optimal | 81 | 34.2% |
| Suboptimal | 21 | 8.9% |
|
| ||
| Patient status | ||
| NED | 35 | 14.8% |
| AWD | 81 | 34.2% |
| DOD | 120 | 50.6% |
| DOOC | 1 | 0.4% |
|
| ||
| Variable | N=268 | (%) |
|
| ||
| Timing of PARPi use | ||
| Upfront maintenance | 55 | 20.4% |
| Recurrent | 213 | 79.6% |
|
| ||
| Any ABX use | ||
| Yes | 58 | 21.6% |
| No | 210 | 78.4% |
|
| ||
| Timing of ABX use | ||
| None | 209 | 78.1% |
| Prior to PARPi | 4 | 1.5% |
| During PARPi | 50 | 18.6% |
| Both prior to and during PARPi | 5 | 1.9% |
|
| ||
| Anti-gram-positive ABX* | ||
| Yes | 17 | 29.3% |
| No | 38 | 62.1% |
|
| ||
| Any PPI use | ||
| Yes | 42 | 15.6% |
| No | 226 | 84.4% |
|
| ||
| Timing of PPI use | ||
| None | 226 | 84.4% |
| Prior to PARPi | 5 | 1.9% |
| During PARPi | 34 | 12.6% |
| Both prior to and during PARPi | 3 | 1.1% |
|
| ||
| Any H2 receptor antagonist use | ||
| Yes | 30 | 11.2% |
| No | 238 | 88.8% |
|
| ||
| Timing of H2 receptor antagonist use | ||
| None | 238 | 88.8% |
| Prior to PARPi | 15 | 5.6% |
| During PARPi | 14 | 5.2% |
| Both prior to and during PARPi | 1 | 0.4% |
|
| ||
| Any steroid use | ||
| Yes | 96 | 36.1% |
| No | 172 | 63.9% |
|
| ||
| Timing of steroid use | ||
| None | 172 | 63.9% |
| Prior to PARPi | 58 | 21.6% |
| During PARPi | 22 | 8.6% |
| Both prior to and during PARPi | 16 | 594.8% |
|
| ||
Indicates that some data may be missing for a given variable
Abbreviations: ECOG PS, Eastern Cooperative Oncology Group Performance Status; FIGO, International Federation of Gynecology and Obstetrics; PPI, proton-pump inhibitor; chemo, chemotherapy; NED, no evidence of disease; AWD, alive with disease; DOD, dead of disease; DOOC, dead of other cause
Table 2 displays the impact of medication use on PFS in patients with OC treated with PARPi therapy. Patients who received any ABX during treatment with PARPi had increased PFS compared to those not treated with ABX (10 vs. 7 months, p=0.0491) on univariate analysis (Figure 1A, Table 2). However, this difference was not significant in multivariate analysis when controlling for the timing of PARPi therapy (front-line versus recurrent) and BRCA-mut/HRD status (p=0.1163) (Table 2). Additionally, the timing of ABX (before, during, or both) did not affect PFS compared to those not receiving ABX (Table 2). Among the ABX cohort, patients who received anti-gram-positive ABX had a non-statistically significant improvement in PFS relative to other ABX (8 vs. 13 months, p=0.72). There was no difference in PFS for PPI use (8 vs. 8 months, p=0.56), H2RA use (8 vs. 8 months, p=0.42), and steroid use 8 vs. 8 months (p=0.56). Table 3 displays details of the cohort who received ABX. There was no difference in PFS based on ABX timing (p=0.67), total duration of ABX (p=0.30), route of ABX (p=0.98), or hospitalization (p=0.37) or ICU admission (p=0.08) at the time of ABX use. PPI, H2RA, and steroid use were not significantly associated with differences in PFS either before or during treatment (Table 2, Figure 1B–C)
Table 2.
Factors associated with progression-free survival of patients after receiving platinum-based adjuvant chemotherapy
|
|
||||||
|---|---|---|---|---|---|---|
| Univariate Analysis | Multivariate Analysis | |||||
|
|
||||||
| Variable | n | No. failed | Median months (95% CI) | p-value | Hazard ratio (95% CI) | p-value |
| Timing of PARPi use | <0.0001 | <0.0001 | ||||
| Upfront maintenance | 55 | 32 | 22 (11-39) | reference | ||
| Recurrent | 213 | 190 | 6 (5-8) | 2.23 (1.52-3.27) | ||
|
| ||||||
| BRCA/HRD status | <0.0001 | 0.0004 | ||||
| BRCA-mut/HRD | 155 | 120 | 11 (8-13) | reference | ||
| HRP/unknown | 113 | 102 | 6 (4-7) | 1.65 (1.25-2.19) | ||
|
| ||||||
| Any ABX use | 0.0491 | 0.1163 | ||||
| Yes | 58 | 12 | 10 (8-19) | reference | ||
| No | 210 | 176 | 7 (6-9) | 1.29 (0.93-1.79) | ||
|
| ||||||
| Timing of ABX use | 0.1326 | |||||
| None | 209 | 176 | 7 (6-9) | |||
| Prior to PARPi | 4 | 3 | 2 (1-NR) | |||
| During PARPi | 50 | 40 | 10 (8-19) | |||
| Both prior to and during PARPi | 5 | 3 | 17 (8-NR) | |||
|
| ||||||
| Anti-gram-positive ABX* | 0.7151 | |||||
| Yes | 17 | 16 | 13 (2-33) | |||
| No | 38 | 27 | 8.5 (7-23) | |||
|
| ||||||
| Any PPI use | 0.5581 | |||||
| Yes | 42 | 38 | 8 (4-12) | |||
| No | 226 | 184 | 8 (6-9) | |||
|
| ||||||
| Timing of PPI use | 0.1211 | |||||
| None | 226 | 184 | 8 (6-9) | |||
| Prior to PARPi | 5 | 5 | 4 (0-19) | |||
| During PARPi | 34 | 30 | 9.5 (6-25) | |||
| Both prior to and during PARPi | 3 | 3 | 3 (2-12) | |||
|
| ||||||
| Any H2 receptor antagonist use | 0.4220 | |||||
| Yes | 30 | 24 | 8 (3-13) | |||
| No | 238 | 198 | 8 (6-10) | |||
|
| ||||||
| Timing of H2 receptor antagonist use | 0.1955 | |||||
| None | 238 | 198 | 8 (6-10) | |||
| Prior to PARPi | 15 | 13 | 6 (1-9) | |||
| During PARPi | 14 | 10 | 11 (2-NR) | |||
| Both prior to and during PARPi | 1 | 1 | 3 | |||
|
| ||||||
| Any steroid use | 0.5587 | |||||
| Yes | 96 | 74 | 8 (5-10) | |||
| No | 172 | 148 | 8 (6-10) | |||
|
| ||||||
| Timing of steroid use | 0.1267 | |||||
| None | 171 | 148 | 8 (6-10) | |||
| Prior to PARPi | 58 | 47 | 5.5 (4-8) | |||
| During PARPi | 23 | 16 | 11 (8-39) | |||
| Both prior to and during PARPi | 16 | 11 | 12 (3-23) | |||
indicates some data may be missing for a given variable
Log-rank p-value used
Abbreviations: PARPi, poly-ADP-ribose polymerase inhibitor; AB, antibiotics; PPI, proton-pump inhibitor
Figure 1.
Impact of Medication Use on PFS in Ovarian Cancer Patients Treated with PARP inhibitors: A) Antibiotics, B) PPI, C) H2RA, D) Steroids.
Table 3.
Univariate analysis of factors associated with PFS for those patients who received ABX either prior to or during PARPi
| Variable | Progression-Free Survival | |||
|---|---|---|---|---|
|
| ||||
| n | No. failed | Median months (95% CI) |
p-value | |
| Timing of ABX use | 0.6706 | |||
| Prior to PARPi | 4 | 3 | 2 (1-NR) | |
| During PARPi | 49 | 40 | 10 (8-19) | |
| Both prior to and during PARPi | 5 | 3 | 17 (8-NR) | |
|
| ||||
| Total days of ABX therapy | 0.2981 | |||
| 7 or less | 29 | 23 | 8 (4-23) | |
| 8-14 | 20 | 15 | 14 (8-41) | |
| 14 or more | 8 | 8 | 9.5 (0-13) | |
|
| ||||
| Route of ABX | 0.9809 | |||
| PO | 36 | 27 | 9 (7-20) | |
| IV | 4 | 3 | 10.5 (4-NR) | |
| IV and PO | 17 | 16 | 13 (2-33) | |
|
| ||||
| Concurrent hospitalization with ABX | 0.3745 | |||
| No | 35 | 25 | 10 (8-23) | |
| Yes | 22 | 21 | 8.5 (4-19) | |
|
| ||||
| Concurrent ICU admission with ABX | 0.0842 | |||
| No | 53 | 42 | 10 (8-19) | |
| Yes | 4 | 4 | 5 (0-19) | |
Log-rank p-value used
Abbreviations: ABX, antibiotics; PO, per oral; IV, intravenous; ICU, intensive care unit; NR, not reached
Discussion
Our single-institution retrospective cohort study investigated the impact of concurrent medications during PARPI therapy in patients with OC. We found that commonly prescribed medications during cancer treatment, including antibiotics, H2RA, PPIs, and steroids, did not impact PFS, supporting the relative safety of these medications during the 2–3-year maintenance period that PARPi are typically prescribed. While prior research shows that ABX, H2RA, PPIs, and steroids negatively impact oncologic outcomes during platinum-based chemotherapy, these findings were not replicated in this study when PARPi therapy was used.
In recent years, increasing research has suggested that concurrent medication use, and drug-drug interactions can influence oncologic outcomes, particularly during cytotoxic chemotherapy and immunotherapy12–20. Specifically, antibiotics, PPIs, and corticosteroids have been associated with altered response and worsened PFS and OS in patients receiving platinum-based chemotherapy or immune checkpoint inhibitors12–20. These effects are hypothesized to result from a combination of pharmacokinetic interactions, modulation of the tumor microenvironment, and, in some contexts, alterations to the gut microbiome7–8.
PARPis are oral medications rapidly absorbed in the gastrointestinal tract at the level of the small intestine21, 22. While the exact pharmacokinetics of PARPi differ among each medication, their function at the cellular level is the same. PARPi primarily act on the PARP-1 protein to inhibit single-strand DNA repair, and ultimately failure of homologous recombination and synthetic lethality of tumor cells23. Mechanistically, medications may interfere with cancer therapies through altered pharmacokinetics, such as drug absorption, distribution, metabolism, and excretion7–8. For example, PPIs can raise gastric pH and potentially affect the solubility and absorption of orally administered agents. Corticosteroids and H2RAs may have immunosuppressive or anti-inflammatory effects that alter the tumor microenvironment, potentially influencing therapeutic efficacy7–8. Antibiotics, while primarily targeting pathogens, can affect the microbiome, gut mucosal barrier function, and digestion7–8.
Notably, while prior studies have linked drug-drug interactions to worsened outcomes in other treatment contexts, we did not observe similar effects in this cohort of PARPi-treated patients. This difference may reflect distinct mechanisms of action between PARPi and other drug classes, reduced pharmacokinetic interaction potential, or population-specific characteristics. This contrasts to previous studies that have been performed in gynecologic oncology evaluating platinum chemotherapy and concurrent medications12–13. Specifically, in a study by Haight et al., worse PFS was seen in patients with endometrial cancer being treated with platinum-based chemotherapy while receiving PPI13. Consistent findings have been demonstrated in non-gynecologic cancer patients. A meta-analysis by Wu et al. found that across multiple types of solid tumors treated with PD-1 or PD-L1 inhibitors, PPI use was associated with shorter OS and PFS. This meta-analysis included studies investigating PPI use in patients with non-small cell lung carcinoma, urothelial carcinoma, and other cancer types treated with these medications19. A different study by Kichenadasse et al. demonstrated the same findings in patients with colorectal cancer treated with fluoropyrimidine-base chemotherapy20.
One possible mechanism for how concurrent medication can impact response to cancer therapy is through the gut microbiome7–8. Although microbiome-related pathways are implicated in treatment response across several therapeutic classes, PARPis are absorbed primarily in the small intestine, where microbial content is relatively low among healthy individuals24, 28. This pharmacologic characteristic may reduce the degree to which microbial metabolism directly affects PARPi pharmacokinetics. There is some very limited data to suggest a possible link between gut microbial composition and PARPi response. In a recent study conducted in mice models, PARPi use led to changes in the gut microbiome composition, an increase in short chain fatty acid (SCFA) producing bacteria Lachnospiraceae NK4A136 and improved OS26. Additionally, in a study of patients undergoing PARPi treatment, increased Phascolarctobacterium was associated with improved OS in BRCA1/2 negative patients being treated with Olaparib27. While this retrospective study did not collect fecal samples from patients to establish a causal relationship between the gut microbiome, medication use and PARPi efficacy, future studies to explore the gut microbiome as a potential contributor to PARPi resistance are necessary.
Limitations of this study include the small sample size, retrospective nature of data collection and the data available from the electronic medical record. Specifically, infectious workup including vital signs, laboratory values, and culture of bodily fluid, tissue, secretions were not routinely or serially collected given the outpatient nature of treatment and low concern for systemic infection in many cases where ABX were prescribed. It is also possible medications were documented incorrectly, taken differently than prescribed, or prescribed/obtained at outside institutions, such as at urgent care or over the counter, and not reported on medication intake. Additionally, it was not possible to collect samples from patients to analyze their gut microbiomes, which guides potential future directions of this research. From these data, we are unable to conclude whether it is the medication or the medical condition itself that is the driver of the observed outcome. Despite these limitations, our study is one of the first to explore concurrent medication use with the PARPi class of medications and is relevant to gynecologic oncology and other oncology specialties where PARPi are utilized. While retrospective, our findings do not support that ABX, PPI, H2RA, and steroids are associated with detriment to PFS in patients with OC on PARPi therapy.
In this single institution, retrospective analysis of patients with OC, our data does not support that concurrent ABX, PPI, H2RA, or steroids are associated with worsened in patients with OC being treated with PARPi. Treatment with ABX at the time of PARPi therapy is associated with improved survival but did not remain significant when controlling for the timing of PARPi use and BRCA/HRD status. Further study is necessary to delineate mechanisms of PARPi resistance, including those that may involve microbiome-driven mechanisms, to improve outcomes in gynecologic and non-gynecologic cancers.
Acknowledgments:
This work was supported by the Ovarian Cancer Research Alliance Early Career Investigator Grant (ECIG-2024-3-1553) for Laura Chambers, DO, and Grant 2021258 for Laura Chambers, DO, from the Doris Duke Charitable Foundation (DDCF) through the COVID-19 Fund to Retain Clinical Scientists collaborative grant program and was made possible through the support of Grant 62288 from the John Templeton Foundation. Additional research support comes from the Paul Calabresi Career Development Award for Clinical Oncology (NIHK12CA133250) for Laura Chambers, DO. The content is solely the authors’ responsibility and does not necessarily reflect the views of The Ohio State University Medical Center. The opinions expressed in this publication are those of the author(s) and do not necessarily reflect the view of the DDCF, the John Templeton Foundation, the OSU College of Medicine, or the Center for Clinical and Translational Science.
Footnotes
Conflicts of Interest Relevant to this Study:
Quinn Kistenfeger MD: None
Paulina J Haight MD: None
Monica Levine MD: None
Heather Wang DO: None
Floor J Backes MD: None
Kristin L Bixel MD: None
Larry J Copeland MD: None
David E Cohn MD: None
Casey M Cosgrove MD: None
David M O’Malley MD: None
Christa I Nagel MD: None
Daniel J Spakowicz PhD: None
Laura M. Chambers, DO: None
References:
- 1.Siegel RL, Kratzer TB, Giaquinto AN, Sung H, Jemal A. Cancer statistics, 2025. CA Cancer J Clin. 2025. Jan-Feb;75(1):10–45. doi: 10.3322/caac.21871. Epub 2025 Jan 16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Orr B, Edwards RP. Diagnosis and Treatment of Ovarian Cancer. Hematol Oncol Clin North Am. 2018. Dec;32(6):943–964. doi: 10.1016/j.hoc.2018.07.010. [DOI] [PubMed] [Google Scholar]
- 3.Tew WP, Lacchetti C, Ellis A, Maxian K, Banerjee S, Bookman M, Jones MB, Lee JM, Lheureux S, Liu JF, Moore KN, Muller C, Rodriguez P, Walsh C, Westin SN, Kohn EC. PARP Inhibitors in the Management of Ovarian Cancer: ASCO Guideline. J Clin Oncol. 2020. Oct 20;38(30):3468–3493. doi: 10.1200/JCO.20.01924. Epub 2020 August 13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.González-Martín A, Pothuri B, Vergote I, DePont Christensen R, Graybill W, Mirza MR, McCormick C, Lorusso D, Hoskins P, Freyer G, Baumann K, Jardon K, Redondo A, Moore RG, Vulsteke C, O’Cearbhaill RE, Lund B, Backes F, Barretina-Ginesta P, Haggerty AF, Rubio-Pérez MJ, Shahin MS, Mangili G, Bradley WH, Bruchim I, Sun K, Malinowska IA, Li Y, Gupta D, Monk BJ; PRIMA/ENGOT-OV26/GOG-3012 Investigators. Niraparib in Patients with Newly Diagnosed Advanced Ovarian Cancer. N Engl J Med. 2019. Dec 19;381(25):2391–2402. doi: 10.1056/NEJMoa1910962. Epub 2019 Sep 28. [DOI] [PubMed] [Google Scholar]
- 5.Liu JF, Xiong N, Wenham RM, Wahner-Hendrickson A, Armstrong DK, Chan N, O’Malley DM, Lee JM, Penson RT, Cristea MC, Abbruzzese JL, Matsuo K, Olawaiye AB, Barry WT, Cheng SC, Polak M, Swisher EM, Shapiro GI, Kohn EC, Ivy SP, Matulonis UA. A phase 2 trial exploring the significance of homologous recombination status in patients with platinum sensitive or platinum resistant relapsed ovarian cancer receiving combination cediranib and olaparib. Gynecol Oncol. 2024. Aug;187:105–112. doi: 10.1016/j.ygyno.2024.05.002. Epub 2024 May 17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Dias MP, Moser SC, Ganesan S, Jonkers J. Understanding and overcoming resistance to PARP inhibitors in cancer therapy. Nat Rev Clin Oncol. 2021. Dec;18(12):773–791. doi: 10.1038/s41571-021-00532-x. Epub 2021 Jul 20. [DOI] [PubMed] [Google Scholar]
- 7.Ismail M, Khan S, Khan F, Noor S, Sajid H, Yar S, Rasheed I. Prevalence and significance of potential drug-drug interactions among cancer patients receiving chemotherapy. BMC Cancer. 2020. Apr 19;20(1):335. doi: 10.1186/s12885-020-06855-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Santamaria F, Roberto M, Buccilli D, Di Civita MA, Giancontieri P, Maltese G, Nicolella F, Torchia A, Scagnoli S, Pisegna S, Barchiesi G, Speranza I, Botticelli A, Santini D. Clinical implications of the Drug-Drug Interaction in Cancer Patients treated with innovative oncological treatments. Crit Rev Oncol Hematol. 2024. Aug;200:104405. doi: 10.1016/j.critrevonc.2024.104405. Epub 2024 Jun 3. [DOI] [PubMed] [Google Scholar]
- 9.Jackson MA, Goodrich JK, Maxan ME, Freedberg DE, Abrams JA, Poole AC, Sutter JL, Welter D, Ley RE, Bell JT, Spector TD, Steves CJ. Proton pump inhibitors alter the composition of the gut microbiota. Gut. 2016. May;65(5):749–56. doi: 10.1136/gutjnl-2015-310861. Epub 2015 December 30. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Maier L, Pruteanu M, Kuhn M, Zeller G, Telzerow A, Anderson EE, Brochado AR, Fernandez KC, Dose H, Mori H, Patil KR, Bork P, Typas A. Extensive impact of non-antibiotic drugs on human gut bacteria. Nature. 2018. Mar 29;555(7698):623–628. doi: 10.1038/nature25979. Epub 2018 March 19. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Chambers LM, Kuznicki M, Yao M, Chichura A, Gruner M, Reizes O, Debernardo R, Rose PG, Michener C, Vargas R. Impact of antibiotic treatment during platinum chemotherapy on survival and recurrence in women with advanced epithelial ovarian cancer. Gynecol Oncol. 2020. Dec;159(3):699–705. doi: 10.1016/j.ygyno.2020.09.010. Epub 2020 September 17. [DOI] [PubMed] [Google Scholar]
- 12.Chambers LM, Michener CM, Rose PG, Reizes O, Yao M, Vargas R. Impact of antibiotic treatment on immunotherapy response in women with recurrent gynecologic cancer. Gynecol Oncol. 2021. Apr;161(1):211–220. doi: 10.1016/j.ygyno.2021.01.015. Epub 2021 January 24. [DOI] [PubMed] [Google Scholar]
- 13.Haight PJ, Kistenfeger Q, Riedinger CJ, Khadraoui W, Backes FJ, Bixel KL, Copeland LJ, Cohn DE, Cosgrove CM, O’Malley DM, Nagel CI, Spakowicz DJ, Chambers LM. The impact of antibiotic and proton pump inhibitor use at the time of adjuvant platinum-based chemotherapy on survival in patients with endometrial cancer. Gynecol Oncol. 2023. Nov;178:14–22. doi: 10.1016/j.ygyno.2023.09.005. Epub 2023 September 21. [DOI] [PubMed] [Google Scholar]
- 14.Mohiuddin JJ, Chu B, Facciabene A, Poirier K, Wang X, Doucette A, Zheng C, Xu W, Anstadt EJ, Amaravadi RK, Karakousis GC, Mitchell TC, Huang AC, Shabason JE, Lin A, Swisher-McClure S, Maity A, Schuchter LM, Lukens JN. Association of Antibiotic Exposure With Survival and Toxicity in Patients With Melanoma Receiving Immunotherapy. J Natl Cancer Inst. 2021. Feb 1;113(2):162–170. doi: 10.1093/jnci/djaa057. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Derosa L, Hellmann MD, Spaziano M, Halpenny D, Fidelle M, Rizvi H, Long N, Plodkowski AJ, Arbour KC, Chaft JE, Rouche JA, Zitvogel L, Zalcman G, Albiges L, Escudier B, Routy B. Negative association of antibiotics on clinical activity of immune checkpoint inhibitors in patients with advanced renal cell and non-small-cell lung cancer. Ann Oncol. 2018. Jun 1;29(6):1437–1444. doi: 10.1093/annonc/mdy103. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Ishiyama Y, Kondo T, Nemoto Y, Kobari Y, Ishihara H, Tachibana H, Yoshida K, Hashimoto Y, Takagi T, Iizuka J, Tanabe K. Antibiotic use and survival of patients receiving pembrolizumab for chemotherapy-resistant metastatic urothelial carcinoma. Urol Oncol. 2021. Dec;39(12):834.e21–834.e28. doi: 10.1016/j.urolonc.2021.05.033. Epub 2021 July 18. [DOI] [PubMed] [Google Scholar]
- 17.Zhang L, Chen C, Chai D, Li C, Guan Y, Liu L, Kuang T, Deng W, Wang W. The association between antibiotic use and outcomes of HCC patients treated with immune checkpoint inhibitors. Front Immunol. 2022. August 17;13:956533. doi: 10.3389/fimmu.2022.956533. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Chambers LM, Esakov Rhoades EL, Bharti R, Braley C, Tewari S, Trestan L, Alali Z, Bayik D, Lathia JD, Sangwan N, Bazeley P, Joehlin-Price AS, Wang Z, Dutta S, Dwidar M, Hajjar A, Ahern PP, Claesen J, Rose P, Vargas R, Brown JM, Michener CM, Reizes O. Disruption of the Gut Microbiota Confers Cisplatin Resistance in Epithelial Ovarian Cancer. Cancer Res. 2022. Dec 16;82(24):4654–4669. doi: 10.1158/0008-5472.CAN-22-0455. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Wu B, Sun C, Sun X, Li X. Effect of proton pump inhibitors on the clinical outcomes of PD-1/PD-L1 inhibitor in solid cancer patients. Medicine (Baltimore). 2022. Sep 9;101(36):e30532. doi: 10.1097/MD.0000000000030532. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Kichenadasse G, Miners JO, Mangoni AA, Karapetis CS, Hopkins AM, Sorich MJ. Proton Pump Inhibitors and Survival in Patients With Colorectal Cancer Receiving Fluoropyrimidine-Based Chemotherapy. J Natl Compr Canc Netw. 2021. May 5;19(9):1037–1044. doi: 10.6004/jnccn.2020.7670. [DOI] [PubMed] [Google Scholar]
- 21.Bruin MAC, Sonke GS, Beijnen JH, Huitema ADR. Pharmacokinetics and Pharmacodynamics of PARP Inhibitors in Oncology. Clin Pharmacokinet. 2022. Dec;61(12):1649–1675. doi: 10.1007/s40262-022-01167-6. Epub 2022 Oct 11. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Fu X, Li P, Zhou Q, He R, Wang G, Zhu S, Bagheri A, Kupfer G, Pei H, Li J. Mechanism of PARP inhibitor resistance and potential overcoming strategies. Genes Dis. 2023. Mar 24;11(1):306–320. doi: 10.1016/j.gendis.2023.02.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Lau CH, Seow KM, Chen KH. The Molecular Mechanisms of Actions, Effects, and Clinical Implications of PARP Inhibitors in Epithelial Ovarian Cancers: A Systematic Review. Int J Mol Sci. 2022. Jul 23;23(15):8125. doi: 10.3390/ijms23158125. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Zhang X, Han Y, Huang W, Jin M, Gao Z. The influence of the gut microbiota on the bioavailability of oral drugs. Acta Pharm Sin B. 2021. Jul;11(7):1789–1812. doi: 10.1016/j.apsb.2020.09.013. Epub 2020 Sep 28. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Vich Vila A, Collij V, Sanna S, Sinha T, Imhann F, Bourgonje AR, Mujagic Z, Jonkers DMAE, Masclee AAM, Fu J, Kurilshikov A, Wijmenga C, Zhernakova A, Weersma RK. Impact of commonly used drugs on the composition and metabolic function of the gut microbiota. Nat Commun. 2020. Jan 17;11(1):362. doi: 10.1038/s41467-019-14177-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Matsumura M, Fujihara H, Maita K, Miyakawa M, Sakai Y, Nakayama R, Ito Y, Hasebe M, Kawaguchi K, Hamada Y. Combinatorial Effects of Cisplatin and PARP Inhibitor Olaparib on Survival, Intestinal Integrity, and Microbiome Modulation in Murine Model. Int J Mol Sci. 2025. Jan 30;26(3):1191. doi: 10.3390/ijms26031191. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Okazawa-Sakai M, Sakai SA, Hyodo I, Horasawa S, Sawada K, Fujisawa T, Yamamoto Y, Boku S, Hayasaki Y, Isobe M, Shintani D, Hasegawa K, Egawa-Takata T, Ito K, Ihira K, Watari H, Takehara K, Yagi H, Kato K, Chiyoda T, Harano K, Nakamura Y, Yamashita R, Yoshino T, Aoki D. Gut microbiome associated with PARP inhibitor efficacy in patients with ovarian cancer. J Gynecol Oncol. 2024. Oct 21. doi: 10.3802/jgo.2025.36.e38. Epub ahead of print. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.O’Malley DM, Krivak TC, Kabil N, Munley J, Moore KN. PARP Inhibitors in Ovarian Cancer: A Review. Target Oncol. 2023. Jul;18(4):471–503. doi: 10.1007/s11523-023-00970-w. Epub 2023 Jun 3. [DOI] [PMC free article] [PubMed] [Google Scholar]