The association of histopathologic features after neoadjuvant chemo-immunotherapy with clinical outcome: Sub-analyses from the randomized double-blinded, placebo-controlled, Phase III IMagyn050/GOG3015/ENGOT-ov39 study.

OBJECTIVE: Histopathologic characteristics after neoadjuvant chemotherapy (NACT) may correlate with outcome. This study evaluates histopathologic features after immunotherapy and NACT/bevacizumab, and associated clinical outcomes. METHODS: Evaluable tissue from IMagyn050/GOG3015/ENGOT-ov39 patients from prespecified anatomic sites from interval cytoreductive surgery (ICS) after NACT/bevacizumab plus atezolizumab/placebo underwent central histopathologic scoring and analyzed with clinical outcomes. RESULTS: The predefined population had 243 evaluable NACT patients, with 48.1% tumors being PD-L1-positive. No statistically significant differences in PFS (16.9 months vs. 19.2 months, p = 0.21) or OS (41.5 months vs. 45.1 months, p = 0.67) between treatment arms were seen. Substantial residual tumor (RT) (3+) was identified in 26% atezolizumab vs. 24% placebo arms (p = 0.94). Most showed no (1+) necrosis (82% vs. 96%, respectively, p = 0.69), moderate (2+) to severe (3+) fibrosis (71% vs. 75%, respectively, p = 0.82), and extensive (2+) inflammation (53% vs. 47% respectively, p = 0.48). No significant histopathologic differences were identified by tissue site or by arm. Multivariate analyses showed increased risk for progression with moderate and substantial RT (13.6 mon vs. 21.1 mon, hazard ratio 2.0, p < 0.01; 13.6 mon vs. 21.1 mon, HR 1.9, p < 0.01, respectively); but decreased risk for death with extensive inflammation (46.9 mon vs. 36.3 mon, HR 0.65, p = 0.02). Inflammation also correlated with greater likelihood of response to NACT/bevacizumab plus immunotherapy (odds ratio 2.9, p < 0.01). Modeling showed inflammation as a consistent but modest predictor for OS. CONCLUSIONS: Detailed histologic assessment of ICS specimens appear to identify characteristics, such as inflammation and residual tumor, that may provide insight to certain clinical outcomes. Future work potentially leveraging emerging tools may provide further insight into outcomes.

Real-world use of immune checkpoint inhibitors in advanced or recurrent endometrial cancer.

OBJECTIVE: The aim of this study was to describe real-world use of immune checkpoint inhibitors for women with advanced or recurrent endometrial cancer. METHODS: Adult women with advanced or recurrent endometrial cancer who received at least one line of systemic treatment between January 1, 2014 and November 1, 2020, then followed to May 31, 2021 in a nationwide electronic health record-derived de-identified database. Chi-Squared test or Welch's 2-sample t-tests were used to compare patient and clinical factors associated with immune checkpoint inhibitor treatment. Time to next treatment analyses were performed based on the treatment line of the immune checkpoint inhibitor. Sankey plots depicted patient-level temporal systemic treatment. RESULTS: During our study period, 326 women received their first immune checkpoint inhibitor treatment, increasing from 12 patients in 2016 to 148 in 2020. Factors associated with ever receiving immune checkpoint inhibitors included disease stage (p=0.002), mismatch repair (MMR)/microsatellite instability (MSI) status (p<0.001), performance status (p=0.001), and prior radiation receipt (p<0.001) and modality (p=0.003). The most common immune checkpoint inhibitor regimen was pembrolizumab (47.9%) followed by pembrolizumab and lenvatinib (34.7%). Immune checkpoint inhibitors were given as first, second, and third or greater lines of therapy in 24.5%, 41.7%, and 46.1% of evaluable patients. The median time to next treatment was significantly longer if given as an earlier line of treatment (p=0.008). There were significant differences in treatment line of immune checkpoint inhibitor by region (p=0.004), stage (p<0.001), and prior radiation receipt (p=0.014) and modality (p=0.009). Among 326 patients who received immune checkpoint inhibitors, 114 (34.9%) received subsequent treatment including chemotherapy (43.9%), additional immune checkpoint inhibitors (29.8%), and other (26.3%) with no differences in demographic or clinical characteristics based on the type of post-immune checkpoint inhibitor treatment. CONCLUSION: In an observational retrospective real-world database study, immune checkpoint inhibitors were used in 14.7% of patients with advanced or recurrent endometrial cancer across multiple lines of treatment, including after initial immune checkpoint inhibitor treatment.

A non-comparative, randomized, phase II trial of atezolizumab or atezolizumab plus tiragolumab for programmed death-ligand 1-positive recurrent cervical cancer (SKYSCRAPER-04).

OBJECTIVE: To evaluate tiragolumab (anti-TIGIT) and atezolizumab (anti-PD-L1) as second- or third-line therapy for PD-L1-positive persistent/recurrent cervical cancer. METHODS: In the open-label, non-comparative, randomized phase II SKYSCRAPER-04 trial (NCT04300647), patients with PD-L1-positive (SP263 tumor area positivity ≥5%) recurrent/persistent cervical cancer after 1-2 chemotherapy lines (≥1 platinum-based) were randomized 3:1 to atezolizumab 1200 mg with/without tiragolumab 600 mg every 3 weeks until disease progression or unacceptable toxicity. Stratification factors were performance status, prior (chemo)radiotherapy, and disease status. The primary endpoint was independent review committee-assessed confirmed objective response rate per RECIST v1.1 in patients receiving tiragolumab plus atezolizumab. An objective response rate ≥21% (one-sample z-test p≤0.0245) was required for statistical significance versus a historical reference. RESULTS: Protocol-defined independent review committee-assessed objective response rates were 19.0% (95% CI 12.6 to 27.0) in 126 patients receiving tiragolumab plus atezolizumab (p=0.0787 vs historical reference) and 15.6% (95% CI 6.5 to 29.5) in 45 atezolizumab-treated patients. Response rates were higher in PD-L1high (tumor area positivity ≥10%) than PD-L1low (tumor area positivity 5%-9%) subgroups with both regimens. At 8.5 months' median follow-up, independent review committee-assessed progression-free survival was 2.8 months (95% CI 1.7 to 4.1) with tiragolumab plus atezolizumab and 1.9 months (95% CI 1.5 to 3.0) with atezolizumab. In post hoc analyses (10.4 months' median follow-up), median overall survival was 11.1 months (95% CI 9.6 to 14.5) with the combination and 10.6 months (95% CI 6.9 to 13.8) with atezolizumab (crossover permitted). In the combination group, 3% of patients had adverse events requiring treatment discontinuation and 8% had grade ≥3 adverse events of special interest; corresponding values in the single-agent arm were 4% and 11%. There were no treatment-related deaths or new safety findings. CONCLUSION: The objective response rate with the tiragolumab-plus-atezolizumab combination was numerically higher than the historical reference but did not reach statistical significance.

Systematic analysis of off-label and off-guideline cancer therapy usage in a real-world cohort of 165,912 US patients.

Patients with cancer may be given treatments that are not officially approved (off-label) or recommended by guidelines (off-guideline). Here we present a data science framework to systematically characterize off-label and off-guideline usages using real-world data from de-identified electronic health records (EHR). We analyze treatment patterns in 165,912 US patients with 14 common cancer types. We find that 18.6% and 4.4% of patients have received at least one line of off-label and off-guideline cancer drugs, respectively. Patients with worse performance status, in later lines, or treated at academic hospitals are significantly more likely to receive off-label and off-guideline drugs. To quantify how predictable off-guideline usage is, we developed machine learning models to predict which drug a patient is likely to receive based on their clinical characteristics and previous treatments. Finally, we demonstrate that our systematic analyses generate hypotheses about patients' response to treatments.