Response-guided neoadjuvant sacituzumab govitecan for localized triple-negative breast cancer: results from the NeoSTAR trial.

BACKGROUND: Sacituzumab govitecan (SG), a novel antibody-drug conjugate (ADC) targeting TROP2, is approved for pre-treated metastatic triple-negative breast cancer (mTNBC). We conducted an investigator-initiated clinical trial evaluating neoadjuvant (NA) SG (NCT04230109), and report primary results. PATIENTS AND METHODS: Participants with early-stage TNBC received NA SG for four cycles. The primary objective was to assess pathological complete response (pCR) rate in breast and lymph nodes (ypT0/isN0) to SG. Secondary objectives included overall response rate (ORR), safety, event-free survival (EFS), and predictive biomarkers. A response-guided approach was utilized, and subsequent systemic therapy decisions were at the discretion of the treating physician. RESULTS: From July 2020 to August 2021, 50 participants were enrolled (median age = 48.5 years; 13 clinical stage I disease, 26 stage II, 11 stage III). Forty-nine (98%) completed four cycles of SG. Overall, the pCR rate with SG alone was 30% [n = 15, 95% confidence interval (CI) 18% to 45%]. The ORR per RECIST V1.1 after SG alone was 64% (n = 32/50, 95% CI 77% to 98%). Higher Ki-67 and tumor-infiltrating lymphocytes (TILs) were predictive of pCR to SG (P = 0.007 for Ki-67 and 0.002 for TILs), while baseline TROP2 expression was not (P = 0.440). Common adverse events were nausea (82%), fatigue (76%), alopecia (76%), neutropenia (44%), and rash (48%). With a median follow-up time of 18.9 months (95% CI 16.3-21.9 months), the 2-year EFS for all participants was 95%. Among participants with a pCR with SG (n = 15), the 2-year EFS was 100%. CONCLUSIONS: In the first NA trial with an ADC in localized TNBC, SG demonstrated single-agent efficacy and feasibility of response-guided escalation/de-escalation. Further research on optimal duration of SG as well as NA combination strategies, including immunotherapy, are needed.

TBCRC 030: a phase II study of preoperative cisplatin versus paclitaxel in triple-negative breast cancer: evaluating the homologous recombination deficiency (HRD) biomarker.

BACKGROUND: Cisplatin and paclitaxel are active in triple-negative breast cancer (TNBC). Despite different mechanisms of action, effective predictive biomarkers to preferentially inform drug selection have not been identified. The homologous recombination deficiency (HRD) assay (Myriad Genetics, Inc.) detects impaired double-strand DNA break repair and may identify patients with BRCA1/2-proficient tumors that are sensitive to DNA-targeting therapy. The primary objective of TBCRC 030 was to detect an association of HRD with pathologic response [residual cancer burden (RCB)-0/1] to single-agent cisplatin or paclitaxel. PATIENTS AND METHODS: This prospective phase II study enrolled patients with germline BRCA1/2 wild-type/unknown stage I-III TNBC in a 12-week randomized study of preoperative cisplatin or paclitaxel. The HRD assay was carried out on baseline tissue; positive HRD was defined as a score ≥33. Crossover to an alternative chemotherapy was offered if there was inadequate response. RESULTS: One hundred and thirty-nine patients were evaluable for response, including 88 (63.3%) who had surgery at 12 weeks and 51 (36.7%) who crossed over to an alternative provider-selected preoperative chemotherapy regimen due to inadequate clinical response. HRD results were available for 104 tumors (74.8%) and 74 (71.1%) were HRD positive. The RCB-0/1 rate was 26.4% with cisplatin and 22.3% with paclitaxel. No significant association was observed between HRD score and RCB response to either cisplatin [odds ratio (OR) for RCB-0/1 if HRD positive 2.22 (95% CI: 0.39-23.68)] or paclitaxel [OR for RCB-0/1 if HRD positive 0.90 (95% CI: 0.19-4.95)]. There was no evidence of an interaction between HRD and pathologic response to chemotherapy. CONCLUSIONS: In this prospective preoperative trial in TNBC, HRD was not predictive of pathologic response. Tumors were similarly responsive to preoperative paclitaxel or cisplatin chemotherapy.

Phase I dose-escalation trial of tucatinib in combination with trastuzumab in patients with HER2-positive breast cancer brain metastases.

BACKGROUND: Brain metastases are frequent in HER2-positive breast cancer. ONT-380 (tucatinib) is a potent selective inhibitor of HER2 with intracranial activity in preclinical models. PATIENTS AND METHODS: This was a phase I study of tucatinib with trastuzumab, without chemotherapy, in patients with progressive, measurable HER2-positive brain metastases. The study tested two schedules of tucatinib: cohort A was twice daily and cohort B was once daily. The primary objective was determination of the maximum tolerated dose (MTD). Secondary end points included objective response (intracranial and extracranial) using modified RECIST and clinical benefit rate (CBR). RESULTS: Overall, 41 patients were enrolled (cohort A, n = 22; cohort B, n = 19). Patients had a median of two prior treatments for metastatic breast cancer and 83% had progressed after prior brain radiation. The MTD of tucatinib for cohort A was 300 mg twice daily and for cohort B was 750 mg once daily. The most common dose-limiting toxicities included thrombocytopenia and aspartate transaminase/alanine aminotransferase elevation. Grade 3/4 aspartate transaminase/alanine aminotransferase elevation occurred in nine of 41 patients (22%). Intracranial responses were observed in two of 17 (12%) patients in cohort A and one of 17 (6%) patients in cohort B treated at the MTD. In cohort A, CBR at 16 weeks was 35% (n = 6). In cohort B, CBR at 16 weeks was 53% (n = 9). Of 15 patients overall who experienced clinical benefit, 12 (80%) had received prior neratinib and/or lapatinib. Median progression-free survival for cohorts A and B was 3.4 and 4.1 months, respectively. CONCLUSION: The combination of tucatinib and trastuzumab is tolerable and demonstrated preliminary evidence of efficacy in patients with HER2-positive brain metastases. CLINICAL TRIAL REGISTRATION: NCT01921335.

Divining responder populations from survival data.

BACKGROUND: Biomarkers that predict treatment response are the foundation of precision medicine in clinical decision-making and have the potential to significantly improve the efficiency of clinical trials. Such biomarkers may be identified before clinical testing but many trials enroll unselected populations. We hypothesized that time-varying treatment effects in unselected trials may result from identifiable responder subpopulations that may have associated biomarkers. MATERIALS AND METHODS: We first simulated scenarios of clinical trials with biomarker populations of varying prevalence and prognostic and predictive associations to illustrate the impact of subgroup-specific effects on overall population estimates. To show a real-world example of time-dependent treatment effects resulting from a prognostic and predictive biomarker, we re-analyzed data from a published clinical trial (RTOG, Radiation Therapy Oncology Group, 9402). We then demonstrated a quantitative framework to fit survival data from clinical trials using statistical models incorporating known estimates of biomarker prevalence and prognostic value to prioritize predictive biomarker hypotheses. RESULTS: Our simulation studies demonstrate how biomarker subgroups that are both predictive and prognostic can manifest as time-dependent treatment effects in overall populations. RTOG 9402 provides a representative example where 1p/19q co-deletion and IDH mutation biomarker-specific effects led to time-varying treatment effects and a considerable deviation from proportional hazards in the overall trial population. Finally, using biomarker data from The Cancer Genome Atlas, we were able to generate statistical models that correctly identified and prioritized a commonly used biomarker through retrospective analysis of published clinical trial data. CONCLUSIONS: Biomarkers that are both predictive and prognostic can result in characteristic changes in survival results. Retrospectively analyzing survival data from clinical trials may highlight potential indications for which an underlying predictive biomarker may be found.

Evaluating newly approved drugs for multidrug-resistant tuberculosis (endTB): study protocol for an adaptive, multi-country randomized controlled trial.

BACKGROUND: Treatment of multidrug- and rifampin-resistant tuberculosis (MDR/RR-TB) is expensive, labour-intensive, and associated with substantial adverse events and poor outcomes. While most MDR/RR-TB patients do not receive treatment, many who do are treated for 18 months or more. A shorter all-oral regimen is currently recommended for only a sub-set of MDR/RR-TB. Its use is only conditionally recommended because of very low-quality evidence underpinning the recommendation. Novel combinations of newer and repurposed drugs bring hope in the fight against MDR/RR-TB, but their use has not been optimized in all-oral, shorter regimens. This has greatly limited their impact on the burden of disease. There is, therefore, dire need for high-quality evidence on the performance of new, shortened, injectable-sparing regimens for MDR-TB which can be adapted to individual patients and different settings. METHODS: endTB is a phase III, pragmatic, multi-country, adaptive, randomized, controlled, parallel, open-label clinical trial evaluating the efficacy and safety of shorter treatment regimens containing new drugs for patients with fluoroquinolone-susceptible, rifampin-resistant tuberculosis. Study participants are randomized to either the control arm, based on the current standard of care for MDR/RR-TB, or to one of five 39-week multi-drug regimens containing newly approved and repurposed drugs. Study participation in all arms lasts at least 73 and up to 104 weeks post-randomization. Randomization is response-adapted using interim Bayesian analysis of efficacy endpoints. The primary objective is to assess whether the efficacy of experimental regimens at 73 weeks is non-inferior to that of the control. A sample size of 750 patients across 6 arms affords at least 80% power to detect the non-inferiority of at least 1 (and up to 3) experimental regimens, with a one-sided alpha of 0.025 and a non-inferiority margin of 12%, against the control in both modified intention-to-treat and per protocol populations. DISCUSSION: The lack of a safe and effective regimen that can be used in all patients is a major obstacle to delivering appropriate treatment to all patients with active MDR/RR-TB. Identifying multiple shorter, safe, and effective regimens has the potential to greatly reduce the burden of this deadly disease worldwide. TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT02754765. Registered on 28 April 2016; the record was last updated for study protocol version 3.3, on 27 August 2019.

Bayesian adaptive randomization in a clinical trial to identify new regimens for MDR-TB: the endTB trial.

BACKGROUND: Evidence-based optimization of treatment for multidrug-resistant tuberculosis (MDR-TB), including integration of new drugs, is urgent. Such optimization would benefit from efficient trial designs requiring fewer patients. Implementation of such innovative designs could accelerate improvements in and access to MDR-TB treatment. OBJECTIVE: To describe the application, advantages, and challenges of Bayesian adaptive randomization in a Phase III non-inferiority trial of MDR-TB treatment. DESIGN: endTB is the first Phase III non-inferiority trial of MDR-TB treatment to use Bayesian adaptive randomization. METHODS: We present a simulation study with assumptions for treatment response at 8, 39, and 73 weeks after randomization, on which sample size calculations are based. We show differences between Bayesian adaptive randomization and balanced randomization designs in sample size and number of patients exposed to ineffective regimens. RESULTS: With 750 participants, 27% fewer than required by balanced randomization, the study had 80% power to detect up to two (of five) novel treatment regimens that are non-inferior (margin 12%) to the control (70% estimated efficacy) at 73 weeks post randomization. Comparing Bayesian adaptive randomization to balanced randomization, up to 25% more participants would receive non-inferior regimens. CONCLUSION: Bayesian adaptive randomization may expose fewer participants to ineffective treatments and enhance the efficiency of MDR-TB treatment trials.