Phase I study of sapanisertib (CB-228/TAK-228/MLN0128) in combination with ziv-aflibercept in patients with advanced solid tumors

BACKGROUND

Sapanisertib is a potent ATP-competitive, dual inhibitor of mTORC1/2. Ziv-aflibercept is a recombinant fusion protein comprising human VEGF receptor extracellular domains fused to human immunoglobulin G1. HIF-1α inhibition in combination with anti-angiogenic therapy is a promising anti-tumor strategy. This Phase 1 dose-escalation/expansion study assessed safety/ tolerability of sapanisertib in combination with ziv-aflibercept in advanced solid tumors.

METHODS

Fifty-five patients with heavily pre-treated advanced metastatic solid tumors resistant or refractory to standard treatment received treatment on a range of dose levels.

RESULTS

Fifty-five patients were enrolled and treated across a range of dose levels. Forty were female (73%), median age was 62 (range: 21-79), and ECOG PS was 0 (9, 16%) or 1 (46, 84%). Most common tumor types included ovarian (8), colorectal (8), sarcoma (8), breast (3), cervical (4), and endometrial (4). Median number of prior lines of therapy was 4 (range 2-11). Sapanisertib 4 mg orally 3 days on and 4 days off plus 3 mg/kg ziv-aflibercept IV every 2 weeks on a 28-day cycle was defined as the maximum tolerated dose. Most frequent treatment-related grade ≥2 adverse events included hypertension, fatigue, anorexia, hypertriglyceridemia, diarrhea, nausea, mucositis, and serum lipase increase. There were no grade 5 events. In patients with evaluable disease (n = 50), 37 patients (74%) achieved stable disease (SD) as best response, two patients (4%) achieved a confirmed partial response (PR); disease control rate (DCR) (CR + SD + PR) was 78%.

CONCLUSION

The combination of sapanisertib and ziv-aflibercept was generally tolerable and demonstrated anti-tumor activity in heavily pre-treated patients with advanced malignancies.

Artificial intelligence in clinical and translational science: Successes, challenges and opportunities

Artificial intelligence (AI) is transforming many domains, including finance, agriculture, defense, and biomedicine. In this paper, we focus on the role of AI in clinical and translational research (CTR), including preclinical research (T1), clinical research (T2), clinical implementation (T3), and public (or population) health (T4). Given the rapid evolution of AI in CTR, we present three complementary perspectives: (1) scoping literature review, (2) survey, and (3) analysis of federally funded projects. For each CTR phase, we addressed challenges, successes, failures, and opportunities for AI. We surveyed Clinical and Translational Science Award (CTSA) hubs regarding AI projects at their institutions. Nineteen of 63 CTSA hubs (30%) responded to the survey. The most common funding source (48.5%) was the federal government. The most common translational phase was T2 (clinical research, 40.2%). Clinicians were the intended users in 44.6% of projects and researchers in 32.3% of projects. The most common computational approaches were supervised machine learning (38.6%) and deep learning (34.2%). The number of projects steadily increased from 2012 to 2020. Finally, we analyzed 2604 AI projects at CTSA hubs using the National Institutes of Health Research Portfolio Online Reporting Tools (RePORTER) database for 2011-2019. We mapped available abstracts to medical subject headings and found that nervous system (16.3%) and mental disorders (16.2) were the most common topics addressed. From a computational perspective, big data (32.3%) and deep learning (30.0%) were most common. This work represents a snapshot in time of the role of AI in the CTSA program.

Survival of patients with metastatic leiomyosarcoma: the MD Anderson Clinical Center for targeted therapy experience

Advanced stage leiomyosarcoma (LMS) is incurable with current systemic antitumor therapies. Therefore, there is clinical interest in exploring novel therapeutic regimens to treat LMS. We reviewed the medical records of 75 consecutive patients with histologically confirmed metastatic LMS, who had been referred to the Clinical Center for Targeted Therapy at MD Anderson Cancer Center. To lay the foundation for potential phase I trials for the treatment of advanced LMS, we analyzed tumor response and survival outcome data. The frequent hotspot gene aberrations that we observed were the TP53 mutation (65%) and RB1 loss/mutation (45%) detected by Sequenom or next-generation sequencing. Among patients treated with gene aberration-related phase I trial therapy, the median progression-free survival was 5.8 months and the median overall survival was 15.9 months, significantly better than in patients without therapy (1.9 months, P = 0.001; and 8.7 months, P = 0.013, respectively). Independent risk factors that predicted shorter overall survival included hemoglobin <10 g/dL, body mass index <30 kg/m2 , serum albumin <3.5 g/dL, and neutrophil above upper limit of normal. The median survivals were 19.9, 7.6, and 0.9 months for patients with 0, 1 or 2, and ≥3 of the above risk factors, respectively (P < 0.001). A prognostic scoring system that included four independent risk factors might predict survival in patients with metastatic LMS who were treated in a phase I trial. Gene aberration-related therapies led to significantly better clinical benefits, supporting that further exploration with novel mechanism-driven therapeutic regimens is warranted.