Clear cell Renal Cell Carcinoma (ccRCC) is characterized by VHL inactivation1,2. Because no other gene is mutated as frequently, and VHL mutations are truncal3, VHL inactivation is regarded as the governing event4. VHL loss activates HIF-2, and constitutive HIF-2 restores tumorigenesis in VHL-reconstituted ccRCC cells5. HIF-2 is implicated in angiogenesis and multiple other processes6–9, but angiogenesis is the main target of drugs like sunitinib10. HIF-2, a transcription factor, has been regarded as undruggable11. A structure-based design approach identified a selective HIF-2 antagonist (PT2399) that we evaluate using a tumorgraft (TG)/PDX platform12,13. PT2399 dissociated HIF-2 (an obligatory heterodimer [HIF-2α/HIF-1β])14 in human ccRCC suppressing tumorigenesis in 56% (10/18) lines. PT2399 had greater activity than sunitinib, was active in sunitinib-progressing tumors, and was better tolerated. Unexpectedly, some VHL-mutant ccRCCs were resistant. Resistance occurred despite HIF-2 dissociation in tumors and evidence of Hif-2 inhibition in the mouse as determined by suppression of circulating erythropoietin, a HIF-2 target15 and possible pharmacodynamic marker. We identified a HIF-2-dependent gene signature in sensitive tumors. Illustrating drug specificity, gene expression was largely unaffected by PT2399 in resistant tumors. Sensitive tumors exhibited a distinguishing gene expression signature, and generally higher HIF-2α levels. Prolonged PT2399 treatment led to resistance. We identified a binding site and second site suppressor mutation in HIF-2α and HIF-1β respectively. Both mutations preserved HIF-2 dimers despite treatment with PT2399. Finally, an extensively pretreated patient with a sensitive TG had disease control for >11 months with the close analogue PT2385. We validate HIF-2 as a target in ccRCC, show that some ccRCC are, unexpectedly, HIF-2 independent, and set the stage for biomarker-driven clinical trials.

Advances in single-cell RNA sequencing (scRNA-Seq) have allowed for comprehensive analyses of single cell data. However, current analyses of scRNA-Seq data usually start from unsupervised clustering or visualization. These methods ignore the prior knowledge of transcriptomes and of the probable structures of the data. Moreover, cell identification heavily relies on subjective and inaccurate human inspection afterwards. To address these analytical challenges, we developed the Semi-supervised Category Identification and Assignment (SCINA) algorithm, a semi-supervised model, for analyses of scRNA-Seq and flow cytometry/CyTOF data, and other data of similar format, by automatically exploiting previously established gene signatures using an expectation–maximization (EM) algorithm. We applied SCINA on a wide range of datasets, and showed its accuracy, stableness and efficiency exceeded most popular unsupervised approaches. SCINA discovered an intermediate stage of oligodendrocyte from mouse brain scRNA-Seq data. SCINA also detected immune cell population shifting in Stk4 knock-out -knockoutmouse cytometry data. Finally, SCINA identified a new kidney tumor clade with similarity to FH-deficient tumors from bulk tumor data. Overall, SCINA provides both methodological advances and biological insights from perspectives different from traditional analytical methods.

Purpose: The heterodimeric transcription factor HIF-2 is arguably the most important driver of clear cell renal cell carcinoma (ccRCC). Although considered undruggable, structural analyses at the University of Texas Southwestern Medical Center (UTSW, Dallas, TX) identified a vulnerability in the a subunit, which heterodimerizes with HIF1b, ultimately leading to the development of PT2385, a first-in-class inhibitor. PT2385 was safe and active in a first-in-human phase I clinical trial of patients with extensively pretreated ccRCC at UTSW and elsewhere. There were no dose-limiting toxicities, and disease control !4 months was achieved in 42% of patients.Patients and Methods: We conducted a prospective companion substudy involving a subset of patients enrolled in the phase I clinical trial at UTSW (n ¼ 10), who were treated at the phase II dose or above, involving multiparametric MRI, blood draws, and serial biopsies for biochemical, whole exome, and RNAsequencing studies.Results: PT2385 inhibited HIF-2 in nontumor tissues, as determined by a reduction in erythropoietin levels (a pharmacodynamic marker), in all but one patient, who had the lowest drug concentrations. PT2385 dissociated HIF-2 complexes in ccRCC metastases, and inhibited HIF-2 target gene expression. In contrast, HIF-1 complexes were unaffected. Prolonged PT2385 treatment resulted in the acquisition of resistance, and we identified a gatekeeper mutation (G323E) in HIF2a, which interferes with drug binding and precluded HIF-2 complex dissociation. In addition, we identified an acquired TP53 mutation elsewhere, suggesting a possible alternate mechanism of resistance.Conclusions: These findings demonstrate a core dependency on HIF-2 in metastatic ccRCC and establish PT2385 as a highly specific HIF-2 inhibitor in humans. New approaches will be required to target mutant HIF-2 beyond PT2385 or the closely related PT2977 (MK-6482).

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Advances in single-cell RNA sequencing (scRNA-Seq) have allowed for comprehensive analyses of single cell data. However, current analyses of scRNA-Seq data usually start from unsupervised clustering or visualization. These methods ignore the prior knowledge of transcriptomes and of the probable structures of the data. Moreover, cell identification heavily relies on subjective and inaccurate human inspection afterwards. We reversed this paradigm and developed SCINA, a semi-supervised model, for analyses of scRNA-Seq and flow cytometry/CyTOF data, and other data of similar format, by automatically exploiting previously established gene signatures using an expectation-maximization (EM) algorithm. We applied SCINA on a wide range of datasets, and showed its accuracy, stableness and efficiency exceeded most popular unsupervised approaches. Notably, SCINA discovered an intermediate stage of oligodendrocyte from mouse brain scRNA-Seq data. SCINA also detected immune cell population shifting in Stk4 knock-out mouse cytometry data. Finally, SCINA identified a new kidney tumor clade with similarity to FH-deficient tumors from bulk tumor data. Overall, SCINA provides both methodological advances and biological insights from perspectives different from traditional analytical methods.