Tumor and immune reprogramming during immunotherapy in advanced renal cell carcinoma
Tumor and immune reprogramming during immunotherapy in advanced renal cell carcinoma is a research paper published in Cancer Cell (2021). On theSindex it has a DataRank of 0.964. It has been cited 617 times.
Abstract
Immune checkpoint blockade (ICB) results in durable disease control in a subset of patients with advanced renal cell carcinoma (RCC), but mechanisms driving resistance are poorly understood. We characterize the single-cell transcriptomes of cancer and immune cells from metastatic RCC patients before or after ICB exposure. In responders, subsets of cytotoxic T cells express higher levels of co-inhibitory receptors and effector molecules. Macrophages from treated biopsies shift toward pro-inflammatory states in response to an interferon-rich microenvironment but also upregulate immunosuppressive markers. In cancer cells, we identify bifurcation into two subpopulations differing in angiogenic signaling and upregulation of immunosuppressive programs after ICB. Expression signatures for cancer cell subpopulations and immune evasion are associated with PBRM1 mutation and survival in primary and ICB-treated advanced RCC. Our findings demonstrate that ICB remodels the RCC microenvironment and modifies the interplay between cancer and immune cell populations critical for understanding response and resistance to ICB.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
DataRank Breakdown
Base Score Contribution
0.964
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.