A review on single-cell analysis of macrophages in cancer
Bibliographic record
Abstract
Cancer is characterized by uncontrolled cell growth and spread into surrounding tissue. According to the North American Association of Central Cancer Registries (NAACCR) and the National Center for Health Statistics, there were 1,918,030 cancer cases and a total of 609,360 deaths estimated in the US in 2022. Although the direct cause of cancer is not fully understood, it is recognized that the immune system plays a major role in the development and progression of tumors. Investigating tumor-associated macrophages (TAMs) is crucial for understanding the immunosuppressive role that they may play in the tumor microenvironment, which makes them a target for immunotherapies to combat cancer. However, angiogenesis, tumor initiation and invasion promoting signals prevent gaining deeper insight into the intricate molecular mechanisms involved in TAMs and the tumor microenvironment. While researchers in the past have used population samples to analyze cells, recent efforts have incorporated single-cell technologies to study the complex mixture of cells in a tumor, monitor the effects of immunotherapies and more. With the revolutionary advent of the first single-cell RNA sequencing (scRNA-seq), researchers have since been able to gain insight into single-cell data with unprecedented accuracy and develop new immunotherapies through different single-cell technologies, such as polymethylsiloxane (PDMS) and Multiplexed Ion Beam Imaging (MIBI) multiplexing. In this review, we underline the benefits of adopting single-cell technologies to identify distinct cell types in complex environments (i.e., tumors) and highlight the potential application of these technologies along with various multiplexing techniques to immunotherapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".