Cancer3.AI › Latest Research

Latest Research

A cross-section of the Cancer3.AI database: the newest publication from each body region first, then the next one from each — so the review spans cancer types instead of running in blocks. Summaries are generated by Claude Sonnet (Anthropic) and link to the original publications.

ICD: C71 WHO Vol. 6 (CNS5, 2021) Central Nervous System (CNS)
2026-09-10

The glioma metabolite, D-2-hydroxyglutarate (D-2-HG), reduces synaptic transmission and epileptiform bursts in neocortical slices.

Shao L, et al

Contrary to a widely held hypothesis, the glioma metabolite D-2-hydroxyglutarate (D-2-HG) does not promote seizures in brain tissue but instead reduces epileptiform burst frequency and suppresses excitatory synaptic transmission in neocortical slices. Using whole-cell patch-clamp electrophysiology, researchers tested acute bath application of D-2-HG (10 mM) on neocortical tissue preserving intact excitatory-inhibitory networks, finding that rather than depolarizing neurons or triggering seizure-like discharges, D-2-HG prolonged inter-burst intervals and diminished both mono- and poly-synaptic excitatory postsynaptic currents. This finding is clinically significant because approximately 80% of patients with IDH-mutant low-grade gliomas experience seizures, and nearly half of those cases are drug-refractory, meaning the assumed mechanism driving epilepsy in these patients must be reconsidered. The results challenge the glutamate-mimicry model and redirect the search for the true cause of seizures in this large population of glioma patients.

Experimental neurology

Source →
ICD: C90 WHO Vol. 11 (2024) Haematolymphoid System
2026-09-10

Patient-derived mesenchymal stromal cells establish an IL-6 axis that marks progression from smouldering to active multiple myeloma.

Blair PE, et al

Researchers have identified a paracrine IL-6 trans-signalling axis — in which bone marrow mesenchymal stromal cells (MSCs) supply the IL-6 ligand while multiple myeloma (MM) cells provide soluble IL-6 receptor (sIL-6R) — as a key mechanism driving progression from smouldering to active myeloma, with MM cells from active disease patients secreting substantially greater quantities of sIL-6R than those from smouldering disease patients, establishing sIL-6R as a marker of biologically active disease. MSCs in advanced disease also display higher basal IL-6 secretion, demonstrating augmentation of the entire pathway as disease progresses. Using Hyper-IL-6 and sgp130, the study showed that trans-signalling through gp130 is the dominant route by which stromal-derived IL-6 protects MM cells from treatment-induced apoptosis, independent of membrane-bound IL-6R expression. These findings propose that targeted inhibition of IL-6 trans-signalling with sgp130 could provide a more precise and potentially safer alternative to total IL-6/IL-6R blockade, and that early intervention in smouldering myeloma — before pathway amplification — may represent a more effective therapeutic strategy.

Cytokine

Source →
ICD: C69.2 WHO — Eye Tumours Eye & Orbit
2026-09-10

Adolescent Unilateral Retinoblastoma in a Low- and Middle-Income Country: Diagnostic Challenges and Barriers to Genetic Cancer Care.

Sarfraz S, et al

A study published in Pediatric Blood & Cancer investigates the diagnostic challenges and systemic barriers to genetic cancer care faced by adolescents with unilateral retinoblastoma in a low- and middle-income country (LMIC) setting, a group that is frequently underrepresented in the retinoblastoma literature. The research highlights that delayed diagnosis and limited access to genetic counseling and molecular testing represent critical unmet needs in this population, where resource constraints compound the inherent difficulty of identifying retinoblastoma in older patients who present atypically compared to young children. These findings underscore the urgent need for improved awareness, referral pathways, and affordable genetic services in LMICs to ensure timely intervention and appropriate family risk assessment for adolescent patients.

Pediatric blood & cancer

Source →
ICD: C64 WHO Vol. 8 Urinary Tract
2026-09-10

L1CAM expression in eosinophilic solid and cystic renal cell carcinoma: clinicopathologic and immunophenotypic insights from a multicenter cohort.

Kabul S, et al

A multicenter retrospective study of 11 eosinophilic solid and cystic renal cell carcinoma (ESC-RCC) cases found that L1CAM protein is expressed in 63.6% of these rare kidney tumors, with 5 cases showing diffuse membranous (3+) staining and 2 cases showing intermediate (2+) staining. The cohort comprised 9 female and 2 male patients with a median age of 50 years (range 39–79), all of whom were alive without evidence of disease at a median follow-up of 10 months. All tumors expressed KRT20 regardless of L1CAM status, SDHB was retained in all 6 tested cases, and GATA3 was negative in all 5 tested cases. Despite expanding the known immunophenotypic spectrum of ESC-RCC, the authors conclude that L1CAM staining is neither sensitive nor specific enough to support or exclude the diagnosis, and does not currently justify routine use in the diagnostic evaluation of morphologically suspected ESC-RCC.

Human pathology

Source →
ICD: C50 WHO Vol. 2 Breast
2026-09-10 • AI

Measuring Radiologist Workload After AI Triage in Breast Cancer Screening.

Sorin V, et al

A new session-level reporting standard is proposed to more accurately quantify radiologist workload following AI triage in breast cancer screening, replacing simple reading counts with total active human interpretation minutes per 1,000 women screened. The authors argue that reading count alone fails to reveal how much radiologist time is genuinely saved or whether work is merely displaced to arbitration, consensus review, or other downstream tasks. To measure these effects rigorously, the paper also introduces a matched-session study design that directly compares workload before and after AI integration. The framework aims to give screening programmes a practical, reproducible tool for evaluating the true impact of AI on clinical workflow and diagnostic outcomes.

Academic radiology

Source →