Leveraging large language models for decision support in personalized oncology

IMPORTANCE: Clinical interpretation of complex biomarkers for precision oncology currently requires manual investigations of previous studies and databases. Conversational large language models (LLMs) might be beneficial as automated tools for assisting clinical decision-making. OBJECTIVE: To assess performance and define their role using 4 recent LLMs as support tools for precision oncology. DESIGN, SETTING, AND PARTICIPANTS: This diagnostic study examined 10 fictional cases of patients with advanced cancer with genetic alterations. Each case was submitted to 4 different LLMs (ChatGPT, Galactica, Perplexity, and BioMedLM) and 1 expert physician to identify personalized treatment options in 2023. Treatment options were masked and presented to a molecular tumor board (MTB), whose members rated the likelihood of a treatment option coming from an LLM on a scale from 0 to 10 (0, extremely unlikely; 10, extremely likely) and decided whether the treatment option was clinically useful. MAIN OUTCOMES AND MEASURES: Number of treatment options, precision, recall, F1 score of LLMs compared with human experts, recognizability, and usefulness of recommendations. RESULTS: For 10 fictional cancer patients (4 with lung cancer, 6 with other; median [IQR] 3.5 [3.0-4.8] molecular alterations per patient), a median (IQR) number of 4.0 (4.0-4.0) compared with 3.0 (3.0-5.0), 7.5 (4.3-9.8), 11.5 (7.8-13.0), and 13.0 (11.3-21.5) treatment options each was identified by the human expert and 4 LLMs, respectively. When considering the expert as a criterion standard, LLM-proposed treatment options reached F1 scores of 0.04, 0.17, 0.14, and 0.19 across all patients combined. Combining treatment options from different LLMs allowed a precision of 0.29 and a recall of 0.29 for an F1 score of 0.29. LLM-generated treatment options were recognized as AI-generated with a median (IQR) 7.5 (5.3-9.0) points in contrast to 2.0 (1.0-3.0) points for manually annotated cases. A crucial reason for identifying AI-generated treatment options was insufficient accompanying evidence. For each patient, at least 1 LLM generated a treatment option that was considered helpful by MTB members. Two unique useful treatment options (including 1 unique treatment strategy) were identified only by LLM. CONCLUSIONS AND RELEVANCE: In this diagnostic study, treatment options of LLMs in precision oncology did not reach the quality and credibility of human experts; however, they generated helpful ideas that might have complemented established procedures. Considering technological progress, LLMs could play an increasingly important role in assisting with screening and selecting relevant biomedical literature to support evidence-based, personalized treatment decisions

Interferon-beta in patients with low-grade astrocytomas--a phase I study

In 3 patients with low-grade astrocytomas clinical pharmacology of interferon-beta (10(7) U/mg protein) was investigated. Interferon-beta with escalating dosage (2.3, 6.9, 23, 69 X 10(6) U/patient) was given to each patient in 4 infusions at weekly time intervals. In these patients dose-dependent plasma-levels of interferon-beta of up to 5800 IU/ml were achieved. Plasma concentrations showed a biphasic decline (T1 1/2:0.095-0.49 hrs and T2 1/2: 5-14.5 hrs). Side effects were: mild fatigue, myalgia, tachycardia, hypertension, and fever; the latter was well controlled by pretreatment application of paracetamol. Hematological changes included lymphopenia (2-6 hrs after infusion) and granulocytosis (3-6 hrs after infusion). Natural Killer cell activity was also monitored: 6 hours after infusion a drop of activity - not clearly dose dependent - was observed to a minimum of 1% pretreatment activity; 24 hrs after infusion activity increased up to a maximum of 400%. In this phase I study high biological activity of interferon-beta could be detected in plasma of astrocytoma patients - clinical tolerance was good and only mild toxicity was observed

Placental growth factor supports neuroendocrine tumor growth and predicts disease prognosis in patients

Placental growth factor (PlGF), a VEGF-homolog implicated in tumor angiogenesis and adaptation to antiangiogenic therapy, is emerging as candidate target in malignancies. Here, we addressed the expression, function and prognostic value of PlGF in neuroendocrine tumors (NETs). PlGF was determined in sera of NET patients collected retrospectively (n=88) and prospectively (n=87) using Roche-Elecsys and correlated with clinicopathological data. Tumoral PlGF was evaluated by immunohistochemistry, effects of PlGF on proliferation and migration in vitro were assessed using different NET cell lines and effects on tumor growth in vivo in orthotopic xenografts. Circulating and tumoral PlGF were elevated in patients with pancreatic NETs (pNETs) as compared to control sera and respective healthy tissue. De novo PlGF expression occurred primarily in the tumor stroma, suggesting paracrine stimulatory circuits. Indeed, PlGF enhanced NET proliferation and migration in vitro and, conversely, neutralizing antibodies to PlGF reduced tumor growth in vivo. Elevated circulating PlGF levels in NET patients correlated with advanced tumor grading and were associated with reduced tumor-related survival in pNETs. Subsequent determinations confirmed and extended our observation of elevated PlGF levels in a prospective cohort of grade 1 and grade 2 pNETs (n=30) and intestinal NETs (n=57). In low-grade pNETs, normal circulating PlGF levels were associated with better survival. In intestinal NETs, circulating PlGF above median emerged as an independent prognostic factor for shorter time-to-progression in multivariate analyses. These data assign to PlGF a novel function in the pathobiology of NETs and propose PlGF as prognostic parameter and therapeutic target

Axon guidance factor Slit2 inhibits neural invasion and metastasis in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) metastasizes by neural, vascular and local invasion routes, which limit patient survival. In nerves and vessels, Slit2 and its Robo receptors constitute repellent guidance cues that also direct epithelial branching. Thus, the Slit2-Robo system may represent a key pinch point to regulate PDAC spread. In this study, we examined the hypothesis that escaping from repellent Slit2-Robo signaling is essential to enable PDAC cells to appropriate their local stromal infrastructure for dissemination. Through immunohistochemical analysis, we detected Slit2 receptors Robo1 and Robo4 on epithelia, nerves and vessels in healthy pancreas and PDAC specimens, respectively. Slit2 mRNA expression was reduced in PDAC compared to non-transformed pancreatic tissues and cell lines, suggesting a reduction in Slit2-Robo pathway activity in PDAC. In support of this interpretation, restoring the Slit2 expression in Slit2-deficient PDAC cells inhibited their bidirectional chemoattraction with neural cells, and more specifically impaired unidirectional PDAC cell navigation along outgrowing neurites in models of neural invasion. Restoring autocrine/paracrine Slit2 signaling was also sufficient to inhibit the directed motility of PDAC cells, but not their random movement. Conversely, RNAi-mediated silencing of Robo1 stimulated the motility of Slit2-competent PDAC cells. Furthermore, culture supernatants from Slit2-competent PDAC cells impaired migration of endothelial cells (HUVEC) whereas an N-terminal Slit2 cleavage fragment stimulated such migration. In vivo investigations of orthotopic pancreatic tumors with restored Slit2 expression demonstrated reduced invasion, metastasis and vascularization, with opposing effects produced by Robo1 silencing in tumor cells or sequestration of endogenous Slit2. Analysis of clinical specimens of PDAC showed that those with low Slit2-mRNA expression exhibited a higher incidence and a higher fraction of tumor-infiltrated lymph nodes. Taken together, our findings argue that disrupting Slit2-Robo signaling in PDAC may enhance metastasis and predispose PDAC cells to neural invasion

A Forward Genetic Screen Targeting the Endothelium Reveals a Regulatory Role for the Lipid Kinase Pi4ka in Myelo- and Erythropoiesis

Given its role as the source of definitive hematopoietic cells, we sought to determine whether mutations initiated in the hemogenic endothelium would yield hematopoietic abnormalities or malignancies. Here, we find that endothelium-specific transposon mutagenesis in mice promotes hematopoietic pathologies that are both myeloid and lymphoid in nature. Frequently mutated genes included previously recognized cancer drivers and additional candidates, such as Pi4ka, a lipid kinase whose mutation was found to promote myeloid and erythroid dysfunction. Subsequent validation experiments showed that targeted inactivation of the Pi4ka catalytic domain or reduction in mRNA expression inhibited myeloid and erythroid cell differentiation in vitro and promoted anemia in vivo through a mechanism involving deregulation of AKT, MAPK, SRC, and JAK-STAT signaling. Finally, we provide evidence linking PI4KAP2, previously considered a pseudogene, to human myeloid and erythroid leukemia