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Nature, Published online: 11 December 2024; doi:10.1038/d41586-024-03855-8
Cancer cells often become unresponsive to multiple types of therapy. It emerges that these ‘cross-resistant’ tumour cells release lipids that reprogram cells called monocytes to stop them from activating tumour-targeting T cells.
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The trial was termed GDFATHER, for GDF-15 antibody-mediated human effector cell relocation (ClinicalTrials.gov NCT04725474). This study was conducted in compliance with the International Council for Harmonisation E6 guideline for Good Clinical Practice and the Declaration of Helsinki. Complete and signed written informed consent was obtained from patients for inclusion in the study. The protocol was approved by the regulatory authorities in Germany (Paul-Ehrlich-Institut), Spain (Agencia Española de Medicamentos y Productos Sanitarios) and Switzerland (Swissmedic) and the local ethics committees in charge of the clinical trial site: Comité de Ética de la Investigación con Medicamentos, Pamplona, Spain; Swissethics, Cantonal Ethics Committee, Zurich, Switzerland; and the Ethics Committee of the University of Würzburg, Würzburg, Germany. The redacted protocol is available in the Supplementary Information and publicly available at the Clinical Trials Information System, a database set up and maintained by the European Commission.
Patients were eligible for enrolment if they had advanced-stage, relapsed or refractory solid tumours; had exhausted available approved standard treatments, including being relapsed or refractory to prior anti-PD-1 or anti-PD-L1 treatment; (for phase 1; part A and selected phase 2a cohorts) presented with biopsy-accessible tumour for serial biopsy taking; were 18 years or older; and had signed the informed consent form.
The study consisted of two parts, part A (phase 1 (dose escalation)) being a classic ‘3 + 3’ dose-escalation study and part B (phase 2a (expansion)) to explore the antitumoural activity of the combination. In part A of the study, a total of 25 patients were enrolled to receive five predefined DLs and received escalating doses of visugromab intravenous infusion (0.3, 1, 3, 10 and 20 mg kg−1) every 2 weeks. The first three patients for each DL received visugromab as monotherapy for one cycle (14 days) followed by the combination of visugromab and nivolumab. Nivolumab was also administered as an intravenous infusion at 240 mg every 2 weeks. Triple tumour biopsies were taken at baseline, day 14 and day 28.
In part B of the study, up to 5 cohorts with up to 27 participants per cohort with defined tumour entities expected to be GDF-15 dependent were treated with a recommended phase 2 dose, and safety and preliminary efficacy of CTL-002 monotherapy and the combination were evaluated further. To rule out significant visugromab-independent antitumoural activity of nivolumab, patients were included only if they were relapsed or refractory to prior, approved anti-PD-1- or anti-PD-L1-containing treatment as per defined, strict criteria. Patients were enrolled only if they had a minimum of 12 weeks of continuous prior exposure to anti-PD-1 or anti-PD-L1, and their relapse or progression on prior approved anti-PD-1- or anti-PD-L1-containing treatment had occurred while this anti-PD-1 or anti-PD-L1 treatment was ongoing. Based on data in the literature, the expected rate of patients responding to retreatment with an approved PD-1 or PD-L1 agent such as nivolumab in monotherapy in these populations is ≤5% for NSCLC10,11,12,13,14,15.
The study was initiated in December 2020 and the first patient was enrolled on 9 December 2020. As of October 2023, phase 1 of the study has been completed and phase 2a is ongoing with a total of 174 patients enrolled overall in the study.
The main endpoints were safety of visugromab (CTL-002) in combination with nivolumab and antitumoural activity. Safety parameters evaluated for this purpose were the number of participants with adverse events, including serious adverse events; clinical laboratory data; vital signs; electrocardiograms; physical examination (including neurological assessment); and Eastern Cooperative Oncology Group performance status. For phase 1 (part A) dose-limiting toxicities and maximum tolerated dose were also evaluated using National Cancer Institute Common Terminology Criteria for Adverse Events (NCI-CTCAE) Version 5.0. Investigator-assessed evaluation of the antitumour activity was performed according to RECIST V1.1 including the assessment of the proportion of participants with tumour shrinkage (declared if RECIST V1.1-defined reduction in target lesions was ≥5% or more and <30%), a confirmed PR (≥30% reduction in target lesions) and/or CR, and ORR and various related parameters such as time to response, time to progression and DOR (measured from the time point of signing the informed consent). Secondary and exploratory endpoints included PK, pharmacodynamics (for example, degree of GDF-15 neutralization achieved and change in immune-cell number and composition in the tumour tissue) and cachexia-related parameters such as change in weight.
The analysis consisted of 30 different cancer types with a varying number of solid tumours analysed. Material included in the TCGA database is derived from primary tumours of untreated (meaning immune checkpoint blockade naive) patients. The full list of abbreviations used, study names and number of samples for solid tumours is available in Extended Data Table 1.
For these indications, gene expression data (RNAseq-v2 raw counts and TPM) were downloaded from http://firebrowse.org on 1 August 2019 (Broad Institute of MIT and Harvard). Duplicated and ambiguous genes were removed, and normalized data were log2-transformed (log2[TPM + 1]). Correlation analyses of normalized GDF15 expression with immune-related genes and signatures (Extended Data Table 2) were performed using Spearman’s rank correlation (test) with the normalized enrichment score from single-sample gene set enrichment analyses or averaged expression levels and visualized as heat maps. GDF15 expression was analysed in different molecular subtypes and in association with clinical parameters in primary tumours of selected indications. Differences in expression between levels of molecular cancer subtypes were tested using a two-sided Wilcoxon rank sum test. Subsequently, P values were adjusted for multiple testing with the Benjamini–Hochberg method.
Fractions of immune infiltrates and other cell types (including tumour and stromal cells) were estimated through quanTIseq38,39 using the immunodeconv R package40 on RNA-sequencing data (TPM) corrected for purity as determined by ESTIMATE41. Average distribution within a subgroup of patients was computed according to GDF15 expression log2[TPM + 1] (low, medium and high terciles) within the respective tumour types. The distribution of immune-cell fractions was averaged over patients in these groups and visualized as a stacked bar plot (including a fraction with other cell types such as tumour and stromal cells).
All calculations, correlations and visualization analyses were performed using the statistical software environment R as well as the resources outlined in Extended Data Table 4.
Serum samples for assessment of chemokine levels were taken at screening and each scheduled visit day from cycle 1 until cycle 3, and at the end-of-treatment visit. At dosing days (day 1), the samples were taken within 30 min before infusion. The serum was isolated using standard procedures and cryopreserved at −80 °C until use. Concentrations of the CXCL9 (MIG) and CXCL10 (IP10) chemokines were determined using validated solid-phase sandwich enzyme-linked immunosorbent assay (ELISA) kits according to the manufacturer’s instructions (R&D Systems; human CXCL9/MIG Quantikine ELISA kit, catalogue no. DCX900; human CXCL10/IP10 Quantikine ELISA kit, catalogue no. DIP100). Both assays were conducted in compliance with the Principles of Good Laboratory Practices regulations.
Serum samples for PK assessment of total visugromab were taken at screening and every treatment cycle within 30 min before dosing and just at the end of each infusion. The serum was isolated using standard procedures and cryopreserved at −80 °C until use. Concentrations of visugromab were determined using a validated electrochemiluminescence assay method. The PK assay was conducted in compliance with the Principles of Good Laboratory Practices regulations.
Serum samples for GDF-15 assessment were taken at screening and every treatment cycle within 30 min before dosing, isolated using standard procedures and cryopreserved at −80 °C until use. Samples from patients at screening were analysed for baseline GDF-15 levels using a validated quantitative solid-phase sandwich ELISA kit according to the manufacturer’s instructions (R&D Systems; human GDF-15 DuoSet ELISA Kit, catalogue no. DY957). Total GDF-15 levels (free GDF-15 plus visugromab-GDF-15 complex) during treatment were determined by a validated ECL method, using a custom visugromab non-competitive anti-GDF-15 nanobody as a capture reagent and, following saturation with visugromab, a custom non-competitive anti-visugromab antibody as a detection reagent. Both GDF-15 assays were conducted in compliance with the Principles of Good Laboratory Practices regulations.
Tumour, serum samples and patient data used in the translational part of the study were provided by the University Cancer Center Frankfurt (UCT). The study was approved by the institutional review boards of the UCT and the responsible ethical committees at the Goethe University Frankfurt (project number SUG-2-2022).
For analysis of serum GDF-15, archived samples, which were taken within 1–89 days before surgery for patients with invasive bladder cancer (n = 34) or invasive upper urinary tract carcinoma (n = 3), as well as before treatment start with systemic therapy and within 1 year before–after biopsy for patients with metastatic urothelial cancer (n = 13), were provided. The serum was analysed for GDF-15 levels using a quantitative solid-phase sandwich ELISA kit according to the manufacturer’s instructions (R&D Systems; human GDF-15 DuoSet ELISA Kit, catalogue no. DY957).
Immunohistochemical (IHC) staining and immunofluorescence staining were performed on 4-µm formalin-fixed and paraffin-embedded (FFPE) sections of tumour tissue from consenting patients. Sections were deparaffinized and pretreated by protease digestion or heat-mediated treatment before antibody incubation. Before conducting any IHC or immunofluorescence analysis, a histology assessment was performed by a board-certified medical pathologist on slides stained with haematoxylin–eosin. The slides were stained using the Ventana HE 600 automated staining system (Roche Diagnostics) and scanned using bright-field imaging on the Leica Aperio AT2 platform (Leica Biosystems) using Scanscope software (console, v102.0.7.5; controller, v102.0.8.60) and a UPlanSapo 20×/0.75 objective (plus Doppler lens for ×40 images). The assessment consisted of confirmation of tumour type, assessment of histological features, presence of invasive margin, and determination of percentage of necrotic area on the whole slide and in the malignant lesion area.
Determination of the intratumoural pro-GDF-15 expression levels was carried out applying a rabbit polyclonal anti-GDF-15 antibody (Sigma Aldrich; product no. HPA011191) as the primary antibody for automated staining using the Ventana BenchMark Ultra platform (Roche; software version no. 12.3.1 and 12.5.4). The binding of the anti-GDF-15 antibody was visualized using a secondary horseradish peroxidase (HRP) enzyme-conjugated antibody (Roche Diagnostics; Ventana optiView Universal DAB Detection Kit, catalogue no. 760-700). This specific antibody–enzyme complex was then visualized with a precipitating enzyme reaction product. Evaluation was carried out by a board-certified pathologist. Evaluation considered cytoplasmic staining (and in cases in which it was applicable, also membranous staining) of tumour cells. Cytoplasmic staining was assessed in four staining intensity categories, ranging from 0 (no staining) to 3+ (intensive staining). The percentage of stained cells per staining intensity category (0 to 3+) was recorded. This classification provided the basis for the calculation of the H score, which describes the GDF-15 protein levels in the tumour. The H score was determined by adding the results of multiplication of the percentage of cells with their respective intensity values as follows:
$${\rm{H}}\,{\rm{score}}=[1\times (\text{percentage}\,\text{of}\,\text{intensity}\,1)]+[2\times (\text{percentage}\,\text{of}\,\text{intensity}\,2)]+[3\times (\text{percentage}\,\text{of}\,\text{intensity}\,3)]$$
Determination of the PD-L1 protein expression level was performed equivalent to the pro-GDF-15 assessment using a rabbit monoclonal anti-PD-L1 (SP263) antibody (Roche Diagnostics; catalogue no. 790-4905) as a primary antibody for automated staining following the manufacturer’s protocol. A rabbit IgG monoclonal antibody (Roche Diagnostics; catalogue no. 790-4795) was used as an isotype control. PD-L1 IHC was evaluated by a board-certified pathologist applying TPS and combined positive score as previously described42.
CD8, CD4 and FOXP3 IHC staining was performed as a triplex IHC assay on one FFPE tissue slide. CD8 staining was carried out using a monoclonal mouse anti-human CD8 antibody (clone C8/144B; Agilent Technologies; product no. M710301-2) as a primary antibody. CD4 staining was carried out using a monoclonal rabbit anti-human CD4 antibody (clone SP35; CellMarque; product no. 104R), and FOXP3 was stained using a monoclonal rabbit anti-human FOXP3 antibody (clone SP97; Abcam; product no. Ab99963).
Depending on the primary antibody, rabbit- or mouse-specific hydroxyquinoxaline (HQ)-conjugated secondary antibodies (Roche Diagnostics; anti-mouse HQ, product no. 760-4814, anti-rabbit HQ, product no. 760-4815) or rabbit-specific nitropyrazole (NP)-conjugated secondary antibodies (Roche Diagnostics; anti-rabbit NP, product no. 760-4817) were used to allow signal amplification. The binding of the specific primary antibody was visualized using a secondary HRP-conjugated antibody or alkaline phosphatase-conjugated antibody (Roche Diagnostics; anti-HQ HRP, product no. 760-4820; anti-NP alkaline phosphatase, product no. 760-4827).
The enzyme coupled to the secondary antibody catalysed a chromogenic reaction at the binding site of the actual primary antibody resulting in teal-, yellow- and purple-coloured precipitation (Roche Diagnostics; DISCOVERY Teal HRP Kit, product no. 760-247; DISCOVERY Yellow Kit, product no. 760-239; DISCOVERY Purple Kit, product no. 760-229). Triplex IHC staining was carried out on a Ventana Discovery Ultra stainer (Roche; software version 12.5.4). For the identification of tumour epithelium, additional staining for pan-cytokeratin IHC analysis was performed on a separate FFPE tissue slide using a mouse anti-human pan-keratin antibody (clone AE1/AE3/PCK26; Roche Diagnostics; product no. 05267145001). For melanoma samples, an additional SOX10 IHC staining was performed on a separate FFPE tissue slide using a monoclonal rabbit anti-human SOX10 antibody (clone SP267; Roche Diagnostics; product no. 760-4968) to support the tumour-stroma separation process in the digital image analysis. Stained slides were scanned on a Leica Aperio AT2 scanner using Scanscope software (console, v102.0.7.5; controller, v102.0.8.60) and a UPlanSapo 20×/0.75 objective (Olympus; plus Doppler lens for ×40 images).
Evaluation of the triplex IHC staining was carried out by pathologist-assisted digital image analysis of representative areas using Visiopharm software (Visiopharm; version no. 2020.01.1 or higher). Visiopharm software uses undisclosed, linear display lookup tables. Visiopharm analysis output gave a readout on the density (positively stained cells per square millimetre) of cells positive for CD4, CD8, FOXP3 and FOXP3 plus CD4 in four different annotated regions of interest (‘tumour’, ‘tumour stroma’, ‘peritumoural stroma’ and adjacent non-neoplastic ‘normal tissue’).
For the evaluation of CD3, GZMB, Ki67, panCK and SOX10 expression and a nuclear counterstain in human FFPE patient samples, the semi-quantitative UltiMapper I/O T-act kit (Ultivue; product no. ULT20104 or ULT20110) was used. The slides were stained on a Leica Bond RX (Leica Biosystems; software version Bond 6.0.0.431 or higher), and scanned on the Zeiss Axio Scan Z1 fluorescence scanner (Hamamatsu Orca Flash, v4.0, camera (Hamamatsu Photonics); Colibri7 LED light source (Carl Zeiss Microscopy)) using Zen Blue (v3.1) software and a Plan-Apochromat 20×/0.8 M27 (Carl Zeiss Microscopy) objective. The slides were subsequently analysed by pathologist-assisted digital image analysis using Visiopharm software (Visiopharm; version no. 2020.01.1 or higher). Visiopharm software uses undisclosed, linear display lookup tables. The output of the digital analysis gave information on cell densities (positively stained cells per square millimetre) in four different annotated regions of interest (‘tumour cells’, ‘tumour stroma’, ‘tumour area’ (combining ‘tumour cells’ + ‘tumour stroma’), and in adjacent non-neoplastic ‘normal tissue’, in cases in which it was applicable).
Slides were stained with haematoxylin and eosin (Sakura Finetek). IHC staining of GDF-15 (HPA011191, polyclonal, 1:100) was performed manually on 4-µm fresh FFPE slides. Semi-quantitative evaluation of IHC results was performed by a pathologist blinded to clinical data using a semi-quantitative approach using the H score.
The multiplex immunofluorescence analysis on whole-slide images was described previously43. Slides were stained with Opal 7‐Color Automation Kits (Akoya Biosciences). We stained a tumour microenvironment panel: panCK (C-11, Abcam), CD45 (polyclonal, Abcam), PD-L1 (SP142, Abcam), αSMA (1A4, Sigma), Ki67 (SP6, Abcam) and vimentin (EPR3776, Abcam); and an immune-cell panel: CD3 (D7A6E, Cell Signaling), CD8 (C8/144B, Dako/Agilent), CD4 (EPR6855, Abcam), FOXP3 (236A/E7, Abcam). The dye 4′,6‐diamidino‐2‐phenylindole (DAPI; SouthernBiotech) was used for staining of nuclei. Corresponding Opal fluorophore antibodies were used for visualization, and images were taken with the PhenoImager HT imaging system (Akoya Biosciences). Representative areas with urothelial cancer were selected and analysed with the inForm software (Akoya Biosciences). Cells were segmented and a machine learning algorithm in the inForm software was trained to identify cell populations.
Gene expression was measured using the NanoString nCounter PanCancer IO 360 Panel (NanoString Technologies). The PanCancer IO 360 Panel consists of 770 genes, including 20 housekeeping genes. Tissue samples were placed on glass slides as 4-μm-thick FFPE sections and five slides were subjected to RNA extraction using the RNeasy FFPE kit (QIAGEN; catalogue no. 73504) and quality control by NanoDrop (Thermo Scientific). The analysis of gene expression was conducted on the nCounter PanCancer IO 360 Panel and NanoString (NanoString Technologies) platform. A quality check had been performed using NanoStringQCpro v1.14.0 (NanoString Technologies). Raw data normalization using the R package NanoStringNorm resulted in very similar relative log expression distributions compared to normalization using nSolver v4.0 (NanoString Technologies) with standard settings. No batch effect between different runs or cartridges was observed by principal component analyses. Normalized expression data were log2-transformed, and housekeeping genes were filtered. Differential gene expression analyses were performed using the R/Bioconductor package limma (linear models for microarray and RNA-sequencing data) between visugromab treatment (day 14) and pretreatment (baseline) by applying a paired moderated t-test. These analyses were performed for two groups of participants, one group of participants with immune-cell influx (IMM) indicated by a more than twofold increase in the numbers of CD8+ and CD4+ T cells, and another group of participants with less than twofold increases in the same T cell subsets (NOIMM). Differential expression of individual genes between IMM and NOIMM was tested using two-sided Wilcoxon rank sum test and visualized as box plots. Volcano plots were generated using the R package EnhancedVolcano. Over-represented Reactome pathways for significantly upregulated genes on visugromab treatment (day 14) versus pretreatment (baseline) in the IMM group were determined using pathway information from ConsensusPathDB (http://cpdb.molgen.mpg.de/CPDB) and a Fisher’s exact test adjusted for the PanCancer IO 360 gene panel in the statistical software environment R v4.3.1 (R Development Core Team). P values were adjusted for the number of Reactome pathways with at least two matching genes using the Benjamini–Hochberg method.
The PK–PD model was derived from non-human primates and describes the distribution, elimination and interaction of visugromab and GDF-15 in the serum compartment and the peripheral compartment. The distribution between the two compartments was modelled with an inter-compartmental clearance. Visugromab clearance was modelled as a first-order elimination from the serum compartment. Mass action kinetics was used to describe the binding of visugromab to GDF-15, forming a complex that was assumed to be eliminated at the same rate as free visugromab from the serum compartment.
Model parameters for non-human primates were scaled to humans using allometric scaling with a 3 kg body weight for non-human primates and 70 kg body weight for human. The allometric scaling coefficients were 1 for volumes of distribution, 0.75 for clearance, 0.667 for the inter-compartmental clearance, and −0.25 for rates.
The scaled human PK–PD model was coupled to a tumour model to predict free tumour GDF-15 levels for the first-in-human dose selection, reflecting visugromab’s mechanism of action and GDF-15 biology. The tumour model included a tumour microvasculature compartment with a blood flow rate of 0.2 ml−1 min−1 g−1, assuming a tumour size of 36 g. Visugromab was entering from the serum side, whereas free GDF-15 was released from the tumour, resulting in reported serum GDF-15 levels in patients with cancer of 0.5, 2 and 10 ng ml−1 (low, medium and severe scenarios). The calculation of free GDF-15 used the duration it takes the blood to flow through the tumour.
The parameters of this PK–PD model were estimated using total visugromab and total GDF-15 concentration measurements from the dose-range-finding and Good Laboratory Practice toxicology studies. Once the clinical phase 1 study data were available, the parameters were re-estimated. Both estimations were performed using the SEAM algorithm with Monolix software version 2019R1.
This phase 1 and 2a trial was largely evaluated on a descriptive basis as antitumoural activity was unknown for this first-in-human trial.
For the phase 2a cohorts, n = 14 response-evaluable participants were to be initially recruited for each tumour indication. Assuming a true response rate of 20%, the probability of observing at least 2 responding patients out of 14 participants was 80%. For an assumed true response of 10%, the probability of observing at least 1/14 responses in the cohort was 77%.
If at least one response was observed, cohort expansion was warranted per the design (5% one-sided α-level, 80% power, 5% maximum response probability of a ‘poor drug’, 20% response probability of a ‘good drug’). An additional n = 13 participants were then to be added to a cohort. Observing at least 4/27 responses would confirm that the drug warrants further investigation in that indication.
To assess the statistical significance between two independent groups, a two-tailed Mann–Whitney test was performed with a P value of ≤0.05 deemed as statistically significant. The statistical significance between three or more groups was determined by repeated-measures ANOVA with Geisser–Greenhouse correction followed by Dunnett’s test corrected for multiple comparisons with a P value of ≤0.05 deemed as statistically significant. The correlation between two parameters was computed using a non-parametric Spearman’s rank correlation with a 95% confidence interval. All statistical analysis was performed using GraphPad Prism software version no. 10.1.2.
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
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Serge Stinckwich is working to understand how to use AI tools sustainably.Credit: UNDESA/DPIDG/UNPOG
There is a lot of interest from inside the United Nations around how artificial intelligence (AI) can be used to speed up progress towards its 17 Sustainable Development Goals (SDGs), says computer scientist Serge Stinckwich.
As head of research at the United Nations University Institute in Macau (UNU Macau), which was established by the UN in 1992 to do research and training on the use of digital technologies in addressing global issues, Stinckwich is interested in how AI can help countries to hit their SDG targets by the 2030 deadline.
Any gains made using AI will come with costs, however. A notoriously power-hungry resource that is vulnerable to bias and inequitable access, AI presents its own challenges.
Stinckwich spoke to Nature Index about how institutions can use AI tools responsibly to power their SDG-related research.
The popularity of large language models (LLMs) has caused a rapid escalation in the amount of data being used to train AI systems. There’s now a scarcity of machine-readable, diverse data on the Internet for training AI algorithms. Synthetic data, which are generated using algorithms and simulations that mimic real-world scenarios, provide a way to train AI models on more data than would usually be possible.
Synthetic data can help to rebalance biased data sets — for example, in a data set skewed towards one gender, synthetic data can be added to balance representation. They can also help to address the problem of scarcity or missing data. This can be particularly useful in medical research, in which people’s health data and personal information can be hard to obtain because of privacy issues.
This approach will become increasingly common. Gartner, a research and consulting firm headquartered in Stamford, Connecticut, predicts that by the end this year, more than 60% of the data used to train machine learning models will be synthetic.
Synthetic data are generated from data sets that already exist. So, biases in the initial data sets could be propagated throughout the synthetic data, and in turn, AI models that have been trained on them. Our work at UNU Macau focuses on understanding the impact of synthetic data used in machine learning, including the risks for sustainable development through research.
Last year, for instance, we published a technology brief in which we tried to identify the benefits and risks of using synthetic data in AI training. On the basis of this work, we proposed guidelines for responsible use of synthetic data in research related to SDGs, especially in poorer countries. This includes using diverse data when creating synthetic data sets, which means including a wide range of demographics, environments and conditions. We also recommend disclosing or watermarking all synthetic data and their sources, disclosing quality metrics for synthetic data and prioritizing the use of non-synthetic data when possible.
2024 Research Leaders
We also recommend that institutions and organizations establish global quality standards, security measures and ethical guidelines for generation and use of synthetic data.
We hope that UN member states and agencies will adopt our guidelines to support policy-making in the global governance of AI.
When I was a researcher at the French Research Institute on Sustainable Development (IRD) in Marseille, I worked on a project called Deep2PDE in Cameroon. Together with colleagues at the local universities, our team used machine-learning tools to understand how competition for light between plant species affects agroforests in which cocoa trees are grown alongside other trees and crops. This helped us to simulate, design and test systems to optimize cocoa production.
There are lots of practical applications of AI, such as this one, that can aid progress towards the SDGs. A big advantage is that these tools can help teams to tailor their work to the needs and contexts of communities; what might be useful for people in Europe or North America might not work in Africa.
We need big computing infrastructure to power AI systems, and this requires resources such as water for cooling systems. This has implications for sustainability, and by extension, the SDGs. So, we have to be cautious. The environmental impacts of AI systems, including on the use of minerals and water and greenhouse-gas emissions, is a big concern. For instance, some research suggests that training an LLM, such as the one powering the chatbot ChatGPT, could produce carbon emissions equivalent to those from roughly 500–600 flights between New York City and Los Angeles, California.
Some technology companies are not keen to share the actual cost of their AI systems and the resources they use. This makes it difficult for researchers to evaluate the environmental impacts of AI and to advise governments and policymakers on how to mitigate them.
Another major issue is one of inequity: AI tools and data are often owned and controlled by companies and institutions in richer countries, so poorer countries are limited in how they can use them to further their SDG-related research.
A big reason for this issue is that most of the progress in building LLMs in the past few years has been done by private companies, not by academics and research institutions. Some potential solutions include creating public–private partnerships and initiatives to democratize access to computing infrastructure.
For example, the Swiss International Computation and AI Network, run by the Swiss Federal Institute of Technology in Zurich, aims to give researchers from low-income settings access to supercomputing resources so that they can develop AI tools that benefit the world. They’re partnering with organizations such as Data Science Africa, a non-profit group in Nairobi, to empower young Africans to use data science to develop solutions for local problems and to help reduce inequalities in data and software infrastructures.
Some online platforms, such as the one run by Hugging Face, a technology company in New York City, make AI-tool-building infrastructure accessible to everyone. It’s open-source, allowing users to share and access resources, including data sets and models developed by others. This approach can help to reduce resource consumption and the environmental impact of AI development.
This interview has been edited for length and clarity.
Nature Index’s news and supplement content is editorially independent of its publisher, Springer Nature. For more information about Nature Index, see the homepage.
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Blood tests are fundamental to modern medicine, and are typically interpreted against broad reference ranges, based on population averages. Yet this standard approach overlooks a crucial point — many blood measurements (biomarkers) are highly individualized and regulated around unique stable values called setpoints, which differ between people. For many people, their ‘normal’ blood values fall within a narrow range — one that is much smaller than the population-based reference range1. Writing in Nature, Foy et al.2 present evidence that underscores the importance of these personalized setpoints, analysing decades of blood-test data across nine key parameters. The authors’ findings suggest that integrating personalized reference intervals into clinical diagnostics could bring about a new level of precision medicine.
S.J.R.M. received research grants, research support, consultancy fees and lecture fees from diagnostic companies, including Roche Diagnostics, Abbott Laboratories and Werfen, all paid to his employer (Maastricht University Medical Center), and unrelated to the topic discussed in this manuscript. K.M.A. has served on advisory boards for Roche Diagnostics, Radiometer, Siemens Healthineers and SpinChip, and received consultant honoraria from CardiNor, lecturing honorarium from Siemens Healthineers, Roche Diagnostics, Mindray and Snibe Diagnostics and research grants from Siemens Healthineers and Roche Diagnostics. K.M.A. is also Associate Editor of Clinical Biochemistry and Chair of the IFCC Committee of Clinical Application of Cardiac Bio-markers.
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The protein GDF-15 is a potent immunosuppressor that is overexpressed in many solid tumours and impedes the effects of cancer immunotherapy. In a first-in-human clinical trial, the GDF-15-targeting antibody visugromab reversed GDF-15-mediated resistance to immunotherapy, resulting in deep, long-lasting tumour regressions in some individuals.
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A rendering of a bottlebrush elastomer and carbon-nanotube composite that researchers believe has potential use as a brain electrode.Credit: Xu, P. et al. Nature Commun. 14, 623 (2023)/CC BY 4.0
When Shuai Xu set out to create a wearable biosensor to monitor the vital signs of premature infants and newborns, he faced a major challenge: the skin of these children is so delicate that the adhesive used to attach a sensor could damage it, potentially leading to infection. The stiff device pulling against the skin as the baby moved, and the wires that might pull it in a different direction, added to the problem. The solution was to build a sensor that was soft and stretchable, with flexible circuit boards and thin, 50-millimetre wires, a huge change from the rigid devices that had long been a mainstay of this type of engineering. It was encased in a bendable silicone, transmitted its readings via Bluetooth, and was stuck to the body using a hydrogel, a polymer-based substance made mostly of water. Xu, a dermatologist, helped develop the device as a postdoctoral researcher in the laboratory of John Rogers, an engineer and materials scientist at Northwestern University in Evanston, Illinois, a pioneer in soft materials.
Xu went on to become a founder and chief executive of Sibel Health in Chicago, Illinois, a medical device company that won Nature’s Spinoff Prize in 2020 and sells wearable sensors for monitoring patients. Xu’s challenges are common among researchers trying to develop biosensors and the materials that go into creating them. The devices must be small and lightweight, and must attach to the body with minimum irritation. In some cases, they require long-lasting batteries and circuitry that can handle a growing suite of artificial-intelligence algorithms that make sense of the data they collect.
Nature Index 2024 Materials science
According to one estimate, the global market for health sensors was worth an estimated US$42.6 billion in 2023 and expected to grow to US$142.2 billion by 2030. The wrist-worn or finger-worn devices that were designed to count steps can now measure heartbeat and blood-oxygen levels, and they’ve been joined by patches that allow diabetics to perform continuous monitoring of their glucose levels.
“That’s nothing to sneeze at,” Xu says. “But there are so many other things that are out there, biochemical and biophysical, that we still can’t do in a practical, continuous way.” Figuring out how to measure a variety of physical and chemical signals cheaply and non-invasively could provide diagnostic information that could reshape medicine. And this might go beyond sensors that take mechanical measurements, such as heart rate. Researchers are also working on chemical sensors that can detect biomarkers in blood, sweat and tears, as well as in fluids that surround cells.
Aida Ebrahimi, a biosensor engineer at Pennsylvania State University in State College, is working on materials that can detect neurotransmitters in saliva or urine such as dopamine, serotonin, adrenaline and noradrenaline, which change in people with diseases such as Parkinson’s or Alzheimer’s. She’s focused on 2D materials, which are only one atomic layer thick, such as molybdenum disulfide. With a material in which, effectively, the “whole thing is surface, you are going to get high sensitivity in the ability to detect a very low concentration of biomolecules”, says Ebrahimi. The material properties of such atomically thin films are also sensitive to surface modification. For example, attaching molecules of manganese gives the material an affinity for dopamine, creating an ultrasensitive detector1.
A soft and stretchable sensor was developed for a newborn’s sensitive skin.Credit: Northwestern University
Similar materials with different molecules attached could be used as sensors for other chemicals that can provide information about health, says Ebrahimi. Her team built a prototype of the sensor in 2020 that they showed could measure dopamine1, but building it and validating it for use could be several years off.
One measuring challenge is that a lot of signalling, particularly in the brain, is performed by the movement of ions, whereas most monitoring equipment is designed to detect electrical currents carried by the flow of electrons. Sahika Inal, a bioengineer at KAUST in Thuwal, Saudi Arabia, is using organic electrochemical transistors (OECTs)2, devices that can detect signals from biomolecules, cells and lipid layers and turn them into readings that can be measured by electronic equipment. OECTs can be built using organic mixed ionic–electronic conductors (OMIECs), which have been the focus of much interest in the past few years. OMIECs are polymers that both ions and electrons can flow across easily. When part of the transistor experiences a small change in a property it is measuring, the OMIEC amplifies that signal. Because it’s an organic polymer, the material is much more compatible with the wet environment of the body than a standard electronic transistor, which has to be encapsulated to protect it from fluids. As a result, electronics can be developed “that can be integrated directly with the biological system,” Inal says.
OECT’s could be printed directly on the skin’s surface to detect biological signals, for instance, or built on top of threads of fabric to create biosensing garments and wraps that could survive washing. They also have the potential to replace the stiff electrodes used in brain implants to control prosthetic devices and monitor electrical activity in seizure patients. Their flexibility and biocompatibility might cause less irritation to brain tissue, which can render the electrodes less sensitive.
At the University of Toronto, mechanical engineer, Xinyu Liu, and chemical engineer, Helen Tran, have developed another material with the softness and flexibility to be used as a brain electrode3. Dubbed the bottlebrush elastomer, their rubber-like substance is made from a molecule that has a long, stiff spine, which maintains its structure, surrounded by short, flexible bristles, for softness. To give the material electrical conductivity, Liu and Tran add a filler — either carbon nanotubes or a mixture of silver flakes and eutectic gallium indium, a semiconductor in liquid form. They worry, though, that the filler could leech out and have toxic effects, so they’d like to eliminate it. “Ultimately, we would like to design a polymer that is soft and electron-conducting,” Tran says. “These demands are often at odds.”
Liu’s lab is also working on wearable sensors. One, based on a hydrogel, is designed to conform to the skin and measure strain when a body part, a knee, for example — is bent4. Such a device could be useful in monitoring an athlete’s performance or assessing arthritis.
Another sensor they are developing places nanowires of zinc oxide on a cotton thread to create electronic textiles that can measure substances such as lactate and sodium in sweat. The material could be woven into a shirt or a sweatband to monitor an athlete’s health5.
Xu sees a lot of opportunities for new biosensors. “AI is generating new algorithms,” he says, that can then be integrated into sensors to learn from, and react, to measurements they’re recording. That would require developing processors that can work with the limited power available in a sensor. Better batteries might help, as would alternatives such as harvesting power from movement or body heat, he says. Devices that can combine readings — glucose levels with heart rate, for instance — could be transformative, he says. He would also like to be able to detect stress hormones that could be used to monitor fatigue, or drug metabolites to check patients have taken medications.
Biosensors have the potential to collect a lot of useful information, and to do it in everyday settings that might give a more realistic picture of health than a one-time doctor’s test. “Whether you’re ill or not”, says Xu, people do not spend most of their time in a clinic or hospital. The ability to track health “and use the technology yourself, I think is really important”.
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Evolutionary geneticist Mary-Claire King did not anticipate the impact of her discovery.Credit: Rina Castelnuovo/New York Times/Redux/eyevine
When Mary-Claire King embarked on a painstaking 17-year-long hunt for a gene linked to breast cancer, she had no inkling that its discovery would be saving lives some three decades later.
King, an evolutionary geneticist, was trying to solve the mystery of why breast cancer was common in some families. This was during the 1970s, decades before the first human genome was sequenced. In the absence of modern tools such as PCR tests, sequencing and mapping genes took a heroic effort. Cancer researchers at the time were mostly studying tumour-causing viruses, but several individuals having the disease across generations of the same family suggested that considerable danger could lurk in the human genome, too.
What is the best time of the month to treat breast cancer?
“The worldwide impact of something like this just never crossed my mind,” says King, who is at the University of Washington in Seattle. “I was absolutely gobsmacked.” King named the gene BRCA1. Since then, it has become clear that mutations in BRCA1 are responsible for about 35% of hereditary breast cancers, and that genetic variants of it and a related gene called BRCA2 are also linked to ovarian, prostate and pancreatic cancers. Drugs have been developed that target cancers with these variants, and genetic tests are available to identify people who are at risk.
But looking back on the impact of BRCA1’s discovery also highlights how far there still is to go. Too few people have access to genetic tests and, even when they do, they have few options to reduce their risk of cancer. Researchers must advocate for and study ways to improve access and to expand the cancer-prevention options available to people who carry BRCA1 and BRCA2 mutations.
Huge leap in breast cancer survival rat
BRCA1 encodes a protein that is important for repairing damaged DNA. Although King identified the BRCA1 gene and pinpointed its location1 in 1990, the team that first sequenced it in 1994 included researchers at the precision-medicine firm Myriad Genetics in Salt Lake City, Utah2. Myriad promptly applied for patents on the gene and used this intellectual property to prevent competitors from developing tests for cancer-associated BRCA1 mutations. The high price tag of Myriad’s genetic tests kept them out of many people’s reach, until a landmark US Supreme Court decision in June 2013 found that such gene patents were invalid.
Following the court’s decision, test prices in the United States plummeted from around US$3,800 to $250, as other providers surged into the field. Yet, testing remains limited, despite studies3 finding that expanding BRCA1 and BRCA2 testing to all women could be cost-effective, particularly for those screened between the ages of 20 and 35. There are several reasons for this, including limited health-care access and concerns about privacy. Lack of awareness among primary-care physicians about genetic testing and conflicting guidelines from professional organizations about who should be tested contribute, too. For now, however, even in places where testing is an option, it is often made available only to those at high risk of carrying a cancer-associated form of BRCA1, including people with a high rate of cancer in their family (see ‘Testing times’).
Source: Ref. 4
Many who are eligible do not get tested for BRCA1 and BRCA2 mutations: one US study4 found that only about 35% of eligible individuals with ovarian cancer and 56% of eligible people with breast cancer had been tested. Other problems limit the tests’ practical benefits, too. Reports provided to physicians and people with cancer are often unnecessarily complicated, because they list not only mutations known to increase risk, but also any other unusual DNA sequences in the genes — even if their relevance is unknown. Many tests also provide data on genes unrelated to cancer, launching fresh medical odysseys for people already dealing with a cancer diagnosis. When King accompanied a friend diagnosed with breast cancer to a clinical appointment, the attending doctor waved off suggestions for BRCA1 testing. “The difficulty with genetic tests,” they said, “is that they simply beget more tests.”
This scientist treated her own cancer with viruses she grew in the lab
Simplifying tests and equipping medical staff with the knowledge to interpret the results could improve uptake. People who learn that they carry worrisome BRCA1 mutations need better options to either prevent cancer from developing or intercept it at an early stage. This is particularly crucial for reducing the risk of ovarian cancer and aiding its early detection. Whereas mammograms can detect some breast tumours early, there is no equivalent test for ovarian cancer, which is often diagnosed at late stages. At present, cancer detection and prevention are typically achieved by careful monitoring or, in some cases, surgery to remove the breasts and ovaries. “When I see a 25-year-old woman newly found to have a BRCA1 mutation, I’m mostly having the same conversations now that I did long ago for her options for risk reduction,” says Susan Domchek, a breast-cancer specialist at the University of Pennsylvania Perelman School of Medicine in Philadelphia. “We have a lot of work to do.”
To improve on this, researchers must develop better means of detecting cancers early, and learn more about the biology of early tumours and why some will go on to become malignant whereas others do not. They must also investigate ways to treat people at earlier stages — an effort that will require learning more about early cancers’ biological hallmarks. By contrast, most treatments are first developed for and tested in people who have advanced disease.
By filling the gaps on testing and giving people with harmful mutations better ways to reduce their risk, BRCA1 and BRCA2 testing could become a model for how genetic tests for other cancer risk factors should be implemented. Then, King’s crucial discovery will save even more lives.
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Huji Xu’s team was on tenterhooks after delivering the first treatments. “We couldn’t sleep, because all these cases are very sick patients,” says Xu, a rheumatologist at the Naval Medical University in Shanghai, China, who published the first results of a revolutionary cellular therapy for autoimmune diseases in September (X. Wang et al. Cell 187, 4890–4904; 2024).
Two weeks after receiving engineered immune cells, the first patient — a woman with a debilitating disorder characterized by extreme muscle weakness — told nurses that she had regained enough strength to lift her arms and comb her hair. Two other recipients, both men, with a different condition, said that their symptoms began fading within days. More than six months later, all three recipients were in remission, according to Xu. “We are a little bit more relaxed” now, he says.
The engineered cells are known as chimeric antigen receptor (CAR) T cells and have been designed to hunt down and eliminate B cells, a type of immune cell that sometimes runs amok in people with autoimmune disorders. CAR-T-cell therapy is widely used to treat blood cancers involving malignant B cells, but it has also shown some promise for autoimmune diseases.
Last year, teams in Germany revealed that they had used CAR T cells to treat at least 15 people with several autoimmune conditions, with stunning success. Xu’s trial differs from these because it used cells taken from an independent donor, whereas the German teams used cells taken from the person being treated. If successful, the donor strategy could allow for mass production of CAR-T-cell treatments, reducing their costs and extending their reach.
Xu trained as a medical doctor in Shanghai. In 1990, he moved to Adelaide, Australia, to start a PhD in immunology and rheumatology, looking at the role of a specific antibody in rheumatic diseases — inflammatory conditions that affect the joints, muscles and bones. Xu went on to research a broad range of subjects, from the biological mechanism of lupus and several types of arthritis, to sudden infant death syndrome (SIDS) and malaria vaccines.
In 2008, he returned to Shanghai and established a large clinical and research centre for rheumatology. The CAR-T-cell trial was a good fit, says Xu, because of his interest in the underlying causes of rheumatic disease. The woman his team treated had refractory inflammatory myopathy. The men had a type of systemic sclerosis that causes the skin to harden and affects many organs.
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Early this year, cases of mpox erupted across Central Africa, killing hundreds. Seeing the events unfold so soon after the still-simmering outbreak of 2022 “felt like scientific amnesia”, says Placide Mbala, an epidemiologist at the National Institute of Biomedical Research in Kinshasa, the capital of the Democratic Republic of the Congo (DRC).
Mbala led a team of researchers who sounded the alarm about the latest outbreak when they spotted a suspicious cluster of mpox cases among young adults and sex workers in an eastern region of the DRC. The team predicted that the disease would move quickly and urged health officials both in the DRC and in neighbouring countries to devise plans to contain the monkeypox virus’s spread.
He and his colleagues analysed the genome of the virus (E. H. Vakaniaki et al. Nature Med. 30, 2791–2795; 2024), revealing that it was a new strain, capable of passing from human to human and distinct from the virus that caused the 2022 outbreak and other previous outbreaks in the DRC. It has since been detected in Sweden, Thailand, India, Germany, the United States, the United Kingdom and six African countries that had never before reported mpox infections.
Mbala has been instrumental in leading these research projects, says Jason Kindrachuk, a virologist at the University of Manitoba in Winnipeg, Canada, who collaborates with Mbala. Furthermore, Mbala has “been coordinating response and community-engagement activities across the country, and doing all this in the least selfish, most diplomatic and democratic ways”, Kindrachuk adds.
It’s a role that Mbala has long trained for. After finishing medical school in 2006 and spending a year as a clinician in Kinshasa, he met Jean-Jacques Muyembe-Tamfum, a microbiologist who directs the centre where Mbala works. Mbala was impressed by Muyembe-Tamfum’s work — in particular his dogged efforts to find the animal reservoir of Ebola, having co-discovered the Ebola virus in 1976. Muyembe-Tamfum took Mbala under his wing, and in 2008 they worked to improve the country’s capacity for mpox testing and treatment. Later, Mbala helped to diagnose and confirm, through genetic sequencing, the first infections with the Ebola virus during the DRC’s 2014 outbreak.
“He’s leaving quite the legacy, and he’s really fitting the shoes of his mentor,” says Nicaise Ndembi, a virologist at the Africa Centres for Disease Control and Prevention in Addis Ababa, who is coordinating the agency’s 2024 mpox response.
For Mbala, it is his personal mission to put an end to the scientific amnesia that allowed conditions such as mpox to linger and re-emerge. He says that the world knew what the monkeypox virus was capable of, and yet, once infections outside Africa dropped below a certain level, the disease became neglected once again. Vaccines and therapeutics that many high-income countries deployed to control the 2022 outbreaks remained out of reach of African nations until last September — when the strain had already spread aggressively throughout the continent.
Mbala aims to better understand how the disease spreads in the DRC and neighbouring countries. His team has found that the virus can spread rampantly in displacement camps and through non-sexual contact; previously, mpox in Central Africa caused small, localized outbreaks and was known to spread to people only through contact with infected animals (D. Mukadi-Bamuleka et al. Preprint at medRxiv https://doi.org/g8dxrz; 2024).
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Nature, Published online: 04 December 2024; doi:10.1038/d41586-024-03846-9
The discovery that peptides from myelin proteins in the brain help the immune system’s T cells to suppress autoimmunity could pave the way to developing therapies for inflammatory brain diseases.
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