Nutrition exerts considerable effects on health, and dietary interventions are commonly used to treat diseases of metabolic aetiology. Although cancer has a substantial metabolic component
, the ...principles that define whether nutrition may be used to influence outcomes of cancer are unclear
. Nevertheless, it is established that targeting metabolic pathways with pharmacological agents or radiation can sometimes lead to controlled therapeutic outcomes. By contrast, whether specific dietary interventions can influence the metabolic pathways that are targeted in standard cancer therapies is not known. Here we show that dietary restriction of the essential amino acid methionine-the reduction of which has anti-ageing and anti-obesogenic properties-influences cancer outcome, through controlled and reproducible changes to one-carbon metabolism. This pathway metabolizes methionine and is the target of a variety of cancer interventions that involve chemotherapy and radiation. Methionine restriction produced therapeutic responses in two patient-derived xenograft models of chemotherapy-resistant RAS-driven colorectal cancer, and in a mouse model of autochthonous soft-tissue sarcoma driven by a G12D mutation in KRAS and knockout of p53 (Kras
;Trp53
) that is resistant to radiation. Metabolomics revealed that the therapeutic mechanisms operate via tumour-cell-autonomous effects on flux through one-carbon metabolism that affects redox and nucleotide metabolism-and thus interact with the antimetabolite or radiation intervention. In a controlled and tolerated feeding study in humans, methionine restriction resulted in effects on systemic metabolism that were similar to those obtained in mice. These findings provide evidence that a targeted dietary manipulation can specifically affect tumour-cell metabolism to mediate broad aspects of cancer outcome.
Accurate risk assessment is essential for the success of population screening programs in breast cancer. Models with high sensitivity and specificity would enable programs to target more elaborate ...screening efforts to high-risk populations, while minimizing overtreatment for the rest. Artificial intelligence (AI)-based risk models have demonstrated a significant advance over risk models used today in clinical practice. However, the responsible deployment of novel AI requires careful validation across diverse populations. To this end, we validate our AI-based model, Mirai, across globally diverse screening populations.
We collected screening mammograms and pathology-confirmed breast cancer outcomes from Massachusetts General Hospital, USA; Novant, USA; Emory, USA; Maccabi-Assuta, Israel; Karolinska, Sweden; Chang Gung Memorial Hospital, Taiwan; and Barretos, Brazil. We evaluated Uno's concordance index for Mirai in predicting risk of breast cancer at one to five years from the mammogram.
A total of 128,793 mammograms from 62,185 patients were collected across the seven sites, of which 3,815 were followed by a cancer diagnosis within 5 years. Mirai obtained concordance indices of 0.75 (95% CI, 0.72 to 0.78), 0.75 (95% CI, 0.70 to 0.80), 0.77 (95% CI, 0.75 to 0.79), 0.77 (95% CI, 0.73 to 0.81), 0.81 (95% CI, 0.79 to 0.82), 0.79 (95% CI, 0.76 to 0.83), and 0.84 (95% CI, 0.81 to 0.88) at Massachusetts General Hospital, Novant, Emory, Maccabi-Assuta, Karolinska, Chang Gung Memorial Hospital, and Barretos, respectively.
Mirai, a mammography-based risk model, maintained its accuracy across globally diverse test sets from seven hospitals across five countries. This is the broadest validation to date of an AI-based breast cancer model and suggests that the technology can offer broad and equitable improvements in care.
Screening programs must balance the benefit of early detection with the cost of overscreening. Here, we introduce a novel reinforcement learning-based framework for personalized screening, Tempo, and ...demonstrate its efficacy in the context of breast cancer. We trained our risk-based screening policies on a large screening mammography dataset from Massachusetts General Hospital (MGH; USA) and validated this dataset in held-out patients from MGH and external datasets from Emory University (Emory; USA), Karolinska Institute (Karolinska; Sweden) and Chang Gung Memorial Hospital (CGMH; Taiwan). Across all test sets, we find that the Tempo policy combined with an image-based artificial intelligence (AI) risk model is significantly more efficient than current regimens used in clinical practice in terms of simulated early detection per screen frequency. Moreover, we show that the same Tempo policy can be easily adapted to a wide range of possible screening preferences, allowing clinicians to select their desired trade-off between early detection and screening costs without training new policies. Finally, we demonstrate that Tempo policies based on AI-based risk models outperform Tempo policies based on less accurate clinical risk models. Altogether, our results show that pairing AI-based risk models with agile AI-designed screening policies has the potential to improve screening programs by advancing early detection while reducing overscreening.
Low-dose computed tomography (LDCT) for lung cancer screening is effective, although most eligible people are not being screened. Tools that provide personalized future cancer risk assessment could ...focus approaches toward those most likely to benefit. We hypothesized that a deep learning model assessing the entire volumetric LDCT data could be built to predict individual risk without requiring additional demographic or clinical data.
We developed a model called Sybil using LDCTs from the National Lung Screening Trial (NLST). Sybil requires only one LDCT and does not require clinical data or radiologist annotations; it can run in real time in the background on a radiology reading station. Sybil was validated on three independent data sets: a heldout set of 6,282 LDCTs from NLST participants, 8,821 LDCTs from Massachusetts General Hospital (MGH), and 12,280 LDCTs from Chang Gung Memorial Hospital (CGMH, which included people with a range of smoking history including nonsmokers).
Sybil achieved area under the receiver-operator curves for lung cancer prediction at 1 year of 0.92 (95% CI, 0.88 to 0.95) on NLST, 0.86 (95% CI, 0.82 to 0.90) on MGH, and 0.94 (95% CI, 0.91 to 1.00) on CGMH external validation sets. Concordance indices over 6 years were 0.75 (95% CI, 0.72 to 0.78), 0.81 (95% CI, 0.77 to 0.85), and 0.80 (95% CI, 0.75 to 0.86) for NLST, MGH, and CGMH, respectively.
Sybil can accurately predict an individual's future lung cancer risk from a single LDCT scan to further enable personalized screening. Future study is required to understand Sybil's clinical applications. Our model and annotations are publicly available.
Media: see text.
Objectives
To compare upgrade rates of ductal carcinoma in situ (DCIS) on digital mammography (DM) versus digital breast tomosynthesis (DBT) and identify patient, imaging, and pathological features ...associated with upgrade risk.
Methods
A retrospective review was performed of 318 women (mean 59 years, range 37–89) with screening-detected DCIS from 2007 to 2011 (DM group) and from 2013 to 2016 (DBT group). Comparisons made between DM and DBT groups using the unpaired
t
test and chi-square test include detection rates of DCIS, upgrade rates to invasive cancer, and pathological features of DCIS and upgraded cases. Patient, imaging, and pathological features associated with upgrade were also determined.
P
values < 0.05 were considered significant.
Results
There was no significant difference in detection rates of DCIS between DM and DBT groups (0.9 versus 1.0 per 1000 examinations,
p
= 0.45). Upgrade rates of DCIS to invasive cancer in DM and DBT groups were similar (17.3% versus 16.8%,
p
= 0.90), despite significant differences in pathological features of DCIS between DM and DBT groups (including nuclear grade, comedonecrosis, and progesterone receptor status
p
≤ 0.01). Among upgraded cases, a higher proportion were high-grade invasive cancers with DBT (36.7% versus 9.5%,
p
= 0.03). In both groups, ultrasound-guided (versus stereotactic) biopsy was associated with higher upgrade risk (
p
≤ 0.03).
Conclusions
There was no significant difference in detection rates or upgrade rates of DCIS on DM versus DBT; however, upgraded cases were more likely to be high grade with DBT, suggesting possible differences in tumor biology between cancers with DM and DBT. In both DM and DBT groups, biopsy modality was associated with upgrade risk.
Key Points
• Detection rates and upgrade rates of ductal carcinoma in situ (DCIS) on digital mammography (DM) versus digital breast tomosynthesis (DBT) are similar.
• A higher proportion of upgraded cases were high-grade invasive cancers with DBT than DM, suggesting possible differences in tumor biology between cancers that are detected with DM and DBT.
• With both DM and DBT, ultrasound-guided biopsy (versus stereotactic biopsy) was associated with a higher risk of upgrade.
Diverse mechanisms have been described for selective enrichment of biomolecules in membrane-bound organelles, but less is known about mechanisms by which molecules are selectively incorporated into ...biomolecular assemblies such as condensates that lack surrounding membranes. The chemical environments within condensates may differ from those outside these bodies, and if these differed among various types of condensate, then the different solvation environments would provide a mechanism for selective distribution among these intracellular bodies. Here we use small molecule probes to show that different condensates have distinct chemical solvating properties and that selective partitioning of probes in condensates can be predicted with deep learning approaches. Our results demonstrate that different condensates harbor distinct chemical environments that influence the distribution of molecules, show that clues to condensate chemical grammar can be ascertained by machine learning and suggest approaches to facilitate development of small molecule therapeutics with optimal subcellular distribution and therapeutic benefit.
Abstract
Controlled human malaria infection (CHMI) using cryopreserved non-attenuated Plasmodium falciparum sporozoites (PfSPZ) offers a unique opportunity to investigate naturally acquired immunity ...(NAI). By analyzing blood samples from 5 malaria-naïve European and 20 African adults with lifelong exposure to malaria, before, 5, and 11 days after direct venous inoculation (DVI) with Sanaria
R
PfSPZ Challenge, we assessed the immunological patterns associated with control of microscopic and submicroscopic parasitemia. All (5/5) European individuals developed parasitemia as defined by thick blood smear (TBS), but 40% (8/20) of the African individuals controlled their parasitemia, and therefore remained thick blood smear-negative (TBS
−
Africans). In the TBS
−
Africans, we observed higher baseline frequencies of CD4
+
T cells producing interferon-gamma (IFNγ) that significantly decreased 5 days after PfSPZ DVI. The TBS
−
Africans, which represent individuals with either very strong and rapid blood-stage immunity or with immunity to liver stages, were stratified into subjects with sub-microscopic parasitemia (TBS
-
PCR
+
) or those with possibly sterilizing immunity (TBS
−
PCR
−
). Higher frequencies of IFNγ
+
TNF
+
CD8
+
γδ T cells at baseline, which later decreased within five days after PfSPZ DVI, were associated with those who remained TBS
−
PCR
−
. These findings suggest that naturally acquired immunity is characterized by different cell types that show varying strengths of malaria parasite control. While the high frequencies of antigen responsive IFNγ
+
CD4
+
T cells in peripheral blood keep the blood-stage parasites to a sub-microscopic level, it is the IFNγ
+
TNF
+
CD8
+
γδ T cells that are associated with either immunity to the liver-stage, or rapid elimination of blood-stage parasites.
Abstract Cellular metabolism is a key determinant of immune cell function. Here we found that CD14 + monocytes from Sub‐Saharan Africans produce higher levels of IL‐10 following TLR‐4 stimulation and ...are bioenergetically distinct from monocytes from Europeans. Through metabolomic profiling, we identified the higher IL‐10 production to be driven by increased baseline production of NADPH oxidase‐dependent reactive oxygen species, supported by enhanced pentose phosphate pathway activity. Together, these data indicate that NADPH oxidase‐derived ROS is a metabolic checkpoint in monocytes that governs their inflammatory profile and uncovers a metabolic basis for immunological differences across geographically distinct populations.
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