Category: 3. Business

  • The transforming role of wharton’s jelly mesenchymal stem cell-derived exosomes for diabetic foot ulcer healing: a randomized controlled clinical trial | Stem Cell Research & Therapy

    The transforming role of wharton’s jelly mesenchymal stem cell-derived exosomes for diabetic foot ulcer healing: a randomized controlled clinical trial | Stem Cell Research & Therapy

    Isolation and characterization of WJ-MSC cells from UC

    After gaining informed consent, the university hospital used aseptic surgery to remove the umbilical cord tissue from a healthy donor. Before dissecting Wharton jelly (WJ), UC tissue was submerged in phosphate-buffered saline (PBS) containing 100 U/ml penicillin, 100 µg/ml streptomycin, and 2 µg/ml amphotericin B [21]. WJ was centrifuged at 340xg after being treated for an hour at 37 °C with collagenase (1 mg/ml type I) and hyaluronidase (0.7 mg/ml). The cell pellet was mixed with DMEM/F12 supplemented with 15% FBS and incubated at 37 °C with 5% CO2. Fresh media was added to the cells every 4 days throughout the 21-day observation period [22]. WJ-MSC cells’ morphology was examined under a microscope. During the culture period, the medium was replaced every 3–4 days to ensure proper cell growth. Cells were split at a 1:4 ratio upon reaching confluence to maintain adequate growth conditions and prevent overcrowding. In WJ-MSC cells during the 21st-day passage, flow cytometric examination revealed the presence of CD14, CD34, CD73, and CD105 labeling. WJ-MSC cell surface receptors CD14, CD34, CD73, and CD105 staining were subjected to the immunofluorescence technique [23].

    Isolation and characterization of exosomes from WJ-MSC

    For 48 h, MSC cells were cultured in 75 cm2 flasks using DMEM/F12 mix that was devoid of FBS (starved). Exosomes secreted from fasting cells during a 48-h period were first obtained by collecting the media. To separate the cells and big vesicles, the fluids were centrifuged for 10 min at 13,000×g and 10 min at 45,000×g. After that, it was centrifuged in an ultracentrifuge for 5 h at 110,000×g (Beckman Coulter) [24]. Ultimately, the pellet was suspended in PBS and the supernatant was discarded [25]. Using flow cytometry, the characteristics of the isolated exosomes were examined for the CD9, CD63, CD81, and HSP70 markers. WJMSC exosomes were incubated with 1.5 × 105 anti-CD63 beads in 50 ml PBS at room temperature for 15 min to perform a flow cytometric analysis. The beads were incubated for the entire night at 4uC with mild stirring after the volume was increased to 300 ml. After 30 min of incubation in 100 mM glycine, the process was halted [26]. Following two rounds of washing, exosome-coated beads were incubated in 50 mg of human IgG (Sigma-Aldrich) for 15 min at 4 °C. They were then treated with anti-CD9 FITC, anti-CD63 PE, anti-CD81 APC, or matched isotype controls (BD Biosciences) and obtained using a FACS Melody (BD Biosciences) [27]. TEM electron microscopy was used to determine the morphology and nano-size of WJMSCs exosomes [28]. The isolation and characterization process were repeated three times to confirm reproducibility and consistency in results.

    Study design

    To assess the effectiveness and safety of WJ-MSC-derived exosome gel in patients with diabetic foot ulcers (DFUs), a randomized double-blind controlled clinical experiment was carried out. The trial protocol was implemented after obtaining the Scientific Research Ethics Committee of Kafr Elsheikh University on 25/3/2024, with final approval by the Committee’s decision No. KESIRB200-175. With Clinical Trial ID. NCT06812637 on ClinicalTrials.gov https://clinicaltrials.gov/study/NCT06812637. All participants provided written informed consent before enrollment.

    Participants

    After 207 patients were assessed, 110 of them satisfied the requirements for inclusion. The patients were then split into three groups:

    1. a.

      Group treated: 40 patients received standard of care (SOC) once weekly for 4 weeks, followed by a 16-week follow-up, using Wharton jelly derived mesenchymal stem cell (WJ-MSC) exosome gel [29].

    2. b.

      Control group: 35 patients had just standard of care (SOC) for 4 weeks, then 16 weeks of follow-up.

    3. c.

      A visually similar saline-based formulation was administered once weekly to 35 patients in the placebo group for 4 weeks, followed by follow-up for 16 weeks, along with SOC [29].

    Analysis After Dropouts:

    • Group treated (SOC + WJ-MSC-derived exosome gel):

      • 30 patients completed the pre-protocol analysis.

      • Dropouts included: 3 withdrew consent, 1 missed 6 consecutive dressings, 5 were lost to follow-up, and 1 developed osteomyelitis.

    • Control Group (SOC only):

      • 24 patients completed the pre-protocol analysis.

      • Dropouts included: 5 withdrew consent, 2 missed 6 consecutive dressings, 2 were lost to follow-up, and 2 developed osteomyelitis.

    • Placebo Group (SOC with vehicle):

      • 31 patients completed the pre-protocol analysis.

      • Dropouts included: 1 death, 1 amputation,1 was lost to follow-up, and 1 developed osteomyelitis.

    A consort flow diagram showing the progress through the trial phases is shown in Fig. 1.

    Fig. 1

    Consort flow diagram showing the progress through the phases of the trial

    Inclusion criteria

    1. 1.

      People with type 2 diabetes who are 42–62 years of age.

    2. 2.

      The existence of a persistent DFU that does not go away after 7 days of standard of care (SOC) treatment or that does not shrink by more than 30%.

    3. 3.

      Ulcers smaller than 30 cm² that are seen on the plantar, medial, or lateral portions of the foot.

    4. 4.

      Individuals suffering from ischemic, neuropathic, or mixed neuropathic-ischemic ulcers.

    5. 5.

      Revascularization performed for ischemic ulcers before enrollment.

    Exclusion criteria

    1. 1.

      Pregnancy or breastfeeding.

    2. 2.

      Type 1 diabetes who are 18 years of age or older.

    3. 3.

      Presence of venous ulcers or active infections.

    4. 4.

      Exposure of bone, ligaments, or tendons.

    Demographic data, comorbidities, and concomitant medications were recorded. All participants received instructions on ulcer care and offloading.

    Methods of evaluation of ulcer treatment outcome

    Classification of ulcer

    Two systems were used for classification: the Wound, Ischemia, and Foot Infection system (WIFI system) and the Site, Ischemia, Neuropathy, Bacterial Infection, Area, Depth system (SINBAD system) [30].

    The SINBAD

    The SINBAD method makes it easier to classify diabetic foot ulcers (DFUs) using a 0–1 point scale. The severity of the ulcer is indicated by a score that ranges from 0 to 6 [31]. This approach is easy to use, gives good intra-observer and modest inter-observer repeatability, and doesn’t require any specific equipment beyond standard clinical examination. The SINBAD system is a useful tool for clinical application, as recommended by the IWGDF, and it efficiently monitors ulcer progression, healing, and amputation risk Table 1.

    The WIFI system

    The WIFI system, developed by the Society for Vascular Surgery in 2014, addresses the limitations of existing classifications by evaluating three major risk factors for amputation: wound characteristics, ischemia (based on ABI scores), and infection. Each factor is scored from 0 to 3, providing a detailed severity assessment as shown in Table 2. While the WIFI system aids in predicting major amputations and guiding interventions like revascularization, its reliance on specialized vascular measurements limits its utility in primary or community care settings, making it more suitable for specialized vascular clinics.

    To classify foot infection, we use the international working group on the diabetic foot (IWGDF)/Infectious diseases society of America (IDSA system) [32]

    The categorization method is used to determine whether a diabetic has a foot infection and how serious it is, as shown in Table 3.

    Table 3 IWGDF-IDSA system

    The presence of clinically significant foot ischemia makes both diagnosis and treatment of infection considerably more difficult.

    1. a.

      Infection refers to any part of the foot.

    2. b.

      In any direction, from the rim of the wound.

    3. c.

      If osteomyelitis is demonstrated in the absence of ≥ 2 signs/symptoms of local or systemic inflammation, classify the foot as either grade 3(O) (if < 2 SIRS criteria) or grade 4(O) if ≥ 2 SIRS criteria).

    Assessment of ulcer healing outcome clinically

    An Android smartphone was used to measure the ulcer’s length, breadth, and surface area exactly [33]. The camera was positioned 25 cm from the ulcer, making sure it was parallel to the wound. After taking the picture, the operator marked the edges of the wound and determined the size of the ulcer. A blinded medical practitioner assessed each wound three times to ensure reproducibility, and statistical analysis was performed using the average of these data [29]. Ulcer size reduction will be computed using the following formula: ulcer size reduction = (Ai – Af)/Ai × 100, where Ai is the initial ulcer area and Af is the ulcer area during follow-up after treatment. Ulcers were assessed and photographed for healing [34].

    Management of ulcer and application of WJ-MSC exosome gel

    Debridement of diabetic foot ulcers was initially performed to remove hyperkeratotic skin or necrotic and infected tissues. Following that, the area was cleaned with regular saline. Before beginning any research, measurements were made of the ulcer’s length, width, and surface. After applying the Wharton jelly derived mesenchymal stem cell (WJ-MSC) exosome gel to the ulcer, the treatment group covered the region with sterile gauze and a non-compressible bandage. After a month of doing every 3 days, the wound was irrigated with regular saline, examined for infection, and then treated with Wharton jelly derived mesenchymal stem cell (WJ-MSC) exosome gel. The SOC was administered to the control and placebo groups, which included removing necrotic, hyperkeratotic, and infected tissues, washing the wound with regular saline, and covering the ulcer with non-compressible bandages and sterile gauze. The patients were instructed to change the bandages every day and wipe the ulcer with regular saline [29].

    Follow-up

    Participants were followed up for 16 weeks with evaluations conducted at 2, 4 weeks, then 6 weeks, and then at 2, 4, and 6 months [29]. At each visit:

    1. 1.

      Ulcers were cleansed and assessed for infection.

    2. 2.

      A record of the interim medical history was kept, which included adverse events and prescription drugs.

    3. 3.

      Photographs of the ulcers were taken at each time point.

    Endpoints

    Primary Endpoint:

    Secondary Endpoints:

    • The average reduction in ulcer size throughout the research.

    • Complete healing rate (100% re-epithelialization without drainage).

    • Safety assessment, including adverse events and tolerability [29].

    Evaluation of outcomes

    The study utilized clinical classification systems, including the SINBAD and WIFI systems, to stratify ulcers by severity, vascular health, and infection risk. Evaluating an ulcer involves several diagnostic tools to uncover underlying complications. To provide a comprehensive picture of systemic health and inflammation, laboratory tests usually include fasting blood sugar levels, glycated hemoglobin (HbA1c), a full metabolic panel, complete blood count (CBC), erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP).

    Imaging studies play a critical role, with plain X-rays used to identify hidden issues such as osteomyelitis, subcutaneous air, fractures, or foreign bodies. When osteomyelitis is suspected, MRI stands out as the preferred diagnostic tool due to its superior accuracy. Peripheral vascular disease can be assessed using arterial Doppler and ankle-brachial index (ABI) measurements, ensuring vascular factors are addressed. The probe-to-bone (PTB) test provides a straightforward but efficient way to identify osteomyelitis at the patient’s bedside: if a sterile metal probe contacts bone while exploring the ulcer, the test is positive. When treating diabetic individuals who may have bone infections, this rapid and accurate test is quite helpful [35].

    All patients in the three groups mentioned above were subjected to

    1. 1.

      To get rid of necrotic or diseased tissues, wounds were debrided.

    2. 2.

      After cleaning the region with regular saline, the therapy was administered.

    3. 3.

      The ulcers were covered with non-compressive bandages and sterile gauze.

    4. 4.

      Participants were instructed to clean and redress ulcers daily.

    Statistical analysis

    The non-parametric Wilcoxon rank-sum test was used to evaluate differences in continuous variables of interest, while the chi-square test (or Fisher’s exact, if more applicable) was employed to compare categorical variables between groups. The Wilcoxon signed-rank test was used for post hoc comparisons to find changes in ulcer area across the timepoints of interest, and the non-parametric Friedman test was used to find differences in ulcer size at baseline, 2 weeks, and 4 weeks for the effects of therapy. Software GraphPad Prism (GraphPad version 8.0.2) was used to conduct statistical analysis. When P was less than 0.05, statistical significance was reached.

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  • GP Bullhound advised IMPECT on its acquisition by Catapult

    GP Bullhound advised IMPECT on its acquisition by Catapult

    Berlin, 13 October 2025 – GP Bullhound acted as the exclusive financial advisor to IMPECT, a global SportsTech data and analytics platform, on its acquisition by Catapult Group International Limited (ASX:CAT), a leading Australian sports performance technology company.

    Catapult will acquire 100% of IMPECT for up to €78 million and raise A$130 million underwritten institutional equity placement to finance the transaction and further strategic M&A.

    Founded in Germany in 2014, IMPECT is a pioneer in football analytics, known for its proprietary Packing® metric, and provides scalable, technology-first solutions that deliver deep tactical insights and advanced contextual data. Its products are used by more than 150 professional clubs across 25+ countries, enabling advanced scouting and tactical decision-making.

    Together, Catapult and IMPECT are building the future of sports performance technology – combining video, tactical, and performance data into a single, unified analytics ecosystem that enhances how teams approach player recruitment, tactical analysis, and performance optimization.

    This transaction reinforces GP Bullhound’s global leadership in SportsTech and data analytics, representing its tenth transaction in 18months in this exciting subsector of tech. Most recent transactions include the the $600m sale of Urban Sports Club to Wellhub, PerfectGym to Sport Alliance, or the acquisitions of Eversports by Verdane, and BRP Systems by Nordic Capital. GP Bullhound continues to build on its strong track record advising category leaders at the intersection of sport, data, and digital innovation.

    Simon Miremadi, Partner at GP Bullhound, said:
    “We are thrilled and incredibly proud to have advised the IMPECT team on this landmark transaction. Stefan, Jens, Lukas, Matthias and the entire IMPECT team have built a global company at the intersection of sport, data, and technology. Catapult is the ideal partner to accelerate IMPECT’s vision, impact and reach across multiple sports.”

    Stefan Reinartz, Co-founder and Managing Director of IMPECT, added:
    “From the moment our founders heard the vision from Will and the team, it became clear that bringing our technology into the Catapult platform represented an opportunity to accelerate our growth, benefit from Catapult’s global scale and industry leadership, whilst maintaining our steadfast culture of innovation. We are deeply grateful to the GP Bullhound team for their outstanding partnership and unwavering commitment throughout the process – their strategic guidance, precision, and relentless execution ensured a highly competitive process and an exceptional outcome for IMPECT.”

    About IMPECT
    IMPECT is a leading football video and data analytics company providing advanced insights to professional clubs and federations worldwide. Founded in 2014 in Germany, IMPECT’s proprietary data model quantifies and visualizes the complexity of football, offering revolutionary tools for player evaluation and tactical analysis.
    Learn more at www.impect.com.

    About Catapult
    Catapult (ASX: CAT) is a global leader in sports performance technology, helping athletes and teams improve performance through data, analytics, and wearable innovation. With headquarters in Australia and operations across the globe, Catapult’s products are trusted by elite teams across football, rugby, basketball, and beyond.
    Visit www.catapult.com.

    About GP Bullhound
    GP Bullhound is a leading technology advisory firm, providing transaction advice and capital to the world’s best entrepreneurs and founders. Founded in 1999 in London and Menlo Park, the firm today has 10 offices spanning Europe, Asia and the US.
    For more information, please visit www.gpbullhound.com.

    Enquiries
    For enquiries, please contact:
    Simon Miremadi, simon.miremadi@gpbullhound.com

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  • TSMC Becomes AI Backbone as Nvidia and AMD Double Down

    TSMC Becomes AI Backbone as Nvidia and AMD Double Down

    This article first appeared on GuruFocus.

    Oct 13 – Taiwan Semiconductor (NYSE:TSM) continues to dominate headlines as its stock slices through new all-time highs, fueled by powerful waves of AI demand. In just a 6 month, the chip giant delivered nearly 80% total return, outpacing the S&P 500 by more than 20%. A jolt came last Friday: news of Trump’s threat of a 100% tariff on China triggered a 6% pullback, though underlying momentum in AI spending still looks firm.

    Hyperscale cloud players continue locking in deals to expand data center capacity ahead of demand. Oracle (NYSE:ORCL) saw its stock jump over 20% recently, backed by explosive year-over-year growth in multicloud database bookings. Its partnerships with Microsoft, Amazon (AMZN), and Google (NASDAQ:GOOGL) have powered that surge.

    Meanwhile, a major pivot in chip supply is likely underway. AMD (AMD) has inked a significant multi-year AI chip contract with OpenAI, agreed to supply about 6 gigawatts of capacity starting 2026, and granted OpenAI the option to acquire up to 10% of its shares. That deal reverberated across the sector, as AI buyers increasingly diversify beyond Nvidia (NASDAQ:NVDA). NVDA, in turn, earlier struck a $100 billion partnership with OpenAI to help build AI infrastructure at scale.

    TSM sits at the fulcrum of this shift: while chip designers like NVDA and AMD lean into R&D and architecture, they depend heavily on TSM for fabrication. Though Intel (NASDAQ:INTC) may try to reenter the race in the long term, its execution gaps leave it playing catch-up for now.

    With that perspective, the TSM FY2026 appears to be very strong due to a number of mega deals with leading chip manufacturers and cloud technology companies. The volatility in the near term could be due to trade-war headlines but the broad AI tailwinds are likely to keep pushing the demand. As market mood might waver, TSM remains at the centre of the next layer of AI infrastructure development due to its fundamental positioning in terms of chip design as well as manufacturing.

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  • Veg oils prices commentary: futures edge mostly lower on higher CPO stocks, plummeting crude

    Veg oils prices commentary: futures edge mostly lower on higher CPO stocks, plummeting crude

    Crude palm oil (CPO) futures reversed direction after a three-day rally to close lower on Friday, as traders digested news from the September Malaysia data released by the Malaysian Palm Oil Board (MPOB) and amid positioning ahead of the weekend.

    The most-active December CPO futures contract on the Bursa Malaysia Derivatives Exchange fell by 0.98% to 4,546 ringgit ($1,077) per tonne after trading between 4,525-4,612 ringgit per tonne and extending losses from earlier in the day, when it closed 10 ringgit per tonne lower at 4,581 ringgit per tonne at the end of morning trading session.

    Losses were extended into the afternoon following data from MPOB which showed a build-up in stocks instead of a previously anticipated decline, though indications of better October exports helped to curb the drop in CPO futures.

    On a weekly basis, the most-active third month contract has rose by 2.34% compared with 4,591 ringgit per tonne on October 3, with support stemming mainly from news of Indonesia’s B50 biodiesel upgrade plans and movement in related oils.

    Chinese vegoil futures closed mixed on Friday after strong increases the previous day, with the most-active January palm olein futures contract on the Dalian Commodity Exchange falling by 0.78% to 9,438 yuan ($1,323) per tonne as it tracked a similar movement with Malaysia palm oil futures.

    The January soybean oil futures on DCE was marginally higher by 8 yuan per tonne to 8,302 yuan per tonne, while the January rapeseed oil futures on the Zhengzhou Commodity Exchange fell by 1.33% to close at 10,061 yuan per tonne.

    On the news front, Malaysia’s palm oil stocks rose for a seventh straight month to 2.36 million tonnes at the end of September, against earlier average market estimates of 2.15 million-2.16 million tonnes, with the resultant increase due to smaller-than-expected declines in production, higher imports and lower local disappearance.

    CPO production was marginally lower by 0.73% to 1.841 million tonnes, while exports rose by 7.69% to 1.43 million tonnes. Imports were recorded at 78,413 tonnes, up by 34% on the month, while local disappearance fell by 33% to 333,432 tonnes.

    Meanwhile, cargo surveyors Intertek Testing Services (ITS) and Amspec Agri Malaysia reported October 1-10 exports for Malaysia at 523,602 tonnes and 495,415 tonnes respectively, 9.86% and 19.4% higher from their respective September estimates. The increase helped limit losses in CPO futures amid optimism for stronger October export demand.

    In the cash market, CPO was traded at $1,162 per tonne CFR Kandla for October shipment and at $1,170 per tonne CFR east coast India for November-December shipment.

    Offers heard toward the close of the day were at $1,165-1,170 per tonne CFR west coast India (WCI) for October cargoes, while offers for November shipment were around $1,175-1,185 per tonne CFR WCI and $1,185 per tonne CFR WCI for December shipment.

    At origin, CPO offers out of Indonesia were limited, while offers for olein were at $1,100 per tonne FOB Indonesia for November shipment, with a trade at $1,095 per tonne FOB Indonesia also heard concluded earlier in the day for November shipment.

    Soyoil futures

    In the Americas, soyoil futures plummeted in the Chicago Mercantile Exchange, borrowing weakness from crude markets.

    Soyoil CME futures for December delivery declined by 2.22% day on day to 49.84 cents per lb at 1pm US Eastern time, erasing weekly increases.

    Crude prices weighed on the soyoil market, with Brent and West Texas Intermediate contracts down by 3.7-4.2% day on day amid risk-off positioning backed by progress in peace negotiations for the conflict in the Gaza Strip.

    The US soybean market also faced headwinds from what seems to be a deteriorating atmosphere for trade negotiations between the US and China. China has been strict on the limitations to its rare earths exports and has imposed retaliatory charges on US vessels docking at Chinese ports, while US President Donald Trump threatened to cancel the meeting he is scheduled to have with Chinese President Xi Jinping in three weeks’ time.

    The US dollar reversed directions from the previous trading day, devaluating when measured against a basket of reserve currencies and putting an end to a four-day rally. This helped capping soyoil losses somewhat.

    December CME soymeal futures fell slightly to $276 per short ton at 1pm US Eastern time, with some underlying support from product spreading dynamics.

    In the physical market in South America, the bases negotiations for both soyoil and soymeal were slower with the holiday in Argentina.

    The November basis was assessed at a discount of 0.75 cents per lb in Argentina and at a premium of 0.90 cents per lb in Brazil, both to December futures.

    On the soymeal front, the November basis was assessed at a discount of $7.50 per short ton in Brazil, while in Argentina the corresponding basis was assessed at a discount of $11.50 per short ton to the same futures contract.

    As of 6:15pm Central European time, Euronext November rapeseed futures were trading at €467.25 ($542) per tonne, down by €4.50 per tonne from the previous session.

    FOB Rotterdam rapeseed oil prices fell on Friday.

    For the November–December–January (NDJ) window, offers were made at €1,087-1,093 per tonne, compared with €1,090-1,105 per tonne on Thursday October 9, while bids were made at €1,061-1,065 per tonne, compared with €1,070 per tonne on Thursday.

    For the February-March-April (FMA) window, offers were made at €1,058-1,070 per tonne and bids were made at €1,048-1,050 per tonne, compared with offers of €1,065-1,082 per tonne and bids at €1,055-1,060 per tonne on Thursday.

    Rapeseed oil was heard to have traded at €1,054 per tonne for the FMA window on Friday.

    FOB sunflower oil prices across six EU ports eased on Friday in a correction after hitting nearly three-year highs.

    For the November-December window, offers were made at $1,355 per tonne with bids of $1,330 per tonne, compared with offers made at $1,360-1,390 per tonne and bids of $1,340 per tonne on Thursday.

    For the January–February–March (JFM) window, offers were made at $1,325 per tonne and drew bids of $1,310 per tonne, compared with offers of $1,327.50-1,330.00 per tonne and bids of $1,300.00-1,322.50 per tonne on Thursday.

    No sunflower oil trades were heard on Friday.

    Black Sea sunflower oil

    The Black Sea sunflower oil market recorded limited activity on Friday, with October–November offers reported around $1,300 per tonne CIF Mersin/Iskenderun. Buyers, recognizing that earlier levels were no longer workable, have raised ideas into the $1,275–1,285 per tonne range compared with softer levels earlier in the week.

    TMO has issued an international tender for 18,000 tonnes of sunflower oil for November delivery. The tender is scheduled for Thursday October 16. In its previous tender on September 12, TMO awarded 18,000 tonnes of crude sunflower oil for October delivery — 6,000 tonnes to Tekirdag at $1,256 per tonne CFR and 12,000 tonnes to Iskenderun/Mersin at $1,253.80 per tonne CFR.

    In other news, Russia’s Agriculture Ministry has revised down its 2025 sunflower harvest forecast to 17.5 million tonnes, compared with 18.3 million tonnes in 2024. The downgrade reflected weaker yields, particularly in the southern regions, despite expectations of record gross production across several other crops this season.

    Fastmarkets’ comprehensive coverage includes a wide range of veg oils and meals, including palm, coconut, cottonseed, peanut, sunflower and canola. Our dedicated team of price reporters and analysts monitors these markets daily to provide you with the most up-to-date pricing information.

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  • Welcome to the Agentic Enterprise: With Agentforce 360, Salesforce Elevates Human Potential in the Age of AI – Salesforce Investor Relations

    1. Welcome to the Agentic Enterprise: With Agentforce 360, Salesforce Elevates Human Potential in the Age of AI  Salesforce Investor Relations
    2. Exclusive: Salesforce announces agents with voice and hybrid reasoning  Axios
    3. Salesforce adds voice calling to Agentforce AI customer service software  CNBC
    4. Salesforce Unveils IT Product, Deepening ServiceNow Rivalry  Bloomberg.com
    5. Salesforce Enhances AI Capabilities with New Data Governance Innovations  Small Business Trends

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  • Report: COVID-19 Vax Risks Managed, Independence Important – Medscape

    1. Report: COVID-19 Vax Risks Managed, Independence Important  Medscape
    2. CDC’s Vaccine Safety Monitoring Office Performed Well During COVID-19 Pandemic; Steps Needed to Safeguard Office’s Independence, Improve Communications  National Academies
    3. CDC’s Vaccine Safety Office Has an Image Problem, Report Says  MedPage Today

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  • Comparative Efficacy of Transurethral Laser Ablation Versus Conventional Methods in the Management of Recurrent Non-Muscle-Invasive Bladder Tumours

    Comparative Efficacy of Transurethral Laser Ablation Versus Conventional Methods in the Management of Recurrent Non-Muscle-Invasive Bladder Tumours


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  • Oil recoups some losses after US-China trade tensions

    Oil recoups some losses after US-China trade tensions

    • Brent and WTI rebound from 5-month lows
    • China September crude imports rise 3.9% on year
    • First Israeli hostages released from Gaza

    LONDON, Oct 13 (Reuters) – Oil prices rose on Monday after hitting five-month lows in the previous session, as investors focused on potential talks between the presidents of the United States and China that could ease trade tensions between the world’s two largest economies.

    Brent crude futures rose $1.08, or 1.7%, to $63.81 a barrel by 1056 GMT. U.S. West Texas Intermediate crude was at $60.03 a barrel, up $1.13, or 1.92%. Both contracts lost around 4% on Friday to settle at their lowest since May.

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    Market sentiment was also boosted by Palestinian militant group Hamas freeing the last 20 surviving Israeli hostages on Monday under a U.S.-brokered ceasefire deal. That was seen as a big step towards ending two years of war in Gaza as U.S. President Donald Trump proclaimed the “historic dawn of a new Middle East”.

    “Last week’s price meltdown was largely on the back of ceasefire in Gaza and return of U.S.-China trade volatility ahead of the November 10 trade truce deadline,” DBS energy analyst Suvro Sarkar said.

    The selloff in markets now looked to be capped by Washington and Beijing’s willingness to negotiate, he said, adding the near-term outlook hinged on the eventual outcome of the trade talks.

    Trade tensions flared up last week after China expanded its rare earth export controls. In response, U.S. President Donald Trump, opens new tab on Friday said he would impose 100% tariffs on China’s U.S.-bound exports.

    An expected meeting between Trump and Chinese President Xi Jinping later this month was in doubt after Trump said on Friday there was no reason to meet his counterpart. U.S. Trade Representative Jamison Greer said on Sunday that a meeting could still happen in South Korea on the sidelines of the Asia-Pacific Economic Cooperation forum.

    Oil prices tumbled in March and April at the height of trade tensions between the two countries.

    On the demand side, China’s crude imports in September rose 3.9% from a year earlier to 11.5 million barrels per day, customs data showed.

    Reporting by Enes Tunagur in London, Florence Tan in Singapore; Editing by Jamie Freed, Kirsten Donovan and Susan Fenton

    Our Standards: The Thomson Reuters Trust Principles., opens new tab

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  • G10 FX Talking: Threat of softer jobs data limits the $ bounce | articles

    G10 FX Talking: Threat of softer jobs data limits the $ bounce | articles

    The dollar is trading towards the top of its trading range, buoyed by the lack of US data and some challenges faced by the euro and the yen. Yet US consumers remain fearful of their employment prospects, and we doubt the Fed will stray from its path toward two more rate cuts this year. Lower US hedging costs and seasonal trends suggest recent $ strength won’t last

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  • Deciphering the cellular tumor microenvironment landscape in salivary gland carcinomas using multiplexed imaging mass cytometry | Journal of Experimental & Clinical Cancer Research

    Deciphering the cellular tumor microenvironment landscape in salivary gland carcinomas using multiplexed imaging mass cytometry | Journal of Experimental & Clinical Cancer Research

    Clinicopathological data

    Overall, 54 patients with SGC were included in this study. The most frequent entity was salivary duct carcinoma (SDC; 43.6%), followed by acinic cell carcinoma (ACC; 24.1%), mucoepidermoid carcinoma (MEC; 22.2%), and secretory carcinoma (Sec; 11.1%). The mean age across the whole cohort was 57.7 (± 17.5) years, and 46.3% of all patients were female. Most patients (57.7%) had T1/2 tumors. Among SDC, 81.1% were androgen receptor (AR)-positive, and 54.5% showed nuclear AR positivity in > 70% of tumor cells (ARhigh), whereas 45.5% showed nuclear AR positivity in ≤ 70% of tumor cells (ARlow). Furthermore, 36.4% of the SDC were positive for human epidermal growth factor receptor 2 (HER2; score 3 + or HER2-amplified). The primary therapy was surgery in 98.1% of cases, and chemoradiation in 1.9% of cases. Neck dissection was performed in 74.1% of the cases, and 57.4% of the patients received adjuvant (chemo-)radiation therapy. Detailed data for the specific entities are presented in Table 1.

    Table 1 Clinicopathological data

    With a median follow-up of 63.5 months for the entire cohort, SDC patients showed the most unfavorable five-year OS (50.6%), followed by MEC (83.3%), ACC (83.9%), and Sec patients (100.0%). Five-year RFS rates were 30.7% for SDC, 69.2% for ACC, 83.3% for MEC, and 66.7% for Sec. Moreover, five-year RFP was 39.9% for SDC, 69.2% for ACC, 91.7% for MEC, and 66.7% for Sec. Lastly, five-year DCR was 47.0% for SDC, 69.2% for ACC, 100.0% for MEC, and 83.3% for Sec.

    A 13-marker IMC panel identifies distinct immune cell and CAF subsets in nonMYO SGC

    We used tissue from SGC primaries and metastases from 54 patients with SGC to create a tissue microarray (TMA). Tissue sections from TMA were stained with a cocktail of 13 metal-conjugated antibodies. Ablation of the tissue slices produced single-channel images that were used to generate multichannel images. After segmentation, the cells were categorized into cellular subsets. The cellular frequencies and results from the spatial analyses were then correlated with clinical parameters and patient outcomes (Fig. 1A).

    Fig. 1

    Phenotyping of the cellular SGC TME. A Overview of the study: SGC samples from 54 patients were captured on a TMA, which was incubated with 13 metal-conjugated antibodies and ablated using a Hyperion system. Through image segmentation, a spatially-resolved single cell dataset was obtained and cell phenotyping was carried out in a semi-supervised fashion with established cell type markers. Cell frequencies and cellular neighborhoods were then correlated with clinical data. B tSNE plots of all 408.939 cells colored by cell type and (C) cell category. D Heatmap depicting the mean marker expression by cell type. E Representative images of the tumor architecture by marker expression (top row) and cellular composition after phenotyping (bottom row). 100-micron scale bars

    After the thorough exclusion of non-tumor-bearing TMA cores, we analyzed 199 SGC TMA cores from 54 SGC, including 188 primaries and 11 metastases from SDC, ACC, MEC, and Sec. A median cell count of 3,019 (ACC), 2,274 (Sec), 2,104 (MEC), and 2,029 (SDC) per 1 mm2 ROI was noted. Using a stepwise Gaussian mixture model of cytokeratin expression and SOM clustering, we measured the expression of 13 different markers in 408,939 cells, (Fig. 1B and C). First, we separated tumor cells (CKAE1/3+; n = 266,172) from endothelial cells (CD31+, CKAE1/3; n = 1,349), immune cells (CKAE1/3, CD45+, or CD138+; n = 50,432), and CAFs (CKAE1/3/CD45/CD31/CD138; n = 90,986). Immune cells were then clustered into plasma cells (CD138+), CD8+ T cells, CD4+ T cells, and weakly CD4-expressing cells, which are most likely monocytes or other myeloid-derived cells (other CD4+cells; [5, 12, 19]). CD4+ T cells were subclassified as CD4+CD74+ T cells, which most likely correspond to effector Tregs [20, 21], proliferating CD4+ T cells (Ki67+), and other CD4+ T cells. Immune cells solely expressing CD45 were classified as “other immune cells.” To classify CAFs, we adopted an IMC-validated classification scheme proposed by Cords et al. [5, 25], separating mCAFs (SMA+Collagen1+), Collagen CAFs (SMA-Collagen1+), SMA-CAFs (SMA+Collagen1), dCAFs (Ki67+, Vimentin+), and apCAFs (CD74+, Vimentin+). We also included CD73, IDO, and CD34 to detect rCAFs, ifnCAF/IDO CAFs, and iCAFs, respectively. However, the CD73 and IDO antibodies used did not produce a specific IMC signal. We could not discriminate iCAFs, since all CD34+ cells co-expressed CD31, a specific vascular marker, which was used to identify endothelia. Two subsets of cells did not display a specific marker profile: cells without expression of any of the markers used were designated as tumor cells, as they were located in tumor cell patches. Regarding the TME-centered focus of this work, we reasoned that this conservative approach would not impact TME-cell classification and thus minimize the chance of false-positive results. Cells with sole expression of Vimentin were classified as “other CAFs” with Vimentin expression (Fig. 1D). Figure 1E displays representative ROIs of the included tumor entities as multiplex images and the corresponding cellular maps after segmentation and cell phenotyping.

    SGC entities are characterized by different TME cell frequencies

    When pooling all ROIs, the cell type distribution across SGC tumor types displayed marked differences with a prominent immune cell compartment in ACC (Supplementary Figure S1C and D). No prominent differences in cell category contributions were noted when comparing primaries and metastases or peripheral and central tumor regions (Supplementary Figure S1E and F).

    To account for differing numbers of ROIs per patient due to the exclusion of non-tumor-bearing and torn TMA cores, we calculated the median cell frequency per patient and compared the distribution of cells across the different SGC entities. We found a significantly varying abundance of immune cell subsets across tumor types (Fig. 2A), which was largely due to significantly elevated levels of CD4+ T cells (p = 0.03), CD8+ T cells (p = 0.04), other CD4+ cells (p = 0.03), and unclassified immune cells (p = 0.04) in ACC. This resulted in a significantly higher frequency of overall immune cells in ACC (p = 0.04; Fig. 2B and C).

    Fig. 2
    figure 2

    Comparison of median cell frequencies per patient across tumor subtypes (A) Immune cell subsets across SGC entities. B Immune cell subsets in ACC vs. other carcinoma types. C Representative core immune marker expression (top row) and selected immune cell subsets in SDC versus ACC. D Frequencies of CAF subtypes across SGC entities. E Examples of the spectrum of core CAF marker expression (top row) and cell categories (bottom row) in SDC. F Frequency of tumor-infiltrating CAFs dependent on AR expression in SDC. G Frequency of tumor-infiltrating apCAFs in ACC versus other SGC subtypes. H Representative images of CD74+ expression in apCAFs in ACC (top row) and corresponding cell categories after segmentation (bottom row). 100-micron scale bars

    Although the overall distribution of CAFs did not significantly vary in SGC types (Fig. 2D), we detected the highest mean frequency of mCAFs in SDC, a CAF subset that has been associated with ECM production. However, in direct comparison with other entities, this elevation was not significant (Supplementary Figure S2A), probably because of the broad spectrum of mCAF frequencies within SDC (Fig. 2D and E).

    We also analyzed the frequency of tumor-infiltrating CAFs using tumor patch detection (Supplementary Figure S2B). As AR and HER2 are frequently expressed in SDC and are molecular targets for systemic therapy approaches [26], we examined the association between AR/HER2 expression and CAF frequency. We were able to show that the number of tumor-infiltrating CAFs was significantly elevated in ARhigh SDC compared to ARlow tumors (p = 0.049; Fig. 2F). No other significant cellular TME alterations were noted when SDC was subgrouped based on AR (Supplementary Figure S2C-E) and HER2 status (Supplementary Figure S2F-H). In contrast, in direct comparison to other tumor types, ACC displayed elevated levels of tumor-infiltrating apCAFs (p = 0.003; Fig. 2G and H, Supplementary Figure S2I). However, the frequency of these cells was generally very low. No significant differences in tumor-infiltrating immune cells were noted across SGC tumor types (Supplementary Figure S2J).

    Spatial interaction analyses reveal a co-localisation of mCAFs with intratumoral vasculature

    The spatial composition of the TME is an integral feature of tumor biology and influences the response to immunotherapy and patient outcomes [27]. Therefore, we leveraged the spatial information preserved in the dataset.

    After computing a spatial interaction graph (examples in Supplementary Figure S3A), we clustered cells based on their 20 nearest neighbors into 9 cellular neighborhoods (CN; Fig. 3A and B). We found that the tumor cell-rich neighborhoods were largely devoid of CAFs and immune cells (CN1 and CN3). In contrast, CD4+ T cells, proliferating CD4+ T cells, other CD4+ cells, and CD8+ T cells formed a distinct lymphocyte-rich cluster (CN9, Fig. 3C). Another immune-related CN mainly consisted of CD4+CD74+ T-cells, apCAFs, and dCAFs.

    Fig. 3
    figure 3

    Spatial analysis of the TME of SGC and correlation with ECM protein modules. A Exemplary ROIs with cells colored by cellular neighborhood (CN). B Scaled cell abundance per CN. C Representative ROI rich in cells that mainly contribute to CN9 (ACC) and CN8 (MEC). D Frequency of CN across SCG types. E Spatial interaction of cell types colored by the number of ROIs with significant co-localization of two cell types. A red color indicates more ROIs with significant interaction while blue tiles indicate more ROIs with significant avoidance of two cell types. F Correlation of cell types with ECM module eigengenes which were published previously. Symbols within the tiles indicate the test significance (* p < 0.05, ns not significant). 100-micron scale bars

    However, as the contributing cell types, the latter CN occurred at very low frequencies (CN2, Supplementary Figure S3B). SMA CAF and mCAFs contributed to two distinct CN with partial overlap (CN 6 and 8, respectively; Supplementary Figure S3C). Interestingly, the matrix-associated CAF-rich (mCAF-rich) CN8 displayed a strong contribution to endothelia (Fig. 3C). No specific marker for vCAFs, another cell type that was found to be associated with endothelia by Cords et al., was available in this panel. However, in contrast to mCAFs, very low expression of SMA and Collagen1 was reported in vCAFs [12], allowing for discrimination between the two cell types.

    Although we did not observe significant differences in CN frequencies across SGC types, immune cell-rich CN was distinctively enriched in ACC. In addition, in SDC, we noted higher frequencies of the mCAF-rich CN8, but not of CNs 5, 6, and 7, which have high contributions of Collagen CAFs and SMA CAFs (Fig. 3D, Supplementary Figure S3C and D).

    The CN analyses were complemented by a more direct spatial interaction analysis, as proposed by Schapiro et al. [22] (Fig. 3E). This approach compares the spatial interactions of cell type pairs with a null distribution in each ROI. The number of ROIs with significant positive (red) and negative (blue) interactions can then be summarized and graphically displayed. This methodically different analysis largely validated the aforementioned results, indicating potential interactions among immune cells and between mCAFs and endothelia as well as SMA CAFs. Again, mutual exclusion of TME and tumor cells was noted.

    In summary, we describe distinct spatial cellular TME neighborhoods in SGC that largely show a mutually exclusive predominance of tumor cells, immune cells, and CAFs. However, mCAFs and Collagen CAFs tend to localize in proximity to the tumor vasculature.

    CAFs and particularly mCAFs are associated with a distinct ECM profile

    Recently, we dissected the ECM of SGC using an unbiased proteomic approach and discovered three protein clusters (“ECM modules”) that explain ECM differences across SGC tumor types via module Eigengenes (i.e., the resulting vector of the module’s proteomic signature) [15]. Using gene set enrichment analysis, these modules were biologically annotated and found to be enriched for classic CAF-associated (CAF-module), basement membrane-associated (BM-module), and peripheral blood-associated (Hem-module) biological terms. The most significant members of the latter include coagulation-related factors such as kallikrein, kininogen, prothrombin, plasminogen, and angiotensin. Since CAFs are considered the main source of ECM in carcinomas, we leveraged an overlap of 40 patients between that and the present SGC cohort and correlated the module Eigengenes with the cell type frequencies (Fig. 3F), thereby establishing a connection between the ECM and the cellular TME. As anticipated, we observed a strong positive correlation between CAFs with a more classic myofibroblastic phenotype (mCAF, Collagen CAF, SMA CAF) and the CAF protein module. However, only the correlation between mCAFs and overall CAFs was significant. In contrast, all immune cell subsets, except for plasma cells, were negatively correlated with the CAF module. Unexpectedly, very similar associations were found for the Hem-module, including an anti-correlation with endothelia and all immune cell subsets except plasma cells. We previously reasoned that this module consists of blood-related factors that are deposited within the ECM. However, the present data indicate that this does not imply exaggerated vasculature or deposition of cellular components of the peripheral blood. The BM module was mainly expressed in SGC with myoepithelial differentiation (e.g., adenoid cystic carcinomas), which were not analyzed in the present study.

    Together, these data clearly link SGC mCAFs, Collagen CAFs, and SMA CAFs to an increase in classical CAF-associated ECM proteins and provide evidence that the ECM components of the CAF and the Hem module participate in an immune-exclusive TME.

    Metastasis-associated signatures are enriched in tumor niches with a co-localization of mCAFs and endothelia and the interaction may be mediated by specific gene signatures

    A significant positive correlation between mCAF-like and pericyte as well as endothelia signatures found in all three ST SDC data sets additionally supported the previously identified spatial co-localization of mCAFs and intratumoral vasculature (Fig. 5A and B; Supplementary Figure S5A and S5B).

    A marked enrichment of metastasis-associated signatures such as “ANASTASSIOU_MULTICANCER_INVASIVENESS_SIGNATURE”, “GILDEA_METASTASIS”, “ROZANOV_MMP14_TARGETS_UP”, and “HEBERT_MATRISOME_TNBC_LUNG_METASTASIS” among others in mCAFhighendotheliahigh and a simultaneous depletion of those signatures in mCAFlowendothelialow ST spots (Fig. 5C) supported our previous clinically-based findings indicating that the spatial interaction of mCAFs and endothelia may be associated with metastasis. Notably, enrichment of metastasis-associated signatures was markedly higher in mCAFhighendotheliahigh than in mCAFhighendothelialow spots. In line with these findings, the invasiveness suppressing signature “RODRIGUES_DCC_TARGETS_UP” [28] was markedly depleted in mCAFhighendotheliahigh tumor niches. Alternative cut-offs for spot classification were evaluated and led to similar results, demonstrating the robustness of this approach (Supplementary Figure S6 A and B).

    DE analysis followed by GSEA for mCAFhighendotheliahigh versus all other spots was performed to identify which factors may mediate the interaction between mCAFs and endothelia and therefore may be associated with metastasis of SDC (Fig. 5D-F and Supplementary Fig. 5 C). Notably, beside several ECM-/fibroblast-/collagen-related signatures, the “metalloendopeptidase activity” signature (enrichment score = 0.59; q-value < 0.001) was among the top-enriched signatures and, accordingly, the pro-metastatic matrix metalloproteinases [29,30,31] (MMP-28 (avg_log2FC = 2.10; p-adj. < 0.001), MMP-9 (avg_log2FC = 2.01; p-adj. < 0.001), MMP-11 (avg_log2FC = 1.69; p-adj. < 0.001), MMP-14 (avg_log2FC = 1.68; p-adj. < 0.001), and MMP-2 (avg_log2FC = 1.64; p-adj. < 0.001) were identified as strongly upregulated genes. More importantly, the “platelet-derived growth factor binding” (enrichment score = 0.84; q-value < 0.001) signature showed the second highest enrichment score among all signatures. Further, the “insulin-like growth factor binding” (enrichment score = 0.67; q-value = 0.007) signature ranked among the top-enriched signatures within mCAFhighendotheliahigh tumor niches. Accordingly, platelet-derived growth factor receptor β (PDGFRβ; avg_log2FC = 2.07; p-adj. < 0.001) and PDGFRα (avg_log2FC = 1.67; p-adj. < 0.001) as well as insulin-like growth factor family member 1 (IGFL1; avg_log2FC = 1.84; p-adj. < 0.001) were strongly upregulated genes within mCAFhighendotheliahigh niches (Supplementary Table S1).

    A higher frequency of mCAFs is an independent prognostic factor for recurrence and distant metastasis in SDC

    Cox regression analysis and log-rank test were performed to identify a potential influence of TME on the prognosis of SDC. The results of the univariate Cox regression model showed that a higher frequency of mCAFs was a prognostic factor for a higher rate of distant metastasis in SDC (p = 0.02; HR (95%CI) = 10.99 (1.37–88.16); Fig. 4A). Regarding RFP, a higher frequency of mCAFs (p = 0.02; HR (95%CI) = 6.23 (1.34–28.93)) and a higher frequency of other CD4+ cells (p = 0.04; HR (95%CI) = 3.82 (1.07–13.69)) were prognostic factors for a higher probability of recurrence in the univariate Cox regression model (Fig. 4B). Multivariate Cox regression revealed a higher frequency of mCAFs as an independent prognostic factor for a higher probability of recurrence (p = 0.032; HR (95%CI) = 7.81 (1.19–51.3); Fig. 4C). The five-year DCR was 75.0% for patients with a low frequency of mCAFs and 24.2% for those with a high frequency of mCAFs (p = 0.0049; Fig. 4D). The five-year RFP was 68.2% for patients with a low frequency and 18.2% for those with a high frequency of mCAFs (p = 0.0077; Fig. 4E). A high frequency of CN8 (mCAF-rich) was marginally non-significant as a prognostic factor for a higher probability of recurrence (p = 0.06; HR (95%CI) = 4.54 (0.94–22.01); Supplementary Fig. 4 A) and distant metastasis (p = 0.07; HR (95%CI) = 3.46 (0.91–13.10); Supplementary Fig. 4B) in the Cox regression model. Nevertheless, the five-year DCR was significantly lower in patients with a high frequency (27.7%) than in those with a low frequency of CN8 (68.2%; p = 0.04; Fig. 4F). Further, the five-year RFP was 20.4% for patients with a high frequency and 61.4% for those with a low frequency of CN8 (p = 0.052; Fig. 4G). The association between higher mCAF frequencies and a decreased DCR as well as RFP was consistent across AR subgroups (Supplementary Fig. 4 C and D). Finally, a significantly higher frequency of mCAFs was found in patients with distant metastatic disease than in those without (p = 0.048). Sex did neither show significance as prognostic factor for DCR (p = 0.23; HR (95%CI) = 0.28 (0.03–2.25), nor for RFP (p = 0.36; HR (95%CI) = 0.48 (0.10–2.28). When including survival data, a higher frequency of mCAFs was not a prognostic factor for OS (p = 0.90; HR (95%CI) = 1.10 (0.34–3.63) or RFS (p = 0.16; HR (95%CI) = 2.10 (0.74–5.91) in univariate cox regression. Furthermore, the five-year OS did not differ significantly between patients with a high frequency of mCAFs (45.5%) and those with a low mCAF frequency (58.3%; p = 0.87). There was no difference in the five-year RFS between patients with a high mCAF frequency (18.2%) and those with a low mCAF frequency (43.7%; p = 0.15). Notably, patients with low mCAF frequencies were markedly older (mean age = 69.17 years) than those with high mCAF frequencies (mean age = 63.64 years).

    Fig. 4
    figure 4

    Association of cell types with distant control rate (DCR), recurrence-free probability (RFP), and frequency of distant metastasis in salivary duct carcinoma (SDC) patients. A Univariate cox proportional hazards model for DCR for cell types and T stage, stratified by median proportion or as negative vs. positive in case median equals zero. N stage was excluded due to complete separation, resulting in an unbounded 95% confidence interval for its odds ratio. B Univariate cox proportional hazards model for RFP for cell types and T stage, stratified by median proportion or as negative vs. positive in case median equals zero. N stage was excluded due to complete separation, resulting in an unbounded 95% confidence interval for its odds ratio. C Multivariate cox proportional hazards model for RFP for mCAFs, AR status (ARhigh > 70% and ARlow ≤ 70 of tumor cells positive for AR), HER2 status, T stage, age, and other CD4+ cells. N stage was excluded due to complete separation, resulting in an unbounded 95% confidence interval for its odds ratio. D Kaplan–Meier plot with log-rank test for DCR comparing patients stratified as high and low based on the median proportion of mCAFs. E Kaplan–Meier plot with log-rank test for RFP comparing patients stratified as high and low based on the median proportion of mCAFs. F Kaplan–Meier plot with log-rank test for DCR comparing patients stratified as high and low based on the median proportion of CN8. G Kaplan–Meier plot with log-rank test for RFP comparing patients stratified as high and low based on the median proportion of CN8. H Boxplot displaying the proportion of mCAFs samples from patients with vs. without distant metastasis

    When testing these results for validity within a previously published independent cohort with RNA-seq data from n = 67 SDC cases [23], we found that patients with low scores of the mCAF-like signature (mCAFhigh) had a significantly lower RFP (median RFP = 10.2 months; two-year survival = 23.5%) than mCAFlow patients (median RFP = 21.6 months; two-year-survival = 46.4%; p = 0.0294; Fig. 5G). Accordingly, mCAFhigh patients had a significantly lower RFS (median RFS = 10.2 months, two-year survival = 23.5%) than mCAFlow patients (median RFS = 21.6 months; two-year-survival = 46.4%; p = 0.049; Fig. 5H). RFS was also less favorable in mCAFhigh compared to mCAFlow patients when using mCAF-like signatures with different cut-offs (Supplementary Fig. 5 D-G).

    Fig. 5
    figure 5

    Validation analyses with RNA-seq and Spatial Transcriptomics (ST) data. A Spearman correlation of the mCAF-like module score with the pericytes and the B) endothelial module score, leveraging ST data of three SDC specimens. C Expression of metastasis-, IL-6- and VEGF-associated module scores in mCAFhighendotheliahigh ST-spots. IL-6- and VEGF-related signatures were not significantly enriched. D Differential expression analysis contrasting mCAFhighendotheliahigh ST-spots with all other ST-spots. The top10 overexpressed genes are highlighted. E Gene set enrichment analysis of DE-results depicted in C) using “Cellular Component” and (F) “Molecular Functions” GO terms. G and H) Kaplan–Meier plots and log-rank tests depicting the prognostic impact of the transcriptomic mCAF-like signature (top20% mCAF marker) after median dichotomization of 67 samples through estimation of the recurrence-free probability (G) and recurrence-free survival (H); LN = lymph node; met. = metastasis

    No prognostic tests were performed for entities other than SDC because of the low absolute number of events within those entities.

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