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In a conversation with CancerNetwork® at the 2025 National Immune Cell Effector Therapy (ICE-T) conference, Jose Sandoval Sus, MD, assistant member of the Malignant Hematology and Cellular Therapy Program at Moffitt Cancer Center, discussed the use considerations for the use of numerous emergent CAR T-cell therapies across a broad spectrum of large B-cell lymphoma variants.¹

He began by highlighting the focus of the presentation he gave at the conference, which he explained recapped the emergence of anti-CD19 CAR T-cell therapies in B-cell non-Hodgkin lymphoma variants, describing it as a “revolution.” Sandoval Sus further highlighted standard treatments for patients with large B-cell lymphomas, disaggregated by disease variant, risk assessment, and line of therapy.

Next, Sandoval Sus touched upon CAR T-cell therapies currently undergoing investigation, showing promise in large B-cell lymphoma, which include agents like rapcabtagene autoleucel (YTB323) in reducing vein-to-vein time among patients undergoing leukapheresis during CAR T-cell collection and dual-targeting CAR T therapies, among others in development. He concluded by discussing considerations for expanding access to CAR T-cell therapies, including optimizing their use outside of academic centers and increasing referrals for CAR T use in the community setting.

CancerNetwork: What was the focus of your presentation on CAR T-cell therapy developments in lymphoma?

Sandoval Sus: My topic [was] a recap of the revolution that anti-CD19 CAR T-cell therapies have brought to the field of B-cell non-Hodgkin lymphoma. [In] large B-cell lymphoma, follicular lymphoma, and a little snippet of a marginal zone lymphoma [MZL], CAR T-cell therapy has been incredibly revolutionary for patients with these diseases. Before, with patients who had relapsed/refractory, large B-cell lymphoma, as an example, the [median] survival of those patients would not be more than 6 months.

Nowadays, at 5 years, we are seeing that [more than] 40% of those patients are alive, and about 30% are alive without their disease coming back. It has been a real revolution. Then for the other indolent lymphomas, like follicular lymphoma and [MZL], we [have] also seen promising outcomes with these constructs. I [gave a] recap of what we have learned up until now with some updated information about [CAR T-cell therapies in B-cell non-Hodgkin lymphoma] up to 2025.

What do current standard treatment options look like for these lymphoma patient populations, and what would you say are some strengths and limitations associated with these strategies?

Sandoval Sus: For diffuse large B-cell lymphoma, we call it large B-cell lymphoma because we understand that diffuse large B-cell lymphoma is not just one disease. It is one diagnosis, but it can behave in multiple different ways. Now, [when] talking about diffuse large B-cell lymphoma not otherwise specified or high grade B-cell lymphoma with high risk features, for example, rearrangements in MEK and BCL2, I firmly believe that for patients who have a primary refractory disease, meaning that they did not have a response through the first chemoimmunotherapy regimen, or the patients with an early relapse, meaning that the disease [recurs] in the first 12 months, CAR T-cell therapies in the US should be the standard of care.

We have 2 constructs that have demonstrated [an improvement in OS]. One of them demonstrated improvement in overall survival in second-line therapy that is called axicabtagene ciloleucel [Yescarta] or otherwise [called] axi-cel. We have another one that is trending that way and has remarkable outcomes as well, which is called lisocabtagene maraleucel [also known] as liso-cel.

Now, in the third line, I believe that every patient should be offered treatment with anti-CD19 CAR T-cell therapy. In that setting, we have 3 constructs. I already talked about axi-cel and liso-cel, which at 5 years, around 35% to 40% of patients were alive, and some of them without [any] disease whatsoever. There is another compound that is called tisagenlecleucel [Kymriah], otherwise known as [tisa-cel], approved for relapsed/refractory large B-cell lymphoma after 2 lines of therapy or more.² In that setting, it shows promising progression-free survival [and] overall survival outcomes in general in patients with large B-cell lymphoma.

In patients [with large B-cell lymphoma], there are a couple of limitations that we need to be aware of. Although these agents are incredibly active, we know that it is limited to centers that specialize in cellular therapy. It used to be that there were fewer than 100 centers across the US, but more institutions are [becoming] specialized in this area of cellular therapy. We still have a long way to go, and a long way to providing more access to these lifesaving therapies to our patients.

Another important limitation is what we call financial toxicity. They’re [quite] expensive agents, and we need to find a way, not only to improve access, but to make these medications less costly in the long run. I believe that there’s a potential for these types of therapies to be taken [before second line, in earlier lines, and] maybe on the front line. We have at least 2 trials that I know of that are evaluating these agents in the front line for high-risk patients with large B-cell lymphoma. If you can imagine that it is going to expand the potential candidates for these treatments if these [outcomes] are positive.

We need not only to improve access but also decrease the financial toxicity [associated with] these medications. That’s an ongoing challenge that [many] of us in the fields are trying to find solutions [for], [including] working with our colleagues in pharmaceuticals.

What CAR T-cell therapies are currently under investigation that show the most promise in treating patients with lymphoma and other diseases, and what data support their use?

Sandoval Sus: There are [many] questions that we need to tackle. One of them is [decreasing] vein-to-vein time. When we leukapherese the patient, we collect their cells, and when the product is ready to be infused for the patient––one of them is a compound called [rapcabtagene autoleucel]––the vein-to-vein time [may] be less than 7 days, [perhaps] around 3 to 5 days, incredibly fast. In the early reports of a phase 2 trial [NCT03960840], the overall response rates [ORRs], complete response [rates], and at least 6 months of progression-free survival [PFS] look comparable with other CD19-targeting CAR T-cell therapy.³ More to see about that, and that looks [quite] promising.

[The other question is] how we can overcome the mechanisms of resistance of these cells. In the summer conferences, several talks were intriguing and quite promising about some agents that have dual-targeting capabilities. Right now, like the name of the CAR T-cells says––anti-CD19––we are only targeting the CD19 antigen right now. The next wave of these CAR T-cells is targeting 2 antigens. One of them is CD19, and the other one is, for example, ECD20.

You can [dual target] in 2 different ways. There’s a construct from [Kite Pharma], for example, that is called bicistronic, which in the same cell, has 2 different CARs, one for CD19, and it keeps the construct of CD19 with the costimulatory molecule, CD28, and they have a different construct on the same cell driving CD20 with a costimulatory 4-1BB molecule. That’s one of them.

There are 2 other compounds that I can think about, one from [Johnson & Johnson] and another one from Miltenyi Biotec; these 2 compounds have differences between how they were manufactured, how fast [they are] manufactured, and how one of them is a fresh product vs another one [being] cryopreserved. [They] have the same mechanism of action, the same concept of the construct, that is what we call a tandem CAR T.In the same construct, they have expression of both [proteins]. They can target with the same construct, both CD19 and CD20. That’s another wave of development. Those constructs are a bit more advanced than others.

There are other concepts that are a bit advanced in development, targeting both antigens, CD19 and CD20, by enriching the product for what we call memory T cells or naïve T cells that have been associated with better clinical responses. There’s a company that is doing that as well.

[Finally,] targeting other B-cell lymphoma [variants] that have been left behind, unfortunately, because we need to devise better strategies to tackle them. For example, in Hodgkin lymphoma or our T-cell lymphomas, there’s an interesting concept right now of an allogeneic natural killer [NK] cell group with a tetravalent bispecific antibody. [This agent targets] CD16 and CD30, that is expressed on Hodgkin lymphomas and some of our T-cell lymphomas. The data [are premature] but show remarkable tolerability and some early efficacy. That is quite intriguing, and we are looking forward to that. There are [many] things that we could say, also [highlight]. Allogenic-CAR T-cells are important [in decreasing] vein-to-vein timing, [and there] already have been some early results [published] in that regard.

How can the field expand access to CAR T-cell therapy for patients with these malignancies?

Sandoval Sus: We have several strategies that [we could] potentially all work on as the field moves forward with [CAR T-cell therapy]. Some of the constructs that we have available right now are quite effective, and some of them we are learning have [fewer] toxicities, especially treatment-related [adverse] effects of interest, such as cytokine release syndrome [CRS], [immune effector cell-associated neurotoxicity syndrome (ICANS)], and [others]. I am talking about both [toxicities] because those are usually the acute toxicities that sometimes limit the generalizability of these products in the community.

Some of these new constructs we might be able to use with an appropriate setup and the appropriate place to take them, more from the inpatient setting to the outpatient setting. Maybe patients can return [sooner], to their oncologist and to the community to be followed there a bit closer. One of the strategies is working more towards taking these CAR T-cell therapies in a safe way for the right patients at the right time to the outpatient setting, and working as we have been doing closely, but doing it [to better accommodate] our colleagues in the community.

The other thing is identifying [which] patients are at higher risk for worse toxicities and working on new strategies to mitigate [them]. We have tried different things, prophylactic steroids, prophylactic tocilizumab [Actemra], another anti-IL1 antibody called anakinra [Kineret], and recently, we also tried other [agents] as prophylaxis to decrease the toxicity of these regimens; for example, JAK inhibitors. That is another strategy working in prophylactic or preventive measures, to decrease the toxicity of the CAR T cells.

Another one is also important for us to talk about [with] the patients and their caregivers, to take home the message to [community oncologists], or the colleagues of a loved one, to offer CAR T cells. It is rare for us to see in the community, but at least to have the opportunity to refer patients early on for a consultation on CAR T-cell therapy that they can determine whether they are going to need this. I believe that a patient with an early relapse of large B-cell lymphoma should get a consultation at a CAR T cell service sooner rather than later. These patients who are at high risk for early death from their disease, or patients with mantle cell lymphoma, especially with high-risk features, should also [receive] an early consultation, [preferably] at their first relapse. Early consultation will expand access for curative therapies like CAR T cells.

Reference

1. Sandoval Sus J. Revolutionizing lymphoma treatment: the latest breakthrough in CAR-T therapy. Presented at the 2025 National Immune Cell Effector Therapy (ICE-T) conference. July 26, 2025.
2. FDA approves tisagenlecleucel for adults with relapsed or refractory large B-cell lymphoma. News release. FDA. May 1, 2018. Accessed August 1, 2025. https://tinyurl.com/5exzpw6p
3. Riedell PA, Kwon M, Finn IW, et al. Rapcabtagene autoleucel (YTB323) in patients (pts) with relapsed/refractory diffuse large B-cell lymphoma (R/R DLBCL): phase II trial clinical update. Blood. 2024;144(suppl 1):67. doi:10.1182/blood-2024-204264

We obtain financial transaction data from ekko²⁰, a sustainable banking FinTech company, alongside several government datasets. These include the Index of Multiple Deprivation (IMD)²¹ , Living Cost and Food Survey (LCFS)²², and UK Multi-Region Input-Output (UKMRIO) data²³. These datasets provide socio-economic context and environmental impact metrics for the customers of the banking initiative. In this section, we describe the dataset and also outline the network construction approach and community detection methodology, aimed at uncovering patterns in consumer behaviour and carbon emissions associated with everyday spending. The code used to apply the methodology for this research is freely available ²⁴.

Financial transaction dataset

We obtained a debit card transaction dataset from ekko²⁰, a sustainable banking FinTech we partnered with, containing tens of thousands of transactions spanning from 2021 to 2023 from 1,362 customers based in the UK. A distinctive feature of these customers is their higher level of environmental consciousness compared to the general population. This is demonstrated by their engagement with ekko, which focuses on promoting environmentally friendly practices and rewards customers for their transaction activity. These rewards are specifically designed to help customers consider the carbon footprint of their everyday transactions. The FinTech’s mobile application allows customers to view in real-time the environmental impact of their spending, along with personalized insights and streamlined features that make it easier to adopt greener choices in their daily lives.

The financial transaction dataset includes key customer metrics such as customer ID, postcode, age, and transaction details including transaction time, Merchant Category Code (MCC), and amount spent. We summarize the age and IMD distributions of the customers and discuss how they compare to national levels in Appendix 1.

The spending categories of customers in our analysis are defined by the Merchant Category Codes (MCCs) assigned by the debit card provider, in this case, Mastercard. Each transaction is linked to a merchant category, described by spending type. An extensive list of merchant categories and the transactions that fall within each is available in Mastercard’s reference documentation²⁵.

However, it is important to note that some spending categories are incompletely represented in the dataset due to the nature of the available transaction data. In particular, utility payments are frequently made via direct debit, which is not included in this dataset. As a result, spending in the MCC 4900 category (“Utilities—Electric, Gas, Heating Oil, Sanitary, Water”) is less than expected. Other services commonly paid via direct debit, such as subscriptions (MCC 4899—“Cable, Satellite, and Other Pay Television and Radio Services”) and rent payments (MCC 6513—“Real Estate Agents and Managers—Rentals”), are also underrepresented in this data. This limitation should be kept in mind when interpreting the absence of energy-related or subscription-based spending and emissions in the figures and cluster analysis.

For the Stochastic Block Modelling (SBM) analysis, we focus on customers who have made a minimum of 30 transactions across 10 distinct categories to ensure we capture consistent and diverse everyday spending patterns. This threshold is set to include only those customers with a sufficiently broad spending behaviour, preventing the inclusion of infrequent or niche spenders that could skew the analysis. With this criterion, we retain a sample of 272 customers, ensuring statistical significance. In Fig. 1 we present the percentage of spending behaviour across MCCs of this sample. To further validate our approach, we apply the model to larger, artificially generated transaction datasets in subsection Applying the model on larger dataset. The results show that although the set of customers may vary with different minimum thresholds for transactions and MCCs, the use of stochastic block modelling still yields clusters with consistent underlying patterns (see subsection Applying the model on larger dataset for more details). The choice of thresholds does not fundamentally alter the overall structure uncovered by the analysis, but it does change the specific composition of the clusters based on the number of customers we allow for.

Government datasets: IMD, LCFS, and UKMRIO

This study relies on several openly available government datasets to contextualise spending patterns and their associated carbon emissions within broader socio-economic and environmental contexts. The datasets used are the Index of Multiple Deprivation (IMD), the Living Cost and Food Survey (LCFS), and the UK Multi-Region Input-Output (UKMRIO) data.

The Index of Multiple Deprivation (IMD) provides a detailed measure of deprivation at a small area level across England, covering indicators such as income, employment, education, health, crime, housing, and the living environment. We use the English indices of deprivation 2019 dataset, published by the UK Ministry of Housing, Communities & Local Government²¹, to understand the socio-economic profile of customers and analyse correlations between deprivation levels and spending behaviour.

The Living Cost and Food Survey (LCFS), collected annually by the Office for National Statistics (ONS), provides detailed data on household expenditure, income, and demographics. The UK Multi-Region Input-Output (UKMRIO) dataset offers a model of the flow of goods and services across UK regions, detailing inter-industry interactions, consumption, and environmental impacts. Together, these datasets allow us to estimate the carbon emissions linked to different categories of expenditure⁶.

Data integration and fusion

To integrate the financial transaction data with these government datasets, we follow a structured process. First, carbon emissions for each transaction are estimated using the third approach described in Trendl et al.⁶, which derives emissions from financial transaction data combined with MRIO-based carbon multipliers. Specifically, we apply carbon intensity multipliers developed by Trendl et al.⁶, based on the LCFS and UKMRIO datasets. These multipliers are linked to COICOP categories, which are then mapped to MCCs in the transaction data using established mappings^2,26,27,28. This approach allows us to estimate emissions for each transaction based on its MCC and spending amount, with adjustments for inflation using Consumer Price Index (CPI) values.

Second, the IMD is linked to the transaction data using customer postcodes, matching each postcode to a Lower Layer Super Output Area (LSOA) in the IMD dataset. This provides deprivation statistics for each customer, enabling an analysis of socio-economic influences on spending behaviour and carbon emissions. We managed to map every customer to the respective level of deprivation of their environment.

By combining these datasets, we ensure a comprehensive analysis of customer spending patterns, socio-economic contexts, and associated carbon emissions.

Bipartite network creation

Bipartite networks are used to represent systems consisting of two distinct types of nodes. In simple bipartite networks, connections only form between nodes of different types, which makes them ideal for modelling relationships in complex datasets. For example, in ecology, bipartite networks can illustrate interactions between species and their environments, helping researchers understand ecosystem dynamics^29,30. Similarly, in recommendation systems, these networks can connect users with items, allowing for tailored recommendations based on user preferences³¹. In economics, bipartite networks can represent relationships between different economic agents, such as consumers and products, facilitating insights into market behaviour³².

By highlighting these applications, we can see that bipartite networks are not just theoretical constructs; they are a practical tool that helps us analyse and understand complex interactions between distinct groups across various fields. They can be used for data mining, pattern recognition, and identifying relationships within complex systems, making them useful in both research and applied contexts. In the context of this research, their role extends to identifying consumer spending patterns and estimating carbon emissions, thereby providing valuable insights for policymakers and financial institutions seeking to implement effective carbon reduction strategies. In this research context, these networks allow customers to be linked with the transaction categories they engage with.

An example of a transaction bipartite network, showing the connection between customers (top) and MCCs (bottom). Each edge represents that the customer has had at least one transaction in that merchant category.

To understand customer spending behaviour, we construct a bipartite network with customers as one type of node and MCCs as the other. An edge is created between a customer and an MCC if the customer has made a transaction in that category. See Fig. 2 for an illustration of this bipartite network structure. This approach allows us to present all the data from a large transaction dataset in a single network, connecting customers and categories based on transaction patterns. We can then apply network analysis techniques to analyse the dataset, which provides additional insights compared to classical statistics and machine learning approaches.

This network approach also accommodates classification systems different from the MCC used by financial institutions, allowing for the exploration of connections and the identification of consumer communities based on their spending patterns. By incorporating various classifications, such as COICOP used by the UN and multiple policymaking institutions, this approach provides broad analysis of consumer behaviour across different contexts.

Additionally, we define two alternative edge-weighting schemes for the bipartite network to better capture different characteristics of the data. These weight assignments provide different ways to quantify the strength of the relationship between customers and merchant categories (MCCs) based on their transaction behaviour.

First, we use the number of transactions between a customer and an MCC as the edge weight. This means that for each customer-category connection, the weight is simply the count of transactions made by that specific customer in that category. This approach ensures that the network structure reflects not only the set of categories a customer transacts in but also the frequency of transactions within each category. By incorporating transaction counts directly as weights, we preserve the raw behavioural patterns without introducing external assumptions.

Second, we define an alternative weighting based on relative spending per category. Here, the edge weight represents the total amount a customer has spent in a given category, normalised by the average spending of all customers in that category. This normalisation ensures that spending behaviour is interpreted in the context of overall category trends, allowing us to identify customers who spend significantly more or less than average in a given category.

These weighting schemes do not introduce arbitrary modifications but rather directly derive from the transaction data itself–either as transaction counts or spending amounts–ensuring a transparent and interpretable network representation. By applying network analysis to these weighted structures, we uncover different aspects of consumer spending behaviour. The results of these analyses and their implications are explored in the Discussion section.

Stochastic block modelling

A Stochastic Block Model (SBM) is a probabilistic model used to analyse the structure of networks by dividing nodes into distinct groups or “blocks” based on connection patterns. Its probabilistic nature makes it well-suited for community detection, allowing us to identify connection patterns within and between groups. Introduced in the 1980s by³³, the canonical SBM views networks as structures composed of blocks of nodes. Connections between nodes are determined by their block memberships and predefined network parameters.

There have been many recent advancements in SBMs expanding their applicability to various networks across different scientific fields. Modifications to the canonical SBM structure include approaches that account for weights in networks³⁴ and hierarchical communities³⁵. Studies have also developed degree-corrected variations³⁶ and overlapping communities³⁷.

These models have been effectively applied to study community formation in various fields, such as in US Senate political cohesion and co-voting networks³⁸, connections in healthy human gut microbiomes³⁹, and relationships in ecology and ethnobiology⁴⁰.

SBMs are particularly useful for analysing large-scale networks encountered in real-world applications, such as large financial transaction datasets. While our study focuses on UK consumption data, this approach is not limited to a single region. SBMs can be applied to transaction datasets from other countries, enabling cross-country comparisons of consumer behaviour and spending patterns by identifying similarities and differences in community structures across regions. Their high resolution limits allow for the identification of numerous communities with specific characteristics^35,41. The probabilistic basis of SBMs ensures reliable community detection results based on observed data, making them an efficient tool for revealing insights into community organization in complex networks.

In contrast to methods like k-means or modularity maximisation, which may struggle with high-dimensional, sparse, or complex network data, SBMs are better suited for detecting meaningful community structures. SBMs identify statistically significant assortative modules by modelling the full probabilistic structure of the network, enabling them to avoid common pitfalls like resolution limits and overfitting^35,42. Moreover, SBM’s hierarchical and nonparametric properties allow the detection of multi-scale community structures and fine-grained behavioural patterns without prior assumptions about the number or shape of the clusters¹⁹. This makes them particularly appropriate for analysing behavioural networks derived from financial transactions.

In this study, we apply a degree-corrected nonparametric hierarchical SBM on the bipartite network we previously described to identify communities of customers based on spending categories. We implement the model using Python and the graph-tool package. For reproducibility, the full code is openly available²⁴. This specific SBM method was initially developed for topic modelling through word clusters in documents^43,44. We apply this method to financial transaction datasets so that we can find communities of customers with similar spending behaviour in bipartite networks of customers and categories. Additionally, we introduce modifications to the code, so that we can use any arbitrary vector of weights between the customer and category nodes instead of solely relying on the number of repeated transactions as the weight.

However, due to its probabilistic nature, running the SBM algorithm only once does not guarantee finding the optimal partition consistently¹⁹. To address this variability, we run the algorithm 100 times, as this is typically sufficient for the entropy in the system to stabilise—meaning that the uncertainty in the community assignments reaches a consistent level. Stabilisation of entropy is desirable because it indicates that the algorithm has converged to a reliable partitioning of the network. However, we can run additional iterations if necessary. We then plot the change in entropy and select the iteration with the highest posterior probability. We choose the clusters generated by the SBM which are optimal because they achieve the lowest possible entropy, indicating a well-defined community structure.

Commissioner Kim issued an August 4 memorandum (Kim Memo) that clarifies how examiners should apply existing SME guidance, especially when analyzing artificial intelligence, machine learning, software, or embedded software (e.g., hardware control) claims.  The memo was directed to Technology Centers 2100 (Computer Architecture Software and Information Security), 2600 (Communications), and 3600 (eCommerce).

For practitioners, this is a very useful guide for rebutting § 101 rejections and has several paragraphs to support reconsideration and argumentation. The Kim Memo narrows the circumstances under which examiners may invoke the mental-process abstract-idea category, emphasizing that claim limitations that cannot practically be performed in the human mind should not be classified as mental processes, and also provides guidance on integration into a practical application, and advises against oversimplification of the claims.  

This is particularly relevant guidance for machine learning operations that involve complex computations, especially in view of a recent Federal Circuit decision and subsequent unfavourable Patent Trial and Appeal Board decisions citing the decision.  

Introduction

The memorandum instructs examiners to carefully distinguish between claims that merely involve a judicial exception and those that actually recite it.  This distinction is crucial because claims that only involve an abstract idea do not require further eligibility analysis under Step 2A Prong One.  Additionally, the Kim Memo stresses that Step 2A Prong Two must consider all claim limitations as a whole, focusing on how these limitations interact to integrate the judicial exception into a practical application.  This holistic approach prevents piecemeal treatment and ensures the totality of the system architecture, data flows, or hardware cooperation is evaluated. 

Examiners are cautioned against over-reliance on the “apply it” rationale, which oversimplifies claim limitations and fails to respect the technical particulars of the implementation.  

The memorandum directs that a § 101 rejection should only be issued when it is more likely than not the claim is ineligible, reinforcing the preponderance standard.  This means if the factual predicates are debatable, the uncertainty mandates withdrawal of the § 101 rejection. 

Each of these points provides valuable advocacy supports for applicants seeking to establish patent-eligible subject matter, offering a robust

for demonstrating that their claims satisfy the requirements of the Alice/USPTO SME framework. 

Relevant memo excerpts

Concluding remarks

The Kim Memo provides applicants with authoritative language to help respond to overly expansive § 101 rejections in relation to technological advances embedded in AI and software innovations.  By weaving these passages into drafting and prosecution strategies, practitioners can more persuasively demonstrate that their claims satisfy each stage of the Alice / USPTO subject matter eligibility framework. The Kim Memo guidance is helpful both for drafting patent applications and preparing responses to examiner rejections.

Company will leverage Upstream AB’s AI capabilities to accelerate innovation in intelligent automation

Miami, FL, August 8, 2025 — Kaseya, the leading global provider of AI-powered IT management and cybersecurity software, today announced its partnership with Upstream AB, a Stockholm-based IT solutions provider serving MSPs and internal IT departments across the Nordics. Upstream AB has a proven track record of delivering best-in-class solutions alongside exceptional Swedish-language support and strategic guidance.

“Upstream is a fantastic addition to the Kaseya ecosystem,” said Dermot McCann, Executive Vice President and General Manager, EMEA at Kaseya. “They’ve earned the trust of leading MSPs and enterprises across the Nordics by combining world-class technology with deep local service. Together, we’re now able to deliver even greater value, faster innovation and stronger partnership to customers across Sweden and the broader region.”

Upstream AB’s partners will gain direct access to Kaseya 365 with integrated solutions that streamline workflows with automation —delivered with seamless local support and onboarding. Customers will also benefit from Upstream’s AI Center of Excellence, built on over 20 years of automation expertise in the RMM space. This capability will accelerate our innovation in intelligent automation and further enhance the power of Kaseya 365.

Customers will see enhanced access to new products, resources and support channels moving forward. McCann continued, “This partnership demonstrates Kaseya’s continued investment in Europe and its long-term strategy to empower local MSPs with global capabilities, while preserving the high-touch service and regional expertise that customers value.”

The Upstream AB team will continue to operate from Stockholm as part of Kaseya’s growing EMEA infrastructure.

About Kaseya
Kaseya is the leading global provider of AI-powered IT management and cybersecurity software. Its Kaseya 365 platform is purpose-built to meet the demands of multifunctional IT professionals, offering a unified solution to manage infrastructure, secure endpoints, back up critical data and streamline operations. Serving nearly 50,000 MSPs and IT departments across more than 170 countries, Kaseya’s portfolio includes trusted brands such as Datto, Unitrends, IT Glue, ConnectBooster, Spanning Cloud Apps, RapidFire Tools and more. Headquartered in Miami, Florida, Kaseya is privately held with operations in more than a dozen countries. To learn more, visit https://www.kaseya.com/.

About Upstream AB
Founded in 1998 and headquartered in Stockholm, Upstream AB is a value-added distributor of IT management, cybersecurity and business continuity solutions. Serving organizations across Sweden, the company is known for its proactive service model, local language support, and broad vendor portfolio tailored to MSPs and internal IT departments.

LAS VEGAS — The Defense Advanced Research Projects Agency on Friday announced the winners of its AI Cyber Challenge, or AIxCC, a two-year-long competition that evaluates AI models built to autonomously identify and patch vulnerabilities in open-source code used in critical infrastructure systems.

Team Atlanta, which includes experts from the Georgia Institute of Technology, Samsung Research, the Korea Advanced Institute of Science & Technology and the Pohang University of Science and Technology, won first place, DARPA announced at the DEF CON hacker convention in Las Vegas, Nevada.

Trail of Bits, a New York City-based small business, won second place. And Theori, a team of AI researchers and security professionals in the U.S. and South Korea, won third place.

Four of the models developed by the seven competing finalist teams have already been made available for use, while three others will become available in the coming weeks, DARPA director Stephen Winchell told a large audience at the convention, where the winners were announced.

“We’re living in a world right now that has ancient digital scaffolding that’s holding everything up. A lot of the code bases, a lot of the languages, a lot of the ways we do business — and everything we’ve built on top of it — is all incurred huge technical debt over the years,” Winchell said. “And the reality is [that] it is a problem that is beyond human scale, and it’s a critical problem that we need to solve right now.”

Open-source tools are free to use and implement, making them convenient for critical infrastructure owners and operators. But they’re particularly vulnerable to cyber exploitation because of the nature of their publicly available code bases. If hackers succeed in infiltrating a code base and leveraging a flaw, it could create cascading impacts on public health and safety.

The two-year competition was partly fueled by the advent of large language models that power popular consumer-facing generative AI tools. Many of the major companies that have rolled out such offerings, like Anthropic and OpenAI, provided their model infrastructure to competitors. The goal of the contest, in essence, was to mesh AI tooling into models that can automatically patch vulnerabilities in open-source code and deploy it at scale to those who may be vulnerable.

The teams at AIxCC uncovered 70 synthetic vulnerabilities built for the competition, along with 18 previously unknown real-world flaws. The latter were not planted in advance and were discovered during the teams’ scans. On average, their models patched flaws in just 45 minutes.

“The teams figured out how to use this technology in better, more innovative ways,” said Andrew Carney, the program manager for AIxCC, speaking on stage at DEF CON. “They also found way more real-world issue bugs — real vulnerabilities — that we are in the process of disclosing to maintainers.”

Open-source projects — which underpin software systems used everywhere — rely on contributions from community members to keep them updated with patches. The updates are often discussed on forums with volunteer software maintainers, who chat with one another about proposed changes.

Historically, community practices have operated under the premise that all contributors are benevolent. That notion was challenged last February when a user dubbed “Jia Tan” tried to quietly plant a backdoor into XZ Utils, a file transfer tool used in several Linux builds that power software in leading global companies.

DARPA and the Advanced Research Projects Agency for Health also distributed an additional $1.4 million in funds to help with additional implementation. The cost per successfully completed competition task was $152, a number that falls significantly below human labor costs. 

“Today, the world is different” because the competition has “fundamentally changed our understanding of what is possible in terms of automatically finding or really, more importantly, fixing vulnerabilities in software,” Kathleen Fisher, DARPA’s Information Innovation Office director, told reporters at a press conference on the sidelines of DEF CON.

(Bloomberg) — Stocks saw their best week since June, with a rally in big tech driving the Nasdaq 100 to all-time highs. Also buoying sentiment were hopes the US and Russia will reach a deal to halt the war in Ukraine. Gold whipsawed.

The S&P 500 approached 6,400, closing on the brink of a record. Apple Inc. saw its best week since 2020 amid optimism that plans to spend an additional $100 billion on domestic manufacturing may help the company avoid tariffs. Fannie Mae and Freddie Mac soared on reports the US is preparing to sell shares in an offering that could start as early as this year.

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The yield on 10-year Treasuries rose three basis points to 4.28%. The dollar barely budged. Oil fluctuated. The Trump administration suggested it would issue a new policy clarifying that imports of gold bars should not face tariffs.

Donald Trump announced that he plans to meet “very shortly” with Russian President Vladimir Putin, as the US president looks to broker a ceasefire agreement in hopes of bringing an end to the war in Ukraine.

Bret Kenwell at eToro said momentum has been strong in equities, with both technicals and fundamentals working in bulls’ favor.

“While an unexpected risk could develop in the second half of 2025, earnings have been better-than-expected and the Fed is inching closer to lower interest rates,” he noted. “As long as the economy holds up, there are catalysts in play for stocks to continue higher.”

Trump said tariffs are “having a huge positive impact on the stock market,” adding that “almost every day, new records are set.” Hundreds of billions of dollars are “pouring into our country’s coffers,” he noted.

“Markets rebounded strongly this week with a clear ‘buy on the dip’ mentality,” said Florian Ielpo at Lombard Odier Investment Managers. “While market sentiment appeared to be waning last week, with subdued reactions to earnings beats, this week clearly demonstrated a different trend.”

And that begs the question: are we close to a solid ceiling?

“Our risk appetite indicator shows improvement from last week, but clearly has room to grow,” Ielpo said.

At Piper Sandler, Craig Johnson says that while the summer doldrums often lead to modest pullbacks in August and September, investors who have doubted this rally are now forced to “buy the dips… and not sell the rips.”

Despite the solid rebound, nearly $28 billion was redeemed from US stocks in the week through Aug. 6, while money market funds attracted about $107 billion, according to a Bank of America Corp. note citing EPFR Global data.

“With the major indexes at or near record highs, valuations are rich, and stock selection and diversification are more important than ever,” said Daniel Skelly at Morgan Stanley’s Wealth Management Market Research & Strategy Team.

On the macro front, BofA’s Michael Hartnett said a majority of the bank’s clients are betting on a “Goldilocks” outcome, which implies an economy that’s running neither too hot nor too cold. He said investors expect a scenario where lower rates would fuel a rally in equities.

Kenwell at eToro says that it would be a healthy price action for stocks to consolidate after a big rally — either by pulling back or digesting the move by trading sideways.

“This pullback would likely be viewed as an opportunity for investors to buy the dip rather than run for the hills,” Kenwell said.

“We believe stocks will stay supported amid solid fundamentals, but fresh headlines in the coming week may challenge investor sentiment that remains vulnerable to tariff, economic, and geopolitical risks,” said Ulrike Hoffmann-Burchardi at UBS Global Wealth Management.

The next significant directional move in the market will be driven by fundamentals, either through macro resilience driving earnings estimates higher or further cracks in the labor market driving increased recession concerns, according to Mark Hackett at Nationwide.

“Given the moderation in technical indicators and the sluggish seasonal shift, a period of consolidation is not unexpected or unhealthy,” he said.

Federal Reserve Bank of St. Louis President Alberto Musalem said he supported last week’s decision by policymakers to leave interest rates steady, adding the US central bank is still missing more on the inflation side of its mandate.

Traders will soon shift their focus to next week’s release of US inflation numbers for clues on the Fed’s next steps.

“We expect the July CPI report to show that core inflation gained additional momentum,” according to strategists at TD Securities.

Corporate Highlights:

Meta Platforms Inc. has selected Pacific Investment Management Co. and Blue Owl Capital Inc. to lead a $29 billion financing for its data center expansion in rural Louisiana as the race for artificial intelligence infrastructure heats up, according to people with knowledge of the matter. Tesla Inc. is disbanding its Dojo team and its leader will leave the company, according to people familiar with the matter, upending the automaker’s effort to build an in-house supercomputer for developing driverless-vehicle technology. Intel Corp. Chief Executive Officer Lip-Bu Tan said he’s got the full backing of the company’s board, responding for the first time to US President Donald Trump’s call for his resignation over conflicts of interest. SoftBank Group Corp. is the buyer taking ownership of Foxconn Technology Group’s electric vehicle plant in Ohio, a move aimed at kick-starting the Japanese company’s $500 billion Stargate data center project with OpenAI and Oracle Corp. Taiwan Semiconductor Manufacturing Co. reported a 26% growth spurt in July, adding to evidence of accelerating spending on artificial intelligence. Expedia Group Inc. raised its full-year sales target after reporting strong second-quarter bookings, fueled mainly by its enterprise business as well as improved demand from US consumers. Pinterest Inc. reported second-quarter sales that beat analysts’ expectations, but earnings for the second quarter were less than Wall Street expected and user growth in the US and Canada, the company’s most lucrative market, was flat. Under Armour Inc. forecast worse-than-expected sales and profit for the current quarter, stalling a turnaround plan that was taking hold. Gilead Sciences Inc. lifted its full-year outlook after strong HIV drug sales in the second quarter helped revenue and earnings modestly beat analyst expectations. Wendy’s Co. cut its full-year sales guidance after posting a bigger-than-expected quarterly decline, highlighting the economic pressures weighing on the chain’s US business. Instacart posted its strongest order growth since 2022 for a second straight quarter and beat earnings estimates for the current period, a sign of resilience in its core delivery business after it rolled out initiatives to cater to price-conscious consumers. Trade Desk Inc. reported second-quarter results that spurred multiple downgrades. Firms note growing concerns about competition from Amazon.com Inc. Sweetgreen Inc. slashed its sales guidance after a second straight quarter of disappointing results, highlighting the salad chain’s struggles to sell $15 salads to budget-strained diners. What Bloomberg Strategists say…

“An improving geopolitical backdrop has become a headwind for oil prices, especially as peace in Ukraine looks closer. Traders will now increasingly look past geopolitical hurdles, leaving the market uncomfortably exposed to uncertain demand and rising supply.”

—Michael Ball, Macro Strategist, Markets Live

For the full analysis, click here.

Some of the main moves in markets:

Stocks

The S&P 500 rose 0.8% as of 4 p.m. New York time The Nasdaq 100 rose 0.9% The Dow Jones Industrial Average rose 0.5% The MSCI World Index rose 0.7% Bloomberg Magnificent 7 Total Return Index rose 1.6% The Russell 2000 Index rose 0.2% Currencies

The Bloomberg Dollar Spot Index was little changed The euro fell 0.2% to $1.1643 The British pound was little changed at $1.3450 The Japanese yen fell 0.4% to 147.75 per dollar Cryptocurrencies

Bitcoin fell 0.7% to $116,464.8 Ether rose 4.8% to $4,062.95 Bonds

The yield on 10-year Treasuries advanced three basis points to 4.28% Germany’s 10-year yield advanced six basis points to 2.69% Britain’s 10-year yield advanced five basis points to 4.60% The yield on 2-year Treasuries advanced three basis points to 3.76% The yield on 30-year Treasuries advanced three basis points to 4.85% Commodities

West Texas Intermediate crude fell 0.4% to $63.64 a barrel Spot gold was little changed ©2025 Bloomberg L.P.