Researchers shown in lab have discovered a new neurodevelopmental genetic syndrome after uncovering its link to a tumor suppressor gene.
Researchers have discovered a new neurodevelopmental disorder after uncovering its link to a tumor suppressor gene. [Source: Edmonton Economic Development Corporation]

Leukemia Non-Responders to CAR-T

Acute lymphoblastic leukemia (ALL) cells from patients whose cancers did not respond to CD19-targeted CAR T therapy had gene regulation signatures that could potentially facilitate treatment resistance, according to results from researchers at the National Cancer Institute (NCI)

“We identified a signature of nonresponse that is present and detectable prior to treatment,” said Katherine Masih, an NIH-Cambridge scholar in the Genetics Branch at the NCI, who presented the study.

“The data support that these leukemias are relatively plastic and exhibit multi-lineage potential, similar to stem cells, which we suspect allows them to more rapidly adapt to the evolutionary pressures of CD19 CAR T cells,” she added.

CAR T-cell therapy is a type of immunotherapy in which immune cells called T cells are harvested from a patient, reprogrammed to target cancer cells, and infused back into the patient to fight the cancer. A common CAR T-cell target is the receptor CD19; however, cancer cells can mutate CD19 or suppress its expression to develop CAR T resistance. While CD19 expression is currently one of the few biomarkers of potential CAR T response, not all patients who develop resistance lose CD19 expression.

“Our study is one of the few studies looking at CD19-independent sources of primary nonresponse to CAR T therapy,” said Javed Khan, MD, senior author and deputy chief of the Genetics Branch at the NCI, explaining that some cells are intrinsically predisposed to treatment resistance. “The main purpose was to identify pre-existing, leukemia-intrinsic mechanisms of resistance.”

The researchers identified three notable features that differentiated the gene regulatory mechanisms of CAR T-resistant leukemia cells from CAR T-responsive leukemia cells. One was a pattern of DNA methylation—a regulatory mark that decreases gene expression—associated with a stem cell-like phenotype; second was a switch from a lymphoid to a myeloid lineage as a mechanism of resistance to CD19-CAR T therapy; and third was decreased expression of genes involved in antigen presentation and processing, pathways that are crucial for mounting an immune response, in cells that did not respond to CAR T therapy.

AI Model Could Predict Adverse Events from New Drug Combinations

Preliminary data from an artificial intelligence model could potentially predict side effects resulting from new combination therapies, according to researchers from the Cancer Center Amsterdam.

“Clinicians are challenged by the real-world problem that new combination therapies could lead to unpredictable outcomes,” said Bart Westerman, PhD, senior author of the study and an associate professor at the Cancer Center Amsterdam. “Our approach can help us understand the relationship between the effects of different drugs in relation to the disease context.”

Many cancer types are increasingly being treated with combination therapies, through which clinicians attempt to maximize efficacy and minimize the chances of treatment resistance. However, such combination therapies can add multiple drugs at once to a patient’s already complicated list of medications. Clinical trials that test new drugs or combinations rarely account for other medications a patient may take outside of the tested treatment regimen.

Westerman and colleagues sought to use machine learning to better predict the adverse events resulting from new drug combinations. They collected data from the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS), a database containing more than 15 million records of adverse events. Using a method called dimensional reduction, they grouped together events that frequently co-occur in order to simplify the analysis and strengthen the associations between a drug and its side-effect profile.

The researchers then fed the data into a convolutional neural network algorithm, a type of machine learning that mimics the way human brains make associations between data. Adverse events for individual therapies were then used to train the algorithm, which identified common patterns between drugs and their side effects. The recognized patterns were encoded into a so-called “latent space” that simplifies calculations by representing each adverse event profile as a string of 225 numbers between 0 and 1, which can be decoded back to the original profile.

To test their model, the researchers provided unseen adverse event profiles of combination therapies to their model, called the “adverse events atlas,” to see whether it could recognize these new profiles and properly decode them using the latent space descriptors. This showed that the model could recognize these new patterns, demonstrating that measured combined profiles could be converted back into those of each drug in the combination therapy.

This, Westerman said, demonstrated that the adverse effects of combination therapy could be easily predicted. “We were able to determine the sum of individual therapy effects through simple algebraic calculation of the latent space descriptors,” he explained. “Since this approach reduces noise in the data because the algorithm is trained to recognize global patterns, it can accurately capture the side effects of combination therapies.”


Patients with advanced solid tumors carrying ATM gene alterations and BRCA1/2 defects had durable and prolonged responses when treated with the oral ataxia-telangiectasia and Rad3–related protein inhibitor elimusertib (Bayer). These results were from a phase 1b trial (NCT03188965) that involved more than 140 patients and was presented by Timothy A. Yap, medical director of The Institute for Applied Cancer Science and associate director of Translational Research at the Khalifa Institute for Personalized Cancer Therapy of the University of Texas MD Anderson Cancer Center in Houston.

Ataxia telangiectasia-mutated (ATM) protein is a key player in the pathways to initiate the DNA damage response (DDR). Elimusertib is a selective ataxia telangiectasia and Rad3-related protein (ATR) inhibitor that targets ATM protein loss and/or ATM in solid tumors. There are currently eight studies of elimusertib (formerly BAY 1895344), all in solid tumors, listed on

In their abstract, the researchers reported that: “Further biomarker analysis is underway to identify potential gene signatures associated with response. Clinical development of elimusertib in combination with checkpoint inhibitors and chemotherapy is ongoing.”

Combination Immunotherapy Effective Before Lung Cancer Surgery

Combination immunotherapy with the anti-PD-L1 monoclonal antibody durvalumab and other novel agents outperforms durvalumab alone in the neoadjuvant (pre-surgical) setting for early-stage non-small-cell lung cancer (NSCLC), according to new research from The University of Texas MD Anderson Cancer Center.

The multicenter, randomized Phase II NeoCOAST clinical trial evaluated neoadjuvant durvalumab alone and in combination with each of the following novel immunotherapies: the anti-CD73 monoclonal antibody oleclumab, the anti-NKG2A monoclonal antibody monalizumab and the anti-STAT3 antisense oligonucleotide danvatirsen. While the study was not statistically powered to compare arms, all combinations resulted in numerically higher major pathological response (MPR) rates than with durvalumab monotherapy.

“This study builds on the growing evidence that combination immunotherapy has a role in the neoadjuvant setting for this patient population,” said Tina Cascone, MD, PhD, assistant professor of Thoracic/Head and Neck Medical Oncology and lead author of the study. “Ultimately, we want to give patients a chance to live longer without their cancer returning.”

The NeoCOAST study enrolled 84 patients with untreated, resectable (>2cm), stage I-IIIA NSCLC, between March 2019 and September 2020. Most patients were male (59.5%) and had a smoking history (89%). The median age was 67.5, and the racial breakdown was 89% white, 6% Black, 2% Asian and 2% other. Eighty-three patients received one 28-day cycle of neoadjuvant durvalumab alone or combined with another therapy.

The primary endpoint was investigator-assessed MPR, defined as ≤10% residual viable tumor cells in the resected tumor tissue and sampled nodes at surgery. The investigators assessed pathological complete response (pCR), or complete disappearance of viable tumor cells, as a secondary endpoint. Exploratory endpoints included tumor, fecal and blood biomarkers.

All combinations had numerically higher rates of MPR and pCR than monotherapy, and there were no statistically significant differences in responses between the combination arms:

  • For the patients who received durvalumab monotherapy, MPR occurred in 11.1% and pCR in 3.7%, which is comparable to results from other monotherapy studies.
  • MPR rates for combination therapy ranged from 19% (oleclumab) to 31.3% (danvatirsen), and pCR rates ranged from 9.5% (with oleclumab) to 12.5% (with danvatirsen). For combination therapy with monalizumab, MPR was 30% and pCR was 10%.

Cancer Therapies Targeting DNA Damage Response Show Promise

Early-stage clinical trials lead by Timoty Yap, Ph.D., associate professor of Investigational Cancer Therapeutics at the University of Texas M. D. Anderson Cancer Centers show two drugs that target the DNA damage response (DDR) pathway in cancers — ATR inhibitor elimusertib and PARP inhibitor AZD5305 — are safe and clinically beneficial in treating patients with advanced solid tumors.

“DDR orchestrates a complex network of mechanisms that detects and repairs damage to DNA, such as double strand breaks and replication stress,” Yap explained. “However, when DDR defects occur, it promotes uncontrolled cancer cell growth and enables cells to evade apoptosis. The studies suggest that PARP1-selective and ATR inhibitors, which block two key mediators of the DDR signaling pathway, are a promising class of new drugs that offer significant therapeutic potential for patients with cancers harboring synthetic lethal genomic alterations in DDR pathways.”

In a Phase Ib expansion trial, elimusertib—a potent and highly selective ATR inhibitor—demonstrated promising antitumor activity against a range of advanced solid tumors with different putative deleterious DDR alterations.

ATR is a critical component of the DDR network that is activated by DNA damage or replication stress. By binding to ATR and blocking ATR-mediated signaling, ATR inhibitors prevent DNA damage checkpoint activation, disrupt DNA damage repair and stop the growth of tumor cells, Yap explained.

Results from the Phase I/IIa PETRA trial showed that AZD5305, a potent and highly selective next-generation PARP1 inhibitor and trapper, achieved maximal target engagement and promising clinical activity with a favorable safety profile. The targeted therapy demonstrated significantly improved pharmacokinetics and exposure above target than could be achieved with first-generation PARP inhibitors.

In addition to blocking PARP enzymatic activity, first-generation PARP inhibitors trap PARP1 and PARP2 — two repair proteins that activate the DDR pathway — to the sites of DNA damage to prevent DNA repair and to selectively kill cancer cells. However, a growing body of evidence shows that only PARP1 inhibition and trapping is required for synthetic lethality in cancers with homologous recombination repair (HRR) deficiency.

“By selectively inhibiting and trapping PARP1, AZD5305 achieved greater antitumor efficacy across select tumor and molecular subtypes, more durable target inhibition and superior tolerability compared to first-generation dual PARP1/2 inhibitors in preclinical models,” Yap said. “These exciting trial results of AZD5305 demonstrate that we can build on the success of first-generation PARP inhibitors by providing important clinical proof of concept for this innovative strategy. We were able to achieve substantially improved safety, pharmacokinetics, pharmacodynamics and promising efficacy in patients with different molecularly-driven cancers with AZD5305.”

Improved Outcomes Predicted via Machine Learning

A new bioinformatics platform predicts optimal cancer treatment combinations based on co-occurring tumor alterations, according to work from researchers at University of Texas MD Anderson Cancer Center. In retrospective validation studies, the tool selected combinations that resulted in improved patient outcomes across both pre-clinical and clinical studies.

“Our ultimate goal is to make precision oncology more effective and create meaningful patient benefit,” said principal investigator Anil Korkut, assistant professor of Bioinformatics and Computational Biology.

The platform, called REcurrent Features LEveraged for Combination Therapy (REFLECT), integrates machine learning and cancer informatics algorithms to analyze biological tumor features, including genetic mutations, copy number changes, gene expression, and protein expression aberrations. It also identifies frequent co-occurring alterations that could be targeted by multiple drugs.

“We believe REFLECT may be one of the tools that can help overcome some of the current challenges in the field by facilitating both the discovery and the selection of combination therapies matched to the molecular composition of tumors,” Korkut said.

Sotorasib May Offer Long-Term Clinical Benefit in Patients with NSCLC

Patients with non-small cell lung cancer (NSCLC) treated with the KRAS G12C inhibitor sotorasib (Lumakras) had a two-year overall survival rate of 32.5 percent, according to data from the CodeBreaK 100 clinical trial. Based on the primary analysis from this trial, the U.S. Food and Drug Administration approved sotorasib in May 2021 for the treatment of patients with locally advanced or metastatic NSCLC whose tumors harbor the KRAS G12C mutation and who have received prior therapies.

“Longer-term follow-up data are important to better define the safety and efficacy of sotorasib since it is the first-in-class KRAS G12C inhibitor therapy to be approved for this patient population,” said presenter Grace K. Dy, MD, chief of thoracic oncology and professor of oncology at Roswell Park Comprehensive Cancer Center, Buffalo, New York. “For this particular analysis, we also sought to determine whether there are potential biomarkers that can identify patients who will derive long-term benefit from sotorasib treatment.”

Dy and colleagues analyzed data from 174 patients who received sotorasib in the combined phase I and phase II studies of the trial. Most patients had received an average of two prior lines of therapy, including anti-PD-1 or anti PD-L1 immunotherapy and platinum-based chemotherapy.

In this updated analysis, which included NSCLC patients receiving the FDA-approved dose of sotorasib at 960mg daily, 40.7% of patients experienced a partial or complete response to sotorasib, with a median duration of response of 12.3 months. The median progression-free survival and overall survival were 6.3 months and 12.5 months, respectively. The overall survival rate was 50.8 percent after one year of treatment and 32.5% after two years.

Long-term treatment with sotorasib was well tolerated, with mild and manageable toxicities and no new safety concerns in patients continuing onto sotorasib beyond one year.

“Given that the majority of NSCLC patients enrolled had previously received immunotherapy and platinum-based chemotherapy, it is notable that the two-year overall survival rate was almost 33 percent, which is very favorable in comparison to historical control treatment,” said Dy. “For example, the two-year overall survival rate in patients with non-squamous NSCLC treated with the chemotherapy agent docetaxel with or without the anti-VEGFR antibody therapy ramucirumab as second-line treatment is expected to range between 15 and 22 percent.”

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