At the core of UT MD Anderson’s approach to exceptional patient care is a virtuous cycle of innovation that's grounded in collaboration, connectivity and data-based science. Our unique environment and vast resources accelerate novel concepts faster than ever before by offering leading scientific minds close proximity to our clinics to ease collaboration with our renowned clinicians and our patient partners.
At the American Association for Cancer Research Annual Meeting, our experts showcase their pacesetting research and join colleagues from across the globe for discussion on the latest advances in cancer science.
Below are highlights from our presentations, details about onsite networking opportunities with our experts and information on open research positions at UT MD Anderson.
Meet the Experts
Visit us at Booth 3317 to discuss hot topics and emerging trends with leading minds in cancer science.
Exhibit Hall Hours
Sunday, April 19: noon–5 p.m.
Monday, April 20: 9 a.m.–5 p.m.
Tuesday, April 21: 9 a.m.–5 p.m.
Wednesday, April 22: 9 a.m.–noon
Additional Featured Articles
AACR: Clinical trial presentations feature advances across cancer care
- Early results for first-in-human and first-in-class therapies including an integrin inhibitor and a genetically engineered tumor infiltrating lymphocyte therapy
- ctDNA monitoring may be changing treatment options for patients
- Next-generation targeted therapies may allow de-escalation of treatment
Researchers from The University of Texas MD Anderson Cancer Center will present initial data from six clinical trials during minisymposia at this week’s American Association for Cancer Research (AACR) Annual Meeting 2026. These abstracts will feature updates on novel targeted and cell therapies, ctDNA for monitoring responses to treatment, and new approaches allowing for de-escalation of therapy.
In addition to the studies below, forthcoming press releases will feature notable oral and plenary session abstracts on promising clinical trial results. More information on all UT MD Anderson AACR Annual Meeting content can be found on MDAnderson.org/AACR.
Can zanidatamab allow patients with early stage HER2+ breast cancer to avoid chemotherapy? (Abstract CT012)
Funda Meric-Bernstam, M.D., chair of Investigational Cancer Therapeutics, will present data from an investigator-initiated Phase II trial evaluating zanidatamab, a HER2-targeted bispecific antibody from Jazz Pharma, as an option to potentially allow some early-stage breast cancer patients to avoid chemotherapy. Meric-Bernstam will present trial results April 18.
“Targeted therapies are becoming so effective that it’s time to start asking if every patient needs the added chemotherapy,” Meric-Bernstam said. “This trial is an important step toward answering that question, because being able to take even a subset of patients off chemotherapy would significantly improve their quality of life during treatment.”
Are there effective biomarkers associated with response to perioperative nivolumab in early-stage lung cancer? (Abstract CT015)
Tina Cascone, M.D., Ph.D., associate professor of Thoracic/Head & Neck Medical Oncology, will present additional findings from the CheckMate 77T trial. The Food and Drug Administration approved perioperative nivolumab in 2024 for the treatment of resectable non-small cell lung cancer based on the initial study results. This presentation will focus on insights derived from biomarker analyses conducted in the trial and their potential to improve outcome prediction. Tina Cascone will present these findings April 18.
“What we’re learning is that while genomic testing provides valuable insight, it should not be considered in isolation,” Cascone said. “We performed a comprehensive, integrated biomarker analysis to better understand what factors may impact outcomes in the setting of perioperative treatment for patients with resectable non-small cell lung cancer. We found that in some patients whose tumors harbor genomic alterations historically associated with worse prognosis, perioperative immunotherapy may still provide benefit, reinforcing the need for improved methods to identify this patient population. We also saw that combined assessment of ctDNA dynamics over time and pathological complete response may provide a more nuanced approach to risk stratification.”
How can the integrin inhibitor PLN-101095 promote better responses to immunotherapy? (Abstract CT002)
Timothy Yap, M.B.B.S, Ph.D., professor of Investigational Cancer Therapeutics and vice president and head of clinical development in UT MD Anderson’s Therapeutics Discovery division, will present the first data disclosure from a potential first-in-class therapy, PLN-101095, an integrin αVβ8 and αVβ1 inhibitor from Pliant Therapeutics. Yap will present the findings April 18.
“This is a unique approach to change the way the tumor microenvironment reacts to immunotherapy, so as to overcome drug resistance,” Yap said. “Many cancers upregulate a specific protein that can suppress responses to immune checkpoint inhibitors, and that protein is activated by integrins. PLN-101095 is thought to work by inhibiting the integrins themselves, which should then promote responsiveness to immunotherapy. This has the potential to stimulate or reinvigorate a cancer immune response, improving the outcomes and lives of our patients.”
Can a genetically engineered TIL demonstrate effectiveness in melanoma? (Abstract CT028)
Rodabe Amaria, M.D., professor of Melanoma Medical Oncology will present the initial data from a trial of genetically engineered tumor-infiltrating lymphocyte (TIL) therapy in solid tumors. The cell therapy, from KSQ Therapeutics, uses CRISPR/Cas9 technology to inactivate a specific gene in patient tumor-derived lymphocytes before they are reintroduced. Amaria will present the data April 19.
“TIL therapies have been successful in the treatment of metastatic melanoma, but they face hurdles for wider effectiveness in solid tumors,” Amaria said. “In preclinical work, KSQ identified a gene that, when inactivated, seemed to significantly increase the anti-tumor activity of T cells. We will be presenting the initial data on use of this gene-edited TIL therapy to test its safety and efficacy in melanoma.”
Can adjuvant immunotherapy prevent recurrence in patients with HR+ inflammatory breast cancer? (Abstract CT172)
Ranjan Upadhyay, M.D., Ph.D., oncology fellow in Cancer Medicine, will present findings of a Phase II trial investigating the potential effectiveness of immunotherapy in preventing recurrence after surgery in hormone receptor positive inflammatory breast cancer (IBC), a very high-risk disease with limited treatment options. Upadhyay will present the data April 20.
“The first question we wanted to answer was whether we could better predict recurrence using tools, like monitoring ctDNA,” Upadhyay said. “From there, based on other biomarkers in the blood and in the resected tumor, is this a setting where immunotherapy could be effective? We know that IBC can be hard to treat once we detect clinical signs of progression, but we hypothesized that there were some patients for whom we could potentially prevent the cancer from coming back by delivering immunotherapy earlier.”
Does the KRAS G12C inhibitor elisrasib offer benefits over first-generation inhibitors? (Abstract CT303)
Kanwal Raghav, M.B.B.S., M.D, professor of Gastrointestinal Medical Oncology, will present data on the next-generation KRAS G12C inhibitor elisrasib from D3 Bio. The study will provide clarity on whether elisrasib can overcome some of the limitations from emerging treatment resistance faced by first-generation KRAS inhibitors in certain cancer types. Raghav will present the findings April 21.
“First-generation KRAS inhibitors have had limited clinical efficacy in colorectal and pancreatic cancers, largely due to the rapid emergence of resistance,” Raghav said. “What’s encouraging is that a new generation of KRAS-targeted therapies are now being developed to address this challenge directly. The current study in patients who have already had multiple treatments is an important step forward and is designed to advance therapy for patients with very few current options.”
AACR: Zedoresertib and lunresertib combination shows promising antitumor activity
- Phase I trial provides early clinical proof-of-concept supporting the combination of zedoresertib and lunresertib in certain advanced solid tumors
- Zedoresertib, which targets WEE1, and lunresertib, a PKMYT1 inhibitor, are both investigational therapies that create synthetic lethality by blocking cell cycle proteins
- Combination achieved notable responses in ovarian cancer at the preliminary recommended Phase II dose, with a 50% overall response rate across all patients and a 60% rate in patients with CCNE1 amplification
- This combination has been granted FDA Fast Track Designation in patients with ovarian cancer harboring CCNE1 amplification, FBXW7 or PPP2R1A deleterious mutations
ABSTRACT: CT022
For patients with advanced solid tumors harboring specific genetic alterations, the first-in-class synthetic lethal combination of WEE1 inhibitor zedoresertib plus PKMYT1 inhibitor lunresertib demonstrated promising antitumor activity and was generally well-tolerated, according to Phase I MYTHIC trial data reported by researchers at The University of Texas MD Anderson Cancer Center.
Based on these data, the combination was granted Food and Drug Administration (FDA) Fast Track Designation in patients with ovarian cancer harboring these genetic alterations. Results were presented today in the clinical trials plenary session at the American Association for Cancer Research (AACR) Annual Meeting 2026 by principal investigator Timothy Yap, M.B.B.S., Ph.D., professor of Investigational Cancer Therapeutics and vice president and head of clinical development in UT MD Anderson’s Therapeutics Discovery division.
“This combination demonstrated strong synergy in preclinical studies, and we have now demonstrated its great potential as a novel therapeutic option for patients across multiple tumor types – especially for those with ovarian cancer,” Yap said. “Patients with cancers harboring CCNE1 amplification and FBXW7 and PPP2R1A mutations represent areas of unmet clinical need, for which this combination could provide a new treatment option.”
More information on all UT MD Anderson AACR Annual Meeting content can be found at MDAnderson.org/AACR.
What are zedoresertib and lunresertib, and how do they work?
Zedoresertib, developed by Debiopharm, is a highly selective and brain-penetrant WEE1 kinase inhibitor. The WEE1 protein is a critical gatekeeper of the cell cycle that can help cancer cells survive DNA damage by ensuring that DNA repair occurs before cell division takes place. Inhibiting WEE1 with zedoresertib, therefore, pushes cancer cells with DNA damage – such as those with CCNE1 amplification-induced replication stress – into mitosis earlier, leading to cell death through apoptosis.
Similarly, lunresertib, developed by Repare Therapeutics and licensed to Debiopharm, is a highly selective PKMYT1 kinase inhibitor that regulates the cell cycle via a different pathway, creating synthetic lethality in tumors with specific genetic mutations, such as FBXW7 or PPP2R1A, or CCNE1 amplification.
Both zedoresertib and lunresertib work well together in multiple tumor types in vivo, showing durable regressions following both continuous and intermittent zedoresertib treatment. The combination was also well-tolerated in preclinical studies.
What were the main objectives and who was treated in the MYTHIC trial?
The primary objectives of the Phase I MYTHIC trial were to determine the safety and tolerability of zedoresertib plus lunresertib, as well as to identify the maximum tolerated dose. Additionally, the researchers evaluated preliminary antitumor activity, pharmacokinetics and pharmacodynamics.
The ongoing trial had enrolled 62 patients at data cutoff with advanced resistant/refractory solid tumors harboring CCNE1 amplification and/or FBXW7 and/or PPP2R1A mutations, including but not limited to patients with ovarian, colorectal, pancreatic and breast cancer. Investigators are examining treatment doses from 150 mg. to 260 mg. daily of zedoresertib plus 60 mg. or 80 mg. of lunresertib on a three-on-four-off schedule.
What were the results of combining zedoresertib and lunresertib?
The overall disease control rate for 54 evaluable patients across all dosing levels was 68.5%. In 51 patients with target lesions, 26 showed tumor shrinkage and 10 patients achieved a radiological response.
Among patients with advanced ovarian cancers harboring CCNE1 amplification, FBXW7 or PPP2R1A mutations, 80% showed consistent tumor shrinkage, with durable responses observed. At least 10 patients (37%) remained on treatment for more than 16 weeks, and five patients (18.5%) remained on treatment for longer than 32 weeks.
The molecular response rate (MRR) was 47% for all patients across all dose levels. In patients with advanced ovarian cancer, the MRR was 67%.
The safety profile was manageable and consistent with either inhibitor as a monotherapy. The most commonly reported side effects were Grade 1 or 2 nausea, vomiting and asthenia, or generalized fatigue.
Given the promising antitumor activity and manageable safety profile, researchers will continue to optimize dosing and scheduling of this combination across the various tumor types represented in this study.
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The study was funded by Debiopharm and Repare Therapeutics. A complete list of collaborating authors and their disclosures can be found with the abstract.
AACR: New platform uses machine learning to predict responses in patients with lung cancer
- Path-IO is a machine learning platform that incorporates pathology data to predict how patients with non-small cell lung cancer will respond to immunotherapy
- Unlike molecular approaches, Path-IO uses pathological data that is already routinely gathered from patients
- Path-IO outperformed the current standard-of-care biomarker for guiding immunotherapy use in non-small cell lung cancer
ABSTRACT: 4003
An artificial intelligence (AI) model developed by researchers at The University of Texas MD Anderson Cancer Center demonstrated the ability to accurately predict responses to immunotherapy for patients with metastatic non-small cell lung cancer (NSCLC). If clinically validated, it could give clinicians much-needed insight into one of the most pressing challenges in oncology.
Details of the model, called Path-IO, were presented today at the American Association for Cancer Research (AACR) Annual Meeting 2026 by Rukhmini Bandyopadhyay, Ph.D., postdoctoral fellow in the lab of Jia Wu, Ph.D., associate professor of Imaging Physics and Thoracic/Head and Neck Medical Oncology.
“There are a number of AI-based approaches that have shown potential in recent years, but Path-IO really stands apart because we designed it from the outset for clinical translation,” Bandyopadhyay said. “For that to happen, a model has to make explainable decisions based on known factors and do it in a way that holds up across data sets. What we show here is, not only can Path-IO do that, but it can do it using data from slides that are already routinely gathered.”
More information on all UT MD Anderson AACR Annual Meeting content can be found on MDAnderson.org/AACR.
What is the significance of Path-IO and how could it contribute to clinical care?
Immunotherapy has been a transformational advancement in cancer care, but not all patients benefit from it. A significant challenge in oncology is determining who is most likely to benefit, so that physicians can tailor treatments and avoid unnecessary therapies.
The current standard-of-care biomarker for immunotherapy outcomes is PD-L1 expression, but this has demonstrated only modest predictive ability. In fact, in some of the validation groups used in this study, PD-L1 expression was as predictive as flipping a coin.
New research is showing that certain intratumoral structures, known as niches, are important biomarkers for predicting response, as well. Using pathology slides, Path-IO looks for these niches and other complex patterns that may be challenging for humans to reliably identify. The model then uses that information to stratify patients in groups based on their risk of disease progression following immunotherapy treatment.
This biology-based approach is one of the things that makes Path-IO unique. Rather than functioning as a “black box” AI that identifies entirely new and often uninterpretable patterns, Path-IO focuses on well-established tissue features and structures that, while difficult to consistently detect and quantify, are known to influence treatment response. This ability to explain the decisions it makes is an important distinction for its potential clinical adoption.
Using a historical data set from UT MD Anderson, Path-IO separated patients into high-risk and low-risk groups. Patients in the high-risk group had double the risk of death or disease progression than those in the low-risk group. For validation, the researchers tested the model on several external data sets with comparable results.
In all, Path-IO was validated in over 1,000 patients across multiple institutions and from multiple countries, and it significantly outperformed PD-L1 testing across all datasets.
What are the next steps for Path-IO?
The next crucial step for this technology will be validating it in a prospective clinical study. To prepare for that, the team already is expanding the testing cohorts to include more diverse groups of patients.
As with most AI tools, the more data Path-IO has to work from, the more accurate its predictions. In this study, researchers already have combined pathology-based predictions with radiomics and clinical data to further improve the prognostic ability of the model.
Soon, Bandyopadhyay believes the model will not only be able to predict whether patients will respond to immunotherapy but will even be able to predict the best immunotherapy strategy, such as an immune checkpoint inhibitor alone or combined with other agents.
Further down the road, Bandyopadhyay hopes that this platform can be fully integrated with additional data into a digital twin model that includes multimodal data, CT imaging, genomic factors and other clinical variables.
“To our knowledge, this is the most rigorously validated deep-learning pathomics framework to date. But we’re really just getting started,” Bandyopadhyay said. “As we continue to integrate more data streams into the model, it will improve and become more specific in its predictive abilities, hopefully becoming a major asset for clinicians who are helping patients make important decisions about their treatment options.”
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This study was funded by the National Institutes of Health, UT MD Anderson institutional funding, The Mugnaini Fund for Lung Cancer Research, the Rexanna’s Foundation for Fighting Lung Cancer, QIAC Partnership in Research (QPR) funding, and Permanent Health Funds. Scientific and financial support for the Cancer Immune Monitoring and Analysis Centers-Cancer Immunologic Data Commons (CIMACs-CIDC) Network was provided by the National Cancer Institute. A full list of authors and their disclosures can be found with the abstract.
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