Catalytic Asymmetric α-Acylation of Tertiary Amines Mediated by a Dual Catalysis Mode: N-Heterocyclic Carbene and Photoredox Catalysis

Department of Chemistry, Colorado State University, Fort Collins, Colorado 80523, United States


Cooperative photoredox catalysis

Xianjun Lang, Jincai Zhao, Xiaodong Chen

School of Materials Science and Engineering

Abstract: Cooperative photoredox catalysis bridges visible-light photoredox catalysis with other types of catalysis like transition-metal catalysis, biocatalysis or electrocatalysis for establishing demanding organic transformations.


The photophysics of photoredox catalysis: a roadmap for catalyst design

Daniela M. Arias-Rotondo, James K. McCusker

Abstract: The photophysical properties of transition metal-based charge-transfer chromophores are discussed with an eye toward their application in organic photoredox catalysis.


Oxidase reactions in photoredox catalysis

National Institute of General Medical Sciences

Abstract: This tutorial review highlights the distinctive mechanistic roles of and practical considerations regarding terminal oxidants in photocatalytic “oxidase”-type reactions.


Breaking Kasha’s Rule to Enable Higher Reactivity in Photoredox Catalysis

Journal of the American Chemical Society

Department of Chemistry, University of Basel, St. Johanns-Ring 19, 4056 Basel, Switzerland


9 Synergistic Visible Light Photoredox Catalysis


Combining Gold and Photoredox Catalysis: Visible Light-Mediated Oxy- and Aminoarylation of Alkenes

Basudev Sahoo, Matthew N. Hopkinson, Frank Glorius

NRW Graduate
School of Chemistry, Organisch-Chemisches Institut, Westfälische Wilhelms-Universität Münster, Corrensstrasse
40, 48149 Münster, Germany


11. Synergistic visible light photoredox catalysis


Site-Selective Functionalization of Methionine Residues via Photoredox Catalysis

Junyong Kim, Beryl X. Li, Richard Y.-C. Huang, Jennifer X. Qiao, William R. Ewing, David W. C. MacMillan

Merck Center for Catalysis at Princeton University, Princeton, New Jersey 08544, United States

National Institute of General Medical Sciences, Princeton Catalysis Initiative


Visible-Light Photoredox Catalysis: Aza-Henry Reactions via C−H Functionalization

Allison G. Condie, José C. González-Gómez, Corey R. J. Stephenson

Department of Chemistry, Boston University, Boston, Massachusetts 02215


Electron-Transfer Photoredox Catalysis: Development of a Tin-Free Reductive Dehalogenation Reaction

Jagan M. R. Narayanam, Joseph W. Tucker, Corey R. J. Stephenson


Mechanistic Origin of Photoredox Catalysis Involving Iron(II) Polypyridyl Chromophores

Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States


Programmable Piperazine Synthesis via Organic Photoredox Catalysis

Alexander J. Boley, Jason C. Genova, David A. Nicewicz

Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-3290, United States


Installing a Single Monomer within Acrylic Polymers Using Photoredox Catalysis

Jared G. Baker, Richard Zhang, C. Adrian Figg

Department of Chemistry and Macromolecules Innovation Institute, Virginia Tech, Blacksburg, Virginia 24061, United States

Virginia Polytechnic Institute and State University


Photoredox Catalysis-Enabled Sulfination of Alcohols and Bromides

William P. Carson II, Patrick J. Sarver, Noelle S. Goudy, David W. C. MacMillan

Princeton University, Division of Graduate Education, Bristol-Myers Squibb, National Institute of General Medical Sciences, Princeton Catalysis Initiative


Highly Regioselective Indoline Synthesis under Nickel/Photoredox Dual Catalysis

Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States

National Institute of General Medical Sciences, Division of Chemistry


Ambient-Temperature Newman–Kwart Rearrangement Mediated by Organic Photoredox Catalysis

Andrew J. Perkowski, Cole L. Cruz, David A. Nicewicz


A General Strategy for Organocatalytic Activation of C–H Bonds via Photoredox Catalysis: Direct Arylation of Benzylic Ethers

Katrine Qvortrup, Danica A. Rankic, David W. C. MacMillan

Merck Center for Catalysis, Princeton University, Princeton, New Jersey 08544, United States


General Strategy for Improving the Quantum Efficiency of Photoredox Hydroamidation Catalysis

Serge Ruccolo, Yangzhong Qin, Christoph Schnedermann, Daniel G. Nocera

Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, Massachusetts 02138−2902, United States


Enantioselective reductive couplings and related transformations under cooperative photoredox catalysis

Key Laboratory of Optic-electric Sensing and Analytical Chemistry for Life Science, MOE, College of Chemistry and Molecular Engineering, Qingdao University of Science and Technology, Qingdao, 266042, P. R. China

National Natural Science Foundation of China, Natural Science Foundation of Shandong Province

Abstract: This tutorial review offers a comprehensive overview of enantioselective reductive couplings and related transformations under cooperative photoredox catalysis.


Generation and Alkylation of α-Carbamyl Radicals via Organic Photoredox Catalysis

Joshua B. McManus, Nicholas P. R. Onuska, David A. Nicewicz


Synthesis of Sterically Hindered Primary Amines by Concurrent Tandem Photoredox Catalysis

Michael C. Nicastri, Dan Lehnherr, Yu-hong Lam, Daniel A. DiRocco, Tomislav Rovis

Department of Chemistry, Columbia University, New York, New York 10027, United States


Synthesis of (−)-Pseudotabersonine, (−)-Pseudovincadifformine, and (+)-Coronaridine Enabled by Photoredox Catalysis in Flow

Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States


Enantioselective, Catalytic Trichloromethylation through Visible-Light-Activated Photoredox Catalysis with a Chiral Iridium Complex

Haohua Huo, Chuanyong Wang, Klaus Harms, Eric Meggers

Fachbereich
Chemie, Philipps-Universität Marburg, Hans-Meerwein-Strasse, 35043 Marburg, Germany


Metal–Organic Layers for Synergistic Lewis Acid and Photoredox Catalysis

Yangjian Quan, Guangxu Lan, Yingjie Fan, Wenjie Shi, Eric You, Wenbin Lin

Department of Chemistry, The University of Chicago, Chicago, Illinois 60637, United States

University of Chicago, National Science Foundation


Late-Stage <i>N</i>-Me Selective Arylation of Trialkylamines Enabled by Ni/Photoredox Dual Catalysis


Unexpected Hydrated Electron Source for Preparative Visible-Light Driven Photoredox Catalysis

Christoph Kerzig, Xingwei Guo, Oliver S. Wenger

Schweizerischer Nationalfonds zur F?rderung der Wissenschaftlichen Forschung


Direct α-Acylation of Alkenes via N-Heterocyclic Carbene, Sulfinate, and Photoredox Cooperative Triple Catalysis

Organisch-Chemisches Institut, Westfälische Wilhelms-Universität, Corrensstrasse 40, 48149 Münster, Germany


Photoredox Catalysis Mediated by Tungsten(0) Arylisocyanides

Javier Fajardo, Alexandra T. Barth, Maryann Morales, Michael K. Takase, Jay R. Winkler, Harry B. Gray

Beckman Institute, California Institute of Technology, Pasadena, California 91125, United States


12. Excited radical anions and excited anions in visible light photoredox catalysis


Mechanistic Insight into the Photoredox Catalysis of Anti-Markovnikov Alkene Hydrofunctionalization Reactions

National Institute of General Medical Sciences, National Science Foundation


Direct C–H Cyanation of Arenes via Organic Photoredox Catalysis

National Institute of General Medical Sciences, Eli Lilly and Company


Enantioselective Photoredox Catalysis Enabled by Proton-Coupled Electron Transfer: Development of an Asymmetric Aza-Pinacol Cyclization

Lydia J. Rono, Hatice G. Yayla, David Y. Wang, Michael F. Armstrong, Robert R. Knowles

Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States


Direct C–F Bond Formation Using Photoredox Catalysis

Montserrat Rueda-Becerril, Olivier Mahé, Myriam Drouin, Marek B. Majewski, Julian G. West, Michael O. Wolf, Glenn M. Sammis, Jean-François Paquin

Department
of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, British Columbia, V6T 1Z1, Canada


Decarboxylative Fluorination of Aliphatic Carboxylic Acids via Photoredox Catalysis

Sandrine Ventre, Filip R. Petronijevic, David W. C. MacMillan

Merck Center for Catalysis at Princeton University, Princeton, New Jersey 08544, United States

National Institute of General Medical Sciences, Amgen, Merck, AbbVie


A Spirobifluorene-Based Covalent Organic Framework for Dual Photoredox and Nickel Catalysis

Yingjie Fan, Dong Won Kang, Steven Labalme, Wenbin Lin

Division of Chemistry, University of Chicago


Spin-State and Reorganization Energy Considerations for Metal-Centered Photoredox Catalysis

Bekah E. Bowers, Björn Pfund, Hayden F. Beissel, Atanu Ghosh, James K. McCusker

Contribution from the Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States


Oxygen Switch in Visible-Light Photoredox Catalysis: Radical Additions and Cyclizations and Unexpected C–C-Bond Cleavage Reactions

Shaoqun Zhu, Arindam Das, Lan Bui, Hanjun Zhou, Dennis P. Curran, Magnus Rueping

RWTH Aachen University, Institute of Organic Chemistry, Landoltweg 1, D-52074 Aachen, Germany


Bifunctional Metal–Organic Layer with Organic Dyes and Iron Centers for Synergistic Photoredox Catalysis

Yangjian Quan, Wenjie Shi, Yang Song, Xiaomin Jiang, Cheng Wang, Wenbin Lin


Two-Photon Excitation Photoredox Catalysis Enabled Atom Transfer Radical Polymerization

Yu-Ying Yang, Pengfei Zhang, Nikos Hadjichristidis

Polymer Synthesis Laboratory, Chemistry Program, KAUST Catalysis Center, Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia


Remote and Proximal Hydroaminoalkylation of Alkenes Enabled by Photoredox/Nickel Dual Catalysis

Songlin Zheng, Wenlong Wang, Weiming Yuan

Key Laboratory of Material Chemistry for Energy Conversion and Storage, Ministry of Education, Hubei Key Laboratory of Bioinorganic Chemistry and Materia Medica, Hubei Key Laboratory of Materials Chemistry and Service Failure, School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology (HUST), 1037 Luoyu Road, Wuhan 430074, China

Guangdong Science and Technology Department, Guangdong Science and Technology Department, Guangdong Provincial Key Laboratory Of Catalysis, Central University Basic Research Fund of China, Huazhong University of Science and Technology


A Luminescent Zirconium(IV) Complex as a Molecular Photosensitizer for Visible Light Photoredox Catalysis

Yu Zhang, Jeffrey L. Petersen, Carsten Milsmann

C. Eugene
Bennett Department
of Chemistry, West Virginia University, 100 Prospect Street, Morgantown, West Virginia 26506, United States

West Virginia University, Don and Linda Brodie Resource Fund for Innovation


Intermolecular Radical Addition to Carbonyls Enabled by Visible Light Photoredox Initiated Hole Catalysis

Lena Pitzer, Frederik Sandfort, Felix Strieth-Kalthoff, Frank Glorius

Organisch-Chemisches Institut, Westfälische Wilhelms-Universität Münster, Corrensstraße 40, 48149 Münster, Germany

Alfried Krupp von Bohlen und Halbach-Stiftung, Deutsche Forschungsgemeinschaft


Diastereoselective Allylation of Aldehydes by Dual Photoredox and Chromium Catalysis

J. Luca Schwarz, Felix Schäfers, Adrian Tlahuext-Aca, Lukas Lückemeier, Frank Glorius

Deutsche Forschungsgemeinschaft, Alfried Krupp von Bohlen and Halbach-Stiftung


Merging Photoredox and Nickel Catalysis: Decarboxylative Cross-Coupling of Carboxylic Acids with Vinyl Halides

Adam Noble, Stefan J. McCarver, David W. C. MacMillan

National Institute of General Medical Sciences


Mechanistic Insights and Kinetic Analysis for the Oxidative Hydroxylation of Arylboronic Acids by Visible Light Photoredox Catalysis: A Metal-Free Alternative

Spencer P. Pitre, Christopher D. McTiernan, Hossein Ismaili, Juan C. Scaiano

Department of Chemistry and Centre
for Catalysis Research
and Innovation, University of Ottawa, 10
Marie Curie, Ottawa K1N 6N5, Canada


High Triplet Energy Iridium(III) Isocyanoborato Complex for Photochemical Upconversion, Photoredox and Energy Transfer Catalysis

Lucius Schmid, Felix Glaser, Raoul Schaer, Oliver S. Wenger

Department of Chemistry, University of Basel, St. Johanns-Ring 19, 4056 Basel, Switzerland


Organic photoredox catalysis for the oxidation of silicates: applications in radical synthesis and dual catalysis

Christophe Lévêque, Ludwig Chenneberg, Vincent Corcé, Cyril Ollivier, Louis Fensterbank

Abstract: Metal free photooxidation of alkyl bis(catecholato)silicates with the organic dye 1,2,3,5-tetrakis(carbazol-9-yl)-4,6-dicyano-benzene (4CzIPN) allows the smooth formation of alkyl radicals.


Highly Effective Near-Infrared Activating Triplet–Triplet Annihilation Upconversion for Photoredox Catalysis

Ling Huang, Wenting Wu, Yang Li, Kai Huang, Le Zeng, Wenhai Lin, Gang Han

Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, United States


KRAS G12R in pancreatic cancer: why this mutation resists current inhibitor strategies

Affiliate Faculty, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego , San Diego, CA 92109,

Abstract: Abstract Background Pancreatic ductal adenocarcinoma (PDAC) remains among the most lethal solid malignancies, with limited therapeutic progress despite decades of cytotoxic chemotherapy. About 90% of PDAC tumors harbor activating mutations in the Kirsten rat sarcoma viral oncogene homolog (KRAS), making oncogenic KRAS signaling a longstanding therapeutic target. Although KRAS G12C inhibitors have demonstrated clinical success in lung and colorectal cancers, this strategy has not translated to the same degree in the glycine-to-arginine substitution at codon 12 (G12R), present in 15%-20% of PDAC tumors. Methods A focused narrative review of the literature was conducted using PubMed and Google Scholar to examine structural, biochemical, and clinical data relevant to KRAS G12R, binding pocket dynamics, therapeutic outcomes, and novel approaches. Results G12R substitution introduces a bulky, positively charged arginine side chain that sterically occludes the binding pocket of KRAS (Switch II pocket), precluding both covalent and competitive inhibitor engagement. In addition to structural inaccessibility, G12R mutations exhibit distinct signaling rewiring, including impaired phosphoinositide 3-kinase alpha (PI3Kα) and mitogen-activated protein kinase (MEK) interactions, along with increased survival driven by amplified autophagy. These features limit the efficacy of pocket-directed inhibitors and downstream MEK blockade. Emerging pan-RAS(ON) ternary complex inhibitors bypass the occluded pocket by recruiting cyclophilin A to block effector signaling and may provide an effective means to address KRAS G12R-mutated disease. Conclusions KRAS G12R defines a structurally and biologically distinct subset of PDAC characterized by binding-pocket occlusion and increased survival pathways. Effective treatment will require pocket-independent strategies or targeting of G12R-specific biological vulnerabilities.


Abstract B90: HSP90 inhibitor NVP-AUY922 sensitizes intrinsic MEK inhibitor Trametinib-resistant NSCLC cells harboring KRAS mutation

American Association for Cancer Research (AACR)

1Univ. of Ulsan Asan Medical Ctr., Seoul, Korea, Republic of;

Abstract: Abstract Introduction: Mutant KRAS is feature of more than 25% of non-small cell lung cancers (NSCLC) and exemplifies one of the most popular oncogenic drivers. Ras-driven tumors are often hard to cure with conventional therapies and are necessary novel treatment strategies. Experiment: Using the HSP90 inhibitor NVP-AUY922, we investigated the potential of HSP90 inhibition to overcome the resistance to Trametinib, MEK inhibitor, in lung cancers harboring K-RAS mutant cell lines (H23, H358, H647, H1944, and A549). The synergistic effect of AUY-922 and trametinib was determined by analysis of the combination index (CI). After performing western blotting, we underwent following xenograft study with 6-week-old Balb/c-nu/nu female using H649 and H1944 cell lines. The mice were randomized into groups of 5, and NVP-AUY922 (5 mg/kg) was administered 3 days/week and GSK1120212 (300 μg/kg) was administered 5 days/week for up to 21 days in total. Result: NSCLC cells harboring G13D KRAS mutation showed intrinsic MEK inhibitor resistance via PI3K activation by highly expressed AKT as a bypass pathway. And then, HSP90 inhibitor AUY-922 suppresses both PI3K-AKT-mTOR and RAF-MEK-ERK reactivated by MEK inhibitor GSK1120212 and finally AUY-922 renders intrinsic MEK inhibitor resistant NSCLC cells sensitive to GSK1120212 with significant synergy. Furthermore, we showed this synergistic effect is occurred on sub-effective dose of either drug. Conclusion: The dual inhibition of HSP90 and MEK signaling pathways on sub-effective dose may constitute a potent therapeutic strategy to treat intrinsic MEK inhibitor resistant G13DKRAS mutant NSCLCs for resolving toxicity problem of dual inhibition of AKT and MEK in clinical trials. Citation Format: Dae Ho Lee, Kang-Seo Park, Bora Oh, Mi-Hee Lee, Hannah Yang. HSP90 inhibitor NVP-AUY922 sensitizes intrinsic MEK inhibitor Trametinib-resistant NSCLC cells harboring KRAS mutation. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr B90.


Sotorasib: First Approved <i>KRAS</i> Mutation Inhibitor for the Treatment of Non-small Cell Lung Cancer


A Pan-KRAS Inhibitor Impedes Common KRAS Oncoproteins and Tumor Growth

Abstract: Abstract A pan-KRAS inhibitor is selective for the inactive state of multiple KRAS mutants, sparing other RAS isoforms.


20 Beyond KRAS G12D Mutation: The Prognostic Significance of The KRAS Mutation Variants on Progression-Free Survival in Pancreatic Ductal Adenocarcinoma

Abstract: Abstract Pancreatic ductal adenocarcinomas (PDACs) are aggressive neoplasms with poor outcomes. The prognostic significance of major driver gene mutation, beyond KRAS G12D, is yet to be explored. A 10-year retrospective comprehensive study of the prognostic significance of major driver genes in PDACs was conducted. Next-generation sequencing was performed on 131 PDACs. The highest mutation rates observed were in KRAS (n = 121; 92.4%), followed by TP53 (n = 92; 70.2%), SMAD4 (n = 38; 29%), and CDKN2A (n = 30; 22.9%). Among KRAS-mutated PDACs that underwent neoadjuvant chemotherapy (n = 96; 73.3%), the presence of SMAD4 and/or TP53 mutations was associated with worse progression-free survival (PFS). Patients with KRAS G12V mutations exhibited a trend where those with SMAD4/TP53 wild-type had better PFS compared to those with the mutated SMAD4/TP53, particularly during the early months of follow-up. KRAS G12R mutations showed the most distinct separation pattern in PFS, with SMAD4/TP53 wild-type PDACs showing relatively better PFS. In contrast, KRAS G12D had a more uniform effect on disease progression, regardless of SMAD4 and TP53 mutation status. Our study shows that the prognostic impact of SMAD4 and TP53 mutations in PDACs may vary depending on the specific KRAS variants. The presence of SMAD4/TP53 mutations was generally associated with poorer survival outcomes in KRAS-mutated cases receiving neoadjuvant chemotherapy, especially among those with the KRAS G12R mutation. Patients with KRAS G12V and G12R mutations had better PFS when SMAD4/TP53 were wild-type, while the impact of KRAS G12D mutations on PFS remained consistent regardless of SMAD4/TP53 mutation status.


Targeting Krasg12c‐mutant cancer with a mutation‐specific inhibitor

J. G. Christensen, P. Olson, T. Briere, C. Wiel, M. O. Bergo

From the Mirati Therapeutics, Inc. San Diego CA USA

Abstract: AbstractThe RAS genes, which include H, N, and KRAS, comprise the most frequently mutated family of oncogenes in cancer. Mutations in KRAS – such as the G12C mutation – are found in most pancreatic, half of colorectal and a third of lung cancer cases and is thus responsible for a substantial proportion of cancer deaths. Consequently, KRAS has been the subject of exhaustive drug‐targeting efforts over the past 3–4 decades. These efforts have included targeting the KRAS protein itself but also its posttranslational modifications, membrane localization, protein–protein interactions and downstream signalling pathways. Most of these strategies have failed and no KRAS‐specific drugs have yet been approved. However, for one specific mutation, KRASG12C, there is light on the horizon. MRTX849 was recently identified as a potent, selective and covalent KRASG12C inhibitor that possesses favourable drug‐like properties. MRTX849 selectively modifies the mutant cysteine residue in GDP‐bound KRASG12C and inhibits GTP‐loading and downstream KRAS‐dependent signalling. The drug inhibits the in vivo growth of multiple KRASG12C‐mutant cell line xenografts, causes tumour regression in patient‐derived xenograft models and shows striking responses in combination with other agents. It has also produced objective responses in patients with mutant‐specific lung and colorectal cancer. In this review, we discuss the history of RAS drug‐targeting efforts, the discovery of MRTX849, and how this drug provides an exciting and long‐awaited opportunity to selectively target mutant KRAS in patients.


A phase I trial of the pan-ERBB inhibitor neratinib combined with the MEK inhibitor trametinib in patients with advanced cancer with EGFR mutation/amplification, HER2 mutation/amplification, HER3/4 mutation or KRAS mutation

Sarina A. Piha-Paul, Chieh Tseng, Hai T. Tran, Meng Gao, Daniel D. Karp, Vivek Subbiah, Apostolia Maria Tsimberidou, Jitesh D. Kawedia, Siqing Fu, Shubham Pant, Timothy A. Yap, Van K. Morris, Bryan K. Kee, Mariela Blum Murphy, JoAnn Lim, Funda Meric-Bernstam

Abstract: Abstract Purpose Aberrant alterations of ERBB receptor tyrosine kinases lead to tumorigenesis. Single agent therapy targeting EGFR or HER2 has shown clinical successes, but drug resistance often develops due to aberrant or compensatory mechanisms. Herein, we sought to determine the feasibility and safety of neratinib and trametinib in patients with EGFR mutation/amplification, HER2 mutation/amplification, HER3/4 mutation and KRAS mutation. Methods Patients with actionable somatic mutations or amplifications in ERBB genes or actionable KRAS mutations were enrolled to receive neratinib and trametinib in this phase I dose escalation trial. The primary endpoint was determination of the maximum tolerated dose (MTD) and dose-limiting toxicity (DLT). Secondary endpoints included pharmacokinetic analysis and preliminary anti-tumor efficacy. Results Twenty patients were enrolled with a median age of 50.5 years and a median of 3 lines of prior therapy. Grade 3 treatment-related toxicities included: diarrhea (25%), vomiting (10%), nausea (5%), fatigue (5%) and malaise (5%). The MTD was dose level (DL) minus 1 (neratinib 160 mg daily with trametinib 1 mg, 5 days on and 2 days off) given 2 DLTs of grade 3 diarrhea in DL1 (neratinib 160 mg daily with trametinib 1 mg daily). The treatment-related toxicities of DL1 included: diarrhea (100%), nausea (55.6%) and rash (55.6%). Pharmacokinetic data showed trametinib clearance was significantly reduced leading to high drug exposures of trametinib. Two patients achieved stable disease (SD) ≥ 4 months. Conclusion Neratinib and trametinib combination was toxic and had limited clinical efficacy. This may be due to suboptimal drug dosing given drug–drug interactions. Trial registration ID: NCT03065387.


Data from Circulating Tumor DNA Dynamics Reveal <i>KRAS G12C</i> Mutation Heterogeneity and Response to Treatment with the KRAS G12C Inhibitor Divarasib in Solid Tumors

Yoonha Choi, Neekesh V. Dharia, Tomi Jun, Julie Chang, Stephanie Royer-Joo, Kenneth K. Yau, Zoe J. Assaf, Junko Aimi, Smruthy Sivakumar, Meagan Montesion, Adrian Sacher, Patricia LoRusso, Jayesh Desai, Jennifer L. Schutzman, Zhen Shi

Abstract:
AbstractPurpose:
To inform prognosis, treatment response, disease biology, and KRAS G12C mutation heterogeneity, we conducted exploratory circulating tumor DNA (ctDNA) profiling on 134 patients with solid tumors harboring a KRAS G12C mutation treated with single-agent divarasib (GDC-6036) in a phase 1 study.
Experimental Design:
Plasma samples were collected for serial ctDNA profiling at baseline (cycle 1 day 1 prior to treatment) and multiple on-treatment time points (cycle 1 day 15 and cycle 3 day 1).
Results:
KRAS G12C ctDNA was detectable from plasma samples in 72.9% (43/59) and 92.6% (50/54) of patients with non–small cell lung cancer and colorectal cancer, respectively, the majority of whom were eligible for study participation based on a local test detecting the KRAS G12C mutation in tumor tissue. Baseline ctDNA tumor fraction was associated with tumor type, disease burden, and metastatic sites. A decline in ctDNA level was observed as early as cycle 1 day 15. Serial assessment showed a decline in ctDNA tumor fraction associated with response and progression-free survival. Except for a few cases of KRAS G12C sub-clonality, on-treatment changes in KRAS G12C variant allele frequency mirrored changes in the overall ctDNA tumor fraction.
Conclusions:
Across tumor types, the KRAS G12C mutation likely represents a truncal mutation in the majority of patients. Rapid and deep decline in ctDNA tumor fraction was observed in patients responding to divarasib treatment. Early on-treatment dynamics of ctDNA were associated with patient outcomes and tumor response to divarasib treatment.


Abstract C108: A novel mitotic kinesin Eg5 inhibitor exerts the growth-inhibitory effect in cancer cells in a manner independent of induction of monoastral formation but dependent of KRAS mutation status

Shizuoka Cancer Center Research Institute, Suntou-gun, Japan.

Abstract: Abstract Mitotic kinesin Eg5 plays an important role in mitosis, as it is critical for proper bipolar spindle assembly. After the discovery of the first Eg5 inhibitor monastrol, a number of Eg5 inhibitors have been developed as anticancer drugs. We have previously synthesized a series of S-trityl-L-cysteine (STLC) derivatives as Eg5 inhibitors and demonstrated potent inhibitory effect on Eg5 ATPase activity and induction of mitotic arrest with characteristic monoastral spindles in HeLa cells. In this study, we evaluated the growth-inhibitory acitivities of novel STLC derivatives, Compound1, Compound2, and Compound3 by MTT assay in a wide range of human cancer cell lines derived from gastric (MKN1, MKN45, MKN74, NUGC3, NUGC4, NCI-N87), colon (C170, DLD1, HCT15, COLO205), pancreatic (AsPC1, BxPC3, SUIT2) and lung (PC-9, PC-14, H1975, H441, A549) cancers and investigated possible predictive biomarkers. Among the derivatives, Compound3 showed the most potent growth-inhibitory effect and sensitivity between cell lines showed marked differences, with IC50 values ranging from 0.134 to 7.14 M. Pancreatic cancer cell line AsPC1 and non-small cell lung cancer (NSCLC) cell lines H441 and A549 which harbor KRAS mutation showed relatively high IC50 values compared to other cell lines without KRAS mutation. Compound3 induced G2/M arrest in all cell lines as early as 8 hours after the treatment by flowcytometry, while the increase of the apoptotic sub-G1 fraction was not observed in these AsPC1, H441 and A549 cells harboring KRAS mutation. These results suggested that differential sensitivity to Compound3 and attenuated induction of apoptosis by Compound3 were attributed to KRAS mutation. Induction of monoastral spindles formation by Compound3 was observed as early as 8 hours after the treatment not only in sensitive cell lines but in refractory cell lines such as AsPC1 and H441 cells by immunocytochemistry, suggesting the induction of monoastral spindles formation does not work as a predictive biomarker for clinical development. There was no correlation observed between the growth-inhibitory effect and the basal Eg5 expression level of each cell line by western blot analysis. No significant difference was observed in the induction of a spindle checkpoint kinase BubR1 phosphorylation among the cells, a possible predictive biomarker in response to the Eg5 inhibitor treatment by western blot analysis. Taken together, a novel mitotic kinesin Eg5 inhibitor Compound3 should be considered for further exploration and development and KRAS mutation status was suggested to be a candidate for a predictive biomarker of poor response to Eg5 inhibitors. Citation Information: Mol Cancer Ther 2009;8(12 Suppl):C108.


KRAS mutation profile in Puerto Rican patients with colorectal cancer: Trends in expression of KRAS mutation in PR between April 2009 to January 2011.

American Society of Clinical Oncology (ASCO)

Yelitza Ruiz, Christine Miranda, Robert F. Hunter

Abstract: e14102 Background: The prevalence of a mutated KRAS gene in colorectal cancer shows ethnic variation with a range between 30-48% among the Caucasian, Asian, and European populations. The purpose of this study was to define the profile of KRAS mutation in colorectal cancer patients in Puerto Rico. Methods: Between April 2009 and January 2011, we collected the expression of KRAS gene among colorectal cancer patients in Puerto Rico. The assay was performed by a central laboratory which began to offer the test in the island in April 2009 (GENOPTIX). Data for the presence and type of mutation, age, sex, cancer histology, site of tumor, and cancer stage were collected. The site of the mutation in codon 12 and 13 were identified by the real time polymerase chain reaction (PCR). The study was approved by IRB. A total of 501 cases were available for study. SPPS version 14 was used for statistical analysis. Results: A mutated KRAS was present in 39% (n=195) of the cohort, more commonly in the 51-69 age group (55.4%; p=0.004), in females (55.4%; p=0.095), in the proximal area of colon (43.8%; p=0.017), in the codon 12 (91.2%), and 8.8% in the codon 13. The majority of mutated genes were in codon 12 (91.2%) with 12 ASP (39.5%) and 12 VAL (25.4%) most often seen. Mucinous histology was associated with 46.7% mutated KRAS, poorly differentiated tumor with 40.0% of KRAS mutation. The mutant expression of KRAS in cases with rectum carcinoma was 39.5%, where the most common mutation was 12 VAL. Conclusions: The prevalence of KRAS mutation in Puerto Rican population is similar to other reported studies. A higher prevalence of 12 VAL mutation in rectal carcinoma and a trend towards a higher mutation rate in females are different in this cohort as compared with published studies. This study was supported by NIH grant numbers G12RR-03035 & 8U54MD007587-03.


Abstract 2667: Trial in progress: Phase 1 study of BI 1823911, an irreversible KRASG12C inhibitor targeting KRAS in its GDP-loaded state, as monotherapy and in combination with the pan-KRAS SOS1 inhibitor BI 1701963 in solid tumors expressing KRASG12C mutation

1Boehringer Ingelheim RCV GmbH & Co KG, Vienna, Austria;

Abstract: Abstract Inhibition of KRASG12C mediated signaling and the therapeutic impact in non-small cell lung cancer (NSCLC) whose tumors carry this mutation was demonstrated clinically by sotorasib and adagrasib leading to approval of sotorasib in KRASG12C mutant NSCLC. These encouraging data are currently changing the treatment paradigm for patients with KRASG12C-mutated tumors. However only a fraction of patients is initially responding to these compounds and patients who achieved an objective response ultimately progressed on-treatment. It became clear from these studies that KRASG12C inhibitors require a combination partner to either achieve a deeper response initially or to prevent development of resistance. Multiple rational combination approaches are currently investigated with the goal to prolong duration of response or to overcome KRASG12C inhibitor resistance. The KRASG12C inhibitor BI 1823911 is more potent compared to sotorasib or adagrasib and showed comparable in vivo efficacy at a dose of 60 mg/kg vs. 100 mg/kg of either sotorasib or adagrasib in preclinical studies. The pan-KRAS SOS1 inhibitor BI 1701963 is the first direct KRAS signaling modifier, which entered phase I clinical trials both as a monotherapy as well as in combination with KRASG12C inhibitors, MEK inhibitors and irinotecan. Pan-KRAS SOS1 inhibitors exhibit activity against a broad spectrum of KRAS alleles, including the major G12D/V/C and G13D oncoproteins, while sparing the interaction of KRAS with SOS2. In the presented combination concept BI 1701963 shifts the balance of KRASG12C to its GDP-loaded form, which is the state to which BI 1823911 covalently binds to. Here, we present pre-clinical data showing enhanced pathway modulation and synergistic anti-tumor effects following vertical pathway inhibition of BI 1823911 in combination with BI 1701963. Our preclinical results supported the start of a phase I trial (NCT04973163), investigating the safety, tolerability, recommended dose and preliminary efficacy of BI 1823911 alone and in combination with the pan-KRAS SOS1 inhibitor BI 1701963. The trial includes cohorts of patients with KRASG12C mutant solid tumors, such as NSCLC, CRC, cholangiocarcinoma, and pancreatic adenocarcinoma, both KRAS therapy naïve or KRAS therapy relapsed. The first patients in the trial were treated in the monotherapy arm, dose escalation started at a dose of 50 mg. The combination therapy part is expected to start in Q1 2022. Primary endpoints include dose-limiting toxicities, treatment-emergent or -related adverse events. Secondary endpoints include pharmacokinetic properties of combination regimens and preliminary efficacy. Citation Format: Irene C. Waizenegger, Hengyu Lu, Claus Thamer, Fabio Savarese, Daniel Gerlach, Dorothea Rudolph, Christopher P. Vellano, Marcelo Marotti, John Heymach, Scott Kopetz, Timothy P. Heffernan, Joseph R. Marszalek, Mark P. Petronczki, Marco H. Hofmann, Norbert Kraut. Trial in progress: Phase 1 study of BI 1823911, an irreversible KRASG12C inhibitor targeting KRAS in its GDP-loaded state, as monotherapy and in combination with the pan-KRAS SOS1 inhibitor BI 1701963 in solid tumors expressing KRASG12C mutation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2667.


Supplementary Figure S3 from Circulating Tumor DNA Dynamics Reveal <i>KRAS G12C</i> Mutation Heterogeneity and Response to Treatment with the KRAS G12C Inhibitor Divarasib in Solid Tumors

Abstract: Supplementary Figure S3


Differences in gene mutation profiles of colon cancer patients with or without a KRAS mutation

Shujian Chang, Yudan Zhou, Ruirong Wu, Xiaosong Ge, Yong Pu

Affiliated Hospital of Jiangnan University

Abstract: Abstract RAS plays an important role in the carcinogenesis of colorectal epithelial cells, but RAS mutations are only present, at most, in approximately 50% of patients. In order to compare the gene mutation profiles of patients with or without a KRAS mutation, the clinicopathological data of 858 patients and NGS test results of 1697 patients with colorectal cancer were used in this analysis. In 858 patients, the mutation rate of KRAS was 50.8% (i.e., 436/858). Only 2 out of 349 (0.5%) KRAS mutant patients had a BRAF mutation, while 25 out of 422 (5.9%) KRAS wild-type patients had a BRAF mutation (p < 0.0001). The median OS of patients with a mutant or wild-type KRAS were 83.47 months and not reached, respectively (HR: 0.8147; 95% CI: 0.6271–1.051; p = 0.1153). The NGS results showed that in RAS mutant patients, genes with a high mutation rate mainly included APC, TP53, PIK3CA, Smad4, and Fbxw7, and in RAS wild-type patients, genes with a high mutation rate mainly included TP53, APC, LRP1B, MYC, and BRAF. The mutation rates of BRAF and EGFR in the RAS wild-type group were 15% and 9%, respectively, while they were only 3% in the RAS mutant group. The mutation rate of PIK3CA in the RAS mutant group was 31%, while that in the RAS wild-type group was 14%. The mutation rate of APC was 72.2% (i.e., 687/952). The mutation rate of the gene, RNF43, in the APC wild-type group was 5.23 times higher than that in the APC mutant group, and the gene, NSD1, in the APC wild-type group was 0.07 times higher than that in the APC mutant group. The mutation rate of TP53 was 78.2% (i.e., 744/952). The mutation rate of MLH1 in the TP53 wild-type group was 8.42 times higher than that in the TP53 mutant group, which was significantly higher than that in the TP53 mutant group. Generally, the gene mutation profiles were significantly different between KRAS mutation and wild-type colorectal cancer patients. A single gene mutation may be sufficient to cause the dysfunction of a signal transduction pathway, and APC, TP53, or RAS are not necessary for the carcinogenesis of sporadic colorectal cancer.


GO42144 INVESTIGATORS AND STUDY GROUP1 from Circulating Tumor DNA Dynamics Reveal <i>KRAS G12C</i> Mutation Heterogeneity and Response to Treatment with the KRAS G12C Inhibitor Divarasib in Solid Tumors

Abstract: GO42144 INVESTIGATORS AND STUDY GROUP


Supplementary Table S1 from Circulating Tumor DNA Dynamics Reveal <i>KRAS G12C</i> Mutation Heterogeneity and Response to Treatment with the KRAS G12C Inhibitor Divarasib in Solid Tumors

Abstract: Representativeness of Study Participants


Supplementary Figure S5 from Circulating Tumor DNA Dynamics Reveal <i>KRAS G12C</i> Mutation Heterogeneity and Response to Treatment with the KRAS G12C Inhibitor Divarasib in Solid Tumors

Abstract: Supplementary Figure S5


Supplementary Figure S2 from Circulating Tumor DNA Dynamics Reveal <i>KRAS G12C</i> Mutation Heterogeneity and Response to Treatment with the KRAS G12C Inhibitor Divarasib in Solid Tumors

Abstract: Supplementary Figure S2


Researchers Reveal Another KRAS Inhibitor

Abstract: Abstract Researchers have developed a KRAS inhibitor, BI-2852, that inhibits ERK phosphorylation and reduces proliferation of KRAS-mutant cancer cells. A drug based on the molecule may be effective in patients who don't respond to the KRASG12C inhibitors currently in clinical trials.


Abstract: Supplementary Figure S5


Abstract: Supplementary Figure S2


Abstract LB-209: Chromatin condensation in KRAS-mutant lung cancer as a therapeutic target: challenging the paradigm of associating KRAS mutation with EGFR inhibitor resistance

1Department of Radiation Oncology, Mass. General Hospital, Boston, MA;

Abstract: Abstract Non-small cell lung cancer (NSCLC) with mutations in the KRAS oncogene is generally resistant to targeted agents directed against the epidermal growth factor receptor (EGFR). Although EGFR is active in KRAS-mutant cancers its role in supporting cell survival and its potential utility as a therapeutic target have remained unclear. EGFR-targeted drugs have been shown to enhance the cytotoxic effects of radiation treatments (RT). However, clinical results have been inconsistent suggesting the need for genomic biomarkers to better identify a subset of patients in whom the combination of EGFR inhibitors and RT can be further developed. By carrying out a screen of 40 human NSCLC-derived cell lines, we establish a surprising positive correlation between the presence of a KRAS mutation and radiosensitization by the EGFR tyrosine kinase inhibitor erlotinib and the monoclonal antibody cetuximab. EGFR signaling in KRAS-mutant NSCLC cells promotes chromatin condensation in-vitro and in-vivo, thereby restricting the number of DNA double-strand breaks (DSB) produced by a given dose of RT. Chromatin condensation in interphase cells is characterized by an unexpected mitosis-like co-localization of serine 10 phosphorylation and lysine 9 trimethylation on histone H3. Aurora B kinase promotes this process in a manner that is co-dependent upon EGFR and protein kinase C alpha (PKCα) in KRAS-mutant but not wild-type cells. In keeping with a recently reported role of PKCα in breast cancer stem cells, disrupting the EGFR-PKCα pathway is particularly effective under experimental conditions that enrich for stem cell-like cancer cells including spheres. Interestingly, PKCα as well as MEK/ERK signaling is required for suppression of DSB-inducible premature senescence by EGFR. Blockade of autophagy results in a mutant KRAS-dependent senescence-to-apoptosis switch in cancer cells or spheres treated with RT. In conclusion, we identify EGFR as a molecular target to overcome a novel chromatin-mediated mechanism of RT resistance in KRAS-mutant tumor cells, thereby challenging the current paradigm of resistance of KRAS-mutant cancers to EGFR inhibitors. Our findings may reposition EGFR-targeted agents for combination with DSB-inducing therapies in KRAS-mutant NSCLC, and yield novel therapeutic opportunities to target chromatin modifications in cancer stem cells. Supported by the Dana-Farber/Harvard Cancer Center SPORE in Lung Cancer, NCI P50 CA090578, NCI R01 CA142698, American Cancer Society 123420RSG-12-224-01-DMC and RSG-12-079-01, UK Wellcome Trust 086357, Deutsche Forschungsgemeinschaft DFG-PAK-190, Federal Share of program income earned by Massachusetts General Hospital on C06 CA059267, Proton Therapy Research and Treatment Center. Citation Format: Meng Wang, Ashley M. Kern, Marieke Hülskötter, Patricia Greninger, Anurag Singh, Yunfeng Pan, Dipanjan Chowdhury, Mechthild Krause, Michael Baumann, Cyril H. Benes, Jason A. Efstathiou, Jeff Settleman, Henning Willers. Chromatin condensation in KRAS-mutant lung cancer as a therapeutic target: challenging the paradigm of associating KRAS mutation with EGFR inhibitor resistance. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr LB-209. doi:10.1158/1538-7445.AM2014-LB-209


Abstract: GO42144 INVESTIGATORS AND STUDY GROUP


Supplementary Figure S4 from Circulating Tumor DNA Dynamics Reveal <i>KRAS G12C</i> Mutation Heterogeneity and Response to Treatment with the KRAS G12C Inhibitor Divarasib in Solid Tumors

Abstract: Supplementary Figure S4


Supplementary Figure S1 from Circulating Tumor DNA Dynamics Reveal <i>KRAS G12C</i> Mutation Heterogeneity and Response to Treatment with the KRAS G12C Inhibitor Divarasib in Solid Tumors

Abstract: Supplementary Figure S1


Abstract: Supplementary Figure S3


Mechanisms of KRAS Inhibitor Resistance Revealed

Abstract: Abstract A recent study revealed diverse mechanisms of acquired resistance in patients with non–small cell lung cancer or colorectal cancer who stop responding to a KRASG12C inhibitor. These patients developed secondary KRAS mutations, MAPK pathway alterations, or genomic rearrangements—and in some cases multiple resistance mechanisms occurred simultaneously.


Abstract 6390: Identification of novel pan-KRAS mutation inhibitor shows significant efficacy in pancreatic cancer with no toxicity, offering hope beyond current therapies

1Houston Methodist Research Institute, Houston, TX;

Abstract: Abstract Pancreatic ductal adenocarcinoma (PDAC) is one of the most challenging cancers to treat, with current therapies providing a median survival of just six months. The intrinsic chemoresistance and genetic heterogeneity of PDAC tumors complicate the development of effective targeted therapies. Approximately 90% of PDAC cases harbor KRAS mutations, which drive tumor initiation and progression, making KRAS a critical therapeutic target. Although the FDA has approved two KRAS inhibitors (Sotorasib and Adagrasib) for lung cancer, these drugs specifically target the KRAS-G12C mutation, found in only 2% of PDAC cases. Most PDAC tumors carry other KRAS mutations, particularly KRAS-G12D, leaving patients with limited targeted treatment options. To address this, we synthesized 21 compounds and identified 3E as a novel pan-KRAS inhibitor targeting KRAS-G12D, KRAS-G12C, and KRAS-G12S. Kaplan-Meier analysis of the TCGA PDAC database (n=177, p=0.00036) showed that elevated KRAS expression correlates with shorter survival, highlighting KRAS's role in PDAC progression. In vitro, treatment with 3E inhibited KRAS signaling in PDAC cells (PANC1, Miapaca-2) and KPC transgenic cells, as indicated by p-ERK suppression. 3E significantly reduced cell proliferation, migration, and invasion. Clonogenic assays demonstrated that 3E was more effective than Adagrasib and Salirasib (a non-specific RAS inhibitor in Phase II trials) at inhibiting PDAC cell growth. Moreover, 3E showed synergy with gemcitabine, a first-line chemotherapeutic agent for PDAC, enhancing its antiproliferative effects. In vivo, systemic delivery of 3E encapsulated in single lipid nanoparticles (SLNP) delayed tumor growth in PDAC xenograft and KPC transgenic mouse models without toxicity, as shown by blood chemistry markers. Tumor analysis after 3E treatment revealed reduced intra-tumoral proliferation (Ki-67 by IHC) and increased apoptosis (TUNEL assay). These findings suggest that 3E is a promising therapeutic candidate for KRAS-driven PDAC and other cancers. Its superior efficacy over FDA-approved KRAS inhibitors and synergy with gemcitabine support its clinical potential as a novel treatment approach for this aggressive cancer. Citation Format: Pinar Atalay, Asli Ture, Nermin Kahraman, Izabela Fokt, Waldemar Priebe, Erdem Buyukbingol, Bulent Ozpolat. Identification of novel pan-KRAS mutation inhibitor shows significant efficacy in pancreatic cancer with no toxicity, offering hope beyond current therapies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6390.


Abstract: Supplementary Figure S1


Abstract: Supplementary Figure S4


Abstract 2441: The myCAF and ECM landscape in KRAS-mutated cancer: Utilizing liquid biomarkers to track KRAS-induced fibrosis and KRAS inhibitor efficacy

Abstract: Abstract Background: KRAS-driven cancer represents ∼25% of cancers, with high rates in pancreatic cancer (PDAC), colorectal cancer (CRC), and non-small cell lung cancer (NSCLC). Mutated KRAS and TGF-β signaling activate cancer-associated fibroblasts (CAFs), creating a fibrotic extracellular matrix (ECM). The fibrotic ECM promotes immunosuppression and tumor growth but is potentially reversible with KRAS inhibitors (KRASi). We have recently demonstrated that myofibroblast CAFs (myCAFs) in KRAS-mutated PDAC express high levels of collagens III, V, VIII, XI, and XII. In this study, we investigated the landscape of KRAS-induced myCAF activation and ECM change across KRAS-mutated cancers. We utilized these changes to identify biomarkers for KRASi efficacy. Methods: Patients with KRAS-mutated PDAC (n=141), CRC (n=223), or NSCLC (n=74) were evaluated for collagen expression and gene-set variation analysis (GSVA) was used for hallmark KRAS and TGF-β signaling. Correlations of KRAS and TGF-β signaling, and collagen expressions across cancers, were evaluated using Spearman correlation. Single-cell RNA-seq data from KRASi treated KPPC mice was analyzed using Seurat. CAFs were subclustered and subtypes identified from marker genes (myCAF, inflammatory CAF, and antigen-presenting CAF). CAF subtype and collagen expression were compared between KRASi and vehicle with Fisher’s exact test and Wilcoxon rank-sum test, respectively. Biomarkers for formation of collagens I (PRO-C1), III (PRO-C3), V (PRO-C5), VIII (PRO-C8), XI (PRO-C11), and XII (PRO-C12) were measured in serum from healthy controls and patients with PDAC, CRC, or NSCLC. Differences between cancer and control were evaluated by Wilcoxon rank-sum test. Results: KRAS signaling was significantly correlated with TGF-β signaling and expression of COL3A1, COL5A2, and COL8A1 across patients with KRAS-mutated PDAC, CRC, and NSCLC (R&gt;0.4, p&lt;0.05). Significantly increased expression of Col3a1, Col5a2, Col8a1, Col11a1, and Col12a1 was observed from fibroblasts in KRASi treated PDAC compared to vehicle (p&lt;0.001). Significantly fewer myCAFs in KRASi treated PDAC (p&lt;0.001) suggested reduced myCAF-derived ECM with KRAS inhibition. Serum biomarkers for formation of myCAF collagens (PRO-C3, PRO-C5, PRO-C8, PRO-C11, and PRO-C12) were significantly upregulated in patients with PDAC, CRC, and NSCLC compared to healthy controls (p&lt;0.001). Type I collagen formation was unaffected (PRO-C1, p=0.37), suggesting KRAS-specific collagen formation. Conclusion: KRAS-mutated tumors have a myCAF-enriched microenvironment with a unique collagen expression profile that is altered by KRAS inhibition. Non-invasive myCAF-derived collagen biomarkers that are elevated in serum from patients with KRAS-driven cancers, can potentially quantify KRAS signaling and track KRASi efficacy to guide clinical decision-making. Citation Format: Martin Birkmose Rasmussen, Rasmus Sund Pedersen, Nicholas Willumsen, Morten Karsdal. The myCAF and ECM landscape in KRAS-mutated cancer: Utilizing liquid biomarkers to track KRAS-induced fibrosis and KRAS inhibitor efficacy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2441.


Sotorasib – an inhibitor of KRAS p.G12c mutation in advanced non-small cell carcinoma

Cancer Research, Statistics, and Treatment

Amit Kumar Agrawal, Ramya Pragya, Amit Choudhary, Anuj Gupta, Kuraparthy Sambasivaiah, Bal Krishna Mishra, Satvik Khaddar, Akhil Kapoor

Abstract: The KRAS p.G12C mutation occurs in seen in 13% of non-small cell lung cancers (NSCLCs) and in approximately 1%–3% of colorectal and other cancers. Until the last decade, there were no approved therapies for targeting the KRAS mutation, but recently, drugs targeting the mutation have been discovered. KRAS is a small protein structurally without any deep pockets making it almost impossible to target. Furthermore, it binds in its active state with the GTP protein, with remarkably close affinity making blockage of the KRAS mutation challenging. Sotorasib is a nanomolecule that selectively and irreversibly targets the KRAS mutation. The phase 2 trial (CodeBreaK100) conducted in a total of 129 patients with advanced solid tumors harboring the KRAS p.G12C mutation showed anticancer activity in patients following multiple lines of treatment. We searched for the articles published online between 2018 and May 2021 with keywords, “KRAS mutation,” “lung cancer,” and “sotorasib.” In this review article, we have discussed the history, pharmacokinetics, dosing, important studies, toxicities, and other pertinent details of sotorasib.


Another KRAS Inhibitor Holds Its Own

Abstract: Abstract According to the KRYSTAL-1 study, the KRASG12c inhibitor adagrasib, also known as MRTX849, is largely well tolerated and shows considerable efficacy in patients with non–small cell lung cancer harboring this mutation. The drug is also active, albeit more modestly, in colorectal cancer and several other solid tumor types.


FDA Approves First KRAS Inhibitor: Sotorasib

Abstract: Abstract The FDA has approved the first KRAS-targeted therapy, sotorasib, for patients with previously treated non-small cell lung cancer with KRASG12C mutations. In a phase II trial, the drug yielded a median progression-free survival of 6.8 months in patients whose disease had advanced despite treatment with standard therapies, namely platinum-based chemotherapy and PD-1–PD-L1 inhibitors.


Comparison of KRAS Mutation Status with Clinical Parameters in Colon Adenocarcinoma

Abstract: KRAS mutations are mutually exclusive with other activating mutations on EGFR pathway. Detection of KRAS mutations associated with tumorigenesis, predicates the lack of other mutations on the same pathway and shows that the application of targeted therapy approaches which target other proteins in EGFR-MAPK pathway ineffective. In this study, frequency of KRAS mutations in colorectal cancer and relationship between KRAS mutation status and other clinical features were assessed. KRAS mutations were detected in 47,7% of the cases included in our study. We determined that 76% of the mutations were located in codon 12, 9% of the mutations were located in codon 13, 9% of the mutations were located in codon 61 and 6% of the mutations located in codon 117 or codon 146. Determination of mutation rates and association of mutations with clinical features for different populations are important for planning of the treatment strategies nationwide. In our study, we have demonstrated that KRAS mutation status and clinical features associated with KRAS mutation is in accordance with the literature. We have determined that there is statistically significant correlation between grade and KRAS mutation status.


KRAS Inhibitor Continues to Impress in NSCLC

Abstract: Abstract Evidence continues to grow that KRAS, once considered an “undruggable” target, can be targeted successfully in non–small cell lung cancer. In a phase I trial, the KRASG12C inhibitor sotorasib elicited responses in about a third of patients with the disease and was generally well tolerated.


D33E mutation reshapes active and inactive conformations of KRAS

Nicholas A. Carton, Maxim Mulligan, Brajesh Narayan, Nicolae-Viorel Buchete

Abstract: GTP-bound KRAS cycles between inactive and active conformations through coordinated rearrangements of Switch I and Switch II, yet the structural and kinetic consequences of many clinically observed mutations remain unresolved. The rare D33E substitution lies within the electrostatic network of Switch I and, despite being chemically conservative, exhibits tumorigenesis. Here, we use extensive atomistic simulations and Markov state models (MSMs) of GTP-bound KRAS to characterize how D33E reshapes its conformational landscape and kinetics, including its propensity to sample conformations associated with CRAF binding. Free energy projections along established distance-based reaction coordinates d 1 and d 2 (distances from C a atoms of residues 60 and 35 to γ phosphate) reveal that D33E stabilises switch GTP engaged conformations. Structural mapping of experimentally derived RAS structures onto the simulated landscape identifies two clusters: an inactive cluster and an active, effector binding competent cluster. MSM analysis reveals that D33E substantially enriches the fully switch engaged S1 ensemble, primarily through a sevenfold reduction in exit rates rather than accelerated entry. The negatively charged E37 side chain adopts a new conformation in low d 2 states, not observed in the wildtype, correlating with movements in E62 towards the GTP nucleotide, promoting the S 1 state. S 1 overlaps with experimentally observed effector-bound and GAP-bound complexes in the structural mapping. Within this binding-competent ensemble, D33E rigidifies both switch regions locally and reduces the frequency at which Q61 adopts the catalytic approach geometry required for GAP-stimulated hydrolysis. Together, these observations are consistent with the biochemical profile of D33E, in which enhanced effector binding coincides with substantially reduced GAP stimulated catalysis. This suggests ensemble level population redistribution as a potential contributing mechanism to the oncogenic signaling of D33E. These results shed new light on understanding how mutations modulate KRAS activity.


Targeting the Undruggable KRAS Mutation in Lung Cancer: A Review

Pharmaceutical Scientist and Molecular Biologist

Abstract: Targeting the KRAS mutation in lung cancer has proven to be a challenging yet essential task due to its intricate signaling mechanisms and high prevalence in non-small cell lung cancer (NSCLC). Despite historical setbacks, recent advancements have led to the approval of targeted therapies such as sotorasib and adagrasib, offering new hope for patients with KRAS-mutated lung cancer. Ongoing research and development efforts have introduced several promising candidates in the pipeline, showcasing promising compounds like a pan-KRAS inhibitor, which could potentially transform the treatment landscape for KRAS-mutant cancers. Artificial intelligence (AI) in accelerating drug discovery for KRAS-targeted therapies, leveraging machine learning algorithms to analyze vast amounts of data and predict the efficacy of potential drug candidates. AI- driven drug discovery is revolutionizing the field by enabling researchers to explore novel treatment strategies and develop innovative therapies for KRAS-mutant cancers. AI may be utilized to develop personalized treatment regimens, predict outcomes, and revolutionize the landscape of precision medicine for lung cancer patients with KRAS mutations. Through a combination of cutting-edge research, combination therapies, and AI-driven innovation, recent advancements illustrate profound hope in the field of targeting the historically undruggable KRAS mutation in lung cancer.


Data from The SOS1 Inhibitor BI 1701963 as Monotherapy or in Combination with Trametinib in Patients with <i>KRAS</i> Mutation-Positive Solid Tumors

Pasi A. Jänne, Melissa L. Johnson, Jin Li, Shubham Pant, Ulrich Dünzinger, Luis Ilia, Kerstin Möldner, Jae Eui Soh, Noboru Yamamoto

Abstract:
Abstract Purpose:
BI 1701963 is a small-molecule son of sevenless 1 (SOS1) inhibitor that selectively binds to SOS1, blocking the protein–protein interaction of SOS1 with guanosine diphosphate-bound rat sarcoma virus (RAS) proteins [Kirsten RAS (KRAS), Harvey RAS, and neuroblastoma RAS], thereby inhibiting the growth of RAS-dependent cancer cells.
 Patients and Methods:
Three phase I dose-escalation studies evaluated BI 1701963 as monotherapy (starting dose of 50 mg) or in combination with trametinib (starting dose of 100 mg/1 mg) in patients with KRAS mutation-positive solid tumors (NCT04111458, USA and Europe; NCT04835714, Japan; and NCT04627142, China). Primary endpoints were maximum tolerated dose (MTD) based on dose-limiting toxicities (DLT; part A NCT04111458) and the number of patients with DLT in the MTD evaluation period (part A NCT04111458 and NCT04835714) or the on-treatment period (Part B NCT04835714).
 Results:
Ninety-four patients were treated: 75 with monotherapy (50–800 mg) and 19 in combination (100 mg/1 mg; 100 mg/1.5 mg; and 200 mg/1 mg). Six monotherapy patients (8%) and six combination patients (31.6%) experienced DLT in the MTD evaluation period. All patients experienced adverse events; of note, three monotherapy patients had interstitial lung disease during the first treatment cycle, leading to death, which was considered drug-related by the investigator. In the monotherapy group, one patient (1.8%) experienced a partial response, and 12 patients (21.8%) had stable disease. In the combination group, four patients (33.3%) had stable disease, carrying different KRAS alleles.
 Conclusions:
BI 1701963 treatment demonstrated limited efficacy as monotherapy. MTD was determined to be 800-mg monotherapy (NCT04111458) and 100 mg/1 mg in combination with trametinib (NCT04111458).


A non-conserved histidine residue on KRAS drives paralog selectivity of the KRAS G12D inhibitor MRTX1133

Miles A. Keats, John J. W. Han, Yeon-Hwa Lee, Chih-Shia Lee, Ji Luo

Abstract: Abstract The discovery of small molecule inhibitors against mutant KRAS protein was a recent breakthrough in targeted therapy. Understanding the mechanism of selectivity by KRAS inhibitors and the mechanism of resistance to them can guide the future development of KRAS inhibitors. MRTX1133 was the first non-covalent inhibitor against the KRAS G12D mutant that demonstrated specificity and potency in pre-clinical tumor models. We used isogenic cell lines expressing a single RAS allele to evaluate the selectivity of this compound. We found that in addition to KRAS G12D, MRTX1133 shows significant activity against several other KRAS mutants as well as wildtype KRAS protein. In contrast, MRTX1133 exhibits no activity against both G12D and wildtype forms of HRAS and NRAS proteins. Functional analysis revealed that the selectivity of MRTX1133 towards KRAS is associated with its binding to histidine 95 on KRAS, a residue that is not conserved in HRAS and NRAS. Reciprocal mutation of amino acid 95 among the three RAS paralogs resulted in reciprocal change in their sensitivity towards MRTX1133. Thus, histidine 95 is an essential selectivity handle for MRTX1133 towards KRAS. This knowledge could aid the development of future KRAS-selective inhibitors.


Resistant KRAS(G12C) Inhibitor Subclones Exhibit Heterogeneous Alterations

Abstract: Abstract KRAS(G12C) inhibitor (G12Ci) resistance exhibits a heterogeneous pattern with no one dominant alteration.


Supplementary Table S4 from The SOS1 Inhibitor BI 1701963 as Monotherapy or in Combination with Trametinib in Patients with <i>KRAS</i> Mutation-Positive Solid Tumors

Abstract: Supplementary Table S4: Pharmacokinetic parameters of BI 1701963 monotherapy (Studies NCT04111458, NCT04835714 and NCT04627142)


Trends in Overall Survival in Lung Adenocarcinoma with EGFR Mutation, KRAS Mutation, or No Mutation

Martin Faehling, Sabine Fallscheer, Birgit Schwenk, Harald Seifarth, Jörn Sträter, Claudia Lengerke, Petros Christopoulos

Clinic of Cardiology and Pneumology, Esslingen Hospital, 73730 Esslingen, Germany

Abstract: Background: Treatment of lung adenocarcinoma has changed and now includes checkpoint inhibitors (CPIs) or, in the case of an EGFR mutation, third-generation EGFR TKI osimertinib. Few data compare the long-term overall survival (OS) of current and historic subgroups. Methods: This real-world analysis (KOMPASS study) included stage IV lung-adenocarcinoma patients with either EGFR, KRAS, or no mutation. Patients were assigned to the “current” EGFR, KRAS, or no-mutation cohort if they had mutation testing using NGS (n = 199; median date of diagnosis 2021). If they had an EGFR PCR test only, they were assigned to the “historic” EGFR or no-mutation cohort (n = 127; median date of diagnosis 2014). Results: Both the current and the historic EGFR cohorts had significantly longer OS than the respective no-mutation cohorts (HR 0.58 and 0.60, respectively). The current no-mutation and EGFR cohorts had a strong trend to longer OS than the respective historic cohorts. In the no-mutation cohorts, the improvement was due to an increase in long-term survivors (HR 0.71), whereas in the EGFR mutation cohorts, the median OS was improved without long-term survivors (HR 0.70). The KRAS cohort showed OS like the no-mutation cohort, with a plateau of long-term survivors around 20%. Conclusions: A comparison of our data with that of the phase III trials KEYNOTE-189 and FLAURA suggests that the improved outcomes are due to the use of CPIs or osimertinib. The clinical trial results are well translated into real-world clinical practice with comparable OS. KRAS patients benefit from CPI treatment like no-mutation patients.


Supplementary Figure S2 from The SOS1 Inhibitor BI 1701963 as Monotherapy or in Combination with Trametinib in Patients with <i>KRAS</i> Mutation-Positive Solid Tumors

Abstract: Supplementary Figure S2. Ki67 Proliferative Index from tumor biopsies taken at screening and at Day 16 of Cycle 1 in patients treated in NCT04111458


Abstract 2197: The AXL kinase inhibitor, TP-0903, demonstrates efficacy in preclinical models of colorectal cancer independent of KRAS mutation status

Abstract: Abstract Colorectal cancer (CRC) is the third leading cause of cancer-related mortality in the US (2017). Most patients present with stage II or III disease at diagnosis, with the 5-year survival rate between 53-89%. Survival in patients with stage IV CRC, however, is a discouraging 11%. Metastasis in CRC is linked to a mesenchymal phenotype, which is associated with chemoresistance. AXL, a receptor tyrosine kinase, promotes the mesenchymal phenotype in cancer cells and its expression is associated with drug resistance and poor outcomes. TP-0903, a clinical-stage, investigational small molecule inhibitor of AXL has been shown to reverse the mesenchymal phenotype and restore drug sensitivity in cells that no longer respond to standard agents in preclinical models. TP-0903 is hypothesized to be an active agent in CRC through reversal of the mesenchymal phenotype. In cell viability assays of CRC lines, TP-0903 treatment resulted in IC50 values ranging from 4.5 – 123 nM. Notably, cell growth inhibition by TP-0903 was independent of KRAS mutation status; the KRAS mutant HCT-116 line was the most sensitive CRC cell line tested. Mesenchymal markers, including Snail, were suppressed by 7.6-fold (mRNA) and 4.9-fold (protein) in the HCT-116 line at 500 nM. TP-0903 activity was also assessed in vivo using two KRAS mutant CRC models: HCT-116 and a patient-derived xenograft (PDX) model. In the HCT-116 xenograft model, single agent TP-0903 treatment achieved 69% tumor growth inhibition (%TGI) with an oral dosing schedule at 40 mg/kg. In a KRAS-mutant PDX model, TP-0903 achieved 44% TGI when mice were dosed at 40 mg/kg. Pharmacodynamic analyses were performed on tissues from the HCT-116 and PDX models. The ligand for AXL, GAS6, was significantly upregulated in tissues after TP-0903 treatment in both CRC in vivo models while soluble AXL and GAS6 were significantly downregulated in plasma in the PDX model. Furthermore, Axin2, a Wnt/β-catenin regulated gene, was downregulated by TP-0903 in tumor tissue from the PDX model, suggesting inhibition of the Wnt/βcatenin pathway. These data support a potential role for AXL in the promotion of the mesenchymal phenotype in CRC, and showed that AXL inhibition by TP-0903 suppressed the mesenchymal phenotype and was effective against CRC cells regardless of KRAS mutation status. These observations support further clinical investigation of TP-0903 as a potential therapeutic agent in metastatic CRC. A Phase I trial with this investigational agent is ongoing, including patients with KRAS mutant CRC (clincaltrials.gov, NCT02729298). Citation Format: Ryan Mangelson, Peter Peterson, Jason M. Foulks, Yuta Matsumura, Lars Mouritsen, Clifford J. Whatcott, David J. Bearss, Steven L. Warner. The AXL kinase inhibitor, TP-0903, demonstrates efficacy in preclinical models of colorectal cancer independent of KRAS mutation status [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2197.


The KRAS G12C mutation in NSCLC: from target to resistance

Alfredo Addeo, Giuseppe Luigi Banna, Alex Friedlaender

Abstract: Lung cancer represents the most common form of cancer accounting for 1.8 million deaths globally in 2020.The 5-year relative survival rate for lung cancer is lower than many other leading cancer types. Over the last decade the treatment for advanced and metastatic non small cell lung cancer have dramatically improved due to the development of immune checkpoint inhibitors and the identification of targetable driver mutations. Recently, potentially effective inhibitors of a hitherto untargetable oncogenic driver mutation in NSCLC, Kirsten Rat Sarcoma (KRAS) have been developed. KRAS mutations are found in 20-25% of NSCLC and represent the most frequent mutation. The mutation is almost exclusively detected in adenocarcinoma and is found among smokers 90% of the time. Along with the development of new drugs that have been showing promising activity, resistance mechanisms have begun to be clarified. The aim of this review is to unwrap the biology of KRAS in NSCLC with a specific focus on primary and secondary resistance mechanisms and their possible clinical implications.


Supplementary Figure S1 from The SOS1 Inhibitor BI 1701963 as Monotherapy or in Combination with Trametinib in Patients with <i>KRAS</i> Mutation-Positive Solid Tumors

Abstract: Supplementary Figure S1: Geometric mean drug plasma concentration-time profiles of BI 1701963 QD after single and multiple administrations.


Supplementary Table S2 from The SOS1 Inhibitor BI 1701963 as Monotherapy or in Combination with Trametinib in Patients with <i>KRAS</i> Mutation-Positive Solid Tumors

Abstract: Supplementary Table S2: Representativeness of study participants with KRAS mutation-positive solid tumors


Abstract IA19: Insight towards therapeutic susceptibility of KRAS-mutant cancers from MRTX1257, a novel KRAS G12C mutant-selective small-molecule inhibitor

Abstract: Abstract The ability to effectively target mutated KRAS has remained elusive despite decades of research. By solving a highly informative set of ligand-complexed co-crystal structures coupled with iterative structure-based drug design, substituted tetrahydropyridopyrimidines were identified as selective, covalent inhibitors of mutant KRAS G12C. MRTX1257 emerged from this chemical series as a research tool compound that demonstrates the ability to irreversibly bind to the KRAS G12C variant, trap it in the inactive GDP-bound state, and inhibit ERK1/2 phosphorylation with an IC50 value of 1 nM. Therefore, a number of studies with MRTX1257 (and other prototype inhibitors) were designed to provide insight towards the breadth of therapeutic response and the underlying molecular mechanisms of sensitivity and resistance to KRAS inhibition. The evaluation of an extended panel of KRAS G12C mutant cell lines in cell viability and survival assays indicated that the vast majority of cell lines responded at concentrations under 50 nM, but that the concentration-response range varied by two orders of magnitude (0.2-62 nM). To extend these analyses to in vivo studies, MRTX1257 demonstrated clear evidence of dose-dependent irreversible modification of KRAS G12C and inhibition of KRAS-dependent signal transduction in multiple KRAS G12C mutant tumor models. MRTX1257 was then evaluated at a fixed dose that yielded near-complete KRAS target inhibition across an additional large panel of KRAS G12C-mutant subcutaneous cell-derived and patient-derived xenografts and demonstrated broad-spectrum antitumor activity including frank tumor regressions (often exceeding 50% tumor reduction) in approximately 80% of all models evaluated (n = 23). In contrast, MRTX1257 was inactive in non-KRAS G12C-mutant cell lines in vitro and tumor models in vivo, indicating that the antitumor activity of this molecule was mediated through binding to and inhibition of the KRAS G12C variant. The antitumor response across tumor models varied in KRAS G12C positive models from complete tumor regression with no evidence of recovery after treatment cessation, to partial response, to tumor stasis. In addition, a small subset of models demonstrated rapid initial tumor regression followed by tumor stasis, suggesting that there may be mechanisms of adaptive tolerance to MRTX1257 treatment. Based on this response pattern, temporal effects on signal transduction and molecules implicated in feedback signaling were evaluated. In a temporal pattern consistent with drug tolerance kinetics, MAP kinase pathway reactivation was observed based on rebound of pERK and pS6 signaling despite continuous treatment. Also consistent with signaling rebound dynamics and drug tolerance, downregulation of dual specificity phosphatases (DUSPs), Sprouty family (SPRY), and ETVs along with decoupling of cell cycle from KRAS regulatory constraints was observed. Based on these observations, a number of combination strategies designed to co-target signaling feedback pathways were evaluated in tumor models and provided direct insight into the importance of rebound pathways and response and the practical value of rational combination approaches. Together, these data indicate the therapeutic susceptibility and broad dependence of KRAS G12C mutation-positive tumors on KRAS for tumor cell growth and survival and provide insight toward the molecular basis of response to single-agent and combinatorial therapies. Citation Format: James G. Christensen. Insight towards therapeutic susceptibility of KRAS-mutant cancers from MRTX1257, a novel KRAS G12C mutant-selective small-molecule inhibitor [abstract]. In: Proceedings of the AACR Special Conference on Targeting RAS-Driven Cancers; 2018 Dec 9-12; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2020;18(5_Suppl):Abstract nr IA19.


Emergent Sentience in Large Language Models Emergent Sentience in Large Language Models: Transformer Architecture and the Neurological Foundations of Consciousness


Comparison and Analysis of Large Language Models Based on Transformer

SCITEPRESS - Science and Technology Publications

Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence

School of Information and Management Sciences, Henan Agricultural University, Zhengzhou, China, --- Select a Country ---


Inside the Transformer: Core Architectures


Security and Risk Implications of Transformer-Based Large Language Models

Abstract: The growing adoption of Large Language Models (LLMs) across a wide range of applications has introduced new challenges in application security, complementing traditional deterministic software approaches rather than replacing them entirely. While this transition enables advanced capabilities in automated reasoning and content generation, it also exposes architectural characteristics that remain insufficiently addressed by many existing cybersecurity frameworks. A core security challenge arises from the use of natural language as a unified representational medium for both instructions and data within modern generative systems. Unlike classical computing architectures, where a strict separation exists between executable instructions and passive data, LLMs process system instructions and user inputs as undifferentiated sequences of natural-language tokens. This architectural property introduces a form of semantic ambiguity in which untrusted data may be interpreted as instructions under certain contextual conditions, leading to unintended model behaviour. This paper examines the security and risk implications of this ambiguity through architectural, operational, and legal lenses. Particular attention is given to agent-based systems and Retrieval-Augmented Generation (RAG) pipelines, where indirect prompt injection, tool abuse, and excessive model agency expand the attack surface beyond purely digital environments into physical systems and complex organizational supply chains. The analysis highlights the limitations of existing security assumptions and motivates the need for systemic resilience strategies tailored to LLM-driven systems.


Transformative Role of Transformer Models in Healthcare

PG & Research Department of Computer Science, Dwaraka Doss Goverdhan Doss Vaishnav College, India

Abstract: Artificial Intelligence (AI) is a field of computer science that enables machines to learn, interpret, and make decisions like humans. In healthcare, AI is driving transformation by introducing systems that simulate human behavior and reasoning. The sector produces vast amounts of structured and unstructured data. AI tools can process this information, integrate data from various sources, and identify patterns to support predictive decision-making. Machine Learning (ML), a subset of AI, and Deep Learning (DL), a subset of ML, play a key role in this advancement. Large Language Models (LLMs), part of DL, help machines analyze and generate human-like language. These models use Transformer architectures, known for capturing long-range dependencies in text. This chapter focuses on transformer-based models, especially BERT (Bidirectional Encoder Representations from Transformers), an encoder-only model. It also includes a practical application of BERT for detecting suicidal ideation in Reddit posts, highlighting its value in clinical sentiment analysis.


Abstract: The growing adoption of Large Language Models (LLMs) across a wide range of applications has introduced new challenges in application security, complementing traditional deterministic software approaches rather than replacing them entirely. While this transition enables advanced capabilities in automated reasoning and content generation, it also exposes architectural characteristics that remain insufficiently addressed by many existing cybersecurity frameworks.A core security challenge arises from the use of natural language as a unified representational medium for both instructions and data within modern generative systems. Unlike classical computing architectures, where a strict separation exists between executable instructions and passive data, LLMs process system instructions and user inputs as undifferentiated sequences of natural-language tokens. This architectural property introduces a form of semantic ambiguity in which untrusted data may be interpreted as instructions under certain contextual conditions, leading to unintended model behaviour.This paper examines the security and risk implications of this ambiguity through architectural, operational, and legal lenses. Particular attention is given to agent-based systems and Retrieval-Augmented Generation (RAG) pipelines, where indirect prompt injection, tool abuse, and excessive model agency expand the attack surface beyond purely digital environments into physical systems and complex organizational supply chains. The analysis highlights the limitations of existing security assumptions and motivates the need for systemic resilience strategies tailored to LLM-driven systems.


AI Content Self-Detection for Transformer-based large Language Models

Abstract: Abstract The usage of generative artificial intelligence (AI)tools based on large language models, including ChatGPT, Bard, and Claude, for text generation, has many exciting applications with the potential for phenomenal productivity gains. One issue is authorship attribution when using AI tools. This is especially important in an academic setting where the inappropriate use regenerative AI tools may hinder student learning or stifle research by, creating a large amount of automatically generated derivative work. Existing plagiarism detection systems can trace the source of submitted text but are not yet equipped with methods to accurately detect AI-generated text. This paper introduces the dea of direct origin detection and evaluates whether generative AI systems can recognize their output and distinguish it from human-written texts. We argue why current transformer-based models may be able to self-detect their own generated text and perform a small empirical study using zero-shot learning to investigate if that is the case. Results reveal varying capabilities of AI systems to identify their generated text. Google’s Bard model exhibits the largest capability of self-detection with an accuracy of 94%, followed by OpenAI’s ChatGPT with 83%.On the other hand, Anthropic’s Claude model seems to be not able to self-detect.


Mathematical Foundations of Hallucination in Transformer-Based Large Language Models for Improvisation

Institute of Electrical and Electronics Engineers (IEEE)

Ahmet Gundogmusler, Fatma Bayindiroglu, Mustafa Karakucukoglu

Abstract: Transformer architectures have revolutionized natural language processing through their ability to handle longrange dependencies and generate contextually coherent text. Despite their impressive capabilities, these models frequently produce outputs that are not directly grounded in the input data, a phenomenon known as hallucination. The paper posits that hallucination is not merely a flaw but a mathematical necessity for enabling improvisation within transformer-based large language models. Through rigorous theoretical analysis, the relationship between hallucination and improvisation is explored, demonstrating that the probabilistic nature of language generation inherently leads to outputs extending beyond the training data. The proofs and lemmas presented substantiate the claim that hallucinations are essential for the model's ability to generate novel and creative responses. The research further delves into the implications of this necessity, suggesting a reevaluation of performance metrics and training strategies to balance accuracy with creativity. The findings highlight the intricate dynamics between hallucination and improvisation, providing foundational insights into the design and evaluation of transformer-based models.


Abstract: Transformer architectures have revolutionized natural language processing through their ability to handle longrange dependencies and generate contextually coherent text. Despite their impressive capabilities, these models frequently produce outputs that are not directly grounded in the input data, a phenomenon known as hallucination. The paper posits that hallucination is not merely a flaw but a mathematical necessity for enabling improvisation within transformer-based large language models. Through rigorous theoretical analysis, the relationship between hallucination and improvisation is explored, demonstrating that the probabilistic nature of language generation inherently leads to outputs extending beyond the training data. The proofs and lemmas presented substantiate the claim that hallucinations are essential for the model’s ability to generate novel and creative responses. The research further delves into the implications of this necessity, suggesting a reevaluation of performance metrics and training strategies to balance accuracy with creativity. The findings highlight the intricate dynamics between hallucination and improvisation, providing foundational insights into the design and evaluation of transformer-based models.


A Systematic Review on Optimization Approaches for Transformer and Large Language Models

Atefeh Hemmati, Fatemeh Bazikar, Amir Masoud Rahmani, Hossein Moosaei

Department of Computer Engineering, Science and Research Branch, Islamic Azad University

Abstract: Large language models (LLMs) are deep learning (DL) algorithms that can perform a range of natural language processing (NLP) tasks. LLMs use transformer models trained using large datasets and have revolutionized NLP tasks. However, their large-scale implementation poses significant computational resources, efficiency, and scalability challenges. This systematic literature review (SLR) examines optimization techniques applied to transformer and LLM models from 2020 to 2024. We analyzed 32 papers to provide an overview of optimization methods and highlight their impact on model performance and computational efficiency. Additionally, we examine the evolution of these techniques, highlight implementation challenges, and propose future research directions for optimization. In addition, we discuss the evaluation factors, environments, and datasets commonly used to evaluate optimization techniques and explore the applications of LLMs in real-world scenarios. Finally, we emphasize the critical role of optimization in improving the performance, reducing costs, and enhancing the scalability of transformer models and LLMs and provide insights into emerging trends and opportunities for future research in this area.


Investigating the Predominance of Large Language Models in Low-Resource Bangla Language Over Transformer Models for Hate Speech Detection: A Comparative Analysis

Fatema Tuj Johora Faria, Laith H. Baniata, Sangwoo Kang

Abstract: The rise of abusive language on social media is a significant threat to mental health and 1 social cohesion. For Bengali speakers, the need for effective detection is critical. However, current 2 methods fall short in addressing the massive volume of content. Improved techniques are urgently 3 needed to combat online hate speech in Bengali. Traditional machine learning techniques, while 4 useful, often require large, linguistically diverse datasets to train models effectively. This paper 5 addresses the urgent need for improved hate speech detection methods in Bengali, aiming to fill the 6 existing research gap. Contextual understanding is crucial in differentiating between harmful speech 7 and benign expressions. Large language models (LLMs) have shown state-of-the-art performance in 8 various natural language tasks due to their extensive training on vast amounts of data. We explore the 9 application of LLMs, specifically GPT-3.5 Turbo and Gemini 1.5 Pro, for Bengali hate speech detection 10 using Zero-Shot and Few-Shot Learning approaches. Unlike conventional methods, Zero-Shot 11 Learning identifies hate speech without task-specific training data, making it highly adaptable to new 12 datasets and languages. Few-Shot Learning, on the other hand, requires minimal labeled examples, 13 allowing for efficient model training with limited resources. Our experimental results show that 14 LLMs outperform traditional approaches. In this study, we evaluated GPT-3.5 Turbo and Gemini 1.5 15 Pro on multiple datasets. To further enhance our study, we considered the distribution of comments 16 in different datasets and the challenge of class imbalance, which can affect model performance. The 17 BD-SHS dataset consists of 35,197 comments in the training set, 7,542 in the validation set, and 7,542 18 in the test set. The Bengali Hate Speech Dataset v1.0 &amp; v2.0 includes comments distributed across 19 various hate categories: personal hate (629), political hate (1,771), religious hate (502), geopolitical hate 20 (1,179), and gender abusive hate (316). The Bengali Hate Dataset comprises 7,500 non-hate and 7,500 21 hate comments. GPT-3.5 Turbo achieved impressive results with 97.33%, 98.42%, and 98.53% accuracy. 22 In contrast, Gemini 1.5 Pro showed lower performance across all datasets. Specifically, GPT-3.5 Turbo 23 excelled with significantly higher accuracy compared to Gemini 1.5 Pro. These outcomes highlight a 24 6.28% increase in accuracy compared to traditional methods, which achieved 92.25%. Our research 25 contributes to the growing body of literature on LLM applications in natural language processing, 26 particularly in the context of low-resource languages.


Contextual Pathway Reconfiguration for Efficient Multimodal Understanding in Transformer-based Large Language Models

Michael Kobrun, Jacob Fitzwilliam, Isabella Montmorency, Alexander Krajewski, Edward Sandringham

Abstract: Contextual pathway reconfiguration introduces a dynamic approach to enhancing the performance and adaptability of transformer-based architectures in multimodal settings. By incorporating hierarchical gating mechanisms and auxiliary embedding layers, the proposed framework efficiently prioritizes and synthesizes information across diverse input modalities, addressing long-standing challenges in feature alignment and integration. Extensive experiments demonstrated consistent improvements in performance metrics across a variety of tasks, including image captioning and video question answering, showcasing the model's ability to generalize effectively while maintaining computational efficiency. The reinforcement learning-driven pathway evaluation mechanism emerged as a critical component, enabling real-time adjustments to computational pathways based on contextual input demands. Comparisons with traditional transformer methodologies highlighted the modular design's superiority in both scalability and robustness, particularly in handling noise-prone and unbalanced datasets. An in-depth analysis of latency and throughput further demonstrated the model's potential for deployment in high-demand, real-time applications. Error analysis revealed specific cases of improvement in cross-modal reasoning while identifying areas where input noise posed residual challenges. Ablation studies validated the significance of individual architectural components, confirming their contribution to overall model performance. The novel approach to dynamically reconfiguring attention mechanisms has broad implications for advancing multimodal applications, offering a versatile solution to integrate textual, visual, and auditory data. Additionally, energy efficiency assessments demonstrated the model's practicality for both training and inference scenarios, enhancing its viability for large-scale use. The findings present a compelling case for redefining multimodal transformer architectures to balance precision, efficiency, and adaptability, paving the way for more robust and versatile implementations.


Large Language Models


Automating Sarcasm Identification from App Reviews Using Transformer-based Language Models

2025 3rd International Conference on Foundation and Large Language Models (FLLM)

Khubaib Amjad Alam, Bisma Shahid, Muhammad Haroon, Qaisar Shafi, Momina Kamal Cheema

Al Ain University,College of Engineering,United Arab Emirates


Transformer Block and Full Transformer Model—It’s Time to Put the Puzzle Together

Building Large Language Models from Scratch


Semantic Layer Reconstruction in Large Language Models Using Multi-Level Transformer Feedback Mechanisms

Charles Welling, Beatrix Longborough, Barnaby Winterbourne, Isabella Eisenhardt

Abstract: Semantic Layer Reconstruction introduces a transformative mechanism for refining the interactions between transformer layers, leveraging recursive feedback to enhance semantic coherence across linguistic tasks. The framework incorporates a multi-level feedback mechanism that recalibrates token embeddings through bidirectional propagation of semantic cues, addressing challenges such as semantic drift and alignment inefficiencies. By dynamically integrating higher-layer abstractions into earlier computational processes, the approach fosters a more coherent synthesis of token-level and contextual representations. Experimental evaluations reveal significant improvements in perplexity, BLEU scores, and semantic similarity indices, highlighting the framework's ability to address complex linguistic phenomena. Layer-wise analyses underscore the critical contributions of middle layers in achieving optimal semantic alignment, while attention-weight distributions demonstrate a more balanced contextual focus. The recursive feedback mechanism also proved instrumental in capturing long-range dependencies, showcasing its utility in tasks requiring deep contextual understanding. Comparative configurations further validated the adaptability of the mechanism, with dynamic recursive intervals outperforming static approaches across all metrics. Noise-resilience experiments highlighted the model's robustness, while visualizations of semantic drift reduction provided compelling evidence of improved coherence. The integration of the mechanism into existing transformer architectures maintained computational efficiency through optimizations such as sparse attention and modularity. The study bridges theoretical innovations with empirical validation, offering a scalable and adaptable solution for advancing the semantic capabilities of transformer-based architectures. The findings provide a foundation for redefining the paradigms of contextual representation and layer-wise interaction in contemporary language modeling.


Enhancing Text Summarization: Evaluating Transformer-Based Models and the Role of Large Language Models like ChatGPT

2023 4th International Informatics and Software Engineering Conference (IISEC)


Contextual Residual Inversion in Transformer-Based Large Language Models: A Reversibility Paradigm for Studying Representational Fidelity

Daniel Arthev, Richard Abercrombie, Clive Fenwick

Abstract: Contextual Residual Inversion introduces a theoretical and computational approach for estimating partial reversibility of internal residual streams in transformer-based LLMs through non-invasive linear inversion operators applied to frozen inference passes. The method defines invertibility over structured prompt families by quantifying layer-wise reconstruction fidelity using projection-constrained residual approximations. Inversion accuracy is examined under varying lexical entropy, token classes, and layer depth, revealing substantial directional drift and energy compression patterns in deeper layers. Semantic stability of inverted representations is further analyzed through KL divergence and centroid shift in token distributions, demonstrating mild correlation between residual fidelity and generative coherence. Residual operators trained on specific prompt clusters show degradation when generalized across structurally divergent prompts, suggesting sensitivity to latent representation regimes not apparent from surface form alone. Metrics including cosine similarity, semantic drift score, and re-alignment ratio provide modest insight into the underlying activation structure governing token-wise representation. Experimental data is drawn from controlled synthetic prompts using a sparse Mixture-of-Experts LLM architecture, enabling reproducible assessments of inversion fragility and directional bias. The findings offer an interpretable, layer-sensitive perspective on the reversibility and geometric constraints of contextual encoding in high-capacity generative systems.


Dynamic Probabilistic Contextual Realignment in Transformer-Based Large Language Models for Semantic Consistency

Yukito Odorico, Kousuke Hayashi, Isamu Kiryu, Kenta Yamagata, Daiki Masuda, Shohei Shinohara

Abstract: The rapid evolution of artificial intelligence has led to the development of sophisticated language models capable of generating human-like text. However, maintaining semantic consistency throughout extended text generation remains a significant challenge. Introducing Dynamic Probabilistic Contextual Realignment (DPCR), a novel methodology that dynamically adjusts contextual embeddings through probabilistic realignment, addresses this issue by mitigating semantic drift. The integration of DPCR into transformer-based large language models (LLMs) has demonstrated substantial improvements in semantic coherence and contextual relevance. Quantitative analyses reveal enhanced performance metrics, while qualitative assessments highlight more coherent and contextually appropriate text generation. The findings demonstrate the potential of DPCR to advance LLM development and applications across various domains, emphasizing the importance of dynamic context management in achieving semantic consistency.


Enhancing Named Entity Recognition in Arabic: Leveraging Transformer-Based Models and Open Data Sources

2024 2nd International Conference on Foundation and Large Language Models (FLLM)

Laboratoire de Recherche LYRIDS,Ecole d’ingénieurs Campus de Paris (ECE),Paris,France


Conversational Topic Recommendation in Counseling and Psychotherapy with Decision Transformer and Large Language Models

Association for Computational Linguistics

Proceedings of the 6th Clinical Natural Language Processing Workshop

Aylin Gunal, Baihan Lin, Djallel Bouneffouf


Advanced Transformer Architectures


Introduction to Large Language Models with OpenAI


Unpacking the Transformer Architecture

The Practical Guide to Large Language Models


Context-Aware Transformer Optimization for Improved Generalization in Large Language Models

2026 IEEE International Conference for Convergence in Computing Technology (I3CTCON)

Webster University,Independent Security Researcher,MO,63119


Quantitative Models with Qualitative Scenarios: Simulation with Transformer and Large Language Models


Hierarchical Classification of Arabic Legal Cases Using Transformer Architectures and Large Language Models

Nourah Alangari, Nouf Alshenaifi, Huda Almuzaini

Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia

Abstract: Automated classification of Arabic legal texts presents unique challenges stemming from the formal register of judicial language, domain-specific Sharī‘a terminology, and the severe class imbalance inherent in hierarchical legal taxonomies. This paper addresses these challenges through a systematic investigation of hierarchical multi-class classification applied to a dataset of 1146 Arabic judicial cases sourced from the Saudi Ministry of Justice open data portal. Cases are annotated at two hierarchical levels: a binary main-class distinction between criminal and civil cases (L1), and a fine-grained sub-category classification across 63 legal topics (L2), exhibiting a class imbalance ratio of 43:1. We evaluate three methodological families under four classification architectures. For encoder-only models, we fine-tune AraBERT, CAMeL-BERT, MARBERTv2, and XLM-RoBERTa under Flat (Bottom-Up), Hierarchical Multi-Task, Two-Stage, and Hierarchy-Aware configurations. For encoder–decoder models, we adapt AraT5v2 under the same four architectures. Finally, we conduct a prompt-based evaluation of GPT-4o under zero-shot and targeted few-shot settings. The experimental results demonstrate that the Hierarchy-Aware architecture consistently achieves the strongest fine-grained classification performance across both model families. AraBERTv2 with Hierarchy-Aware training achieves the best overall L2 accuracy of 83.04% and a Macro-F1 of 76.07%, while the Single Multi-Task configuration achieves the highest L1 accuracy of 99.57%. GPT-4o under 5-shot prompting achieves 99.13% L1 accuracy and a competitive L2 Macro-F1 of 74.30% without task-specific fine-tuning, though supervised models maintain stronger overall fine-grained performance. These findings highlight the importance of explicit hierarchical supervision and domain-adapted pre-training for Arabic legal text classification, and establish strong baselines for future research in this underexplored domain.


Introduction to Large Language Models


Artificial Intelligence-Powered Large Language Transformer Models for Opioid Abuse and Social Determinants of Health Detection for the Underserved Population

Proceedings of the 13th International Conference on Data Science, Technology and Applications

Don Roosan, Yanting Wu, Jay Chok, Christopher Sanine, Tiffany Khou, Yawen Li, Hasiba Khan

Western University of Health Sciences, College of Pharmacy, 309 E 2nd Street, Pomona, CA, U.S.A.


Seamlessly Integrating Tree-Based Positional Embeddings into Transformer Models for Source Code Representation

Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025)


In the Engine Room: Transformer Models

Synthesis Lectures on Human-Centered Informatics


What Are Large Language Models?

Introduction to Python and Large Language Models