Caris Life Sciences Publishes Study Showing AI Signature-positive Breast Cancer Patients Live Almost Twice as Long as AI-negative Patients When Treated with a Checkpoint Inhibitor
Caris Life Sciences (NASDAQ: CAI) has published a groundbreaking study in Communications Medicine demonstrating the superior effectiveness of their AI-based image analysis model in predicting cancer biomarkers and patient survival. The study, analyzing data from over 35,000 patients, showed remarkable results in breast cancer patients treated with pembrolizumab, achieving a hazard ratio of 0.511 (p<0.001) compared to 0.882 (p>0.1) for traditional PD-L1 testing methods.
The AI model's analysis of H&E images demonstrated nearly doubled overall survival for AI signature-positive breast cancer patients treated with checkpoint inhibitors. In colorectal cancer, the AI successfully predicted mismatch repair deficiency and microsatellite instability comparable to traditional scoring methods.
Caris Life Sciences (NASDAQ: CAI) ha pubblicato uno studio innovativo su Communications Medicine che dimostra l'efficacia superiore del loro modello di analisi delle immagini basato su intelligenza artificiale nella previsione dei biomarcatori tumorali e della sopravvivenza dei pazienti. Lo studio, che ha analizzato i dati di oltre 35.000 pazienti, ha evidenziato risultati straordinari nei pazienti con tumore al seno trattati con pembrolizumab, raggiungendo un hazard ratio di 0,511 (p<0,001) rispetto a 0,882 (p>0,1) per i metodi tradizionali di test PD-L1.
L'analisi del modello AI sulle immagini H&E ha mostrato un raddoppio quasi completo della sopravvivenza complessiva per i pazienti con tumore al seno positivi alla firma AI trattati con inibitori del checkpoint. Nel cancro colorettale, l'AI ha previsto con successo la carenza di riparazione degli errori di appaiamento e l'instabilit脿 dei microsatelliti in modo comparabile ai metodi di valutazione tradizionali.
Caris Life Sciences (NASDAQ: CAI) ha publicado un estudio pionero en Communications Medicine que demuestra la efectividad superior de su modelo de an谩lisis de im谩genes basado en inteligencia artificial para predecir biomarcadores de c谩ncer y la supervivencia de los pacientes. El estudio, que analiz贸 datos de m谩s de 35,000 pacientes, mostr贸 resultados notables en pacientes con c谩ncer de mama tratados con pembrolizumab, alcanzando una raz贸n de riesgo de 0.511 (p<0.001) en comparaci贸n con 0.882 (p>0.1) para los m茅todos tradicionales de prueba PD-L1.
El an谩lisis del modelo de IA sobre im谩genes H&E demostr贸 casi el doble de supervivencia general para pacientes con c谩ncer de mama positivos a la firma de IA tratados con inhibidores de puntos de control. En el c谩ncer colorrectal, la IA predijo con 茅xito la deficiencia de reparaci贸n por desajuste y la inestabilidad de microsat茅lites de manera comparable a los m茅todos tradicionales de puntuaci贸n.
Caris Life Sciences (NASDAQ: CAI)電� Communications Medicine鞐� 順侅嫚鞝侅澑 鞐瓣惮毳� 氚滍憸頃橃棳 鞎� 氚旍澊鞓る旎れ檧 頇橃瀽 靸濎〈鞙� 鞓堨浮鞐� 鞛堨柎 AI 旮半皹 鞚措歆� 攵勳劃 氇嵏鞚� 鞖办垬頃� 須臣毳� 鞛呾頄堨姷雼堧嫟. 35,000氇� 鞚挫儊鞚� 頇橃瀽 雿办澊韯半ゼ 攵勳劃頃� 鞚� 鞐瓣惮電� 韼笇搿るΜ欤茧鞙茧 旃橂氚涭潃 鞙犽癌鞎� 頇橃瀽鞐愳劀 旮办〈 PD-L1 瓴�靷� 氚╇矔 雽�牍� 0.511 (p<0.001)鞚� 鞙勴棙牍勲ゼ 雼劚頃橂┌ 霙办柎雮� 瓴瓣臣毳� 氤挫榾鞀惦媹雼� (旮办〈 氚╇矔鞚� 0.882, p>0.1).
AI 氇嵏鞚� H&E 鞚措歆�毳� 攵勳劃頃� 瓴瓣臣, 觳错伂韽澑韸� 鞏奠牅鞝滊 旃橂氚涭潃 AI 鞁滉犯雼堨矘 鞏戩劚 鞙犽癌鞎� 頇橃瀽鞚� 鞝勳泊 靸濎〈鞙澊 瓯办潣 霊� 氚� 臧�旯岇澊 歃濌皜頄堨姷雼堧嫟. 雽�鞛レ晹鞐愳劀電� AI臧� 旮办〈 韽夑皜 氚╇矔瓿� 鞙犾偓頃橁矊 攵堨澕旃� 氤店惮 瓴绊暔瓿� 毵堨澊韥靸堩媭霛检澊韸� 攵堨晥鞝曥潉 靹标车鞝侅溂搿� 鞓堨浮頄堨姷雼堧嫟.
Caris Life Sciences (NASDAQ: CAI) a publi茅 une 茅tude r茅volutionnaire dans Communications Medicine d茅montrant l'efficacit茅 sup茅rieure de leur mod猫le d'analyse d'images bas茅 sur l'intelligence artificielle pour pr茅dire les biomarqueurs du cancer et la survie des patients. L'茅tude, analysant les donn茅es de plus de 35 000 patients, a montr茅 des r茅sultats remarquables chez les patientes atteintes de cancer du sein trait茅es par pembrolizumab, avec un rapport de risque de 0,511 (p<0,001) compar茅 脿 0,882 (p>0,1) pour les m茅thodes traditionnelles de test PD-L1.
L'analyse du mod猫le IA des images H&E a d茅montr茅 une survie globale presque doubl茅e chez les patientes atteintes d'un cancer du sein positives 脿 la signature IA trait茅es par inhibiteurs de points de contr么le. Dans le cancer colorectal, l'IA a pr茅dit avec succ猫s la d茅ficience de r茅paration des m茅sappariements et l'instabilit茅 des microsatellites, de mani猫re comparable aux m茅thodes traditionnelles d'茅valuation.
Caris Life Sciences (NASDAQ: CAI) hat eine bahnbrechende Studie in Communications Medicine ver枚ffentlicht, die die 眉berlegene Wirksamkeit ihres KI-basierten Bildanalysemodells bei der Vorhersage von Krebs-Biomarkern und dem 脺berleben von Patienten zeigt. Die Studie, die Daten von 眉ber 35.000 Patienten analysierte, zeigte bemerkenswerte Ergebnisse bei Brustkrebspatienten, die mit Pembrolizumab behandelt wurden, mit einem Hazard Ratio von 0,511 (p<0,001) im Vergleich zu 0,882 (p>0,1) bei herk枚mmlichen PD-L1-Testmethoden.
Die Analyse des KI-Modells von H&E-Bildern zeigte nahezu eine Verdopplung des Gesamt眉berlebens bei KI-Signatur-positiven Brustkrebspatienten, die mit Checkpoint-Inhibitoren behandelt wurden. Beim kolorektalen Krebs sagte die KI erfolgreich die Mismatch-Reparaturdefizienz und Mikrosatelliteninstabilit盲t 盲hnlich wie traditionelle Bewertungsmethoden vorher.
- AI model demonstrates almost double survival rate for breast cancer patients compared to traditional testing methods
- Successfully analyzed large-scale data from over 35,000 patients
- AI technology shows equivalent accuracy to traditional scoring in colorectal cancer detection
- Potential to improve efficiency and accuracy of cancer patient evaluation
- Technology still requires further clinical validation and regulatory approvals
- Implementation may face reimbursement challenges from third-party payers
Insights
Caris' AI model demonstrates superior prediction of immunotherapy response in cancer patients, potentially revolutionizing treatment selection and doubling survival rates.
This publication in Communications Medicine represents a significant advancement in precision oncology. Caris Life Sciences has developed an AI model that analyzes standard H&E stained tissue images to predict biomarker status with greater accuracy than conventional companion diagnostic methods. The most striking finding is in breast cancer patients treated with pembrolizumab (an immune checkpoint inhibitor), where the AI-positive signature correlated with nearly doubled overall survival compared to traditional PD-L1 testing methods.
The hazard ratio of 0.511 (p<0.001) for the AI model versus 0.882 (p>0.1) for traditional PD-L1 immunohistochemistry indicates substantially improved predictive power. This is particularly important because conventional PD-L1 testing has known limitations, especially near the critical 1% expression threshold that determines treatment eligibility. By more accurately identifying responsive patients, this AI approach could significantly expand the population benefiting from immunotherapy while sparing non-responders from unnecessary treatment.
The study's scale is impressive, analyzing over 35,000 patient samples from Caris' clinico-genomic database. Beyond breast cancer, the AI model also demonstrated equivalent accuracy to traditional methods in predicting mismatch repair deficiency and microsatellite instability in colorectal cancer - both critical biomarkers for immunotherapy response.
This research exemplifies how AI can enhance pathologist capabilities rather than replace them, potentially streamlining the diagnostic workflow while improving accuracy. If validated in prospective studies, this approach could transform immunotherapy patient selection paradigms and represents a significant competitive advantage for Caris in the precision oncology market.
Caris Life Sciences has achieved a potentially game-changing breakthrough in AI-driven cancer diagnostics with significant market implications. Their AI model demonstrates superior predictive power compared to current gold-standard diagnostic methods, particularly for immunotherapy response in breast cancer patients.
The most commercially significant finding is the hazard ratio of 0.511 for overall survival when using their AI model to predict immunotherapy response, compared to just 0.882 with traditional methods. This translates to patients identified by Caris' AI living almost twice as long when receiving pembrolizumab - a dramatic improvement in patient selection that could drive significant adoption.
This technology addresses critical limitations in the current
For Caris, which recently went public in 2025, this publication in a prestigious Nature portfolio journal provides strong validation of their AI TechBio strategy. The study's massive scale (35,000+ patients) demonstrates Caris' data advantage and computational capabilities - key differentiators in the competitive precision medicine landscape.
While regulatory approval and clinical adoption will take time, this publication positions Caris to potentially capture significant market share in the rapidly growing precision oncology sector. The technology could become an essential component of immunotherapy patient selection, creating new revenue opportunities through both testing services and potential licensing to pharmaceutical partners developing checkpoint inhibitors.
Groundbreaking study in Nature Communications highlights AI's role in transforming immunotherapy decisions and precision oncology
For this study, Caris' AI model analyzed data from over 35,000 patients in the Caris clinico-genomic database. In breast cancer, the AI model scored PD-L1 positive phenotype status using an H&E image alone and assessed overall survival of patients treated with pembrolizumab, achieving a hazard ratio (HR) for overall survival of 0.511 (p<0.001), compared to an HR of 0.882 (p>0.1) for traditionally scored PD-L1 IHCs, a result consistent with an almost doubling of overall survival for patients treated with pembrolizumab. In colorectal cancer, AI predicted mismatch repair deficiency (MMRd) and microsatellite instability (MSI) equivalent to traditional scoring.
"Traditional PD-L1 testing can听undercall positive cases, especially near the
"This study highlights how AI can significantly improve the accuracy and efficiency of tissue sample evaluation, and down the line, this has the potential to guide immunotherapy decisions and enhance patient outcomes," said , Caris EVP and Chief Medical Officer.
The publication can be viewed in its entirety on the
About Caris Life Sciences听
Caris Life Sciences庐鈥�(Caris) is a leading, patient-centric, next-generation AI TechBio company and precision medicine pioneer that is actively developing and commercializing innovative solutions to transform healthcare. Through comprehensive molecular profiling (Whole Exome and Whole Transcriptome Sequencing) and the application of advanced AI and machine learning algorithms at scale, Caris has created the large-scale, multimodal clinico-genomic database and computing capability needed to analyze and further unravel the molecular complexity of disease. This convergence of next-generation sequencing, AI and machine learning technologies, and high-performance computing provides a differentiated platform to develop the latest generation of advanced precision medicine diagnostic solutions for early detection, diagnosis, monitoring, therapy selection and drug development.听
Caris was founded with the belief and vision that combining a vast set of consistently generated molecular information with robust data-driven insights could realize the potential of precision medicine for patients. Headquartered in
Forward Looking Statements
This press release contains forward-looking statements, within the meaning of the federal securities laws, about Caris Life Sciences and its business. All statements other than statements of historical facts contained in this press release are forward-looking statements. In some cases forward-looking statements can be identified by words such as "may," "will," "should," "would," "expect," "plan," "anticipate," "could," "intend," "target," "project," "potential," "contemplate," "believe," "estimate," "predict," "potential," "supports" or "continue" or similar expressions.听听听
You should not rely upon forward-looking statements as predictions of future events.听 Although we believe that the expectations reflected in these forward-looking statements are reasonable based on information currently available to us, we cannot guarantee that the future results, discoveries, levels of activity, performance or events and circumstances reflected in forward-looking statements will be achieved or occur. Forward-looking statements involve known and unknown risks and uncertainties, some of which are beyond our control.听 Risks and uncertainties that could cause our actual results to differ materially from those indicated or implied by the forward-looking statements in this press release include, among other things: developments in the precision oncology industry; future financial performance, results of operations or other operational results or metrics; development, validation and timing of future solutions; the rapidly evolving competitive environment in which we operate; third-party payer reimbursement and coverage decisions; our ability to protect and enhance our intellectual property; regulatory requirements, decisions or approvals (including the timing and conditions thereof) related to our solutions;听 our compliance with laws and regulations; and our ability to hire and retain key personnel as well as risks, uncertainties, and other factors described in the section titled "Risk Factors" and elsewhere in the prospectus for our initial public offering filed with the Securities and Exchange Commission on June 20, 2025, and in our other filings we make with the SEC from time to time.听 We undertake no obligation to update any forward-looking statements to reflect changes in events, circumstances or our beliefs after the date of this press release, except as required by law.听
Caris Life Sciences Media:
Corporate Communications
[email protected]
214.294.5606
View original content to download multimedia:
SOURCE Caris Life Sciences
FAQ
What are the key findings of Caris Life Sciences' (NASDAQ: CAI) AI study in breast cancer treatment?
How many patients were included in Caris' AI model analysis?
What advantages does Caris' AI model offer over traditional PD-L1 testing?
How effective is Caris' AI technology in colorectal cancer detection?
What is the potential impact of Caris' AI technology on cancer treatment?
