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<p data-start="39" data-end="225"><span style="font-family: georgia, palatino, serif; font-size: 12pt;">A groundbreaking AI tool developed by an international team of researchers is set to transform personalised cancer treatment by identifying and mapping cellular diversity within tumours.</span></p>
<p data-start="227" data-end="456"><span style="font-family: georgia, palatino, serif; font-size: 12pt;">The new tool addresses one of the most persistent challenges in oncology&mdash;tumour heterogeneity&mdash;where different types of cancer cells within the same tumour respond differently to treatment, often leading to resistance and relapse.</span></p>
<p data-start="458" data-end="816"><span style="font-family: georgia, palatino, serif; font-size: 12pt;">Named AAnet, the AI system was created through a collaboration between Australia&rsquo;s Garvan Institute of Medical Research and Yale School of Medicine in the US. Using deep learning techniques, AAnet analyzes gene activity at the single-cell level and identifies five distinct types of cells within tumours, each with unique behaviours and metastatic risks.</span></p>
<p data-start="818" data-end="1249"><span style="font-family: georgia, palatino, serif; font-size: 12pt;">This level of precision offers a significant improvement over traditional approaches that treat all cancer cells the same. According to Associate Professor Christine Chaffer from the Garvan Institute, &ldquo;Heterogeneity poses a major issue in cancer therapy. By assuming all tumour cells are identical, a single therapy may fail to eliminate subpopulations of cells that don&rsquo;t share the same vulnerabilities&mdash;allowing cancer to return.&rdquo;</span></p>
<p data-start="1251" data-end="1417"><span style="font-family: georgia, palatino, serif; font-size: 12pt;">AAnet enables doctors to pinpoint these diverse cell types and design combination treatments that target each group simultaneously, potentially preventing recurrence.</span></p>
<p data-start="1419" data-end="1672"><span style="font-family: georgia, palatino, serif; font-size: 12pt;">Yale University&rsquo;s Associate Professor Smita Krishnaswamy, who co-developed the tool, said that AAnet is the first method to break down complex tumour biology into usable cellular archetypes, opening the door for major advancements in precision medicine.</span></p>
<p data-start="1674" data-end="1946"><span style="font-family: georgia, palatino, serif; font-size: 12pt;">Validated in breast cancer studies, the technology is already considered ready for clinical implementation. Researchers plan to integrate it with conventional diagnostic tools to deliver more targeted and personalised treatments based on a tumour's unique cellular makeup.</span></p>
<p data-start="1948" data-end="2108" data-is-last-node="" data-is-only-node=""><span style="font-family: georgia, palatino, serif; font-size: 12pt;">The AI tool also shows promise for expanding into other cancers and even autoimmune diseases, according to findings published in the journal <em data-start="2089" data-end="2107">Cancer Discovery</em>.</span></p>
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<div class="pointer-events-none h-px w-px" aria-hidden="true" data-edge="true"><span style="font-family: georgia, palatino, serif; font-size: 12pt;">[Except for the headline, this article has not been edited by the Benefit News editorial team and is published directly from the IANS news feed]</span></div>

New AI detects tumour cell diversity, paving the way for precise and personalised cancer therapies

AI tool maps tumour cells for better cancer treatment

New AI detects tumour cell diversity, paving the way for precise and personalised cancer therapies

June 27, 2025 | 18:37 IST