Scientists have been working with pathologists to build an artificial intelligence tool that can predict which patients with lung cancer have a higher risk of the disease coming back after treatment. The tool was able to differentiate between cancer and immune cells, which enabled the researchers to better understand how lung cancers evolve in response to the immune system in individual patients.
Although its utility research is still in the early stages, the approach could speed up how doctors predict which patients are more likely to have their cancer return and can help them tailor their treatment plans.
A tool to quantify immune “hot and cold regions”
The AI tool was developed by researchers at The Institute of Cancer Research in collaboration with scientists at the University College London Cancer Institute and the Francis Crick Institute.
The tool was trained by pathologists to pick out immune cells from the cancer cells. This maps out areas in tumours where the number of immune cells was higher compared to cancer cells, which allowed the team to identify tumour areas packed with immune cells, “hot regions”, and other parts which were devoid of them – “cold regions”.
Following this, the researchers found that patients with a higher number of cold regions had a higher risk of relapse. The paper was published in Nature yesterday and is part of the TRACERx (Tracking Cancer Evolution through therapy Rx) lung study, funded by Cancer Research UK.
The team, led by Dr YinYin Yuan, combined the AI pathology image-mapping tool with next generation sequencing (NGS) and compared the extent to which the patient’s tumour genetic makeup differed when comparing immune hot or cold areas within the same tumours, giving clues to how they had changed in response to evolutionary pressures.
The work revealed that cancer cells in the immune “cold” regions may have evolved more recently than those in the immune hot regions. The researchers suggested that the cold areas may have developed a cloaking mechanism under evolutionary pressure to hide from the body’s natural immune defences.
The tool can assess how many regions in the tumour that are being “hidden” from the immune system which is crucial as these are associated with relapse. Dr Yinyin Yuan, one of the authors and team leaders at ICR said that they “applied artificial intelligence with genetic data and pathology images to create the tool, that could in the future help pick out patients who are at highest risk of their cancer coming back” and that their research has given fresh insights into why some lung cancers are difficult to treat.
Wataalamu wa magonjwa ya mapafu wamekuwa wakifanya kazi na wataalamu wa magonjwa ya magonjwa ya magonjwa ya magonjwa ya magonjwa ya magonjwa ya magonjwa ya magonjwa ya magonjwa ya magonjwa ya magonjwa ya magonjwa ya magonjwa ya magonjwa ya magonjwa ya magonjwa ya magonjwa ya magonjwa ya magonjwa ya magonjwa ya magonjwa ya magonjwa ya magonjwa ya magonjwa ya magonjwa. Kwa hivyo, chombo hicho kiliweza kutofautisha kati ya seli za saratani na seli za kinga, na hivyo kuwawezesha watafiti kuelewa vizuri jinsi saratani ya mapafu inavyoendelea kujibu mfumo wa kinga kwa wagonjwa binafsi. Ingawa utafiti wake wa matumizi bado uko katika hatua za mapema, mbinu hiyo inaweza kuharakisha jinsi madaktari wanavyotabiri ni wagonjwa gani wana uwezekano mkubwa wa kurudi kwa saratani na inaweza kuwasaidia kurekebisha mipango yao ya matibabu. "Kama ""kiwango cha juu cha kinga"" kwa ajili ya utafiti wa saratani, AI imebuniwa na watafiti wa Taasisi ya Utafiti wa Saratani, kwa kushirikiana na wanasayansi wa Chuo Kikuu cha Chuo Kikuu cha London, Taasisi ya Saratani na Taasisi ya Francis Crick." Kifaa hicho kilizoezwa na wataalamu wa magonjwa ya magonjwa kuchagua chembe za kinga kutoka kwa chembe za kansa. "Hii inaonyesha maeneo katika uvimbe ambapo idadi ya seli za kinga ilikuwa ya juu ikilinganishwa na seli za saratani, ambayo iliruhusu timu kutambua maeneo ya uvimbe yaliyojaa seli za kinga, ""mikoa moto"" na sehemu nyingine ambazo hazikuwa nazo." Baada ya hapo, watafiti walipata kwamba wagonjwa walio na idadi kubwa ya maeneo baridi walikuwa na hatari kubwa ya kurudi nyuma. Utafiti huo ulichapishwa katika jarida la Nature na ni sehemu ya utafiti wa TRACERX (Tracking Cancer Evolution through Therapy Rx) uliofadhiliwa na Cancer Research UK. Timu iliyoongozwa na Dk Yin Yin Yuan ilichanganya chombo cha AI cha picha ya pathology na mfululizo wa kizazi kijacho (NGS) na kulinganisha kiwango ambacho muundo wa maumbile ya ugonjwa wa mgonjwa ulitofautiana wakati wa kulinganisha maeneo ya joto au baridi ya kinga ndani ya uvimbe huo huo, ikitoa dalili za jinsi walivyobadilika kwa majibu ya shinikizo la mageuzi. Kazi hiyo ilifunua kwamba chembe za kansa katika maeneo ya kinga "ya baridi" huenda zilikuwa zimebadilika hivi karibuni kuliko zile katika maeneo ya kinga yenye joto. Watafiti wanasema kuwa maeneo ya baridi yanaweza kuwa na utaratibu wa kuficha chini ya shinikizo la mageuzi ili kujificha kutoka kwa ulinzi wa asili wa kinga ya mwili. "Kifaa hiki kinaweza kutathmini ni maeneo gani katika uvimbe ambayo yanafichwa kutoka kwa mfumo wa kinga, ambayo ni muhimu kwani haya yanahusishwa na kurudia.""" "Daktari Yinyin Yuan, mmoja wa waandishi na viongozi wa timu katika ICR, alisema kwamba ""waliitumia akili bandia na data ya maumbile na picha za patholojia kuunda chombo ambacho kinaweza kusaidia kuchagua wagonjwa ambao wana hatari kubwa ya saratani yao kurudi"" na kwamba utafiti wao umetoa ufahamu mpya juu ya kwa nini saratani za mapafu ni ngumu kutibu."