The complex nature of the tumour microenvironment poses a great challenge for the extraction of biomarkers of immune response and immunotherapy efficacy. To overcome this challenge, a recent study used RNA-sequencing (RNA-seq) data to derive sequence-based signatures of the tumour microenvironment. The researchers used machine learning to extract mechanistic biomarkers of antitumour immune responses that effectively predict patient response to immune-checkpoint blockers.
In recent years, immunotherapy has revolutionised cancer treatment. Immune-checkpoint blockers (ICBs) boost the patient’s immune system to effectively recognise and attack cancerous cells. Patients treated with immune-based therapies have shown promising results, especially in terms of long-term patient survival and curative potential. However, a major drawback is that only a minority of patients achieve complete response. Additionally, high immunological toxicity and considerable costs per patient are limiting the use of immune-checkpoint blockers. Therefore, it is important for clinicians to identify biomarkers, sparing unnecessary and potentially harmful treatments in patients that are unlikely to respond to ICBs.
The tumour microenvironment and biomarkers for immunotherapy
Different mechanisms in the tumour microenvironment (TME) are involved in mediating the immune response and affect the efficacy of ICB therapies. The first important aspect of the TME is the cell type composition. Different types of TME cells can have a pro- or antitumour role in regulating cancer progression and response to treatment. Another important aspect is the inter- and intracellular regulation of cellular functions that are responsible for shaping the anticancer immune response. These aspects should be taken into account when providing a comprehensive description of the TME.
A holistic approach to deriving biomarkers of immune response can inform clinicians on the efficacy of ICB treatments in patients. Different emerging omics technologies have allowed researchers to take snapshots of the TME in bulk tumours, in single cells and from images of tumour slides. The combination of these tools with computational approaches has the potential to provide a complete picture of the TME, which can shed light on how complex cellular and intercellular mechanisms orchestrate the immune response.
However, these technologies are not yet widely available, and the accompanying computational tools are still in their infancy and have shown difficulty in training the models. For precision medicine to be improved, researchers urgently need different approaches to derive a comprehensive description of the TME and how it regulates the immune response in individual patients, using available patient data.
Using RNA-seq data to predict biomarkers for immunotherapy
In this study, the researchers used an approach based on RNA-seq data combined with different types of prior knowledge to derive a holistic description of the tumour microenvironment. The team searched the TME using the Bayesian efficient multiple-kernel learning model and RNA-seq data from 7,550 cancer patients across 18 solid cancers contained within The Cancer Genome Atlas (TCGA). RNA-seq datasets are publicly available, but the information on which patients respond to ICB therapy is only available for a small subset of patients and cancer types, therefore this their method aimed to overcome this data problem. The researchers chose several substitute immune responses from the same datasets to be used as an indicator of the effectiveness of ICBs. By using substitute immune responses in the training process the researchers were able to overcome the issues with applying computational models for biomarker identification in immunotherapy.
They used the machine learning model to look for associations between the derived system-based features and the immune response, estimated using 14 predictors (proxies) that were obtained from recent publications. The proxies were considered as different tasks to be predicted by their machine learning model and the researchers used multi-task learning algorithms to learn all tasks jointly. An overall description of the approach can be seen in figure 1, which was taken directly from the article by Lapuente-Santana et al.
Significantly, the researchers found that their machine learning model outperforms biomarkers currently used in the clinical setting to assess ICB treatments, demonstrating that their method could be used to successfully assess patients that would respond well to immunotherapy treatments.
This study used computational models to assess RNA-seq data, from a large dataset of patients and tumour subtypes, to predict biomarkers for immunotherapy response. Their model was shown to outperform biomarkers that are currently being used by clinicians to assess immunotherapy response, demonstrating the success and utility of their approach. Moreover, the researchers hope to utilise emerging single-cell methodologies, which will allow them to look at the TME from a different angle to provide complementary insights into intra- and extracellular interactions, which could help them to adapt their framework further.
Image credit: kjpargeter – Freepik
Asili tata ya microenvironment ya uvimbe inaweka changamoto kubwa kwa ajili ya uchimbaji wa biomarkers ya majibu ya kinga na ufanisi immunotherapy. Ili kukabiliana na changamoto hii, utafiti wa hivi karibuni ulitumia data ya RNA-sequencing (RNA-seq) kupata saini za msingi wa mfululizo wa mazingira madogo ya uvimbe. Watafiti walitumia kujifunza mashine kutoa biomarkers ya utaratibu wa majibu ya kinga ya antitumor ambayo kwa ufanisi kutabiri majibu ya mgonjwa kwa blockers ya kinga-checkpoint. Katika miaka ya hivi karibuni, tiba ya kinga imebadili kabisa matibabu ya kansa. Vizuizi vya mfumo wa kinga (ICBs) huongeza mfumo wa kinga ya mgonjwa kutambua na kushambulia seli za kansa. Matibabu ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga ya kinga. Hata hivyo, kasoro kubwa ni kwamba ni idadi ndogo tu ya wagonjwa wanaopata majibu kamili. Kwa kuongezea, sumu ya juu ya kinga na gharama kubwa kwa kila mgonjwa zinapunguza matumizi ya vizuizi vya kinga. Kwa hivyo, ni muhimu kwa madaktari kutambua biomarkers, kuokoa matibabu yasiyo ya lazima na yenye madhara kwa wagonjwa ambao hawawezi kujibu kwa ICBs. Tumour microenvironment na biomarkers kwa ajili ya immunotherapy: Utaratibu tofauti katika tumour microenvironment (TME) ni kushiriki katika kuingilia kati majibu ya kinga na kuathiri ufanisi wa matibabu ICB. Sehemu muhimu ya TME ni aina ya seli. Aina tofauti za seli za TME zinaweza kuwa na jukumu la pro- au antitumor katika kudhibiti maendeleo ya kansa na majibu ya matibabu. Sehemu nyingine muhimu ni udhibiti wa ndani na ndani ya seli ya kazi za seli ambazo zinawajibika kwa kuunda majibu ya kinga ya saratani. Mambo haya yanapaswa kuzingatiwa wakati wa kutoa maelezo ya kina ya TME. Njia ya jumla ya kupata biomarkers ya majibu ya kinga inaweza kuwa na uwezo wa kuwajulisha waganga juu ya ufanisi wa matibabu ya ICB kwa wagonjwa. Teknolojia mpya za omics zimewawezesha watafiti kuchukua picha za TME katika uvimbe wa wingi, katika seli moja na kutoka kwa picha za slides za uvimbe. Mchanganyiko wa zana hizi na mbinu za kompyuta una uwezo wa kutoa picha kamili ya TME, ambayo inaweza kutoa mwanga juu ya jinsi utaratibu tata wa seli na intercellular unavyoongoza majibu ya kinga. Hata hivyo, teknolojia hizi bado si inapatikana kwa wingi, na kuandamana zana computational ni bado katika utotoni yao na wameonyesha ugumu katika mafunzo ya mifano. Ili kuboresha dawa za usahihi, watafiti wanahitaji haraka mbinu tofauti za kupata maelezo kamili ya TME na jinsi inavyodhibiti majibu ya kinga kwa wagonjwa mmoja-mmoja, kwa kutumia data ya wagonjwa inayopatikana. Kwa kutumia data ya RNA-sequence kutabiri biomarkers kwa ajili ya immunotherapy, watafiti walitumia mbinu ya msingi ya data ya RNA-sequence pamoja na aina mbalimbali za maarifa ya awali kupata maelezo ya jumla ya microenvironment ya uvimbe. Timu hiyo ilichunguza TME kwa kutumia mfano wa kujifunza wa Bayesian na data ya RNA-seq kutoka kwa wagonjwa wa saratani 7,550 katika saratani 18 thabiti zilizomo ndani ya Atlas ya Genome ya Saratani (TCGA). "Kulingana na utafiti wa utafiti wa utafiti wa ""RNA-seq,"" data za watu wanaojibu kwa matibabu ya ICB zinapatikana tu kwa sehemu ndogo ya wagonjwa na aina za kansa, na kwa hivyo njia yao inakusudia kushinda tatizo hili la data." Watafiti walichagua majibu kadhaa ya kinga ya badala kutoka kwa seti sawa za data kutumika kama kiashiria cha ufanisi wa ICBs. Kwa kutumia majibu ya kinga ya badala katika mchakato wa mafunzo, watafiti waliweza kushinda masuala ya kutumia mifano ya kompyuta kwa utambulisho wa biomarker katika immunotherapy. Kwa mfano, utafiti wa utafiti wa mashine ulionyesha uhusiano kati ya sifa za mfumo na majibu ya kinga, kulingana na vipimo vya 14 vya utabiri vilivyopatikana katika machapisho ya hivi karibuni. "Wanafunzi walichunguza kazi za ""proxy"" na kuamua ni nini kinachohitajika kwa ajili ya kujifunza mashine, na watafiti walitumia algorithms ya kujifunza kazi nyingi kujifunza kazi zote kwa pamoja." Maelezo ya jumla ya mbinu inaweza kuonekana katika takwimu 1, ambayo ilichukuliwa moja kwa moja kutoka makala na Lapuente-Santana et al. Kwa kweli, watafiti walipata kwamba mfano wao wa kujifunza mashine unazidi biomarkers zinazotumiwa sasa katika mazingira ya kliniki kutathmini matibabu ya ICB, kuonyesha kwamba mbinu yao inaweza kutumika kwa mafanikio kutathmini wagonjwa ambao watarejea vizuri matibabu ya immunotherapy. Utafiti huu ulitumia mifano ya kompyuta kutathmini data ya RNA-seq, kutoka kwa seti kubwa ya data ya wagonjwa na aina ndogo za uvimbe, kutabiri biomarkers kwa majibu ya immunotherapy. Mfano wao ulionyesha kuwa unafanya vizuri kuliko viashiria vya kibiolojia ambavyo kwa sasa vinatumiwa na madaktari kutathmini majibu ya immunotherapy, ikionyesha mafanikio na manufaa ya mbinu yao. Zaidi ya hayo, watafiti wanatarajia kutumia mbinu za kiini kimoja zinazojitokeza, ambazo zitawaruhusu kuangalia TME kutoka pembe tofauti ili kutoa ufahamu wa ziada juu ya mwingiliano wa ndani na nje ya seli, ambayo inaweza kuwasaidia kurekebisha mfumo wao zaidi. Picha: KJ Pargeter