The outcome in patients with cancer is determined by a combination of numerous factors. The personalized treatment of a cancer patient depends on the site of origin of the cancer, the morphologic type, and prognostic factors specific to the particular patient and cancer. While this blog post focuses on prognostic factors in colorectal cancer, it’s helpful to know about the immense influence prognostic factors have on cancer outcome in general (Gospodarowicz & O’sullivan, 2003). According to the TNM project committee, prognostic factors are classified into three groups.
- Tumor-related prognostic factors that directly relate to the tumor itself
- Host-related prognostic factors include inherent demographic characteristics such as age, gender, race, performance status, comorbidity, and immune status
- Environment-related prognostic factors such as choice of treatment, quality of treatment, access to care, or health-care policy (Gospodarowicz & O’sullivan, 2003)
Colorectal cancer accounts for nearly 1 mio deaths in 2020
Colorectal cancer (CRC) is one of the most commonly diagnosed cancers worldwide. Although the overall survival has risen due to new treatments and drugs, CRC is still one of the leading causes of cancer-related death (Martins et al., 2019; Divitiis et al., 2014). According to GLOBOCAN data, in 2020, there were an estimated 19.3 million new cases and 10 million cancer deaths worldwide, of which CRC contributed about 1.93 million (10 %) further incidences and 0.94 million (9.4 %) deaths (Hossain et al., 2022). As early diagnosis of CRC is entirely treatable, accurate determination of tumor-related prognostic factors is of infinite importance.
The development of CRC is related to non-modifiable host- and environmental factors, such as age, gender, race, family history, or rare inherited conditions (Hossain et al., 2022). However, to plan the treatment of an individual CRC patient, tumor-related prognostic factors like growth, invasion, and metastasis are fundamental (Gospodarowicz & O’sullivan, 2003). For this reason, the stage of the cancer is determined via the internationally accepted TNM classification.
- The T(umor) category describes the primary tumor site and size,
- The N(node) category describes the regional lymph node involvement
- The M(etastasis) category describes the metastatic spread.
In addition, numbers describe in more detail if and how much cancer is in the body. The earliest stage CRC can have is stage 0, then range from stages I (1) through IV (4). Altogether, combined T, N, and M categories provide detailed information about the stage groups for CRC summarized in the above figure and described in more detail in the following table.
|0||“Cancer in situ”. The cancer cells are only in the mucosa, or the inner lining, of the|
colon or rectum.
|I||The cancer has grown through the mucosa and has invaded the muscular layer of |
the colon or rectum. It has not spread into nearby tissue or lymph nodes (T1 or T2,
|IIA||The cancer has grown through the wall of the colon or rectum but has not spread |
to nearby tissue or to the nearby lymph nodes (T3, N0, M0).
|IIB||The cancer has grown through the layers of the muscle to the lining of the |
abdomen, called the visceral peritoneum. It has not spread to the nearby lymph
nodes or elsewhere (T4a, N0, M0).
|IIC||The cancer has spread through the wall of the colon or rectum and has grown into |
nearby structures. It has not spread to the nearby lymph nodes or elsewhere (T4b,
|IIIA||The cancer has grown through the inner lining or into the muscle layers of the |
intestine. It has spread to 1 to 3 lymph nodes or to a nodule of tumor cells in
tissues around the colon or rectum that do not appear to be lymph nodes, but has
not spread to other parts of the body (T1 or T2, N1 or N1c, M0; or T1, N2a, M0).
|IIIB||The cancer has grown through the bowel wall or to surrounding organs and into 1 |
to 3 lymph nodes or to a nodule of tumor in tissues around the colon or rectum
that do not appear to be lymph nodes. It has not spread to other parts of the
body (T3 or T4a, N1 or N1c, M0; T2 or T3, N2a, M0; or T1 or T2, N2b, M0).
|IIIC||The cancer of the colon, regardless of how deep it has grown, has spread to 4 or |
more lymph nodes but not to other distant parts of the body (T4a, N2a, M0; T3 or
T4a, N2b, M0; or T4b, N1 or N2, M0).
|IVA||The cancer has spread to a single distant part of the body, such as the liver or |
lungs (any T, any N, M1a).
|IVB||The cancer has spread to more than 1 part of the body (any T, any N, M1b).|
|IVC||The cancer has spread to the peritoneum. It may also have spread to other sites or|
organs (any T, any N, M1c).
Apart from tumor growth, invasion, and metastasis, biological markers (biomarkers) also reveal significant information about a person’s cancer. Biomarkers are genes, proteins, or other substances that can be measured in blood, tissue, or other fluids functioning as indicators of pathogenic processes, or pharmacologic responses to a therapeutic intervention. The most common prognostic biomarkers in CRC used for diagnosis, progression, prognosis, and treatment are summarized in the following figure.
|BRAF||10-15 % of patients||Stimulates growth and division of cells||Mutation causes uncontrolled growth of cells, which cannot be |
(not a germline mutation) →
will not pass from one generation to another.
|The BRAF V600E mutation is a prognostic biomarker of aggressive |
tumor growth. Patients need aggressive treatment with chemotherapy.
Patients can receive BRAF inhibitors given together with additional
inhibitors as a second or third line therapy
|KRAS||40-45 % of patients||Acts as an “on/off” switch that instructs cells to grow and divide or mature||The KRAS gene is an oncogene →|
mutations cause normal cells to become cancerous.
|Patients with mutated or unknown KRAS status should receive |
chemotherapy including FOLFOX, CAPOX, or FOLFIRI with
or without bevacizumab.
Patients should not receive EGFR-inhibitors like cetuximab
or panitumumab alone or in combination with chemotherapy
|NRAS||5 % of patients||Acts as an “on/off” switch to monitor cell growth, division, and movement||Mutation causes the protein to be locked in the “on” position |
driving constant and uncontrolled cell division
|Patients should receive chemotherapy (some examples are FOLFOX,|
CAPOX, and FOLFIRI), with or without Bevacizumab
|HER2||3-4 % of patients||Receptor on the surface of almost all the cells in our body. |
It is responsible for the communication between the cells
to promote their growth, division, repair, and survival
|Mutation causes overexpression resulting in too many |
HER2 receptors → uncontrolled growth and division of cells
|Treatment with dual-targeted therapy against HER2 →|
combination of trastuzumab with either pertuzumab
or lapatinib alone or with chemotherapy
|NTRK||< 1 % of patients||TRK proteins are receptors mostly found on the surface of cells |
of the nervous system receiving signals from neighboring
neurons to instruct cells to grow and divide
|NTRK genes can fuse with other non-related genes producing new|
NTRK fusion proteins that promote uncontrolled cell growth
and division in cancer cells
|Treatment with one of two FDA-approved NTRK inhibitors: |
larotrectinib or entrectinib
|MSI-H||15 % of patients||Microsatellites (MS) are short repetitive DNA sequences providing |
instructions for our cells on how to grow, carry out specific activities,
divide, or die → number of microsatellite repeats has to remain
the same in all cells guaranteed by the DNA mismatch repair (MMR) process
|cancer cells are “deficient” in mismatch repair (MMR) |
→ tumor cells end up with too many microsatellite DNA repeats
→ high microsatellite instability (MSI-H)
|Treatment with immune checkpoint inhibitors |
(Pembrolizumab and/or Nivolumab)
|UGT1A1||45 % of African patients|
31 % of European patients
|The protein UDP glucuronosyltransferases is an enzyme |
that is most active in the liver and converts toxic irinotecan (chemotherapy drug)
to a non-toxic form
|Patients with a UGT1A1 mutation cannot convert toxic irinotecan’s |
leading to severe toxicity (especially liver toxicity)
|Germline mutation →|
will pass from one generation to another
|Irinotecan is a chemotherapy drug commonly used to treat |
metastatic CRC → Patients experience severe toxicity
during treatment with FOLFIRI, FOLFIRINOX, or irinotecan alone
|PIK3CA||10-20 % of patients||Protein is part of an enzyme called phosphatidylinositol 3-kinase (PI3K) |
→ PI3K promotes and regulates cell proliferation, migration or movement,
apoptosis and survival
|Mutation drives the growth of cells.||Not |
|Patients should receive standard chemotherapy such as: |
FOLFOX, CAPOX, or FOLFIRI with or without bevacizumab
|DPYD||5-18 % of patients||Protein converts toxic fluorouracil-based (chemotherapy drugs |
used in colorectal cancer treatment) to a non-toxic molecule which
can be removed from the body.
|Patients with a gene mutation either have a less efficient enzyme |
or complete loss of the enzyme
→ no metabolization of chemotherapy drugs possible
|Germline mutation →|
will pass from one generation to another
|Patients cannot metabolize fluoropyrimidine-based chemotherapies |
(FOLFOX, FOLFIRI, FOLFIRINOX, capecitabine, and TAS-102).
Patients with less-efficient enzyme activity experience severe
side effects like neutropenia, diarrhea, and mucositis.
Patients with no enzyme activity at all may experience
|PTCH1||4-5 % of patients||Tumor suppressor gene that is part of the hedgehog signaling pathway||Mutations cause loss of protein function |
→ hedgehog signaling pathway leads to cancerous cells
and tumor growth.
|No FDA approved therapies targeting PTCH1 mutations to date|
Digital pathology plays a key role in the diagnosis of prognostic factors in oncological diseases. Pathologists have to evaluate a large amount of histological data with steadily growing case numbers. Here, Artificial Intelligence (AI) offers the possibility to compensate the increasing challenges by supporting the pathologist in their daily routines. For instance, AI could be used to perform repetitive tasks such as the analysis of multiple slides of lymph node biopsies to detect (micro) metastases. In addition, it could already been shown that tumor grading and characterization of an invasive tumor front is more precise and reliable if AI was used. Other works have displayed that even genetic mutations, in particular MSI, can be predicted directly from the HE slide.
The transfer of AI solutions to pathology can enable a reduction in workload as it analyzes more data in less time without producing biases. Our MICAIA® pathology software offers the ideal computational support to address your individual image analysis tasks. Use the preinstalled AI-based apps to view, annotate, process and analyze your single or whole-slide images in brightfield or fluorescence. If you are missing a feature or require an individual analysis services, we will help you to achieve your research goals.